





from __future__ import annotations







class      lowerCamelCase:
     '''simple docstring'''


     def __init__(    self				,     snake_case_=None							):
         _A     =							data
         _A     =							None




     def __repr__(    self							):
         _A     =							[]
         _A     =							self
         while temp:
             string_rep.append(F"{temp.data}"							)
             _A     =							temp.next
         return "->".join(snake_case_							)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    if not elements_list:
        raise Exception('The Elements List is empty'				)

    _A     =							_A     =							Node(elements_list[0]				)
    for i in range(1							,     len(_SCREAMING_SNAKE_CASE				)				):
        _A     =							Node(elements_list[i]				)
        _A     =							current.next
    return head





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> None:




    """simple docstring"""




    if head_node is not None and isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        print_reverse(head_node.next				)
        print(head_node.data				)





def 				__lowerCAmelCase(       )							-> Any:




    """simple docstring"""




    from doctest import testmod

    testmod()

    _A     =							make_linked_list([14, 52, 14, 12, 43]				)
    print('Linked List:'				)
    print(_SCREAMING_SNAKE_CASE				)
    print('Elements in Reverse:'				)
    print_reverse(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
 main()












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      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         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        lowerCAmelCase__		(    self							):
         _A     =							self.dummy_uncond_unet
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

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

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=2				,     generator=snake_case_				,     output_type='numpy'				,     return_dict=snake_case_							)[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([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      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         _A     =							'google/ncsnpp-celebahq-256'
         _A     =							UNetaDModel.from_pretrained(snake_case_							)
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

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

         _A     =							image[0, -3:, -3:, -1]
         assert image.shape == (1, 256, 256, 3)
         _A     =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]							)

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





