from typing import TYPE_CHECKING

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


__snake_case			      =    {
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case			      =    ['''MPNetTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case			      =    [
					    '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''MPNetForMaskedLM''',
					    '''MPNetForMultipleChoice''',
					    '''MPNetForQuestionAnswering''',
					    '''MPNetForSequenceClassification''',
					    '''MPNetForTokenClassification''',
					    '''MPNetLayer''',
					    '''MPNetModel''',
					    '''MPNetPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case			      =    [
					    '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFMPNetEmbeddings''',
					    '''TFMPNetForMaskedLM''',
					    '''TFMPNetForMultipleChoice''',
					    '''TFMPNetForQuestionAnswering''',
					    '''TFMPNetForSequenceClassification''',
					    '''TFMPNetForTokenClassification''',
					    '''TFMPNetMainLayer''',
					    '''TFMPNetModel''',
					    '''TFMPNetPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
					from .tokenization_mpnet import MPNetTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mpnet_fast import MPNetTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mpnet import (
										    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MPNetForMaskedLM,
										    MPNetForMultipleChoice,
										    MPNetForQuestionAnswering,
										    MPNetForSequenceClassification,
										    MPNetForTokenClassification,
										    MPNetLayer,
										    MPNetModel,
										    MPNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mpnet import (
										    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFMPNetEmbeddings,
										    TFMPNetForMaskedLM,
										    TFMPNetForMultipleChoice,
										    TFMPNetForQuestionAnswering,
										    TFMPNetForSequenceClassification,
										    TFMPNetForTokenClassification,
										    TFMPNetMainLayer,
										    TFMPNetModel,
										    TFMPNetPreTrainedModel,
										)

else:
					import sys

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

__snake_case			      =    [[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 			__snake_case   :
 def __init__(  self		,   snake_case__						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =name
       UpperCAmelCase    : Tuple      =[]
       UpperCAmelCase    : str      =[]
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       self.inbound.append(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Dict:
       '''simple docstring'''





       self.outbound.append(snake_case__						)






 def __repr__(  self						)	->							Union[str, Any]:
       '''simple docstring'''





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





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase=3		,					__lowerCAmelCase=0.85						)->  Any:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      ={}
      for node in nodes:
            UpperCAmelCase    : str      =1

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

      for i in range(__lowerCAmelCase						):
            print(f'''======= Iteration {i + 1} ======='''						)
            for _, node in enumerate(__lowerCAmelCase						):
                  UpperCAmelCase    : Optional[int]      =(1 - d) + d * sum(
                      ranks[ib] / outbounds[ib] for ib in node.inbound						)
            print(__lowerCAmelCase						)





def   lowerCAmelCase_ (				)->  Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase    : Union[str, Any]      =list(input('''Enter Names of the Nodes: '''						).split()						)

      UpperCAmelCase    : str      =[Node(__lowerCAmelCase						) for name in names]

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

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

      page_rank(__lowerCAmelCase						)


if __name__ == "__main__":
     main()
