'''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_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: str          =   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: List[Any]          =   0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Optional[Any]  =					len([g for position, g in enumerate(UpperCAmelCase     ) if g == main_target[position]]     )
 return (item, float(UpperCAmelCase     ))
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Optional[Any]  =					random.randint(0		,     len(UpperCAmelCase     ) - 1     )
 lowercase__				:    Tuple  =					parent_a[:random_slice] + parent_a[random_slice:]
 lowercase__				:    Union[str, Any]  =					parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    List[str]  =					list(UpperCAmelCase     )
 if random.uniform(0		,     1     ) < MUTATION_PROBABILITY:
  lowercase__				:    Dict  =					random.choice(UpperCAmelCase     )
 return "".join(UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     ):
 lowercase__				:    Tuple  =					[]
 # Generate more children proportionally to the fitness score.
 lowercase__				:    Any  =					int(parent_a[1] * 100     ) + 1
 lowercase__				:    Tuple  =					10 if child_n >= 10 else child_n
 for _ in range(UpperCAmelCase     ):
  lowercase__				:    Optional[int]  =					population_score[random.randint(0		,     UpperCAmelCase     )][0]

  lowercase__ ,							lowercase__				:    Union[str, 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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = True     ):

 # Verify if N_POPULATION is bigger than N_SELECTED
 if N_POPULATION < N_SELECTED:
  lowercase__				:    List[str]  =					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.
 lowercase__				:    Dict  =					sorted({c for c in target if c not in genes}     )
 if not_in_genes_list:
  lowercase__				:    List[str]  =					F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
  raise ValueError(UpperCAmelCase     )

 # Generate random starting population.
 lowercase__				:    List[Any]  =					[]
 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.
 lowercase__ ,							lowercase__				:    Tuple  =					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.
  lowercase__				:    Optional[Any]  =					[evaluate(UpperCAmelCase		,     UpperCAmelCase     ) for item in population]

  # Check if there is a matching evolution.
  lowercase__				:    Dict  =					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.
  lowercase__				:    int  =					population[: int(N_POPULATION / 3     )]
  population.clear()
  population.extend(UpperCAmelCase     )
  # Normalize population score to be between 0 and 1.
  lowercase__				:    Any  =					[
      (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: int          =   (
      """This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
  )
  __a: Any          =   list(
      """ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
      """nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"""
  )
  __a   ,			__a   ,			__a: Optional[int]          =   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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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: List[str]          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    ["pixel_values"]

       def __init__(						self	,   __lowerCAmelCase = True	,   __lowerCAmelCase = None	,   __lowerCAmelCase = PILImageResampling.BICUBIC	,   __lowerCAmelCase = True	,   __lowerCAmelCase = 1 / 255	,   __lowerCAmelCase = True	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = True	,   **__lowerCAmelCase	,   )  ->  None:
        super().__init__(**__lowerCAmelCase )
        lowercase__				:    Optional[int]  =					size if size is not None else {'''height''': 384, '''width''': 384}
        lowercase__				:    Optional[Any]  =					get_size_dict(__lowerCAmelCase	,   default_to_square=__lowerCAmelCase )

        lowercase__				:    Dict  =					do_resize
        lowercase__				:    int  =					size
        lowercase__				:    int  =					resample
        lowercase__				:    Tuple  =					do_rescale
        lowercase__				:    int  =					rescale_factor
        lowercase__				:    int  =					do_normalize
        lowercase__				:    Optional[int]  =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowercase__				:    Optional[int]  =					image_std if image_std is not None else OPENAI_CLIP_STD
        lowercase__				:    Tuple  =					do_convert_rgb

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = PILImageResampling.BICUBIC	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   )  ->  np.ndarray:
        lowercase__				:    Union[str, Any]  =					get_size_dict(__lowerCAmelCase	,   default_to_square=__lowerCAmelCase )
        if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
        lowercase__				:    Any  =					(size['''height'''], size['''width'''])
        return resize(__lowerCAmelCase	,   size=__lowerCAmelCase	,   resample=__lowerCAmelCase	,   data_format=__lowerCAmelCase	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   )  ->  str:
        return rescale(__lowerCAmelCase	,   scale=__lowerCAmelCase	,   data_format=__lowerCAmelCase	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   )  ->  np.ndarray:
        return normalize(__lowerCAmelCase	,   mean=__lowerCAmelCase	,   std=__lowerCAmelCase	,   data_format=__lowerCAmelCase	,   **__lowerCAmelCase )


       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = ChannelDimension.FIRST	,   **__lowerCAmelCase	,   )  ->  PIL.Image.Image:
        lowercase__				:    Union[str, Any]  =					do_resize if do_resize is not None else self.do_resize
        lowercase__				:    Any  =					resample if resample is not None else self.resample
        lowercase__				:    int  =					do_rescale if do_rescale is not None else self.do_rescale
        lowercase__				:    List[Any]  =					rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase__				:    List[Any]  =					do_normalize if do_normalize is not None else self.do_normalize
        lowercase__				:    Dict  =					image_mean if image_mean is not None else self.image_mean
        lowercase__				:    Dict  =					image_std if image_std is not None else self.image_std
        lowercase__				:    Dict  =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowercase__				:    Optional[int]  =					size if size is not None else self.size
        lowercase__				:    int  =					get_size_dict(__lowerCAmelCase	,   default_to_square=__lowerCAmelCase )

        lowercase__				:    str  =					make_list_of_images(__lowerCAmelCase )

        if not valid_images(__lowerCAmelCase ):
         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_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.''' )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
         lowercase__				:    Optional[Any]  =					[convert_to_rgb(__lowerCAmelCase ) for image in images]

        # All transformations expect numpy arrays.
        lowercase__				:    Any  =					[to_numpy_array(__lowerCAmelCase ) for image in images]

        if do_resize:
         lowercase__				:    Tuple  =					[self.resize(image=__lowerCAmelCase	,   size=__lowerCAmelCase	,   resample=__lowerCAmelCase ) for image in images]

        if do_rescale:
         lowercase__				:    List[str]  =					[self.rescale(image=__lowerCAmelCase	,   scale=__lowerCAmelCase ) for image in images]

        if do_normalize:
         lowercase__				:    Tuple  =					[self.normalize(image=__lowerCAmelCase	,   mean=__lowerCAmelCase	,   std=__lowerCAmelCase ) for image in images]

        lowercase__				:    List[str]  =					[to_channel_dimension_format(__lowerCAmelCase	,   __lowerCAmelCase ) for image in images]

        lowercase__				:    Optional[Any]  =					BatchFeature(data={'''pixel_values''': images}	,   tensor_type=__lowerCAmelCase )

        return encoded_outputs

