






"""simple docstring"""





def   a    (     __UpperCAmelCase      :    List[Any]  )    ->							str:
						__magic_name__:	Optional[int]							=       [0] * len(__UpperCAmelCase  )
						__magic_name__:	str							=       []
						__magic_name__:	Any							=       []
						__magic_name__:	Union[str, Any]							=       0

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(__UpperCAmelCase  )  ):
												if indegree[i] == 0:
																		queue.append(__UpperCAmelCase  )

						while queue:
												__magic_name__:	Optional[Any]							=       queue.pop(0  )
												cnt += 1
												topo.append(__UpperCAmelCase  )
												for x in graph[vertex]:
																		indegree[x] -= 1
																		if indegree[x] == 0:
																								queue.append(__UpperCAmelCase  )

						if cnt != len(__UpperCAmelCase  ):
												print("""Cycle exists"""  )
						else:
												print(__UpperCAmelCase  )


# Adjacency List of Graph
__lowerCamelCase							=  {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}

topological_sort(graph)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase  :      Union[str, Any]			 ={
    """configuration_clap""": [
        """CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ClapAudioConfig""",
        """ClapConfig""",
        """ClapTextConfig""",
    ],
    """processing_clap""": ["""ClapProcessor"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Union[str, Any]			 =[
					    """CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ClapModel""",
					    """ClapPreTrainedModel""",
					    """ClapTextModel""",
					    """ClapTextModelWithProjection""",
					    """ClapAudioModel""",
					    """ClapAudioModelWithProjection""",
					]
					_lowerCAmelCase  :      List[str]			 =["""ClapFeatureExtractor"""]

if TYPE_CHECKING:
					from .configuration_clap import (
					    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
					    ClapAudioConfig,
					    ClapConfig,
					    ClapTextConfig,
					)
					from .processing_clap import ClapProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clap import ClapFeatureExtractor
										from .modeling_clap import (
										    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ClapAudioModel,
										    ClapAudioModelWithProjection,
										    ClapModel,
										    ClapPreTrainedModel,
										    ClapTextModel,
										    ClapTextModelWithProjection,
										)


else:
					import sys

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