


'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def      lowerCamelCase					(      lowerCamelCase  :      Optional[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 >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
          or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F)  #
          or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F)  #
          or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F)  #
          or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F)  #
          or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F)  #
          or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
          or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F)  #
      ):  #
            return True

      return False




def      lowerCamelCase					(      lowerCamelCase  :      str):
      # word like '180' or '身高' or '神'
      for char in word:
            A_						:  Optional[Any]          =				ord(lowerCamelCase)
            if not _is_chinese_char(lowerCamelCase):
                  return 0
      return 1




def      lowerCamelCase					(      lowerCamelCase  :      List[str]):
      A_						:  Any          =				set()

      for token in tokens:
            A_						:  str          =				len(lowerCamelCase) > 1 and is_chinese(lowerCamelCase)
            if chinese_word:
                  word_set.add(lowerCamelCase)
      A_						:  Any          =				list(lowerCamelCase)
      return word_list




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      set()):
      if not chinese_word_set:
            return bert_tokens
      A_						:  Any          =				max([len(lowerCamelCase) for w in chinese_word_set])

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




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      LTP							,						lowerCamelCase  :      BertTokenizer):
      A_						:  Union[str, Any]          =				[]

      for i in range(0							,						len(lowerCamelCase)							,						100):
            A_						:  List[Any]          =				ltp_tokenizer.pipeline(lines[i : i + 100]							,						tasks=["""cws"""]).cws
            A_						:  int          =				[get_chinese_word(lowerCamelCase) for r in res]
            ltp_res.extend(lowerCamelCase)
      assert len(lowerCamelCase) == len(lowerCamelCase)

      A_						:  List[Any]          =				[]
      for i in range(0							,						len(lowerCamelCase)							,						100):
            A_						:  Dict          =				bert_tokenizer(lines[i : i + 100]							,						add_special_tokens=lowerCamelCase							,						truncation=lowerCamelCase							,						max_length=512)
            bert_res.extend(res["""input_ids"""])
      assert len(lowerCamelCase) == len(lowerCamelCase)

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

      assert len(lowerCamelCase) == len(lowerCamelCase)

      return ref_ids




def      lowerCamelCase					(      lowerCamelCase  :      Tuple):
      # 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:
            A_						:  Optional[int]          =				f.readlines()
      A_						:  Union[str, Any]          =				[line.strip() for line in data if len(lowerCamelCase) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      A_						:  Optional[Any]          =				LTP(args.ltp)  # faster in GPU device
      A_						:  Dict          =				BertTokenizer.from_pretrained(args.bert)

      A_						:  str          =				prepare_ref(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)

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


if __name__ == "__main__":
     __magic_name__			=      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',
     )

     __magic_name__			=      parser.parse_args()
     main(args)




'''simple docstring'''


import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
     from ...processing_utils import ProcessorMixin
     from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""owlvit_text_model"""






  def __init__(							self :       Union[str, Any]	,_a :       Any=49408	,_a :       Any=512	,_a :       Tuple=2048	,_a :       Dict=12	,_a :       Optional[int]=8	,_a :       Tuple=16	,_a :       Tuple="quick_gelu"	,_a :       Optional[Any]=1e-5	,_a :       List[Any]=0.0	,_a :       Optional[int]=0.02	,_a :       Dict=1.0	,_a :       Dict=0	,_a :       Any=49406	,_a :       Tuple=49407	,**_a :       List[Any]	,):

        '''simple docstring'''




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

        A_						:  Tuple          =				vocab_size
        A_						:  int          =				hidden_size
        A_						:  Optional[int]          =				intermediate_size
        A_						:  Optional[int]          =				num_hidden_layers
        A_						:  Union[str, Any]          =				num_attention_heads
        A_						:  int          =				max_position_embeddings
        A_						:  str          =				hidden_act
        A_						:  Union[str, Any]          =				layer_norm_eps
        A_						:  Tuple          =				attention_dropout
        A_						:  Union[str, Any]          =				initializer_range
        A_						:  List[Any]          =				initializer_factor






  @classmethod
  def        _a							(							cls :       List[str]	,_a :       Union[str, os.PathLike]	,**_a :       str ):

        '''simple docstring'''




        cls._set_token_in_kwargs(_a )

        A_   ,					A_						:  int          =				cls.get_config_dict(_a	,**_a )

        # get the text config dict if we are loading from OwlViTConfig
        if config_dict.get("""model_type""" ) == "owlvit":
              A_						:  Union[str, Any]          =				config_dict["""text_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(_a	,**_a )







