




"""simple docstring"""




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    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      lowerCAmelCase_						(a__			, a__			, unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase				:						List[Any] 	=       StableDiffusionXLPipeline
				__UpperCamelCase				:						str 	=       TEXT_TO_IMAGE_PARAMS
				__UpperCamelCase				:						Any 	=       TEXT_TO_IMAGE_BATCH_PARAMS
				__UpperCamelCase				:						List[str] 	=       TEXT_TO_IMAGE_IMAGE_PARAMS
				__UpperCamelCase				:						Optional[int] 	=       TEXT_TO_IMAGE_IMAGE_PARAMS



				def      __magic_name__   (self   ) ->		str:



											"""simple docstring"""




											torch.manual_seed(0   )
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       UNetaDConditionModel(
											    block_out_channels=(32, 64)       ,	layers_per_block=2       ,	sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")       ,	attention_head_dim=(2, 4)       ,	use_linear_projection=SCREAMING_SNAKE_CASE__       ,	addition_embed_type="""text_time"""       ,	addition_time_embed_dim=8       ,	transformer_layers_per_block=(1, 2)       ,	projection_class_embeddings_input_dim=80       ,	cross_attention_dim=64       ,	)
											SCREAMING_SNAKE_CASE__						:	str									=       EulerDiscreteScheduler(
											    beta_start=0.00085       ,	beta_end=0.012       ,	steps_offset=1       ,	beta_schedule="""scaled_linear"""       ,	timestep_spacing="""leading"""       ,	)
											torch.manual_seed(0   )
											SCREAMING_SNAKE_CASE__						:	int									=       AutoencoderKL(
											    block_out_channels=[32, 64]       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,	latent_channels=4       ,	sample_size=1_28       ,	)
											torch.manual_seed(0   )
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       CLIPTextConfig(
											    bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=32       ,	intermediate_size=37       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=10_00       ,	hidden_act="""gelu"""       ,	projection_dim=32       ,	)
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       CLIPTextModel(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       ,	local_files_only=SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Any									=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       ,	local_files_only=SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """vae""": vae,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """text_encoder_2""": text_encoder_a,
											    """tokenizer_2""": tokenizer_a,
											    # "safety_checker": None,
											    # "feature_extractor": None,
											}
											return components



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=0   ) ->		Optional[Any]:



											"""simple docstring"""




											if str(SCREAMING_SNAKE_CASE__   ).startswith("""mps"""   ):
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.manual_seed(SCREAMING_SNAKE_CASE__   )
											else:
																		SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.Generator(device=SCREAMING_SNAKE_CASE__   ).manual_seed(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       {
											    """prompt""": """A painting of a squirrel eating a burger""",
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """guidance_scale""": 5.0,
											    """output_type""": """numpy""",
											}
											return inputs



				def      __magic_name__   (self   ) ->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.get_dummy_components()
											SCREAMING_SNAKE_CASE__						:	str									=       StableDiffusionXLPipeline(**SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	str									=       sd_pipe.to(SCREAMING_SNAKE_CASE__   )
											sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	int									=       self.get_dummy_inputs(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       sd_pipe(**SCREAMING_SNAKE_CASE__   ).images
											SCREAMING_SNAKE_CASE__						:	List[str]									=       image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											SCREAMING_SNAKE_CASE__						:	Dict									=       np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747]   )

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



				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       self.get_dummy_components()
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       StableDiffusionXLPipeline(**SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       sd_pipe.to(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Tuple									=       sd_pipe.to(SCREAMING_SNAKE_CASE__   )
											sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

											# forward without prompt embeds
											SCREAMING_SNAKE_CASE__						:	str									=       self.get_dummy_inputs(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	List[str]									=       3 * ["""this is a negative prompt"""]
											SCREAMING_SNAKE_CASE__						:	int									=       negative_prompt
											SCREAMING_SNAKE_CASE__						:	List[str]									=       3 * [inputs["""prompt"""]]

