


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 		_snake_case  (				unittest.TestCase							):




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:Union[str, Any]										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Any										=	self.dummy_uncond_unet
       a						:Tuple										=	PNDMScheduler()

       a						:Dict										=	PNDMPipeline(unet=_lowerCamelCase ,  scheduler=_lowerCamelCase   )
       pndm.to(_lowerCamelCase   )
       pndm.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Dict										=	torch.manual_seed(0   )
       a						:List[Any]										=	pndm(generator=_lowerCamelCase ,  num_inference_steps=20 ,  output_type='''numpy'''   ).images

       a						:str										=	torch.manual_seed(0   )
       a						:Optional[int]										=	pndm(generator=_lowerCamelCase ,  num_inference_steps=20 ,  output_type='''numpy''' ,  return_dict=_lowerCamelCase   )[0]

       a						:Optional[Any]										=	image[0, -3:, -3:, -1]
       a						:int										=	image_from_tuple[0, -3:, -3:, -1]

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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	'''google/ddpm-cifar10-32'''

       a						:int										=	UNetaDModel.from_pretrained(_lowerCamelCase   )
       a						:Optional[int]										=	PNDMScheduler()

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

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

       assert image.shape == (1, 32, 32, 3)
       a						:Dict										=	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









def     __lowerCamelCase						(			UpperCAmelCase_						:		str						):




      """simple docstring"""





      if n_term == "":
            return []
      a						:list										=	[]
      for temp in range(int(UpperCAmelCase_						)						):
            series.append(F'''1/{temp + 1}''' if series else '''1'''						)
      return series


if __name__ == "__main__":
     snake_case    :   Tuple	    =					input('''Enter the last number (nth term) of the Harmonic Series''')
     print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
     print(harmonic_series(nth_term))





