





'''simple docstring'''

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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase							:	List[str]			=logging.get_logger(__name__)  # pylint: disable=invalid-name





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[List, PIL.Image.Image, torch.Tensor]       ):
    warnings.warn(
        "The preprocess method is deprecated and will be removed in a future version. Please"
        " use VaeImageProcessor.preprocess instead"						,       _lowerCamelCase						,       )
    if isinstance(_lowerCamelCase						,       torch.Tensor       ):
        return image
    elif isinstance(_lowerCamelCase						,       PIL.Image.Image       ):
        A__				=			[image]

    if isinstance(image[0]						,       PIL.Image.Image       ):
        A__,	A__				=			image[0].size
        A__,	A__				=			(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

        A__				=			[np.array(i.resize((w, h)						,       resample=PIL_INTERPOLATION["lanczos"]       )       )[None, :] for i in image]
        A__				=			np.concatenate(_lowerCamelCase						,       axis=0       )
        A__				=			np.array(_lowerCamelCase       ).astype(np.floataa       ) / 2_5_5.0
        A__				=			image.transpose(0						,       3						,       1						,       2       )
        A__				=			2.0 * image - 1.0
        A__				=			torch.from_numpy(_lowerCamelCase       )
    elif isinstance(image[0]						,       torch.Tensor       ):
        A__				=			torch.cat(_lowerCamelCase						,       dim=0       )
    return image





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[List, PIL.Image.Image, torch.Tensor]       ):
    if isinstance(_lowerCamelCase						,       torch.Tensor       ):
        return mask
    elif isinstance(_lowerCamelCase						,       PIL.Image.Image       ):
        A__				=			[mask]

    if isinstance(mask[0]						,       PIL.Image.Image       ):
        A__,	A__				=			mask[0].size
        A__,	A__				=			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
        A__				=			[np.array(m.convert("L"       ).resize((w, h)						,       resample=PIL_INTERPOLATION["nearest"]       )       )[None, :] for m in mask]
        A__				=			np.concatenate(_lowerCamelCase						,       axis=0       )
        A__				=			mask.astype(np.floataa       ) / 2_5_5.0
        A__				=			0
        A__				=			1
        A__				=			torch.from_numpy(_lowerCamelCase       )
    elif isinstance(mask[0]						,       torch.Tensor       ):
        A__				=			torch.cat(_lowerCamelCase						,       dim=0       )
    return mask






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				42
 __lowercase   		=				42
 def __init__(				self      :str						,       lowercase_      :Union[str, Any]						,       lowercase_      :Tuple							)->							Dict:
     super().__init__()
     self.register_modules(unet=lowercase_						,       scheduler=lowercase_							)



 @torch.no_grad()
 def __call__(				self      :str						,       lowercase_      :Union[torch.Tensor, PIL.Image.Image]						,       lowercase_      :Union[torch.Tensor, PIL.Image.Image]						,       lowercase_      :int = 2_50						,       lowercase_      :float = 0.0						,       lowercase_      :int = 10						,       lowercase_      :int = 10						,       lowercase_      :Optional[Union[torch.Generator, List[torch.Generator]]] = None						,       lowercase_      :Optional[str] = "pil"						,       lowercase_      :bool = True						,       )->							Union[ImagePipelineOutput, Tuple]:
     A__				=			image

     A__				=			_preprocess_image(lowercase_							)
     A__				=			original_image.to(device=self.device						,       dtype=self.unet.dtype							)
     A__				=			_preprocess_mask(lowercase_							)
     A__				=			mask_image.to(device=self.device						,       dtype=self.unet.dtype							)

     A__				=			original_image.shape[0]

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

     A__				=			original_image.shape
     A__				=			randn_tensor(lowercase_						,       generator=lowercase_						,       device=self.device						,       dtype=self.unet.dtype							)

     # set step values
     self.scheduler.set_timesteps(lowercase_						,       lowercase_						,       lowercase_						,       self.device							)
     A__				=			eta

     A__				=			self.scheduler.timesteps[0] + 1
     A__				=			generator[0] if isinstance(lowercase_						,       lowercase_							) else generator
     for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
         if t < t_last:
             # predict the noise residual
             A__				=			self.unet(lowercase_						,       lowercase_							).sample
             # compute previous image: x_t -> x_t-1
             A__				=			self.scheduler.step(lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       lowercase_							).prev_sample

         else:
             # compute the reverse: x_t-1 -> x_t
             A__				=			self.scheduler.undo_step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			t

     A__				=			(image / 2 + 0.5).clamp(0						,       1							)
     A__				=			image.cpu().permute(0						,       2						,       3						,       1							).numpy()
     if output_type == "pil":
         A__				=			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 						UpperCamelCase				(							_lowerCamelCase					:					list[list[float]]       ):
    A__				=			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
        A__				=			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
        A__				=			[[0.0, 0.0], [0.0, 0.0]]
        A__,	A__				=			matrix[1][1], matrix[0][0]
        A__,	A__				=			-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
        A__				=			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
        A__				=			[
            [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       )],
        ]
        A__				=			(d(matrix[1][1]       ) * d(matrix[2][2]       )) - (
            d(matrix[1][2]       ) * d(matrix[2][1]       )
        )
        A__				=			-(
            (d(matrix[1][0]       ) * d(matrix[2][2]       )) - (d(matrix[1][2]       ) * d(matrix[2][0]       ))
        )
        A__				=			(d(matrix[1][0]       ) * d(matrix[2][1]       )) - (
            d(matrix[1][1]       ) * d(matrix[2][0]       )
        )
        A__				=			-(
            (d(matrix[0][1]       ) * d(matrix[2][2]       )) - (d(matrix[0][2]       ) * d(matrix[2][1]       ))
        )
        A__				=			(d(matrix[0][0]       ) * d(matrix[2][2]       )) - (
            d(matrix[0][2]       ) * d(matrix[2][0]       )
        )
        A__				=			-(
            (d(matrix[0][0]       ) * d(matrix[2][1]       )) - (d(matrix[0][1]       ) * d(matrix[2][0]       ))
        )
        A__				=			(d(matrix[0][1]       ) * d(matrix[1][2]       )) - (
            d(matrix[0][2]       ) * d(matrix[1][1]       )
        )
        A__				=			-(
            (d(matrix[0][0]       ) * d(matrix[1][2]       )) - (d(matrix[0][2]       ) * d(matrix[1][0]       ))
        )
        A__				=			(d(matrix[0][0]       ) * d(matrix[1][1]       )) - (
            d(matrix[0][1]       ) * d(matrix[1][0]       )
        )

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

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
        A__				=			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."       )



