




"""simple docstring"""

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
a							      =     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.
a							      =     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.
a							      =     0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))






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






      '''simple docstring'''


      _A     =  len([g for position, g in enumerate(_snake_case				) if g == main_target[position]]				)
      return (item, float(_snake_case				))






def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   str				)       ->   tuple[str, str]:






      '''simple docstring'''


      _A     =  random.randint(0							,    len(_snake_case				) - 1				)
      _A     =  parent_a[:random_slice] + parent_a[random_slice:]
      _A     =  parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)






def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   list[str]				)       ->   str:






      '''simple docstring'''


      _A     =  list(_snake_case				)
      if random.uniform(0							,    1				) < MUTATION_PROBABILITY:
            _A     =  random.choice(_snake_case				)
      return "".join(_snake_case				)






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






      '''simple docstring'''


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

            _A  ,       _A     =  crossover(parent_a[0]							,    _snake_case				)
            # Append new string to the population list.
            pop.append(mutate(_snake_case							,    _snake_case				)				)
            pop.append(mutate(_snake_case							,    _snake_case				)				)
      return pop






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






      '''simple docstring'''


      if N_POPULATION < N_SELECTED:
            _A     =  F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
            raise ValueError(_snake_case				)
      # Verify that the target contains no genes besides the ones inside genes variable.
      _A     =  sorted({c for c in target if c not in genes}				)
      if not_in_genes_list:
            _A     =  F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
            raise ValueError(_snake_case				)

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

      # Just some logs to know what the algorithms is doing.
      _A  ,       _A     =  0, 0

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

            # 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.
            _A     =  [evaluate(_snake_case							,    _snake_case				) for item in population]

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

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


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







"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	UNetaDModel
 UpperCAmelCase       :	KarrasVeScheduler

 def __init__(					self			:			Any       ,				_UpperCAmelCase			:			UNetaDModel       ,				_UpperCAmelCase			:			KarrasVeScheduler						):
       super().__init__()
       self.register_modules(unet=_UpperCAmelCase       ,				scheduler=_UpperCAmelCase						)



 @torch.no_grad()
 def __call__(					self			:			Optional[int]       ,				_UpperCAmelCase			:			int = 1       ,				_UpperCAmelCase			:			int = 50       ,				_UpperCAmelCase			:			Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,				_UpperCAmelCase			:			Optional[str] = "pil"       ,				_UpperCAmelCase			:			bool = True       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       _A     =  self.unet.config.sample_size
       _A     =  (batch_size, 3, img_size, img_size)

       _A     =  self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       _A     =  randn_tensor(_UpperCAmelCase       ,				generator=_UpperCAmelCase       ,				device=self.device						) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(_UpperCAmelCase						)

       for t in self.progress_bar(self.scheduler.timesteps						):
             # here sigma_t == t_i from the paper
             _A     =  self.scheduler.schedule[t]
             _A     =  self.scheduler.schedule[t - 1] if t > 0 else 0

             # 1. Select temporarily increased noise level sigma_hat
             # 2. Add new noise to move from sample_i to sample_hat
             _A  ,       _A     =  self.scheduler.add_noise_to_input(_UpperCAmelCase       ,				_UpperCAmelCase       ,				generator=_UpperCAmelCase						)

             # 3. Predict the noise residual given the noise magnitude `sigma_hat`
             # The model inputs and output are adjusted by following eq. (213) in [1].
             _A     =  (sigma_hat / 2) * model((sample_hat + 1) / 2       ,				sigma_hat / 2						).sample

             # 4. Evaluate dx/dt at sigma_hat
             # 5. Take Euler step from sigma to sigma_prev
             _A     =  self.scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)

             if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   _A     =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,				sigma_prev / 2						).sample
                   _A     =  self.scheduler.step_correct(
                       _UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				step_output.prev_sample       ,				step_output['derivative']       ,				)
             _A     =  step_output.prev_sample

       _A     =  (sample / 2 + 0.5).clamp(0       ,				1						)
       _A     =  sample.cpu().permute(0       ,				2       ,				3       ,				1						).numpy()
       if output_type == "pil":
             _A     =  self.numpy_to_pil(_UpperCAmelCase						)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase						)

