





'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import TvltImageProcessor



def  lowercase__(		_UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : Tuple=10 ,   _UpperCamelCase  : List[Any]=10 ,   _UpperCamelCase  : str=False ,   _UpperCamelCase  : int=False				)->   Union[str, Any]:






					"""simple docstring"""

					_UpperCamelCase											=   []
					for i in range(image_processor_tester.num_frames				):
										video.append(np.random.randint(255 ,   size=(image_processor_tester.num_channels, width, height) ,   dtype=np.uinta				)				)

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										_UpperCamelCase											=   [Image.fromarray(np.moveaxis(__snake_case ,   0 ,   -1				)				) for frame in video]

					if torchify:
										_UpperCamelCase											=   [torch.from_numpy(__snake_case				) for frame in video]

					return video



def  lowercase__(		_UpperCamelCase  : Tuple ,   _UpperCamelCase  : Union[str, Any]=False ,   _UpperCamelCase  : List[str]=False ,   _UpperCamelCase  : Optional[Any]=False				)->   Union[str, Any]:






					"""simple docstring"""

					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					_UpperCamelCase											=   []
					for i in range(image_processor_tester.batch_size				):
										if equal_resolution:
															_UpperCamelCase											=   _UpperCamelCase											=   image_processor_tester.max_resolution
										else:
															_UpperCamelCase				,	_UpperCamelCase											=   np.random.choice(
															    np.arange(image_processor_tester.min_resolution ,   image_processor_tester.max_resolution				) ,   2				)
															_UpperCamelCase											=   prepare_video(
															    image_processor_tester=__snake_case ,   width=__snake_case ,   height=__snake_case ,   numpify=__snake_case ,   torchify=__snake_case ,   )
										video_inputs.append(__snake_case				)

					return video_inputs

class     A_ (  unittest.TestCase  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_=7			,     A_=3			,     A_=4			,     A_=18			,     A_=30			,     A_=4_00			,     A_=True			,     A_=None			,     A_=True			,     A_=[0.5, 0.5, 0.5]			,     A_=[0.5, 0.5, 0.5]			,     A_=True			,     A_=None			,     ):
								_UpperCamelCase											=   size if size is not None else {"shortest_edge": 18}
								_UpperCamelCase											=   crop_size if crop_size is not None else {"height": 18, "width": 18}

								_UpperCamelCase											=   parent
								_UpperCamelCase											=   batch_size
								_UpperCamelCase											=   num_channels
								_UpperCamelCase											=   num_frames
								_UpperCamelCase											=   image_size
								_UpperCamelCase											=   min_resolution
								_UpperCamelCase											=   max_resolution
								_UpperCamelCase											=   do_resize
								_UpperCamelCase											=   size
								_UpperCamelCase											=   do_normalize
								_UpperCamelCase											=   image_mean
								_UpperCamelCase											=   image_std
								_UpperCamelCase											=   do_center_crop
								_UpperCamelCase											=   crop_size




			def       a	(					self   ):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}




@require_torch
@require_vision
class     A_ (  lowerCAmelCase_    ,			unittest.TestCase  ):

			'''simple docstring'''





			_lowerCAmelCase						=    TvltImageProcessor if is_vision_available() else None




			def       a	(					self   ):
								_UpperCamelCase											=   TvltImageProcessorTester(self   )




			@property
			def       a	(					self   ):
								return self.image_processor_tester.prepare_image_processor_dict()




			def       a	(					self   ):
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(A_			,     "image_mean"   )   )
								self.assertTrue(hasattr(A_			,     "image_std"   )   )
								self.assertTrue(hasattr(A_			,     "do_normalize"   )   )
								self.assertTrue(hasattr(A_			,     "do_resize"   )   )
								self.assertTrue(hasattr(A_			,     "do_center_crop"   )   )
								self.assertTrue(hasattr(A_			,     "size"   )   )




			def       a	(					self   ):
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random PIL videos
								_UpperCamelCase											=   prepare_video_inputs(self.image_processor_tester			,     equal_resolution=A_   )
								for video in video_inputs:
													self.assertIsInstance(A_			,     A_   )
													self.assertIsInstance(video[0]			,     Image.Image   )

