






from __future__ import annotations

from typing import Any


class 							__UpperCamelCase     :







	def __init__(	self   :				Optional[Any]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				float = 0      )		->			None:
		"""simple docstring"""







		__lowercase							,    __lowercase   							=	row, column
		__lowercase   							=	[[default_value for c in range(_lowerCAmelCase      )] for r in range(_lowerCAmelCase      )]







	def __str__(	self   :				List[Any]      )		->			str:
		"""simple docstring"""







		__lowercase   							=	F'Matrix consist of {self.row} rows and {self.column} columns\n'

		# Make string identifier
		__lowercase   							=	0
		for row_vector in self.array:
			for obj in row_vector:
				__lowercase   							=	max(_lowerCAmelCase							,					len(str(_lowerCAmelCase      )      )      )
		__lowercase   							=	F'%{max_element_length}s'

		# Make string and return
		def single_line(_lowerCAmelCase   :				list[float]      ) -> str:
			nonlocal string_format_identifier
			__lowercase   							=	"""["""
			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   :				int      )		->			str:
		"""simple docstring"""







		return str(self      )







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				tuple[int, int]      )		->			bool:
		"""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   :				List[str]							,					_lowerCAmelCase   :				tuple[int, int]      )		->			Any:
		"""simple docstring"""







		assert self.validate_indicies(_lowerCAmelCase      )
		return self.array[loc[0]][loc[1]]







	def __setitem__(	self   :				List[Any]							,					_lowerCAmelCase   :				tuple[int, int]							,					_lowerCAmelCase   :				float      )		->			None:
		"""simple docstring"""







		assert self.validate_indicies(_lowerCAmelCase      )
		__lowercase   							=	value







	def __add__(	self   :				Any							,					_lowerCAmelCase   :				Matrix      )		->			Matrix:
		"""simple docstring"""







		assert isinstance(_lowerCAmelCase							,					_lowerCAmelCase      )
		assert self.row == another.row and self.column == another.column

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







	def __neg__(	self   :				str      )		->			Matrix:
		"""simple docstring"""







		__lowercase   							=	Matrix(self.row							,					self.column      )
		for r in range(self.row      ):
			for c in range(self.column      ):
				__lowercase   							=	-self[r, c]
		return result







	def __sub__(	self   :				Tuple							,					_lowerCAmelCase   :				Matrix      )		->			Matrix:
		"""simple docstring"""







		return self + (-another)







	def __mul__(	self   :				Dict							,					_lowerCAmelCase   :				int | float | Matrix      )		->			Matrix:
		"""simple docstring"""







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







	def 			_a (	self   :				Any      )		->			Matrix:
		"""simple docstring"""







		__lowercase   							=	Matrix(self.column							,					self.row      )
		for r in range(self.row      ):
			for c in range(self.column      ):
				__lowercase   							=	self[r, c]
		return result







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				Matrix							,					_lowerCAmelCase   :				Matrix      )		->			Any:
		"""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
		__lowercase   							=	v.transpose()
		__lowercase   							=	(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 					snake_case	(  ):




							'''simple docstring'''







							__lowercase   							=	Matrix(3					,	3					,	0       )
							for i in range(3       ):
								__lowercase   							=	1
							print(F'a^(-1) is {ainv}'       )
							# u, v
							__lowercase   							=	Matrix(3					,	1					,	0       )
							__lowercase							,    __lowercase							,    __lowercase   							=	1, 2, -3
							__lowercase   							=	Matrix(3					,	1					,	0       )
							__lowercase							,    __lowercase							,    __lowercase   							=	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(lowerCamelCase					,	lowerCamelCase       )}'       )





						def 					snake_case	(  ):




							'''simple docstring'''







							import doctest

							doctest.testmod()




						testa()




import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





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



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_euler'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe([prompt] ,   generator=lowercase__ ,   guidance_scale=9.0 ,   num_inference_steps=2_0 ,   output_type='''np'''     )

             __A    =output.images

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]     )

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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_euler'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe([prompt] ,   generator=lowercase__ ,   guidance_scale=9.0 ,   num_inference_steps=2_0 ,   output_type='''np'''     )

             __A    =output.images

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]     )

             assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5E-1


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''     )
             __A    =sd_pipe.to(lowercase__     )
             sd_pipe.set_progress_bar_config(disable=lowercase__     )

             sd_pipe.set_scheduler('''sample_dpmpp_2m'''     )

             __A    ='''A painting of a squirrel eating a burger'''
             __A    =torch.manual_seed(0     )
             __A    =sd_pipe(
                 [prompt] ,   generator=lowercase__ ,   guidance_scale=7.5 ,   num_inference_steps=1_5 ,   output_type='''np''' ,   use_karras_sigmas=lowercase__ ,   )

             __A    =output.images

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

             assert image.shape == (1, 5_1_2, 5_1_2, 3)
             __A    =np.array(
                 [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]     )

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


