From 061eeb9f6174163fc4f1e015228c27cdbd3d3590 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 16 Jun 2024 13:10:54 +0300
Subject: [PATCH] talk-llama : sync llama.cpp

---
 examples/talk-llama/llama.cpp        | 3288 +++++----
 examples/talk-llama/llama.h          |   96 +-
 examples/talk-llama/unicode-data.cpp | 9138 ++++++++++++++++++++------
 examples/talk-llama/unicode-data.h   |   27 +-
 examples/talk-llama/unicode.cpp      |  200 +-
 examples/talk-llama/unicode.h        |   56 +-
 6 files changed, 9112 insertions(+), 3693 deletions(-)

diff --git a/examples/talk-llama/llama.cpp b/examples/talk-llama/llama.cpp
index e91ad72..3bf9b66 100644
--- a/examples/talk-llama/llama.cpp
+++ b/examples/talk-llama/llama.cpp
@@ -7,10 +7,12 @@
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 
+#ifdef GGML_USE_RPC
+#  include "ggml-rpc.h"
+#endif
+
 #ifdef GGML_USE_CUDA
 #  include "ggml-cuda.h"
-#elif defined(GGML_USE_CLBLAST)
-#  include "ggml-opencl.h"
 #elif defined(GGML_USE_VULKAN)
 #  include "ggml-vulkan.h"
 #elif defined(GGML_USE_SYCL)
@@ -19,19 +21,16 @@
 #   include "ggml-kompute.h"
 #endif
 
+#ifdef GGML_USE_BLAS
+#  include "ggml-blas.h"
+#endif
+
 #ifdef GGML_USE_METAL
 #  include "ggml-metal.h"
 #endif
-#ifdef GGML_USE_MPI
-#  include "ggml-mpi.h"
-#endif
-#ifndef QK_K
-#  ifdef GGML_QKK_64
-#    define QK_K 64
-#  else
-#    define QK_K 256
-#  endif
-#endif
+
+// TODO: replace with ggml API call
+#define QK_K 256
 
 #ifdef __has_include
     #if __has_include(<unistd.h>)
@@ -106,14 +105,14 @@
 #endif
 
 #define LLAMA_MAX_NODES   8192
-#define LLAMA_MAX_EXPERTS 60
+#define LLAMA_MAX_EXPERTS 160
 
 //
 // logging
 //
 
 LLAMA_ATTRIBUTE_FORMAT(2, 3)
-static void llama_log_internal        (ggml_log_level level, const char* format, ...);
+static void llama_log_internal        (ggml_log_level level, const char * format, ...);
 static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);
 
 #define LLAMA_LOG_INFO(...)  llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
@@ -201,7 +200,6 @@ enum llm_arch {
     LLM_ARCH_GPTNEOX,
     LLM_ARCH_MPT,
     LLM_ARCH_STARCODER,
-    LLM_ARCH_PERSIMMON,
     LLM_ARCH_REFACT,
     LLM_ARCH_BERT,
     LLM_ARCH_NOMIC_BERT,
@@ -225,6 +223,8 @@ enum llm_arch {
     LLM_ARCH_COMMAND_R,
     LLM_ARCH_DBRX,
     LLM_ARCH_OLMO,
+    LLM_ARCH_ARCTIC,
+    LLM_ARCH_DEEPSEEK2,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -238,7 +238,6 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_MPT,             "mpt"          },
     { LLM_ARCH_BAICHUAN,        "baichuan"     },
     { LLM_ARCH_STARCODER,       "starcoder"    },
-    { LLM_ARCH_PERSIMMON,       "persimmon"    },
     { LLM_ARCH_REFACT,          "refact"       },
     { LLM_ARCH_BERT,            "bert"         },
     { LLM_ARCH_NOMIC_BERT,      "nomic-bert"   },
@@ -262,6 +261,8 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_COMMAND_R,       "command-r"    },
     { LLM_ARCH_DBRX,            "dbrx"         },
     { LLM_ARCH_OLMO,            "olmo"         },
+    { LLM_ARCH_ARCTIC,          "arctic"       },
+    { LLM_ARCH_DEEPSEEK2,       "deepseek2"    },
     { LLM_ARCH_UNKNOWN,         "(unknown)"    },
 };
 
@@ -282,11 +283,15 @@ enum llm_kv {
     LLM_KV_CONTEXT_LENGTH,
     LLM_KV_EMBEDDING_LENGTH,
     LLM_KV_BLOCK_COUNT,
+    LLM_KV_LEADING_DENSE_BLOCK_COUNT,
     LLM_KV_FEED_FORWARD_LENGTH,
+    LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
     LLM_KV_USE_PARALLEL_RESIDUAL,
     LLM_KV_TENSOR_DATA_LAYOUT,
     LLM_KV_EXPERT_COUNT,
     LLM_KV_EXPERT_USED_COUNT,
+    LLM_KV_EXPERT_SHARED_COUNT,
+    LLM_KV_EXPERT_WEIGHTS_SCALE,
     LLM_KV_POOLING_TYPE,
     LLM_KV_LOGIT_SCALE,
 
@@ -299,14 +304,18 @@ enum llm_kv {
     LLM_KV_ATTENTION_LAYERNORM_EPS,
     LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
     LLM_KV_ATTENTION_CAUSAL,
+    LLM_KV_ATTENTION_Q_LORA_RANK,
+    LLM_KV_ATTENTION_KV_LORA_RANK,
 
     LLM_KV_ROPE_DIMENSION_COUNT,
     LLM_KV_ROPE_FREQ_BASE,
     LLM_KV_ROPE_SCALE_LINEAR,
     LLM_KV_ROPE_SCALING_TYPE,
     LLM_KV_ROPE_SCALING_FACTOR,
+    LLM_KV_ROPE_SCALING_ATTN_FACTOR,
     LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
     LLM_KV_ROPE_SCALING_FINETUNED,
+    LLM_KV_ROPE_SCALING_YARN_LOG_MUL,
 
     LLM_KV_SPLIT_NO,
     LLM_KV_SPLIT_COUNT,
@@ -355,17 +364,21 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_GENERAL_SOURCE_URL,            "general.source.url"                    },
     { LLM_KV_GENERAL_SOURCE_HF_REPO,        "general.source.huggingface.repository" },
 
-    { LLM_KV_VOCAB_SIZE,                    "%s.vocab_size"            },
-    { LLM_KV_CONTEXT_LENGTH,                "%s.context_length"        },
-    { LLM_KV_EMBEDDING_LENGTH,              "%s.embedding_length"      },
-    { LLM_KV_BLOCK_COUNT,                   "%s.block_count"           },
-    { LLM_KV_FEED_FORWARD_LENGTH,           "%s.feed_forward_length"   },
-    { LLM_KV_USE_PARALLEL_RESIDUAL,         "%s.use_parallel_residual" },
-    { LLM_KV_TENSOR_DATA_LAYOUT,            "%s.tensor_data_layout"    },
-    { LLM_KV_EXPERT_COUNT,                  "%s.expert_count"          },
-    { LLM_KV_EXPERT_USED_COUNT,             "%s.expert_used_count"     },
-    { LLM_KV_POOLING_TYPE ,                 "%s.pooling_type"          },
-    { LLM_KV_LOGIT_SCALE,                   "%s.logit_scale"           },
+    { LLM_KV_VOCAB_SIZE,                    "%s.vocab_size"                 },
+    { LLM_KV_CONTEXT_LENGTH,                "%s.context_length"             },
+    { LLM_KV_EMBEDDING_LENGTH,              "%s.embedding_length"           },
+    { LLM_KV_BLOCK_COUNT,                   "%s.block_count"                },
+    { LLM_KV_LEADING_DENSE_BLOCK_COUNT,     "%s.leading_dense_block_count"  },
+    { LLM_KV_FEED_FORWARD_LENGTH,           "%s.feed_forward_length"        },
+    { LLM_KV_EXPERT_FEED_FORWARD_LENGTH,    "%s.expert_feed_forward_length" },
+    { LLM_KV_USE_PARALLEL_RESIDUAL,         "%s.use_parallel_residual"      },
+    { LLM_KV_TENSOR_DATA_LAYOUT,            "%s.tensor_data_layout"         },
+    { LLM_KV_EXPERT_COUNT,                  "%s.expert_count"               },
+    { LLM_KV_EXPERT_USED_COUNT,             "%s.expert_used_count"          },
+    { LLM_KV_EXPERT_SHARED_COUNT,           "%s.expert_shared_count"        },
+    { LLM_KV_EXPERT_WEIGHTS_SCALE,          "%s.expert_weights_scale"       },
+    { LLM_KV_POOLING_TYPE ,                 "%s.pooling_type"               },
+    { LLM_KV_LOGIT_SCALE,                   "%s.logit_scale"                },
 
     { LLM_KV_ATTENTION_HEAD_COUNT,          "%s.attention.head_count"             },
     { LLM_KV_ATTENTION_HEAD_COUNT_KV,       "%s.attention.head_count_kv"          },
@@ -376,14 +389,18 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_LAYERNORM_EPS,       "%s.attention.layer_norm_epsilon"     },
     { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,   "%s.attention.layer_norm_rms_epsilon" },
     { LLM_KV_ATTENTION_CAUSAL,              "%s.attention.causal"                 },
+    { LLM_KV_ATTENTION_Q_LORA_RANK,         "%s.attention.q_lora_rank"            },
+    { LLM_KV_ATTENTION_KV_LORA_RANK,        "%s.attention.kv_lora_rank"           },
 
     { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
     { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
     { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"                    },
     { LLM_KV_ROPE_SCALING_TYPE,             "%s.rope.scaling.type"                    },
     { LLM_KV_ROPE_SCALING_FACTOR,           "%s.rope.scaling.factor"                  },
+    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,      "%s.rope.scaling.attn_factor"             },
     { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,     "%s.rope.scaling.original_context_length" },
     { LLM_KV_ROPE_SCALING_FINETUNED,        "%s.rope.scaling.finetuned"               },
+    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL,     "%s.rope.scaling.yarn_log_multiplier"     },
 
     { LLM_KV_SPLIT_NO,                      "split.no"            },
     { LLM_KV_SPLIT_COUNT,                   "split.count"         },
@@ -437,6 +454,8 @@ enum llm_tensor {
     LLM_TENSOR_OUTPUT,
     LLM_TENSOR_OUTPUT_NORM,
     LLM_TENSOR_ROPE_FREQS,
+    LLM_TENSOR_ROPE_FACTORS_LONG,
+    LLM_TENSOR_ROPE_FACTORS_SHORT,
     LLM_TENSOR_ATTN_Q,
     LLM_TENSOR_ATTN_K,
     LLM_TENSOR_ATTN_V,
@@ -456,6 +475,7 @@ enum llm_tensor {
     LLM_TENSOR_FFN_DOWN_EXP,  // split experts for backward compatibility
     LLM_TENSOR_FFN_GATE_EXP,
     LLM_TENSOR_FFN_UP_EXP,
+    LLM_TENSOR_FFN_NORM_EXPS,
     LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
     LLM_TENSOR_FFN_GATE_EXPS,
     LLM_TENSOR_FFN_UP_EXPS,
@@ -472,6 +492,12 @@ enum llm_tensor {
     LLM_TENSOR_SSM_A,
     LLM_TENSOR_SSM_D,
     LLM_TENSOR_SSM_OUT,
+    LLM_TENSOR_ATTN_Q_A,
+    LLM_TENSOR_ATTN_Q_B,
+    LLM_TENSOR_ATTN_KV_A_MQA,
+    LLM_TENSOR_ATTN_KV_B,
+    LLM_TENSOR_ATTN_Q_A_NORM,
+    LLM_TENSOR_ATTN_KV_A_NORM,
 };
 
 static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@@ -594,23 +620,6 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
-    {
-        LLM_ARCH_PERSIMMON,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd"},
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm"},
-            { LLM_TENSOR_OUTPUT,          "output"},
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm"},
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv"},
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output"},
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm"},
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm"},
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm"},
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down"},
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up"},
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd"},
-        },
-    },
     {
         LLM_ARCH_MPT,
         {
@@ -699,6 +708,7 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
             { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
             { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
+            { LLM_TENSOR_ATTN_NORM_2,     "blk.%d.attn_norm_2" },
             { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
             { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
             { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
@@ -821,18 +831,20 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
     {
         LLM_ARCH_PHI3,
         {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
+            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,           "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
         },
     },
     {
@@ -1048,6 +1060,57 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_ARCTIC,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_NORM_EXPS,   "blk.%d.ffn_norm_exps" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+        },
+    },
+    {
+        LLM_ARCH_DEEPSEEK2,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q_A_NORM,      "blk.%d.attn_q_a_norm" },
+            { LLM_TENSOR_ATTN_KV_A_NORM,     "blk.%d.attn_kv_a_norm" },
+            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_Q_A,           "blk.%d.attn_q_a" },
+            { LLM_TENSOR_ATTN_Q_B,           "blk.%d.attn_q_b" },
+            { LLM_TENSOR_ATTN_KV_A_MQA,      "blk.%d.attn_kv_a_mqa" },
+            { LLM_TENSOR_ATTN_KV_B,          "blk.%d.attn_kv_b" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
+        },
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
@@ -1642,12 +1705,13 @@ struct llama_mlock {
 };
 using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
 
-static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
+// NOTE: avoid ever using this except for building the token_to_piece caches
+static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) {
     std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
+    const int n_tokens = llama_token_to_piece(model, token, result.data(), result.size(), special);
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
+        int check = llama_token_to_piece(model, token, result.data(), result.size(), special);
         GGML_ASSERT(check == -n_tokens);
     }
     else {
@@ -1685,91 +1749,6 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer
     GGML_UNUSED(host_buffer);
 }
 
-static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) {
-    ggml_backend_buffer_type_t buft = nullptr;
-
-#ifdef GGML_USE_METAL
-    buft = ggml_backend_metal_buffer_type();
-#elif defined(GGML_USE_CUDA)
-    buft = ggml_backend_cuda_buffer_type(gpu);
-#elif defined(GGML_USE_VULKAN)
-    buft = ggml_backend_vk_buffer_type(gpu);
-#elif defined(GGML_USE_SYCL)
-    buft = ggml_backend_sycl_buffer_type(gpu);
-#elif defined(GGML_USE_CLBLAST)
-    buft = ggml_backend_opencl_buffer_type();
-#elif defined(GGML_USE_KOMPUTE)
-    buft = ggml_backend_kompute_buffer_type(gpu);
-    if (buft == nullptr) {
-        LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu);
-    }
-#endif
-
-    if (buft == nullptr) {
-        buft = llama_default_buffer_type_cpu(true);
-    }
-    return buft;
-
-    GGML_UNUSED(gpu);
-}
-
-static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_gpu, const float * tensor_split) {
-    ggml_backend_buffer_type_t buft = nullptr;
-
-#ifdef GGML_USE_CUDA
-    if (ggml_backend_cuda_get_device_count() > 1) {
-        buft = ggml_backend_cuda_split_buffer_type(tensor_split);
-    }
-#endif
-
-#ifdef GGML_USE_SYCL
-    if (ggml_backend_sycl_get_device_count() > 1) {
-        buft = ggml_backend_sycl_split_buffer_type(tensor_split);
-    }
-#endif
-
-    if (buft == nullptr) {
-        buft = llama_default_buffer_type_offload(fallback_gpu);
-    }
-    return buft;
-
-    GGML_UNUSED(tensor_split);
-}
-
-static size_t llama_get_device_count() {
-#if defined(GGML_USE_CUDA)
-    return ggml_backend_cuda_get_device_count();
-#elif defined(GGML_USE_SYCL)
-    return ggml_backend_sycl_get_device_count();
-#elif defined(GGML_USE_VULKAN)
-    return ggml_backend_vk_get_device_count();
-#else
-    return 1;
-#endif
-}
-
-static size_t llama_get_device_memory(int device) {
-#if defined(GGML_USE_CUDA)
-    size_t total;
-    size_t free;
-    ggml_backend_cuda_get_device_memory(device, &free, &total);
-    return free;
-#elif defined(GGML_USE_SYCL)
-    size_t total;
-    size_t free;
-    ggml_backend_sycl_get_device_memory(device, &free, &total);
-    return free;
-#elif defined(GGML_USE_VULKAN)
-    size_t total;
-    size_t free;
-    ggml_backend_vk_get_device_memory(device, &free, &total);
-    return free;
-#else
-    return 1;
-    GGML_UNUSED(device);
-#endif
-}
-
 //
 // globals
 //
@@ -1778,6 +1757,8 @@ struct llama_state {
     llama_state() {
 #ifdef GGML_USE_METAL
         ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data);
+#elif defined(GGML_USE_CUDA)
+        ggml_backend_cuda_log_set_callback(log_callback, log_callback_user_data);
 #endif
     }
 
@@ -1791,23 +1772,31 @@ static llama_state g_state;
 // available llama models
 enum e_model {
     MODEL_UNKNOWN,
+    MODEL_14M,
     MODEL_17M,
     MODEL_22M,
     MODEL_33M,
+    MODEL_70M,
     MODEL_109M,
     MODEL_137M,
+    MODEL_160M,
     MODEL_335M,
+    MODEL_410M,
     MODEL_0_5B,
     MODEL_1B,
+    MODEL_1_4B,
     MODEL_2B,
+    MODEL_2_8B,
     MODEL_3B,
     MODEL_4B,
+    MODEL_6_9B,
     MODEL_7B,
     MODEL_8B,
     MODEL_12B,
     MODEL_13B,
     MODEL_14B,
     MODEL_15B,
+    MODEL_16B,
     MODEL_20B,
     MODEL_30B,
     MODEL_34B,
@@ -1815,6 +1804,7 @@ enum e_model {
     MODEL_40B,
     MODEL_65B,
     MODEL_70B,
+    MODEL_236B,
     MODEL_314B,
     MODEL_SMALL,
     MODEL_MEDIUM,
@@ -1824,6 +1814,7 @@ enum e_model {
     MODEL_8x7B,
     MODEL_8x22B,
     MODEL_16x12B,
+    MODEL_10B_128x3_66B,
 };
 
 static const size_t kiB = 1024;
@@ -1833,6 +1824,7 @@ static const size_t GiB = 1024*MiB;
 struct llama_hparams {
     bool vocab_only;
     bool rope_finetuned;
+    bool use_par_res;
 
     uint32_t n_vocab;
     uint32_t n_ctx_train; // context size the model was trained on
@@ -1848,12 +1840,21 @@ struct llama_hparams {
     uint32_t n_expert_used = 0;
     uint32_t n_vocab_type = 0; // for BERT-style token types
 
+    uint32_t n_layer_dense_lead = 0;
+    uint32_t n_lora_q = 0;
+    uint32_t n_lora_kv = 0;
+    uint32_t n_ff_exp = 0;
+    uint32_t n_expert_shared = 0;
+    float    expert_weights_scale = 0.0;
+
     float f_norm_eps;
     float f_norm_rms_eps;
 
+    float    rope_attn_factor = 1.0f;
     float    rope_freq_base_train;
     float    rope_freq_scale_train;
-    uint32_t n_yarn_orig_ctx;
+    uint32_t n_ctx_orig_yarn;
+    float    rope_yarn_log_mul;
 
     // for State Space Models
     uint32_t ssm_d_conv  = 0;
@@ -1887,8 +1888,14 @@ struct llama_hparams {
         if (this->n_expert      != other.n_expert)      return true;
         if (this->n_expert_used != other.n_expert_used) return true;
 
+        if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true;
+        if (this->n_lora_q           != other.n_lora_q)           return true;
+        if (this->n_lora_kv          != other.n_lora_kv)          return true;
+        if (this->n_ff_exp           != other.n_ff_exp)           return true;
+        if (this->n_expert_shared    != other.n_expert_shared)    return true;
+
         if (this->rope_finetuned  != other.rope_finetuned)  return true;
-        if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;
+        if (this->n_ctx_orig_yarn != other.n_ctx_orig_yarn) return true;
 
         if (this->ssm_d_conv  != other.ssm_d_conv)  return true;
         if (this->ssm_d_inner != other.ssm_d_inner) return true;
@@ -1899,8 +1906,11 @@ struct llama_hparams {
 
         if (!is_float_close(this->f_norm_eps,            other.f_norm_eps,            EPSILON)) return true;
         if (!is_float_close(this->f_norm_rms_eps,        other.f_norm_rms_eps,        EPSILON)) return true;
+        if (!is_float_close(this->rope_attn_factor,      other.rope_attn_factor,      EPSILON)) return true;
         if (!is_float_close(this->rope_freq_base_train,  other.rope_freq_base_train,  EPSILON)) return true;
         if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true;
+        if (!is_float_close(this->expert_weights_scale,  other.expert_weights_scale,  EPSILON)) return true;
+        if (!is_float_close(this->rope_yarn_log_mul,     other.rope_yarn_log_mul,     EPSILON)) return true;
 
         return false;
     }
@@ -1944,7 +1954,7 @@ struct llama_cparams {
     float rope_freq_base;
     float rope_freq_scale;
 
-    uint32_t n_yarn_orig_ctx;
+    uint32_t n_ctx_orig_yarn;
     // These hyperparameters are not exposed in GGUF, because all
     // existing YaRN models use the same values for them.
     float yarn_ext_factor;
@@ -1976,6 +1986,8 @@ struct llama_layer {
     struct ggml_tensor * attn_k_norm_b;
     struct ggml_tensor * attn_out_norm;
     struct ggml_tensor * attn_out_norm_b;
+    struct ggml_tensor * attn_q_a_norm;
+    struct ggml_tensor * attn_kv_a_norm;
 
     // attention
     struct ggml_tensor * wq;
@@ -1983,6 +1995,10 @@ struct llama_layer {
     struct ggml_tensor * wv;
     struct ggml_tensor * wo;
     struct ggml_tensor * wqkv;
+    struct ggml_tensor * wq_a;
+    struct ggml_tensor * wq_b;
+    struct ggml_tensor * wkv_a_mqa;
+    struct ggml_tensor * wkv_b;
 
     // attention bias
     struct ggml_tensor * bq;
@@ -1996,6 +2012,7 @@ struct llama_layer {
     struct ggml_tensor * ffn_norm_b;
     struct ggml_tensor * layer_out_norm;
     struct ggml_tensor * layer_out_norm_b;
+    struct ggml_tensor * ffn_norm_exps;
 
     // ff
     struct ggml_tensor * ffn_gate; // w1
@@ -2015,8 +2032,9 @@ struct llama_layer {
     struct ggml_tensor * ffn_up_shexp;
 
     // ff bias
-    struct ggml_tensor * ffn_down_b; // b2
-    struct ggml_tensor * ffn_up_b;   // b3
+    struct ggml_tensor * ffn_gate_b = nullptr;
+    struct ggml_tensor * ffn_down_b = nullptr; // b2
+    struct ggml_tensor * ffn_up_b   = nullptr; // b3
     struct ggml_tensor * ffn_act;
 
     // mamba proj
@@ -2033,6 +2051,10 @@ struct llama_layer {
     // mamba bias
     struct ggml_tensor * ssm_conv1d_b;
     struct ggml_tensor * ssm_dt_b;
+
+    // long rope factors
+    struct ggml_tensor * rope_long  = nullptr;
+    struct ggml_tensor * rope_short = nullptr;
 };
 
 struct llama_kv_cell {
@@ -2130,12 +2152,12 @@ struct llama_control_vector {
 struct llama_vocab {
     using id    = int32_t;
     using token = std::string;
-    using ttype = llama_token_type;
+    using tattr = llama_token_attr;
 
     struct token_data {
         token text;
         float score;
-        ttype type;
+        tattr attr;
     };
 
     enum llama_vocab_type     type     = LLAMA_VOCAB_TYPE_SPM;
@@ -2144,7 +2166,8 @@ struct llama_vocab {
     std::unordered_map<token, id> token_to_id;
     std::vector<token_data>       id_to_token;
 
-    std::unordered_map<token, id> special_tokens_cache;
+    std::vector<id>    cache_special_tokens;
+    std::vector<token> cache_token_to_piece; // llama_token_to_piece(special = true);
 
     std::map<std::pair<std::string, std::string>, int> bpe_ranks;
 
@@ -2210,6 +2233,8 @@ struct llama_model {
     int main_gpu;
     int n_gpu_layers;
 
+    std::vector<std::string> rpc_servers;
+
     // gguf metadata
     std::unordered_map<std::string, std::string> gguf_kv;
 
@@ -2278,9 +2303,13 @@ struct llama_context {
     std::vector<ggml_backend_t> backends;
 #ifdef GGML_USE_METAL
     ggml_backend_t backend_metal = nullptr;
+#endif
+#ifdef GGML_USE_BLAS
+    ggml_backend_t backend_blas = nullptr;
 #endif
     ggml_backend_t backend_cpu = nullptr;
 
+
     const llama_model & model;
 
     // key + value cache for the self attention
@@ -2347,12 +2376,115 @@ struct llama_context {
 
     // control vectors
     struct llama_control_vector cvec;
-
-#ifdef GGML_USE_MPI
-    ggml_mpi_context * ctx_mpi = NULL;
-#endif
 };
 
+static size_t llama_get_device_count(const llama_model & model) {
+    size_t count = 1;
+#if defined(GGML_USE_CUDA)
+    count = ggml_backend_cuda_get_device_count();
+#elif defined(GGML_USE_SYCL)
+    count = ggml_backend_sycl_get_device_count();
+#elif defined(GGML_USE_VULKAN)
+    count = ggml_backend_vk_get_device_count();
+#endif
+#if defined(GGML_USE_RPC)
+    count += model.rpc_servers.size();
+#endif
+    return count;
+    GGML_UNUSED(model);
+}
+
+static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int gpu) {
+    ggml_backend_buffer_type_t buft = nullptr;
+
+#if defined(GGML_USE_RPC)
+    int dev_count = (int)llama_get_device_count(model);
+    int rpc_count = (int)model.rpc_servers.size();
+    if (gpu >= dev_count - rpc_count) {
+        const char * endpoint = model.rpc_servers[gpu - dev_count + rpc_count].c_str();
+        return ggml_backend_rpc_buffer_type(endpoint);
+    }
+#endif
+#if defined(GGML_USE_METAL)
+    buft = ggml_backend_metal_buffer_type();
+#elif defined(GGML_USE_CUDA)
+    buft = ggml_backend_cuda_buffer_type(gpu);
+#elif defined(GGML_USE_VULKAN)
+    buft = ggml_backend_vk_buffer_type(gpu);
+#elif defined(GGML_USE_SYCL)
+    buft = ggml_backend_sycl_buffer_type(gpu);
+#elif defined(GGML_USE_KOMPUTE)
+    buft = ggml_backend_kompute_buffer_type(gpu);
+    if (buft == nullptr) {
+        LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu);
+    }
+#endif
+
+    if (buft == nullptr) {
+        buft = llama_default_buffer_type_cpu(true);
+    }
+    return buft;
+    GGML_UNUSED(model);
+    GGML_UNUSED(gpu);
+}
+
+static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_model & model, int fallback_gpu, const float * tensor_split) {
+    ggml_backend_buffer_type_t buft = nullptr;
+
+#ifdef GGML_USE_CUDA
+    if (ggml_backend_cuda_get_device_count() > 1) {
+        buft = ggml_backend_cuda_split_buffer_type(tensor_split);
+    }
+#endif
+
+#ifdef GGML_USE_SYCL
+    if (ggml_backend_sycl_get_device_count() > 1) {
+        buft = ggml_backend_sycl_split_buffer_type(tensor_split);
+    }
+#endif
+
+    if (buft == nullptr) {
+        buft = llama_default_buffer_type_offload(model, fallback_gpu);
+    }
+    return buft;
+
+    GGML_UNUSED(tensor_split);
+}
+
+static size_t llama_get_device_memory(const llama_model & model, int device) {
+#if defined(GGML_USE_RPC)
+    int dev_count = (int)llama_get_device_count(model);
+    int rpc_count = (int)model.rpc_servers.size();
+    if (device >= dev_count - rpc_count) {
+        size_t total;
+        size_t free;
+        const char * endpoint = model.rpc_servers[device - dev_count + rpc_count].c_str();
+        ggml_backend_rpc_get_device_memory(endpoint, &free, &total);
+        return free;
+    }
+#endif
+#if defined(GGML_USE_CUDA)
+    size_t total;
+    size_t free;
+    ggml_backend_cuda_get_device_memory(device, &free, &total);
+    return free;
+#elif defined(GGML_USE_SYCL)
+    size_t total;
+    size_t free;
+    ggml_backend_sycl_get_device_memory(device, &free, &total);
+    return free;
+#elif defined(GGML_USE_VULKAN)
+    size_t total;
+    size_t free;
+    ggml_backend_vk_get_device_memory(device, &free, &total);
+    return free;
+#else
+    return 1;
+#endif
+    GGML_UNUSED(model);
+    GGML_UNUSED(device);
+}
+
 //
 // kv cache helpers
 //
@@ -2403,10 +2535,6 @@ static bool llama_kv_cache_init(
         }
     }
 
-#ifdef GGML_USE_CLBLAST
-    offload = false;
-#endif
-
     // count used buffer types
     std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
     if (offload) {
@@ -2472,7 +2600,6 @@ static bool llama_kv_cache_init(
 static bool llama_kv_cache_find_slot(
            struct llama_kv_cache & cache,
         const struct llama_batch & batch) {
-    const uint32_t n_ctx    = cache.size;
     const uint32_t n_tokens = batch.n_tokens;
 
     if (cache.recurrent) {
@@ -2523,16 +2650,16 @@ static bool llama_kv_cache_find_slot(
     }
     // otherwise, one cell per token.
 
-    if (n_tokens > n_ctx) {
-        LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx);
+    if (n_tokens > cache.size) {
+        LLAMA_LOG_ERROR("%s: n_tokens=%d > cache.size=%d\n", __func__, n_tokens, cache.size);
         return false;
     }
 
     uint32_t n_tested = 0;
 
     while (true) {
-        if (cache.head + n_tokens > n_ctx) {
-            n_tested += n_ctx - cache.head;
+        if (cache.head + n_tokens > cache.size) {
+            n_tested += cache.size - cache.head;
             cache.head = 0;
             continue;
         }
@@ -2551,7 +2678,7 @@ static bool llama_kv_cache_find_slot(
             break;
         }
 
-        if (n_tested >= n_ctx) {
+        if (n_tested >= cache.size) {
             //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
             return false;
         }
@@ -2805,6 +2932,11 @@ static void llama_kv_cache_defrag(struct llama_kv_cache & cache) {
     cache.do_defrag = true;
 }
 
+static uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams) {
+    // the FA kernels require padding to avoid extra runtime boundary checks
+    return cparams.flash_attn ? 256u : 32u;
+}
+
 //
 // model loading and saving
 //
@@ -3306,6 +3438,39 @@ struct llama_model_loader {
         return get_arr_n(llm_kv(kid), result, required);
     }
 
+    template<typename T>
+    bool get_arr(const std::string & key, std::vector<T> & result, const bool required = true) {
+        const int kid = gguf_find_key(meta, key.c_str());
+
+        if (kid < 0) {
+            if (required) {
+                throw std::runtime_error(format("key not found in model: %s", key.c_str()));
+            }
+            return false;
+        }
+
+        struct GGUFMeta::ArrayInfo arr_info =
+            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
+
+        if (arr_info.gt != GGUF_TYPE_FLOAT32 && arr_info.gt != GGUF_TYPE_INT32) {
+            throw std::runtime_error(format("%s is not a float32 or int32 array", key.c_str()));
+        }
+
+        // GGML_ASSERT(gguf_type_size(arr_info.gt) == sizeof(T));
+        GGML_ASSERT((arr_info.gt != GGUF_TYPE_FLOAT32 || std::is_same<T, float>::value));
+        GGML_ASSERT((arr_info.gt != GGUF_TYPE_INT32   || std::is_same<T, int>::value));
+
+        result.resize(arr_info.length);
+        result.assign((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length);
+
+        return true;
+    }
+
+    template<typename T>
+    bool get_arr(const enum llm_kv kid, T& result, const bool required = true) {
+        return get_arr(llm_kv(kid), result, required);
+    }
+
     template<typename T>
     bool get_key(const std::string & key, T & result, const bool required = true) {
         auto it = kv_overrides.find(key);
@@ -3380,11 +3545,15 @@ struct llama_model_loader {
         return get_tensor_meta(get_tensor_name(i));
     }
 
-    struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur) {
+    struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur, bool duplicated) {
         struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
         ggml_set_name(tensor, ggml_get_name(cur));
 
-        n_created++;
+        if (duplicated) {
+            size_data += ggml_nbytes(cur);
+        } else {
+            n_created++;
+        }
 
         return tensor;
     }
@@ -3419,14 +3588,17 @@ struct llama_model_loader {
         return cur;
     }
 
-    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, bool required = true) {
-        const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
+    static const int TENSOR_NOT_REQUIRED = 1;
+    static const int TENSOR_DUPLICATED   = 2;
+
+    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, int flags = 0) {
+        const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED));
 
         if (cur == NULL) {
             return NULL;
         }
 
-        return create_tensor_for(ctx, cur);
+        return create_tensor_for(ctx, cur, flags & TENSOR_DUPLICATED);
     }
 
     struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::vector<int64_t> & ne, size_t offset, bool required = true) {
@@ -3726,37 +3898,50 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
 
 static const char * llama_model_type_name(e_model type) {
     switch (type) {
-        case MODEL_22M:    return "22M";
-        case MODEL_33M:    return "33M";
-        case MODEL_109M:   return "109M";
-        case MODEL_137M:   return "137M";
-        case MODEL_0_5B:   return "0.5B";
-        case MODEL_1B:     return "1B";
-        case MODEL_2B:     return "2B";
-        case MODEL_3B:     return "3B";
-        case MODEL_7B:     return "7B";
-        case MODEL_8B:     return "8B";
-        case MODEL_12B:    return "12B";
-        case MODEL_13B:    return "13B";
-        case MODEL_14B:    return "14B";
-        case MODEL_15B:    return "15B";
-        case MODEL_20B:    return "20B";
-        case MODEL_30B:    return "30B";
-        case MODEL_34B:    return "34B";
-        case MODEL_35B:    return "35B";
-        case MODEL_40B:    return "40B";
-        case MODEL_65B:    return "65B";
-        case MODEL_70B:    return "70B";
-        case MODEL_314B:   return "314B";
-        case MODEL_SMALL:  return "0.1B";
-        case MODEL_MEDIUM: return "0.4B";
-        case MODEL_LARGE:  return "0.8B";
-        case MODEL_XL:     return "1.5B";
-        case MODEL_A2_7B:  return "A2.7B";
-        case MODEL_8x7B:   return "8x7B";
-        case MODEL_8x22B:  return "8x22B";
-        case MODEL_16x12B: return "16x12B";
-        default:           return "?B";
+        case MODEL_14M:           return "14M";
+        case MODEL_17M:           return "17M";
+        case MODEL_22M:           return "22M";
+        case MODEL_33M:           return "33M";
+        case MODEL_70M:           return "70M";
+        case MODEL_109M:          return "109M";
+        case MODEL_137M:          return "137M";
+        case MODEL_160M:          return "160M";
+        case MODEL_335M:          return "335M";
+        case MODEL_410M:          return "410M";
+        case MODEL_0_5B:          return "0.5B";
+        case MODEL_1B:            return "1B";
+        case MODEL_1_4B:          return "1.4B";
+        case MODEL_2B:            return "2B";
+        case MODEL_2_8B:          return "2.8B";
+        case MODEL_3B:            return "3B";
+        case MODEL_4B:            return "4B";
+        case MODEL_6_9B:          return "6.9B";
+        case MODEL_7B:            return "7B";
+        case MODEL_8B:            return "8B";
+        case MODEL_12B:           return "12B";
+        case MODEL_13B:           return "13B";
+        case MODEL_14B:           return "14B";
+        case MODEL_15B:           return "15B";
+        case MODEL_16B:           return "16B";
+        case MODEL_20B:           return "20B";
+        case MODEL_30B:           return "30B";
+        case MODEL_34B:           return "34B";
+        case MODEL_35B:           return "35B";
+        case MODEL_40B:           return "40B";
+        case MODEL_65B:           return "65B";
+        case MODEL_70B:           return "70B";
+        case MODEL_236B:          return "236B";
+        case MODEL_314B:          return "314B";
+        case MODEL_SMALL:         return "0.1B";
+        case MODEL_MEDIUM:        return "0.4B";
+        case MODEL_LARGE:         return "0.8B";
+        case MODEL_XL:            return "1.5B";
+        case MODEL_A2_7B:         return "A2.7B";
+        case MODEL_8x7B:          return "8x7B";
+        case MODEL_8x22B:         return "8x22B";
+        case MODEL_16x12B:        return "16x12B";
+        case MODEL_10B_128x3_66B: return "10B+128x3.66B";
+        default:                  return "?B";
     }
 }
 
@@ -3829,8 +4014,8 @@ static void llm_load_hparams(
     ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
     hparams.rope_finetuned = rope_finetuned;
 
-    hparams.n_yarn_orig_ctx = hparams.n_ctx_train;
-    ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_yarn_orig_ctx, false);
+    hparams.n_ctx_orig_yarn = hparams.n_ctx_train;
+    ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_ctx_orig_yarn, false);
 
     // rope_freq_base (optional)
     hparams.rope_freq_base_train = 10000.0f;
@@ -3849,6 +4034,8 @@ static void llm_load_hparams(
     }
     hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
 
+    ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
+
     // sanity check for n_rot (optional)
     {
         hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
@@ -3886,7 +4073,9 @@ static void llm_load_hparams(
                     switch (hparams.n_layer) {
                         case 22: model.type = e_model::MODEL_1B; break;
                         case 26: model.type = e_model::MODEL_3B; break;
-                        case 32: model.type = hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B; break;
+                        // granite uses a vocab with len 49152
+                        case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break;
+                        case 36: model.type = e_model::MODEL_8B; break; // granite
                         case 40: model.type = e_model::MODEL_13B; break;
                         case 48: model.type = e_model::MODEL_34B; break;
                         case 60: model.type = e_model::MODEL_30B; break;
@@ -3948,14 +4137,6 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
-        case LLM_ARCH_PERSIMMON:
-            {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                switch (hparams.n_layer) {
-                    case 36: model.type = e_model::MODEL_8B; break;
-                    default: model.type = e_model::MODEL_UNKNOWN;
-                }
-            } break;
         case LLM_ARCH_REFACT:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -4097,6 +4278,7 @@ static void llm_load_hparams(
                 switch (hparams.n_layer) {
                     case 24: model.type = e_model::MODEL_1B; break;
                     case 32: model.type = e_model::MODEL_3B; break;
+                    case 40: model.type = e_model::MODEL_14B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
@@ -4163,6 +4345,8 @@ static void llm_load_hparams(
                     case 30: model.type = e_model::MODEL_3B; break;
                     case 32: model.type = e_model::MODEL_7B; break;
                     case 40: model.type = e_model::MODEL_15B; break;
+                    case 52: model.type = e_model::MODEL_20B; break; // granite
+                    case 88: model.type = e_model::MODEL_34B; break; // granite
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
@@ -4237,6 +4421,85 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_GPTNEOX:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res);
+                switch (hparams.n_layer) {
+                    case 6:
+                        switch (hparams.n_ff) {
+                            case 512: model.type = e_model::MODEL_14M; break;
+                            case 2048: model.type = e_model::MODEL_70M; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 12:
+                        switch (hparams.n_ff) {
+                            case 3072: model.type = e_model::MODEL_160M; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 16:
+                        switch (hparams.n_ff) {
+                            case 8192: model.type = e_model::MODEL_1B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 24:
+                        switch (hparams.n_ff) {
+                            case 4096: model.type = e_model::MODEL_410M; break;
+                            case 8192: model.type = e_model::MODEL_1_4B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 32:
+                        switch (hparams.n_ff) {
+                            case 10240: model.type = e_model::MODEL_2_8B; break;
+                            case 16384: model.type = e_model::MODEL_6_9B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 36:
+                        switch (hparams.n_ff) {
+                            case 20480: model.type = e_model::MODEL_12B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 44:
+                        switch (hparams.n_ff) {
+                            case 24576: model.type = e_model::MODEL_20B; break;
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_ARCTIC:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                if (hparams.n_expert == 128) {
+                    switch (hparams.n_layer) {
+                        case 35: model.type = e_model::MODEL_10B_128x3_66B; break;
+                        default: model.type = e_model::MODEL_UNKNOWN;
+                    }
+                } else {
+                    model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
+        case LLM_ARCH_DEEPSEEK2:
+            {
+                bool is_lite = (hparams.n_layer == 27);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
+                if (!is_lite) {
+                    ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
+                }
+                ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
+                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul);
+
+                switch (hparams.n_layer) {
+                    case 27: model.type = e_model::MODEL_16B; break;
+                    case 60: model.type = e_model::MODEL_236B; break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         default: (void)0;
     }
 
@@ -4298,35 +4561,6 @@ static void llm_load_vocab(
             vocab.special_cls_id  = -1;
             vocab.special_mask_id = -1;
 
-            // For Fill-In-the-Middle (FIM)/infill models which where converted
-            // prior to support of FIM special tokens in GGUF, the following
-            // will allow those models to continue to work. The general names
-            // of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and
-            // CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once
-            // new versions of these models have been published.
-            std::string gen_name;
-            ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false);
-
-            std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(),
-                [](unsigned char c){ return std::tolower(c); });
-
-            if (gen_name.find("code") != std::string::npos) {
-                if (model.arch == LLM_ARCH_LLAMA) {
-                    vocab.special_prefix_id = 32007;
-                    vocab.special_suffix_id = 32008;
-                    vocab.special_middle_id = 32009;
-                    vocab.special_eot_id    = 32010;
-                } else if (model.arch == LLM_ARCH_GEMMA) {
-                    vocab.special_prefix_id = 67;
-                    vocab.special_suffix_id = 69;
-                    vocab.special_middle_id = 68;
-                    // TODO: this is not EOT, it is "file separator" token, needs fix
-                    //       https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
-                    //vocab.special_eot_id    = 70;
-                    vocab.special_eot_id    = 107;
-                }
-            }
-
             const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
             if (add_space_prefix_keyidx != -1) {
                 vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
@@ -4343,15 +4577,14 @@ static void llm_load_vocab(
             vocab.special_cls_id  = 101;
             vocab.special_mask_id = 103;
             vocab.add_space_prefix = false;
-        } else {
-            if (tokenizer_model == "gpt2") {
-                vocab.type = LLAMA_VOCAB_TYPE_BPE;
-            } else {
-                LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str());
-                LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);
-                vocab.type = LLAMA_VOCAB_TYPE_SPM;
-                return;
+        } else if (tokenizer_model == "gpt2") {
+            vocab.type = LLAMA_VOCAB_TYPE_BPE;
+
+            const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
+            if (add_space_prefix_keyidx != -1) {
+                vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
             }
+
             // read bpe merges and populate bpe ranks
             const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
             if (merges_keyidx == -1) {
@@ -4385,6 +4618,8 @@ static void llm_load_vocab(
             vocab.special_pad_id  = -1;
             vocab.special_cls_id  = -1;
             vocab.special_mask_id = -1;
+        } else {
+            throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
         }
 
         // for now, only BPE models have pre-tokenizers
@@ -4398,8 +4633,7 @@ static void llm_load_vocab(
                 LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
                 LLAMA_LOG_WARN("%s:                                             \n", __func__);
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-            } else if (
-                    tokenizer_pre == "default") {
+            } else if (tokenizer_pre == "default") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
             } else if (
                     tokenizer_pre == "llama3"   ||
@@ -4424,7 +4658,10 @@ static void llm_load_vocab(
             } else if (
                     tokenizer_pre == "gpt-2"   ||
                     tokenizer_pre == "jina-es" ||
-                    tokenizer_pre == "jina-de") {
+                    tokenizer_pre == "jina-de" ||
+                    tokenizer_pre == "jina-v2-es" ||
+                    tokenizer_pre == "jina-v2-de" ||
+                    tokenizer_pre == "jina-v2-code") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2;
             } else if (
                     tokenizer_pre == "refact") {
@@ -4435,12 +4672,21 @@ static void llm_load_vocab(
             } else if (
                 tokenizer_pre == "qwen2") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
+            } else if (
+                tokenizer_pre == "stablelm2") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STABLELM2;
             } else if (
                 tokenizer_pre == "olmo") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_OLMO;
             } else if (
                 tokenizer_pre == "dbrx") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DBRX;
+            } else if (
+                tokenizer_pre == "smaug-bpe") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMAUG;
+            } else if (
+                tokenizer_pre == "poro-chat") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
@@ -4479,12 +4725,64 @@ static void llm_load_vocab(
         auto & token_data = vocab.id_to_token[i];
         token_data.text  = std::move(word);
         token_data.score = scores ? scores[i] : 0.0f;
-        token_data.type  = toktypes ? (llama_token_type) toktypes[i] : LLAMA_TOKEN_TYPE_NORMAL;
+        token_data.attr  = LLAMA_TOKEN_ATTR_NORMAL;
+
+        if (toktypes) {  //TODO: remove, required until per token attributes are available from GGUF file
+            switch(toktypes[i]) {
+                case LLAMA_TOKEN_TYPE_UNKNOWN:      token_data.attr = LLAMA_TOKEN_ATTR_UNKNOWN;      break;
+                case LLAMA_TOKEN_TYPE_UNUSED:       token_data.attr = LLAMA_TOKEN_ATTR_UNUSED;       break;
+                case LLAMA_TOKEN_TYPE_NORMAL:       token_data.attr = LLAMA_TOKEN_ATTR_NORMAL;       break;
+                case LLAMA_TOKEN_TYPE_CONTROL:      token_data.attr = LLAMA_TOKEN_ATTR_CONTROL;      break;
+                case LLAMA_TOKEN_TYPE_USER_DEFINED: token_data.attr = LLAMA_TOKEN_ATTR_USER_DEFINED; break;
+                case LLAMA_TOKEN_TYPE_BYTE:         token_data.attr = LLAMA_TOKEN_ATTR_BYTE;         break;
+                case LLAMA_TOKEN_TYPE_UNDEFINED:    token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED;    break;
+                default:                            token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED;    break;
+            }
+        }
     }
     GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());
 
     // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
     if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
+        // For Fill-In-the-Middle (FIM)/infill models which where converted
+        // prior to support of FIM special tokens in GGUF, the following
+        // will allow those models to continue to work. The general names
+        // of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and
+        // CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once
+        // new versions of these models have been published.
+        std::string gen_name;
+        ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false);
+
+        std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(),
+            [](unsigned char c){ return std::tolower(c); });
+
+        if (gen_name.find("code") != std::string::npos) {
+            if (model.arch == LLM_ARCH_LLAMA
+              && 32010 < vocab.id_to_token.size()
+              && vocab.id_to_token[32007].text == "<PRE>"
+              && vocab.id_to_token[32008].text == "<SUF>"
+              && vocab.id_to_token[32009].text == "<MID>"
+              && vocab.id_to_token[32010].text == "<EOT>") {
+                vocab.special_prefix_id = 32007;
+                vocab.special_suffix_id = 32008;
+                vocab.special_middle_id = 32009;
+                vocab.special_eot_id    = 32010;
+            } else if (model.arch == LLM_ARCH_GEMMA
+              && 107 < vocab.id_to_token.size()
+              && vocab.id_to_token[67].text == "<|fim_prefix|>"
+              && vocab.id_to_token[69].text == "<|fim_suffix|>"
+              && vocab.id_to_token[68].text == "<|fim_middle|>"
+              && vocab.id_to_token[107].text == "<end_of_turn>") {
+                vocab.special_prefix_id = 67;
+                vocab.special_suffix_id = 69;
+                vocab.special_middle_id = 68;
+                // TODO: this is not EOT, it is "file separator" token, needs fix
+                //       https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
+                //vocab.special_eot_id    = 70;
+                vocab.special_eot_id    = 107;
+            }
+        }
+
         try {
             vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
         } catch (const std::exception & e) {
@@ -4556,7 +4854,8 @@ static void llm_load_vocab(
                         (t.first == "<|eot_id|>" ||
                          t.first == "<|im_end|>" ||
                          t.first == "<|end|>" ||
-                         t.first == "<end_of_turn>"
+                         t.first == "<end_of_turn>" ||
+                         t.first == "<|endoftext|>"
                         )
                    ) {
                     vocab.special_eot_id = t.second;
@@ -4568,96 +4867,88 @@ static void llm_load_vocab(
 
     // build special tokens cache
     {
-        // TODO: It is unclear (to me) at this point, whether special tokes are guaranteed to be of a deterministic type,
-        //  and will always be correctly labeled in 'added_tokens.json' etc.
-        // The assumption is, since special tokens aren't meant to be exposed to end user, they are designed
-        //  to be unmatchable by the tokenizer, therefore tokens from the vocab, which are unmatchable by the tokenizer
-        //  are special tokens.
-        // From testing, this appears to correlate 1:1 with special tokens.
-        //
-
-        // Counting special tokens and verifying in only one direction
-        //  is sufficient to detect difference in those two sets.
-        //
-        uint32_t special_tokens_count_by_type = 0;
-        uint32_t special_tokens_count_from_verification = 0;
-
-        bool special_tokens_definition_mismatch = false;
-
-        for (const auto & t : vocab.token_to_id) {
-            const auto & token = t.first;
-            const auto & id    = t.second;
-
-            // Count all non-normal tokens in the vocab while iterating
-            if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) {
-                special_tokens_count_by_type++;
-            }
-
-            // Skip single character tokens
-            if (token.length() > 1) {
-                bool is_tokenizable = false;
-
-                // Split token string representation in two, in all possible ways
-                //  and check if both halves can be matched to a valid token
-                for (unsigned i = 1; i < token.length();) {
-                    const auto left  = token.substr(0, i);
-                    const auto right = token.substr(i);
-
-                    // check if we didnt partition in the middle of a utf sequence
-                    auto utf = utf8_len(left.at(left.length() - 1));
-
-                    if (utf == 1) {
-                        if (vocab.token_to_id.find(left)  != vocab.token_to_id.end() &&
-                            vocab.token_to_id.find(right) != vocab.token_to_id.end() ) {
-                            is_tokenizable = true;
-                            break;
-                        }
-                        i++;
-                    } else {
-                        // skip over the rest of multibyte utf sequence
-                        i += utf - 1;
-                    }
-                }
-
-                if (!is_tokenizable) {
-                    // Some tokens are multibyte, but they are utf sequences with equivalent text length of 1
-                    //  it's faster to re-filter them here, since there are way less candidates now
-
-                    // Calculate a total "utf" length of a token string representation
-                    size_t utf8_str_len = 0;
-                    for (unsigned i = 0; i < token.length();) {
-                        utf8_str_len++;
-                        i += utf8_len(token.at(i));
-                    }
-
-                    // And skip the ones which are one character
-                    if (utf8_str_len > 1) {
-                        // At this point what we have left are special tokens only
-                        vocab.special_tokens_cache[token] = id;
-
-                        // Count manually found special tokens
-                        special_tokens_count_from_verification++;
-
-                        // If this manually found special token is not marked as such, flag a mismatch
-                        if (vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL) {
-                            special_tokens_definition_mismatch = true;
-                        }
-                    }
-                }
+        for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
+            if (!(vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL)) {
+                vocab.cache_special_tokens.push_back(id);
             }
         }
 
-        if (special_tokens_definition_mismatch || special_tokens_count_from_verification != special_tokens_count_by_type) {
-            LLAMA_LOG_WARN("%s: mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n",
-                __func__,
-                special_tokens_count_from_verification, vocab.id_to_token.size(),
-                special_tokens_count_by_type, vocab.id_to_token.size()
-            );
-        } else {
-            LLAMA_LOG_INFO("%s: special tokens definition check successful ( %u/%zu ).\n",
-                __func__,
-                special_tokens_count_from_verification, vocab.id_to_token.size()
-            );
+        std::sort( vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(),
+            [&] (const llama_vocab::id a, const llama_vocab::id b) {
+                return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size();
+            }
+        );
+
+        LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t)vocab.cache_special_tokens.size());
+    }
+
+    // build token to piece cache
+    {
+        size_t size_cache = 0;
+
+        std::vector<llama_vocab::token> cache_token_to_piece(n_vocab);
+
+        for (uint32_t id = 0; id < n_vocab; ++id) {
+            cache_token_to_piece[id] = llama_token_to_piece(&model, id, true);
+
+            size_cache += cache_token_to_piece[id].size();
+        }
+
+        std::swap(vocab.cache_token_to_piece, cache_token_to_piece);
+
+        LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
+    }
+
+    // Handle per token attributes
+    //NOTE: Each model customizes per token attributes.
+    //NOTE: Per token attributes are missing from the GGUF file.
+    //TODO: Extract attributes from GGUF file.
+    {
+        auto _contains_any = [] (const std::string &str, const std::vector<std::string> &substrs) -> bool {
+            for (auto substr : substrs) {
+                if (str.find(substr) < std::string::npos) {
+                    return true;
+                }
+            }
+            return false;
+        };
+
+        auto _set_tokenid_attr = [&] (const llama_vocab::id id, llama_token_attr attr, bool value) {
+            uint32_t current = vocab.id_to_token.at(id).attr;
+            current = value ? (current | attr) : (current & ~attr);
+            vocab.id_to_token[id].attr = (llama_token_attr) current;
+        };
+
+        auto _set_token_attr = [&] (const std::string & token, llama_token_attr attr, bool value) {
+            _set_tokenid_attr(vocab.token_to_id.at(token), attr, value);
+        };
+
+        std::string model_name;
+        std::string tokenizer_pre;
+
+        ml.get_key(LLM_KV_GENERAL_NAME, model_name, false);
+        ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
+
+        // model name to lowercase
+        std::transform(model_name.begin(), model_name.end(), model_name.begin(),
+            [] (const std::string::value_type x) {
+                return std::tolower(x);
+            }
+        );
+
+        // set attributes by model/tokenizer name
+        if (_contains_any(tokenizer_pre, {"jina-v2-es", "jina-v2-de"})) {
+            _set_token_attr("<mask>", LLAMA_TOKEN_ATTR_LSTRIP, true);
+        } else if (_contains_any(model_name, {"phi-3", "phi3"})) {
+            for (auto id : vocab.cache_special_tokens) {
+                _set_tokenid_attr(id, LLAMA_TOKEN_ATTR_RSTRIP, true);
+            }
+            for (auto token : {"</s>"}) {
+                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, true);
+            }
+            for (auto token : {"<unk>", "<s>", "<|endoftext|>"}) {
+                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false);
+            }
         }
     }
 }
@@ -4699,7 +4990,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
     LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
     LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
-    LLAMA_LOG_INFO("%s: n_yarn_orig_ctx  = %u\n",     __func__, hparams.n_yarn_orig_ctx);
+    LLAMA_LOG_INFO("%s: n_ctx_orig_yarn  = %u\n",     __func__, hparams.n_ctx_orig_yarn);
     LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
     LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
     LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
@@ -4739,6 +5030,16 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token        = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); }
     if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token        = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
     if (vocab.special_eot_id    != -1) { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,    vocab.id_to_token[vocab.special_eot_id].text.c_str() );    }
+
+    if (model.arch == LLM_ARCH_DEEPSEEK2) {
+        LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
+        LLAMA_LOG_INFO("%s: n_lora_q             = %d\n",     __func__, hparams.n_lora_q);
+        LLAMA_LOG_INFO("%s: n_lora_kv            = %d\n",     __func__, hparams.n_lora_kv);
+        LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
+        LLAMA_LOG_INFO("%s: n_expert_shared      = %d\n",     __func__, hparams.n_expert_shared);
+        LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
+        LLAMA_LOG_INFO("%s: rope_yarn_log_mul    = %.4f\n",   __func__, hparams.rope_yarn_log_mul);
+    }
 }
 
 // Returns false if cancelled by progress_callback
@@ -4784,13 +5085,13 @@ static bool llm_load_tensors(
 
     if (split_mode == LLAMA_SPLIT_MODE_LAYER) {
         // calculate the split points
-        int device_count = llama_get_device_count();
+        int device_count = llama_get_device_count(model);
         bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
         std::vector<float> splits(device_count);
         if (all_zero) {
             // default split, by free memory
             for (int i = 0; i < device_count; ++i) {
-                splits[i] = llama_get_device_memory(i);
+                splits[i] = llama_get_device_memory(model, i);
             }
         } else {
             std::copy(tensor_split, tensor_split + device_count, splits.begin());
@@ -4810,35 +5111,35 @@ static bool llm_load_tensors(
         int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
         for (int64_t i = i_gpu_start; i < n_layer; ++i) {
             int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
-            model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu);
+            model.buft_layer[i] = llama_default_buffer_type_offload(model, layer_gpu);
         }
         // assign the output layer
         if (n_gpu_layers > n_layer) {
             int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin();
-            model.buft_output = llama_default_buffer_type_offload(layer_gpu);
+            model.buft_output = llama_default_buffer_type_offload(model, layer_gpu);
         } else {
             model.buft_output = llama_default_buffer_type_cpu(true);
         }
     } else {
         ggml_backend_buffer_type_t split_buft;
         if (split_mode == LLAMA_SPLIT_MODE_ROW) {
-            split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
+            split_buft = llama_default_buffer_type_split(model, main_gpu, tensor_split);
         } else {
             // LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported
-            split_buft = llama_default_buffer_type_offload(main_gpu);
+            split_buft = llama_default_buffer_type_offload(model, main_gpu);
         }
         // assign the repeating layers
         for (int64_t i = i_gpu_start; i < n_layer; ++i) {
             model.buft_layer[i] = {
                 split_buft,
-                llama_default_buffer_type_offload(main_gpu)
+                llama_default_buffer_type_offload(model, main_gpu)
             };
         }
         // assign the output layer
         if (n_gpu_layers > n_layer) {
             model.buft_output = {
                 split_buft,
-                llama_default_buffer_type_offload(main_gpu)
+                llama_default_buffer_type_offload(model, main_gpu)
             };
         } else {
             model.buft_output = llama_default_buffer_type_cpu(true);
@@ -4882,6 +5183,7 @@ static bool llm_load_tensors(
     // create tensors for the weights
     {
         const int64_t n_embd       = hparams.n_embd;
+        const int64_t n_embd_head  = n_embd / hparams.n_head;
         const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
         const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
         const int64_t n_embd_gqa   = n_embd_v_gqa;
@@ -4894,8 +5196,6 @@ static bool llm_load_tensors(
             throw std::runtime_error("model has expert layers but no expert layers are used");
         }
 
-        GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
-
         ggml_context * ctx_input        = ctx_map.at(model.buft_input.buft);
         ggml_context * ctx_output       = ctx_map.at(model.buft_output.buft);
         ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix);
@@ -4915,14 +5215,10 @@ static bool llm_load_tensors(
                     // output
                     {
                         model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        if (model.arch != LLM_ARCH_MINICPM){
-                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
-                            // if output is NULL, init from the input tok embed
-                            if (model.output == NULL) {
-                                model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                                ml.n_created--; // artificial tensor
-                                ml.size_data += ggml_nbytes(model.output);
-                            }
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
                     }
 
@@ -4940,10 +5236,10 @@ static bool llm_load_tensors(
                         layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
                         // optional bias tensors
-                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     false);
-                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, false);
-                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, false);
-                        layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     false);
+                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
 
@@ -4951,10 +5247,15 @@ static bool llm_load_tensors(
                             layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                             layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                             layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+
+                            // optional MLP bias
+                            layer.ffn_gate_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                            layer.ffn_down_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                            layer.ffn_up_b   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         } else {
                             layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
 
-                            layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, false);
+                            layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
                             if (layer.ffn_gate_exps) {
                                 layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert});
                                 layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert});
@@ -4996,12 +5297,10 @@ static bool llm_load_tensors(
                     // output
                     {
                         model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         // if output is NULL, init from the input tok embed
                         if (model.output == NULL) {
-                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
                     }
 
@@ -5024,7 +5323,7 @@ static bool llm_load_tensors(
 
                         layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
 
-                        layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false);
+                        layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         if (layer.ffn_gate_exps) {
                             layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert});
                             layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert});
@@ -5126,11 +5425,9 @@ static bool llm_load_tensors(
                         model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                         model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
 
-                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         if (!model.output) {
-                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU
                         }
                     }
 
@@ -5143,8 +5440,8 @@ static bool llm_load_tensors(
                         layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                         layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
 
-                        layer.attn_norm_2   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, false);
-                        layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, false);
+                        layer.attn_norm_2   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                         layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
@@ -5162,7 +5459,12 @@ static bool llm_load_tensors(
                     {
                         model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                         model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
-                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        if (!model.output) {
+                            // needs to be on GPU
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+                        }
+
                     }
 
                     for (int i = 0; i < n_layer; ++i) {
@@ -5190,47 +5492,6 @@ static bool llm_load_tensors(
                         layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff});
                     }
                 } break;
-            case LLM_ARCH_PERSIMMON:
-                {
-                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"),  {n_embd, n_vocab});
-
-                    {
-                        model.output_norm    = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output_norm_b  = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
-                        model.output         = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
-                    }
-
-                    for (int i = 0; i < n_layer; ++i) {
-                        ggml_context * ctx_layer = ctx_for_layer(i);
-                        ggml_context * ctx_split = ctx_for_layer_split(i);
-
-                        auto & layer = model.layers[i];
-
-                        layer.attn_norm     = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd});
-                        layer.attn_norm_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "bias",   i), {n_embd});
-
-                        layer.wqkv          = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV,    "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
-                        layer.bqkv          = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV,    "bias",   i), {n_embd + 2*n_embd_gqa});
-
-                        layer.wo            = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,    "weight", i), {n_embd, n_embd});
-                        layer.bo            = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT,    "bias",   i), {n_embd});
-
-                        layer.ffn_down      = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN,    "weight", i), {n_ff, n_embd});
-                        layer.ffn_down_b    = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN,    "bias",   i), {n_embd});
-
-                        layer.ffn_up        = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,      "weight", i), {n_embd, n_ff});
-                        layer.ffn_up_b      = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,      "bias",   i), {n_ff});
-
-                        layer.ffn_norm      = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM,    "weight", i), {n_embd});
-                        layer.ffn_norm_b    = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM,    "bias",   i), {n_embd});
-
-                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64});
-                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {64});
-
-                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64});
-                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {64});
-                    }
-                } break;
             case LLM_ARCH_BERT:
             case LLM_ARCH_NOMIC_BERT:
                 {
@@ -5276,7 +5537,7 @@ static bool llm_load_tensors(
 
                             layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
                         } else {
-                            layer.ffn_gate   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                            layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
                         }
 
                         layer.layer_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
@@ -5299,14 +5560,14 @@ static bool llm_load_tensors(
                         layer.wq   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
                         layer.bq   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i),   {n_embd});
 
-                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false);
-                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false);
+                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.wk   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
                         layer.bk   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i),   {n_embd_gqa});
 
-                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false);
-                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false);
+                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.wv   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
                         layer.bv   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i),   {n_embd_gqa});
@@ -5317,6 +5578,9 @@ static bool llm_load_tensors(
                         layer.attn_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm
                         layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd});
 
+                        layer.attn_norm_2   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
                         layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff});
                         layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE,    "weight", i), {n_embd, n_ff});
 
@@ -5368,18 +5632,16 @@ static bool llm_load_tensors(
             case LLM_ARCH_MPT:
                 {
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, hparams.n_ctx_train}, false);
+                    model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, hparams.n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                     // output
                     {
                         model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, false);
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         if (!model.output) {
-                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU
                         }
                     }
 
@@ -5390,31 +5652,31 @@ static bool llm_load_tensors(
                         auto & layer = model.layers[i];
 
                         layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
-                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, false);
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
-                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, false);
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
-                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, false);
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
-                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, false);
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
-                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, false);
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
-                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, false);
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false);
-                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, false);
+                        layer.attn_q_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
-                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false);
-                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, false);
+                        layer.attn_k_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         // AWQ ScaleActivation layer
-                        layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false);
+                        layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                     }
                 } break;
             case LLM_ARCH_STABLELM:
@@ -5443,17 +5705,17 @@ static bool llm_load_tensors(
                         layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
 
                         // optional bias tensors, present in Stable LM 2 1.6B
-                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     false);
-                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, false);
-                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, false);
+                        layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         // optional q and k layernorms, present in StableLM 2 12B
-                        layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, false);
-                        layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, false);
+                        layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         // optional FFN norm, not present in StableLM 2 12B which uses parallel residual
-                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, false);
-                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, false);
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                         layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
@@ -5496,12 +5758,10 @@ static bool llm_load_tensors(
                     // output
                     {
                         model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, false);
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         // if output is NULL, init from the input tok embed
                         if (model.output == NULL) {
-                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
                     }
 
@@ -5599,8 +5859,8 @@ static bool llm_load_tensors(
                         layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
                         layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
 
-                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, false);
-                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, false);
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         if (layer.wqkv == nullptr) {
                             layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
@@ -5637,17 +5897,20 @@ static bool llm_load_tensors(
                         ggml_context* ctx_layer = ctx_for_layer(i);
                         ggml_context* ctx_split = ctx_for_layer_split(i);
 
-                        auto& layer = model.layers[i];
+                        auto & layer = model.layers[i];
 
