
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  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           torch.manual_seed(0	)
           UpperCamelCase__     : str											=					UNetaDModel(
               block_out_channels=(3_2, 6_4)    ,    layers_per_block=2    ,    sample_size=3_2    ,    in_channels=3    ,    out_channels=3    ,    down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,    up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,    )
           return model






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.dummy_uncond_unet
           UpperCamelCase__     : str											=					KarrasVeScheduler()

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

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

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

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

           assert image.shape == (1, 3_2, 3_2, 3)
           UpperCamelCase__     : 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 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







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

           UpperCamelCase__     : 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	)

           UpperCamelCase__     : List[str]											=					torch.manual_seed(0	)
           UpperCamelCase__     : Optional[int]											=					pipe(num_inference_steps=2_0    ,    generator=__SCREAMING_SNAKE_CASE    ,    output_type='''numpy'''	).images

           UpperCamelCase__     : str											=					image[0, -3:, -3:, -1]
           assert image.shape == (1, 2_5_6, 2_5_6, 3)
           UpperCamelCase__     : int											=					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




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)))


