





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment






class       __A					(					unittest.TestCase				):

			'''simple docstring'''







			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	inspect.getfile(accelerate.test_utils							)
				lowerCamelCase__              =	os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_script.py''']							)
				lowerCamelCase__              =	os.path.sep.join(
				    mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']							)
				lowerCamelCase__              =	os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_ops.py''']							)





			@require_multi_gpu
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				print(F'Found {torch.cuda.device_count()} devices.'							)
				lowerCamelCase__              =	['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
				with patch_environment(omp_num_threads=1							):
					execute_subprocess_async(_SCREAMING_SNAKE_CASE ,						env=os.environ.copy()							)





			@require_multi_gpu
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				print(F'Found {torch.cuda.device_count()} devices.'							)
				lowerCamelCase__              =	['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
				print(F'Command: {cmd}'							)
				with patch_environment(omp_num_threads=1							):
					execute_subprocess_async(_SCREAMING_SNAKE_CASE ,						env=os.environ.copy()							)





			@require_multi_gpu
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__							)]
				with patch_environment(omp_num_threads=1							):
					execute_subprocess_async(_SCREAMING_SNAKE_CASE ,						env=os.environ.copy()							)





			@require_multi_gpu
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				print(F'Found {torch.cuda.device_count()} devices, using 2 devices only'							)
				lowerCamelCase__              =	['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
				with patch_environment(omp_num_threads=1 ,						cuda_visible_devices='''0,1'''							):
					execute_subprocess_async(_SCREAMING_SNAKE_CASE ,						env=os.environ.copy()							)


if __name__ == "__main__":
			_a						=   Accelerator()
			_a						=   (accelerator.state.process_index + 2, 10)
			_a						=   torch.randint(0, 10, shape).to(accelerator.device)

			_a						=   ""

			_a						=   accelerator.pad_across_processes(tensor)
			if tensora.shape[0] != accelerator.state.num_processes + 1:
						error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
			if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
						error_msg += "Tensors have different values."
			if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
						error_msg += "Padding was not done with the right value (0)."

			_a						=   accelerator.pad_across_processes(tensor, pad_first=True)
			if tensora.shape[0] != accelerator.state.num_processes + 1:
						error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
			_a						=   accelerator.state.num_processes - accelerator.state.process_index - 1
			if not torch.equal(tensora[index:], tensor):
						error_msg += "Tensors have different values."
			if not torch.all(tensora[:index] == 0):
						error_msg += "Padding was not done with the right value (0)."

			# Raise error at the end to make sure we don't stop at the first failure.
			if len(error_msg) > 0:
						raise ValueError(error_msg)










from queue import PriorityQueue
from typing import Any

import numpy as np





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,)       ->   float | int:







	'''simple docstring'''







	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		lowerCamelCase__              =	cst_fwd.get(__snake_case ,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 lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   int:







	'''simple docstring'''







	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(__snake_case							)

		lowerCamelCase__       ,			lowerCamelCase__              =	queue_backward.get()
		visited_backward.add(__snake_case							)

		lowerCamelCase__              =	pass_and_relaxation(
		    __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,)

		lowerCamelCase__              =	pass_and_relaxation(
		    __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,)

		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()



