





'''simple docstring'''



import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator



def  lowercase__(		_UpperCamelCase  : Dict				)->   Tuple:






					"""simple docstring"""

					_UpperCamelCase											=   fname.split(os.path.sep				)[-1]
					return re.search(R"^(.*)_\d+\.jpg$" ,   _UpperCamelCase				).groups()[0]

class     A_ (  lowerCAmelCase_  ):

			'''simple docstring'''





			def __init__(					self			,     A_			,     A_=None			,     A_=None   ):
								_UpperCamelCase											=   file_names
								_UpperCamelCase											=   image_transform
								_UpperCamelCase											=   label_to_id




			def __len__(					self   ):
								return len(self.file_names   )




			def __getitem__(					self			,     A_   ):
								_UpperCamelCase											=   self.file_names[idx]
								_UpperCamelCase											=   PIL.Image.open(A_   )
								_UpperCamelCase											=   raw_image.convert("RGB"   )
								if self.image_transform is not None:
													_UpperCamelCase											=   self.image_transform(A_   )
								_UpperCamelCase											=   extract_label(A_   )
								if self.label_to_id is not None:
													_UpperCamelCase											=   self.label_to_id[label]
								return {"image": image, "label": label}




def  lowercase__(		_UpperCamelCase  : List[str] ,   _UpperCamelCase  : str				)->   Any:






					"""simple docstring"""

					_UpperCamelCase											=   Accelerator(cpu=args.cpu ,   mixed_precision=args.mixed_precision				)

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_UpperCamelCase											=   config["lr"]
					_UpperCamelCase											=   int(config["num_epochs"]				)
					_UpperCamelCase											=   int(config["seed"]				)
					_UpperCamelCase											=   int(config["batch_size"]				)
					_UpperCamelCase											=   config["image_size"]
					if not isinstance(_UpperCamelCase ,   (list, tuple)				):
										_UpperCamelCase											=   (image_size, image_size)

					# Grab all the image filenames
					_UpperCamelCase											=   [os.path.join(args.data_dir ,   _UpperCamelCase				) for fname in os.listdir(args.data_dir				) if fname.endswith(".jpg"				)]

					# Build the label correspondences
					_UpperCamelCase											=   [extract_label(_UpperCamelCase				) for fname in file_names]
					_UpperCamelCase											=   list(set(_UpperCamelCase				)				)
					id_to_label.sort()
					_UpperCamelCase											=   {lbl: i for i, lbl in enumerate(_UpperCamelCase				)}

					# Set the seed before splitting the data.
					np.random.seed(_UpperCamelCase				)
					torch.manual_seed(_UpperCamelCase				)
					torch.cuda.manual_seed_all(_UpperCamelCase				)

					# Split our filenames between train and validation
					_UpperCamelCase											=   np.random.permutation(len(_UpperCamelCase				)				)
					_UpperCamelCase											=   int(0.8 * len(_UpperCamelCase				)				)
					_UpperCamelCase											=   random_perm[:cut]
					_UpperCamelCase											=   random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					_UpperCamelCase											=   Compose([RandomResizedCrop(_UpperCamelCase ,   scale=(0.5, 1.0)				), ToTensor()]				)
					_UpperCamelCase											=   PetsDataset(
					    [file_names[i] for i in train_split] ,   image_transform=_UpperCamelCase ,   label_to_id=_UpperCamelCase				)

					# For evaluation, we use a deterministic Resize
					_UpperCamelCase											=   Compose([Resize(_UpperCamelCase				), ToTensor()]				)
					_UpperCamelCase											=   PetsDataset([file_names[i] for i in eval_split] ,   image_transform=_UpperCamelCase ,   label_to_id=_UpperCamelCase				)

