



'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class      snake_case__       (__lowerCamelCase      ,			unittest.TestCase	):







						"""simple docstring"""




						__lowerCAmelCase  :Optional[int]			      =  KandinskyPipeline
						__lowerCAmelCase  :Dict			      =  [
						    'prompt',
						    'image_embeds',
						    'negative_image_embeds',
						]
						__lowerCAmelCase  :str			      =  ['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
						__lowerCAmelCase  :Any			      =  [
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						__lowerCAmelCase  :Any			      =  False

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[int]:
									"""simple docstring"""




									return 3_2

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[int]:
									"""simple docstring"""




									return 3_2

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[Any]:
									"""simple docstring"""




									return self.time_input_dim

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Union[str, Any]:
									"""simple docstring"""




									return self.time_input_dim * 4

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[Any]:
									"""simple docstring"""




									return 1_0_0

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Dict:
									"""simple docstring"""




									a__     :		Any		 =						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""						)
									return tokenizer

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[Any]:
									"""simple docstring"""




									torch.manual_seed(0						)
									a__     :		List[Any]		 =						MCLIPConfig(
									    numDims=self.cross_attention_dim      ,     transformerDimensions=self.text_embedder_hidden_size      ,     hidden_size=self.text_embedder_hidden_size      ,     intermediate_size=3_7      ,     num_attention_heads=4      ,     num_hidden_layers=5      ,     vocab_size=1_0_0_5      ,     )

									a__     :		int		 =						MultilingualCLIP(__lowercase						)
									a__     :		Optional[int]		 =						text_encoder.eval()

									return text_encoder

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[int]:
									"""simple docstring"""




									torch.manual_seed(0						)

									a__     :		int		 =						{
									    """in_channels""": 4,
									    # Out channels is double in channels because predicts mean and variance
									    """out_channels""": 8,
									    """addition_embed_type""": """text_image""",
									    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
									    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
									    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
									    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    """layers_per_block""": 1,
									    """encoder_hid_dim""": self.text_embedder_hidden_size,
									    """encoder_hid_dim_type""": """text_image_proj""",
									    """cross_attention_dim""": self.cross_attention_dim,
									    """attention_head_dim""": 4,
									    """resnet_time_scale_shift""": """scale_shift""",
									    """class_embed_type""": None,
									}

									a__     :		Optional[int]		 =						UNetaDConditionModel(**__lowercase						)
									return model

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
									"""simple docstring"""




									return {
									    "block_out_channels": [3_2, 6_4],
									    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 1_2,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[Any]:
									"""simple docstring"""




									torch.manual_seed(0						)
									a__     :		List[str]		 =						VQModel(**self.dummy_movq_kwargs						)
									return model

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Tuple:
									"""simple docstring"""




									a__     :		Optional[int]		 =						self.dummy_text_encoder
									a__     :		List[Any]		 =						self.dummy_tokenizer
									a__     :		Optional[Any]		 =						self.dummy_unet
									a__     :		Any		 =						self.dummy_movq

									a__     :		Dict		 =						DDIMScheduler(
									    num_train_timesteps=1_0_0_0      ,     beta_schedule="""linear"""      ,     beta_start=0.0_0_0_8_5      ,     beta_end=0.0_1_2      ,     clip_sample=__lowercase      ,     set_alpha_to_one=__lowercase      ,     steps_offset=1      ,     prediction_type="""epsilon"""      ,     thresholding=__lowercase      ,     )

									a__     :		Tuple		 =						{
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """movq""": movq,
									}
									return components

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase=0						)      ->   Optional[Any]:
									"""simple docstring"""




									a__     :		Tuple		 =						floats_tensor((1, self.cross_attention_dim)      ,     rng=random.Random(__lowercase						)						).to(__lowercase						)
									a__     :		Dict		 =						floats_tensor((1, self.cross_attention_dim)      ,     rng=random.Random(seed + 1						)						).to(__lowercase						)
									if str(__lowercase						).startswith("""mps"""						):
												a__     :		Union[str, Any]		 =						torch.manual_seed(__lowercase						)
									else:
												a__     :		int		 =						torch.Generator(device=__lowercase						).manual_seed(__lowercase						)
									a__     :		Optional[Any]		 =						{
									    """prompt""": """horse""",
									    """image_embeds""": image_embeds,
									    """negative_image_embeds""": negative_image_embeds,
									    """generator""": generator,
									    """height""": 6_4,
									    """width""": 6_4,
									    """guidance_scale""": 4.0,
									    """num_inference_steps""": 2,
									    """output_type""": """np""",
									}
									return inputs