                         layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd });
 
-                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, false);
-                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd });
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd });
 
                         layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd });
 
                         layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd });
                         layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff });
+
+                        layer.rope_long  = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+                        layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                     }
                 } break;
             case LLM_ARCH_PLAMO:
@@ -5816,9 +6079,7 @@ static bool llm_load_tensors(
 
                     // output
                     model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                    model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading
-                    ml.n_created--; // artificial tensor
-                    ml.size_data += ggml_nbytes(model.output);
+                    model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
 
                     const int64_t n_ff          = hparams.n_ff;
                     const int64_t n_embd_head_k = hparams.n_embd_head_k;
@@ -5853,12 +6114,10 @@ static bool llm_load_tensors(
                         model.output_norm   = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                         model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
 
-                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         // if output is NULL, init from the input tok embed
                         if (model.output == NULL) {
-                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
 
                     }
@@ -5909,12 +6168,10 @@ static bool llm_load_tensors(
                     {
                         model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
 
-                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         // if output is NULL, init from the input tok embed, duplicated to allow offloading
                         if (model.output == NULL) {
-                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
                     }
 
@@ -5975,9 +6232,7 @@ static bool llm_load_tensors(
                     {
                         model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                         // init output from the input tok embed
-                        model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                        ml.n_created--; // artificial tensor
-                        ml.size_data += ggml_nbytes(model.output);
+                        model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                     }
 
                     for (int i = 0; i < n_layer; ++i) {
@@ -6009,12 +6264,10 @@ static bool llm_load_tensors(
 
                     // output
                     {
-                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         // if output is NULL, init from the input tok embed
                         if (model.output == NULL) {
-                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
-                            ml.n_created--; // artificial tensor
-                            ml.size_data += ggml_nbytes(model.output);
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
                     }
 
@@ -6034,6 +6287,145 @@ static bool llm_load_tensors(
                         layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                     }
                 } break;
+            case LLM_ARCH_GPTNEOX:
+                {
+                    model.tok_embd   = ml.create_tensor(ctx_input,  tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab});
+                    // output
+                    {
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd});
+
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});
+
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
+
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
+
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
+
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
+                    }
+                } break;
+            case LLM_ARCH_ARCTIC:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+                        }
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_embd});
+
+                        layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+                        layer.ffn_norm_exps = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd});
+                        layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, false);
+                        layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert});
+                        layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert});
+                    }
+                } break;
+            case LLM_ARCH_DEEPSEEK2:
+                {
+                    bool is_lite = (hparams.n_layer == 27);
+
+                    const uint32_t n_embd_head_qk_rope = hparams.n_rot;
+                    const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+                    const uint32_t q_lora_rank = hparams.n_lora_q;
+                    const uint32_t kv_lora_rank = hparams.n_lora_kv;
+                    const uint32_t n_ff_exp = hparams.n_ff_exp;
+
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        if (!is_lite) {
+                            layer.attn_q_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank});
+                        }
+                        layer.attn_kv_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank});
+
+                        if (!is_lite) {
+                            layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A,   "weight", i), {n_embd, q_lora_rank});
+                            layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B,   "weight", i), {q_lora_rank, hparams.n_head * hparams.n_embd_head_k});
+                        } else {
+                            layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa});
+                        }
+                        layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA,   "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope});
+                        layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B,   "weight", i), {kv_lora_rank, hparams.n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {hparams.n_head * hparams.n_embd_head_v, n_embd});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+                        if ((uint32_t) i < hparams.n_layer_dense_lead) {
+                            layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                            layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                            layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                        } else {
+                            layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+
+                            GGML_ASSERT(hparams.n_expert      > 0);
+                            GGML_ASSERT(hparams.n_expert_used > 0);
+
+                            // MoE branch
+                            layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert});
+                            layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert});
+                            layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert});
+
+                            // Shared expert branch
+                            layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd,   n_ff_exp * hparams.n_expert_shared});
+                            layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {  n_ff_exp * hparams.n_expert_shared, n_embd});
+                            layer.ffn_up_shexp   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd,   n_ff_exp * hparams.n_expert_shared});
+                        }
+                    }
+                } break;
             default:
                 throw std::runtime_error("unknown architecture");
         }
@@ -6233,16 +6625,6 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
         }
 #endif
 
-#ifdef GGML_USE_SYCL
-        if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
-            ggml_backend_sycl_set_single_device_mode(params.main_gpu);
-            //SYCL use device index (0, 1, 2) directly, uer input device id, then convert to device index.
-            params.main_gpu = ggml_backend_sycl_get_device_index(params.main_gpu);
-        } else {
-            ggml_backend_sycl_set_mul_device_mode();
-        }
-#endif
-
         if (!llm_load_tensors(
             ml, model, params.n_gpu_layers, params.split_mode,  params.main_gpu, params.tensor_split, params.use_mlock,
             params.progress_callback, params.progress_callback_user_data
@@ -6298,10 +6680,7 @@ static struct ggml_tensor * llm_build_inp_embd(
 
         inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
     } else {
-#ifdef GGML_USE_MPI
-        GGML_ASSERT(false && "not implemented");
-#endif
-        lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
+       lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
         inpL = lctx.inp_embd;
         ggml_set_input(lctx.inp_embd);
     }
@@ -6491,6 +6870,8 @@ static struct ggml_tensor * llm_build_moe_ffn(
                     int64_t   n_expert_used,
             llm_ffn_op_type   type_op,
                        bool   norm_w,
+                       bool   scale_w,
+                      float   w_scale,
          const llm_build_cb & cb,
                         int   il) {
     int64_t n_embd = cur->ne[0];
@@ -6522,6 +6903,10 @@ static struct ggml_tensor * llm_build_moe_ffn(
 
         weights = ggml_reshape_3d(ctx, weights, 1, n_expert_used, n_tokens);
     }
+    if (scale_w) {
+        weights = ggml_scale(ctx, weights, w_scale);
+        cb(weights, "ffn_moe_weights_scaled", il);
+    }
 
     cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens);
     ggml_tensor * up = ggml_mul_mat_id(ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
@@ -6596,6 +6981,7 @@ static struct ggml_tensor * llm_build_kqv(
     const int64_t n_embd_head_k = hparams.n_embd_head_k;
     const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
     const int64_t n_embd_head_v = hparams.n_embd_head_v;
+    const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
 
     struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
     cb(q, "q", il);
@@ -6618,23 +7004,23 @@ static struct ggml_tensor * llm_build_kqv(
         struct ggml_tensor * v =
             ggml_view_3d(ctx, kv.v_l[il],
                     n_embd_head_v, n_kv, n_head_kv,
-                    ggml_row_size(kv.v_l[il]->type, n_embd_k_gqa),
-                    ggml_row_size(kv.v_l[il]->type, n_embd_head_k),
+                    ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa),
+                    ggml_row_size(kv.v_l[il]->type, n_embd_head_v),
                     0);
         cb(v, "v", il);
 
         cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias);
 
-        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) {
+        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) {
             ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
         }
 
-        cur = ggml_reshape_2d(ctx, cur, n_embd_head_k*n_head, n_tokens);
+        cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens);
     } else {
         struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
         cb(kq, "kq", il);
 
-        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) {
+        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) {
             // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
             // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
             ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
@@ -6674,7 +7060,7 @@ static struct ggml_tensor * llm_build_kqv(
         struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
         cb(kqv_merged, "kqv_merged", il);
 
-        cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens);
+        cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_v*n_head, n_tokens);
         cb(cur, "kqv_merged_cont", il);
     }
 
@@ -6763,7 +7149,7 @@ struct llm_build_context {
     const int32_t n_kv;     // size of KV cache to consider (n_kv <= kv_self.size)
     const int32_t n_outputs;
     const int32_t kv_head;  // index of where we store new KV data in the cache
-    const int32_t n_orig_ctx;
+    const int32_t n_ctx_orig;
 
     const bool flash_attn;
 
@@ -6812,7 +7198,7 @@ struct llm_build_context {
         n_kv             (worst_case ? kv_self.size : kv_self.n),
         n_outputs        (worst_case ? n_tokens : lctx.n_outputs),
         kv_head          (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head),
-        n_orig_ctx       (cparams.n_yarn_orig_ctx),
+        n_ctx_orig       (cparams.n_ctx_orig_yarn),
         flash_attn       (cparams.flash_attn),
         pooling_type     (cparams.pooling_type),
         rope_type        (hparams.rope_type),
@@ -6859,17 +7245,20 @@ struct llm_build_context {
         cb(lctx.inp_K_shift, "K_shift", -1);
         ggml_set_input(lctx.inp_K_shift);
 
+
         for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * rope_factors = build_rope_factors(il);
             struct ggml_tensor * tmp =
                 // we rotate only the first n_rot dimensions
-                ggml_rope_custom_inplace(ctx0,
+                ggml_rope_ext_inplace(ctx0,
                         ggml_view_3d(ctx0, kv_self.k_l[il],
                             n_embd_head_k, n_head_kv, n_ctx,
                             ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
                             ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
                             0),
-                        lctx.inp_K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        lctx.inp_K_shift, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
+
             cb(tmp, "K_shifted", il);
             ggml_build_forward_expand(gf, tmp);
         }
@@ -6972,6 +7361,17 @@ struct llm_build_context {
         return lctx.inp_pos;
     }
 
+    struct ggml_tensor * build_rope_factors(int il) {
+        // choose long/short freq factors based on the context size
+        const auto n_ctx_pre_seq = cparams.n_ctx / cparams.n_seq_max;
+
+        if (n_ctx_pre_seq > hparams.n_ctx_orig_yarn) {
+            return model.layers[il].rope_long;
+        }
+
+        return model.layers[il].rope_short;
+    }
+
     struct ggml_tensor * build_inp_out_ids() {
         lctx.inp_out_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs);
         cb(lctx.inp_out_ids, "inp_out_ids", -1);
@@ -7079,16 +7479,16 @@ struct llm_build_context {
                     cb(Vcur, "Vcur", il);
                 }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -7117,9 +7517,9 @@ struct llm_build_context {
                 cb(cur, "ffn_norm", il);
 
                 cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                         NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
@@ -7137,6 +7537,7 @@ struct llm_build_context {
                         model.layers[il].ffn_down_exps,
                         n_expert, n_expert_used,
                         LLM_FFN_SILU, true,
+                        false, 0.0,
                         cb, il);
                 cb(cur, "ffn_moe_out", il);
             }
@@ -7209,14 +7610,14 @@ struct llm_build_context {
 
                 switch (model.type) {
                     case MODEL_7B:
-                        Qcur = ggml_rope_custom(
-                            ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                            n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        Qcur = ggml_rope_ext(
+                            ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                             ext_factor, attn_factor, beta_fast, beta_slow
                         );
-                        Kcur = ggml_rope_custom(
-                            ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                            n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        Kcur = ggml_rope_ext(
+                            ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                             ext_factor, attn_factor, beta_fast, beta_slow
                         );
                         break;
@@ -7321,16 +7722,16 @@ struct llm_build_context {
                 struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -7442,14 +7843,14 @@ struct llm_build_context {
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
                 // using mode = 2 for neox mode
-                Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -7565,16 +7966,16 @@ struct llm_build_context {
                     cb(Vcur, "Vcur", il);
                 }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -7618,6 +8019,7 @@ struct llm_build_context {
                     model.layers[il].ffn_down_exps,
                     n_expert, n_expert_used,
                     LLM_FFN_GELU, true,
+                    false, 0.0,
                     cb, il);
             cb(cur, "ffn_moe_out", il);
 
@@ -7717,16 +8119,16 @@ struct llm_build_context {
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -7761,6 +8163,7 @@ struct llm_build_context {
                     model.layers[il].ffn_down_exps,
                     n_expert, n_expert_used,
                     LLM_FFN_SILU, true,
+                    false, 0.0,
                     cb, il);
             cb(cur, "ffn_moe_out", il);
 
@@ -7894,213 +8297,6 @@ struct llm_build_context {
         return gf;
     }
 
-    struct ggml_cgraph * build_persimmon() {
-        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
-
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        GGML_ASSERT(n_embd_head   == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head/2 == hparams.n_rot);
-
-        struct ggml_tensor * cur;
-        struct ggml_tensor * inpL;
-
-        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
-
-        // inp_pos - contains the positions
-        struct ggml_tensor * inp_pos = build_inp_pos();
-
-        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
-
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * residual = inpL;
-
-            cur = llm_build_norm(ctx0, inpL, hparams,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, cb, il);
-            cb(cur, "attn_norm", il);
-
-            // self attention
-            {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                cb(cur, "bqkv", il);
-
-                // split qkv
-                GGML_ASSERT(n_head_kv == n_head);
-
-                struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
-                cb(tmpqkv, "tmpqkv", il);
-
-                struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
-                cb(tmpqkv_perm, "tmpqkv", il);
-
-                struct ggml_tensor * tmpq = ggml_view_3d(
-                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
-                        0
-                        );
-                cb(tmpq, "tmpq", il);
-
-                struct ggml_tensor * tmpk = ggml_view_3d(
-                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
-                        );
-                cb(tmpk, "tmpk", il);
-
-                // Q/K Layernorm
-                tmpq = llm_build_norm(ctx0, tmpq, hparams,
-                        model.layers[il].attn_q_norm,
-                        model.layers[il].attn_q_norm_b,
-                        LLM_NORM, cb, il);
-                cb(tmpq, "tmpq", il);
-
-                tmpk = llm_build_norm(ctx0, tmpk, hparams,
-                        model.layers[il].attn_k_norm,
-                        model.layers[il].attn_k_norm_b,
-                        LLM_NORM, cb, il);
-                cb(tmpk, "tmpk", il);
-
-                // RoPE the first n_rot of q/k, pass the other half, and concat.
-                struct ggml_tensor * qrot = ggml_view_3d(
-                        ctx0, tmpq, n_rot, n_head, n_tokens,
-                        ggml_element_size(tmpq) * n_embd_head,
-                        ggml_element_size(tmpq) * n_embd_head * n_head,
-                        0
-                        );
-                cb(qrot, "qrot", il);
-
-                struct ggml_tensor * krot = ggml_view_3d(
-                        ctx0, tmpk, n_rot, n_head, n_tokens,
-                        ggml_element_size(tmpk) * n_embd_head,
-                        ggml_element_size(tmpk) * n_embd_head * n_head,
-                        0
-                        );
-                cb(krot, "krot", il);
-
-                // get the second half of tmpq, e.g tmpq[n_rot:, :, :]
-                struct ggml_tensor * qpass = ggml_view_3d(
-                        ctx0, tmpq, n_rot, n_head, n_tokens,
-                        ggml_element_size(tmpq) * n_embd_head,
-                        ggml_element_size(tmpq) * n_embd_head * n_head,
-                        ggml_element_size(tmpq) * n_rot
-                        );
-                cb(qpass, "qpass", il);
-
-                struct ggml_tensor * kpass = ggml_view_3d(
-                        ctx0, tmpk, n_rot, n_head, n_tokens,
-                        ggml_element_size(tmpk) * n_embd_head,
-                        ggml_element_size(tmpk) * n_embd_head * n_head,
-                        ggml_element_size(tmpk) * n_rot
-                        );
-                cb(kpass, "kpass", il);
-
-                struct ggml_tensor * qrotated = ggml_rope_custom(
-                    ctx0, qrot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
-                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(qrotated, "qrotated", il);
-
-                struct ggml_tensor * krotated = ggml_rope_custom(
-                    ctx0, krot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
-                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(krotated, "krotated", il);
-
-                // ggml currently only supports concatenation on dim=2
-                // so we need to permute qrot, qpass, concat, then permute back.
-                qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
-                cb(qrotated, "qrotated", il);
-
-                krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
-                cb(krotated, "krotated", il);
-
-                qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
-                cb(qpass, "qpass", il);
-
-                kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
-                cb(kpass, "kpass", il);
-
-                struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
-                cb(Qcur, "Qcur", il);
-
-                struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
-                cb(Kcur, "Kcur", il);
-
-                struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3));
-                cb(Q, "Q", il);
-
-                Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
-                cb(Kcur, "Kcur", il);
-
-                struct ggml_tensor * Vcur = ggml_view_3d(
-                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
-                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
-                        );
-                cb(Vcur, "Vcur", il);
-
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Q, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
-            }
-
-            if (il == n_layer - 1) {
-                // skip computing output for unused tokens
-                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-                cur      = ggml_get_rows(ctx0,      cur, inp_out_ids);
-                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
-            }
-
-            struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            {
-                cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                        model.layers[il].ffn_norm,
-                        model.layers[il].ffn_norm_b,
-                        LLM_NORM, cb, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
-                        NULL,                      NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
-                        NULL,
-                        LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "l_out", il);
-
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = llm_build_norm(ctx0, cur, hparams,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, cb, -1);
-        cb(cur, "result_norm", -1);
-
-        cur = ggml_mul_mat(ctx0, model.output, cur);
-        cb(cur, "result_output", -1);
-
-        ggml_build_forward_expand(gf, cur);
-
-        return gf;
-    }
-
     struct ggml_cgraph * build_refact() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
@@ -8277,16 +8473,16 @@ struct llm_build_context {
                 cb(Kcur, "Kcur", il);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -8338,6 +8534,11 @@ struct llm_build_context {
             // attention layer norm
             cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il);
 
+            if (model.layers[il].attn_norm_2 != nullptr) {
+                cur = ggml_add(ctx0, cur, inpL); // re-add the layer input
+                cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_norm_2, model.layers[il].attn_norm_2_b, LLM_NORM, cb, il);
+            }
+
             struct ggml_tensor * ffn_inp = cur;
             cb(ffn_inp, "ffn_inp", il);
 
@@ -8717,16 +8918,16 @@ struct llm_build_context {
                 }
 
 
-                Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -8837,14 +9038,14 @@ struct llm_build_context {
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
                 // using mode = 2 for neox mode
-                Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -8948,16 +9149,16 @@ struct llm_build_context {
                 Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -9062,16 +9263,16 @@ struct llm_build_context {
                 Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -9106,6 +9307,7 @@ struct llm_build_context {
                         model.layers[il].ffn_down_exps,
                         n_expert, n_expert_used,
                         LLM_FFN_SILU, false,
+                        false, 0.0,
                         cb, il);
             cb(cur, "ffn_moe_out", il);
 
@@ -9214,8 +9416,8 @@ struct llm_build_context {
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
@@ -9225,8 +9427,8 @@ struct llm_build_context {
                 Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head)));
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -9302,6 +9504,9 @@ struct llm_build_context {
 
             // self-attention
             {
+                // rope freq factors for 128k context
+                struct ggml_tensor * rope_factors = build_rope_factors(il);
+
                 struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
                     model.layers[il].attn_norm,
                     NULL,
@@ -9333,8 +9538,8 @@ struct llm_build_context {
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
-                Qcur = ggml_rope_custom(
-                    ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
@@ -9342,8 +9547,8 @@ struct llm_build_context {
                 Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
+                Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -9449,15 +9654,15 @@ struct llm_build_context {
                 struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head,    n_tokens), inp_pos,
-                        n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head,    n_tokens), inp_pos, nullptr,
+                        n_embd_head, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos,
-                        n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos, nullptr,
+                        n_embd_head, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur", il);
 
@@ -9657,16 +9862,16 @@ struct llm_build_context {
                 cb(tmpk, "tmpk", il);
                 cb(Vcur, "Vcur", il);
 
-                struct ggml_tensor * Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                struct ggml_tensor * Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                struct ggml_tensor * Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -9773,16 +9978,16 @@ struct llm_build_context {
                 //     cb(Vcur, "Vcur", il);
                 // }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -9890,16 +10095,16 @@ struct llm_build_context {
                     cb(Vcur, "Vcur", il);
                 }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -10020,16 +10225,16 @@ struct llm_build_context {
                     cb(Vcur, "Vcur", il);
                 }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -10094,7 +10299,7 @@ struct llm_build_context {
         cb(cur, "lmhead_scaling", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
+        cur = ggml_mul_mat(ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10140,18 +10345,18 @@ struct llm_build_context {
                 struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
-                Qcur = ggml_rope_custom(
-                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos,
-                        n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos, nullptr,
+                        n_embd_head_k, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Qcur, "Qcur", il);
 
                 Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));
                 cb(Qcur, "Qcur_scaled", il);
 
-                Kcur = ggml_rope_custom(
-                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos,
-                        n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr,
+                        n_embd_head_k, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur", il);
 
@@ -10260,16 +10465,16 @@ struct llm_build_context {
                     cb(Vcur, "Vcur", il);
                 }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -10550,16 +10755,16 @@ struct llm_build_context {
                     cb(Kcur, "Kcur", il);
                 }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -10681,16 +10886,16 @@ struct llm_build_context {
                     cb(Vcur, "Vcur", il);
                 }
 
-                Qcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
-                Kcur = ggml_rope_custom(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -10753,6 +10958,508 @@ struct llm_build_context {
 
         return gf;
     }
+
+    struct ggml_cgraph * build_gptneox() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
+
+                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+
+            // ffn
+            if (hparams.use_par_res) {
+                // attention and ffn are computed in parallel
+                // x = x + attn(ln1(x)) + ffn(ln2(x))
+
+                struct ggml_tensor * attn_out = cur;
+
+                cur = llm_build_norm(ctx0, inpL, hparams,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                        NULL,                      NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+                cb(cur, "ffn_out", il);
+
+                cur = ggml_add(ctx0, cur, inpL);
+                cb(cur, "ffn_out", il);
+
+                inpL = ggml_add(ctx0, cur, attn_out);
+                cb(inpL, "l_out", il);
+            } else {
+                // attention and ffn are computed sequentially
+                // x = x + attn(ln1(x))
+                // x = x + ffn(ln2(x))
+
+                struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+                cb(ffn_inp, "ffn_inp", il);
+
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                        NULL,                      NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+                cb(cur, "ffn_out", il);
+
+                inpL = ggml_add(ctx0, cur, ffn_inp);
+                cb(inpL, "l_out", il);
+            }
+        }
+
+        cur = llm_build_norm(ctx0, inpL, hparams,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_arctic() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   NULL,
+                    model.layers[il].ffn_gate, NULL,
+                    model.layers[il].ffn_down, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_out", il);
+
+            struct ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp);
+            cb(ffn_out, "ffn_out", il);
+
+            // MoE
+            cur = llm_build_norm(ctx0, inpSA, hparams,
+                    model.layers[il].ffn_norm_exps, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm_exps", il);
+
+            cur = llm_build_moe_ffn(ctx0, cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, true,
+                    false, 0.0,
+                    cb, il);
+            cb(cur, "ffn_moe_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_out);
+            cb(cur, "ffn_out", il);
+
+            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+            if (layer_dir != nullptr) {
+                cur = ggml_add(ctx0, cur, layer_dir);
+            }
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_deepseek2() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        bool is_lite = (hparams.n_layer == 27);
+
+        // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
+        // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
+        const float mscale = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
+        const float kq_scale = 1.0f*mscale*mscale/sqrtf(float(hparams.n_embd_head_k));
+        const float attn_factor_scaled = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale));
+
+        const uint32_t n_embd_head_qk_rope = hparams.n_rot;
+        const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+        const uint32_t kv_lora_rank = hparams.n_lora_kv;
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        // {n_embd, n_tokens}
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                struct ggml_tensor * q = NULL;
+                if (!is_lite) {
+                    // {n_embd, q_lora_rank} * {n_embd, n_tokens} -> {q_lora_rank, n_tokens}
+                    q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
+                    cb(q, "q", il);
+
+                    q = llm_build_norm(ctx0, q, hparams,
+                            model.layers[il].attn_q_a_norm, NULL,
+                            LLM_NORM_RMS, cb, il);
+                    cb(q, "q", il);
+
+                    // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens}
+                    q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
+                    cb(q, "q", il);
+                } else {
+                    q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                    cb(q, "q", il);
+                }
+
+                // split into {n_head * n_embd_head_qk_nope, n_tokens}
+                struct ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
+                        ggml_row_size(q->type, hparams.n_embd_head_k),
+                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+                        0);
+                cb(q_nope, "q_nope", il);
+
+                // and {n_head * n_embd_head_qk_rope, n_tokens}
+                struct ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
+                        ggml_row_size(q->type, hparams.n_embd_head_k),
+                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+                        ggml_row_size(q->type, n_embd_head_qk_nope));
+                cb(q_pe, "q_pe", il);
+
+                // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
+                struct ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
+                cb(kv_pe_compresseed, "kv_pe_compresseed", il);
+
+                // split into {kv_lora_rank, n_tokens}
+                struct ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,
+                        kv_pe_compresseed->nb[1],
+                        0);
+                cb(kv_compressed, "kv_compressed", il);
+
+                // and {n_embd_head_qk_rope, n_tokens}
+                struct ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,
+                        kv_pe_compresseed->nb[1],
+                        kv_pe_compresseed->nb[1],
+                        ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
+                cb(k_pe, "k_pe", il);
+
+                kv_compressed = ggml_cont(ctx0, kv_compressed); // TODO: the CUDA backend does not support non-contiguous norm
+                kv_compressed = llm_build_norm(ctx0, kv_compressed, hparams,
+                        model.layers[il].attn_kv_a_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(kv_compressed, "kv_compressed", il);
+
+                // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}
+                struct ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);
+                cb(kv, "kv", il);
+
+                // split into {n_head * n_embd_head_qk_nope, n_tokens}
+                struct ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
+                        ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
+                        ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+                        0);
+                cb(k_nope, "k_nope", il);
+
+                // and {n_head * n_embd_head_v, n_tokens}
+                struct ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens,
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head),
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope)));
+                cb(v_states, "v_states", il);
+
+                v_states = ggml_cont(ctx0, v_states);
+                cb(v_states, "v_states", il);
+
+                v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens,
+                    ggml_row_size(kv->type, hparams.n_embd_head_v * n_head),
+                    0);
+                cb(v_states, "v_states", il);
+
+                q_pe = ggml_cont(ctx0, q_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
+                q_pe = ggml_rope_ext(
+                    ctx0, q_pe, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor_scaled, beta_fast, beta_slow
+                );
+                cb(q_pe, "q_pe", il);
+
+                // shared RoPE key
+                k_pe = ggml_cont(ctx0, k_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
+                k_pe = ggml_rope_ext(
+                    ctx0, k_pe, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor_scaled, beta_fast, beta_slow
+                );
+                cb(k_pe, "k_pe", il);
+
+                struct ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
+                cb(q_states, "q_states", il);
+
+                struct ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
+                cb(k_states, "k_states", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        k_states, v_states, q_states, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            if ((uint32_t) il < hparams.n_layer_dense_lead) {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            } else {
+                // MoE branch
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                ggml_tensor * moe_out =
+                        llm_build_moe_ffn(ctx0, cur,
+                            model.layers[il].ffn_gate_inp,
+                            model.layers[il].ffn_up_exps,
+                            model.layers[il].ffn_gate_exps,
+                            model.layers[il].ffn_down_exps,
+                            n_expert, n_expert_used,
+                            LLM_FFN_SILU, false,
+                            true, hparams.expert_weights_scale,
+                            cb, il);
+                cb(moe_out, "ffn_moe_out", il);
+
+                // FFN shared expert
+                {
+                    ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, cur,
+                            model.layers[il].ffn_up_shexp,   NULL,
+                            model.layers[il].ffn_gate_shexp, NULL,
+                            model.layers[il].ffn_down_shexp, NULL,
+                            NULL,
+                            LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                    cb(ffn_shexp, "ffn_shexp", il);
+
+                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                    cb(cur, "ffn_out", il);
+                }
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
 };
 
 static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
@@ -10833,7 +11540,8 @@ static struct ggml_cgraph * llama_build_graph(
         if (batch.n_tokens < 32 || full_offload) {
             if (il != -1 && strcmp(name, "norm") == 0) {
                 for (auto * backend : lctx.backends) {
-                    if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) {
+                    if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft) &&
+                        (ggml_backend_supports_op(backend, cur) || ggml_backend_offload_op(backend, cur))) {
                         ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend);
                         break;
                     }
@@ -10869,10 +11577,6 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_starcoder();
             } break;
-        case LLM_ARCH_PERSIMMON:
-            {
-                result = llm.build_persimmon();
-            } break;
         case LLM_ARCH_REFACT:
             {
                 result = llm.build_refact();
@@ -10967,6 +11671,18 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_olmo();
             } break;
+        case LLM_ARCH_GPTNEOX:
+            {
+                result = llm.build_gptneox();
+            } break;
+        case LLM_ARCH_ARCTIC:
+            {
+                result = llm.build_arctic();
+            } break;
+        case LLM_ARCH_DEEPSEEK2:
+            {
+                result = llm.build_deepseek2();
+            } break;
         default:
             GGML_ASSERT(false);
     }
@@ -11312,11 +12028,6 @@ static void llama_graph_compute(
         llama_context & lctx,
           ggml_cgraph * gf,
                   int   n_threads) {
-#ifdef GGML_USE_MPI
-    const int64_t n_layer = lctx.model.hparams.n_layer;
-    ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
-#endif
-
 #ifdef GGML_USE_METAL
     if (ggml_backend_is_metal(lctx.backend_metal)) {
         ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads);
@@ -11327,14 +12038,15 @@ static void llama_graph_compute(
         ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
         ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data);
     }
+#ifdef GGML_USE_BLAS
+    if (lctx.backend_blas != nullptr) {
+        ggml_backend_blas_set_n_threads(lctx.backend_blas, n_threads);
+    }
+#endif
 
     ggml_backend_sched_graph_compute_async(lctx.sched, gf);
 
     // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
-
-#ifdef GGML_USE_MPI
-    ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
-#endif
 }
 
 // decode a batch of tokens by evaluating the transformer
@@ -11372,12 +12084,6 @@ static int llama_decode_internal(
     }
     lctx.n_queued_tokens += n_tokens_all;
 
-#ifdef GGML_USE_MPI
-    // TODO: needs fix after #3228
-    GGML_ASSERT(false && "not implemented");
-    //ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
-#endif
-
     auto & kv_self = lctx.kv_self;
 
     const int64_t n_embd  = hparams.n_embd;
@@ -11508,7 +12214,8 @@ static int llama_decode_internal(
                 // a heuristic, to avoid attending the full cache if it is not yet utilized
                 // after enough generations, the benefit from this heuristic disappears
                 // if we start defragmenting the cache, the benefit from this will be more important
-                kv_self.n = std::min(kv_self.size, std::max(256u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 256)));
+                const uint32_t pad = llama_kv_cache_get_padding(cparams);
+                kv_self.n = std::min(kv_self.size, std::max(pad, GGML_PAD(llama_kv_cache_cell_max(kv_self), pad)));
                 //kv_self.n = llama_kv_cache_cell_max(kv_self);
             }
         }
@@ -11558,17 +12265,6 @@ static int llama_decode_internal(
         }
         // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
 
-        // for big prompts, if BLAS is enabled, it is better to use only one thread
-        // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
-        // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
-        //       we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
-        //       with the BLAS calls. need a better solution
-        // MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is
-        //                   being processed then Accelerate/BLAS will not be involved, so capping would limit performance.
-        if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
-            n_threads = std::min(4, n_threads);
-        }
-
         ggml_backend_sched_alloc_graph(lctx.sched, gf);
 
         llama_set_inputs(lctx, u_batch);
@@ -11997,27 +12693,27 @@ static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
 
 static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
     GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL;
 }
 
 static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
     GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNKNOWN;
 }
 
 static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
     GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_CONTROL;
 }
 
 static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
     GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_BYTE;
 }
 
 static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
     GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_USER_DEFINED;
 }
 
 static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
@@ -12270,6 +12966,7 @@ struct llm_tokenizer_bpe {
                         });
                         break;
                     case LLAMA_VOCAB_PRE_TYPE_DBRX:
+                    case LLAMA_VOCAB_PRE_TYPE_SMAUG:
                         word_collection = unicode_regex_split(text, {
                             // same as llama3
                             "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
@@ -12326,6 +13023,7 @@ struct llm_tokenizer_bpe {
                             "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
                         });
                         break;
+                    case LLAMA_VOCAB_PRE_TYPE_STABLELM2:
                     case LLAMA_VOCAB_PRE_TYPE_QWEN2:
                         word_collection = unicode_regex_split(text, {
                             // original regex from tokenizer.json
@@ -12333,6 +13031,11 @@ struct llm_tokenizer_bpe {
                             "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
                         });
                         break;
+                    case LLAMA_VOCAB_PRE_TYPE_PORO:
+                        word_collection = unicode_regex_split(text, {
+                            " ?[^(\\s|.,!?…。，、।۔،)]+",
+                        });
+                        break;
                     default:
                         // default regex for BPE tokenization pre-processing
                         word_collection = unicode_regex_split(text, {
@@ -12491,7 +13194,7 @@ struct llm_tokenizer_wpm {
     llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {}
 
     void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
-        auto * token_map = &vocab.token_to_id;
+        const auto & token_map = vocab.token_to_id;
 
         // normalize and split by whitespace
         std::vector<std::string> words = preprocess(text);
@@ -12506,108 +13209,89 @@ struct llm_tokenizer_wpm {
             }
 
             // prepend phantom space
-            std::string word1 = "\xe2\x96\x81" + word;
-            int n = word1.size();
+            const std::string word1 = "\xe2\x96\x81" + word;
+            const int n = word1.size();
+
+            const size_t current_tokens = output.size();
 
             // we're at the start of a new word
-            int i = 0;
-            bool match_any = false;
-
             // move through character position in word
-            while (i < n) {
+            for (int i = 0; i < n; ++i) {
                 // loop through possible match length
                 bool match = false;
                 for (int j = n; j > i; j--) {
-                    auto it = token_map->find(word1.substr(i, j - i));
-                    if (it != token_map->end()) {
+                    auto it = token_map.find(word1.substr(i, j - i));
+                    if (it != token_map.end()) {
                         output.push_back(it->second);
                         match = true;
-                        match_any = true;
-                        i = j;
+                        i = j - 1;
                         break;
                     }
                 }
 
-                // must be an unknown character
-                if (!match) {
-                    i++;
+                if (!match) { // discard all
+                    output.resize(current_tokens);
+                    break;  // and discard next tokens
                 }
             }
 
             // we didn't find any matches for this word
-            if (!match_any) {
+            if (current_tokens == output.size()) {
                 output.push_back(vocab.special_unk_id);
             }
         }
     }
 
     std::vector<std::string> preprocess(const std::string & text) {
-        std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
+        const std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
+        std::vector<std::string> words(1, "");
 
-        // strip accents, strip control, uniformize whitespace,
-        // to lowercase, pad chinese characters, pad punctuation
-        std::string new_str = "";
-        for (uint32_t code : cpts_nfd) {
-            int type = unicode_cpt_type(code);
-            if (type == CODEPOINT_TYPE_ACCENT_MARK || type == CODEPOINT_TYPE_CONTROL) {
+        for (const char32_t cpt : cpts_nfd) {
+            const auto flags = unicode_cpt_flags(cpt);
+
+            if (flags.is_whitespace) {
+                if (words.back().size()) {  // finish previous word if any
+                    words.emplace_back();
+                }
                 continue;
             }
-            code = unicode_tolower(code);
-            if (type == CODEPOINT_TYPE_SEPARATOR) {
-                code = ' ';
+
+            assert (!flags.is_separator);
+            if (cpt == 0 || cpt == 0xFFFD || flags.is_control) {
+                continue;
             }
-            std::string s = unicode_cpt_to_utf8(code);
-            if (type == CODEPOINT_TYPE_PUNCTUATION || is_ascii_punct(code) || is_chinese_char(code)) {
-                new_str += " ";
-                new_str += s;
-                new_str += " ";
+
+            const std::string s = unicode_cpt_to_utf8(unicode_tolower(cpt));
+            if (flags.is_punctuation || ( cpt < 0x7F && flags.is_symbol ) || is_chinese_char(cpt)) {
+                if (words.back().size()) {  // finish previous word if any
+                    words.emplace_back();
+                }
+                words.back() = s;       // single char word
+                words.emplace_back();   // start a new word
             } else {
-                new_str += s;
+                words.back() += s;  // append char to word
             }
         }
 
-        // split by whitespace
-        uint64_t l = 0;
-        uint64_t r = 0;
-        std::vector<std::string> words;
-        while (r < new_str.size()) {
-            // if is whitespace
-            if (isspace(new_str[r], std::locale::classic())) {
-                if (r > l) words.push_back(new_str.substr(l, (r - l)));
-                l = r + 1;
-                r = l;
-            } else {
-                r += 1;
-            }
-        }
-        if (r > l) {
-            words.push_back(new_str.substr(l, (r - l)));
+        if (!words.back().size()) {
+            words.pop_back();
         }
+
         return words;
     }
 
-    bool is_ascii_punct(uint32_t code) {
-        if (code > 0xFF) {
-            return false;
-        }
-        auto c = char(static_cast<unsigned char>(code));
-        return ispunct(c, std::locale::classic());
-    }
-
-    bool is_chinese_char(uint32_t cpt) {
-        if ((cpt >= 0x4E00  && cpt <= 0x9FFF)  ||
-            (cpt >= 0x3400  && cpt <= 0x4DBF)  ||
+    static bool is_chinese_char(uint32_t cpt) {
+        return
+            (cpt >= 0x04E00 && cpt <= 0x09FFF) ||
+            (cpt >= 0x03400 && cpt <= 0x04DBF) ||
             (cpt >= 0x20000 && cpt <= 0x2A6DF) ||
             (cpt >= 0x2A700 && cpt <= 0x2B73F) ||
             (cpt >= 0x2B740 && cpt <= 0x2B81F) ||
             (cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
-            (cpt >= 0xF900  && cpt <= 0xFAFF)  ||
-            (cpt >= 0x2F800 && cpt <= 0x2FA1F) ||
-            (cpt >= 0x3000  && cpt <= 0x303F)  ||
-            (cpt >= 0xFF00  && cpt <= 0xFFEF)) {
-            return true; // NOLINT
-        }
-        return false;
+            (cpt >= 0x0F900 && cpt <= 0x0FAFF) ||
+            (cpt >= 0x2F800 && cpt <= 0x2FA1F);
+            //(cpt >= 0x3000  && cpt <= 0x303F)  ||
+            //(cpt >= 0xFF00  && cpt <= 0xFFEF);
     }
 
     const llama_vocab & vocab;
@@ -12651,9 +13335,9 @@ struct fragment_buffer_variant {
 
 static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer) {
     // for each special token
-    for (const auto & st: vocab.special_tokens_cache) {
-        const auto & special_token = st.first;
-        const auto & special_id    = st.second;
+    for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
+        const auto & data = vocab.id_to_token[special_id];
+        const auto & special_token = data.text;
 
         // for each text fragment
         std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
@@ -12662,7 +13346,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
 
             // if a fragment is text ( not yet processed )
             if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                auto * raw_text = &(fragment.raw_text);
+                auto & raw_text = fragment.raw_text;
 
                 auto raw_text_base_offset = fragment.offset;
                 auto raw_text_base_length = fragment.length;
@@ -12672,7 +13356,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
                     // find the first occurrence of a given special token in this fragment
                     //  passing offset argument only limit the "search area" but match coordinates
                     //  are still relative to the source full raw_text
-                    auto match = raw_text->find(special_token, raw_text_base_offset);
+                    auto match = raw_text.find(special_token, raw_text_base_offset);
 
                     // no occurrences found, stop processing this fragment for a given special token
                     if (match == std::string::npos) break;
@@ -12690,13 +13374,22 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
                     if (match > raw_text_base_offset) {
                         // left
                         const int64_t left_reminder_offset = raw_text_base_offset + 0;
-                        const int64_t left_reminder_length = match - raw_text_base_offset;
-                        buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length);
+                        int64_t left_reminder_length = match - raw_text_base_offset;
+
+                        if (data.attr & LLAMA_TOKEN_ATTR_LSTRIP) {
+                            while (left_reminder_length > 0 && isspace(raw_text[left_reminder_offset + left_reminder_length - 1])) {
+                                left_reminder_length--;
+                            }
+                        }
+
+                        if (left_reminder_length > 0) {
+                            buffer.emplace_after(it, raw_text, left_reminder_offset, left_reminder_length);
+                            it++;
+                        }
 
 #ifdef PRETOKENIZERDEBUG
                         LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
 #endif
-                        it++;
                     }
 
                     // special token
@@ -12705,16 +13398,25 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
 
                     // right
                     if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
-                        const int64_t right_reminder_offset = match + special_token.length();
-                        const int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
-                        buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length);
+                        int64_t right_reminder_offset = match + special_token.length();
+                        int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
+
+                        if (data.attr & LLAMA_TOKEN_ATTR_RSTRIP) {
+                            while (right_reminder_length > 0 && isspace(raw_text[right_reminder_offset])) {
+                                right_reminder_offset++;
+                                right_reminder_length--;
+                            }
+                        }
+
+                        if (right_reminder_length > 0) {
+                            buffer.emplace_after(it, raw_text, right_reminder_offset, right_reminder_length);
+                            it++;
+                        }
 
 #ifdef PRETOKENIZERDEBUG
                         LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
 #endif
 
-                        it++;
-
                         if (source == 0) {
                             buffer.erase_after(buffer.before_begin());
                         } else {
@@ -12760,23 +13462,21 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
                 // tokenizer.encode('', add_special_tokens=True)  returns [1]
                 // tokenizer.encode('', add_special_tokens=False) returns []
 
+                bool is_prev_special = false;
+
                 if (add_special && vocab.special_add_bos != 0) {
                     GGML_ASSERT(vocab.special_bos_id != -1);
                     output.push_back(vocab.special_bos_id);
+                    is_prev_special = true;
                 }
 
                 for (const auto & fragment : fragment_buffer) {
                     if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        // without adding this leading whitespace, we do not get the same results as the original tokenizer
-
-                        // TODO: It's likely possible to get rid of this string copy entirely
-                        //  by modifying llm_tokenizer_x to operate with string offsets like pre-tokenizer
-                        //  and passing 'add space prefix' as bool argument
-                        //
                         auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
-                        if (&fragment == &fragment_buffer.front()) {
-                            if (vocab.add_space_prefix) {
-                                raw_text = " " + raw_text; // prefix with space if the first token is not special
+
+                        if (vocab.add_space_prefix) {
+                            if (!output.size() || is_prev_special) {  // prefix with space if first token
+                                raw_text = " " + raw_text;
                             }
                         }
 
@@ -12788,9 +13488,17 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
                         tokenizer.tokenize(raw_text, output);
                     } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                         output.push_back(fragment.token);
+                        is_prev_special = true;
                     }
                 }
 
+                if (add_special && vocab.special_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) {
+                    LLAMA_LOG_WARN(
+                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
+                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
+                        "Are you sure this is what you want?\n", __FUNCTION__);
+                }
+
                 if (add_special && vocab.special_add_eos == 1) {
                     GGML_ASSERT(vocab.special_eos_id != -1);
                     output.push_back(vocab.special_eos_id);
@@ -12817,6 +13525,13 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
                     }
                 }
 
+                if (add_special && vocab.special_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) {
+                    LLAMA_LOG_WARN(
+                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
+                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
+                        "Are you sure this is what you want?\n", __FUNCTION__);
+                }
+
                 if (add_special && vocab.special_add_eos == 1) {
                     GGML_ASSERT(vocab.special_add_eos != -1);
                     output.push_back(vocab.special_eos_id);
@@ -12936,7 +13651,7 @@ static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
         const uint32_t                chr) {
 
     bool found            = false;
-    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
+    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR || pos->type == LLAMA_GRETYPE_CHAR_ANY;
 
     GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT); // NOLINT
 
@@ -12945,6 +13660,10 @@ static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
             // inclusive range, e.g. [a-z]
             found = found || (pos->value <= chr && chr <= pos[1].value);
             pos += 2;
+        } else if (pos->type == LLAMA_GRETYPE_CHAR_ANY) {
+            // Any character matches "."
+            found = true;
+            pos += 1;
         } else {
             // exact char match, e.g. [a] or "a"
             found = found || pos->value == chr;
@@ -12962,7 +13681,7 @@ static bool llama_grammar_match_partial_char(
         const llama_grammar_element * pos,
         const llama_partial_utf8      partial_utf8) {
 
-    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
+    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR || pos->type == LLAMA_GRETYPE_CHAR_ANY;
     GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
 
     uint32_t partial_value = partial_utf8.value;
@@ -12992,6 +13711,9 @@ static bool llama_grammar_match_partial_char(
                 return is_positive_char;
             }
             pos += 2;
+        } else if (pos->type == LLAMA_GRETYPE_CHAR_ANY) {
+            // Any character matches "."
+            return true;
         } else {
             // exact char match, e.g. [a] or "a"
             if (low <= pos->value && pos->value <= high) {
@@ -13052,6 +13774,7 @@ static void llama_grammar_advance_stack(
         }
         case LLAMA_GRETYPE_CHAR:
         case LLAMA_GRETYPE_CHAR_NOT:
+        case LLAMA_GRETYPE_CHAR_ANY:
             if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
                 // only add the stack if it's not a duplicate of one we already have
                 new_stacks.emplace_back(stack);
@@ -13174,6 +13897,58 @@ static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
     return rejects;
 }
 
+static bool llama_grammar_detect_left_recursion(
+        const std::vector<std::vector<llama_grammar_element>> & rules,
+        size_t                                                  rule_index,
+        std::vector<bool>                                     * rules_visited,
+        std::vector<bool>                                     * rules_in_progress,
+        std::vector<bool>                                     * rules_may_be_empty) {
+    if ((*rules_in_progress)[rule_index]) {
+        return true;
+    }
+
+    (*rules_in_progress)[rule_index] = true;
+
+    const std::vector<llama_grammar_element> & rule = rules[rule_index];
+
+    // First check if the rule might produce the empty string. This could be done combined with the second
+    // step but it's more readable as two steps.
+    bool at_rule_start = true;
+    for (size_t i = 0; i < rule.size(); i++) {
+        if (llama_grammar_is_end_of_sequence(&rule[i])) {
+            if (at_rule_start) {
+                (*rules_may_be_empty)[rule_index] = true;
+                break;
+            }
+            at_rule_start = true;
+        } else {
+            at_rule_start = false;
+        }
+    }
+
+    // Second, recurse into leftmost nonterminals (or next-leftmost as long as the previous nonterminal may
+    // be empty)
+    bool recurse_into_nonterminal = true;
+    for (size_t i = 0; i < rule.size(); i++) {
+        if (rule[i].type == LLAMA_GRETYPE_RULE_REF && recurse_into_nonterminal) {
+            if (llama_grammar_detect_left_recursion(rules, (size_t)rule[i].value, rules_visited, rules_in_progress, rules_may_be_empty)) {
+                return true;
+            }
+            if (!((*rules_may_be_empty)[(size_t)rule[i].value])) {
+                recurse_into_nonterminal = false;
+            }
+        } else if (llama_grammar_is_end_of_sequence(&rule[i])) {
+            recurse_into_nonterminal = true;
+        } else {
+            recurse_into_nonterminal = false;
+        }
+    }
+
+    (*rules_in_progress)[rule_index] = false;
+    (*rules_visited)[rule_index] = true;
+    return false;
+}
+
 //
 // grammar - external
 //
@@ -13193,6 +13968,19 @@ struct llama_grammar * llama_grammar_init(
         vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
     }
 
+    // Check for left recursion
+    std::vector<bool> rules_visited(n_rules);
+    std::vector<bool> rules_in_progress(n_rules);
+    std::vector<bool> rules_may_be_empty(n_rules);
+    for (size_t i = 0; i < n_rules; i++) {
+        if (rules_visited[i]) {
+            continue;
+        }
+        if (llama_grammar_detect_left_recursion(vec_rules, i, &rules_visited, &rules_in_progress, &rules_may_be_empty)) {
+            throw std::runtime_error(format("unsupported grammar, left recursion detected for nonterminal at index %zu", i));
+        }
+    }
+
     // loop over alternates of start rule to build initial stacks
     std::vector<std::vector<const llama_grammar_element *>> stacks;
     pos = vec_rules[start_rule_index].data();
@@ -13215,6 +14003,9 @@ struct llama_grammar * llama_grammar_init(
         }
     } while (true);
 
+    // Important: vec_rules has to be moved here, not copied, because stacks contains
+    // pointers to elements of vec_rules. If vec_rules were copied into llama_grammar
+    // then the pointers would be invalidated when the local vec_rules goes out of scope.
     return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} };
 }
 
@@ -13706,7 +14497,7 @@ void llama_sample_repetition_penalties(
 
 void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
     GGML_ASSERT(ctx);
-    const int64_t t_start_sample_us = ggml_time_us();
+    int64_t t_start_sample_us = ggml_time_us();
 
     bool allow_eog = false;
     for (const auto & stack : grammar->stacks) {
@@ -13718,12 +14509,13 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
 
     std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
     candidates_decoded.reserve(candidates->size);
-    std::vector<llama_grammar_candidate>                              candidates_grammar;
+
+    std::vector<llama_grammar_candidate> candidates_grammar;
     candidates_grammar.reserve(candidates->size);
 
     for (size_t i = 0; i < candidates->size; ++i) {
-        const llama_token id    = candidates->data[i].id;
-        const std::string piece = llama_token_to_piece(ctx, id, false);
+        const llama_token id      = candidates->data[i].id;
+        const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(id);
 
         if (llama_token_is_eog(&ctx->model, id)) {
             if (!allow_eog) {
@@ -13809,9 +14601,7 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_
 
     // Sample the next word X using top-k sampling
     llama_sample_top_k(nullptr, candidates, int(k), 1);
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
+    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
     llama_token X = llama_sample_token(ctx, candidates);
     t_start_sample_us = ggml_time_us();
 
@@ -13825,9 +14615,7 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_
     // Update mu using the learning rate and error
     *mu = *mu - eta * e;
 
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
+    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
     return X;
 }
 
@@ -13927,7 +14715,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
         GGML_ASSERT(false);
     }
 
-    const std::string piece = llama_token_to_piece(ctx, token, false);
+    const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(token);
 
     // Note terminating 0 in decoded string
     const auto   decoded     = decode_utf8(piece, grammar->partial_utf8);
@@ -13943,260 +14731,6 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
     ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
 }
 
-//
-// Beam search
-//
-
-struct llama_beam {
-    std::vector<llama_token> tokens;
-    float p;  // Cumulative beam probability (renormalized relative to all beams)
-    bool eob; // Initialize end-of-beam to false. Callback sets this to true.
-    // Sort beams by probability. In case of ties, prefer beams at eob.
-    bool operator<(const llama_beam & rhs) const {
-        return std::make_pair(p, eob) < std::make_pair(rhs.p, rhs.eob);
-    }
-    // Shift off first n tokens and discard them.
-    void shift_tokens(const size_t n) {
-        if (n) {
-            std::copy(tokens.begin() + n, tokens.end(), tokens.begin());
-            tokens.resize(tokens.size() - n);
-        }
-    }
-    llama_beam_view view() const { return {tokens.data(), tokens.size(), p, eob}; }
-};
-
-// A struct for calculating logit-related info.
-struct llama_logit_info {
-    const float * const logits;
-    const int n_vocab;
-    const float max_l;
-    const float normalizer;
-    struct sum_exp {
-        float max_l;
-        float operator()(float sum, float l) const { return sum + std::exp(l - max_l); }
-    };
-    llama_logit_info(llama_context * ctx)
-      : logits(llama_get_logits(ctx))
-      , n_vocab(llama_n_vocab(llama_get_model(ctx)))
-      , max_l(*std::max_element(logits, logits + n_vocab))
-      , normalizer(1.0f / std::accumulate(logits, logits + n_vocab, 0.0f, sum_exp{max_l}))
-      { }
-    llama_token_data get_token_data(const llama_token token_id) const {
-        constexpr auto p = std::numeric_limits<float>::quiet_NaN();  // never used
-        return {token_id, logits[token_id], p};
-    }
-    // Return top k token_data by logit.
-    std::vector<llama_token_data> top_k(size_t k) {
-        std::vector<llama_token_data> min_heap;  // min-heap by logit
-        const llama_token k_min = std::min(static_cast<llama_token>(k), n_vocab);
-        min_heap.reserve(k_min);
-        for (llama_token token_id = 0 ; token_id < k_min ; ++token_id) {
-            min_heap.push_back(get_token_data(token_id));
-        }
-        auto comp = [](const llama_token_data & a, const llama_token_data & b) { return a.logit > b.logit; };
-        std::make_heap(min_heap.begin(), min_heap.end(), comp);
-        for (llama_token token_id = k_min ; token_id < n_vocab ; ++token_id) {
-            if (min_heap.front().logit < logits[token_id]) {
-                std::pop_heap(min_heap.begin(), min_heap.end(), comp);
-                min_heap.back().id = token_id;
-                min_heap.back().logit = logits[token_id];
-                std::push_heap(min_heap.begin(), min_heap.end(), comp);
-            }
-        }
-        return min_heap;
-    }
-    float probability_from_logit(float logit) const {
-        return normalizer * std::exp(logit - max_l);
-    }
-};
-
-struct llama_beam_search_data {
-    llama_context * ctx;
-    size_t n_beams;
-    int n_past;
-    int n_predict;
-    std::vector<llama_beam> beams;
-    std::vector<llama_beam> next_beams;
-
-    // Re-calculated on each loop iteration
-    size_t common_prefix_length;
-
-    // Used to communicate to/from callback on beams state.
-    std::vector<llama_beam_view> beam_views;
-
-    llama_beam_search_data(llama_context * ctx, size_t n_beams, int n_past, int n_predict)
-      : ctx(ctx)
-      , n_beams(n_beams)
-      , n_past(n_past)
-      , n_predict(n_predict)
-      , beam_views(n_beams) {
-        beams.reserve(n_beams);
-        next_beams.reserve(n_beams);
-    }
-
-    // Collapse beams to a single beam given by index.
-    void collapse_beams(const size_t beam_idx) {
-        if (0u < beam_idx) {
-            std::swap(beams[0], beams[beam_idx]);
-        }
-        beams.resize(1);
-    }
-
-    // Min-heaps are used to efficiently collect the top-k elements (k=n_beams).
-    // The repetitive patterns below reflect the 2 stages of heaps:
-    //  * Gather elements until the vector is full, then call std::make_heap() on it.
-    //  * If the heap is full and a new element is found that should be included, pop the
-    //    least element to the back(), replace it with the new, then push it into the heap.
-    void fill_next_beams_by_top_probabilities(llama_beam & beam) {
-        // Min-heaps use a greater-than comparator.
-        const auto comp = [](const llama_beam & a, const llama_beam & b) { return a.p > b.p; };
-        if (beam.eob) {
-            // beam is at end-of-sentence, so just copy it to next_beams if its probability is high enough.
-            if (next_beams.size() < n_beams) {
-                next_beams.push_back(std::move(beam));
-                if (next_beams.size() == n_beams) {
-                    std::make_heap(next_beams.begin(), next_beams.end(), comp);
-                }
-            } else if (next_beams.front().p < beam.p) {
-                std::pop_heap(next_beams.begin(), next_beams.end(), comp);
-                next_beams.back() = std::move(beam);
-                std::push_heap(next_beams.begin(), next_beams.end(), comp);
-            }
-        } else {
-            // beam is not at end-of-sentence, so branch with next top_k tokens.
-            if (!beam.tokens.empty()) {
-                llama_decode(ctx, llama_batch_get_one(beam.tokens.data(), beam.tokens.size(), n_past, 0));
-            }
-            llama_logit_info logit_info(ctx);
-            std::vector<llama_token_data> next_tokens = logit_info.top_k(n_beams);
-
-            // Clear the kv slot so that other beams may try different tokens at this position. The llama_decode()
-            // call in loop() will conclusively fill in the kv slot once the beams converge at this position.
-            llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
-
-            size_t i=0;
-            if (next_beams.size() < n_beams) {
-                for (; next_beams.size() < n_beams ; ++i) {
-                    llama_beam next_beam = beam;
-                    next_beam.tokens.push_back(next_tokens[i].id);
-                    next_beam.p *= logit_info.probability_from_logit(next_tokens[i].logit);
-                    next_beams.push_back(std::move(next_beam));
-                }
-                std::make_heap(next_beams.begin(), next_beams.end(), comp);
-            } else {
-                for (; next_beams.front().p == 0.0f ; ++i) {
-                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
-                    next_beams.back() = beam;
-                    next_beams.back().tokens.push_back(next_tokens[i].id);
-                    next_beams.back().p *= logit_info.probability_from_logit(next_tokens[i].logit);
-                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
-                }
-            }
-            for (; i < n_beams ; ++i) {
-                const float next_p = beam.p * logit_info.probability_from_logit(next_tokens[i].logit);
-                if (next_beams.front().p < next_p) {
-                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
-                    next_beams.back() = beam;
-                    next_beams.back().tokens.push_back(next_tokens[i].id);
-                    next_beams.back().p = next_p;
-                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
-                }
-            }
-        }
-    }
-
-    // Find common_prefix_length based on beams.
-    // Requires beams is not empty.
-    size_t find_common_prefix_length() {
-        size_t common_prefix_length = beams[0].tokens.size();
-        for (size_t i = 1 ; i < beams.size() ; ++i) {
-            common_prefix_length = std::min(common_prefix_length, beams[i].tokens.size());
-            for (size_t j = 0 ; j < common_prefix_length ; ++j) {
-                if (beams[0].tokens[j] != beams[i].tokens[j]) {
-                    common_prefix_length = j;
-                    break;
-                }
-            }
-        }
-        return common_prefix_length;
-    }
-
-    // Construct beams_state to send back to caller via the callback function.
-    // Side effect: set common_prefix_length = find_common_prefix_length();
-    llama_beams_state get_beams_state(const bool last_call) {
-        for (size_t i = 0 ; i < beams.size() ; ++i) {
-            beam_views[i] = beams[i].view();
-        }
-        common_prefix_length = find_common_prefix_length();
-        return {beam_views.data(), beams.size(), common_prefix_length, last_call};
-    }
-
-    // Loop:
-    //  * while i < n_predict, AND
-    //  * any of the beams have not yet reached end-of-beam (eob), AND
-    //  * the highest probability beam(s) (plural in case of ties) are not at end-of-sentence
-    //    (since all other beam probabilities can only decrease)
-    void loop(const llama_beam_search_callback_fn_t callback, void * const callback_data) {
-        beams.push_back({{}, 1.0f, false});  // Start with one empty beam w/ probability = 1.0 and !eob.
-        const auto not_eob = [](const llama_beam & beam) { return !beam.eob; };
-        for (int i = 0 ; i < n_predict && std::any_of(beams.begin(),beams.end(),not_eob) &&
-                       !beams[top_beam_index()].eob ; ++i) {
-            callback(callback_data, get_beams_state(false));  // Sets common_prefix_length
-            update_beams_from_beam_views();   // Update values (p,eob) that callback may have changed.
-            if (common_prefix_length) {
-                llama_decode(ctx, llama_batch_get_one(beams[0].tokens.data(), common_prefix_length, n_past, 0));
-                n_past += common_prefix_length;
-            }
-            // Zero-out next_beam probabilities to place them last in following min-heap.
-            std::for_each(next_beams.begin(), next_beams.end(), [](llama_beam & beam) { beam.p = 0.0f; });
-            for (llama_beam & beam : beams) {
-                beam.shift_tokens(common_prefix_length);
-                fill_next_beams_by_top_probabilities(beam);
-            }
-            // next_beams become the beams of next/final iteration. Swap them to re-use memory.
-            beams.swap(next_beams);
-            renormalize_beam_probabilities(beams);
-        }
-        collapse_beams(top_beam_index());
-        callback(callback_data, get_beams_state(true));
-    }
-
-    // As beams grow, the cumulative probabilities decrease.
-    // Renormalize them to avoid floating point underflow.
-    static void renormalize_beam_probabilities(std::vector<llama_beam> & beams) {
-        const auto sum_p = [](float sum, llama_beam & beam) { return sum + beam.p; };
-        const float inv_sum = 1.0f / std::accumulate(beams.begin(), beams.end(), 0.0f, sum_p);
-        std::for_each(beams.begin(), beams.end(), [=](llama_beam & beam) { beam.p *= inv_sum; });
-    }
-
-    // Assumes beams is non-empty.  Uses llama_beam::operator<() for ordering.
-    size_t top_beam_index() {
-        return std::max_element(beams.begin(), beams.end()) - beams.begin();
-    }
-
-    // Copy (p,eob) for each beam which may have been changed by the callback.
-    void update_beams_from_beam_views() {
-        for (size_t i = 0 ; i < beams.size() ; ++i) {
-            beams[i].p = beam_views[i].p;
-            beams[i].eob = beam_views[i].eob;
-        }
-    }
-};
-
-void llama_beam_search(llama_context * ctx,
-                       llama_beam_search_callback_fn_t callback, void * callback_data,
-                       size_t n_beams, int n_past, int n_predict) {
-    assert(ctx);
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    llama_beam_search_data beam_search_data(ctx, n_beams, n_past, n_predict);
-
-    beam_search_data.loop(callback, callback_data);
-
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    ctx->n_sample++;
-}
-
 //
 // quantization
 //
@@ -14412,8 +14946,6 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
         else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
                 use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
         else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
-        else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
-                (qs.i_attention_wv < qs.n_attention_wv/8 || qs.i_attention_wv >= 7*qs.n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
         if (qs.model.type == MODEL_70B) {
             // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
             // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
@@ -14716,6 +15248,14 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         if (imatrix_data) {
             LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
             qs.has_imatrix = true;
+            // check imatrix for nans or infs
+            for (const auto & kv : *imatrix_data) {
+                for (float f : kv.second) {
+                    if (!std::isfinite(f)) {
+                        throw std::runtime_error(format("imatrix contains non-finite value %f\n", f));
+                    }
+                }
+            }
         }
     }
 
@@ -15314,6 +15854,7 @@ struct llama_model_params llama_model_default_params() {
         /*.split_mode                  =*/ LLAMA_SPLIT_MODE_LAYER,
         /*.main_gpu                    =*/ 0,
         /*.tensor_split                =*/ nullptr,
+        /*.rpc_servers                 =*/ nullptr,
         /*.progress_callback           =*/ nullptr,
         /*.progress_callback_user_data =*/ nullptr,
         /*.kv_overrides                =*/ nullptr,
@@ -15384,7 +15925,9 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
 }
 
 size_t llama_max_devices(void) {
-#if defined(GGML_USE_METAL)
+#if defined(GGML_USE_RPC)
+    return GGML_RPC_MAX_SERVERS;
+#elif defined(GGML_USE_METAL)
     return 1;
 #elif defined(GGML_USE_CUDA)
     return GGML_CUDA_MAX_DEVICES;
@@ -15406,8 +15949,8 @@ bool llama_supports_mlock(void) {
 }
 
 bool llama_supports_gpu_offload(void) {
-#if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
-    defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE)
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_METAL)   || defined(GGML_USE_VULKAN) || \
+    defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC)
     // Defined when llama.cpp is compiled with support for offloading model layers to GPU.
     return true;
 #else
@@ -15424,10 +15967,6 @@ void llama_backend_init(void) {
         struct ggml_context * ctx = ggml_init(params);
         ggml_free(ctx);
     }
-
-#ifdef GGML_USE_MPI
-    ggml_mpi_backend_init();
-#endif
 }
 
 void llama_numa_init(enum ggml_numa_strategy numa) {
@@ -15437,9 +15976,6 @@ void llama_numa_init(enum ggml_numa_strategy numa) {
 }
 
 void llama_backend_free(void) {
-#ifdef GGML_USE_MPI
-    ggml_mpi_backend_free();
-#endif
     ggml_quantize_free();
 }
 
@@ -15470,7 +16006,17 @@ struct llama_model * llama_load_model_from_file(
             return true;
         };
     }
-
+    if (params.rpc_servers != nullptr && params.rpc_servers[0] != '\0') {
+        // split the servers set them into model->rpc_servers
+        std::string servers(params.rpc_servers);
+        size_t pos = 0;
+        while ((pos = servers.find(",")) != std::string::npos) {
+            std::string server = servers.substr(0, pos);
+            model->rpc_servers.push_back(server);
+            servers.erase(0, pos + 1);
+        }
+        model->rpc_servers.push_back(servers);
+    }
     int status = llama_model_load(path_model, *model, params);
     GGML_ASSERT(status <= 0);
     if (status < 0) {
@@ -15509,6 +16055,16 @@ struct llama_context * llama_new_context_with_model(
         return nullptr;
     }
 
+    if (params.flash_attn && model->arch == LLM_ARCH_GROK) {
+        LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__);
+        params.flash_attn = false;
+    }
+
+    if (params.type_v != GGML_TYPE_F16 && !params.flash_attn) {
+        LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__);
+        return nullptr;
+    }
+
     llama_context * ctx = new llama_context(*model);
 
     const auto & hparams = model->hparams;
@@ -15532,7 +16088,7 @@ struct llama_context * llama_new_context_with_model(
     cparams.rope_freq_scale  = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;
 
     // this is necessary due to kv_self.n being padded later during inference
-    cparams.n_ctx            = GGML_PAD(cparams.n_ctx, 256);
+    cparams.n_ctx            = GGML_PAD(cparams.n_ctx, llama_kv_cache_get_padding(cparams));
 
