




'''simple docstring'''


import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
  pass

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


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}





class    __lowercase						(  __magic_name__ ):
   _a							=      """align_text_model"""







   def __init__(	self      ,							UpperCamelCase=3_0522      ,							UpperCamelCase=768      ,							UpperCamelCase=12      ,							UpperCamelCase=12      ,							UpperCamelCase=3072      ,							UpperCamelCase="gelu"      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.1      ,							UpperCamelCase=512      ,							UpperCamelCase=2      ,							UpperCamelCase=0.02      ,							UpperCamelCase=1e-12      ,							UpperCamelCase=0      ,							UpperCamelCase="absolute"      ,							UpperCamelCase=True      ,							**UpperCamelCase      ,							)				->    Tuple:
     super().__init__(**UpperCamelCase      )

     __a			=					vocab_size
     __a			=					hidden_size
     __a			=					num_hidden_layers
     __a			=					num_attention_heads
     __a			=					hidden_act
     __a			=					intermediate_size
     __a			=					hidden_dropout_prob
     __a			=					attention_probs_dropout_prob
     __a			=					max_position_embeddings
     __a			=					type_vocab_size
     __a			=					initializer_range
     __a			=					layer_norm_eps
     __a			=					position_embedding_type
     __a			=					use_cache
     __a			=					pad_token_id







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    "PretrainedConfig":
     cls._set_token_in_kwargs(UpperCamelCase      )

     __a	,       __a			=					cls.get_config_dict(UpperCamelCase      ,							**UpperCamelCase      )

     # get the text config dict if we are loading from AlignConfig
     if config_dict.get('model_type'      ) == "align":
       __a			=					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(UpperCamelCase      ,							**UpperCamelCase      )





class    __lowercase						(  __magic_name__ ):
   _a							=      """align_vision_model"""







   def __init__(	self      ,							UpperCamelCase = 3      ,							UpperCamelCase = 600      ,							UpperCamelCase = 2.0      ,							UpperCamelCase = 3.1      ,							UpperCamelCase = 8      ,							UpperCamelCase = [3, 3, 5, 3, 5, 5, 3]      ,							UpperCamelCase = [32, 16, 24, 40, 80, 112, 192]      ,							UpperCamelCase = [16, 24, 40, 80, 112, 192, 320]      ,							UpperCamelCase = []      ,							UpperCamelCase = [1, 2, 2, 2, 1, 2, 1]      ,							UpperCamelCase = [1, 2, 2, 3, 3, 4, 1]      ,							UpperCamelCase = [1, 6, 6, 6, 6, 6, 6]      ,							UpperCamelCase = 0.25      ,							UpperCamelCase = "swish"      ,							UpperCamelCase = 2560      ,							UpperCamelCase = "mean"      ,							UpperCamelCase = 0.02      ,							UpperCamelCase = 0.001      ,							UpperCamelCase = 0.99      ,							UpperCamelCase = 0.2      ,							**UpperCamelCase      ,							)				->    int:
     super().__init__(**UpperCamelCase      )

     __a			=					num_channels
     __a			=					image_size
     __a			=					width_coefficient
     __a			=					depth_coefficient
     __a			=					depth_divisor
     __a			=					kernel_sizes
     __a			=					in_channels
     __a			=					out_channels
     __a			=					depthwise_padding
     __a			=					strides
     __a			=					num_block_repeats
     __a			=					expand_ratios
     __a			=					squeeze_expansion_ratio
     __a			=					hidden_act
     __a			=					hidden_dim
     __a			=					pooling_type
     __a			=					initializer_range
     __a			=					batch_norm_eps
     __a			=					batch_norm_momentum
     __a			=					drop_connect_rate
     __a			=					sum(UpperCamelCase      ) * 4







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    "PretrainedConfig":
     cls._set_token_in_kwargs(UpperCamelCase      )

     __a	,       __a			=					cls.get_config_dict(UpperCamelCase      ,							**UpperCamelCase      )

     # get the vision config dict if we are loading from AlignConfig
     if config_dict.get('model_type'      ) == "align":
       __a			=					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(UpperCamelCase      ,							**UpperCamelCase      )





class    __lowercase						(  __magic_name__ ):
   _a							=      """align"""
   _a							=      True







   def __init__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=640      ,							UpperCamelCase=1.0      ,							UpperCamelCase=0.02      ,							**UpperCamelCase      ,							)				->    int:
     super().__init__(**UpperCamelCase      )

     if text_config is None:
       __a			=					{}
       logger.info('text_config is None. Initializing the AlignTextConfig with default values.'      )

     if vision_config is None:
       __a			=					{}
       logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'      )

     __a			=					AlignTextConfig(**UpperCamelCase      )
     __a			=					AlignVisionConfig(**UpperCamelCase      )

     __a			=					projection_dim
     __a			=					temperature_init_value
     __a			=					initializer_range







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    Union[str, Any]:
     return cls(text_config=text_config.to_dict()      ,							vision_config=vision_config.to_dict()      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					copy.deepcopy(self.__dict__      )
     __a			=					self.text_config.to_dict()
     __a			=					self.vision_config.to_dict()
     __a			=					self.__class__.model_type
     return output









'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  # 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 >= 0X20000 and cp <= 0X2a6df)  #
      or (cp >= 0X2a700 and cp <= 0X2b73f)  #
      or (cp >= 0X2b740 and cp <= 0X2b81f)  #
      or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
      or (cp >= 0Xf900 and cp <= 0Xfaff)
      or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
  ):  #
    return True

  return False


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


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]			):
  __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 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							set()			):
  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 						SCREAMING_SNAKE_CASE       (      a_	:							List[str]				,					a_	:							LTP				,					a_	:							BertTokenizer			):
  __a			=					[]

  for i in range(0				,					len(a_			)				,					100			):
    __a			=					ltp_tokenizer.seg(lines[i : i + 100]			)[0]
    __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 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  # 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			=					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__":
  UpperCAmelCase_ 		=      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")

  UpperCAmelCase_ 		=      parser.parse_args()
  main(args)



