





from __future__ import annotations

import random

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







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  str     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Dict   =					len([g for position, g in enumerate(_lowerCamelCase     ) if g == main_target[position]]     )
						return (item, float(_lowerCamelCase     ))







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  str     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					random.randint(0			,   len(_lowerCamelCase     ) - 1     )
						SCREAMING_SNAKE_CASE__     :				Tuple   =					parent_a[:random_slice] + parent_a[random_slice:]
						SCREAMING_SNAKE_CASE__     :				Any   =					parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  list[str]     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					list(_lowerCamelCase     )
						if random.uniform(0			,   1     ) < MUTATION_PROBABILITY:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					random.choice(_lowerCamelCase     )
						return "".join(_lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  tuple[str, float]			,   _lowerCamelCase      :  list[tuple[str, float]]			,   _lowerCamelCase      :  list[str]			,   ):





						'''simple docstring'''


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

												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					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       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  list[str]			,   _lowerCamelCase      :  bool = True     ):





						'''simple docstring'''


						if N_POPULATION < N_SELECTED:
												SCREAMING_SNAKE_CASE__     :				int   =					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.
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					sorted({c for c in target if c not in genes}     )
						if not_in_genes_list:
												SCREAMING_SNAKE_CASE__     :				int   =					f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
												raise ValueError(_lowerCamelCase     )

						# Generate random starting population.
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
						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.
						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					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.
												SCREAMING_SNAKE_CASE__     :				List[str]   =					[evaluate(_lowerCamelCase			,   _lowerCamelCase     ) for item in population]

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






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor



class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     Any							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     int = 32							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     bool = True							,	a				:     Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]							,	a				:     Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]							,	a				:     bool = True							,	a				:     Any=7							,	a				:     str=30							,	a				:     Dict=4_00							,	a				:     Optional[int]=3							,	)	->int:
													SCREAMING_SNAKE_CASE__     :				int   =					parent
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_resize
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size if size is not None else {"shortest_edge": 2_88}
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					size_divisor
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Tuple   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_mean
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_std
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_pad
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					batch_size
													SCREAMING_SNAKE_CASE__     :				int   =					num_channels
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					min_resolution
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					max_resolution

							def      A_  (      self				:     List[str]    )	->Tuple:
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "size_divisor": self.size_divisor,
													}






							def      A_  (      self				:     int							,	a				:     Optional[int]							,	a				:     Union[str, Any]=False    )	->Optional[Any]:
													if not batched:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.size["shortest_edge"]
																			SCREAMING_SNAKE_CASE__     :				Dict   =					image_inputs[0]
																			if isinstance(a							,	Image.Image    ):
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					image.size
																			else:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					image.shape[1], image.shape[2]
																			SCREAMING_SNAKE_CASE__     :				Any   =					size / min(a							,	a    )
																			if h < w:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					size, scale * w
																			else:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					scale * h, size

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					int((13_33 / 8_00) * size    )
																			if max(a							,	a    ) > max_size:
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					max_size / max(a							,	a    )
																									SCREAMING_SNAKE_CASE__     :				int   =					newh * scale
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					neww * scale

																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					int(newh + 0.5    ), int(neww + 0.5    )
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					(
																			    newh // self.size_divisor * self.size_divisor,
																			    neww // self.size_divisor * self.size_divisor,
																			)

													else:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
																			for image in image_inputs:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.get_expected_values([image]    )
																									expected_values.append((expected_height, expected_width)    )
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					max(a							,	key=lambda a    : item[0]    )[0]
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					max(a							,	key=lambda a    : item[1]    )[1]

													return expected_height, expected_width







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






							"""simple docstring"""

							snake_case_							 =						BridgeTowerImageProcessor if is_vision_available() else None

							def      A_  (      self				:     List[Any]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Any   =					BridgeTowerImageProcessingTester(self    )

							@property
							def      A_  (      self				:     Optional[int]    )	->Optional[Any]:
													return self.image_processor_tester.prepare_image_processor_dict()

							def      A_  (      self				:     Tuple    )	->str:
													SCREAMING_SNAKE_CASE__     :				int   =					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							,	"size"    )    )
													self.assertTrue(hasattr(a							,	"size_divisor"    )    )

							def      A_  (      self				:     List[Any]    )	->List[Any]:
													pass

							def      A_  (      self				:     Tuple    )	->Optional[Any]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	Image.Image    )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				int   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)

							def      A_  (      self				:     Optional[int]    )	->Any:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				str   =					self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													SCREAMING_SNAKE_CASE__     :				str   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a							,	numpify=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	np.ndarray    )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)






							def      A_  (      self				:     str    )	->Optional[int]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a							,	torchify=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	torch.Tensor    )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Tuple   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				Any   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)