





'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       @property
       def snake_case__						(							self       :		List[str]					)   ->					List[str]:

            '''simple docstring'''




            torch.manual_seed(0					)
            _UpperCamelCase       =	UNetaDModel(
                block_out_channels=(32, 64)	,			layers_per_block=2	,			sample_size=32	,			in_channels=3	,			out_channels=3	,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')	,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')	,			)
            return model







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

            '''simple docstring'''




            _UpperCamelCase       =	self.dummy_uncond_unet
            _UpperCamelCase       =	KarrasVeScheduler()

            _UpperCamelCase       =	KarrasVePipeline(unet=lowerCAmelCase__	,			scheduler=lowerCAmelCase__					)
            pipe.to(lowerCAmelCase__					)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

            _UpperCamelCase       =	torch.manual_seed(0					)
            _UpperCamelCase       =	pipe(num_inference_steps=2	,			generator=lowerCAmelCase__	,			output_type='''numpy'''					).images

            _UpperCamelCase       =	torch.manual_seed(0					)
            _UpperCamelCase       =	pipe(num_inference_steps=2	,			generator=lowerCAmelCase__	,			output_type='''numpy'''	,			return_dict=lowerCAmelCase__					)[0]

            _UpperCamelCase       =	image[0, -3:, -3:, -1]
            _UpperCamelCase       =	image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            _UpperCamelCase       =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

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

@slow
@require_torch
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		Any					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	'''google/ncsnpp-celebahq-256'''
            _UpperCamelCase       =	UNetaDModel.from_pretrained(lowerCAmelCase__					)
            _UpperCamelCase       =	KarrasVeScheduler()

            _UpperCamelCase       =	KarrasVePipeline(unet=lowerCAmelCase__	,			scheduler=lowerCAmelCase__					)
            pipe.to(lowerCAmelCase__					)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

            _UpperCamelCase       =	torch.manual_seed(0					)
            _UpperCamelCase       =	pipe(num_inference_steps=20	,			generator=lowerCAmelCase__	,			output_type='''numpy'''					).images

            _UpperCamelCase       =	image[0, -3:, -3:, -1]
            assert image.shape == (1, 256, 256, 3)
            _UpperCamelCase       =	np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]					)

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










'''simple docstring'''

from __future__ import annotations







def  a__    (    lowercase :					list[int], lowercase :					list[int], lowercase :					int				)		->		tuple[float, list[float]]:





     """simple docstring"""

     _UpperCamelCase       =	list(range(len(lowercase				)				)				)
     _UpperCamelCase       =	[v / w for v, w in zip(lowercase, lowercase				)]
     index.sort(key=lambda lowercase				: ratio[i], reverse=lowercase				)

     _UpperCamelCase       =	0
     _UpperCamelCase       =	[0] * len(lowercase				)
     for i in index:
          if weight[i] <= capacity:
               _UpperCamelCase       =	1
               max_value += value[i]
               capacity -= weight[i]
          else:
               _UpperCamelCase       =	capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
   import doctest

   doctest.testmod()



