






"""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 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







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

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





class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(	self :     Tuple,       _lowerCamelCase :     Dict,       _lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     super().__init__()

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

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


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple,       _lowerCamelCase :     Dict  ):

     '''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 :     Dict,       _lowerCamelCase :     Any,       _lowerCamelCase :     Dict,       _lowerCamelCase :     Optional[Any]  ):

     '''simple docstring'''
     # get the original timestep using init_timestep
     __A									= min(int(num_inference_steps * strength  ),       _lowerCamelCase  )

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

     return timesteps, num_inference_steps - t_start


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict,       _lowerCamelCase :     Dict,       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     Dict,       _lowerCamelCase :     str,       _lowerCamelCase :     str,       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     if not isinstance(_lowerCamelCase,       (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(_lowerCamelCase  )}'  )

     __A									= image.to(device=_lowerCamelCase,       dtype=_lowerCamelCase  )

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

     __A									= init_latents.shape
     __A									= randn_tensor(_lowerCamelCase,       generator=_lowerCamelCase,       device=_lowerCamelCase,       dtype=_lowerCamelCase  )

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

     return latents






   @torch.no_grad()
   def __call__(	self :     Dict,       _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,       ):

     '''simple docstring'''
     self.check_inputs(_lowerCamelCase  )

     # 2. Preprocess image
     __A									= preprocess(_lowerCamelCase  )

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

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

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

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

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

     return ImagePipelineOutput(images=_lowerCamelCase  )












"""simple docstring"""


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     list[int]  ):

     '''simple docstring'''
     __A									= len(_lowerCamelCase  )
     __A									= [0] * len_array

     if len_array > 0:
       __A									= array[0]

     for i in range(1,       _lowerCamelCase  ):
       __A									= self.prefix_sum[i - 1] + array[i]


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     int,       _lowerCamelCase :     int  ):

     '''simple docstring'''
     if start == 0:
       return self.prefix_sum[end]

     return self.prefix_sum[end] - self.prefix_sum[start - 1]






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     int  ):

     '''simple docstring'''
     __A									= {0}
     for sum_item in self.prefix_sum:
       if sum_item - target_sum in sums:
         return True

       sums.add(_lowerCamelCase  )

     return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()




