



"""simple docstring"""





class  lowercase__  :







       '''simple docstring'''


       def __init__(				self     :    List[Any]						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    Any						,							_UpperCAmelCase     :    str       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				name
             UpperCAmelCase_     					=				value
             UpperCAmelCase_     					=				weight



       def __repr__(				self     :    List[str]       )					->		Any:


             '''simple docstring'''




             return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""



       def 	lowercase__							(				self     :    List[Any]       )					->		Optional[Any]:


             '''simple docstring'''




             return self.value



       def 	lowercase__							(				self     :    str       )					->		Dict:


             '''simple docstring'''




             return self.name



       def 	lowercase__							(				self     :    Optional[Any]       )					->		Tuple:


             '''simple docstring'''




             return self.weight







       def 	lowercase__							(				self     :    Tuple       )					->		Any:


             '''simple docstring'''




             return self.value / self.weight



def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				[]
      for i in range(len(lowercase__ ) ):
            menu.append(Things(name[i]      ,     value[i]      ,     weight[i] ) )
      return menu
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				sorted(lowercase__      ,     key=lowercase__      ,     reverse=lowercase__ )
      UpperCAmelCase_     					=				[]
      UpperCAmelCase_					,       UpperCAmelCase_     					=				0.0, 0.0
      for i in range(len(lowercase__ ) ):
            if (total_cost + items_copy[i].get_weight()) <= max_cost:
                  result.append(items_copy[i] )
                  total_cost += items_copy[i].get_weight()
                  total_value += items_copy[i].get_value()
      return (result, total_value)
def   a__      ( ):
      pass







if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 	lowerCAmelCase  (   unittest.TestCase ):







       @property
       def       snake_case (				self      :			str   ):







            """simple docstring"""







            torch.manual_seed(0   )
            __lowercase      		=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      :			int   ):







            """simple docstring"""







            __lowercase      		=self.dummy_uncond_unet
            __lowercase      		=PNDMScheduler()

            __lowercase      		=PNDMPipeline(unet=__lowercase    ,   scheduler=__lowercase   )
            pndm.to(__lowercase   )
            pndm.set_progress_bar_config(disable=__lowercase   )

            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   num_inference_steps=20    ,   output_type='numpy'   ).images

            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   num_inference_steps=20    ,   output_type='numpy'    ,   return_dict=__lowercase   )[0]

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

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







       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		='google/ddpm-cifar10-32'

            __lowercase      		=UNetaDModel.from_pretrained(__lowercase   )
            __lowercase      		=PNDMScheduler()

            __lowercase      		=PNDMPipeline(unet=__lowercase    ,   scheduler=__lowercase   )
            pndm.to(__lowercase   )
            pndm.set_progress_bar_config(disable=__lowercase   )
            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   output_type='numpy'   ).images

            __lowercase      		=image[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            __lowercase      		=np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]   )

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


