
"""simple docstring"""


from queue import PriorityQueue
from typing import Any

import numpy as np






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,):
			for nxt, d in graph[v]:
						if nxt in visited_forward:
									continue
						lowerCamelCase_					=  cst_fwd.get(lowerCAmelCase__	,np.inf						)
						lowerCamelCase_					=  cst_fwd[v] + d
						if new_cost_f < old_cost_f:
									queue.put((new_cost_f, nxt)						)
									lowerCamelCase_					=  new_cost_f
									lowerCamelCase_					=  v
						if nxt in visited_backward:
									if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
												lowerCamelCase_					=  cst_fwd[v] + d + cst_bwd[nxt]
			return shortest_distance






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  -1

			lowerCamelCase_					=  set()
			lowerCamelCase_					=  set()
			lowerCamelCase_					=  {source: 0}
			lowerCamelCase_					=  {destination: 0}
			lowerCamelCase_					=  {source: None}
			lowerCamelCase_					=  {destination: None}
			lowerCamelCase_					=  PriorityQueue()
			lowerCamelCase_					=  PriorityQueue()

			lowerCamelCase_					=  np.inf

			queue_forward.put((0, source)						)
			queue_backward.put((0, destination)						)

			if source == destination:
						return 0

			while not queue_forward.empty() and not queue_backward.empty():
						lowerCamelCase_		, lowerCamelCase_					=  queue_forward.get()
						visited_forward.add(lowerCAmelCase__						)

						lowerCamelCase_		, lowerCamelCase_					=  queue_backward.get()
						visited_backward.add(lowerCAmelCase__						)

						lowerCamelCase_					=  pass_and_relaxation(
						    lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,)

						lowerCamelCase_					=  pass_and_relaxation(
						    lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,)

						if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
									break

			if shortest_distance != np.inf:
						lowerCamelCase_					=  shortest_distance
			return shortest_path_distance


A_    							=					{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
A_    							=					{
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
						import doctest

						doctest.testmod()





'''simple docstring'''
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__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""marian"""
		lowerCamelCase     							=						["""past_key_values"""]
		lowerCamelCase     							=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(      self				:			Any  ,	UpperCamelCase__				:			Dict=5_8101  ,	UpperCamelCase__				:			int=None  ,	UpperCamelCase__				:			Union[str, Any]=1024  ,	UpperCamelCase__				:			List[Any]=12  ,	UpperCamelCase__				:			str=4096  ,	UpperCamelCase__				:			Optional[Any]=16  ,	UpperCamelCase__				:			str=12  ,	UpperCamelCase__				:			Tuple=4096  ,	UpperCamelCase__				:			Any=16  ,	UpperCamelCase__				:			Tuple=0.0  ,	UpperCamelCase__				:			Dict=0.0  ,	UpperCamelCase__				:			Dict=True  ,	UpperCamelCase__				:			Any=True  ,	UpperCamelCase__				:			List[Any]="gelu"  ,	UpperCamelCase__				:			Tuple=1024  ,	UpperCamelCase__				:			Optional[Any]=0.1  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			Optional[int]=0.0  ,	UpperCamelCase__				:			Optional[Any]=0.02  ,	UpperCamelCase__				:			Tuple=5_8100  ,	UpperCamelCase__				:			Union[str, Any]=False  ,	UpperCamelCase__				:			str=5_8100  ,	UpperCamelCase__				:			List[str]=0  ,	UpperCamelCase__				:			int=0  ,	UpperCamelCase__				:			Dict=True  ,	**UpperCamelCase__				:			int  ,	)						->			int:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						vocab_size
				snake_case  :       Optional[int]     			=						decoder_vocab_size or vocab_size
				snake_case  :       int     			=						max_position_embeddings
				snake_case  :       Tuple     			=						d_model
				snake_case  :       int     			=						encoder_ffn_dim
				snake_case  :       Optional[int]     			=						encoder_layers
				snake_case  :       Union[str, Any]     			=						encoder_attention_heads
				snake_case  :       List[str]     			=						decoder_ffn_dim
				snake_case  :       List[str]     			=						decoder_layers
				snake_case  :       List[str]     			=						decoder_attention_heads
				snake_case  :       Any     			=						dropout
				snake_case  :       Optional[Any]     			=						attention_dropout
				snake_case  :       Tuple     			=						activation_dropout
				snake_case  :       Union[str, Any]     			=						activation_function
				snake_case  :       int     			=						init_std
				snake_case  :       Dict     			=						encoder_layerdrop
				snake_case  :       Dict     			=						decoder_layerdrop
				snake_case  :       List[Any]     			=						use_cache
				snake_case  :       int     			=						encoder_layers
				snake_case  :       List[str]     			=						scale_embedding  # scale factor will be sqrt(d_model) if True
				snake_case  :       str     			=						share_encoder_decoder_embeddings
				super().__init__(
				    pad_token_id=UpperCamelCase__  ,	eos_token_id=UpperCamelCase__  ,	is_encoder_decoder=UpperCamelCase__  ,	decoder_start_token_id=UpperCamelCase__  ,	forced_eos_token_id=UpperCamelCase__  ,	**UpperCamelCase__  ,	)







