
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												=	False


class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							pass


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







							'''simple docstring'''

							def 	lowercase							(self  )					->       Dict:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							def 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""  )
														# remove text_unet
														pipe.remove_unused_weights()
														pipe.to(UpperCAmelCase  )
														pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  """A painting of a squirrel eating a burger """
														_snake_case       =  torch.manual_seed(0  )
														_snake_case       =  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  )
																					_snake_case       =  VersatileDiffusionTextToImagePipeline.from_pretrained(UpperCAmelCase  )
														pipe.to(UpperCAmelCase  )
														pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  generator.manual_seed(0  )
														_snake_case       =  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 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  VersatileDiffusionTextToImagePipeline.from_pretrained(
														    """shi-labs/versatile-diffusion"""  ,  torch_dtype=torch.floataa  )
														pipe.to(UpperCAmelCase  )
														pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  """A painting of a squirrel eating a burger """
														_snake_case       =  torch.manual_seed(0  )
														_snake_case       =  pipe(
														    prompt=UpperCAmelCase  ,  generator=UpperCAmelCase  ,  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'''
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):

							if not (isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				) and isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)):
														raise ValueError("""longest_common_substring() takes two strings for inputs"""				)

							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)

							_snake_case       =  [[0] * (texta_length + 1) for _ in range(texta_length + 1				)]
							_snake_case       =  0
							_snake_case       =  0

							for i in range(1							, texta_length + 1				):
														for j in range(1							, texta_length + 1				):
																					if texta[i - 1] == texta[j - 1]:
																												_snake_case       =  1 + dp[i - 1][j - 1]
																												if dp[i][j] > ans_length:
																																			_snake_case       =  i
																																			_snake_case       =  dp[i][j]

							return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
				import doctest

				doctest.testmod()