
from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   def __init__(   self	:				List[str]     ,     lowerCAmelCase_	:				List[Any]     ,     lowerCAmelCase_	:				Optional[int]	):

      """simple docstring"""
      super().__init__()
      self.register_modules(unet=lowerCAmelCase_     ,     scheduler=lowerCAmelCase_	)


   @torch.no_grad()
   def __call__(   self	:				List[str]     ,     lowerCAmelCase_	:				int = 1     ,     lowerCAmelCase_	:				Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,     lowerCAmelCase_	:				int = 1_0_0_0     ,     lowerCAmelCase_	:				Optional[str] = "pil"     ,     lowerCAmelCase_	:				bool = True     ,     ):

      """simple docstring"""
      # Sample gaussian noise to begin loop
      if isinstance(self.unet.config.sample_size     ,     lowerCAmelCase_	):
         _A:    Any						     =			(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
      else:
         _A:    Any						     =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

      if self.device.type == "mps":
         # randn does not work reproducibly on mps
         _A:    str						     =			randn_tensor(lowerCAmelCase_     ,     generator=lowerCAmelCase_	)
         _A:    List[Any]						     =			image.to(self.device	)
      else:
         _A:    Optional[Any]						     =			randn_tensor(lowerCAmelCase_     ,     generator=lowerCAmelCase_     ,     device=self.device	)

      # set step values
      self.scheduler.set_timesteps(lowerCAmelCase_	)

      for t in self.progress_bar(self.scheduler.timesteps	):
         # 1. predict noise model_output
         _A:    Dict						     =			self.unet(lowerCAmelCase_     ,     lowerCAmelCase_	).sample

         # 2. compute previous image: x_t -> x_t-1
         _A:    Union[str, Any]						     =			self.scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     generator=lowerCAmelCase_	).prev_sample

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

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=lowerCAmelCase_	)





import math as m






def     lowerCamelCase__				(   a						,						a						,						a=0.001 ) ->  Optional[int]:
   return (f(a + h ) - f(a - h )) / (2 * h)






def     lowerCamelCase__				(   a						,						a=0						,						a=1_00						,						a=0.0001						,						a=1E-6						,						a=False ) ->  List[Any]:
   _A:    Optional[Any]						     =			xa  # set the initial guess
   _A:    Dict						     =			[a]
   _A:    Tuple						     =			abs(f(a ) )
   _A:    Tuple						     =			lambda a : calc_derivative(a						,						a						,						h=a )  # noqa: E731  Derivative of f(x)
   for _ in range(a ):
      if fa(a ) == 0:
         raise ValueError('''No converging solution found''' )
      _A:    Any						     =			a - f(a ) / fa(a )  # Calculate the next estimate
      if logsteps:
         steps.append(a )
      if error < maxerror:
         break
   else:
      raise ValueError('''Iteration limit reached, no converging solution found''' )
   if logsteps:
      # If logstep is true, then log intermediate steps
      return a, error, steps
   return a, error


if __name__ == "__main__":
   from matplotlib import pyplot as plt

   UpperCAmelCase__						:	Optional[Any]	     = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__						:	str	     = newton_raphson(
       f, xa=10, maxiter=1000, step=1E-6, logsteps=True
   )
   plt.plot([abs(f(x)) for x in steps])
   plt.xlabel('step')
   plt.ylabel('error')
   plt.show()
   print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")