     // with causal attention, the batch size is limited by the context size
     cparams.n_batch          = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;
@@ -15547,8 +16103,8 @@ struct llama_context * llama_new_context_with_model(
 
     cparams.n_ubatch         = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
 
-    cparams.n_yarn_orig_ctx  = params.yarn_orig_ctx    != 0 ? params.yarn_orig_ctx    :
-                               hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx :
+    cparams.n_ctx_orig_yarn  = params.yarn_orig_ctx    != 0 ? params.yarn_orig_ctx    :
+                               hparams.n_ctx_orig_yarn != 0 ? hparams.n_ctx_orig_yarn :
                                                               hparams.n_ctx_train;
 
     cparams.cb_eval           = params.cb_eval;
@@ -15567,6 +16123,7 @@ struct llama_context * llama_new_context_with_model(
         cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
     }
 
+    cparams.yarn_attn_factor *= hparams.rope_attn_factor;
     cparams.causal_attn = hparams.causal_attn;
 
     if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
@@ -15577,11 +16134,6 @@ struct llama_context * llama_new_context_with_model(
         }
     }
 
-    if (cparams.flash_attn && model->arch == LLM_ARCH_GROK) {
-        LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__);
-        cparams.flash_attn = false;
-    }
-
     if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
     }
@@ -15617,7 +16169,7 @@ struct llama_context * llama_new_context_with_model(
 
     if (!hparams.vocab_only) {
         // initialize backends
-#ifdef GGML_USE_METAL
+#if defined(GGML_USE_METAL)
         if (model->n_gpu_layers > 0) {
             ctx->backend_metal = ggml_backend_metal_init();
             if (ctx->backend_metal == nullptr) {
@@ -15656,7 +16208,7 @@ struct llama_context * llama_new_context_with_model(
             return nullptr;
         }
         if (model->split_mode == LLAMA_SPLIT_MODE_NONE) {
-            ggml_backend_t backend = ggml_backend_vk_init(0);
+            ggml_backend_t backend = ggml_backend_vk_init(model->main_gpu);
             if (backend == nullptr) {
                 LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
                 llama_free(ctx);
@@ -15679,8 +16231,7 @@ struct llama_context * llama_new_context_with_model(
         if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
             ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu);
             if (backend == nullptr) {
-                int main_gpu_id = ggml_backend_sycl_get_device_id(model->main_gpu);
-                LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, main_gpu_id, model->main_gpu);
+                LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d backend\n", __func__, model->main_gpu);
                 llama_free(ctx);
                 return nullptr;
             }
@@ -15710,6 +16261,29 @@ struct llama_context * llama_new_context_with_model(
             ctx->backends.push_back(backend);
         }
 #endif
+
+#ifdef GGML_USE_BLAS
+        ctx->backend_blas = ggml_backend_blas_init();
+        if (ctx->backend_blas == nullptr) {
+            LLAMA_LOG_WARN("%s: failed to initialize BLAS backend\n", __func__);
+        } else {
+            ctx->backends.push_back(ctx->backend_blas);
+        }
+#endif
+
+#if defined(GGML_USE_RPC)
+        if (model->n_gpu_layers > 0) {
+            for (const auto & endpoint : model->rpc_servers) {
+                ggml_backend_t backend = ggml_backend_rpc_init(endpoint.c_str());
+                if (backend == nullptr) {
+                    LLAMA_LOG_ERROR("%s: failed to initialize RPC to '%s'\n", __func__, endpoint.c_str());
+                    llama_free(ctx);
+                    return nullptr;
+                }
+                ctx->backends.push_back(backend);
+            }
+        }
+#endif
         ctx->backend_cpu = ggml_backend_cpu_init();
         if (ctx->backend_cpu == nullptr) {
             LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__);
@@ -15773,7 +16347,11 @@ struct llama_context * llama_new_context_with_model(
             ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead_custom(LLAMA_MAX_NODES, false));
 
             // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
-            bool pipeline_parallel = llama_get_device_count() > 1 && model->n_gpu_layers > (int)model->hparams.n_layer && model->split_mode == LLAMA_SPLIT_MODE_LAYER;
+            bool pipeline_parallel =
+                llama_get_device_count(*model) > 1 &&
+                model->n_gpu_layers > (int)model->hparams.n_layer &&
+                model->split_mode == LLAMA_SPLIT_MODE_LAYER &&
+                params.offload_kqv;
 #ifndef GGML_USE_CUDA
             // pipeline parallelism requires support for async compute and events
             // currently this is only implemented in the CUDA backend
@@ -15816,20 +16394,6 @@ struct llama_context * llama_new_context_with_model(
         }
     }
 
-#ifdef GGML_USE_MPI
-    ctx->ctx_mpi = ggml_mpi_init();
-
-    if (ggml_mpi_rank(ctx->ctx_mpi) > 0) {
-        // Enter a blocking eval loop with dummy input, letting rank=0 drive the process
-        // TODO: needs fix after #3228
-        GGML_ASSERT(false && "not implemented");
-        //const std::vector<llama_token> tmp(ctx->model.hparams.n_ctx, llama_token_bos(ctx));
-        //while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {};
-        llama_backend_free();
-        exit(1);
-    }
-#endif
-
     return ctx;
 }
 
@@ -15866,7 +16430,6 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         // these models do not use RoPE
         case LLM_ARCH_GPT2:
         case LLM_ARCH_GPTJ:
-        case LLM_ARCH_GPTNEOX:
         case LLM_ARCH_MPT:
         case LLM_ARCH_REFACT:
         case LLM_ARCH_BLOOM:
@@ -15886,13 +16449,14 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_XVERSE:
         case LLM_ARCH_COMMAND_R:
         case LLM_ARCH_OLMO:
+        case LLM_ARCH_ARCTIC:
+        case LLM_ARCH_DEEPSEEK2:
             return LLAMA_ROPE_TYPE_NORM;
 
         // the pairs of head values are offset by n_rot/2
         case LLM_ARCH_FALCON:
         case LLM_ARCH_GROK:
         case LLM_ARCH_DBRX:
-        case LLM_ARCH_PERSIMMON:
         case LLM_ARCH_BERT:
         case LLM_ARCH_NOMIC_BERT:
         case LLM_ARCH_STABLELM:
@@ -15903,6 +16467,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_PHI3:
         case LLM_ARCH_GEMMA:
         case LLM_ARCH_STARCODER2:
+        case LLM_ARCH_GPTNEOX:
             return LLAMA_ROPE_TYPE_NEOX;
 
         // all model arches should be listed explicitly here
@@ -16062,6 +16627,7 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const
     }
 
     // make tensors
+    cvec.tensors.reserve(model.hparams.n_layer);
     cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
     for (size_t il = 1; il < model.hparams.n_layer; il++) {
         struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft);
@@ -16070,6 +16636,8 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const
     }
 
     // allocate tensors / buffers and zero
+    cvec.ctxs.reserve(ctx_map.size());
+    cvec.bufs.reserve(ctx_map.size());
     for (auto it : ctx_map) {
         ggml_backend_buffer_type_t buft = it.first;
         ggml_context * ctx = it.second;
@@ -16893,13 +17461,13 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam
             }
             else {
                 if (cell_range_begin != kv_self.size) {
-                    cell_ranges.push_back({ cell_range_begin, i });
+                    cell_ranges.emplace_back(cell_range_begin, i);
                     cell_range_begin = kv_self.size;
                 }
             }
         }
         if (cell_range_begin != kv_self.size) {
-            cell_ranges.push_back({ cell_range_begin, kv_self.size });
+            cell_ranges.emplace_back(cell_range_begin, kv_self.size);
         }
 
         // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
@@ -17278,6 +17846,14 @@ void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_
     ctx->cparams.n_threads_batch = n_threads_batch;
 }
 
+uint32_t llama_n_threads(struct llama_context * ctx) {
+    return ctx->cparams.n_threads;
+}
+
+uint32_t llama_n_threads_batch(struct llama_context * ctx) {
+    return ctx->cparams.n_threads_batch;
+}
+
 void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) {
     ctx->abort_callback      = abort_callback;
     ctx->abort_callback_data = abort_callback_data;
@@ -17489,9 +18065,9 @@ float llama_token_get_score(const struct llama_model * model, llama_token token)
     return model->vocab.id_to_token[token].score;
 }
 
-llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
+llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token) {
     GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return model->vocab.id_to_token[token].type;
+    return model->vocab.id_to_token[token].attr;
 }
 
 bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
@@ -17501,6 +18077,10 @@ bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
     );
 }
 
+bool llama_token_is_control(const struct llama_model * model, llama_token token) {
+    return llama_is_control_token(model->vocab, token);
+}
+
 llama_token llama_token_bos(const struct llama_model * model) {
     return model->vocab.special_bos_id;
 }
@@ -17572,7 +18152,16 @@ static std::string llama_decode_text(const std::string & text) {
 
     const auto cpts = unicode_cpts_from_utf8(text);
     for (const auto cpt : cpts) {
-        decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(cpt));
+        const auto utf8 = unicode_cpt_to_utf8(cpt);
+        try {
+            decoded_text += unicode_utf8_to_byte(utf8);
+        } catch (const std::out_of_range & e) {
+            decoded_text += "[UNK_BYTE_0x";
+            for (const auto c : utf8) {
+                decoded_text += format("%02x", (uint8_t) c);
+            }
+            decoded_text += text + "]";
+        }
     }
 
     return decoded_text;
@@ -17580,69 +18169,88 @@ static std::string llama_decode_text(const std::string & text) {
 
 // does not write null-terminator to buf
 int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) {
+    // ref: https://github.com/ggerganov/llama.cpp/pull/7587#discussion_r1620983843
+    if (!special && llama_is_control_token(model->vocab, token)) {
+        return 0;
+    }
+
+    // if we have a cache - use it
+    {
+        const auto & cache = model->vocab.cache_token_to_piece;
+
+        if (!cache.empty()) {
+            const auto & res = cache.at(token);
+            if (length < (int) res.size()) {
+                return -(int) res.size();
+            }
+            memcpy(buf, res.c_str(), res.size());
+            return res.size();
+        }
+    }
+
     if (0 <= token && token < llama_n_vocab(model)) {
         switch (llama_vocab_get_type(model->vocab)) {
-        case LLAMA_VOCAB_TYPE_WPM:
-        case LLAMA_VOCAB_TYPE_SPM: {
-            // NOTE: we accept all unsupported token types,
-            // suppressing them like CONTROL tokens.
-            if (llama_is_normal_token(model->vocab, token)) {
-                std::string result = model->vocab.id_to_token[token].text;
-                llama_unescape_whitespace(result);
-                if (length < (int) result.length()) {
-                    return -(int) result.length();
+            case LLAMA_VOCAB_TYPE_WPM:
+            case LLAMA_VOCAB_TYPE_SPM: {
+                // NOTE: we accept all unsupported token types,
+                // suppressing them like CONTROL tokens.
+                if (llama_is_normal_token(model->vocab, token)) {
+                    std::string result = model->vocab.id_to_token[token].text;
+                    llama_unescape_whitespace(result);
+                    if (length < (int) result.length()) {
+                        return -(int) result.length();
+                    }
+                    memcpy(buf, result.c_str(), result.length());
+                    return result.length();
+                } else if (
+                        (llama_is_user_defined_token(model->vocab, token)) ||
+                        (llama_is_control_token     (model->vocab, token) && special)) {
+                    std::string result = model->vocab.id_to_token[token].text;
+                    if (length < (int) result.length()) {
+                        return -(int) result.length();
+                    }
+                    memcpy(buf, result.c_str(), result.length());
+                    return result.length();
+                } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
+                    if (length < 3) {
+                        return -3;
+                    }
+                    memcpy(buf, "\xe2\x96\x85", 3);
+                    return 3;
+                } else if (llama_is_byte_token(model->vocab, token)) {
+                    if (length < 1) {
+                        return -1;
+                    }
+                    buf[0] = llama_token_to_byte(model->vocab, token);
+                    return 1;
                 }
-                memcpy(buf, result.c_str(), result.length());
-                return result.length();
-            } else if (
-                    (llama_is_user_defined_token(model->vocab, token)) ||
-                    (llama_is_control_token     (model->vocab, token) && special)) {
-                std::string result = model->vocab.id_to_token[token].text;
-                if (length < (int) result.length()) {
-                    return -(int) result.length();
-                }
-                memcpy(buf, result.c_str(), result.length());
-                return result.length();
-            } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
-                if (length < 3) {
-                    return -3;
-                }
-                memcpy(buf, "\xe2\x96\x85", 3);
-                return 3;
-            } else if (llama_is_byte_token(model->vocab, token)) {
-                if (length < 1) {
-                    return -1;
-                }
-                buf[0] = llama_token_to_byte(model->vocab, token);
-                return 1;
+                break;
             }
-            break;
-        }
-        case LLAMA_VOCAB_TYPE_BPE: {
-            // NOTE: we accept all unsupported token types,
-            // suppressing them like CONTROL tokens.
-            if (llama_is_normal_token(model->vocab, token)) {
-                std::string result = model->vocab.id_to_token[token].text;
-                result = llama_decode_text(result);
-                if (length < (int) result.length()) {
-                    return -(int) result.length();
+            case LLAMA_VOCAB_TYPE_BPE: {
+                // NOTE: we accept all unsupported token types,
+                // suppressing them like CONTROL tokens.
+                if (llama_is_normal_token(model->vocab, token)) {
+                    std::string result = model->vocab.id_to_token[token].text;
+                    result = llama_decode_text(result);
+                    if (length < (int) result.length()) {
+                        return -(int) result.length();
+                    }
+                    memcpy(buf, result.c_str(), result.length());
+                    return result.length();
+                } else if (
+                        (llama_is_user_defined_token(model->vocab, token)) ||
+                        (llama_is_control_token     (model->vocab, token) && special)) {
+                    std::string result = model->vocab.id_to_token[token].text;
+                    if (length < (int) result.length()) {
+                        return -(int) result.length();
+                    }
+                    memcpy(buf, result.c_str(), result.length());
+                    return result.length();
                 }
-                memcpy(buf, result.c_str(), result.length());
-                return result.length();
-            } else if (
-                    (llama_is_user_defined_token(model->vocab, token)) ||
-                    (llama_is_control_token     (model->vocab, token) && special)) {
-                std::string result = model->vocab.id_to_token[token].text;
-                if (length < (int) result.length()) {
-                    return -(int) result.length();
-                }
-                memcpy(buf, result.c_str(), result.length());
-                return result.length();
+                break;
             }
-            break;
-        }
-        default:
-            GGML_ASSERT(false);
+            default:
+                GGML_ASSERT(false);
         }
     }
     return 0;
@@ -17712,6 +18320,15 @@ static int32_t llama_chat_apply_template_internal(
             }
         }
         // llama2 templates seem to not care about "add_generation_prompt"
+    } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos)) {
+        // Phi 3
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>\n" << message->content << "<|end|>\n";
+        }
+        if (add_ass) {
+            ss << "<|assistant|>\n";
+        }
     } else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) {
         // zephyr template
         for (auto message : chat) {
@@ -17844,15 +18461,6 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
         }
-    } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos )) {
-        // Phi 3
-        for (auto message : chat) {
-            std::string role(message->role);
-            ss << "<|" << role << "|>\n" << trim(message->content) << "<|end|>\n";
-        }
-        if (add_ass) {
-            ss << "<|assistant|>\n";
-        }
     } else {
         // template not supported
         return -1;
@@ -17974,8 +18582,10 @@ const char * llama_print_system_info(void) {
     s += "AVX512 = "      + std::to_string(ggml_cpu_has_avx512())      + " | ";
     s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | ";
     s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | ";
+    s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | ";
     s += "FMA = "         + std::to_string(ggml_cpu_has_fma())         + " | ";
     s += "NEON = "        + std::to_string(ggml_cpu_has_neon())        + " | ";
+    s += "SVE = "         + std::to_string(ggml_cpu_has_sve())         + " | ";
     s += "ARM_FMA = "     + std::to_string(ggml_cpu_has_arm_fma())     + " | ";
     s += "F16C = "        + std::to_string(ggml_cpu_has_f16c())        + " | ";
     s += "FP16_VA = "     + std::to_string(ggml_cpu_has_fp16_va())     + " | ";
@@ -18034,6 +18644,8 @@ void llama_log_set(ggml_log_callback log_callback, void * user_data) {
     g_state.log_callback_user_data = user_data;
 #ifdef GGML_USE_METAL
     ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
+#elif defined(GGML_USE_CUDA)
+    ggml_backend_cuda_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
 #endif
 }
 
diff --git a/examples/talk-llama/llama.h b/examples/talk-llama/llama.h
index 0b2e708..da310ff 100644
--- a/examples/talk-llama/llama.h
+++ b/examples/talk-llama/llama.h
@@ -81,9 +81,12 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_GPT2           = 7,
         LLAMA_VOCAB_PRE_TYPE_REFACT         = 8,
         LLAMA_VOCAB_PRE_TYPE_COMMAND_R      = 9,
-        LLAMA_VOCAB_PRE_TYPE_QWEN2          = 10,
-        LLAMA_VOCAB_PRE_TYPE_OLMO           = 11,
-        LLAMA_VOCAB_PRE_TYPE_DBRX           = 12,
+        LLAMA_VOCAB_PRE_TYPE_STABLELM2      = 10,
+        LLAMA_VOCAB_PRE_TYPE_QWEN2          = 11,
+        LLAMA_VOCAB_PRE_TYPE_OLMO           = 12,
+        LLAMA_VOCAB_PRE_TYPE_DBRX           = 13,
+        LLAMA_VOCAB_PRE_TYPE_SMAUG          = 14,
+        LLAMA_VOCAB_PRE_TYPE_PORO           = 15,
     };
 
     // note: these values should be synchronized with ggml_rope
@@ -95,7 +98,7 @@ extern "C" {
         LLAMA_ROPE_TYPE_GLM  =  4,
     };
 
-    enum llama_token_type {
+    enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file
         LLAMA_TOKEN_TYPE_UNDEFINED    = 0,
         LLAMA_TOKEN_TYPE_NORMAL       = 1,
         LLAMA_TOKEN_TYPE_UNKNOWN      = 2,
@@ -105,6 +108,20 @@ extern "C" {
         LLAMA_TOKEN_TYPE_BYTE         = 6,
     };
 
+    enum llama_token_attr {
+        LLAMA_TOKEN_ATTR_UNDEFINED    = 0,
+        LLAMA_TOKEN_ATTR_UNKNOWN      = 1 << 0,
+        LLAMA_TOKEN_ATTR_UNUSED       = 1 << 1,
+        LLAMA_TOKEN_ATTR_NORMAL       = 1 << 2,
+        LLAMA_TOKEN_ATTR_CONTROL      = 1 << 3,  // SPECIAL?
+        LLAMA_TOKEN_ATTR_USER_DEFINED = 1 << 4,
+        LLAMA_TOKEN_ATTR_BYTE         = 1 << 5,
+        LLAMA_TOKEN_ATTR_NORMALIZED   = 1 << 6,
+        LLAMA_TOKEN_ATTR_LSTRIP       = 1 << 7,
+        LLAMA_TOKEN_ATTR_RSTRIP       = 1 << 8,
+        LLAMA_TOKEN_ATTR_SINGLE_WORD  = 1 << 9,
+    };
+
     // model file types
     enum llama_ftype {
         LLAMA_FTYPE_ALL_F32              = 0,
@@ -242,6 +259,9 @@ extern "C" {
         // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
         const float * tensor_split;
 
+        // comma separated list of RPC servers to use for offloading
+        const char * rpc_servers;
+
         // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
         // If the provided progress_callback returns true, model loading continues.
         // If it returns false, model loading is immediately aborted.
@@ -260,6 +280,8 @@ extern "C" {
         bool check_tensors; // validate model tensor data
     };
 
+    // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
+    //       https://github.com/ggerganov/llama.cpp/pull/7544
     struct llama_context_params {
         uint32_t seed;              // RNG seed, -1 for random
         uint32_t n_ctx;             // text context, 0 = from model
@@ -286,14 +308,14 @@ extern "C" {
         ggml_backend_sched_eval_callback cb_eval;
         void * cb_eval_user_data;
 
-        enum ggml_type type_k; // data type for K cache
-        enum ggml_type type_v; // data type for V cache
+        enum ggml_type type_k; // data type for K cache [EXPERIMENTAL]
+        enum ggml_type type_v; // data type for V cache [EXPERIMENTAL]
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
         bool logits_all;  // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
         bool embeddings;  // if true, extract embeddings (together with logits)
         bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
-        bool flash_attn;  // whether to use flash attention
+        bool flash_attn;  // whether to use flash attention [EXPERIMENTAL]
 
         // Abort callback
         // if it returns true, execution of llama_decode() will be aborted
@@ -344,6 +366,9 @@ extern "C" {
         // modifies a preceding LLAMA_GRETYPE_CHAR or
         // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
         LLAMA_GRETYPE_CHAR_ALT       = 6,
+
+        // any character (.)
+        LLAMA_GRETYPE_CHAR_ANY       = 7,
     };
 
     typedef struct llama_grammar_element {
@@ -417,8 +442,8 @@ extern "C" {
 
     LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);
 
-    LLAMA_API enum llama_vocab_type   llama_vocab_type  (const struct llama_model   * model);
-    LLAMA_API enum llama_rope_type    llama_rope_type   (const struct llama_model   * model);
+    LLAMA_API enum llama_vocab_type   llama_vocab_type  (const struct llama_model * model);
+    LLAMA_API enum llama_rope_type    llama_rope_type   (const struct llama_model * model);
 
     LLAMA_API int32_t llama_n_vocab    (const struct llama_model * model);
     LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
@@ -755,6 +780,12 @@ extern "C" {
     // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
     LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
 
+    // Get the number of threads used for generation of a single token.
+    LLAMA_API uint32_t llama_n_threads(struct llama_context * ctx);
+
+    // Get the number of threads used for prompt and batch processing (multiple token).
+    LLAMA_API uint32_t llama_n_threads_batch(struct llama_context * ctx);
+
     // Set whether to use causal attention or not
     // If set to true, the model will only attend to the past tokens
     LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
@@ -808,11 +839,14 @@ extern "C" {
 
     LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
 
-    LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
+    LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token);
 
     // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
     LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token);
 
+    // Identify if Token Id is a control token or a render-able token
+    LLAMA_API bool llama_token_is_control(const struct llama_model * model, llama_token token);
+
     // Special tokens
     LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
     LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
@@ -1026,49 +1060,9 @@ extern "C" {
                      llama_token   token);
 
     //
-    // Beam search
+    // Model split
     //
 
-    struct llama_beam_view {
-        const llama_token * tokens;
-
-        size_t n_tokens;
-        float  p;        // Cumulative beam probability (renormalized relative to all beams)
-        bool   eob;      // Callback should set this to true when a beam is at end-of-beam.
-    };
-
-    // Passed to beam_search_callback function.
-    // Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
-    // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
-    // These pointers are valid only during the synchronous callback, so should not be saved.
-    struct llama_beams_state {
-        struct llama_beam_view * beam_views;
-
-        size_t n_beams;               // Number of elements in beam_views[].
-        size_t common_prefix_length;  // Current max length of prefix tokens shared by all beams.
-        bool   last_call;             // True iff this is the last callback invocation.
-    };
-
-    // Type of pointer to the beam_search_callback function.
-    // void* callback_data is any custom data passed to llama_beam_search, that is subsequently
-    // passed back to beam_search_callback. This avoids having to use global variables in the callback.
-    typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
-
-    /// @details Deterministically returns entire sentence constructed by a beam search.
-    /// @param ctx Pointer to the llama_context.
-    /// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
-    /// @param callback_data A pointer that is simply passed back to callback.
-    /// @param n_beams Number of beams to use.
-    /// @param n_past Number of tokens already evaluated.
-    /// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
-    LLAMA_API void llama_beam_search(
-                   struct llama_context * ctx,
-        llama_beam_search_callback_fn_t   callback,
-                                   void * callback_data,
-                                 size_t   n_beams,
-                                int32_t   n_past,
-                                int32_t   n_predict);
-
     /// @details Build a split GGUF final path for this chunk.
     ///          llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf"
     //  Returns the split_path length.
diff --git a/examples/talk-llama/unicode-data.cpp b/examples/talk-llama/unicode-data.cpp
index c54175f..d7c1c89 100644
--- a/examples/talk-llama/unicode-data.cpp
+++ b/examples/talk-llama/unicode-data.cpp
@@ -1,2183 +1,6983 @@
-#include "unicode-data.h"
+// generated with scripts/gen-unicode-data.py
+
+#include "unicode-data.h"
 
 #include <cstdint>
-#include <map>
-#include <utility>
 #include <vector>
+#include <unordered_map>
+#include <unordered_set>
 
-// generated with scripts/gen-unicode-data.py
-//
-// TODO: generate unicode_map_nfd
-
-const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_number = {
-{0x00000030, 0x00000039}, {0x000000B2, 0x000000B3}, {0x000000B9, 0x000000B9}, {0x000000BC, 0x000000BE},
-{0x00000660, 0x00000669}, {0x000006F0, 0x000006F9}, {0x000007C0, 0x000007C9}, {0x00000966, 0x0000096F},
-{0x000009E6, 0x000009EF}, {0x000009F4, 0x000009F9}, {0x00000A66, 0x00000A6F}, {0x00000AE6, 0x00000AEF},
-{0x00000B66, 0x00000B6F}, {0x00000B72, 0x00000B77}, {0x00000BE6, 0x00000BF2}, {0x00000C66, 0x00000C6F},
-{0x00000C78, 0x00000C7E}, {0x00000CE6, 0x00000CEF}, {0x00000D58, 0x00000D5E}, {0x00000D66, 0x00000D78},
-{0x00000DE6, 0x00000DEF}, {0x00000E50, 0x00000E59}, {0x00000ED0, 0x00000ED9}, {0x00000F20, 0x00000F33},
-{0x00001040, 0x00001049}, {0x00001090, 0x00001099}, {0x00001369, 0x0000137C}, {0x000016EE, 0x000016F0},
-{0x000017E0, 0x000017E9}, {0x000017F0, 0x000017F9}, {0x00001810, 0x00001819}, {0x00001946, 0x0000194F},
-{0x000019D0, 0x000019DA}, {0x00001A80, 0x00001A89}, {0x00001A90, 0x00001A99}, {0x00001B50, 0x00001B59},
-{0x00001BB0, 0x00001BB9}, {0x00001C40, 0x00001C49}, {0x00001C50, 0x00001C59}, {0x00002070, 0x00002070},
-{0x00002074, 0x00002079}, {0x00002080, 0x00002089}, {0x00002150, 0x00002182}, {0x00002185, 0x00002189},
-{0x00002460, 0x0000249B}, {0x000024EA, 0x000024FF}, {0x00002776, 0x00002793}, {0x00002CFD, 0x00002CFD},
-{0x00003007, 0x00003007}, {0x00003021, 0x00003029}, {0x00003038, 0x0000303A}, {0x00003192, 0x00003195},
-{0x00003220, 0x00003229}, {0x00003248, 0x0000324F}, {0x00003251, 0x0000325F}, {0x00003280, 0x00003289},
-{0x000032B1, 0x000032BF}, {0x0000A620, 0x0000A629}, {0x0000A6E6, 0x0000A6EF}, {0x0000A830, 0x0000A835},
-{0x0000A8D0, 0x0000A8D9}, {0x0000A900, 0x0000A909}, {0x0000A9D0, 0x0000A9D9}, {0x0000A9F0, 0x0000A9F9},
-{0x0000AA50, 0x0000AA59}, {0x0000ABF0, 0x0000ABF9}, {0x0000FF10, 0x0000FF19}, {0x00010107, 0x00010133},
-{0x00010140, 0x00010178}, {0x0001018A, 0x0001018B}, {0x000102E1, 0x000102FB}, {0x00010320, 0x00010323},
-{0x00010341, 0x00010341}, {0x0001034A, 0x0001034A}, {0x000103D1, 0x000103D5}, {0x000104A0, 0x000104A9},
-{0x00010858, 0x0001085F}, {0x00010879, 0x0001087F}, {0x000108A7, 0x000108AF}, {0x000108FB, 0x000108FF},
-{0x00010916, 0x0001091B}, {0x000109BC, 0x000109BD}, {0x000109C0, 0x000109CF}, {0x000109D2, 0x000109FF},
-{0x00010A40, 0x00010A48}, {0x00010A7D, 0x00010A7E}, {0x00010A9D, 0x00010A9F}, {0x00010AEB, 0x00010AEF},
-{0x00010B58, 0x00010B5F}, {0x00010B78, 0x00010B7F}, {0x00010BA9, 0x00010BAF}, {0x00010CFA, 0x00010CFF},
-{0x00010D30, 0x00010D39}, {0x00010E60, 0x00010E7E}, {0x00010F1D, 0x00010F26}, {0x00010F51, 0x00010F54},
-{0x00010FC5, 0x00010FCB}, {0x00011052, 0x0001106F}, {0x000110F0, 0x000110F9}, {0x00011136, 0x0001113F},
-{0x000111D0, 0x000111D9}, {0x000111E1, 0x000111F4}, {0x000112F0, 0x000112F9}, {0x00011450, 0x00011459},
-{0x000114D0, 0x000114D9}, {0x00011650, 0x00011659}, {0x000116C0, 0x000116C9}, {0x00011730, 0x0001173B},
-{0x000118E0, 0x000118F2}, {0x00011950, 0x00011959}, {0x00011C50, 0x00011C6C}, {0x00011D50, 0x00011D59},
-{0x00011DA0, 0x00011DA9}, {0x00011F50, 0x00011F59}, {0x00011FC0, 0x00011FD4}, {0x00012400, 0x0001246E},
-{0x00016A60, 0x00016A69}, {0x00016AC0, 0x00016AC9}, {0x00016B50, 0x00016B59}, {0x00016B5B, 0x00016B61},
-{0x00016E80, 0x00016E96}, {0x0001D2C0, 0x0001D2D3}, {0x0001D2E0, 0x0001D2F3}, {0x0001D360, 0x0001D378},
-{0x0001D7CE, 0x0001D7FF}, {0x0001E140, 0x0001E149}, {0x0001E2F0, 0x0001E2F9}, {0x0001E4F0, 0x0001E4F9},
-{0x0001E8C7, 0x0001E8CF}, {0x0001E950, 0x0001E959}, {0x0001EC71, 0x0001ECAB}, {0x0001ECAD, 0x0001ECAF},
-{0x0001ECB1, 0x0001ECB4}, {0x0001ED01, 0x0001ED2D}, {0x0001ED2F, 0x0001ED3D}, {0x0001F100, 0x0001F10C},
-{0x0001FBF0, 0x0001FBF9},
+const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags = {  // start, flags // last=next_start-1
+{0x000000, 0x0080},
+{0x000020, 0x0008},
+{0x000021, 0x0020},
+{0x000024, 0x0040},
+{0x000025, 0x0020},
+{0x00002B, 0x0040},
+{0x00002C, 0x0020},
+{0x000030, 0x0002},
+{0x00003A, 0x0020},
+{0x00003C, 0x0040},
+{0x00003F, 0x0020},
+{0x000041, 0x0004},
+{0x00005B, 0x0020},
+{0x00005E, 0x0040},
+{0x00005F, 0x0020},
+{0x000060, 0x0040},
+{0x000061, 0x0004},
+{0x00007B, 0x0020},
+{0x00007C, 0x0040},
+{0x00007D, 0x0020},
+{0x00007E, 0x0040},
+{0x00007F, 0x0080},
+{0x0000A0, 0x0008},
+{0x0000A1, 0x0020},
+{0x0000A2, 0x0040},
+{0x0000A7, 0x0020},
+{0x0000A8, 0x0040},
+{0x0000AA, 0x0004},
+{0x0000AB, 0x0020},
+{0x0000AC, 0x0040},
+{0x0000AD, 0x0080},
+{0x0000AE, 0x0040},
+{0x0000B2, 0x0002},
+{0x0000B4, 0x0040},
+{0x0000B5, 0x0004},
+{0x0000B6, 0x0020},
+{0x0000B8, 0x0040},
+{0x0000B9, 0x0002},
+{0x0000BA, 0x0004},
+{0x0000BB, 0x0020},
+{0x0000BC, 0x0002},
+{0x0000BF, 0x0020},
+{0x0000C0, 0x0004},
+{0x0000D7, 0x0040},
+{0x0000D8, 0x0004},
+{0x0000F7, 0x0040},
+{0x0000F8, 0x0004},
+{0x0002C2, 0x0040},
+{0x0002C6, 0x0004},
+{0x0002D2, 0x0040},
+{0x0002E0, 0x0004},
+{0x0002E5, 0x0040},
+{0x0002EC, 0x0004},
+{0x0002ED, 0x0040},
+{0x0002EE, 0x0004},
+{0x0002EF, 0x0040},
+{0x000300, 0x0010},
+{0x000370, 0x0004},
+{0x000375, 0x0040},
+{0x000376, 0x0004},
+{0x000378, 0x0080},
+{0x00037A, 0x0004},
+{0x00037E, 0x0020},
+{0x00037F, 0x0004},
+{0x000380, 0x0080},
+{0x000384, 0x0040},
+{0x000386, 0x0004},
+{0x000387, 0x0020},
+{0x000388, 0x0004},
+{0x00038B, 0x0080},
+{0x00038C, 0x0004},
+{0x00038D, 0x0080},
+{0x00038E, 0x0004},
+{0x0003A2, 0x0080},
+{0x0003A3, 0x0004},
+{0x0003F6, 0x0040},
+{0x0003F7, 0x0004},
+{0x000482, 0x0040},
+{0x000483, 0x0010},
+{0x00048A, 0x0004},
+{0x000530, 0x0080},
+{0x000531, 0x0004},
+{0x000557, 0x0080},
+{0x000559, 0x0004},
+{0x00055A, 0x0020},
+{0x000560, 0x0004},
+{0x000589, 0x0020},
+{0x00058B, 0x0080},
+{0x00058D, 0x0040},
+{0x000590, 0x0080},
+{0x000591, 0x0010},
+{0x0005BE, 0x0020},
+{0x0005BF, 0x0010},
+{0x0005C0, 0x0020},
+{0x0005C1, 0x0010},
+{0x0005C3, 0x0020},
+{0x0005C4, 0x0010},
+{0x0005C6, 0x0020},
+{0x0005C7, 0x0010},
+{0x0005C8, 0x0080},
+{0x0005D0, 0x0004},
+{0x0005EB, 0x0080},
+{0x0005EF, 0x0004},
+{0x0005F3, 0x0020},
+{0x0005F5, 0x0080},
+{0x000606, 0x0040},
+{0x000609, 0x0020},
+{0x00060B, 0x0040},
+{0x00060C, 0x0020},
+{0x00060E, 0x0040},
+{0x000610, 0x0010},
+{0x00061B, 0x0020},
+{0x00061C, 0x0080},
+{0x00061D, 0x0020},
+{0x000620, 0x0004},
+{0x00064B, 0x0010},
+{0x000660, 0x0002},
+{0x00066A, 0x0020},
+{0x00066E, 0x0004},
+{0x000670, 0x0010},
+{0x000671, 0x0004},
+{0x0006D4, 0x0020},
+{0x0006D5, 0x0004},
+{0x0006D6, 0x0010},
+{0x0006DD, 0x0080},
+{0x0006DE, 0x0040},
+{0x0006DF, 0x0010},
+{0x0006E5, 0x0004},
+{0x0006E7, 0x0010},
+{0x0006E9, 0x0040},
+{0x0006EA, 0x0010},
+{0x0006EE, 0x0004},
+{0x0006F0, 0x0002},
+{0x0006FA, 0x0004},
+{0x0006FD, 0x0040},
+{0x0006FF, 0x0004},
+{0x000700, 0x0020},
+{0x00070E, 0x0080},
+{0x000710, 0x0004},
+{0x000711, 0x0010},
+{0x000712, 0x0004},
+{0x000730, 0x0010},
+{0x00074B, 0x0080},
+{0x00074D, 0x0004},
+{0x0007A6, 0x0010},
+{0x0007B1, 0x0004},
+{0x0007B2, 0x0080},
+{0x0007C0, 0x0002},
+{0x0007CA, 0x0004},
+{0x0007EB, 0x0010},
+{0x0007F4, 0x0004},
+{0x0007F6, 0x0040},
+{0x0007F7, 0x0020},
+{0x0007FA, 0x0004},
+{0x0007FB, 0x0080},
+{0x0007FD, 0x0010},
+{0x0007FE, 0x0040},
+{0x000800, 0x0004},
+{0x000816, 0x0010},
+{0x00081A, 0x0004},
+{0x00081B, 0x0010},
+{0x000824, 0x0004},
+{0x000825, 0x0010},
+{0x000828, 0x0004},
+{0x000829, 0x0010},
+{0x00082E, 0x0080},
+{0x000830, 0x0020},
+{0x00083F, 0x0080},
+{0x000840, 0x0004},
+{0x000859, 0x0010},
+{0x00085C, 0x0080},
+{0x00085E, 0x0020},
+{0x00085F, 0x0080},
+{0x000860, 0x0004},
+{0x00086B, 0x0080},
+{0x000870, 0x0004},
+{0x000888, 0x0040},
+{0x000889, 0x0004},
+{0x00088F, 0x0080},
+{0x000898, 0x0010},
+{0x0008A0, 0x0004},
+{0x0008CA, 0x0010},
+{0x0008E2, 0x0080},
+{0x0008E3, 0x0010},
+{0x000904, 0x0004},
+{0x00093A, 0x0010},
+{0x00093D, 0x0004},
+{0x00093E, 0x0010},
+{0x000950, 0x0004},
+{0x000951, 0x0010},
+{0x000958, 0x0004},
+{0x000962, 0x0010},
+{0x000964, 0x0020},
+{0x000966, 0x0002},
+{0x000970, 0x0020},
+{0x000971, 0x0004},
+{0x000981, 0x0010},
+{0x000984, 0x0080},
+{0x000985, 0x0004},
+{0x00098D, 0x0080},
+{0x00098F, 0x0004},
+{0x000991, 0x0080},
+{0x000993, 0x0004},
+{0x0009A9, 0x0080},
+{0x0009AA, 0x0004},
+{0x0009B1, 0x0080},
+{0x0009B2, 0x0004},
+{0x0009B3, 0x0080},
+{0x0009B6, 0x0004},
+{0x0009BA, 0x0080},
+{0x0009BC, 0x0010},
+{0x0009BD, 0x0004},
+{0x0009BE, 0x0010},
+{0x0009C5, 0x0080},
+{0x0009C7, 0x0010},
+{0x0009C9, 0x0080},
+{0x0009CB, 0x0010},
+{0x0009CE, 0x0004},
+{0x0009CF, 0x0080},
+{0x0009D7, 0x0010},
+{0x0009D8, 0x0080},
+{0x0009DC, 0x0004},
+{0x0009DE, 0x0080},
+{0x0009DF, 0x0004},
+{0x0009E2, 0x0010},
+{0x0009E4, 0x0080},
+{0x0009E6, 0x0002},
+{0x0009F0, 0x0004},
+{0x0009F2, 0x0040},
+{0x0009F4, 0x0002},
+{0x0009FA, 0x0040},
+{0x0009FC, 0x0004},
+{0x0009FD, 0x0020},
+{0x0009FE, 0x0010},
+{0x0009FF, 0x0080},
+{0x000A01, 0x0010},
+{0x000A04, 0x0080},
+{0x000A05, 0x0004},
+{0x000A0B, 0x0080},
+{0x000A0F, 0x0004},
+{0x000A11, 0x0080},
+{0x000A13, 0x0004},
+{0x000A29, 0x0080},
+{0x000A2A, 0x0004},
+{0x000A31, 0x0080},
+{0x000A32, 0x0004},
+{0x000A34, 0x0080},
+{0x000A35, 0x0004},
+{0x000A37, 0x0080},
+{0x000A38, 0x0004},
+{0x000A3A, 0x0080},
+{0x000A3C, 0x0010},
+{0x000A3D, 0x0080},
+{0x000A3E, 0x0010},
+{0x000A43, 0x0080},
+{0x000A47, 0x0010},
+{0x000A49, 0x0080},
+{0x000A4B, 0x0010},
+{0x000A4E, 0x0080},
+{0x000A51, 0x0010},
+{0x000A52, 0x0080},
+{0x000A59, 0x0004},
+{0x000A5D, 0x0080},
+{0x000A5E, 0x0004},
+{0x000A5F, 0x0080},
+{0x000A66, 0x0002},
+{0x000A70, 0x0010},
+{0x000A72, 0x0004},
+{0x000A75, 0x0010},
+{0x000A76, 0x0020},
+{0x000A77, 0x0080},
+{0x000A81, 0x0010},
+{0x000A84, 0x0080},
+{0x000A85, 0x0004},
+{0x000A8E, 0x0080},
+{0x000A8F, 0x0004},
+{0x000A92, 0x0080},
+{0x000A93, 0x0004},
+{0x000AA9, 0x0080},
+{0x000AAA, 0x0004},
+{0x000AB1, 0x0080},
+{0x000AB2, 0x0004},
+{0x000AB4, 0x0080},
+{0x000AB5, 0x0004},
+{0x000ABA, 0x0080},
+{0x000ABC, 0x0010},
+{0x000ABD, 0x0004},
+{0x000ABE, 0x0010},
+{0x000AC6, 0x0080},
+{0x000AC7, 0x0010},
+{0x000ACA, 0x0080},
+{0x000ACB, 0x0010},
+{0x000ACE, 0x0080},
+{0x000AD0, 0x0004},
+{0x000AD1, 0x0080},
+{0x000AE0, 0x0004},
+{0x000AE2, 0x0010},
+{0x000AE4, 0x0080},
+{0x000AE6, 0x0002},
+{0x000AF0, 0x0020},
+{0x000AF1, 0x0040},
+{0x000AF2, 0x0080},
+{0x000AF9, 0x0004},
+{0x000AFA, 0x0010},
+{0x000B00, 0x0080},
+{0x000B01, 0x0010},
+{0x000B04, 0x0080},
+{0x000B05, 0x0004},
+{0x000B0D, 0x0080},
+{0x000B0F, 0x0004},
+{0x000B11, 0x0080},
+{0x000B13, 0x0004},
+{0x000B29, 0x0080},
+{0x000B2A, 0x0004},
+{0x000B31, 0x0080},
+{0x000B32, 0x0004},
+{0x000B34, 0x0080},
+{0x000B35, 0x0004},
+{0x000B3A, 0x0080},
+{0x000B3C, 0x0010},
+{0x000B3D, 0x0004},
+{0x000B3E, 0x0010},
+{0x000B45, 0x0080},
+{0x000B47, 0x0010},
+{0x000B49, 0x0080},
+{0x000B4B, 0x0010},
+{0x000B4E, 0x0080},
+{0x000B55, 0x0010},
+{0x000B58, 0x0080},
+{0x000B5C, 0x0004},
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+{0x01E7E0, 0x0004},
+{0x01E7E7, 0x0080},
+{0x01E7E8, 0x0004},
+{0x01E7EC, 0x0080},
+{0x01E7ED, 0x0004},
+{0x01E7EF, 0x0080},
+{0x01E7F0, 0x0004},
+{0x01E7FF, 0x0080},
+{0x01E800, 0x0004},
+{0x01E8C5, 0x0080},
+{0x01E8C7, 0x0002},
+{0x01E8D0, 0x0010},
+{0x01E8D7, 0x0080},
+{0x01E900, 0x0004},
+{0x01E944, 0x0010},
+{0x01E94B, 0x0004},
+{0x01E94C, 0x0080},
+{0x01E950, 0x0002},
+{0x01E95A, 0x0080},
+{0x01E95E, 0x0020},
+{0x01E960, 0x0080},
+{0x01EC71, 0x0002},
+{0x01ECAC, 0x0040},
+{0x01ECAD, 0x0002},
+{0x01ECB0, 0x0040},
+{0x01ECB1, 0x0002},
+{0x01ECB5, 0x0080},
+{0x01ED01, 0x0002},
+{0x01ED2E, 0x0040},
+{0x01ED2F, 0x0002},
+{0x01ED3E, 0x0080},
+{0x01EE00, 0x0004},
+{0x01EE04, 0x0080},
+{0x01EE05, 0x0004},
+{0x01EE20, 0x0080},
+{0x01EE21, 0x0004},
+{0x01EE23, 0x0080},
+{0x01EE24, 0x0004},
+{0x01EE25, 0x0080},
+{0x01EE27, 0x0004},
+{0x01EE28, 0x0080},
+{0x01EE29, 0x0004},
+{0x01EE33, 0x0080},
+{0x01EE34, 0x0004},
+{0x01EE38, 0x0080},
+{0x01EE39, 0x0004},
+{0x01EE3A, 0x0080},
+{0x01EE3B, 0x0004},
+{0x01EE3C, 0x0080},
+{0x01EE42, 0x0004},
+{0x01EE43, 0x0080},
+{0x01EE47, 0x0004},
+{0x01EE48, 0x0080},
+{0x01EE49, 0x0004},
+{0x01EE4A, 0x0080},
+{0x01EE4B, 0x0004},
+{0x01EE4C, 0x0080},
+{0x01EE4D, 0x0004},
+{0x01EE50, 0x0080},
+{0x01EE51, 0x0004},
+{0x01EE53, 0x0080},
+{0x01EE54, 0x0004},
+{0x01EE55, 0x0080},
+{0x01EE57, 0x0004},
+{0x01EE58, 0x0080},
+{0x01EE59, 0x0004},
+{0x01EE5A, 0x0080},
+{0x01EE5B, 0x0004},
+{0x01EE5C, 0x0080},
+{0x01EE5D, 0x0004},
+{0x01EE5E, 0x0080},
+{0x01EE5F, 0x0004},
+{0x01EE60, 0x0080},
+{0x01EE61, 0x0004},
+{0x01EE63, 0x0080},
+{0x01EE64, 0x0004},
+{0x01EE65, 0x0080},
+{0x01EE67, 0x0004},
+{0x01EE6B, 0x0080},
+{0x01EE6C, 0x0004},
+{0x01EE73, 0x0080},
+{0x01EE74, 0x0004},
+{0x01EE78, 0x0080},
+{0x01EE79, 0x0004},
+{0x01EE7D, 0x0080},
+{0x01EE7E, 0x0004},
+{0x01EE7F, 0x0080},
+{0x01EE80, 0x0004},
+{0x01EE8A, 0x0080},
+{0x01EE8B, 0x0004},
+{0x01EE9C, 0x0080},
+{0x01EEA1, 0x0004},
+{0x01EEA4, 0x0080},
+{0x01EEA5, 0x0004},
+{0x01EEAA, 0x0080},
+{0x01EEAB, 0x0004},
+{0x01EEBC, 0x0080},
+{0x01EEF0, 0x0040},
+{0x01EEF2, 0x0080},
+{0x01F000, 0x0040},
+{0x01F02C, 0x0080},
+{0x01F030, 0x0040},
+{0x01F094, 0x0080},
+{0x01F0A0, 0x0040},
+{0x01F0AF, 0x0080},
+{0x01F0B1, 0x0040},
+{0x01F0C0, 0x0080},
+{0x01F0C1, 0x0040},
+{0x01F0D0, 0x0080},
+{0x01F0D1, 0x0040},
+{0x01F0F6, 0x0080},
+{0x01F100, 0x0002},
+{0x01F10D, 0x0040},
+{0x01F1AE, 0x0080},
+{0x01F1E6, 0x0040},
+{0x01F203, 0x0080},
+{0x01F210, 0x0040},
+{0x01F23C, 0x0080},
+{0x01F240, 0x0040},
+{0x01F249, 0x0080},
+{0x01F250, 0x0040},
+{0x01F252, 0x0080},
+{0x01F260, 0x0040},
+{0x01F266, 0x0080},
+{0x01F300, 0x0040},
+{0x01F6D8, 0x0080},
+{0x01F6DC, 0x0040},
+{0x01F6ED, 0x0080},
+{0x01F6F0, 0x0040},
+{0x01F6FD, 0x0080},
+{0x01F700, 0x0040},
+{0x01F777, 0x0080},
+{0x01F77B, 0x0040},
+{0x01F7DA, 0x0080},
+{0x01F7E0, 0x0040},
+{0x01F7EC, 0x0080},
+{0x01F7F0, 0x0040},
+{0x01F7F1, 0x0080},
+{0x01F800, 0x0040},
+{0x01F80C, 0x0080},
+{0x01F810, 0x0040},
+{0x01F848, 0x0080},
+{0x01F850, 0x0040},
+{0x01F85A, 0x0080},
+{0x01F860, 0x0040},
+{0x01F888, 0x0080},
+{0x01F890, 0x0040},
+{0x01F8AE, 0x0080},
+{0x01F8B0, 0x0040},
+{0x01F8B2, 0x0080},
+{0x01F900, 0x0040},
+{0x01FA54, 0x0080},
+{0x01FA60, 0x0040},
+{0x01FA6E, 0x0080},
+{0x01FA70, 0x0040},
+{0x01FA7D, 0x0080},
+{0x01FA80, 0x0040},
+{0x01FA89, 0x0080},
+{0x01FA90, 0x0040},
+{0x01FABE, 0x0080},
+{0x01FABF, 0x0040},
+{0x01FAC6, 0x0080},
+{0x01FACE, 0x0040},
+{0x01FADC, 0x0080},
+{0x01FAE0, 0x0040},
+{0x01FAE9, 0x0080},
+{0x01FAF0, 0x0040},
+{0x01FAF9, 0x0080},
+{0x01FB00, 0x0040},
+{0x01FB93, 0x0080},
+{0x01FB94, 0x0040},
+{0x01FBCB, 0x0080},
+{0x01FBF0, 0x0002},
+{0x01FBFA, 0x0080},
+{0x020000, 0x0004},
+{0x02A6E0, 0x0080},
+{0x02A700, 0x0004},
+{0x02B73A, 0x0080},
+{0x02B740, 0x0004},
+{0x02B81E, 0x0080},
+{0x02B820, 0x0004},
+{0x02CEA2, 0x0080},
+{0x02CEB0, 0x0004},
+{0x02EBE1, 0x0080},
+{0x02EBF0, 0x0004},
+{0x02EE5E, 0x0080},
+{0x02F800, 0x0004},
+{0x02FA1E, 0x0080},
+{0x030000, 0x0004},
+{0x03134B, 0x0080},
+{0x031350, 0x0004},
+{0x0323B0, 0x0080},
+{0x0E0100, 0x0010},
+{0x0E01F0, 0x0080},
+{0x110000, 0x0000},
 };
 
-const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_letter = {
-{0x00000041, 0x0000005A}, {0x00000061, 0x0000007A}, {0x000000AA, 0x000000AA}, {0x000000B5, 0x000000B5},
-{0x000000BA, 0x000000BA}, {0x000000C0, 0x000000D6}, {0x000000D8, 0x000000F6}, {0x000000F8, 0x000002C1},
-{0x000002C6, 0x000002D1}, {0x000002E0, 0x000002E4}, {0x000002EC, 0x000002EC}, {0x000002EE, 0x000002EE},
-{0x00000370, 0x00000374}, {0x00000376, 0x00000377}, {0x0000037A, 0x0000037D}, {0x0000037F, 0x0000037F},
-{0x00000386, 0x00000386}, {0x00000388, 0x0000038A}, {0x0000038C, 0x0000038C}, {0x0000038E, 0x000003A1},
-{0x000003A3, 0x000003F5}, {0x000003F7, 0x00000481}, {0x0000048A, 0x0000052F}, {0x00000531, 0x00000556},
-{0x00000559, 0x00000559}, {0x00000560, 0x00000588}, {0x000005D0, 0x000005EA}, {0x000005EF, 0x000005F2},
-{0x00000620, 0x0000064A}, {0x0000066E, 0x0000066F}, {0x00000671, 0x000006D3}, {0x000006D5, 0x000006D5},
-{0x000006E5, 0x000006E6}, {0x000006EE, 0x000006EF}, {0x000006FA, 0x000006FC}, {0x000006FF, 0x000006FF},
-{0x00000710, 0x00000710}, {0x00000712, 0x0000072F}, {0x0000074D, 0x000007A5}, {0x000007B1, 0x000007B1},
-{0x000007CA, 0x000007EA}, {0x000007F4, 0x000007F5}, {0x000007FA, 0x000007FA}, {0x00000800, 0x00000815},
-{0x0000081A, 0x0000081A}, {0x00000824, 0x00000824}, {0x00000828, 0x00000828}, {0x00000840, 0x00000858},
-{0x00000860, 0x0000086A}, {0x00000870, 0x00000887}, {0x00000889, 0x0000088E}, {0x000008A0, 0x000008C9},
-{0x00000904, 0x00000939}, {0x0000093D, 0x0000093D}, {0x00000950, 0x00000950}, {0x00000958, 0x00000961},
-{0x00000971, 0x00000980}, {0x00000985, 0x0000098C}, {0x0000098F, 0x00000990}, {0x00000993, 0x000009A8},
-{0x000009AA, 0x000009B0}, {0x000009B2, 0x000009B2}, {0x000009B6, 0x000009B9}, {0x000009BD, 0x000009BD},
-{0x000009CE, 0x000009CE}, {0x000009DC, 0x000009DD}, {0x000009DF, 0x000009E1}, {0x000009F0, 0x000009F1},
-{0x000009FC, 0x000009FC}, {0x00000A05, 0x00000A0A}, {0x00000A0F, 0x00000A10}, {0x00000A13, 0x00000A28},
-{0x00000A2A, 0x00000A30}, {0x00000A32, 0x00000A33}, {0x00000A35, 0x00000A36}, {0x00000A38, 0x00000A39},
-{0x00000A59, 0x00000A5C}, {0x00000A5E, 0x00000A5E}, {0x00000A72, 0x00000A74}, {0x00000A85, 0x00000A8D},
-{0x00000A8F, 0x00000A91}, {0x00000A93, 0x00000AA8}, {0x00000AAA, 0x00000AB0}, {0x00000AB2, 0x00000AB3},
-{0x00000AB5, 0x00000AB9}, {0x00000ABD, 0x00000ABD}, {0x00000AD0, 0x00000AD0}, {0x00000AE0, 0x00000AE1},
-{0x00000AF9, 0x00000AF9}, {0x00000B05, 0x00000B0C}, {0x00000B0F, 0x00000B10}, {0x00000B13, 0x00000B28},
-{0x00000B2A, 0x00000B30}, {0x00000B32, 0x00000B33}, {0x00000B35, 0x00000B39}, {0x00000B3D, 0x00000B3D},
-{0x00000B5C, 0x00000B5D}, {0x00000B5F, 0x00000B61}, {0x00000B71, 0x00000B71}, {0x00000B83, 0x00000B83},
-{0x00000B85, 0x00000B8A}, {0x00000B8E, 0x00000B90}, {0x00000B92, 0x00000B95}, {0x00000B99, 0x00000B9A},
-{0x00000B9C, 0x00000B9C}, {0x00000B9E, 0x00000B9F}, {0x00000BA3, 0x00000BA4}, {0x00000BA8, 0x00000BAA},
-{0x00000BAE, 0x00000BB9}, {0x00000BD0, 0x00000BD0}, {0x00000C05, 0x00000C0C}, {0x00000C0E, 0x00000C10},
-{0x00000C12, 0x00000C28}, {0x00000C2A, 0x00000C39}, {0x00000C3D, 0x00000C3D}, {0x00000C58, 0x00000C5A},
-{0x00000C5D, 0x00000C5D}, {0x00000C60, 0x00000C61}, {0x00000C80, 0x00000C80}, {0x00000C85, 0x00000C8C},
-{0x00000C8E, 0x00000C90}, {0x00000C92, 0x00000CA8}, {0x00000CAA, 0x00000CB3}, {0x00000CB5, 0x00000CB9},
-{0x00000CBD, 0x00000CBD}, {0x00000CDD, 0x00000CDE}, {0x00000CE0, 0x00000CE1}, {0x00000CF1, 0x00000CF2},
-{0x00000D04, 0x00000D0C}, {0x00000D0E, 0x00000D10}, {0x00000D12, 0x00000D3A}, {0x00000D3D, 0x00000D3D},
-{0x00000D4E, 0x00000D4E}, {0x00000D54, 0x00000D56}, {0x00000D5F, 0x00000D61}, {0x00000D7A, 0x00000D7F},
-{0x00000D85, 0x00000D96}, {0x00000D9A, 0x00000DB1}, {0x00000DB3, 0x00000DBB}, {0x00000DBD, 0x00000DBD},
-{0x00000DC0, 0x00000DC6}, {0x00000E01, 0x00000E30}, {0x00000E32, 0x00000E33}, {0x00000E40, 0x00000E46},
-{0x00000E81, 0x00000E82}, {0x00000E84, 0x00000E84}, {0x00000E86, 0x00000E8A}, {0x00000E8C, 0x00000EA3},
-{0x00000EA5, 0x00000EA5}, {0x00000EA7, 0x00000EB0}, {0x00000EB2, 0x00000EB3}, {0x00000EBD, 0x00000EBD},
-{0x00000EC0, 0x00000EC4}, {0x00000EC6, 0x00000EC6}, {0x00000EDC, 0x00000EDF}, {0x00000F00, 0x00000F00},
-{0x00000F40, 0x00000F47}, {0x00000F49, 0x00000F6C}, {0x00000F88, 0x00000F8C}, {0x00001000, 0x0000102A},
-{0x0000103F, 0x0000103F}, {0x00001050, 0x00001055}, {0x0000105A, 0x0000105D}, {0x00001061, 0x00001061},
-{0x00001065, 0x00001066}, {0x0000106E, 0x00001070}, {0x00001075, 0x00001081}, {0x0000108E, 0x0000108E},
-{0x000010A0, 0x000010C5}, {0x000010C7, 0x000010C7}, {0x000010CD, 0x000010CD}, {0x000010D0, 0x000010FA},
-{0x000010FC, 0x00001248}, {0x0000124A, 0x0000124D}, {0x00001250, 0x00001256}, {0x00001258, 0x00001258},
-{0x0000125A, 0x0000125D}, {0x00001260, 0x00001288}, {0x0000128A, 0x0000128D}, {0x00001290, 0x000012B0},
-{0x000012B2, 0x000012B5}, {0x000012B8, 0x000012BE}, {0x000012C0, 0x000012C0}, {0x000012C2, 0x000012C5},
-{0x000012C8, 0x000012D6}, {0x000012D8, 0x00001310}, {0x00001312, 0x00001315}, {0x00001318, 0x0000135A},
-{0x00001380, 0x0000138F}, {0x000013A0, 0x000013F5}, {0x000013F8, 0x000013FD}, {0x00001401, 0x0000166C},
-{0x0000166F, 0x0000167F}, {0x00001681, 0x0000169A}, {0x000016A0, 0x000016EA}, {0x000016F1, 0x000016F8},
-{0x00001700, 0x00001711}, {0x0000171F, 0x00001731}, {0x00001740, 0x00001751}, {0x00001760, 0x0000176C},
-{0x0000176E, 0x00001770}, {0x00001780, 0x000017B3}, {0x000017D7, 0x000017D7}, {0x000017DC, 0x000017DC},
-{0x00001820, 0x00001878}, {0x00001880, 0x00001884}, {0x00001887, 0x000018A8}, {0x000018AA, 0x000018AA},
-{0x000018B0, 0x000018F5}, {0x00001900, 0x0000191E}, {0x00001950, 0x0000196D}, {0x00001970, 0x00001974},
-{0x00001980, 0x000019AB}, {0x000019B0, 0x000019C9}, {0x00001A00, 0x00001A16}, {0x00001A20, 0x00001A54},
-{0x00001AA7, 0x00001AA7}, {0x00001B05, 0x00001B33}, {0x00001B45, 0x00001B4C}, {0x00001B83, 0x00001BA0},
-{0x00001BAE, 0x00001BAF}, {0x00001BBA, 0x00001BE5}, {0x00001C00, 0x00001C23}, {0x00001C4D, 0x00001C4F},
-{0x00001C5A, 0x00001C7D}, {0x00001C80, 0x00001C88}, {0x00001C90, 0x00001CBA}, {0x00001CBD, 0x00001CBF},
-{0x00001CE9, 0x00001CEC}, {0x00001CEE, 0x00001CF3}, {0x00001CF5, 0x00001CF6}, {0x00001CFA, 0x00001CFA},
-{0x00001D00, 0x00001DBF}, {0x00001E00, 0x00001F15}, {0x00001F18, 0x00001F1D}, {0x00001F20, 0x00001F45},
-{0x00001F48, 0x00001F4D}, {0x00001F50, 0x00001F57}, {0x00001F59, 0x00001F59}, {0x00001F5B, 0x00001F5B},
-{0x00001F5D, 0x00001F5D}, {0x00001F5F, 0x00001F7D}, {0x00001F80, 0x00001FB4}, {0x00001FB6, 0x00001FBC},
-{0x00001FBE, 0x00001FBE}, {0x00001FC2, 0x00001FC4}, {0x00001FC6, 0x00001FCC}, {0x00001FD0, 0x00001FD3},
-{0x00001FD6, 0x00001FDB}, {0x00001FE0, 0x00001FEC}, {0x00001FF2, 0x00001FF4}, {0x00001FF6, 0x00001FFC},
-{0x00002071, 0x00002071}, {0x0000207F, 0x0000207F}, {0x00002090, 0x0000209C}, {0x00002102, 0x00002102},
-{0x00002107, 0x00002107}, {0x0000210A, 0x00002113}, {0x00002115, 0x00002115}, {0x00002119, 0x0000211D},
-{0x00002124, 0x00002124}, {0x00002126, 0x00002126}, {0x00002128, 0x00002128}, {0x0000212A, 0x0000212D},
-{0x0000212F, 0x00002139}, {0x0000213C, 0x0000213F}, {0x00002145, 0x00002149}, {0x0000214E, 0x0000214E},
-{0x00002183, 0x00002184}, {0x00002C00, 0x00002CE4}, {0x00002CEB, 0x00002CEE}, {0x00002CF2, 0x00002CF3},
-{0x00002D00, 0x00002D25}, {0x00002D27, 0x00002D27}, {0x00002D2D, 0x00002D2D}, {0x00002D30, 0x00002D67},
-{0x00002D6F, 0x00002D6F}, {0x00002D80, 0x00002D96}, {0x00002DA0, 0x00002DA6}, {0x00002DA8, 0x00002DAE},
-{0x00002DB0, 0x00002DB6}, {0x00002DB8, 0x00002DBE}, {0x00002DC0, 0x00002DC6}, {0x00002DC8, 0x00002DCE},
-{0x00002DD0, 0x00002DD6}, {0x00002DD8, 0x00002DDE}, {0x00002E2F, 0x00002E2F}, {0x00003005, 0x00003006},
-{0x00003031, 0x00003035}, {0x0000303B, 0x0000303C}, {0x00003041, 0x00003096}, {0x0000309D, 0x0000309F},
-{0x000030A1, 0x000030FA}, {0x000030FC, 0x000030FF}, {0x00003105, 0x0000312F}, {0x00003131, 0x0000318E},
-{0x000031A0, 0x000031BF}, {0x000031F0, 0x000031FF}, {0x00003400, 0x00004DBF}, {0x00004E00, 0x0000A48C},
-{0x0000A4D0, 0x0000A4FD}, {0x0000A500, 0x0000A60C}, {0x0000A610, 0x0000A61F}, {0x0000A62A, 0x0000A62B},
-{0x0000A640, 0x0000A66E}, {0x0000A67F, 0x0000A69D}, {0x0000A6A0, 0x0000A6E5}, {0x0000A717, 0x0000A71F},
-{0x0000A722, 0x0000A788}, {0x0000A78B, 0x0000A7CA}, {0x0000A7D0, 0x0000A7D1}, {0x0000A7D3, 0x0000A7D3},
-{0x0000A7D5, 0x0000A7D9}, {0x0000A7F2, 0x0000A801}, {0x0000A803, 0x0000A805}, {0x0000A807, 0x0000A80A},
-{0x0000A80C, 0x0000A822}, {0x0000A840, 0x0000A873}, {0x0000A882, 0x0000A8B3}, {0x0000A8F2, 0x0000A8F7},
-{0x0000A8FB, 0x0000A8FB}, {0x0000A8FD, 0x0000A8FE}, {0x0000A90A, 0x0000A925}, {0x0000A930, 0x0000A946},
-{0x0000A960, 0x0000A97C}, {0x0000A984, 0x0000A9B2}, {0x0000A9CF, 0x0000A9CF}, {0x0000A9E0, 0x0000A9E4},
-{0x0000A9E6, 0x0000A9EF}, {0x0000A9FA, 0x0000A9FE}, {0x0000AA00, 0x0000AA28}, {0x0000AA40, 0x0000AA42},
-{0x0000AA44, 0x0000AA4B}, {0x0000AA60, 0x0000AA76}, {0x0000AA7A, 0x0000AA7A}, {0x0000AA7E, 0x0000AAAF},
-{0x0000AAB1, 0x0000AAB1}, {0x0000AAB5, 0x0000AAB6}, {0x0000AAB9, 0x0000AABD}, {0x0000AAC0, 0x0000AAC0},
-{0x0000AAC2, 0x0000AAC2}, {0x0000AADB, 0x0000AADD}, {0x0000AAE0, 0x0000AAEA}, {0x0000AAF2, 0x0000AAF4},
-{0x0000AB01, 0x0000AB06}, {0x0000AB09, 0x0000AB0E}, {0x0000AB11, 0x0000AB16}, {0x0000AB20, 0x0000AB26},
-{0x0000AB28, 0x0000AB2E}, {0x0000AB30, 0x0000AB5A}, {0x0000AB5C, 0x0000AB69}, {0x0000AB70, 0x0000ABE2},
-{0x0000AC00, 0x0000D7A3}, {0x0000D7B0, 0x0000D7C6}, {0x0000D7CB, 0x0000D7FB}, {0x0000F900, 0x0000FA6D},
-{0x0000FA70, 0x0000FAD9}, {0x0000FB00, 0x0000FB06}, {0x0000FB13, 0x0000FB17}, {0x0000FB1D, 0x0000FB1D},
-{0x0000FB1F, 0x0000FB28}, {0x0000FB2A, 0x0000FB36}, {0x0000FB38, 0x0000FB3C}, {0x0000FB3E, 0x0000FB3E},
-{0x0000FB40, 0x0000FB41}, {0x0000FB43, 0x0000FB44}, {0x0000FB46, 0x0000FBB1}, {0x0000FBD3, 0x0000FD3D},
-{0x0000FD50, 0x0000FD8F}, {0x0000FD92, 0x0000FDC7}, {0x0000FDF0, 0x0000FDFB}, {0x0000FE70, 0x0000FE74},
-{0x0000FE76, 0x0000FEFC}, {0x0000FF21, 0x0000FF3A}, {0x0000FF41, 0x0000FF5A}, {0x0000FF66, 0x0000FFBE},
-{0x0000FFC2, 0x0000FFC7}, {0x0000FFCA, 0x0000FFCF}, {0x0000FFD2, 0x0000FFD7}, {0x0000FFDA, 0x0000FFDC},
-{0x00010000, 0x0001000B}, {0x0001000D, 0x00010026}, {0x00010028, 0x0001003A}, {0x0001003C, 0x0001003D},
-{0x0001003F, 0x0001004D}, {0x00010050, 0x0001005D}, {0x00010080, 0x000100FA}, {0x00010280, 0x0001029C},
-{0x000102A0, 0x000102D0}, {0x00010300, 0x0001031F}, {0x0001032D, 0x00010340}, {0x00010342, 0x00010349},
-{0x00010350, 0x00010375}, {0x00010380, 0x0001039D}, {0x000103A0, 0x000103C3}, {0x000103C8, 0x000103CF},
-{0x00010400, 0x0001049D}, {0x000104B0, 0x000104D3}, {0x000104D8, 0x000104FB}, {0x00010500, 0x00010527},
-{0x00010530, 0x00010563}, {0x00010570, 0x0001057A}, {0x0001057C, 0x0001058A}, {0x0001058C, 0x00010592},
-{0x00010594, 0x00010595}, {0x00010597, 0x000105A1}, {0x000105A3, 0x000105B1}, {0x000105B3, 0x000105B9},
-{0x000105BB, 0x000105BC}, {0x00010600, 0x00010736}, {0x00010740, 0x00010755}, {0x00010760, 0x00010767},
-{0x00010780, 0x00010785}, {0x00010787, 0x000107B0}, {0x000107B2, 0x000107BA}, {0x00010800, 0x00010805},
-{0x00010808, 0x00010808}, {0x0001080A, 0x00010835}, {0x00010837, 0x00010838}, {0x0001083C, 0x0001083C},
-{0x0001083F, 0x00010855}, {0x00010860, 0x00010876}, {0x00010880, 0x0001089E}, {0x000108E0, 0x000108F2},
-{0x000108F4, 0x000108F5}, {0x00010900, 0x00010915}, {0x00010920, 0x00010939}, {0x00010980, 0x000109B7},
-{0x000109BE, 0x000109BF}, {0x00010A00, 0x00010A00}, {0x00010A10, 0x00010A13}, {0x00010A15, 0x00010A17},
-{0x00010A19, 0x00010A35}, {0x00010A60, 0x00010A7C}, {0x00010A80, 0x00010A9C}, {0x00010AC0, 0x00010AC7},
-{0x00010AC9, 0x00010AE4}, {0x00010B00, 0x00010B35}, {0x00010B40, 0x00010B55}, {0x00010B60, 0x00010B72},
-{0x00010B80, 0x00010B91}, {0x00010C00, 0x00010C48}, {0x00010C80, 0x00010CB2}, {0x00010CC0, 0x00010CF2},
-{0x00010D00, 0x00010D23}, {0x00010E80, 0x00010EA9}, {0x00010EB0, 0x00010EB1}, {0x00010F00, 0x00010F1C},
-{0x00010F27, 0x00010F27}, {0x00010F30, 0x00010F45}, {0x00010F70, 0x00010F81}, {0x00010FB0, 0x00010FC4},
-{0x00010FE0, 0x00010FF6}, {0x00011003, 0x00011037}, {0x00011071, 0x00011072}, {0x00011075, 0x00011075},
-{0x00011083, 0x000110AF}, {0x000110D0, 0x000110E8}, {0x00011103, 0x00011126}, {0x00011144, 0x00011144},
-{0x00011147, 0x00011147}, {0x00011150, 0x00011172}, {0x00011176, 0x00011176}, {0x00011183, 0x000111B2},
-{0x000111C1, 0x000111C4}, {0x000111DA, 0x000111DA}, {0x000111DC, 0x000111DC}, {0x00011200, 0x00011211},
-{0x00011213, 0x0001122B}, {0x0001123F, 0x00011240}, {0x00011280, 0x00011286}, {0x00011288, 0x00011288},
-{0x0001128A, 0x0001128D}, {0x0001128F, 0x0001129D}, {0x0001129F, 0x000112A8}, {0x000112B0, 0x000112DE},
-{0x00011305, 0x0001130C}, {0x0001130F, 0x00011310}, {0x00011313, 0x00011328}, {0x0001132A, 0x00011330},
-{0x00011332, 0x00011333}, {0x00011335, 0x00011339}, {0x0001133D, 0x0001133D}, {0x00011350, 0x00011350},
-{0x0001135D, 0x00011361}, {0x00011400, 0x00011434}, {0x00011447, 0x0001144A}, {0x0001145F, 0x00011461},
-{0x00011480, 0x000114AF}, {0x000114C4, 0x000114C5}, {0x000114C7, 0x000114C7}, {0x00011580, 0x000115AE},
-{0x000115D8, 0x000115DB}, {0x00011600, 0x0001162F}, {0x00011644, 0x00011644}, {0x00011680, 0x000116AA},
-{0x000116B8, 0x000116B8}, {0x00011700, 0x0001171A}, {0x00011740, 0x00011746}, {0x00011800, 0x0001182B},
-{0x000118A0, 0x000118DF}, {0x000118FF, 0x00011906}, {0x00011909, 0x00011909}, {0x0001190C, 0x00011913},
-{0x00011915, 0x00011916}, {0x00011918, 0x0001192F}, {0x0001193F, 0x0001193F}, {0x00011941, 0x00011941},
-{0x000119A0, 0x000119A7}, {0x000119AA, 0x000119D0}, {0x000119E1, 0x000119E1}, {0x000119E3, 0x000119E3},
-{0x00011A00, 0x00011A00}, {0x00011A0B, 0x00011A32}, {0x00011A3A, 0x00011A3A}, {0x00011A50, 0x00011A50},
-{0x00011A5C, 0x00011A89}, {0x00011A9D, 0x00011A9D}, {0x00011AB0, 0x00011AF8}, {0x00011C00, 0x00011C08},
-{0x00011C0A, 0x00011C2E}, {0x00011C40, 0x00011C40}, {0x00011C72, 0x00011C8F}, {0x00011D00, 0x00011D06},
-{0x00011D08, 0x00011D09}, {0x00011D0B, 0x00011D30}, {0x00011D46, 0x00011D46}, {0x00011D60, 0x00011D65},
-{0x00011D67, 0x00011D68}, {0x00011D6A, 0x00011D89}, {0x00011D98, 0x00011D98}, {0x00011EE0, 0x00011EF2},
-{0x00011F02, 0x00011F02}, {0x00011F04, 0x00011F10}, {0x00011F12, 0x00011F33}, {0x00011FB0, 0x00011FB0},
-{0x00012000, 0x00012399}, {0x00012480, 0x00012543}, {0x00012F90, 0x00012FF0}, {0x00013000, 0x0001342F},
-{0x00013441, 0x00013446}, {0x00014400, 0x00014646}, {0x00016800, 0x00016A38}, {0x00016A40, 0x00016A5E},
-{0x00016A70, 0x00016ABE}, {0x00016AD0, 0x00016AED}, {0x00016B00, 0x00016B2F}, {0x00016B40, 0x00016B43},
-{0x00016B63, 0x00016B77}, {0x00016B7D, 0x00016B8F}, {0x00016E40, 0x00016E7F}, {0x00016F00, 0x00016F4A},
-{0x00016F50, 0x00016F50}, {0x00016F93, 0x00016F9F}, {0x00016FE0, 0x00016FE1}, {0x00016FE3, 0x00016FE3},
-{0x00017000, 0x000187F7}, {0x00018800, 0x00018CD5}, {0x00018D00, 0x00018D08}, {0x0001AFF0, 0x0001AFF3},
-{0x0001AFF5, 0x0001AFFB}, {0x0001AFFD, 0x0001AFFE}, {0x0001B000, 0x0001B122}, {0x0001B132, 0x0001B132},
-{0x0001B150, 0x0001B152}, {0x0001B155, 0x0001B155}, {0x0001B164, 0x0001B167}, {0x0001B170, 0x0001B2FB},
-{0x0001BC00, 0x0001BC6A}, {0x0001BC70, 0x0001BC7C}, {0x0001BC80, 0x0001BC88}, {0x0001BC90, 0x0001BC99},
-{0x0001D400, 0x0001D454}, {0x0001D456, 0x0001D49C}, {0x0001D49E, 0x0001D49F}, {0x0001D4A2, 0x0001D4A2},
-{0x0001D4A5, 0x0001D4A6}, {0x0001D4A9, 0x0001D4AC}, {0x0001D4AE, 0x0001D4B9}, {0x0001D4BB, 0x0001D4BB},
-{0x0001D4BD, 0x0001D4C3}, {0x0001D4C5, 0x0001D505}, {0x0001D507, 0x0001D50A}, {0x0001D50D, 0x0001D514},
-{0x0001D516, 0x0001D51C}, {0x0001D51E, 0x0001D539}, {0x0001D53B, 0x0001D53E}, {0x0001D540, 0x0001D544},
-{0x0001D546, 0x0001D546}, {0x0001D54A, 0x0001D550}, {0x0001D552, 0x0001D6A5}, {0x0001D6A8, 0x0001D6C0},
-{0x0001D6C2, 0x0001D6DA}, {0x0001D6DC, 0x0001D6FA}, {0x0001D6FC, 0x0001D714}, {0x0001D716, 0x0001D734},
-{0x0001D736, 0x0001D74E}, {0x0001D750, 0x0001D76E}, {0x0001D770, 0x0001D788}, {0x0001D78A, 0x0001D7A8},
-{0x0001D7AA, 0x0001D7C2}, {0x0001D7C4, 0x0001D7CB}, {0x0001DF00, 0x0001DF1E}, {0x0001DF25, 0x0001DF2A},
-{0x0001E030, 0x0001E06D}, {0x0001E100, 0x0001E12C}, {0x0001E137, 0x0001E13D}, {0x0001E14E, 0x0001E14E},
-{0x0001E290, 0x0001E2AD}, {0x0001E2C0, 0x0001E2EB}, {0x0001E4D0, 0x0001E4EB}, {0x0001E7E0, 0x0001E7E6},
-{0x0001E7E8, 0x0001E7EB}, {0x0001E7ED, 0x0001E7EE}, {0x0001E7F0, 0x0001E7FE}, {0x0001E800, 0x0001E8C4},
-{0x0001E900, 0x0001E943}, {0x0001E94B, 0x0001E94B}, {0x0001EE00, 0x0001EE03}, {0x0001EE05, 0x0001EE1F},
-{0x0001EE21, 0x0001EE22}, {0x0001EE24, 0x0001EE24}, {0x0001EE27, 0x0001EE27}, {0x0001EE29, 0x0001EE32},
-{0x0001EE34, 0x0001EE37}, {0x0001EE39, 0x0001EE39}, {0x0001EE3B, 0x0001EE3B}, {0x0001EE42, 0x0001EE42},
-{0x0001EE47, 0x0001EE47}, {0x0001EE49, 0x0001EE49}, {0x0001EE4B, 0x0001EE4B}, {0x0001EE4D, 0x0001EE4F},
-{0x0001EE51, 0x0001EE52}, {0x0001EE54, 0x0001EE54}, {0x0001EE57, 0x0001EE57}, {0x0001EE59, 0x0001EE59},
-{0x0001EE5B, 0x0001EE5B}, {0x0001EE5D, 0x0001EE5D}, {0x0001EE5F, 0x0001EE5F}, {0x0001EE61, 0x0001EE62},
-{0x0001EE64, 0x0001EE64}, {0x0001EE67, 0x0001EE6A}, {0x0001EE6C, 0x0001EE72}, {0x0001EE74, 0x0001EE77},
-{0x0001EE79, 0x0001EE7C}, {0x0001EE7E, 0x0001EE7E}, {0x0001EE80, 0x0001EE89}, {0x0001EE8B, 0x0001EE9B},
-{0x0001EEA1, 0x0001EEA3}, {0x0001EEA5, 0x0001EEA9}, {0x0001EEAB, 0x0001EEBB}, {0x00020000, 0x0002A6DF},
-{0x0002A700, 0x0002B739}, {0x0002B740, 0x0002B81D}, {0x0002B820, 0x0002CEA1}, {0x0002CEB0, 0x0002EBE0},
-{0x0002EBF0, 0x0002EE5D}, {0x0002F800, 0x0002FA1D}, {0x00030000, 0x0003134A}, {0x00031350, 0x000323AF},
+const std::unordered_set<uint32_t> unicode_set_whitespace = {
+0x000009, 0x00000A, 0x00000B, 0x00000C, 0x00000D, 0x000020, 0x000085, 0x0000A0, 0x001680, 0x002000, 0x002001, 0x002002, 0x002003, 0x002004, 0x002005, 0x002006, 0x002007, 0x002008, 0x002009, 0x00200A, 0x002028, 0x002029, 0x00202F, 0x00205F, 0x003000
 };
 
