






"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class    lowercase__			(							UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   unittest.TestCase   ):
			_UpperCAmelCase		:Tuple   			= StableDiffusionInstructPixaPixPipeline
			_UpperCAmelCase		:Any   			= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
			_UpperCAmelCase		:Any   			= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCAmelCase		:Any   			= IMAGE_TO_IMAGE_IMAGE_PARAMS
			_UpperCAmelCase		:Any   			= IMAGE_TO_IMAGE_IMAGE_PARAMS


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										torch.manual_seed(0					)
										lowerCamelCase_       :							Optional[Any]									=UNetaDConditionModel(
										    block_out_channels=(32, 64)   ,       layers_per_block=2   ,       sample_size=32   ,       in_channels=8   ,       out_channels=4   ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   ,       cross_attention_dim=32   ,       )
										lowerCamelCase_       :							Dict									=PNDMScheduler(skip_prk_steps=__lowercase					)
										torch.manual_seed(0					)
										lowerCamelCase_       :							Tuple									=AutoencoderKL(
										    block_out_channels=[32, 64]   ,       in_channels=3   ,       out_channels=3   ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,       latent_channels=4   ,       )
										torch.manual_seed(0					)
										lowerCamelCase_       :							Union[str, Any]									=CLIPTextConfig(
										    bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=32   ,       intermediate_size=37   ,       layer_norm_eps=1E-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=1000   ,       )
										lowerCamelCase_       :							List[Any]									=CLIPTextModel(__lowercase					)
										lowerCamelCase_       :							str									=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

										lowerCamelCase_       :							List[str]									={
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components


			def 	UpperCAmelCase__						(      self     :  List[Any]   ,       snake_case__     :  List[Any]   ,       snake_case__     :  str=0					):
										lowerCamelCase_       :							Optional[Any]									=floats_tensor((1, 3, 32, 32)   ,       rng=random.Random(__lowercase					)					).to(__lowercase					)
										lowerCamelCase_       :							Union[str, Any]									=image.cpu().permute(0   ,       2   ,       3   ,       1					)[0]
										lowerCamelCase_       :							Tuple									=Image.fromarray(np.uinta(__lowercase					)					).convert("RGB"					)
										if str(__lowercase					).startswith("mps"					):
																	lowerCamelCase_       :							List[Any]									=torch.manual_seed(__lowercase					)
										else:
																	lowerCamelCase_       :							Any									=torch.Generator(device=__lowercase					).manual_seed(__lowercase					)
										lowerCamelCase_       :							Dict									={
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''image''': image,
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 6.0,
										    '''image_guidance_scale''': 1,
										    '''output_type''': '''numpy''',
										}
										return inputs


			def 	UpperCAmelCase__						(      self     :  List[Any]					):
										lowerCamelCase_       :							List[str]									='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase_       :							Dict									=self.get_dummy_components()
										lowerCamelCase_       :							List[Any]									=StableDiffusionInstructPixaPixPipeline(**__lowercase					)
										lowerCamelCase_       :							List[str]									=sd_pipe.to(__lowercase					)
										sd_pipe.set_progress_bar_config(disable=__lowercase					)

										lowerCamelCase_       :							List[str]									=self.get_dummy_inputs(__lowercase					)
										lowerCamelCase_       :							int									=sd_pipe(**__lowercase					).images
										lowerCamelCase_       :							int									=image[0, -3:, -3:, -1]
										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase_       :							List[Any]									=np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3


			def 	UpperCAmelCase__						(      self     :  str					):
										lowerCamelCase_       :							int									='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase_       :							Dict									=self.get_dummy_components()
										lowerCamelCase_       :							int									=StableDiffusionInstructPixaPixPipeline(**__lowercase					)
										lowerCamelCase_       :							List[Any]									=sd_pipe.to(__lowercase					)
										sd_pipe.set_progress_bar_config(disable=__lowercase					)

										lowerCamelCase_       :							Any									=self.get_dummy_inputs(__lowercase					)
										lowerCamelCase_       :							int									='''french fries'''
										lowerCamelCase_       :							Tuple									=sd_pipe(**__lowercase   ,       negative_prompt=__lowercase					)
										lowerCamelCase_       :							Dict									=output.images
										lowerCamelCase_       :							Any									=image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase_       :							List[Any]									=np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3


			def 	UpperCAmelCase__						(      self     :  Union[str, Any]					):
										lowerCamelCase_       :							int									='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase_       :							str									=self.get_dummy_components()
										lowerCamelCase_       :							str									=StableDiffusionInstructPixaPixPipeline(**__lowercase					)
										lowerCamelCase_       :							List[Any]									=sd_pipe.to(__lowercase					)
										sd_pipe.set_progress_bar_config(disable=__lowercase					)

										lowerCamelCase_       :							List[Any]									=self.get_dummy_inputs(__lowercase					)
										lowerCamelCase_       :							Tuple									=[inputs['''prompt''']] * 2

