
"""simple docstring"""



from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
a					:	Tuple							=		2_0_0
# 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					:	Dict							=		5_0
# 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					:	Optional[int]							=		0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))


def  _UpperCamelCase	(						_A					,      _A      )    ->  tuple[str, float]:





    """simple docstring"""

    _UpperCAmelCase          =   len([g for position, g in enumerate(_A      ) if g == main_target[position]]      )
    return (item, float(_A      ))


def  _UpperCamelCase	(						_A					,      _A      )    ->  tuple[str, str]:





    """simple docstring"""

    _UpperCAmelCase          =   random.randint(0					,      len(_A      ) - 1      )
    _UpperCAmelCase          =   parent_a[:random_slice] + parent_a[random_slice:]
    _UpperCAmelCase          =   parent_a[:random_slice] + parent_a[random_slice:]
    return (child_a, child_a)


def  _UpperCamelCase	(						_A					,      _A      )    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   list(_A      )
    if random.uniform(0					,      1      ) < MUTATION_PROBABILITY:
        _UpperCAmelCase          =   random.choice(_A      )
    return "".join(_A      )


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      )    ->  list[str]:





    """simple docstring"""

    _UpperCAmelCase          =   []
    # Generate more children proportionally to the fitness score.
    _UpperCAmelCase          =   int(parent_a[1] * 1_0_0      ) + 1
    _UpperCAmelCase          =   1_0 if child_n >= 1_0 else child_n
    for _ in range(_A      ):
        _UpperCAmelCase          =   population_score[random.randint(0					,      _A      )][0]

        _UpperCAmelCase    ,_UpperCAmelCase          =   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  _UpperCamelCase	(						_A					,      _A					,      _A = True      )    ->  tuple[int, int, str]:





    """simple docstring"""

    if N_POPULATION < N_SELECTED:
        _UpperCAmelCase          =   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.
    _UpperCAmelCase          =   sorted({c for c in target if c not in genes}      )
    if not_in_genes_list:
        _UpperCAmelCase          =   F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
        raise ValueError(_A      )

    # Generate random starting population.
    _UpperCAmelCase          =   []
    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.
    _UpperCAmelCase    ,_UpperCAmelCase          =   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.
        _UpperCAmelCase          =   [evaluate(_A					,      _A      ) for item in population]

        # Check if there is a matching evolution.
        _UpperCAmelCase          =   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 % 1_0 == 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          =   population[: int(N_POPULATION / 3      )]
        population.clear()
        population.extend(_A      )
        # Normalize population score to be between 0 and 1.
        _UpperCAmelCase          =   [
            (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__":
 a					:	str							=		(
     '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
 )
 a					:	int							=		list(
     ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
     '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
 )
 a					:	str							=		basic(target_str, genes_list)
 print(
     F"\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"
 )

"""simple docstring"""



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	):







    @property
    def      _snake_case					(			self			:       Dict     )		->int:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   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      _snake_case					(			self			:       Optional[Any]     )		->str:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   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      _snake_case					(			self			:       List[Any]     )		->Optional[int]:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   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=10_00     ,					)
        return CLIPTextModel(__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.dummy_uncond_unet
        _UpperCAmelCase          =   DDIMScheduler()
        _UpperCAmelCase          =   self.dummy_vq_model

        _UpperCAmelCase          =   LDMPipeline(unet=__UpperCamelCase     ,					vqvae=__UpperCamelCase     ,					scheduler=__UpperCamelCase     )
        ldm.to(__UpperCamelCase     )
        ldm.set_progress_bar_config(disable=__UpperCamelCase     )

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=2     ,					output_type="""numpy"""     ).images

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=2     ,					output_type="""numpy"""     ,					return_dict=__UpperCamelCase     )[0]

        _UpperCAmelCase          =   image[0, -3:, -3:, -1]
        _UpperCAmelCase          =   image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _UpperCAmelCase          =   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]     )
        _UpperCAmelCase          =   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	):







    def      _snake_case					(			self			:       List[Any]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""     )
        ldm.to(__UpperCamelCase     )
        ldm.set_progress_bar_config(disable=__UpperCamelCase     )

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=5     ,					output_type="""numpy"""     ).images

        _UpperCAmelCase          =   image[0, -3:, -3:, -1]

        assert image.shape == (1, 2_56, 2_56, 3)
        _UpperCAmelCase          =   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]     )
        _UpperCAmelCase          =   1e-2 if torch_device != """mps""" else 3e-2

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