-const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_separator = {
-{0x00000020, 0x00000020}, {0x000000A0, 0x000000A0}, {0x00001680, 0x00001680}, {0x00002000, 0x0000200A},
-{0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, {0x0000205F, 0x0000205F}, {0x00003000, 0x00003000},
+const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase = {
+{0x000041, 0x000061},
+{0x000042, 0x000062},
+{0x000043, 0x000063},
+{0x000044, 0x000064},
+{0x000045, 0x000065},
+{0x000046, 0x000066},
+{0x000047, 0x000067},
+{0x000048, 0x000068},
+{0x000049, 0x000069},
+{0x00004A, 0x00006A},
+{0x00004B, 0x00006B},
+{0x00004C, 0x00006C},
+{0x00004D, 0x00006D},
+{0x00004E, 0x00006E},
+{0x00004F, 0x00006F},
+{0x000050, 0x000070},
+{0x000051, 0x000071},
+{0x000052, 0x000072},
+{0x000053, 0x000073},
+{0x000054, 0x000074},
+{0x000055, 0x000075},
+{0x000056, 0x000076},
+{0x000057, 0x000077},
+{0x000058, 0x000078},
+{0x000059, 0x000079},
+{0x00005A, 0x00007A},
+{0x0000C0, 0x0000E0},
+{0x0000C1, 0x0000E1},
+{0x0000C2, 0x0000E2},
+{0x0000C3, 0x0000E3},
+{0x0000C4, 0x0000E4},
+{0x0000C5, 0x0000E5},
+{0x0000C6, 0x0000E6},
+{0x0000C7, 0x0000E7},
+{0x0000C8, 0x0000E8},
+{0x0000C9, 0x0000E9},
+{0x0000CA, 0x0000EA},
+{0x0000CB, 0x0000EB},
+{0x0000CC, 0x0000EC},
+{0x0000CD, 0x0000ED},
+{0x0000CE, 0x0000EE},
+{0x0000CF, 0x0000EF},
+{0x0000D0, 0x0000F0},
+{0x0000D1, 0x0000F1},
+{0x0000D2, 0x0000F2},
+{0x0000D3, 0x0000F3},
+{0x0000D4, 0x0000F4},
+{0x0000D5, 0x0000F5},
+{0x0000D6, 0x0000F6},
+{0x0000D8, 0x0000F8},
+{0x0000D9, 0x0000F9},
+{0x0000DA, 0x0000FA},
+{0x0000DB, 0x0000FB},
+{0x0000DC, 0x0000FC},
+{0x0000DD, 0x0000FD},
+{0x0000DE, 0x0000FE},
+{0x000100, 0x000101},
+{0x000102, 0x000103},
+{0x000104, 0x000105},
+{0x000106, 0x000107},
+{0x000108, 0x000109},
+{0x00010A, 0x00010B},
+{0x00010C, 0x00010D},
+{0x00010E, 0x00010F},
+{0x000110, 0x000111},
+{0x000112, 0x000113},
+{0x000114, 0x000115},
+{0x000116, 0x000117},
+{0x000118, 0x000119},
+{0x00011A, 0x00011B},
+{0x00011C, 0x00011D},
+{0x00011E, 0x00011F},
+{0x000120, 0x000121},
+{0x000122, 0x000123},
+{0x000124, 0x000125},
+{0x000126, 0x000127},
+{0x000128, 0x000129},
+{0x00012A, 0x00012B},
+{0x00012C, 0x00012D},
+{0x00012E, 0x00012F},
+{0x000130, 0x000069},
+{0x000132, 0x000133},
+{0x000134, 0x000135},
+{0x000136, 0x000137},
+{0x000139, 0x00013A},
+{0x00013B, 0x00013C},
+{0x00013D, 0x00013E},
+{0x00013F, 0x000140},
+{0x000141, 0x000142},
+{0x000143, 0x000144},
+{0x000145, 0x000146},
+{0x000147, 0x000148},
+{0x00014A, 0x00014B},
+{0x00014C, 0x00014D},
+{0x00014E, 0x00014F},
+{0x000150, 0x000151},
+{0x000152, 0x000153},
+{0x000154, 0x000155},
+{0x000156, 0x000157},
+{0x000158, 0x000159},
+{0x00015A, 0x00015B},
+{0x00015C, 0x00015D},
+{0x00015E, 0x00015F},
+{0x000160, 0x000161},
+{0x000162, 0x000163},
+{0x000164, 0x000165},
+{0x000166, 0x000167},
+{0x000168, 0x000169},
+{0x00016A, 0x00016B},
+{0x00016C, 0x00016D},
+{0x00016E, 0x00016F},
+{0x000170, 0x000171},
+{0x000172, 0x000173},
+{0x000174, 0x000175},
+{0x000176, 0x000177},
+{0x000178, 0x0000FF},
+{0x000179, 0x00017A},
+{0x00017B, 0x00017C},
+{0x00017D, 0x00017E},
+{0x000181, 0x000253},
+{0x000182, 0x000183},
+{0x000184, 0x000185},
+{0x000186, 0x000254},
+{0x000187, 0x000188},
+{0x000189, 0x000256},
+{0x00018A, 0x000257},
+{0x00018B, 0x00018C},
+{0x00018E, 0x0001DD},
+{0x00018F, 0x000259},
+{0x000190, 0x00025B},
+{0x000191, 0x000192},
+{0x000193, 0x000260},
+{0x000194, 0x000263},
+{0x000196, 0x000269},
+{0x000197, 0x000268},
+{0x000198, 0x000199},
+{0x00019C, 0x00026F},
+{0x00019D, 0x000272},
+{0x00019F, 0x000275},
+{0x0001A0, 0x0001A1},
+{0x0001A2, 0x0001A3},
+{0x0001A4, 0x0001A5},
+{0x0001A6, 0x000280},
+{0x0001A7, 0x0001A8},
+{0x0001A9, 0x000283},
+{0x0001AC, 0x0001AD},
+{0x0001AE, 0x000288},
+{0x0001AF, 0x0001B0},
+{0x0001B1, 0x00028A},
+{0x0001B2, 0x00028B},
+{0x0001B3, 0x0001B4},
+{0x0001B5, 0x0001B6},
+{0x0001B7, 0x000292},
+{0x0001B8, 0x0001B9},
+{0x0001BC, 0x0001BD},
+{0x0001C4, 0x0001C6},
+{0x0001C5, 0x0001C6},
+{0x0001C7, 0x0001C9},
+{0x0001C8, 0x0001C9},
+{0x0001CA, 0x0001CC},
+{0x0001CB, 0x0001CC},
+{0x0001CD, 0x0001CE},
+{0x0001CF, 0x0001D0},
+{0x0001D1, 0x0001D2},
+{0x0001D3, 0x0001D4},
+{0x0001D5, 0x0001D6},
+{0x0001D7, 0x0001D8},
+{0x0001D9, 0x0001DA},
+{0x0001DB, 0x0001DC},
+{0x0001DE, 0x0001DF},
+{0x0001E0, 0x0001E1},
+{0x0001E2, 0x0001E3},
+{0x0001E4, 0x0001E5},
+{0x0001E6, 0x0001E7},
+{0x0001E8, 0x0001E9},
+{0x0001EA, 0x0001EB},
+{0x0001EC, 0x0001ED},
+{0x0001EE, 0x0001EF},
+{0x0001F1, 0x0001F3},
+{0x0001F2, 0x0001F3},
+{0x0001F4, 0x0001F5},
+{0x0001F6, 0x000195},
+{0x0001F7, 0x0001BF},
+{0x0001F8, 0x0001F9},
+{0x0001FA, 0x0001FB},
+{0x0001FC, 0x0001FD},
+{0x0001FE, 0x0001FF},
+{0x000200, 0x000201},
+{0x000202, 0x000203},
+{0x000204, 0x000205},
+{0x000206, 0x000207},
+{0x000208, 0x000209},
+{0x00020A, 0x00020B},
+{0x00020C, 0x00020D},
+{0x00020E, 0x00020F},
+{0x000210, 0x000211},
+{0x000212, 0x000213},
+{0x000214, 0x000215},
+{0x000216, 0x000217},
+{0x000218, 0x000219},
+{0x00021A, 0x00021B},
+{0x00021C, 0x00021D},
+{0x00021E, 0x00021F},
+{0x000220, 0x00019E},
+{0x000222, 0x000223},
+{0x000224, 0x000225},
+{0x000226, 0x000227},
+{0x000228, 0x000229},
+{0x00022A, 0x00022B},
+{0x00022C, 0x00022D},
+{0x00022E, 0x00022F},
+{0x000230, 0x000231},
+{0x000232, 0x000233},
+{0x00023A, 0x002C65},
+{0x00023B, 0x00023C},
+{0x00023D, 0x00019A},
+{0x00023E, 0x002C66},
+{0x000241, 0x000242},
+{0x000243, 0x000180},
+{0x000244, 0x000289},
+{0x000245, 0x00028C},
+{0x000246, 0x000247},
+{0x000248, 0x000249},
+{0x00024A, 0x00024B},
+{0x00024C, 0x00024D},
+{0x00024E, 0x00024F},
+{0x000370, 0x000371},
+{0x000372, 0x000373},
+{0x000376, 0x000377},
+{0x00037F, 0x0003F3},
+{0x000386, 0x0003AC},
+{0x000388, 0x0003AD},
+{0x000389, 0x0003AE},
+{0x00038A, 0x0003AF},
+{0x00038C, 0x0003CC},
+{0x00038E, 0x0003CD},
+{0x00038F, 0x0003CE},
+{0x000391, 0x0003B1},
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+{0x010C88, 0x010CC8},
+{0x010C89, 0x010CC9},
+{0x010C8A, 0x010CCA},
+{0x010C8B, 0x010CCB},
+{0x010C8C, 0x010CCC},
+{0x010C8D, 0x010CCD},
+{0x010C8E, 0x010CCE},
+{0x010C8F, 0x010CCF},
+{0x010C90, 0x010CD0},
+{0x010C91, 0x010CD1},
+{0x010C92, 0x010CD2},
+{0x010C93, 0x010CD3},
+{0x010C94, 0x010CD4},
+{0x010C95, 0x010CD5},
+{0x010C96, 0x010CD6},
+{0x010C97, 0x010CD7},
+{0x010C98, 0x010CD8},
+{0x010C99, 0x010CD9},
+{0x010C9A, 0x010CDA},
+{0x010C9B, 0x010CDB},
+{0x010C9C, 0x010CDC},
+{0x010C9D, 0x010CDD},
+{0x010C9E, 0x010CDE},
+{0x010C9F, 0x010CDF},
+{0x010CA0, 0x010CE0},
+{0x010CA1, 0x010CE1},
+{0x010CA2, 0x010CE2},
+{0x010CA3, 0x010CE3},
+{0x010CA4, 0x010CE4},
+{0x010CA5, 0x010CE5},
+{0x010CA6, 0x010CE6},
+{0x010CA7, 0x010CE7},
+{0x010CA8, 0x010CE8},
+{0x010CA9, 0x010CE9},
+{0x010CAA, 0x010CEA},
+{0x010CAB, 0x010CEB},
+{0x010CAC, 0x010CEC},
+{0x010CAD, 0x010CED},
+{0x010CAE, 0x010CEE},
+{0x010CAF, 0x010CEF},
+{0x010CB0, 0x010CF0},
+{0x010CB1, 0x010CF1},
+{0x010CB2, 0x010CF2},
+{0x0118A0, 0x0118C0},
+{0x0118A1, 0x0118C1},
+{0x0118A2, 0x0118C2},
+{0x0118A3, 0x0118C3},
+{0x0118A4, 0x0118C4},
+{0x0118A5, 0x0118C5},
+{0x0118A6, 0x0118C6},
+{0x0118A7, 0x0118C7},
+{0x0118A8, 0x0118C8},
+{0x0118A9, 0x0118C9},
+{0x0118AA, 0x0118CA},
+{0x0118AB, 0x0118CB},
+{0x0118AC, 0x0118CC},
+{0x0118AD, 0x0118CD},
+{0x0118AE, 0x0118CE},
+{0x0118AF, 0x0118CF},
+{0x0118B0, 0x0118D0},
+{0x0118B1, 0x0118D1},
+{0x0118B2, 0x0118D2},
+{0x0118B3, 0x0118D3},
+{0x0118B4, 0x0118D4},
+{0x0118B5, 0x0118D5},
+{0x0118B6, 0x0118D6},
+{0x0118B7, 0x0118D7},
+{0x0118B8, 0x0118D8},
+{0x0118B9, 0x0118D9},
+{0x0118BA, 0x0118DA},
+{0x0118BB, 0x0118DB},
+{0x0118BC, 0x0118DC},
+{0x0118BD, 0x0118DD},
+{0x0118BE, 0x0118DE},
+{0x0118BF, 0x0118DF},
+{0x016E40, 0x016E60},
+{0x016E41, 0x016E61},
+{0x016E42, 0x016E62},
+{0x016E43, 0x016E63},
+{0x016E44, 0x016E64},
+{0x016E45, 0x016E65},
+{0x016E46, 0x016E66},
+{0x016E47, 0x016E67},
+{0x016E48, 0x016E68},
+{0x016E49, 0x016E69},
+{0x016E4A, 0x016E6A},
+{0x016E4B, 0x016E6B},
+{0x016E4C, 0x016E6C},
+{0x016E4D, 0x016E6D},
+{0x016E4E, 0x016E6E},
+{0x016E4F, 0x016E6F},
+{0x016E50, 0x016E70},
+{0x016E51, 0x016E71},
+{0x016E52, 0x016E72},
+{0x016E53, 0x016E73},
+{0x016E54, 0x016E74},
+{0x016E55, 0x016E75},
+{0x016E56, 0x016E76},
+{0x016E57, 0x016E77},
+{0x016E58, 0x016E78},
+{0x016E59, 0x016E79},
+{0x016E5A, 0x016E7A},
+{0x016E5B, 0x016E7B},
+{0x016E5C, 0x016E7C},
+{0x016E5D, 0x016E7D},
+{0x016E5E, 0x016E7E},
+{0x016E5F, 0x016E7F},
+{0x01E900, 0x01E922},
+{0x01E901, 0x01E923},
+{0x01E902, 0x01E924},
+{0x01E903, 0x01E925},
+{0x01E904, 0x01E926},
+{0x01E905, 0x01E927},
+{0x01E906, 0x01E928},
+{0x01E907, 0x01E929},
+{0x01E908, 0x01E92A},
+{0x01E909, 0x01E92B},
+{0x01E90A, 0x01E92C},
+{0x01E90B, 0x01E92D},
+{0x01E90C, 0x01E92E},
+{0x01E90D, 0x01E92F},
+{0x01E90E, 0x01E930},
+{0x01E90F, 0x01E931},
+{0x01E910, 0x01E932},
+{0x01E911, 0x01E933},
+{0x01E912, 0x01E934},
+{0x01E913, 0x01E935},
+{0x01E914, 0x01E936},
+{0x01E915, 0x01E937},
+{0x01E916, 0x01E938},
+{0x01E917, 0x01E939},
+{0x01E918, 0x01E93A},
+{0x01E919, 0x01E93B},
+{0x01E91A, 0x01E93C},
+{0x01E91B, 0x01E93D},
+{0x01E91C, 0x01E93E},
+{0x01E91D, 0x01E93F},
+{0x01E91E, 0x01E940},
+{0x01E91F, 0x01E941},
+{0x01E920, 0x01E942},
+{0x01E921, 0x01E943},
 };
 
-const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_whitespace = {
-{0x00000009, 0x0000000D}, {0x00000020, 0x00000020}, {0x00000085, 0x00000085}, {0x000000A0, 0x000000A0},
-{0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, {0x00002028, 0x00002029}, {0x0000202F, 0x0000202F},
-{0x0000205F, 0x0000205F}, {0x00003000, 0x00003000},
+const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {
+{0x000061, 0x000041},
+{0x000062, 0x000042},
+{0x000063, 0x000043},
+{0x000064, 0x000044},
+{0x000065, 0x000045},
+{0x000066, 0x000046},
+{0x000067, 0x000047},
+{0x000068, 0x000048},
+{0x000069, 0x000049},
+{0x00006A, 0x00004A},
+{0x00006B, 0x00004B},
+{0x00006C, 0x00004C},
+{0x00006D, 0x00004D},
+{0x00006E, 0x00004E},
+{0x00006F, 0x00004F},
+{0x000070, 0x000050},
+{0x000071, 0x000051},
+{0x000072, 0x000052},
+{0x000073, 0x000053},
+{0x000074, 0x000054},
+{0x000075, 0x000055},
+{0x000076, 0x000056},
+{0x000077, 0x000057},
+{0x000078, 0x000058},
+{0x000079, 0x000059},
+{0x00007A, 0x00005A},
+{0x0000B5, 0x00039C},
+{0x0000DF, 0x000053},
+{0x0000E0, 0x0000C0},
+{0x0000E1, 0x0000C1},
+{0x0000E2, 0x0000C2},
+{0x0000E3, 0x0000C3},
+{0x0000E4, 0x0000C4},
+{0x0000E5, 0x0000C5},
+{0x0000E6, 0x0000C6},
+{0x0000E7, 0x0000C7},
+{0x0000E8, 0x0000C8},
+{0x0000E9, 0x0000C9},
+{0x0000EA, 0x0000CA},
+{0x0000EB, 0x0000CB},
+{0x0000EC, 0x0000CC},
+{0x0000ED, 0x0000CD},
+{0x0000EE, 0x0000CE},
+{0x0000EF, 0x0000CF},
+{0x0000F0, 0x0000D0},
+{0x0000F1, 0x0000D1},
+{0x0000F2, 0x0000D2},
+{0x0000F3, 0x0000D3},
+{0x0000F4, 0x0000D4},
+{0x0000F5, 0x0000D5},
+{0x0000F6, 0x0000D6},
+{0x0000F8, 0x0000D8},
+{0x0000F9, 0x0000D9},
+{0x0000FA, 0x0000DA},
+{0x0000FB, 0x0000DB},
+{0x0000FC, 0x0000DC},
+{0x0000FD, 0x0000DD},
+{0x0000FE, 0x0000DE},
+{0x0000FF, 0x000178},
+{0x000101, 0x000100},
+{0x000103, 0x000102},
+{0x000105, 0x000104},
+{0x000107, 0x000106},
+{0x000109, 0x000108},
+{0x00010B, 0x00010A},
+{0x00010D, 0x00010C},
+{0x00010F, 0x00010E},
+{0x000111, 0x000110},
+{0x000113, 0x000112},
+{0x000115, 0x000114},
+{0x000117, 0x000116},
+{0x000119, 0x000118},
+{0x00011B, 0x00011A},
+{0x00011D, 0x00011C},
+{0x00011F, 0x00011E},
+{0x000121, 0x000120},
+{0x000123, 0x000122},
+{0x000125, 0x000124},
+{0x000127, 0x000126},
+{0x000129, 0x000128},
+{0x00012B, 0x00012A},
+{0x00012D, 0x00012C},
+{0x00012F, 0x00012E},
+{0x000131, 0x000049},
+{0x000133, 0x000132},
+{0x000135, 0x000134},
+{0x000137, 0x000136},
+{0x00013A, 0x000139},
+{0x00013C, 0x00013B},
+{0x00013E, 0x00013D},
+{0x000140, 0x00013F},
+{0x000142, 0x000141},
+{0x000144, 0x000143},
+{0x000146, 0x000145},
+{0x000148, 0x000147},
+{0x000149, 0x0002BC},
+{0x00014B, 0x00014A},
+{0x00014D, 0x00014C},
+{0x00014F, 0x00014E},
+{0x000151, 0x000150},
+{0x000153, 0x000152},
+{0x000155, 0x000154},
+{0x000157, 0x000156},
+{0x000159, 0x000158},
+{0x00015B, 0x00015A},
+{0x00015D, 0x00015C},
+{0x00015F, 0x00015E},
+{0x000161, 0x000160},
+{0x000163, 0x000162},
+{0x000165, 0x000164},
+{0x000167, 0x000166},
+{0x000169, 0x000168},
+{0x00016B, 0x00016A},
+{0x00016D, 0x00016C},
+{0x00016F, 0x00016E},
+{0x000171, 0x000170},
+{0x000173, 0x000172},
+{0x000175, 0x000174},
+{0x000177, 0x000176},
+{0x00017A, 0x000179},
+{0x00017C, 0x00017B},
+{0x00017E, 0x00017D},
+{0x00017F, 0x000053},
+{0x000180, 0x000243},
+{0x000183, 0x000182},
+{0x000185, 0x000184},
+{0x000188, 0x000187},
+{0x00018C, 0x00018B},
+{0x000192, 0x000191},
+{0x000195, 0x0001F6},
+{0x000199, 0x000198},
+{0x00019A, 0x00023D},
+{0x00019E, 0x000220},
+{0x0001A1, 0x0001A0},
+{0x0001A3, 0x0001A2},
+{0x0001A5, 0x0001A4},
+{0x0001A8, 0x0001A7},
+{0x0001AD, 0x0001AC},
+{0x0001B0, 0x0001AF},
+{0x0001B4, 0x0001B3},
+{0x0001B6, 0x0001B5},
+{0x0001B9, 0x0001B8},
+{0x0001BD, 0x0001BC},
+{0x0001BF, 0x0001F7},
+{0x0001C5, 0x0001C4},
+{0x0001C6, 0x0001C4},
+{0x0001C8, 0x0001C7},
+{0x0001C9, 0x0001C7},
+{0x0001CB, 0x0001CA},
+{0x0001CC, 0x0001CA},
+{0x0001CE, 0x0001CD},
+{0x0001D0, 0x0001CF},
+{0x0001D2, 0x0001D1},
+{0x0001D4, 0x0001D3},
+{0x0001D6, 0x0001D5},
+{0x0001D8, 0x0001D7},
+{0x0001DA, 0x0001D9},
+{0x0001DC, 0x0001DB},
+{0x0001DD, 0x00018E},
+{0x0001DF, 0x0001DE},
+{0x0001E1, 0x0001E0},
+{0x0001E3, 0x0001E2},
+{0x0001E5, 0x0001E4},
+{0x0001E7, 0x0001E6},
+{0x0001E9, 0x0001E8},
+{0x0001EB, 0x0001EA},
+{0x0001ED, 0x0001EC},
+{0x0001EF, 0x0001EE},
+{0x0001F0, 0x00004A},
+{0x0001F2, 0x0001F1},
+{0x0001F3, 0x0001F1},
+{0x0001F5, 0x0001F4},
+{0x0001F9, 0x0001F8},
+{0x0001FB, 0x0001FA},
+{0x0001FD, 0x0001FC},
+{0x0001FF, 0x0001FE},
+{0x000201, 0x000200},
+{0x000203, 0x000202},
+{0x000205, 0x000204},
+{0x000207, 0x000206},
+{0x000209, 0x000208},
+{0x00020B, 0x00020A},
+{0x00020D, 0x00020C},
+{0x00020F, 0x00020E},
+{0x000211, 0x000210},
+{0x000213, 0x000212},
+{0x000215, 0x000214},
+{0x000217, 0x000216},
+{0x000219, 0x000218},
+{0x00021B, 0x00021A},
+{0x00021D, 0x00021C},
+{0x00021F, 0x00021E},
+{0x000223, 0x000222},
+{0x000225, 0x000224},
+{0x000227, 0x000226},
+{0x000229, 0x000228},
+{0x00022B, 0x00022A},
+{0x00022D, 0x00022C},
+{0x00022F, 0x00022E},
+{0x000231, 0x000230},
+{0x000233, 0x000232},
+{0x00023C, 0x00023B},
+{0x00023F, 0x002C7E},
+{0x000240, 0x002C7F},
+{0x000242, 0x000241},
+{0x000247, 0x000246},
+{0x000249, 0x000248},
+{0x00024B, 0x00024A},
+{0x00024D, 0x00024C},
+{0x00024F, 0x00024E},
+{0x000250, 0x002C6F},
+{0x000251, 0x002C6D},
+{0x000252, 0x002C70},
+{0x000253, 0x000181},
+{0x000254, 0x000186},
+{0x000256, 0x000189},
+{0x000257, 0x00018A},
+{0x000259, 0x00018F},
+{0x00025B, 0x000190},
+{0x00025C, 0x00A7AB},
+{0x000260, 0x000193},
+{0x000261, 0x00A7AC},
+{0x000263, 0x000194},
+{0x000265, 0x00A78D},
+{0x000266, 0x00A7AA},
+{0x000268, 0x000197},
+{0x000269, 0x000196},
+{0x00026A, 0x00A7AE},
+{0x00026B, 0x002C62},
+{0x00026C, 0x00A7AD},
+{0x00026F, 0x00019C},
+{0x000271, 0x002C6E},
+{0x000272, 0x00019D},
+{0x000275, 0x00019F},
+{0x00027D, 0x002C64},
+{0x000280, 0x0001A6},
+{0x000282, 0x00A7C5},
+{0x000283, 0x0001A9},
+{0x000287, 0x00A7B1},
+{0x000288, 0x0001AE},
+{0x000289, 0x000244},
+{0x00028A, 0x0001B1},
+{0x00028B, 0x0001B2},
+{0x00028C, 0x000245},
+{0x000292, 0x0001B7},
+{0x00029D, 0x00A7B2},
+{0x00029E, 0x00A7B0},
+{0x000345, 0x000399},
+{0x000371, 0x000370},
+{0x000373, 0x000372},
+{0x000377, 0x000376},
+{0x00037B, 0x0003FD},
+{0x00037C, 0x0003FE},
+{0x00037D, 0x0003FF},
+{0x000390, 0x000399},
+{0x0003AC, 0x000386},
+{0x0003AD, 0x000388},
+{0x0003AE, 0x000389},
+{0x0003AF, 0x00038A},
+{0x0003B0, 0x0003A5},
+{0x0003B1, 0x000391},
+{0x0003B2, 0x000392},
+{0x0003B3, 0x000393},
+{0x0003B4, 0x000394},
+{0x0003B5, 0x000395},
+{0x0003B6, 0x000396},
+{0x0003B7, 0x000397},
+{0x0003B8, 0x000398},
+{0x0003B9, 0x000399},
+{0x0003BA, 0x00039A},
+{0x0003BB, 0x00039B},
+{0x0003BC, 0x00039C},
+{0x0003BD, 0x00039D},
+{0x0003BE, 0x00039E},
+{0x0003BF, 0x00039F},
+{0x0003C0, 0x0003A0},
+{0x0003C1, 0x0003A1},
+{0x0003C2, 0x0003A3},
+{0x0003C3, 0x0003A3},
+{0x0003C4, 0x0003A4},
+{0x0003C5, 0x0003A5},
+{0x0003C6, 0x0003A6},
+{0x0003C7, 0x0003A7},
+{0x0003C8, 0x0003A8},
+{0x0003C9, 0x0003A9},
+{0x0003CA, 0x0003AA},
+{0x0003CB, 0x0003AB},
+{0x0003CC, 0x00038C},
+{0x0003CD, 0x00038E},
+{0x0003CE, 0x00038F},
+{0x0003D0, 0x000392},
+{0x0003D1, 0x000398},
+{0x0003D5, 0x0003A6},
+{0x0003D6, 0x0003A0},
+{0x0003D7, 0x0003CF},
+{0x0003D9, 0x0003D8},
+{0x0003DB, 0x0003DA},
+{0x0003DD, 0x0003DC},
+{0x0003DF, 0x0003DE},
+{0x0003E1, 0x0003E0},
+{0x0003E3, 0x0003E2},
+{0x0003E5, 0x0003E4},
+{0x0003E7, 0x0003E6},
+{0x0003E9, 0x0003E8},
+{0x0003EB, 0x0003EA},
+{0x0003ED, 0x0003EC},
+{0x0003EF, 0x0003EE},
+{0x0003F0, 0x00039A},
+{0x0003F1, 0x0003A1},
+{0x0003F2, 0x0003F9},
+{0x0003F3, 0x00037F},
+{0x0003F5, 0x000395},
+{0x0003F8, 0x0003F7},
+{0x0003FB, 0x0003FA},
+{0x000430, 0x000410},
+{0x000431, 0x000411},
+{0x000432, 0x000412},
+{0x000433, 0x000413},
+{0x000434, 0x000414},
+{0x000435, 0x000415},
+{0x000436, 0x000416},
+{0x000437, 0x000417},
+{0x000438, 0x000418},
+{0x000439, 0x000419},
+{0x00043A, 0x00041A},
+{0x00043B, 0x00041B},
+{0x00043C, 0x00041C},
+{0x00043D, 0x00041D},
+{0x00043E, 0x00041E},
+{0x00043F, 0x00041F},
+{0x000440, 0x000420},
+{0x000441, 0x000421},
+{0x000442, 0x000422},
+{0x000443, 0x000423},
+{0x000444, 0x000424},
+{0x000445, 0x000425},
+{0x000446, 0x000426},
+{0x000447, 0x000427},
+{0x000448, 0x000428},
+{0x000449, 0x000429},
+{0x00044A, 0x00042A},
+{0x00044B, 0x00042B},
+{0x00044C, 0x00042C},
+{0x00044D, 0x00042D},
+{0x00044E, 0x00042E},
+{0x00044F, 0x00042F},
+{0x000450, 0x000400},
+{0x000451, 0x000401},
+{0x000452, 0x000402},
+{0x000453, 0x000403},
+{0x000454, 0x000404},
+{0x000455, 0x000405},
+{0x000456, 0x000406},
+{0x000457, 0x000407},
+{0x000458, 0x000408},
+{0x000459, 0x000409},
+{0x00045A, 0x00040A},
+{0x00045B, 0x00040B},
+{0x00045C, 0x00040C},
+{0x00045D, 0x00040D},
+{0x00045E, 0x00040E},
+{0x00045F, 0x00040F},
+{0x000461, 0x000460},
+{0x000463, 0x000462},
+{0x000465, 0x000464},
+{0x000467, 0x000466},
+{0x000469, 0x000468},
+{0x00046B, 0x00046A},
+{0x00046D, 0x00046C},
+{0x00046F, 0x00046E},
+{0x000471, 0x000470},
+{0x000473, 0x000472},
+{0x000475, 0x000474},
+{0x000477, 0x000476},
+{0x000479, 0x000478},
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+{0x010CEE, 0x010CAE},
+{0x010CEF, 0x010CAF},
+{0x010CF0, 0x010CB0},
+{0x010CF1, 0x010CB1},
+{0x010CF2, 0x010CB2},
+{0x0118C0, 0x0118A0},
+{0x0118C1, 0x0118A1},
+{0x0118C2, 0x0118A2},
+{0x0118C3, 0x0118A3},
+{0x0118C4, 0x0118A4},
+{0x0118C5, 0x0118A5},
+{0x0118C6, 0x0118A6},
+{0x0118C7, 0x0118A7},
+{0x0118C8, 0x0118A8},
+{0x0118C9, 0x0118A9},
+{0x0118CA, 0x0118AA},
+{0x0118CB, 0x0118AB},
+{0x0118CC, 0x0118AC},
+{0x0118CD, 0x0118AD},
+{0x0118CE, 0x0118AE},
+{0x0118CF, 0x0118AF},
+{0x0118D0, 0x0118B0},
+{0x0118D1, 0x0118B1},
+{0x0118D2, 0x0118B2},
+{0x0118D3, 0x0118B3},
+{0x0118D4, 0x0118B4},
+{0x0118D5, 0x0118B5},
+{0x0118D6, 0x0118B6},
+{0x0118D7, 0x0118B7},
+{0x0118D8, 0x0118B8},
+{0x0118D9, 0x0118B9},
+{0x0118DA, 0x0118BA},
+{0x0118DB, 0x0118BB},
+{0x0118DC, 0x0118BC},
+{0x0118DD, 0x0118BD},
+{0x0118DE, 0x0118BE},
+{0x0118DF, 0x0118BF},
+{0x016E60, 0x016E40},
+{0x016E61, 0x016E41},
+{0x016E62, 0x016E42},
+{0x016E63, 0x016E43},
+{0x016E64, 0x016E44},
+{0x016E65, 0x016E45},
+{0x016E66, 0x016E46},
+{0x016E67, 0x016E47},
+{0x016E68, 0x016E48},
+{0x016E69, 0x016E49},
+{0x016E6A, 0x016E4A},
+{0x016E6B, 0x016E4B},
+{0x016E6C, 0x016E4C},
+{0x016E6D, 0x016E4D},
+{0x016E6E, 0x016E4E},
+{0x016E6F, 0x016E4F},
+{0x016E70, 0x016E50},
+{0x016E71, 0x016E51},
+{0x016E72, 0x016E52},
+{0x016E73, 0x016E53},
+{0x016E74, 0x016E54},
+{0x016E75, 0x016E55},
+{0x016E76, 0x016E56},
+{0x016E77, 0x016E57},
+{0x016E78, 0x016E58},
+{0x016E79, 0x016E59},
+{0x016E7A, 0x016E5A},
+{0x016E7B, 0x016E5B},
+{0x016E7C, 0x016E5C},
+{0x016E7D, 0x016E5D},
+{0x016E7E, 0x016E5E},
+{0x016E7F, 0x016E5F},
+{0x01E922, 0x01E900},
+{0x01E923, 0x01E901},
+{0x01E924, 0x01E902},
+{0x01E925, 0x01E903},
+{0x01E926, 0x01E904},
+{0x01E927, 0x01E905},
+{0x01E928, 0x01E906},
+{0x01E929, 0x01E907},
+{0x01E92A, 0x01E908},
+{0x01E92B, 0x01E909},
+{0x01E92C, 0x01E90A},
+{0x01E92D, 0x01E90B},
+{0x01E92E, 0x01E90C},
+{0x01E92F, 0x01E90D},
+{0x01E930, 0x01E90E},
+{0x01E931, 0x01E90F},
+{0x01E932, 0x01E910},
+{0x01E933, 0x01E911},
+{0x01E934, 0x01E912},
+{0x01E935, 0x01E913},
+{0x01E936, 0x01E914},
+{0x01E937, 0x01E915},
+{0x01E938, 0x01E916},
+{0x01E939, 0x01E917},
+{0x01E93A, 0x01E918},
+{0x01E93B, 0x01E919},
+{0x01E93C, 0x01E91A},
+{0x01E93D, 0x01E91B},
+{0x01E93E, 0x01E91C},
+{0x01E93F, 0x01E91D},
+{0x01E940, 0x01E91E},
+{0x01E941, 0x01E91F},
+{0x01E942, 0x01E920},
+{0x01E943, 0x01E921},
 };
 
