




from __future__ import annotations
class       a__   :






						def __init__(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	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(_A      ) != 0:
																__lowerCAmelCase           =	len(rows[0]      )
																if cols == 0:
																					raise error
																for row in rows:
																					if len(_A      ) != cols:
																										raise error
																					for value in row:
																										if not isinstance(_A		,      (int, float)      ):
																															raise error
																__lowerCAmelCase           =	rows
											else:
																__lowerCAmelCase           =	[]






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return len(self.rows      )






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return len(self.rows[0]      )






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return (self.num_rows, self.num_columns)






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	[
											    [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(_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return bool(self.determinant()      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	[
											    [
											        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(_A      ).determinant()






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	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      ):




											"""simple docstring"""



											return str(self.rows      )






						def __str__(       self      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None      ):




											"""simple docstring"""



											__lowerCAmelCase           =	TypeError("Row must be a list containing all ints and/or floats"      )
											if not isinstance(_A		,      _A      ):
																raise type_error
											for value in row:
																if not isinstance(_A		,      (int, float)      ):
																					raise type_error
											if len(_A      ) != 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(_A      )
											else:
																__lowerCAmelCase           =	self.rows[0:position] + [row] + self.rows[position:]






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None      ):




											"""simple docstring"""



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






						def __eq__(       self		,      _A      ):




											"""simple docstring"""



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






						def __ne__(       self		,      _A      ):




											"""simple docstring"""



											return not self == other






						def __neg__(       self      ):




											"""simple docstring"""



											return self * -1






						def __add__(       self		,      _A      ):




											"""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		,      _A      ):




											"""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		,      _A      ):




											"""simple docstring"""



											if isinstance(_A		,      (int, float)      ):
																return Matrix(
																    [[int(element * other      ) for element in row] for row in self.rows]      )
											elif isinstance(_A		,      _A      ):
																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(_A		,      _A      ) 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		,      _A      ):




											"""simple docstring"""



											if not isinstance(_A		,      _A      ):
																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"      )
											__lowerCAmelCase           =	self
											for _ in range(other - 1      ):
																result *= self
											return result






						@classmethod
						def     __SCREAMING_SNAKE_CASE(       cls		,      _A		,      _A      ):




											"""simple docstring"""



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


if __name__ == "__main__":
					import doctest

					doctest.testmod()










import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class       a__   ( unittest.TestCase				):






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											torch.manual_seed(0      )
											__lowerCAmelCase           =	UNetaDModel(
											    block_out_channels=(3_2, 6_4)		,      layers_per_block=2		,      sample_size=3_2		,      in_channels=3		,      out_channels=3		,      down_block_types=("DownBlock2D", "AttnDownBlock2D")		,      up_block_types=("AttnUpBlock2D", "UpBlock2D")		,      )
											return model






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.dummy_uncond_unet
											__lowerCAmelCase           =	ScoreSdeVeScheduler()

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A		,      return_dict=_A      )[
											    0
											]

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]
											__lowerCAmelCase           =	image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowerCAmelCase           =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2






@slow
@require_torch
class       a__   ( unittest.TestCase				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"google/ncsnpp-church-256"
											__lowerCAmelCase           =	UNetaDModel.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVeScheduler.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=1_0		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]

											assert image.shape == (1, 2_5_6, 2_5_6, 3)

											__lowerCAmelCase           =	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




