



from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray






def  lowerCamelCase_	(      lowerCAmelCase__	:	NDArray[floataa]			,	lowerCAmelCase__	:	NDArray[floataa]			,	lowerCAmelCase__	:	list[int]			,	lowerCAmelCase__	:	int			,	)       ->							list[float]:

							'''simple docstring'''






							A						,   A      						=    coefficient_matrix.shape
							A						,   A      						=    constant_matrix.shape

							if rowsa != colsa:
														A      						=    F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
														raise ValueError(lowerCAmelCase__				)

							if colsa != 1:
														A      						=    F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
														raise ValueError(lowerCAmelCase__				)

							if rowsa != rowsa:
														A      						=    (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
														)
														raise ValueError(lowerCAmelCase__				)

							if len(lowerCAmelCase__				) != rowsa:
														A      						=    (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'''matrix but received {len(lowerCAmelCase__				)} and {rowsa}'''
														)
														raise ValueError(lowerCAmelCase__				)

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'				)

							A      						=    np.concatenate(
							    (coefficient_matrix, constant_matrix)			,	axis=1				)

							A						,   A      						=    table.shape

							strictly_diagonally_dominant(lowerCAmelCase__				)

							# Iterates the whole matrix for given number of times
							for _ in range(lowerCAmelCase__				):
														A      						=    []
														for row in range(lowerCAmelCase__				):
																					A      						=    0
																					for col in range(lowerCAmelCase__				):
																												if col == row:
																																			A      						=    table[row][col]
																												elif col == cols - 1:
																																			A      						=    table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					A      						=    (temp + val) / denom
																					new_val.append(lowerCAmelCase__				)
														A      						=    new_val

							return [float(lowerCAmelCase__				) for i in new_val]






def  lowerCamelCase_	(      lowerCAmelCase__	:	NDArray[floataa]				)       ->							bool:

							'''simple docstring'''






							A						,   A      						=    table.shape

							A      						=    True

							for i in range(0			,	lowerCAmelCase__				):
														A      						=    0
														for j in range(0			,	cols - 1				):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'				)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
				import doctest

				doctest.testmod()




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


__snake_case     :str			       =False



class        lowerCAmelCase__					(  unittest.TestCase       ):
				pass





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


				def __UpperCamelCase						(							self      :  Dict       )		->     List[Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											A      						=    VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

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

											A      						=    torch.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt='first prompt'						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=2						, output_type='numpy'						, ).images

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

											A      						=    generator.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt='first prompt'						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, 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 __UpperCamelCase						(							self      :  Tuple       )		->     List[str]:
											A      						=    VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    'cyberpunk 2077'
											A      						=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
											A      						=    torch.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt=__UpperCamelCase						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=50						, output_type='numpy'						, ).images

											A      						=    image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]       )

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

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

											A      						=    image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]       )

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

											A      						=    pipe.image_variation(__UpperCamelCase						, generator=__UpperCamelCase						, output_type='numpy'       ).images

											A      						=    image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]       )

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