


from math import pow, sqrt


def 				lowerCamelCase__	(      *a	:   float				)    ->	bool:





			"""simple docstring"""
			a__						  =    len(a				) > 0 and all(value > 0.0 for value in values				)
			return result


def 				lowerCamelCase__	(      a	:   float	,  a	:   float				)    ->	float | ValueError:





			"""simple docstring"""
			return (
			    round(sqrt(molar_mass_a / molar_mass_a				)	,  6				)
			    if validate(a	,  a				)
			    else ValueError("Input Error: Molar mass values must greater than 0."				)
			)


def 				lowerCamelCase__	(      a	:   float	,  a	:   float	,  a	:   float				)    ->	float | ValueError:





			"""simple docstring"""
			return (
			    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a				)	,  6				)
			    if validate(a	,  a	,  a				)
			    else ValueError(
			        "Input Error: Molar mass and effusion rate values must greater than 0."				)
			)


def 				lowerCamelCase__	(      a	:   float	,  a	:   float	,  a	:   float				)    ->	float | ValueError:





			"""simple docstring"""
			return (
			    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a				)	,  6				)
			    if validate(a	,  a	,  a				)
			    else ValueError(
			        "Input Error: Molar mass and effusion rate values must greater than 0."				)
			)


def 				lowerCamelCase__	(      a	:   float	,  a	:   float	,  a	:   float				)    ->	float | ValueError:





			"""simple docstring"""
			return (
			    round(molar_mass / pow(effusion_rate_a / effusion_rate_a	,  2				)	,  6				)
			    if validate(a	,  a	,  a				)
			    else ValueError(
			        "Input Error: Molar mass and effusion rate values must greater than 0."				)
			)







def 				lowerCamelCase__	(      a	:   float	,  a	:   float	,  a	:   float				)    ->	float | ValueError:





			"""simple docstring"""
			return (
			    round(pow(effusion_rate_a / effusion_rate_a	,  2				) / molar_mass	,  6				)
			    if validate(a	,  a	,  a				)
			    else ValueError(
			        "Input Error: Molar mass and effusion rate values must greater than 0."				)
			)






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


snake_case__			 =						False


class 					lowerCAmelCase_ (       unittest.TestCase):
						pass






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




						def  _snake_case					(	self						:   Tuple						)      ->Optional[int]:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Dict						)      ->Any:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :List[Any]						  =    "A painting of a squirrel eating a burger "
									a__ :Optional[Any]						  =    torch.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=2   ,     output_type="numpy"						).images

									with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(__A						)
												a__ :List[Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(__A						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Optional[int]						  =    generator.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     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						:   Optional[Any]						)      ->List[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(
									    "shi-labs/versatile-diffusion"   ,     torch_dtype=torch.floataa						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Tuple						  =    "A painting of a squirrel eating a burger "
									a__ :Tuple						  =    torch.manual_seed(0						)
									a__ :Optional[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=50   ,     output_type="numpy"						).images

									a__ :Tuple						  =    image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									a__ :Tuple						  =    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]						)

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


