



"""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_						:    str				=      transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def  __snake_case							(	__A      :						Any     )     ->       Optional[int]:






				'''simple docstring'''
				if isinstance(__A							,		torch.Tensor     ):
								return image
				elif isinstance(__A							,		PIL.Image.Image     ):
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [image]

				SCREAMING_SNAKE_CASE						:							Dict							=     [trans(img.convert('RGB'     )     ) for img in image]
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.stack(__A     )
				return image







class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	def __init__(      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]    )						->	List[str]:





					"""simple docstring"""


					super().__init__()

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

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





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	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 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     min(int(num_inference_steps * strength    )     ,       _SCREAMING_SNAKE_CASE    )

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

					return timesteps, num_inference_steps - t_start





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       List[str]=None    )						->	Any:





					"""simple docstring"""


					if not isinstance(_SCREAMING_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(_SCREAMING_SNAKE_CASE    )}"""    )

					SCREAMING_SNAKE_CASE						:							int							=     image.to(device=_SCREAMING_SNAKE_CASE     ,       dtype=_SCREAMING_SNAKE_CASE    )

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

					SCREAMING_SNAKE_CASE						:							List[Any]							=     init_latents.shape
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     randn_tensor(_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=_SCREAMING_SNAKE_CASE    )

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

					return latents







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





					"""simple docstring"""


					self.check_inputs(_SCREAMING_SNAKE_CASE    )

					# 2. Preprocess image
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     preprocess(_SCREAMING_SNAKE_CASE    )

					# 3. set timesteps
					self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE     ,       device=self.device    )
					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[Any]							=     self.get_timesteps(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       self.device    )
					SCREAMING_SNAKE_CASE						:							str							=     timesteps[:1].repeat(_SCREAMING_SNAKE_CASE    )

					# 4. Prepare latent variables
					SCREAMING_SNAKE_CASE						:							str							=     self.prepare_latents(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       self.unet.dtype     ,       self.device     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     latents

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

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

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

					return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE    )










"""simple docstring"""

def  __snake_case							(	__A      :						int							,		__A      :						int     )     ->       float:






				'''simple docstring'''
				return base * power(__A							,		(exponent - 1)     ) if exponent else 1


if __name__ == "__main__":
							print('Raise base to the power of exponent using recursion...')
							A_						:    str				=      int(input('Enter the base: ').strip())
							A_						:    Dict				=      int(input('Enter the exponent: ').strip())
							A_						:    List[str]				=      power(base, abs(exponent))
							if exponent < 0:  # power() does not properly deal w/ negative exponents
														A_						:    Any				=      1 / result
							print(f'''{base} to the power of {exponent} is {result}''')





