






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

import yaml


class       __SCREAMING_SNAKE_CASE   (				yaml.SafeLoader):






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






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







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=False				):






												"""simple docstring"""
												lowerCAmelCase__        =  super().construct_mapping(_UpperCamelCase       ,  deep=_UpperCamelCase				)
												self._check_no_duplicates_on_constructed_node(_UpperCamelCase				)
												return mapping







def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				Tuple[Optional[str], str]:

						"""simple docstring"""



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

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


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						# class attributes
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   {'''train_eval_index'''}  # train-eval-index in the YAML metadata






						@classmethod
						def  UpperCamelCase__ (		cls       ,  _UpperCamelCase				):






												"""simple docstring"""
												with open(_UpperCamelCase       ,  encoding='utf-8'				) as readme_file:
																		lowerCAmelCase__  ,    lowerCAmelCase__        =  _split_yaml_from_readme(readme_file.read()				)
												if yaml_string is not None:
																		return cls.from_yaml_string(_UpperCamelCase				)
												else:
																		return cls()






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												if path.exists():
																		with open(_UpperCamelCase       ,  encoding='utf-8'				) as readme_file:
																								lowerCAmelCase__        =  readme_file.read()
												else:
																		lowerCAmelCase__        =  None
												lowerCAmelCase__        =  self._to_readme(_UpperCamelCase				)
												with open(_UpperCamelCase       ,  'w'       ,  encoding='utf-8'				) as readme_file:
																		readme_file.write(_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												if readme_content is not None:
																		lowerCAmelCase__  ,    lowerCAmelCase__        =  _split_yaml_from_readme(_UpperCamelCase				)
																		lowerCAmelCase__        =  '---\n' + self.to_yaml_string() + '---\n' + content
												else:
																		lowerCAmelCase__        =  '---\n' + self.to_yaml_string() + '---\n'
												return full_content






						@classmethod
						def  UpperCamelCase__ (		cls       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  yaml.load(_UpperCamelCase       ,  Loader=_NoDuplicateSafeLoader				) or {}

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







						def  UpperCamelCase__ (		self				):






												"""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=_UpperCamelCase       ,  allow_unicode=_UpperCamelCase       ,  encoding='utf-8'       ,  ).decode('utf-8'				)




__snake_case      :      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

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

					__snake_case      :      Dict             =       Path(args.readme_filepath)
					__snake_case      :      int             =       DatasetMetadata.from_readme(readme_filepath)
					print(dataset_metadata)
					dataset_metadata.to_readme(readme_filepath)













from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





