code: 
  import argparse
  import gc
  import os
  import threading
  
  import evaluate
  import psutil
  import torch
  from datasets import load_dataset
  from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
  from torch.utils.data import DataLoader
  from transformers import (
      AutoModelForSequenceClassification,
      AutoTokenizer,
      get_linear_schedule_with_warmup,
      set_seed,
  )
  
  from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin
  
  
  ########################################################################
  # 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)
  #   - FSDP
  #
  # 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
  #
  ########################################################################
  
  
  snake_case				:	Any							   =						16
  snake_case				:	Union[str, Any]							   =						32
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	Any:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	return int(x / 2**20     )
  
  
  
  
  
  class    _snake_case      :
  
  
  
  
  
  
  
  			def __enter__(       self	):
  				gc.collect()
  				torch.cuda.empty_cache()
  				torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
  				__magic_name__	:	List[Any]			     =  torch.cuda.memory_allocated()
  				__magic_name__	:	Dict			     =  psutil.Process()
  
  				__magic_name__	:	List[str]			     =  self.cpu_mem_used()
  				__magic_name__	:	Optional[int]			     =  True
  				__magic_name__	:	int			     =  threading.Thread(target=self.peak_monitor_func	)
  				__magic_name__	:	str			     =  True
  				peak_monitor_thread.start()
  				return self
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return self.process.memory_info().rss
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Dict			     =  -1
  
  				while True:
  					__magic_name__	:	Optional[Any]			     =  max(self.cpu_mem_used()		,     self.cpu_peak	)
  
  					# can't sleep or will not catch the peak right (this comment is here on purpose)
  					# time.sleep(0.001) # 1msec
  
  					if not self.peak_monitoring:
  						break
  
  
  
  
  
  
  
  			def __exit__(       self		,     *_a	):
  				__magic_name__	:	List[Any]			     =  False
  
  				gc.collect()
  				torch.cuda.empty_cache()
  				__magic_name__	:	Union[str, Any]			     =  torch.cuda.memory_allocated()
  				__magic_name__	:	Any			     =  torch.cuda.max_memory_allocated()
  				__magic_name__	:	List[Any]			     =  bamb(self.end - self.begin	)
  				__magic_name__	:	List[str]			     =  bamb(self.peak - self.begin	)
  
  				__magic_name__	:	List[str]			     =  self.cpu_mem_used()
  				__magic_name__	:	int			     =  bamb(self.cpu_end - self.cpu_begin	)
  				__magic_name__	:	List[Any]			     =  bamb(self.cpu_peak - self.cpu_begin	)
  				# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
  
  
  # For testing only
  if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
  				from accelerate.test_utils.training import mocked_dataloaders
  
  				snake_case				:	Tuple							   =						mocked_dataloaders  # noqa: F811
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						str   ,     _snake_case   :						Dict     )       ->	Dict:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if os.environ.get("TESTING_MOCKED_DATALOADERS"   ,     _snake_case     ) == "1":
  		__magic_name__	:	Optional[Any]			     =  2
  
  	# New Code #
  	# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  	__magic_name__	:	Dict			     =  FullyShardedDataParallelPlugin(
  	    state_dict_config=FullStateDictConfig(offload_to_cpu=_snake_case   ,     ranka_only=_snake_case     )   ,     optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_snake_case   ,     ranka_only=_snake_case     )   ,     )
  
  	# Initialize accelerator
  	if args.with_tracking:
  		__magic_name__	:	Union[str, Any]			     =  Accelerator(
  		    cpu=args.cpu   ,     mixed_precision=args.mixed_precision   ,     log_with="wandb"   ,     project_dir=args.logging_dir   ,     fsdp_plugin=_snake_case   ,     )
  	else:
  		__magic_name__	:	List[str]			     =  Accelerator(fsdp_plugin=_snake_case     )
  	accelerator.print(accelerator.distributed_type     )
  
  	if hasattr(args.checkpointing_steps   ,     "isdigit"     ):
  		if args.checkpointing_steps == "epoch":
  			__magic_name__	:	List[Any]			     =  args.checkpointing_steps
  		elif args.checkpointing_steps.isdigit():
  			__magic_name__	:	List[Any]			     =  int(args.checkpointing_steps     )
  		else:
  			raise ValueError(
  			    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''     )
  	else:
  		__magic_name__	:	List[str]			     =  None
  	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  	__magic_name__	:	List[Any]			     =  config["lr"]
  	__magic_name__	:	Optional[Any]			     =  int(config["num_epochs"]     )
  	__magic_name__	:	Optional[int]			     =  int(config["seed"]     )
  	__magic_name__	:	int			     =  int(config["batch_size"]     )
  
  	# We need to initialize the trackers we use, and also store our configuration
  	if args.with_tracking:
  		__magic_name__	:	List[Any]			     =  vars(_snake_case     )
  		accelerator.init_trackers("fsdp_glue_no_trainer"   ,     _snake_case     )
  
