



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__    =	{
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    =	[
      """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RobertaForCausalLM""",
      """RobertaForMaskedLM""",
      """RobertaForMultipleChoice""",
      """RobertaForQuestionAnswering""",
      """RobertaForSequenceClassification""",
      """RobertaForTokenClassification""",
      """RobertaModel""",
      """RobertaPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    =	[
      """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRobertaForCausalLM""",
      """TFRobertaForMaskedLM""",
      """TFRobertaForMultipleChoice""",
      """TFRobertaForQuestionAnswering""",
      """TFRobertaForSequenceClassification""",
      """TFRobertaForTokenClassification""",
      """TFRobertaMainLayer""",
      """TFRobertaModel""",
      """TFRobertaPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    =	[
      """FlaxRobertaForCausalLM""",
      """FlaxRobertaForMaskedLM""",
      """FlaxRobertaForMultipleChoice""",
      """FlaxRobertaForQuestionAnswering""",
      """FlaxRobertaForSequenceClassification""",
      """FlaxRobertaForTokenClassification""",
      """FlaxRobertaModel""",
      """FlaxRobertaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
  from .tokenization_roberta import RobertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roberta_fast import RobertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta import (
        ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaForCausalLM,
        RobertaForMaskedLM,
        RobertaForMultipleChoice,
        RobertaForQuestionAnswering,
        RobertaForSequenceClassification,
        RobertaForTokenClassification,
        RobertaModel,
        RobertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta import (
        TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        TFRobertaForMultipleChoice,
        TFRobertaForQuestionAnswering,
        TFRobertaForSequenceClassification,
        TFRobertaForTokenClassification,
        TFRobertaMainLayer,
        TFRobertaModel,
        TFRobertaPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta import (
        FlaxRobertaForCausalLM,
        FlaxRobertaForMaskedLM,
        FlaxRobertaForMultipleChoice,
        FlaxRobertaForQuestionAnswering,
        FlaxRobertaForSequenceClassification,
        FlaxRobertaForTokenClassification,
        FlaxRobertaModel,
        FlaxRobertaPreTrainedModel,
    )

else:
  import sys

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





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Union[str, Any]:
 _snake_case    :  str					=					[False] * len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					[-1] * len(SCREAMING_SNAKE_CASE__				)

 def dfs(SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				):
  _snake_case    :  Optional[Any]					=					True
  _snake_case    :  Tuple					=					c
  for u in graph[v]:
   if not visited[u]:
    dfs(SCREAMING_SNAKE_CASE__				,       1 - c				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  if not visited[i]:
   dfs(SCREAMING_SNAKE_CASE__				,       0				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  for j in graph[i]:
   if color[i] == color[j]:
    return False

 return True


# Adjacency list of graph
a__    =	{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}

print(check_bipartite_dfs(graph))
