



"""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


A:     int										=	8

def    _snake_case		(		UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	str=BITS		):
 UpperCAmelCase			:      Tuple			=			x.device

 UpperCAmelCase			:      List[str]			=			(x * 255).int().clamp(0    ,	255		)

 UpperCAmelCase			:      Optional[int]			=			2 ** torch.arange(bits - 1    ,	-1    ,	-1    ,	device=UpperCamelCase		)
 UpperCAmelCase			:      Any			=			rearrange(UpperCamelCase    ,	"""d -> d 1 1"""		)
 UpperCAmelCase			:      Union[str, Any]			=			rearrange(UpperCamelCase    ,	"""b c h w -> b c 1 h w"""		)

 UpperCAmelCase			:      Any			=			((x & mask) != 0).float()
 UpperCAmelCase			:      str			=			rearrange(UpperCamelCase    ,	"""b c d h w -> b (c d) h w"""		)
 UpperCAmelCase			:      List[Any]			=			bits * 2 - 1
 return bits

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	Dict=BITS		):
 UpperCAmelCase			:      Tuple			=			x.device

 UpperCAmelCase			:      List[Any]			=			(x > 0).int()
 UpperCAmelCase			:      Dict			=			2 ** torch.arange(bits - 1    ,	-1    ,	-1    ,	device=UpperCamelCase    ,	dtype=torch.intaa		)

 UpperCAmelCase			:      int			=			rearrange(UpperCamelCase    ,	"""d -> d 1 1"""		)
 UpperCAmelCase			:      str			=			rearrange(UpperCamelCase    ,	"""b (c d) h w -> b c d h w"""    ,	d=8		)
 UpperCAmelCase			:      Optional[int]			=			reduce(x * mask    ,	"""b c d h w -> b c h w"""    ,	"""sum"""		)
 return (dec / 255).clamp(0.0    ,	1.0		)

def    _snake_case		(		self		:	Dict    ,	UpperCamelCase		:	torch.FloatTensor    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	torch.FloatTensor    ,	UpperCamelCase		:	float = 0.0    ,	UpperCamelCase		:	bool = True    ,	UpperCamelCase		:	Dict=None    ,	UpperCamelCase		:	bool = True    ,	):
 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)
 UpperCAmelCase			:      Optional[Any]			=			timestep - self.config.num_train_timesteps // self.num_inference_steps

 # 2. compute alphas, betas
 UpperCAmelCase			:      Any			=			self.alphas_cumprod[timestep]
 UpperCAmelCase			:      List[str]			=			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

 UpperCAmelCase			:      str			=			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
 UpperCAmelCase			:      Optional[Any]			=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

 # 4. Clip "predicted x_0"
 UpperCAmelCase			:      Optional[int]			=			self.bit_scale
 if self.config.clip_sample:
  UpperCAmelCase			:      str			=			torch.clamp(UpperCamelCase    ,	-scale    ,	UpperCamelCase		)

 # 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)
 UpperCAmelCase			:      List[str]			=			self._get_variance(UpperCamelCase    ,	UpperCamelCase		)
 UpperCAmelCase			:      str			=			eta * variance ** 0.5

 if use_clipped_model_output:
  # the model_output is always re-derived from the clipped x_0 in Glide
  UpperCAmelCase			:      List[Any]			=			(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
 UpperCAmelCase			:      Optional[int]			=			(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
 UpperCAmelCase			:      str			=			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
  UpperCAmelCase			:      Union[str, Any]			=			model_output.device if torch.is_tensor(UpperCamelCase		) else """cpu"""
  UpperCAmelCase			:      Optional[int]			=			torch.randn(model_output.shape    ,	dtype=model_output.dtype    ,	generator=UpperCamelCase		).to(UpperCamelCase		)
  UpperCAmelCase			:      Union[str, Any]			=			self._get_variance(UpperCamelCase    ,	UpperCamelCase		) ** 0.5 * eta * noise

  UpperCAmelCase			:      Optional[int]			=			prev_sample + variance

 if not return_dict:
  return (prev_sample,)

 return DDIMSchedulerOutput(prev_sample=UpperCamelCase    ,	pred_original_sample=UpperCamelCase		)

def    _snake_case		(		self		:	List[str]    ,	UpperCamelCase		:	torch.FloatTensor    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	torch.FloatTensor    ,	UpperCamelCase		:	List[Any]="epsilon"    ,	UpperCamelCase		:	Optional[Any]=None    ,	UpperCamelCase		:	bool = True    ,	):
 UpperCAmelCase			:      int			=			timestep

 if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
  UpperCAmelCase			,	UpperCAmelCase			:      Union[str, Any]			=			torch.split(UpperCamelCase    ,	sample.shape[1]    ,	dim=1		)
 else:
  UpperCAmelCase			:      Optional[int]			=			None

