

"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					list			):

     def merge(UpperCAmelCase_  :					list					,   UpperCAmelCase_  :					list			) -> list:

          def _merge():
               while left and right:
                    yield (left if left[0] <= right[0] else right).pop(0			)
               yield from left
               yield from right

          return list(_merge()			)

     if len(UpperCAmelCase_			) <= 1:
          return collection
     A__										=  len(UpperCAmelCase_			) // 2
     return merge(merge_sort(collection[:mid]			)					,   merge_sort(collection[mid:]			)			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 SCREAMING_SNAKE_CASE_					:	List[Any]							  =						input('Enter numbers separated by a comma:\n').strip()
 SCREAMING_SNAKE_CASE_					:	List[str]							  =						[int(item) for item in user_input.split(',')]
 print(*merge_sort(unsorted), sep=',')









"""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 					a						(       unittest.TestCase				):



    """simple docstring"""



    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         torch.manual_seed(0       )
         A__										=  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"""


         A__										=  self.dummy_uncond_unet
         A__										=  PNDMScheduler()

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

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""       ).images

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""    ,						return_dict=UpperCamelCase       )[0]

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

         assert image.shape == (1, 32, 32, 3)
         A__										=  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 					a						(       unittest.TestCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  """google/ddpm-cifar10-32"""

         A__										=  UNetaDModel.from_pretrained(UpperCamelCase       )
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )
         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						output_type="""numpy"""       ).images

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

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]       )

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