					# Instantiate dataloaders.
					_UpperCamelCase											=   DataLoader(_UpperCamelCase ,   shuffle=_UpperCamelCase ,   batch_size=_UpperCamelCase ,   num_workers=4				)
					_UpperCamelCase											=   DataLoader(_UpperCamelCase ,   shuffle=_UpperCamelCase ,   batch_size=_UpperCamelCase ,   num_workers=4				)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					_UpperCamelCase											=   create_model("resnet50d" ,   pretrained=_UpperCamelCase ,   num_classes=len(_UpperCamelCase				)				)

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					_UpperCamelCase											=   model.to(accelerator.device				)

					# Freezing the base model
					for param in model.parameters():
										_UpperCamelCase											=   False
					for param in model.get_classifier().parameters():
										_UpperCamelCase											=   True

					# We normalize the batches of images to be a bit faster.
					_UpperCamelCase											=   torch.tensor(model.default_cfg["mean"]				)[None, :, None, None].to(accelerator.device				)
					_UpperCamelCase											=   torch.tensor(model.default_cfg["std"]				)[None, :, None, None].to(accelerator.device				)

					# Instantiate optimizer
					_UpperCamelCase											=   torch.optim.Adam(params=model.parameters() ,   lr=lr / 25				)

					# Instantiate learning rate scheduler
					_UpperCamelCase											=   OneCycleLR(optimizer=_UpperCamelCase ,   max_lr=_UpperCamelCase ,   epochs=_UpperCamelCase ,   steps_per_epoch=len(_UpperCamelCase				)				)

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   accelerator.prepare(
					    _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)

					# Now we train the model
					for epoch in range(_UpperCamelCase				):
										model.train()
										for step, batch in enumerate(_UpperCamelCase				):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															_UpperCamelCase											=   {k: v.to(accelerator.device				) for k, v in batch.items()}
															_UpperCamelCase											=   (batch["image"] - mean) / std
															_UpperCamelCase											=   model(_UpperCamelCase				)
															_UpperCamelCase											=   torch.nn.functional.cross_entropy(_UpperCamelCase ,   batch["label"]				)
															accelerator.backward(_UpperCamelCase				)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

										model.eval()
										_UpperCamelCase											=   0
										_UpperCamelCase											=   0
										for _, batch in enumerate(_UpperCamelCase				):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															_UpperCamelCase											=   {k: v.to(accelerator.device				) for k, v in batch.items()}
															_UpperCamelCase											=   (batch["image"] - mean) / std
															with torch.no_grad():
																				_UpperCamelCase											=   model(_UpperCamelCase				)
															_UpperCamelCase											=   outputs.argmax(dim=-1				)
															_UpperCamelCase				,	_UpperCamelCase											=   accelerator.gather_for_metrics((predictions, batch["label"])				)
															_UpperCamelCase											=   predictions == references
															num_elems += accurate_preds.shape[0]
															accurate += accurate_preds.long().sum()

										_UpperCamelCase											=   accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}"				)



def  lowercase__(		)->   Tuple:






					"""simple docstring"""

					_UpperCamelCase											=   argparse.ArgumentParser(description="Simple example of training script."				)
					parser.add_argument("--data_dir" ,   required=_UpperCamelCase ,   help="The data folder on disk."				)
					parser.add_argument(
					    "--mixed_precision" ,   type=_UpperCamelCase ,   default=_UpperCamelCase ,   choices=["no", "fp16", "bf16", "fp8"] ,   help="Whether to use mixed precision. Choose"
					    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
					    "and an Nvidia Ampere GPU." ,   )
					parser.add_argument(
					    "--checkpointing_steps" ,   type=_UpperCamelCase ,   default=_UpperCamelCase ,   help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch." ,   )
					parser.add_argument("--cpu" ,   action="store_true" ,   help="If passed, will train on the CPU."				)
					_UpperCamelCase											=   parser.parse_args()
					_UpperCamelCase											=   {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
					training_function(_UpperCamelCase ,   _UpperCamelCase				)


if __name__ == "__main__":
			main()










'''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_   )   )   )



