


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]    ):
     # This defines a "chinese character" as anything in the CJK Unicode block:
     #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
     #
     # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
     # despite its name. The modern Korean Hangul alphabet is a different block,
     # as is Japanese Hiragana and Katakana. Those alphabets are used to write
     # space-separated words, so they are not treated specially and handled
     # like the all of the other languages.
     if (
         (cp >= 0X4e00 and cp <= 0X9fff)
         or (cp >= 0X3400 and cp <= 0X4dbf)  #
         or (cp >= 0X2_0000 and cp <= 0X2_a6df)  #
         or (cp >= 0X2_a700 and cp <= 0X2_b73f)  #
         or (cp >= 0X2_b740 and cp <= 0X2_b81f)  #
         or (cp >= 0X2_b820 and cp <= 0X2_ceaf)  #
         or (cp >= 0Xf900 and cp <= 0Xfaff)
         or (cp >= 0X2_f800 and cp <= 0X2_fa1f)  #
     ):  #
          return True

     return False

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str    ):
     # word like '180' or '身高' or '神'
     for char in word:
          __UpperCamelCase   							=ord(SCREAMING_SNAKE_CASE__    )
          if not _is_chinese_char(SCREAMING_SNAKE_CASE__    ):
               return 0
     return 1

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]    ):
     __UpperCamelCase   							=set()

     for token in tokens:
          __UpperCamelCase   							=len(SCREAMING_SNAKE_CASE__    ) > 1 and is_chinese(SCREAMING_SNAKE_CASE__    )
          if chinese_word:
               word_set.add(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=list(SCREAMING_SNAKE_CASE__    )
     return word_list

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : set()    ):
     if not chinese_word_set:
          return bert_tokens
     __UpperCamelCase   							=max([len(SCREAMING_SNAKE_CASE__    ) for w in chinese_word_set]    )

     __UpperCamelCase   							=bert_tokens
     __UpperCamelCase							,				__UpperCamelCase   							=0, len(SCREAMING_SNAKE_CASE__    )
     while start < end:
          __UpperCamelCase   							=True
          if is_chinese(bert_word[start]    ):
               __UpperCamelCase   							=min(end - start      ,					SCREAMING_SNAKE_CASE__    )
               for i in range(SCREAMING_SNAKE_CASE__      ,					1      ,					-1    ):
                    __UpperCamelCase   							=''.join(bert_word[start : start + i]    )
                    if whole_word in chinese_word_set:
                         for j in range(start + 1      ,					start + i    ):
                              __UpperCamelCase   							='##' + bert_word[j]
                         __UpperCamelCase   							=start + i
                         __UpperCamelCase   							=False
                         break
          if single_word:
               start += 1
     return bert_word

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : LTP      ,					SCREAMING_SNAKE_CASE__      : BertTokenizer    ):
     __UpperCamelCase   							=[]

     for i in range(0      ,					len(SCREAMING_SNAKE_CASE__    )      ,					1_00    ):
          __UpperCamelCase   							=ltp_tokenizer.seg(lines[i : i + 1_00]    )[0]
          __UpperCamelCase   							=[get_chinese_word(SCREAMING_SNAKE_CASE__    ) for r in res]
          ltp_res.extend(SCREAMING_SNAKE_CASE__    )
     assert len(SCREAMING_SNAKE_CASE__    ) == len(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=[]
     for i in range(0      ,					len(SCREAMING_SNAKE_CASE__    )      ,					1_00    ):
          __UpperCamelCase   							=bert_tokenizer(lines[i : i + 1_00]      ,					add_special_tokens=SCREAMING_SNAKE_CASE__      ,					truncation=SCREAMING_SNAKE_CASE__      ,					max_length=5_12    )
          bert_res.extend(res['input_ids']    )
     assert len(SCREAMING_SNAKE_CASE__    ) == len(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=[]
     for input_ids, chinese_word in zip(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
          __UpperCamelCase   							=[]
          for id in input_ids:
               __UpperCamelCase   							=bert_tokenizer._convert_id_to_token(SCREAMING_SNAKE_CASE__    )
               input_tokens.append(SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=add_sub_symbol(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=[]
          # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
          for i, token in enumerate(SCREAMING_SNAKE_CASE__    ):
               if token[:2] == "##":
                    __UpperCamelCase   							=token[2:]
                    # save chinese tokens' pos
                    if len(SCREAMING_SNAKE_CASE__    ) == 1 and _is_chinese_char(ord(SCREAMING_SNAKE_CASE__    )    ):
                         ref_id.append(SCREAMING_SNAKE_CASE__    )
          ref_ids.append(SCREAMING_SNAKE_CASE__    )

     assert len(SCREAMING_SNAKE_CASE__    ) == len(SCREAMING_SNAKE_CASE__    )

     return ref_ids

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]    ):
     # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
     # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
     with open(args.file_name      ,					'r'      ,					encoding='utf-8'    ) as f:
          __UpperCamelCase   							=f.readlines()
     __UpperCamelCase   							=[line.strip() for line in data if len(SCREAMING_SNAKE_CASE__    ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
     __UpperCamelCase   							=LTP(args.ltp    )  # faster in GPU device
     __UpperCamelCase   							=BertTokenizer.from_pretrained(args.bert    )

     __UpperCamelCase   							=prepare_ref(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

     with open(args.save_path      ,					'w'      ,					encoding='utf-8'    ) as f:
          __UpperCamelCase   							=[json.dumps(SCREAMING_SNAKE_CASE__    ) + '\n' for ref in ref_ids]
          f.writelines(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
   _A			=						argparse.ArgumentParser(description='prepare_chinese_ref')
   parser.add_argument(
       '--file_name',
       type=str,
       default='./resources/chinese-demo.txt',
       help='file need process, same as training data in lm',
   )
   parser.add_argument(
       '--ltp', type=str, default='./resources/ltp', help='resources for LTP tokenizer, usually a path'
   )
   parser.add_argument('--bert', type=str, default='./resources/robert', help='resources for Bert tokenizer')
   parser.add_argument('--save_path', type=str, default='./resources/ref.txt', help='path to save res')

   _A			=						parser.parse_args()
   main(args)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_A			=						{'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						['ViTFeatureExtractor']
   _A			=						['ViTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTForImageClassification',
       'ViTForMaskedImageModeling',
       'ViTModel',
       'ViTPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'TFViTForImageClassification',
       'TFViTModel',
       'TFViTPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'FlaxViTForImageClassification',
       'FlaxViTModel',
       'FlaxViTPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vit import ViTFeatureExtractor
      from .image_processing_vit import ViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit import (
          VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
          ViTModel,
          ViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
   import sys

   _A			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





