


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

import yaml




class A  (   yaml.SafeLoader     ):






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     str )					->						Any:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     List[Any]			,   lowercase_						:     Optional[Any]			,   lowercase_						:     Optional[Any]=False )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		str		=super().construct_mapping(lowercase_			,   deep=lowercase_ )
             self._check_no_duplicates_on_constructed_node(lowercase_ )
             return mapping




def 	a_      (    SCREAMING_SNAKE_CASE__					:      str     ):
       '''simple docstring'''





       _lowerCamelCase       :		Any		=list(readme_content.splitlines()     )
       if full_content and full_content[0] == "---" and "---" in full_content[1:]:
              _lowerCamelCase       :		List[str]		=full_content[1:].index('---'     ) + 1
              _lowerCamelCase       :		str		="\n".join(full_content[1:sep_idx]     )
              return yamlblock, "\n".join(full_content[sep_idx + 1 :]     )

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




class A  (   __A     ):
      # class attributes
      UpperCamelCase__							:  Dict      ={"train_eval_index"}  # train-eval-index in the YAML metadata






      @classmethod
      def        lowerCamelCase       (					cls						:     List[str]			,   lowercase_						:     Path )					->						"DatasetMetadata":
             """simple docstring"""




             with open(lowercase_			,   encoding='utf-8' ) as readme_file:
                    _lowerCamelCase       :		List[str]		=_split_yaml_from_readme(readme_file.read() )
             if yaml_string is not None:
                    return cls.from_yaml_string(lowercase_ )
             else:
                    return cls()






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     Path )					->						Optional[int]:
             """simple docstring"""




             if path.exists():
                    with open(lowercase_			,   encoding='utf-8' ) as readme_file:
                           _lowerCamelCase       :		Optional[Any]		=readme_file.read()
             else:
                    _lowerCamelCase       :		Any		=None
             _lowerCamelCase       :		Any		=self._to_readme(lowercase_ )
             with open(lowercase_			,   'w'			,   encoding='utf-8' ) as readme_file:
                    readme_file.write(lowercase_ )






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     Optional[str] = None )					->						str:
             """simple docstring"""




             if readme_content is not None:
                    _lowerCamelCase       :		Tuple		=_split_yaml_from_readme(lowercase_ )
                    _lowerCamelCase       :		str		="---\n" + self.to_yaml_string() + "---\n" + content
             else:
                    _lowerCamelCase       :		Optional[int]		="---\n" + self.to_yaml_string() + "---\n"
             return full_content






      @classmethod
      def        lowerCamelCase       (					cls						:     List[str]			,   lowercase_						:     str )					->						"DatasetMetadata":
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=yaml.load(lowercase_			,   Loader=_NoDuplicateSafeLoader ) or {}

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






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """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=lowercase_			,   allow_unicode=lowercase_			,   encoding='utf-8'			,   ).decode('utf-8' )




lowerCamelCase											=       {
    '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

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

 lowerCamelCase											=       Path(args.readme_filepath)
 lowerCamelCase											=       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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase  							=						{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						['''ElectraTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ElectraForCausalLM''',
          '''ElectraForMaskedLM''',
          '''ElectraForMultipleChoice''',
          '''ElectraForPreTraining''',
          '''ElectraForQuestionAnswering''',
          '''ElectraForSequenceClassification''',
          '''ElectraForTokenClassification''',
          '''ElectraModel''',
          '''ElectraPreTrainedModel''',
          '''load_tf_weights_in_electra''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFElectraForMaskedLM''',
          '''TFElectraForMultipleChoice''',
          '''TFElectraForPreTraining''',
          '''TFElectraForQuestionAnswering''',
          '''TFElectraForSequenceClassification''',
          '''TFElectraForTokenClassification''',
          '''TFElectraModel''',
          '''TFElectraPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''FlaxElectraForCausalLM''',
          '''FlaxElectraForMaskedLM''',
          '''FlaxElectraForMultipleChoice''',
          '''FlaxElectraForPreTraining''',
          '''FlaxElectraForQuestionAnswering''',
          '''FlaxElectraForSequenceClassification''',
          '''FlaxElectraForTokenClassification''',
          '''FlaxElectraModel''',
          '''FlaxElectraPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
      from .tokenization_electra import ElectraTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_electra_fast import ElectraTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_electra import (
                ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                ElectraForCausalLM,
                ElectraForMaskedLM,
                ElectraForMultipleChoice,
                ElectraForPreTraining,
                ElectraForQuestionAnswering,
                ElectraForSequenceClassification,
                ElectraForTokenClassification,
                ElectraModel,
                ElectraPreTrainedModel,
                load_tf_weights_in_electra,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_electra import (
                TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFElectraForMaskedLM,
                TFElectraForMultipleChoice,
                TFElectraForPreTraining,
                TFElectraForQuestionAnswering,
                TFElectraForSequenceClassification,
                TFElectraForTokenClassification,
                TFElectraModel,
                TFElectraPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_electra import (
                FlaxElectraForCausalLM,
                FlaxElectraForMaskedLM,
                FlaxElectraForMultipleChoice,
                FlaxElectraForPreTraining,
                FlaxElectraForQuestionAnswering,
                FlaxElectraForSequenceClassification,
                FlaxElectraForTokenClassification,
                FlaxElectraModel,
                FlaxElectraPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase  							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