										lowerCamelCase_       :							Dict									=np.array(inputs["image"]					).astype(np.floataa					) / 255.0
										lowerCamelCase_       :							Tuple									=torch.from_numpy(__lowercase					).unsqueeze(0					).to(__lowercase					)
										lowerCamelCase_       :							int									=image / 2 + 0.5
										lowerCamelCase_       :							List[str]									=image.permute(0   ,       3   ,       1   ,       2					)
										lowerCamelCase_       :							Dict									=image.repeat(2   ,       1   ,       1   ,       1					)

										lowerCamelCase_       :							Tuple									=sd_pipe(**__lowercase					).images
										lowerCamelCase_       :							Optional[int]									=image[-1, -3:, -3:, -1]

										assert image.shape == (2, 32, 32, 3)
										lowerCamelCase_       :							List[str]									=np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3


			def 	UpperCAmelCase__						(      self     :  Any					):
										lowerCamelCase_       :							List[Any]									='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase_       :							Union[str, Any]									=self.get_dummy_components()
										lowerCamelCase_       :							Union[str, Any]									=EulerAncestralDiscreteScheduler(
										    beta_start=0.00_085   ,       beta_end=0.012   ,       beta_schedule="scaled_linear"					)
										lowerCamelCase_       :							Any									=StableDiffusionInstructPixaPixPipeline(**__lowercase					)
										lowerCamelCase_       :							Union[str, Any]									=sd_pipe.to(__lowercase					)
										sd_pipe.set_progress_bar_config(disable=__lowercase					)

										lowerCamelCase_       :							Optional[Any]									=self.get_dummy_inputs(__lowercase					)
										lowerCamelCase_       :							int									=sd_pipe(**__lowercase					).images
										lowerCamelCase_       :							List[Any]									=image[0, -3:, -3:, -1]

										lowerCamelCase_       :							str									=[round(__lowercase   ,       4					) for x in image_slice.flatten().tolist()]
										print(",".join([str(__lowercase					) for x in slice]					)					)

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase_       :							str									=np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3


			def 	UpperCAmelCase__						(      self     :  Dict					):
										super().test_inference_batch_single_identical(expected_max_diff=3E-3					)


			def 	UpperCAmelCase__						(      self     :  Optional[int]					):
										lowerCamelCase_       :							int									=self.get_dummy_components()
										lowerCamelCase_       :							Any									=StableDiffusionInstructPixaPixPipeline(**__lowercase					)
										lowerCamelCase_       :							Union[str, Any]									=VaeImageProcessor(do_resize=__lowercase   ,       do_normalize=__lowercase					)
										lowerCamelCase_       :							Dict									=pipe.to(__lowercase					)
										pipe.set_progress_bar_config(disable=__lowercase					)

										lowerCamelCase_       :							Any									=pipe(**self.get_dummy_inputs_by_type(__lowercase   ,       input_image_type="pt"					)					)[0]

										lowerCamelCase_       :							int									=components['''vae''']
										lowerCamelCase_       :							Dict									=self.get_dummy_inputs_by_type(__lowercase   ,       input_image_type="pt"					)

										for image_param in self.image_latents_params:
																	if image_param in inputs.keys():
																								lowerCamelCase_       :							str									=vae.encode(inputs[image_param]					).latent_dist.mode()

										lowerCamelCase_       :							Tuple									=pipe(**__lowercase					)[0]

										lowerCamelCase_       :							Optional[Any]									=np.abs(out - out_latents_inputs					).max()
										self.assertLess(__lowercase   ,       1E-4   ,       "passing latents as image input generate different result from passing image"					)







@slow
@require_torch_gpu
class    lowercase__			(							unittest.TestCase   ):


			def 	UpperCAmelCase__						(      self     :  str					):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


			def 	UpperCAmelCase__						(      self     :  str   ,       snake_case__     :  Optional[Any]=0					):
										lowerCamelCase_       :							Optional[int]									=torch.manual_seed(__lowercase					)
										lowerCamelCase_       :							Optional[Any]									=load_image(
										    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"					)
										lowerCamelCase_       :							Any									={
										    '''prompt''': '''turn him into a cyborg''',
										    '''image''': image,
										    '''generator''': generator,
										    '''num_inference_steps''': 3,
										    '''guidance_scale''': 7.5,
										    '''image_guidance_scale''': 1.0,
										    '''output_type''': '''numpy''',
										}
										return inputs


			def 	UpperCAmelCase__						(      self     :  Union[str, Any]					):
										lowerCamelCase_       :							Any									=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    "timbrooks/instruct-pix2pix"   ,       safety_checker=__lowercase					)
										pipe.to(__lowercase					)
										pipe.set_progress_bar_config(disable=__lowercase					)
										pipe.enable_attention_slicing()

										lowerCamelCase_       :							Union[str, Any]									=self.get_inputs()
										lowerCamelCase_       :							Dict									=pipe(**__lowercase					).images
										lowerCamelCase_       :							Union[str, Any]									=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase_       :							Any									=np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]					)

										assert np.abs(expected_slice - image_slice					).max() < 1E-3


			def 	UpperCAmelCase__						(      self     :  List[str]					):
										lowerCamelCase_       :							int									=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    "timbrooks/instruct-pix2pix"   ,       safety_checker=__lowercase					)
										lowerCamelCase_       :							Dict									=LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
										pipe.to(__lowercase					)
										pipe.set_progress_bar_config(disable=__lowercase					)
										pipe.enable_attention_slicing()

