



"""simple docstring"""
def       _snake_case						(						UpperCamelCase						:		Optional[int]						):
 UpperCAmelCase	:					List[str]	=	[0] * len(__SCREAMING_SNAKE_CASE						)
 UpperCAmelCase	:					Union[str, Any]	=	[]
 UpperCAmelCase	:					int	=	[]
 UpperCAmelCase	:					Optional[int]	=	0

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

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

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

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


# Adjacency List of Graph
A:   Dict								=		{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, is_vision_available


_lowercase :  Union[str, Any]    							=       {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Optional[int]    							=       ["Mask2FormerImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  str    							=       [
							    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Mask2FormerForUniversalSegmentation",
							    "Mask2FormerModel",
							    "Mask2FormerPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_maskaformer import MaskaFormerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskaformer import (
														    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskaFormerForUniversalSegmentation,
														    MaskaFormerModel,
														    MaskaFormerPreTrainedModel,
														)


else:
							import sys

							_lowercase :  Dict    							=       _LazyModule(__name__, globals()["__file__"], _import_structure)



