





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

import yaml







class   _UpperCamelCase  (       yaml.SafeLoader  ):







					'''simple docstring'''

					def        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     [self.constructed_objects[key_node] for key_node, _ in node.value]
						UpperCAmelCase_								=     [tuple(__lowercase		) if isinstance(__lowercase ,					__lowercase		) else key for key in keys]
						UpperCAmelCase_								=     Counter(__lowercase		)
						UpperCAmelCase_								=     [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    :  Dict ,					__lowercase    :  List[Any] ,					__lowercase    :  Optional[Any]=False		):






						'''simple docstring'''







						UpperCAmelCase_								=     super().construct_mapping(__lowercase ,					deep=__lowercase		)
						self._check_no_duplicates_on_constructed_node(__lowercase		)
						return mapping




def 					A_(			A   ):
	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(A   )







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Dict			    =       {'train_eval_index'}  # train-eval-index in the YAML metadata





					@classmethod
					def        SCREAMING_SNAKE_CASE							(						cls    :  Union[str, Any] ,					__lowercase    :  Path		):






						'''simple docstring'''







						with open(__lowercase ,					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(__lowercase		)
						else:
							return cls()





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  Path		):






						'''simple docstring'''







						if path.exists():
							with open(__lowercase ,					encoding="""utf-8"""		) as readme_file:
								UpperCAmelCase_								=     readme_file.read()
						else:
							UpperCAmelCase_								=     None
						UpperCAmelCase_								=     self._to_readme(__lowercase		)
						with open(__lowercase ,					"""w""" ,					encoding="""utf-8"""		) as readme_file:
							readme_file.write(__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Optional[str] = None		):






						'''simple docstring'''







						if readme_content is not None:
							UpperCAmelCase_						,	UpperCAmelCase_								=     _split_yaml_from_readme(__lowercase		)
							UpperCAmelCase_								=     """---\n""" + self.to_yaml_string() + """---\n""" + content
						else:
							UpperCAmelCase_								=     """---\n""" + self.to_yaml_string() + """---\n"""
						return full_content





					@classmethod
					def        SCREAMING_SNAKE_CASE							(						cls    :  Optional[int] ,					__lowercase    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     yaml.load(__lowercase ,					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(**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''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"""		)


UpperCamelCase__   : Optional[int]					  = {
    """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__   : Any					  = ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
				ap.add_argument("""readme_filepath""")
				UpperCamelCase__   : Optional[int]					  = ap.parse_args()

				UpperCamelCase__   : Optional[int]					  = Path(args.readme_filepath)
				UpperCamelCase__   : Dict					  = DatasetMetadata.from_readme(readme_filepath)
				print(dataset_metadata)
				dataset_metadata.to_readme(readme_filepath)









import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def    A_(			A				,      A				,      **A   ):
	UpperCAmelCase_								=     AutoConfig.from_pretrained(A				,      **A   )
	UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_config(A   )
	model.save_pretrained(A   )
	AutoTokenizer.from_pretrained(A   ).save_pretrained(A   )
	return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)


