



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

import yaml
class 			_lowerCAmelCase   ( yaml.SafeLoader			):






			"""simple docstring"""





			def 		snake_case__			(				self	:       List[Any]			,     lowercase	:       List[str]   )						->					Tuple:
									"""simple docstring"""







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







			def 		snake_case__			(				self	:       Any			,     lowercase	:       Tuple			,     lowercase	:       Union[str, Any]=False   )						->					str:
									"""simple docstring"""







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






def  UpperCAmelCase__    (    lowercase__   ) ->      Tuple[Optional[str], str]:
						__lowercase					=				list(readme_content.splitlines()   )
						if full_content and full_content[0] == "---" and "---" in full_content[1:]:
												__lowercase					=				full_content[1:].index("""---"""   ) + 1
												__lowercase					=				"""\n""".join(full_content[1:sep_idx]   )
												return yamlblock, "\n".join(full_content[sep_idx + 1 :]   )

						return None, "\n".join(lowercase__   )
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Optional[Any]      =			{"""train_eval_index"""}  # train-eval-index in the YAML metadata





			@classmethod
			def 		snake_case__			(				cls	:       str			,     lowercase	:       Path   )						->					"DatasetMetadata":
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       str			,     lowercase	:       Path   )						->					Union[str, Any]:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       List[str]			,     lowercase	:       Optional[str] = None   )						->					str:
									"""simple docstring"""







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





			@classmethod
			def 		snake_case__			(				cls	:       Optional[int]			,     lowercase	:       str   )						->					"DatasetMetadata":
									"""simple docstring"""







									__lowercase					=				yaml.load(lowercase			,     Loader=_NoDuplicateSafeLoader   ) or {}

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







			def 		snake_case__			(				self	:       List[str]   )						->					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=lowercase			,     allow_unicode=lowercase			,     encoding="""utf-8"""			,     ).decode("""utf-8"""   )





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

			UpperCamelCase__ =						Path(args.readme_filepath)
			UpperCamelCase__ =						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_torch_available


UpperCamelCase__ =						{
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "CTRLForSequenceClassification",
			    "CTRLLMHeadModel",
			    "CTRLModel",
			    "CTRLPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFCTRLForSequenceClassification",
			    "TFCTRLLMHeadModel",
			    "TFCTRLModel",
			    "TFCTRLPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
			from .tokenization_ctrl import CTRLTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ctrl import (
						    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CTRLForSequenceClassification,
						    CTRLLMHeadModel,
						    CTRLModel,
						    CTRLPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_ctrl import (
						    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCTRLForSequenceClassification,
						    TFCTRLLMHeadModel,
						    TFCTRLModel,
						    TFCTRLPreTrainedModel,
						)

else:
			import sys

			UpperCamelCase__ =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





