



'''simple docstring'''





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def lowerCAmelCase  (__A):



     """simple docstring"""



     if (
         (cp >= 0x4_E00 and cp <= 0x9_FFF)
         or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
         or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
         or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
         or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
         or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
         or (cp >= 0xF_900 and cp <= 0xF_AFF)
         or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
     ):  #
          return True

     return False




def lowerCAmelCase  (__A):



     """simple docstring"""



     for char in word:
          _a      =  ord(__A)
          if not _is_chinese_char(__A):
               return 0
     return 1




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  set()

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




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     if not chinese_word_set:
          return bert_tokens
     _a      =  max([len(__A) for w in chinese_word_set])

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




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  []

     for i in range(0  ,	len(__A)  ,	100):
          _a      =  ltp_tokenizer.pipeline(lines[i : i + 100]  ,	tasks=['''cws''']).cws
          _a      =  [get_chinese_word(__A) for r in res]
          ltp_res.extend(__A)
     assert len(__A) == len(__A)

     _a      =  []
     for i in range(0  ,	len(__A)  ,	100):
          _a      =  bert_tokenizer(lines[i : i + 100]  ,	add_special_tokens=__A  ,	truncation=__A  ,	max_length=512)
          bert_res.extend(res['''input_ids'''])
     assert len(__A) == len(__A)

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

     assert len(__A) == len(__A)

     return ref_ids




def lowerCAmelCase  (__A):



     """simple docstring"""



     with open(args.file_name  ,	'''r'''  ,	encoding='''utf-8''') as f:
          _a      =  f.readlines()
     _a      =  [line.strip() for line in data if len(__A) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
     _a      =  LTP(args.ltp)  # faster in GPU device
     _a      =  BertTokenizer.from_pretrained(args.bert)

     _a      =  prepare_ref(__A  ,	__A  ,	__A)

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


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

  lowercase_		       =  parser.parse_args()
  main(args)








'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Tuple          =       'resnet'
 __lowerCamelCase    :       Any          =       ['basic', 'bottleneck']



 def __init__(self ,   A=3 ,   A=64 ,   A=[256, 512, 1_024, 2_048] ,   A=[3, 4, 6, 3] ,   A="bottleneck" ,   A="relu" ,   A=False ,   A=None ,   A=None ,   **A ,   )    ->     Dict:


      """simple docstring"""



      super().__init__(**A       )
      if layer_type not in self.layer_types:
           raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types       )}'''       )
      _a      =  num_channels
      _a      =  embedding_size
      _a      =  hidden_sizes
      _a      =  depths
      _a      =  layer_type
      _a      =  hidden_act
      _a      =  downsample_in_first_stage
      _a      =  ['''stem'''] + [f'''stage{idx}''' for idx in range(1 ,   len(A       ) + 1       )]
      _a  ,    _a      =  get_aligned_output_features_output_indices(
          out_features=A ,   out_indices=A ,   stage_names=self.stage_names       )






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       version.parse('1.11'						)



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
          ]       )



 @property
 def 					a__  (self       )    ->     float:


      """simple docstring"""



      return 1E-3



