





from queue import PriorityQueue
from typing import Any

import numpy as np


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				,	__a				,	__a				,	__a				,	__a				,	__a				,	):
	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		snake_case_						:     Any     =		cst_fwd.get(__a				,	np.inf				)
		snake_case_						:     Optional[int]     =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt)				)
			snake_case_						:     Optional[Any]     =		new_cost_f
			snake_case_						:     List[Any]     =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				snake_case_						:     str     =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				):
	snake_case_						:     Union[str, Any]     =		-1

	snake_case_						:     List[Any]     =		set()
	snake_case_						:     str     =		set()
	snake_case_						:     List[Any]     =		{source: 0}
	snake_case_						:     Union[str, Any]     =		{destination: 0}
	snake_case_						:     Optional[Any]     =		{source: None}
	snake_case_						:     Union[str, Any]     =		{destination: None}
	snake_case_						:     PriorityQueue[Any]     =		PriorityQueue()
	snake_case_						:     PriorityQueue[Any]     =		PriorityQueue()

	snake_case_						:     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():
		snake_case_  ,snake_case_						:     List[Any]     =		queue_forward.get()
		visited_forward.add(__a				)

		snake_case_  ,snake_case_						:     Dict     =		queue_backward.get()
		visited_backward.add(__a				)

		snake_case_						:     Dict     =		pass_and_relaxation(
		    __a				,	__a				,	__a				,	__a				,	__a				,	__a				,	__a				,	__a				,	__a				,	)

		snake_case_						:     str     =		pass_and_relaxation(
		    __a				,	__a				,	__a				,	__a				,	__a				,	__a				,	__a				,	__a				,	__a				,	)

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

	if shortest_distance != np.inf:
		snake_case_						:     Optional[int]     =		shortest_distance
	return shortest_path_distance


_SCREAMING_SNAKE_CASE 		=						{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
_SCREAMING_SNAKE_CASE 		=						{
    """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()








_SCREAMING_SNAKE_CASE 		=						{
    """Pillow""": """Pillow""",
    """accelerate""": """accelerate>=0.11.0""",
    """compel""": """compel==0.1.8""",
    """black""": """black~=23.1""",
    """datasets""": """datasets""",
    """filelock""": """filelock""",
    """flax""": """flax>=0.4.1""",
    """hf-doc-builder""": """hf-doc-builder>=0.3.0""",
    """huggingface-hub""": """huggingface-hub>=0.13.2""",
    """requests-mock""": """requests-mock==1.10.0""",
    """importlib_metadata""": """importlib_metadata""",
    """invisible-watermark""": """invisible-watermark""",
    """isort""": """isort>=5.5.4""",
    """jax""": """jax>=0.2.8,!=0.3.2""",
    """jaxlib""": """jaxlib>=0.1.65""",
    """Jinja2""": """Jinja2""",
    """k-diffusion""": """k-diffusion>=0.0.12""",
    """torchsde""": """torchsde""",
    """note_seq""": """note_seq""",
    """librosa""": """librosa""",
    """numpy""": """numpy""",
    """omegaconf""": """omegaconf""",
    """parameterized""": """parameterized""",
    """protobuf""": """protobuf>=3.20.3,<4""",
    """pytest""": """pytest""",
    """pytest-timeout""": """pytest-timeout""",
    """pytest-xdist""": """pytest-xdist""",
    """ruff""": """ruff>=0.0.241""",
    """safetensors""": """safetensors""",
    """sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""",
    """scipy""": """scipy""",
    """onnx""": """onnx""",
    """regex""": """regex!=2019.12.17""",
    """requests""": """requests""",
    """tensorboard""": """tensorboard""",
    """torch""": """torch>=1.4""",
    """torchvision""": """torchvision""",
    """transformers""": """transformers>=4.25.1""",
    """urllib3""": """urllib3<=2.0.0""",
}

