




from __future__ import annotations

import random

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: str  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: str  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: list[str]  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: tuple[str, float]			,							lowercase			: list[tuple[str, float]]			,							lowercase			: list[str]			,							):


      '''simple docstring'''



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

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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: list[str]			,							lowercase			: bool = True  ):


      '''simple docstring'''



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

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

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

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

            # 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.
            lowerCamelCase_	  =					[evaluate(lowercase			,							lowercase  ) for item in population]

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

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


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









from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase       :	List[Any]									=     logging.get_logger(__name__)







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		['''pixel_values''']

 def __init__(       self      :						List[Any]		,							A_      :						bool = True		,							A_      :						Dict[str, int] = None		,							A_      :						PILImageResampling = PILImageResampling.BICUBIC		,							A_      :						bool = True		,							A_      :						Dict[str, int] = None		,							A_      :						bool = True		,							A_      :						Union[int, float] = 1 / 255		,							A_      :						bool = True		,							A_      :						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN		,							A_      :						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD		,							**A_      :						Tuple		,							)     ->			None:

       """simple docstring"""






       super().__init__(**A_						)
       lowerCamelCase_	  =					size if size is not None else {'shortest_edge': 224}
       lowerCamelCase_	  =					get_size_dict(A_		,							default_to_square=A_						)
       lowerCamelCase_	  =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_	  =					get_size_dict(A_		,							param_name='crop_size'						)

       lowerCamelCase_	  =					do_resize
       lowerCamelCase_	  =					size
       lowerCamelCase_	  =					resample
       lowerCamelCase_	  =					do_center_crop
       lowerCamelCase_	  =					crop_size
       lowerCamelCase_	  =					do_rescale
       lowerCamelCase_	  =					rescale_factor
       lowerCamelCase_	  =					do_normalize
       lowerCamelCase_	  =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       lowerCamelCase_	  =					image_std if image_std is not None else IMAGENET_DEFAULT_STD

 def        a__				(       self      :						Optional[Any]		,							A_      :						np.ndarray		,							A_      :						Dict[str, int]		,							A_      :						PILImageResampling = PILImageResampling.BICUBIC		,							A_      :						Optional[Union[str, ChannelDimension]] = None		,							**A_      :						Tuple		,							)     ->			np.ndarray:

       """simple docstring"""






       lowerCamelCase_	  =					get_size_dict(A_		,							default_to_square=A_						)
       # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
       if "shortest_edge" in size:
             lowerCamelCase_	  =					int((256 / 224) * size['shortest_edge']						)
             lowerCamelCase_	  =					get_resize_output_image_size(A_		,							size=A_		,							default_to_square=A_						)
             lowerCamelCase_	  =					{'height': output_size[0], 'width': output_size[1]}
       if "height" not in size_dict or "width" not in size_dict:
             raise ValueError(
                 f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""						)
       return resize(
           A_		,							size=(size_dict['height'], size_dict['width'])		,							resample=A_		,							data_format=A_		,							**A_						)

 def        a__				(       self      :						Any		,							A_      :						np.ndarray		,							A_      :						Dict[str, int]		,							A_      :						Optional[Union[str, ChannelDimension]] = None		,							**A_      :						Any		,							)     ->			np.ndarray:

       """simple docstring"""






       lowerCamelCase_	  =					get_size_dict(A_						)
       if "height" not in size or "width" not in size:
             raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""						)
       return center_crop(A_		,							size=(size['height'], size['width'])		,							data_format=A_		,							**A_						)

 def        a__				(       self      :						Optional[Any]		,							A_      :						np.ndarray		,							A_      :						Union[int, float]		,							A_      :						Optional[Union[str, ChannelDimension]] = None		,							**A_      :						Optional[int]		,							)     ->			np.ndarray:

       """simple docstring"""






       return rescale(A_		,							scale=A_		,							data_format=A_		,							**A_						)

 def        a__				(       self      :						List[str]		,							A_      :						np.ndarray		,							A_      :						Union[float, List[float]]		,							A_      :						Union[float, List[float]]		,							A_      :						Optional[Union[str, ChannelDimension]] = None		,							**A_      :						str		,							)     ->			np.ndarray:

       """simple docstring"""






       return normalize(A_		,							mean=A_		,							std=A_		,							data_format=A_		,							**A_						)





 def        a__				(       self      :						Optional[int]		,							A_      :						ImageInput		,							A_      :						Optional[bool] = None		,							A_      :						Optional[Dict[str, int]] = None		,							A_      :						PILImageResampling = None		,							A_      :						Optional[bool] = None		,							A_      :						Optional[Dict[str, int]] = None		,							A_      :						Optional[bool] = None		,							A_      :						Optional[float] = None		,							A_      :						Optional[bool] = None		,							A_      :						Optional[Union[float, Iterable[float]]] = None		,							A_      :						Optional[Union[float, Iterable[float]]] = None		,							A_      :						Optional[TensorType] = None		,							A_      :						ChannelDimension = ChannelDimension.FIRST		,							**A_      :						List[Any]		,							)     ->			BatchFeature:

       """simple docstring"""






       lowerCamelCase_	  =					do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_	  =					resample if resample is not None else self.resample
       lowerCamelCase_	  =					do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCamelCase_	  =					do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_	  =					rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_	  =					do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_	  =					image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_	  =					image_std if image_std is not None else self.image_std

       lowerCamelCase_	  =					size if size is not None else self.size
       lowerCamelCase_	  =					get_size_dict(A_		,							default_to_square=A_						)
       lowerCamelCase_	  =					crop_size if crop_size is not None else self.crop_size
       lowerCamelCase_	  =					get_size_dict(A_		,							param_name='crop_size'						)

       lowerCamelCase_	  =					make_list_of_images(A_						)

       if not valid_images(A_						):
             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:
             raise ValueError('Size must be specified if do_resize is True.'						)

       if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop 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.'						)

       # All transformations expect numpy arrays.
       lowerCamelCase_	  =					[to_numpy_array(A_						) for image in images]

       if do_resize:
             lowerCamelCase_	  =					[self.resize(A_		,							A_		,							A_						) for image in images]

       if do_center_crop:
             lowerCamelCase_	  =					[self.center_crop(A_		,							A_						) for image in images]

       if do_rescale:
             lowerCamelCase_	  =					[self.rescale(A_		,							A_						) for image in images]

       if do_normalize:
             lowerCamelCase_	  =					[self.normalize(A_		,							A_		,							A_						) for image in images]

       lowerCamelCase_	  =					[to_channel_dimension_format(A_		,							A_						) for image in images]

       lowerCamelCase_	  =					{'pixel_values': images}
       return BatchFeature(data=A_		,							tensor_type=A_						)



