
lowercase__				:				Any						=      {
    0: "underwear",
    1: "pants",
    2: "belt",
    3: "suit",
    4: "shoe",
    5: "socks",
    6: "shirt",
    7: "tie",
    8: "watch",
}

lowercase__				:				List[Any]						=      [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

lowercase__				:				int						=      [0 for x in range(len(graph))]
lowercase__				:				str						=      []




def 						A_			(				snake_case				: Any			,   snake_case				: List[str]			)   ->     Any:



						'''simple docstring'''

						__UpperCamelCase  	=      1
						while stack:
												__UpperCamelCase  	=      stack.pop()
												print(snake_case			,   clothes[current_clothing]			)
												order += 1




def 						A_			(				snake_case				: Dict			,   snake_case				: Union[str, Any]			,   snake_case				: Optional[Any]			)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      1
						for v in graph[u]:
												if not visited[v]:
																		depth_first_search(snake_case			,   snake_case			,   snake_case			)

						stack.append(snake_case			)




def 						A_			(				snake_case				: List[str]			,   snake_case				: Any			)   ->     Union[str, Any]:



						'''simple docstring'''

						for v in range(len(snake_case			)			):
												if not visited[v]:
																		depth_first_search(snake_case			,   snake_case			,   snake_case			)


if __name__ == "__main__":
				topological_sort(graph, visited)
				print(stack)
				print_stack(stack, clothes)









"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase					:					Any 		=		{
    "configuration_unispeech_sat": ["UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Any 		=		[
		    "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "UniSpeechSatForAudioFrameClassification",
		    "UniSpeechSatForCTC",
		    "UniSpeechSatForPreTraining",
		    "UniSpeechSatForSequenceClassification",
		    "UniSpeechSatForXVector",
		    "UniSpeechSatModel",
		    "UniSpeechSatPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech_sat import (
				    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechSatForAudioFrameClassification,
				    UniSpeechSatForCTC,
				    UniSpeechSatForPreTraining,
				    UniSpeechSatForSequenceClassification,
				    UniSpeechSatForXVector,
				    UniSpeechSatModel,
				    UniSpeechSatPreTrainedModel,
				)

else:
		import sys

		_lowercase					:					int 		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




