

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class 			UpperCAmelCase__						(			unittest.TestCase			):






  """simple docstring"""


  a 	=   StableDiffusionLDMaDPipeline
  a 	=   TEXT_TO_IMAGE_PARAMS
  a 	=   TEXT_TO_IMAGE_BATCH_PARAMS
  a 	=   TEXT_TO_IMAGE_IMAGE_PARAMS






  def  lowercase_						(				self		:						Optional[int]   )						->				str:
       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       UNetaDConditionModel(
           block_out_channels=(32, 64)      ,  layers_per_block=2      ,  sample_size=32      ,  in_channels=4      ,  out_channels=4      ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,  cross_attention_dim=32      ,  )
       SCREAMING_SNAKE_CASE__       =       DDIMScheduler(
           beta_start=0.00085      ,  beta_end=0.012      ,  beta_schedule='''scaled_linear'''      ,  clip_sample=__lowerCamelCase      ,  set_alpha_to_one=__lowerCamelCase      ,  )
       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       AutoencoderKL(
           block_out_channels=[32, 64]      ,  in_channels=6      ,  out_channels=6      ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,  latent_channels=4      ,  )
       torch.manual_seed(0   )
       SCREAMING_SNAKE_CASE__       =       CLIPTextConfig(
           bos_token_id=0      ,  eos_token_id=2      ,  hidden_size=32      ,  intermediate_size=37      ,  layer_norm_eps=1e-05      ,  num_attention_heads=4      ,  num_hidden_layers=5      ,  pad_token_id=1      ,  vocab_size=1000      ,  )
       SCREAMING_SNAKE_CASE__       =       CLIPTextModel(__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       SCREAMING_SNAKE_CASE__       =       {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components






  def  lowercase_						(				self		:						Union[str, Any]      ,  __lowerCamelCase		:						List[Any]      ,  __lowerCamelCase		:						int=0   )						->				Optional[int]:






       if str(__lowerCamelCase   ).startswith('''mps'''   ):
            SCREAMING_SNAKE_CASE__       =       torch.manual_seed(__lowerCamelCase   )
       else:
            SCREAMING_SNAKE_CASE__       =       torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs






  def  lowercase_						(				self		:						Dict   )						->				Optional[Any]:
       SCREAMING_SNAKE_CASE__       =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator

       SCREAMING_SNAKE_CASE__       =       self.get_dummy_components()
       SCREAMING_SNAKE_CASE__       =       StableDiffusionLDMaDPipeline(**__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       ldmad_pipe.to(__lowerCamelCase   )
       ldmad_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

       SCREAMING_SNAKE_CASE__       =       self.get_dummy_inputs(__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       ldmad_pipe(**__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__       =       output.rgb, output.depth

       SCREAMING_SNAKE_CASE__       =       rgb[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__       =       depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       SCREAMING_SNAKE_CASE__       =       np.array(
           [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]   )
       SCREAMING_SNAKE_CASE__       =       np.array([103.4_6727, 85.812004, 87.849236]   )

       assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb   ).max() < 1e-2
       assert np.abs(image_slice_depth.flatten() - expected_slice_depth   ).max() < 1e-2






  def  lowercase_						(				self		:						Union[str, Any]   )						->				Dict:
       SCREAMING_SNAKE_CASE__       =       self.get_dummy_components()
       SCREAMING_SNAKE_CASE__       =       StableDiffusionLDMaDPipeline(**__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       ldmad_pipe.to(__lowerCamelCase   )
       ldmad_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

       SCREAMING_SNAKE_CASE__       =       self.get_dummy_inputs(__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       3 * [inputs['''prompt''']]

       # forward
       SCREAMING_SNAKE_CASE__       =       ldmad_pipe(**__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__       =       output.rgb, output.depth
       SCREAMING_SNAKE_CASE__       =       rgb_slice_a[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__       =       depth_slice_a[0, -3:, -1]

       SCREAMING_SNAKE_CASE__       =       self.get_dummy_inputs(__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       3 * [inputs.pop('''prompt'''   )]

       SCREAMING_SNAKE_CASE__       =       ldmad_pipe.tokenizer(
           __lowerCamelCase      ,  padding='''max_length'''      ,  max_length=ldmad_pipe.tokenizer.model_max_length      ,  truncation=__lowerCamelCase      ,  return_tensors='''pt'''      ,  )
       SCREAMING_SNAKE_CASE__       =       text_inputs['''input_ids'''].to(__lowerCamelCase   )

       SCREAMING_SNAKE_CASE__       =       ldmad_pipe.text_encoder(__lowerCamelCase   )[0]

