


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest



@require_torchsde
class 	__lowerCamelCase			(_a						):
       _lowercase											=     (DPMSolverSDEScheduler,)
       _lowercase											=     10







       def 		snake_case_	(			self: Tuple,**A_: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			{
                'num_train_timesteps': 1100,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'noise_sampler_seed': 0,
            }

            config.update(**A_					)
            return config







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=A_					)







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],[0.0_0_0_2, 0.0_0_2, 0.0_2]					):
                 self.check_over_configs(beta_start=A_,beta_end=A_					)







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=A_					)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=A_					)







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.scheduler_classes[0]
            __UpperCamelCase =			self.get_scheduler_config()
            __UpperCamelCase =			scheduler_class(**A_					)

            scheduler.set_timesteps(self.num_inference_steps					)

            __UpperCamelCase =			self.dummy_model()
            __UpperCamelCase =			self.dummy_sample_deter * scheduler.init_noise_sigma
            __UpperCamelCase =			sample.to(A_					)

            for i, t in enumerate(scheduler.timesteps					):
                 __UpperCamelCase =			scheduler.scale_model_input(A_,A_					)

                 __UpperCamelCase =			model(A_,A_					)

                 __UpperCamelCase =			scheduler.step(A_,A_,A_					)
                 __UpperCamelCase =			output.prev_sample

            __UpperCamelCase =			torch.sum(torch.abs(A_					)					)
            __UpperCamelCase =			torch.mean(torch.abs(A_					)					)

            if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5					) < 1E-2
                 assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7					) < 1E-3
            elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6					) < 1E-2
                 assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2					) < 1E-3
            else:
                 assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2					) < 1E-2
                 assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6					) < 1E-3







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.scheduler_classes[0]
            __UpperCamelCase =			self.get_scheduler_config(prediction_type='v_prediction'					)
            __UpperCamelCase =			scheduler_class(**A_					)

            scheduler.set_timesteps(self.num_inference_steps					)

            __UpperCamelCase =			self.dummy_model()
            __UpperCamelCase =			self.dummy_sample_deter * scheduler.init_noise_sigma
            __UpperCamelCase =			sample.to(A_					)

            for i, t in enumerate(scheduler.timesteps					):
                 __UpperCamelCase =			scheduler.scale_model_input(A_,A_					)

                 __UpperCamelCase =			model(A_,A_					)

                 __UpperCamelCase =			scheduler.step(A_,A_,A_					)
                 __UpperCamelCase =			output.prev_sample

            __UpperCamelCase =			torch.sum(torch.abs(A_					)					)
            __UpperCamelCase =			torch.mean(torch.abs(A_					)					)

            if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3					) < 1E-2
                 assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4					) < 1E-3
            elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3					) < 1E-2
                 assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7					) < 1E-3
            else:
                 assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5					) < 1E-2
                 assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1					) < 1E-3







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.scheduler_classes[0]
            __UpperCamelCase =			self.get_scheduler_config()
            __UpperCamelCase =			scheduler_class(**A_					)

            scheduler.set_timesteps(self.num_inference_steps,device=A_					)

            __UpperCamelCase =			self.dummy_model()
            __UpperCamelCase =			self.dummy_sample_deter.to(A_					) * scheduler.init_noise_sigma

            for t in scheduler.timesteps:
                 __UpperCamelCase =			scheduler.scale_model_input(A_,A_					)

                 __UpperCamelCase =			model(A_,A_					)

                 __UpperCamelCase =			scheduler.step(A_,A_,A_					)
                 __UpperCamelCase =			output.prev_sample

            __UpperCamelCase =			torch.sum(torch.abs(A_					)					)
            __UpperCamelCase =			torch.mean(torch.abs(A_					)					)

            if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8					) < 1E-2
                 assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5					) < 1E-3
            elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2					) < 1E-2
                 assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1					) < 1E-3
            else:
                 assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2					) < 1E-2
                 assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6					) < 1E-3







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.scheduler_classes[0]
            __UpperCamelCase =			self.get_scheduler_config()
            __UpperCamelCase =			scheduler_class(**A_,use_karras_sigmas=A_					)

            scheduler.set_timesteps(self.num_inference_steps,device=A_					)

            __UpperCamelCase =			self.dummy_model()
            __UpperCamelCase =			self.dummy_sample_deter.to(A_					) * scheduler.init_noise_sigma
            __UpperCamelCase =			sample.to(A_					)

            for t in scheduler.timesteps:
                 __UpperCamelCase =			scheduler.scale_model_input(A_,A_					)

                 __UpperCamelCase =			model(A_,A_					)

                 __UpperCamelCase =			scheduler.step(A_,A_,A_					)
                 __UpperCamelCase =			output.prev_sample

            __UpperCamelCase =			torch.sum(torch.abs(A_					)					)
            __UpperCamelCase =			torch.mean(torch.abs(A_					)					)

            if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8					) < 1E-2
                 assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1					) < 1E-2
            elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5					) < 1E-2
                 assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1					) < 1E-2
            else:
                 assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2					) < 1E-2
                 assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1					) < 1E-2





'''simple docstring'''





