
'''simple docstring'''


from __future__ import annotations

import random

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



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       str):
   UpperCamelCase       =  len([g for position, g in enumerate(_UpperCAmelCase) if g == main_target[position]])
   return (item, float(_UpperCAmelCase))



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       str):
   UpperCamelCase       =  random.randint(0,      len(_UpperCAmelCase) - 1)
   UpperCamelCase       =  parent_a[:random_slice] + parent_a[random_slice:]
   UpperCamelCase       =  parent_a[:random_slice] + parent_a[random_slice:]
   return (child_a, child_a)



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       list[str]):
   UpperCamelCase       =  list(_UpperCAmelCase)
   if random.uniform(0,      1) < MUTATION_PROBABILITY:
      UpperCamelCase       =  random.choice(_UpperCAmelCase)
   return "".join(_UpperCAmelCase)



def  __snake_case     (    _UpperCAmelCase					:       tuple[str, float],      _UpperCAmelCase					:       list[tuple[str, float]],      _UpperCAmelCase					:       list[str],      ):
   UpperCamelCase       =  []
   # Generate more children proportionally to the fitness score.
   UpperCamelCase       =  int(parent_a[1] * 100) + 1
   UpperCamelCase       =  10 if child_n >= 10 else child_n
   for _ in range(_UpperCAmelCase):
      UpperCamelCase       =  population_score[random.randint(0,      _UpperCAmelCase)][0]

      UpperCamelCase     , UpperCamelCase       =  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  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       list[str],      _UpperCAmelCase					:       bool = True):

   # Verify if N_POPULATION is bigger than N_SELECTED
   if N_POPULATION < N_SELECTED:
      UpperCamelCase       =  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.
   UpperCamelCase       =  sorted({c for c in target if c not in genes})
   if not_in_genes_list:
      UpperCamelCase       =  f'{not_in_genes_list} is not in genes list, evolution cannot converge'
      raise ValueError(_UpperCAmelCase)

   # Generate random starting population.
   UpperCamelCase       =  []
   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.
   UpperCamelCase     , UpperCamelCase       =  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.
      UpperCamelCase       =  [evaluate(_UpperCAmelCase,      _UpperCAmelCase) for item in population]

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







'''simple docstring'''


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import LevitImageProcessor


class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=7 ,   lowerCamelCase__=3 ,   lowerCamelCase__=1_8 ,   lowerCamelCase__=3_0 ,   lowerCamelCase__=4_0_0 ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   ):

      '''simple docstring'''







      UpperCamelCase       =  size if size is not None else {'''shortest_edge''': 1_8}
      UpperCamelCase       =  crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
      UpperCamelCase       =  parent
      UpperCamelCase       =  batch_size
      UpperCamelCase       =  num_channels
      UpperCamelCase       =  image_size
      UpperCamelCase       =  min_resolution
      UpperCamelCase       =  max_resolution
      UpperCamelCase       =  do_resize
      UpperCamelCase       =  size
      UpperCamelCase       =  do_center_crop
      UpperCamelCase       =  crop_size
      UpperCamelCase       =  do_normalize
      UpperCamelCase       =  image_mean
      UpperCamelCase       =  image_std







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "do_center_crop": self.do_center_crop,
          "size": self.size,
          "crop_size": self.crop_size,
      }




@require_torch
@require_vision
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							LevitImageProcessor if is_vision_available() else None







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  LevitImageProcessingTester(self   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.image_processor_tester.prepare_image_processor_dict()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''image_mean'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''image_std'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_normalize'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_resize'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_center_crop'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''size'''   )   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 1_8}   )
      self.assertEqual(image_processor.crop_size ,   {'''height''': 1_8, '''width''': 1_8}   )

      UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict ,   size=4_2 ,   crop_size=8_4   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 4_2}   )
      self.assertEqual(image_processor.crop_size ,   {'''height''': 8_4, '''width''': 8_4}   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      pass







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   Image.Image   )

      # Test not batched input
      UpperCamelCase       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase__ ,   numpify=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   np.ndarray   )

      # Test not batched input
      UpperCamelCase       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase__ ,   torchify=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   torch.Tensor   )

      # Test not batched input
      UpperCamelCase       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          ) ,   )





