


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


SCREAMING_SNAKE_CASE			: int				   =					False






class _lowerCamelCase(		unittest.TestCase  ):
       pass






@nightly
@require_torch_gpu
class _lowerCamelCase(		unittest.TestCase  ):
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

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

           _lowercase			:				Union[str, Any] 						=						'A painting of a squirrel eating a burger '
           _lowercase			:				List[Any] 						=						torch.manual_seed(0)
           _lowercase			:				str 						=						pipe(
               prompt=lowerCamelCase, generator=lowerCamelCase, guidance_scale=7.5, num_inference_steps=2, output_type='numpy').images

           with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(lowerCamelCase)
               _lowercase			:				Tuple 						=						VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase)
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase			:				Optional[int] 						=						generator.manual_seed(0)
           _lowercase			:				Dict 						=						pipe(
               prompt=lowerCamelCase, generator=lowerCamelCase, 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)			->  Any:




           """simple docstring"""

           _lowercase			:				Any 						=						VersatileDiffusionTextToImagePipeline.from_pretrained(
               'shi-labs/versatile-diffusion', torch_dtype=torch.floataa)
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase			:				Union[str, Any] 						=						'A painting of a squirrel eating a burger '
           _lowercase			:				List[Any] 						=						torch.manual_seed(0)
           _lowercase			:				Optional[int] 						=						pipe(
               prompt=lowerCamelCase, generator=lowerCamelCase, guidance_scale=7.5, num_inference_steps=50, output_type='numpy').images

           _lowercase			:				List[Any] 						=						image[0, 2_53:2_56, 2_53:2_56, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase			:				List[str] 						=						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-2






def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:

    if not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        raise ValueError('multiplicative_persistence() only accepts integral values'					)
    if num < 0:
        raise ValueError('multiplicative_persistence() does not accept negative values'					)

    _lowercase			:				List[str] 						=						0
    _lowercase			:				Optional[int] 						=						str(lowerCamelCase_					)

    while len(lowerCamelCase_					) != 1:
        _lowercase			:				Any 						=						[int(lowerCamelCase_					) for i in num_string]

        _lowercase			:				List[Any] 						=						1
        for i in range(0				,					len(lowerCamelCase_					)					):
            total *= numbers[i]

        _lowercase			:				Optional[Any] 						=						str(lowerCamelCase_					)

        steps += 1
    return steps



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:

    if not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        raise ValueError('additive_persistence() only accepts integral values'					)
    if num < 0:
        raise ValueError('additive_persistence() does not accept negative values'					)

    _lowercase			:				Optional[int] 						=						0
    _lowercase			:				str 						=						str(lowerCamelCase_					)

    while len(lowerCamelCase_					) != 1:
        _lowercase			:				Dict 						=						[int(lowerCamelCase_					) for i in num_string]

        _lowercase			:				Any 						=						0
        for i in range(0				,					len(lowerCamelCase_					)					):
            total += numbers[i]

        _lowercase			:				Dict 						=						str(lowerCamelCase_					)

        steps += 1
    return steps


if __name__ == "__main__":
 import doctest

 doctest.testmod()


