code: 
  '''simple docstring'''
  
  
  
  import argparse
  import os
  
  import evaluate
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
  
  from accelerate import Accelerator, DistributedType
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate
  #
  # This example trains a Bert base model on GLUE MRPC
  # in any of the following settings (with the same script):
  #   - single CPU or single GPU
  #   - multi GPUS (using PyTorch distributed mode)
  #   - (multi) TPUs
  #   - fp16 (mixed-precision) or fp32 (normal precision)
  #
  # This example also demonstrates the checkpointing and sharding capabilities
  #
  # To run it in each of these various modes, follow the instructions
  # in the readme for examples:
  # https://github.com/huggingface/accelerate/tree/main/examples
  #
  ########################################################################
  
  
  __A          =    16
  __A          =    32
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						# Initialize accelerator
  						if args.with_tracking:
  												lowercase__							 =       Accelerator(
  												    cpu=args.cpu		,		mixed_precision=args.mixed_precision		,		log_with="""all"""		,		project_dir=args.project_dir	)
  						else:
  												lowercase__							 =       Accelerator(cpu=args.cpu		,		mixed_precision=args.mixed_precision	)
  
  						if hasattr(args.checkpointing_steps		,		"""isdigit"""	):
  												if args.checkpointing_steps == "epoch":
  																		lowercase__							 =       args.checkpointing_steps
  												elif args.checkpointing_steps.isdigit():
  																		lowercase__							 =       int(args.checkpointing_steps	)
  												else:
  																		raise ValueError(
  																		    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
  						else:
  												lowercase__							 =       None
  						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  						lowercase__							 =       config["""lr"""]
  						lowercase__							 =       int(config["""num_epochs"""]	)
  						lowercase__							 =       int(config["""seed"""]	)
  						lowercase__							 =       int(config["""batch_size"""]	)
  
  						# We need to initialize the trackers we use, and also store our configuration
  						if args.with_tracking:
  												lowercase__							 =       os.path.split(lowercase__	)[-1].split("""."""	)[0]
  												accelerator.init_trackers(lowercase__		,		lowercase__	)
  
  						lowercase__							 =       AutoTokenizer.from_pretrained("""bert-base-cased"""	)
  						lowercase__							 =       load_dataset("""glue"""		,		"""mrpc"""	)
  						lowercase__							 =       evaluate.load("""glue"""		,		"""mrpc"""	)
  
  						def tokenize_function(lowercase__	):
  												# max_length=None => use the model max length (it's actually the default)
  												lowercase__							 =       tokenizer(examples["""sentence1"""]		,		examples["""sentence2"""]		,		truncation=lowercase__		,		max_length=lowercase__	)
  												return outputs
  
  						# Apply the method we just defined to all the examples in all the splits of the dataset
  						# starting with the main process first:
  						with accelerator.main_process_first():
  												lowercase__							 =       datasets.map(
  												    lowercase__		,		batched=lowercase__		,		remove_columns=["""idx""", """sentence1""", """sentence2"""]		,		)
  
  						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  						# transformers library
  						lowercase__							 =       tokenized_datasets.rename_column("""label"""		,		"""labels"""	)
  
  						# If the batch size is too big we use gradient accumulation
  						lowercase__							 =       1
  						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  												lowercase__							 =       batch_size // MAX_GPU_BATCH_SIZE
  												lowercase__							 =       MAX_GPU_BATCH_SIZE
  
  						def collate_fn(lowercase__	):
  												# On TPU it's best to pad everything to the same length or training will be very slow.
  												lowercase__							 =       128 if accelerator.distributed_type == DistributedType.TPU else None
  												# When using mixed precision we want round multiples of 8/16
  												if accelerator.mixed_precision == "fp8":
  																		lowercase__							 =       16
  												elif accelerator.mixed_precision != "no":
  																		lowercase__							 =       8
  												else:
  																		lowercase__							 =       None
  
  												return tokenizer.pad(
  												    lowercase__		,		padding="""longest"""		,		max_length=lowercase__		,		pad_to_multiple_of=lowercase__		,		return_tensors="""pt"""		,		)
  
  						# Instantiate dataloaders.
  						lowercase__							 =       DataLoader(
  						    tokenized_datasets["""train"""]		,		shuffle=lowercase__		,		collate_fn=lowercase__		,		batch_size=lowercase__	)
  						lowercase__							 =       DataLoader(
  						    tokenized_datasets["""validation"""]		,		shuffle=lowercase__		,		collate_fn=lowercase__		,		batch_size=lowercase__	)
  
  						set_seed(lowercase__	)
  
