




"""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


lowercase__     						=						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def      _snake_case			(				lowercase__		):
				if isinstance(lowercase__							,    torch.Tensor		):
								return image
				elif isinstance(lowercase__							,    PIL.Image.Image		):
								_lowerCamelCase     :			Optional[int]    =    [image]

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






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase  ):
							super().__init__()

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

							self.register_modules(unet=lowercase  ,      scheduler=lowercase  )
			def 					A_    (  self  ,      lowercase  ):
							if strength < 0 or strength > 1:
											raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							# get the original timestep using init_timestep
							_lowerCamelCase     :			Any    =    min(int(num_inference_steps * strength  )  ,      lowercase  )

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

							return timesteps, num_inference_steps - t_start
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase=None  ):
							if not isinstance(lowercase  ,      (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(lowercase  )}'''  )

							_lowerCamelCase     :			Union[str, Any]    =    image.to(device=lowercase  ,      dtype=lowercase  )

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

							_lowerCamelCase     :			List[str]    =    init_latents.shape
							_lowerCamelCase     :			Optional[Any]    =    randn_tensor(lowercase  ,      generator=lowercase  ,      device=lowercase  ,      dtype=lowercase  )

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

							return latents






			@torch.no_grad()
			def __call__(  self  ,      lowercase = None  ,      lowercase = 0.8  ,      lowercase = 1  ,      lowercase = None  ,      lowercase = 0.0  ,      lowercase = 50  ,      lowercase = None  ,      lowercase = "pil"  ,      lowercase = True  ,      ):
							self.check_inputs(lowercase  )

							# 2. Preprocess image
							_lowerCamelCase     :			int    =    preprocess(lowercase  )

							# 3. set timesteps
							self.scheduler.set_timesteps(lowercase  ,      device=self.device  )
							_lowerCamelCase, _lowerCamelCase     :			Dict    =    self.get_timesteps(lowercase  ,      lowercase  ,      self.device  )
							_lowerCamelCase     :			Union[str, Any]    =    timesteps[:1].repeat(lowercase  )

							# 4. Prepare latent variables
							_lowerCamelCase     :			List[str]    =    self.prepare_latents(lowercase  ,      lowercase  ,      lowercase  ,      self.unet.dtype  ,      self.device  ,      lowercase  )
							_lowerCamelCase     :			Optional[int]    =    latents

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

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

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

							return ImagePipelineOutput(images=lowercase  )





"""simple docstring"""






import math







def      _snake_case			(				lowercase__		):
				return math.sqrt(lowercase__		) * math.sqrt(lowercase__		) == num







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Optional[int]    =    0
				_lowerCamelCase     :			List[Any]    =    n
				while left <= right:
								_lowerCamelCase     :			str    =    (left + right) // 2
								if mid**2 == n:
												return True
								elif mid**2 > n:
												_lowerCamelCase     :			str    =    mid - 1
								else:
												_lowerCamelCase     :			Optional[int]    =    mid + 1
				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()