




a_										=       [[0, 1, 1], [0, 0, 1], [1, 0, 0]]

class 	_lowerCamelCase  :

      """simple docstring"""

      def __init__(       self   :	str	,    snake_case   :	Any			):
          __UpperCamelCase											=  name
          __UpperCamelCase											=  []
          __UpperCamelCase											=  []







      def  snake_case		(       self   :	Any	,    snake_case   :	Dict			):
          self.inbound.append(snake_case			)







      def  snake_case		(       self   :	Tuple	,    snake_case   :	Dict			):
          self.outbound.append(snake_case			)







      def __repr__(       self   :	List[str]			):
          return F"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"







def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_=3   ,       lowercase_=0.85 )    ->		Tuple:

    """simple docstring"""


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

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

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




def    __SCREAMING_SNAKE_CASE				(					)    ->		int:

    """simple docstring"""


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

    __UpperCamelCase											=  [Node(lowercase_ ) for name in names]

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

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

    page_rank(lowercase_ )


if __name__ == "__main__":
 main()












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,
)


a_										=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       [
     "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MarianForCausalLM",
     "MarianModel",
     "MarianMTModel",
     "MarianPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
 from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_marian import MarianTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_marian import (
      MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarianForCausalLM,
      MarianModel,
      MarianMTModel,
      MarianPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
 import sys

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






