
'''simple docstring'''





from __future__ import annotations







class        lowerCAmelCase__      :

						def __init__(			self       ,							a				)					->      Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	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:
										_UpperCamelCase						    =	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
										_UpperCamelCase						    =	rows
								else:
										_UpperCamelCase						    =	[]

						def  A_				(			self				)					->      list[list[int]]:


								'''simple docstring'''

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

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return len(self.rows				)

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return len(self.rows[0]				)

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


								'''simple docstring'''

								return (self.num_rows, self.num_columns)

						@property
						def  A_				(			self				)					->      bool:


								'''simple docstring'''

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

						def  A_				(			self				)					->      Matrix:


								'''simple docstring'''

								_UpperCamelCase						    =	[
								    [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  A_				(			self				)					->      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  A_				(			self				)					->      bool:


								'''simple docstring'''

								return bool(self.determinant()				)

						def  A_				(			self       ,							a       ,							a				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	[
								    [
								        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  A_				(			self       ,							a       ,							a				)					->      int:


								'''simple docstring'''

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

						def  A_				(			self				)					->      Matrix:


								'''simple docstring'''

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

						def  A_				(			self				)					->      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  A_				(			self				)					->      Matrix:


								'''simple docstring'''

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

						def  A_				(			self				)					->      Matrix:


								'''simple docstring'''

								_UpperCamelCase						    =	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				)					->      str:


								'''simple docstring'''

								return str(self.rows				)

						def __str__(			self				)					->      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(a				) for value in row]				) + """.]"""
								            for row in self.rows
								        ]				)
								    + "]"
								)

						def  A_				(			self       ,							a       ,							a = None				)					->      None:


								'''simple docstring'''

								_UpperCamelCase						    =	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:
										_UpperCamelCase						    =	self.rows[0:position] + [row] + self.rows[position:]

						def  A_				(			self       ,							a       ,							a = None				)					->      None:


								'''simple docstring'''

								_UpperCamelCase						    =	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:
										_UpperCamelCase						    =	[self.rows[i] + [column[i]] for i in range(self.num_rows				)]
								else:
										_UpperCamelCase						    =	[
										    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
										    for i in range(self.num_rows				)
										]

						def __eq__(			self       ,							a				)					->      bool:


								'''simple docstring'''

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

						def __ne__(			self       ,							a				)					->      bool:


								'''simple docstring'''

								return not self == other

						def __neg__(			self				)					->      Matrix:


								'''simple docstring'''

								return self * -1

						def __add__(			self       ,							a				)					->      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       ,							a				)					->      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       ,							a				)					->      Matrix:


								'''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				)					->      Matrix:


								'''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"""				)
								_UpperCamelCase						    =	self
								for _ in range(other - 1				):
										result *= self
								return result







						@classmethod
						def  A_				(			cls       ,							a       ,							a				)					->      int:


								'''simple docstring'''

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class        lowerCAmelCase__      (   __lowercase		,	__lowercase		,	__lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: List[Any]	=		StableDiffusionInpaintPipeline
						UpperCamelCase_							: int	=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						UpperCamelCase_							: Union[str, Any]	=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						UpperCamelCase_							: Tuple	=		frozenset(
						    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						UpperCamelCase_							: int	=		frozenset([]	)

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


								'''simple docstring'''

								torch.manual_seed(0				)
								_UpperCamelCase						    =	UNetaDConditionModel(
								    block_out_channels=(32, 64)       ,							layers_per_block=2       ,							sample_size=32       ,							in_channels=9       ,							out_channels=4       ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")       ,							cross_attention_dim=32       ,							attention_head_dim=(2, 4)       ,							use_linear_projection=a       ,							)
								_UpperCamelCase						    =	PNDMScheduler(skip_prk_steps=a				)
								torch.manual_seed(0				)
								_UpperCamelCase						    =	AutoencoderKL(
								    block_out_channels=[32, 64]       ,							in_channels=3       ,							out_channels=3       ,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,							latent_channels=4       ,							sample_size=1_28       ,							)
								torch.manual_seed(0				)
								_UpperCamelCase						    =	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=10_00       ,							hidden_act="""gelu"""       ,							projection_dim=5_12       ,							)
								_UpperCamelCase						    =	CLIPTextModel(a				)
								_UpperCamelCase						    =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

