

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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     tuple[str, float]:



  """simple docstring"""






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






def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     tuple[str, str]:



  """simple docstring"""






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






def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     str:



  """simple docstring"""






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






def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	)							->     list[str]:



  """simple docstring"""






  _snake_case						=							[]
  # Generate more children proportionally to the fitness score.
  _snake_case						=							int(parent_a[1] * 100					) + 1
  _snake_case						=							10 if child_n >= 10 else child_n
  for _ in range(lowerCamelCase__					):
    _snake_case						=							population_score[random.randint(0			,	lowerCamelCase__					)][0]

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






def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = True					)							->     tuple[int, int, str]:



  """simple docstring"""






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

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

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

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

    # 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.
    _snake_case						=							[evaluate(lowerCamelCase__			,	lowerCamelCase__					) for item in population]

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

    # This is selection
    for i in range(lowerCamelCase__					):
      population.extend(select(population_score[int(lowerCamelCase__					)]			,	lowerCamelCase__			,	lowerCamelCase__					)					)
      # 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(lowerCamelCase__					) > 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}'''
 )




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_video_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import VivitImageProcessor



class   lowercase_   (  unittest.TestCase					):



      def __init__(      self				:    Union[str, Any]      , A__				:    int      , A__				:    List[str]=7      , A__				:    Tuple=3      , A__				:    List[str]=10      , A__				:    Optional[int]=18      , A__				:    int=30      , A__				:    Tuple=400      , A__				:    Dict=True      , A__				:    str=None      , A__				:    str=True      , A__				:    List[str]=[0.5, 0.5, 0.5]      , A__				:    int=[0.5, 0.5, 0.5]      , A__				:    List[Any]=None      , )   ->					int:
        _snake_case						=							size if size is not None else {'''shortest_edge''': 18}
        _snake_case						=							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							num_channels
        _snake_case						=							num_frames
        _snake_case						=							image_size
        _snake_case						=							min_resolution
        _snake_case						=							max_resolution
        _snake_case						=							do_resize
        _snake_case						=							size
        _snake_case						=							do_normalize
        _snake_case						=							image_mean
        _snake_case						=							image_std
        _snake_case						=							crop_size







      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Union[str, Any]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Tuple										=    VivitImageProcessor if is_vision_available() else None



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[str]:
        _snake_case						=							VivitImageProcessingTester(self		)



      @property
      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Optional[Any]:
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(A__      , '''image_mean'''		)		)
        self.assertTrue(hasattr(A__      , '''image_std'''		)		)
        self.assertTrue(hasattr(A__      , '''do_normalize'''		)		)
        self.assertTrue(hasattr(A__      , '''do_resize'''		)		)
        self.assertTrue(hasattr(A__      , '''do_center_crop'''		)		)
        self.assertTrue(hasattr(A__      , '''size'''		)		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					List[Any]:
        _snake_case						=							self.image_processing_class.from_dict(self.image_processor_dict		)
        self.assertEqual(image_processor.size      , {'''shortest_edge''': 18}		)
        self.assertEqual(image_processor.crop_size      , {'''height''': 18, '''width''': 18}		)

        _snake_case						=							self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84		)
        self.assertEqual(image_processor.size      , {'''shortest_edge''': 42}		)
        self.assertEqual(image_processor.crop_size      , {'''height''': 84, '''width''': 84}		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[Any]:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PIL videos
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , Image.Image		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )



      def 						UpperCamelCase_		(      self				:    Any		)   ->					List[str]:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__      , numpify=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , np.ndarray		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )







      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					int:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__      , torchify=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , torch.Tensor		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

