code: 
  
  
  """simple docstring"""
  
  
  
  
  from sklearn.metrics import roc_auc_score
  
  import datasets
  
  
  __SCREAMING_SNAKE_CASE  		=						"""\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"""
  
  __SCREAMING_SNAKE_CASE  		=						"""\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"""
  
  __SCREAMING_SNAKE_CASE  		=						"""\\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      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            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							:Union[str, Any]				,  UpperCamelCase__							:int				,  UpperCamelCase__							:str				,  UpperCamelCase__							:str="macro"				,  UpperCamelCase__							:Dict=None				,  UpperCamelCase__							:int=None				,  UpperCamelCase__							:Dict="raise"				,  UpperCamelCase__							:List[Any]=None				,  ):
            return {
                "roc_auc": roc_auc_score(
                    UpperCamelCase__				,  UpperCamelCase__				,  average=UpperCamelCase__				,  sample_weight=UpperCamelCase__				,  max_fpr=UpperCamelCase__				,  multi_class=UpperCamelCase__				,  labels=UpperCamelCase__				,  )
            }
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import time
  
  import torch
  import transformers
  from measures_util import end_measure, log_measures, start_measure
  from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer
  
  from accelerate.utils import compute_module_sizes
  
  
  __SCREAMING_SNAKE_CASE					=						{
      """gpt-j-6b""": {"""is_causal""": True, """model""": """sgugger/sharded-gpt-j-6B""", """tokenizer""": """EleutherAI/gpt-j-6B"""},
      """gpt-neox""": {"""is_causal""": True, """model""": """EleutherAI/gpt-neox-20b"""},
      """opt""": {"""is_causal""": True, """model""": """facebook/opt-30b"""},
      """T0pp""": {"""is_causal""": False, """model""": """bigscience/T0pp""", """model_revision""": """sharded"""},
  }
  
  __SCREAMING_SNAKE_CASE					=						[
      """Hello, my name is""",
      """Are unicorns real? Unicorns are""",
      """For the first time in several years,""",
      """My name is Julien and I am""",
      """The goal of life is""",
      """Whenever I'm sad, I like to""",
  ]
  
  
  
  def 						__a      (  ):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate."					)
         parser.add_argument("model_name",		type=a,		default=a,		help="The name of the model to try."					)
         parser.add_argument(
             "--tokenizer_name",		type=a,		default=a,		help="The name of the tokenizer (if different from the model."					)
         parser.add_argument("--is_causal",		type=a,		default=a,		help="Whether or not the model is causal."					)
         parser.add_argument(
             "--model_revision",		type=a,		default=a,		help="The revision to use for the model checkpoint."					)
         parser.add_argument("--torch_dtype",		type=a,		default=a,		help="The dtype for the model."					)
         parser.add_argument("--disk_offload",		action="store_true"					)
  
         _a          =				parser.parse_args()
  
         # Sanitize args
         if args.model_name in DEFAULT_MODELS:
                _a          =				DEFAULT_MODELS[args.model_name]
                _a          =				defaults["model"]
                if args.tokenizer_name is None:
                       _a          =				defaults.get("tokenizer",		args.model_name					)
                if args.is_causal is None:
                       _a          =				defaults["is_causal"]
                if args.model_revision is None:
                       _a          =				defaults.get("model_revision",		"main"					)
  
         if args.is_causal is None:
                raise ValueError("Could not infer the default for `--is_causal`, pass either True or False for it."					)
         if args.tokenizer_name is None:
                _a          =				args.model_name
         if args.model_revision is None:
                _a          =				"main"
  
         return args
  
  
  
  def 						__a      (  ):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         transformers.utils.logging.set_verbosity_error()
         _a          =				parse_args()
  
         if args.torch_dtype is None:
                _a          =				AutoConfig.from_pretrained(args.model_name					)
                _a          =				getattr(a,		"torch_dtype",		torch.floataa					)
         else:
                _a          =				getattr(a,		args.torch_dtype					)
         _a          =				AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
         _a          =				{
             "torch_dtype": torch_dtype,
             "revision": args.model_revision,
         }
         if args.disk_offload:
                _a          =				"tmp_offload"
                _a          =				True
  
         _a          =				start_measure()
         _a          =				model_cls.from_pretrained(args.model_name,		device_map="auto",		**a					)
         _a          =				end_measure(a					)
         log_measures(a,		"Model loading"					)
  
         _a          =				compute_module_sizes(a					)
         _a          =				{v: 0 for v in model.hf_device_map.values()}
         for module, device in model.hf_device_map.items():
                device_size[device] += module_sizes[module]
         _a          =				"\n".join([F'- {device}: {size // 2**2_0}MiB' for device, size in device_size.items()]					)
         print(F'\nTheoretical use:\n{message}'					)
  
         _a          =				AutoTokenizer.from_pretrained(args.tokenizer_name					)
  
         _a          =				start_measure()
         _a          =				[]
         _a          =				[]
         _a          =				[]
         for prompt in PROMPTS:
                _a          =				tokenizer(a,		return_tensors="pt"					).to(0					)
                _a          =				inputs["input_ids"][0].tolist()
                _a          =				time.time()
                _a          =				model.generate(inputs["input_ids"]					)
                _a          =				time.time()
                _a          =				outputs[0].tolist()
                _a          =				len(a					) if outputs[: len(a					)] != tokens else len(a					) - len(a					)
                _a          =				after_generate - before_generate
  
                _a          =				tokenizer.decode(a,		skip_special_tokens=a					)
                texts_outs.append(a					)
                generation_times.append(a					)
                gen_tokens.append(a					)
                print(F'Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'					)
  
         _a          =				end_measure(a					)
         log_measures(a,		"Model generation"					)
  
         _a          =				[gen / tok for gen, tok in zip(a,		a					)]
         _a          =				sum(a					) / len(a					)
         print(F'Average time of generation per token: {avg_gen:.2f}s'					)
         print(F'First generation (avg time per token): {generation_times_per_token[0]:.2f}s'					)
         _a          =				sum(generation_times_per_token[1:]					) / (len(a					) - 1)
         print(F'Average time of generation per token (excluding the first): {avg_gen:.2f}s'					)
  
  
  if __name__ == "__main__":
     main()
  