
'''simple docstring'''







from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowercase    :							Optional[Any]      =					8







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=BITS	)       ->	Tuple:
	_snake_case   						=		x.device

	_snake_case   						=		(x * 255).int().clamp(0			,				255	)

	_snake_case   						=		2 ** torch.arange(bits - 1			,				-1			,				-1			,				device=__A	)
	_snake_case   						=		rearrange(__A			,				'd -> d 1 1'	)
	_snake_case   						=		rearrange(__A			,				'b c h w -> b c 1 h w'	)

	_snake_case   						=		((x & mask) != 0).float()
	_snake_case   						=		rearrange(__A			,				'b c d h w -> b (c d) h w'	)
	_snake_case   						=		bits * 2 - 1
	return bits







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=BITS	)       ->	Optional[int]:
	_snake_case   						=		x.device

	_snake_case   						=		(x > 0).int()
	_snake_case   						=		2 ** torch.arange(bits - 1			,				-1			,				-1			,				device=__A			,				dtype=torch.intaa	)

	_snake_case   						=		rearrange(__A			,				'd -> d 1 1'	)
	_snake_case   						=		rearrange(__A			,				'b (c d) h w -> b c d h w'			,				d=8	)
	_snake_case   						=		reduce(x * mask			,				'b c d h w -> b c h w'			,				'sum'	)
	return (dec / 255).clamp(0.0			,				1.0	)







def 				SCREAMING_SNAKE_CASE__	(   self			,				__A			,				__A			,				__A			,				__A = 0.0			,				__A = True			,				__A=None			,				__A = True			,				)       ->	Union[DDIMSchedulerOutput, Tuple]:
	if self.num_inference_steps is None:
		raise ValueError(
		    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'	)

	# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
	# Ideally, read DDIM paper in-detail understanding

	# Notation (<variable name> -> <name in paper>
	# - pred_noise_t -> e_theta(x_t, t)
	# - pred_original_sample -> f_theta(x_t, t) or x_0
	# - std_dev_t -> sigma_t
	# - eta -> η
	# - pred_sample_direction -> "direction pointing to x_t"
	# - pred_prev_sample -> "x_t-1"

	# 1. get previous step value (=t-1)
	_snake_case   						=		timestep - self.config.num_train_timesteps // self.num_inference_steps

	# 2. compute alphas, betas
	_snake_case   						=		self.alphas_cumprod[timestep]
	_snake_case   						=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

	_snake_case   						=		1 - alpha_prod_t

	# 3. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	_snake_case   						=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

	# 4. Clip "predicted x_0"
	_snake_case   						=		self.bit_scale
	if self.config.clip_sample:
		_snake_case   						=		torch.clamp(__A			,				-scale			,				__A	)

	# 5. compute variance: "sigma_t(η)" -> see formula (16)
	# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
	_snake_case   						=		self._get_variance(__A			,				__A	)
	_snake_case   						=		eta * variance ** 0.5

	if use_clipped_model_output:
		# the model_output is always re-derived from the clipped x_0 in Glide
		_snake_case   						=		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

	# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	_snake_case   						=		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

	# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	_snake_case   						=		alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

	if eta > 0:
		# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
		_snake_case   						=		model_output.device if torch.is_tensor(__A	) else 'cpu'
		_snake_case   						=		torch.randn(model_output.shape			,				dtype=model_output.dtype			,				generator=__A	).to(__A	)
		_snake_case   						=		self._get_variance(__A			,				__A	) ** 0.5 * eta * noise

		_snake_case   						=		prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=__A			,				pred_original_sample=__A	)







def 				SCREAMING_SNAKE_CASE__	(   self			,				__A			,				__A			,				__A			,				__A="epsilon"			,				__A=None			,				__A = True			,				)       ->	Union[DDPMSchedulerOutput, Tuple]:
	_snake_case   						=		timestep

	if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
		_snake_case						, _snake_case   						=		torch.split(__A			,				sample.shape[1]			,				dim=1	)
	else:
		_snake_case   						=		None

	# 1. compute alphas, betas
	_snake_case   						=		self.alphas_cumprod[t]
	_snake_case   						=		self.alphas_cumprod[t - 1] if t > 0 else self.one
	_snake_case   						=		1 - alpha_prod_t
	_snake_case   						=		1 - alpha_prod_t_prev

	# 2. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
	if prediction_type == "epsilon":
		_snake_case   						=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
	elif prediction_type == "sample":
		_snake_case   						=		model_output
	else:
		raise ValueError(F'Unsupported prediction_type {prediction_type}.'	)

