code: 
  from sklearn.metrics import roc_auc_score
  
  import datasets
  
  
  snake_case				:	List[Any]							   =						"\nThis metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.\n\nThis metric has three separate use cases:\n    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.\n    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.\n    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.\n"
  
  snake_case				:	Any							   =						"\nArgs:\n- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples,)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples, n_classes)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:\n    - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.\n    - `'macro'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.\n    - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).\n    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.\n    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.\n- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.\n- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.\n- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:\n    - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.\n    - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.\n- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in\n    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in\n    `prediction_scores` is used. Defaults to `None`.\nReturns:\n    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.\nExamples:\n    Example 1:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\")\n        >>> refs = [1, 0, 1, 1, 0, 0]\n        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)\n        >>> print(round(results['roc_auc'], 2))\n        0.78\n\n    Example 2:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")\n        >>> refs = [1, 0, 1, 2, 2, 0]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')\n        >>> print(round(results['roc_auc'], 2))\n        0.85\n\n    Example 3:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")\n        >>> refs = [[1, 1, 0],\n        ...         [1, 1, 0],\n        ...         [0, 1, 0],\n        ...         [0, 0, 1],\n        ...         [0, 1, 1],\n        ...         [1, 0, 1]]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)\n        >>> print([round(res, 2) for res in results['roc_auc']])\n        [0.83, 0.38, 0.94]\n"
  
  snake_case				:	List[Any]							   =						"\\n@article{doi:10.1177/0272989X8900900307,\nauthor = {Donna Katzman McClish},\ntitle ={Analyzing a Portion of the ROC Curve},\njournal = {Medical Decision Making},\nvolume = {9},\nnumber = {3},\npages = {190-195},\nyear = {1989},\ndoi = {10.1177/0272989X8900900307},\n    note ={PMID: 2668680},\nURL = {https://doi.org/10.1177/0272989X8900900307},\neprint = {https://doi.org/10.1177/0272989X8900900307}\n}\n\n\n@article{10.1023/A:1010920819831,\nauthor = {Hand, David J. and Till, Robert J.},\ntitle = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},\nyear = {2001},\nissue_date = {November 2001},\npublisher = {Kluwer Academic Publishers},\naddress = {USA},\nvolume = {45},\nnumber = {2},\nissn = {0885-6125},\nurl = {https://doi.org/10.1023/A:1010920819831},\ndoi = {10.1023/A:1010920819831},\njournal = {Mach. Learn.},\nmonth = {oct},\npages = {171–186},\nnumpages = {16},\nkeywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}\n}\n\n\n@article{scikit-learn,\ntitle={Scikit-learn: Machine Learning in {P}ython},\nauthor={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\njournal={Journal of Machine Learning Research},\nvolume={12},\npages={2825--2830},\nyear={2011}\n}\n"
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,		_KWARGS_DESCRIPTION       )
  class    _snake_case      (  datasets.Metric       ):
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return datasets.MetricInfo(
  				    description=_DESCRIPTION		,     citation=_CITATION		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
  				        {
  				            "prediction_scores": datasets.Sequence(datasets.Value("float"	)	),
  				            "references": datasets.Value("int32"	),
  				        }
  				        if self.config_name == "multiclass"
  				        else {
  				            "references": datasets.Sequence(datasets.Value("int32"	)	),
  				            "prediction_scores": datasets.Sequence(datasets.Value("float"	)	),
  				        }
  				        if self.config_name == "multilabel"
  				        else {
  				            "references": datasets.Value("int32"	),
  				            "prediction_scores": datasets.Value("float"	),
  				        }	)		,     reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html"]		,     )
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a="macro"		,     _a=None		,     _a=None		,     _a="raise"		,     _a=None		,     ):
  				return {
  				    "roc_auc": roc_auc_score(
  				        _a		,     _a		,     average=_a		,     sample_weight=_a		,     max_fpr=_a		,     multi_class=_a		,     labels=_a		,     )
  				}
  
