


"""simple docstring"""

from collections import deque


def lowerCAmelCase_      (				UpperCamelCase__	: Dict ):






 """simple docstring"""







 __lowercase									=	len(UpperCamelCase__ )
 __lowercase									=	deque()
 __lowercase									=	[False for _ in range(UpperCamelCase__ )]
 __lowercase									=	[-1 for _ in range(UpperCamelCase__ )]
 __lowercase									=	index_of[:]

 def strong_connect(UpperCamelCase__	: List[Any]				,   UpperCamelCase__	: Dict				,   UpperCamelCase__	: Tuple ):
  __lowercase									=	index  # the number when this node is seen
  __lowercase									=	index  # lowest rank node reachable from here
  index += 1
  stack.append(UpperCamelCase__ )
  __lowercase									=	True

  for w in g[v]:
   if index_of[w] == -1:
    __lowercase									=	strong_connect(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )
    __lowercase									=	(
        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
    )
   elif on_stack[w]:
    __lowercase									=	(
        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
    )

  if lowlink_of[v] == index_of[v]:
   __lowercase									=	[]
   __lowercase									=	stack.pop()
   __lowercase									=	False
   component.append(UpperCamelCase__ )
   while w != v:
    __lowercase									=	stack.pop()
    __lowercase									=	False
    component.append(UpperCamelCase__ )
   components.append(UpperCamelCase__ )
  return index

 __lowercase									=	[]
 for v in range(UpperCamelCase__ ):
  if index_of[v] == -1:
   strong_connect(UpperCamelCase__				,   0				,   UpperCamelCase__ )

 return components


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple				,   UpperCamelCase__	: Any ):






 """simple docstring"""







 __lowercase									=	[[] for _ in range(UpperCamelCase__ )]
 for u, v in edges:
  g[u].append(UpperCamelCase__ )
 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_tf_available, is_torch_available


UpperCAmelCase__  							={
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase__  							=[
           "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Data2VecAudioForAudioFrameClassification",
           "Data2VecAudioForCTC",
           "Data2VecAudioForSequenceClassification",
           "Data2VecAudioForXVector",
           "Data2VecAudioModel",
           "Data2VecAudioPreTrainedModel",
       ]
       UpperCAmelCase__  							=[
           "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Data2VecTextForCausalLM",
           "Data2VecTextForMaskedLM",
           "Data2VecTextForMultipleChoice",
           "Data2VecTextForQuestionAnswering",
           "Data2VecTextForSequenceClassification",
           "Data2VecTextForTokenClassification",
           "Data2VecTextModel",
           "Data2VecTextPreTrainedModel",
       ]
       UpperCAmelCase__  							=[
           "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Data2VecVisionForImageClassification",
           "Data2VecVisionForMaskedImageModeling",
           "Data2VecVisionForSemanticSegmentation",
           "Data2VecVisionModel",
           "Data2VecVisionPreTrainedModel",
       ]

if is_tf_available():
       UpperCAmelCase__  							=[
           "TFData2VecVisionForImageClassification",
           "TFData2VecVisionForSemanticSegmentation",
           "TFData2VecVisionModel",
           "TFData2VecVisionPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
       from .configuration_dataavec_text import (
           DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           DataaVecTextConfig,
           DataaVecTextOnnxConfig,
       )
       from .configuration_dataavec_vision import (
           DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
           DataaVecVisionConfig,
           DataaVecVisionOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dataavec_audio import (
                  DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DataaVecAudioForAudioFrameClassification,
                  DataaVecAudioForCTC,
                  DataaVecAudioForSequenceClassification,
                  DataaVecAudioForXVector,
                  DataaVecAudioModel,
                  DataaVecAudioPreTrainedModel,
              )
              from .modeling_dataavec_text import (
                  DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DataaVecTextForCausalLM,
                  DataaVecTextForMaskedLM,
                  DataaVecTextForMultipleChoice,
                  DataaVecTextForQuestionAnswering,
                  DataaVecTextForSequenceClassification,
                  DataaVecTextForTokenClassification,
                  DataaVecTextModel,
                  DataaVecTextPreTrainedModel,
              )
              from .modeling_dataavec_vision import (
                  DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DataaVecVisionForImageClassification,
                  DataaVecVisionForMaskedImageModeling,
                  DataaVecVisionForSemanticSegmentation,
                  DataaVecVisionModel,
                  DataaVecVisionPreTrainedModel,
              )
       if is_tf_available():
              from .modeling_tf_dataavec_vision import (
                  TFDataaVecVisionForImageClassification,
                  TFDataaVecVisionForSemanticSegmentation,
                  TFDataaVecVisionModel,
                  TFDataaVecVisionPreTrainedModel,
              )

else:
       import sys

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