


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


snake_case    :   str	    =					transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def     __lowerCamelCase						(			UpperCAmelCase_						:		int						):




      """simple docstring"""





      if isinstance(UpperCAmelCase_			,  torch.Tensor						):
            return image
      elif isinstance(UpperCAmelCase_			,  PIL.Image.Image						):
            a						:Union[str, Any]										=	[image]

      a						:int										=	[trans(img.convert('''RGB'''						)						) for img in image]
      a						:Optional[int]										=	torch.stack(UpperCAmelCase_						)
      return image







class 		_snake_case  (				_snake_case							):




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

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

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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       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 ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       # get the original timestep using init_timestep
       a						:List[str]										=	min(int(num_inference_steps * strength   ) ,  _lowerCamelCase   )

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

       return timesteps, num_inference_steps - t_start




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase=None   ):
       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						:Any										=	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						:List[str]										=	init_latents.shape
       a						:Tuple										=	randn_tensor(_lowerCamelCase ,  generator=_lowerCamelCase ,  device=_lowerCamelCase ,  dtype=_lowerCamelCase   )

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

       return latents




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

       # 2. Preprocess image
       a						:Tuple										=	preprocess(_lowerCamelCase   )

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

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

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

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

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

       return ImagePipelineOutput(images=_lowerCamelCase   )









def     __lowerCamelCase						(			UpperCAmelCase_						:		str						):




      """simple docstring"""





      if n_term == "":
            return []
      a						:list										=	[]
      for temp in range(int(UpperCAmelCase_						)						):
            series.append(F'''1/{temp + 1}''' if series else '''1'''						)
      return series


if __name__ == "__main__":
     snake_case    :   Tuple	    =					input('''Enter the last number (nth term) of the Harmonic Series''')
     print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
     print(harmonic_series(nth_term))





