



'''simple docstring'''

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import (
			    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
			    VideoMAEForPreTraining,
			    VideoMAEForVideoClassification,
			    VideoMAEModel,
			)
			from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from transformers import VideoMAEImageProcessor

class      __magic_name__						:







							def __init__(							self :	Dict     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Optional[Any]=13     ,_UpperCAmelCase :	str=10     ,_UpperCAmelCase :	Optional[int]=3     ,_UpperCAmelCase :	List[str]=2     ,_UpperCAmelCase :	Tuple=2     ,_UpperCAmelCase :	Dict=2     ,_UpperCAmelCase :	Union[str, Any]=True     ,_UpperCAmelCase :	Optional[Any]=True     ,_UpperCAmelCase :	List[Any]=32     ,_UpperCAmelCase :	Optional[int]=5     ,_UpperCAmelCase :	Union[str, Any]=4     ,_UpperCAmelCase :	Any=37     ,_UpperCAmelCase :	Optional[Any]="gelu"     ,_UpperCAmelCase :	Dict=0.1     ,_UpperCAmelCase :	List[str]=0.1     ,_UpperCAmelCase :	Union[str, Any]=10     ,_UpperCAmelCase :	Optional[int]=0.02     ,_UpperCAmelCase :	Optional[int]=0.9     ,_UpperCAmelCase :	Tuple=None     ,):
									_a					:				Optional[int]				=							parent
									_a					:				List[str]				=							batch_size
									_a					:				Any				=							image_size
									_a					:				List[str]				=							num_channels
									_a					:				Tuple				=							patch_size
									_a					:				Tuple				=							tubelet_size
									_a					:				str				=							num_frames
									_a					:				List[str]				=							is_training
									_a					:				Optional[Any]				=							use_labels
									_a					:				int				=							hidden_size
									_a					:				Optional[int]				=							num_hidden_layers
									_a					:				Optional[Any]				=							num_attention_heads
									_a					:				Dict				=							intermediate_size
									_a					:				str				=							hidden_act
									_a					:				Union[str, Any]				=							hidden_dropout_prob
									_a					:				Optional[Any]				=							attention_probs_dropout_prob
									_a					:				Tuple				=							type_sequence_label_size
									_a					:				int				=							initializer_range
									_a					:				Any				=							mask_ratio
									_a					:				Union[str, Any]				=							scope

									# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
									_a					:				Union[str, Any]				=							(image_size // patch_size) ** 2
									_a					:				Optional[Any]				=							(num_frames // tubelet_size) * self.num_patches_per_frame

									# use this variable to define bool_masked_pos
									_a					:				Optional[int]				=							int(mask_ratio * self.seq_length		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				List[Any]				=							floats_tensor(
									    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]		)

									_a					:				Any				=							None
									if self.use_labels:
											_a					:				Tuple				=							ids_tensor([self.batch_size]     ,self.type_sequence_label_size		)

									_a					:				int				=							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase      (							self :	str		):
									return VideoMAEConfig(
									    image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,num_frames=self.num_frames     ,tubelet_size=self.tubelet_size     ,hidden_size=self.hidden_size     ,num_hidden_layers=self.num_hidden_layers     ,num_attention_heads=self.num_attention_heads     ,intermediate_size=self.intermediate_size     ,hidden_act=self.hidden_act     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,is_decoder=_UpperCAmelCase     ,initializer_range=self.initializer_range     ,)







							def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Union[str, Any]		):
									_a					:				List[str]				=							VideoMAEModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				int				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	List[str]		):
									_a					:				Any				=							VideoMAEForPreTraining(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									# important: each video needs to have the same number of masked patches
									# hence we define a single mask, which we then repeat for each example in the batch
									_a					:				Union[str, Any]				=							torch.ones((self.num_masks,)		)
									_a					:				Any				=							torch.cat([mask, torch.zeros(self.seq_length - mask.size(0		)		)]		)
									_a					:				str				=							mask.expand(self.batch_size     ,-1		).bool()

