





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        snake_case_		(			unittest.TestCase):







		def 			__lowercase   (		self   )				->   Optional[Any]:
				lowerCamelCase   : Dict								=inspect.getfile(accelerate.test_utils   )
				lowerCamelCase   : List[Any]								=os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_script.py''']   )
				lowerCamelCase   : Any								=os.path.sep.join(
				    mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']   )
				lowerCamelCase   : str								=os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_ops.py''']   )







		@require_multi_gpu
		def 			__lowercase   (		self   )				->   Any:
				print(F"Found {torch.cuda.device_count()} devices."   )
				lowerCamelCase   : int								=['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
				with patch_environment(omp_num_threads=1   ):
						execute_subprocess_async(__lowercase			,     env=os.environ.copy()   )







		@require_multi_gpu
		def 			__lowercase   (		self   )				->   Union[str, Any]:
				print(F"Found {torch.cuda.device_count()} devices."   )
				lowerCamelCase   : Dict								=['''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(__lowercase			,     env=os.environ.copy()   )







		@require_multi_gpu
		def 			__lowercase   (		self   )				->   List[str]:
				lowerCamelCase   : List[Any]								=['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__   )]
				with patch_environment(omp_num_threads=1   ):
						execute_subprocess_async(__lowercase			,     env=os.environ.copy()   )







		@require_multi_gpu
		def 			__lowercase   (		self   )				->   Optional[int]:
				print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"   )
				lowerCamelCase   : Any								=['''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(__lowercase			,     env=os.environ.copy()   )


if __name__ == "__main__":
				snake_case_							=			Accelerator()
				snake_case_							=			(accelerator.state.process_index + 2, 1_0)
				snake_case_							=			torch.randint(0, 1_0, shape).to(accelerator.device)

				snake_case_							=			''''''

				snake_case_							=			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)."

				snake_case_							=			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."
				snake_case_							=			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)








import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = math.inf							,       SCREAMING_SNAKE_CASE_ = -math.inf							,       SCREAMING_SNAKE_CASE_ = math.inf							,       SCREAMING_SNAKE_CASE_ = -math.inf							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = 1_0_0							,       SCREAMING_SNAKE_CASE_ = 0.0_1							,       SCREAMING_SNAKE_CASE_ = 1							,       )   ->     Any:
		lowerCamelCase   : Union[str, Any]								=False
		lowerCamelCase   : int								=search_prob
		lowerCamelCase   : int								=start_temperate
		lowerCamelCase   : Any								=[]
		lowerCamelCase   : Any								=0
		lowerCamelCase   : Union[str, Any]								=None

		while not search_end:
				lowerCamelCase   : Dict								=current_state.score()
				if best_state is None or current_score > best_state.score():
						lowerCamelCase   : Tuple								=current_state
				scores.append(SCREAMING_SNAKE_CASE_		)
				iterations += 1
				lowerCamelCase   : Union[str, Any]								=None
				lowerCamelCase   : int								=current_state.get_neighbors()
				while (
				    next_state is None and neighbors
				):  # till we do not find a neighbor that we can move to
						lowerCamelCase   : Union[str, Any]								=random.randint(0							,       len(SCREAMING_SNAKE_CASE_		) - 1		)  # picking a random neighbor
						lowerCamelCase   : List[str]								=neighbors.pop(SCREAMING_SNAKE_CASE_		)
						lowerCamelCase   : Any								=picked_neighbor.score() - current_score

						if (
						    picked_neighbor.x > max_x
						    or picked_neighbor.x < min_x
						    or picked_neighbor.y > max_y
						    or picked_neighbor.y < min_y
						):
								continue  # neighbor outside our bounds

						if not find_max:
								lowerCamelCase   : Dict								=change * -1  # in case we are finding minimum
						if change > 0:  # improves the solution
								lowerCamelCase   : Optional[int]								=picked_neighbor
						else:
								lowerCamelCase   : Any								=(math.e) ** (
								    change / current_temp
								)  # probability generation function
								if random.random() < probability:  # random number within probability
										lowerCamelCase   : Union[str, Any]								=picked_neighbor
				lowerCamelCase   : Union[str, Any]								=current_temp - (current_temp * rate_of_decrease)

				if current_temp < threshold_temp or next_state is None:
						# temperature below threshold, or could not find a suitable neighbor
						lowerCamelCase   : Optional[int]								=True
				else:
						lowerCamelCase   : List[str]								=next_state

		if visualization:
				from matplotlib import pyplot as plt

				plt.plot(range(SCREAMING_SNAKE_CASE_		)							,       SCREAMING_SNAKE_CASE_		)
				plt.xlabel('''Iterations'''		)
				plt.ylabel('''Function values'''		)
				plt.show()
		return best_state


if __name__ == "__main__":


				def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     List[str]:
						return (x**2) + (y**2)

				# starting the problem with initial coordinates (12, 47)
				snake_case_							=			SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			simulated_annealing(
				    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
				)
				print(
				    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
				    F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
				)

				# starting the problem with initial coordinates (12, 47)
				snake_case_							=			SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			simulated_annealing(
				    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
				)
				print(
				    '''The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
				    F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
				)


				def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Tuple:
						return (3 * x**2) - (6 * y)

				snake_case_							=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			simulated_annealing(prob, find_max=False, visualization=True)
				print(
				    '''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
				    F"""{local_min.score()}"""
				)

				snake_case_							=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			simulated_annealing(prob, find_max=True, visualization=True)



				print(
				    '''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
				    F"""{local_min.score()}"""
				)

