


from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat




def     __SCREAMING_SNAKE_CASE							(       )		->     tuple[list[int], int]:




						'''simple docstring'''







						__UpperCAmelCase			:							List[Any]	    =	[randint(-1000      ,       1000      ) for i in range(10      )]
						__UpperCAmelCase			:							Dict	    =	randint(-5000      ,       5000      )
						return (arr, r)


lowerCAmelCase       		=    make_dataset()




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     tuple[int, ...]:




						'''simple docstring'''







						for triplet in permutations(lowercase_      ,       3      ):
												if sum(lowercase_      ) == target:
																		return tuple(sorted(lowercase_      )      )
						return (0, 0, 0)




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     tuple[int, int, int]:




						'''simple docstring'''







						arr.sort()
						__UpperCAmelCase			:							Union[str, Any]	    =	len(lowercase_      )
						for i in range(n - 1      ):
												__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	i + 1, n - 1
												while left < right:
																		if arr[i] + arr[left] + arr[right] == target:
																								return (arr[i], arr[left], arr[right])
																		elif arr[i] + arr[left] + arr[right] < target:
																								left += 1
																		elif arr[i] + arr[left] + arr[right] > target:
																								right -= 1
						return (0, 0, 0)




def     __SCREAMING_SNAKE_CASE							(       )		->     tuple[float, float]:




						'''simple docstring'''







						__UpperCAmelCase			:							Union[str, Any]	    =	'''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
						__UpperCAmelCase			:							int	    =	'''
triplet_sum1(*dataset)
'''
						__UpperCAmelCase			:							Union[str, Any]	    =	'''
triplet_sum2(*dataset)
'''
						__UpperCAmelCase			:							Any	    =	repeat(setup=lowercase_      ,       stmt=lowercase_      ,       repeat=5      ,       number=10000      )
						__UpperCAmelCase			:							Dict	    =	repeat(setup=lowercase_      ,       stmt=lowercase_      ,       repeat=5      ,       number=10000      )
						return (min(lowercase_      ), min(lowercase_      ))


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				lowerCAmelCase       		=    solution_times()
				print(F'The time for naive implementation is {times[0]}.')
				print(F'The time for optimized implementation is {times[1]}.')





import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()




class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Optional[Any]										=						StableUnCLIPImgaImgPipeline
	_lowerCAmelCase     :		Any										=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS
	_lowerCAmelCase     :		List[Any]										=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	_lowerCAmelCase     :		Dict										=						frozenset(
	    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	_lowerCAmelCase     :		str										=						frozenset([] )


	def  A(    self):
							__UpperCAmelCase			:							Any	    =	3_2
							__UpperCAmelCase			:							Union[str, Any]	    =	embedder_hidden_size

							# image encoding components

							__UpperCAmelCase			:							Union[str, Any]	    =	CLIPImageProcessor(crop_size=3_2				,     size=3_2)

							torch.manual_seed(0)
							__UpperCAmelCase			:							List[str]	    =	CLIPVisionModelWithProjection(
							    CLIPVisionConfig(
							        hidden_size=lowercase__				,     projection_dim=lowercase__				,     num_hidden_layers=5				,     num_attention_heads=4				,     image_size=3_2				,     intermediate_size=3_7				,     patch_size=1				,     ))

							# regular denoising components

							torch.manual_seed(0)
							__UpperCAmelCase			:							List[Any]	    =	StableUnCLIPImageNormalizer(embedding_dim=lowercase__)
							__UpperCAmelCase			:							Any	    =	DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')

							torch.manual_seed(0)
							__UpperCAmelCase			:							Dict	    =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

							torch.manual_seed(0)
							__UpperCAmelCase			:							int	    =	CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0				,     eos_token_id=2				,     hidden_size=lowercase__				,     projection_dim=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				,     ))

							torch.manual_seed(0)
							__UpperCAmelCase			:							Tuple	    =	UNetaDConditionModel(
							    sample_size=3_2				,     in_channels=4				,     out_channels=4				,     down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')				,     up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')				,     block_out_channels=(3_2, 6_4)				,     attention_head_dim=(2, 4)				,     class_embed_type='''projection'''				,     projection_class_embeddings_input_dim=embedder_projection_dim * 2				,     cross_attention_dim=lowercase__				,     layers_per_block=1				,     upcast_attention=lowercase__				,     use_linear_projection=lowercase__				,     )

							torch.manual_seed(0)
							__UpperCAmelCase			:							Optional[Any]	    =	DDIMScheduler(
							    beta_schedule='''scaled_linear'''				,     beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     prediction_type='''v_prediction'''				,     set_alpha_to_one=lowercase__				,     steps_offset=1				,     )

							torch.manual_seed(0)
							__UpperCAmelCase			:							Optional[Any]	    =	AutoencoderKL()

