



from __future__ import annotations

from typing import Any


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				Optional[int]       ,	snake_case    :				int       ,	snake_case    :				int       ,	snake_case    :				float = 0	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[int]						=				row, column
											SCREAMING_SNAKE_CASE							:     List[Any]						=				[[default_value for c in range(snake_case	)] for r in range(snake_case	)]







						def __str__(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

											# Make string identifier
											SCREAMING_SNAKE_CASE							:     int						=				0
											for row_vector in self.array:
																for obj in row_vector:
																					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				max(snake_case       ,	len(str(snake_case	)	)	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				f'''%{max_element_length}s'''

											# Make string and return
											def single_line(snake_case    :				list[float]	) -> str:
																nonlocal string_format_identifier
																SCREAMING_SNAKE_CASE							:     Tuple						=				'['
																line += ", ".join(string_format_identifier % (obj,) for obj in row_vector	)
																line += "]"
																return line

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







						def __repr__(  self    :				List[str]	):



											'''simple docstring'''


											return str(self	)







						def       lowerCamelCase_				(  self    :				List[str]       ,	snake_case    :				tuple[int, int]	):



											'''simple docstring'''


											if not (isinstance(snake_case       ,	(list, tuple)	) and len(snake_case	) == 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[Any]       ,	snake_case    :				tuple[int, int]	):



											'''simple docstring'''


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







						def __setitem__(  self    :				List[str]       ,	snake_case    :				tuple[int, int]       ,	snake_case    :				float	):



											'''simple docstring'''


											assert self.validate_indicies(snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				value







						def __add__(  self    :				List[str]       ,	snake_case    :				Matrix	):



											'''simple docstring'''


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

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







						def __neg__(  self    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				Matrix(self.row       ,	self.column	)
											for r in range(self.row	):
																for c in range(self.column	):
																					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				-self[r, c]
											return result







						def __sub__(  self    :				Optional[int]       ,	snake_case    :				Matrix	):



											'''simple docstring'''


											return self + (-another)







						def __mul__(  self    :				int       ,	snake_case    :				int | float | Matrix	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				Matrix(self.column       ,	self.row	)
											for r in range(self.row	):
																for c in range(self.column	):
																					SCREAMING_SNAKE_CASE							:     List[str]						=				self[r, c]
											return result







						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				Matrix       ,	snake_case    :				Matrix	):



											'''simple docstring'''


											assert isinstance(snake_case       ,	snake_case	) and isinstance(snake_case       ,	snake_case	)
											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
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				v.transpose()
											SCREAMING_SNAKE_CASE							:     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        __a			(   )     ->     None:
								# a^(-1)
								SCREAMING_SNAKE_CASE							:     List[Any]						=				Matrix(3      ,	3      ,	0   )
								for i in range(3   ):
													SCREAMING_SNAKE_CASE							:     Any						=				1
								print(F'''a^(-1) is {ainv}'''   )
								# u, v
								SCREAMING_SNAKE_CASE							:     Dict						=				Matrix(3      ,	1      ,	0   )
								SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				1, 2, -3
								SCREAMING_SNAKE_CASE							:     Optional[Any]						=				Matrix(3      ,	1      ,	0   )
								SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[int]						=				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        __a			(   )     ->     None:
								import doctest

								doctest.testmod()



			testa()




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase    :		Any		      =    False


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						pass


@nightly
@require_torch_gpu
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Dict						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(snake_case	)
																SCREAMING_SNAKE_CASE							:     Any						=				VersatileDiffusionPipeline.from_pretrained(snake_case       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				generator.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'cyberpunk 2077'
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Any						=				pipe.dual_guided(
											    prompt=snake_case       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'       ,	).images

											SCREAMING_SNAKE_CASE							:     List[Any]						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Tuple						=				np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]	)

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

											SCREAMING_SNAKE_CASE							:     Any						=				'A painting of a squirrel eating a burger '
											SCREAMING_SNAKE_CASE							:     Any						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				pipe.text_to_image(
											    prompt=snake_case       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     Dict						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

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

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				pipe.image_variation(snake_case       ,	generator=snake_case       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]	)

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