


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   _lowerCAmelCase				(   UpperCamelCase__:     Union[List, PIL.Image.Image, torch.Tensor]						) ->   Union[str, Any]:





	"""simple docstring"""







	warnings.warn(
	    """The preprocess method is deprecated and will be removed in a future version. Please"""
	    """ use VaeImageProcessor.preprocess instead"""							,					UpperCamelCase__							,					)
	if isinstance(UpperCamelCase__							,					torch.Tensor						):
		return image
	elif isinstance(UpperCamelCase__							,					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(UpperCamelCase__							,					axis=0						)
		A								=				np.array(UpperCamelCase__						).astype(np.floataa						) / 2_55.0
		A								=				image.transpose(0							,					3							,					1							,					2						)
		A								=				2.0 * image - 1.0
		A								=				torch.from_numpy(UpperCamelCase__						)
	elif isinstance(image[0]							,					torch.Tensor						):
		A								=				torch.cat(UpperCamelCase__							,					dim=0						)
	return image





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[List, PIL.Image.Image, torch.Tensor]						) ->   Dict:





	"""simple docstring"""







	if isinstance(UpperCamelCase__							,					torch.Tensor						):
		return mask
	elif isinstance(UpperCamelCase__							,					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(UpperCamelCase__							,					axis=0						)
		A								=				mask.astype(np.floataa						) / 2_55.0
		A								=				0
		A								=				1
		A								=				torch.from_numpy(UpperCamelCase__						)
	elif isinstance(mask[0]							,					torch.Tensor						):
		A								=				torch.cat(UpperCamelCase__							,					dim=0						)
	return mask






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 4_2
				lowerCAmelCase 		= 4_2

				def __init__(						self				,   a__				,   a__   )	->				List[Any]:
					super().__init__()
					self.register_modules(unet=a__				,   scheduler=a__   )

				@torch.no_grad()
				def __call__(						self				,   a__				,   a__				,   a__ = 250				,   a__ = 0.0				,   a__ = 10				,   a__ = 10				,   a__ = None				,   a__ = "pil"				,   a__ = True				,   )	->				Union[ImagePipelineOutput, Tuple]:
					A								=				image

					A								=				_preprocess_image(a__   )
					A								=				original_image.to(device=self.device				,   dtype=self.unet.dtype   )
					A								=				_preprocess_mask(a__   )
					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(a__				,   a__   ) and len(a__   ) != batch_size:
						raise ValueError(
						    f'You have passed a list of generators of length {len(a__   )}, 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(a__				,   generator=a__				,   device=self.device				,   dtype=self.unet.dtype   )

					# set step values
					self.scheduler.set_timesteps(a__				,   a__				,   a__				,   self.device   )
					A								=				eta

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

						else:
							# compute the reverse: x_t-1 -> x_t
							A								=				self.scheduler.undo_step(a__				,   a__				,   a__   )
						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(a__   )

					if not return_dict:
						return (image,)

					return ImagePipelineOutput(images=a__   )



from __future__ import annotations

from typing import Any






class       _UpperCamelCase    :

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__				,   a__ = 0   )	->				None:
					A					,			A								=				row, column
					A								=				[[default_value for c in range(a__   )] for r in range(a__   )]

				def __str__(						self   )	->				str:
					A								=				f'Matrix consist of {self.row} rows and {self.column} columns\n'

					# Make string identifier
					A								=				0
					for row_vector in self.array:
						for obj in row_vector:
							A								=				max(a__				,   len(str(a__   )   )   )
					A								=				f'%{max_element_length}s'

					# Make string and return
					def single_line(a__   ) -> str:
						nonlocal string_format_identifier
						A								=				"""["""
						line += ", ".join(string_format_identifier % (obj,) for obj in row_vector   )
						line += "]"
						return line

					s += "\n".join(single_line(a__   ) for row_vector in self.array   )
					return s

				def __repr__(						self   )	->				str:
					return str(self   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				bool:
					if not (isinstance(a__				,   (list, tuple)   ) and len(a__   ) == 2):
						return False
					elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
						return False
					else:
						return True

				def __getitem__(						self				,   a__   )	->				Any:
					assert self.validate_indicies(a__   )
					return self.array[loc[0]][loc[1]]

				def __setitem__(						self				,   a__				,   a__   )	->				None:
					assert self.validate_indicies(a__   )
					A								=				value

				def __add__(						self				,   a__   )	->				Matrix:
					assert isinstance(a__				,   a__   )
					assert self.row == another.row and self.column == another.column

					# Add
					A								=				Matrix(self.row				,   self.column   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				self[r, c] + another[r, c]
					return result

				def __neg__(						self   )	->				Matrix:
					A								=				Matrix(self.row				,   self.column   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				-self[r, c]
					return result

				def __sub__(						self				,   a__   )	->				Matrix:
					return self + (-another)

				def __mul__(						self				,   a__   )	->				Matrix:

					if isinstance(a__				,   (int, float)   ):  # Scalar multiplication
						A								=				Matrix(self.row				,   self.column   )
						for r in range(self.row   ):
							for c in range(self.column   ):
								A								=				self[r, c] * another
						return result
					elif isinstance(a__				,   a__   ):  # Matrix multiplication
						assert self.column == another.row
						A								=				Matrix(self.row				,   another.column   )
						for r in range(self.row   ):
							for c in range(another.column   ):
								for i in range(self.column   ):
									result[r, c] += self[r, i] * another[i, c]
						return result
					else:
						A								=				f'Unsupported type given for another ({type(a__   )})'
						raise TypeError(a__   )

				def      _UpperCAmelCase					(						self   )	->				Matrix:
					A								=				Matrix(self.column				,   self.row   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				self[r, c]
					return result

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Any:
					assert isinstance(a__				,   a__   ) and isinstance(a__				,   a__   )
					assert self.row == self.column == u.row == v.row  # u, v should be column vector
					assert u.column == v.column == 1  # u, v should be column vector

					# Calculate
					A								=				v.transpose()
					A								=				(v_t * self * u)[0, 0] + 1
					if numerator_factor == 0:
						return None  # It's not invertable
					return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





				def   _lowerCAmelCase				(   ) ->   None:





					"""simple docstring"""







					A								=				Matrix(3							,					3							,					0						)
					for i in range(3						):
						A								=				1
					print(f'a^(-1) is {ainv}'						)
					# u, v
					A								=				Matrix(3							,					1							,					0						)
					A					,			A					,			A								=				1, 2, -3
					A								=				Matrix(3							,					1							,					0						)
					A					,			A					,			A								=				4, -2, 5
					print(f'u is {u}'						)
					print(f'v is {v}'						)
					print(f'uv^T is {u * v.transpose()}'						)
					# Sherman Morrison
					print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(UpperCamelCase__							,					UpperCamelCase__						)}'						)





				def   _lowerCAmelCase				(   ) ->   None:





					"""simple docstring"""







					import doctest

					doctest.testmod()
				testa()



