from typing import TYPE_CHECKING

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


__a  :					List[str]          =  {
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :					Any          =  ["""CamembertTokenizer"""]

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :					Optional[Any]          =  ["""CamembertTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :					Tuple          =  [
		    """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """CamembertForCausalLM""",
		    """CamembertForMaskedLM""",
		    """CamembertForMultipleChoice""",
		    """CamembertForQuestionAnswering""",
		    """CamembertForSequenceClassification""",
		    """CamembertForTokenClassification""",
		    """CamembertModel""",
		    """CamembertPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :					int          =  [
		    """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFCamembertForCausalLM""",
		    """TFCamembertForMaskedLM""",
		    """TFCamembertForMultipleChoice""",
		    """TFCamembertForQuestionAnswering""",
		    """TFCamembertForSequenceClassification""",
		    """TFCamembertForTokenClassification""",
		    """TFCamembertModel""",
		    """TFCamembertPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_camembert import CamembertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_camembert_fast import CamembertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_camembert import (
				    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CamembertForCausalLM,
				    CamembertForMaskedLM,
				    CamembertForMultipleChoice,
				    CamembertForQuestionAnswering,
				    CamembertForSequenceClassification,
				    CamembertForTokenClassification,
				    CamembertModel,
				    CamembertPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_camembert import (
				    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCamembertForCausalLM,
				    TFCamembertForMaskedLM,
				    TFCamembertForMultipleChoice,
				    TFCamembertForQuestionAnswering,
				    TFCamembertForSequenceClassification,
				    TFCamembertForTokenClassification,
				    TFCamembertModel,
				    TFCamembertPreTrainedModel,
				)

else:
		import sys

		__a  :					Optional[int]          =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
__a  :					Union[str, Any]          =  [[0, 1, 1], [0, 0, 1], [1, 0, 0]]




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							name
							__lowercase			      =							[]
							__lowercase			      =							[]





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							self.inbound.append(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							self.outbound.append(lowerCAmelCase__						)





			def __repr__(       self						)					-> Tuple:

							'''simple docstring'''


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





def     UpperCAmelCase						(   lowercase							,      lowercase=3							,      lowercase=0.85							):







				"""simple docstring"""



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

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

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





def     UpperCAmelCase						(   ):







				"""simple docstring"""



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

				__lowercase			      =							[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()