
"""simple docstring"""






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor




class 				lowercase__(				unittest.TestCase   ):





	def __init__(						self    :Any		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :List[Any]=7		, lowerCamelCase_    :List[Any]=3		, lowerCamelCase_    :Dict=30		, lowerCamelCase_    :Union[str, Any]=4_00		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :Dict=None		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :Any=[0.5, 0.5, 0.5]		, lowerCamelCase_    :Union[str, Any]=[0.5, 0.5, 0.5]		, lowerCamelCase_    :Optional[Any]=True		, lowerCamelCase_    :Dict=1 / 2_55		, lowerCamelCase_    :str=True		, )							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
			SCREAMING_SNAKE_CASE       :     Optional[int]				= parent
			SCREAMING_SNAKE_CASE       :     Any				= batch_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= num_channels
			SCREAMING_SNAKE_CASE       :     Tuple				= min_resolution
			SCREAMING_SNAKE_CASE       :     Tuple				= max_resolution
			SCREAMING_SNAKE_CASE       :     Tuple				= do_resize
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= size
			SCREAMING_SNAKE_CASE       :     List[str]				= do_normalize
			SCREAMING_SNAKE_CASE       :     List[str]				= image_mean
			SCREAMING_SNAKE_CASE       :     Any				= image_std
			SCREAMING_SNAKE_CASE       :     List[Any]				= do_rescale
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= rescale_factor
			SCREAMING_SNAKE_CASE       :     int				= do_pad





	def   __lowerCAmelCase (						self    :str					)							->			List[str]:







			'''simple docstring'''




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







	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Tuple=False					)							->			List[str]:







			'''simple docstring'''




			if not batched:
					SCREAMING_SNAKE_CASE       :     str				= image_inputs[0]
					if isinstance(A__		, Image.Image					):
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Dict				= image.size
					else:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= image.shape[1], image.shape[2]
					if w < h:
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= int(self.size['''shortest_edge'''] * h / w					)
							SCREAMING_SNAKE_CASE       :     str				= self.size['''shortest_edge''']
					elif w > h:
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.size['''shortest_edge''']
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= int(self.size['''shortest_edge'''] * w / h					)
					else:
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.size['''shortest_edge''']
							SCREAMING_SNAKE_CASE       :     List[str]				= self.size['''shortest_edge''']

			else:
					SCREAMING_SNAKE_CASE       :     str				= []
					for image in image_inputs:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= self.get_expected_values([image]					)
							expected_values.append((expected_height, expected_width)					)
					SCREAMING_SNAKE_CASE       :     List[str]				= max(A__		, key=lambda lowerCamelCase_					: item[0]					)[0]
					SCREAMING_SNAKE_CASE       :     Any				= max(A__		, key=lambda lowerCamelCase_					: item[1]					)[1]

			return expected_height, expected_width





@require_torch
@require_vision
class 				lowercase__(				_lowerCamelCase		,						unittest.TestCase   ):
	UpperCamelCase				=  DetaImageProcessor if is_vision_available() else None





	def   __lowerCAmelCase (						self    :Any					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= DetaImageProcessingTester(self					)





	@property
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			int:







			'''simple docstring'''




			return self.image_processor_tester.prepare_image_processor_dict()





	def   __lowerCAmelCase (						self    :Any					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= 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_rescale'''					)					)
			self.assertTrue(hasattr(A__		, '''do_pad'''					)					)
			self.assertTrue(hasattr(A__		, '''size'''					)					)





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.image_processing_class.from_dict(self.image_processor_dict					)
			self.assertEqual(image_processor.size		, {'''shortest_edge''': 18, '''longest_edge''': 13_33}					)
			self.assertEqual(image_processor.do_pad		, A__					)





	def   __lowerCAmelCase (						self    :Dict					)							->			List[str]:







			'''simple docstring'''




			pass





	def   __lowerCAmelCase (						self    :List[str]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.image_processing_class(**self.image_processor_dict					)
			# create random PIL images
			SCREAMING_SNAKE_CASE       :     Dict				= 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       :     Tuple				= image_processing(image_inputs[0]		, return_tensors='''pt'''					).pixel_values

			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= 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,					SCREAMING_SNAKE_CASE       :     str				= self.image_processor_tester.get_expected_values(A__		, batched=A__					)

			SCREAMING_SNAKE_CASE       :     List[str]				= image_processing(A__		, return_tensors='''pt'''					).pixel_values
			self.assertEqual(
			    encoded_images.shape		, (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    )		, )





	def   __lowerCAmelCase (						self    :List[str]					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.image_processing_class(**self.image_processor_dict					)
			# create random numpy tensors
			SCREAMING_SNAKE_CASE       :     Tuple				= 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       :     str				= 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       :     int				= 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   __lowerCAmelCase (						self    :Optional[int]					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.image_processing_class(**self.image_processor_dict					)
			# create random PyTorch tensors
			SCREAMING_SNAKE_CASE       :     List[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       :     str				= 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       :     Optional[Any]				= image_processing(A__		, return_tensors='''pt'''					).pixel_values

			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     str				= 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,
			    )		, )





	@slow
	def   __lowerCAmelCase (						self    :Any					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
			with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		, '''r'''					) as f:
					SCREAMING_SNAKE_CASE       :     List[Any]				= json.loads(f.read()					)

			SCREAMING_SNAKE_CASE       :     Tuple				= {'''image_id''': 3_97_69, '''annotations''': target}