       SCREAMING_SNAKE_CASE__       =       prompt_embeds

       # forward
       SCREAMING_SNAKE_CASE__       =       ldmad_pipe(**__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__       =       output.rgb, output.depth
       SCREAMING_SNAKE_CASE__       =       rgb_slice_a[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__       =       depth_slice_a[0, -3:, -1]

       assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()   ).max() < 1e-4
       assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()   ).max() < 1e-4







  def  lowercase_						(				self		:						int   )						->				Any:
       SCREAMING_SNAKE_CASE__       =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__       =       self.get_dummy_components()
       SCREAMING_SNAKE_CASE__       =       PNDMScheduler(skip_prk_steps=__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       StableDiffusionLDMaDPipeline(**__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       ldmad_pipe.to(__lowerCamelCase   )
       ldmad_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

       SCREAMING_SNAKE_CASE__       =       self.get_dummy_inputs(__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       '''french fries'''
       SCREAMING_SNAKE_CASE__       =       ldmad_pipe(**__lowerCamelCase      ,  negative_prompt=__lowerCamelCase   )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__       =       output.rgb, output.depth
       SCREAMING_SNAKE_CASE__       =       rgb[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__       =       depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       SCREAMING_SNAKE_CASE__       =       np.array(
           [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]   )
       SCREAMING_SNAKE_CASE__       =       np.array([107.8_4738, 84.62802, 89.962135]   )
       assert np.abs(rgb_slice.flatten() - expected_slice_rgb   ).max() < 1e-2
       assert np.abs(depth_slice.flatten() - expected_slice_depth   ).max() < 1e-2






@slow
@require_torch_gpu
class 			UpperCAmelCase__						(			unittest.TestCase			):






  """simple docstring"""


  def  lowercase_						(				self		:						Tuple   )						->				Optional[int]:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  def  lowercase_						(				self		:						Any      ,  __lowerCamelCase		:						Any      ,  __lowerCamelCase		:						int="cpu"      ,  __lowerCamelCase		:						Union[str, Any]=torch.floataa      ,  __lowerCamelCase		:						Optional[int]=0   )						->				List[Any]:
       SCREAMING_SNAKE_CASE__       =       torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       np.random.RandomState(__lowerCamelCase   ).standard_normal((1, 4, 64, 64)   )
       SCREAMING_SNAKE_CASE__       =       torch.from_numpy(__lowerCamelCase   ).to(device=__lowerCamelCase      ,  dtype=__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       {
           '''prompt''': '''a photograph of an astronaut riding a horse''',
           '''latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs







  def  lowercase_						(				self		:						Any   )						->				Union[str, Any]:
       SCREAMING_SNAKE_CASE__       =       StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''   )
       SCREAMING_SNAKE_CASE__       =       ldmad_pipe.to(__lowerCamelCase   )
       ldmad_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

       SCREAMING_SNAKE_CASE__       =       self.get_inputs(__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       ldmad_pipe(**__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__       =       output.rgb, output.depth
       SCREAMING_SNAKE_CASE__       =       rgb[0, -3:, -3:, -1].flatten()
       SCREAMING_SNAKE_CASE__       =       rgb[0, -3:, -1].flatten()

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512)

       SCREAMING_SNAKE_CASE__       =       np.array(
           [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]   )
       SCREAMING_SNAKE_CASE__       =       np.array(
           [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]   )
       assert np.abs(rgb_slice - expected_slice_rgb   ).max() < 3e-3
       assert np.abs(depth_slice - expected_slice_depth   ).max() < 3e-3






@nightly
@require_torch_gpu
class 			UpperCAmelCase__						(			unittest.TestCase			):






  """simple docstring"""


  def  lowercase_						(				self		:						List[Any]   )						->				Dict:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  def  lowercase_						(				self		:						Any      ,  __lowerCamelCase		:						Any      ,  __lowerCamelCase		:						Optional[Any]="cpu"      ,  __lowerCamelCase		:						str=torch.floataa      ,  __lowerCamelCase		:						Optional[Any]=0   )						->				Union[str, Any]:
       SCREAMING_SNAKE_CASE__       =       torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       np.random.RandomState(__lowerCamelCase   ).standard_normal((1, 4, 64, 64)   )
       SCREAMING_SNAKE_CASE__       =       torch.from_numpy(__lowerCamelCase   ).to(device=__lowerCamelCase      ,  dtype=__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       {
           '''prompt''': '''a photograph of an astronaut riding a horse''',
           '''latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 50,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs






  def  lowercase_						(				self		:						Dict   )						->				Optional[int]:
       SCREAMING_SNAKE_CASE__       =       StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''   ).to(__lowerCamelCase   )
       ldmad_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

