



'''simple docstring'''





from collections import deque

def 			a_	(				_lowerCAmelCase			) -> Optional[Any]:
  __lowerCamelCase			:			Dict       			=					len(_lowerCAmelCase			)
  __lowerCamelCase			:			Tuple       			=					deque()
  __lowerCamelCase			:			Tuple       			=					[False for _ in range(_lowerCAmelCase			)]
  __lowerCamelCase			:			str       			=					[-1 for _ in range(_lowerCAmelCase			)]
  __lowerCamelCase			:			Union[str, Any]       			=					index_of[:]

  def strong_connect(_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			):
    __lowerCamelCase			:			Dict       			=					index  # the number when this node is seen
    __lowerCamelCase			:			Optional[Any]       			=					index  # lowest rank node reachable from here
    index += 1
    stack.append(_lowerCAmelCase			)
    __lowerCamelCase			:			Optional[int]       			=					True

    for w in g[v]:
      if index_of[w] == -1:
        __lowerCamelCase			:			Tuple       			=					strong_connect(_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			)
        __lowerCamelCase			:			Dict       			=					(
            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
        )
      elif on_stack[w]:
        __lowerCamelCase			:			List[str]       			=					(
            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
        )

    if lowlink_of[v] == index_of[v]:
      __lowerCamelCase			:			Optional[int]       			=					[]
      __lowerCamelCase			:			List[str]       			=					stack.pop()
      __lowerCamelCase			:			Optional[Any]       			=					False
      component.append(_lowerCAmelCase			)
      while w != v:
        __lowerCamelCase			:			int       			=					stack.pop()
        __lowerCamelCase			:			Dict       			=					False
        component.append(_lowerCAmelCase			)
      components.append(_lowerCAmelCase			)
    return index

  __lowerCamelCase			:			str       			=					[]
  for v in range(_lowerCAmelCase			):
    if index_of[v] == -1:
      strong_connect(_lowerCAmelCase			,0			,_lowerCAmelCase			)

  return components

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> List[Any]:
  __lowerCamelCase			:			Optional[int]       			=					[[] for _ in range(_lowerCAmelCase			)]
  for u, v in edges:
    g[u].append(_lowerCAmelCase			)
  return g


if __name__ == "__main__":
   # Test
   _UpperCamelCase											=		7
   _UpperCamelCase											=		[0, 0, 1, 2, 3, 3, 4, 4, 6]
   _UpperCamelCase											=		[1, 3, 2, 0, 1, 4, 5, 6, 5]
   _UpperCamelCase											=		[(u, v) for u, v in zip(source, target)]
   _UpperCamelCase											=		create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)











'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase											=		{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		['BridgeTowerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		[
       'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BridgeTowerForContrastiveLearning',
       'BridgeTowerForImageAndTextRetrieval',
       'BridgeTowerForMaskedLM',
       'BridgeTowerModel',
       'BridgeTowerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=		_LazyModule(__name__, globals()['__file__'], _import_structure)






