

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


__lowercase     : Optional[Any]       =						logging.get_logger(__name__)  # pylint: disable=invalid-name







def       lowercase					(	__A :					Union[List, PIL.Image.Image, torch.Tensor]		)				->			Any:





    '''simple docstring'''




    warnings.warn(
        """The preprocess method is deprecated and will be removed in a future version. Please"""
        """ use VaeImageProcessor.preprocess instead""" ,    __A ,    )
    if isinstance(__A ,    torch.Tensor		):
        return image
    elif isinstance(__A ,    PIL.Image.Image		):
        snake_case							:		Optional[int]        =		[image]

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

        snake_case							:		List[str]        =		[np.array(i.resize((w, h) ,    resample=PIL_INTERPOLATION["""lanczos"""]		)		)[None, :] for i in image]
        snake_case							:		Tuple        =		np.concatenate(__A ,    axis=0		)
        snake_case							:		str        =		np.array(__A		).astype(np.floataa		) / 255.0
        snake_case							:		Any        =		image.transpose(0 ,    3 ,    1 ,    2		)
        snake_case							:		List[str]        =		2.0 * image - 1.0
        snake_case							:		int        =		torch.from_numpy(__A		)
    elif isinstance(image[0] ,    torch.Tensor		):
        snake_case							:		Tuple        =		torch.cat(__A ,    dim=0		)
    return image







def       lowercase					(	__A :					Union[List, PIL.Image.Image, torch.Tensor]		)				->			Optional[int]:





    '''simple docstring'''




    if isinstance(__A ,    torch.Tensor		):
        return mask
    elif isinstance(__A ,    PIL.Image.Image		):
        snake_case							:		int        =		[mask]

    if isinstance(mask[0] ,    PIL.Image.Image		):
        snake_case      ,		snake_case							:		List[str]        =		mask[0].size
        snake_case      ,		snake_case							:		Any        =		(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
        snake_case							:		Optional[int]        =		[np.array(m.convert("""L"""		).resize((w, h) ,    resample=PIL_INTERPOLATION["""nearest"""]		)		)[None, :] for m in mask]
        snake_case							:		List[Any]        =		np.concatenate(__A ,    axis=0		)
        snake_case							:		Optional[Any]        =		mask.astype(np.floataa		) / 255.0
        snake_case							:		Dict        =		0
        snake_case							:		List[Any]        =		1
        snake_case							:		Dict        =		torch.from_numpy(__A		)
    elif isinstance(mask[0] ,    torch.Tensor		):
        snake_case							:		List[Any]        =		torch.cat(__A ,    dim=0		)
    return mask



class 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      UNetaDModel
  __lowerCamelCase			:      RePaintScheduler





  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      super().__init__()
      self.register_modules(unet=SCREAMING_SNAKE_CASE_   ,scheduler=SCREAMING_SNAKE_CASE_    )





  @torch.no_grad()
  def __call__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = 250   ,SCREAMING_SNAKE_CASE_ = 0.0   ,SCREAMING_SNAKE_CASE_ = 10   ,SCREAMING_SNAKE_CASE_ = 10   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = "pil"   ,SCREAMING_SNAKE_CASE_ = True   ,):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		image

      snake_case							:		int        =		_preprocess_image(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		original_image.to(device=self.device   ,dtype=self.unet.dtype    )
      snake_case							:		int        =		_preprocess_mask(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		mask_image.to(device=self.device   ,dtype=self.unet.dtype    )

      snake_case							:		Optional[int]        =		original_image.shape[0]

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

      snake_case							:		int        =		original_image.shape
      snake_case							:		str        =		randn_tensor(SCREAMING_SNAKE_CASE_   ,generator=SCREAMING_SNAKE_CASE_   ,device=self.device   ,dtype=self.unet.dtype    )

      # set step values
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,self.device    )
      snake_case							:		int        =		eta