									_a					:				Any				=							model(_UpperCAmelCase     ,_UpperCAmelCase		)
									# model only returns predictions for masked patches
									_a					:				Dict				=							mask.sum().item()
									_a					:				List[Any]				=							3 * self.tubelet_size * self.patch_size**2
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, num_masked_patches, decoder_num_labels)		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Tuple				=							self.prepare_config_and_inputs()
									_a					:				Any				=							config_and_inputs
									_a					:				Tuple				=							{"pixel_values": pixel_values}
									return config, inputs_dict

@require_torch
class      __magic_name__						(   lowerCamelCase__      ,						lowerCamelCase__      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Optional[Any]           = (
							    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
							)
							lowerCAmelCase	:   Tuple           = (
							    {'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase	:   Optional[Any]           = False
							lowerCAmelCase	:   List[Any]           = False
							lowerCAmelCase	:   Union[str, Any]           = False
							lowerCAmelCase	:   Optional[int]           = False







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				int				=							VideoMAEModelTester(self		)
									_a					:				Dict				=							ConfigTester(self     ,config_class=_UpperCAmelCase     ,has_text_modality=_UpperCAmelCase     ,hidden_size=37		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	int=False		):
									_a					:				Tuple				=							copy.deepcopy(_UpperCAmelCase		)

									if model_class == VideoMAEForPreTraining:
											# important: each video needs to have the same number of masked patches
											# hence we define a single mask, which we then repeat for each example in the batch
											_a					:				int				=							torch.ones((self.model_tester.num_masks,)		)
											_a					:				int				=							torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0		)		)]		)
											_a					:				str				=							mask.expand(self.model_tester.batch_size     ,-1		).bool()
											_a					:				Optional[int]				=							bool_masked_pos.to(_UpperCAmelCase		)

									if return_labels:
											if model_class in [
											    *get_values(_UpperCAmelCase		),
											]:
													_a					:				Any				=							torch.zeros(
													    self.model_tester.batch_size     ,dtype=torch.long     ,device=_UpperCAmelCase		)

									return inputs_dict







							def 							__lowercase      (							self :	Tuple		):
									self.config_tester.run_common_tests()







							@unittest.skip(reason='VideoMAE does not use inputs_embeds'		)
							def 							__lowercase      (							self :	List[str]		):
									pass







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Optional[int]				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				Optional[Any]				=							model_class(_UpperCAmelCase		)
											self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)		)
											_a					:				List[str]				=							model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_UpperCAmelCase     ,nn.Linear		)		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				Optional[Any]				=							model_class(_UpperCAmelCase		)
											_a					:				Optional[int]				=							inspect.signature(model.forward		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a					:				List[str]				=							[*signature.parameters.keys()]

											_a					:				Tuple				=							["pixel_values"]
											self.assertListEqual(arg_names[:1]     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[str]		):
									_a					:				Union[str, Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):
									_a					:				Optional[int]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase		)







							@slow
							def 							__lowercase      (							self :	List[str]		):




									for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_a					:				List[str]				=							VideoMAEModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):




									if not self.has_attentions:
											pass

									else:
											_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs_for_common()
											_a					:				Dict				=							True

											for model_class in self.all_model_classes:
													_a					:				List[Any]				=							self.model_tester.seq_length - self.model_tester.num_masks
													_a					:				int				=							(
													    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
													)

													_a					:				int				=							True
													_a					:				Optional[Any]				=							False
													_a					:				Optional[int]				=							True
													_a					:				int				=							model_class(_UpperCAmelCase		)
													model.to(_UpperCAmelCase		)
													model.eval()
													with torch.no_grad():
															_a					:				Tuple				=							model(**self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase		)		)
													_a					:				Optional[int]				=							outputs.attentions
													self.assertEqual(len(_UpperCAmelCase		)     ,self.model_tester.num_hidden_layers		)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													_a					:				Union[str, Any]				=							True
													_a					:				Union[str, Any]				=							model_class(_UpperCAmelCase		)
													model.to(_UpperCAmelCase		)
													model.eval()
													with torch.no_grad():
															_a					:				List[Any]				=							model(**self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase		)		)
													_a					:				Union[str, Any]				=							outputs.attentions
													self.assertEqual(len(_UpperCAmelCase		)     ,self.model_tester.num_hidden_layers		)

