






from __future__ import annotations

import math

def _UpperCAmelCase   (	a						:	List[str]  ,					a						:	int ):





							if len(UpperCamelCase__ ) != 2 or len(a[0] ) != 2 or len(UpperCamelCase__ ) != 2 or len(b[0] ) != 2:
														raise Exception("""Matrices are not 2x2""" )
							snake_case__	  =       [
							    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
							    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
							]
							return new_matrix

def _UpperCAmelCase   (	a						:	str  ,					a						:	Tuple ):
							return [
							    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
							    for row in range(len(UpperCamelCase__ ) )
							]

def _UpperCAmelCase   (	a						:	Dict  ,					a						:	Tuple ):
							return [
							    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
							    for row in range(len(UpperCamelCase__ ) )
							]

def _UpperCAmelCase   (	a						:	Optional[Any] ):





							if len(UpperCamelCase__ ) % 2 != 0 or len(a[0] ) % 2 != 0:
														raise Exception("""Odd matrices are not supported!""" )

							snake_case__	  =       len(UpperCamelCase__ )
							snake_case__	  =       matrix_length // 2

							snake_case__	  =       [[a[i][j] for j in range(UpperCamelCase__  ,					UpperCamelCase__ )] for i in range(UpperCamelCase__ )]
							snake_case__	  =       [
							    [a[i][j] for j in range(UpperCamelCase__  ,					UpperCamelCase__ )] for i in range(UpperCamelCase__  ,					UpperCamelCase__ )
							]

							snake_case__	  =       [[a[i][j] for j in range(UpperCamelCase__ )] for i in range(UpperCamelCase__ )]
							snake_case__	  =       [[a[i][j] for j in range(UpperCamelCase__ )] for i in range(UpperCamelCase__  ,					UpperCamelCase__ )]

							return top_left, top_right, bot_left, bot_right

def _UpperCAmelCase   (	a						:	Optional[int] ):
							return len(UpperCamelCase__ ), len(matrix[0] )

def _UpperCAmelCase   (	a						:	Optional[int] ):
							print("""\n""".join(str(UpperCamelCase__ ) for line in matrix ) )

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Optional[int] ):





							if matrix_dimensions(UpperCamelCase__ ) == (2, 2):
														return default_matrix_multiplication(UpperCamelCase__  ,					UpperCamelCase__ )

							snake_case__  , snake_case__  , snake_case__  , snake_case__	  =       split_matrix(UpperCamelCase__ )
							snake_case__  , snake_case__  , snake_case__  , snake_case__	  =       split_matrix(UpperCamelCase__ )

							snake_case__	  =       actual_strassen(UpperCamelCase__  ,					matrix_subtraction(UpperCamelCase__  ,					UpperCamelCase__ ) )
							snake_case__	  =       actual_strassen(matrix_addition(UpperCamelCase__  ,					UpperCamelCase__ )  ,					UpperCamelCase__ )
							snake_case__	  =       actual_strassen(matrix_addition(UpperCamelCase__  ,					UpperCamelCase__ )  ,					UpperCamelCase__ )
							snake_case__	  =       actual_strassen(UpperCamelCase__  ,					matrix_subtraction(UpperCamelCase__  ,					UpperCamelCase__ ) )
							snake_case__	  =       actual_strassen(matrix_addition(UpperCamelCase__  ,					UpperCamelCase__ )  ,					matrix_addition(UpperCamelCase__  ,					UpperCamelCase__ ) )
							snake_case__	  =       actual_strassen(matrix_subtraction(UpperCamelCase__  ,					UpperCamelCase__ )  ,					matrix_addition(UpperCamelCase__  ,					UpperCamelCase__ ) )
							snake_case__	  =       actual_strassen(matrix_subtraction(UpperCamelCase__  ,					UpperCamelCase__ )  ,					matrix_addition(UpperCamelCase__  ,					UpperCamelCase__ ) )

							snake_case__	  =       matrix_addition(matrix_subtraction(matrix_addition(UpperCamelCase__  ,					UpperCamelCase__ )  ,					UpperCamelCase__ )  ,					UpperCamelCase__ )
							snake_case__	  =       matrix_addition(UpperCamelCase__  ,					UpperCamelCase__ )
							snake_case__	  =       matrix_addition(UpperCamelCase__  ,					UpperCamelCase__ )
							snake_case__	  =       matrix_subtraction(matrix_subtraction(matrix_addition(UpperCamelCase__  ,					UpperCamelCase__ )  ,					UpperCamelCase__ )  ,					UpperCamelCase__ )

