






"""simple docstring"""







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

import yaml







class  lowerCamelCase__ (	yaml.SafeLoader			):






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	[self.constructed_objects[key_node] for key_node, _ in node.value]
          UpperCAmelCase          =	[tuple(A ) if isinstance(A     ,A ) else key for key in keys]
          UpperCAmelCase          =	Counter(A )
          UpperCAmelCase          =	[key for key in counter if counter[key] > 1]
          if duplicate_keys:
                raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}''' )






    def     _UpperCamelCase							(			self     ,A     ,A=False ):
          UpperCAmelCase          =	super().construct_mapping(A     ,deep=A )
          self._check_no_duplicates_on_constructed_node(A )
          return mapping







def _a			(		_snake_case							):







      """simple docstring"""






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

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







class  lowerCamelCase__ (	snake_case			):
    # class attributes
    SCREAMING_SNAKE_CASE		=      {'''train_eval_index'''}  # train-eval-index in the YAML metadata






    @classmethod
    def     _UpperCamelCase							(			cls     ,A ):
          with open(A     ,encoding="""utf-8""" ) as readme_file:
                UpperCAmelCase      , UpperCAmelCase          =	_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     ,A ):
          if path.exists():
                with open(A     ,encoding="""utf-8""" ) as readme_file:
                      UpperCAmelCase          =	readme_file.read()
          else:
                UpperCAmelCase          =	None
          UpperCAmelCase          =	self._to_readme(A )
          with open(A     ,"""w"""     ,encoding="""utf-8""" ) as readme_file:
                readme_file.write(A )






    def     _UpperCamelCase							(			self     ,A = None ):
          if readme_content is not None:
                UpperCAmelCase      , UpperCAmelCase          =	_split_yaml_from_readme(A )
                UpperCAmelCase          =	"""---\n""" + self.to_yaml_string() + """---\n""" + content
          else:
                UpperCAmelCase          =	"""---\n""" + self.to_yaml_string() + """---\n"""
          return full_content






    @classmethod
    def     _UpperCamelCase							(			cls     ,A ):
          UpperCAmelCase          =	yaml.load(A     ,Loader=_NoDuplicateSafeLoader ) or {}

          # Convert the YAML keys to DatasetMetadata fields
          UpperCAmelCase          =	{
              (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 ):
          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""" )







_UpperCamelCase					      =  {
    """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

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

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











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase					      =  {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""RoFormerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RoFormerForCausalLM""",
      """RoFormerForMaskedLM""",
      """RoFormerForMultipleChoice""",
      """RoFormerForQuestionAnswering""",
      """RoFormerForSequenceClassification""",
      """RoFormerForTokenClassification""",
      """RoFormerLayer""",
      """RoFormerModel""",
      """RoFormerPreTrainedModel""",
      """load_tf_weights_in_roformer""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRoFormerForCausalLM""",
      """TFRoFormerForMaskedLM""",
      """TFRoFormerForMultipleChoice""",
      """TFRoFormerForQuestionAnswering""",
      """TFRoFormerForSequenceClassification""",
      """TFRoFormerForTokenClassification""",
      """TFRoFormerLayer""",
      """TFRoFormerModel""",
      """TFRoFormerPreTrainedModel""",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FlaxRoFormerForMaskedLM""",
      """FlaxRoFormerForMultipleChoice""",
      """FlaxRoFormerForQuestionAnswering""",
      """FlaxRoFormerForSequenceClassification""",
      """FlaxRoFormerForTokenClassification""",
      """FlaxRoFormerModel""",
      """FlaxRoFormerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
  from .tokenization_roformer import RoFormerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roformer_fast import RoFormerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roformer import (
        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoFormerForCausalLM,
        RoFormerForMaskedLM,
        RoFormerForMultipleChoice,
        RoFormerForQuestionAnswering,
        RoFormerForSequenceClassification,
        RoFormerForTokenClassification,
        RoFormerLayer,
        RoFormerModel,
        RoFormerPreTrainedModel,
        load_tf_weights_in_roformer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roformer import (
        TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerLayer,
        TFRoFormerModel,
        TFRoFormerPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roformer import (
        FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
        FlaxRoFormerPreTrainedModel,
    )


else:
  import sys

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



