




lowercase_		=							[[0, 1, 1], [0, 0, 1], [1, 0, 0]]





class 		A__				:






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




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							name
						__magic_name__					:							Optional[Any]		   =							[]
						__magic_name__					:							Dict		   =							[]






	def  lowercase      (	self				,     lowerCamelCase						)      ->   int:




						"""simple docstring"""






						self.inbound.append(lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase						)      ->   Tuple:




						"""simple docstring"""






						self.outbound.append(lowerCamelCase						)






	def __repr__(	self						)      ->   Optional[Any]:




						"""simple docstring"""






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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase=3,	UpperCAmelCase=0.85       )  ->Union[str, Any]:






					"""simple docstring"""






					__magic_name__					:							Any		   =							{}
					for node in nodes:
										__magic_name__					:							int		   =							1

					__magic_name__					:							Optional[Any]		   =							{}
					for node in nodes:
										__magic_name__					:							Optional[int]		   =							len(node.outbound       )

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







def  lowerCAmelCase  (	)  ->Optional[int]:






					"""simple docstring"""






					__magic_name__					:							List[str]		   =							list(input('''Enter Names of the Nodes: '''       ).split()       )

					__magic_name__					:							Union[str, Any]		   =							[Node(UpperCAmelCase       ) for name in names]

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

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

					page_rank(UpperCAmelCase       )


if __name__ == "__main__":
				main()











from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase     :							List[str]	     =			logging.get_logger(__name__)

lowercase     :							int	     =			{
    '''google/bigbird-pegasus-large-arxiv''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-pubmed''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-bigpatent''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'''
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						A   :      List[str]						 =    'bigbird_pegasus'
						A   :      Optional[int]						 =    ['past_key_values']
						A   :      Tuple						 =    {
						    'num_attention_heads': 'encoder_attention_heads',
						    'hidden_size': 'd_model',
						    'attention_probs_dropout_prob': 'attention_dropout',
						}






						def __init__(		self						, _SCREAMING_SNAKE_CASE=9_6103						, _SCREAMING_SNAKE_CASE=4096						, _SCREAMING_SNAKE_CASE=16						, _SCREAMING_SNAKE_CASE=4096						, _SCREAMING_SNAKE_CASE=16						, _SCREAMING_SNAKE_CASE=16						, _SCREAMING_SNAKE_CASE=4096						, _SCREAMING_SNAKE_CASE=16						, _SCREAMING_SNAKE_CASE=0.0						, _SCREAMING_SNAKE_CASE=0.0						, _SCREAMING_SNAKE_CASE=True						, _SCREAMING_SNAKE_CASE=True						, _SCREAMING_SNAKE_CASE="gelu_new"						, _SCREAMING_SNAKE_CASE=1024						, _SCREAMING_SNAKE_CASE=0.1						, _SCREAMING_SNAKE_CASE=0.0						, _SCREAMING_SNAKE_CASE=0.0						, _SCREAMING_SNAKE_CASE=0.02						, _SCREAMING_SNAKE_CASE=2						, _SCREAMING_SNAKE_CASE=0.0						, _SCREAMING_SNAKE_CASE=True						, _SCREAMING_SNAKE_CASE=0						, _SCREAMING_SNAKE_CASE=2						, _SCREAMING_SNAKE_CASE=1						, _SCREAMING_SNAKE_CASE="block_sparse"						, _SCREAMING_SNAKE_CASE=64						, _SCREAMING_SNAKE_CASE=3						, _SCREAMING_SNAKE_CASE=False						, **_SCREAMING_SNAKE_CASE						, )       -> Dict:
								snake_case_ :							str         =				vocab_size
								snake_case_ :							Tuple         =				max_position_embeddings
								snake_case_ :							List[Any]         =				d_model
								snake_case_ :							List[str]         =				encoder_ffn_dim
								snake_case_ :							List[str]         =				encoder_layers
								snake_case_ :							int         =				encoder_attention_heads
								snake_case_ :							Tuple         =				decoder_ffn_dim
								snake_case_ :							List[Any]         =				decoder_layers
								snake_case_ :							Optional[int]         =				decoder_attention_heads
								snake_case_ :							Any         =				dropout
								snake_case_ :							Tuple         =				attention_dropout
								snake_case_ :							List[str]         =				activation_dropout
								snake_case_ :							Any         =				activation_function
								snake_case_ :							Any         =				init_std
								snake_case_ :							Optional[int]         =				encoder_layerdrop
								snake_case_ :							Optional[Any]         =				decoder_layerdrop
								snake_case_ :							Tuple         =				classifier_dropout
								snake_case_ :							str         =				use_cache
								snake_case_ :							Dict         =				encoder_layers
								snake_case_ :							List[str]         =				scale_embedding  # scale factor will be sqrt(d_model) if True