  
  
  
style_context: 
  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()
  
  snake_case				:	int							   =						logging.get_logger(__name__)
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						List[Any]   ,     _snake_case   :						bool = True     )       ->	Tuple:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	List[Any]			     =  {}
  	for name in model.attn_processors.keys():
  		__magic_name__	:	List[Any]			     =  name.endswith("attn2.processor"     ) and not (
  		    name.startswith("transformer_in"     ) or "temp_attentions" in name.split("."     )
  		)
  		__magic_name__	:	List[str]			     =  model.config.cross_attention_dim if has_cross_attention else None
  		if name.startswith("mid_block"     ):
  			__magic_name__	:	Tuple			     =  model.config.block_out_channels[-1]
  		elif name.startswith("up_blocks"     ):
  			__magic_name__	:	Tuple			     =  int(name[len("up_blocks."     )]     )
  			__magic_name__	:	Optional[Any]			     =  list(reversed(model.config.block_out_channels     )     )[block_id]
  		elif name.startswith("down_blocks"     ):
  			__magic_name__	:	Optional[int]			     =  int(name[len("down_blocks."     )]     )
  			__magic_name__	:	str			     =  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
  			__magic_name__	:	List[Any]			     =  8 * model.config.attention_head_dim
  
  		__magic_name__	:	Union[str, Any]			     =  LoRAAttnProcessor(hidden_size=_snake_case   ,     cross_attention_dim=_snake_case     )
  		__magic_name__	:	str			     =  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    _snake_case      (  snake_case       ,		snake_case       ,		unittest.TestCase       ):
  			UpperCamelCase__					   =					UNetaDConditionModel
  			UpperCamelCase__					   =					'sample'
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[Any]			     =  4
  				__magic_name__	:	str			     =  4
  				__magic_name__	:	List[str]			     =  4
  				__magic_name__	:	Optional[int]			     =  (32, 32)
  
  				__magic_name__	:	Optional[Any]			     =  floats_tensor((batch_size, num_channels, num_frames) + sizes	).to(_a	)
  				__magic_name__	:	Union[str, Any]			     =  torch.tensor([10]	).to(_a	)
  				__magic_name__	:	List[str]			     =  floats_tensor((batch_size, 4, 32)	).to(_a	)
  
  				return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return (4, 4, 32, 32)
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return (4, 4, 32, 32)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[Any]			     =  {
  				    "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,
  				}
  				__magic_name__	:	List[str]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Any			     =  self.prepare_init_args_and_inputs_for_common()
  				__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
  
  				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     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Tuple			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Any			     =  32
  
  				__magic_name__	:	Dict			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				with torch.no_grad():
  					__magic_name__	:	Any			     =  model(**_a	)
  
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	Any			     =  output.sample
  
  				self.assertIsNotNone(_a	)
  				__magic_name__	:	Union[str, Any]			     =  inputs_dict["sample"].shape
  				self.assertEqual(output.shape		,     _a		,     "Input and output shapes do not match"	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.prepare_init_args_and_inputs_for_common()
  				__magic_name__	:	int			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				with torch.no_grad():
  					# Warmup pass when using mps (see #372)
  					if torch_device == "mps" and isinstance(_a		,     _a	):
  						model(**self.dummy_input	)
  
  					__magic_name__	:	str			     =  model(**_a	)
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	str			     =  first.sample
  
  					__magic_name__	:	Tuple			     =  model(**_a	)
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	str			     =  second.sample
  
  				__magic_name__	:	Optional[int]			     =  first.cpu().numpy()
  				__magic_name__	:	Any			     =  second.cpu().numpy()
  				__magic_name__	:	List[Any]			     =  out_a[~np.isnan(_a	)]
  				__magic_name__	:	Any			     =  out_a[~np.isnan(_a	)]
  				__magic_name__	:	Tuple			     =  np.amax(np.abs(out_a - out_a	)	)
  				self.assertLessEqual(_a		,     1e-5	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Union[str, Any]			     =  8
  
