





'''simple docstring'''

from collections import deque





def lowerCamelCase						(     __lowerCamelCase :    str    )						->List[str]:
    _SCREAMING_SNAKE_CASE									=			len(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			deque()
    _SCREAMING_SNAKE_CASE									=			[False for _ in range(__lowerCamelCase    )]
    _SCREAMING_SNAKE_CASE									=			[-1 for _ in range(__lowerCamelCase    )]
    _SCREAMING_SNAKE_CASE									=			index_of[:]

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

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

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

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

    return components





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


if __name__ == "__main__":
      # Test
      lowercase_					= 7
      lowercase_					= [0, 0, 1, 2, 3, 3, 4, 4, 6]
      lowercase_					= [1, 3, 2, 0, 1, 4, 5, 6, 5]
      lowercase_					= [(u, v) for u, v in zip(source, target)]
      lowercase_					= 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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_					= {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= ["""MBartTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= ["""MBartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MBartForCausalLM""",
          """MBartForConditionalGeneration""",
          """MBartForQuestionAnswering""",
          """MBartForSequenceClassification""",
          """MBartModel""",
          """MBartPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """TFMBartForConditionalGeneration""",
          """TFMBartModel""",
          """TFMBartPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """FlaxMBartForConditionalGeneration""",
          """FlaxMBartForQuestionAnswering""",
          """FlaxMBartForSequenceClassification""",
          """FlaxMBartModel""",
          """FlaxMBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart import MBartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart_fast import MBartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mbart import (
                MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                MBartForCausalLM,
                MBartForConditionalGeneration,
                MBartForQuestionAnswering,
                MBartForSequenceClassification,
                MBartModel,
                MBartPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mbart import (
                FlaxMBartForConditionalGeneration,
                FlaxMBartForQuestionAnswering,
                FlaxMBartForSequenceClassification,
                FlaxMBartModel,
                FlaxMBartPreTrainedModel,
            )

else:
      import sys

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