-const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_accent_mark = {
-{0x00000300, 0x0000036F}, {0x00000483, 0x00000489}, {0x00000591, 0x000005BD}, {0x000005BF, 0x000005BF},
-{0x000005C1, 0x000005C2}, {0x000005C4, 0x000005C5}, {0x000005C7, 0x000005C7}, {0x00000610, 0x0000061A},
-{0x0000064B, 0x0000065F}, {0x00000670, 0x00000670}, {0x000006D6, 0x000006DC}, {0x000006DF, 0x000006E4},
-{0x000006E7, 0x000006E8}, {0x000006EA, 0x000006ED}, {0x00000711, 0x00000711}, {0x00000730, 0x0000074A},
-{0x000007A6, 0x000007B0}, {0x000007EB, 0x000007F3}, {0x000007FD, 0x000007FD}, {0x00000816, 0x00000819},
-{0x0000081B, 0x00000823}, {0x00000825, 0x00000827}, {0x00000829, 0x0000082D}, {0x00000859, 0x0000085B},
-{0x00000898, 0x0000089F}, {0x000008CA, 0x000008E1}, {0x000008E3, 0x00000903}, {0x0000093A, 0x0000093C},
-{0x0000093E, 0x0000094F}, {0x00000951, 0x00000957}, {0x00000962, 0x00000963}, {0x00000981, 0x00000983},
-{0x000009BC, 0x000009BC}, {0x000009BE, 0x000009C4}, {0x000009C7, 0x000009C8}, {0x000009CB, 0x000009CD},
-{0x000009D7, 0x000009D7}, {0x000009E2, 0x000009E3}, {0x000009FE, 0x000009FE}, {0x00000A01, 0x00000A03},
-{0x00000A3C, 0x00000A3C}, {0x00000A3E, 0x00000A42}, {0x00000A47, 0x00000A48}, {0x00000A4B, 0x00000A4D},
-{0x00000A51, 0x00000A51}, {0x00000A70, 0x00000A71}, {0x00000A75, 0x00000A75}, {0x00000A81, 0x00000A83},
-{0x00000ABC, 0x00000ABC}, {0x00000ABE, 0x00000AC5}, {0x00000AC7, 0x00000AC9}, {0x00000ACB, 0x00000ACD},
-{0x00000AE2, 0x00000AE3}, {0x00000AFA, 0x00000AFF}, {0x00000B01, 0x00000B03}, {0x00000B3C, 0x00000B3C},
-{0x00000B3E, 0x00000B44}, {0x00000B47, 0x00000B48}, {0x00000B4B, 0x00000B4D}, {0x00000B55, 0x00000B57},
-{0x00000B62, 0x00000B63}, {0x00000B82, 0x00000B82}, {0x00000BBE, 0x00000BC2}, {0x00000BC6, 0x00000BC8},
-{0x00000BCA, 0x00000BCD}, {0x00000BD7, 0x00000BD7}, {0x00000C00, 0x00000C04}, {0x00000C3C, 0x00000C3C},
-{0x00000C3E, 0x00000C44}, {0x00000C46, 0x00000C48}, {0x00000C4A, 0x00000C4D}, {0x00000C55, 0x00000C56},
-{0x00000C62, 0x00000C63}, {0x00000C81, 0x00000C83}, {0x00000CBC, 0x00000CBC}, {0x00000CBE, 0x00000CC4},
-{0x00000CC6, 0x00000CC8}, {0x00000CCA, 0x00000CCD}, {0x00000CD5, 0x00000CD6}, {0x00000CE2, 0x00000CE3},
-{0x00000CF3, 0x00000CF3}, {0x00000D00, 0x00000D03}, {0x00000D3B, 0x00000D3C}, {0x00000D3E, 0x00000D44},
-{0x00000D46, 0x00000D48}, {0x00000D4A, 0x00000D4D}, {0x00000D57, 0x00000D57}, {0x00000D62, 0x00000D63},
-{0x00000D81, 0x00000D83}, {0x00000DCA, 0x00000DCA}, {0x00000DCF, 0x00000DD4}, {0x00000DD6, 0x00000DD6},
-{0x00000DD8, 0x00000DDF}, {0x00000DF2, 0x00000DF3}, {0x00000E31, 0x00000E31}, {0x00000E34, 0x00000E3A},
-{0x00000E47, 0x00000E4E}, {0x00000EB1, 0x00000EB1}, {0x00000EB4, 0x00000EBC}, {0x00000EC8, 0x00000ECE},
-{0x00000F18, 0x00000F19}, {0x00000F35, 0x00000F35}, {0x00000F37, 0x00000F37}, {0x00000F39, 0x00000F39},
-{0x00000F3E, 0x00000F3F}, {0x00000F71, 0x00000F84}, {0x00000F86, 0x00000F87}, {0x00000F8D, 0x00000F97},
-{0x00000F99, 0x00000FBC}, {0x00000FC6, 0x00000FC6}, {0x0000102B, 0x0000103E}, {0x00001056, 0x00001059},
-{0x0000105E, 0x00001060}, {0x00001062, 0x00001064}, {0x00001067, 0x0000106D}, {0x00001071, 0x00001074},
-{0x00001082, 0x0000108D}, {0x0000108F, 0x0000108F}, {0x0000109A, 0x0000109D}, {0x0000135D, 0x0000135F},
-{0x00001712, 0x00001715}, {0x00001732, 0x00001734}, {0x00001752, 0x00001753}, {0x00001772, 0x00001773},
-{0x000017B4, 0x000017D3}, {0x000017DD, 0x000017DD}, {0x0000180B, 0x0000180D}, {0x0000180F, 0x0000180F},
-{0x00001885, 0x00001886}, {0x000018A9, 0x000018A9}, {0x00001920, 0x0000192B}, {0x00001930, 0x0000193B},
-{0x00001A17, 0x00001A1B}, {0x00001A55, 0x00001A5E}, {0x00001A60, 0x00001A7C}, {0x00001A7F, 0x00001A7F},
-{0x00001AB0, 0x00001ACE}, {0x00001B00, 0x00001B04}, {0x00001B34, 0x00001B44}, {0x00001B6B, 0x00001B73},
-{0x00001B80, 0x00001B82}, {0x00001BA1, 0x00001BAD}, {0x00001BE6, 0x00001BF3}, {0x00001C24, 0x00001C37},
-{0x00001CD0, 0x00001CD2}, {0x00001CD4, 0x00001CE8}, {0x00001CED, 0x00001CED}, {0x00001CF4, 0x00001CF4},
-{0x00001CF7, 0x00001CF9}, {0x00001DC0, 0x00001DFF}, {0x000020D0, 0x000020F0}, {0x00002CEF, 0x00002CF1},
-{0x00002D7F, 0x00002D7F}, {0x00002DE0, 0x00002DFF}, {0x0000302A, 0x0000302F}, {0x00003099, 0x0000309A},
-{0x0000A66F, 0x0000A672}, {0x0000A674, 0x0000A67D}, {0x0000A69E, 0x0000A69F}, {0x0000A6F0, 0x0000A6F1},
-{0x0000A802, 0x0000A802}, {0x0000A806, 0x0000A806}, {0x0000A80B, 0x0000A80B}, {0x0000A823, 0x0000A827},
-{0x0000A82C, 0x0000A82C}, {0x0000A880, 0x0000A881}, {0x0000A8B4, 0x0000A8C5}, {0x0000A8E0, 0x0000A8F1},
-{0x0000A8FF, 0x0000A8FF}, {0x0000A926, 0x0000A92D}, {0x0000A947, 0x0000A953}, {0x0000A980, 0x0000A983},
-{0x0000A9B3, 0x0000A9C0}, {0x0000A9E5, 0x0000A9E5}, {0x0000AA29, 0x0000AA36}, {0x0000AA43, 0x0000AA43},
-{0x0000AA4C, 0x0000AA4D}, {0x0000AA7B, 0x0000AA7D}, {0x0000AAB0, 0x0000AAB0}, {0x0000AAB2, 0x0000AAB4},
-{0x0000AAB7, 0x0000AAB8}, {0x0000AABE, 0x0000AABF}, {0x0000AAC1, 0x0000AAC1}, {0x0000AAEB, 0x0000AAEF},
-{0x0000AAF5, 0x0000AAF6}, {0x0000ABE3, 0x0000ABEA}, {0x0000ABEC, 0x0000ABED}, {0x0000FB1E, 0x0000FB1E},
-{0x0000FE00, 0x0000FE0F}, {0x0000FE20, 0x0000FE2F}, {0x000101FD, 0x000101FD}, {0x000102E0, 0x000102E0},
-{0x00010376, 0x0001037A}, {0x00010A01, 0x00010A03}, {0x00010A05, 0x00010A06}, {0x00010A0C, 0x00010A0F},
-{0x00010A38, 0x00010A3A}, {0x00010A3F, 0x00010A3F}, {0x00010AE5, 0x00010AE6}, {0x00010D24, 0x00010D27},
-{0x00010EAB, 0x00010EAC}, {0x00010EFD, 0x00010EFF}, {0x00010F46, 0x00010F50}, {0x00010F82, 0x00010F85},
-{0x00011000, 0x00011002}, {0x00011038, 0x00011046}, {0x00011070, 0x00011070}, {0x00011073, 0x00011074},
-{0x0001107F, 0x00011082}, {0x000110B0, 0x000110BA}, {0x000110C2, 0x000110C2}, {0x00011100, 0x00011102},
-{0x00011127, 0x00011134}, {0x00011145, 0x00011146}, {0x00011173, 0x00011173}, {0x00011180, 0x00011182},
-{0x000111B3, 0x000111C0}, {0x000111C9, 0x000111CC}, {0x000111CE, 0x000111CF}, {0x0001122C, 0x00011237},
-{0x0001123E, 0x0001123E}, {0x00011241, 0x00011241}, {0x000112DF, 0x000112EA}, {0x00011300, 0x00011303},
-{0x0001133B, 0x0001133C}, {0x0001133E, 0x00011344}, {0x00011347, 0x00011348}, {0x0001134B, 0x0001134D},
-{0x00011357, 0x00011357}, {0x00011362, 0x00011363}, {0x00011366, 0x0001136C}, {0x00011370, 0x00011374},
-{0x00011435, 0x00011446}, {0x0001145E, 0x0001145E}, {0x000114B0, 0x000114C3}, {0x000115AF, 0x000115B5},
-{0x000115B8, 0x000115C0}, {0x000115DC, 0x000115DD}, {0x00011630, 0x00011640}, {0x000116AB, 0x000116B7},
-{0x0001171D, 0x0001172B}, {0x0001182C, 0x0001183A}, {0x00011930, 0x00011935}, {0x00011937, 0x00011938},
-{0x0001193B, 0x0001193E}, {0x00011940, 0x00011940}, {0x00011942, 0x00011943}, {0x000119D1, 0x000119D7},
-{0x000119DA, 0x000119E0}, {0x000119E4, 0x000119E4}, {0x00011A01, 0x00011A0A}, {0x00011A33, 0x00011A39},
-{0x00011A3B, 0x00011A3E}, {0x00011A47, 0x00011A47}, {0x00011A51, 0x00011A5B}, {0x00011A8A, 0x00011A99},
-{0x00011C2F, 0x00011C36}, {0x00011C38, 0x00011C3F}, {0x00011C92, 0x00011CA7}, {0x00011CA9, 0x00011CB6},
-{0x00011D31, 0x00011D36}, {0x00011D3A, 0x00011D3A}, {0x00011D3C, 0x00011D3D}, {0x00011D3F, 0x00011D45},
-{0x00011D47, 0x00011D47}, {0x00011D8A, 0x00011D8E}, {0x00011D90, 0x00011D91}, {0x00011D93, 0x00011D97},
-{0x00011EF3, 0x00011EF6}, {0x00011F00, 0x00011F01}, {0x00011F03, 0x00011F03}, {0x00011F34, 0x00011F3A},
-{0x00011F3E, 0x00011F42}, {0x00013440, 0x00013440}, {0x00013447, 0x00013455}, {0x00016AF0, 0x00016AF4},
-{0x00016B30, 0x00016B36}, {0x00016F4F, 0x00016F4F}, {0x00016F51, 0x00016F87}, {0x00016F8F, 0x00016F92},
-{0x00016FE4, 0x00016FE4}, {0x00016FF0, 0x00016FF1}, {0x0001BC9D, 0x0001BC9E}, {0x0001CF00, 0x0001CF2D},
-{0x0001CF30, 0x0001CF46}, {0x0001D165, 0x0001D169}, {0x0001D16D, 0x0001D172}, {0x0001D17B, 0x0001D182},
-{0x0001D185, 0x0001D18B}, {0x0001D1AA, 0x0001D1AD}, {0x0001D242, 0x0001D244}, {0x0001DA00, 0x0001DA36},
-{0x0001DA3B, 0x0001DA6C}, {0x0001DA75, 0x0001DA75}, {0x0001DA84, 0x0001DA84}, {0x0001DA9B, 0x0001DA9F},
-{0x0001DAA1, 0x0001DAAF}, {0x0001E000, 0x0001E006}, {0x0001E008, 0x0001E018}, {0x0001E01B, 0x0001E021},
-{0x0001E023, 0x0001E024}, {0x0001E026, 0x0001E02A}, {0x0001E08F, 0x0001E08F}, {0x0001E130, 0x0001E136},
-{0x0001E2AE, 0x0001E2AE}, {0x0001E2EC, 0x0001E2EF}, {0x0001E4EC, 0x0001E4EF}, {0x0001E8D0, 0x0001E8D6},
-{0x0001E944, 0x0001E94A}, {0x000E0100, 0x000E01EF},
+const std::vector<range_nfd> unicode_ranges_nfd = {  // start, last, nfd
+{0x000000, 0x000000, 0x000000},
+{0x0000C0, 0x0000C5, 0x000041},
+{0x0000C7, 0x0000C7, 0x000043},
+{0x0000C8, 0x0000CB, 0x000045},
+{0x0000CC, 0x0000CF, 0x000049},
+{0x0000D1, 0x0000D1, 0x00004E},
+{0x0000D2, 0x0000D6, 0x00004F},
+{0x0000D9, 0x0000DC, 0x000055},
+{0x0000DD, 0x0000DD, 0x000059},
+{0x0000E0, 0x0000E5, 0x000061},
+{0x0000E7, 0x0000E7, 0x000063},
+{0x0000E8, 0x0000EB, 0x000065},
+{0x0000EC, 0x0000EF, 0x000069},
+{0x0000F1, 0x0000F1, 0x00006E},
+{0x0000F2, 0x0000F6, 0x00006F},
+{0x0000F9, 0x0000FC, 0x000075},
+{0x0000FD, 0x0000FD, 0x000079},
+{0x0000FF, 0x0000FF, 0x000079},
+{0x000100, 0x000100, 0x000041},
+{0x000101, 0x000101, 0x000061},
+{0x000102, 0x000102, 0x000041},
+{0x000103, 0x000103, 0x000061},
+{0x000104, 0x000104, 0x000041},
+{0x000105, 0x000105, 0x000061},
+{0x000106, 0x000106, 0x000043},
+{0x000107, 0x000107, 0x000063},
+{0x000108, 0x000108, 0x000043},
+{0x000109, 0x000109, 0x000063},
+{0x00010A, 0x00010A, 0x000043},
+{0x00010B, 0x00010B, 0x000063},
+{0x00010C, 0x00010C, 0x000043},
+{0x00010D, 0x00010D, 0x000063},
+{0x00010E, 0x00010E, 0x000044},
+{0x00010F, 0x00010F, 0x000064},
+{0x000112, 0x000112, 0x000045},
+{0x000113, 0x000113, 0x000065},
+{0x000114, 0x000114, 0x000045},
+{0x000115, 0x000115, 0x000065},
+{0x000116, 0x000116, 0x000045},
+{0x000117, 0x000117, 0x000065},
+{0x000118, 0x000118, 0x000045},
+{0x000119, 0x000119, 0x000065},
+{0x00011A, 0x00011A, 0x000045},
+{0x00011B, 0x00011B, 0x000065},
+{0x00011C, 0x00011C, 0x000047},
+{0x00011D, 0x00011D, 0x000067},
+{0x00011E, 0x00011E, 0x000047},
+{0x00011F, 0x00011F, 0x000067},
+{0x000120, 0x000120, 0x000047},
+{0x000121, 0x000121, 0x000067},
+{0x000122, 0x000122, 0x000047},
+{0x000123, 0x000123, 0x000067},
+{0x000124, 0x000124, 0x000048},
+{0x000125, 0x000125, 0x000068},
+{0x000128, 0x000128, 0x000049},
+{0x000129, 0x000129, 0x000069},
+{0x00012A, 0x00012A, 0x000049},
+{0x00012B, 0x00012B, 0x000069},
+{0x00012C, 0x00012C, 0x000049},
+{0x00012D, 0x00012D, 0x000069},
+{0x00012E, 0x00012E, 0x000049},
+{0x00012F, 0x00012F, 0x000069},
+{0x000130, 0x000130, 0x000049},
+{0x000134, 0x000134, 0x00004A},
+{0x000135, 0x000135, 0x00006A},
+{0x000136, 0x000136, 0x00004B},
+{0x000137, 0x000137, 0x00006B},
+{0x000139, 0x000139, 0x00004C},
+{0x00013A, 0x00013A, 0x00006C},
+{0x00013B, 0x00013B, 0x00004C},
+{0x00013C, 0x00013C, 0x00006C},
+{0x00013D, 0x00013D, 0x00004C},
+{0x00013E, 0x00013E, 0x00006C},
+{0x000143, 0x000143, 0x00004E},
+{0x000144, 0x000144, 0x00006E},
+{0x000145, 0x000145, 0x00004E},
+{0x000146, 0x000146, 0x00006E},
+{0x000147, 0x000147, 0x00004E},
+{0x000148, 0x000148, 0x00006E},
+{0x00014C, 0x00014C, 0x00004F},
+{0x00014D, 0x00014D, 0x00006F},
+{0x00014E, 0x00014E, 0x00004F},
+{0x00014F, 0x00014F, 0x00006F},
+{0x000150, 0x000150, 0x00004F},
+{0x000151, 0x000151, 0x00006F},
+{0x000154, 0x000154, 0x000052},
+{0x000155, 0x000155, 0x000072},
+{0x000156, 0x000156, 0x000052},
+{0x000157, 0x000157, 0x000072},
+{0x000158, 0x000158, 0x000052},
+{0x000159, 0x000159, 0x000072},
+{0x00015A, 0x00015A, 0x000053},
+{0x00015B, 0x00015B, 0x000073},
+{0x00015C, 0x00015C, 0x000053},
+{0x00015D, 0x00015D, 0x000073},
+{0x00015E, 0x00015E, 0x000053},
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+{0x02F8FC, 0x02F8FC, 0x006CBF},
+{0x02F8FD, 0x02F8FD, 0x006CCD},
+{0x02F8FE, 0x02F8FE, 0x006C67},
+{0x02F8FF, 0x02F8FF, 0x006D16},
+{0x02F900, 0x02F900, 0x006D3E},
+{0x02F901, 0x02F901, 0x006D77},
+{0x02F902, 0x02F902, 0x006D41},
+{0x02F903, 0x02F903, 0x006D69},
+{0x02F904, 0x02F904, 0x006D78},
+{0x02F905, 0x02F905, 0x006D85},
+{0x02F906, 0x02F906, 0x023D1E},
+{0x02F907, 0x02F907, 0x006D34},
+{0x02F908, 0x02F908, 0x006E2F},
+{0x02F909, 0x02F909, 0x006E6E},
+{0x02F90A, 0x02F90A, 0x003D33},
+{0x02F90B, 0x02F90B, 0x006ECB},
+{0x02F90C, 0x02F90C, 0x006EC7},
+{0x02F90D, 0x02F90D, 0x023ED1},
+{0x02F90E, 0x02F90E, 0x006DF9},
+{0x02F90F, 0x02F90F, 0x006F6E},
+{0x02F910, 0x02F910, 0x023F5E},
+{0x02F911, 0x02F911, 0x023F8E},
+{0x02F912, 0x02F912, 0x006FC6},
+{0x02F913, 0x02F913, 0x007039},
+{0x02F914, 0x02F914, 0x00701E},
+{0x02F915, 0x02F915, 0x00701B},
+{0x02F916, 0x02F916, 0x003D96},
+{0x02F917, 0x02F917, 0x00704A},
+{0x02F918, 0x02F918, 0x00707D},
+{0x02F919, 0x02F919, 0x007077},
+{0x02F91A, 0x02F91A, 0x0070AD},
+{0x02F91B, 0x02F91B, 0x020525},
+{0x02F91C, 0x02F91C, 0x007145},
+{0x02F91D, 0x02F91D, 0x024263},
+{0x02F91E, 0x02F91E, 0x00719C},
+{0x02F91F, 0x02F91F, 0x0243AB},
+{0x02F920, 0x02F920, 0x007228},
+{0x02F921, 0x02F921, 0x007235},
+{0x02F922, 0x02F922, 0x007250},
+{0x02F923, 0x02F923, 0x024608},
+{0x02F924, 0x02F924, 0x007280},
+{0x02F925, 0x02F925, 0x007295},
+{0x02F926, 0x02F926, 0x024735},
+{0x02F927, 0x02F927, 0x024814},
+{0x02F928, 0x02F928, 0x00737A},
+{0x02F929, 0x02F929, 0x00738B},
+{0x02F92A, 0x02F92A, 0x003EAC},
+{0x02F92B, 0x02F92B, 0x0073A5},
+{0x02F92C, 0x02F92D, 0x003EB8},
+{0x02F92E, 0x02F92E, 0x007447},
+{0x02F92F, 0x02F92F, 0x00745C},
+{0x02F930, 0x02F930, 0x007471},
+{0x02F931, 0x02F931, 0x007485},
+{0x02F932, 0x02F932, 0x0074CA},
+{0x02F933, 0x02F933, 0x003F1B},
+{0x02F934, 0x02F934, 0x007524},
+{0x02F935, 0x02F935, 0x024C36},
+{0x02F936, 0x02F936, 0x00753E},
+{0x02F937, 0x02F937, 0x024C92},
+{0x02F938, 0x02F938, 0x007570},
+{0x02F939, 0x02F939, 0x02219F},
+{0x02F93A, 0x02F93A, 0x007610},
+{0x02F93B, 0x02F93B, 0x024FA1},
+{0x02F93C, 0x02F93C, 0x024FB8},
+{0x02F93D, 0x02F93D, 0x025044},
+{0x02F93E, 0x02F93E, 0x003FFC},
+{0x02F93F, 0x02F93F, 0x004008},
+{0x02F940, 0x02F940, 0x0076F4},
+{0x02F941, 0x02F941, 0x0250F3},
+{0x02F942, 0x02F942, 0x0250F2},
+{0x02F943, 0x02F943, 0x025119},
+{0x02F944, 0x02F944, 0x025133},
+{0x02F945, 0x02F945, 0x00771E},
+{0x02F946, 0x02F947, 0x00771F},
+{0x02F948, 0x02F948, 0x00774A},
+{0x02F949, 0x02F949, 0x004039},
+{0x02F94A, 0x02F94A, 0x00778B},
+{0x02F94B, 0x02F94B, 0x004046},
+{0x02F94C, 0x02F94C, 0x004096},
+{0x02F94D, 0x02F94D, 0x02541D},
+{0x02F94E, 0x02F94E, 0x00784E},
+{0x02F94F, 0x02F94F, 0x00788C},
+{0x02F950, 0x02F950, 0x0078CC},
+{0x02F951, 0x02F951, 0x0040E3},
+{0x02F952, 0x02F952, 0x025626},
+{0x02F953, 0x02F953, 0x007956},
+{0x02F954, 0x02F954, 0x02569A},
+{0x02F955, 0x02F955, 0x0256C5},
+{0x02F956, 0x02F956, 0x00798F},
+{0x02F957, 0x02F957, 0x0079EB},
+{0x02F958, 0x02F958, 0x00412F},
+{0x02F959, 0x02F959, 0x007A40},
+{0x02F95A, 0x02F95A, 0x007A4A},
+{0x02F95B, 0x02F95B, 0x007A4F},
+{0x02F95C, 0x02F95C, 0x02597C},
+{0x02F95D, 0x02F95E, 0x025AA7},
+{0x02F95F, 0x02F95F, 0x007AEE},
+{0x02F960, 0x02F960, 0x004202},
+{0x02F961, 0x02F961, 0x025BAB},
+{0x02F962, 0x02F962, 0x007BC6},
+{0x02F963, 0x02F963, 0x007BC9},
+{0x02F964, 0x02F964, 0x004227},
+{0x02F965, 0x02F965, 0x025C80},
+{0x02F966, 0x02F966, 0x007CD2},
+{0x02F967, 0x02F967, 0x0042A0},
+{0x02F968, 0x02F968, 0x007CE8},
+{0x02F969, 0x02F969, 0x007CE3},
+{0x02F96A, 0x02F96A, 0x007D00},
+{0x02F96B, 0x02F96B, 0x025F86},
+{0x02F96C, 0x02F96C, 0x007D63},
+{0x02F96D, 0x02F96D, 0x004301},
+{0x02F96E, 0x02F96E, 0x007DC7},
+{0x02F96F, 0x02F96F, 0x007E02},
+{0x02F970, 0x02F970, 0x007E45},
+{0x02F971, 0x02F971, 0x004334},
+{0x02F972, 0x02F972, 0x026228},
+{0x02F973, 0x02F973, 0x026247},
+{0x02F974, 0x02F974, 0x004359},
+{0x02F975, 0x02F975, 0x0262D9},
+{0x02F976, 0x02F976, 0x007F7A},
+{0x02F977, 0x02F977, 0x02633E},
+{0x02F978, 0x02F978, 0x007F95},
+{0x02F979, 0x02F979, 0x007FFA},
+{0x02F97A, 0x02F97A, 0x008005},
+{0x02F97B, 0x02F97B, 0x0264DA},
+{0x02F97C, 0x02F97C, 0x026523},
+{0x02F97D, 0x02F97D, 0x008060},
+{0x02F97E, 0x02F97E, 0x0265A8},
+{0x02F97F, 0x02F97F, 0x008070},
+{0x02F980, 0x02F980, 0x02335F},
+{0x02F981, 0x02F981, 0x0043D5},
+{0x02F982, 0x02F982, 0x0080B2},
+{0x02F983, 0x02F983, 0x008103},
+{0x02F984, 0x02F984, 0x00440B},
+{0x02F985, 0x02F985, 0x00813E},
+{0x02F986, 0x02F986, 0x005AB5},
+{0x02F987, 0x02F987, 0x0267A7},
+{0x02F988, 0x02F988, 0x0267B5},
+{0x02F989, 0x02F989, 0x023393},
+{0x02F98A, 0x02F98A, 0x02339C},
+{0x02F98B, 0x02F98B, 0x008201},
+{0x02F98C, 0x02F98C, 0x008204},
+{0x02F98D, 0x02F98D, 0x008F9E},
+{0x02F98E, 0x02F98E, 0x00446B},
+{0x02F98F, 0x02F98F, 0x008291},
+{0x02F990, 0x02F990, 0x00828B},
+{0x02F991, 0x02F991, 0x00829D},
+{0x02F992, 0x02F992, 0x0052B3},
+{0x02F993, 0x02F993, 0x0082B1},
+{0x02F994, 0x02F994, 0x0082B3},
+{0x02F995, 0x02F995, 0x0082BD},
+{0x02F996, 0x02F996, 0x0082E6},
+{0x02F997, 0x02F997, 0x026B3C},
+{0x02F998, 0x02F998, 0x0082E5},
+{0x02F999, 0x02F999, 0x00831D},
+{0x02F99A, 0x02F99A, 0x008363},
+{0x02F99B, 0x02F99B, 0x0083AD},
+{0x02F99C, 0x02F99C, 0x008323},
+{0x02F99D, 0x02F99D, 0x0083BD},
+{0x02F99E, 0x02F99E, 0x0083E7},
+{0x02F99F, 0x02F99F, 0x008457},
+{0x02F9A0, 0x02F9A0, 0x008353},
+{0x02F9A1, 0x02F9A1, 0x0083CA},
+{0x02F9A2, 0x02F9A2, 0x0083CC},
+{0x02F9A3, 0x02F9A3, 0x0083DC},
+{0x02F9A4, 0x02F9A4, 0x026C36},
+{0x02F9A5, 0x02F9A5, 0x026D6B},
+{0x02F9A6, 0x02F9A6, 0x026CD5},
+{0x02F9A7, 0x02F9A7, 0x00452B},
+{0x02F9A8, 0x02F9A8, 0x0084F1},
+{0x02F9A9, 0x02F9A9, 0x0084F3},
+{0x02F9AA, 0x02F9AA, 0x008516},
+{0x02F9AB, 0x02F9AB, 0x0273CA},
+{0x02F9AC, 0x02F9AC, 0x008564},
+{0x02F9AD, 0x02F9AD, 0x026F2C},
+{0x02F9AE, 0x02F9AE, 0x00455D},
+{0x02F9AF, 0x02F9AF, 0x004561},
+{0x02F9B0, 0x02F9B0, 0x026FB1},
+{0x02F9B1, 0x02F9B1, 0x0270D2},
+{0x02F9B2, 0x02F9B2, 0x00456B},
+{0x02F9B3, 0x02F9B3, 0x008650},
+{0x02F9B4, 0x02F9B4, 0x00865C},
+{0x02F9B5, 0x02F9B5, 0x008667},
+{0x02F9B6, 0x02F9B6, 0x008669},
+{0x02F9B7, 0x02F9B7, 0x0086A9},
+{0x02F9B8, 0x02F9B8, 0x008688},
+{0x02F9B9, 0x02F9B9, 0x00870E},
+{0x02F9BA, 0x02F9BA, 0x0086E2},
+{0x02F9BB, 0x02F9BB, 0x008779},
+{0x02F9BC, 0x02F9BC, 0x008728},
+{0x02F9BD, 0x02F9BD, 0x00876B},
+{0x02F9BE, 0x02F9BE, 0x008786},
+{0x02F9BF, 0x02F9BF, 0x0045D7},
+{0x02F9C0, 0x02F9C0, 0x0087E1},
+{0x02F9C1, 0x02F9C1, 0x008801},
+{0x02F9C2, 0x02F9C2, 0x0045F9},
+{0x02F9C3, 0x02F9C3, 0x008860},
+{0x02F9C4, 0x02F9C4, 0x008863},
+{0x02F9C5, 0x02F9C5, 0x027667},
+{0x02F9C6, 0x02F9C6, 0x0088D7},
+{0x02F9C7, 0x02F9C7, 0x0088DE},
+{0x02F9C8, 0x02F9C8, 0x004635},
+{0x02F9C9, 0x02F9C9, 0x0088FA},
+{0x02F9CA, 0x02F9CA, 0x0034BB},
+{0x02F9CB, 0x02F9CB, 0x0278AE},
+{0x02F9CC, 0x02F9CC, 0x027966},
+{0x02F9CD, 0x02F9CD, 0x0046BE},
+{0x02F9CE, 0x02F9CE, 0x0046C7},
+{0x02F9CF, 0x02F9CF, 0x008AA0},
+{0x02F9D0, 0x02F9D0, 0x008AED},
+{0x02F9D1, 0x02F9D1, 0x008B8A},
+{0x02F9D2, 0x02F9D2, 0x008C55},
+{0x02F9D3, 0x02F9D3, 0x027CA8},
+{0x02F9D4, 0x02F9D4, 0x008CAB},
+{0x02F9D5, 0x02F9D5, 0x008CC1},
+{0x02F9D6, 0x02F9D6, 0x008D1B},
+{0x02F9D7, 0x02F9D7, 0x008D77},
+{0x02F9D8, 0x02F9D8, 0x027F2F},
+{0x02F9D9, 0x02F9D9, 0x020804},
+{0x02F9DA, 0x02F9DA, 0x008DCB},
+{0x02F9DB, 0x02F9DB, 0x008DBC},
+{0x02F9DC, 0x02F9DC, 0x008DF0},
+{0x02F9DD, 0x02F9DD, 0x0208DE},
+{0x02F9DE, 0x02F9DE, 0x008ED4},
+{0x02F9DF, 0x02F9DF, 0x008F38},
+{0x02F9E0, 0x02F9E0, 0x0285D2},
+{0x02F9E1, 0x02F9E1, 0x0285ED},
+{0x02F9E2, 0x02F9E2, 0x009094},
+{0x02F9E3, 0x02F9E3, 0x0090F1},
+{0x02F9E4, 0x02F9E4, 0x009111},
+{0x02F9E5, 0x02F9E5, 0x02872E},
+{0x02F9E6, 0x02F9E6, 0x00911B},
+{0x02F9E7, 0x02F9E7, 0x009238},
+{0x02F9E8, 0x02F9E8, 0x0092D7},
+{0x02F9E9, 0x02F9E9, 0x0092D8},
+{0x02F9EA, 0x02F9EA, 0x00927C},
+{0x02F9EB, 0x02F9EB, 0x0093F9},
+{0x02F9EC, 0x02F9EC, 0x009415},
+{0x02F9ED, 0x02F9ED, 0x028BFA},
+{0x02F9EE, 0x02F9EE, 0x00958B},
+{0x02F9EF, 0x02F9EF, 0x004995},
+{0x02F9F0, 0x02F9F0, 0x0095B7},
+{0x02F9F1, 0x02F9F1, 0x028D77},
+{0x02F9F2, 0x02F9F2, 0x0049E6},
+{0x02F9F3, 0x02F9F3, 0x0096C3},
+{0x02F9F4, 0x02F9F4, 0x005DB2},
+{0x02F9F5, 0x02F9F5, 0x009723},
+{0x02F9F6, 0x02F9F6, 0x029145},
+{0x02F9F7, 0x02F9F7, 0x02921A},
+{0x02F9F8, 0x02F9F8, 0x004A6E},
+{0x02F9F9, 0x02F9F9, 0x004A76},
+{0x02F9FA, 0x02F9FA, 0x0097E0},
+{0x02F9FB, 0x02F9FB, 0x02940A},
+{0x02F9FC, 0x02F9FC, 0x004AB2},
+{0x02F9FD, 0x02F9FD, 0x029496},
+{0x02F9FE, 0x02F9FF, 0x00980B},
+{0x02FA00, 0x02FA00, 0x009829},
+{0x02FA01, 0x02FA01, 0x0295B6},
+{0x02FA02, 0x02FA02, 0x0098E2},
+{0x02FA03, 0x02FA03, 0x004B33},
+{0x02FA04, 0x02FA04, 0x009929},
+{0x02FA05, 0x02FA05, 0x0099A7},
+{0x02FA06, 0x02FA06, 0x0099C2},
+{0x02FA07, 0x02FA07, 0x0099FE},
+{0x02FA08, 0x02FA08, 0x004BCE},
+{0x02FA09, 0x02FA09, 0x029B30},
+{0x02FA0A, 0x02FA0A, 0x009B12},
+{0x02FA0B, 0x02FA0B, 0x009C40},
+{0x02FA0C, 0x02FA0C, 0x009CFD},
+{0x02FA0D, 0x02FA0D, 0x004CCE},
+{0x02FA0E, 0x02FA0E, 0x004CED},
+{0x02FA0F, 0x02FA0F, 0x009D67},
+{0x02FA10, 0x02FA10, 0x02A0CE},
+{0x02FA11, 0x02FA11, 0x004CF8},
+{0x02FA12, 0x02FA12, 0x02A105},
+{0x02FA13, 0x02FA13, 0x02A20E},
+{0x02FA14, 0x02FA14, 0x02A291},
+{0x02FA15, 0x02FA15, 0x009EBB},
+{0x02FA16, 0x02FA16, 0x004D56},
+{0x02FA17, 0x02FA17, 0x009EF9},
+{0x02FA18, 0x02FA18, 0x009EFE},
+{0x02FA19, 0x02FA19, 0x009F05},
+{0x02FA1A, 0x02FA1A, 0x009F0F},
+{0x02FA1B, 0x02FA1B, 0x009F16},
+{0x02FA1C, 0x02FA1C, 0x009F3B},
+{0x02FA1D, 0x02FA1D, 0x02A600},
 };
 
