



'''simple docstring'''
def 		_SCREAMING_SNAKE_CASE				(					lowerCamelCase__ :   int					):


			'''simple docstring'''







			A:   Any								= [0] * len(lowerCamelCase__					)
			A:   List[str]								= []
			A:   Any								= []
			A:   List[str]								= 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:
						A:   int								= 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
__SCREAMING_SNAKE_CASE			:   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_flax_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE_            =    {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''BeitFeatureExtractor''']
   SCREAMING_SNAKE_CASE_            =    ['''BeitImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BeitForImageClassification''',
       '''BeitForMaskedImageModeling''',
       '''BeitForSemanticSegmentation''',
       '''BeitModel''',
       '''BeitPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''FlaxBeitForImageClassification''',
       '''FlaxBeitForMaskedImageModeling''',
       '''FlaxBeitModel''',
       '''FlaxBeitPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_beit import BeitFeatureExtractor
      from .image_processing_beit import BeitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_beit import (
          BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
          BeitPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_beit import (
          FlaxBeitForImageClassification,
          FlaxBeitForMaskedImageModeling,
          FlaxBeitModel,
          FlaxBeitPreTrainedModel,
      )


else:
   import sys

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



