





'''simple docstring'''







from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase_					:    List[str]      	=			transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def  _lowerCamelCase				( lowercase						:      List[Any]						)	->	str:
				if isinstance(lowercase    ,							torch.Tensor						):
								return image
				elif isinstance(lowercase    ,							PIL.Image.Image						):
								_a								= [image]

				_a								= [trans(img.convert("RGB"						)						) for img in image]
				_a								= torch.stack(lowercase						)
				return image



class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __init__(  self     :					Union[str, Any]					,				__a     :					str					,				__a     :					Dict					):
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								_a								= DDIMScheduler.from_config(scheduler.config					)

								self.register_modules(unet=__a					,				scheduler=__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					Dict					):
								if strength < 0 or strength > 1:
												raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}'					)




				def   UpperCamelCase__   (  self     :					Dict					,				__a     :					Union[str, Any]					,				__a     :					int					,				__a     :					int					):
								# get the original timestep using init_timestep
								_a								= min(int(num_inference_steps * strength					)					,				__a					)

								_a								= max(num_inference_steps - init_timestep					,				0					)
								_a								= self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					int					,				__a     :					int					,				__a     :					Tuple					,				__a     :					List[str]					,				__a     :					List[str]					,				__a     :					str=None					):
								if not isinstance(__a					,				(torch.Tensor, PIL.Image.Image, list)					):
												raise ValueError(
												    f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__a					)}'					)

								_a								= image.to(device=__a					,				dtype=__a					)

								if isinstance(__a					,				__a					) and len(__a					) != batch_size:
												raise ValueError(
												    f'You have passed a list of generators of length {len(__a					)}, but requested an effective batch'
												    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'					)

								_a								= init_latents.shape
								_a								= randn_tensor(__a					,				generator=__a					,				device=__a					,				dtype=__a					)

								# get latents
								print("add noise to latents at timestep"					,				__a					)
								_a								= self.scheduler.add_noise(__a					,				__a					,				__a					)
								_a								= init_latents

								return latents




				@torch.no_grad()
				def __call__(  self     :					List[str]					,				__a     :					Union[torch.FloatTensor, PIL.Image.Image] = None					,				__a     :					float = 0.8					,				__a     :					int = 1					,				__a     :					Optional[Union[torch.Generator, List[torch.Generator]]] = None					,				__a     :					float = 0.0					,				__a     :					int = 50					,				__a     :					Optional[bool] = None					,				__a     :					Optional[str] = "pil"					,				__a     :					bool = True					,				):
								self.check_inputs(__a					)

								# 2. Preprocess image
								_a								= preprocess(__a					)

								# 3. set timesteps
								self.scheduler.set_timesteps(__a					,				device=self.device					)
								_a	,					_a								= self.get_timesteps(__a					,				__a					,				self.device					)
								_a								= timesteps[:1].repeat(__a					)

								# 4. Prepare latent variables
								_a								= self.prepare_latents(__a					,				__a					,				__a					,				self.unet.dtype					,				self.device					,				__a					)
								_a								= latents

								# 5. Denoising loop
								for t in self.progress_bar(__a					):
												# 1. predict noise model_output
												_a								= self.unet(__a					,				__a					).sample

												# 2. predict previous mean of image x_t-1 and add variance depending on eta
												# eta corresponds to η in paper and should be between [0, 1]
												# do x_t -> x_t-1
												_a								= self.scheduler.step(
												    __a					,				__a					,				__a					,				eta=__a					,				use_clipped_model_output=__a					,				generator=__a					,				).prev_sample

								_a								= (image / 2 + 0.5).clamp(0					,				1					)
								_a								= image.cpu().permute(0					,				2					,				3					,				1					).numpy()
								if output_type == "pil":
												_a								= self.numpy_to_pil(__a					)

								if not return_dict:
												return (image, latent_timestep.item())

								return ImagePipelineOutput(images=__a					)







'''simple docstring'''







import math
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					list[list[float]]					,				__a     :					list[int]					):
								_a								= 0.0
								_a								= 0.0
								for i in range(len(__a					)					):
												da += math.pow((sample[i] - weights[0][i])					,				2					)
												da += math.pow((sample[i] - weights[1][i])					,				2					)
												return 0 if da > da else 1
								return 0




				def   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					list[list[int | float]]					,				__a     :					list[int]					,				__a     :					int					,				__a     :					float					):
								for i in range(len(__a					)					):
												weights[j][i] += alpha * (sample[i] - weights[j][i])
								return weights






def  _lowerCamelCase				( )	->	None:
				# Training Examples ( m, n )
				_a								= [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

				# weight initialization ( n, C )
				_a								= [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

				# training
				_a								= SelfOrganizingMap()
				_a								= 3
				_a								= 0.5

				for _ in range(lowercase						):
								for j in range(len(lowercase						)						):
												# training sample
												_a								= training_samples[j]

												# Compute the winning vector
												_a								= self_organizing_map.get_winner(lowercase    ,							lowercase						)

												# Update the winning vector
												_a								= self_organizing_map.update(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

    # classify test sample
				_a								= [0, 0, 0, 1]
				_a								= self_organizing_map.get_winner(lowercase    ,							lowercase						)

				# results
				print(F'Clusters that the test sample belongs to : {winner}'						)
				print(F'Weights that have been trained : {weights}'						)


# running the main() function
if __name__ == "__main__":
					main()