								_UpperCamelCase						    =	{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}
								return components

						def  A_				(			self       ,							a       ,							a=0				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	floats_tensor((1, 3, 32, 32)       ,							rng=random.Random(a				)				).to(a				)
								_UpperCamelCase						    =	image.cpu().permute(0       ,							2       ,							3       ,							1				)[0]
								_UpperCamelCase						    =	Image.fromarray(np.uinta(a				)				).convert("""RGB"""				).resize((64, 64)				)
								_UpperCamelCase						    =	Image.fromarray(np.uinta(image + 4				)				).convert("""RGB"""				).resize((64, 64)				)
								if str(a				).startswith("""mps"""				):
										_UpperCamelCase						    =	torch.manual_seed(a				)
								else:
										_UpperCamelCase						    =	torch.Generator(device=a				).manual_seed(a				)
								_UpperCamelCase						    =	{
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """image""": init_image,
								    """mask_image""": mask_image,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								}
								return inputs

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								_UpperCamelCase						    =	self.get_dummy_components()
								_UpperCamelCase						    =	StableDiffusionInpaintPipeline(**a				)
								_UpperCamelCase						    =	sd_pipe.to(a				)
								sd_pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)
								_UpperCamelCase						    =	sd_pipe(**a				).images
								_UpperCamelCase						    =	image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								_UpperCamelCase						    =	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]				)

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







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								super().test_inference_batch_single_identical(expected_max_diff=3e-3				)







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

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


								'''simple docstring'''

								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png"""				)
								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)
								_UpperCamelCase						    =	load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
								    """/yellow_cat_sitting_on_a_park_bench.npy"""				)

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	StableDiffusionInpaintPipeline.from_pretrained(a       ,							safety_checker=a				)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)
								pipe.enable_attention_slicing()

								_UpperCamelCase						    =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(
								    prompt=a       ,							image=a       ,							mask_image=a       ,							generator=a       ,							output_type="""np"""       ,							)
								_UpperCamelCase						    =	output.images[0]

								assert image.shape == (5_12, 5_12, 3)
								assert np.abs(expected_image - image				).max() < 9e-3

						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png"""				)
								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)
								_UpperCamelCase						    =	load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
								    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""				)

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	StableDiffusionInpaintPipeline.from_pretrained(
								    a       ,							torch_dtype=torch.floataa       ,							safety_checker=a       ,							)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)
								pipe.enable_attention_slicing()

								_UpperCamelCase						    =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(
								    prompt=a       ,							image=a       ,							mask_image=a       ,							generator=a       ,							output_type="""np"""       ,							)
								_UpperCamelCase						    =	output.images[0]

								assert image.shape == (5_12, 5_12, 3)
								assert np.abs(expected_image - image				).max() < 5e-1







						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png"""				)
								_UpperCamelCase						    =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""				)

								_UpperCamelCase						    =	"""stabilityai/stable-diffusion-2-inpainting"""
								_UpperCamelCase						    =	PNDMScheduler.from_pretrained(a       ,							subfolder="""scheduler"""				)
								_UpperCamelCase						    =	StableDiffusionInpaintPipeline.from_pretrained(
								    a       ,							safety_checker=a       ,							scheduler=a       ,							torch_dtype=torch.floataa       ,							)
								pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)
								pipe.enable_attention_slicing(1				)
								pipe.enable_sequential_cpu_offload()

								_UpperCamelCase						    =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(
								    prompt=a       ,							image=a       ,							mask_image=a       ,							generator=a       ,							num_inference_steps=2       ,							output_type="""np"""       ,							)

								_UpperCamelCase						    =	torch.cuda.max_memory_allocated()
								# make sure that less than 2.65 GB is allocated
								assert mem_bytes < 2.65 * 10**9



