'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class     __UpperCAmelCase       ( unittest.TestCase		):





  @property
  def 						UpperCAmelCase_	(	self ):
     torch.manual_seed(0 )
     lowerCAmelCase_			=      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 ):
     lowerCAmelCase_			=      self.dummy_uncond_unet
     lowerCAmelCase_			=      KarrasVeScheduler()

     lowerCAmelCase_			=      KarrasVePipeline(unet=_lowerCamelCase    ,     scheduler=_lowerCamelCase )
     pipe.to(_lowerCamelCase )
     pipe.set_progress_bar_config(disable=_lowerCamelCase )

     lowerCAmelCase_			=      torch.manual_seed(0 )
     lowerCAmelCase_			=      pipe(num_inference_steps=2    ,     generator=_lowerCamelCase    ,     output_type='''numpy''' ).images

     lowerCAmelCase_			=      torch.manual_seed(0 )
     lowerCAmelCase_			=      pipe(num_inference_steps=2    ,     generator=_lowerCamelCase    ,     output_type='''numpy'''    ,     return_dict=_lowerCamelCase )[0]

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

     assert image.shape == (1, 32, 32, 3)
     lowerCAmelCase_			=      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     __UpperCAmelCase       ( unittest.TestCase		):





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''google/ncsnpp-celebahq-256'''
     lowerCAmelCase_			=      UNetaDModel.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      KarrasVeScheduler()

     lowerCAmelCase_			=      KarrasVePipeline(unet=_lowerCamelCase    ,     scheduler=_lowerCamelCase )
     pipe.to(_lowerCamelCase )
     pipe.set_progress_bar_config(disable=_lowerCamelCase )

     lowerCAmelCase_			=      torch.manual_seed(0 )
     lowerCAmelCase_			=      pipe(num_inference_steps=20    ,     generator=_lowerCamelCase    ,     output_type='''numpy''' ).images

     lowerCAmelCase_			=      image[0, -3:, -3:, -1]
     assert image.shape == (1, 256, 256, 3)
     lowerCAmelCase_			=      np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86] )

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






'''simple docstring'''

def snake_case_      (					__snake_case  :							int	,   __snake_case  :							int	,   __snake_case  :							int)			->					float:
   lowerCAmelCase_			=      (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
   # formula for sum of series
   return total



def snake_case_      (					)			->					str:
   print(sum_of_series(1	,   1	,   10))


if __name__ == "__main__":
 import doctest

 doctest.testmod()