							# construct the new matrix from our 4 quadrants
							snake_case__	  =       []
							for i in range(len(UpperCamelCase__ ) ):
														new_matrix.append(top_left[i] + top_right[i] )
							for i in range(len(UpperCamelCase__ ) ):
														new_matrix.append(bot_left[i] + bot_right[i] )
							return new_matrix

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Any ):





							if matrix_dimensions(UpperCamelCase__ )[1] != matrix_dimensions(UpperCamelCase__ )[0]:
														snake_case__	  =       (
														    """Unable to multiply these matrices, please check the dimensions.\n"""
														    F'''Matrix A: {matrixa}\n'''
														    F'''Matrix B: {matrixa}'''
														)
														raise Exception(UpperCamelCase__ )
							snake_case__	  =       matrix_dimensions(UpperCamelCase__ )
							snake_case__	  =       matrix_dimensions(UpperCamelCase__ )

							if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
														return [matrixa, matrixa]

							snake_case__	  =       max(*UpperCamelCase__  ,					*UpperCamelCase__ )
							snake_case__	  =       int(math.pow(2  ,					math.ceil(math.loga(UpperCamelCase__ ) ) ) )
							snake_case__	  =       matrixa
							snake_case__	  =       matrixa

							# Adding zeros to the matrices so that the arrays dimensions are the same and also
							# power of 2
							for i in range(0  ,					UpperCamelCase__ ):
														if i < dimensiona[0]:
																					for _ in range(dimensiona[1]  ,					UpperCamelCase__ ):
																												new_matrixa[i].append(0 )
														else:
																					new_matrixa.append([0] * maxim )
														if i < dimensiona[0]:
																					for _ in range(dimensiona[1]  ,					UpperCamelCase__ ):
																												new_matrixa[i].append(0 )
														else:
																					new_matrixa.append([0] * maxim )

							snake_case__	  =       actual_strassen(UpperCamelCase__  ,					UpperCamelCase__ )

							# Removing the additional zeros
							for i in range(0  ,					UpperCamelCase__ ):
														if i < dimensiona[0]:
																					for _ in range(dimensiona[1]  ,					UpperCamelCase__ ):
																												final_matrix[i].pop()
														else:
																					final_matrix.pop()
							return final_matrix


if __name__ == "__main__":
	a__     :   Tuple       =     [
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 2, 3, 1],
	]
	a__     :   Optional[Any]       =     [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
	print(strassen(matrixa, matrixa))








import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class    _lowerCAmelCase			(		lowercase_     ,      lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   Optional[int]      =  VQModel
				_lowercase				:   str      =  '''sample'''



				@property
				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Tuple=(3_2, 3_2)):






											'''simple docstring'''







											snake_case__	  =       4
											snake_case__	  =       3

											snake_case__	  =       floats_tensor((batch_size, num_channels) + sizes).to(UpperCamelCase__)

											return {"sample": image}



				@property
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											return (3, 3_2, 3_2)



				@property
				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											return (3, 3_2, 3_2)



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       {
											    """block_out_channels""": [3_2, 6_4],
											    """in_channels""": 3,
											    """out_channels""": 3,
											    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
											    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
											    """latent_channels""": 3,
											}
											snake_case__	  =       self.dummy_input
											return init_dict, inputs_dict



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       VQModel.from_pretrained("""fusing/vqgan-dummy"""			,		output_loading_info=UpperCamelCase__)
											self.assertIsNotNone(UpperCamelCase__)
											self.assertEqual(len(loading_info["""missing_keys"""])			,		0)

											model.to(UpperCamelCase__)
											snake_case__	  =       model(**self.dummy_input)

											assert image is not None, "Make sure output is not None"



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       VQModel.from_pretrained("""fusing/vqgan-dummy""")
											model.to(UpperCamelCase__).eval()

											torch.manual_seed(0)
											if torch.cuda.is_available():
																		torch.cuda.manual_seed_all(0)

											snake_case__	  =       torch.randn(1			,		model.config.in_channels			,		model.config.sample_size			,		model.config.sample_size)
											snake_case__	  =       image.to(UpperCamelCase__)
											with torch.no_grad():
																		snake_case__	  =       model(UpperCamelCase__).sample

											snake_case__	  =       output[0, -1, -3:, -3:].flatten().cpu()
											# fmt: off
											snake_case__	  =       torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43])
											# fmt: on
											self.assertTrue(torch.allclose(UpperCamelCase__			,		UpperCamelCase__			,		atol=1E-3))
