


def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				len(__a	)
  lowerCamelCase :					Optional[int]    =				sum(__a	)

  lowerCamelCase :					Dict    =				[[False for x in range(s + 1	)] for y in range(n + 1	)]

  for i in range(1      ,						n + 1	):
    lowerCamelCase :					int    =				True

  for i in range(1      ,						s + 1	):
    lowerCamelCase :					Dict    =				False

  for i in range(1      ,						n + 1	):
    for j in range(1      ,						s + 1	):
      lowerCamelCase :					Tuple    =				dp[i][j - 1]

      if arr[i - 1] <= j:
        lowerCamelCase :					List[str]    =				dp[i][j] or dp[i - 1][j - arr[i - 1]]

  for j in range(int(s / 2	)      ,						-1      ,						-1	):
    if dp[n][j] is True:
      lowerCamelCase :					str    =				s - 2 * j
      break

  return diff





'''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 UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	int			=   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   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	str			=   self.dummy_uncond_unet
     _a				:	int			=   PNDMScheduler()

     _a				:	str			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Any			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	List[Any]			=   image[0, -3:, -3:, -1]
     _a				:	Any			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	List[Any]			=   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 UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[str]			=   'google/ddpm-cifar10-32'

     _a				:	str			=   UNetaDModel.from_pretrained(_a       )
     _a				:	Union[str, Any]			=   PNDMScheduler()

     _a				:	Tuple			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )
     _a				:	str			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,output_type='numpy'       ).images

     _a				:	Dict			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	Tuple			=   np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

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



