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






def        a_		(       __lowercase   :	Tuple ) ->  Optional[Any]:






			if isinstance(__a       ,					torch.Tensor ):
						return image
			elif isinstance(__a       ,					PIL.Image.Image ):
						_snake_case		=			[image]

			_snake_case		=			[trans(img.convert('RGB' ) ) for img in image]
			_snake_case		=			torch.stack(__a )
			return image




class    SCREAMING_SNAKE_CASE__     (	A__					):





	'''simple docstring'''



	def __init__(  self	:    int			,  lowercase	:    Any			,  lowercase	:    int	):


				'''simple docstring'''


				super().__init__()

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

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



	def  A							(  self	:    Optional[Any]			,  lowercase	:    int	):


				'''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  A							(  self	:    Dict			,  lowercase	:    Optional[Any]			,  lowercase	:    Union[str, Any]			,  lowercase	:    Any	):


				'''simple docstring'''


				_snake_case		=			min(int(num_inference_steps * strength	)			,  UpperCamelCase_	)

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

				return timesteps, num_inference_steps - t_start



	def  A							(  self	:    Optional[Any]			,  lowercase	:    List[str]			,  lowercase	:    str			,  lowercase	:    Any			,  lowercase	:    int			,  lowercase	:    Any			,  lowercase	:    str=None	):


				'''simple docstring'''


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

				_snake_case		=			image.to(device=UpperCamelCase_			,  dtype=UpperCamelCase_	)

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

				_snake_case		=			init_latents.shape
				_snake_case		=			randn_tensor(UpperCamelCase_			,  generator=UpperCamelCase_			,  device=UpperCamelCase_			,  dtype=UpperCamelCase_	)

				# get latents
				print('add noise to latents at timestep'			,  UpperCamelCase_	)
				_snake_case		=			self.scheduler.add_noise(UpperCamelCase_			,  UpperCamelCase_			,  UpperCamelCase_	)
				_snake_case		=			init_latents

				return latents





	@torch.no_grad()
	def __call__(  self	:    List[Any]			,  lowercase	:    Optional[Any] = None			,  lowercase	:    List[str] = 0.8			,  lowercase	:    int = 1			,  lowercase	:    List[Any] = None			,  lowercase	:    Tuple = 0.0			,  lowercase	:    Dict = 50			,  lowercase	:    List[str] = None			,  lowercase	:    List[str] = "pil"			,  lowercase	:    Tuple = True			,  ):


				'''simple docstring'''


				self.check_inputs(UpperCamelCase_	)

				# 2. Preprocess image
				_snake_case		=			preprocess(UpperCamelCase_	)

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

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

				# 5. Denoising loop
				for t in self.progress_bar(UpperCamelCase_	):
							# 1. predict noise model_output
							_snake_case		=			self.unet(UpperCamelCase_			,  UpperCamelCase_	).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		=			self.scheduler.step(
							    UpperCamelCase_			,  UpperCamelCase_			,  UpperCamelCase_			,  eta=UpperCamelCase_			,  use_clipped_model_output=UpperCamelCase_			,  generator=UpperCamelCase_			,  ).prev_sample

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

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

				return ImagePipelineOutput(images=UpperCamelCase_	)



'''simple docstring'''
def 			a      (			__a ,					__a   )  ->	int:



					'''simple docstring'''






					if len(__a   ) != len(__a   ):
										raise ValueError('''String lengths must match!'''   )

					UpperCamelCase__   :Union[str, Any]												= 0

					for chara, chara in zip(__a ,					__a   ):
										if chara != chara:
															count += 1

					return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()