

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


snake_case 				=       logging.get_logger(__name__)

snake_case 				=       {"""vocab_file""": """vocab.txt"""}

snake_case 				=       {
    """vocab_file""": {
        """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt""",
        """YituTech/conv-bert-medium-small""": (
            """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"""
        ),
        """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt""",
    }
}

snake_case 				=       {
    """YituTech/conv-bert-base""": 5_1_2,
    """YituTech/conv-bert-medium-small""": 5_1_2,
    """YituTech/conv-bert-small""": 5_1_2,
}


snake_case 				=       {
    """YituTech/conv-bert-base""": {"""do_lower_case""": True},
    """YituTech/conv-bert-medium-small""": {"""do_lower_case""": True},
    """YituTech/conv-bert-small""": {"""do_lower_case""": True},
}





class 				A_				(      UpperCamelCase__	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Any      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_      : Optional[int]      =  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_      : List[Any]      =  PRETRAINED_INIT_CONFIGURATION
       SCREAMING_SNAKE_CASE_      : Tuple      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_      : Dict      =  ConvBertTokenizer







       def __init__(    self	:	Dict						,__A	:	Dict=None						,__A	:	Dict=None						,__A	:	Tuple=True						,__A	:	Union[str, Any]="[UNK]"						,__A	:	Optional[Any]="[SEP]"						,__A	:	Union[str, Any]="[PAD]"						,__A	:	Optional[int]="[CLS]"						,__A	:	Optional[int]="[MASK]"						,__A	:	List[Any]=True						,__A	:	Any=None						,**__A	:	int						,)						->					Any:
         super().__init__(
             _a						,tokenizer_file=_a						,do_lower_case=_a						,unk_token=_a						,sep_token=_a						,pad_token=_a						,cls_token=_a						,mask_token=_a						,tokenize_chinese_chars=_a						,strip_accents=_a						,**_a						,)

         _lowercase							= json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
         if (
             normalizer_state.get('lowercase'						,_a  ) != do_lower_case
             or normalizer_state.get('strip_accents'						,_a  ) != strip_accents
             or normalizer_state.get('handle_chinese_chars'						,_a  ) != tokenize_chinese_chars
         ):
           _lowercase							= getattr(_a						,normalizer_state.pop('type'  )  )
           _lowercase							= do_lower_case
           _lowercase							= strip_accents
           _lowercase							= tokenize_chinese_chars
           _lowercase							= normalizer_class(**_a  )

         _lowercase							= do_lower_case







       def    __UpperCAmelCase (    self	:	str						,__A	:	Tuple						,__A	:	Union[str, Any]=None  )						->					Dict:
         _lowercase							= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
           output += token_ids_a + [self.sep_token_id]

         return output







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	Tuple						,__A	:	Dict = None  )						->					List[int]:
         _lowercase							= [self.sep_token_id]
         _lowercase							= [self.cls_token_id]
         if token_ids_a is None:
           return len(cls + token_ids_a + sep  ) * [0]
         return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	str						,__A	:	str = None  )						->					Tuple[str]:
         _lowercase							= self._tokenizer.model.save(_a						,name=_a  )
         return tuple(_a  )






from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml





class 				A_				(      yaml.SafeLoader	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Dict						,__A	:	List[Any]  )						->					Any:
         _lowercase							= [self.constructed_objects[key_node] for key_node, _ in node.value]
         _lowercase							= [tuple(__A  ) if isinstance(__A						,__A  ) else key for key in keys]
         _lowercase							= Counter(__A  )
         _lowercase							= [key for key in counter if counter[key] > 1]
         if duplicate_keys:
           raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	int						,__A	:	Dict=False  )						->					Union[str, Any]:
         _lowercase							= super().construct_mapping(__A						,deep=__A  )
         self._check_no_duplicates_on_constructed_node(__A  )
         return mapping

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str     )      ->						Tuple[Optional[str], str]:
  _lowercase							= list(readme_content.splitlines()     )
  if full_content and full_content[0] == "---" and "---" in full_content[1:]:
    _lowercase							= full_content[1:].index('---'     ) + 1
    _lowercase							= '\n'.join(full_content[1:sep_idx]     )
    return yamlblock, "\n".join(full_content[sep_idx + 1 :]     )

  return None, "\n".join(snake_case__     )





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Any      =  {'''train_eval_index'''}  # train-eval-index in the YAML metadata







       @classmethod
       def    __UpperCAmelCase (    cls	:	List[Any]						,__A	:	Path  )						->					"DatasetMetadata":
         with open(__A						,encoding='utf-8'  ) as readme_file:
           _lowercase						,		_lowercase							= _split_yaml_from_readme(readme_file.read()  )
         if yaml_string is not None:
           return cls.from_yaml_string(__A  )
         else:
           return cls()







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	Path  )						->					Optional[Any]:
         if path.exists():
           with open(__A						,encoding='utf-8'  ) as readme_file:
             _lowercase							= readme_file.read()
         else:
           _lowercase							= None
         _lowercase							= self._to_readme(__A  )
         with open(__A						,'w'						,encoding='utf-8'  ) as readme_file:
           readme_file.write(__A  )







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Optional[str] = None  )						->					str:
         if readme_content is not None:
           _lowercase						,		_lowercase							= _split_yaml_from_readme(__A  )
           _lowercase							= '---\n' + self.to_yaml_string() + '---\n' + content
         else:
           _lowercase							= '---\n' + self.to_yaml_string() + '---\n'
         return full_content







       @classmethod
       def    __UpperCAmelCase (    cls	:	List[Any]						,__A	:	str  )						->					"DatasetMetadata":
         _lowercase							= yaml.load(__A						,Loader=_NoDuplicateSafeLoader  ) or {}

         # Convert the YAML keys to DatasetMetadata fields
         _lowercase							= {
             (key.replace('-'						,'_'  ) if key.replace('-'						,'_'  ) in cls._FIELDS_WITH_DASHES else key): value
             for key, value in metadata_dict.items()
         }
         return cls(**__A  )







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					str:
         return yaml.safe_dump(
             {
                 (key.replace('_'						,'-'  ) if key in self._FIELDS_WITH_DASHES else key): value
                 for key, value in self.items()
             }						,sort_keys=__A						,allow_unicode=__A						,encoding='utf-8'						,).decode('utf-8'  )
snake_case 				=       {
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      snake_case 				=       ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
      ap.add_argument("""readme_filepath""")
      snake_case 				=       ap.parse_args()

      snake_case 				=       Path(args.readme_filepath)
      snake_case 				=       DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)