								# Test not batched input
								_UpperCamelCase											=   image_processor(video_inputs[0]			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_videos.shape			,     (
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )			,     )

								# Test batched
								_UpperCamelCase											=   image_processor(A_			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_videos.shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )			,     )




			def       a	(					self   ):
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random numpy tensors
								_UpperCamelCase											=   prepare_video_inputs(self.image_processor_tester			,     equal_resolution=A_			,     numpify=A_   )
								for video in video_inputs:
													self.assertIsInstance(A_			,     A_   )
													self.assertIsInstance(video[0]			,     np.ndarray   )

								# Test not batched input
								_UpperCamelCase											=   image_processor(video_inputs[0]			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_videos.shape			,     (
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )			,     )

								# Test batched
								_UpperCamelCase											=   image_processor(A_			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_videos.shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )			,     )




			def       a	(					self   ):
								_UpperCamelCase											=   self.image_processing_class(**self.image_processor_dict   )
								# create random PyTorch tensors
								_UpperCamelCase											=   prepare_video_inputs(self.image_processor_tester			,     equal_resolution=A_			,     torchify=A_   )
								for video in video_inputs:
													self.assertIsInstance(A_			,     A_   )
													self.assertIsInstance(video[0]			,     torch.Tensor   )

								# Test not batched input
								_UpperCamelCase											=   image_processor(video_inputs[0]			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_videos.shape			,     (
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )			,     )

								# Test batched
								_UpperCamelCase											=   image_processor(A_			,     return_tensors="pt"   ).pixel_values
								self.assertEqual(
								    encoded_videos.shape			,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )			,     )










'''simple docstring'''



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__(		_UpperCamelCase  : nn.Module				)->   Any:






					"""simple docstring"""

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



def  lowercase__(		_UpperCamelCase  : nn.Module				)->   List[Any]:






					"""simple docstring"""

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



def  lowercase__(		_UpperCamelCase  : nn.Module				)->   Dict:






					"""simple docstring"""

					_UpperCamelCase											=   create_text_encoder_lora_attn_procs(_UpperCamelCase				)
					_UpperCamelCase											=   AttnProcsLayers(_UpperCamelCase				)
					return text_encoder_lora_layers



def  lowercase__(		_UpperCamelCase  : List[str] ,   _UpperCamelCase  : Optional[int]=False				)->   Optional[int]:






					"""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"				):
																				_UpperCamelCase											=   (
																				    torch.randn_like(layer_module.up.weight				)
																				    if randn_weight
																				    else torch.zeros_like(layer_module.up.weight				)
																				)
																				_UpperCamelCase											=   torch.nn.Parameter(_UpperCamelCase				)

class     A_ (  unittest.TestCase  ):

			'''simple docstring'''





			def       a	(					self   ):
								torch.manual_seed(0   )
								_UpperCamelCase											=   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			,     )
								_UpperCamelCase											=   DDIMScheduler(
								    beta_start=0.0_0085			,     beta_end=0.012			,     beta_schedule="scaled_linear"			,     clip_sample=A_			,     set_alpha_to_one=A_			,     steps_offset=1			,     )
								torch.manual_seed(0   )
								_UpperCamelCase											=   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			,     )
								_UpperCamelCase											=   CLIPTextConfig(
								    bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     intermediate_size=37			,     layer_norm_eps=1e-0_5			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=10_00			,     )
								_UpperCamelCase											=   CLIPTextModel(A_   )
								_UpperCamelCase											=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

								_UpperCamelCase				,	_UpperCamelCase											=   create_unet_lora_layers(A_   )
								_UpperCamelCase											=   create_text_encoder_lora_layers(A_   )