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

          else:
              # compute the reverse: x_t-1 -> x_t
              snake_case							:		Optional[Any]        =		self.scheduler.undo_step(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
          snake_case							:		Union[str, Any]        =		t

      snake_case							:		Any        =		(image / 2 + 0.5).clamp(0   ,1    )
      snake_case							:		Optional[Any]        =		image.cpu().permute(0   ,2   ,3   ,1    ).numpy()
      if output_type == "pil":
          snake_case							:		Optional[int]        =		self.numpy_to_pil(SCREAMING_SNAKE_CASE_    )

      if not return_dict:
          return (image,)

      return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_    )










'''simple docstring'''

from __future__ import annotations







class __a			:





   def __init__(       self					:      Optional[int]					,lowerCamelCase					:      list[list[int]]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TypeError(
          """Matrices must be formed from a list of zero or more lists containing at """
          """least one and the same number of values, each of which must be of type """
          """int or float."""     )
      if len(lowerCamelCase     ) != 0:
         __SCREAMING_SNAKE_CASE   				=							len(rows[0]     )
         if cols == 0:
            raise error
         for row in rows:
            if len(lowerCamelCase     ) != cols:
               raise error
            for value in row:
               if not isinstance(lowerCamelCase					,(int, float)     ):
                  raise error
         __SCREAMING_SNAKE_CASE   				=							rows
      else:
         __SCREAMING_SNAKE_CASE   				=							[]





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      return [[row[i] for row in self.rows] for i in range(len(self.rows[0]     )     )]





   @property
   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return len(self.rows     )





   @property
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      return len(self.rows[0]     )





   @property
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      return (self.num_rows, self.num_columns)





   @property
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return self.order[0] == self.order[1]





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [0 if column_num != row_num else 1 for column_num in range(self.num_rows     )]
          for row_num in range(self.num_rows     )
      ]
      return Matrix(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      if not self.is_square:
         return 0
      if self.order == (0, 0):
         return 1
      if self.order == (1, 1):
         return int(self.rows[0][0]     )
      if self.order == (2, 2):
         return int(
             (self.rows[0][0] * self.rows[1][1])
             - (self.rows[0][1] * self.rows[1][0])     )
      else:
         return sum(
             self.rows[0][column] * self.cofactors().rows[0][column]
             for column in range(self.num_columns     )     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return bool(self.determinant()     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [
              self.rows[other_row][other_column]
              for other_column in range(self.num_columns     )
              if other_column != column
          ]
          for other_row in range(self.num_rows     )
          if other_row != row
      ]
      return Matrix(lowerCamelCase     ).determinant()





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      int					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      if (row + column) % 2 == 0:
         return self.get_minor(lowerCamelCase					,lowerCamelCase     )
      return -1 * self.get_minor(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      return Matrix(
          [
              [self.get_minor(lowerCamelCase					,lowerCamelCase     ) for column in range(self.num_columns     )]
              for row in range(self.num_rows     )
          ]     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return Matrix(
          [
              [
                  self.minors().rows[row][column]
                  if (row + column) % 2 == 0
                  else self.minors().rows[row][column] * -1
                  for column in range(self.minors().num_columns     )
              ]
              for row in range(self.minors().num_rows     )
          ]     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [self.cofactors().rows[column][row] for column in range(self.num_columns     )]
          for row in range(self.num_rows     )
      ]
      return Matrix(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.determinant()
      if not determinant:
         raise TypeError("""Only matrices with a non-zero determinant have an inverse"""     )
      return self.adjugate() * (1 / determinant)





   def __repr__(       self					:      List[Any]     ):

      '''simple docstring'''

      return str(self.rows     )





   def __str__(       self					:      List[str]     ):

      '''simple docstring'''

      if self.num_rows == 0:
         return "[]"
      if self.num_rows == 1:
         return "[[" + ". ".join(str(self.rows[0]     )     ) + "]]"
      return (
          "["
          + "\n ".join(
              [
                  """[""" + """. """.join([str(lowerCamelCase     ) for value in row]     ) + """.]"""
                  for row in self.rows
              ]     )
          + "]"
      )