  	__magic_name__	:	Dict			     =  AutoTokenizer.from_pretrained(args.model_name_or_path     )
  	__magic_name__	:	List[Any]			     =  load_dataset("glue"   ,     "mrpc"     )
  	__magic_name__	:	Union[str, Any]			     =  evaluate.load("glue"   ,     "mrpc"     )
  
  	def tokenize_function(_snake_case   :						List[Any]     ):
  		# max_length=None => use the model max length (it's actually the default)
  		__magic_name__	:	Union[str, Any]			     =  tokenizer(examples["sentence1"]   ,     examples["sentence2"]   ,     truncation=_snake_case   ,     max_length=_snake_case     )
  		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():
  		__magic_name__	:	int			     =  datasets.map(
  		    _snake_case   ,     batched=_snake_case   ,     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
  	__magic_name__	:	Optional[Any]			     =  tokenized_datasets.rename_column("label"   ,     "labels"     )
  
  	# If the batch size is too big we use gradient accumulation
  	__magic_name__	:	Dict			     =  1
  	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  		__magic_name__	:	List[Any]			     =  batch_size // MAX_GPU_BATCH_SIZE
  		__magic_name__	:	Dict			     =  MAX_GPU_BATCH_SIZE
  
  	def collate_fn(_snake_case   :						Union[str, Any]     ):
  		# On TPU it's best to pad everything to the same length or training will be very slow.
  		__magic_name__	:	Tuple			     =  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":
  			__magic_name__	:	List[str]			     =  16
  		elif accelerator.mixed_precision != "no":
  			__magic_name__	:	Any			     =  8
  		else:
  			__magic_name__	:	Tuple			     =  None
  
  		return tokenizer.pad(
  		    _snake_case   ,     padding="longest"   ,     max_length=_snake_case   ,     pad_to_multiple_of=_snake_case   ,     return_tensors="pt"   ,     )
  
  	# Instantiate dataloaders.
  	__magic_name__	:	Optional[int]			     =  DataLoader(
  	    tokenized_datasets["train"]   ,     shuffle=_snake_case   ,     collate_fn=_snake_case   ,     batch_size=_snake_case     )
  	__magic_name__	:	Dict			     =  DataLoader(
  	    tokenized_datasets["validation"]   ,     shuffle=_snake_case   ,     collate_fn=_snake_case   ,     batch_size=_snake_case     )
  
  	set_seed(_snake_case     )
  
  	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  	__magic_name__	:	Optional[Any]			     =  AutoModelForSequenceClassification.from_pretrained(
  	    args.model_name_or_path   ,     return_dict=_snake_case   ,     low_cpu_mem_usage=_snake_case     )
  
  	# New Code #
  	# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
  	__magic_name__	:	List[str]			     =  accelerator.prepare(_snake_case     )
  	accelerator.print(_snake_case     )
  
  	# Instantiate optimizer
  	# New Code #
  	# For FSDP feature, at present it doesn't support multiple parameter groups,
  	# so we need to create a single parameter group for the whole model
  	__magic_name__	:	Optional[Any]			     =  torch.optim.AdamW(params=model.parameters()   ,     lr=_snake_case   ,     weight_decay=2E-4     )
  
  	# Instantiate scheduler
  	__magic_name__	:	Any			     =  get_linear_schedule_with_warmup(
  	    optimizer=_snake_case   ,     num_warmup_steps=10   ,     num_training_steps=(len(_snake_case     ) * num_epochs) // gradient_accumulation_steps   ,     )
  
  	# New Code #
  	# For FSDP feature, prepare everything except the model as we have already prepared the model
  	# before creating the optimizer
  	# 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.
  	__magic_name__     ,					__magic_name__     ,					__magic_name__     ,					__magic_name__	:	Dict			     =  accelerator.prepare(
  	    _snake_case   ,     _snake_case   ,     _snake_case   ,     _snake_case     )
  
  	__magic_name__	:	Union[str, Any]			     =  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     )
  			__magic_name__	:	Optional[Any]			     =  os.path.basename(args.resume_from_checkpoint     )
  		else:
  			# Get the most recent checkpoint
  			__magic_name__	:	Tuple			     =  [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
  			dirs.sort(key=os.path.getctime     )
  			__magic_name__	:	Tuple			     =  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  		# Extract `epoch_{i}` or `step_{i}`
  		__magic_name__	:	Dict			     =  os.path.splitext(_snake_case     )[0]
  
  		if "epoch" in training_difference:
  			num_epochs -= int(training_difference.replace("epoch_"   ,     ""     )     )
  			__magic_name__	:	List[Any]			     =  None
  		else:
  			__magic_name__	:	List[str]			     =  int(training_difference.replace("step_"   ,     ""     )     )
  			num_epochs -= resume_step // len(_snake_case     )
  			# If resuming by step, we also need to know exactly how far into the DataLoader we went
  			__magic_name__	:	Tuple			     =  (num_epochs * len(_snake_case     )) - resume_step
  