								_UpperCamelCase											=   {
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "safety_checker": None,
								    "feature_extractor": None,
								}
								_UpperCamelCase											=   {
								    "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   ):
								_UpperCamelCase											=   1
								_UpperCamelCase											=   10
								_UpperCamelCase											=   4
								_UpperCamelCase											=   (32, 32)

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

								_UpperCamelCase											=   {
								    "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   ):
								_UpperCamelCase											=   77

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

								_UpperCamelCase											=   {}
								_UpperCamelCase											=   inputs
								return prepared_inputs




			def       a	(					self			,     A_   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								LoraLoaderMixin.save_lora_weights(
								    save_directory=A_			,     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(A_			,     "pytorch_lora_weights.bin"   )   )   )




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

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

								with tempfile.TemporaryDirectory() as tmpdirname:
													LoraLoaderMixin.save_lora_weights(
													    save_directory=A_			,     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(A_			,     "pytorch_lora_weights.bin"   )   )   )
													sd_pipe.load_lora_weights(A_   )

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

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




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

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

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

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

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




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   lora_components["unet_lora_attn_procs"]
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

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

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

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

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




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )

								_UpperCamelCase											=   self.get_dummy_tokens()

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

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

								# monkey patch
								pipe._modify_text_encoder(A_   )

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

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

								assert torch.allclose(
								    A_			,     A_   ), "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
								_UpperCamelCase											=   create_text_encoder_lora_attn_procs(pipe.text_encoder   )
								set_lora_up_weights(A_			,     randn_weight=A_   )

								# monkey patch
								pipe._modify_text_encoder(A_   )

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

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

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




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )

								_UpperCamelCase											=   self.get_dummy_tokens()

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

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

								# monkey patch
								pipe._modify_text_encoder(A_   )

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

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

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

								# remove monkey patch
								pipe._remove_text_encoder_monkey_patch()

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

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




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

								with tempfile.TemporaryDirectory() as tmpdirname:
													LoraLoaderMixin.save_lora_weights(
													    save_directory=A_			,     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(A_			,     "pytorch_lora_weights.bin"   )   )   )
													sd_pipe.load_lora_weights(A_   )

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

								_UpperCamelCase											=   sd_pipe(**A_			,     cross_attention_kwargs={"scale": 0.5}   ).images
								_UpperCamelCase											=   lora_images_with_scale[0, -3:, -3:, -1]

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




			def       a	(					self   ):
								with tempfile.TemporaryDirectory() as tmpdirname:
													self.create_lora_weight_file(A_   )

													_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
													_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
													_UpperCamelCase											=   sd_pipe.to(A_   )
													sd_pipe.set_progress_bar_config(disable=A_   )

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

            # load LoRA weight file
													sd_pipe.load_lora_weights(A_   )

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




			@unittest.skipIf(torch_device != "cuda"			,     "This test is supposed to run on GPU"   )
			def       a	(					self   ):
								with tempfile.TemporaryDirectory() as tmpdirname:
													self.create_lora_weight_file(A_   )

													_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
													_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
													_UpperCamelCase											=   sd_pipe.to(A_   )
													sd_pipe.set_progress_bar_config(disable=A_   )

													# 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(A_			,     A_   ):
																							self.assertIsInstance(module.processor			,     A_   )

            # load LoRA weight file
													sd_pipe.load_lora_weights(A_   )

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




			@unittest.skipIf(torch_device != "cuda"			,     "This test is supposed to run on GPU"   )
			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

								# enable XFormers
								sd_pipe.enable_xformers_memory_efficient_attention()

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

								with tempfile.TemporaryDirectory() as tmpdirname:
													LoraLoaderMixin.save_lora_weights(
													    save_directory=A_			,     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(A_			,     "pytorch_lora_weights.bin"   )   )   )
													sd_pipe.load_lora_weights(A_   )

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

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



