



"""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


snake_case   					=				transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def 			snake_case		(		lowerCAmelCase_	)						->				Tuple:
  if isinstance(lowerCAmelCase_  ,						torch.Tensor	):
    return image
  elif isinstance(lowerCAmelCase_  ,						PIL.Image.Image	):
    _snake_case   	=      [image]

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







class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	str							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     super().__init__()

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

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



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	Tuple				):



     """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    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     # get the original timestep using init_timestep
     _snake_case   	=      min(int(num_inference_steps * strength				)							,       __lowerCamelCase				)

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

     return timesteps, num_inference_steps - t_start



   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	List[Any]=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				)}"""				)

     _snake_case   	=      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."""				)

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

     # get latents
     print('''add noise to latents at timestep'''							,       __lowerCamelCase				)
     _snake_case   	=      self.scheduler.add_noise(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      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 = 5_0							,       __lowerCamelCase    :	Optional[bool] = None							,       __lowerCamelCase    :	Optional[str] = "pil"							,       __lowerCamelCase    :	bool = True							,       ):



     """simple docstring"""
     self.check_inputs(__lowerCamelCase				)

     # 2. Preprocess image
     _snake_case   	=      preprocess(__lowerCamelCase				)

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

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

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

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

     return ImagePipelineOutput(images=__lowerCamelCase				)











'''simple docstring'''


import math
import random



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					bool = False				):




     """simple docstring"""


     if deriv:
          return value * (1 - value)
     return 1 / (1 + math.exp(-value				))


# Initial Value
lowerCAmelCase_      :  Optional[Any]				     =  0.02



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							Tuple     						=  float(2 * (random.randint(1 ,    100				)) - 1				)

     for _ in range(UpperCamelCase__				):
          # Forward propagation
          a_     :							Dict     						=  sigmoid_function(INITIAL_VALUE * weight				)
          # How much did we miss?
          a_     :							Dict     						=  (expected / 100) - layer_a
          # Error delta
          a_     :							Optional[Any]     						=  layer_1_error * sigmoid_function(UpperCamelCase__ ,    UpperCamelCase__				)
          # Update weight
          weight += INITIAL_VALUE * layer_1_delta

     return layer_a * 100


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase_      :  str				     =  int(input('Expected value: '))
 lowerCAmelCase_      :  str				     =  int(input('Number of propagations: '))
 print(forward_propagation(expected, number_propagations))
