


'''simple docstring'''





from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar




class 							UpperCAmelCase ( _snake_case	):


 @abstractmethod
 def __lt__(			self       :  T ,					__lowerCamelCase       :  T   ):
    pass


 @abstractmethod
 def __gt__(			self       :  T ,					__lowerCamelCase       :  T   ):
    pass





 @abstractmethod
 def __eq__(			self       :  T ,					__lowerCamelCase       :  object   ):
    pass

__lowerCamelCase       				=	TypeVar('''T''', bound=Comparable)




class 							UpperCAmelCase ( Generic[T]	):


 def __init__(			self       :  Any   ):
    UpperCAmelCase__  :list[T]            =			[]
    UpperCAmelCase__  :int            =			0


 def __repr__(			self       :  Union[str, Any]   ):
    return str(self.h   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  int   ):
    if child_idx > 0:
       return (child_idx - 1) // 2
    return None


 def 					__SCREAMING_SNAKE_CASE				(			self       :  int ,					__lowerCamelCase       :  int   ):
    UpperCAmelCase__  :Any            =			2 * parent_idx + 1
    if left_child_index < self.heap_size:
       return left_child_index
    return None


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Any ,					__lowerCamelCase       :  int   ):
    UpperCAmelCase__  :Any            =			2 * parent_idx + 2
    if right_child_index < self.heap_size:
       return right_child_index
    return None


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Any ,					__lowerCamelCase       :  int   ):
    if index < self.heap_size:
       UpperCAmelCase__  :int            =			index
       UpperCAmelCase__  :Any            =			self.left_child_idx(__lowerCamelCase   )
       UpperCAmelCase__  :Optional[int]            =			self.right_child_idx(__lowerCamelCase   )
       # check which child is larger than its parent
       if left_child is not None and self.h[left_child] > self.h[violation]:
          UpperCAmelCase__  :List[str]            =			left_child
       if right_child is not None and self.h[right_child] > self.h[violation]:
          UpperCAmelCase__  :List[str]            =			right_child
       # if violation indeed exists
       if violation != index:
          # swap to fix the violation
          UpperCAmelCase__      ,	UpperCAmelCase__  :Tuple            =			self.h[index], self.h[violation]
          # fix the subsequent violation recursively if any
          self.max_heapify(__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str] ,					__lowerCamelCase       :  Iterable[T]   ):
    UpperCAmelCase__  :Tuple            =			list(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			len(self.h   )
    if self.heap_size > 1:
       # max_heapify from right to left but exclude leaves (last level)
       for i in range(self.heap_size // 2 - 1 ,					-1 ,					-1   ):
          self.max_heapify(__lowerCamelCase   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    if self.heap_size >= 2:
       UpperCAmelCase__  :str            =			self.h[0]
       UpperCAmelCase__  :str            =			self.h.pop(-1   )
       self.heap_size -= 1
       self.max_heapify(0   )
       return me
    elif self.heap_size == 1:
       self.heap_size -= 1
       return self.h.pop(-1   )
    else:
       raise Exception('''Empty heap'''   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int] ,					__lowerCamelCase       :  T   ):
    self.h.append(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[Any]            =			(self.heap_size - 1) // 2
    self.heap_size += 1
    while idx >= 0:
       self.max_heapify(__lowerCamelCase   )
       UpperCAmelCase__  :Tuple            =			(idx - 1) // 2





 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    UpperCAmelCase__  :List[Any]            =			self.heap_size
    for j in range(size - 1 ,					0 ,					-1   ):
       UpperCAmelCase__      ,	UpperCAmelCase__  :Tuple            =			self.h[j], self.h[0]
       self.heap_size -= 1
       self.max_heapify(0   )
    UpperCAmelCase__  :Dict            =			size


if __name__ == "__main__":
    import doctest

    # run doc test
    doctest.testmod()

    # demo
    for unsorted in [
        [0],
        [2],
        [3, 5],
        [5, 3],
        [5, 5],
        [0, 0, 0, 0],
        [1, 1, 1, 1],
        [2, 2, 3, 5],
        [0, 2, 2, 3, 5],
        [2, 5, 3, 0, 2, 3, 0, 3],
        [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
        [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
        [-45, -2, -5],
    ]:
        print(F'''unsorted array: {unsorted}''')

        __lowerCamelCase       				=	Heap()
        heap.build_max_heap(unsorted)
        print(F'''after build heap: {heap}''')

        print(F'''max value: {heap.extract_max()}''')
        print(F'''after max value removed: {heap}''')

        heap.insert(100)
        print(F'''after new value 100 inserted: {heap}''')

        heap.heap_sort()
        print(F'''heap-sorted array: {heap}\n''')







'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 							UpperCAmelCase ( _snake_case	,   _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				StableDiffusionParadigmsPipeline
 UpperCAmelCase				=				TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase				=				TEXT_TO_IMAGE_IMAGE_PARAMS


