






from __future__ import annotations

from typing import Any
class 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = 0     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:Any        =      row, column
								__magic_name__		:Union[str, Any]        =      [[default_value for c in range(__lowerCAmelCase     )] for r in range(__lowerCAmelCase     )]







				def __str__(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

								# Make string identifier
								__magic_name__		:Dict        =      0
								for row_vector in self.array:
												for obj in row_vector:
																__magic_name__		:Dict        =      max(__lowerCAmelCase       ,					len(str(__lowerCAmelCase     )     )     )
								__magic_name__		:List[str]        =      F'''%{max_element_length}s'''

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

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







				def __repr__(						self     ):
								"""simple docstring"""
								return str(self     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								if not (isinstance(__lowerCAmelCase       ,					(list, tuple)     ) and len(__lowerCAmelCase     ) == 2):
												return False
								elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
												return False
								else:
												return True







				def __getitem__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert self.validate_indicies(__lowerCAmelCase     )
								return self.array[loc[0]][loc[1]]







				def __setitem__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert self.validate_indicies(__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      value







				def __add__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     )
								assert self.row == another.row and self.column == another.column

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







				def __neg__(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Matrix(self.row       ,					self.column     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:Any        =      -self[r, c]
								return result







				def __sub__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return self + (-another)







				def __mul__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""

								if isinstance(__lowerCAmelCase       ,					(int, float)     ):  # Scalar multiplication
												__magic_name__		:List[str]        =      Matrix(self.row       ,					self.column     )
												for r in range(self.row     ):
																for c in range(self.column     ):
																				__magic_name__		:Tuple        =      self[r, c] * another
												return result
								elif isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):  # Matrix multiplication
												assert self.column == another.row
												__magic_name__		:List[Any]        =      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:
												__magic_name__		:List[str]        =      F'''Unsupported type given for another ({type(__lowerCAmelCase     )})'''
												raise TypeError(__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Matrix(self.column       ,					self.row     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:List[str]        =      self[r, c]
								return result







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) and isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     )
								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
								__magic_name__		:int        =      v.transpose()
								__magic_name__		:str        =      (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      __lowercase					( ):







									"""simple docstring"""






									__magic_name__		:Optional[int]        =      Matrix(3,  3,  0 )
									for i in range(3 ):
													__magic_name__		:Tuple        =      1
									print(f'''a^(-1) is {ainv}''' )
									# u, v
									__magic_name__		:Optional[int]        =      Matrix(3,  1,  0 )
									__magic_name__     ,				__magic_name__     ,				__magic_name__		:Optional[int]        =      1, 2, -3
									__magic_name__		:int        =      Matrix(3,  1,  0 )
									__magic_name__     ,				__magic_name__     ,				__magic_name__		:str        =      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(snake_case,  snake_case )}''' )

					def      __lowercase					( ):







									"""simple docstring"""






									import doctest

									doctest.testmod()

					testa()














import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 						lowerCamelCase_				(      unittest.TestCase ):







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		:Dict        =      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







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		:List[str]        =      VQModel(
								    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       ,					)
								return model







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		:int        =      CLIPTextConfig(
								    bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					)
								return CLIPTextModel(__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.dummy_uncond_unet
								__magic_name__		:Optional[int]        =      DDIMScheduler()
								__magic_name__		:List[str]        =      self.dummy_vq_model

								__magic_name__		:Tuple        =      LDMPipeline(unet=__lowerCAmelCase       ,					vqvae=__lowerCAmelCase       ,					scheduler=__lowerCAmelCase     )
								ldm.to(__lowerCAmelCase     )
								ldm.set_progress_bar_config(disable=__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      torch.manual_seed(0     )
								__magic_name__		:List[str]        =      ldm(generator=__lowerCAmelCase       ,					num_inference_steps=2       ,					output_type='''numpy'''     ).images

								__magic_name__		:List[Any]        =      torch.manual_seed(0     )
								__magic_name__		:Any        =      ldm(generator=__lowerCAmelCase       ,					num_inference_steps=2       ,					output_type='''numpy'''       ,					return_dict=__lowerCAmelCase     )[0]

								__magic_name__		:Any        =      image[0, -3:, -3:, -1]
								__magic_name__		:Dict        =      image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)
								__magic_name__		:Union[str, Any]        =      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]     )
								__magic_name__		:Any        =      1E-2 if torch_device != '''mps''' else 3E-2

								assert np.abs(image_slice.flatten() - expected_slice     ).max() < tolerance
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < tolerance







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







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''     )
								ldm.to(__lowerCAmelCase     )
								ldm.set_progress_bar_config(disable=__lowerCAmelCase     )

								__magic_name__		:Optional[Any]        =      torch.manual_seed(0     )
								__magic_name__		:Optional[int]        =      ldm(generator=__lowerCAmelCase       ,					num_inference_steps=5       ,					output_type='''numpy'''     ).images

								__magic_name__		:Optional[Any]        =      image[0, -3:, -3:, -1]

								assert image.shape == (1, 2_5_6, 2_5_6, 3)
								__magic_name__		:List[str]        =      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]     )
								__magic_name__		:Tuple        =      1E-2 if torch_device != '''mps''' else 3E-2

								assert np.abs(image_slice.flatten() - expected_slice     ).max() < tolerance






