




def lowercase						(     ) ->       int:


			'''simple docstring'''







			for a in range(3_00   ):
						for b in range(a + 1					,						4_00   ):
									for c in range(b + 1					,						5_00   ):
												if (a + b + c) == 10_00 and (a**2) + (b**2) == (c**2):
															return a * b * c

			return -1

def lowercase						(     ) ->       int:


			'''simple docstring'''







			for a in range(3_00   ):
						for b in range(4_00   ):
									SCREAMING_SNAKE_CASE_			=	10_00 - a - b
									if a < b < c and (a**2) + (b**2) == (c**2):
												return a * b * c

			return -1

def lowercase						(     ) ->       None:


			'''simple docstring'''







			import timeit

			print(
			    timeit.timeit('solution()'					,						setup='from __main__ import solution'					,						number=10_00   )   )
			print(
			    timeit.timeit(
			        'solution_fast()'					,						setup='from __main__ import solution_fast'					,						number=10_00   )   )


if __name__ == "__main__":
				print(f"""{solution() = }""")






import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device

def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       int:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	{}
			for name in unet.attn_processors.keys():
						SCREAMING_SNAKE_CASE_			=	None if name.endswith('attn1.processor'   ) else unet.config.cross_attention_dim
						if name.startswith('mid_block'   ):
									SCREAMING_SNAKE_CASE_			=	unet.config.block_out_channels[-1]
						elif name.startswith('up_blocks'   ):
									SCREAMING_SNAKE_CASE_			=	int(name[len('up_blocks.'   )]   )
									SCREAMING_SNAKE_CASE_			=	list(reversed(unet.config.block_out_channels   )   )[block_id]
						elif name.startswith('down_blocks'   ):
									SCREAMING_SNAKE_CASE_			=	int(name[len('down_blocks.'   )]   )
									SCREAMING_SNAKE_CASE_			=	unet.config.block_out_channels[block_id]
						SCREAMING_SNAKE_CASE_			=	(
						    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE					,						'scaled_dot_product_attention'   ) else LoRAAttnProcessor
						)
						SCREAMING_SNAKE_CASE_			=	lora_attn_processor_class(
						    hidden_size=SCREAMING_SNAKE_CASE					,						cross_attention_dim=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE_			=	AttnProcsLayers(SCREAMING_SNAKE_CASE   )
			return lora_attn_procs, unet_lora_layers

def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Optional[Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	{}
			SCREAMING_SNAKE_CASE_			=	(
			    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE					,						'scaled_dot_product_attention'   ) else LoRAAttnProcessor
			)
			for name, module in text_encoder.named_modules():
						if name.endswith(SCREAMING_SNAKE_CASE   ):
									SCREAMING_SNAKE_CASE_			=	lora_attn_processor_class(
									    hidden_size=module.out_proj.out_features					,						cross_attention_dim=SCREAMING_SNAKE_CASE   )
			return text_lora_attn_procs

def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE_			=	AttnProcsLayers(SCREAMING_SNAKE_CASE   )
			return text_encoder_lora_layers

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=False   ) ->       Dict:


			'''simple docstring'''







			for _, attn_proc in text_lora_attn_procs.items():
						# set up.weights
						for layer_name, layer_module in attn_proc.named_modules():
									if layer_name.endswith('_lora'   ):
												SCREAMING_SNAKE_CASE_			=	(
												    torch.randn_like(layer_module.up.weight   )
												    if randn_weight
												    else torch.zeros_like(layer_module.up.weight   )
												)
												SCREAMING_SNAKE_CASE_			=	torch.nn.Parameter(SCREAMING_SNAKE_CASE   )



class 			a_					(    unittest.TestCase    ):





	def 	A_(       self			)     ->				str:




				"""simple docstring"""



				torch.manual_seed(0			)
				SCREAMING_SNAKE_CASE_			=	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')			,    cross_attention_dim=32			,    )
				SCREAMING_SNAKE_CASE_			=	DDIMScheduler(
				    beta_start=0.0_0_0_8_5			,    beta_end=0.0_1_2			,    beta_schedule='scaled_linear'			,    clip_sample=SCREAMING_SNAKE_CASE			,    set_alpha_to_one=SCREAMING_SNAKE_CASE			,    steps_offset=1			,    )
				torch.manual_seed(0			)
				SCREAMING_SNAKE_CASE_			=	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			,    )
				SCREAMING_SNAKE_CASE_			=	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=1000			,    )
				SCREAMING_SNAKE_CASE_			=	CLIPTextModel(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	create_unet_lora_layers(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_layers(SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	{
				    'unet': unet,
				    'scheduler': scheduler,
				    'vae': vae,
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'safety_checker': None,
				    'feature_extractor': None,
				}
				SCREAMING_SNAKE_CASE_			=	{
				    'unet_lora_layers': unet_lora_layers,
				    'text_encoder_lora_layers': text_encoder_lora_layers,
				    'unet_lora_attn_procs': unet_lora_attn_procs,
				}
				return pipeline_components, lora_components





	def 	A_(       self			)     ->				Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	1
				SCREAMING_SNAKE_CASE_			=	10
				SCREAMING_SNAKE_CASE_			=	4
				SCREAMING_SNAKE_CASE_			=	(32, 32)