													self.assertListEqual(
													    list(attentions[0].shape[-3:]		)     ,[self.model_tester.num_attention_heads, seq_len, seq_len]     ,)
													_a					:				Any				=							len(_UpperCAmelCase		)

													# Check attention is always last and order is fine
													_a					:				int				=							True
													_a					:				Optional[Any]				=							True
													_a					:				Tuple				=							model_class(_UpperCAmelCase		)
													model.to(_UpperCAmelCase		)
													model.eval()
													with torch.no_grad():
															_a					:				Tuple				=							model(**self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase		)		)

													self.assertEqual(out_len + 1     ,len(_UpperCAmelCase		)		)

													_a					:				int				=							outputs.attentions

													self.assertEqual(len(_UpperCAmelCase		)     ,self.model_tester.num_hidden_layers		)
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]		)     ,[self.model_tester.num_attention_heads, seq_len, seq_len]     ,)







							def 							__lowercase      (							self :	Union[str, Any]		):




									def check_hidden_states_output(_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	int		):
											_a					:				Dict				=							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.eval()

											with torch.no_grad():
													_a					:				Tuple				=							model(**self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase		)		)

											_a					:				Optional[Any]				=							outputs.hidden_states
											_a					:				str				=							self.model_tester.num_hidden_layers + 1
											self.assertEqual(len(_UpperCAmelCase		)     ,_UpperCAmelCase		)

											_a					:				List[str]				=							self.model_tester.seq_length - self.model_tester.num_masks
											_a					:				Union[str, Any]				=							num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]		)     ,[seq_length, self.model_tester.hidden_size]     ,)

									_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				Optional[Any]				=							True
											check_hidden_states_output(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											_a					:				int				=							True

											check_hidden_states_output(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)







							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
							def 							__lowercase      (							self :	List[Any]		):
									pass



def 					__lowerCamelCase					(				)      ->				Any:
		_a					:				List[str]				=							hf_hub_download(
		    repo_id='hf-internal-testing/spaghetti-video'						,      filename='eating_spaghetti.npy'						,      repo_type='dataset'  )
		_a					:				Any				=							np.load(_lowerCAmelCase  )
		return list(_lowerCAmelCase  )

@require_torch
@require_vision
class      __magic_name__						(   unittest.TestCase       ):







							@cached_property
							def 							__lowercase      (							self :	Union[str, Any]		):
									return (
									    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]     ,image_std=[0.5, 0.5, 0.5]		)
									    if is_vision_available()
									    else None
									)







							@slow
							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Optional[Any]				=							VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics'		).to(
									    _UpperCAmelCase		)

									_a					:				Dict				=							self.default_image_processor
									_a					:				Optional[int]				=							prepare_video()
									_a					:				Union[str, Any]				=							image_processor(_UpperCAmelCase     ,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a					:				List[str]				=							model(**_UpperCAmelCase		)

									# verify the logits
									_a					:				Tuple				=							torch.Size((1, 400)		)
									self.assertEqual(outputs.logits.shape     ,_UpperCAmelCase		)

									_a					:				Tuple				=							torch.tensor([0.36_69, -0.06_88, -0.24_21]		).to(_UpperCAmelCase		)

									self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,_UpperCAmelCase     ,atol=1E-4		)		)







							@slow
							def 							__lowercase      (							self :	Tuple		):
									_a					:				Optional[Any]				=							VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'		).to(_UpperCAmelCase		)

									_a					:				int				=							self.default_image_processor
									_a					:				Tuple				=							prepare_video()
									_a					:				List[Any]				=							image_processor(_UpperCAmelCase     ,return_tensors='pt'		).to(_UpperCAmelCase		)