  						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  						lowercase__							 =       AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		,		return_dict=lowercase__	)
  
  						# 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).
  						lowercase__							 =       model.to(accelerator.device	)
  
  						# Instantiate optimizer
  						lowercase__							 =       AdamW(params=model.parameters()		,		lr=lowercase__	)
  
  						# Instantiate scheduler
  						lowercase__							 =       get_linear_schedule_with_warmup(
  						    optimizer=lowercase__		,		num_warmup_steps=100		,		num_training_steps=(len(lowercase__	) * num_epochs) // gradient_accumulation_steps		,		)
  
  						# 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.
  						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       accelerator.prepare(
  						    lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__	)
  
  						# We need to keep track of how many total steps we have iterated over
  						lowercase__							 =       0
  						# We also need to keep track of the stating epoch so files are named properly
  						lowercase__							 =       0
  
  						# Potentially load in the weights and states from a previous save
  						if args.resume_from_checkpoint:
  												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
  																		accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''	)
  																		accelerator.load_state(args.resume_from_checkpoint	)
  																		lowercase__							 =       os.path.basename(args.resume_from_checkpoint	)
  												else:
  																		# Get the most recent checkpoint
  																		lowercase__							 =       [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
  																		dirs.sort(key=os.path.getctime	)
  																		lowercase__							 =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  												# Extract `epoch_{i}` or `step_{i}`
  												lowercase__							 =       os.path.splitext(lowercase__	)[0]
  
  												if "epoch" in training_difference:
  																		lowercase__							 =       int(training_difference.replace("""epoch_"""		,		""""""	)	) + 1
  																		lowercase__							 =       None
  												else:
  																		lowercase__							 =       int(training_difference.replace("""step_"""		,		""""""	)	)
  																		lowercase__							 =       resume_step // len(lowercase__	)
  																		resume_step -= starting_epoch * len(lowercase__	)
  
      # Now we train the model
  						for epoch in range(lowercase__		,		lowercase__	):
  												model.train()
  												if args.with_tracking:
  																		lowercase__							 =       0
  												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
  																		# We need to skip steps until we reach the resumed step
  																		lowercase__							 =       accelerator.skip_first_batches(lowercase__		,		lowercase__	)
  																		overall_step += resume_step
  												else:
  																		# After the first iteration though, we need to go back to the original dataloader
  																		lowercase__							 =       train_dataloader
  												for step, batch in enumerate(lowercase__	):
  																		# We could avoid this line since we set the accelerator with `device_placement=True`.
  																		batch.to(accelerator.device	)
  																		lowercase__							 =       model(**lowercase__	)
  																		lowercase__							 =       outputs.loss
  																		lowercase__							 =       loss / gradient_accumulation_steps
  																		# We keep track of the loss at each epoch
  																		if args.with_tracking:
  																								total_loss += loss.detach().float()
  																		accelerator.backward(lowercase__	)
  																		if step % gradient_accumulation_steps == 0:
  																								optimizer.step()
  																								lr_scheduler.step()
  																								optimizer.zero_grad()
  
  																		overall_step += 1
  
  																		if isinstance(lowercase__		,		lowercase__	):
  																								lowercase__							 =       f'''step_{overall_step}'''
  																								if overall_step % checkpointing_steps == 0:
  																														if args.output_dir is not None:
  																																				lowercase__							 =       os.path.join(args.output_dir		,		lowercase__	)
  																														accelerator.save_state(lowercase__	)
  
  												model.eval()
  												for step, batch in enumerate(lowercase__	):
  																		# We could avoid this line since we set the accelerator with `device_placement=True`.
  																		batch.to(accelerator.device	)
  																		with torch.no_grad():
  																								lowercase__							 =       model(**lowercase__	)
  																		lowercase__							 =       outputs.logits.argmax(dim=-1	)
  																		lowercase__ ,						lowercase__							 =       accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
  																		metric.add_batch(
  																		    predictions=lowercase__		,		references=lowercase__		,		)
  
  												lowercase__							 =       metric.compute()
  												# Use accelerator.print to print only on the main process.
  												accelerator.print(f'''epoch {epoch}:'''		,		lowercase__	)
  												if args.with_tracking:
  																		accelerator.log(
  																		    {
  																		        """accuracy""": eval_metric["""accuracy"""],
  																		        """f1""": eval_metric["""f1"""],
  																		        """train_loss""": total_loss.item() / len(lowercase__	),
  																		        """epoch""": epoch,
  																		    }		,		step=lowercase__		,		)
  
  												if checkpointing_steps == "epoch":
  																		lowercase__							 =       f'''epoch_{epoch}'''
  																		if args.output_dir is not None:
  																								lowercase__							 =       os.path.join(args.output_dir		,		lowercase__	)
  																		accelerator.save_state(lowercase__	)
  
  						if args.with_tracking:
  												accelerator.end_training()
  