				SCREAMING_SNAKE_CASE_			=	torch.manual_seed(0			)
				SCREAMING_SNAKE_CASE_			=	floats_tensor((batch_size, num_channels) + sizes			)
				SCREAMING_SNAKE_CASE_			=	torch.randint(1			,    SCREAMING_SNAKE_CASE			,    size=(batch_size, sequence_length)			,    generator=SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	{
				    'prompt': 'A painting of a squirrel eating a burger',
				    'generator': generator,
				    'num_inference_steps': 2,
				    'guidance_scale': 6.0,
				    'output_type': 'numpy',
				}

				return noise, input_ids, pipeline_inputs

				# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb





	def 	A_(       self			)     ->				Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	77

				SCREAMING_SNAKE_CASE_			=	torch.randint(2			,    56			,    size=(1, max_seq_length)			,    generator=torch.manual_seed(0			)			)

				SCREAMING_SNAKE_CASE_			=	{}
				SCREAMING_SNAKE_CASE_			=	inputs
				return prepared_inputs





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
				LoraLoaderMixin.save_lora_weights(
				    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    )
				self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)





	def 	A_(       self			)     ->				Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()

				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]

				with tempfile.TemporaryDirectory() as tmpdirname:
							LoraLoaderMixin.save_lora_weights(
							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    )
							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]

				# Outputs shouldn't match.
				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)





	def 	A_(       self			)     ->				Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()

				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]

				with tempfile.TemporaryDirectory() as tmpdirname:
							LoraLoaderMixin.save_lora_weights(
							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    safe_serialization=SCREAMING_SNAKE_CASE			,    )
							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.safetensors'			)			)			)
							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]

				# Outputs shouldn't match.
				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)





	def 	A_(       self			)     ->				Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
				SCREAMING_SNAKE_CASE_			=	lora_components['unet_lora_attn_procs']
				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()

				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]

				with tempfile.TemporaryDirectory() as tmpdirname:
							SCREAMING_SNAKE_CASE_			=	sd_pipe.unet
							unet.set_attn_processor(SCREAMING_SNAKE_CASE			)
							unet.save_attn_procs(SCREAMING_SNAKE_CASE			)
							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]

				# Outputs shouldn't match.
				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)





	def 	A_(       self			)     ->				str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	self.get_dummy_tokens()

				# inference without lora
				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
				assert outputs_without_lora.shape == (1, 77, 32)

				# create lora_attn_procs with zeroed out up.weights
				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)

				# monkey patch
				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)

				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
				del text_attn_procs
				gc.collect()

				# inference with lora
				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
				assert outputs_with_lora.shape == (1, 77, 32)

				assert torch.allclose(
				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

				# create lora_attn_procs with randn up.weights
				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)

				# monkey patch
				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)

				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
				del text_attn_procs
				gc.collect()

				# inference with lora
				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
				assert outputs_with_lora.shape == (1, 77, 32)

				assert not torch.allclose(
				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"





	def 	A_(       self			)     ->				Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	self.get_dummy_tokens()

				# inference without lora
				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
				assert outputs_without_lora.shape == (1, 77, 32)

				# create lora_attn_procs with randn up.weights
				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)

				# monkey patch
				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)

				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
				del text_attn_procs
				gc.collect()

				# inference with lora
				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
				assert outputs_with_lora.shape == (1, 77, 32)

				assert not torch.allclose(
				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora outputs should be different to without lora outputs"

				# remove monkey patch
				pipe._remove_text_encoder_monkey_patch()

				# inference with removed lora
				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
				assert outputs_without_lora_removed.shape == (1, 77, 32)

				assert torch.allclose(
				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "remove lora monkey patch should restore the original outputs"





	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()

				with tempfile.TemporaryDirectory() as tmpdirname:
							LoraLoaderMixin.save_lora_weights(
							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    )
							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]

				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			,    cross_attention_kwargs={'scale': 0.5}			).images
				SCREAMING_SNAKE_CASE_			=	lora_images_with_scale[0, -3:, -3:, -1]

				# Outputs shouldn't match.
				self.assertFalse(
				    torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)





	def 	A_(       self			)     ->				Union[str, Any]:




				"""simple docstring"""



				with tempfile.TemporaryDirectory() as tmpdirname:
							self.create_lora_weight_file(SCREAMING_SNAKE_CASE			)

							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
							SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
							sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)

							# check if vanilla attention processors are used
							for _, module in sd_pipe.unet.named_modules():
										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
													self.assertIsInstance(module.processor			,    (AttnProcessor, AttnProcessor2_0)			)

            # load LoRA weight file
							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)

							# check if lora attention processors are used
							for _, module in sd_pipe.unet.named_modules():
										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
													SCREAMING_SNAKE_CASE_			=	(
													    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE			,    'scaled_dot_product_attention'			) else LoRAAttnProcessor
													)
													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)





	@unittest.skipIf(torch_device != 'cuda'			,    'This test is supposed to run on GPU'			)
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				with tempfile.TemporaryDirectory() as tmpdirname:
							self.create_lora_weight_file(SCREAMING_SNAKE_CASE			)

							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
							SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
							sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)

							# enable XFormers
							sd_pipe.enable_xformers_memory_efficient_attention()

							# check if xFormers attention processors are used
							for _, module in sd_pipe.unet.named_modules():
										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)

            # load LoRA weight file
							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)

							# check if lora attention processors are used
							for _, module in sd_pipe.unet.named_modules():
										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)





	@unittest.skipIf(torch_device != 'cuda'			,    'This test is supposed to run on GPU'			)
	def 	A_(       self			)     ->				str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()

				# enable XFormers
				sd_pipe.enable_xformers_memory_efficient_attention()

				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]

				with tempfile.TemporaryDirectory() as tmpdirname:
							LoraLoaderMixin.save_lora_weights(
							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    )
							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]

				# Outputs shouldn't match.
				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