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   str:
									"""simple docstring"""




									a__     :		int		 =						"""cpu"""

									a__     :		int		 =						self.get_dummy_components()

									a__     :		Union[str, Any]		 =						self.pipeline_class(**__lowercase						)
									a__     :		List[Any]		 =						pipe.to(__lowercase						)

									pipe.set_progress_bar_config(disable=__lowercase						)

									a__     :		List[str]		 =						pipe(**self.get_dummy_inputs(__lowercase						)						)
									a__     :		Union[str, Any]		 =						output.images

									a__     :		Union[str, Any]		 =						pipe(
									    **self.get_dummy_inputs(__lowercase						)      ,     return_dict=__lowercase      ,     )[0]

									a__     :		Any		 =						image[0, -3:, -3:, -1]
									a__     :		Tuple		 =						image_from_tuple[0, -3:, -3:, -1]

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

									a__     :		Optional[int]		 =						np.array(
									    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]						)

									assert (
									    np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
									), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
									assert (
									    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2
									), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''




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







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Any:
									"""simple docstring"""




									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   str:
									"""simple docstring"""




									a__     :		List[Any]		 =						load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""						)

									a__     :		List[Any]		 =						KandinskyPriorPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1-prior"""      ,     torch_dtype=torch.floataa						)
									pipe_prior.to(__lowercase						)

									a__     :		Tuple		 =						KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""      ,     torch_dtype=torch.floataa						)
									a__     :		Union[str, Any]		 =						pipeline.to(__lowercase						)
									pipeline.set_progress_bar_config(disable=__lowercase						)

									a__     :		Dict		 =						"""red cat, 4k photo"""

									a__     :		str		 =						torch.Generator(device="""cuda"""						).manual_seed(0						)
									a__ ,		a__     :		Tuple		 =						pipe_prior(
									    __lowercase      ,     generator=__lowercase      ,     num_inference_steps=5      ,     negative_prompt=""""""      ,     ).to_tuple()

									a__     :		List[str]		 =						torch.Generator(device="""cuda"""						).manual_seed(0						)
									a__     :		str		 =						pipeline(
									    __lowercase      ,     image_embeds=__lowercase      ,     negative_image_embeds=__lowercase      ,     generator=__lowercase      ,     num_inference_steps=1_0_0      ,     output_type="""np"""      ,     )

									a__     :		Optional[Any]		 =						output.images[0]

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

									assert_mean_pixel_difference(__lowercase      ,     __lowercase						)









from __future__ import annotations

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

class      snake_case__       (A__	):







						"""simple docstring"""




						@abstractmethod
						def __lt__(     self      ,     __lowercase						)      ->   bool:
									"""simple docstring"""




									pass

						@abstractmethod
						def __gt__(     self      ,     __lowercase						)      ->   bool:
									"""simple docstring"""




									pass

						@abstractmethod
						def __eq__(     self      ,     __lowercase						)      ->   bool:
									"""simple docstring"""




									pass




_lowercase	:   List[str]    	=TypeVar("T", bound=Comparable)

class      snake_case__       (Generic[T]	):







						"""simple docstring"""




						def __init__(     self						)      ->   None:
									"""simple docstring"""




									a__     :		list[T]		 =						[]
									a__     :		int		 =						0

						def __repr__(     self						)      ->   str:
									"""simple docstring"""




									return str(self.h						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int | None:
									"""simple docstring"""




									if child_idx > 0:
												return (child_idx - 1) // 2
									return None

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int | None:
									"""simple docstring"""




									a__     :		int		 =						2 * parent_idx + 1
									if left_child_index < self.heap_size:
												return left_child_index
									return None

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int | None:
									"""simple docstring"""




									a__     :		Optional[Any]		 =						2 * parent_idx + 2
									if right_child_index < self.heap_size:
												return right_child_index
									return None

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   None:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   None:
									"""simple docstring"""




									a__     :		Tuple		 =						list(__lowercase						)
									a__     :		Union[str, Any]		 =						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(__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   T:
									"""simple docstring"""




									if self.heap_size >= 2:
												a__     :		Tuple		 =						self.h[0]
												a__     :		Union[str, Any]		 =						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      ,     __lowercase						)      ->   None:
									"""simple docstring"""




									self.h.append(__lowercase						)
									a__     :		Optional[int]		 =						(self.heap_size - 1) // 2
									self.heap_size += 1
									while idx >= 0:
												self.max_heapify(__lowercase						)
												a__     :		Union[str, Any]		 =						(idx - 1) // 2

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   None:
									"""simple docstring"""




									a__     :		Any		 =						self.heap_size
									for j in range(size - 1      ,     0      ,     -1						):
												a__ ,		a__     :		Dict		 =						self.h[j], self.h[0]
												self.heap_size -= 1
												self.max_heapify(0						)
									a__     :		List[Any]		 =						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}')

				_lowercase	:   Heap[int]    	=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')