 # 1. compute alphas, betas
 UpperCAmelCase			:      Dict			=			self.alphas_cumprod[t]
 UpperCAmelCase			:      List[str]			=			self.alphas_cumprod[t - 1] if t > 0 else self.one
 UpperCAmelCase			:      List[str]			=			1 - alpha_prod_t
 UpperCAmelCase			:      Optional[Any]			=			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":
  UpperCAmelCase			:      Optional[Any]			=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
 elif prediction_type == "sample":
  UpperCAmelCase			:      List[str]			=			model_output
 else:
  raise ValueError(F"Unsupported prediction_type {prediction_type}."		)

 # 3. Clip "predicted x_0"
 UpperCAmelCase			:      str			=			self.bit_scale
 if self.config.clip_sample:
  UpperCAmelCase			:      Tuple			=			torch.clamp(UpperCamelCase    ,	-scale    ,	UpperCamelCase		)

 # 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
 UpperCAmelCase			:      str			=			(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
 UpperCAmelCase			:      Optional[Any]			=			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
 UpperCAmelCase			:      Optional[Any]			=			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

 # 6. Add noise
 UpperCAmelCase			:      Any			=			0
 if t > 0:
  UpperCAmelCase			:      str			=			torch.randn(
      model_output.size()    ,	dtype=model_output.dtype    ,	layout=model_output.layout    ,	generator=UpperCamelCase		).to(model_output.device		)
  UpperCAmelCase			:      List[str]			=			(self._get_variance(UpperCamelCase    ,	predicted_variance=UpperCamelCase		) ** 0.5) * noise

 UpperCAmelCase			:      List[str]			=			pred_prev_sample + variance

 if not return_dict:
  return (pred_prev_sample,)

 return DDPMSchedulerOutput(prev_sample=UpperCamelCase    ,	pred_original_sample=UpperCamelCase		)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = 1.0   ,  )						->   Any:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      Optional[int]			=			bit_scale
    UpperCAmelCase			:      Optional[Any]			=			(
        ddim_bit_scheduler_step if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else ddpm_bit_scheduler_step
    )

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




   @torch.no_grad()
   def __call__(     self   ,  _SCREAMING_SNAKE_CASE = 256   ,  _SCREAMING_SNAKE_CASE = 256   ,  _SCREAMING_SNAKE_CASE = 50   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = 1   ,  _SCREAMING_SNAKE_CASE = "pil"   ,  _SCREAMING_SNAKE_CASE = True   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Union[Tuple, ImagePipelineOutput]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			torch.randn(
        (batch_size, self.unet.config.in_channels, height, width)   ,  generator=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Tuple			=			decimal_to_bits(_SCREAMING_SNAKE_CASE		) * self.bit_scale
    UpperCAmelCase			:      str			=			latents.to(self.device		)

    self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

    for t in self.progress_bar(self.scheduler.timesteps		):
     # predict the noise residual
     UpperCAmelCase			:      List[Any]			=			self.unet(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		).sample

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase			:      Optional[int]			=			self.scheduler.step(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		).prev_sample

    UpperCAmelCase			:      Optional[Any]			=			bits_to_decimal(_SCREAMING_SNAKE_CASE		)

    if output_type == "pil":
     UpperCAmelCase			:      Optional[int]			=			self.numpy_to_pil(_SCREAMING_SNAKE_CASE		)

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE		)





"""simple docstring"""
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A:     Union[str, Any]										=	2_0_0
# 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.
A:     int										=	5_0
# 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.
A:     Tuple										=	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      Tuple			=			len([g for position, g in enumerate(UpperCamelCase		) if g == main_target[position]]		)
 return (item, float(UpperCamelCase		))

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      List[str]			=			random.randint(0    ,	len(UpperCamelCase		) - 1		)
 UpperCAmelCase			:      List[str]			=			parent_a[:random_slice] + parent_a[random_slice:]
 UpperCAmelCase			:      List[str]			=			parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	list[str]		):
 UpperCAmelCase			:      str			=			list(UpperCamelCase		)
 if random.uniform(0    ,	1		) < MUTATION_PROBABILITY:
  UpperCAmelCase			:      int			=			random.choice(UpperCamelCase		)
 return "".join(UpperCamelCase		)

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

  UpperCAmelCase			,	UpperCAmelCase			:      Any			=			crossover(parent_a[0]    ,	UpperCamelCase		)
  # Append new string to the population list.
  pop.append(mutate(UpperCamelCase    ,	UpperCamelCase		)		)
  pop.append(mutate(UpperCamelCase    ,	UpperCamelCase		)		)
 return pop

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	list[str]    ,	UpperCamelCase		:	bool = True		):

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

 # Generate random starting population.
 UpperCAmelCase			:      Optional[int]			=			[]
 for _ in range(UpperCamelCase		):
  population.append("""""".join([random.choice(UpperCamelCase		) for i in range(len(UpperCamelCase		)		)]		)		)

 # Just some logs to know what the algorithms is doing.
 UpperCAmelCase			,	UpperCAmelCase			:      Any			=			0, 0

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

  # 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.
  UpperCAmelCase			:      str			=			[evaluate(UpperCamelCase    ,	UpperCamelCase		) for item in population]

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

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


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