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
UpperCamelCase_    						=       200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
UpperCamelCase_    						=       50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
UpperCamelCase_    						=       0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  tuple[str, float]:
						lowercase :							int    		=len([g for position, g in enumerate(__magic_name__						) if g == main_target[position]]						)
						return (item, float(__magic_name__						))






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  tuple[str, str]:
						lowercase :							Any    		=random.randint(0   , len(__magic_name__						) - 1						)
						lowercase :							Tuple    		=parent_a[:random_slice] + parent_a[random_slice:]
						lowercase :							List[str]    		=parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     list[str]						)				->  str:
						lowercase :							Union[str, Any]    		=list(__magic_name__						)
						if random.uniform(0   , 1						) < MUTATION_PROBABILITY:
												lowercase :							Dict    		=random.choice(__magic_name__						)
						return "".join(__magic_name__						)






def 				_lowerCAmelCase			(   __magic_name__						:     tuple[str, float]   , __magic_name__						:     list[tuple[str, float]]   , __magic_name__						:     list[str]   , )				->  list[str]:
						lowercase :							Any    		=[]
						# Generate more children proportionally to the fitness score.
						lowercase :							Dict    		=int(parent_a[1] * 100						) + 1
						lowercase :							List[str]    		=10 if child_n >= 10 else child_n
						for _ in range(__magic_name__						):
												lowercase :							List[str]    		=population_score[random.randint(0   , __magic_name__						)][0]

												lowercase		,	lowercase :							Dict    		=crossover(parent_a[0]   , __magic_name__						)
												# Append new string to the population list.
												pop.append(mutate(__magic_name__   , __magic_name__						)						)
												pop.append(mutate(__magic_name__   , __magic_name__						)						)
						return pop






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     list[str]   , __magic_name__						:     bool = True						)				->  tuple[int, int, str]:

						# Verify if N_POPULATION is bigger than N_SELECTED
						if N_POPULATION < N_SELECTED:
												lowercase :							List[str]    		=f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
												raise ValueError(__magic_name__						)
						# Verify that the target contains no genes besides the ones inside genes variable.
						lowercase :							Optional[int]    		=sorted({c for c in target if c not in genes}						)
						if not_in_genes_list:
												lowercase :							Dict    		=f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
												raise ValueError(__magic_name__						)

						# Generate random starting population.
						lowercase :							int    		=[]
						for _ in range(__magic_name__						):
												population.append(''''''.join([random.choice(__magic_name__						) for i in range(len(__magic_name__						)						)]						)						)

						# Just some logs to know what the algorithms is doing.
						lowercase		,	lowercase :							Optional[int]    		=0, 0

						# This loop will end when we find a perfect match for our target.
						while True:
												generation += 1
												total_population += len(__magic_name__						)

												# Random population created. Now it's time to evaluate.

												# Adding a bit of concurrency can make everything faster,
												#
												# import concurrent.futures
												# population_score: list[tuple[str, float]] = []
												# with concurrent.futures.ThreadPoolExecutor(
												#                                   max_workers=NUM_WORKERS) as executor:
												#     futures = {executor.submit(evaluate, item) for item in population}
												#     concurrent.futures.wait(futures)
												#     population_score = [item.result() for item in futures]
												#
												# but with a simple algorithm like this, it will probably be slower.
												# We just need to call evaluate for every item inside the population.
												lowercase :							List[str]    		=[evaluate(__magic_name__   , __magic_name__						) for item in population]

												# Check if there is a matching evolution.
												lowercase :							int    		=sorted(__magic_name__   , key=lambda __magic_name__						: x[1]   , reverse=__magic_name__						)
												if population_score[0][0] == target:
																		return (generation, total_population, population_score[0][0])

												# Print the best result every 10 generation.
												# Just to know that the algorithm is working.
												if debug and generation % 10 == 0:
																		print(
																		    f'''\nGeneration: {generation}'''
																		    f'''\nTotal Population:{total_population}'''
																		    f'''\nBest score: {population_score[0][1]}'''
																		    f'''\nBest string: {population_score[0][0]}'''						)

												# Flush the old population, keeping some of the best evolutions.
												# Keeping this avoid regression of evolution.
												lowercase :							Any    		=population[: int(N_POPULATION / 3						)]
												population.clear()
												population.extend(__magic_name__						)
												# Normalize population score to be between 0 and 1.
												lowercase :							Dict    		=[
												    (item, score / len(__magic_name__						)) for item, score in population_score
												]

												# This is selection
												for i in range(__magic_name__						):
																		population.extend(select(population_score[int(__magic_name__						)]   , __magic_name__   , __magic_name__						)						)
																		# Check if the population has already reached the maximum value and if so,
																		# break the cycle.  If this check is disabled, the algorithm will take
																		# forever to compute large strings, but will also calculate small strings in
																		# a far fewer generations.
																		if len(__magic_name__						) > N_POPULATION:
																								break


if __name__ == "__main__":
							UpperCamelCase_    						=       (
							    """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
							)
							UpperCamelCase_    						=       list(
							    """ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
							    """nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
							)
							UpperCamelCase_						,   UpperCamelCase_						,   UpperCamelCase_    						=       basic(target_str, genes_list)
							print(
							    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
							)