											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       sd_pipe(**SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Any									=       output.images[0, -3:, -3:, -1]

											# forward with prompt embeds
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       self.get_dummy_inputs(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	List[str]									=       3 * ["""this is a negative prompt"""]
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       3 * [inputs.pop("""prompt"""   )]

											(
											    SCREAMING_SNAKE_CASE__				
											)						:	int									=       sd_pipe.encode_prompt(SCREAMING_SNAKE_CASE__       ,	negative_prompt=SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       sd_pipe(
											    **SCREAMING_SNAKE_CASE__       ,	prompt_embeds=SCREAMING_SNAKE_CASE__       ,	negative_prompt_embeds=SCREAMING_SNAKE_CASE__       ,	pooled_prompt_embeds=SCREAMING_SNAKE_CASE__       ,	negative_pooled_prompt_embeds=SCREAMING_SNAKE_CASE__       ,	)
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       output.images[0, -3:, -3:, -1]

											# make sure that it's equal
											assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1E-4



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



											"""simple docstring"""




											super().test_attention_slicing_forward_pass(expected_max_diff=3E-3   )



				def      __magic_name__   (self   ) ->		int:



											"""simple docstring"""




											super().test_inference_batch_single_identical(expected_max_diff=3E-3   )






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


				"""simple docstring"""



				def      __magic_name__   (self   ) ->		List[str]:



											"""simple docstring"""




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



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__="cpu"       ,	SCREAMING_SNAKE_CASE__=torch.floataa       ,	SCREAMING_SNAKE_CASE__=0   ) ->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.Generator(device=SCREAMING_SNAKE_CASE__   ).manual_seed(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       np.random.RandomState(SCREAMING_SNAKE_CASE__   ).standard_normal((1, 4, 64, 64)   )
											SCREAMING_SNAKE_CASE__						:	Dict									=       torch.from_numpy(SCREAMING_SNAKE_CASE__   ).to(device=SCREAMING_SNAKE_CASE__       ,	dtype=SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Any									=       {
											    """prompt""": """a photograph of an astronaut riding a horse""",
											    """latents""": latents,
											    """generator""": generator,
											    """num_inference_steps""": 3,
											    """guidance_scale""": 7.5,
											    """output_type""": """numpy""",
											}
											return inputs



				def      __magic_name__   (self   ) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[str]									=       DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""   )
											pipe.to(SCREAMING_SNAKE_CASE__   )
											pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.get_inputs(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	str									=       pipe(**SCREAMING_SNAKE_CASE__   ).images
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_12, 5_12, 3)
											SCREAMING_SNAKE_CASE__						:	List[str]									=       np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]   )
											assert np.abs(image_slice - expected_slice   ).max() < 7E-3






"""simple docstring"""




def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Any									=       index
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       2 * index + 1
							SCREAMING_SNAKE_CASE__						:	Any									=       2 * index + 2
							if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
														SCREAMING_SNAKE_CASE__						:	str									=       left_index

							if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
														SCREAMING_SNAKE_CASE__						:	Any									=       right_index

							if largest != index:
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Tuple									=       unsorted[index], unsorted[largest]
														heapify(_snake_case       ,_snake_case       ,_snake_case						)

def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       len(_snake_case						)
							for i in range(n // 2 - 1       ,-1       ,-1						):
														heapify(_snake_case       ,_snake_case       ,_snake_case						)
							for i in range(n - 1       ,0       ,-1						):
														SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Any									=       unsorted[i], unsorted[0]
														heapify(_snake_case       ,0       ,_snake_case						)
							return unsorted


if __name__ == "__main__":
					UpperCAmelCase__				:   Optional[Any]   							=			input('Enter numbers separated by a comma:\n').strip()
					UpperCAmelCase__				:   Optional[int]   							=			[int(item) for item in user_input.split(',')]
					print(heap_sort(unsorted))