										lowerCamelCase_       :							Any									=self.get_inputs()
										lowerCamelCase_       :							Union[str, Any]									=pipe(**__lowercase					).images
										lowerCamelCase_       :							int									=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase_       :							List[str]									=np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]					)

										assert np.abs(expected_slice - image_slice					).max() < 1E-3


			def 	UpperCAmelCase__						(      self     :  Tuple					):
										lowerCamelCase_       :							Dict									=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    "timbrooks/instruct-pix2pix"   ,       safety_checker=__lowercase					)
										lowerCamelCase_       :							List[Any]									=DDIMScheduler.from_config(pipe.scheduler.config					)
										pipe.to(__lowercase					)
										pipe.set_progress_bar_config(disable=__lowercase					)
										pipe.enable_attention_slicing()

										lowerCamelCase_       :							Any									=self.get_inputs()
										lowerCamelCase_       :							Dict									=pipe(**__lowercase					).images
										lowerCamelCase_       :							List[Any]									=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase_       :							List[str]									=np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]					)

										assert np.abs(expected_slice - image_slice					).max() < 1E-3


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_       :							Dict									=0

										def callback_fn(snake_case__     :  List[str]   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  List[Any]					) -> None:
																	lowerCamelCase_       :							Any									=True
																	nonlocal number_of_steps
																	number_of_steps += 1
																	if step == 1:
																								lowerCamelCase_       :							str									=latents.detach().cpu().numpy()
																								assert latents.shape == (1, 4, 64, 64)
																								lowerCamelCase_       :							Optional[int]									=latents[0, -3:, -3:, -1]
																								lowerCamelCase_       :							Tuple									=np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]					)

																								assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2
																	elif step == 2:
																								lowerCamelCase_       :							Optional[Any]									=latents.detach().cpu().numpy()
																								assert latents.shape == (1, 4, 64, 64)
																								lowerCamelCase_       :							str									=latents[0, -3:, -3:, -1]
																								lowerCamelCase_       :							int									=np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]					)

																								assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2

										lowerCamelCase_       :							Tuple									=False

										lowerCamelCase_       :							Optional[Any]									=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    "timbrooks/instruct-pix2pix"   ,       safety_checker=__lowercase   ,       torch_dtype=torch.floataa					)
										lowerCamelCase_       :							List[str]									=pipe.to(__lowercase					)
										pipe.set_progress_bar_config(disable=__lowercase					)
										pipe.enable_attention_slicing()

										lowerCamelCase_       :							Dict									=self.get_inputs()
										pipe(**__lowercase   ,       callback=__lowercase   ,       callback_steps=1					)
										assert callback_fn.has_been_called
										assert number_of_steps == 3


			def 	UpperCAmelCase__						(      self     :  int					):
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										lowerCamelCase_       :							Dict									=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    "timbrooks/instruct-pix2pix"   ,       safety_checker=__lowercase   ,       torch_dtype=torch.floataa					)
										lowerCamelCase_       :							Tuple									=pipe.to(__lowercase					)
										pipe.set_progress_bar_config(disable=__lowercase					)
										pipe.enable_attention_slicing(1					)
										pipe.enable_sequential_cpu_offload()

										lowerCamelCase_       :							Any									=self.get_inputs()
										lowerCamelCase_       :							Tuple									=pipe(**__lowercase					)

										lowerCamelCase_       :							Union[str, Any]									=torch.cuda.max_memory_allocated()
										# make sure that less than 2.2 GB is allocated
										assert mem_bytes < 2.2 * 10**9


			def 	UpperCAmelCase__						(      self     :  Optional[int]					):
										lowerCamelCase_       :							Any									=self.get_inputs()
										# resize to resolution that is divisible by 8 but not 16 or 32
										lowerCamelCase_       :							str									=inputs['''image'''].resize((504, 504)					)

										lowerCamelCase_       :							str									='''timbrooks/instruct-pix2pix'''
										lowerCamelCase_       :							str									=StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    __lowercase   ,       safety_checker=__lowercase   ,       )
										pipe.to(__lowercase					)
										pipe.set_progress_bar_config(disable=__lowercase					)
										pipe.enable_attention_slicing()

										lowerCamelCase_       :							int									=pipe(**__lowercase					)
										lowerCamelCase_       :							Tuple									=output.images[0]

										lowerCamelCase_       :							Dict									=image[255:258, 383:386, -1]

										assert image.shape == (504, 504, 3)
										lowerCamelCase_       :							List[str]									=np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-3




from __future__ import annotations

import random

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		list(SCREAMING_SNAKE_CASE	)
    if random.uniform(0  ,			1	) < MUTATION_PROBABILITY:
        __UpperCamelCase     :str			  =		random.choice(SCREAMING_SNAKE_CASE	)
    return "".join(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			):
    '''simple docstring'''







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

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = True	):
    '''simple docstring'''







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

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

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

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

        # 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.
        __UpperCamelCase     :Tuple			  =		[evaluate(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	) for item in population]

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

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


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





