
"""simple docstring"""






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (DDPMParallelScheduler,)





	def   __lowerCAmelCase (						self    :int		, **lowerCamelCase_    :Dict					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= {
			    '''num_train_timesteps''': 10_00,
			    '''beta_start''': 0.0_0_0_1,
			    '''beta_end''': 0.0_2,
			    '''beta_schedule''': '''linear''',
			    '''variance_type''': '''fixed_small''',
			    '''clip_sample''': True,
			}

			config.update(**lowerCamelCase_					)
			return config





	def   __lowerCAmelCase (						self    :Any					)							->			int:







			'''simple docstring'''




			for timesteps in [1, 5, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Optional[int]:







			'''simple docstring'''




			for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]		, [0.0_0_2, 0.0_2, 0.2, 2]					):
					self.check_over_configs(beta_start=lowerCamelCase_		, beta_end=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			List[str]:







			'''simple docstring'''




			for schedule in ["linear", "squaredcos_cap_v2"]:
					self.check_over_configs(beta_schedule=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Dict					)							->			str:







			'''simple docstring'''




			for variance in ["fixed_small", "fixed_large", "other"]:
					self.check_over_configs(variance_type=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Optional[int]:







			'''simple docstring'''




			for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :List[Any]					)							->			Union[str, Any]:







			'''simple docstring'''




			self.check_over_configs(thresholding=lowerCamelCase_					)
			for threshold in [0.5, 1.0, 2.0]:
					for prediction_type in ["epsilon", "sample", "v_prediction"]:
							self.check_over_configs(
							    thresholding=lowerCamelCase_		, prediction_type=lowerCamelCase_		, sample_max_value=lowerCamelCase_		, )





	def   __lowerCAmelCase (						self    :int					)							->			List[str]:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Dict					)							->			Union[str, Any]:







			'''simple docstring'''




			for t in [0, 5_00, 9_99]:
					self.check_over_forward(time_step=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     Tuple				= scheduler_class(**lowerCamelCase_					)

			assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(4_87					) - 0.0_0_9_7_9					)					) < 1E-5
			assert torch.sum(torch.abs(scheduler._get_variance(9_99					) - 0.0_2					)					) < 1E-5





	def   __lowerCAmelCase (						self    :List[Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Any				= len(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     Dict				= self.dummy_sample_deter
			SCREAMING_SNAKE_CASE       :     Dict				= self.dummy_sample_deter + 0.1
			SCREAMING_SNAKE_CASE       :     int				= self.dummy_sample_deter - 0.1

			SCREAMING_SNAKE_CASE       :     List[str]				= samplea.shape[0]
			SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.stack([samplea, samplea, samplea]		, dim=0					)
			SCREAMING_SNAKE_CASE       :     List[str]				= torch.arange(lowerCamelCase_					)[0:3, None].repeat(1		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= model(samples.flatten(0		, 1					)		, timesteps.flatten(0		, 1					)					)
			SCREAMING_SNAKE_CASE       :     List[str]				= scheduler.batch_step_no_noise(lowerCamelCase_		, timesteps.flatten(0		, 1					)		, samples.flatten(0		, 1					)					)

			SCREAMING_SNAKE_CASE       :     Any				= torch.sum(torch.abs(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     Any				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_sum.item() - 1_1_5_3.1_8_3_3					) < 1E-2
			assert abs(result_mean.item() - 0.5_0_0_5					) < 1E-3





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     List[Any]				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     Dict				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[str]				= len(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.dummy_sample_deter
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.manual_seed(0					)

			for t in reversed(range(lowerCamelCase_					)					):
					# 1. predict noise residual
					SCREAMING_SNAKE_CASE       :     Tuple				= model(lowerCamelCase_		, lowerCamelCase_					)

					# 2. predict previous mean of sample x_t-1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, generator=lowerCamelCase_					).prev_sample

					SCREAMING_SNAKE_CASE       :     Tuple				= pred_prev_sample

			SCREAMING_SNAKE_CASE       :     Dict				= torch.sum(torch.abs(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     str				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_sum.item() - 2_5_8.9_6_0_6					) < 1E-2
			assert abs(result_mean.item() - 0.3_3_7_2					) < 1E-3





	def   __lowerCAmelCase (						self    :List[str]					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.get_scheduler_config(prediction_type='''v_prediction'''					)
			SCREAMING_SNAKE_CASE       :     Any				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= len(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= self.dummy_model()
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.dummy_sample_deter
			SCREAMING_SNAKE_CASE       :     List[str]				= torch.manual_seed(0					)

			for t in reversed(range(lowerCamelCase_					)					):
					# 1. predict noise residual
					SCREAMING_SNAKE_CASE       :     Optional[int]				= model(lowerCamelCase_		, lowerCamelCase_					)

					# 2. predict previous mean of sample x_t-1
					SCREAMING_SNAKE_CASE       :     Tuple				= scheduler.step(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, generator=lowerCamelCase_					).prev_sample

