

import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def    A_  (							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    A_  (							a  ):

      """simple docstring"""



      for char in word:
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     ord(a  )
            if not _is_chinese_char(a  ):
                  return 0
      return 1

def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Any           =     set()

      for token in tokens:
            SCREAMING_SNAKE_CASE_				:  Dict           =     len(a  ) > 1 and is_chinese(a  )
            if chinese_word:
                  word_set.add(a  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     list(a  )
      return word_list

def    A_  (							a					,   a  ):

      """simple docstring"""



      if not chinese_word_set:
            return bert_tokens
      SCREAMING_SNAKE_CASE_				:  Any           =     max([len(a  ) for w in chinese_word_set]  )

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

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     []

      for i in range(0					,   len(a  )					,   1_0_0  ):
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     ltp_tokenizer.pipeline(lines[i : i + 1_0_0]					,   tasks=['cws']  ).cws
            SCREAMING_SNAKE_CASE_				:  Dict           =     [get_chinese_word(a  ) for r in res]
            ltp_res.extend(a  )
      assert len(a  ) == len(a  )

      SCREAMING_SNAKE_CASE_				:  int           =     []
      for i in range(0					,   len(a  )					,   1_0_0  ):
            SCREAMING_SNAKE_CASE_				:  List[str]           =     bert_tokenizer(lines[i : i + 1_0_0]					,   add_special_tokens=a					,   truncation=a					,   max_length=5_1_2  )
            bert_res.extend(res['input_ids']  )
      assert len(a  ) == len(a  )

      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     []
      for input_ids, chinese_word in zip(a					,   a  ):
            SCREAMING_SNAKE_CASE_				:  Tuple           =     []
            for id in input_ids:
                  SCREAMING_SNAKE_CASE_				:  Tuple           =     bert_tokenizer._convert_id_to_token(a  )
                  input_tokens.append(a  )
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     add_sub_symbol(a					,   a  )
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     []
            # 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] == "##":
                        SCREAMING_SNAKE_CASE_				:  Any           =     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    A_  (							a  ):

      """simple docstring"""



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

      SCREAMING_SNAKE_CASE_				:  Any           =     prepare_ref(a					,   a					,   a  )

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


if __name__ == "__main__":
    lowerCAmelCase     :   Dict						     =  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',
    )

    lowerCAmelCase     :   List[str]						     =  parser.parse_args()
    main(args)









import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase     :   Optional[int]						     =  logging.get_logger(__name__)

lowerCAmelCase     :   str						     =  {
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  '''git_vision_model'''

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=768 ,					_SCREAMING_SNAKE_CASE=3072 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=224 ,					_SCREAMING_SNAKE_CASE=16 ,					_SCREAMING_SNAKE_CASE="quick_gelu" ,					_SCREAMING_SNAKE_CASE=1e-5 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.02 ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        super().__init__(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     hidden_size
        SCREAMING_SNAKE_CASE_				:  Any           =     intermediate_size
        SCREAMING_SNAKE_CASE_				:  List[str]           =     num_hidden_layers
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     num_attention_heads
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     num_channels
        SCREAMING_SNAKE_CASE_				:  Dict           =     patch_size
        SCREAMING_SNAKE_CASE_				:  Any           =     image_size
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     initializer_range
        SCREAMING_SNAKE_CASE_				:  Any           =     attention_dropout
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     layer_norm_eps
        SCREAMING_SNAKE_CASE_				:  Tuple           =     hidden_act



  @classmethod
  def  UpperCAmelCase    (				cls ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  str           =     cls.get_config_dict(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

        # get the vision config dict if we are loading from GITConfig
        if config_dict.get('model_type'					) == "git":
              SCREAMING_SNAKE_CASE_				:  str           =     config_dict['vision_config']

        if "model_type" in config_dict and hasattr(cls ,					'model_type'					) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                  f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."					)

        return cls.from_dict(_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  '''git'''

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=3_0522 ,					_SCREAMING_SNAKE_CASE=768 ,					_SCREAMING_SNAKE_CASE=6 ,					_SCREAMING_SNAKE_CASE=12 ,					_SCREAMING_SNAKE_CASE=3072 ,					_SCREAMING_SNAKE_CASE="gelu" ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=1024 ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=1e-12 ,					_SCREAMING_SNAKE_CASE=0 ,					_SCREAMING_SNAKE_CASE="absolute" ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=101 ,					_SCREAMING_SNAKE_CASE=102 ,					_SCREAMING_SNAKE_CASE=None ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        super().__init__(bos_token_id=_SCREAMING_SNAKE_CASE ,					eos_token_id=_SCREAMING_SNAKE_CASE ,					pad_token_id=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

        if vision_config is None:
              SCREAMING_SNAKE_CASE_				:  Any           =     {}
              logger.info('vision_config is None. initializing the GitVisionConfig with default values.'					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     GitVisionConfig(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     vocab_size
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     hidden_size
        SCREAMING_SNAKE_CASE_				:  Dict           =     num_hidden_layers
        SCREAMING_SNAKE_CASE_				:  List[str]           =     num_attention_heads
        SCREAMING_SNAKE_CASE_				:  List[str]           =     hidden_act
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     intermediate_size
        SCREAMING_SNAKE_CASE_				:  Any           =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_				:  Tuple           =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_				:  int           =     max_position_embeddings
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     initializer_range
        SCREAMING_SNAKE_CASE_				:  int           =     layer_norm_eps
        SCREAMING_SNAKE_CASE_				:  List[str]           =     position_embedding_type
        SCREAMING_SNAKE_CASE_				:  List[str]           =     use_cache
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tie_word_embeddings
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     num_image_with_embedding

        SCREAMING_SNAKE_CASE_				:  Tuple           =     bos_token_id
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     eos_token_id



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     copy.deepcopy(self.__dict__					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.vision_config.to_dict()
        SCREAMING_SNAKE_CASE_				:  int           =     self.__class__.model_type
        return output






