



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

import yaml







class 		snake_case						(	yaml.SafeLoader  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			Dict				,		lowerCAmelCase							:			str)				->				int:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					[self.constructed_objects[key_node] for key_node, _ in node.value]
    _snake_case    :  List[str]					=					[tuple(_A) if isinstance(_A				,		_A) else key for key in keys]
    _snake_case    :  Tuple					=					Counter(_A)
    _snake_case    :  Tuple					=					[key for key in counter if counter[key] > 1]
    if duplicate_keys:
     raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}''')


   def      UpperCamelCase_  (					self							:			List[Any]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Any=False)				->				List[Any]:

    """simple docstring"""







    _snake_case    :  int					=					super().construct_mapping(_A				,		deep=_A)
    self._check_no_duplicates_on_constructed_node(_A)
    return mapping



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

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







class 		snake_case						(	snake_case__  ):



   '''simple docstring'''






   snake_case_					: int          =		{'''train_eval_index'''}  # train-eval-index in the YAML metadata

   @classmethod
   def      UpperCamelCase_  (					cls							:			Optional[int]				,		lowerCAmelCase							:			Union[str, Any])				->				List[str]:

    """simple docstring"""







    with open(_A				,		encoding="""utf-8""") as readme_file:
     _snake_case    :  int					=					_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							:			Dict				,		lowerCAmelCase							:			Optional[Any])				->				Optional[int]:

    """simple docstring"""







    if path.exists():
     with open(_A				,		encoding="""utf-8""") as readme_file:
      _snake_case    :  Tuple					=					readme_file.read()
    else:
     _snake_case    :  Dict					=					None
    _snake_case    :  str					=					self._to_readme(_A)
    with open(_A				,		"""w"""				,		encoding="""utf-8""") as readme_file:
     readme_file.write(_A)

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			Optional[int] = None)				->				Optional[int]:

    """simple docstring"""







    if readme_content is not None:
     _snake_case    :  str					=					_split_yaml_from_readme(_A)
     _snake_case    :  Dict					=					'---\n' + self.to_yaml_string() + '---\n' + content
    else:
     _snake_case    :  Optional[Any]					=					'---\n' + self.to_yaml_string() + '---\n'
    return full_content

   @classmethod
   def      UpperCamelCase_  (					cls							:			Union[str, Any]				,		lowerCAmelCase							:			Any)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  List[Any]					=					yaml.load(_A				,		Loader=_NoDuplicateSafeLoader) or {}

    # Convert the YAML keys to DatasetMetadata fields
    _snake_case    :  List[str]					=					{
        (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])				->				Dict:

    """simple docstring"""







    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)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__    =	{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    =	[
      """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CanineForMultipleChoice""",
      """CanineForQuestionAnswering""",
      """CanineForSequenceClassification""",
      """CanineForTokenClassification""",
      """CanineLayer""",
      """CanineModel""",
      """CaninePreTrainedModel""",
      """load_tf_weights_in_canine""",
  ]


if TYPE_CHECKING:
  from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
  from .tokenization_canine import CanineTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_canine import (
        CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
        CanineForMultipleChoice,
        CanineForQuestionAnswering,
        CanineForSequenceClassification,
        CanineForTokenClassification,
        CanineLayer,
        CanineModel,
        CaninePreTrainedModel,
        load_tf_weights_in_canine,
    )


else:
  import sys

  a__    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