-const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_punctuation = {
-{0x00000021, 0x00000023}, {0x00000025, 0x0000002A}, {0x0000002C, 0x0000002F}, {0x0000003A, 0x0000003B},
-{0x0000003F, 0x00000040}, {0x0000005B, 0x0000005D}, {0x0000005F, 0x0000005F}, {0x0000007B, 0x0000007B},
-{0x0000007D, 0x0000007D}, {0x000000A1, 0x000000A1}, {0x000000A7, 0x000000A7}, {0x000000AB, 0x000000AB},
-{0x000000B6, 0x000000B7}, {0x000000BB, 0x000000BB}, {0x000000BF, 0x000000BF}, {0x0000037E, 0x0000037E},
-{0x00000387, 0x00000387}, {0x0000055A, 0x0000055F}, {0x00000589, 0x0000058A}, {0x000005BE, 0x000005BE},
-{0x000005C0, 0x000005C0}, {0x000005C3, 0x000005C3}, {0x000005C6, 0x000005C6}, {0x000005F3, 0x000005F4},
-{0x00000609, 0x0000060A}, {0x0000060C, 0x0000060D}, {0x0000061B, 0x0000061B}, {0x0000061D, 0x0000061F},
-{0x0000066A, 0x0000066D}, {0x000006D4, 0x000006D4}, {0x00000700, 0x0000070D}, {0x000007F7, 0x000007F9},
-{0x00000830, 0x0000083E}, {0x0000085E, 0x0000085E}, {0x00000964, 0x00000965}, {0x00000970, 0x00000970},
-{0x000009FD, 0x000009FD}, {0x00000A76, 0x00000A76}, {0x00000AF0, 0x00000AF0}, {0x00000C77, 0x00000C77},
-{0x00000C84, 0x00000C84}, {0x00000DF4, 0x00000DF4}, {0x00000E4F, 0x00000E4F}, {0x00000E5A, 0x00000E5B},
-{0x00000F04, 0x00000F12}, {0x00000F14, 0x00000F14}, {0x00000F3A, 0x00000F3D}, {0x00000F85, 0x00000F85},
-{0x00000FD0, 0x00000FD4}, {0x00000FD9, 0x00000FDA}, {0x0000104A, 0x0000104F}, {0x000010FB, 0x000010FB},
-{0x00001360, 0x00001368}, {0x00001400, 0x00001400}, {0x0000166E, 0x0000166E}, {0x0000169B, 0x0000169C},
-{0x000016EB, 0x000016ED}, {0x00001735, 0x00001736}, {0x000017D4, 0x000017D6}, {0x000017D8, 0x000017DA},
-{0x00001800, 0x0000180A}, {0x00001944, 0x00001945}, {0x00001A1E, 0x00001A1F}, {0x00001AA0, 0x00001AA6},
-{0x00001AA8, 0x00001AAD}, {0x00001B5A, 0x00001B60}, {0x00001B7D, 0x00001B7E}, {0x00001BFC, 0x00001BFF},
-{0x00001C3B, 0x00001C3F}, {0x00001C7E, 0x00001C7F}, {0x00001CC0, 0x00001CC7}, {0x00001CD3, 0x00001CD3},
-{0x00002010, 0x00002027}, {0x00002030, 0x00002043}, {0x00002045, 0x00002051}, {0x00002053, 0x0000205E},
-{0x0000207D, 0x0000207E}, {0x0000208D, 0x0000208E}, {0x00002308, 0x0000230B}, {0x00002329, 0x0000232A},
-{0x00002768, 0x00002775}, {0x000027C5, 0x000027C6}, {0x000027E6, 0x000027EF}, {0x00002983, 0x00002998},
-{0x000029D8, 0x000029DB}, {0x000029FC, 0x000029FD}, {0x00002CF9, 0x00002CFC}, {0x00002CFE, 0x00002CFF},
-{0x00002D70, 0x00002D70}, {0x00002E00, 0x00002E2E}, {0x00002E30, 0x00002E4F}, {0x00002E52, 0x00002E5D},
-{0x00003001, 0x00003003}, {0x00003008, 0x00003011}, {0x00003014, 0x0000301F}, {0x00003030, 0x00003030},
-{0x0000303D, 0x0000303D}, {0x000030A0, 0x000030A0}, {0x000030FB, 0x000030FB}, {0x0000A4FE, 0x0000A4FF},
-{0x0000A60D, 0x0000A60F}, {0x0000A673, 0x0000A673}, {0x0000A67E, 0x0000A67E}, {0x0000A6F2, 0x0000A6F7},
-{0x0000A874, 0x0000A877}, {0x0000A8CE, 0x0000A8CF}, {0x0000A8F8, 0x0000A8FA}, {0x0000A8FC, 0x0000A8FC},
-{0x0000A92E, 0x0000A92F}, {0x0000A95F, 0x0000A95F}, {0x0000A9C1, 0x0000A9CD}, {0x0000A9DE, 0x0000A9DF},
-{0x0000AA5C, 0x0000AA5F}, {0x0000AADE, 0x0000AADF}, {0x0000AAF0, 0x0000AAF1}, {0x0000ABEB, 0x0000ABEB},
-{0x0000FD3E, 0x0000FD3F}, {0x0000FE10, 0x0000FE19}, {0x0000FE30, 0x0000FE52}, {0x0000FE54, 0x0000FE61},
-{0x0000FE63, 0x0000FE63}, {0x0000FE68, 0x0000FE68}, {0x0000FE6A, 0x0000FE6B}, {0x0000FF01, 0x0000FF03},
-{0x0000FF05, 0x0000FF0A}, {0x0000FF0C, 0x0000FF0F}, {0x0000FF1A, 0x0000FF1B}, {0x0000FF1F, 0x0000FF20},
-{0x0000FF3B, 0x0000FF3D}, {0x0000FF3F, 0x0000FF3F}, {0x0000FF5B, 0x0000FF5B}, {0x0000FF5D, 0x0000FF5D},
-{0x0000FF5F, 0x0000FF65}, {0x00010100, 0x00010102}, {0x0001039F, 0x0001039F}, {0x000103D0, 0x000103D0},
-{0x0001056F, 0x0001056F}, {0x00010857, 0x00010857}, {0x0001091F, 0x0001091F}, {0x0001093F, 0x0001093F},
-{0x00010A50, 0x00010A58}, {0x00010A7F, 0x00010A7F}, {0x00010AF0, 0x00010AF6}, {0x00010B39, 0x00010B3F},
-{0x00010B99, 0x00010B9C}, {0x00010EAD, 0x00010EAD}, {0x00010F55, 0x00010F59}, {0x00010F86, 0x00010F89},
-{0x00011047, 0x0001104D}, {0x000110BB, 0x000110BC}, {0x000110BE, 0x000110C1}, {0x00011140, 0x00011143},
-{0x00011174, 0x00011175}, {0x000111C5, 0x000111C8}, {0x000111CD, 0x000111CD}, {0x000111DB, 0x000111DB},
-{0x000111DD, 0x000111DF}, {0x00011238, 0x0001123D}, {0x000112A9, 0x000112A9}, {0x0001144B, 0x0001144F},
-{0x0001145A, 0x0001145B}, {0x0001145D, 0x0001145D}, {0x000114C6, 0x000114C6}, {0x000115C1, 0x000115D7},
-{0x00011641, 0x00011643}, {0x00011660, 0x0001166C}, {0x000116B9, 0x000116B9}, {0x0001173C, 0x0001173E},
-{0x0001183B, 0x0001183B}, {0x00011944, 0x00011946}, {0x000119E2, 0x000119E2}, {0x00011A3F, 0x00011A46},
-{0x00011A9A, 0x00011A9C}, {0x00011A9E, 0x00011AA2}, {0x00011B00, 0x00011B09}, {0x00011C41, 0x00011C45},
-{0x00011C70, 0x00011C71}, {0x00011EF7, 0x00011EF8}, {0x00011F43, 0x00011F4F}, {0x00011FFF, 0x00011FFF},
-{0x00012470, 0x00012474}, {0x00012FF1, 0x00012FF2}, {0x00016A6E, 0x00016A6F}, {0x00016AF5, 0x00016AF5},
-{0x00016B37, 0x00016B3B}, {0x00016B44, 0x00016B44}, {0x00016E97, 0x00016E9A}, {0x00016FE2, 0x00016FE2},
-{0x0001BC9F, 0x0001BC9F}, {0x0001DA87, 0x0001DA8B}, {0x0001E95E, 0x0001E95F},
-};
-
-const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_symbol = {
-{0x00000024, 0x00000024}, {0x0000002B, 0x0000002B}, {0x0000003C, 0x0000003E}, {0x0000005E, 0x0000005E},
-{0x00000060, 0x00000060}, {0x0000007C, 0x0000007C}, {0x0000007E, 0x0000007E}, {0x000000A2, 0x000000A6},
-{0x000000A8, 0x000000A9}, {0x000000AC, 0x000000AC}, {0x000000AE, 0x000000B1}, {0x000000B4, 0x000000B4},
-{0x000000B8, 0x000000B8}, {0x000000D7, 0x000000D7}, {0x000000F7, 0x000000F7}, {0x000002C2, 0x000002C5},
-{0x000002D2, 0x000002DF}, {0x000002E5, 0x000002EB}, {0x000002ED, 0x000002ED}, {0x000002EF, 0x000002FF},
-{0x00000375, 0x00000375}, {0x00000384, 0x00000385}, {0x000003F6, 0x000003F6}, {0x00000482, 0x00000482},
-{0x0000058D, 0x0000058F}, {0x00000606, 0x00000608}, {0x0000060B, 0x0000060B}, {0x0000060E, 0x0000060F},
-{0x000006DE, 0x000006DE}, {0x000006E9, 0x000006E9}, {0x000006FD, 0x000006FE}, {0x000007F6, 0x000007F6},
-{0x000007FE, 0x000007FF}, {0x00000888, 0x00000888}, {0x000009F2, 0x000009F3}, {0x000009FA, 0x000009FB},
-{0x00000AF1, 0x00000AF1}, {0x00000B70, 0x00000B70}, {0x00000BF3, 0x00000BFA}, {0x00000C7F, 0x00000C7F},
-{0x00000D4F, 0x00000D4F}, {0x00000D79, 0x00000D79}, {0x00000E3F, 0x00000E3F}, {0x00000F01, 0x00000F03},
-{0x00000F13, 0x00000F13}, {0x00000F15, 0x00000F17}, {0x00000F1A, 0x00000F1F}, {0x00000F34, 0x00000F34},
-{0x00000F36, 0x00000F36}, {0x00000F38, 0x00000F38}, {0x00000FBE, 0x00000FC5}, {0x00000FC7, 0x00000FCC},
-{0x00000FCE, 0x00000FCF}, {0x00000FD5, 0x00000FD8}, {0x0000109E, 0x0000109F}, {0x00001390, 0x00001399},
-{0x0000166D, 0x0000166D}, {0x000017DB, 0x000017DB}, {0x00001940, 0x00001940}, {0x000019DE, 0x000019FF},
-{0x00001B61, 0x00001B6A}, {0x00001B74, 0x00001B7C}, {0x00001FBD, 0x00001FBD}, {0x00001FBF, 0x00001FC1},
-{0x00001FCD, 0x00001FCF}, {0x00001FDD, 0x00001FDF}, {0x00001FED, 0x00001FEF}, {0x00001FFD, 0x00001FFE},
-{0x00002044, 0x00002044}, {0x00002052, 0x00002052}, {0x0000207A, 0x0000207C}, {0x0000208A, 0x0000208C},
-{0x000020A0, 0x000020C0}, {0x00002100, 0x00002101}, {0x00002103, 0x00002106}, {0x00002108, 0x00002109},
-{0x00002114, 0x00002114}, {0x00002116, 0x00002118}, {0x0000211E, 0x00002123}, {0x00002125, 0x00002125},
-{0x00002127, 0x00002127}, {0x00002129, 0x00002129}, {0x0000212E, 0x0000212E}, {0x0000213A, 0x0000213B},
-{0x00002140, 0x00002144}, {0x0000214A, 0x0000214D}, {0x0000214F, 0x0000214F}, {0x0000218A, 0x0000218B},
-{0x00002190, 0x00002307}, {0x0000230C, 0x00002328}, {0x0000232B, 0x00002426}, {0x00002440, 0x0000244A},
-{0x0000249C, 0x000024E9}, {0x00002500, 0x00002767}, {0x00002794, 0x000027C4}, {0x000027C7, 0x000027E5},
-{0x000027F0, 0x00002982}, {0x00002999, 0x000029D7}, {0x000029DC, 0x000029FB}, {0x000029FE, 0x00002B73},
-{0x00002B76, 0x00002B95}, {0x00002B97, 0x00002BFF}, {0x00002CE5, 0x00002CEA}, {0x00002E50, 0x00002E51},
-{0x00002E80, 0x00002E99}, {0x00002E9B, 0x00002EF3}, {0x00002F00, 0x00002FD5}, {0x00002FF0, 0x00002FFF},
-{0x00003004, 0x00003004}, {0x00003012, 0x00003013}, {0x00003020, 0x00003020}, {0x00003036, 0x00003037},
-{0x0000303E, 0x0000303F}, {0x0000309B, 0x0000309C}, {0x00003190, 0x00003191}, {0x00003196, 0x0000319F},
-{0x000031C0, 0x000031E3}, {0x000031EF, 0x000031EF}, {0x00003200, 0x0000321E}, {0x0000322A, 0x00003247},
-{0x00003250, 0x00003250}, {0x00003260, 0x0000327F}, {0x0000328A, 0x000032B0}, {0x000032C0, 0x000033FF},
-{0x00004DC0, 0x00004DFF}, {0x0000A490, 0x0000A4C6}, {0x0000A700, 0x0000A716}, {0x0000A720, 0x0000A721},
-{0x0000A789, 0x0000A78A}, {0x0000A828, 0x0000A82B}, {0x0000A836, 0x0000A839}, {0x0000AA77, 0x0000AA79},
-{0x0000AB5B, 0x0000AB5B}, {0x0000AB6A, 0x0000AB6B}, {0x0000FB29, 0x0000FB29}, {0x0000FBB2, 0x0000FBC2},
-{0x0000FD40, 0x0000FD4F}, {0x0000FDCF, 0x0000FDCF}, {0x0000FDFC, 0x0000FDFF}, {0x0000FE62, 0x0000FE62},
-{0x0000FE64, 0x0000FE66}, {0x0000FE69, 0x0000FE69}, {0x0000FF04, 0x0000FF04}, {0x0000FF0B, 0x0000FF0B},
-{0x0000FF1C, 0x0000FF1E}, {0x0000FF3E, 0x0000FF3E}, {0x0000FF40, 0x0000FF40}, {0x0000FF5C, 0x0000FF5C},
-{0x0000FF5E, 0x0000FF5E}, {0x0000FFE0, 0x0000FFE6}, {0x0000FFE8, 0x0000FFEE}, {0x0000FFFC, 0x0000FFFD},
-{0x00010137, 0x0001013F}, {0x00010179, 0x00010189}, {0x0001018C, 0x0001018E}, {0x00010190, 0x0001019C},
-{0x000101A0, 0x000101A0}, {0x000101D0, 0x000101FC}, {0x00010877, 0x00010878}, {0x00010AC8, 0x00010AC8},
-{0x0001173F, 0x0001173F}, {0x00011FD5, 0x00011FF1}, {0x00016B3C, 0x00016B3F}, {0x00016B45, 0x00016B45},
-{0x0001BC9C, 0x0001BC9C}, {0x0001CF50, 0x0001CFC3}, {0x0001D000, 0x0001D0F5}, {0x0001D100, 0x0001D126},
-{0x0001D129, 0x0001D164}, {0x0001D16A, 0x0001D16C}, {0x0001D183, 0x0001D184}, {0x0001D18C, 0x0001D1A9},
-{0x0001D1AE, 0x0001D1EA}, {0x0001D200, 0x0001D241}, {0x0001D245, 0x0001D245}, {0x0001D300, 0x0001D356},
-{0x0001D6C1, 0x0001D6C1}, {0x0001D6DB, 0x0001D6DB}, {0x0001D6FB, 0x0001D6FB}, {0x0001D715, 0x0001D715},
-{0x0001D735, 0x0001D735}, {0x0001D74F, 0x0001D74F}, {0x0001D76F, 0x0001D76F}, {0x0001D789, 0x0001D789},
-{0x0001D7A9, 0x0001D7A9}, {0x0001D7C3, 0x0001D7C3}, {0x0001D800, 0x0001D9FF}, {0x0001DA37, 0x0001DA3A},
-{0x0001DA6D, 0x0001DA74}, {0x0001DA76, 0x0001DA83}, {0x0001DA85, 0x0001DA86}, {0x0001E14F, 0x0001E14F},
-{0x0001E2FF, 0x0001E2FF}, {0x0001ECAC, 0x0001ECAC}, {0x0001ECB0, 0x0001ECB0}, {0x0001ED2E, 0x0001ED2E},
-{0x0001EEF0, 0x0001EEF1}, {0x0001F000, 0x0001F02B}, {0x0001F030, 0x0001F093}, {0x0001F0A0, 0x0001F0AE},
-{0x0001F0B1, 0x0001F0BF}, {0x0001F0C1, 0x0001F0CF}, {0x0001F0D1, 0x0001F0F5}, {0x0001F10D, 0x0001F1AD},
-{0x0001F1E6, 0x0001F202}, {0x0001F210, 0x0001F23B}, {0x0001F240, 0x0001F248}, {0x0001F250, 0x0001F251},
-{0x0001F260, 0x0001F265}, {0x0001F300, 0x0001F6D7}, {0x0001F6DC, 0x0001F6EC}, {0x0001F6F0, 0x0001F6FC},
-{0x0001F700, 0x0001F776}, {0x0001F77B, 0x0001F7D9}, {0x0001F7E0, 0x0001F7EB}, {0x0001F7F0, 0x0001F7F0},
-{0x0001F800, 0x0001F80B}, {0x0001F810, 0x0001F847}, {0x0001F850, 0x0001F859}, {0x0001F860, 0x0001F887},
-{0x0001F890, 0x0001F8AD}, {0x0001F8B0, 0x0001F8B1}, {0x0001F900, 0x0001FA53}, {0x0001FA60, 0x0001FA6D},
-{0x0001FA70, 0x0001FA7C}, {0x0001FA80, 0x0001FA88}, {0x0001FA90, 0x0001FABD}, {0x0001FABF, 0x0001FAC5},
-{0x0001FACE, 0x0001FADB}, {0x0001FAE0, 0x0001FAE8}, {0x0001FAF0, 0x0001FAF8}, {0x0001FB00, 0x0001FB92},
-{0x0001FB94, 0x0001FBCA},
-};
-
-const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_control = {
-{0x00000000, 0x0000001F}, {0x0000007F, 0x0000009F}, {0x000000AD, 0x000000AD}, {0x00000378, 0x00000379},
-{0x00000380, 0x00000383}, {0x0000038B, 0x0000038B}, {0x0000038D, 0x0000038D}, {0x000003A2, 0x000003A2},
-{0x00000530, 0x00000530}, {0x00000557, 0x00000558}, {0x0000058B, 0x0000058C}, {0x00000590, 0x00000590},
-{0x000005C8, 0x000005CF}, {0x000005EB, 0x000005EE}, {0x000005F5, 0x00000605}, {0x0000061C, 0x0000061C},
-{0x000006DD, 0x000006DD}, {0x0000070E, 0x0000070F}, {0x0000074B, 0x0000074C}, {0x000007B2, 0x000007BF},
-{0x000007FB, 0x000007FC}, {0x0000082E, 0x0000082F}, {0x0000083F, 0x0000083F}, {0x0000085C, 0x0000085D},
-{0x0000085F, 0x0000085F}, {0x0000086B, 0x0000086F}, {0x0000088F, 0x00000897}, {0x000008E2, 0x000008E2},
-{0x00000984, 0x00000984}, {0x0000098D, 0x0000098E}, {0x00000991, 0x00000992}, {0x000009A9, 0x000009A9},
-{0x000009B1, 0x000009B1}, {0x000009B3, 0x000009B5}, {0x000009BA, 0x000009BB}, {0x000009C5, 0x000009C6},
-{0x000009C9, 0x000009CA}, {0x000009CF, 0x000009D6}, {0x000009D8, 0x000009DB}, {0x000009DE, 0x000009DE},
-{0x000009E4, 0x000009E5}, {0x000009FF, 0x00000A00}, {0x00000A04, 0x00000A04}, {0x00000A0B, 0x00000A0E},
-{0x00000A11, 0x00000A12}, {0x00000A29, 0x00000A29}, {0x00000A31, 0x00000A31}, {0x00000A34, 0x00000A34},
-{0x00000A37, 0x00000A37}, {0x00000A3A, 0x00000A3B}, {0x00000A3D, 0x00000A3D}, {0x00000A43, 0x00000A46},
-{0x00000A49, 0x00000A4A}, {0x00000A4E, 0x00000A50}, {0x00000A52, 0x00000A58}, {0x00000A5D, 0x00000A5D},
-{0x00000A5F, 0x00000A65}, {0x00000A77, 0x00000A80}, {0x00000A84, 0x00000A84}, {0x00000A8E, 0x00000A8E},
-{0x00000A92, 0x00000A92}, {0x00000AA9, 0x00000AA9}, {0x00000AB1, 0x00000AB1}, {0x00000AB4, 0x00000AB4},
-{0x00000ABA, 0x00000ABB}, {0x00000AC6, 0x00000AC6}, {0x00000ACA, 0x00000ACA}, {0x00000ACE, 0x00000ACF},
-{0x00000AD1, 0x00000ADF}, {0x00000AE4, 0x00000AE5}, {0x00000AF2, 0x00000AF8}, {0x00000B00, 0x00000B00},
-{0x00000B04, 0x00000B04}, {0x00000B0D, 0x00000B0E}, {0x00000B11, 0x00000B12}, {0x00000B29, 0x00000B29},
-{0x00000B31, 0x00000B31}, {0x00000B34, 0x00000B34}, {0x00000B3A, 0x00000B3B}, {0x00000B45, 0x00000B46},
-{0x00000B49, 0x00000B4A}, {0x00000B4E, 0x00000B54}, {0x00000B58, 0x00000B5B}, {0x00000B5E, 0x00000B5E},
-{0x00000B64, 0x00000B65}, {0x00000B78, 0x00000B81}, {0x00000B84, 0x00000B84}, {0x00000B8B, 0x00000B8D},
-{0x00000B91, 0x00000B91}, {0x00000B96, 0x00000B98}, {0x00000B9B, 0x00000B9B}, {0x00000B9D, 0x00000B9D},
-{0x00000BA0, 0x00000BA2}, {0x00000BA5, 0x00000BA7}, {0x00000BAB, 0x00000BAD}, {0x00000BBA, 0x00000BBD},
-{0x00000BC3, 0x00000BC5}, {0x00000BC9, 0x00000BC9}, {0x00000BCE, 0x00000BCF}, {0x00000BD1, 0x00000BD6},
-{0x00000BD8, 0x00000BE5}, {0x00000BFB, 0x00000BFF}, {0x00000C0D, 0x00000C0D}, {0x00000C11, 0x00000C11},
-{0x00000C29, 0x00000C29}, {0x00000C3A, 0x00000C3B}, {0x00000C45, 0x00000C45}, {0x00000C49, 0x00000C49},
-{0x00000C4E, 0x00000C54}, {0x00000C57, 0x00000C57}, {0x00000C5B, 0x00000C5C}, {0x00000C5E, 0x00000C5F},
-{0x00000C64, 0x00000C65}, {0x00000C70, 0x00000C76}, {0x00000C8D, 0x00000C8D}, {0x00000C91, 0x00000C91},
-{0x00000CA9, 0x00000CA9}, {0x00000CB4, 0x00000CB4}, {0x00000CBA, 0x00000CBB}, {0x00000CC5, 0x00000CC5},
-{0x00000CC9, 0x00000CC9}, {0x00000CCE, 0x00000CD4}, {0x00000CD7, 0x00000CDC}, {0x00000CDF, 0x00000CDF},
-{0x00000CE4, 0x00000CE5}, {0x00000CF0, 0x00000CF0}, {0x00000CF4, 0x00000CFF}, {0x00000D0D, 0x00000D0D},
-{0x00000D11, 0x00000D11}, {0x00000D45, 0x00000D45}, {0x00000D49, 0x00000D49}, {0x00000D50, 0x00000D53},
-{0x00000D64, 0x00000D65}, {0x00000D80, 0x00000D80}, {0x00000D84, 0x00000D84}, {0x00000D97, 0x00000D99},
-{0x00000DB2, 0x00000DB2}, {0x00000DBC, 0x00000DBC}, {0x00000DBE, 0x00000DBF}, {0x00000DC7, 0x00000DC9},
-{0x00000DCB, 0x00000DCE}, {0x00000DD5, 0x00000DD5}, {0x00000DD7, 0x00000DD7}, {0x00000DE0, 0x00000DE5},
-{0x00000DF0, 0x00000DF1}, {0x00000DF5, 0x00000E00}, {0x00000E3B, 0x00000E3E}, {0x00000E5C, 0x00000E80},
-{0x00000E83, 0x00000E83}, {0x00000E85, 0x00000E85}, {0x00000E8B, 0x00000E8B}, {0x00000EA4, 0x00000EA4},
-{0x00000EA6, 0x00000EA6}, {0x00000EBE, 0x00000EBF}, {0x00000EC5, 0x00000EC5}, {0x00000EC7, 0x00000EC7},
-{0x00000ECF, 0x00000ECF}, {0x00000EDA, 0x00000EDB}, {0x00000EE0, 0x00000EFF}, {0x00000F48, 0x00000F48},
-{0x00000F6D, 0x00000F70}, {0x00000F98, 0x00000F98}, {0x00000FBD, 0x00000FBD}, {0x00000FCD, 0x00000FCD},
-{0x00000FDB, 0x00000FFF}, {0x000010C6, 0x000010C6}, {0x000010C8, 0x000010CC}, {0x000010CE, 0x000010CF},
-{0x00001249, 0x00001249}, {0x0000124E, 0x0000124F}, {0x00001257, 0x00001257}, {0x00001259, 0x00001259},
-{0x0000125E, 0x0000125F}, {0x00001289, 0x00001289}, {0x0000128E, 0x0000128F}, {0x000012B1, 0x000012B1},
-{0x000012B6, 0x000012B7}, {0x000012BF, 0x000012BF}, {0x000012C1, 0x000012C1}, {0x000012C6, 0x000012C7},
-{0x000012D7, 0x000012D7}, {0x00001311, 0x00001311}, {0x00001316, 0x00001317}, {0x0000135B, 0x0000135C},
-{0x0000137D, 0x0000137F}, {0x0000139A, 0x0000139F}, {0x000013F6, 0x000013F7}, {0x000013FE, 0x000013FF},
-{0x0000169D, 0x0000169F}, {0x000016F9, 0x000016FF}, {0x00001716, 0x0000171E}, {0x00001737, 0x0000173F},
-{0x00001754, 0x0000175F}, {0x0000176D, 0x0000176D}, {0x00001771, 0x00001771}, {0x00001774, 0x0000177F},
-{0x000017DE, 0x000017DF}, {0x000017EA, 0x000017EF}, {0x000017FA, 0x000017FF}, {0x0000180E, 0x0000180E},
-{0x0000181A, 0x0000181F}, {0x00001879, 0x0000187F}, {0x000018AB, 0x000018AF}, {0x000018F6, 0x000018FF},
-{0x0000191F, 0x0000191F}, {0x0000192C, 0x0000192F}, {0x0000193C, 0x0000193F}, {0x00001941, 0x00001943},
-{0x0000196E, 0x0000196F}, {0x00001975, 0x0000197F}, {0x000019AC, 0x000019AF}, {0x000019CA, 0x000019CF},
-{0x000019DB, 0x000019DD}, {0x00001A1C, 0x00001A1D}, {0x00001A5F, 0x00001A5F}, {0x00001A7D, 0x00001A7E},
-{0x00001A8A, 0x00001A8F}, {0x00001A9A, 0x00001A9F}, {0x00001AAE, 0x00001AAF}, {0x00001ACF, 0x00001AFF},
-{0x00001B4D, 0x00001B4F}, {0x00001B7F, 0x00001B7F}, {0x00001BF4, 0x00001BFB}, {0x00001C38, 0x00001C3A},
-{0x00001C4A, 0x00001C4C}, {0x00001C89, 0x00001C8F}, {0x00001CBB, 0x00001CBC}, {0x00001CC8, 0x00001CCF},
-{0x00001CFB, 0x00001CFF}, {0x00001F16, 0x00001F17}, {0x00001F1E, 0x00001F1F}, {0x00001F46, 0x00001F47},
-{0x00001F4E, 0x00001F4F}, {0x00001F58, 0x00001F58}, {0x00001F5A, 0x00001F5A}, {0x00001F5C, 0x00001F5C},
-{0x00001F5E, 0x00001F5E}, {0x00001F7E, 0x00001F7F}, {0x00001FB5, 0x00001FB5}, {0x00001FC5, 0x00001FC5},
-{0x00001FD4, 0x00001FD5}, {0x00001FDC, 0x00001FDC}, {0x00001FF0, 0x00001FF1}, {0x00001FF5, 0x00001FF5},
-{0x00001FFF, 0x00001FFF}, {0x0000200B, 0x0000200F}, {0x0000202A, 0x0000202E}, {0x00002060, 0x0000206F},
-{0x00002072, 0x00002073}, {0x0000208F, 0x0000208F}, {0x0000209D, 0x0000209F}, {0x000020C1, 0x000020CF},
-{0x000020F1, 0x000020FF}, {0x0000218C, 0x0000218F}, {0x00002427, 0x0000243F}, {0x0000244B, 0x0000245F},
-{0x00002B74, 0x00002B75}, {0x00002B96, 0x00002B96}, {0x00002CF4, 0x00002CF8}, {0x00002D26, 0x00002D26},
-{0x00002D28, 0x00002D2C}, {0x00002D2E, 0x00002D2F}, {0x00002D68, 0x00002D6E}, {0x00002D71, 0x00002D7E},
-{0x00002D97, 0x00002D9F}, {0x00002DA7, 0x00002DA7}, {0x00002DAF, 0x00002DAF}, {0x00002DB7, 0x00002DB7},
-{0x00002DBF, 0x00002DBF}, {0x00002DC7, 0x00002DC7}, {0x00002DCF, 0x00002DCF}, {0x00002DD7, 0x00002DD7},
-{0x00002DDF, 0x00002DDF}, {0x00002E5E, 0x00002E7F}, {0x00002E9A, 0x00002E9A}, {0x00002EF4, 0x00002EFF},
-{0x00002FD6, 0x00002FEF}, {0x00003040, 0x00003040}, {0x00003097, 0x00003098}, {0x00003100, 0x00003104},
-{0x00003130, 0x00003130}, {0x0000318F, 0x0000318F}, {0x000031E4, 0x000031EE}, {0x0000321F, 0x0000321F},
-{0x0000A48D, 0x0000A48F}, {0x0000A4C7, 0x0000A4CF}, {0x0000A62C, 0x0000A63F}, {0x0000A6F8, 0x0000A6FF},
-{0x0000A7CB, 0x0000A7CF}, {0x0000A7D2, 0x0000A7D2}, {0x0000A7D4, 0x0000A7D4}, {0x0000A7DA, 0x0000A7F1},
-{0x0000A82D, 0x0000A82F}, {0x0000A83A, 0x0000A83F}, {0x0000A878, 0x0000A87F}, {0x0000A8C6, 0x0000A8CD},
-{0x0000A8DA, 0x0000A8DF}, {0x0000A954, 0x0000A95E}, {0x0000A97D, 0x0000A97F}, {0x0000A9CE, 0x0000A9CE},
-{0x0000A9DA, 0x0000A9DD}, {0x0000A9FF, 0x0000A9FF}, {0x0000AA37, 0x0000AA3F}, {0x0000AA4E, 0x0000AA4F},
-{0x0000AA5A, 0x0000AA5B}, {0x0000AAC3, 0x0000AADA}, {0x0000AAF7, 0x0000AB00}, {0x0000AB07, 0x0000AB08},
-{0x0000AB0F, 0x0000AB10}, {0x0000AB17, 0x0000AB1F}, {0x0000AB27, 0x0000AB27}, {0x0000AB2F, 0x0000AB2F},
-{0x0000AB6C, 0x0000AB6F}, {0x0000ABEE, 0x0000ABEF}, {0x0000ABFA, 0x0000ABFF}, {0x0000D7A4, 0x0000D7AF},
-{0x0000D7C7, 0x0000D7CA}, {0x0000D7FC, 0x0000F8FF}, {0x0000FA6E, 0x0000FA6F}, {0x0000FADA, 0x0000FAFF},
-{0x0000FB07, 0x0000FB12}, {0x0000FB18, 0x0000FB1C}, {0x0000FB37, 0x0000FB37}, {0x0000FB3D, 0x0000FB3D},
-{0x0000FB3F, 0x0000FB3F}, {0x0000FB42, 0x0000FB42}, {0x0000FB45, 0x0000FB45}, {0x0000FBC3, 0x0000FBD2},
-{0x0000FD90, 0x0000FD91}, {0x0000FDC8, 0x0000FDCE}, {0x0000FDD0, 0x0000FDEF}, {0x0000FE1A, 0x0000FE1F},
-{0x0000FE53, 0x0000FE53}, {0x0000FE67, 0x0000FE67}, {0x0000FE6C, 0x0000FE6F}, {0x0000FE75, 0x0000FE75},
-{0x0000FEFD, 0x0000FF00}, {0x0000FFBF, 0x0000FFC1}, {0x0000FFC8, 0x0000FFC9}, {0x0000FFD0, 0x0000FFD1},
-{0x0000FFD8, 0x0000FFD9}, {0x0000FFDD, 0x0000FFDF}, {0x0000FFE7, 0x0000FFE7}, {0x0000FFEF, 0x0000FFFB},
-{0x0000FFFE, 0x0000FFFF}, {0x0001000C, 0x0001000C}, {0x00010027, 0x00010027}, {0x0001003B, 0x0001003B},
-{0x0001003E, 0x0001003E}, {0x0001004E, 0x0001004F}, {0x0001005E, 0x0001007F}, {0x000100FB, 0x000100FF},
-{0x00010103, 0x00010106}, {0x00010134, 0x00010136}, {0x0001018F, 0x0001018F}, {0x0001019D, 0x0001019F},
-{0x000101A1, 0x000101CF}, {0x000101FE, 0x0001027F}, {0x0001029D, 0x0001029F}, {0x000102D1, 0x000102DF},
-{0x000102FC, 0x000102FF}, {0x00010324, 0x0001032C}, {0x0001034B, 0x0001034F}, {0x0001037B, 0x0001037F},
-{0x0001039E, 0x0001039E}, {0x000103C4, 0x000103C7}, {0x000103D6, 0x000103FF}, {0x0001049E, 0x0001049F},
-{0x000104AA, 0x000104AF}, {0x000104D4, 0x000104D7}, {0x000104FC, 0x000104FF}, {0x00010528, 0x0001052F},
-{0x00010564, 0x0001056E}, {0x0001057B, 0x0001057B}, {0x0001058B, 0x0001058B}, {0x00010593, 0x00010593},
-{0x00010596, 0x00010596}, {0x000105A2, 0x000105A2}, {0x000105B2, 0x000105B2}, {0x000105BA, 0x000105BA},
-{0x000105BD, 0x000105FF}, {0x00010737, 0x0001073F}, {0x00010756, 0x0001075F}, {0x00010768, 0x0001077F},
-{0x00010786, 0x00010786}, {0x000107B1, 0x000107B1}, {0x000107BB, 0x000107FF}, {0x00010806, 0x00010807},
-{0x00010809, 0x00010809}, {0x00010836, 0x00010836}, {0x00010839, 0x0001083B}, {0x0001083D, 0x0001083E},
-{0x00010856, 0x00010856}, {0x0001089F, 0x000108A6}, {0x000108B0, 0x000108DF}, {0x000108F3, 0x000108F3},
-{0x000108F6, 0x000108FA}, {0x0001091C, 0x0001091E}, {0x0001093A, 0x0001093E}, {0x00010940, 0x0001097F},
-{0x000109B8, 0x000109BB}, {0x000109D0, 0x000109D1}, {0x00010A04, 0x00010A04}, {0x00010A07, 0x00010A0B},
-{0x00010A14, 0x00010A14}, {0x00010A18, 0x00010A18}, {0x00010A36, 0x00010A37}, {0x00010A3B, 0x00010A3E},
-{0x00010A49, 0x00010A4F}, {0x00010A59, 0x00010A5F}, {0x00010AA0, 0x00010ABF}, {0x00010AE7, 0x00010AEA},
-{0x00010AF7, 0x00010AFF}, {0x00010B36, 0x00010B38}, {0x00010B56, 0x00010B57}, {0x00010B73, 0x00010B77},
-{0x00010B92, 0x00010B98}, {0x00010B9D, 0x00010BA8}, {0x00010BB0, 0x00010BFF}, {0x00010C49, 0x00010C7F},
-{0x00010CB3, 0x00010CBF}, {0x00010CF3, 0x00010CF9}, {0x00010D28, 0x00010D2F}, {0x00010D3A, 0x00010E5F},
-{0x00010E7F, 0x00010E7F}, {0x00010EAA, 0x00010EAA}, {0x00010EAE, 0x00010EAF}, {0x00010EB2, 0x00010EFC},
-{0x00010F28, 0x00010F2F}, {0x00010F5A, 0x00010F6F}, {0x00010F8A, 0x00010FAF}, {0x00010FCC, 0x00010FDF},
-{0x00010FF7, 0x00010FFF}, {0x0001104E, 0x00011051}, {0x00011076, 0x0001107E}, {0x000110BD, 0x000110BD},
-{0x000110C3, 0x000110CF}, {0x000110E9, 0x000110EF}, {0x000110FA, 0x000110FF}, {0x00011135, 0x00011135},
-{0x00011148, 0x0001114F}, {0x00011177, 0x0001117F}, {0x000111E0, 0x000111E0}, {0x000111F5, 0x000111FF},
-{0x00011212, 0x00011212}, {0x00011242, 0x0001127F}, {0x00011287, 0x00011287}, {0x00011289, 0x00011289},
-{0x0001128E, 0x0001128E}, {0x0001129E, 0x0001129E}, {0x000112AA, 0x000112AF}, {0x000112EB, 0x000112EF},
-{0x000112FA, 0x000112FF}, {0x00011304, 0x00011304}, {0x0001130D, 0x0001130E}, {0x00011311, 0x00011312},
-{0x00011329, 0x00011329}, {0x00011331, 0x00011331}, {0x00011334, 0x00011334}, {0x0001133A, 0x0001133A},
-{0x00011345, 0x00011346}, {0x00011349, 0x0001134A}, {0x0001134E, 0x0001134F}, {0x00011351, 0x00011356},
-{0x00011358, 0x0001135C}, {0x00011364, 0x00011365}, {0x0001136D, 0x0001136F}, {0x00011375, 0x000113FF},
-{0x0001145C, 0x0001145C}, {0x00011462, 0x0001147F}, {0x000114C8, 0x000114CF}, {0x000114DA, 0x0001157F},
-{0x000115B6, 0x000115B7}, {0x000115DE, 0x000115FF}, {0x00011645, 0x0001164F}, {0x0001165A, 0x0001165F},
-{0x0001166D, 0x0001167F}, {0x000116BA, 0x000116BF}, {0x000116CA, 0x000116FF}, {0x0001171B, 0x0001171C},
-{0x0001172C, 0x0001172F}, {0x00011747, 0x000117FF}, {0x0001183C, 0x0001189F}, {0x000118F3, 0x000118FE},
-{0x00011907, 0x00011908}, {0x0001190A, 0x0001190B}, {0x00011914, 0x00011914}, {0x00011917, 0x00011917},
-{0x00011936, 0x00011936}, {0x00011939, 0x0001193A}, {0x00011947, 0x0001194F}, {0x0001195A, 0x0001199F},
-{0x000119A8, 0x000119A9}, {0x000119D8, 0x000119D9}, {0x000119E5, 0x000119FF}, {0x00011A48, 0x00011A4F},
-{0x00011AA3, 0x00011AAF}, {0x00011AF9, 0x00011AFF}, {0x00011B0A, 0x00011BFF}, {0x00011C09, 0x00011C09},
-{0x00011C37, 0x00011C37}, {0x00011C46, 0x00011C4F}, {0x00011C6D, 0x00011C6F}, {0x00011C90, 0x00011C91},
-{0x00011CA8, 0x00011CA8}, {0x00011CB7, 0x00011CFF}, {0x00011D07, 0x00011D07}, {0x00011D0A, 0x00011D0A},
-{0x00011D37, 0x00011D39}, {0x00011D3B, 0x00011D3B}, {0x00011D3E, 0x00011D3E}, {0x00011D48, 0x00011D4F},
-{0x00011D5A, 0x00011D5F}, {0x00011D66, 0x00011D66}, {0x00011D69, 0x00011D69}, {0x00011D8F, 0x00011D8F},
-{0x00011D92, 0x00011D92}, {0x00011D99, 0x00011D9F}, {0x00011DAA, 0x00011EDF}, {0x00011EF9, 0x00011EFF},
-{0x00011F11, 0x00011F11}, {0x00011F3B, 0x00011F3D}, {0x00011F5A, 0x00011FAF}, {0x00011FB1, 0x00011FBF},
-{0x00011FF2, 0x00011FFE}, {0x0001239A, 0x000123FF}, {0x0001246F, 0x0001246F}, {0x00012475, 0x0001247F},
-{0x00012544, 0x00012F8F}, {0x00012FF3, 0x00012FFF}, {0x00013430, 0x0001343F}, {0x00013456, 0x000143FF},
-{0x00014647, 0x000167FF}, {0x00016A39, 0x00016A3F}, {0x00016A5F, 0x00016A5F}, {0x00016A6A, 0x00016A6D},
-{0x00016ABF, 0x00016ABF}, {0x00016ACA, 0x00016ACF}, {0x00016AEE, 0x00016AEF}, {0x00016AF6, 0x00016AFF},
-{0x00016B46, 0x00016B4F}, {0x00016B5A, 0x00016B5A}, {0x00016B62, 0x00016B62}, {0x00016B78, 0x00016B7C},
-{0x00016B90, 0x00016E3F}, {0x00016E9B, 0x00016EFF}, {0x00016F4B, 0x00016F4E}, {0x00016F88, 0x00016F8E},
-{0x00016FA0, 0x00016FDF}, {0x00016FE5, 0x00016FEF}, {0x00016FF2, 0x00016FFF}, {0x000187F8, 0x000187FF},
-{0x00018CD6, 0x00018CFF}, {0x00018D09, 0x0001AFEF}, {0x0001AFF4, 0x0001AFF4}, {0x0001AFFC, 0x0001AFFC},
-{0x0001AFFF, 0x0001AFFF}, {0x0001B123, 0x0001B131}, {0x0001B133, 0x0001B14F}, {0x0001B153, 0x0001B154},
-{0x0001B156, 0x0001B163}, {0x0001B168, 0x0001B16F}, {0x0001B2FC, 0x0001BBFF}, {0x0001BC6B, 0x0001BC6F},
-{0x0001BC7D, 0x0001BC7F}, {0x0001BC89, 0x0001BC8F}, {0x0001BC9A, 0x0001BC9B}, {0x0001BCA0, 0x0001CEFF},
-{0x0001CF2E, 0x0001CF2F}, {0x0001CF47, 0x0001CF4F}, {0x0001CFC4, 0x0001CFFF}, {0x0001D0F6, 0x0001D0FF},
-{0x0001D127, 0x0001D128}, {0x0001D173, 0x0001D17A}, {0x0001D1EB, 0x0001D1FF}, {0x0001D246, 0x0001D2BF},
-{0x0001D2D4, 0x0001D2DF}, {0x0001D2F4, 0x0001D2FF}, {0x0001D357, 0x0001D35F}, {0x0001D379, 0x0001D3FF},
-{0x0001D455, 0x0001D455}, {0x0001D49D, 0x0001D49D}, {0x0001D4A0, 0x0001D4A1}, {0x0001D4A3, 0x0001D4A4},
-{0x0001D4A7, 0x0001D4A8}, {0x0001D4AD, 0x0001D4AD}, {0x0001D4BA, 0x0001D4BA}, {0x0001D4BC, 0x0001D4BC},
-{0x0001D4C4, 0x0001D4C4}, {0x0001D506, 0x0001D506}, {0x0001D50B, 0x0001D50C}, {0x0001D515, 0x0001D515},
-{0x0001D51D, 0x0001D51D}, {0x0001D53A, 0x0001D53A}, {0x0001D53F, 0x0001D53F}, {0x0001D545, 0x0001D545},
-{0x0001D547, 0x0001D549}, {0x0001D551, 0x0001D551}, {0x0001D6A6, 0x0001D6A7}, {0x0001D7CC, 0x0001D7CD},
-{0x0001DA8C, 0x0001DA9A}, {0x0001DAA0, 0x0001DAA0}, {0x0001DAB0, 0x0001DEFF}, {0x0001DF1F, 0x0001DF24},
-{0x0001DF2B, 0x0001DFFF}, {0x0001E007, 0x0001E007}, {0x0001E019, 0x0001E01A}, {0x0001E022, 0x0001E022},
-{0x0001E025, 0x0001E025}, {0x0001E02B, 0x0001E02F}, {0x0001E06E, 0x0001E08E}, {0x0001E090, 0x0001E0FF},
-{0x0001E12D, 0x0001E12F}, {0x0001E13E, 0x0001E13F}, {0x0001E14A, 0x0001E14D}, {0x0001E150, 0x0001E28F},
-{0x0001E2AF, 0x0001E2BF}, {0x0001E2FA, 0x0001E2FE}, {0x0001E300, 0x0001E4CF}, {0x0001E4FA, 0x0001E7DF},
-{0x0001E7E7, 0x0001E7E7}, {0x0001E7EC, 0x0001E7EC}, {0x0001E7EF, 0x0001E7EF}, {0x0001E7FF, 0x0001E7FF},
-{0x0001E8C5, 0x0001E8C6}, {0x0001E8D7, 0x0001E8FF}, {0x0001E94C, 0x0001E94F}, {0x0001E95A, 0x0001E95D},
-{0x0001E960, 0x0001EC70}, {0x0001ECB5, 0x0001ED00}, {0x0001ED3E, 0x0001EDFF}, {0x0001EE04, 0x0001EE04},
-{0x0001EE20, 0x0001EE20}, {0x0001EE23, 0x0001EE23}, {0x0001EE25, 0x0001EE26}, {0x0001EE28, 0x0001EE28},
-{0x0001EE33, 0x0001EE33}, {0x0001EE38, 0x0001EE38}, {0x0001EE3A, 0x0001EE3A}, {0x0001EE3C, 0x0001EE41},
-{0x0001EE43, 0x0001EE46}, {0x0001EE48, 0x0001EE48}, {0x0001EE4A, 0x0001EE4A}, {0x0001EE4C, 0x0001EE4C},
-{0x0001EE50, 0x0001EE50}, {0x0001EE53, 0x0001EE53}, {0x0001EE55, 0x0001EE56}, {0x0001EE58, 0x0001EE58},
-{0x0001EE5A, 0x0001EE5A}, {0x0001EE5C, 0x0001EE5C}, {0x0001EE5E, 0x0001EE5E}, {0x0001EE60, 0x0001EE60},
-{0x0001EE63, 0x0001EE63}, {0x0001EE65, 0x0001EE66}, {0x0001EE6B, 0x0001EE6B}, {0x0001EE73, 0x0001EE73},
-{0x0001EE78, 0x0001EE78}, {0x0001EE7D, 0x0001EE7D}, {0x0001EE7F, 0x0001EE7F}, {0x0001EE8A, 0x0001EE8A},
-{0x0001EE9C, 0x0001EEA0}, {0x0001EEA4, 0x0001EEA4}, {0x0001EEAA, 0x0001EEAA}, {0x0001EEBC, 0x0001EEEF},
-{0x0001EEF2, 0x0001EFFF}, {0x0001F02C, 0x0001F02F}, {0x0001F094, 0x0001F09F}, {0x0001F0AF, 0x0001F0B0},
-{0x0001F0C0, 0x0001F0C0}, {0x0001F0D0, 0x0001F0D0}, {0x0001F0F6, 0x0001F0FF}, {0x0001F1AE, 0x0001F1E5},
-{0x0001F203, 0x0001F20F}, {0x0001F23C, 0x0001F23F}, {0x0001F249, 0x0001F24F}, {0x0001F252, 0x0001F25F},
-{0x0001F266, 0x0001F2FF}, {0x0001F6D8, 0x0001F6DB}, {0x0001F6ED, 0x0001F6EF}, {0x0001F6FD, 0x0001F6FF},
-{0x0001F777, 0x0001F77A}, {0x0001F7DA, 0x0001F7DF}, {0x0001F7EC, 0x0001F7EF}, {0x0001F7F1, 0x0001F7FF},
-{0x0001F80C, 0x0001F80F}, {0x0001F848, 0x0001F84F}, {0x0001F85A, 0x0001F85F}, {0x0001F888, 0x0001F88F},
-{0x0001F8AE, 0x0001F8AF}, {0x0001F8B2, 0x0001F8FF}, {0x0001FA54, 0x0001FA5F}, {0x0001FA6E, 0x0001FA6F},
-{0x0001FA7D, 0x0001FA7F}, {0x0001FA89, 0x0001FA8F}, {0x0001FABE, 0x0001FABE}, {0x0001FAC6, 0x0001FACD},
-{0x0001FADC, 0x0001FADF}, {0x0001FAE9, 0x0001FAEF}, {0x0001FAF9, 0x0001FAFF}, {0x0001FB93, 0x0001FB93},
-{0x0001FBCB, 0x0001FBEF}, {0x0001FBFA, 0x0001FFFF}, {0x0002A6E0, 0x0002A6FF}, {0x0002B73A, 0x0002B73F},
-{0x0002B81E, 0x0002B81F}, {0x0002CEA2, 0x0002CEAF}, {0x0002EBE1, 0x0002EBEF}, {0x0002EE5E, 0x0002F7FF},
-{0x0002FA1E, 0x0002FFFF}, {0x0003134B, 0x0003134F}, {0x000323B0, 0x000E00FF}, {0x000E01F0, 0x0010FFFF},
-};
-
-const std::map<char32_t, char32_t> unicode_map_lowercase = {
-{0x00000041, 0x00000061}, {0x00000042, 0x00000062}, {0x00000043, 0x00000063}, {0x00000044, 0x00000064},
-{0x00000045, 0x00000065}, {0x00000046, 0x00000066}, {0x00000047, 0x00000067}, {0x00000048, 0x00000068},
-{0x00000049, 0x00000069}, {0x0000004A, 0x0000006A}, {0x0000004B, 0x0000006B}, {0x0000004C, 0x0000006C},
-{0x0000004D, 0x0000006D}, {0x0000004E, 0x0000006E}, {0x0000004F, 0x0000006F}, {0x00000050, 0x00000070},
-{0x00000051, 0x00000071}, {0x00000052, 0x00000072}, {0x00000053, 0x00000073}, {0x00000054, 0x00000074},
-{0x00000055, 0x00000075}, {0x00000056, 0x00000076}, {0x00000057, 0x00000077}, {0x00000058, 0x00000078},
-{0x00000059, 0x00000079}, {0x0000005A, 0x0000007A}, {0x000000C0, 0x000000E0}, {0x000000C1, 0x000000E1},
-{0x000000C2, 0x000000E2}, {0x000000C3, 0x000000E3}, {0x000000C4, 0x000000E4}, {0x000000C5, 0x000000E5},
-{0x000000C6, 0x000000E6}, {0x000000C7, 0x000000E7}, {0x000000C8, 0x000000E8}, {0x000000C9, 0x000000E9},
-{0x000000CA, 0x000000EA}, {0x000000CB, 0x000000EB}, {0x000000CC, 0x000000EC}, {0x000000CD, 0x000000ED},
-{0x000000CE, 0x000000EE}, {0x000000CF, 0x000000EF}, {0x000000D0, 0x000000F0}, {0x000000D1, 0x000000F1},
-{0x000000D2, 0x000000F2}, {0x000000D3, 0x000000F3}, {0x000000D4, 0x000000F4}, {0x000000D5, 0x000000F5},
-{0x000000D6, 0x000000F6}, {0x000000D8, 0x000000F8}, {0x000000D9, 0x000000F9}, {0x000000DA, 0x000000FA},
-{0x000000DB, 0x000000FB}, {0x000000DC, 0x000000FC}, {0x000000DD, 0x000000FD}, {0x000000DE, 0x000000FE},
-{0x00000100, 0x00000101}, {0x00000102, 0x00000103}, {0x00000104, 0x00000105}, {0x00000106, 0x00000107},
-{0x00000108, 0x00000109}, {0x0000010A, 0x0000010B}, {0x0000010C, 0x0000010D}, {0x0000010E, 0x0000010F},
-{0x00000110, 0x00000111}, {0x00000112, 0x00000113}, {0x00000114, 0x00000115}, {0x00000116, 0x00000117},
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-{0x00001E26, 0x00001E27}, {0x00001E28, 0x00001E29}, {0x00001E2A, 0x00001E2B}, {0x00001E2C, 0x00001E2D},
-{0x00001E2E, 0x00001E2F}, {0x00001E30, 0x00001E31}, {0x00001E32, 0x00001E33}, {0x00001E34, 0x00001E35},
-{0x00001E36, 0x00001E37}, {0x00001E38, 0x00001E39}, {0x00001E3A, 0x00001E3B}, {0x00001E3C, 0x00001E3D},
-{0x00001E3E, 0x00001E3F}, {0x00001E40, 0x00001E41}, {0x00001E42, 0x00001E43}, {0x00001E44, 0x00001E45},
-{0x00001E46, 0x00001E47}, {0x00001E48, 0x00001E49}, {0x00001E4A, 0x00001E4B}, {0x00001E4C, 0x00001E4D},
-{0x00001E4E, 0x00001E4F}, {0x00001E50, 0x00001E51}, {0x00001E52, 0x00001E53}, {0x00001E54, 0x00001E55},
-{0x00001E56, 0x00001E57}, {0x00001E58, 0x00001E59}, {0x00001E5A, 0x00001E5B}, {0x00001E5C, 0x00001E5D},
-{0x00001E5E, 0x00001E5F}, {0x00001E60, 0x00001E61}, {0x00001E62, 0x00001E63}, {0x00001E64, 0x00001E65},
-{0x00001E66, 0x00001E67}, {0x00001E68, 0x00001E69}, {0x00001E6A, 0x00001E6B}, {0x00001E6C, 0x00001E6D},
-{0x00001E6E, 0x00001E6F}, {0x00001E70, 0x00001E71}, {0x00001E72, 0x00001E73}, {0x00001E74, 0x00001E75},
-{0x00001E76, 0x00001E77}, {0x00001E78, 0x00001E79}, {0x00001E7A, 0x00001E7B}, {0x00001E7C, 0x00001E7D},
-{0x00001E7E, 0x00001E7F}, {0x00001E80, 0x00001E81}, {0x00001E82, 0x00001E83}, {0x00001E84, 0x00001E85},
-{0x00001E86, 0x00001E87}, {0x00001E88, 0x00001E89}, {0x00001E8A, 0x00001E8B}, {0x00001E8C, 0x00001E8D},
-{0x00001E8E, 0x00001E8F}, {0x00001E90, 0x00001E91}, {0x00001E92, 0x00001E93}, {0x00001E94, 0x00001E95},
-{0x00001E9E, 0x000000DF}, {0x00001EA0, 0x00001EA1}, {0x00001EA2, 0x00001EA3}, {0x00001EA4, 0x00001EA5},
-{0x00001EA6, 0x00001EA7}, {0x00001EA8, 0x00001EA9}, {0x00001EAA, 0x00001EAB}, {0x00001EAC, 0x00001EAD},
-{0x00001EAE, 0x00001EAF}, {0x00001EB0, 0x00001EB1}, {0x00001EB2, 0x00001EB3}, {0x00001EB4, 0x00001EB5},
-{0x00001EB6, 0x00001EB7}, {0x00001EB8, 0x00001EB9}, {0x00001EBA, 0x00001EBB}, {0x00001EBC, 0x00001EBD},
-{0x00001EBE, 0x00001EBF}, {0x00001EC0, 0x00001EC1}, {0x00001EC2, 0x00001EC3}, {0x00001EC4, 0x00001EC5},
-{0x00001EC6, 0x00001EC7}, {0x00001EC8, 0x00001EC9}, {0x00001ECA, 0x00001ECB}, {0x00001ECC, 0x00001ECD},
-{0x00001ECE, 0x00001ECF}, {0x00001ED0, 0x00001ED1}, {0x00001ED2, 0x00001ED3}, {0x00001ED4, 0x00001ED5},
-{0x00001ED6, 0x00001ED7}, {0x00001ED8, 0x00001ED9}, {0x00001EDA, 0x00001EDB}, {0x00001EDC, 0x00001EDD},
-{0x00001EDE, 0x00001EDF}, {0x00001EE0, 0x00001EE1}, {0x00001EE2, 0x00001EE3}, {0x00001EE4, 0x00001EE5},
-{0x00001EE6, 0x00001EE7}, {0x00001EE8, 0x00001EE9}, {0x00001EEA, 0x00001EEB}, {0x00001EEC, 0x00001EED},
-{0x00001EEE, 0x00001EEF}, {0x00001EF0, 0x00001EF1}, {0x00001EF2, 0x00001EF3}, {0x00001EF4, 0x00001EF5},
-{0x00001EF6, 0x00001EF7}, {0x00001EF8, 0x00001EF9}, {0x00001EFA, 0x00001EFB}, {0x00001EFC, 0x00001EFD},
-{0x00001EFE, 0x00001EFF}, {0x00001F08, 0x00001F00}, {0x00001F09, 0x00001F01}, {0x00001F0A, 0x00001F02},
-{0x00001F0B, 0x00001F03}, {0x00001F0C, 0x00001F04}, {0x00001F0D, 0x00001F05}, {0x00001F0E, 0x00001F06},
-{0x00001F0F, 0x00001F07}, {0x00001F18, 0x00001F10}, {0x00001F19, 0x00001F11}, {0x00001F1A, 0x00001F12},
-{0x00001F1B, 0x00001F13}, {0x00001F1C, 0x00001F14}, {0x00001F1D, 0x00001F15}, {0x00001F28, 0x00001F20},
-{0x00001F29, 0x00001F21}, {0x00001F2A, 0x00001F22}, {0x00001F2B, 0x00001F23}, {0x00001F2C, 0x00001F24},
-{0x00001F2D, 0x00001F25}, {0x00001F2E, 0x00001F26}, {0x00001F2F, 0x00001F27}, {0x00001F38, 0x00001F30},
-{0x00001F39, 0x00001F31}, {0x00001F3A, 0x00001F32}, {0x00001F3B, 0x00001F33}, {0x00001F3C, 0x00001F34},
-{0x00001F3D, 0x00001F35}, {0x00001F3E, 0x00001F36}, {0x00001F3F, 0x00001F37}, {0x00001F48, 0x00001F40},
-{0x00001F49, 0x00001F41}, {0x00001F4A, 0x00001F42}, {0x00001F4B, 0x00001F43}, {0x00001F4C, 0x00001F44},
-{0x00001F4D, 0x00001F45}, {0x00001F59, 0x00001F51}, {0x00001F5B, 0x00001F53}, {0x00001F5D, 0x00001F55},
-{0x00001F5F, 0x00001F57}, {0x00001F68, 0x00001F60}, {0x00001F69, 0x00001F61}, {0x00001F6A, 0x00001F62},
-{0x00001F6B, 0x00001F63}, {0x00001F6C, 0x00001F64}, {0x00001F6D, 0x00001F65}, {0x00001F6E, 0x00001F66},
-{0x00001F6F, 0x00001F67}, {0x00001F88, 0x00001F80}, {0x00001F89, 0x00001F81}, {0x00001F8A, 0x00001F82},
-{0x00001F8B, 0x00001F83}, {0x00001F8C, 0x00001F84}, {0x00001F8D, 0x00001F85}, {0x00001F8E, 0x00001F86},
-{0x00001F8F, 0x00001F87}, {0x00001F98, 0x00001F90}, {0x00001F99, 0x00001F91}, {0x00001F9A, 0x00001F92},
-{0x00001F9B, 0x00001F93}, {0x00001F9C, 0x00001F94}, {0x00001F9D, 0x00001F95}, {0x00001F9E, 0x00001F96},
-{0x00001F9F, 0x00001F97}, {0x00001FA8, 0x00001FA0}, {0x00001FA9, 0x00001FA1}, {0x00001FAA, 0x00001FA2},
-{0x00001FAB, 0x00001FA3}, {0x00001FAC, 0x00001FA4}, {0x00001FAD, 0x00001FA5}, {0x00001FAE, 0x00001FA6},
-{0x00001FAF, 0x00001FA7}, {0x00001FB8, 0x00001FB0}, {0x00001FB9, 0x00001FB1}, {0x00001FBA, 0x00001F70},
-{0x00001FBB, 0x00001F71}, {0x00001FBC, 0x00001FB3}, {0x00001FC8, 0x00001F72}, {0x00001FC9, 0x00001F73},
-{0x00001FCA, 0x00001F74}, {0x00001FCB, 0x00001F75}, {0x00001FCC, 0x00001FC3}, {0x00001FD8, 0x00001FD0},
-{0x00001FD9, 0x00001FD1}, {0x00001FDA, 0x00001F76}, {0x00001FDB, 0x00001F77}, {0x00001FE8, 0x00001FE0},
-{0x00001FE9, 0x00001FE1}, {0x00001FEA, 0x00001F7A}, {0x00001FEB, 0x00001F7B}, {0x00001FEC, 0x00001FE5},
-{0x00001FF8, 0x00001F78}, {0x00001FF9, 0x00001F79}, {0x00001FFA, 0x00001F7C}, {0x00001FFB, 0x00001F7D},
-{0x00001FFC, 0x00001FF3}, {0x00002126, 0x000003C9}, {0x0000212A, 0x0000006B}, {0x0000212B, 0x000000E5},
-{0x00002132, 0x0000214E}, {0x00002160, 0x00002170}, {0x00002161, 0x00002171}, {0x00002162, 0x00002172},
-{0x00002163, 0x00002173}, {0x00002164, 0x00002174}, {0x00002165, 0x00002175}, {0x00002166, 0x00002176},
-{0x00002167, 0x00002177}, {0x00002168, 0x00002178}, {0x00002169, 0x00002179}, {0x0000216A, 0x0000217A},
-{0x0000216B, 0x0000217B}, {0x0000216C, 0x0000217C}, {0x0000216D, 0x0000217D}, {0x0000216E, 0x0000217E},
-{0x0000216F, 0x0000217F}, {0x00002183, 0x00002184}, {0x000024B6, 0x000024D0}, {0x000024B7, 0x000024D1},
-{0x000024B8, 0x000024D2}, {0x000024B9, 0x000024D3}, {0x000024BA, 0x000024D4}, {0x000024BB, 0x000024D5},
-{0x000024BC, 0x000024D6}, {0x000024BD, 0x000024D7}, {0x000024BE, 0x000024D8}, {0x000024BF, 0x000024D9},
-{0x000024C0, 0x000024DA}, {0x000024C1, 0x000024DB}, {0x000024C2, 0x000024DC}, {0x000024C3, 0x000024DD},
-{0x000024C4, 0x000024DE}, {0x000024C5, 0x000024DF}, {0x000024C6, 0x000024E0}, {0x000024C7, 0x000024E1},
-{0x000024C8, 0x000024E2}, {0x000024C9, 0x000024E3}, {0x000024CA, 0x000024E4}, {0x000024CB, 0x000024E5},
-{0x000024CC, 0x000024E6}, {0x000024CD, 0x000024E7}, {0x000024CE, 0x000024E8}, {0x000024CF, 0x000024E9},
-{0x00002C00, 0x00002C30}, {0x00002C01, 0x00002C31}, {0x00002C02, 0x00002C32}, {0x00002C03, 0x00002C33},
-{0x00002C04, 0x00002C34}, {0x00002C05, 0x00002C35}, {0x00002C06, 0x00002C36}, {0x00002C07, 0x00002C37},
-{0x00002C08, 0x00002C38}, {0x00002C09, 0x00002C39}, {0x00002C0A, 0x00002C3A}, {0x00002C0B, 0x00002C3B},
-{0x00002C0C, 0x00002C3C}, {0x00002C0D, 0x00002C3D}, {0x00002C0E, 0x00002C3E}, {0x00002C0F, 0x00002C3F},
-{0x00002C10, 0x00002C40}, {0x00002C11, 0x00002C41}, {0x00002C12, 0x00002C42}, {0x00002C13, 0x00002C43},
-{0x00002C14, 0x00002C44}, {0x00002C15, 0x00002C45}, {0x00002C16, 0x00002C46}, {0x00002C17, 0x00002C47},
-{0x00002C18, 0x00002C48}, {0x00002C19, 0x00002C49}, {0x00002C1A, 0x00002C4A}, {0x00002C1B, 0x00002C4B},
-{0x00002C1C, 0x00002C4C}, {0x00002C1D, 0x00002C4D}, {0x00002C1E, 0x00002C4E}, {0x00002C1F, 0x00002C4F},
-{0x00002C20, 0x00002C50}, {0x00002C21, 0x00002C51}, {0x00002C22, 0x00002C52}, {0x00002C23, 0x00002C53},
-{0x00002C24, 0x00002C54}, {0x00002C25, 0x00002C55}, {0x00002C26, 0x00002C56}, {0x00002C27, 0x00002C57},
-{0x00002C28, 0x00002C58}, {0x00002C29, 0x00002C59}, {0x00002C2A, 0x00002C5A}, {0x00002C2B, 0x00002C5B},
-{0x00002C2C, 0x00002C5C}, {0x00002C2D, 0x00002C5D}, {0x00002C2E, 0x00002C5E}, {0x00002C60, 0x00002C61},
-{0x00002C62, 0x0000026B}, {0x00002C63, 0x00001D7D}, {0x00002C64, 0x0000027D}, {0x00002C67, 0x00002C68},
-{0x00002C69, 0x00002C6A}, {0x00002C6B, 0x00002C6C}, {0x00002C6D, 0x00000251}, {0x00002C6E, 0x00000271},
-{0x00002C6F, 0x00000250}, {0x00002C70, 0x00000252}, {0x00002C72, 0x00002C73}, {0x00002C75, 0x00002C76},
-{0x00002C7E, 0x0000023F}, {0x00002C7F, 0x00000240}, {0x00002C80, 0x00002C81}, {0x00002C82, 0x00002C83},
-{0x00002C84, 0x00002C85}, {0x00002C86, 0x00002C87}, {0x00002C88, 0x00002C89}, {0x00002C8A, 0x00002C8B},
-{0x00002C8C, 0x00002C8D}, {0x00002C8E, 0x00002C8F}, {0x00002C90, 0x00002C91}, {0x00002C92, 0x00002C93},
-{0x00002C94, 0x00002C95}, {0x00002C96, 0x00002C97}, {0x00002C98, 0x00002C99}, {0x00002C9A, 0x00002C9B},
-{0x00002C9C, 0x00002C9D}, {0x00002C9E, 0x00002C9F}, {0x00002CA0, 0x00002CA1}, {0x00002CA2, 0x00002CA3},
-{0x00002CA4, 0x00002CA5}, {0x00002CA6, 0x00002CA7}, {0x00002CA8, 0x00002CA9}, {0x00002CAA, 0x00002CAB},
-{0x00002CAC, 0x00002CAD}, {0x00002CAE, 0x00002CAF}, {0x00002CB0, 0x00002CB1}, {0x00002CB2, 0x00002CB3},
-{0x00002CB4, 0x00002CB5}, {0x00002CB6, 0x00002CB7}, {0x00002CB8, 0x00002CB9}, {0x00002CBA, 0x00002CBB},
-{0x00002CBC, 0x00002CBD}, {0x00002CBE, 0x00002CBF}, {0x00002CC0, 0x00002CC1}, {0x00002CC2, 0x00002CC3},
-{0x00002CC4, 0x00002CC5}, {0x00002CC6, 0x00002CC7}, {0x00002CC8, 0x00002CC9}, {0x00002CCA, 0x00002CCB},
-{0x00002CCC, 0x00002CCD}, {0x00002CCE, 0x00002CCF}, {0x00002CD0, 0x00002CD1}, {0x00002CD2, 0x00002CD3},
-{0x00002CD4, 0x00002CD5}, {0x00002CD6, 0x00002CD7}, {0x00002CD8, 0x00002CD9}, {0x00002CDA, 0x00002CDB},
-{0x00002CDC, 0x00002CDD}, {0x00002CDE, 0x00002CDF}, {0x00002CE0, 0x00002CE1}, {0x00002CE2, 0x00002CE3},
-{0x00002CEB, 0x00002CEC}, {0x00002CED, 0x00002CEE}, {0x00002CF2, 0x00002CF3}, {0x0000A640, 0x0000A641},
-{0x0000A642, 0x0000A643}, {0x0000A644, 0x0000A645}, {0x0000A646, 0x0000A647}, {0x0000A648, 0x0000A649},
-{0x0000A64A, 0x0000A64B}, {0x0000A64C, 0x0000A64D}, {0x0000A64E, 0x0000A64F}, {0x0000A650, 0x0000A651},
-{0x0000A652, 0x0000A653}, {0x0000A654, 0x0000A655}, {0x0000A656, 0x0000A657}, {0x0000A658, 0x0000A659},
-{0x0000A65A, 0x0000A65B}, {0x0000A65C, 0x0000A65D}, {0x0000A65E, 0x0000A65F}, {0x0000A660, 0x0000A661},
-{0x0000A662, 0x0000A663}, {0x0000A664, 0x0000A665}, {0x0000A666, 0x0000A667}, {0x0000A668, 0x0000A669},
-{0x0000A66A, 0x0000A66B}, {0x0000A66C, 0x0000A66D}, {0x0000A680, 0x0000A681}, {0x0000A682, 0x0000A683},
-{0x0000A684, 0x0000A685}, {0x0000A686, 0x0000A687}, {0x0000A688, 0x0000A689}, {0x0000A68A, 0x0000A68B},
-{0x0000A68C, 0x0000A68D}, {0x0000A68E, 0x0000A68F}, {0x0000A690, 0x0000A691}, {0x0000A692, 0x0000A693},
-{0x0000A694, 0x0000A695}, {0x0000A696, 0x0000A697}, {0x0000A698, 0x0000A699}, {0x0000A69A, 0x0000A69B},
-{0x0000A722, 0x0000A723}, {0x0000A724, 0x0000A725}, {0x0000A726, 0x0000A727}, {0x0000A728, 0x0000A729},
-{0x0000A72A, 0x0000A72B}, {0x0000A72C, 0x0000A72D}, {0x0000A72E, 0x0000A72F}, {0x0000A732, 0x0000A733},
-{0x0000A734, 0x0000A735}, {0x0000A736, 0x0000A737}, {0x0000A738, 0x0000A739}, {0x0000A73A, 0x0000A73B},
-{0x0000A73C, 0x0000A73D}, {0x0000A73E, 0x0000A73F}, {0x0000A740, 0x0000A741}, {0x0000A742, 0x0000A743},
-{0x0000A744, 0x0000A745}, {0x0000A746, 0x0000A747}, {0x0000A748, 0x0000A749}, {0x0000A74A, 0x0000A74B},
-{0x0000A74C, 0x0000A74D}, {0x0000A74E, 0x0000A74F}, {0x0000A750, 0x0000A751}, {0x0000A752, 0x0000A753},
-{0x0000A754, 0x0000A755}, {0x0000A756, 0x0000A757}, {0x0000A758, 0x0000A759}, {0x0000A75A, 0x0000A75B},
-{0x0000A75C, 0x0000A75D}, {0x0000A75E, 0x0000A75F}, {0x0000A760, 0x0000A761}, {0x0000A762, 0x0000A763},
-{0x0000A764, 0x0000A765}, {0x0000A766, 0x0000A767}, {0x0000A768, 0x0000A769}, {0x0000A76A, 0x0000A76B},
-{0x0000A76C, 0x0000A76D}, {0x0000A76E, 0x0000A76F}, {0x0000A779, 0x0000A77A}, {0x0000A77B, 0x0000A77C},
-{0x0000A77D, 0x00001D79}, {0x0000A77E, 0x0000A77F}, {0x0000A780, 0x0000A781}, {0x0000A782, 0x0000A783},
-{0x0000A784, 0x0000A785}, {0x0000A786, 0x0000A787}, {0x0000A78B, 0x0000A78C}, {0x0000A78D, 0x00000265},
-{0x0000A790, 0x0000A791}, {0x0000A792, 0x0000A793}, {0x0000A796, 0x0000A797}, {0x0000A798, 0x0000A799},
-{0x0000A79A, 0x0000A79B}, {0x0000A79C, 0x0000A79D}, {0x0000A79E, 0x0000A79F}, {0x0000A7A0, 0x0000A7A1},
-{0x0000A7A2, 0x0000A7A3}, {0x0000A7A4, 0x0000A7A5}, {0x0000A7A6, 0x0000A7A7}, {0x0000A7A8, 0x0000A7A9},
-{0x0000A7AA, 0x00000266}, {0x0000A7AB, 0x0000025C}, {0x0000A7AC, 0x00000261}, {0x0000A7AD, 0x0000026C},
-{0x0000A7AE, 0x0000026A}, {0x0000A7B0, 0x0000029E}, {0x0000A7B1, 0x00000287}, {0x0000A7B2, 0x0000029D},
-{0x0000A7B3, 0x0000AB53}, {0x0000A7B4, 0x0000A7B5}, {0x0000A7B6, 0x0000A7B7}, {0x0000A7B8, 0x0000A7B9},
-{0x0000A7BA, 0x0000A7BB}, {0x0000A7BC, 0x0000A7BD}, {0x0000A7BE, 0x0000A7BF}, {0x0000A7C2, 0x0000A7C3},
-{0x0000A7C4, 0x0000A794}, {0x0000A7C5, 0x00000282}, {0x0000A7C6, 0x00001D8E}, {0x0000A7C7, 0x0000A7C8},
-{0x0000A7C9, 0x0000A7CA}, {0x0000A7F5, 0x0000A7F6}, {0x0000FF21, 0x0000FF41}, {0x0000FF22, 0x0000FF42},
-{0x0000FF23, 0x0000FF43}, {0x0000FF24, 0x0000FF44}, {0x0000FF25, 0x0000FF45}, {0x0000FF26, 0x0000FF46},
-{0x0000FF27, 0x0000FF47}, {0x0000FF28, 0x0000FF48}, {0x0000FF29, 0x0000FF49}, {0x0000FF2A, 0x0000FF4A},
-{0x0000FF2B, 0x0000FF4B}, {0x0000FF2C, 0x0000FF4C}, {0x0000FF2D, 0x0000FF4D}, {0x0000FF2E, 0x0000FF4E},
-{0x0000FF2F, 0x0000FF4F}, {0x0000FF30, 0x0000FF50}, {0x0000FF31, 0x0000FF51}, {0x0000FF32, 0x0000FF52},
-{0x0000FF33, 0x0000FF53}, {0x0000FF34, 0x0000FF54}, {0x0000FF35, 0x0000FF55}, {0x0000FF36, 0x0000FF56},
-{0x0000FF37, 0x0000FF57}, {0x0000FF38, 0x0000FF58}, {0x0000FF39, 0x0000FF59}, {0x0000FF3A, 0x0000FF5A},
-{0x00010400, 0x00010428}, {0x00010401, 0x00010429}, {0x00010402, 0x0001042A}, {0x00010403, 0x0001042B},
-{0x00010404, 0x0001042C}, {0x00010405, 0x0001042D}, {0x00010406, 0x0001042E}, {0x00010407, 0x0001042F},
-{0x00010408, 0x00010430}, {0x00010409, 0x00010431}, {0x0001040A, 0x00010432}, {0x0001040B, 0x00010433},
-{0x0001040C, 0x00010434}, {0x0001040D, 0x00010435}, {0x0001040E, 0x00010436}, {0x0001040F, 0x00010437},
-{0x00010410, 0x00010438}, {0x00010411, 0x00010439}, {0x00010412, 0x0001043A}, {0x00010413, 0x0001043B},
-{0x00010414, 0x0001043C}, {0x00010415, 0x0001043D}, {0x00010416, 0x0001043E}, {0x00010417, 0x0001043F},
-{0x00010418, 0x00010440}, {0x00010419, 0x00010441}, {0x0001041A, 0x00010442}, {0x0001041B, 0x00010443},
-{0x0001041C, 0x00010444}, {0x0001041D, 0x00010445}, {0x0001041E, 0x00010446}, {0x0001041F, 0x00010447},
-{0x00010420, 0x00010448}, {0x00010421, 0x00010449}, {0x00010422, 0x0001044A}, {0x00010423, 0x0001044B},
-{0x00010424, 0x0001044C}, {0x00010425, 0x0001044D}, {0x00010426, 0x0001044E}, {0x00010427, 0x0001044F},
-{0x000104B0, 0x000104D8}, {0x000104B1, 0x000104D9}, {0x000104B2, 0x000104DA}, {0x000104B3, 0x000104DB},
-{0x000104B4, 0x000104DC}, {0x000104B5, 0x000104DD}, {0x000104B6, 0x000104DE}, {0x000104B7, 0x000104DF},
-{0x000104B8, 0x000104E0}, {0x000104B9, 0x000104E1}, {0x000104BA, 0x000104E2}, {0x000104BB, 0x000104E3},
-{0x000104BC, 0x000104E4}, {0x000104BD, 0x000104E5}, {0x000104BE, 0x000104E6}, {0x000104BF, 0x000104E7},
-{0x000104C0, 0x000104E8}, {0x000104C1, 0x000104E9}, {0x000104C2, 0x000104EA}, {0x000104C3, 0x000104EB},
-{0x000104C4, 0x000104EC}, {0x000104C5, 0x000104ED}, {0x000104C6, 0x000104EE}, {0x000104C7, 0x000104EF},
-{0x000104C8, 0x000104F0}, {0x000104C9, 0x000104F1}, {0x000104CA, 0x000104F2}, {0x000104CB, 0x000104F3},
-{0x000104CC, 0x000104F4}, {0x000104CD, 0x000104F5}, {0x000104CE, 0x000104F6}, {0x000104CF, 0x000104F7},
-{0x000104D0, 0x000104F8}, {0x000104D1, 0x000104F9}, {0x000104D2, 0x000104FA}, {0x000104D3, 0x000104FB},
-{0x00010C80, 0x00010CC0}, {0x00010C81, 0x00010CC1}, {0x00010C82, 0x00010CC2}, {0x00010C83, 0x00010CC3},
-{0x00010C84, 0x00010CC4}, {0x00010C85, 0x00010CC5}, {0x00010C86, 0x00010CC6}, {0x00010C87, 0x00010CC7},
-{0x00010C88, 0x00010CC8}, {0x00010C89, 0x00010CC9}, {0x00010C8A, 0x00010CCA}, {0x00010C8B, 0x00010CCB},
-{0x00010C8C, 0x00010CCC}, {0x00010C8D, 0x00010CCD}, {0x00010C8E, 0x00010CCE}, {0x00010C8F, 0x00010CCF},
-{0x00010C90, 0x00010CD0}, {0x00010C91, 0x00010CD1}, {0x00010C92, 0x00010CD2}, {0x00010C93, 0x00010CD3},
-{0x00010C94, 0x00010CD4}, {0x00010C95, 0x00010CD5}, {0x00010C96, 0x00010CD6}, {0x00010C97, 0x00010CD7},
-{0x00010C98, 0x00010CD8}, {0x00010C99, 0x00010CD9}, {0x00010C9A, 0x00010CDA}, {0x00010C9B, 0x00010CDB},
-{0x00010C9C, 0x00010CDC}, {0x00010C9D, 0x00010CDD}, {0x00010C9E, 0x00010CDE}, {0x00010C9F, 0x00010CDF},
-{0x00010CA0, 0x00010CE0}, {0x00010CA1, 0x00010CE1}, {0x00010CA2, 0x00010CE2}, {0x00010CA3, 0x00010CE3},
-{0x00010CA4, 0x00010CE4}, {0x00010CA5, 0x00010CE5}, {0x00010CA6, 0x00010CE6}, {0x00010CA7, 0x00010CE7},
-{0x00010CA8, 0x00010CE8}, {0x00010CA9, 0x00010CE9}, {0x00010CAA, 0x00010CEA}, {0x00010CAB, 0x00010CEB},
-{0x00010CAC, 0x00010CEC}, {0x00010CAD, 0x00010CED}, {0x00010CAE, 0x00010CEE}, {0x00010CAF, 0x00010CEF},
-{0x00010CB0, 0x00010CF0}, {0x00010CB1, 0x00010CF1}, {0x00010CB2, 0x00010CF2}, {0x000118A0, 0x000118C0},
-{0x000118A1, 0x000118C1}, {0x000118A2, 0x000118C2}, {0x000118A3, 0x000118C3}, {0x000118A4, 0x000118C4},
-{0x000118A5, 0x000118C5}, {0x000118A6, 0x000118C6}, {0x000118A7, 0x000118C7}, {0x000118A8, 0x000118C8},
-{0x000118A9, 0x000118C9}, {0x000118AA, 0x000118CA}, {0x000118AB, 0x000118CB}, {0x000118AC, 0x000118CC},
-{0x000118AD, 0x000118CD}, {0x000118AE, 0x000118CE}, {0x000118AF, 0x000118CF}, {0x000118B0, 0x000118D0},
-{0x000118B1, 0x000118D1}, {0x000118B2, 0x000118D2}, {0x000118B3, 0x000118D3}, {0x000118B4, 0x000118D4},
-{0x000118B5, 0x000118D5}, {0x000118B6, 0x000118D6}, {0x000118B7, 0x000118D7}, {0x000118B8, 0x000118D8},
-{0x000118B9, 0x000118D9}, {0x000118BA, 0x000118DA}, {0x000118BB, 0x000118DB}, {0x000118BC, 0x000118DC},
-{0x000118BD, 0x000118DD}, {0x000118BE, 0x000118DE}, {0x000118BF, 0x000118DF}, {0x00016E40, 0x00016E60},
-{0x00016E41, 0x00016E61}, {0x00016E42, 0x00016E62}, {0x00016E43, 0x00016E63}, {0x00016E44, 0x00016E64},
-{0x00016E45, 0x00016E65}, {0x00016E46, 0x00016E66}, {0x00016E47, 0x00016E67}, {0x00016E48, 0x00016E68},
-{0x00016E49, 0x00016E69}, {0x00016E4A, 0x00016E6A}, {0x00016E4B, 0x00016E6B}, {0x00016E4C, 0x00016E6C},
-{0x00016E4D, 0x00016E6D}, {0x00016E4E, 0x00016E6E}, {0x00016E4F, 0x00016E6F}, {0x00016E50, 0x00016E70},
-{0x00016E51, 0x00016E71}, {0x00016E52, 0x00016E72}, {0x00016E53, 0x00016E73}, {0x00016E54, 0x00016E74},
-{0x00016E55, 0x00016E75}, {0x00016E56, 0x00016E76}, {0x00016E57, 0x00016E77}, {0x00016E58, 0x00016E78},
-{0x00016E59, 0x00016E79}, {0x00016E5A, 0x00016E7A}, {0x00016E5B, 0x00016E7B}, {0x00016E5C, 0x00016E7C},
-{0x00016E5D, 0x00016E7D}, {0x00016E5E, 0x00016E7E}, {0x00016E5F, 0x00016E7F}, {0x0001E900, 0x0001E922},
-{0x0001E901, 0x0001E923}, {0x0001E902, 0x0001E924}, {0x0001E903, 0x0001E925}, {0x0001E904, 0x0001E926},
-{0x0001E905, 0x0001E927}, {0x0001E906, 0x0001E928}, {0x0001E907, 0x0001E929}, {0x0001E908, 0x0001E92A},
-{0x0001E909, 0x0001E92B}, {0x0001E90A, 0x0001E92C}, {0x0001E90B, 0x0001E92D}, {0x0001E90C, 0x0001E92E},
-{0x0001E90D, 0x0001E92F}, {0x0001E90E, 0x0001E930}, {0x0001E90F, 0x0001E931}, {0x0001E910, 0x0001E932},
-{0x0001E911, 0x0001E933}, {0x0001E912, 0x0001E934}, {0x0001E913, 0x0001E935}, {0x0001E914, 0x0001E936},
-{0x0001E915, 0x0001E937}, {0x0001E916, 0x0001E938}, {0x0001E917, 0x0001E939}, {0x0001E918, 0x0001E93A},
-{0x0001E919, 0x0001E93B}, {0x0001E91A, 0x0001E93C}, {0x0001E91B, 0x0001E93D}, {0x0001E91C, 0x0001E93E},
-{0x0001E91D, 0x0001E93F}, {0x0001E91E, 0x0001E940}, {0x0001E91F, 0x0001E941}, {0x0001E920, 0x0001E942},
-{0x0001E921, 0x0001E943},
-};
-
-const std::map<char32_t, char32_t> unicode_map_uppercase = {
-{0x00000061, 0x00000041}, {0x00000062, 0x00000042}, {0x00000063, 0x00000043}, {0x00000064, 0x00000044},
-{0x00000065, 0x00000045}, {0x00000066, 0x00000046}, {0x00000067, 0x00000047}, {0x00000068, 0x00000048},
-{0x00000069, 0x00000049}, {0x0000006A, 0x0000004A}, {0x0000006B, 0x0000004B}, {0x0000006C, 0x0000004C},
-{0x0000006D, 0x0000004D}, {0x0000006E, 0x0000004E}, {0x0000006F, 0x0000004F}, {0x00000070, 0x00000050},
-{0x00000071, 0x00000051}, {0x00000072, 0x00000052}, {0x00000073, 0x00000053}, {0x00000074, 0x00000054},
-{0x00000075, 0x00000055}, {0x00000076, 0x00000056}, {0x00000077, 0x00000057}, {0x00000078, 0x00000058},
-{0x00000079, 0x00000059}, {0x0000007A, 0x0000005A}, {0x000000B5, 0x0000039C}, {0x000000DF, 0x00000053},
-{0x000000E0, 0x000000C0}, {0x000000E1, 0x000000C1}, {0x000000E2, 0x000000C2}, {0x000000E3, 0x000000C3},
-{0x000000E4, 0x000000C4}, {0x000000E5, 0x000000C5}, {0x000000E6, 0x000000C6}, {0x000000E7, 0x000000C7},
-{0x000000E8, 0x000000C8}, {0x000000E9, 0x000000C9}, {0x000000EA, 0x000000CA}, {0x000000EB, 0x000000CB},
-{0x000000EC, 0x000000CC}, {0x000000ED, 0x000000CD}, {0x000000EE, 0x000000CE}, {0x000000EF, 0x000000CF},
-{0x000000F0, 0x000000D0}, {0x000000F1, 0x000000D1}, {0x000000F2, 0x000000D2}, {0x000000F3, 0x000000D3},
-{0x000000F4, 0x000000D4}, {0x000000F5, 0x000000D5}, {0x000000F6, 0x000000D6}, {0x000000F8, 0x000000D8},
-{0x000000F9, 0x000000D9}, {0x000000FA, 0x000000DA}, {0x000000FB, 0x000000DB}, {0x000000FC, 0x000000DC},
-{0x000000FD, 0x000000DD}, {0x000000FE, 0x000000DE}, {0x000000FF, 0x00000178}, {0x00000101, 0x00000100},
-{0x00000103, 0x00000102}, {0x00000105, 0x00000104}, {0x00000107, 0x00000106}, {0x00000109, 0x00000108},
-{0x0000010B, 0x0000010A}, {0x0000010D, 0x0000010C}, {0x0000010F, 0x0000010E}, {0x00000111, 0x00000110},
-{0x00000113, 0x00000112}, {0x00000115, 0x00000114}, {0x00000117, 0x00000116}, {0x00000119, 0x00000118},
-{0x0000011B, 0x0000011A}, {0x0000011D, 0x0000011C}, {0x0000011F, 0x0000011E}, {0x00000121, 0x00000120},
-{0x00000123, 0x00000122}, {0x00000125, 0x00000124}, {0x00000127, 0x00000126}, {0x00000129, 0x00000128},
-{0x0000012B, 0x0000012A}, {0x0000012D, 0x0000012C}, {0x0000012F, 0x0000012E}, {0x00000131, 0x00000049},
-{0x00000133, 0x00000132}, {0x00000135, 0x00000134}, {0x00000137, 0x00000136}, {0x0000013A, 0x00000139},
-{0x0000013C, 0x0000013B}, {0x0000013E, 0x0000013D}, {0x00000140, 0x0000013F}, {0x00000142, 0x00000141},
-{0x00000144, 0x00000143}, {0x00000146, 0x00000145}, {0x00000148, 0x00000147}, {0x00000149, 0x000002BC},
-{0x0000014B, 0x0000014A}, {0x0000014D, 0x0000014C}, {0x0000014F, 0x0000014E}, {0x00000151, 0x00000150},
-{0x00000153, 0x00000152}, {0x00000155, 0x00000154}, {0x00000157, 0x00000156}, {0x00000159, 0x00000158},
-{0x0000015B, 0x0000015A}, {0x0000015D, 0x0000015C}, {0x0000015F, 0x0000015E}, {0x00000161, 0x00000160},
-{0x00000163, 0x00000162}, {0x00000165, 0x00000164}, {0x00000167, 0x00000166}, {0x00000169, 0x00000168},
-{0x0000016B, 0x0000016A}, {0x0000016D, 0x0000016C}, {0x0000016F, 0x0000016E}, {0x00000171, 0x00000170},
-{0x00000173, 0x00000172}, {0x00000175, 0x00000174}, {0x00000177, 0x00000176}, {0x0000017A, 0x00000179},
-{0x0000017C, 0x0000017B}, {0x0000017E, 0x0000017D}, {0x0000017F, 0x00000053}, {0x00000180, 0x00000243},
-{0x00000183, 0x00000182}, {0x00000185, 0x00000184}, {0x00000188, 0x00000187}, {0x0000018C, 0x0000018B},
-{0x00000192, 0x00000191}, {0x00000195, 0x000001F6}, {0x00000199, 0x00000198}, {0x0000019A, 0x0000023D},
-{0x0000019E, 0x00000220}, {0x000001A1, 0x000001A0}, {0x000001A3, 0x000001A2}, {0x000001A5, 0x000001A4},
-{0x000001A8, 0x000001A7}, {0x000001AD, 0x000001AC}, {0x000001B0, 0x000001AF}, {0x000001B4, 0x000001B3},
-{0x000001B6, 0x000001B5}, {0x000001B9, 0x000001B8}, {0x000001BD, 0x000001BC}, {0x000001BF, 0x000001F7},
-{0x000001C5, 0x000001C4}, {0x000001C6, 0x000001C4}, {0x000001C8, 0x000001C7}, {0x000001C9, 0x000001C7},
-{0x000001CB, 0x000001CA}, {0x000001CC, 0x000001CA}, {0x000001CE, 0x000001CD}, {0x000001D0, 0x000001CF},
-{0x000001D2, 0x000001D1}, {0x000001D4, 0x000001D3}, {0x000001D6, 0x000001D5}, {0x000001D8, 0x000001D7},
-{0x000001DA, 0x000001D9}, {0x000001DC, 0x000001DB}, {0x000001DD, 0x0000018E}, {0x000001DF, 0x000001DE},
-{0x000001E1, 0x000001E0}, {0x000001E3, 0x000001E2}, {0x000001E5, 0x000001E4}, {0x000001E7, 0x000001E6},
-{0x000001E9, 0x000001E8}, {0x000001EB, 0x000001EA}, {0x000001ED, 0x000001EC}, {0x000001EF, 0x000001EE},
-{0x000001F0, 0x0000004A}, {0x000001F2, 0x000001F1}, {0x000001F3, 0x000001F1}, {0x000001F5, 0x000001F4},
-{0x000001F9, 0x000001F8}, {0x000001FB, 0x000001FA}, {0x000001FD, 0x000001FC}, {0x000001FF, 0x000001FE},
-{0x00000201, 0x00000200}, {0x00000203, 0x00000202}, {0x00000205, 0x00000204}, {0x00000207, 0x00000206},
-{0x00000209, 0x00000208}, {0x0000020B, 0x0000020A}, {0x0000020D, 0x0000020C}, {0x0000020F, 0x0000020E},
-{0x00000211, 0x00000210}, {0x00000213, 0x00000212}, {0x00000215, 0x00000214}, {0x00000217, 0x00000216},
-{0x00000219, 0x00000218}, {0x0000021B, 0x0000021A}, {0x0000021D, 0x0000021C}, {0x0000021F, 0x0000021E},
-{0x00000223, 0x00000222}, {0x00000225, 0x00000224}, {0x00000227, 0x00000226}, {0x00000229, 0x00000228},
-{0x0000022B, 0x0000022A}, {0x0000022D, 0x0000022C}, {0x0000022F, 0x0000022E}, {0x00000231, 0x00000230},
-{0x00000233, 0x00000232}, {0x0000023C, 0x0000023B}, {0x0000023F, 0x00002C7E}, {0x00000240, 0x00002C7F},
-{0x00000242, 0x00000241}, {0x00000247, 0x00000246}, {0x00000249, 0x00000248}, {0x0000024B, 0x0000024A},
-{0x0000024D, 0x0000024C}, {0x0000024F, 0x0000024E}, {0x00000250, 0x00002C6F}, {0x00000251, 0x00002C6D},
-{0x00000252, 0x00002C70}, {0x00000253, 0x00000181}, {0x00000254, 0x00000186}, {0x00000256, 0x00000189},
-{0x00000257, 0x0000018A}, {0x00000259, 0x0000018F}, {0x0000025B, 0x00000190}, {0x0000025C, 0x0000A7AB},
-{0x00000260, 0x00000193}, {0x00000261, 0x0000A7AC}, {0x00000263, 0x00000194}, {0x00000265, 0x0000A78D},
-{0x00000266, 0x0000A7AA}, {0x00000268, 0x00000197}, {0x00000269, 0x00000196}, {0x0000026A, 0x0000A7AE},
-{0x0000026B, 0x00002C62}, {0x0000026C, 0x0000A7AD}, {0x0000026F, 0x0000019C}, {0x00000271, 0x00002C6E},
-{0x00000272, 0x0000019D}, {0x00000275, 0x0000019F}, {0x0000027D, 0x00002C64}, {0x00000280, 0x000001A6},
-{0x00000282, 0x0000A7C5}, {0x00000283, 0x000001A9}, {0x00000287, 0x0000A7B1}, {0x00000288, 0x000001AE},
-{0x00000289, 0x00000244}, {0x0000028A, 0x000001B1}, {0x0000028B, 0x000001B2}, {0x0000028C, 0x00000245},
-{0x00000292, 0x000001B7}, {0x0000029D, 0x0000A7B2}, {0x0000029E, 0x0000A7B0}, {0x00000345, 0x00000399},
-{0x00000371, 0x00000370}, {0x00000373, 0x00000372}, {0x00000377, 0x00000376}, {0x0000037B, 0x000003FD},
-{0x0000037C, 0x000003FE}, {0x0000037D, 0x000003FF}, {0x00000390, 0x00000399}, {0x000003AC, 0x00000386},
-{0x000003AD, 0x00000388}, {0x000003AE, 0x00000389}, {0x000003AF, 0x0000038A}, {0x000003B0, 0x000003A5},
-{0x000003B1, 0x00000391}, {0x000003B2, 0x00000392}, {0x000003B3, 0x00000393}, {0x000003B4, 0x00000394},
-{0x000003B5, 0x00000395}, {0x000003B6, 0x00000396}, {0x000003B7, 0x00000397}, {0x000003B8, 0x00000398},
-{0x000003B9, 0x00000399}, {0x000003BA, 0x0000039A}, {0x000003BB, 0x0000039B}, {0x000003BC, 0x0000039C},
-{0x000003BD, 0x0000039D}, {0x000003BE, 0x0000039E}, {0x000003BF, 0x0000039F}, {0x000003C0, 0x000003A0},
-{0x000003C1, 0x000003A1}, {0x000003C2, 0x000003A3}, {0x000003C3, 0x000003A3}, {0x000003C4, 0x000003A4},
-{0x000003C5, 0x000003A5}, {0x000003C6, 0x000003A6}, {0x000003C7, 0x000003A7}, {0x000003C8, 0x000003A8},
-{0x000003C9, 0x000003A9}, {0x000003CA, 0x000003AA}, {0x000003CB, 0x000003AB}, {0x000003CC, 0x0000038C},
-{0x000003CD, 0x0000038E}, {0x000003CE, 0x0000038F}, {0x000003D0, 0x00000392}, {0x000003D1, 0x00000398},
-{0x000003D5, 0x000003A6}, {0x000003D6, 0x000003A0}, {0x000003D7, 0x000003CF}, {0x000003D9, 0x000003D8},
-{0x000003DB, 0x000003DA}, {0x000003DD, 0x000003DC}, {0x000003DF, 0x000003DE}, {0x000003E1, 0x000003E0},
-{0x000003E3, 0x000003E2}, {0x000003E5, 0x000003E4}, {0x000003E7, 0x000003E6}, {0x000003E9, 0x000003E8},
-{0x000003EB, 0x000003EA}, {0x000003ED, 0x000003EC}, {0x000003EF, 0x000003EE}, {0x000003F0, 0x0000039A},
-{0x000003F1, 0x000003A1}, {0x000003F2, 0x000003F9}, {0x000003F3, 0x0000037F}, {0x000003F5, 0x00000395},
-{0x000003F8, 0x000003F7}, {0x000003FB, 0x000003FA}, {0x00000430, 0x00000410}, {0x00000431, 0x00000411},
-{0x00000432, 0x00000412}, {0x00000433, 0x00000413}, {0x00000434, 0x00000414}, {0x00000435, 0x00000415},
-{0x00000436, 0x00000416}, {0x00000437, 0x00000417}, {0x00000438, 0x00000418}, {0x00000439, 0x00000419},
-{0x0000043A, 0x0000041A}, {0x0000043B, 0x0000041B}, {0x0000043C, 0x0000041C}, {0x0000043D, 0x0000041D},
-{0x0000043E, 0x0000041E}, {0x0000043F, 0x0000041F}, {0x00000440, 0x00000420}, {0x00000441, 0x00000421},
-{0x00000442, 0x00000422}, {0x00000443, 0x00000423}, {0x00000444, 0x00000424}, {0x00000445, 0x00000425},
-{0x00000446, 0x00000426}, {0x00000447, 0x00000427}, {0x00000448, 0x00000428}, {0x00000449, 0x00000429},
-{0x0000044A, 0x0000042A}, {0x0000044B, 0x0000042B}, {0x0000044C, 0x0000042C}, {0x0000044D, 0x0000042D},
-{0x0000044E, 0x0000042E}, {0x0000044F, 0x0000042F}, {0x00000450, 0x00000400}, {0x00000451, 0x00000401},
-{0x00000452, 0x00000402}, {0x00000453, 0x00000403}, {0x00000454, 0x00000404}, {0x00000455, 0x00000405},
-{0x00000456, 0x00000406}, {0x00000457, 0x00000407}, {0x00000458, 0x00000408}, {0x00000459, 0x00000409},
-{0x0000045A, 0x0000040A}, {0x0000045B, 0x0000040B}, {0x0000045C, 0x0000040C}, {0x0000045D, 0x0000040D},
-{0x0000045E, 0x0000040E}, {0x0000045F, 0x0000040F}, {0x00000461, 0x00000460}, {0x00000463, 0x00000462},
-{0x00000465, 0x00000464}, {0x00000467, 0x00000466}, {0x00000469, 0x00000468}, {0x0000046B, 0x0000046A},
-{0x0000046D, 0x0000046C}, {0x0000046F, 0x0000046E}, {0x00000471, 0x00000470}, {0x00000473, 0x00000472},
-{0x00000475, 0x00000474}, {0x00000477, 0x00000476}, {0x00000479, 0x00000478}, {0x0000047B, 0x0000047A},
-{0x0000047D, 0x0000047C}, {0x0000047F, 0x0000047E}, {0x00000481, 0x00000480}, {0x0000048B, 0x0000048A},
-{0x0000048D, 0x0000048C}, {0x0000048F, 0x0000048E}, {0x00000491, 0x00000490}, {0x00000493, 0x00000492},
-{0x00000495, 0x00000494}, {0x00000497, 0x00000496}, {0x00000499, 0x00000498}, {0x0000049B, 0x0000049A},
-{0x0000049D, 0x0000049C}, {0x0000049F, 0x0000049E}, {0x000004A1, 0x000004A0}, {0x000004A3, 0x000004A2},
-{0x000004A5, 0x000004A4}, {0x000004A7, 0x000004A6}, {0x000004A9, 0x000004A8}, {0x000004AB, 0x000004AA},
-{0x000004AD, 0x000004AC}, {0x000004AF, 0x000004AE}, {0x000004B1, 0x000004B0}, {0x000004B3, 0x000004B2},
-{0x000004B5, 0x000004B4}, {0x000004B7, 0x000004B6}, {0x000004B9, 0x000004B8}, {0x000004BB, 0x000004BA},
-{0x000004BD, 0x000004BC}, {0x000004BF, 0x000004BE}, {0x000004C2, 0x000004C1}, {0x000004C4, 0x000004C3},
-{0x000004C6, 0x000004C5}, {0x000004C8, 0x000004C7}, {0x000004CA, 0x000004C9}, {0x000004CC, 0x000004CB},
-{0x000004CE, 0x000004CD}, {0x000004CF, 0x000004C0}, {0x000004D1, 0x000004D0}, {0x000004D3, 0x000004D2},
-{0x000004D5, 0x000004D4}, {0x000004D7, 0x000004D6}, {0x000004D9, 0x000004D8}, {0x000004DB, 0x000004DA},
-{0x000004DD, 0x000004DC}, {0x000004DF, 0x000004DE}, {0x000004E1, 0x000004E0}, {0x000004E3, 0x000004E2},
-{0x000004E5, 0x000004E4}, {0x000004E7, 0x000004E6}, {0x000004E9, 0x000004E8}, {0x000004EB, 0x000004EA},
-{0x000004ED, 0x000004EC}, {0x000004EF, 0x000004EE}, {0x000004F1, 0x000004F0}, {0x000004F3, 0x000004F2},
-{0x000004F5, 0x000004F4}, {0x000004F7, 0x000004F6}, {0x000004F9, 0x000004F8}, {0x000004FB, 0x000004FA},
-{0x000004FD, 0x000004FC}, {0x000004FF, 0x000004FE}, {0x00000501, 0x00000500}, {0x00000503, 0x00000502},
-{0x00000505, 0x00000504}, {0x00000507, 0x00000506}, {0x00000509, 0x00000508}, {0x0000050B, 0x0000050A},
-{0x0000050D, 0x0000050C}, {0x0000050F, 0x0000050E}, {0x00000511, 0x00000510}, {0x00000513, 0x00000512},
-{0x00000515, 0x00000514}, {0x00000517, 0x00000516}, {0x00000519, 0x00000518}, {0x0000051B, 0x0000051A},
-{0x0000051D, 0x0000051C}, {0x0000051F, 0x0000051E}, {0x00000521, 0x00000520}, {0x00000523, 0x00000522},
-{0x00000525, 0x00000524}, {0x00000527, 0x00000526}, {0x00000529, 0x00000528}, {0x0000052B, 0x0000052A},
-{0x0000052D, 0x0000052C}, {0x0000052F, 0x0000052E}, {0x00000561, 0x00000531}, {0x00000562, 0x00000532},
-{0x00000563, 0x00000533}, {0x00000564, 0x00000534}, {0x00000565, 0x00000535}, {0x00000566, 0x00000536},
-{0x00000567, 0x00000537}, {0x00000568, 0x00000538}, {0x00000569, 0x00000539}, {0x0000056A, 0x0000053A},
-{0x0000056B, 0x0000053B}, {0x0000056C, 0x0000053C}, {0x0000056D, 0x0000053D}, {0x0000056E, 0x0000053E},
-{0x0000056F, 0x0000053F}, {0x00000570, 0x00000540}, {0x00000571, 0x00000541}, {0x00000572, 0x00000542},
-{0x00000573, 0x00000543}, {0x00000574, 0x00000544}, {0x00000575, 0x00000545}, {0x00000576, 0x00000546},
-{0x00000577, 0x00000547}, {0x00000578, 0x00000548}, {0x00000579, 0x00000549}, {0x0000057A, 0x0000054A},
-{0x0000057B, 0x0000054B}, {0x0000057C, 0x0000054C}, {0x0000057D, 0x0000054D}, {0x0000057E, 0x0000054E},
-{0x0000057F, 0x0000054F}, {0x00000580, 0x00000550}, {0x00000581, 0x00000551}, {0x00000582, 0x00000552},
-{0x00000583, 0x00000553}, {0x00000584, 0x00000554}, {0x00000585, 0x00000555}, {0x00000586, 0x00000556},
-{0x00000587, 0x00000535}, {0x000010D0, 0x00001C90}, {0x000010D1, 0x00001C91}, {0x000010D2, 0x00001C92},
-{0x000010D3, 0x00001C93}, {0x000010D4, 0x00001C94}, {0x000010D5, 0x00001C95}, {0x000010D6, 0x00001C96},
-{0x000010D7, 0x00001C97}, {0x000010D8, 0x00001C98}, {0x000010D9, 0x00001C99}, {0x000010DA, 0x00001C9A},
-{0x000010DB, 0x00001C9B}, {0x000010DC, 0x00001C9C}, {0x000010DD, 0x00001C9D}, {0x000010DE, 0x00001C9E},
-{0x000010DF, 0x00001C9F}, {0x000010E0, 0x00001CA0}, {0x000010E1, 0x00001CA1}, {0x000010E2, 0x00001CA2},
-{0x000010E3, 0x00001CA3}, {0x000010E4, 0x00001CA4}, {0x000010E5, 0x00001CA5}, {0x000010E6, 0x00001CA6},
-{0x000010E7, 0x00001CA7}, {0x000010E8, 0x00001CA8}, {0x000010E9, 0x00001CA9}, {0x000010EA, 0x00001CAA},
-{0x000010EB, 0x00001CAB}, {0x000010EC, 0x00001CAC}, {0x000010ED, 0x00001CAD}, {0x000010EE, 0x00001CAE},
-{0x000010EF, 0x00001CAF}, {0x000010F0, 0x00001CB0}, {0x000010F1, 0x00001CB1}, {0x000010F2, 0x00001CB2},
-{0x000010F3, 0x00001CB3}, {0x000010F4, 0x00001CB4}, {0x000010F5, 0x00001CB5}, {0x000010F6, 0x00001CB6},
-{0x000010F7, 0x00001CB7}, {0x000010F8, 0x00001CB8}, {0x000010F9, 0x00001CB9}, {0x000010FA, 0x00001CBA},
-{0x000010FD, 0x00001CBD}, {0x000010FE, 0x00001CBE}, {0x000010FF, 0x00001CBF}, {0x000013F8, 0x000013F0},
-{0x000013F9, 0x000013F1}, {0x000013FA, 0x000013F2}, {0x000013FB, 0x000013F3}, {0x000013FC, 0x000013F4},
-{0x000013FD, 0x000013F5}, {0x00001C80, 0x00000412}, {0x00001C81, 0x00000414}, {0x00001C82, 0x0000041E},
-{0x00001C83, 0x00000421}, {0x00001C84, 0x00000422}, {0x00001C85, 0x00000422}, {0x00001C86, 0x0000042A},
-{0x00001C87, 0x00000462}, {0x00001C88, 0x0000A64A}, {0x00001D79, 0x0000A77D}, {0x00001D7D, 0x00002C63},
-{0x00001D8E, 0x0000A7C6}, {0x00001E01, 0x00001E00}, {0x00001E03, 0x00001E02}, {0x00001E05, 0x00001E04},
-{0x00001E07, 0x00001E06}, {0x00001E09, 0x00001E08}, {0x00001E0B, 0x00001E0A}, {0x00001E0D, 0x00001E0C},
-{0x00001E0F, 0x00001E0E}, {0x00001E11, 0x00001E10}, {0x00001E13, 0x00001E12}, {0x00001E15, 0x00001E14},
-{0x00001E17, 0x00001E16}, {0x00001E19, 0x00001E18}, {0x00001E1B, 0x00001E1A}, {0x00001E1D, 0x00001E1C},
-{0x00001E1F, 0x00001E1E}, {0x00001E21, 0x00001E20}, {0x00001E23, 0x00001E22}, {0x00001E25, 0x00001E24},
-{0x00001E27, 0x00001E26}, {0x00001E29, 0x00001E28}, {0x00001E2B, 0x00001E2A}, {0x00001E2D, 0x00001E2C},
-{0x00001E2F, 0x00001E2E}, {0x00001E31, 0x00001E30}, {0x00001E33, 0x00001E32}, {0x00001E35, 0x00001E34},
-{0x00001E37, 0x00001E36}, {0x00001E39, 0x00001E38}, {0x00001E3B, 0x00001E3A}, {0x00001E3D, 0x00001E3C},
-{0x00001E3F, 0x00001E3E}, {0x00001E41, 0x00001E40}, {0x00001E43, 0x00001E42}, {0x00001E45, 0x00001E44},
-{0x00001E47, 0x00001E46}, {0x00001E49, 0x00001E48}, {0x00001E4B, 0x00001E4A}, {0x00001E4D, 0x00001E4C},
-{0x00001E4F, 0x00001E4E}, {0x00001E51, 0x00001E50}, {0x00001E53, 0x00001E52}, {0x00001E55, 0x00001E54},
-{0x00001E57, 0x00001E56}, {0x00001E59, 0x00001E58}, {0x00001E5B, 0x00001E5A}, {0x00001E5D, 0x00001E5C},
-{0x00001E5F, 0x00001E5E}, {0x00001E61, 0x00001E60}, {0x00001E63, 0x00001E62}, {0x00001E65, 0x00001E64},
-{0x00001E67, 0x00001E66}, {0x00001E69, 0x00001E68}, {0x00001E6B, 0x00001E6A}, {0x00001E6D, 0x00001E6C},
-{0x00001E6F, 0x00001E6E}, {0x00001E71, 0x00001E70}, {0x00001E73, 0x00001E72}, {0x00001E75, 0x00001E74},
