

'''simple docstring'''






import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  def __init__(      self   :     Tuple					,lowercase__   :     VQModel					,lowercase__   :     UNetaDModel					,lowercase__   :     DDIMScheduler			):
   super().__init__()
   self.register_modules(vqvae=lowercase__					,unet=lowercase__					,scheduler=lowercase__			)






  @torch.no_grad()
  def __call__(      self   :     Tuple					,lowercase__   :     int = 1					,lowercase__   :     Optional[Union[torch.Generator, List[torch.Generator]]] = None					,lowercase__   :     float = 0.0					,lowercase__   :     int = 5_0					,lowercase__   :     Optional[str] = "pil"					,lowercase__   :     bool = True					,**lowercase__   :     List[str]					,):
   __lowercase							  = randn_tensor(
       (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,generator=lowercase__					,)
   __lowercase							  = latents.to(self.device			)

   # scale the initial noise by the standard deviation required by the scheduler
   __lowercase							  = latents * self.scheduler.init_noise_sigma

   self.scheduler.set_timesteps(lowercase__			)

   # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
   __lowercase							  = '''eta''' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

   __lowercase							  = {}
   if accepts_eta:
    __lowercase							  = eta

   for t in self.progress_bar(self.scheduler.timesteps			):
    __lowercase							  = self.scheduler.scale_model_input(lowercase__					,lowercase__			)
    # predict the noise residual
    __lowercase							  = self.unet(lowercase__					,lowercase__			).sample
    # compute the previous noisy sample x_t -> x_t-1
    __lowercase							  = self.scheduler.step(lowercase__					,lowercase__					,lowercase__					,**lowercase__			).prev_sample

   # decode the image latents with the VAE
   __lowercase							  = self.vqvae.decode(lowercase__			).sample

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

   if not return_dict:
    return (image,)

   return ImagePipelineOutput(images=lowercase__			)




'''simple docstring'''






from __future__ import annotations

from decimal import Decimal

from numpy import array


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = Decimal

 # Check if the provided matrix has 2 rows and 2 columns
 # since this implementation only works for 2x2 matrices
 if len(A__      ) == 2 and len(matrix[0]      ) == 2 and len(matrix[1]      ) == 2:
  # Calculate the determinant of the matrix
  __lowercase							  = float(
      d(matrix[0][0]      ) * d(matrix[1][1]      ) - d(matrix[1][0]      ) * d(matrix[0][1]      )      )
  if determinant == 0:
   raise ValueError('''This matrix has no inverse.'''      )

  # Creates a copy of the matrix with swapped positions of the elements
  __lowercase							  = [[0.0, 0.0], [0.0, 0.0]]
  __lowercase  ,	__lowercase							  = matrix[1][1], matrix[0][0]
  __lowercase  ,	__lowercase							  = -matrix[1][0], -matrix[0][1]

  # Calculate the inverse of the matrix
  return [
      [(float(d(A__      )      ) / determinant) or 0.0 for n in row] for row in swapped_matrix
  ]
 elif (
     len(A__      ) == 3
     and len(matrix[0]      ) == 3
     and len(matrix[1]      ) == 3
     and len(matrix[2]      ) == 3
 ):
  # Calculate the determinant of the matrix using Sarrus rule
  __lowercase							  = float(
      (
          (d(matrix[0][0]      ) * d(matrix[1][1]      ) * d(matrix[2][2]      ))
          + (d(matrix[0][1]      ) * d(matrix[1][2]      ) * d(matrix[2][0]      ))
          + (d(matrix[0][2]      ) * d(matrix[1][0]      ) * d(matrix[2][1]      ))
      )
      - (
          (d(matrix[0][2]      ) * d(matrix[1][1]      ) * d(matrix[2][0]      ))
          + (d(matrix[0][1]      ) * d(matrix[1][0]      ) * d(matrix[2][2]      ))
          + (d(matrix[0][0]      ) * d(matrix[1][2]      ) * d(matrix[2][1]      ))
      )      )
  if determinant == 0:
   raise ValueError('''This matrix has no inverse.'''      )

  # Creating cofactor matrix
  __lowercase							  = [
      [d(0.0      ), d(0.0      ), d(0.0      )],
      [d(0.0      ), d(0.0      ), d(0.0      )],
      [d(0.0      ), d(0.0      ), d(0.0      )],
  ]
  __lowercase							  = (d(matrix[1][1]      ) * d(matrix[2][2]      )) - (
      d(matrix[1][2]      ) * d(matrix[2][1]      )
  )
  __lowercase							  = -(
      (d(matrix[1][0]      ) * d(matrix[2][2]      )) - (d(matrix[1][2]      ) * d(matrix[2][0]      ))
  )
  __lowercase							  = (d(matrix[1][0]      ) * d(matrix[2][1]      )) - (
      d(matrix[1][1]      ) * d(matrix[2][0]      )
  )
  __lowercase							  = -(
      (d(matrix[0][1]      ) * d(matrix[2][2]      )) - (d(matrix[0][2]      ) * d(matrix[2][1]      ))
  )
  __lowercase							  = (d(matrix[0][0]      ) * d(matrix[2][2]      )) - (
      d(matrix[0][2]      ) * d(matrix[2][0]      )
  )
  __lowercase							  = -(
      (d(matrix[0][0]      ) * d(matrix[2][1]      )) - (d(matrix[0][1]      ) * d(matrix[2][0]      ))
  )
  __lowercase							  = (d(matrix[0][1]      ) * d(matrix[1][2]      )) - (
      d(matrix[0][2]      ) * d(matrix[1][1]      )
  )
  __lowercase							  = -(
      (d(matrix[0][0]      ) * d(matrix[1][2]      )) - (d(matrix[0][2]      ) * d(matrix[1][0]      ))
  )
  __lowercase							  = (d(matrix[0][0]      ) * d(matrix[1][1]      )) - (
      d(matrix[0][1]      ) * d(matrix[1][0]      )
  )

  # Transpose the cofactor matrix (Adjoint matrix)
  __lowercase							  = array(A__      )
  for i in range(3      ):
   for j in range(3      ):
    __lowercase							  = cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
  __lowercase							  = array(A__      )
  for i in range(3      ):
   for j in range(3      ):
    inverse_matrix[i][j] /= d(A__      )

        # Calculate the inverse of the matrix
  return [[float(d(A__      )      ) or 0.0 for n in row] for row in inverse_matrix]
 raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''      )

