
_UpperCAmelCase    			=					[[0, 1, 1], [0, 0, 1], [1, 0, 0]]

class 		__magic_name__				:


  """simple docstring"""





  def __init__(					self	,		a__		):
    _lowerCamelCase      =						name
    _lowerCamelCase      =						[]
    _lowerCamelCase      =						[]


  def     _UpperCAmelCase  (					self	,		a__		):
    self.inbound.append(_lowerCamelCase		)


  def     _UpperCAmelCase  (					self	,		a__		):
    self.outbound.append(_lowerCamelCase		)


  def __repr__(					self		):
    return f'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''







def 		_lowerCamelCase       (   _a			,   _a=3			,   _a=0.85  ):
  """simple docstring"""




  _lowerCamelCase      =						{}
  for node in nodes:
    _lowerCamelCase      =						1

  _lowerCamelCase      =						{}
  for node in nodes:
    _lowerCamelCase      =						len(node.outbound  )

  for i in range(a_  ):
    print(F'''======= Iteration {i + 1} ======='''  )
    for _, node in enumerate(a_  ):
      _lowerCamelCase      =						(1 - d) + d * sum(
          ranks[ib] / outbounds[ib] for ib in node.inbound  )
    print(a_  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						list(input('''Enter Names of the Nodes: '''  ).split()  )

  _lowerCamelCase      =						[Node(a_  ) for name in names]

  for ri, row in enumerate(a_  ):
    for ci, col in enumerate(a_  ):
      if col == 1:
        nodes[ci].add_inbound(names[ri]  )
        nodes[ri].add_outbound(names[ci]  )

  print('''======= Nodes ======='''  )
  for node in nodes:
    print(a_  )

  page_rank(a_  )


if __name__ == "__main__":
  main()









'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase__    :     List[Any]			  =   {
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Any			  =   ['LayoutLMTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Optional[Any]			  =   [
      'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'LayoutLMForMaskedLM',
      'LayoutLMForSequenceClassification',
      'LayoutLMForTokenClassification',
      'LayoutLMForQuestionAnswering',
      'LayoutLMModel',
      'LayoutLMPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     str			  =   [
      'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLayoutLMForMaskedLM',
      'TFLayoutLMForSequenceClassification',
      'TFLayoutLMForTokenClassification',
      'TFLayoutLMForQuestionAnswering',
      'TFLayoutLMMainLayer',
      'TFLayoutLMModel',
      'TFLayoutLMPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
  from .tokenization_layoutlm import LayoutLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )

else:
  import sys

  UpperCamelCase__    :     List[str]			  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