-{0x00001E77, 0x00001E76}, {0x00001E79, 0x00001E78}, {0x00001E7B, 0x00001E7A}, {0x00001E7D, 0x00001E7C},
-{0x00001E7F, 0x00001E7E}, {0x00001E81, 0x00001E80}, {0x00001E83, 0x00001E82}, {0x00001E85, 0x00001E84},
-{0x00001E87, 0x00001E86}, {0x00001E89, 0x00001E88}, {0x00001E8B, 0x00001E8A}, {0x00001E8D, 0x00001E8C},
-{0x00001E8F, 0x00001E8E}, {0x00001E91, 0x00001E90}, {0x00001E93, 0x00001E92}, {0x00001E95, 0x00001E94},
-{0x00001E96, 0x00000048}, {0x00001E97, 0x00000054}, {0x00001E98, 0x00000057}, {0x00001E99, 0x00000059},
-{0x00001E9A, 0x00000041}, {0x00001E9B, 0x00001E60}, {0x00001EA1, 0x00001EA0}, {0x00001EA3, 0x00001EA2},
-{0x00001EA5, 0x00001EA4}, {0x00001EA7, 0x00001EA6}, {0x00001EA9, 0x00001EA8}, {0x00001EAB, 0x00001EAA},
-{0x00001EAD, 0x00001EAC}, {0x00001EAF, 0x00001EAE}, {0x00001EB1, 0x00001EB0}, {0x00001EB3, 0x00001EB2},
-{0x00001EB5, 0x00001EB4}, {0x00001EB7, 0x00001EB6}, {0x00001EB9, 0x00001EB8}, {0x00001EBB, 0x00001EBA},
-{0x00001EBD, 0x00001EBC}, {0x00001EBF, 0x00001EBE}, {0x00001EC1, 0x00001EC0}, {0x00001EC3, 0x00001EC2},
-{0x00001EC5, 0x00001EC4}, {0x00001EC7, 0x00001EC6}, {0x00001EC9, 0x00001EC8}, {0x00001ECB, 0x00001ECA},
-{0x00001ECD, 0x00001ECC}, {0x00001ECF, 0x00001ECE}, {0x00001ED1, 0x00001ED0}, {0x00001ED3, 0x00001ED2},
-{0x00001ED5, 0x00001ED4}, {0x00001ED7, 0x00001ED6}, {0x00001ED9, 0x00001ED8}, {0x00001EDB, 0x00001EDA},
-{0x00001EDD, 0x00001EDC}, {0x00001EDF, 0x00001EDE}, {0x00001EE1, 0x00001EE0}, {0x00001EE3, 0x00001EE2},
-{0x00001EE5, 0x00001EE4}, {0x00001EE7, 0x00001EE6}, {0x00001EE9, 0x00001EE8}, {0x00001EEB, 0x00001EEA},
-{0x00001EED, 0x00001EEC}, {0x00001EEF, 0x00001EEE}, {0x00001EF1, 0x00001EF0}, {0x00001EF3, 0x00001EF2},
-{0x00001EF5, 0x00001EF4}, {0x00001EF7, 0x00001EF6}, {0x00001EF9, 0x00001EF8}, {0x00001EFB, 0x00001EFA},
-{0x00001EFD, 0x00001EFC}, {0x00001EFF, 0x00001EFE}, {0x00001F00, 0x00001F08}, {0x00001F01, 0x00001F09},
-{0x00001F02, 0x00001F0A}, {0x00001F03, 0x00001F0B}, {0x00001F04, 0x00001F0C}, {0x00001F05, 0x00001F0D},
-{0x00001F06, 0x00001F0E}, {0x00001F07, 0x00001F0F}, {0x00001F10, 0x00001F18}, {0x00001F11, 0x00001F19},
-{0x00001F12, 0x00001F1A}, {0x00001F13, 0x00001F1B}, {0x00001F14, 0x00001F1C}, {0x00001F15, 0x00001F1D},
-{0x00001F20, 0x00001F28}, {0x00001F21, 0x00001F29}, {0x00001F22, 0x00001F2A}, {0x00001F23, 0x00001F2B},
-{0x00001F24, 0x00001F2C}, {0x00001F25, 0x00001F2D}, {0x00001F26, 0x00001F2E}, {0x00001F27, 0x00001F2F},
-{0x00001F30, 0x00001F38}, {0x00001F31, 0x00001F39}, {0x00001F32, 0x00001F3A}, {0x00001F33, 0x00001F3B},
-{0x00001F34, 0x00001F3C}, {0x00001F35, 0x00001F3D}, {0x00001F36, 0x00001F3E}, {0x00001F37, 0x00001F3F},
-{0x00001F40, 0x00001F48}, {0x00001F41, 0x00001F49}, {0x00001F42, 0x00001F4A}, {0x00001F43, 0x00001F4B},
-{0x00001F44, 0x00001F4C}, {0x00001F45, 0x00001F4D}, {0x00001F50, 0x000003A5}, {0x00001F51, 0x00001F59},
-{0x00001F52, 0x000003A5}, {0x00001F53, 0x00001F5B}, {0x00001F54, 0x000003A5}, {0x00001F55, 0x00001F5D},
-{0x00001F56, 0x000003A5}, {0x00001F57, 0x00001F5F}, {0x00001F60, 0x00001F68}, {0x00001F61, 0x00001F69},
-{0x00001F62, 0x00001F6A}, {0x00001F63, 0x00001F6B}, {0x00001F64, 0x00001F6C}, {0x00001F65, 0x00001F6D},
-{0x00001F66, 0x00001F6E}, {0x00001F67, 0x00001F6F}, {0x00001F70, 0x00001FBA}, {0x00001F71, 0x00001FBB},
-{0x00001F72, 0x00001FC8}, {0x00001F73, 0x00001FC9}, {0x00001F74, 0x00001FCA}, {0x00001F75, 0x00001FCB},
-{0x00001F76, 0x00001FDA}, {0x00001F77, 0x00001FDB}, {0x00001F78, 0x00001FF8}, {0x00001F79, 0x00001FF9},
-{0x00001F7A, 0x00001FEA}, {0x00001F7B, 0x00001FEB}, {0x00001F7C, 0x00001FFA}, {0x00001F7D, 0x00001FFB},
-{0x00001F80, 0x00001F08}, {0x00001F81, 0x00001F09}, {0x00001F82, 0x00001F0A}, {0x00001F83, 0x00001F0B},
-{0x00001F84, 0x00001F0C}, {0x00001F85, 0x00001F0D}, {0x00001F86, 0x00001F0E}, {0x00001F87, 0x00001F0F},
-{0x00001F88, 0x00001F08}, {0x00001F89, 0x00001F09}, {0x00001F8A, 0x00001F0A}, {0x00001F8B, 0x00001F0B},
-{0x00001F8C, 0x00001F0C}, {0x00001F8D, 0x00001F0D}, {0x00001F8E, 0x00001F0E}, {0x00001F8F, 0x00001F0F},
-{0x00001F90, 0x00001F28}, {0x00001F91, 0x00001F29}, {0x00001F92, 0x00001F2A}, {0x00001F93, 0x00001F2B},
-{0x00001F94, 0x00001F2C}, {0x00001F95, 0x00001F2D}, {0x00001F96, 0x00001F2E}, {0x00001F97, 0x00001F2F},
-{0x00001F98, 0x00001F28}, {0x00001F99, 0x00001F29}, {0x00001F9A, 0x00001F2A}, {0x00001F9B, 0x00001F2B},
-{0x00001F9C, 0x00001F2C}, {0x00001F9D, 0x00001F2D}, {0x00001F9E, 0x00001F2E}, {0x00001F9F, 0x00001F2F},
-{0x00001FA0, 0x00001F68}, {0x00001FA1, 0x00001F69}, {0x00001FA2, 0x00001F6A}, {0x00001FA3, 0x00001F6B},
-{0x00001FA4, 0x00001F6C}, {0x00001FA5, 0x00001F6D}, {0x00001FA6, 0x00001F6E}, {0x00001FA7, 0x00001F6F},
-{0x00001FA8, 0x00001F68}, {0x00001FA9, 0x00001F69}, {0x00001FAA, 0x00001F6A}, {0x00001FAB, 0x00001F6B},
-{0x00001FAC, 0x00001F6C}, {0x00001FAD, 0x00001F6D}, {0x00001FAE, 0x00001F6E}, {0x00001FAF, 0x00001F6F},
-{0x00001FB0, 0x00001FB8}, {0x00001FB1, 0x00001FB9}, {0x00001FB2, 0x00001FBA}, {0x00001FB3, 0x00000391},
-{0x00001FB4, 0x00000386}, {0x00001FB6, 0x00000391}, {0x00001FB7, 0x00000391}, {0x00001FBC, 0x00000391},
-{0x00001FBE, 0x00000399}, {0x00001FC2, 0x00001FCA}, {0x00001FC3, 0x00000397}, {0x00001FC4, 0x00000389},
-{0x00001FC6, 0x00000397}, {0x00001FC7, 0x00000397}, {0x00001FCC, 0x00000397}, {0x00001FD0, 0x00001FD8},
-{0x00001FD1, 0x00001FD9}, {0x00001FD2, 0x00000399}, {0x00001FD3, 0x00000399}, {0x00001FD6, 0x00000399},
-{0x00001FD7, 0x00000399}, {0x00001FE0, 0x00001FE8}, {0x00001FE1, 0x00001FE9}, {0x00001FE2, 0x000003A5},
-{0x00001FE3, 0x000003A5}, {0x00001FE4, 0x000003A1}, {0x00001FE5, 0x00001FEC}, {0x00001FE6, 0x000003A5},
-{0x00001FE7, 0x000003A5}, {0x00001FF2, 0x00001FFA}, {0x00001FF3, 0x000003A9}, {0x00001FF4, 0x0000038F},
-{0x00001FF6, 0x000003A9}, {0x00001FF7, 0x000003A9}, {0x00001FFC, 0x000003A9}, {0x0000214E, 0x00002132},
-{0x00002170, 0x00002160}, {0x00002171, 0x00002161}, {0x00002172, 0x00002162}, {0x00002173, 0x00002163},
-{0x00002174, 0x00002164}, {0x00002175, 0x00002165}, {0x00002176, 0x00002166}, {0x00002177, 0x00002167},
-{0x00002178, 0x00002168}, {0x00002179, 0x00002169}, {0x0000217A, 0x0000216A}, {0x0000217B, 0x0000216B},
-{0x0000217C, 0x0000216C}, {0x0000217D, 0x0000216D}, {0x0000217E, 0x0000216E}, {0x0000217F, 0x0000216F},
-{0x00002184, 0x00002183}, {0x000024D0, 0x000024B6}, {0x000024D1, 0x000024B7}, {0x000024D2, 0x000024B8},
-{0x000024D3, 0x000024B9}, {0x000024D4, 0x000024BA}, {0x000024D5, 0x000024BB}, {0x000024D6, 0x000024BC},
-{0x000024D7, 0x000024BD}, {0x000024D8, 0x000024BE}, {0x000024D9, 0x000024BF}, {0x000024DA, 0x000024C0},
-{0x000024DB, 0x000024C1}, {0x000024DC, 0x000024C2}, {0x000024DD, 0x000024C3}, {0x000024DE, 0x000024C4},
-{0x000024DF, 0x000024C5}, {0x000024E0, 0x000024C6}, {0x000024E1, 0x000024C7}, {0x000024E2, 0x000024C8},
-{0x000024E3, 0x000024C9}, {0x000024E4, 0x000024CA}, {0x000024E5, 0x000024CB}, {0x000024E6, 0x000024CC},
-{0x000024E7, 0x000024CD}, {0x000024E8, 0x000024CE}, {0x000024E9, 0x000024CF}, {0x00002C30, 0x00002C00},
-{0x00002C31, 0x00002C01}, {0x00002C32, 0x00002C02}, {0x00002C33, 0x00002C03}, {0x00002C34, 0x00002C04},
-{0x00002C35, 0x00002C05}, {0x00002C36, 0x00002C06}, {0x00002C37, 0x00002C07}, {0x00002C38, 0x00002C08},
-{0x00002C39, 0x00002C09}, {0x00002C3A, 0x00002C0A}, {0x00002C3B, 0x00002C0B}, {0x00002C3C, 0x00002C0C},
-{0x00002C3D, 0x00002C0D}, {0x00002C3E, 0x00002C0E}, {0x00002C3F, 0x00002C0F}, {0x00002C40, 0x00002C10},
-{0x00002C41, 0x00002C11}, {0x00002C42, 0x00002C12}, {0x00002C43, 0x00002C13}, {0x00002C44, 0x00002C14},
-{0x00002C45, 0x00002C15}, {0x00002C46, 0x00002C16}, {0x00002C47, 0x00002C17}, {0x00002C48, 0x00002C18},
-{0x00002C49, 0x00002C19}, {0x00002C4A, 0x00002C1A}, {0x00002C4B, 0x00002C1B}, {0x00002C4C, 0x00002C1C},
-{0x00002C4D, 0x00002C1D}, {0x00002C4E, 0x00002C1E}, {0x00002C4F, 0x00002C1F}, {0x00002C50, 0x00002C20},
-{0x00002C51, 0x00002C21}, {0x00002C52, 0x00002C22}, {0x00002C53, 0x00002C23}, {0x00002C54, 0x00002C24},
-{0x00002C55, 0x00002C25}, {0x00002C56, 0x00002C26}, {0x00002C57, 0x00002C27}, {0x00002C58, 0x00002C28},
-{0x00002C59, 0x00002C29}, {0x00002C5A, 0x00002C2A}, {0x00002C5B, 0x00002C2B}, {0x00002C5C, 0x00002C2C},
-{0x00002C5D, 0x00002C2D}, {0x00002C5E, 0x00002C2E}, {0x00002C61, 0x00002C60}, {0x00002C65, 0x0000023A},
-{0x00002C66, 0x0000023E}, {0x00002C68, 0x00002C67}, {0x00002C6A, 0x00002C69}, {0x00002C6C, 0x00002C6B},
-{0x00002C73, 0x00002C72}, {0x00002C76, 0x00002C75}, {0x00002C81, 0x00002C80}, {0x00002C83, 0x00002C82},
-{0x00002C85, 0x00002C84}, {0x00002C87, 0x00002C86}, {0x00002C89, 0x00002C88}, {0x00002C8B, 0x00002C8A},
-{0x00002C8D, 0x00002C8C}, {0x00002C8F, 0x00002C8E}, {0x00002C91, 0x00002C90}, {0x00002C93, 0x00002C92},
-{0x00002C95, 0x00002C94}, {0x00002C97, 0x00002C96}, {0x00002C99, 0x00002C98}, {0x00002C9B, 0x00002C9A},
-{0x00002C9D, 0x00002C9C}, {0x00002C9F, 0x00002C9E}, {0x00002CA1, 0x00002CA0}, {0x00002CA3, 0x00002CA2},
-{0x00002CA5, 0x00002CA4}, {0x00002CA7, 0x00002CA6}, {0x00002CA9, 0x00002CA8}, {0x00002CAB, 0x00002CAA},
-{0x00002CAD, 0x00002CAC}, {0x00002CAF, 0x00002CAE}, {0x00002CB1, 0x00002CB0}, {0x00002CB3, 0x00002CB2},
-{0x00002CB5, 0x00002CB4}, {0x00002CB7, 0x00002CB6}, {0x00002CB9, 0x00002CB8}, {0x00002CBB, 0x00002CBA},
-{0x00002CBD, 0x00002CBC}, {0x00002CBF, 0x00002CBE}, {0x00002CC1, 0x00002CC0}, {0x00002CC3, 0x00002CC2},
-{0x00002CC5, 0x00002CC4}, {0x00002CC7, 0x00002CC6}, {0x00002CC9, 0x00002CC8}, {0x00002CCB, 0x00002CCA},
-{0x00002CCD, 0x00002CCC}, {0x00002CCF, 0x00002CCE}, {0x00002CD1, 0x00002CD0}, {0x00002CD3, 0x00002CD2},
-{0x00002CD5, 0x00002CD4}, {0x00002CD7, 0x00002CD6}, {0x00002CD9, 0x00002CD8}, {0x00002CDB, 0x00002CDA},
-{0x00002CDD, 0x00002CDC}, {0x00002CDF, 0x00002CDE}, {0x00002CE1, 0x00002CE0}, {0x00002CE3, 0x00002CE2},
-{0x00002CEC, 0x00002CEB}, {0x00002CEE, 0x00002CED}, {0x00002CF3, 0x00002CF2}, {0x00002D00, 0x000010A0},
-{0x00002D01, 0x000010A1}, {0x00002D02, 0x000010A2}, {0x00002D03, 0x000010A3}, {0x00002D04, 0x000010A4},
-{0x00002D05, 0x000010A5}, {0x00002D06, 0x000010A6}, {0x00002D07, 0x000010A7}, {0x00002D08, 0x000010A8},
-{0x00002D09, 0x000010A9}, {0x00002D0A, 0x000010AA}, {0x00002D0B, 0x000010AB}, {0x00002D0C, 0x000010AC},
-{0x00002D0D, 0x000010AD}, {0x00002D0E, 0x000010AE}, {0x00002D0F, 0x000010AF}, {0x00002D10, 0x000010B0},
-{0x00002D11, 0x000010B1}, {0x00002D12, 0x000010B2}, {0x00002D13, 0x000010B3}, {0x00002D14, 0x000010B4},
-{0x00002D15, 0x000010B5}, {0x00002D16, 0x000010B6}, {0x00002D17, 0x000010B7}, {0x00002D18, 0x000010B8},
-{0x00002D19, 0x000010B9}, {0x00002D1A, 0x000010BA}, {0x00002D1B, 0x000010BB}, {0x00002D1C, 0x000010BC},
-{0x00002D1D, 0x000010BD}, {0x00002D1E, 0x000010BE}, {0x00002D1F, 0x000010BF}, {0x00002D20, 0x000010C0},
-{0x00002D21, 0x000010C1}, {0x00002D22, 0x000010C2}, {0x00002D23, 0x000010C3}, {0x00002D24, 0x000010C4},
-{0x00002D25, 0x000010C5}, {0x00002D27, 0x000010C7}, {0x00002D2D, 0x000010CD}, {0x0000A641, 0x0000A640},
-{0x0000A643, 0x0000A642}, {0x0000A645, 0x0000A644}, {0x0000A647, 0x0000A646}, {0x0000A649, 0x0000A648},
-{0x0000A64B, 0x0000A64A}, {0x0000A64D, 0x0000A64C}, {0x0000A64F, 0x0000A64E}, {0x0000A651, 0x0000A650},
-{0x0000A653, 0x0000A652}, {0x0000A655, 0x0000A654}, {0x0000A657, 0x0000A656}, {0x0000A659, 0x0000A658},
-{0x0000A65B, 0x0000A65A}, {0x0000A65D, 0x0000A65C}, {0x0000A65F, 0x0000A65E}, {0x0000A661, 0x0000A660},
-{0x0000A663, 0x0000A662}, {0x0000A665, 0x0000A664}, {0x0000A667, 0x0000A666}, {0x0000A669, 0x0000A668},
-{0x0000A66B, 0x0000A66A}, {0x0000A66D, 0x0000A66C}, {0x0000A681, 0x0000A680}, {0x0000A683, 0x0000A682},
-{0x0000A685, 0x0000A684}, {0x0000A687, 0x0000A686}, {0x0000A689, 0x0000A688}, {0x0000A68B, 0x0000A68A},
-{0x0000A68D, 0x0000A68C}, {0x0000A68F, 0x0000A68E}, {0x0000A691, 0x0000A690}, {0x0000A693, 0x0000A692},
-{0x0000A695, 0x0000A694}, {0x0000A697, 0x0000A696}, {0x0000A699, 0x0000A698}, {0x0000A69B, 0x0000A69A},
-{0x0000A723, 0x0000A722}, {0x0000A725, 0x0000A724}, {0x0000A727, 0x0000A726}, {0x0000A729, 0x0000A728},
-{0x0000A72B, 0x0000A72A}, {0x0000A72D, 0x0000A72C}, {0x0000A72F, 0x0000A72E}, {0x0000A733, 0x0000A732},
-{0x0000A735, 0x0000A734}, {0x0000A737, 0x0000A736}, {0x0000A739, 0x0000A738}, {0x0000A73B, 0x0000A73A},
-{0x0000A73D, 0x0000A73C}, {0x0000A73F, 0x0000A73E}, {0x0000A741, 0x0000A740}, {0x0000A743, 0x0000A742},
-{0x0000A745, 0x0000A744}, {0x0000A747, 0x0000A746}, {0x0000A749, 0x0000A748}, {0x0000A74B, 0x0000A74A},
-{0x0000A74D, 0x0000A74C}, {0x0000A74F, 0x0000A74E}, {0x0000A751, 0x0000A750}, {0x0000A753, 0x0000A752},
-{0x0000A755, 0x0000A754}, {0x0000A757, 0x0000A756}, {0x0000A759, 0x0000A758}, {0x0000A75B, 0x0000A75A},
-{0x0000A75D, 0x0000A75C}, {0x0000A75F, 0x0000A75E}, {0x0000A761, 0x0000A760}, {0x0000A763, 0x0000A762},
-{0x0000A765, 0x0000A764}, {0x0000A767, 0x0000A766}, {0x0000A769, 0x0000A768}, {0x0000A76B, 0x0000A76A},
-{0x0000A76D, 0x0000A76C}, {0x0000A76F, 0x0000A76E}, {0x0000A77A, 0x0000A779}, {0x0000A77C, 0x0000A77B},
-{0x0000A77F, 0x0000A77E}, {0x0000A781, 0x0000A780}, {0x0000A783, 0x0000A782}, {0x0000A785, 0x0000A784},
-{0x0000A787, 0x0000A786}, {0x0000A78C, 0x0000A78B}, {0x0000A791, 0x0000A790}, {0x0000A793, 0x0000A792},
-{0x0000A794, 0x0000A7C4}, {0x0000A797, 0x0000A796}, {0x0000A799, 0x0000A798}, {0x0000A79B, 0x0000A79A},
-{0x0000A79D, 0x0000A79C}, {0x0000A79F, 0x0000A79E}, {0x0000A7A1, 0x0000A7A0}, {0x0000A7A3, 0x0000A7A2},
-{0x0000A7A5, 0x0000A7A4}, {0x0000A7A7, 0x0000A7A6}, {0x0000A7A9, 0x0000A7A8}, {0x0000A7B5, 0x0000A7B4},
-{0x0000A7B7, 0x0000A7B6}, {0x0000A7B9, 0x0000A7B8}, {0x0000A7BB, 0x0000A7BA}, {0x0000A7BD, 0x0000A7BC},
-{0x0000A7BF, 0x0000A7BE}, {0x0000A7C3, 0x0000A7C2}, {0x0000A7C8, 0x0000A7C7}, {0x0000A7CA, 0x0000A7C9},
-{0x0000A7F6, 0x0000A7F5}, {0x0000AB53, 0x0000A7B3}, {0x0000AB70, 0x000013A0}, {0x0000AB71, 0x000013A1},
-{0x0000AB72, 0x000013A2}, {0x0000AB73, 0x000013A3}, {0x0000AB74, 0x000013A4}, {0x0000AB75, 0x000013A5},
-{0x0000AB76, 0x000013A6}, {0x0000AB77, 0x000013A7}, {0x0000AB78, 0x000013A8}, {0x0000AB79, 0x000013A9},
-{0x0000AB7A, 0x000013AA}, {0x0000AB7B, 0x000013AB}, {0x0000AB7C, 0x000013AC}, {0x0000AB7D, 0x000013AD},
-{0x0000AB7E, 0x000013AE}, {0x0000AB7F, 0x000013AF}, {0x0000AB80, 0x000013B0}, {0x0000AB81, 0x000013B1},
-{0x0000AB82, 0x000013B2}, {0x0000AB83, 0x000013B3}, {0x0000AB84, 0x000013B4}, {0x0000AB85, 0x000013B5},
-{0x0000AB86, 0x000013B6}, {0x0000AB87, 0x000013B7}, {0x0000AB88, 0x000013B8}, {0x0000AB89, 0x000013B9},
-{0x0000AB8A, 0x000013BA}, {0x0000AB8B, 0x000013BB}, {0x0000AB8C, 0x000013BC}, {0x0000AB8D, 0x000013BD},
-{0x0000AB8E, 0x000013BE}, {0x0000AB8F, 0x000013BF}, {0x0000AB90, 0x000013C0}, {0x0000AB91, 0x000013C1},
-{0x0000AB92, 0x000013C2}, {0x0000AB93, 0x000013C3}, {0x0000AB94, 0x000013C4}, {0x0000AB95, 0x000013C5},
-{0x0000AB96, 0x000013C6}, {0x0000AB97, 0x000013C7}, {0x0000AB98, 0x000013C8}, {0x0000AB99, 0x000013C9},
-{0x0000AB9A, 0x000013CA}, {0x0000AB9B, 0x000013CB}, {0x0000AB9C, 0x000013CC}, {0x0000AB9D, 0x000013CD},
-{0x0000AB9E, 0x000013CE}, {0x0000AB9F, 0x000013CF}, {0x0000ABA0, 0x000013D0}, {0x0000ABA1, 0x000013D1},
-{0x0000ABA2, 0x000013D2}, {0x0000ABA3, 0x000013D3}, {0x0000ABA4, 0x000013D4}, {0x0000ABA5, 0x000013D5},
-{0x0000ABA6, 0x000013D6}, {0x0000ABA7, 0x000013D7}, {0x0000ABA8, 0x000013D8}, {0x0000ABA9, 0x000013D9},
-{0x0000ABAA, 0x000013DA}, {0x0000ABAB, 0x000013DB}, {0x0000ABAC, 0x000013DC}, {0x0000ABAD, 0x000013DD},
-{0x0000ABAE, 0x000013DE}, {0x0000ABAF, 0x000013DF}, {0x0000ABB0, 0x000013E0}, {0x0000ABB1, 0x000013E1},
-{0x0000ABB2, 0x000013E2}, {0x0000ABB3, 0x000013E3}, {0x0000ABB4, 0x000013E4}, {0x0000ABB5, 0x000013E5},
-{0x0000ABB6, 0x000013E6}, {0x0000ABB7, 0x000013E7}, {0x0000ABB8, 0x000013E8}, {0x0000ABB9, 0x000013E9},
-{0x0000ABBA, 0x000013EA}, {0x0000ABBB, 0x000013EB}, {0x0000ABBC, 0x000013EC}, {0x0000ABBD, 0x000013ED},
-{0x0000ABBE, 0x000013EE}, {0x0000ABBF, 0x000013EF}, {0x0000FB00, 0x00000046}, {0x0000FB01, 0x00000046},
-{0x0000FB02, 0x00000046}, {0x0000FB03, 0x00000046}, {0x0000FB04, 0x00000046}, {0x0000FB05, 0x00000053},
-{0x0000FB06, 0x00000053}, {0x0000FB13, 0x00000544}, {0x0000FB14, 0x00000544}, {0x0000FB15, 0x00000544},
-{0x0000FB16, 0x0000054E}, {0x0000FB17, 0x00000544}, {0x0000FF41, 0x0000FF21}, {0x0000FF42, 0x0000FF22},
-{0x0000FF43, 0x0000FF23}, {0x0000FF44, 0x0000FF24}, {0x0000FF45, 0x0000FF25}, {0x0000FF46, 0x0000FF26},
-{0x0000FF47, 0x0000FF27}, {0x0000FF48, 0x0000FF28}, {0x0000FF49, 0x0000FF29}, {0x0000FF4A, 0x0000FF2A},
-{0x0000FF4B, 0x0000FF2B}, {0x0000FF4C, 0x0000FF2C}, {0x0000FF4D, 0x0000FF2D}, {0x0000FF4E, 0x0000FF2E},
-{0x0000FF4F, 0x0000FF2F}, {0x0000FF50, 0x0000FF30}, {0x0000FF51, 0x0000FF31}, {0x0000FF52, 0x0000FF32},
-{0x0000FF53, 0x0000FF33}, {0x0000FF54, 0x0000FF34}, {0x0000FF55, 0x0000FF35}, {0x0000FF56, 0x0000FF36},
-{0x0000FF57, 0x0000FF37}, {0x0000FF58, 0x0000FF38}, {0x0000FF59, 0x0000FF39}, {0x0000FF5A, 0x0000FF3A},
-{0x00010428, 0x00010400}, {0x00010429, 0x00010401}, {0x0001042A, 0x00010402}, {0x0001042B, 0x00010403},
-{0x0001042C, 0x00010404}, {0x0001042D, 0x00010405}, {0x0001042E, 0x00010406}, {0x0001042F, 0x00010407},
-{0x00010430, 0x00010408}, {0x00010431, 0x00010409}, {0x00010432, 0x0001040A}, {0x00010433, 0x0001040B},
-{0x00010434, 0x0001040C}, {0x00010435, 0x0001040D}, {0x00010436, 0x0001040E}, {0x00010437, 0x0001040F},
-{0x00010438, 0x00010410}, {0x00010439, 0x00010411}, {0x0001043A, 0x00010412}, {0x0001043B, 0x00010413},
-{0x0001043C, 0x00010414}, {0x0001043D, 0x00010415}, {0x0001043E, 0x00010416}, {0x0001043F, 0x00010417},
-{0x00010440, 0x00010418}, {0x00010441, 0x00010419}, {0x00010442, 0x0001041A}, {0x00010443, 0x0001041B},
-{0x00010444, 0x0001041C}, {0x00010445, 0x0001041D}, {0x00010446, 0x0001041E}, {0x00010447, 0x0001041F},
-{0x00010448, 0x00010420}, {0x00010449, 0x00010421}, {0x0001044A, 0x00010422}, {0x0001044B, 0x00010423},
-{0x0001044C, 0x00010424}, {0x0001044D, 0x00010425}, {0x0001044E, 0x00010426}, {0x0001044F, 0x00010427},
-{0x000104D8, 0x000104B0}, {0x000104D9, 0x000104B1}, {0x000104DA, 0x000104B2}, {0x000104DB, 0x000104B3},
-{0x000104DC, 0x000104B4}, {0x000104DD, 0x000104B5}, {0x000104DE, 0x000104B6}, {0x000104DF, 0x000104B7},
-{0x000104E0, 0x000104B8}, {0x000104E1, 0x000104B9}, {0x000104E2, 0x000104BA}, {0x000104E3, 0x000104BB},
-{0x000104E4, 0x000104BC}, {0x000104E5, 0x000104BD}, {0x000104E6, 0x000104BE}, {0x000104E7, 0x000104BF},
-{0x000104E8, 0x000104C0}, {0x000104E9, 0x000104C1}, {0x000104EA, 0x000104C2}, {0x000104EB, 0x000104C3},
-{0x000104EC, 0x000104C4}, {0x000104ED, 0x000104C5}, {0x000104EE, 0x000104C6}, {0x000104EF, 0x000104C7},
-{0x000104F0, 0x000104C8}, {0x000104F1, 0x000104C9}, {0x000104F2, 0x000104CA}, {0x000104F3, 0x000104CB},
-{0x000104F4, 0x000104CC}, {0x000104F5, 0x000104CD}, {0x000104F6, 0x000104CE}, {0x000104F7, 0x000104CF},
-{0x000104F8, 0x000104D0}, {0x000104F9, 0x000104D1}, {0x000104FA, 0x000104D2}, {0x000104FB, 0x000104D3},
-{0x00010CC0, 0x00010C80}, {0x00010CC1, 0x00010C81}, {0x00010CC2, 0x00010C82}, {0x00010CC3, 0x00010C83},
-{0x00010CC4, 0x00010C84}, {0x00010CC5, 0x00010C85}, {0x00010CC6, 0x00010C86}, {0x00010CC7, 0x00010C87},
-{0x00010CC8, 0x00010C88}, {0x00010CC9, 0x00010C89}, {0x00010CCA, 0x00010C8A}, {0x00010CCB, 0x00010C8B},
-{0x00010CCC, 0x00010C8C}, {0x00010CCD, 0x00010C8D}, {0x00010CCE, 0x00010C8E}, {0x00010CCF, 0x00010C8F},
-{0x00010CD0, 0x00010C90}, {0x00010CD1, 0x00010C91}, {0x00010CD2, 0x00010C92}, {0x00010CD3, 0x00010C93},
-{0x00010CD4, 0x00010C94}, {0x00010CD5, 0x00010C95}, {0x00010CD6, 0x00010C96}, {0x00010CD7, 0x00010C97},
-{0x00010CD8, 0x00010C98}, {0x00010CD9, 0x00010C99}, {0x00010CDA, 0x00010C9A}, {0x00010CDB, 0x00010C9B},
-{0x00010CDC, 0x00010C9C}, {0x00010CDD, 0x00010C9D}, {0x00010CDE, 0x00010C9E}, {0x00010CDF, 0x00010C9F},
-{0x00010CE0, 0x00010CA0}, {0x00010CE1, 0x00010CA1}, {0x00010CE2, 0x00010CA2}, {0x00010CE3, 0x00010CA3},
-{0x00010CE4, 0x00010CA4}, {0x00010CE5, 0x00010CA5}, {0x00010CE6, 0x00010CA6}, {0x00010CE7, 0x00010CA7},
-{0x00010CE8, 0x00010CA8}, {0x00010CE9, 0x00010CA9}, {0x00010CEA, 0x00010CAA}, {0x00010CEB, 0x00010CAB},
-{0x00010CEC, 0x00010CAC}, {0x00010CED, 0x00010CAD}, {0x00010CEE, 0x00010CAE}, {0x00010CEF, 0x00010CAF},
-{0x00010CF0, 0x00010CB0}, {0x00010CF1, 0x00010CB1}, {0x00010CF2, 0x00010CB2}, {0x000118C0, 0x000118A0},
-{0x000118C1, 0x000118A1}, {0x000118C2, 0x000118A2}, {0x000118C3, 0x000118A3}, {0x000118C4, 0x000118A4},
-{0x000118C5, 0x000118A5}, {0x000118C6, 0x000118A6}, {0x000118C7, 0x000118A7}, {0x000118C8, 0x000118A8},
-{0x000118C9, 0x000118A9}, {0x000118CA, 0x000118AA}, {0x000118CB, 0x000118AB}, {0x000118CC, 0x000118AC},
-{0x000118CD, 0x000118AD}, {0x000118CE, 0x000118AE}, {0x000118CF, 0x000118AF}, {0x000118D0, 0x000118B0},
-{0x000118D1, 0x000118B1}, {0x000118D2, 0x000118B2}, {0x000118D3, 0x000118B3}, {0x000118D4, 0x000118B4},
-{0x000118D5, 0x000118B5}, {0x000118D6, 0x000118B6}, {0x000118D7, 0x000118B7}, {0x000118D8, 0x000118B8},
-{0x000118D9, 0x000118B9}, {0x000118DA, 0x000118BA}, {0x000118DB, 0x000118BB}, {0x000118DC, 0x000118BC},
-{0x000118DD, 0x000118BD}, {0x000118DE, 0x000118BE}, {0x000118DF, 0x000118BF}, {0x00016E60, 0x00016E40},
-{0x00016E61, 0x00016E41}, {0x00016E62, 0x00016E42}, {0x00016E63, 0x00016E43}, {0x00016E64, 0x00016E44},
-{0x00016E65, 0x00016E45}, {0x00016E66, 0x00016E46}, {0x00016E67, 0x00016E47}, {0x00016E68, 0x00016E48},
-{0x00016E69, 0x00016E49}, {0x00016E6A, 0x00016E4A}, {0x00016E6B, 0x00016E4B}, {0x00016E6C, 0x00016E4C},
-{0x00016E6D, 0x00016E4D}, {0x00016E6E, 0x00016E4E}, {0x00016E6F, 0x00016E4F}, {0x00016E70, 0x00016E50},
-{0x00016E71, 0x00016E51}, {0x00016E72, 0x00016E52}, {0x00016E73, 0x00016E53}, {0x00016E74, 0x00016E54},
-{0x00016E75, 0x00016E55}, {0x00016E76, 0x00016E56}, {0x00016E77, 0x00016E57}, {0x00016E78, 0x00016E58},
-{0x00016E79, 0x00016E59}, {0x00016E7A, 0x00016E5A}, {0x00016E7B, 0x00016E5B}, {0x00016E7C, 0x00016E5C},
-{0x00016E7D, 0x00016E5D}, {0x00016E7E, 0x00016E5E}, {0x00016E7F, 0x00016E5F}, {0x0001E922, 0x0001E900},
-{0x0001E923, 0x0001E901}, {0x0001E924, 0x0001E902}, {0x0001E925, 0x0001E903}, {0x0001E926, 0x0001E904},
-{0x0001E927, 0x0001E905}, {0x0001E928, 0x0001E906}, {0x0001E929, 0x0001E907}, {0x0001E92A, 0x0001E908},
-{0x0001E92B, 0x0001E909}, {0x0001E92C, 0x0001E90A}, {0x0001E92D, 0x0001E90B}, {0x0001E92E, 0x0001E90C},
-{0x0001E92F, 0x0001E90D}, {0x0001E930, 0x0001E90E}, {0x0001E931, 0x0001E90F}, {0x0001E932, 0x0001E910},
-{0x0001E933, 0x0001E911}, {0x0001E934, 0x0001E912}, {0x0001E935, 0x0001E913}, {0x0001E936, 0x0001E914},
-{0x0001E937, 0x0001E915}, {0x0001E938, 0x0001E916}, {0x0001E939, 0x0001E917}, {0x0001E93A, 0x0001E918},
-{0x0001E93B, 0x0001E919}, {0x0001E93C, 0x0001E91A}, {0x0001E93D, 0x0001E91B}, {0x0001E93E, 0x0001E91C},
-{0x0001E93F, 0x0001E91D}, {0x0001E940, 0x0001E91E}, {0x0001E941, 0x0001E91F}, {0x0001E942, 0x0001E920},
-{0x0001E943, 0x0001E921},
-};
-
-const std::multimap<uint32_t, uint32_t> unicode_map_nfd = {
-{0x000000C0, 0x00000041}, {0x000000C0, 0x00000300}, {0x000000C1, 0x00000041}, {0x000000C1, 0x00000301},
-{0x000000C2, 0x00000041}, {0x000000C2, 0x00000302}, {0x000000C3, 0x00000041}, {0x000000C3, 0x00000303},
-{0x000000C4, 0x00000041}, {0x000000C4, 0x00000308}, {0x000000C5, 0x00000041}, {0x000000C5, 0x0000030A},
-{0x000000C7, 0x00000043}, {0x000000C7, 0x00000327}, {0x000000C8, 0x00000045}, {0x000000C8, 0x00000300},
-{0x000000C9, 0x00000045}, {0x000000C9, 0x00000301}, {0x000000CA, 0x00000045}, {0x000000CA, 0x00000302},
-{0x000000CB, 0x00000045}, {0x000000CB, 0x00000308}, {0x000000CC, 0x00000049}, {0x000000CC, 0x00000300},
-{0x000000CD, 0x00000049}, {0x000000CD, 0x00000301}, {0x000000CE, 0x00000049}, {0x000000CE, 0x00000302},
-{0x000000CF, 0x00000049}, {0x000000CF, 0x00000308}, {0x000000D1, 0x0000004E}, {0x000000D1, 0x00000303},
-{0x000000D2, 0x0000004F}, {0x000000D2, 0x00000300}, {0x000000D3, 0x0000004F}, {0x000000D3, 0x00000301},
-{0x000000D4, 0x0000004F}, {0x000000D4, 0x00000302}, {0x000000D5, 0x0000004F}, {0x000000D5, 0x00000303},
-{0x000000D6, 0x0000004F}, {0x000000D6, 0x00000308}, {0x000000D9, 0x00000055}, {0x000000D9, 0x00000300},
-{0x000000DA, 0x00000055}, {0x000000DA, 0x00000301}, {0x000000DB, 0x00000055}, {0x000000DB, 0x00000302},
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-{0x00001ED0, 0x00000301}, {0x00001ED1, 0x0000006F}, {0x00001ED1, 0x00000302}, {0x00001ED1, 0x00000301},
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-{0x00001ED8, 0x00000302}, {0x00001ED9, 0x0000006F}, {0x00001ED9, 0x00000323}, {0x00001ED9, 0x00000302},
-{0x00001EDA, 0x0000004F}, {0x00001EDA, 0x0000031B}, {0x00001EDA, 0x00000301}, {0x00001EDB, 0x0000006F},
-{0x00001EDB, 0x0000031B}, {0x00001EDB, 0x00000301}, {0x00001EDC, 0x0000004F}, {0x00001EDC, 0x0000031B},
-{0x00001EDC, 0x00000300}, {0x00001EDD, 0x0000006F}, {0x00001EDD, 0x0000031B}, {0x00001EDD, 0x00000300},
-{0x00001EDE, 0x0000004F}, {0x00001EDE, 0x0000031B}, {0x00001EDE, 0x00000309}, {0x00001EDF, 0x0000006F},
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-{0x00001EE0, 0x00000303}, {0x00001EE1, 0x0000006F}, {0x00001EE1, 0x0000031B}, {0x00001EE1, 0x00000303},
-{0x00001EE2, 0x0000004F}, {0x00001EE2, 0x0000031B}, {0x00001EE2, 0x00000323}, {0x00001EE3, 0x0000006F},
-{0x00001EE3, 0x0000031B}, {0x00001EE3, 0x00000323}, {0x00001EE4, 0x00000055}, {0x00001EE4, 0x00000323},
-{0x00001EE5, 0x00000075}, {0x00001EE5, 0x00000323}, {0x00001EE6, 0x00000055}, {0x00001EE6, 0x00000309},
-{0x00001EE7, 0x00000075}, {0x00001EE7, 0x00000309}, {0x00001EE8, 0x00000055}, {0x00001EE8, 0x0000031B},
-{0x00001EE8, 0x00000301}, {0x00001EE9, 0x00000075}, {0x00001EE9, 0x0000031B}, {0x00001EE9, 0x00000301},
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-{0x00001EEF, 0x0000031B}, {0x00001EEF, 0x00000303}, {0x00001EF0, 0x00000055}, {0x00001EF0, 0x0000031B},
-{0x00001EF0, 0x00000323}, {0x00001EF1, 0x00000075}, {0x00001EF1, 0x0000031B}, {0x00001EF1, 0x00000323},
-{0x00001EF2, 0x00000059}, {0x00001EF2, 0x00000300}, {0x00001EF3, 0x00000079}, {0x00001EF3, 0x00000300},
-{0x00001EF4, 0x00000059}, {0x00001EF4, 0x00000323}, {0x00001EF5, 0x00000079}, {0x00001EF5, 0x00000323},
-{0x00001EF6, 0x00000059}, {0x00001EF6, 0x00000309}, {0x00001EF7, 0x00000079}, {0x00001EF7, 0x00000309},
-{0x00001EF8, 0x00000059}, {0x00001EF8, 0x00000303}, {0x00001EF9, 0x00000079}, {0x00001EF9, 0x00000303},
-{0x00001F00, 0x000003B1}, {0x00001F00, 0x00000313}, {0x00001F01, 0x000003B1}, {0x00001F01, 0x00000314},
-{0x00001F02, 0x000003B1}, {0x00001F02, 0x00000313}, {0x00001F02, 0x00000300}, {0x00001F03, 0x000003B1},
-{0x00001F03, 0x00000314}, {0x00001F03, 0x00000300}, {0x00001F04, 0x000003B1}, {0x00001F04, 0x00000313},
-{0x00001F04, 0x00000301}, {0x00001F05, 0x000003B1}, {0x00001F05, 0x00000314}, {0x00001F05, 0x00000301},
-{0x00001F06, 0x000003B1}, {0x00001F06, 0x00000313}, {0x00001F06, 0x00000342}, {0x00001F07, 0x000003B1},
-{0x00001F07, 0x00000314}, {0x00001F07, 0x00000342}, {0x00001F08, 0x00000391}, {0x00001F08, 0x00000313},
-{0x00001F09, 0x00000391}, {0x00001F09, 0x00000314}, {0x00001F0A, 0x00000391}, {0x00001F0A, 0x00000313},
-{0x00001F0A, 0x00000300}, {0x00001F0B, 0x00000391}, {0x00001F0B, 0x00000314}, {0x00001F0B, 0x00000300},
-{0x00001F0C, 0x00000391}, {0x00001F0C, 0x00000313}, {0x00001F0C, 0x00000301}, {0x00001F0D, 0x00000391},
-{0x00001F0D, 0x00000314}, {0x00001F0D, 0x00000301}, {0x00001F0E, 0x00000391}, {0x00001F0E, 0x00000313},
-{0x00001F0E, 0x00000342}, {0x00001F0F, 0x00000391}, {0x00001F0F, 0x00000314}, {0x00001F0F, 0x00000342},
-{0x00001F10, 0x000003B5}, {0x00001F10, 0x00000313}, {0x00001F11, 0x000003B5}, {0x00001F11, 0x00000314},
-{0x00001F12, 0x000003B5}, {0x00001F12, 0x00000313}, {0x00001F12, 0x00000300}, {0x00001F13, 0x000003B5},
-{0x00001F13, 0x00000314}, {0x00001F13, 0x00000300}, {0x00001F14, 0x000003B5}, {0x00001F14, 0x00000313},
-{0x00001F14, 0x00000301}, {0x00001F15, 0x000003B5}, {0x00001F15, 0x00000314}, {0x00001F15, 0x00000301},
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-{0x00001F3A, 0x00000300}, {0x00001F3B, 0x00000399}, {0x00001F3B, 0x00000314}, {0x00001F3B, 0x00000300},
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-{0x00001F3E, 0x00000342}, {0x00001F3F, 0x00000399}, {0x00001F3F, 0x00000314}, {0x00001F3F, 0x00000342},
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-{0x00001F44, 0x00000301}, {0x00001F45, 0x000003BF}, {0x00001F45, 0x00000314}, {0x00001F45, 0x00000301},
-{0x00001F48, 0x0000039F}, {0x00001F48, 0x00000313}, {0x00001F49, 0x0000039F}, {0x00001F49, 0x00000314},
-{0x00001F4A, 0x0000039F}, {0x00001F4A, 0x00000313}, {0x00001F4A, 0x00000300}, {0x00001F4B, 0x0000039F},
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-{0x00001FB1, 0x00000304}, {0x00001FB2, 0x000003B1}, {0x00001FB2, 0x00000300}, {0x00001FB2, 0x00000345},
-{0x00001FB3, 0x000003B1}, {0x00001FB3, 0x00000345}, {0x00001FB4, 0x000003B1}, {0x00001FB4, 0x00000301},
-{0x00001FB4, 0x00000345}, {0x00001FB6, 0x000003B1}, {0x00001FB6, 0x00000342}, {0x00001FB7, 0x000003B1},
-{0x00001FB7, 0x00000342}, {0x00001FB7, 0x00000345}, {0x00001FB8, 0x00000391}, {0x00001FB8, 0x00000306},
-{0x00001FB9, 0x00000391}, {0x00001FB9, 0x00000304}, {0x00001FBA, 0x00000391}, {0x00001FBA, 0x00000300},
-{0x00001FBB, 0x00000391}, {0x00001FBB, 0x00000301}, {0x00001FBC, 0x00000391}, {0x00001FBC, 0x00000345},
-{0x00001FBE, 0x000003B9}, {0x00001FC1, 0x000000A8}, {0x00001FC1, 0x00000342}, {0x00001FC2, 0x000003B7},
-{0x00001FC2, 0x00000300}, {0x00001FC2, 0x00000345}, {0x00001FC3, 0x000003B7}, {0x00001FC3, 0x00000345},
-{0x00001FC4, 0x000003B7}, {0x00001FC4, 0x00000301}, {0x00001FC4, 0x00000345}, {0x00001FC6, 0x000003B7},
-{0x00001FC6, 0x00000342}, {0x00001FC7, 0x000003B7}, {0x00001FC7, 0x00000342}, {0x00001FC7, 0x00000345},
-{0x00001FC8, 0x00000395}, {0x00001FC8, 0x00000300}, {0x00001FC9, 0x00000395}, {0x00001FC9, 0x00000301},
-{0x00001FCA, 0x00000397}, {0x00001FCA, 0x00000300}, {0x00001FCB, 0x00000397}, {0x00001FCB, 0x00000301},
-{0x00001FCC, 0x00000397}, {0x00001FCC, 0x00000345}, {0x00001FCD, 0x00001FBF}, {0x00001FCD, 0x00000300},
-{0x00001FCE, 0x00001FBF}, {0x00001FCE, 0x00000301}, {0x00001FCF, 0x00001FBF}, {0x00001FCF, 0x00000342},
-{0x00001FD0, 0x000003B9}, {0x00001FD0, 0x00000306}, {0x00001FD1, 0x000003B9}, {0x00001FD1, 0x00000304},
-{0x00001FD2, 0x000003B9}, {0x00001FD2, 0x00000308}, {0x00001FD2, 0x00000300}, {0x00001FD3, 0x000003B9},
-{0x00001FD3, 0x00000308}, {0x00001FD3, 0x00000301}, {0x00001FD6, 0x000003B9}, {0x00001FD6, 0x00000342},
-{0x00001FD7, 0x000003B9}, {0x00001FD7, 0x00000308}, {0x00001FD7, 0x00000342}, {0x00001FD8, 0x00000399},
-{0x00001FD8, 0x00000306}, {0x00001FD9, 0x00000399}, {0x00001FD9, 0x00000304}, {0x00001FDA, 0x00000399},
-{0x00001FDA, 0x00000300}, {0x00001FDB, 0x00000399}, {0x00001FDB, 0x00000301}, {0x00001FDD, 0x00001FFE},
-{0x00001FDD, 0x00000300}, {0x00001FDE, 0x00001FFE}, {0x00001FDE, 0x00000301}, {0x00001FDF, 0x00001FFE},
-{0x00001FDF, 0x00000342}, {0x00001FE0, 0x000003C5}, {0x00001FE0, 0x00000306}, {0x00001FE1, 0x000003C5},
-{0x00001FE1, 0x00000304}, {0x00001FE2, 0x000003C5}, {0x00001FE2, 0x00000308}, {0x00001FE2, 0x00000300},
-{0x00001FE3, 0x000003C5}, {0x00001FE3, 0x00000308}, {0x00001FE3, 0x00000301}, {0x00001FE4, 0x000003C1},
-{0x00001FE4, 0x00000313}, {0x00001FE5, 0x000003C1}, {0x00001FE5, 0x00000314}, {0x00001FE6, 0x000003C5},
-{0x00001FE6, 0x00000342}, {0x00001FE7, 0x000003C5}, {0x00001FE7, 0x00000308}, {0x00001FE7, 0x00000342},
-{0x00001FE8, 0x000003A5}, {0x00001FE8, 0x00000306}, {0x00001FE9, 0x000003A5}, {0x00001FE9, 0x00000304},
-{0x00001FEA, 0x000003A5}, {0x00001FEA, 0x00000300}, {0x00001FEB, 0x000003A5}, {0x00001FEB, 0x00000301},
-{0x00001FEC, 0x000003A1}, {0x00001FEC, 0x00000314}, {0x00001FED, 0x000000A8}, {0x00001FED, 0x00000300},
-{0x00001FEE, 0x000000A8}, {0x00001FEE, 0x00000301}, {0x00001FEF, 0x00000060}, {0x00001FF2, 0x000003C9},
-{0x00001FF2, 0x00000300}, {0x00001FF2, 0x00000345}, {0x00001FF3, 0x000003C9}, {0x00001FF3, 0x00000345},
-{0x00001FF4, 0x000003C9}, {0x00001FF4, 0x00000301}, {0x00001FF4, 0x00000345}, {0x00001FF6, 0x000003C9},
-{0x00001FF6, 0x00000342}, {0x00001FF7, 0x000003C9}, {0x00001FF7, 0x00000342}, {0x00001FF7, 0x00000345},
-{0x00001FF8, 0x0000039F}, {0x00001FF8, 0x00000300}, {0x00001FF9, 0x0000039F}, {0x00001FF9, 0x00000301},
-{0x00001FFA, 0x000003A9}, {0x00001FFA, 0x00000300}, {0x00001FFB, 0x000003A9}, {0x00001FFB, 0x00000301},
-{0x00001FFC, 0x000003A9}, {0x00001FFC, 0x00000345}, {0x00001FFD, 0x000000B4}, {0x00002000, 0x00002002},
-{0x00002001, 0x00002003}, {0x00002126, 0x000003A9}, {0x0000212A, 0x0000004B}, {0x0000212B, 0x00000041},
-{0x0000212B, 0x0000030A}, {0x0000219A, 0x00002190}, {0x0000219A, 0x00000338}, {0x0000219B, 0x00002192},
-{0x0000219B, 0x00000338}, {0x000021AE, 0x00002194}, {0x000021AE, 0x00000338}, {0x000021CD, 0x000021D0},
-{0x000021CD, 0x00000338}, {0x000021CE, 0x000021D4}, {0x000021CE, 0x00000338}, {0x000021CF, 0x000021D2},
-{0x000021CF, 0x00000338}, {0x00002204, 0x00002203}, {0x00002204, 0x00000338}, {0x00002209, 0x00002208},
-{0x00002209, 0x00000338}, {0x0000220C, 0x0000220B}, {0x0000220C, 0x00000338}, {0x00002224, 0x00002223},
-{0x00002224, 0x00000338}, {0x00002226, 0x00002225}, {0x00002226, 0x00000338}, {0x00002241, 0x0000223C},
-{0x00002241, 0x00000338}, {0x00002244, 0x00002243}, {0x00002244, 0x00000338}, {0x00002247, 0x00002245},
-{0x00002247, 0x00000338}, {0x00002249, 0x00002248}, {0x00002249, 0x00000338}, {0x00002260, 0x0000003D},
-{0x00002260, 0x00000338}, {0x00002262, 0x00002261}, {0x00002262, 0x00000338}, {0x0000226D, 0x0000224D},
-{0x0000226D, 0x00000338}, {0x0000226E, 0x0000003C}, {0x0000226E, 0x00000338}, {0x0000226F, 0x0000003E},
-{0x0000226F, 0x00000338}, {0x00002270, 0x00002264}, {0x00002270, 0x00000338}, {0x00002271, 0x00002265},
-{0x00002271, 0x00000338}, {0x00002274, 0x00002272}, {0x00002274, 0x00000338}, {0x00002275, 0x00002273},
-{0x00002275, 0x00000338}, {0x00002278, 0x00002276}, {0x00002278, 0x00000338}, {0x00002279, 0x00002277},
-{0x00002279, 0x00000338}, {0x00002280, 0x0000227A}, {0x00002280, 0x00000338}, {0x00002281, 0x0000227B},
-{0x00002281, 0x00000338}, {0x00002284, 0x00002282}, {0x00002284, 0x00000338}, {0x00002285, 0x00002283},
-{0x00002285, 0x00000338}, {0x00002288, 0x00002286}, {0x00002288, 0x00000338}, {0x00002289, 0x00002287},
-{0x00002289, 0x00000338}, {0x000022AC, 0x000022A2}, {0x000022AC, 0x00000338}, {0x000022AD, 0x000022A8},
-{0x000022AD, 0x00000338}, {0x000022AE, 0x000022A9}, {0x000022AE, 0x00000338}, {0x000022AF, 0x000022AB},
-{0x000022AF, 0x00000338}, {0x000022E0, 0x0000227C}, {0x000022E0, 0x00000338}, {0x000022E1, 0x0000227D},
-{0x000022E1, 0x00000338}, {0x000022E2, 0x00002291}, {0x000022E2, 0x00000338}, {0x000022E3, 0x00002292},
-{0x000022E3, 0x00000338}, {0x000022EA, 0x000022B2}, {0x000022EA, 0x00000338}, {0x000022EB, 0x000022B3},
-{0x000022EB, 0x00000338}, {0x000022EC, 0x000022B4}, {0x000022EC, 0x00000338}, {0x000022ED, 0x000022B5},
-{0x000022ED, 0x00000338}, {0x00002329, 0x00003008}, {0x0000232A, 0x00003009}, {0x00002ADC, 0x00002ADD},
-{0x00002ADC, 0x00000338}, {0x0000304C, 0x0000304B}, {0x0000304C, 0x00003099}, {0x0000304E, 0x0000304D},
-{0x0000304E, 0x00003099}, {0x00003050, 0x0000304F}, {0x00003050, 0x00003099}, {0x00003052, 0x00003051},
-{0x00003052, 0x00003099}, {0x00003054, 0x00003053}, {0x00003054, 0x00003099}, {0x00003056, 0x00003055},
-{0x00003056, 0x00003099}, {0x00003058, 0x00003057}, {0x00003058, 0x00003099}, {0x0000305A, 0x00003059},
-{0x0000305A, 0x00003099}, {0x0000305C, 0x0000305B}, {0x0000305C, 0x00003099}, {0x0000305E, 0x0000305D},
-{0x0000305E, 0x00003099}, {0x00003060, 0x0000305F}, {0x00003060, 0x00003099}, {0x00003062, 0x00003061},
-{0x00003062, 0x00003099}, {0x00003065, 0x00003064}, {0x00003065, 0x00003099}, {0x00003067, 0x00003066},
-{0x00003067, 0x00003099}, {0x00003069, 0x00003068}, {0x00003069, 0x00003099}, {0x00003070, 0x0000306F},
-{0x00003070, 0x00003099}, {0x00003071, 0x0000306F}, {0x00003071, 0x0000309A}, {0x00003073, 0x00003072},
-{0x00003073, 0x00003099}, {0x00003074, 0x00003072}, {0x00003074, 0x0000309A}, {0x00003076, 0x00003075},
-{0x00003076, 0x00003099}, {0x00003077, 0x00003075}, {0x00003077, 0x0000309A}, {0x00003079, 0x00003078},
-{0x00003079, 0x00003099}, {0x0000307A, 0x00003078}, {0x0000307A, 0x0000309A}, {0x0000307C, 0x0000307B},
-{0x0000307C, 0x00003099}, {0x0000307D, 0x0000307B}, {0x0000307D, 0x0000309A}, {0x00003094, 0x00003046},
-{0x00003094, 0x00003099}, {0x0000309E, 0x0000309D}, {0x0000309E, 0x00003099}, {0x000030AC, 0x000030AB},
-{0x000030AC, 0x00003099}, {0x000030AE, 0x000030AD}, {0x000030AE, 0x00003099}, {0x000030B0, 0x000030AF},
-{0x000030B0, 0x00003099}, {0x000030B2, 0x000030B1}, {0x000030B2, 0x00003099}, {0x000030B4, 0x000030B3},
-{0x000030B4, 0x00003099}, {0x000030B6, 0x000030B5}, {0x000030B6, 0x00003099}, {0x000030B8, 0x000030B7},
-{0x000030B8, 0x00003099}, {0x000030BA, 0x000030B9}, {0x000030BA, 0x00003099}, {0x000030BC, 0x000030BB},
-{0x000030BC, 0x00003099}, {0x000030BE, 0x000030BD}, {0x000030BE, 0x00003099}, {0x000030C0, 0x000030BF},
-{0x000030C0, 0x00003099}, {0x000030C2, 0x000030C1}, {0x000030C2, 0x00003099}, {0x000030C5, 0x000030C4},
-{0x000030C5, 0x00003099}, {0x000030C7, 0x000030C6}, {0x000030C7, 0x00003099}, {0x000030C9, 0x000030C8},
-{0x000030C9, 0x00003099}, {0x000030D0, 0x000030CF}, {0x000030D0, 0x00003099}, {0x000030D1, 0x000030CF},
-{0x000030D1, 0x0000309A}, {0x000030D3, 0x000030D2}, {0x000030D3, 0x00003099}, {0x000030D4, 0x000030D2},
-{0x000030D4, 0x0000309A}, {0x000030D6, 0x000030D5}, {0x000030D6, 0x00003099}, {0x000030D7, 0x000030D5},
-{0x000030D7, 0x0000309A}, {0x000030D9, 0x000030D8}, {0x000030D9, 0x00003099}, {0x000030DA, 0x000030D8},
-{0x000030DA, 0x0000309A}, {0x000030DC, 0x000030DB}, {0x000030DC, 0x00003099}, {0x000030DD, 0x000030DB},
-{0x000030DD, 0x0000309A}, {0x000030F4, 0x000030A6}, {0x000030F4, 0x00003099}, {0x000030F7, 0x000030EF},
-{0x000030F7, 0x00003099}, {0x000030F8, 0x000030F0}, {0x000030F8, 0x00003099}, {0x000030F9, 0x000030F1},
-{0x000030F9, 0x00003099}, {0x000030FA, 0x000030F2}, {0x000030FA, 0x00003099}, {0x000030FE, 0x000030FD},
-{0x000030FE, 0x00003099}, {0x0000F900, 0x00008C48}, {0x0000F901, 0x000066F4}, {0x0000F902, 0x00008ECA},
-{0x0000F903, 0x00008CC8}, {0x0000F904, 0x00006ED1}, {0x0000F905, 0x00004E32}, {0x0000F906, 0x000053E5},
-{0x0000F907, 0x00009F9C}, {0x0000F908, 0x00009F9C}, {0x0000F909, 0x00005951}, {0x0000F90A, 0x000091D1},
-{0x0000F90B, 0x00005587}, {0x0000F90C, 0x00005948}, {0x0000F90D, 0x000061F6}, {0x0000F90E, 0x00007669},
-{0x0000F90F, 0x00007F85}, {0x0000F910, 0x0000863F}, {0x0000F911, 0x000087BA}, {0x0000F912, 0x000088F8},
-{0x0000F913, 0x0000908F}, {0x0000F914, 0x00006A02}, {0x0000F915, 0x00006D1B}, {0x0000F916, 0x000070D9},
-{0x0000F917, 0x000073DE}, {0x0000F918, 0x0000843D}, {0x0000F919, 0x0000916A}, {0x0000F91A, 0x000099F1},
-{0x0000F91B, 0x00004E82}, {0x0000F91C, 0x00005375}, {0x0000F91D, 0x00006B04}, {0x0000F91E, 0x0000721B},
-{0x0000F91F, 0x0000862D}, {0x0000F920, 0x00009E1E}, {0x0000F921, 0x00005D50}, {0x0000F922, 0x00006FEB},
-{0x0000F923, 0x000085CD}, {0x0000F924, 0x00008964}, {0x0000F925, 0x000062C9}, {0x0000F926, 0x000081D8},
-{0x0000F927, 0x0000881F}, {0x0000F928, 0x00005ECA}, {0x0000F929, 0x00006717}, {0x0000F92A, 0x00006D6A},
-{0x0000F92B, 0x000072FC}, {0x0000F92C, 0x000090CE}, {0x0000F92D, 0x00004F86}, {0x0000F92E, 0x000051B7},
-{0x0000F92F, 0x000052DE}, {0x0000F930, 0x000064C4}, {0x0000F931, 0x00006AD3}, {0x0000F932, 0x00007210},
-{0x0000F933, 0x000076E7}, {0x0000F934, 0x00008001}, {0x0000F935, 0x00008606}, {0x0000F936, 0x0000865C},
-{0x0000F937, 0x00008DEF}, {0x0000F938, 0x00009732}, {0x0000F939, 0x00009B6F}, {0x0000F93A, 0x00009DFA},
-{0x0000F93B, 0x0000788C}, {0x0000F93C, 0x0000797F}, {0x0000F93D, 0x00007DA0}, {0x0000F93E, 0x000083C9},
-{0x0000F93F, 0x00009304}, {0x0000F940, 0x00009E7F}, {0x0000F941, 0x00008AD6}, {0x0000F942, 0x000058DF},
-{0x0000F943, 0x00005F04}, {0x0000F944, 0x00007C60}, {0x0000F945, 0x0000807E}, {0x0000F946, 0x00007262},
-{0x0000F947, 0x000078CA}, {0x0000F948, 0x00008CC2}, {0x0000F949, 0x000096F7}, {0x0000F94A, 0x000058D8},
-{0x0000F94B, 0x00005C62}, {0x0000F94C, 0x00006A13}, {0x0000F94D, 0x00006DDA}, {0x0000F94E, 0x00006F0F},
-{0x0000F94F, 0x00007D2F}, {0x0000F950, 0x00007E37}, {0x0000F951, 0x0000964B}, {0x0000F952, 0x000052D2},
-{0x0000F953, 0x0000808B}, {0x0000F954, 0x000051DC}, {0x0000F955, 0x000051CC}, {0x0000F956, 0x00007A1C},
-{0x0000F957, 0x00007DBE}, {0x0000F958, 0x000083F1}, {0x0000F959, 0x00009675}, {0x0000F95A, 0x00008B80},
-{0x0000F95B, 0x000062CF}, {0x0000F95C, 0x00006A02}, {0x0000F95D, 0x00008AFE}, {0x0000F95E, 0x00004E39},
-{0x0000F95F, 0x00005BE7}, {0x0000F960, 0x00006012}, {0x0000F961, 0x00007387}, {0x0000F962, 0x00007570},
-{0x0000F963, 0x00005317}, {0x0000F964, 0x000078FB}, {0x0000F965, 0x00004FBF}, {0x0000F966, 0x00005FA9},
-{0x0000F967, 0x00004E0D}, {0x0000F968, 0x00006CCC}, {0x0000F969, 0x00006578}, {0x0000F96A, 0x00007D22},
-{0x0000F96B, 0x000053C3}, {0x0000F96C, 0x0000585E}, {0x0000F96D, 0x00007701}, {0x0000F96E, 0x00008449},
-{0x0000F96F, 0x00008AAA}, {0x0000F970, 0x00006BBA}, {0x0000F971, 0x00008FB0}, {0x0000F972, 0x00006C88},
-{0x0000F973, 0x000062FE}, {0x0000F974, 0x000082E5}, {0x0000F975, 0x000063A0}, {0x0000F976, 0x00007565},
-{0x0000F977, 0x00004EAE}, {0x0000F978, 0x00005169}, {0x0000F979, 0x000051C9}, {0x0000F97A, 0x00006881},
-{0x0000F97B, 0x00007CE7}, {0x0000F97C, 0x0000826F}, {0x0000F97D, 0x00008AD2}, {0x0000F97E, 0x000091CF},
-{0x0000F97F, 0x000052F5}, {0x0000F980, 0x00005442}, {0x0000F981, 0x00005973}, {0x0000F982, 0x00005EEC},
-{0x0000F983, 0x000065C5}, {0x0000F984, 0x00006FFE}, {0x0000F985, 0x0000792A}, {0x0000F986, 0x000095AD},
-{0x0000F987, 0x00009A6A}, {0x0000F988, 0x00009E97}, {0x0000F989, 0x00009ECE}, {0x0000F98A, 0x0000529B},
-{0x0000F98B, 0x000066C6}, {0x0000F98C, 0x00006B77}, {0x0000F98D, 0x00008F62}, {0x0000F98E, 0x00005E74},
-{0x0000F98F, 0x00006190}, {0x0000F990, 0x00006200}, {0x0000F991, 0x0000649A}, {0x0000F992, 0x00006F23},
-{0x0000F993, 0x00007149}, {0x0000F994, 0x00007489}, {0x0000F995, 0x000079CA}, {0x0000F996, 0x00007DF4},
-{0x0000F997, 0x0000806F}, {0x0000F998, 0x00008F26}, {0x0000F999, 0x000084EE}, {0x0000F99A, 0x00009023},
-{0x0000F99B, 0x0000934A}, {0x0000F99C, 0x00005217}, {0x0000F99D, 0x000052A3}, {0x0000F99E, 0x000054BD},
-{0x0000F99F, 0x000070C8}, {0x0000F9A0, 0x000088C2}, {0x0000F9A1, 0x00008AAA}, {0x0000F9A2, 0x00005EC9},
-{0x0000F9A3, 0x00005FF5}, {0x0000F9A4, 0x0000637B}, {0x0000F9A5, 0x00006BAE}, {0x0000F9A6, 0x00007C3E},
-{0x0000F9A7, 0x00007375}, {0x0000F9A8, 0x00004EE4}, {0x0000F9A9, 0x000056F9}, {0x0000F9AA, 0x00005BE7},
-{0x0000F9AB, 0x00005DBA}, {0x0000F9AC, 0x0000601C}, {0x0000F9AD, 0x000073B2}, {0x0000F9AE, 0x00007469},
-{0x0000F9AF, 0x00007F9A}, {0x0000F9B0, 0x00008046}, {0x0000F9B1, 0x00009234}, {0x0000F9B2, 0x000096F6},
-{0x0000F9B3, 0x00009748}, {0x0000F9B4, 0x00009818}, {0x0000F9B5, 0x00004F8B}, {0x0000F9B6, 0x000079AE},
-{0x0000F9B7, 0x000091B4}, {0x0000F9B8, 0x000096B8}, {0x0000F9B9, 0x000060E1}, {0x0000F9BA, 0x00004E86},
-{0x0000F9BB, 0x000050DA}, {0x0000F9BC, 0x00005BEE}, {0x0000F9BD, 0x00005C3F}, {0x0000F9BE, 0x00006599},
-{0x0000F9BF, 0x00006A02}, {0x0000F9C0, 0x000071CE}, {0x0000F9C1, 0x00007642}, {0x0000F9C2, 0x000084FC},
-{0x0000F9C3, 0x0000907C}, {0x0000F9C4, 0x00009F8D}, {0x0000F9C5, 0x00006688}, {0x0000F9C6, 0x0000962E},
-{0x0000F9C7, 0x00005289}, {0x0000F9C8, 0x0000677B}, {0x0000F9C9, 0x000067F3}, {0x0000F9CA, 0x00006D41},
-{0x0000F9CB, 0x00006E9C}, {0x0000F9CC, 0x00007409}, {0x0000F9CD, 0x00007559}, {0x0000F9CE, 0x0000786B},
-{0x0000F9CF, 0x00007D10}, {0x0000F9D0, 0x0000985E}, {0x0000F9D1, 0x0000516D}, {0x0000F9D2, 0x0000622E},
-{0x0000F9D3, 0x00009678}, {0x0000F9D4, 0x0000502B}, {0x0000F9D5, 0x00005D19}, {0x0000F9D6, 0x00006DEA},
-{0x0000F9D7, 0x00008F2A}, {0x0000F9D8, 0x00005F8B}, {0x0000F9D9, 0x00006144}, {0x0000F9DA, 0x00006817},
-{0x0000F9DB, 0x00007387}, {0x0000F9DC, 0x00009686}, {0x0000F9DD, 0x00005229}, {0x0000F9DE, 0x0000540F},
-{0x0000F9DF, 0x00005C65}, {0x0000F9E0, 0x00006613}, {0x0000F9E1, 0x0000674E}, {0x0000F9E2, 0x000068A8},
-{0x0000F9E3, 0x00006CE5}, {0x0000F9E4, 0x00007406}, {0x0000F9E5, 0x000075E2}, {0x0000F9E6, 0x00007F79},
-{0x0000F9E7, 0x000088CF}, {0x0000F9E8, 0x000088E1}, {0x0000F9E9, 0x000091CC}, {0x0000F9EA, 0x000096E2},
-{0x0000F9EB, 0x0000533F}, {0x0000F9EC, 0x00006EBA}, {0x0000F9ED, 0x0000541D}, {0x0000F9EE, 0x000071D0},
-{0x0000F9EF, 0x00007498}, {0x0000F9F0, 0x000085FA}, {0x0000F9F1, 0x000096A3}, {0x0000F9F2, 0x00009C57},
-{0x0000F9F3, 0x00009E9F}, {0x0000F9F4, 0x00006797}, {0x0000F9F5, 0x00006DCB}, {0x0000F9F6, 0x000081E8},
-{0x0000F9F7, 0x00007ACB}, {0x0000F9F8, 0x00007B20}, {0x0000F9F9, 0x00007C92}, {0x0000F9FA, 0x000072C0},
-{0x0000F9FB, 0x00007099}, {0x0000F9FC, 0x00008B58}, {0x0000F9FD, 0x00004EC0}, {0x0000F9FE, 0x00008336},
-{0x0000F9FF, 0x0000523A}, {0x0000FA00, 0x00005207}, {0x0000FA01, 0x00005EA6}, {0x0000FA02, 0x000062D3},
-{0x0000FA03, 0x00007CD6}, {0x0000FA04, 0x00005B85}, {0x0000FA05, 0x00006D1E}, {0x0000FA06, 0x000066B4},
-{0x0000FA07, 0x00008F3B}, {0x0000FA08, 0x0000884C}, {0x0000FA09, 0x0000964D}, {0x0000FA0A, 0x0000898B},
-{0x0000FA0B, 0x00005ED3}, {0x0000FA0C, 0x00005140}, {0x0000FA0D, 0x000055C0}, {0x0000FA10, 0x0000585A},
-{0x0000FA12, 0x00006674}, {0x0000FA15, 0x000051DE}, {0x0000FA16, 0x0000732A}, {0x0000FA17, 0x000076CA},
-{0x0000FA18, 0x0000793C}, {0x0000FA19, 0x0000795E}, {0x0000FA1A, 0x00007965}, {0x0000FA1B, 0x0000798F},
-{0x0000FA1C, 0x00009756}, {0x0000FA1D, 0x00007CBE}, {0x0000FA1E, 0x00007FBD}, {0x0000FA20, 0x00008612},
-{0x0000FA22, 0x00008AF8}, {0x0000FA25, 0x00009038}, {0x0000FA26, 0x000090FD}, {0x0000FA2A, 0x000098EF},
-{0x0000FA2B, 0x000098FC}, {0x0000FA2C, 0x00009928}, {0x0000FA2D, 0x00009DB4}, {0x0000FA2E, 0x000090DE},
-{0x0000FA2F, 0x000096B7}, {0x0000FA30, 0x00004FAE}, {0x0000FA31, 0x000050E7}, {0x0000FA32, 0x0000514D},
-{0x0000FA33, 0x000052C9}, {0x0000FA34, 0x000052E4}, {0x0000FA35, 0x00005351}, {0x0000FA36, 0x0000559D},
-{0x0000FA37, 0x00005606}, {0x0000FA38, 0x00005668}, {0x0000FA39, 0x00005840}, {0x0000FA3A, 0x000058A8},
-{0x0000FA3B, 0x00005C64}, {0x0000FA3C, 0x00005C6E}, {0x0000FA3D, 0x00006094}, {0x0000FA3E, 0x00006168},
-{0x0000FA3F, 0x0000618E}, {0x0000FA40, 0x000061F2}, {0x0000FA41, 0x0000654F}, {0x0000FA42, 0x000065E2},
-{0x0000FA43, 0x00006691}, {0x0000FA44, 0x00006885}, {0x0000FA45, 0x00006D77}, {0x0000FA46, 0x00006E1A},
-{0x0000FA47, 0x00006F22}, {0x0000FA48, 0x0000716E}, {0x0000FA49, 0x0000722B}, {0x0000FA4A, 0x00007422},
-{0x0000FA4B, 0x00007891}, {0x0000FA4C, 0x0000793E}, {0x0000FA4D, 0x00007949}, {0x0000FA4E, 0x00007948},
-{0x0000FA4F, 0x00007950}, {0x0000FA50, 0x00007956}, {0x0000FA51, 0x0000795D}, {0x0000FA52, 0x0000798D},
-{0x0000FA53, 0x0000798E}, {0x0000FA54, 0x00007A40}, {0x0000FA55, 0x00007A81}, {0x0000FA56, 0x00007BC0},
-{0x0000FA57, 0x00007DF4}, {0x0000FA58, 0x00007E09}, {0x0000FA59, 0x00007E41}, {0x0000FA5A, 0x00007F72},
-{0x0000FA5B, 0x00008005}, {0x0000FA5C, 0x000081ED}, {0x0000FA5D, 0x00008279}, {0x0000FA5E, 0x00008279},
-{0x0000FA5F, 0x00008457}, {0x0000FA60, 0x00008910}, {0x0000FA61, 0x00008996}, {0x0000FA62, 0x00008B01},
-{0x0000FA63, 0x00008B39}, {0x0000FA64, 0x00008CD3}, {0x0000FA65, 0x00008D08}, {0x0000FA66, 0x00008FB6},
-{0x0000FA67, 0x00009038}, {0x0000FA68, 0x000096E3}, {0x0000FA69, 0x000097FF}, {0x0000FA6A, 0x0000983B},
-{0x0000FA6B, 0x00006075}, {0x0000FA6C, 0x000242EE}, {0x0000FA6D, 0x00008218}, {0x0000FA70, 0x00004E26},
-{0x0000FA71, 0x000051B5}, {0x0000FA72, 0x00005168}, {0x0000FA73, 0x00004F80}, {0x0000FA74, 0x00005145},
-{0x0000FA75, 0x00005180}, {0x0000FA76, 0x000052C7}, {0x0000FA77, 0x000052FA}, {0x0000FA78, 0x0000559D},
-{0x0000FA79, 0x00005555}, {0x0000FA7A, 0x00005599}, {0x0000FA7B, 0x000055E2}, {0x0000FA7C, 0x0000585A},
-{0x0000FA7D, 0x000058B3}, {0x0000FA7E, 0x00005944}, {0x0000FA7F, 0x00005954}, {0x0000FA80, 0x00005A62},
-{0x0000FA81, 0x00005B28}, {0x0000FA82, 0x00005ED2}, {0x0000FA83, 0x00005ED9}, {0x0000FA84, 0x00005F69},
-{0x0000FA85, 0x00005FAD}, {0x0000FA86, 0x000060D8}, {0x0000FA87, 0x0000614E}, {0x0000FA88, 0x00006108},
-{0x0000FA89, 0x0000618E}, {0x0000FA8A, 0x00006160}, {0x0000FA8B, 0x000061F2}, {0x0000FA8C, 0x00006234},
-{0x0000FA8D, 0x000063C4}, {0x0000FA8E, 0x0000641C}, {0x0000FA8F, 0x00006452}, {0x0000FA90, 0x00006556},
-{0x0000FA91, 0x00006674}, {0x0000FA92, 0x00006717}, {0x0000FA93, 0x0000671B}, {0x0000FA94, 0x00006756},
-{0x0000FA95, 0x00006B79}, {0x0000FA96, 0x00006BBA}, {0x0000FA97, 0x00006D41}, {0x0000FA98, 0x00006EDB},
-{0x0000FA99, 0x00006ECB}, {0x0000FA9A, 0x00006F22}, {0x0000FA9B, 0x0000701E}, {0x0000FA9C, 0x0000716E},
-{0x0000FA9D, 0x000077A7}, {0x0000FA9E, 0x00007235}, {0x0000FA9F, 0x000072AF}, {0x0000FAA0, 0x0000732A},
-{0x0000FAA1, 0x00007471}, {0x0000FAA2, 0x00007506}, {0x0000FAA3, 0x0000753B}, {0x0000FAA4, 0x0000761D},
-{0x0000FAA5, 0x0000761F}, {0x0000FAA6, 0x000076CA}, {0x0000FAA7, 0x000076DB}, {0x0000FAA8, 0x000076F4},
-{0x0000FAA9, 0x0000774A}, {0x0000FAAA, 0x00007740}, {0x0000FAAB, 0x000078CC}, {0x0000FAAC, 0x00007AB1},
-{0x0000FAAD, 0x00007BC0}, {0x0000FAAE, 0x00007C7B}, {0x0000FAAF, 0x00007D5B}, {0x0000FAB0, 0x00007DF4},
-{0x0000FAB1, 0x00007F3E}, {0x0000FAB2, 0x00008005}, {0x0000FAB3, 0x00008352}, {0x0000FAB4, 0x000083EF},
-{0x0000FAB5, 0x00008779}, {0x0000FAB6, 0x00008941}, {0x0000FAB7, 0x00008986}, {0x0000FAB8, 0x00008996},
-{0x0000FAB9, 0x00008ABF}, {0x0000FABA, 0x00008AF8}, {0x0000FABB, 0x00008ACB}, {0x0000FABC, 0x00008B01},
-{0x0000FABD, 0x00008AFE}, {0x0000FABE, 0x00008AED}, {0x0000FABF, 0x00008B39}, {0x0000FAC0, 0x00008B8A},
-{0x0000FAC1, 0x00008D08}, {0x0000FAC2, 0x00008F38}, {0x0000FAC3, 0x00009072}, {0x0000FAC4, 0x00009199},
-{0x0000FAC5, 0x00009276}, {0x0000FAC6, 0x0000967C}, {0x0000FAC7, 0x000096E3}, {0x0000FAC8, 0x00009756},
-{0x0000FAC9, 0x000097DB}, {0x0000FACA, 0x000097FF}, {0x0000FACB, 0x0000980B}, {0x0000FACC, 0x0000983B},
-{0x0000FACD, 0x00009B12}, {0x0000FACE, 0x00009F9C}, {0x0000FACF, 0x0002284A}, {0x0000FAD0, 0x00022844},
-{0x0000FAD1, 0x000233D5}, {0x0000FAD2, 0x00003B9D}, {0x0000FAD3, 0x00004018}, {0x0000FAD4, 0x00004039},
-{0x0000FAD5, 0x00025249}, {0x0000FAD6, 0x00025CD0}, {0x0000FAD7, 0x00027ED3}, {0x0000FAD8, 0x00009F43},
-{0x0000FAD9, 0x00009F8E}, {0x0000FB1D, 0x000005D9}, {0x0000FB1D, 0x000005B4}, {0x0000FB1F, 0x000005F2},
-{0x0000FB1F, 0x000005B7}, {0x0000FB2A, 0x000005E9}, {0x0000FB2A, 0x000005C1}, {0x0000FB2B, 0x000005E9},
-{0x0000FB2B, 0x000005C2}, {0x0000FB2C, 0x000005E9}, {0x0000FB2C, 0x000005BC}, {0x0000FB2C, 0x000005C1},
-{0x0000FB2D, 0x000005E9}, {0x0000FB2D, 0x000005BC}, {0x0000FB2D, 0x000005C2}, {0x0000FB2E, 0x000005D0},
-{0x0000FB2E, 0x000005B7}, {0x0000FB2F, 0x000005D0}, {0x0000FB2F, 0x000005B8}, {0x0000FB30, 0x000005D0},
-{0x0000FB30, 0x000005BC}, {0x0000FB31, 0x000005D1}, {0x0000FB31, 0x000005BC}, {0x0000FB32, 0x000005D2},
-{0x0000FB32, 0x000005BC}, {0x0000FB33, 0x000005D3}, {0x0000FB33, 0x000005BC}, {0x0000FB34, 0x000005D4},
-{0x0000FB34, 0x000005BC}, {0x0000FB35, 0x000005D5}, {0x0000FB35, 0x000005BC}, {0x0000FB36, 0x000005D6},
-{0x0000FB36, 0x000005BC}, {0x0000FB38, 0x000005D8}, {0x0000FB38, 0x000005BC}, {0x0000FB39, 0x000005D9},
-{0x0000FB39, 0x000005BC}, {0x0000FB3A, 0x000005DA}, {0x0000FB3A, 0x000005BC}, {0x0000FB3B, 0x000005DB},
-{0x0000FB3B, 0x000005BC}, {0x0000FB3C, 0x000005DC}, {0x0000FB3C, 0x000005BC}, {0x0000FB3E, 0x000005DE},
-{0x0000FB3E, 0x000005BC}, {0x0000FB40, 0x000005E0}, {0x0000FB40, 0x000005BC}, {0x0000FB41, 0x000005E1},
-{0x0000FB41, 0x000005BC}, {0x0000FB43, 0x000005E3}, {0x0000FB43, 0x000005BC}, {0x0000FB44, 0x000005E4},
-{0x0000FB44, 0x000005BC}, {0x0000FB46, 0x000005E6}, {0x0000FB46, 0x000005BC}, {0x0000FB47, 0x000005E7},
-{0x0000FB47, 0x000005BC}, {0x0000FB48, 0x000005E8}, {0x0000FB48, 0x000005BC}, {0x0000FB49, 0x000005E9},
-{0x0000FB49, 0x000005BC}, {0x0000FB4A, 0x000005EA}, {0x0000FB4A, 0x000005BC}, {0x0000FB4B, 0x000005D5},
-{0x0000FB4B, 0x000005B9}, {0x0000FB4C, 0x000005D1}, {0x0000FB4C, 0x000005BF}, {0x0000FB4D, 0x000005DB},
-{0x0000FB4D, 0x000005BF}, {0x0000FB4E, 0x000005E4}, {0x0000FB4E, 0x000005BF}, {0x0001109A, 0x00011099},
-{0x0001109A, 0x000110BA}, {0x0001109C, 0x0001109B}, {0x0001109C, 0x000110BA}, {0x000110AB, 0x000110A5},
-{0x000110AB, 0x000110BA}, {0x0001112E, 0x00011131}, {0x0001112E, 0x00011127}, {0x0001112F, 0x00011132},
-{0x0001112F, 0x00011127}, {0x0001134B, 0x00011347}, {0x0001134B, 0x0001133E}, {0x0001134C, 0x00011347},
-{0x0001134C, 0x00011357}, {0x000114BB, 0x000114B9}, {0x000114BB, 0x000114BA}, {0x000114BC, 0x000114B9},
-{0x000114BC, 0x000114B0}, {0x000114BE, 0x000114B9}, {0x000114BE, 0x000114BD}, {0x000115BA, 0x000115B8},
-{0x000115BA, 0x000115AF}, {0x000115BB, 0x000115B9}, {0x000115BB, 0x000115AF}, {0x0001D15E, 0x0001D157},
-{0x0001D15E, 0x0001D165}, {0x0001D15F, 0x0001D158}, {0x0001D15F, 0x0001D165}, {0x0001D160, 0x0001D158},
-{0x0001D160, 0x0001D165}, {0x0001D160, 0x0001D16E}, {0x0001D161, 0x0001D158}, {0x0001D161, 0x0001D165},
-{0x0001D161, 0x0001D16F}, {0x0001D162, 0x0001D158}, {0x0001D162, 0x0001D165}, {0x0001D162, 0x0001D170},
-{0x0001D163, 0x0001D158}, {0x0001D163, 0x0001D165}, {0x0001D163, 0x0001D171}, {0x0001D164, 0x0001D158},
-{0x0001D164, 0x0001D165}, {0x0001D164, 0x0001D172}, {0x0001D1BB, 0x0001D1B9}, {0x0001D1BB, 0x0001D165},
-{0x0001D1BC, 0x0001D1BA}, {0x0001D1BC, 0x0001D165}, {0x0001D1BD, 0x0001D1B9}, {0x0001D1BD, 0x0001D165},
-{0x0001D1BD, 0x0001D16E}, {0x0001D1BE, 0x0001D1BA}, {0x0001D1BE, 0x0001D165}, {0x0001D1BE, 0x0001D16E},
-{0x0001D1BF, 0x0001D1B9}, {0x0001D1BF, 0x0001D165}, {0x0001D1BF, 0x0001D16F}, {0x0001D1C0, 0x0001D1BA},
-{0x0001D1C0, 0x0001D165}, {0x0001D1C0, 0x0001D16F}, {0x0002F800, 0x00004E3D}, {0x0002F801, 0x00004E38},
-{0x0002F802, 0x00004E41}, {0x0002F803, 0x00020122}, {0x0002F804, 0x00004F60}, {0x0002F805, 0x00004FAE},
-{0x0002F806, 0x00004FBB}, {0x0002F807, 0x00005002}, {0x0002F808, 0x0000507A}, {0x0002F809, 0x00005099},
-{0x0002F80A, 0x000050E7}, {0x0002F80B, 0x000050CF}, {0x0002F80C, 0x0000349E}, {0x0002F80D, 0x0002063A},
-{0x0002F80E, 0x0000514D}, {0x0002F80F, 0x00005154}, {0x0002F810, 0x00005164}, {0x0002F811, 0x00005177},
-{0x0002F812, 0x0002051C}, {0x0002F813, 0x000034B9}, {0x0002F814, 0x00005167}, {0x0002F815, 0x0000518D},
-{0x0002F816, 0x0002054B}, {0x0002F817, 0x00005197}, {0x0002F818, 0x000051A4}, {0x0002F819, 0x00004ECC},
-{0x0002F81A, 0x000051AC}, {0x0002F81B, 0x000051B5}, {0x0002F81C, 0x000291DF}, {0x0002F81D, 0x000051F5},
-{0x0002F81E, 0x00005203}, {0x0002F81F, 0x000034DF}, {0x0002F820, 0x0000523B}, {0x0002F821, 0x00005246},
-{0x0002F822, 0x00005272}, {0x0002F823, 0x00005277}, {0x0002F824, 0x00003515}, {0x0002F825, 0x000052C7},
-{0x0002F826, 0x000052C9}, {0x0002F827, 0x000052E4}, {0x0002F828, 0x000052FA}, {0x0002F829, 0x00005305},
-{0x0002F82A, 0x00005306}, {0x0002F82B, 0x00005317}, {0x0002F82C, 0x00005349}, {0x0002F82D, 0x00005351},
-{0x0002F82E, 0x0000535A}, {0x0002F82F, 0x00005373}, {0x0002F830, 0x0000537D}, {0x0002F831, 0x0000537F},
-{0x0002F832, 0x0000537F}, {0x0002F833, 0x0000537F}, {0x0002F834, 0x00020A2C}, {0x0002F835, 0x00007070},
-{0x0002F836, 0x000053CA}, {0x0002F837, 0x000053DF}, {0x0002F838, 0x00020B63}, {0x0002F839, 0x000053EB},
-{0x0002F83A, 0x000053F1}, {0x0002F83B, 0x00005406}, {0x0002F83C, 0x0000549E}, {0x0002F83D, 0x00005438},
-{0x0002F83E, 0x00005448}, {0x0002F83F, 0x00005468}, {0x0002F840, 0x000054A2}, {0x0002F841, 0x000054F6},
-{0x0002F842, 0x00005510}, {0x0002F843, 0x00005553}, {0x0002F844, 0x00005563}, {0x0002F845, 0x00005584},
-{0x0002F846, 0x00005584}, {0x0002F847, 0x00005599}, {0x0002F848, 0x000055AB}, {0x0002F849, 0x000055B3},
-{0x0002F84A, 0x000055C2}, {0x0002F84B, 0x00005716}, {0x0002F84C, 0x00005606}, {0x0002F84D, 0x00005717},
-{0x0002F84E, 0x00005651}, {0x0002F84F, 0x00005674}, {0x0002F850, 0x00005207}, {0x0002F851, 0x000058EE},
-{0x0002F852, 0x000057CE}, {0x0002F853, 0x000057F4}, {0x0002F854, 0x0000580D}, {0x0002F855, 0x0000578B},
-{0x0002F856, 0x00005832}, {0x0002F857, 0x00005831}, {0x0002F858, 0x000058AC}, {0x0002F859, 0x000214E4},
-{0x0002F85A, 0x000058F2}, {0x0002F85B, 0x000058F7}, {0x0002F85C, 0x00005906}, {0x0002F85D, 0x0000591A},
-{0x0002F85E, 0x00005922}, {0x0002F85F, 0x00005962}, {0x0002F860, 0x000216A8}, {0x0002F861, 0x000216EA},
-{0x0002F862, 0x000059EC}, {0x0002F863, 0x00005A1B}, {0x0002F864, 0x00005A27}, {0x0002F865, 0x000059D8},
-{0x0002F866, 0x00005A66}, {0x0002F867, 0x000036EE}, {0x0002F868, 0x000036FC}, {0x0002F869, 0x00005B08},
-{0x0002F86A, 0x00005B3E}, {0x0002F86B, 0x00005B3E}, {0x0002F86C, 0x000219C8}, {0x0002F86D, 0x00005BC3},
-{0x0002F86E, 0x00005BD8}, {0x0002F86F, 0x00005BE7}, {0x0002F870, 0x00005BF3}, {0x0002F871, 0x00021B18},
-{0x0002F872, 0x00005BFF}, {0x0002F873, 0x00005C06}, {0x0002F874, 0x00005F53}, {0x0002F875, 0x00005C22},
-{0x0002F876, 0x00003781}, {0x0002F877, 0x00005C60}, {0x0002F878, 0x00005C6E}, {0x0002F879, 0x00005CC0},
-{0x0002F87A, 0x00005C8D}, {0x0002F87B, 0x00021DE4}, {0x0002F87C, 0x00005D43}, {0x0002F87D, 0x00021DE6},
-{0x0002F87E, 0x00005D6E}, {0x0002F87F, 0x00005D6B}, {0x0002F880, 0x00005D7C}, {0x0002F881, 0x00005DE1},
-{0x0002F882, 0x00005DE2}, {0x0002F883, 0x0000382F}, {0x0002F884, 0x00005DFD}, {0x0002F885, 0x00005E28},
-{0x0002F886, 0x00005E3D}, {0x0002F887, 0x00005E69}, {0x0002F888, 0x00003862}, {0x0002F889, 0x00022183},
-{0x0002F88A, 0x0000387C}, {0x0002F88B, 0x00005EB0}, {0x0002F88C, 0x00005EB3}, {0x0002F88D, 0x00005EB6},
-{0x0002F88E, 0x00005ECA}, {0x0002F88F, 0x0002A392}, {0x0002F890, 0x00005EFE}, {0x0002F891, 0x00022331},
-{0x0002F892, 0x00022331}, {0x0002F893, 0x00008201}, {0x0002F894, 0x00005F22}, {0x0002F895, 0x00005F22},
-{0x0002F896, 0x000038C7}, {0x0002F897, 0x000232B8}, {0x0002F898, 0x000261DA}, {0x0002F899, 0x00005F62},
-{0x0002F89A, 0x00005F6B}, {0x0002F89B, 0x000038E3}, {0x0002F89C, 0x00005F9A}, {0x0002F89D, 0x00005FCD},
-{0x0002F89E, 0x00005FD7}, {0x0002F89F, 0x00005FF9}, {0x0002F8A0, 0x00006081}, {0x0002F8A1, 0x0000393A},
-{0x0002F8A2, 0x0000391C}, {0x0002F8A3, 0x00006094}, {0x0002F8A4, 0x000226D4}, {0x0002F8A5, 0x000060C7},
-{0x0002F8A6, 0x00006148}, {0x0002F8A7, 0x0000614C}, {0x0002F8A8, 0x0000614E}, {0x0002F8A9, 0x0000614C},
-{0x0002F8AA, 0x0000617A}, {0x0002F8AB, 0x0000618E}, {0x0002F8AC, 0x000061B2}, {0x0002F8AD, 0x000061A4},
-{0x0002F8AE, 0x000061AF}, {0x0002F8AF, 0x000061DE}, {0x0002F8B0, 0x000061F2}, {0x0002F8B1, 0x000061F6},
-{0x0002F8B2, 0x00006210}, {0x0002F8B3, 0x0000621B}, {0x0002F8B4, 0x0000625D}, {0x0002F8B5, 0x000062B1},
-{0x0002F8B6, 0x000062D4}, {0x0002F8B7, 0x00006350}, {0x0002F8B8, 0x00022B0C}, {0x0002F8B9, 0x0000633D},
-{0x0002F8BA, 0x000062FC}, {0x0002F8BB, 0x00006368}, {0x0002F8BC, 0x00006383}, {0x0002F8BD, 0x000063E4},
-{0x0002F8BE, 0x00022BF1}, {0x0002F8BF, 0x00006422}, {0x0002F8C0, 0x000063C5}, {0x0002F8C1, 0x000063A9},
-{0x0002F8C2, 0x00003A2E}, {0x0002F8C3, 0x00006469}, {0x0002F8C4, 0x0000647E}, {0x0002F8C5, 0x0000649D},
-{0x0002F8C6, 0x00006477}, {0x0002F8C7, 0x00003A6C}, {0x0002F8C8, 0x0000654F}, {0x0002F8C9, 0x0000656C},
-{0x0002F8CA, 0x0002300A}, {0x0002F8CB, 0x000065E3}, {0x0002F8CC, 0x000066F8}, {0x0002F8CD, 0x00006649},
-{0x0002F8CE, 0x00003B19}, {0x0002F8CF, 0x00006691}, {0x0002F8D0, 0x00003B08}, {0x0002F8D1, 0x00003AE4},
-{0x0002F8D2, 0x00005192}, {0x0002F8D3, 0x00005195}, {0x0002F8D4, 0x00006700}, {0x0002F8D5, 0x0000669C},
-{0x0002F8D6, 0x000080AD}, {0x0002F8D7, 0x000043D9}, {0x0002F8D8, 0x00006717}, {0x0002F8D9, 0x0000671B},
-{0x0002F8DA, 0x00006721}, {0x0002F8DB, 0x0000675E}, {0x0002F8DC, 0x00006753}, {0x0002F8DD, 0x000233C3},
-{0x0002F8DE, 0x00003B49}, {0x0002F8DF, 0x000067FA}, {0x0002F8E0, 0x00006785}, {0x0002F8E1, 0x00006852},
-{0x0002F8E2, 0x00006885}, {0x0002F8E3, 0x0002346D}, {0x0002F8E4, 0x0000688E}, {0x0002F8E5, 0x0000681F},
-{0x0002F8E6, 0x00006914}, {0x0002F8E7, 0x00003B9D}, {0x0002F8E8, 0x00006942}, {0x0002F8E9, 0x000069A3},
-{0x0002F8EA, 0x000069EA}, {0x0002F8EB, 0x00006AA8}, {0x0002F8EC, 0x000236A3}, {0x0002F8ED, 0x00006ADB},
-{0x0002F8EE, 0x00003C18}, {0x0002F8EF, 0x00006B21}, {0x0002F8F0, 0x000238A7}, {0x0002F8F1, 0x00006B54},
-{0x0002F8F2, 0x00003C4E}, {0x0002F8F3, 0x00006B72}, {0x0002F8F4, 0x00006B9F}, {0x0002F8F5, 0x00006BBA},
-{0x0002F8F6, 0x00006BBB}, {0x0002F8F7, 0x00023A8D}, {0x0002F8F8, 0x00021D0B}, {0x0002F8F9, 0x00023AFA},
-{0x0002F8FA, 0x00006C4E}, {0x0002F8FB, 0x00023CBC}, {0x0002F8FC, 0x00006CBF}, {0x0002F8FD, 0x00006CCD},
-{0x0002F8FE, 0x00006C67}, {0x0002F8FF, 0x00006D16}, {0x0002F900, 0x00006D3E}, {0x0002F901, 0x00006D77},
-{0x0002F902, 0x00006D41}, {0x0002F903, 0x00006D69}, {0x0002F904, 0x00006D78}, {0x0002F905, 0x00006D85},
-{0x0002F906, 0x00023D1E}, {0x0002F907, 0x00006D34}, {0x0002F908, 0x00006E2F}, {0x0002F909, 0x00006E6E},
-{0x0002F90A, 0x00003D33}, {0x0002F90B, 0x00006ECB}, {0x0002F90C, 0x00006EC7}, {0x0002F90D, 0x00023ED1},
-{0x0002F90E, 0x00006DF9}, {0x0002F90F, 0x00006F6E}, {0x0002F910, 0x00023F5E}, {0x0002F911, 0x00023F8E},
-{0x0002F912, 0x00006FC6}, {0x0002F913, 0x00007039}, {0x0002F914, 0x0000701E}, {0x0002F915, 0x0000701B},
-{0x0002F916, 0x00003D96}, {0x0002F917, 0x0000704A}, {0x0002F918, 0x0000707D}, {0x0002F919, 0x00007077},
-{0x0002F91A, 0x000070AD}, {0x0002F91B, 0x00020525}, {0x0002F91C, 0x00007145}, {0x0002F91D, 0x00024263},
-{0x0002F91E, 0x0000719C}, {0x0002F91F, 0x000243AB}, {0x0002F920, 0x00007228}, {0x0002F921, 0x00007235},
-{0x0002F922, 0x00007250}, {0x0002F923, 0x00024608}, {0x0002F924, 0x00007280}, {0x0002F925, 0x00007295},
-{0x0002F926, 0x00024735}, {0x0002F927, 0x00024814}, {0x0002F928, 0x0000737A}, {0x0002F929, 0x0000738B},
-{0x0002F92A, 0x00003EAC}, {0x0002F92B, 0x000073A5}, {0x0002F92C, 0x00003EB8}, {0x0002F92D, 0x00003EB8},
-{0x0002F92E, 0x00007447}, {0x0002F92F, 0x0000745C}, {0x0002F930, 0x00007471}, {0x0002F931, 0x00007485},
-{0x0002F932, 0x000074CA}, {0x0002F933, 0x00003F1B}, {0x0002F934, 0x00007524}, {0x0002F935, 0x00024C36},
-{0x0002F936, 0x0000753E}, {0x0002F937, 0x00024C92}, {0x0002F938, 0x00007570}, {0x0002F939, 0x0002219F},
-{0x0002F93A, 0x00007610}, {0x0002F93B, 0x00024FA1}, {0x0002F93C, 0x00024FB8}, {0x0002F93D, 0x00025044},
-{0x0002F93E, 0x00003FFC}, {0x0002F93F, 0x00004008}, {0x0002F940, 0x000076F4}, {0x0002F941, 0x000250F3},
-{0x0002F942, 0x000250F2}, {0x0002F943, 0x00025119}, {0x0002F944, 0x00025133}, {0x0002F945, 0x0000771E},
-{0x0002F946, 0x0000771F}, {0x0002F947, 0x0000771F}, {0x0002F948, 0x0000774A}, {0x0002F949, 0x00004039},
-{0x0002F94A, 0x0000778B}, {0x0002F94B, 0x00004046}, {0x0002F94C, 0x00004096}, {0x0002F94D, 0x0002541D},
-{0x0002F94E, 0x0000784E}, {0x0002F94F, 0x0000788C}, {0x0002F950, 0x000078CC}, {0x0002F951, 0x000040E3},
-{0x0002F952, 0x00025626}, {0x0002F953, 0x00007956}, {0x0002F954, 0x0002569A}, {0x0002F955, 0x000256C5},
-{0x0002F956, 0x0000798F}, {0x0002F957, 0x000079EB}, {0x0002F958, 0x0000412F}, {0x0002F959, 0x00007A40},
-{0x0002F95A, 0x00007A4A}, {0x0002F95B, 0x00007A4F}, {0x0002F95C, 0x0002597C}, {0x0002F95D, 0x00025AA7},
-{0x0002F95E, 0x00025AA7}, {0x0002F95F, 0x00007AEE}, {0x0002F960, 0x00004202}, {0x0002F961, 0x00025BAB},
-{0x0002F962, 0x00007BC6}, {0x0002F963, 0x00007BC9}, {0x0002F964, 0x00004227}, {0x0002F965, 0x00025C80},
-{0x0002F966, 0x00007CD2}, {0x0002F967, 0x000042A0}, {0x0002F968, 0x00007CE8}, {0x0002F969, 0x00007CE3},
-{0x0002F96A, 0x00007D00}, {0x0002F96B, 0x00025F86}, {0x0002F96C, 0x00007D63}, {0x0002F96D, 0x00004301},
-{0x0002F96E, 0x00007DC7}, {0x0002F96F, 0x00007E02}, {0x0002F970, 0x00007E45}, {0x0002F971, 0x00004334},
-{0x0002F972, 0x00026228}, {0x0002F973, 0x00026247}, {0x0002F974, 0x00004359}, {0x0002F975, 0x000262D9},
-{0x0002F976, 0x00007F7A}, {0x0002F977, 0x0002633E}, {0x0002F978, 0x00007F95}, {0x0002F979, 0x00007FFA},
-{0x0002F97A, 0x00008005}, {0x0002F97B, 0x000264DA}, {0x0002F97C, 0x00026523}, {0x0002F97D, 0x00008060},
-{0x0002F97E, 0x000265A8}, {0x0002F97F, 0x00008070}, {0x0002F980, 0x0002335F}, {0x0002F981, 0x000043D5},
-{0x0002F982, 0x000080B2}, {0x0002F983, 0x00008103}, {0x0002F984, 0x0000440B}, {0x0002F985, 0x0000813E},
-{0x0002F986, 0x00005AB5}, {0x0002F987, 0x000267A7}, {0x0002F988, 0x000267B5}, {0x0002F989, 0x00023393},
-{0x0002F98A, 0x0002339C}, {0x0002F98B, 0x00008201}, {0x0002F98C, 0x00008204}, {0x0002F98D, 0x00008F9E},
-{0x0002F98E, 0x0000446B}, {0x0002F98F, 0x00008291}, {0x0002F990, 0x0000828B}, {0x0002F991, 0x0000829D},
-{0x0002F992, 0x000052B3}, {0x0002F993, 0x000082B1}, {0x0002F994, 0x000082B3}, {0x0002F995, 0x000082BD},
-{0x0002F996, 0x000082E6}, {0x0002F997, 0x00026B3C}, {0x0002F998, 0x000082E5}, {0x0002F999, 0x0000831D},
-{0x0002F99A, 0x00008363}, {0x0002F99B, 0x000083AD}, {0x0002F99C, 0x00008323}, {0x0002F99D, 0x000083BD},
-{0x0002F99E, 0x000083E7}, {0x0002F99F, 0x00008457}, {0x0002F9A0, 0x00008353}, {0x0002F9A1, 0x000083CA},
-{0x0002F9A2, 0x000083CC}, {0x0002F9A3, 0x000083DC}, {0x0002F9A4, 0x00026C36}, {0x0002F9A5, 0x00026D6B},
-{0x0002F9A6, 0x00026CD5}, {0x0002F9A7, 0x0000452B}, {0x0002F9A8, 0x000084F1}, {0x0002F9A9, 0x000084F3},
-{0x0002F9AA, 0x00008516}, {0x0002F9AB, 0x000273CA}, {0x0002F9AC, 0x00008564}, {0x0002F9AD, 0x00026F2C},
-{0x0002F9AE, 0x0000455D}, {0x0002F9AF, 0x00004561}, {0x0002F9B0, 0x00026FB1}, {0x0002F9B1, 0x000270D2},
-{0x0002F9B2, 0x0000456B}, {0x0002F9B3, 0x00008650}, {0x0002F9B4, 0x0000865C}, {0x0002F9B5, 0x00008667},
-{0x0002F9B6, 0x00008669}, {0x0002F9B7, 0x000086A9}, {0x0002F9B8, 0x00008688}, {0x0002F9B9, 0x0000870E},
-{0x0002F9BA, 0x000086E2}, {0x0002F9BB, 0x00008779}, {0x0002F9BC, 0x00008728}, {0x0002F9BD, 0x0000876B},
-{0x0002F9BE, 0x00008786}, {0x0002F9BF, 0x000045D7}, {0x0002F9C0, 0x000087E1}, {0x0002F9C1, 0x00008801},
-{0x0002F9C2, 0x000045F9}, {0x0002F9C3, 0x00008860}, {0x0002F9C4, 0x00008863}, {0x0002F9C5, 0x00027667},
-{0x0002F9C6, 0x000088D7}, {0x0002F9C7, 0x000088DE}, {0x0002F9C8, 0x00004635}, {0x0002F9C9, 0x000088FA},
-{0x0002F9CA, 0x000034BB}, {0x0002F9CB, 0x000278AE}, {0x0002F9CC, 0x00027966}, {0x0002F9CD, 0x000046BE},
-{0x0002F9CE, 0x000046C7}, {0x0002F9CF, 0x00008AA0}, {0x0002F9D0, 0x00008AED}, {0x0002F9D1, 0x00008B8A},
-{0x0002F9D2, 0x00008C55}, {0x0002F9D3, 0x00027CA8}, {0x0002F9D4, 0x00008CAB}, {0x0002F9D5, 0x00008CC1},
-{0x0002F9D6, 0x00008D1B}, {0x0002F9D7, 0x00008D77}, {0x0002F9D8, 0x00027F2F}, {0x0002F9D9, 0x00020804},
-{0x0002F9DA, 0x00008DCB}, {0x0002F9DB, 0x00008DBC}, {0x0002F9DC, 0x00008DF0}, {0x0002F9DD, 0x000208DE},
-{0x0002F9DE, 0x00008ED4}, {0x0002F9DF, 0x00008F38}, {0x0002F9E0, 0x000285D2}, {0x0002F9E1, 0x000285ED},
-{0x0002F9E2, 0x00009094}, {0x0002F9E3, 0x000090F1}, {0x0002F9E4, 0x00009111}, {0x0002F9E5, 0x0002872E},
-{0x0002F9E6, 0x0000911B}, {0x0002F9E7, 0x00009238}, {0x0002F9E8, 0x000092D7}, {0x0002F9E9, 0x000092D8},
-{0x0002F9EA, 0x0000927C}, {0x0002F9EB, 0x000093F9}, {0x0002F9EC, 0x00009415}, {0x0002F9ED, 0x00028BFA},
-{0x0002F9EE, 0x0000958B}, {0x0002F9EF, 0x00004995}, {0x0002F9F0, 0x000095B7}, {0x0002F9F1, 0x00028D77},
-{0x0002F9F2, 0x000049E6}, {0x0002F9F3, 0x000096C3}, {0x0002F9F4, 0x00005DB2}, {0x0002F9F5, 0x00009723},
-{0x0002F9F6, 0x00029145}, {0x0002F9F7, 0x0002921A}, {0x0002F9F8, 0x00004A6E}, {0x0002F9F9, 0x00004A76},
-{0x0002F9FA, 0x000097E0}, {0x0002F9FB, 0x0002940A}, {0x0002F9FC, 0x00004AB2}, {0x0002F9FD, 0x00029496},
-{0x0002F9FE, 0x0000980B}, {0x0002F9FF, 0x0000980B}, {0x0002FA00, 0x00009829}, {0x0002FA01, 0x000295B6},
-{0x0002FA02, 0x000098E2}, {0x0002FA03, 0x00004B33}, {0x0002FA04, 0x00009929}, {0x0002FA05, 0x000099A7},
-{0x0002FA06, 0x000099C2}, {0x0002FA07, 0x000099FE}, {0x0002FA08, 0x00004BCE}, {0x0002FA09, 0x00029B30},
-{0x0002FA0A, 0x00009B12}, {0x0002FA0B, 0x00009C40}, {0x0002FA0C, 0x00009CFD}, {0x0002FA0D, 0x00004CCE},
-{0x0002FA0E, 0x00004CED}, {0x0002FA0F, 0x00009D67}, {0x0002FA10, 0x0002A0CE}, {0x0002FA11, 0x00004CF8},
-{0x0002FA12, 0x0002A105}, {0x0002FA13, 0x0002A20E}, {0x0002FA14, 0x0002A291}, {0x0002FA15, 0x00009EBB},
-{0x0002FA16, 0x00004D56}, {0x0002FA17, 0x00009EF9}, {0x0002FA18, 0x00009EFE}, {0x0002FA19, 0x00009F05},
-{0x0002FA1A, 0x00009F0F}, {0x0002FA1B, 0x00009F16}, {0x0002FA1D, 0x0002A600},
-};
diff --git a/examples/talk-llama/unicode-data.h b/examples/talk-llama/unicode-data.h
index 3cccf20..e27fe17 100644
--- a/examples/talk-llama/unicode-data.h
+++ b/examples/talk-llama/unicode-data.h
@@ -1,17 +1,20 @@
 #pragma once
 
