



"""simple docstring"""

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

import yaml
class     lowerCAmelCase__						(	yaml.SafeLoader ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     [self.constructed_objects[key_node] for key_node, _ in node.value]
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [tuple(_SCREAMING_SNAKE_CASE    ) if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) else key for key in keys]
					SCREAMING_SNAKE_CASE						:							Any							=     Counter(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     [key for key in counter if counter[key] > 1]
					if duplicate_keys:
									raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""    )







	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       Dict=False    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     super().construct_mapping(_SCREAMING_SNAKE_CASE     ,       deep=_SCREAMING_SNAKE_CASE    )
					self._check_no_duplicates_on_constructed_node(_SCREAMING_SNAKE_CASE    )
					return mapping




def  __snake_case							(	__A      :						str     )     ->       Tuple[Optional[str], str]:






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

				return None, "\n".join(__A     )
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		str  						= {'''train_eval_index'''}  # train-eval-index in the YAML metadata





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Path    )						->	"DatasetMetadata":





					"""simple docstring"""


					with open(_SCREAMING_SNAKE_CASE     ,       encoding='utf-8'    ) as readme_file:
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     _split_yaml_from_readme(readme_file.read()    )
					if yaml_string is not None:
									return cls.from_yaml_string(_SCREAMING_SNAKE_CASE    )
					else:
									return cls()





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Path    )						->	Optional[Any]:





					"""simple docstring"""


					if path.exists():
									with open(_SCREAMING_SNAKE_CASE     ,       encoding='utf-8'    ) as readme_file:
													SCREAMING_SNAKE_CASE						:							Dict							=     readme_file.read()
					else:
									SCREAMING_SNAKE_CASE						:							int							=     None
					SCREAMING_SNAKE_CASE						:							Tuple							=     self._to_readme(_SCREAMING_SNAKE_CASE    )
					with open(_SCREAMING_SNAKE_CASE     ,       'w'     ,       encoding='utf-8'    ) as readme_file:
									readme_file.write(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None    )						->	str:





					"""simple docstring"""


					if readme_content is not None:
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     _split_yaml_from_readme(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							str							=     '---\n' + self.to_yaml_string() + '---\n' + content
					else:
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     '---\n' + self.to_yaml_string() + '---\n'
					return full_content





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       int     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	"DatasetMetadata":





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     yaml.load(_SCREAMING_SNAKE_CASE     ,       Loader=_NoDuplicateSafeLoader    ) or {}

					# Convert the YAML keys to DatasetMetadata fields
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     {
					    (key.replace('-'     ,       '_'    ) if key.replace('-'     ,       '_'    ) in cls._FIELDS_WITH_DASHES else key): value
					    for key, value in metadata_dict.items()
					}
					return cls(**_SCREAMING_SNAKE_CASE    )







	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	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=_SCREAMING_SNAKE_CASE     ,       allow_unicode=_SCREAMING_SNAKE_CASE     ,       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_						:    Dict				=      ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
							ap.add_argument('readme_filepath')
							A_						:    Optional[Any]				=      ap.parse_args()

							A_						:    int				=      Path(args.readme_filepath)
							A_						:    List[str]				=      DatasetMetadata.from_readme(readme_filepath)
							print(dataset_metadata)
							dataset_metadata.to_readme(readme_filepath)










"""simple docstring"""

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)





