




'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a  :				List[Any]									=  False






class 						a				(   unittest.TestCase     ):
				pass







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


				def   A_ (		self    :					Tuple					):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


				def   A_ (		self    :					int					):
									snake_case_									=				VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''					)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				'''A painting of a squirrel eating a burger '''
									snake_case_									=				torch.manual_seed(0					)
									snake_case_									=				pipe(
									    prompt=lowercase_       ,				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_									=				VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase_					)
									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				generator.manual_seed(0					)
									snake_case_									=				pipe(
									    prompt=lowercase_       ,				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   A_ (		self    :					Tuple					):
									snake_case_									=				VersatileDiffusionTextToImagePipeline.from_pretrained(
									    '''shi-labs/versatile-diffusion'''       ,				torch_dtype=torch.floataa					)
									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

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

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

									assert image.shape == (1, 512, 512, 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-2







'''simple docstring'''

import math
from collections.abc import Callable


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					float:

					'''simple docstring'''

					snake_case_									=				xa
					snake_case_									=				xa
					while True:
										if x_n == x_na or function(__UpperCAmelCase	) == function(__UpperCAmelCase	):
															raise ZeroDivisionError('''float division by zero, could not find root'''	)
										snake_case_									=				x_na - (
										    function(__UpperCAmelCase	) / ((function(__UpperCAmelCase	) - function(__UpperCAmelCase	)) / (x_na - x_n))
										)
										if abs(x_na - x_na	) < 10**-5:
															return x_na
										snake_case_									=				x_na
										snake_case_									=				x_na


def 		__magic_name__    (	__UpperCAmelCase	)		->					float:

					'''simple docstring'''

					return math.pow(__UpperCAmelCase,      3	) - (2 * x) - 5


if __name__ == "__main__":
							print(intersection(f, 3, 3.5))