 #include <cstdint>
-#include <map>
-#include <utility>
 #include <vector>
+#include <unordered_map>
+#include <unordered_set>
 
-extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_number;
-extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_letter;
-extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_separator;
-extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_whitespace;
-extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_accent_mark;
-extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_punctuation;
-extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_symbol;
-extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_control;
-extern const std::multimap<uint32_t, uint32_t>          unicode_map_nfd;
-extern const std::map<char32_t, char32_t>               unicode_map_lowercase;
+struct range_nfd {
+    uint32_t first;
+    uint32_t last;
+    uint32_t nfd;
+};
+
+static const uint32_t MAX_CODEPOINTS = 0x110000;
+
+extern const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags;
+extern const std::unordered_set<uint32_t> unicode_set_whitespace;
+extern const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase;
+extern const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase;
+extern const std::vector<range_nfd> unicode_ranges_nfd;
diff --git a/examples/talk-llama/unicode.cpp b/examples/talk-llama/unicode.cpp
index ca03c49..056a4c7 100644
--- a/examples/talk-llama/unicode.cpp
+++ b/examples/talk-llama/unicode.cpp
@@ -1,4 +1,4 @@
-#include "unicode.h"
+#include "unicode.h"
 #include "unicode-data.h"
 
 #include <cassert>
@@ -109,57 +109,49 @@ static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset)
 //    return result;
 //}
 
-static std::unordered_map<uint32_t, int> unicode_cpt_type_map() {
-    std::unordered_map<uint32_t, int> cpt_types;
-    for (auto p : unicode_ranges_number) {
-        for (auto i = p.first; i <= p.second; ++i) {
-            cpt_types[i] = CODEPOINT_TYPE_NUMBER;
+static std::vector<codepoint_flags> unicode_cpt_flags_array() {
+    std::vector<codepoint_flags> cpt_flags(MAX_CODEPOINTS, codepoint_flags::UNDEFINED);
+
+    assert (unicode_ranges_flags.front().first == 0);
+    assert (unicode_ranges_flags.back().first == MAX_CODEPOINTS);
+    for (size_t i = 1; i < unicode_ranges_flags.size(); ++i) {
+        const auto range_ini = unicode_ranges_flags[i-1];  // codepoint_ini, flags
+        const auto range_end = unicode_ranges_flags[i];    // codepoint_end, flags
+        for (uint32_t cpt = range_ini.first; cpt < range_end.first; ++cpt) {
+            cpt_flags[cpt] = range_ini.second;
         }
     }
-    for (auto p : unicode_ranges_letter) {
-        for (auto i = p.first; i <= p.second; ++i) {
-            cpt_types[i] = CODEPOINT_TYPE_LETTER;
-        }
+
+    for (auto cpt : unicode_set_whitespace) {
+        cpt_flags[cpt].is_whitespace = true;
     }
-    for (auto p : unicode_ranges_separator) {
-        for (auto i = p.first; i <= p.second; ++i) {
-            cpt_types[i] = CODEPOINT_TYPE_SEPARATOR;
-        }
+
+    for (auto p : unicode_map_lowercase) {
+        cpt_flags[p.second].is_lowercase = true;
     }
-    for (auto p : unicode_ranges_accent_mark) {
-        for (auto i = p.first; i <= p.second; ++i) {
-            cpt_types[i] = CODEPOINT_TYPE_ACCENT_MARK;
-        }
+
+    for (auto p : unicode_map_uppercase) {
+        cpt_flags[p.second].is_uppercase = true;
     }
-    for (auto p : unicode_ranges_punctuation) {
-        for (auto i = p.first; i <= p.second; ++i) {
-            cpt_types[i] = CODEPOINT_TYPE_PUNCTUATION;
-        }
+
+    for (auto &range : unicode_ranges_nfd) {  // start, last, nfd
+        cpt_flags[range.nfd].is_nfd = true;
     }
-    for  (auto p : unicode_ranges_symbol) {
-        for (auto i = p.first; i <= p.second; ++i) {
-            cpt_types[i] = CODEPOINT_TYPE_SYMBOL;
-        }
-    }
-    for (auto p : unicode_ranges_control) {
-        for (auto i = p.first; i <= p.second; ++i) {
-            cpt_types[i] = CODEPOINT_TYPE_CONTROL;
-        }
-    }
-    return cpt_types;
+
+    return cpt_flags;
 }
 
 static std::unordered_map<uint8_t, std::string> unicode_byte_to_utf8_map() {
     std::unordered_map<uint8_t, std::string> map;
-    for (int ch = u'!'; ch <= u'~'; ++ch) {
+    for (int ch = 0x21; ch <= 0x7E; ++ch) {  // u'!' to u'~'
         assert(0 <= ch && ch < 256);
         map[ch] = unicode_cpt_to_utf8(ch);
     }
-    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
+    for (int ch = 0xA1; ch <= 0xAC; ++ch) {  // u'¡' to u'¬'
         assert(0 <= ch && ch < 256);
         map[ch] = unicode_cpt_to_utf8(ch);
     }
-    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
+    for (int ch = 0xAE; ch <= 0xFF; ++ch) {  // u'®' to u'ÿ'
         assert(0 <= ch && ch < 256);
         map[ch] = unicode_cpt_to_utf8(ch);
     }
@@ -175,15 +167,15 @@ static std::unordered_map<uint8_t, std::string> unicode_byte_to_utf8_map() {
 
 static std::unordered_map<std::string, uint8_t> unicode_utf8_to_byte_map() {
     std::unordered_map<std::string, uint8_t> map;
-    for (int ch = u'!'; ch <= u'~'; ++ch) {
+    for (int ch = 0x21; ch <= 0x7E; ++ch) {  // u'!' to u'~'
         assert(0 <= ch && ch < 256);
         map[unicode_cpt_to_utf8(ch)] = ch;
     }
-    for (int ch = u'¡'; ch <= u'¬'; ++ch) {
+    for (int ch = 0xA1; ch <= 0xAC; ++ch) {  // u'¡' to u'¬'
         assert(0 <= ch && ch < 256);
         map[unicode_cpt_to_utf8(ch)] = ch;
     }
-    for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
+    for (int ch = 0xAE; ch <= 0xFF; ++ch) {  // u'®' to u'ÿ'
         assert(0 <= ch && ch < 256);
         map[unicode_cpt_to_utf8(ch)] = ch;
     }
@@ -238,8 +230,9 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t
             return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0;
         };
 
-        auto _get_cpt_type = [&] (const size_t pos) -> int {
-            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED;
+        auto _get_flags = [&] (const size_t pos) -> codepoint_flags {
+            static const codepoint_flags undef(codepoint_flags::UNDEFINED);
+            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef;
         };
 
         size_t _prev_end = offset_ini;
@@ -261,7 +254,7 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t
 
         for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
             const char32_t cpt = _get_cpt(pos);
-            const int cpt_type = _get_cpt_type(pos);
+            const auto flags = _get_flags(pos);
 
             // regex: 's|'t|'re|'ve|'m|'ll|'d
             if (cpt == '\'' && pos+1 < offset_end) {
@@ -281,39 +274,37 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t
                 }
             }
 
-            char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt);
-            int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type);
+            auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags);
             // regex: <space>?\p{L}+
-            if (cpt2_type == CODEPOINT_TYPE_LETTER) {
+            if (flags2.is_letter) {
                 pos += (cpt == ' ');
-                while (cpt2_type == CODEPOINT_TYPE_LETTER) {
-                    cpt2_type = _get_cpt_type(++pos);
+                while (flags2.is_letter) {
+                    flags2 = _get_flags(++pos);
                 }
                 _add_token(pos);
                 continue;
             }
             // regex: <space>?\p{N}+
-            if (cpt2_type == CODEPOINT_TYPE_NUMBER) {
+            if (flags2.is_number) {
                 pos += (cpt == ' ');
-                while (cpt2_type == CODEPOINT_TYPE_NUMBER) {
-                    cpt2_type = _get_cpt_type(++pos);
+                while (flags2.is_number) {
+                    flags2 = _get_flags(++pos);
                 }
                 _add_token(pos);
                 continue;
             }
             // regex: <space>?[^\s\p{L}\p{N}]+
-            if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) {
+            if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
                 pos += (cpt == ' ');
-                while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) {
-                    cpt2_type = _get_cpt_type(++pos);
-                    cpt2 = _get_cpt(pos);
+                while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
+                    flags2 = _get_flags(++pos);
                 }
                 _add_token(pos);
                 continue;
             }
 
             size_t num_whitespaces = 0;
-            while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) {
+            while (_get_flags(pos+num_whitespaces).is_whitespace) {
                 num_whitespaces++;
             }
 
@@ -357,8 +348,9 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
             return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0;
         };
 
-        auto _get_cpt_type = [&] (const size_t pos) -> int {
-            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED;
+        auto _get_flags = [&] (const size_t pos) -> codepoint_flags {
+            static const codepoint_flags undef(codepoint_flags::UNDEFINED);
+            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef;
         };
 
         size_t _prev_end = offset_ini;
@@ -380,7 +372,7 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
 
         for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
             const char32_t cpt = _get_cpt(pos);
-            const int cpt_type = _get_cpt_type(pos);
+            const auto flags = _get_flags(pos);
 
             // regex: (?i:'s|'t|'re|'ve|'m|'ll|'d) // case insensitive
             if (cpt == '\'' && pos+1 < offset_end) {
@@ -401,10 +393,10 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
             }
 
             // regex: [^\r\n\p{L}\p{N}]?\p{L}+  //####FIXME: the first \p{L} is correct?
-            if (cpt != '\r' && cpt != '\n' && /*cpt_type != CODEPOINT_TYPE_LETTER &&*/ cpt_type != CODEPOINT_TYPE_NUMBER) {
-                if (cpt_type == CODEPOINT_TYPE_LETTER || _get_cpt_type(pos+1) == CODEPOINT_TYPE_LETTER) {  // one or more letters
+            if (!(cpt == '\r' || cpt == '\n' || /*flags.is_letter |*/ flags.is_number)) {
+                if (flags.is_letter || _get_flags(pos+1).is_letter) {  // one or more letters
                     pos++;
-                    while (_get_cpt_type(pos) == CODEPOINT_TYPE_LETTER) {
+                    while (_get_flags(pos).is_letter) {
                         pos++;
                     }
                     _add_token(pos);
@@ -413,9 +405,9 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
             }
 
             // regex: \p{N}{1,3}
-            if (cpt_type == CODEPOINT_TYPE_NUMBER) {
+            if (flags.is_number) {
                 size_t ini = pos;
-                while (_get_cpt_type(pos) == CODEPOINT_TYPE_NUMBER) {
+                while (_get_flags(pos).is_number) {
                     if (++pos - ini >= 3 ) {
                         _add_token(pos);
                         ini = pos;
@@ -426,14 +418,13 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
             }
 
             // regex: <space>?[^\s\p{L}\p{N}]+[\r\n]*
-            char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt);
-            int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type);
-            if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) {
+            auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags);
+            if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
                 pos += (cpt == ' ');
-                while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) {
-                    cpt2_type = _get_cpt_type(++pos);
-                    cpt2 = _get_cpt(pos);
+                while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
+                    flags2 = _get_flags(++pos);
                 }
+                char32_t cpt2 = _get_cpt(pos);
                 while (cpt2 == '\r' || cpt2 == '\n') {
                     cpt2 = _get_cpt(++pos);
                 }
@@ -443,7 +434,7 @@ static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string &
 
             size_t num_whitespaces = 0;
             size_t last_end_r_or_n = 0;
-            while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) {
+            while (_get_flags(pos+num_whitespaces).is_whitespace) {
                 char32_t cpt2 = _get_cpt(pos+num_whitespaces);
                 if (cpt2 == '\r' || cpt2 == '\n') {
                     last_end_r_or_n = pos + num_whitespaces + 1;
@@ -589,15 +580,14 @@ std::string unicode_cpt_to_utf8(uint32_t cp) {
 }
 
 std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts) {
-    std::vector<uint32_t> result;
-    result.reserve(cpts.size());
+    auto comp = [] (const uint32_t cpt, const range_nfd & range) {
+        return cpt < range.first;
+    };
+    std::vector<uint32_t> result(cpts.size());
     for (size_t i = 0; i < cpts.size(); ++i) {
-        auto it = unicode_map_nfd.find(cpts[i]);
-        if (it == unicode_map_nfd.end()) {
-            result.push_back(cpts[i]);
-        } else {
-            result.push_back(it->second);
-        }
+        const uint32_t cpt = cpts[i];
+        auto it = std::upper_bound(unicode_ranges_nfd.cbegin(), unicode_ranges_nfd.cend(), cpt, comp) - 1;
+        result[i] = (it->first <= cpt && cpt <= it->last) ? it->nfd : cpt;
     }
     return result;
 }
@@ -611,31 +601,19 @@ std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8) {
     return result;
 }
 
-int unicode_cpt_type(uint32_t cp) {
-    static std::unordered_map<uint32_t, int> cpt_types = unicode_cpt_type_map();
-    const auto it = cpt_types.find(cp);
-    return it == cpt_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : it->second;
+codepoint_flags unicode_cpt_flags(const uint32_t cp) {
+    static const codepoint_flags undef(codepoint_flags::UNDEFINED);
+    static const auto cpt_flags = unicode_cpt_flags_array();
+    return cp < cpt_flags.size() ? cpt_flags[cp] : undef;
 }
 
-int unicode_cpt_type(const std::string & utf8) {
-    if (utf8.length() == 0) {
-        return CODEPOINT_TYPE_UNIDENTIFIED;
+codepoint_flags unicode_cpt_flags(const std::string & utf8) {
+    static const codepoint_flags undef(codepoint_flags::UNDEFINED);
+    if (utf8.empty()) {
+        return undef;  // undefined
     }
     size_t offset = 0;
-    return unicode_cpt_type(unicode_cpt_from_utf8(utf8, offset));
-}
-
-bool unicode_cpt_is_whitespace(uint32_t cp) {
-    static const std::unordered_set<uint32_t> is_whitespace = [] {
-        std::unordered_set<uint32_t> is_whitespace;
-        for (auto p : unicode_ranges_whitespace) {
-            for (auto i = p.first; i <= p.second; ++i) {
-                is_whitespace.insert(i);
-            }
-        }
-        return is_whitespace;
-    }();
-    return (bool)is_whitespace.count(cp);
+    return unicode_cpt_flags(unicode_cpt_from_utf8(utf8, offset));
 }
 
 std::string unicode_byte_to_utf8(uint8_t byte) {
@@ -656,21 +634,21 @@ char32_t unicode_tolower(char32_t cp) {
 std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs) {
     // unicode categories
     static const std::map<std::string, int> k_ucat_enum = {
-        { "\\p{N}", CODEPOINT_TYPE_NUMBER },
-        { "\\p{L}", CODEPOINT_TYPE_LETTER },
-        { "\\p{P}", CODEPOINT_TYPE_PUNCTUATION },
+        { "\\p{N}", codepoint_flags::NUMBER },
+        { "\\p{L}", codepoint_flags::LETTER },
+        { "\\p{P}", codepoint_flags::PUNCTUATION },
     };
 
     static const std::map<int, int> k_ucat_cpt = {
-        { CODEPOINT_TYPE_NUMBER,        0xD1 },
-        { CODEPOINT_TYPE_LETTER,        0xD2 },
-        { CODEPOINT_TYPE_PUNCTUATION,   0xD3 },
+        { codepoint_flags::NUMBER,        0xD1 },
+        { codepoint_flags::LETTER,        0xD2 },
+        { codepoint_flags::PUNCTUATION,   0xD3 },
     };
 
     static const std::map<int, std::string> k_ucat_map = {
-        { CODEPOINT_TYPE_NUMBER,        "\x30-\x39" }, // 0-9
-        { CODEPOINT_TYPE_LETTER,        "\x41-\x5A\x61-\x7A" }, // A-Za-z
-        { CODEPOINT_TYPE_PUNCTUATION,   "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\}
+        { codepoint_flags::NUMBER,        "\x30-\x39" }, // 0-9
+        { codepoint_flags::LETTER,        "\x41-\x5A\x61-\x7A" }, // A-Za-z
+        { codepoint_flags::PUNCTUATION,   "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\}
     };
 
     // compute collapsed codepoints only if needed by at least one regex
@@ -701,10 +679,10 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
                 continue;
             }
 
-            const int cpt_type = unicode_cpt_type(cpts[i]);
+            const int cpt_flag = unicode_cpt_flags(cpts[i]).category_flag();
 
-            if (k_ucat_cpt.find(cpt_type) != k_ucat_cpt.end()) {
-                text_collapsed[i] = k_ucat_cpt.at(cpt_type);
+            if (k_ucat_cpt.find(cpt_flag) != k_ucat_cpt.end()) {
+                text_collapsed[i] = k_ucat_cpt.at(cpt_flag);
             } else {
                 text_collapsed[i] = (char) 0xD0; // fallback
             }
diff --git a/examples/talk-llama/unicode.h b/examples/talk-llama/unicode.h
index d6a14d4..7513be4 100644
--- a/examples/talk-llama/unicode.h
+++ b/examples/talk-llama/unicode.h
@@ -4,24 +4,56 @@
 #include <string>
 #include <vector>
 
-#define CODEPOINT_TYPE_UNIDENTIFIED 0
-#define CODEPOINT_TYPE_NUMBER       1
-#define CODEPOINT_TYPE_LETTER       2
-#define CODEPOINT_TYPE_SEPARATOR    3
-#define CODEPOINT_TYPE_ACCENT_MARK  4
-#define CODEPOINT_TYPE_PUNCTUATION  5
-#define CODEPOINT_TYPE_SYMBOL       6
-#define CODEPOINT_TYPE_CONTROL      7
+struct codepoint_flags {
+    enum {
+        UNDEFINED       = 0x0001,
+        NUMBER          = 0x0002,  // regex: \p{N}
+        LETTER          = 0x0004,  // regex: \p{L}
+        SEPARATOR       = 0x0008,  // regex: \p{Z}
+        ACCENT_MARK     = 0x0010,  // regex: \p{M}
+        PUNCTUATION     = 0x0020,  // regex: \p{P}
+        SYMBOL          = 0x0040,  // regex: \p{S}
+        CONTROL         = 0x0080,  // regex: \p{C}
+        MASK_CATEGORIES = 0x00FF,
+    };
+
+    // codepoint type
+    uint16_t is_undefined   : 1;
+    uint16_t is_number      : 1;  // regex: \p{N}
+    uint16_t is_letter      : 1;  // regex: \p{L}
+    uint16_t is_separator   : 1;  // regex: \p{Z}
+    uint16_t is_accent_mark : 1;  // regex: \p{M}
+    uint16_t is_punctuation : 1;  // regex: \p{P}
+    uint16_t is_symbol      : 1;  // regex: \p{S}
+    uint16_t is_control     : 1;  // regex: \p{C}
+    // helper flags
+    uint16_t is_whitespace  : 1;  // regex: \s
+    uint16_t is_lowercase   : 1;
+    uint16_t is_uppercase   : 1;
+    uint16_t is_nfd         : 1;
+
+    // decode from uint16
+    inline codepoint_flags(const uint16_t flags=0) {
+        *reinterpret_cast<uint16_t*>(this) = flags;
+    }
+
+    inline uint16_t as_uint() const {
+        return *reinterpret_cast<const uint16_t*>(this);
+    }
+
+    inline uint16_t category_flag() const {
+        return this->as_uint() & MASK_CATEGORIES;
+    }
+};
+
 
 std::string unicode_cpt_to_utf8(uint32_t cp);
 std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8);
 
 std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts);
 
-int unicode_cpt_type(uint32_t cp);
-int unicode_cpt_type(const std::string & utf8);
-
-bool unicode_cpt_is_whitespace(uint32_t cp);
+codepoint_flags unicode_cpt_flags(const uint32_t cp);
+codepoint_flags unicode_cpt_flags(const std::string & utf8);
 
 std::string unicode_byte_to_utf8(uint8_t byte);
 uint8_t unicode_utf8_to_byte(const std::string & utf8);