  
  
  
  
  def  _A		(							):
  						lowercase__							 =       argparse.ArgumentParser(description="""Simple example of training script."""	)
  						parser.add_argument(
  						    """--mixed_precision"""		,		type=lowercase__		,		default=lowercase__		,		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("""--cpu"""		,		action="""store_true"""		,		help="""If passed, will train on the CPU."""	)
  						parser.add_argument(
  						    """--checkpointing_steps"""		,		type=lowercase__		,		default=lowercase__		,		help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""		,		)
  						parser.add_argument(
  						    """--resume_from_checkpoint"""		,		type=lowercase__		,		default=lowercase__		,		help="""If the training should continue from a checkpoint folder."""		,		)
  						parser.add_argument(
  						    """--with_tracking"""		,		action="""store_true"""		,		help="""Whether to load in all available experiment trackers from the environment and use them for logging."""		,		)
  						parser.add_argument(
  						    """--output_dir"""		,		type=lowercase__		,		default="""."""		,		help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""		,		)
  						parser.add_argument(
  						    """--project_dir"""		,		type=lowercase__		,		default="""logs"""		,		help="""Location on where to store experiment tracking logs` and relevent project information"""		,		)
  						lowercase__							 =       parser.parse_args()
  						lowercase__							 =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  						training_function(lowercase__		,		lowercase__	)
  
  
  if __name__ == "__main__":
  				main()
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import os
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers.models import ModelMixin, UNetaDConditionModel
  from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
  from diffusers.utils import (
      floats_tensor,
      logging,
      skip_mps,
      torch_device,
  )
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism
  
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
  
  
  enable_full_determinism()
  
  __A          =    logging.get_logger(__name__)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__ = True	):
  						lowercase__							 =       {}
  						for name in model.attn_processors.keys():
  												lowercase__							 =       name.endswith("""attn2.processor"""	) and not (
  												    name.startswith("""transformer_in"""	) or """temp_attentions""" in name.split("""."""	)
  												)
  												lowercase__							 =       model.config.cross_attention_dim if has_cross_attention else None
  												if name.startswith("""mid_block"""	):
  																		lowercase__							 =       model.config.block_out_channels[-1]
  												elif name.startswith("""up_blocks"""	):
  																		lowercase__							 =       int(name[len("""up_blocks."""	)]	)
  																		lowercase__							 =       list(reversed(model.config.block_out_channels	)	)[block_id]
  												elif name.startswith("""down_blocks"""	):
  																		lowercase__							 =       int(name[len("""down_blocks."""	)]	)
  																		lowercase__							 =       model.config.block_out_channels[block_id]
  												elif name.startswith("""transformer_in"""	):
  																		# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
  																		lowercase__							 =       8 * model.config.attention_head_dim
  
  												lowercase__							 =       LoRAAttnProcessor(hidden_size=lowercase__		,		cross_attention_dim=lowercase__	)
  												lowercase__							 =       lora_attn_procs[name].to(model.device	)
  
  												if mock_weights:
  																		# add 1 to weights to mock trained weights
  																		with torch.no_grad():
  																								lora_attn_procs[name].to_q_lora.up.weight += 1
  																								lora_attn_procs[name].to_k_lora.up.weight += 1
  																								lora_attn_procs[name].to_v_lora.up.weight += 1
  																								lora_attn_procs[name].to_out_lora.up.weight += 1
  
  						return lora_attn_procs
  
  
  
  
  
  
  
  @skip_mps
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							List[Any]       		=  UNetaDConditionModel
  			lowerCamelCase							:							int       		=  """sample"""
  
  
  
  			@property
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       4
  									lowercase__							 =       4
  									lowercase__							 =       4
  									lowercase__							 =       (32, 32)
  
  									lowercase__							 =       floats_tensor((batch_size, num_channels, num_frames) + sizes       ).to(lowerCamelCase__       )
  									lowercase__							 =       torch.tensor([10]       ).to(lowerCamelCase__       )
  									lowercase__							 =       floats_tensor((batch_size, 4, 32)       ).to(lowerCamelCase__       )
  
  									return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  			@property
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (4, 4, 32, 32)
  