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""owlvit_vision_model"""






  def __init__(							self :       List[Any]	,_a :       Optional[Any]=768	,_a :       Tuple=3072	,_a :       Dict=12	,_a :       int=12	,_a :       Dict=3	,_a :       Tuple=768	,_a :       int=32	,_a :       int="quick_gelu"	,_a :       List[Any]=1e-5	,_a :       Tuple=0.0	,_a :       List[Any]=0.02	,_a :       str=1.0	,**_a :       int	,):

        '''simple docstring'''




        super().__init__(**_a )

        A_						:  List[str]          =				hidden_size
        A_						:  Union[str, Any]          =				intermediate_size
        A_						:  Union[str, Any]          =				num_hidden_layers
        A_						:  Optional[Any]          =				num_attention_heads
        A_						:  int          =				num_channels
        A_						:  str          =				image_size
        A_						:  List[Any]          =				patch_size
        A_						:  int          =				hidden_act
        A_						:  List[Any]          =				layer_norm_eps
        A_						:  List[str]          =				attention_dropout
        A_						:  str          =				initializer_range
        A_						:  str          =				initializer_factor






  @classmethod
  def        _a							(							cls :       List[Any]	,_a :       Union[str, os.PathLike]	,**_a :       str ):

        '''simple docstring'''




        cls._set_token_in_kwargs(_a )

        A_   ,					A_						:  Optional[int]          =				cls.get_config_dict(_a	,**_a )

        # get the vision config dict if we are loading from OwlViTConfig
        if config_dict.get("""model_type""" ) == "owlvit":
              A_						:  List[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(_a	,**_a )







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""owlvit"""
  a_  					=							True






  def __init__(							self :       Union[str, Any]	,_a :       List[str]=None	,_a :       List[str]=None	,_a :       Dict=512	,_a :       List[Any]=2.6592	,_a :       Optional[Any]=True	,**_a :       Optional[int]	,):

        '''simple docstring'''




        super().__init__(**_a )

        if text_config is None:
              A_						:  List[Any]          =				{}
              logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )

        if vision_config is None:
              A_						:  Tuple          =				{}
              logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )

        A_						:  Dict          =				OwlViTTextConfig(**_a )
        A_						:  Dict          =				OwlViTVisionConfig(**_a )

        A_						:  Any          =				projection_dim
        A_						:  Optional[int]          =				logit_scale_init_value
        A_						:  Optional[int]          =				return_dict
        A_						:  Dict          =				1.0






  @classmethod
  def        _a							(							cls :       Union[str, Any]	,_a :       Union[str, os.PathLike]	,**_a :       Optional[int] ):

        '''simple docstring'''




        cls._set_token_in_kwargs(_a )

        A_   ,					A_						:  List[Any]          =				cls.get_config_dict(_a	,**_a )

        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(_a	,**_a )






  @classmethod
  def        _a							(							cls :       int	,_a :       Dict	,_a :       Dict	,**_a :       List[str] ):

        '''simple docstring'''




        A_						:  str          =				{}
        A_						:  int          =				text_config
        A_						:  Union[str, Any]          =				vision_config

        return cls.from_dict(_a	,**_a )






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        A_						:  Dict          =				copy.deepcopy(self.__dict__ )
        A_						:  str          =				self.text_config.to_dict()
        A_						:  Optional[int]          =				self.vision_config.to_dict()
        A_						:  List[Any]          =				self.__class__.model_type
        return output







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       int ):

        '''simple docstring'''




        return OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
            ] )






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return OrderedDict(
            [
                ("""logits_per_image""", {0: """batch"""}),
                ("""logits_per_text""", {0: """batch"""}),
                ("""text_embeds""", {0: """batch"""}),
                ("""image_embeds""", {0: """batch"""}),
            ] )






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return 1e-4






  def        _a							(							self :       int	,_a :       "ProcessorMixin"	,_a :       int = -1	,_a :       int = -1	,_a :       Optional["TensorType"] = None	,):

        '''simple docstring'''




        A_						:  Any          =				super().generate_dummy_inputs(
            processor.tokenizer	,batch_size=_a	,seq_length=_a	,framework=_a )
        A_						:  Any          =				super().generate_dummy_inputs(
            processor.image_processor	,batch_size=_a	,framework=_a )
        return {**text_input_dict, **image_input_dict}






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return 14
