


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 # 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__						):
 # word like '180' or '身高' or '神'
 for char in word:
  __lowerCamelCase		:							Union[str, Any]		 =						ord(SCREAMING_SNAKE_CASE__						)
  if not _is_chinese_char(SCREAMING_SNAKE_CASE__						):
   return 0
 return 1
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Union[str, Any]		 =						set()

 for token in tokens:
  __lowerCamelCase		:							Any		 =						len(SCREAMING_SNAKE_CASE__						) > 1 and is_chinese(SCREAMING_SNAKE_CASE__						)
  if chinese_word:
   word_set.add(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Any		 =						list(SCREAMING_SNAKE_CASE__						)
 return word_list
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 if not chinese_word_set:
  return bert_tokens
 __lowerCamelCase		:							Any		 =						max([len(SCREAMING_SNAKE_CASE__						) for w in chinese_word_set]						)

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

 for i in range(0      ,	len(SCREAMING_SNAKE_CASE__						)      ,	100						):
  __lowerCamelCase		:							List[Any]		 =						ltp_tokenizer.seg(lines[i : i + 100]						)[0]
  __lowerCamelCase		:							List[Any]		 =						[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__						)

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

 __lowerCamelCase		:							str		 =						[]
 for input_ids, chinese_word in zip(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							int		 =						[]
  for id in input_ids:
   __lowerCamelCase		:							Any		 =						bert_tokenizer._convert_id_to_token(SCREAMING_SNAKE_CASE__						)
   input_tokens.append(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Union[str, Any]		 =						add_sub_symbol(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							List[Any]		 =						[]
  # 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] == "##":
    __lowerCamelCase		:							List[str]		 =						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__						):
 # 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:
  __lowerCamelCase		:							Optional[Any]		 =						f.readlines()
 __lowerCamelCase		:							Optional[Any]		 =						[line.strip() for line in data if len(SCREAMING_SNAKE_CASE__						) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
 __lowerCamelCase		:							int		 =						LTP(args.ltp						)  # faster in GPU device
 __lowerCamelCase		:							Dict		 =						BertTokenizer.from_pretrained(args.bert						)

 __lowerCamelCase		:							Tuple		 =						prepare_ref(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

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


if __name__ == "__main__":
       lowercase_	   =     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')

       lowercase_	   =     parser.parse_args()
       main(args)





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
       from ... import PreTrainedTokenizerBase, TensorType

lowercase_	   =     logging.get_logger(__name__)


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """vision-encoder-decoder"""
      __snake_case 	=       True




      def __init__(    self:	Union[str, Any]						,				**a:	Optional[Any]    ):
       super().__init__(**a    )
       if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            F'A configuraton of type {self.model_type} cannot be instantiated because '
            F'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'    )

       __lowerCamelCase		:							Dict		 =						kwargs.pop('encoder'    )
       __lowerCamelCase		:							int		 =						encoder_config.pop('model_type'    )
       __lowerCamelCase		:							Any		 =						kwargs.pop('decoder'    )
       __lowerCamelCase		:							Union[str, Any]		 =						decoder_config.pop('model_type'    )

       __lowerCamelCase		:							Optional[Any]		 =						AutoConfig.for_model(a						,				**a    )
       __lowerCamelCase		:							List[Any]		 =						AutoConfig.for_model(a						,				**a    )
       __lowerCamelCase		:							Tuple		 =						True




      @classmethod
      def 					_snake_case     (    cls:	Optional[Any]						,				a:	PretrainedConfig						,				a:	PretrainedConfig						,				**a:	Dict    ):
       logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'    )
       __lowerCamelCase		:							Optional[Any]		 =						True
       __lowerCamelCase		:							Optional[Any]		 =						True

       return cls(encoder=encoder_config.to_dict()						,				decoder=decoder_config.to_dict()						,				**a    )




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							int		 =						copy.deepcopy(self.__dict__    )
       __lowerCamelCase		:							Dict		 =						self.encoder.to_dict()
       __lowerCamelCase		:							Union[str, Any]		 =						self.decoder.to_dict()
       __lowerCamelCase		:							Optional[int]		 =						self.__class__.model_type
       return output



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       version.parse("""1.11"""   )




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]    )




      @property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return 1e-4




      @property
      def 					_snake_case     (    self:	List[str]    ):
       return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase		:							Union[str, Any]		 =						OrderedDict()
       __lowerCamelCase		:							Union[str, Any]		 =						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
       __lowerCamelCase		:							Optional[Any]		 =						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
       __lowerCamelCase		:							Dict		 =						{0: 'batch', 1: 'encoder_sequence'}

       return common_inputs




      def 					_snake_case     (    self:	List[Any]						,				a:	"PreTrainedTokenizerBase"						,				a:	int = -1						,				a:	int = -1						,				a:	bool = False						,				a:	Optional["TensorType"] = None						,				):
       import torch

       __lowerCamelCase		:							str		 =						OrderedDict()

       __lowerCamelCase		:							List[Any]		 =						super().generate_dummy_inputs(
           a						,				batch_size=a						,				seq_length=a						,				is_pair=a						,				framework=a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						dummy_input['input_ids'].shape
       __lowerCamelCase		:							str		 =						(batch, encoder_sequence, self._config.encoder_hidden_size)
       __lowerCamelCase		:							str		 =						dummy_input.pop('input_ids'    )
       __lowerCamelCase		:							Union[str, Any]		 =						dummy_input.pop('attention_mask'    )
       __lowerCamelCase		:							str		 =						torch.zeros(a    )

       return common_inputs



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	List[Any]    ):
       pass




      def 					_snake_case     (    self:	List[str]						,				a:	PretrainedConfig    ):
       return VisionEncoderDecoderEncoderOnnxConfig(a    )




      def 					_snake_case     (    self:	Optional[int]						,				a:	PretrainedConfig						,				a:	PretrainedConfig						,				a:	str = "default"    ):
       __lowerCamelCase		:							List[Any]		 =						encoder_config.hidden_size
       return VisionEncoderDecoderDecoderOnnxConfig(a						,				a    )

