



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

import yaml

class        snake_case__    (yaml.SafeLoader		):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase=False  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Tuple							=  super().construct_mapping(__lowercase ,       deep=__lowercase  )
									self._check_no_duplicates_on_constructed_node(__lowercase  )
									return mapping




def      lowerCAmelCase_					(  _lowercase    :       str)							->	Tuple[Optional[str], str]:







			"""simple docstring"""
			a__       :						Tuple							=  list(readme_content.splitlines())
			if full_content and full_content[0] == "---" and "---" in full_content[1:]:
						a__       :						int							=  full_content[1:].index("""---""") + 1
						a__       :						List[str]							=  """\n""".join(full_content[1:sep_idx])
						return yamlblock, "\n".join(full_content[sep_idx + 1 :])

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

class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:str      =					{"train_eval_index"}  # train-eval-index in the YAML metadata

						@classmethod
						def 							SCREAMING_SNAKE_CASE__(       cls ,       __lowercase  ) ->				"DatasetMetadata":
									"""simple docstring"""




									with open(__lowercase ,       encoding="""utf-8"""  ) as readme_file:
												a__	,						a__       :						str							=  _split_yaml_from_readme(readme_file.read()  )
									if yaml_string is not None:
												return cls.from_yaml_string(__lowercase  )
									else:
												return cls()

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									if path.exists():
												with open(__lowercase ,       encoding="""utf-8"""  ) as readme_file:
															a__       :						Tuple							=  readme_file.read()
									else:
												a__       :						Union[str, Any]							=  None
									a__       :						str							=  self._to_readme(__lowercase  )
									with open(__lowercase ,       """w""" ,       encoding="""utf-8"""  ) as readme_file:
												readme_file.write(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase = None  ) ->				str:
									"""simple docstring"""




									if readme_content is not None:
												a__	,						a__       :						List[Any]							=  _split_yaml_from_readme(__lowercase  )
												a__       :						Optional[Any]							=  """---\n""" + self.to_yaml_string() + """---\n""" + content
									else:
												a__       :						List[Any]							=  """---\n""" + self.to_yaml_string() + """---\n"""
									return full_content

						@classmethod
						def 							SCREAMING_SNAKE_CASE__(       cls ,       __lowercase  ) ->				"DatasetMetadata":
									"""simple docstring"""




									a__       :						List[str]							=  yaml.load(__lowercase ,       Loader=_NoDuplicateSafeLoader  ) or {}

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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				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"""  )




_lowercase		:				Dict      		={
    "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

		_lowercase		:				Any      		=ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
		ap.add_argument("readme_filepath")
		_lowercase		:				List[Any]      		=ap.parse_args()

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









import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


_lowercase		:				Union[str, Any]      		=logging.get_logger(__name__)

class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       *__lowercase ,       **__lowercase  ) ->				None:
									"""simple docstring"""




									warnings.warn(
									    """The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
									    """ Please use MobileViTImageProcessor instead.""" ,       __lowercase ,       )
									super().__init__(*__lowercase ,       **__lowercase  )




