



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


__lowerCamelCase  :				int					=					False





class 			__magic_name__ (				unittest.TestCase    ):
		pass





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






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[Any]   )					->       int:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_      (    self   :  List[Any]   )					->       Tuple:
								'''simple docstring'''







								UpperCAmelCase								=  VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"   )
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								UpperCAmelCase								=  'A painting of a squirrel eating a burger '
								UpperCAmelCase								=  torch.manual_seed(0   )
								UpperCAmelCase								=  pipe(
								    prompt=UpperCamelCase__					,		generator=UpperCamelCase__					,		guidance_scale=7.5					,		num_inference_steps=2					,		output_type="numpy"   ).images

								with tempfile.TemporaryDirectory() as tmpdirname:
														pipe.save_pretrained(UpperCamelCase__   )
														UpperCAmelCase								=  VersatileDiffusionTextToImagePipeline.from_pretrained(UpperCamelCase__   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								UpperCAmelCase								=  generator.manual_seed(0   )
								UpperCAmelCase								=  pipe(
								    prompt=UpperCamelCase__					,		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 SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]   )					->       Any:
								'''simple docstring'''







								UpperCAmelCase								=  VersatileDiffusionTextToImagePipeline.from_pretrained(
								    "shi-labs/versatile-diffusion"					,		torch_dtype=torch.floataa   )
								pipe.to(UpperCamelCase__   )
								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

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

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

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCAmelCase								=  np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]   )

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







from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					1.0_5457_1817E-34  # unit of ℏ : J * s

SCREAMING_SNAKE_CASE			: int				   =					3E8  # unit of c : m * s^-1



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		dict[str, float]:

    if (force, area, distance).count(0					) != 1:
        raise ValueError('One and only one argument must be 0'					)
    if force < 0:
        raise ValueError('Magnitude of force can not be negative'					)
    if distance < 0:
        raise ValueError('Distance can not be negative'					)
    if area < 0:
        raise ValueError('Area can not be negative'					)
    if force == 0:
        _lowercase			:				int 						=						(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
            240 * (distance) ** 4
        )
        return {"force": force}
    elif area == 0:
        _lowercase			:				List[Any] 						=						(240 * force * (distance) ** 4) / (
            REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
        )
        return {"area": area}
    elif distance == 0:
        _lowercase			:				List[Any] 						=						(
            (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
        ) ** (1 / 4)
        return {"distance": distance}
    raise ValueError('One and only one argument must be 0'					)


# Run doctest
if __name__ == "__main__":
 import doctest

 doctest.testmod()


