




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


lowercase__							:	Optional[Any]											=		False
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						pass







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







						'''simple docstring'''





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






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"			,      torch_dtype=torch.floataa		)
							pipe.to(__lowercase		)
							pipe.set_progress_bar_config(disable=__lowercase		)

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

							snake_case_						       =	torch.manual_seed(0		)
							snake_case_						       =	pipe.dual_guided(
							    prompt="first prompt"			,      image=__lowercase			,      text_to_image_strength=0.75			,      generator=__lowercase			,      guidance_scale=7.5			,      num_inference_steps=2			,      output_type="numpy"			,      ).images

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

							snake_case_						       =	generator.manual_seed(0		)
							snake_case_						       =	pipe.dual_guided(
							    prompt="first prompt"			,      image=__lowercase			,      text_to_image_strength=0.75			,      generator=__lowercase			,      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 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"			,      torch_dtype=torch.floataa		)
							pipe.to(__lowercase		)
							pipe.set_progress_bar_config(disable=__lowercase		)

							snake_case_						       =	"cyberpunk 2077"
							snake_case_						       =	load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
							snake_case_						       =	torch.manual_seed(0		)
							snake_case_						       =	pipe.dual_guided(
							    prompt=__lowercase			,      image=__lowercase			,      text_to_image_strength=0.75			,      generator=__lowercase			,      guidance_scale=7.5			,      num_inference_steps=50			,      output_type="numpy"			,      ).images

							snake_case_						       =	image[0, 2_53:2_56, 2_53:2_56, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							snake_case_						       =	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

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

							snake_case_						       =	image[0, 2_53:2_56, 2_53:2_56, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							snake_case_						       =	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

							snake_case_						       =	pipe.image_variation(__lowercase			,      generator=__lowercase			,      output_type="numpy"		).images

							snake_case_						       =	image[0, 2_53:2_56, 2_53:2_56, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							snake_case_						       =	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






from __future__ import annotations

from collections.abc import Callable

lowercase__							:	Optional[Any]											=		list[list[float | int]]


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(size + 1				)] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for row in range(_A				):
		for col in range(_A				):
			snake_case_						       =	matrix[row][col]

		snake_case_						       =	vector[row][0]

	snake_case_						       =	0
	snake_case_						       =	0
	while row < size and col < size:
		# pivoting
		snake_case_						       =	max((abs(augmented[rowa][col]				), rowa) for rowa in range(_A	,      _A				)				)[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			snake_case_       , snake_case_						       =	augmented[pivot_row], augmented[row]

		for rowa in range(row + 1	,      _A				):
			snake_case_						       =	augmented[rowa][col] / augmented[row][col]
			snake_case_						       =	0
			for cola in range(col + 1	,      size + 1				):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1	,      _A				):
		for row in range(_A				):
			snake_case_						       =	augmented[row][col] / augmented[col][col]
			for cola in range(_A	,      size + 1				):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]	,      10				)] for row in range(_A				)
	]


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(_A				)] for _ in range(_A				)]
	snake_case_						       =	[[0] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for x_val, y_val in enumerate(_A				):
		for col in range(_A				):
			snake_case_						       =	(x_val + 1) ** (size - col - 1)
		snake_case_						       =	y_val

	snake_case_						       =	solve(_A	,      _A				)

	def interpolated_func(_A				) -> int:
		return sum(
		    round(coeffs[x_val][0]				) * (var ** (size - x_val - 1))
		    for x_val in range(_A				)				)

	return interpolated_func


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)


def 		lowerCamelCase__       (						_A = question_function	,      _A = 10				):







	'''simple docstring'''





	snake_case_						       =	[func(_A				) for x_val in range(1	,      order + 1				)]

	snake_case_						       =	[
	    interpolate(data_points[:max_coeff]				) for max_coeff in range(1	,      order + 1				)
	]

	snake_case_						       =	0
	snake_case_						       =	42
	snake_case_						       =	42

	for poly in polynomials:
		snake_case_						       =	1
		while func(_A				) == poly(_A				):
			x_val += 1

		ret += poly(_A				)

	return ret


if __name__ == "__main__":
		print(f'''{solution() = }''')