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@property
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				if self.task in ["default", "seq2seq-lm"]:
						snake_case  :       Any     			=						OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]      )

						if self.use_past:
								snake_case  :       List[Any]     			=						{0: '''batch'''}
								snake_case  :       Optional[Any]     			=						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
								snake_case  :       List[Any]     			=						{0: '''batch''', 1: '''decoder_sequence'''}
								snake_case  :       Any     			=						{0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
								self.fill_with_past_key_values_(UpperCamelCase__  ,	direction='''inputs'''      )
				elif self.task == "causal-lm":
						# TODO: figure this case out.
						snake_case  :       Any     			=						OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]      )
						if self.use_past:
								snake_case			,snake_case  :       Union[str, Any]     			=						self.num_layers
								for i in range(UpperCamelCase__      ):
										snake_case  :       Optional[Any]     			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
										snake_case  :       List[str]     			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
				else:
						snake_case  :       Dict     			=						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
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
		def       lowerCAmelCase				(      self				:			Any      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				if self.task in ["default", "seq2seq-lm"]:
						snake_case  :       Any     			=						super().outputs
				else:
						snake_case  :       int     			=						super(UpperCamelCase__  ,	self      ).outputs
						if self.use_past:
								snake_case			,snake_case  :       Optional[Any]     			=						self.num_layers
								for i in range(UpperCamelCase__      ):
										snake_case  :       Optional[Any]     			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
										snake_case  :       Optional[Any]     			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
				return common_outputs



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			PreTrainedTokenizer  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[TensorType] = None  ,	)						->			Mapping[str, Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self._generate_dummy_inputs_for_encoder_and_decoder(
				    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

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

				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  :       Dict     			=						common_inputs['''input_ids'''].shape
						snake_case  :       Any     			=						common_inputs['''decoder_input_ids'''].shape[1]
						snake_case			,snake_case  :       Tuple     			=						self.num_attention_heads
						snake_case  :       str     			=						(
						    batch,
						    num_encoder_attention_heads,
						    encoder_seq_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						snake_case  :       Union[str, Any]     			=						decoder_seq_length + 3
						snake_case  :       Union[str, Any]     			=						(
						    batch,
						    num_decoder_attention_heads,
						    decoder_past_length,
						    self._config.hidden_size // num_decoder_attention_heads,
						)

						snake_case  :       Optional[int]     			=						torch.cat(
						    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase__  ,	UpperCamelCase__      )]  ,	dim=1      )

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

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



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			PreTrainedTokenizer  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[TensorType] = None  ,	)						->			Mapping[str, Any]:






				"""simple docstring"""







				snake_case  :       str     			=						self._generate_dummy_inputs_for_encoder_and_decoder(
				    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				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  :       Optional[Any]     			=						common_inputs['''input_ids'''].shape
						# Not using the same length for past_key_values
						snake_case  :       int     			=						seqlen + 2
						snake_case			,snake_case  :       int     			=						self.num_layers
						snake_case			,snake_case  :       List[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  :       Tuple     			=						common_inputs['''attention_mask'''].dtype
						snake_case  :       Optional[int]     			=						torch.cat(
						    [common_inputs['''attention_mask'''], torch.ones(UpperCamelCase__  ,	UpperCamelCase__  ,	dtype=UpperCamelCase__      )]  ,	dim=1      )
						snake_case  :       str     			=						[
						    (torch.zeros(UpperCamelCase__      ), torch.zeros(UpperCamelCase__      )) for _ in range(UpperCamelCase__      )
						]
				return common_inputs



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			PreTrainedTokenizer  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[TensorType] = None  ,	)						->			Mapping[str, Any]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						compute_effective_axis_dimension(
				    UpperCamelCase__  ,	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  :       List[str]     			=						tokenizer.num_special_tokens_to_add(UpperCamelCase__      )
				snake_case  :       str     			=						compute_effective_axis_dimension(
				    UpperCamelCase__  ,	fixed_dimension=OnnxConfig.default_fixed_sequence  ,	num_token_to_add=UpperCamelCase__      )

				# Generate dummy inputs according to compute batch and sequence
				snake_case  :       str     			=						[''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
				snake_case  :       Union[str, Any]     			=						dict(tokenizer(UpperCamelCase__  ,	return_tensors=UpperCamelCase__      )      )
				return common_inputs



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			PreTrainedTokenizer  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[TensorType] = None  ,	)						->			Mapping[str, Any]:






				"""simple docstring"""







				if self.task in ["default", "seq2seq-lm"]:
						snake_case  :       Dict     			=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    UpperCamelCase__  ,	batch_size=UpperCamelCase__  ,	seq_length=UpperCamelCase__  ,	is_pair=UpperCamelCase__  ,	framework=UpperCamelCase__      )

				else:
						snake_case  :       Tuple     			=						self._generate_dummy_inputs_for_causal_lm(
						    UpperCamelCase__  ,	batch_size=UpperCamelCase__  ,	seq_length=UpperCamelCase__  ,	is_pair=UpperCamelCase__  ,	framework=UpperCamelCase__      )

				return common_inputs



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Union[str, Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				if self.task in ["default", "seq2seq-lm"]:
						snake_case  :       List[Any]     			=						super()._flatten_past_key_values_(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				else:
						snake_case  :       Optional[int]     			=						super(UpperCamelCase__  ,	self      )._flatten_past_key_values_(
						    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			float:






				"""simple docstring"""







				return 1e-4


