


'''simple docstring'''


def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       Tuple								=      [0] * len(__lowerCAmelCase   )
		_UpperCAmelCase		:       Dict								=      []
		_UpperCAmelCase		:       Optional[Any]								=      []
		_UpperCAmelCase		:       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:
				_UpperCAmelCase		:       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
lowerCamelCase__    	=     {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_torch_available


lowerCamelCase__    	=     {
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__    	=     [
						    'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'InstructBlipQFormerModel',
						    'InstructBlipPreTrainedModel',
						    'InstructBlipForConditionalGeneration',
						    'InstructBlipVisionModel',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						lowerCamelCase__    	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



