





'''simple docstring'''






def       lowercase_      (		lowercase__						)   ->Dict:
						_snake_case:   Tuple  					=		[0] * len(__lowerCAmelCase						)
						_snake_case:   Dict  					=		[]
						_snake_case:   Optional[Any]  					=		[]
						_snake_case:   Any  					=		0

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

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

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

						if cnt != len(__lowerCAmelCase						):
												print('Cycle exists'						)
						else:
												print(__lowerCAmelCase						)


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



topological_sort(graph)






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A   : Dict             =			{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			['SpeechEncoderDecoderModel']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Union[str, Any]             =			['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
		from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
		import sys

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