			# encode them
			SCREAMING_SNAKE_CASE       :     Dict				= DetaImageProcessor()
			SCREAMING_SNAKE_CASE       :     Any				= image_processing(images=A__		, annotations=A__		, return_tensors='''pt'''					)

			# verify pixel values
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.Size([1, 3, 8_00, 10_66]					)
			self.assertEqual(encoding['''pixel_values'''].shape		, A__					)

			SCREAMING_SNAKE_CASE       :     List[str]				= torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]					)
			self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		, A__		, atol=1E-4					)					)

			# verify area
			SCREAMING_SNAKE_CASE       :     str				= torch.tensor([5_8_8_7.9_6_0_0, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		, A__					)					)
			# verify boxes
			SCREAMING_SNAKE_CASE       :     Tuple				= torch.Size([6, 4]					)
			self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, A__					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		, A__		, atol=1E-3					)					)
			# verify image_id
			SCREAMING_SNAKE_CASE       :     List[str]				= torch.tensor([3_97_69]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, A__					)					)
			# verify is_crowd
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.tensor([0, 0, 0, 0, 0, 0]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, A__					)					)
			# verify class_labels
			SCREAMING_SNAKE_CASE       :     int				= torch.tensor([75, 75, 63, 65, 17, 17]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		, A__					)					)
			# verify orig_size
			SCREAMING_SNAKE_CASE       :     Tuple				= torch.tensor([4_80, 6_40]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, A__					)					)
			# verify size
			SCREAMING_SNAKE_CASE       :     int				= torch.tensor([8_00, 10_66]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, A__					)					)







	@slow
	def   __lowerCAmelCase (						self    :List[str]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
			with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''		, '''r'''					) as f:
					SCREAMING_SNAKE_CASE       :     List[str]				= json.loads(f.read()					)

			SCREAMING_SNAKE_CASE       :     Dict				= {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

			SCREAMING_SNAKE_CASE       :     Optional[int]				= pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''					)

			# encode them
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= DetaImageProcessor(format='''coco_panoptic'''					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= image_processing(images=A__		, annotations=A__		, masks_path=A__		, return_tensors='''pt'''					)

			# verify pixel values
			SCREAMING_SNAKE_CASE       :     str				= torch.Size([1, 3, 8_00, 10_66]					)
			self.assertEqual(encoding['''pixel_values'''].shape		, A__					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]					)
			self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		, A__		, atol=1E-4					)					)

			# verify area
			SCREAMING_SNAKE_CASE       :     Tuple				= torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		, A__					)					)
			# verify boxes
			SCREAMING_SNAKE_CASE       :     int				= torch.Size([6, 4]					)
			self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, A__					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		, A__		, atol=1E-3					)					)
			# verify image_id
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.tensor([3_97_69]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, A__					)					)
			# verify is_crowd
			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.tensor([0, 0, 0, 0, 0, 0]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, A__					)					)
			# verify class_labels
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.tensor([17, 17, 63, 75, 75, 93]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		, A__					)					)
			# verify masks
			SCREAMING_SNAKE_CASE       :     List[str]				= 82_28_73
			self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		, A__					)
			# verify orig_size
			SCREAMING_SNAKE_CASE       :     List[str]				= torch.tensor([4_80, 6_40]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, A__					)					)
			# verify size
			SCREAMING_SNAKE_CASE       :     Tuple				= torch.tensor([8_00, 10_66]					)
			self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, A__					)					)







"""simple docstring"""






from __future__ import annotations

import random

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





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




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= len([g for position, g in enumerate(a_     ) if g == main_target[position]]     )
		return (item, float(a_     ))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= random.randint(0		,		len(a_     ) - 1     )
		SCREAMING_SNAKE_CASE       :     str				= parent_a[:random_slice] + parent_a[random_slice:]
		SCREAMING_SNAKE_CASE       :     Dict				= parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)





def __A						(   a_ :							str		,		a_ :							list[str]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= list(a_     )
		if random.uniform(0		,		1     ) < MUTATION_PROBABILITY:
				SCREAMING_SNAKE_CASE       :     Any				= random.choice(a_     )
		return "".join(a_     )





def __A						(   a_ :							tuple[str, float]		,		a_ :							list[tuple[str, float]]		,		a_ :							list[str]		,		)->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= []
		# Generate more children proportionally to the fitness score.
		SCREAMING_SNAKE_CASE       :     List[str]				= int(parent_a[1] * 1_00     ) + 1
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10 if child_n >= 10 else child_n
		for _ in range(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= population_score[random.randint(0		,		a_     )][0]

				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= crossover(parent_a[0]		,		a_     )
				# Append new string to the population list.
				pop.append(mutate(a_		,		a_     )     )
				pop.append(mutate(a_		,		a_     )     )
		return pop





def __A						(   a_ :							str		,		a_ :							list[str]		,		a_ :							bool = True     )->							tuple[int, int, str]:




		'''simple docstring'''






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

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

		# Just some logs to know what the algorithms is doing.
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= 0, 0

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

				# 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       :     int				= [evaluate(a_		,		a_     ) for item in population]

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

				# This is selection
				for i in range(a_     ):
						population.extend(select(population_score[int(a_     )]		,		a_		,		a_     )     )
						# 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(a_     ) > N_POPULATION:
								break


if __name__ == "__main__":
					lowerCamelCase__ :       Dict						     =			(
					    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
					)
					lowerCamelCase__ :       int						     =			list(
					    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
					    "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
					)
					lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__ :       Dict						     =			basic(target_str, genes_list)
					print(
					    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
					)