								# extra config
								snake_case_ :							Union[str, Any]         =				attention_type
								snake_case_ :							Tuple         =				block_size
								snake_case_ :							Any         =				num_random_blocks
								snake_case_ :							List[str]         =				use_bias

								super().__init__(
								    pad_token_id=_SCREAMING_SNAKE_CASE						, bos_token_id=_SCREAMING_SNAKE_CASE						, eos_token_id=_SCREAMING_SNAKE_CASE						, is_encoder_decoder=_SCREAMING_SNAKE_CASE						, decoder_start_token_id=_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE						, )




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						@property
						def 					_lowerCAmelCase				(		self )       -> Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
										snake_case_ :							str         =				OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
										        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
										    ] )

										if self.use_past:
												snake_case_ :							List[str]         =				{0: "batch"}
												snake_case_ :							Dict         =				{0: "batch", 1: "past_decoder_sequence + sequence"}
										else:
												snake_case_ :							Any         =				{0: "batch", 1: "decoder_sequence"}
												snake_case_ :							int         =				{0: "batch", 1: "decoder_sequence"}

										if self.use_past:
												self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE						, direction="inputs" )
								elif self.task == "causal-lm":
										# TODO: figure this case out.
										snake_case_ :							Union[str, Any]         =				OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
										        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
										    ] )
										if self.use_past:
												snake_case_       ,				snake_case_ :							Tuple         =				self.num_layers
												for i in range(_SCREAMING_SNAKE_CASE ):
														snake_case_ :							int         =				{0: "batch", 2: "past_sequence + sequence"}
														snake_case_ :							Dict         =				{0: "batch", 2: "past_sequence + sequence"}
								else:
										snake_case_ :							Union[str, Any]         =				OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
										        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
										        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
										        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
										    ] )

								return common_inputs






						@property
						def 					_lowerCAmelCase				(		self )       -> Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
										snake_case_ :							str         =				super().outputs
								else:
										snake_case_ :							Optional[Any]         =				super(_SCREAMING_SNAKE_CASE						, self ).outputs
										if self.use_past:
												snake_case_       ,				snake_case_ :							Optional[int]         =				self.num_layers
												for i in range(_SCREAMING_SNAKE_CASE ):
														snake_case_ :							Tuple         =				{0: "batch", 2: "past_sequence + sequence"}
														snake_case_ :							Union[str, Any]         =				{0: "batch", 2: "past_sequence + sequence"}
								return common_outputs






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = -1						, _SCREAMING_SNAKE_CASE = -1						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = None						, )       -> Mapping[str, Any]:
								snake_case_ :							List[str]         =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )

								# Generate decoder inputs
								snake_case_ :							Optional[int]         =				seq_length if not self.use_past else 1
								snake_case_ :							Dict         =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )
								snake_case_ :							List[Any]         =				{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
								snake_case_ :							str         =				dict(**_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE )

								if self.use_past:
										if not is_torch_available():
												raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
										else:
												import torch
										snake_case_       ,				snake_case_ :							str         =				common_inputs["input_ids"].shape
										snake_case_ :							List[str]         =				common_inputs["decoder_input_ids"].shape[1]
										snake_case_       ,				snake_case_ :							Tuple         =				self.num_attention_heads
										snake_case_ :							List[Any]         =				(
										    batch,
										    num_encoder_attention_heads,
										    encoder_seq_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										snake_case_ :							Optional[Any]         =				decoder_seq_length + 3
										snake_case_ :							Any         =				(
										    batch,
										    num_decoder_attention_heads,
										    decoder_past_length,
										    self._config.hidden_size // num_decoder_attention_heads,
										)

										snake_case_ :							Optional[Any]         =				torch.cat(
										    [common_inputs["decoder_attention_mask"], torch.ones(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )]						, dim=1 )

										snake_case_ :							int         =				[]
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										snake_case_       ,				snake_case_ :							Any         =				self.num_layers
										snake_case_ :							Any         =				min(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )
										snake_case_ :							Dict         =				max(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE ) - min_num_layers
										snake_case_ :							Dict         =				"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

										for _ in range(_SCREAMING_SNAKE_CASE ):
												common_inputs["past_key_values"].append(
												    (
												        torch.zeros(_SCREAMING_SNAKE_CASE ),
												        torch.zeros(_SCREAMING_SNAKE_CASE ),
												        torch.zeros(_SCREAMING_SNAKE_CASE ),
												        torch.zeros(_SCREAMING_SNAKE_CASE ),
												    ) )
										# TODO: test this.
										snake_case_ :							Optional[int]         =				encoder_shape if remaining_side_name == "encoder" else decoder_shape
										for _ in range(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE ):
												common_inputs["past_key_values"].append((torch.zeros(_SCREAMING_SNAKE_CASE ), torch.zeros(_SCREAMING_SNAKE_CASE )) )
								return common_inputs






