




'''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 					_a (unittest.TestCase	):





     '''simple docstring'''



     @property
     def 			snake_case_      (				self							)      ->				int:
          torch.manual_seed(0							)
          snake_case			:      str        =			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 			snake_case_      (				self							)      ->				Union[str, Any]:
          snake_case			:      Union[str, Any]        =			self.dummy_uncond_unet
          snake_case			:      str        =			KarrasVeScheduler()

          snake_case			:      Dict        =			KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE							,scheduler=__SCREAMING_SNAKE_CASE							)
          pipe.to(__SCREAMING_SNAKE_CASE							)
          pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

          snake_case			:      Union[str, Any]        =			torch.manual_seed(0							)
          snake_case			:      Dict        =			pipe(num_inference_steps=2							,generator=__SCREAMING_SNAKE_CASE							,output_type="""numpy"""							).images

          snake_case			:      Tuple        =			torch.manual_seed(0							)
          snake_case			:      Union[str, Any]        =			pipe(num_inference_steps=2							,generator=__SCREAMING_SNAKE_CASE							,output_type="""numpy"""							,return_dict=__SCREAMING_SNAKE_CASE							)[0]

          snake_case			:      List[Any]        =			image[0, -3:, -3:, -1]
          snake_case			:      List[Any]        =			image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          snake_case			:      Optional[int]        =			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 					_a (unittest.TestCase	):





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				List[Any]:
          snake_case			:      Optional[int]        =			'''google/ncsnpp-celebahq-256'''
          snake_case			:      List[Any]        =			UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
          snake_case			:      Union[str, Any]        =			KarrasVeScheduler()

          snake_case			:      List[Any]        =			KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE							,scheduler=__SCREAMING_SNAKE_CASE							)
          pipe.to(__SCREAMING_SNAKE_CASE							)
          pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

          snake_case			:      List[str]        =			torch.manual_seed(0							)
          snake_case			:      Optional[int]        =			pipe(num_inference_steps=20							,generator=__SCREAMING_SNAKE_CASE							,output_type="""numpy"""							).images

          snake_case			:      str        =			image[0, -3:, -3:, -1]
          assert image.shape == (1, 256, 256, 3)
          snake_case			:      int        =			np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]							)

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





from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

lowerCamelCase  =(3, 9, -1_1, 0, 7, 5, 1, -1)
lowerCamelCase  =(4, 6, 2, 0, 8, 1_0, 3, -2)




@dataclass
class 					_lowerCamelCase			:





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	42
     SCREAMING_SNAKE_CASE_		 =	42




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> None:




           """simple docstring"""







           UpperCamelCase__     : Node | None											=					None
           for i in sorted(__SCREAMING_SNAKE_CASE    ,    reverse=__SCREAMING_SNAKE_CASE	):
                 UpperCamelCase__     : List[str]											=					Node(__SCREAMING_SNAKE_CASE    ,    self.head	)






     def __iter__(				self	)	-> Iterator[int]:




           """simple docstring"""







           UpperCamelCase__     : int											=					self.head
           while node:
                 yield node.data
                 UpperCamelCase__     : List[Any]											=					node.next_node






     def __len__(				self	)	-> int:




           """simple docstring"""







           return sum(1 for _ in self	)






     def __str__(				self	)	-> str:




           """simple docstring"""







           return " -> ".join([str(__SCREAMING_SNAKE_CASE	) for node in self]	)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      return SortedLinkedList(list(UpperCamelCase__		) + list(UpperCamelCase__		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    lowerCamelCase  =SortedLinkedList
    print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))


