


'''simple docstring'''

import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()


class    a__					(			unittest.TestCase   ):







				"""simple docstring"""



				@property
				def        _snake_case	(self		):
										__lowerCAmelCase											=					1
										__lowerCAmelCase											=					3
										__lowerCAmelCase											=					(32, 32)

										__lowerCAmelCase											=					floats_tensor((batch_size, num_channels) + sizes							,      rng=random.Random(0		)		).to(__lowercase		)
										return image




				@property
				def        _snake_case	(self		):
										torch.manual_seed(0		)
										__lowerCAmelCase											=					UNetaDModel(
										    block_out_channels=(32, 64)							,      layers_per_block=2							,      sample_size=32							,      in_channels=6							,      out_channels=3							,      down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')							,      up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')							,      )
										return model




				@property
				def        _snake_case	(self		):
										torch.manual_seed(0		)
										__lowerCAmelCase											=					VQModel(
										    block_out_channels=[32, 64]							,      in_channels=3							,      out_channels=3							,      down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,      latent_channels=3							,      )
										return model




				def        _snake_case	(self		):
										__lowerCAmelCase											=					'''cpu'''
										__lowerCAmelCase											=					self.dummy_uncond_unet
										__lowerCAmelCase											=					DDIMScheduler()
										__lowerCAmelCase											=					self.dummy_vq_model

										__lowerCAmelCase											=					LDMSuperResolutionPipeline(unet=__lowercase							,      vqvae=__lowercase							,      scheduler=__lowercase		)
										ldm.to(__lowercase		)
										ldm.set_progress_bar_config(disable=__lowercase		)

										__lowerCAmelCase											=					self.dummy_image.to(__lowercase		)

										__lowerCAmelCase											=					torch.Generator(device=__lowercase		).manual_seed(0		)
										__lowerCAmelCase											=					ldm(image=__lowercase							,      generator=__lowercase							,      num_inference_steps=2							,      output_type='''numpy'''		).images

										__lowerCAmelCase											=					image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										__lowerCAmelCase											=					np.array([0.8_6_7_8, 0.8_2_4_5, 0.6_3_8_1, 0.6_8_3_0, 0.4_3_8_5, 0.5_5_9_9, 0.4_6_4_1, 0.6_2_0_1, 0.5_1_5_0]		)

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




				@unittest.skipIf(torch_device != '''cuda'''							,      '''This test requires a GPU'''		)
				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.dummy_uncond_unet
										__lowerCAmelCase											=					DDIMScheduler()
										__lowerCAmelCase											=					self.dummy_vq_model

										# put models in fp16
										__lowerCAmelCase											=					unet.half()
										__lowerCAmelCase											=					vqvae.half()

										__lowerCAmelCase											=					LDMSuperResolutionPipeline(unet=__lowercase							,      vqvae=__lowercase							,      scheduler=__lowercase		)
										ldm.to(__lowercase		)
										ldm.set_progress_bar_config(disable=__lowercase		)

										__lowerCAmelCase											=					self.dummy_image.to(__lowercase		)

										__lowerCAmelCase											=					ldm(__lowercase							,      num_inference_steps=2							,      output_type='''numpy'''		).images

										assert image.shape == (1, 64, 64, 3)


@slow
@require_torch
class    a__					(			unittest.TestCase   ):







				"""simple docstring"""



				def        _snake_case	(self		):
										__lowerCAmelCase											=					load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/vq_diffusion/teddy_bear_pool.png'''		)
										__lowerCAmelCase											=					init_image.resize((64, 64)							,      resample=PIL_INTERPOLATION['''lanczos''']		)

										__lowerCAmelCase											=					LDMSuperResolutionPipeline.from_pretrained('''duongna/ldm-super-resolution'''							,      device_map='''auto'''		)
										ldm.set_progress_bar_config(disable=__lowercase		)

										__lowerCAmelCase											=					torch.manual_seed(0		)
										__lowerCAmelCase											=					ldm(image=__lowercase							,      generator=__lowercase							,      num_inference_steps=20							,      output_type='''numpy'''		).images

										__lowerCAmelCase											=					image[0, -3:, -3:, -1]

										assert image.shape == (1, 2_56, 2_56, 3)
										__lowerCAmelCase											=					np.array([0.7_6_4_4, 0.7_6_7_9, 0.7_6_4_2, 0.7_6_3_3, 0.7_6_6_6, 0.7_5_6_0, 0.7_4_2_5, 0.7_2_5_7, 0.6_9_0_7]		)

										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

_UpperCAmelCase					:			Any				     =					TypeVar("""T""", bound=bool)


class    a__					(			Generic[T]   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										__lowerCAmelCase											=					value
										__lowerCAmelCase											=					None
										__lowerCAmelCase											=					None




				@property
				def        _snake_case	(self		):
										return self._value




				@staticmethod
				def        _snake_case	(__lowercase							,      __lowercase		):
										if not roota:
																return roota

										if not roota:
																return roota

										if roota.value > roota.value:
																__lowerCAmelCase							,       __lowerCAmelCase											=					roota, roota

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

										__lowerCAmelCase											=					RandomizedHeapNode.merge(roota.left							,      __lowercase		)

										return roota


class    a__					(			Generic[T]   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase = ()		):
										__lowerCAmelCase											=					None

										if data:
																for item in data:
																						self.insert(__lowercase		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					RandomizedHeapNode.merge(self._root							,      RandomizedHeapNode(__lowercase		)		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.top()

										if self._root is None:
																return None

										__lowerCAmelCase											=					RandomizedHeapNode.merge(self._root.left							,      self._root.right		)

										return result




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




				def        _snake_case	(self		):
										__lowerCAmelCase											=					None




				def        _snake_case	(self		):
										__lowerCAmelCase											=					[]
										while self:
																result.append(self.pop()		)

										return result




				def __bool__(self		):
										return self._root is not None


if __name__ == "__main__":
	import doctest

	doctest.testmod()

