

"""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    ):

  @property
  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      torch.manual_seed(0   )
      _lowerCAmelCase							: List[str]	=     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 __lowerCamelCase		(		self   ):
      '''simple docstring'''

      _lowerCAmelCase							: List[Any]	=     self.dummy_uncond_unet
      _lowerCAmelCase							: Dict	=     PNDMScheduler()

      _lowerCAmelCase							: Optional[Any]	=     PNDMPipeline(unet=_A     ,scheduler=_A   )
      pndm.to(_A   )
      pndm.set_progress_bar_config(disable=_A   )

      _lowerCAmelCase							: List[str]	=     torch.manual_seed(0   )
      _lowerCAmelCase							: Dict	=     pndm(generator=_A     ,num_inference_steps=20     ,output_type='numpy'   ).images

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

      _lowerCAmelCase							: Union[str, Any]	=     image[0, -3:, -3:, -1]
      _lowerCAmelCase							: Any	=     image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _lowerCAmelCase							: Union[str, 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    ):

  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      _lowerCAmelCase							: Tuple	=     'google/ddpm-cifar10-32'

      _lowerCAmelCase							: Dict	=     UNetaDModel.from_pretrained(_A   )
      _lowerCAmelCase							: Tuple	=     PNDMScheduler()

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

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

      assert image.shape == (1, 32, 32, 3)
      _lowerCAmelCase							: Optional[int]	=     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







from __future__ import annotations


def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					list[list[int]]:

		"""simple docstring"""






		_UpperCamelCase         =						[]
		_UpperCamelCase         =						[]
		_UpperCamelCase         =						0
		_UpperCamelCase         =						sum(lowerCAmelCase			)
		create_state_space_tree(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase			)
		return result


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	)		->					None:

		"""simple docstring"""






		if sum(lowerCAmelCase			) > max_sum or (remaining_nums_sum + sum(lowerCAmelCase			)) < max_sum:
				return
		if sum(lowerCAmelCase			) == max_sum:
				result.append(lowerCAmelCase			)
				return
		for index in range(lowerCAmelCase    ,	len(lowerCAmelCase			)			):
				create_state_space_tree(
				    lowerCAmelCase    ,	lowerCAmelCase    ,	index + 1    ,	[*path, nums[index]]    ,	lowerCAmelCase    ,	remaining_nums_sum - nums[index]    ,	)
lowerCamelCase__						=				[3, 34, 4, 12, 5, 2]
lowerCamelCase__						=				9
lowerCamelCase__						=				generate_sum_of_subsets_soln(nums, max_sum)







print(*result)



