

'''simple docstring'''

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A_       						=							2_00
# 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, 10_00))





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					len([g for position, g in enumerate(snake_case ) if g == main_target[position]] )
  return (item, float(snake_case ))





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:int						      =					random.randint(0      ,       len(snake_case ) - 1 )
  SCREAMING_SNAKE_CASE:Dict						      =					parent_a[:random_slice] + parent_a[random_slice:]
  SCREAMING_SNAKE_CASE:Tuple						      =					parent_a[:random_slice] + parent_a[random_slice:]
  return (child_a, child_a)





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:List[str]						      =					list(snake_case )
  if random.uniform(0      ,       1 ) < MUTATION_PROBABILITY:
    SCREAMING_SNAKE_CASE:Optional[int]						      =					random.choice(snake_case )
  return "".join(snake_case )





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

    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					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    A_  ( snake_case      ,       snake_case      ,       snake_case = True ):

  # Verify if N_POPULATION is bigger than N_SELECTED
  if N_POPULATION < N_SELECTED:
    SCREAMING_SNAKE_CASE:Tuple						      =					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.
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					sorted({c for c in target if c not in genes} )
  if not_in_genes_list:
    SCREAMING_SNAKE_CASE:Dict						      =					F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
    raise ValueError(snake_case )

  # Generate random starting population.
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[]
  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.
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					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.
    SCREAMING_SNAKE_CASE:List[str]						      =					[evaluate(snake_case      ,       snake_case ) for item in population]

    # Check if there is a matching evolution.
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					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.
    SCREAMING_SNAKE_CASE:List[str]						      =					population[: int(N_POPULATION / 3 )]
    population.clear()
    population.extend(snake_case )
    # Normalize population score to be between 0 and 1.
    SCREAMING_SNAKE_CASE:List[str]						      =					[
        (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 Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     Union[str, Any]       =					['''pixel_values''']
   def __init__( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BILINEAR     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Union[int, float] = 1 / 255     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,**SCREAMING_SNAKE_CASE__			:      List[str]     ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					size if size is not None else {"shortest_edge": 384}
     SCREAMING_SNAKE_CASE:List[str]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					do_resize
     SCREAMING_SNAKE_CASE:List[Any]						      =					size
     # Default value set here for backwards compatibility where the value in config is None
     SCREAMING_SNAKE_CASE:Dict						      =					crop_pct if crop_pct is not None else 224 / 256
     SCREAMING_SNAKE_CASE:Any						      =					resample
     SCREAMING_SNAKE_CASE:Dict						      =					do_rescale
     SCREAMING_SNAKE_CASE:Optional[int]						      =					rescale_factor
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					do_normalize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE:Tuple						      =					image_std if image_std is not None else IMAGENET_STANDARD_STD
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      float     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      int     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     if "shortest_edge" not in size:
       raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''							)
     SCREAMING_SNAKE_CASE:Any						      =					size["shortest_edge"]

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       SCREAMING_SNAKE_CASE:Any						      =					int(shortest_edge / crop_pct							)
       SCREAMING_SNAKE_CASE:Any						      =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[int, float]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     return rescale(SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     return normalize(SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      ImageInput     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, TensorType]] = None     ,SCREAMING_SNAKE_CASE__			:      ChannelDimension = ChannelDimension.FIRST     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:List[str]						      =					crop_pct if crop_pct is not None else self.crop_pct
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:List[str]						      =					do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:str						      =					rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:str						      =					do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:Any						      =					image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE:Optional[int]						      =					size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Tuple						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					make_list_of_images(SCREAMING_SNAKE_CASE__							)

     if not valid_images(SCREAMING_SNAKE_CASE__							):
       raise ValueError(
           "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
           "torch.Tensor, tf.Tensor or jax.ndarray."							)

     if do_resize and size is None or resample is None:
       raise ValueError("Size and resample must be specified if do_resize is True."							)

     if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
       raise ValueError("crop_pct must be specified if size < 384."							)

     if do_rescale and rescale_factor is None:
       raise ValueError("Rescale factor must be specified if do_rescale is True."							)

     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError("Image mean and std must be specified if do_normalize is True."							)

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE:int						      =					[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					[self.resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,crop_pct=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.rescale(image=SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					[self.normalize(image=SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:Any						      =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:int						      =					{"pixel_values": images}
     return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,tensor_type=SCREAMING_SNAKE_CASE__							)