      # Now we train the model
  	for epoch in range(_snake_case     ):
  		# New Code #
  		# context manager to track the peak memory usage during the training epoch
  		with TorchTracemalloc() as tracemalloc:
  			model.train()
  			if args.with_tracking:
  				__magic_name__	:	List[Any]			     =  0
  			for step, batch in enumerate(_snake_case     ):
  				# We need to skip steps until we reach the resumed step
  				if args.resume_from_checkpoint and epoch == 0:
  					if resume_step is not None and step < resume_step:
  						pass
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
  				batch.to(accelerator.device     )
  				__magic_name__	:	List[Any]			     =  model(**_snake_case     )
  				__magic_name__	:	Optional[int]			     =  outputs.loss
  				__magic_name__	:	Tuple			     =  loss / gradient_accumulation_steps
  				# We keep track of the loss at each epoch
  				if args.with_tracking:
  					total_loss += loss.detach().float()
  				accelerator.backward(_snake_case     )
  				if step % gradient_accumulation_steps == 0:
  					optimizer.step()
  					lr_scheduler.step()
  					optimizer.zero_grad()
  					# accelerator.print(lr_scheduler.get_lr())
  
  				overall_step += 1
  
  				if isinstance(_snake_case   ,     _snake_case     ):
  					__magic_name__	:	List[Any]			     =  F'''step_{overall_step}'''
  					if overall_step % checkpointing_steps == 0:
  						if args.output_dir is not None:
  							__magic_name__	:	List[str]			     =  os.path.join(args.output_dir   ,     _snake_case     )
  						accelerator.save_state(_snake_case     )
          # New Code #
          # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  		accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin     )     )     )
  		accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used     )     )
  		accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked     )     )
  		accelerator.print(
  		    "Total Peak Memory consumed during the train (max): {}".format(
  		        tracemalloc.peaked + bamb(tracemalloc.begin     )     )     )
  		# Logging the peak memory usage of the GPU to the tracker
  		if args.with_tracking:
  			accelerator.log(
  			    {
  			        "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin     ),
  			    }   ,     step=_snake_case   ,     )
  
  		# New Code #
  		# context manager to track the peak memory usage during the evaluation
  		with TorchTracemalloc() as tracemalloc:
  			model.eval()
  			for step, batch in enumerate(_snake_case     ):
  				# We could avoid this line since we set the accelerator with `device_placement=True`.
  				batch.to(accelerator.device     )
  				with torch.no_grad():
  					__magic_name__	:	Tuple			     =  model(**_snake_case     )
  				__magic_name__	:	Any			     =  outputs.logits.argmax(dim=-1     )
  				__magic_name__     ,					__magic_name__	:	Any			     =  accelerator.gather_for_metrics((predictions, batch["labels"])     )
  				metric.add_batch(
  				    predictions=_snake_case   ,     references=_snake_case   ,     )
  
  			__magic_name__	:	Tuple			     =  metric.compute()
  			# Use accelerator.print to print only on the main process.
  			accelerator.print(F'''epoch {epoch}:'''   ,     _snake_case     )
  			if args.with_tracking:
  				accelerator.log(
  				    {
  				        "accuracy": eval_metric["accuracy"],
  				        "f1": eval_metric["f1"],
  				        "train_loss": total_loss.item() / len(_snake_case     ),
  				    }   ,     step=_snake_case   ,     )
  
  			if checkpointing_steps == "epoch":
  				__magic_name__	:	Dict			     =  F'''epoch_{epoch}'''
  				if args.output_dir is not None:
  					__magic_name__	:	Dict			     =  os.path.join(args.output_dir   ,     _snake_case     )
  				accelerator.save_state(_snake_case     )
          # New Code #
          # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  		accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin     )     )     )
  		accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used     )     )
  		accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked     )     )
  		accelerator.print(
  		    "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin     )     )     )
  		# Logging the peak memory usage of the GPU to the tracker
  		if args.with_tracking:
  			accelerator.log(
  			    {
  			        "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin     ),
  			    }   ,     step=_snake_case   ,     )
  
  	if args.with_tracking:
  		accelerator.end_training()
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      )       ->	Optional[int]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	List[str]			     =  argparse.ArgumentParser(description="Simple example of training script."     )
  	parser.add_argument(
  	    "--mixed_precision"   ,     type=_snake_case   ,     default=_snake_case   ,     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=_snake_case   ,     default=_snake_case   ,     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=_snake_case   ,     default=_snake_case   ,     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=_snake_case   ,     default="."   ,     help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."   ,     )
  	parser.add_argument(
  	    "--logging_dir"   ,     type=_snake_case   ,     default="logs"   ,     help="Location on where to store experiment tracking logs`"   ,     )
  	parser.add_argument(
  	    "--model_name_or_path"   ,     type=_snake_case   ,     help="Path to pretrained model or model identifier from huggingface.co/models."   ,     required=_snake_case   ,     )
  	__magic_name__	:	str			     =  parser.parse_args()
  	__magic_name__	:	Dict			     =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  	training_function(_snake_case   ,     _snake_case     )
  
  
  if __name__ == "__main__":
  				main()
  
style_context: 
  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		,     )
  				}
  