

import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					bool = True					, lowerCAmelCase:					float = math.inf					, lowerCAmelCase:					float = -math.inf					, lowerCAmelCase:					float = math.inf					, lowerCAmelCase:					float = -math.inf					, lowerCAmelCase:					bool = False					, lowerCAmelCase:					float = 100					, lowerCAmelCase:					float = 0.01					, lowerCAmelCase:					float = 1					, )				->    Any:
				_UpperCAmelCase    :						str							=      False
				_UpperCAmelCase    :						Optional[int]							=      search_prob
				_UpperCAmelCase    :						Dict							=      start_temperate
				_UpperCAmelCase    :						Dict							=      []
				_UpperCAmelCase    :						List[Any]							=      0
				_UpperCAmelCase    :						int							=      None

				while not search_end:
								_UpperCAmelCase    :						str							=      current_state.score()
								if best_state is None or current_score > best_state.score():
												_UpperCAmelCase    :						Tuple							=      current_state
								scores.append(lowerCAmelCase	)
								iterations += 1
								_UpperCAmelCase    :						Union[str, Any]							=      None
								_UpperCAmelCase    :						Optional[int]							=      current_state.get_neighbors()
								while (
								    next_state is None and neighbors
								):  # till we do not find a neighbor that we can move to
												_UpperCAmelCase    :						int							=      random.randint(0					, len(lowerCAmelCase	) - 1	)  # picking a random neighbor
												_UpperCAmelCase    :						List[Any]							=      neighbors.pop(lowerCAmelCase	)
												_UpperCAmelCase    :						str							=      picked_neighbor.score() - current_score

												if (
												    picked_neighbor.x > max_x
												    or picked_neighbor.x < min_x
												    or picked_neighbor.y > max_y
												    or picked_neighbor.y < min_y
												):
																continue  # neighbor outside our bounds

												if not find_max:
																_UpperCAmelCase    :						List[Any]							=      change * -1  # in case we are finding minimum
												if change > 0:  # improves the solution
																_UpperCAmelCase    :						List[str]							=      picked_neighbor
												else:
																_UpperCAmelCase    :						Union[str, Any]							=      (math.e) ** (
																    change / current_temp
																)  # probability generation function
																if random.random() < probability:  # random number within probability
																				_UpperCAmelCase    :						Optional[Any]							=      picked_neighbor
								_UpperCAmelCase    :						int							=      current_temp - (current_temp * rate_of_decrease)

								if current_temp < threshold_temp or next_state is None:
												# temperature below threshold, or could not find a suitable neighbor
												_UpperCAmelCase    :						List[str]							=      True
								else:
												_UpperCAmelCase    :						Union[str, Any]							=      next_state

				if visualization:
								from matplotlib import pyplot as plt

								plt.plot(range(lowerCAmelCase	)					, lowerCAmelCase	)
								plt.xlabel("Iterations"	)
								plt.ylabel("Function values"	)
								plt.show()
				return best_state


if __name__ == "__main__":

					def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					Any	)				->    str:
									return (x**2) + (y**2)

					# starting the problem with initial coordinates (12, 47)
					SCREAMING_SNAKE_CASE_            = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE_            = simulated_annealing(
					    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
					)
					print(
					    'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
					    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
					)

					# starting the problem with initial coordinates (12, 47)
					SCREAMING_SNAKE_CASE_            = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE_            = simulated_annealing(
					    prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
					)
					print(
					    'The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
					    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
					)

					def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Dict					, lowerCAmelCase:					Dict	)				->    Optional[int]:
									return (3 * x**2) - (6 * y)

					SCREAMING_SNAKE_CASE_            = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE_            = simulated_annealing(prob, find_max=False, visualization=True)
					print(
					    'The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
					    F'''{local_min.score()}'''
					)

					SCREAMING_SNAKE_CASE_            = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE_            = simulated_annealing(prob, find_max=True, visualization=True)





					print(
					    'The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
					    F'''{local_min.score()}'''
					)





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)


