



'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 				__lowerCAmelCase(							unittest.TestCase      ):






			@property
			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_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 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      'cpu'
					SCREAMING_SNAKE_CASE_						:int										=      self.dummy_uncond_unet
					SCREAMING_SNAKE_CASE_						:str										=      DDPMScheduler()

					SCREAMING_SNAKE_CASE_						:Tuple										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:str										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'      ,       return_dict=SCREAMING_SNAKE_CASE	)[0]

					SCREAMING_SNAKE_CASE_						:Tuple										=      image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE_						:Dict										=      image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Any										=      np.array(
					    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.0_00, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04]	)

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






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.dummy_uncond_unet
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      DDPMScheduler(prediction_type='sample'	)

					SCREAMING_SNAKE_CASE_						:Any										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:int										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	)[0]

					SCREAMING_SNAKE_CASE_						:List[Any]										=      image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE_						:Dict										=      image_eps[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Tuple										=      1E-2 if torch_device != 'mps' else 3E-2
					assert np.abs(image_slice.flatten() - image_eps_slice.flatten()	).max() < tolerance




@slow
@require_torch_gpu
class 				__lowerCAmelCase(							unittest.TestCase      ):






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      'google/ddpm-cifar10-32'

					SCREAMING_SNAKE_CASE_						:Any										=      UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DDPMScheduler.from_pretrained(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Dict										=      np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]	)
					assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

"""simple docstring"""







from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCamelCase      : List[str]       =TypeVar('''T''', bound=bool)






class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					T								=		value
         __A					:					RandomizedHeapNode[T] | None								=		None
         __A					:					RandomizedHeapNode[T] | None								=		None
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._value

    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if not roota:
              return roota

         if not roota:
              return roota

         if roota.value > roota.value:
              __A						,		__A					:					List[str]								=		roota, roota

         if random.choice([True, False]	):
              __A						,		__A					:					Dict								=		roota.right, roota.left

         __A					:					Dict								=		RandomizedHeapNode.merge(roota.left					,       __lowerCamelCase	)

         return roota







class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase = ()	):




         '''simple docstring'''



         __A					:					RandomizedHeapNode[T] | None								=		None

         if data:
              for item in data:
                   self.insert(__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[str]								=		RandomizedHeapNode.merge(self._root					,       RandomizedHeapNode(__lowerCamelCase	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		self.top()

         if self._root is None:
              return None

         __A					:					Dict								=		RandomizedHeapNode.merge(self._root.left					,       self._root.right	)

         return result
    def 						_a							(  self	):




         '''simple docstring'''



         if not self._root:
              raise IndexError('Can\'t get top element for the empty heap.'	)
         return self._root.value
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		None
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		[]
         while self:
              result.append(self.pop()	)

         return result

    def __bool__(  self	):




         '''simple docstring'''



         return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()



