

from __future__ import annotations

import random

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int					, lowerCAmelCase:					int	):
				_UpperCAmelCase    :						Union[str, Any]							=      len([g for position, g in enumerate(_lowercase	) if g == main_target[position]]	)
				return (item, float(_lowercase	))

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[Any]					, lowerCAmelCase:					Optional[int]	):
				_UpperCAmelCase    :						Any							=      random.randint(0					, len(_lowercase	) - 1	)
				_UpperCAmelCase    :						str							=      parent_a[:random_slice] + parent_a[random_slice:]
				_UpperCAmelCase    :						Optional[Any]							=      parent_a[:random_slice] + parent_a[random_slice:]
				return (child_a, child_a)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Union[str, Any]					, lowerCAmelCase:					Union[str, Any]	):
				_UpperCAmelCase    :						Tuple							=      list(_lowercase	)
				if random.uniform(0					, 1	) < MUTATION_PROBABILITY:
								_UpperCAmelCase    :						str							=      random.choice(_lowercase	)
				return "".join(_lowercase	)

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

								_UpperCAmelCase						,							_UpperCAmelCase    :						List[Any]							=      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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]					, lowerCAmelCase:					Union[str, Any]					, lowerCAmelCase:					List[Any] = True	):







				if N_POPULATION < N_SELECTED:
								_UpperCAmelCase    :						List[str]							=      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.
				_UpperCAmelCase    :						Tuple							=      sorted({c for c in target if c not in genes}	)
				if not_in_genes_list:
								_UpperCAmelCase    :						Union[str, Any]							=      F'{not_in_genes_list} is not in genes list, evolution cannot converge'
								raise ValueError(_lowercase	)

				# Generate random starting population.
				_UpperCAmelCase    :						Dict							=      []
				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.
				_UpperCAmelCase						,							_UpperCAmelCase    :						Tuple							=      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.
								_UpperCAmelCase    :						Optional[int]							=      [evaluate(_lowercase					, _lowercase	) for item in population]

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





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()

					parser.add_argument(
					    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
					)
					# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
					parser.add_argument(
					    '--original_config_file',
					    default=None,
					    type=str,
					    help='The YAML config file corresponding to the original architecture.',
					)
					parser.add_argument(
					    '--num_in_channels',
					    default=None,
					    type=int,
					    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
					)
					parser.add_argument(
					    '--scheduler_type',
					    default='pndm',
					    type=str,
					    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
					)
					parser.add_argument(
					    '--pipeline_type',
					    default=None,
					    type=str,
					    help=(
					        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
					        '. If `None` pipeline will be automatically inferred.'
					    ),
					)
					parser.add_argument(
					    '--image_size',
					    default=None,
					    type=int,
					    help=(
					        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
					        ' Base. Use 768 for Stable Diffusion v2.'
					    ),
					)
					parser.add_argument(
					    '--prediction_type',
					    default=None,
					    type=str,
					    help=(
					        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
					        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
					    ),
					)
					parser.add_argument(
					    '--extract_ema',
					    action='store_true',
					    help=(
					        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
					        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
					        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
					    ),
					)
					parser.add_argument(
					    '--upcast_attention',
					    action='store_true',
					    help=(
					        'Whether the attention computation should always be upcasted. This is necessary when running stable'
					        ' diffusion 2.1.'
					    ),
					)
					parser.add_argument(
					    '--from_safetensors',
					    action='store_true',
					    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
					)
					parser.add_argument(
					    '--to_safetensors',
					    action='store_true',
					    help='Whether to store pipeline in safetensors format or not.',
					)
					parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
					parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
					parser.add_argument(
					    '--stable_unclip',
					    type=str,
					    default=None,
					    required=False,
					    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
					)
					parser.add_argument(
					    '--stable_unclip_prior',
					    type=str,
					    default=None,
					    required=False,
					    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
					)
					parser.add_argument(
					    '--clip_stats_path',
					    type=str,
					    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
					    required=False,
					)
					parser.add_argument(
					    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
					)
					parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
					parser.add_argument(
					    '--vae_path',
					    type=str,
					    default=None,
					    required=False,
					    help='Set to a path, hub id to an already converted vae to not convert it again.',
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()

					SCREAMING_SNAKE_CASE_            = download_from_original_stable_diffusion_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    prediction_type=args.prediction_type,
					    model_type=args.pipeline_type,
					    extract_ema=args.extract_ema,
					    scheduler_type=args.scheduler_type,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    stable_unclip=args.stable_unclip,
					    stable_unclip_prior=args.stable_unclip_prior,
					    clip_stats_path=args.clip_stats_path,
					    controlnet=args.controlnet,
					    vae_path=args.vae_path,
					)

					if args.half:
										pipe.to(torch_dtype=torch.floataa)

					if args.controlnet:
										# only save the controlnet model
										pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
					else:
										pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


