





'''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


A   : Any             =			transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def       lowercase_      (		lowercase__						)   ->Dict:
						if isinstance(lowercase__				,  torch.Tensor						):
												return image
						elif isinstance(lowercase__				,  PIL.Image.Image						):
												_snake_case:   Tuple  					=		[image]

						_snake_case:   Optional[int]  					=		[trans(img.convert('RGB'						)						) for img in image]
						_snake_case:   int  					=		torch.stack(lowercase__						)
						return image







class 			lowerCamelCase					(						__UpperCAmelCase  ):


					def __init__(					self	:  Optional[Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Dict       ):





											'''simple docstring'''





											super().__init__()

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

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     __snake_case	:  List[Any]       ):





											'''simple docstring'''





											if strength < 0 or strength > 1:
																	raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}'''       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Dict       ,     __snake_case	:  str       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		min(int(num_inference_steps * strength       )       ,     __snake_case       )

											_snake_case:   str  					=		max(num_inference_steps - init_timestep       ,     0       )
											_snake_case:   List[Any]  					=		self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  List[Any]       ,     __snake_case	:  str       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Tuple       ,     __snake_case	:  Tuple=None       ):





											'''simple docstring'''





											if not isinstance(__snake_case       ,     (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(__snake_case       )}'''       )

											_snake_case:   Optional[int]  					=		image.to(device=__snake_case       ,     dtype=__snake_case       )

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

											_snake_case:   Optional[Any]  					=		init_latents.shape
											_snake_case:   str  					=		randn_tensor(__snake_case       ,     generator=__snake_case       ,     device=__snake_case       ,     dtype=__snake_case       )

											# get latents
											print('add noise to latents at timestep'       ,     __snake_case       )
											_snake_case:   Any  					=		self.scheduler.add_noise(__snake_case       ,     __snake_case       ,     __snake_case       )
											_snake_case:   List[str]  					=		init_latents

											return latents




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





											'''simple docstring'''





											self.check_inputs(__snake_case       )

											# 2. Preprocess image
											_snake_case:   Optional[int]  					=		preprocess(__snake_case       )

											# 3. set timesteps
											self.scheduler.set_timesteps(__snake_case       ,     device=self.device       )
											_snake_case					,						_snake_case:   str  					=		self.get_timesteps(__snake_case       ,     __snake_case       ,     self.device       )
											_snake_case:   int  					=		timesteps[:1].repeat(__snake_case       )

											# 4. Prepare latent variables
											_snake_case:   List[Any]  					=		self.prepare_latents(__snake_case       ,     __snake_case       ,     __snake_case       ,     self.unet.dtype       ,     self.device       ,     __snake_case       )
											_snake_case:   Tuple  					=		latents

											# 5. Denoising loop
											for t in self.progress_bar(__snake_case       ):
																	# 1. predict noise model_output
																	_snake_case:   Optional[int]  					=		self.unet(__snake_case       ,     __snake_case       ).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
																	_snake_case:   str  					=		self.scheduler.step(
																	    __snake_case       ,     __snake_case       ,     __snake_case       ,     eta=__snake_case       ,     use_clipped_model_output=__snake_case       ,     generator=__snake_case       ,     ).prev_sample

											_snake_case:   Dict  					=		(image / 2 + 0.5).clamp(0       ,     1       )
											_snake_case:   Optional[int]  					=		image.cpu().permute(0       ,     2       ,     3       ,     1       ).numpy()
											if output_type == "pil":
																	_snake_case:   Union[str, Any]  					=		self.numpy_to_pil(__snake_case       )

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

											return ImagePipelineOutput(images=__snake_case       )












'''simple docstring'''






def       lowercase_      (		lowercase__ = 1				,  lowercase__ = 1000						)   ->int:
						_snake_case:   int  					=		1
						_snake_case:   Union[str, Any]  					=		0

						for divide_by_number in range(lowercase__				,  digit + 1						):
												_snake_case:   list[int]  					=		[]
												_snake_case:   int  					=		numerator
												for _ in range(1				,  digit + 1						):
																		if now_divide in has_been_divided:
																								if longest_list_length < len(lowercase__						):
																														_snake_case:   Union[str, Any]  					=		len(lowercase__						)
																														_snake_case:   Tuple  					=		divide_by_number
																		else:
																								has_been_divided.append(lowercase__						)
																								_snake_case:   Union[str, Any]  					=		now_divide * 10 % divide_by_number

						return the_digit


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()





