



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






def  __lowerCAmelCase (__lowerCAmelCase   ):

		if isinstance(_A      , torch.Tensor   ):
				return image
		elif isinstance(_A      , PIL.Image.Image   ):
				_UpperCAmelCase		:       List[Any]								=      [image]

		_UpperCAmelCase		:       Optional[Any]								=      [trans(img.convert("RGB"   )   ) for img in image]
		_UpperCAmelCase		:       Tuple								=      torch.stack(_A   )
		return image






class 	lowerCAmelCase__			(			_UpperCAmelCase	):




			def __init__(		self  :     Tuple	,  lowerCamelCase__  :     Any	,  lowerCamelCase__  :     Any )   ->Optional[Any]:



					'''simple docstring'''







					super().__init__()

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

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




			def 							lowerCAmelCase__    (		self  :     Any	,  lowerCamelCase__  :     Union[str, Any] )   ->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  :     Dict	,  lowerCamelCase__  :     Any	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     int )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      min(int(num_inference_steps * strength )	,  __UpperCamelCase )

					_UpperCAmelCase		:       str								=      max(num_inference_steps - init_timestep	,  0 )
					_UpperCAmelCase		:       Optional[int]								=      self.scheduler.timesteps[t_start:]

					return timesteps, num_inference_steps - t_start




			def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     Any	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     Dict	,  lowerCamelCase__  :     str=None )   ->Tuple:



					'''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 )}""" )

					_UpperCAmelCase		:       str								=      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.""" )

					_UpperCAmelCase		:       Union[str, Any]								=      init_latents.shape
					_UpperCAmelCase		:       Optional[int]								=      randn_tensor(__UpperCamelCase	,  generator=__UpperCamelCase	,  device=__UpperCamelCase	,  dtype=__UpperCamelCase )

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

					return latents






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



					'''simple docstring'''







					self.check_inputs(__UpperCamelCase )

					# 2. Preprocess image
					_UpperCAmelCase		:       Union[str, Any]								=      preprocess(__UpperCamelCase )

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

					# 4. Prepare latent variables
					_UpperCAmelCase		:       Tuple								=      self.prepare_latents(__UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  self.unet.dtype	,  self.device	,  __UpperCamelCase )
					_UpperCAmelCase		:       int								=      latents

					# 5. Denoising loop
					for t in self.progress_bar(__UpperCamelCase ):
							# 1. predict noise model_output
							_UpperCAmelCase		:       Dict								=      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
							_UpperCAmelCase		:       int								=      self.scheduler.step(
							    __UpperCamelCase	,  __UpperCamelCase	,  __UpperCamelCase	,  eta=__UpperCamelCase	,  use_clipped_model_output=__UpperCamelCase	,  generator=__UpperCamelCase	,  ).prev_sample

					_UpperCAmelCase		:       List[Any]								=      (image / 2 + 0.5).clamp(0	,  1 )
					_UpperCAmelCase		:       Tuple								=      image.cpu().permute(0	,  2	,  3	,  1 ).numpy()
					if output_type == "pil":
							_UpperCAmelCase		:       Tuple								=      self.numpy_to_pil(__UpperCamelCase )

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

					return ImagePipelineOutput(images=__UpperCamelCase )







'''simple docstring'''


import os






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       List[Any]								=      os.path.join(os.path.dirname(__lowerCAmelCase   )      , "num.txt"   )
		with open(__lowerCAmelCase   ) as file_hand:
				return str(sum(int(__lowerCAmelCase   ) for line in file_hand   )   )[:10]


if __name__ == "__main__":
						print(solution())



