code: 
  '''simple docstring'''
  
  
  
  import argparse
  import logging
  import os
  import sys
  import time
  
  import tensorflow as tf
  from datasets import load_dataset
  from tqdm import tqdm
  
  from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
  from transformers.utils import is_sagemaker_dp_enabled
  
  
  if os.environ.get("SDP_ENABLED") or is_sagemaker_dp_enabled():
  				__A          =    True
  				__A          =    "p3dn.24xlarge"
  				import smdistributed.dataparallel.tensorflow as sdp
  else:
  				__A          =    False
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__=None	):
  						lowercase__							 =       tqdm(lowercase__	)
  						for i, batch in enumerate(lowercase__	):
  												with tf.GradientTape() as tape:
  																		lowercase__ ,						lowercase__							 =       batch
  																		lowercase__							 =       model(lowercase__	)
  																		lowercase__							 =       loss(lowercase__		,		outputs.logits	)
  
  												if SDP_ENABLED:
  																		lowercase__							 =       sdp.DistributedGradientTape(lowercase__		,		sparse_as_dense=lowercase__	)
  
  												lowercase__							 =       tape.gradient(lowercase__		,		model.trainable_variables	)
  												opt.apply_gradients(zip(lowercase__		,		model.trainable_variables	)	)
  
  												pbar.set_description(f'''Loss: {loss_value:.4f}'''	)
  
  												if SDP_ENABLED and i == 0:
  																		sdp.broadcast_variables(model.variables		,		root_rank=0	)
  																		sdp.broadcast_variables(opt.variables()		,		root_rank=0	)
  
  												if max_steps and i >= max_steps:
  																		break
  
  						lowercase__							 =       {"""loss""": loss_value.numpy()}
  						return train_results
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__		,		lowercase__	):
  						# Load dataset
  						lowercase__ ,						lowercase__							 =       load_dataset("""imdb"""		,		split=["""train""", """test"""]	)
  
  						# Preprocess train dataset
  						lowercase__							 =       train_dataset.map(
  						    lambda lowercase__	: tokenizer(e["""text"""]		,		truncation=lowercase__		,		padding="""max_length"""	)		,		batched=lowercase__	)
  						train_dataset.set_format(type="""tensorflow"""		,		columns=["""input_ids""", """attention_mask""", """label"""]	)
  
  						lowercase__							 =       {
  						    x: train_dataset[x].to_tensor(default_value=0		,		shape=[None, tokenizer.model_max_length]	)
  						    for x in ["""input_ids""", """attention_mask"""]
  						}
  						lowercase__							 =       tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])	)
  
  						# Preprocess test dataset
  						lowercase__							 =       test_dataset.map(
  						    lambda lowercase__	: tokenizer(e["""text"""]		,		truncation=lowercase__		,		padding="""max_length"""	)		,		batched=lowercase__	)
  						test_dataset.set_format(type="""tensorflow"""		,		columns=["""input_ids""", """attention_mask""", """label"""]	)
  
  						lowercase__							 =       {
  						    x: test_dataset[x].to_tensor(default_value=0		,		shape=[None, tokenizer.model_max_length]	)
  						    for x in ["""input_ids""", """attention_mask"""]
  						}
  						lowercase__							 =       tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])	)
  
  						if SDP_ENABLED:
  												lowercase__							 =       tf_train_dataset.shard(sdp.size()		,		sdp.rank()	)
  												lowercase__							 =       tf_test_dataset.shard(sdp.size()		,		sdp.rank()	)
  						lowercase__							 =       tf_train_dataset.batch(lowercase__		,		drop_remainder=lowercase__	)
  						lowercase__							 =       tf_test_dataset.batch(lowercase__		,		drop_remainder=lowercase__	)
  
  						return tf_train_dataset, tf_test_dataset
  
  
  if __name__ == "__main__":
  				__A          =    argparse.ArgumentParser()
  
  				# Hyperparameters sent by the client are passed as command-line arguments to the script.
  				parser.add_argument("--epochs", type=int, default=3)
  				parser.add_argument("--per_device_train_batch_size", type=int, default=16)
  				parser.add_argument("--per_device_eval_batch_size", type=int, default=8)
  				parser.add_argument("--model_name_or_path", type=str)
  				parser.add_argument("--learning_rate", type=str, default=5E-5)
  				parser.add_argument("--do_train", type=bool, default=True)
  				parser.add_argument("--do_eval", type=bool, default=True)
  				parser.add_argument("--output_dir", type=str)
  				parser.add_argument("--max_steps", type=int, default=None)
  
  				# Data, model, and output directories
  				parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"])
  				parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"])
  				parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"])
  
  				__A    ,       __A          =    parser.parse_known_args()
  
  				# Set up logging
  				__A          =    logging.getLogger(__name__)
  
  				logging.basicConfig(
  				    level=logging.getLevelName("INFO"),
  				    handlers=[logging.StreamHandler(sys.stdout)],
  				    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
  				)
  
  				if SDP_ENABLED:
  								sdp.init()
  
  								__A          =    tf.config.experimental.list_physical_devices("GPU")
  								for gpu in gpus:
  												tf.config.experimental.set_memory_growth(gpu, True)
  								if gpus:
  												tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], "GPU")
  
      # Load model and tokenizer
  				__A          =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  				__A          =    AutoTokenizer.from_pretrained(args.model_name_or_path)
  
  				# get datasets
  				__A    ,       __A          =    get_datasets(
  				    tokenizer=tokenizer,
  				    train_batch_size=args.per_device_train_batch_size,
  				    eval_batch_size=args.per_device_eval_batch_size,
  				)
  
  				# fine optimizer and loss
  				__A          =    tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  				__A          =    tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  				__A          =    [tf.keras.metrics.SparseCategoricalAccuracy()]
  				model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
  
  				# Training
  				if args.do_train:
  								# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
  								__A          =    time.time()
  								__A          =    fit(
  								    model,
  								    loss,
  								    optimizer,
  								    tf_train_dataset,
  								    args.epochs,
  								    args.per_device_train_batch_size,
  								    max_steps=args.max_steps,
  								)
  								__A          =    time.time() - start_train_time
  								logger.info("*** Train ***")
  								logger.info(F'''train_runtime = {end_train_time}''')
  
  								__A          =    os.path.join(args.output_dir, "train_results.txt")
  
  								if not SDP_ENABLED or sdp.rank() == 0:
  												with open(output_eval_file, "w") as writer:
  																logger.info("***** Train results *****")
  																logger.info(train_results)
  																for key, value in train_results.items():
  																				logger.info(F'''  {key} = {value}''')
  																				writer.write(F'''{key} = {value}\n''')
  
      # Evaluation
  				if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
  								__A          =    model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  								logger.info("*** Evaluate ***")
  
  								__A          =    os.path.join(args.output_dir, "eval_results.txt")
  
  								with open(output_eval_file, "w") as writer:
  												logger.info("***** Eval results *****")
  												logger.info(result)
  												for key, value in result.items():
  																logger.info(F'''  {key} = {value}''')
  																writer.write(F'''{key} = {value}\n''')
  
      # Save result
  				if SDP_ENABLED:
  								if sdp.rank() == 0:
  												model.save_pretrained(args.output_dir)
  												tokenizer.save_pretrained(args.output_dir)
  				else:
  								model.save_pretrained(args.output_dir)
  								tokenizer.save_pretrained(args.output_dir)
  
  
  
  
  
  
  
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.
  
  
  
  
  
  
  