






'''simple docstring'''




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase_												=					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.
lowerCamelCase_												=					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_												=					0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						tuple[str, float]:

      '''simple docstring'''


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

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						tuple[str, str]:

      '''simple docstring'''


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

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						str:

      '''simple docstring'''


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

def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase   ,  )       ->						list[str]:

      '''simple docstring'''


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

            _A     ,	_A			     =	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       )       ->						tuple[int, int, str]:

      '''simple docstring'''


      if N_POPULATION < N_SELECTED:
            _A			     =	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.
      _A			     =	sorted({c for c in target if c not in genes}       )
      if not_in_genes_list:
            _A			     =	F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
            raise ValueError(__lowercase       )

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

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






'''simple docstring'''




import unittest

import numpy as np

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

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 DPTImageProcessor






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=7       ,    __lowerCAmelCase=3       ,    __lowerCAmelCase=18       ,    __lowerCAmelCase=30       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=[0.5, 0.5, 0.5]       ,    __lowerCAmelCase=[0.5, 0.5, 0.5]       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  size if size is not None else {"""height""": 18, """width""": 18}
					lowerCAmelCase   					=  parent
					lowerCAmelCase   					=  batch_size
					lowerCAmelCase   					=  num_channels
					lowerCAmelCase   					=  image_size
					lowerCAmelCase   					=  min_resolution
					lowerCAmelCase   					=  max_resolution
					lowerCAmelCase   					=  do_resize
					lowerCAmelCase   					=  size
					lowerCAmelCase   					=  do_normalize
					lowerCAmelCase   					=  image_mean
					lowerCAmelCase   					=  image_std







			def 							a_       (   self):




					"""simple docstring"""


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







@require_torch
@require_vision
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Tuple      =       DPTImageProcessor if is_vision_available() else None



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  DPTImageProcessingTester(self)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  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       ,    """size"""))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size       ,    {"""height""": 18, """width""": 18})

					lowerCAmelCase   					=  self.image_processing_class.from_dict(self.image_processor_dict       ,    size=42)
					self.assertEqual(image_processor.size       ,    {"""height""": 42, """width""": 42})



			def 							a_       (   self):




					"""simple docstring"""


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

					# Test not batched input
					lowerCAmelCase   					=  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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					lowerCAmelCase   					=  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
					lowerCAmelCase   					=  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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					lowerCAmelCase   					=  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
					lowerCAmelCase   					=  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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )

					# Test batched
					lowerCAmelCase   					=  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.size["""height"""],
					        self.image_processor_tester.size["""width"""],
					    )       ,    )




