






from typing import TYPE_CHECKING

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


snake_case    				=		{
    """configuration_conditional_detr""": [
        """CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConditionalDetrConfig""",
        """ConditionalDetrOnnxConfig""",
    ]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		["""ConditionalDetrFeatureExtractor"""]
   snake_case    				=		["""ConditionalDetrImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ConditionalDetrForObjectDetection""",
       """ConditionalDetrForSegmentation""",
       """ConditionalDetrModel""",
       """ConditionalDetrPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_conditional_detr import (
       CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
       ConditionalDetrConfig,
       ConditionalDetrOnnxConfig,
   )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
      from .image_processing_conditional_detr import ConditionalDetrImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_conditional_detr import (
          CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConditionalDetrForObjectDetection,
          ConditionalDetrForSegmentation,
          ConditionalDetrModel,
          ConditionalDetrPreTrainedModel,
      )

else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)














def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    [0] * len(lowercase				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    []
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    []
  SCREAMING_SNAKE_CASE       :      Any							=    0

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

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

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

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


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

topological_sort(graph)