									# add boolean mask, indicating which patches to mask
									_a					:				Optional[int]				=							hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'     ,filename='bool_masked_pos.pt'		)
									_a					:				Any				=							torch.load(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a					:				Union[str, Any]				=							model(**_UpperCAmelCase		)

									# verify the logits
									_a					:				Any				=							torch.Size([1, 1408, 1536]		)
									_a					:				Optional[int]				=							torch.tensor(
									    [[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]]     ,device=_UpperCAmelCase		)
									self.assertEqual(outputs.logits.shape     ,_UpperCAmelCase		)
									self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]     ,_UpperCAmelCase     ,atol=1E-4		)		)

									# verify the loss (`config.norm_pix_loss` = `True`)
									_a					:				Union[str, Any]				=							torch.tensor([0.51_42]     ,device=_UpperCAmelCase		)
									self.assertTrue(torch.allclose(outputs.loss     ,_UpperCAmelCase     ,atol=1E-4		)		)

									# verify the loss (`config.norm_pix_loss` = `False`)
									_a					:				Optional[int]				=							VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'     ,norm_pix_loss=_UpperCAmelCase		).to(
									    _UpperCAmelCase		)

									with torch.no_grad():
											_a					:				Optional[int]				=							model(**_UpperCAmelCase		)

									_a					:				List[Any]				=							torch.tensor(torch.tensor([0.64_69]		)     ,device=_UpperCAmelCase		)
									self.assertTrue(torch.allclose(outputs.loss     ,_UpperCAmelCase     ,atol=1E-4		)		)









'''simple docstring'''

from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCAmelCase						      =       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						      =       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, 1_000))



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				tuple[str, float]:
		_a					:				List[Any]				=							len([g for position, g in enumerate(lowerCAmelCase_  ) if g == main_target[position]]  )
		return (item, float(lowerCAmelCase_  ))



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				tuple[str, str]:
		_a					:				Dict				=							random.randint(0						,      len(lowerCAmelCase_  ) - 1  )
		_a					:				Optional[int]				=							parent_a[:random_slice] + parent_a[random_slice:]
		_a					:				Optional[int]				=							parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		_a					:				Optional[Any]				=							list(lowerCAmelCase_  )
		if random.uniform(0						,      1  ) < MUTATION_PROBABILITY:
				_a					:				Optional[int]				=							random.choice(lowerCAmelCase_  )
		return "".join(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )      ->				list[str]:
		_a					:				List[str]				=							[]
		# Generate more children proportionally to the fitness score.
		_a					:				Tuple				=							int(parent_a[1] * 100  ) + 1
		_a					:				Tuple				=							10 if child_n >= 10 else child_n
		for _ in range(lowerCAmelCase_  ):
				_a					:				Any				=							population_score[random.randint(0						,      lowerCAmelCase_  )][0]

				_a	, _a					:				Tuple				=							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 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = True  )      ->				tuple[int, int, str]:

		# Verify if N_POPULATION is bigger than N_SELECTED
		if N_POPULATION < N_SELECTED:
				_a					:				Dict				=							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.
		_a					:				Optional[int]				=							sorted({c for c in target if c not in genes}  )
		if not_in_genes_list:
				_a					:				List[Any]				=							f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
				raise ValueError(lowerCAmelCase_  )

		# Generate random starting population.
		_a					:				Union[str, 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.
		_a	, _a					:				Union[str, Any]				=							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.
				_a					:				Optional[Any]				=							[evaluate(lowerCAmelCase_						,      lowerCAmelCase_  ) for item in population]

				# Check if there is a matching evolution.
				_a					:				Tuple				=							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.
				_a					:				Dict				=							population[: int(N_POPULATION / 3  )]
				population.clear()
				population.extend(lowerCAmelCase_  )
				# Normalize population score to be between 0 and 1.
				_a					:				Tuple				=							[
				    (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__":
							__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}"""
							)




