
"""simple docstring"""
import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase = True ,    _lowerCAmelCase = math.inf ,    _lowerCAmelCase = -math.inf ,    _lowerCAmelCase = math.inf ,    _lowerCAmelCase = -math.inf ,    _lowerCAmelCase = False ,    _lowerCAmelCase = 100 ,    _lowerCAmelCase = 0.01 ,    _lowerCAmelCase = 1 ,    ):
						__lowerCAmelCase     =  False
						__lowerCAmelCase     =  search_prob
						__lowerCAmelCase     =  start_temperate
						__lowerCAmelCase     =  []
						__lowerCAmelCase     =  0
						__lowerCAmelCase     =  None

						while not search_end:
												__lowerCAmelCase     =  current_state.score()
												if best_state is None or current_score > best_state.score():
																		__lowerCAmelCase     =  current_state
												scores.append(_lowerCAmelCase						)
												iterations += 1
												__lowerCAmelCase     =  None
												__lowerCAmelCase     =  current_state.get_neighbors()
												while (
												    next_state is None and neighbors
												):  # till we do not find a neighbor that we can move to
																		__lowerCAmelCase     =  random.randint(0 ,    len(_lowerCAmelCase						) - 1						)  # picking a random neighbor
																		__lowerCAmelCase     =  neighbors.pop(_lowerCAmelCase						)
																		__lowerCAmelCase     =  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     =  change * -1  # in case we are finding minimum
																		if change > 0:  # improves the solution
																								__lowerCAmelCase     =  picked_neighbor
																		else:
																								__lowerCAmelCase     =  (math.e) ** (
																								    change / current_temp
																								)  # probability generation function
																								if random.random() < probability:  # random number within probability
																														__lowerCAmelCase     =  picked_neighbor
												__lowerCAmelCase     =  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     =  True
												else:
																		__lowerCAmelCase     =  next_state

						if visualization:
												from matplotlib import pyplot as plt

												plt.plot(range(_lowerCAmelCase						) ,    _lowerCAmelCase						)
												plt.xlabel("""Iterations"""						)
												plt.ylabel("""Function values"""						)
												plt.show()
						return best_state


if __name__ == "__main__":

			def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
									return (x**2) + (y**2)

			# starting the problem with initial coordinates (12, 47)
			SCREAMING_SNAKE_CASE_					     =	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
			SCREAMING_SNAKE_CASE_					     =	simulated_annealing(
			    prob, find_max=False, max_x=100, min_x=5, max_y=50, 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)
			SCREAMING_SNAKE_CASE_					     =	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
			SCREAMING_SNAKE_CASE_					     =	simulated_annealing(
			    prob, find_max=True, max_x=100, min_x=5, max_y=50, 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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
									return (3 * x**2) - (6 * y)

			SCREAMING_SNAKE_CASE_					     =	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			SCREAMING_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()}"
			)

			SCREAMING_SNAKE_CASE_					     =	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			SCREAMING_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()}"
			)


"""simple docstring"""
import operator as op


SCREAMING_SNAKE_CASE_					     =	'''scaler.pt'''
SCREAMING_SNAKE_CASE_					     =	'''pytorch_model'''
SCREAMING_SNAKE_CASE_					     =	'''random_states'''
SCREAMING_SNAKE_CASE_					     =	'''optimizer'''
SCREAMING_SNAKE_CASE_					     =	'''scheduler'''
SCREAMING_SNAKE_CASE_					     =	'''pytorch_model.bin'''
SCREAMING_SNAKE_CASE_					     =	'''pytorch_model.bin.index.json'''
SCREAMING_SNAKE_CASE_					     =	'''model.safetensors'''
SCREAMING_SNAKE_CASE_					     =	'''model.safetensors.index.json'''
SCREAMING_SNAKE_CASE_					     =	'''1.10.2'''
SCREAMING_SNAKE_CASE_					     =	'''py38'''
SCREAMING_SNAKE_CASE_					     =	'''4.17.0'''
SCREAMING_SNAKE_CASE_					     =	['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
SCREAMING_SNAKE_CASE_					     =	['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
SCREAMING_SNAKE_CASE_					     =	['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
SCREAMING_SNAKE_CASE_					     =	['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
SCREAMING_SNAKE_CASE_					     =	['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
SCREAMING_SNAKE_CASE_					     =	'''2.0.1'''
SCREAMING_SNAKE_CASE_					     =	['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
SCREAMING_SNAKE_CASE_					     =	['''default''', '''reduce-overhead''', '''max-autotune''']

SCREAMING_SNAKE_CASE_					     =	{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
SCREAMING_SNAKE_CASE_					     =	[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

SCREAMING_SNAKE_CASE_					     =	['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
SCREAMING_SNAKE_CASE_					     =	['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']