					SCREAMING_SNAKE_CASE       :     Optional[int]				= pred_prev_sample

			SCREAMING_SNAKE_CASE       :     Any				= torch.sum(torch.abs(lowerCamelCase_					)					)
			SCREAMING_SNAKE_CASE       :     Dict				= torch.mean(torch.abs(lowerCamelCase_					)					)

			assert abs(result_sum.item() - 2_0_2.0_2_9_6					) < 1E-2
			assert abs(result_mean.item() - 0.2_6_3_1					) < 1E-3





	def   __lowerCAmelCase (						self    :List[Any]					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     Optional[int]				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Any				= [1_00, 87, 50, 1, 0]

			scheduler.set_timesteps(timesteps=lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= scheduler.timesteps

			for i, timestep in enumerate(lowerCamelCase_					):
					if i == len(lowerCamelCase_					) - 1:
							SCREAMING_SNAKE_CASE       :     List[str]				= -1
					else:
							SCREAMING_SNAKE_CASE       :     int				= timesteps[i + 1]

					SCREAMING_SNAKE_CASE       :     List[Any]				= scheduler.previous_timestep(lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= prev_t.item()

					self.assertEqual(lowerCamelCase_		, lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Tuple				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= [1_00, 87, 50, 51, 0]

			with self.assertRaises(lowerCamelCase_		, msg='''`custom_timesteps` must be in descending order.'''					):
					scheduler.set_timesteps(timesteps=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Tuple					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     int				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Tuple				= [1_00, 87, 50, 1, 0]
			SCREAMING_SNAKE_CASE       :     Optional[int]				= len(lowerCamelCase_					)

			with self.assertRaises(lowerCamelCase_		, msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''					):
					scheduler.set_timesteps(num_inference_steps=lowerCamelCase_		, timesteps=lowerCamelCase_					)







	def   __lowerCAmelCase (						self    :Tuple					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.scheduler_classes[0]
			SCREAMING_SNAKE_CASE       :     Any				= self.get_scheduler_config()
			SCREAMING_SNAKE_CASE       :     List[Any]				= scheduler_class(**lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= [scheduler.config.num_train_timesteps]

			with self.assertRaises(
			    lowerCamelCase_		, msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''		, ):
					scheduler.set_timesteps(timesteps=lowerCamelCase_					)







"""simple docstring"""






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase__ :       Optional[Any]						     =			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.
lowerCamelCase__ :       Optional[int]						     =			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.
lowerCamelCase__ :       Optional[Any]						     =			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, float]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= len([g for position, g in enumerate(a_     ) if g == main_target[position]]     )
		return (item, float(a_     ))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= random.randint(0		,		len(a_     ) - 1     )
		SCREAMING_SNAKE_CASE       :     str				= parent_a[:random_slice] + parent_a[random_slice:]
		SCREAMING_SNAKE_CASE       :     Dict				= parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)





def __A						(   a_ :							str		,		a_ :							list[str]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= list(a_     )
		if random.uniform(0		,		1     ) < MUTATION_PROBABILITY:
				SCREAMING_SNAKE_CASE       :     Any				= random.choice(a_     )
		return "".join(a_     )





def __A						(   a_ :							tuple[str, float]		,		a_ :							list[tuple[str, float]]		,		a_ :							list[str]		,		)->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= []
		# Generate more children proportionally to the fitness score.
		SCREAMING_SNAKE_CASE       :     List[str]				= int(parent_a[1] * 1_00     ) + 1
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10 if child_n >= 10 else child_n
		for _ in range(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= population_score[random.randint(0		,		a_     )][0]

				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= crossover(parent_a[0]		,		a_     )
				# Append new string to the population list.
				pop.append(mutate(a_		,		a_     )     )
				pop.append(mutate(a_		,		a_     )     )
		return pop





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




		'''simple docstring'''






		if N_POPULATION < N_SELECTED:
				SCREAMING_SNAKE_CASE       :     List[Any]				= F"{N_POPULATION} must be bigger than {N_SELECTED}"
				raise ValueError(a_     )
		# Verify that the target contains no genes besides the ones inside genes variable.
		SCREAMING_SNAKE_CASE       :     List[str]				= sorted({c for c in target if c not in genes}     )
		if not_in_genes_list:
				SCREAMING_SNAKE_CASE       :     str				= F"{not_in_genes_list} is not in genes list, evolution cannot converge"
				raise ValueError(a_     )

		# Generate random starting population.
		SCREAMING_SNAKE_CASE       :     Tuple				= []
		for _ in range(a_     ):
				population.append(''''''.join([random.choice(a_     ) for i in range(len(a_     )     )]     )     )

		# Just some logs to know what the algorithms is doing.
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= 0, 0

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

				# 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.
				SCREAMING_SNAKE_CASE       :     int				= [evaluate(a_		,		a_     ) for item in population]

				# Check if there is a matching evolution.
				SCREAMING_SNAKE_CASE       :     List[Any]				= sorted(a_		,		key=lambda a_     : x[1]		,		reverse=a_     )
				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.
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= population[: int(N_POPULATION / 3     )]
				population.clear()
				population.extend(a_     )
				# Normalize population score to be between 0 and 1.
				SCREAMING_SNAKE_CASE       :     Optional[int]				= [
				    (item, score / len(a_     )) for item, score in population_score
				]

				# This is selection
				for i in range(a_     ):
						population.extend(select(population_score[int(a_     )]		,		a_		,		a_     )     )
						# 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(a_     ) > N_POPULATION:
								break


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





