
'''simple docstring'''




import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    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 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    for char in word:
        A       :   Optional[int]					=						ord(snake_case__						)
        if not _is_chinese_char(snake_case__						):
            return 0
    return 1







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Optional[Any]					=						set()

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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    if not chinese_word_set:
        return bert_tokens
    A       :   Tuple					=						max([len(snake_case__						) for w in chinese_word_set]						)

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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Tuple					=						[]

    for i in range(0		,				len(snake_case__						)		,				100						):
        A       :   Optional[Any]					=						ltp_tokenizer.seg(lines[i : i + 100]						)[0]
        A       :   List[str]					=						[get_chinese_word(snake_case__						) for r in res]
        ltp_res.extend(snake_case__						)
    assert len(snake_case__						) == len(snake_case__						)

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

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

    assert len(snake_case__						) == len(snake_case__						)

    return ref_ids







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



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

    A       :   Union[str, Any]					=						prepare_ref(snake_case__		,				snake_case__		,				snake_case__						)

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


if __name__ == "__main__":
      lowercase			:				Any    		=			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			:				Union[str, Any]    		=			parser.parse_args()
      main(args)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


lowercase			:				Dict    		=			{
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[Any]    		=			['Speech2TextTokenizer']

try:
      if not is_speech_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				List[str]    		=			['Speech2TextFeatureExtractor']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Dict    		=			[
          'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSpeech2TextForConditionalGeneration',
          'TFSpeech2TextModel',
          'TFSpeech2TextPreTrainedModel',
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:				Any    		=			[
          'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Speech2TextForConditionalGeneration',
          'Speech2TextModel',
          'Speech2TextPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
      from .processing_speech_to_text import SpeechaTextProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speech_to_text import SpeechaTextTokenizer

      try:
            if not is_speech_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_speech_to_text import (
                TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSpeechaTextForConditionalGeneration,
                TFSpeechaTextModel,
                TFSpeechaTextPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text import (
                SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextForConditionalGeneration,
                SpeechaTextModel,
                SpeechaTextPreTrainedModel,
            )

else:
      import sys

      lowercase			:				List[Any]    		=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




