




'''simple docstring'''







from typing import TYPE_CHECKING

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


A					       ={
    'configuration_dpr': ['DPR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPRConfig'],
    'tokenization_dpr': [
        'DPRContextEncoderTokenizer',
        'DPRQuestionEncoderTokenizer',
        'DPRReaderOutput',
        'DPRReaderTokenizer',
    ],
}


try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A					       =[
       'DPRContextEncoderTokenizerFast',
       'DPRQuestionEncoderTokenizerFast',
       'DPRReaderTokenizerFast',
   ]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A					       =[
       'DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DPRContextEncoder',
       'DPRPretrainedContextEncoder',
       'DPRPreTrainedModel',
       'DPRPretrainedQuestionEncoder',
       'DPRPretrainedReader',
       'DPRQuestionEncoder',
       'DPRReader',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A					       =[
       'TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFDPRContextEncoder',
       'TFDPRPretrainedContextEncoder',
       'TFDPRPretrainedQuestionEncoder',
       'TFDPRPretrainedReader',
       'TFDPRQuestionEncoder',
       'TFDPRReader',
   ]


if TYPE_CHECKING:
   from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
   from .tokenization_dpr import (
       DPRContextEncoderTokenizer,
       DPRQuestionEncoderTokenizer,
       DPRReaderOutput,
       DPRReaderTokenizer,
   )

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_dpr_fast import (
          DPRContextEncoderTokenizerFast,
          DPRQuestionEncoderTokenizerFast,
          DPRReaderTokenizerFast,
      )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dpr import (
          DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPRContextEncoder,
          DPRPretrainedContextEncoder,
          DPRPreTrainedModel,
          DPRPretrainedQuestionEncoder,
          DPRPretrainedReader,
          DPRQuestionEncoder,
          DPRReader,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_dpr import (
          TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDPRContextEncoder,
          TFDPRPretrainedContextEncoder,
          TFDPRPretrainedQuestionEncoder,
          TFDPRPretrainedReader,
          TFDPRQuestionEncoder,
          TFDPRReader,
      )

else:
   import sys

   A					       =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










'''simple docstring'''







A					       =[[0, 1, 1], [0, 0, 1], [1, 0, 0]]


class      _a    :
       def __init__(      self		:      Optional[int]  ,							lowercase		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						name
           UpperCAmelCase     			=						[]
           UpperCAmelCase     			=						[]
       def        A (      self		:      Union[str, Any]  ,							lowercase		:      int							):



           '''simple docstring'''

           self.inbound.append(lowercase							)
       def        A (      self		:      List[str]  ,							lowercase		:      Optional[Any]							):



           '''simple docstring'''

           self.outbound.append(lowercase							)







       def __repr__(      self		:      Any							):



           '''simple docstring'''

           return f"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"






def 		snake_case_				(_a      :				str       ,  _a      :				str=3       ,  _a      :				Any=0.85	):
    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						1

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

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





def 		snake_case_				():
    UpperCAmelCase     			=						list(input('''Enter Names of the Nodes: '''	).split()	)

    UpperCAmelCase     			=						[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()




