



"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       def __init__(							self    :						List[Any]	,       _UpperCAmelCase    :						List[str]	,       _UpperCAmelCase    :						Tuple					)				->		List[Any]:


             '''simple docstring'''




             super().__init__()

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

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







       @torch.no_grad()
       def __call__(							self    :						Optional[Any]	,       _UpperCAmelCase    :						int = 1	,       _UpperCAmelCase    :						Optional[Union[torch.Generator, List[torch.Generator]]] = None	,       _UpperCAmelCase    :						float = 0.0	,       _UpperCAmelCase    :						int = 50	,       _UpperCAmelCase    :						Optional[bool] = None	,       _UpperCAmelCase    :						Optional[str] = "pil"	,       _UpperCAmelCase    :						bool = True	,       )				->		Union[ImagePipelineOutput, Tuple]:


             '''simple docstring'''




             if isinstance(self.unet.config.sample_size	,       _UpperCAmelCase					):
                   UpperCAmelCase_    				=							(
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size,
                       self.unet.config.sample_size,
                   )
             else:
                   UpperCAmelCase_    				=							(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

             UpperCAmelCase_    				=							randn_tensor(_UpperCAmelCase	,       generator=_UpperCAmelCase	,       device=self.device	,       dtype=self.unet.dtype					)

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

             for t in self.progress_bar(self.scheduler.timesteps					):
                   # 1. predict noise model_output
                   UpperCAmelCase_    				=							self.unet(_UpperCAmelCase	,       _UpperCAmelCase					).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
                   UpperCAmelCase_    				=							self.scheduler.step(
                       _UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase	,       eta=_UpperCAmelCase	,       use_clipped_model_output=_UpperCAmelCase	,       generator=_UpperCAmelCase					).prev_sample

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

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=_UpperCAmelCase					)









"""simple docstring"""





from __future__ import annotations

from decimal import Decimal

from numpy import array
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							Decimal

      # Check if the provided matrix has 2 rows and 2 columns
      # since this implementation only works for 2x2 matrices
      if len(lowerCAmelCase__   ) == 2 and len(matrix[0]   ) == 2 and len(matrix[1]   ) == 2:
            # Calculate the determinant of the matrix
            UpperCAmelCase_    				=							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
            UpperCAmelCase_    				=							[[0.0, 0.0], [0.0, 0.0]]
            UpperCAmelCase_				,					UpperCAmelCase_    				=							matrix[1][1], matrix[0][0]
            UpperCAmelCase_				,					UpperCAmelCase_    				=							-matrix[1][0], -matrix[0][1]

            # Calculate the inverse of the matrix
            return [
                [(float(d(lowerCAmelCase__   )   ) / determinant) or 0.0 for n in row] for row in swapped_matrix
            ]
      elif (
          len(lowerCAmelCase__   ) == 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
            UpperCAmelCase_    				=							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
            UpperCAmelCase_    				=							[
                [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   )],
            ]
            UpperCAmelCase_    				=							(d(matrix[1][1]   ) * d(matrix[2][2]   )) - (
                d(matrix[1][2]   ) * d(matrix[2][1]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[1][0]   ) * d(matrix[2][2]   )) - (d(matrix[1][2]   ) * d(matrix[2][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[1][0]   ) * d(matrix[2][1]   )) - (
                d(matrix[1][1]   ) * d(matrix[2][0]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][1]   ) * d(matrix[2][2]   )) - (d(matrix[0][2]   ) * d(matrix[2][1]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][0]   ) * d(matrix[2][2]   )) - (
                d(matrix[0][2]   ) * d(matrix[2][0]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][0]   ) * d(matrix[2][1]   )) - (d(matrix[0][1]   ) * d(matrix[2][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][1]   ) * d(matrix[1][2]   )) - (
                d(matrix[0][2]   ) * d(matrix[1][1]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][0]   ) * d(matrix[1][2]   )) - (d(matrix[0][2]   ) * d(matrix[1][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][0]   ) * d(matrix[1][1]   )) - (
                d(matrix[0][1]   ) * d(matrix[1][0]   )
            )

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

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

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