  				__magic_name__	:	List[str]			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				model.set_attention_slice("auto"	)
  				with torch.no_grad():
  					__magic_name__	:	List[Any]			     =  model(**_a	)
  				assert output is not None
  
  				model.set_attention_slice("max"	)
  				with torch.no_grad():
  					__magic_name__	:	Tuple			     =  model(**_a	)
  				assert output is not None
  
  				model.set_attention_slice(2	)
  				with torch.no_grad():
  					__magic_name__	:	Union[str, Any]			     =  model(**_a	)
  				assert output is not None
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	str			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	List[Any]			     =  8
  
  				__magic_name__	:	Dict			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Union[str, Any]			     =  model(**_a	).sample
  
  				__magic_name__	:	int			     =  create_lora_layers(_a	)
  
  				# make sure we can set a list of attention processors
  				model.set_attn_processor(_a	)
  				model.to(_a	)
  
  				# test that attn processors can be set to itself
  				model.set_attn_processor(model.attn_processors	)
  
  				with torch.no_grad():
  					__magic_name__	:	int			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.0}	).sample
  					__magic_name__	:	Optional[Any]			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  					__magic_name__	:	Union[str, Any]			     =  model(**_a		,     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     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Optional[int]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Tuple			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Tuple			     =  model(**_a	).sample
  
  				__magic_name__	:	Union[str, Any]			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Optional[Any]			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					new_model.load_attn_procs(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	int			     =  new_model(**_a		,     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     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Optional[int]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Optional[int]			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	str			     =  model(**_a	).sample
  
  				__magic_name__	:	Union[str, Any]			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Dict			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a		,     safe_serialization=_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.safetensors"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	List[str]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					new_model.load_attn_procs(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	List[Any]			     =  new_model(**_a		,     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     SCREAMING_SNAKE_CASE					(       self	):
  				# enable deterministic behavior for gradient checkpointing
  				__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	List[str]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Tuple			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				__magic_name__	:	Any			     =  create_lora_layers(_a		,     mock_weights=_a	)
  				model.set_attn_processor(_a	)
  				# Saving as torch, properly reloads with directly filename
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	List[Any]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					new_model.load_attn_procs(_a		,     weight_name="pytorch_lora_weights.bin"	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	str			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	List[str]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				__magic_name__	:	str			     =  create_lora_layers(_a		,     mock_weights=_a	)
  				model.set_attn_processor(_a	)
  				# Saving as torch, properly reloads with directly filename
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	List[Any]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					with self.assertRaises(_a	) as e:
  						new_model.load_attn_procs(_a		,     use_safetensors=_a	)
  					self.assertIn("Error no file named pytorch_lora_weights.safetensors"		,     str(e.exception	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Any			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	List[str]			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Any			     =  model(**_a	).sample
  
  				__magic_name__	:	int			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Dict			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.0}	).sample
  
  				model.set_attn_processor(AttnProcessor()	)
  
  				with torch.no_grad():
  					__magic_name__	:	Optional[Any]			     =  model(**_a	).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     SCREAMING_SNAKE_CASE					(       self	):
  				# enable deterministic behavior for gradient checkpointing
  				__magic_name__     ,					__magic_name__	:	int			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Optional[int]			     =  4
  
  				torch.manual_seed(0	)
  				__magic_name__	:	str			     =  self.model_class(**_a	)
  				model.to(_a	)
  				__magic_name__	:	Any			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				# default
  				with torch.no_grad():
  					__magic_name__	:	int			     =  model(**_a	).sample
  
  					model.enable_xformers_memory_efficient_attention()
  					__magic_name__	:	str			     =  model(**_a	).sample
  
  					model.disable_xformers_memory_efficient_attention()
  					__magic_name__	:	Optional[Any]			     =  model(**_a	).sample
  
  				assert (sample - on_sample).abs().max() < 1e-4
  				assert (sample - off_sample).abs().max() < 1e-4
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	int			     =  self.prepare_init_args_and_inputs_for_common()
  				__magic_name__	:	str			     =  32
  
  				__magic_name__	:	int			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				with torch.no_grad():
  					__magic_name__	:	Optional[Any]			     =  model(**_a	)[0]
  
  				model.enable_forward_chunking()
  				with torch.no_grad():
  					__magic_name__	:	int			     =  model(**_a	)[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.
  