




import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


_lowerCamelCase	:		int							       =   logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       List[Any]:



       """simple docstring"""

       def constraint_to_multiple_of(lowercase_ ,				lowercase_ ,				lowercase_=0 ,				lowercase_=None						):
              A__       =    round(val / multiple						) * multiple

              if max_val is not None and x > max_val:
                     A__       =    math.floor(val / multiple						) * multiple

              if x < min_val:
                     A__       =    math.ceil(val / multiple						) * multiple

              return x

       A__       =    (output_size, output_size) if isinstance(lowercase__ ,				lowercase__						) else output_size

       A__       =    get_image_size(lowercase__						)
       A__       =    output_size

       # determine new height and width
       A__       =    output_height / input_height
       A__       =    output_width / input_width

       if keep_aspect_ratio:
              # scale as little as possible
              if abs(1 - scale_width						) < abs(1 - scale_height						):
                     # fit width
                     A__       =    scale_width
              else:
                     # fit height
                     A__       =    scale_height

       A__       =    constraint_to_multiple_of(scale_height * input_height ,				multiple=lowercase__						)
       A__       =    constraint_to_multiple_of(scale_width * input_width ,				multiple=lowercase__						)

       return (new_height, new_width)


class 				UpperCamelCase_    ( _UpperCAmelCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      ['''pixel_values''']


    def __init__(			self      :	str					, UpperCAmelCase__      :	Tuple = True					, UpperCAmelCase__      :	Optional[Any] = None					, UpperCAmelCase__      :	Optional[int] = PILImageResampling.BILINEAR					, UpperCAmelCase__      :	Optional[Any] = False					, UpperCAmelCase__      :	Optional[int] = 1					, UpperCAmelCase__      :	Dict = True					, UpperCAmelCase__      :	Dict = 1 / 255					, UpperCAmelCase__      :	Tuple = True					, UpperCAmelCase__      :	Any = None					, UpperCAmelCase__      :	List[Any] = None					, **UpperCAmelCase__      :	Union[str, Any]					, )						->None:





           '''simple docstring'''

           super().__init__(**A_)
           A__       =    size if size is not None else {'''height''': 384, '''width''': 384}
           A__       =    get_size_dict(A_)
           A__       =    do_resize
           A__       =    size
           A__       =    keep_aspect_ratio
           A__       =    ensure_multiple_of
           A__       =    resample
           A__       =    do_rescale
           A__       =    rescale_factor
           A__       =    do_normalize
           A__       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           A__       =    image_std if image_std is not None else IMAGENET_STANDARD_STD


    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	List[str] = False					, UpperCAmelCase__      :	Tuple = 1					, UpperCAmelCase__      :	Optional[Any] = PILImageResampling.BICUBIC					, UpperCAmelCase__      :	Dict = None					, **UpperCAmelCase__      :	List[str]					, )						->np.ndarray:





           '''simple docstring'''

           A__       =    get_size_dict(A_)
           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()}""")
           A__       =    get_resize_output_image_size(
               A_					, output_size=(size['''height'''], size['''width'''])					, keep_aspect_ratio=A_					, multiple=A_					, )
           return resize(A_					, size=A_					, resample=A_					, data_format=A_					, **A_)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	int					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	List[str] = None					, **UpperCAmelCase__      :	int					, )						->Dict:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Union[str, Any]					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	Tuple					, UpperCAmelCase__      :	int = None					, **UpperCAmelCase__      :	Optional[int]					, )						->np.ndarray:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	Tuple					, UpperCAmelCase__      :	int = None					, UpperCAmelCase__      :	List[str] = None					, UpperCAmelCase__      :	Union[str, Any] = None					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	Union[str, Any] = None					, UpperCAmelCase__      :	str = None					, UpperCAmelCase__      :	Tuple = None					, UpperCAmelCase__      :	List[Any] = None					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	List[str] = None					, UpperCAmelCase__      :	Dict = None					, UpperCAmelCase__      :	List[str] = ChannelDimension.FIRST					, **UpperCAmelCase__      :	List[Any]					, )						->PIL.Image.Image:





           '''simple docstring'''

           A__       =    do_resize if do_resize is not None else self.do_resize
           A__       =    size if size is not None else self.size
           A__       =    get_size_dict(A_)
           A__       =    keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
           A__       =    ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
           A__       =    resample if resample is not None else self.resample
           A__       =    do_rescale if do_rescale is not None else self.do_rescale
           A__       =    rescale_factor if rescale_factor is not None else self.rescale_factor
           A__       =    do_normalize if do_normalize is not None else self.do_normalize
           A__       =    image_mean if image_mean is not None else self.image_mean
           A__       =    image_std if image_std is not None else self.image_std

           A__       =    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 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.''')

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

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

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

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

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

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





    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	Tuple					, UpperCAmelCase__      :	str = None)						->Any:





           '''simple docstring'''

           A__       =    outputs.logits

           # Resize logits and compute semantic segmentation maps
           if target_sizes is not None:
                  if len(A_) != len(A_):
                         raise ValueError(
                             '''Make sure that you pass in as many target sizes as the batch dimension of the logits''')

                  if is_torch_tensor(A_):
                         A__       =    target_sizes.numpy()

                  A__       =    []

                  for idx in range(len(A_)):
                         A__       =    torch.nn.functional.interpolate(
                             logits[idx].unsqueeze(dim=0)					, size=target_sizes[idx]					, mode='''bilinear'''					, align_corners=A_)
                         A__       =    resized_logits[0].argmax(dim=0)
                         semantic_segmentation.append(A_)
           else:
                  A__       =    logits.argmax(dim=1)
                  A__       =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

           return semantic_segmentation





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_UpperCamelCase 			=		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.
_UpperCamelCase 			=		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.
_UpperCamelCase 			=		0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						List[str]					=					len([g for position, g in enumerate(lowercase__						) if g == main_target[position]]						)
      return (item, float(lowercase__						))







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						str					=					random.randint(0   ,	len(lowercase__						) - 1						)
      __lowerCAmelCase					:						int					=					parent_a[:random_slice] + parent_a[random_slice:]
      __lowerCAmelCase					:						Dict					=					parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						List[str]					=					list(lowercase__						)
      if random.uniform(0   ,	1						) < MUTATION_PROBABILITY:
            __lowerCAmelCase					:						int					=					random.choice(lowercase__						)
      return "".join(lowercase__						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	):
      __lowerCAmelCase					:						str					=					[]
      # Generate more children proportionally to the fitness score.
      __lowerCAmelCase					:						str					=					int(parent_a[1] * 1_0_0						) + 1
      __lowerCAmelCase					:						Optional[Any]					=					1_0 if child_n >= 1_0 else child_n
      for _ in range(lowercase__						):
            __lowerCAmelCase					:						List[Any]					=					population_score[random.randint(0   ,	lowercase__						)][0]

            __lowerCAmelCase,							__lowerCAmelCase					:						Dict					=					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 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__ = True						):

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

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

            # Check if there is a matching evolution.
            __lowerCAmelCase					:						Union[str, Any]					=					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 % 1_0 == 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					:						Tuple					=					population[: int(N_POPULATION / 3						)]
            population.clear()
            population.extend(lowercase__						)
            # Normalize population score to be between 0 and 1.
            __lowerCAmelCase					:						List[Any]					=					[
                (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__":
 _UpperCamelCase 			=		(
     "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
 )
 _UpperCamelCase 			=		list(
     " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
     "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
 )
 _UpperCamelCase			,			_UpperCamelCase			,			_UpperCamelCase 			=		basic(target_str, genes_list)
 print(
     F"\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"
 )