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = -1						, _SCREAMING_SNAKE_CASE = -1						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = None						, )       -> Mapping[str, Any]:
								snake_case_ :							Union[str, Any]         =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )

								if self.use_past:
										if not is_torch_available():
												raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
										else:
												import torch
										snake_case_       ,				snake_case_ :							List[str]         =				common_inputs["input_ids"].shape
										# Not using the same length for past_key_values
										snake_case_ :							Union[str, Any]         =				seqlen + 2
										snake_case_       ,				snake_case_ :							Any         =				self.num_layers
										snake_case_       ,				snake_case_ :							Optional[Any]         =				self.num_attention_heads
										snake_case_ :							Optional[int]         =				(
										    batch,
										    num_encoder_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)

										snake_case_ :							str         =				common_inputs["attention_mask"].dtype
										snake_case_ :							Union[str, Any]         =				torch.cat(
										    [common_inputs["attention_mask"], torch.ones(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, dtype=_SCREAMING_SNAKE_CASE )]						, dim=1 )
										snake_case_ :							Optional[int]         =				[
										    (torch.zeros(_SCREAMING_SNAKE_CASE ), torch.zeros(_SCREAMING_SNAKE_CASE )) for _ in range(_SCREAMING_SNAKE_CASE )
										]
								return common_inputs






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = -1						, _SCREAMING_SNAKE_CASE = -1						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = None						, )       -> Mapping[str, Any]:
								# Copied from OnnxConfig.generate_dummy_inputs
								# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
								# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
								snake_case_ :							List[str]         =				compute_effective_axis_dimension(
								    _SCREAMING_SNAKE_CASE						, fixed_dimension=OnnxConfig.default_fixed_batch						, num_token_to_add=0 )

								# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
								snake_case_ :							Any         =				tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE )
								snake_case_ :							int         =				compute_effective_axis_dimension(
								    _SCREAMING_SNAKE_CASE						, fixed_dimension=OnnxConfig.default_fixed_sequence						, num_token_to_add=_SCREAMING_SNAKE_CASE )

								# Generate dummy inputs according to compute batch and sequence
								snake_case_ :							List[Any]         =				[" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
								snake_case_ :							Tuple         =				dict(tokenizer(_SCREAMING_SNAKE_CASE						, return_tensors=_SCREAMING_SNAKE_CASE ) )
								return common_inputs






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = -1						, _SCREAMING_SNAKE_CASE = -1						, _SCREAMING_SNAKE_CASE = False						, _SCREAMING_SNAKE_CASE = None						, )       -> Mapping[str, Any]:
								if self.task in ["default", "seq2seq-lm"]:
										snake_case_ :							List[Any]         =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
										    _SCREAMING_SNAKE_CASE						, batch_size=_SCREAMING_SNAKE_CASE						, seq_length=_SCREAMING_SNAKE_CASE						, is_pair=_SCREAMING_SNAKE_CASE						, framework=_SCREAMING_SNAKE_CASE )

								elif self.task == "causal-lm":
										snake_case_ :							Optional[Any]         =				self._generate_dummy_inputs_for_causal_lm(
										    _SCREAMING_SNAKE_CASE						, batch_size=_SCREAMING_SNAKE_CASE						, seq_length=_SCREAMING_SNAKE_CASE						, is_pair=_SCREAMING_SNAKE_CASE						, framework=_SCREAMING_SNAKE_CASE )
								else:
										snake_case_ :							Any         =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    _SCREAMING_SNAKE_CASE						, batch_size=_SCREAMING_SNAKE_CASE						, seq_length=_SCREAMING_SNAKE_CASE						, is_pair=_SCREAMING_SNAKE_CASE						, framework=_SCREAMING_SNAKE_CASE )

								return common_inputs






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )       -> Any:
								if self.task in ["default", "seq2seq-lm"]:
										snake_case_ :							int         =				super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )
								else:
										snake_case_ :							Optional[Any]         =				super(_SCREAMING_SNAKE_CASE						, self )._flatten_past_key_values_(
										    _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )
