

"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_a				:		Union[str, Any]	   =       False
class 						__A							(			unittest.TestCase		):
     pass







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






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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""    ,					torch_dtype=torch.floataa  )
       pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:	Dict        =					load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""  )

       _lowerCAmelCase						:	Union[str, Any]        =					torch.manual_seed(0  )
       _lowerCAmelCase						:	int        =					pipe.dual_guided(
           prompt="""first prompt"""    ,					image=a__    ,					text_to_image_strength=0.7_5    ,					generator=a__    ,					guidance_scale=7.5    ,					num_inference_steps=2    ,					output_type="""numpy"""    ,					).images

       with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(a__  )
         _lowerCAmelCase						:	Dict        =					VersatileDiffusionPipeline.from_pretrained(a__    ,					torch_dtype=torch.floataa  )
       pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:	int        =					generator.manual_seed(0  )
       _lowerCAmelCase						:	List[str]        =					pipe.dual_guided(
           prompt="""first prompt"""    ,					image=a__    ,					text_to_image_strength=0.7_5    ,					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    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""    ,					torch_dtype=torch.floataa  )
       pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:	List[Any]        =					"""cyberpunk 2077"""
       _lowerCAmelCase						:	Optional[Any]        =					load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""  )
       _lowerCAmelCase						:	Any        =					torch.manual_seed(0  )
       _lowerCAmelCase						:	Union[str, Any]        =					pipe.dual_guided(
           prompt=a__    ,					image=a__    ,					text_to_image_strength=0.7_5    ,					generator=a__    ,					guidance_scale=7.5    ,					num_inference_steps=50    ,					output_type="""numpy"""    ,					).images

       _lowerCAmelCase						:	int        =					image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:	List[Any]        =					np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]  )

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

       _lowerCAmelCase						:	Union[str, Any]        =					"""A painting of a squirrel eating a burger """
       _lowerCAmelCase						:	str        =					torch.manual_seed(0  )
       _lowerCAmelCase						:	int        =					pipe.text_to_image(
           prompt=a__    ,					generator=a__    ,					guidance_scale=7.5    ,					num_inference_steps=50    ,					output_type="""numpy"""  ).images

       _lowerCAmelCase						:	int        =					image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:	int        =					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-1

       _lowerCAmelCase						:	List[str]        =					pipe.image_variation(a__    ,					generator=a__    ,					output_type="""numpy"""  ).images

       _lowerCAmelCase						:	Optional[int]        =					image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:	int        =					np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]  )

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





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int = 100     )	->				int:
  _lowerCAmelCase						:	Optional[Any]        =					n * (n + 1) * (2 * n + 1) / 6
  _lowerCAmelCase						:	Tuple        =					(n * (n + 1) / 2) ** 2
  return int(square_of_sum - sum_of_squares     )


if __name__ == "__main__":
     print(F"""{solution() = }""")




