



'''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


__snake_case       :	Optional[Any]            =False



class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		pass



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




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	,torch_dtype=torch.floataa				)
						pipe.to(__lowerCamelCase				)
						pipe.set_progress_bar_config(disable=__lowerCamelCase				)

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

						lowerCAmelCase__  :       List[str]				=  torch.manual_seed(0				)
						lowerCAmelCase__  :       Optional[int]				=  pipe.dual_guided(
						    prompt='''first prompt'''	,image=__lowerCamelCase	,text_to_image_strength=0.75	,generator=__lowerCamelCase	,guidance_scale=7.5	,num_inference_steps=2	,output_type='''numpy'''	,).images

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

						lowerCAmelCase__  :       List[str]				=  generator.manual_seed(0				)
						lowerCAmelCase__  :       Dict				=  pipe.dual_guided(
						    prompt='''first prompt'''	,image=__lowerCamelCase	,text_to_image_strength=0.75	,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 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	,torch_dtype=torch.floataa				)
						pipe.to(__lowerCamelCase				)
						pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Tuple				=  '''cyberpunk 2077'''
						lowerCAmelCase__  :       List[str]				=  load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
						lowerCAmelCase__  :       str				=  torch.manual_seed(0				)
						lowerCAmelCase__  :       List[Any]				=  pipe.dual_guided(
						    prompt=__lowerCamelCase	,image=__lowerCamelCase	,text_to_image_strength=0.75	,generator=__lowerCamelCase	,guidance_scale=7.5	,num_inference_steps=50	,output_type='''numpy'''	,).images

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

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__  :       Union[str, Any]				=  np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]				)

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

						lowerCAmelCase__  :       Tuple				=  '''A painting of a squirrel eating a burger '''
						lowerCAmelCase__  :       int				=  torch.manual_seed(0				)
						lowerCAmelCase__  :       Tuple				=  pipe.text_to_image(
						    prompt=__lowerCamelCase	,generator=__lowerCamelCase	,guidance_scale=7.5	,num_inference_steps=50	,output_type='''numpy'''				).images

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

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__  :       Optional[Any]				=  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-1

						lowerCAmelCase__  :       List[Any]				=  pipe.image_variation(__lowerCamelCase	,generator=__lowerCamelCase	,output_type='''numpy'''				).images

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

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__  :       str				=  np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]				)

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






from bisect import bisect
from itertools import accumulate






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Any				=  sorted(zip(lowerCamelCase_ ,lowerCamelCase_) ,key=lambda lowerCamelCase_: x[0] / x[1] ,reverse=lowerCamelCase_)
				lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  [i[0] for i in r], [i[1] for i in r]
				lowerCAmelCase__  :       Tuple				=  list(accumulate(lowerCamelCase_))
				lowerCAmelCase__  :       str				=  bisect(lowerCamelCase_ ,lowerCamelCase_)
				return (
				    0
				    if k == 0
				    else sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
				    if k != n
				    else sum(vl[:k])
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