	# 3. Clip "predicted x_0"
	_snake_case   						=		self.bit_scale
	if self.config.clip_sample:
		_snake_case   						=		torch.clamp(__A			,				-scale			,				__A	)

	# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
	# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
	_snake_case   						=		(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
	_snake_case   						=		self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

	# 5. Compute predicted previous sample µ_t
	# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
	_snake_case   						=		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

	# 6. Add noise
	_snake_case   						=		0
	if t > 0:
		_snake_case   						=		torch.randn(
		    model_output.size()			,				dtype=model_output.dtype			,				layout=model_output.layout			,				generator=__A	).to(model_output.device	)
		_snake_case   						=		(self._get_variance(__A			,				predicted_variance=__A	) ** 0.5) * noise

	_snake_case   						=		pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=__A			,				pred_original_sample=__A	)





class 						__UpperCAmelCase      (					_lowerCamelCase						):


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = 1.0    ,   ):





								"""simple docstring"""






								super().__init__()
								_snake_case   						=		bit_scale
								_snake_case   						=		(
								    ddim_bit_scheduler_step if isinstance(lowerCAmelCase_    ,   lowerCAmelCase_  ) else ddpm_bit_scheduler_step
								)

								self.register_modules(unet=lowerCAmelCase_    ,   scheduler=lowerCAmelCase_  )




							@torch.no_grad()
							def __call__(       self    ,   lowerCAmelCase_ = 2_56    ,   lowerCAmelCase_ = 2_56    ,   lowerCAmelCase_ = 50    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = 1    ,   lowerCAmelCase_ = "pil"    ,   lowerCAmelCase_ = True    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		torch.randn(
								    (batch_size, self.unet.config.in_channels, height, width)    ,   generator=lowerCAmelCase_    ,   )
								_snake_case   						=		decimal_to_bits(lowerCAmelCase_  ) * self.bit_scale
								_snake_case   						=		latents.to(self.device  )

								self.scheduler.set_timesteps(lowerCAmelCase_  )

								for t in self.progress_bar(self.scheduler.timesteps  ):
									# predict the noise residual
									_snake_case   						=		self.unet(lowerCAmelCase_    ,   lowerCAmelCase_  ).sample

									# compute the previous noisy sample x_t -> x_t-1
									_snake_case   						=		self.scheduler.step(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ).prev_sample

								_snake_case   						=		bits_to_decimal(lowerCAmelCase_  )

								if output_type == "pil":
									_snake_case   						=		self.numpy_to_pil(lowerCAmelCase_  )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase_  )



'''simple docstring'''







from __future__ import annotations

import random

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[str, float]:
	_snake_case   						=		len([g for position, g in enumerate(__A	) if g == main_target[position]]	)
	return (item, float(__A	))







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[str, str]:
	_snake_case   						=		random.randint(0			,				len(__A	) - 1	)
	_snake_case   						=		parent_a[:random_slice] + parent_a[random_slice:]
	_snake_case   						=		parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str:
	_snake_case   						=		list(__A	)
	if random.uniform(0			,				1	) < MUTATION_PROBABILITY:
		_snake_case   						=		random.choice(__A	)
	return "".join(__A	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				)       ->	list[str]:
	_snake_case   						=		[]
	# Generate more children proportionally to the fitness score.
	_snake_case   						=		int(parent_a[1] * 100	) + 1
	_snake_case   						=		10 if child_n >= 10 else child_n
	for _ in range(__A	):
		_snake_case   						=		population_score[random.randint(0			,				__A	)][0]

		_snake_case						, _snake_case   						=		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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A = True	)       ->	tuple[int, int, str]:

	# Verify if N_POPULATION is bigger than N_SELECTED
	if N_POPULATION < N_SELECTED:
		_snake_case   						=		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.
	_snake_case   						=		sorted({c for c in target if c not in genes}	)
	if not_in_genes_list:
		_snake_case   						=		F'{not_in_genes_list} is not in genes list, evolution cannot converge'
		raise ValueError(__A	)

	# Generate random starting population.
	_snake_case   						=		[]
	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.
	_snake_case						, _snake_case   						=		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.
		_snake_case   						=		[evaluate(__A			,				__A	) for item in population]

		# Check if there is a matching evolution.
		_snake_case   						=		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.
		_snake_case   						=		population[: int(N_POPULATION / 3	)]
		population.clear()
		population.extend(__A	)
		# Normalize population score to be between 0 and 1.
		_snake_case   						=		[
		    (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__":
					lowercase    :							str      =					(
					    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
					)
					lowercase    :							str      =					list(
					    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
					    "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
					)
					lowercase      ,  lowercase      ,  lowercase    :							Tuple      =					basic(target_str, genes_list)
					print(
					    F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
					)