  
  
  			@property
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (4, 4, 32, 32)
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """block_out_channels""": (32, 64),
  									    """down_block_types""": (
  									        """CrossAttnDownBlock3D""",
  									        """DownBlock3D""",
  									    ),
  									    """up_block_types""": ("""UpBlock3D""", """CrossAttnUpBlock3D"""),
  									    """cross_attention_dim""": 32,
  									    """attention_head_dim""": 8,
  									    """out_channels""": 4,
  									    """in_channels""": 4,
  									    """layers_per_block""": 1,
  									    """sample_size""": 32,
  									}
  									lowercase__							 =       self.dummy_input
  									return init_dict, inputs_dict
  
  
  
  			@unittest.skipIf(
  			    torch_device != """cuda""" or not is_xformers_available()       ,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,     )
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  
  									model.enable_xformers_memory_efficient_attention()
  
  									assert (
  									    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
  									    == "XFormersAttnProcessor"
  									), "xformers is not enabled"
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       32
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       output.sample
  
  									self.assertIsNotNone(lowerCamelCase__       )
  									lowercase__							 =       inputs_dict["""sample"""].shape
  									self.assertEqual(output.shape       ,     lowerCamelCase__       ,     """Input and output shapes do not match"""       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															# Warmup pass when using mps (see #372)
  															if torch_device == "mps" and isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					model(**self.dummy_input       )
  
  															lowercase__							 =       model(**lowerCamelCase__       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       first.sample
  
  															lowercase__							 =       model(**lowerCamelCase__       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       second.sample
  
  									lowercase__							 =       first.cpu().numpy()
  									lowercase__							 =       second.cpu().numpy()
  									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
  									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
  									lowercase__							 =       np.amax(np.abs(out_a - out_a       )       )
  									self.assertLessEqual(lowerCamelCase__       ,     1e-5       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									model.set_attention_slice("""auto"""       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  									model.set_attention_slice("""max"""       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  									model.set_attention_slice(2       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  
  									# make sure we can set a list of attention processors
  									model.set_attn_processor(lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									# test that attn processors can be set to itself
  									model.set_attn_processor(model.attn_processors       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									assert (samplea - samplea).abs().max() < 3e-3
  									assert (samplea - samplea).abs().max() < 3e-3
  
  									# sample 2 and sample 3 should be different
  									assert (samplea - samplea).abs().max() > 3e-3
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       new_model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									assert (sample - new_sample).abs().max() < 1e-3
  
  									# LoRA and no LoRA should NOT be the same
  									assert (sample - old_sample).abs().max() > 1e-4
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       ,     safe_serialization=lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.safetensors"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       new_model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									assert (sample - new_sample).abs().max() < 3e-3
  
  									# LoRA and no LoRA should NOT be the same
  									assert (sample - old_sample).abs().max() > 1e-4
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  									# Saving as torch, properly reloads with directly filename
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       ,     weight_name="""pytorch_lora_weights.bin"""       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  									# Saving as torch, properly reloads with directly filename
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															with self.assertRaises(lowerCamelCase__       ) as e:
  																					new_model.load_attn_procs(lowerCamelCase__       ,     use_safetensors=lowerCamelCase__       )
  															self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""       ,     str(e.exception       )       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
  
  									model.set_attn_processor(AttnProcessor()       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									assert (sample - new_sample).abs().max() < 1e-4
  									assert (sample - old_sample).abs().max() < 3e-3
  
  
  
  			@unittest.skipIf(
  			    torch_device != """cuda""" or not is_xformers_available()       ,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""       ,     )
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       4
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									# default
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  															model.enable_xformers_memory_efficient_attention()
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  															model.disable_xformers_memory_efficient_attention()
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									assert (sample - on_sample).abs().max() < 1e-4
  									assert (sample - off_sample).abs().max() < 1e-4
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       32
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )[0]
  
  									model.enable_forward_chunking()
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )[0]
  
  									self.assertEqual(output.shape       ,     output_a.shape       ,     """Shape doesn't match"""       )
  									assert np.abs(output.cpu() - output_a.cpu()       ).max() < 1e-2
  
  
  # (todo: sayakpaul) implement SLOW tests.
  
  
  
  
  
  
  