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      int | None = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TypeError("""Row must be a list containing all ints and/or floats"""     )
      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         raise type_error
      for value in row:
         if not isinstance(lowerCamelCase					,(int, float)     ):
            raise type_error
      if len(lowerCamelCase     ) != self.num_columns:
         raise ValueError(
             """Row must be equal in length to the other rows in the matrix"""     )
      if position is None:
         self.rows.append(lowerCamelCase     )
      else:
         __SCREAMING_SNAKE_CASE   				=							self.rows[0:position] + [row] + self.rows[position:]





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      int | None = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TypeError(
          """Column must be a list containing all ints and/or floats"""     )
      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         raise type_error
      for value in column:
         if not isinstance(lowerCamelCase					,(int, float)     ):
            raise type_error
      if len(lowerCamelCase     ) != self.num_rows:
         raise ValueError(
             """Column must be equal in length to the other columns in the matrix"""     )
      if position is None:
         __SCREAMING_SNAKE_CASE   				=							[self.rows[i] + [column[i]] for i in range(self.num_rows     )]
      else:
         __SCREAMING_SNAKE_CASE   				=							[
             self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
             for i in range(self.num_rows     )
         ]





   def __eq__(       self					:      int					,lowerCamelCase					:      object     ):

      '''simple docstring'''

      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         return NotImplemented
      return self.rows == other.rows





   def __ne__(       self					:      Any					,lowerCamelCase					:      object     ):

      '''simple docstring'''

      return not self == other





   def __neg__(       self					:      Any     ):

      '''simple docstring'''

      return self * -1





   def __add__(       self					:      List[Any]					,lowerCamelCase					:      Matrix     ):

      '''simple docstring'''

      if self.order != other.order:
         raise ValueError("""Addition requires matrices of the same order"""     )
      return Matrix(
          [
              [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns     )]
              for i in range(self.num_rows     )
          ]     )





   def __sub__(       self					:      Any					,lowerCamelCase					:      Matrix     ):

      '''simple docstring'''

      if self.order != other.order:
         raise ValueError("""Subtraction requires matrices of the same order"""     )
      return Matrix(
          [
              [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns     )]
              for i in range(self.num_rows     )
          ]     )





   def __mul__(       self					:      Any					,lowerCamelCase					:      Matrix | int | float     ):

      '''simple docstring'''

      if isinstance(lowerCamelCase					,(int, float)     ):
         return Matrix(
             [[int(element * other     ) for element in row] for row in self.rows]     )
      elif isinstance(lowerCamelCase					,lowerCamelCase     ):
         if self.num_columns != other.num_rows:
            raise ValueError(
                """The number of columns in the first matrix must """
                """be equal to the number of rows in the second"""     )
         return Matrix(
             [
                 [Matrix.dot_product(lowerCamelCase					,lowerCamelCase     ) for column in other.columns()]
                 for row in self.rows
             ]     )
      else:
         raise TypeError(
             """A Matrix can only be multiplied by an int, float, or another matrix"""     )





   def __pow__(       self					:      Optional[int]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         raise TypeError("""A Matrix can only be raised to the power of an int"""     )
      if not self.is_square:
         raise ValueError("""Only square matrices can be raised to a power"""     )
      if other == 0:
         return self.identity()
      if other < 0:
         if self.is_invertable():
            return self.inverse() ** (-other)
         raise ValueError(
             """Only invertable matrices can be raised to a negative power"""     )
      __SCREAMING_SNAKE_CASE   				=							self
      for _ in range(other - 1     ):
         result *= self
      return result





   @classmethod
   def   UpperCAmelCase__  (       cls					:      str					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      list[int]     ):

      '''simple docstring'''

      return sum(row[i] * column[i] for i in range(len(lowerCamelCase     )     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