 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    torch.manual_seed(0   )
    UpperCAmelCase__  :Optional[int]            =			UNetaDConditionModel(
        block_out_channels=(3_2, 6_4) ,					layers_per_block=2 ,					sample_size=3_2 ,					in_channels=4 ,					out_channels=4 ,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,					cross_attention_dim=3_2 ,					attention_head_dim=(2, 4) ,					use_linear_projection=__lowerCamelCase ,					)
    UpperCAmelCase__  :Union[str, Any]            =			DDIMParallelScheduler(
        beta_start=0.0_00_85 ,					beta_end=0.0_12 ,					beta_schedule='''scaled_linear''' ,					clip_sample=__lowerCamelCase ,					set_alpha_to_one=__lowerCamelCase ,					)
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			AutoencoderKL(
        block_out_channels=[3_2, 6_4] ,					in_channels=3 ,					out_channels=3 ,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,					latent_channels=4 ,					sample_size=1_2_8 ,					)
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			CLIPTextConfig(
        bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=3_2 ,					intermediate_size=3_7 ,					layer_norm_eps=1e-05 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1_0_0_0 ,					hidden_act='''gelu''' ,					projection_dim=5_1_2 ,					)
    UpperCAmelCase__  :List[Any]            =			CLIPTextModel(__lowerCamelCase   )
    UpperCAmelCase__  :Tuple            =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

    UpperCAmelCase__  :int            =			{
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components


 def 					__SCREAMING_SNAKE_CASE				(			self       :  str ,					__lowerCamelCase       :  List[Any] ,					__lowerCamelCase       :  Any=0   ):
    if str(__lowerCamelCase   ).startswith('''mps'''   ):
       UpperCAmelCase__  :str            =			torch.manual_seed(__lowerCamelCase   )
    else:
       UpperCAmelCase__  :Union[str, Any]            =			torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
    UpperCAmelCase__  :Union[str, Any]            =			{
        '''prompt''': '''a photograph of an astronaut riding a horse''',
        '''generator''': generator,
        '''num_inference_steps''': 1_0,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
        '''parallel''': 3,
        '''debug''': True,
    }
    return inputs


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :Dict            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :Dict            =			self.get_dummy_components()
    UpperCAmelCase__  :str            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :List[Any]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Optional[int]            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Any            =			sd_pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :Union[str, Any]            =			image[0, -3:, -3:, -1]
    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :List[Any]            =			np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]   )

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


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :Any            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :Dict            =			self.get_dummy_components()
    torch.manual_seed(0   )
    UpperCAmelCase__  :Tuple            =			DDPMParallelScheduler()
    torch.manual_seed(0   )
    UpperCAmelCase__  :List[str]            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Optional[int]            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[Any]            =			sd_pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :Dict            =			image[0, -3:, -3:, -1]
    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :List[str]            =			np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]   )

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


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    super().test_inference_batch_consistent(batch_sizes=[1, 2]   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    super().test_inference_batch_single_identical(batch_size=2 ,					expected_max_diff=3e-3   )





 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    UpperCAmelCase__  :int            =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase__  :str            =			self.get_dummy_components()
    UpperCAmelCase__  :Any            =			StableDiffusionParadigmsPipeline(**__lowerCamelCase   )
    UpperCAmelCase__  :List[str]            =			sd_pipe.to(__lowerCamelCase   )
    sd_pipe.set_progress_bar_config(disable=__lowerCamelCase   )

    UpperCAmelCase__  :Tuple            =			self.get_dummy_inputs(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			'''french fries'''
    UpperCAmelCase__  :Any            =			sd_pipe(**__lowerCamelCase ,					negative_prompt=__lowerCamelCase   )
    UpperCAmelCase__  :int            =			output.images
    UpperCAmelCase__  :Tuple            =			image[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)

    UpperCAmelCase__  :Tuple            =			np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]   )

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




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


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any] ,					__lowerCamelCase       :  Tuple=0   ):
    UpperCAmelCase__  :List[Any]            =			torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )
    UpperCAmelCase__  :List[str]            =			{
        '''prompt''': '''a photograph of an astronaut riding a horse''',
        '''generator''': generator,
        '''num_inference_steps''': 1_0,
        '''guidance_scale''': 7.5,
        '''output_type''': '''numpy''',
        '''parallel''': 3,
        '''debug''': True,
    }
    return inputs





 def 					__SCREAMING_SNAKE_CASE				(			self       :  int   ):
    UpperCAmelCase__  :Dict            =			'''stabilityai/stable-diffusion-2-base'''
    UpperCAmelCase__  :Union[str, Any]            =			DDIMParallelScheduler.from_pretrained(__lowerCamelCase ,					subfolder='''scheduler'''   )
    UpperCAmelCase__  :Dict            =			StableDiffusionParadigmsPipeline.from_pretrained(__lowerCamelCase ,					scheduler=__lowerCamelCase ,					safety_checker=__lowerCamelCase   )
    pipe.to(__lowerCamelCase   )
    pipe.set_progress_bar_config(disable=__lowerCamelCase   )
    pipe.enable_attention_slicing()

    UpperCAmelCase__  :Dict            =			self.get_inputs()
    UpperCAmelCase__  :int            =			pipe(**__lowerCamelCase   ).images
    UpperCAmelCase__  :List[str]            =			image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 5_1_2, 5_1_2, 3)

    UpperCAmelCase__  :Optional[int]            =			np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]   )

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



