






def        _snake_case   (_snake_case				:       Optional[int]      ,    _snake_case				:       Tuple      ,    _snake_case				:       Dict      ,    _snake_case				:       Dict      ,    _snake_case				:       Tuple      ,    _snake_case				:       str)					->	Dict:
   if index == r:
      for j in range(_snake_case):
         print(data[j]      ,    end=' ')
      print(' ')
      return
   #  When no more elements are there to put in data[]
   if i >= n:
      return
   # current is included, put next at next location
   _lowercase              =arr[i]
   combination_util(_snake_case      ,    _snake_case      ,    _snake_case      ,    index + 1      ,    _snake_case      ,    i + 1)
   # current is excluded, replace it with
   # next (Note that i+1 is passed, but
   # index is not changed)
   combination_util(_snake_case      ,    _snake_case      ,    _snake_case      ,    _snake_case      ,    _snake_case      ,    i + 1)
   # The main function that prints all combinations
   # of size r in arr[] of size n. This function
   # mainly uses combinationUtil()





def        _snake_case   (_snake_case				:       Any      ,    _snake_case				:       List[str]      ,    _snake_case				:       List[Any])					->	int:
   # A temporary array to store all combination one by one
   _lowercase              =[0] * r
   # Print all combination using temporary array 'data[]'
   combination_util(_snake_case      ,    _snake_case      ,    _snake_case      ,    0      ,    _snake_case      ,    0)


if __name__ == "__main__":
    # Driver code to check the function above
    _SCREAMING_SNAKE_CASE   =  [10, 20, 30, 40, 50]
    print_combination(arr, len(arr), 3)
    # This code is contributed by Ambuj sahu










import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """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 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.dummy_uncond_unet
          _lowercase              =ScoreSdeVeScheduler()

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case).images

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case,      return_dict=snake_case)[
              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([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 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='google/ncsnpp-church-256'
          _lowercase              =UNetaDModel.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVeScheduler.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=10,      output_type='numpy',      generator=snake_case).images

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

          assert image.shape == (1, 256, 256, 3)

          _lowercase              =np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

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


