from typing import TYPE_CHECKING

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


A     :  List[str]					     =       {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  str					     =       ["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Union[str, Any]					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      A     :  str					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



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

import yaml


class   lowerCamelCase					(yaml.SafeLoader					):
  """simple docstring"""







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







  def    __A     (							self :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]=False   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  super().construct_mapping(__magic_name__  ,     deep=__magic_name__   )
        self._check_no_duplicates_on_constructed_node(__magic_name__   )
        return mapping



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

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


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						{'''train_eval_index'''}  # train-eval-index in the YAML metadata







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       Path   )  ->	"DatasetMetadata":
        with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(readme_file.read()   )
        if yaml_string is not None:
              return cls.from_yaml_string(__magic_name__   )
        else:
              return cls()







  def    __A     (							self :       str  ,     __magic_name__ :       Path   )  ->	List[str]:
        if path.exists():
              with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
                    SCREAMING_SNAKE_CASE_          =  readme_file.read()
        else:
              SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  self._to_readme(__magic_name__   )
        with open(__magic_name__  ,     "w"  ,     encoding="utf-8"   ) as readme_file:
              readme_file.write(__magic_name__   )







  def    __A     (							self :       Any  ,     __magic_name__ :       Optional[str] = None   )  ->	str:
        if readme_content is not None:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n" + content
        else:
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n"
        return full_content







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

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







  def    __A     (							self :       Optional[Any]   )  ->	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=__magic_name__  ,     allow_unicode=__magic_name__  ,     encoding="utf-8"  ,     ).decode("utf-8"   )



A     :  List[Any]					     =       {
    "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

      A     :  Optional[Any]					     =       ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
      ap.add_argument("readme_filepath")
      A     :  Union[str, Any]					     =       ap.parse_args()

      A     :  Union[str, Any]					     =       Path(args.readme_filepath)
      A     :  List[Any]					     =       DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)


