

from __future__ import annotations

import random

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

def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: str					)     ->      tuple[str, float]:
	A__							:    Tuple        =		len([g for position, g in enumerate(lowercase_					) if g == main_target[position]]					)
	return (item, float(lowercase_					))

def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: str					)     ->      tuple[str, str]:
	A__							:    Dict        =		random.randint(0    ,      len(lowercase_					) - 1					)
	A__							:    Any        =		parent_a[:random_slice] + parent_a[random_slice:]
	A__							:    Tuple        =		parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)

def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: list[str]					)     ->      str:
	A__							:    Union[str, Any]        =		list(lowercase_					)
	if random.uniform(0    ,      1					) < MUTATION_PROBABILITY:
		A__							:    Any        =		random.choice(lowercase_					)
	return "".join(lowercase_					)

def 						UpperCamelCase					(lowercase_: tuple[str, float]    ,      lowercase_: list[tuple[str, float]]    ,      lowercase_: list[str]    ,      )     ->      list[str]:
	A__							:    Any        =		[]
	# Generate more children proportionally to the fitness score.
	A__							:    Union[str, Any]        =		int(parent_a[1] * 100					) + 1
	A__							:    Tuple        =		10 if child_n >= 10 else child_n
	for _ in range(lowercase_					):
		A__							:    Optional[int]        =		population_score[random.randint(0    ,      lowercase_					)][0]

		A__   ,   A__							:    Optional[int]        =		crossover(parent_a[0]    ,      lowercase_					)
		# Append new string to the population list.
		pop.append(mutate(lowercase_    ,      lowercase_					)					)
		pop.append(mutate(lowercase_    ,      lowercase_					)					)
	return pop

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

	# Verify if N_POPULATION is bigger than N_SELECTED
	if N_POPULATION < N_SELECTED:
		A__							:    Tuple        =		f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
		raise ValueError(lowercase_					)
	# Verify that the target contains no genes besides the ones inside genes variable.
	A__							:    Union[str, Any]        =		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(lowercase_					)

	# Generate random starting population.
	A__							:    Any        =		[]
	for _ in range(lowercase_					):
		population.append("""""".join([random.choice(lowercase_					) for i in range(len(lowercase_					)					)]					)					)

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

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

		# Random population created. Now it's time to evaluate.

		# Adding a bit of concurrency can make everything faster,
		#
		# import concurrent.futures
		# population_score: list[tuple[str, float]] = []
		# with concurrent.futures.ThreadPoolExecutor(
		#                                   max_workers=NUM_WORKERS) as executor:
		#     futures = {executor.submit(evaluate, item) for item in population}
		#     concurrent.futures.wait(futures)
		#     population_score = [item.result() for item in futures]
		#
		# but with a simple algorithm like this, it will probably be slower.
		# We just need to call evaluate for every item inside the population.
		A__							:    Tuple        =		[evaluate(lowercase_    ,      lowercase_					) for item in population]

		# Check if there is a matching evolution.
		A__							:    str        =		sorted(lowercase_    ,      key=lambda lowercase_					: x[1]    ,      reverse=lowercase_					)
		if population_score[0][0] == target:
			return (generation, total_population, population_score[0][0])

		# Print the best result every 10 generation.
		# Just to know that the algorithm is working.
		if debug and generation % 10 == 0:
			print(
			    f"""\nGeneration: {generation}"""
			    f"""\nTotal Population:{total_population}"""
			    f"""\nBest score: {population_score[0][1]}"""
			    f"""\nBest string: {population_score[0][0]}"""					)

		# Flush the old population, keeping some of the best evolutions.
		# Keeping this avoid regression of evolution.
		A__							:    Any        =		population[: int(N_POPULATION / 3					)]
		population.clear()
		population.extend(lowercase_					)
		# Normalize population score to be between 0 and 1.
		A__							:    Optional[Any]        =		[
		    (item, score / len(lowercase_					)) for item, score in population_score
		]

		# This is selection
		for i in range(lowercase_					):
			population.extend(select(population_score[int(lowercase_					)]    ,      lowercase_    ,      lowercase_					)					)
			# Check if the population has already reached the maximum value and if so,
			# break the cycle.  If this check is disabled, the algorithm will take
			# forever to compute large strings, but will also calculate small strings in
			# a far fewer generations.
			if len(lowercase_					) > N_POPULATION:
				break


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






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		UNetaDModel(
						    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,       up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    Dict        =		VQModel(
						    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=3    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		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    ,       )
						return CLIPTextModel(A__			)






					def   __A			( self			):
						A__							:    int        =		self.dummy_uncond_unet
						A__							:    Optional[int]        =		DDIMScheduler()
						A__							:    List[Any]        =		self.dummy_vq_model

						A__							:    Optional[int]        =		LDMPipeline(unet=A__    ,       vqvae=A__    ,       scheduler=A__			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""			).images

						A__							:    Optional[int]        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""    ,       return_dict=A__			)[0]

						A__							:    str        =		image[0, -3:, -3:, -1]
						A__							:    Tuple        =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__							:    Dict        =		np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]			)
						A__							:    List[Any]        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < tolerance



@slow
@require_torch
class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Optional[Any]        =		LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=5    ,       output_type="""numpy"""			).images

						A__							:    int        =		image[0, -3:, -3:, -1]

						assert image.shape == (1, 256, 256, 3)
						A__							:    Any        =		np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]			)
						A__							:    int        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance



