from queue import PriorityQueue
from typing import Any

import numpy as np

def    UpperCamelCase						(	_A,   _A,   _A,   _A,   _A,   _A,   _A,   _A,   _A,   ):






						"""simple docstring"""







						for nxt, d in graph[v]:
												if nxt in visited_forward:
																		continue
												__magic_name__      :       int       						= cst_fwd.get(UpperCAmelCase__,   np.inf			)
												__magic_name__      :       Tuple       						= cst_fwd[v] + d
												if new_cost_f < old_cost_f:
																		queue.put((new_cost_f, nxt)			)
																		__magic_name__      :       Optional[int]       						= new_cost_f
																		__magic_name__      :       Tuple       						= v
												if nxt in visited_backward:
																		if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																								__magic_name__      :       int       						= cst_fwd[v] + d + cst_bwd[nxt]
						return shortest_distance

def    UpperCamelCase						(	_A,   _A,   _A,   _A			):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= -1

						__magic_name__      :       str       						= set()
						__magic_name__      :       Optional[int]       						= set()
						__magic_name__      :       Tuple       						= {source: 0}
						__magic_name__      :       Optional[int]       						= {destination: 0}
						__magic_name__      :       Any       						= {source: None}
						__magic_name__      :       Tuple       						= {destination: None}
						__magic_name__      :       int       						= PriorityQueue()
						__magic_name__      :       Optional[int]       						= PriorityQueue()

						__magic_name__      :       Tuple       						= 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():
												__magic_name__						,__magic_name__      :       Optional[int]       						= queue_forward.get()
												visited_forward.add(UpperCAmelCase__			)

												__magic_name__						,__magic_name__      :       Union[str, Any]       						= queue_backward.get()
												visited_backward.add(UpperCAmelCase__			)

												__magic_name__      :       List[str]       						= pass_and_relaxation(
												    UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   )

												__magic_name__      :       List[str]       						= pass_and_relaxation(
												    UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   )

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

						if shortest_distance != np.inf:
												__magic_name__      :       Any       						= shortest_distance
						return shortest_path_distance


__magic_name__:					Union[str, Any]							=     {
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}
__magic_name__:					Any							=     {
    "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()








import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
		import optax
		from flax.training.common_utils import onehot

		from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
		from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 	snake_case__ (							unittest.TestCase				):
	@slow
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Any       						= FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""			)
							__magic_name__      :       Dict       						= AutoTokenizer.from_pretrained("""google/mt5-small"""			)

							__magic_name__      :       Tuple       						= tokenizer("""Hello there"""      ,       return_tensors="""np"""			).input_ids
							__magic_name__      :       List[Any]       						= tokenizer("""Hi I am"""      ,       return_tensors="""np"""			).input_ids

							__magic_name__      :       Any       						= shift_tokens_right(lowerCAmelCase__      ,       model.config.pad_token_id      ,       model.config.decoder_start_token_id			)

							__magic_name__      :       List[Any]       						= model(lowerCAmelCase__      ,       decoder_input_ids=lowerCAmelCase__			).logits
							__magic_name__      :       Tuple       						= optax.softmax_cross_entropy(lowerCAmelCase__      ,       onehot(lowerCAmelCase__      ,       logits.shape[-1]			)			).mean()

							__magic_name__      :       List[Any]       						= -(labels.shape[-1] * loss.item())

							__magic_name__      :       List[Any]       						= -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1e-4			)