							__UpperCAmelCase			:							Optional[Any]	    =	{
							    # image encoding components
							    '''feature_extractor''': feature_extractor,
							    '''image_encoder''': image_encoder.eval(),
							    # image noising components
							    '''image_normalizer''': image_normalizer.eval(),
							    '''image_noising_scheduler''': image_noising_scheduler,
							    # regular denoising components
							    '''tokenizer''': tokenizer,
							    '''text_encoder''': text_encoder.eval(),
							    '''unet''': unet.eval(),
							    '''scheduler''': scheduler,
							    '''vae''': vae.eval(),
							}

							return components


	def  A(    self				,     lowercase__				,     lowercase__=0				,     lowercase__=True):
							if str(lowercase__).startswith('''mps'''):
													__UpperCAmelCase			:							Union[str, Any]	    =	torch.manual_seed(lowercase__)
							else:
													__UpperCAmelCase			:							int	    =	torch.Generator(device=lowercase__).manual_seed(lowercase__)

							__UpperCAmelCase			:							Dict	    =	floats_tensor((1, 3, 3_2, 3_2)				,     rng=random.Random(lowercase__)).to(lowercase__)

							if pil_image:
													__UpperCAmelCase			:							int	    =	input_image * 0.5 + 0.5
													__UpperCAmelCase			:							str	    =	input_image.clamp(0				,     1)
													__UpperCAmelCase			:							Optional[int]	    =	input_image.cpu().permute(0				,     2				,     3				,     1).float().numpy()
													__UpperCAmelCase			:							Union[str, Any]	    =	DiffusionPipeline.numpy_to_pil(lowercase__)[0]

							return {
							    "prompt": "An anime racoon running a marathon",
							    "image": input_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "np",
							}


	@skip_mps
	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							__UpperCAmelCase			:							Dict	    =	self.get_dummy_components()
							__UpperCAmelCase			:							List[Any]	    =	StableUnCLIPImgaImgPipeline(**lowercase__)
							__UpperCAmelCase			:							List[str]	    =	sd_pipe.to(lowercase__)
							sd_pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Dict	    =	self.get_dummy_inputs(lowercase__)
							inputs.update({'''image_embeds''': None})
							__UpperCAmelCase			:							Any	    =	sd_pipe(**lowercase__).images
							__UpperCAmelCase			:							Union[str, Any]	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 3_2, 3_2, 3)
							__UpperCAmelCase			:							Union[str, Any]	    =	np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	torch_device in ['''cpu''', '''mps''']

							self._test_attention_slicing_forward_pass(test_max_difference=lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							str	    =	torch_device in ['''cpu''', '''mps''']

							self._test_inference_batch_single_identical(test_max_difference=lowercase__)


	@unittest.skipIf(
	    torch_device != '''cuda''' or not is_xformers_available()				,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,     )
	def  A(    self):
							self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowercase__)






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


	def  A(    self):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

							__UpperCAmelCase			:							List[str]	    =	load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy''')

							__UpperCAmelCase			:							Optional[int]	    =	StableUnCLIPImgaImgPipeline.from_pretrained(
							    '''fusing/stable-unclip-2-1-l-img2img'''				,     torch_dtype=torch.floataa)
							pipe.to(lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__UpperCAmelCase			:							Optional[Any]	    =	torch.Generator(device='''cpu''').manual_seed(0)
							__UpperCAmelCase			:							Any	    =	pipe(lowercase__				,     '''anime turle'''				,     generator=lowercase__				,     output_type='''np''')

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

							assert image.shape == (7_6_8, 7_6_8, 3)

							assert_mean_pixel_difference(lowercase__				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							str	    =	load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

							__UpperCAmelCase			:							Union[str, Any]	    =	load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy''')

							__UpperCAmelCase			:							Union[str, Any]	    =	StableUnCLIPImgaImgPipeline.from_pretrained(
							    '''fusing/stable-unclip-2-1-h-img2img'''				,     torch_dtype=torch.floataa)
							pipe.to(lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__UpperCAmelCase			:							Dict	    =	torch.Generator(device='''cpu''').manual_seed(0)
							__UpperCAmelCase			:							int	    =	pipe(lowercase__				,     '''anime turle'''				,     generator=lowercase__				,     output_type='''np''')

							__UpperCAmelCase			:							List[str]	    =	output.images[0]

							assert image.shape == (7_6_8, 7_6_8, 3)

							assert_mean_pixel_difference(lowercase__				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							str	    =	load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							__UpperCAmelCase			:							Optional[int]	    =	StableUnCLIPImgaImgPipeline.from_pretrained(
							    '''fusing/stable-unclip-2-1-h-img2img'''				,     torch_dtype=torch.floataa)
							__UpperCAmelCase			:							Optional[int]	    =	pipe.to(lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__UpperCAmelCase			:							int	    =	pipe(
							    lowercase__				,     '''anime turtle'''				,     num_inference_steps=2				,     output_type='''np'''				,     )

							__UpperCAmelCase			:							Optional[Any]	    =	torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 1_0**9

