

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

import yaml

class   _a					(			yaml.SafeLoader				):




      """simple docstring"""

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







      def 		__A	(   self							:				Optional[Any]						,			UpperCAmelCase							:				Any						,			UpperCAmelCase							:				Union[str, Any]=False	):
            A_						  =				super().construct_mapping(UpperCAmelCase						,			deep=UpperCAmelCase	)
            self._check_no_duplicates_on_constructed_node(UpperCAmelCase	)
            return mapping




def       __snake_case						( __UpperCamelCase	: str    ):



      """simple docstring"""

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

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

class   _a					(			snake_case_				):




      """simple docstring"""

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





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





      def 		__A	(   self							:				Optional[int]						,			UpperCAmelCase							:				Path	):
            if path.exists():
                  with open(UpperCAmelCase						,			encoding="utf-8"	) as readme_file:
                        A_						  =				readme_file.read()
            else:
                  A_						  =				None
            A_						  =				self._to_readme(UpperCAmelCase	)
            with open(UpperCAmelCase						,			"w"						,			encoding="utf-8"	) as readme_file:
                  readme_file.write(UpperCAmelCase	)





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





      @classmethod
      def 		__A	(   cls							:				Tuple						,			UpperCAmelCase							:				str	):
            A_						  =				yaml.load(UpperCAmelCase						,			Loader=_NoDuplicateSafeLoader	) or {}

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







      def 		__A	(   self							:				List[Any]	):
            return yaml.safe_dump(
                {
                    (key.replace("_"						,			"-"	) if key in self._FIELDS_WITH_DASHES else key): value
                    for key, value in self.items()
                }						,			sort_keys=UpperCAmelCase						,			allow_unicode=UpperCAmelCase						,			encoding="utf-8"						,			).decode("utf-8"	)




__a    :Optional[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[int]      =			ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
      ap.add_argument('readme_filepath')
      __a    :str      =			ap.parse_args()

      __a    :Optional[Any]      =			Path(args.readme_filepath)
      __a    :Optional[int]      =			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_torch_available, is_vision_available


a  :      int            =    {
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      Any            =    ["""BridgeTowerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      str            =    [
     """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """BridgeTowerForContrastiveLearning""",
     """BridgeTowerForImageAndTextRetrieval""",
     """BridgeTowerForMaskedLM""",
     """BridgeTowerModel""",
     """BridgeTowerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 a  :      List[Any]            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)