       SCREAMING_SNAKE_CASE__       =       self.get_inputs(__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       ldmad_pipe(**__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__       =       output.rgb, output.depth

       SCREAMING_SNAKE_CASE__       =       0.495586
       SCREAMING_SNAKE_CASE__       =       0.33795515
       SCREAMING_SNAKE_CASE__       =       112.4_8518
       SCREAMING_SNAKE_CASE__       =       98.489746
       assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1e-3
       assert np.abs(expected_rgb_std - rgb.std()   ) < 1e-3
       assert np.abs(expected_depth_mean - depth.mean()   ) < 1e-3
       assert np.abs(expected_depth_std - depth.std()   ) < 1e-3







  def  lowercase_						(				self		:						Tuple   )						->				str:
       SCREAMING_SNAKE_CASE__       =       StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''   ).to(__lowerCamelCase   )
       ldmad_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

       SCREAMING_SNAKE_CASE__       =       self.get_inputs(__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       ldmad_pipe(**__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__       =       output.rgb, output.depth

       SCREAMING_SNAKE_CASE__       =       0.4194127
       SCREAMING_SNAKE_CASE__       =       0.35375586
       SCREAMING_SNAKE_CASE__       =       0.5638502
       SCREAMING_SNAKE_CASE__       =       0.34686103

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512, 1)
       assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1e-3
       assert np.abs(expected_rgb_std - rgb.std()   ) < 1e-3
       assert np.abs(expected_depth_mean - depth.mean()   ) < 1e-3
       assert np.abs(expected_depth_std - depth.std()   ) < 1e-3




from __future__ import annotations





class     lowerCamelCase      :







	def __init__(     self      :List[Any]   ,     lowercase      :list[list[int]]			)		->      Dict:







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







	def  snake_case__     (     self      :List[str]			)		->      list[list[int]]:







				"""simple docstring"""






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







	@property
	def  snake_case__     (     self      :Any			)		->      int:







				"""simple docstring"""






				return len(self.rows			)







	@property
	def  snake_case__     (     self      :List[Any]			)		->      int:







				"""simple docstring"""






				return len(self.rows[0]			)







	@property
	def  snake_case__     (     self      :Dict			)		->      tuple[int, int]:







				"""simple docstring"""






				return (self.num_rows, self.num_columns)







	@property
	def  snake_case__     (     self      :Any			)		->      bool:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Tuple			)		->      Matrix:







				"""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(lowercase			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      int:







				"""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  snake_case__     (     self      :int			)		->      bool:







				"""simple docstring"""






				return bool(self.determinant()			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      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(lowercase			).determinant()







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :int   ,     lowercase      :int			)		->      int:







				"""simple docstring"""






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







	def  snake_case__     (     self      :List[Any]			)		->      Matrix:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Dict			)		->      Matrix:







				"""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  snake_case__     (     self      :str			)		->      Matrix:







				"""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(lowercase			)







	def  snake_case__     (     self      :Any			)		->      Matrix:







				"""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      :Union[str, Any]			)		->      str:







				"""simple docstring"""






				return str(self.rows			)







	def __str__(     self      :List[str]			)		->      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(lowercase			) for value in row]			) + '''.]'''
				            for row in self.rows
				        ]			)
				    + "]"
				)







	def  snake_case__     (     self      :int   ,     lowercase      :list[int]   ,     lowercase      :int | None = None			)		->      None:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :list[int]   ,     lowercase      :int | None = None			)		->      None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= TypeError(
				    '''Column must be a list containing all ints and/or floats'''			)
				if not isinstance(lowercase   ,     lowercase			):
							raise type_error
				for value in column:
							if not isinstance(lowercase   ,     (int, float)			):
										raise type_error
				if len(lowercase			) != 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      :Union[str, Any]   ,     lowercase      :object			)		->      bool:







				"""simple docstring"""






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







	def __ne__(     self      :int   ,     lowercase      :object			)		->      bool:







				"""simple docstring"""






				return not self == other







	def __neg__(     self      :int			)		->      Matrix:







				"""simple docstring"""






				return self * -1







	def __add__(     self      :Optional[Any]   ,     lowercase      :Matrix			)		->      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      :Optional[Any]   ,     lowercase      :Matrix			)		->      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      :Optional[int]   ,     lowercase      :Matrix | int | float			)		->      Matrix:







				"""simple docstring"""






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







				"""simple docstring"""






				if not isinstance(lowercase   ,     lowercase			):
							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  snake_case__     (     cls      :List[Any]   ,     lowercase      :list[int]   ,     lowercase      :list[int]			)		->      int:







				"""simple docstring"""






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


if __name__ == "__main__":
							import doctest

							doctest.testmod()