



"""simple docstring"""







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

import yaml

class       _a  ( yaml.SafeLoader):



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				Dict		)							->					Union[str, Any]:
									snake_case   :			Tuple						    = [self.constructed_objects[key_node] for key_node, _ in node.value]
									snake_case   :			List[Any]						    = [tuple(_lowercase		) if isinstance(_lowercase   ,	_lowercase		) else key for key in keys]
									snake_case   :			List[str]						    = Counter(_lowercase		)
									snake_case   :			Any						    = [key for key in counter if counter[key] > 1]
									if duplicate_keys:
															raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''		)



			def 				__lowercase					(			self							:				Any   ,	_lowercase							:				Dict   ,	_lowercase							:				Tuple=False		)							->					Dict:
									snake_case   :			Optional[int]						    = super().construct_mapping(_lowercase   ,	deep=_lowercase		)
									self._check_no_duplicates_on_constructed_node(_lowercase		)
									return mapping



def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






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

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

class       _a  ( SCREAMING_SNAKE_CASE__):
			# class attributes
			__magic_name__     					=     {"""train_eval_index"""}  # train-eval-index in the YAML metadata



			@classmethod
			def 				__lowercase					(			cls							:				str   ,	_lowercase							:				Path		)							->					"DatasetMetadata":
									with open(_lowercase   ,	encoding="utf-8"		) as readme_file:
															snake_case    ,			snake_case   :			Tuple						    = _split_yaml_from_readme(readme_file.read()		)
									if yaml_string is not None:
															return cls.from_yaml_string(_lowercase		)
									else:
															return cls()



			def 				__lowercase					(			self							:				Any   ,	_lowercase							:				Path		)							->					Tuple:
									if path.exists():
															with open(_lowercase   ,	encoding="utf-8"		) as readme_file:
																					snake_case   :			Any						    = readme_file.read()
									else:
															snake_case   :			List[str]						    = None
									snake_case   :			Optional[Any]						    = self._to_readme(_lowercase		)
									with open(_lowercase   ,	"w"   ,	encoding="utf-8"		) as readme_file:
															readme_file.write(_lowercase		)



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				Optional[str] = None		)							->					str:
									if readme_content is not None:
															snake_case    ,			snake_case   :			Any						    = _split_yaml_from_readme(_lowercase		)
															snake_case   :			Union[str, Any]						    = "---\n" + self.to_yaml_string() + "---\n" + content
									else:
															snake_case   :			int						    = "---\n" + self.to_yaml_string() + "---\n"
									return full_content



			@classmethod
			def 				__lowercase					(			cls							:				Optional[int]   ,	_lowercase							:				str		)							->					"DatasetMetadata":
									snake_case   :			int						    = yaml.load(_lowercase   ,	Loader=_NoDuplicateSafeLoader		) or {}

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



			def 				__lowercase					(			self							:				List[str]		)							->					str:
									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"		)



A		=						{
    '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		=						ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
			ap.add_argument('readme_filepath')
			A		=						ap.parse_args()

			A		=						Path(args.readme_filepath)
			A		=						DatasetMetadata.from_readme(readme_filepath)
			print(dataset_metadata)
			dataset_metadata.to_readme(readme_filepath)








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A		=						{'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						['ViTFeatureExtractor']
			A		=						['ViTImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ViTForImageClassification',
			    'ViTForMaskedImageModeling',
			    'ViTModel',
			    'ViTPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'TFViTForImageClassification',
			    'TFViTModel',
			    'TFViTPreTrainedModel',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'FlaxViTForImageClassification',
			    'FlaxViTModel',
			    'FlaxViTPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_vit import ViTFeatureExtractor
						from .image_processing_vit import ViTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit import (
						    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTForImageClassification,
						    ViTForMaskedImageModeling,
						    ViTModel,
						    ViTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
			import sys

			A		=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



