


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

import yaml

class 		lowerCAmelCase     ( yaml.SafeLoader			):

					'''simple docstring'''

					def       lowerCAmelCase				(  self				:	int		,     __a				:	int							)			->  Dict:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Any		,     __a				:	Optional[int]		,     __a				:	Tuple=False							)			->  str:


												"""simple docstring"""

												__lowercase      :							int				=     super().construct_mapping(_A		,     deep=_A							)
												self._check_no_duplicates_on_constructed_node(_A							)
												return mapping







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

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

class 		lowerCAmelCase     ( snake_case__			):

					'''simple docstring'''

					_A						:			int									=      {'''train_eval_index'''}  # train-eval-index in the YAML metadata



					@classmethod
					def       lowerCAmelCase				(  cls				:	Dict		,     __a				:	str							)			->  str:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	str		,     __a				:	Optional[Any]							)			->  Union[str, Any]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Any		,     __a				:	List[str] = None							)			->  Union[str, Any]:


												"""simple docstring"""

												if readme_content is not None:
																			__lowercase      :							str				=     _split_yaml_from_readme(_A							)
																			__lowercase      :							Dict				=     '---\n' + self.to_yaml_string() + '---\n' + content
												else:
																			__lowercase      :							Optional[Any]				=     '---\n' + self.to_yaml_string() + '---\n'
												return full_content



					@classmethod
					def       lowerCAmelCase				(  cls				:	List[Any]		,     __a				:	Any							)			->  Dict:


												"""simple docstring"""

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

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



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Union[str, 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=_A		,     allow_unicode=_A		,     encoding="""utf-8"""		,     ).decode("""utf-8"""							)







lowerCamelCase : Tuple						   =       {
    '''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

	lowerCamelCase : int						   =       ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
	ap.add_argument('''readme_filepath''')
	lowerCamelCase : Dict						   =       ap.parse_args()

	lowerCamelCase : Tuple						   =       Path(args.readme_filepath)
	lowerCamelCase : Tuple						   =       DatasetMetadata.from_readme(readme_filepath)
	print(dataset_metadata)
	dataset_metadata.to_readme(readme_filepath)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase : int						   =       {
    '''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
    '''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
    '''tokenization_funnel''': ['''FunnelTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : List[str]						   =       ['''FunnelTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : Union[str, Any]						   =       [
	    '''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FunnelBaseModel''',
	    '''FunnelForMaskedLM''',
	    '''FunnelForMultipleChoice''',
	    '''FunnelForPreTraining''',
	    '''FunnelForQuestionAnswering''',
	    '''FunnelForSequenceClassification''',
	    '''FunnelForTokenClassification''',
	    '''FunnelModel''',
	    '''FunnelPreTrainedModel''',
	    '''load_tf_weights_in_funnel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : str						   =       [
	    '''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFFunnelBaseModel''',
	    '''TFFunnelForMaskedLM''',
	    '''TFFunnelForMultipleChoice''',
	    '''TFFunnelForPreTraining''',
	    '''TFFunnelForQuestionAnswering''',
	    '''TFFunnelForSequenceClassification''',
	    '''TFFunnelForTokenClassification''',
	    '''TFFunnelModel''',
	    '''TFFunnelPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
	from .tokenization_funnel import FunnelTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_funnel_fast import FunnelTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_funnel import (
		    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FunnelBaseModel,
		    FunnelForMaskedLM,
		    FunnelForMultipleChoice,
		    FunnelForPreTraining,
		    FunnelForQuestionAnswering,
		    FunnelForSequenceClassification,
		    FunnelForTokenClassification,
		    FunnelModel,
		    FunnelPreTrainedModel,
		    load_tf_weights_in_funnel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_funnel import (
		    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFunnelBaseModel,
		    TFFunnelForMaskedLM,
		    TFFunnelForMultipleChoice,
		    TFFunnelForPreTraining,
		    TFFunnelForQuestionAnswering,
		    TFFunnelForSequenceClassification,
		    TFFunnelForTokenClassification,
		    TFFunnelModel,
		    TFFunnelPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase : Any						   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)