






from sklearn.metrics import roc_auc_score

import datasets


__UpperCamelCase   :						Dict							=  """
This 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.

This metric has three separate use cases:
    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
"""

__UpperCamelCase   :						Union[str, Any]							=  """
Args:
- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples,)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples, n_classes)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:
    - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).
    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
- 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`.
- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
    - `'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.
    - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.
- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
    `prediction_scores` is used. Defaults to `None`.
Returns:
    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.
Examples:
    Example 1:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\")
        >>> refs = [1, 0, 1, 1, 0, 0]
        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
        >>> print(round(results['roc_auc'], 2))
        0.78

    Example 2:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")
        >>> refs = [1, 0, 1, 2, 2, 0]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')
        >>> print(round(results['roc_auc'], 2))
        0.85

    Example 3:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")
        >>> refs = [[1, 1, 0],
        ...         [1, 1, 0],
        ...         [0, 1, 0],
        ...         [0, 0, 1],
        ...         [0, 1, 1],
        ...         [1, 0, 1]]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
        >>> print([round(res, 2) for res in results['roc_auc']])
        [0.83, 0.38, 0.94]
"""

__UpperCamelCase   :						int							=  """\
@article{doi:10.1177/0272989X8900900307,
author = {Donna Katzman McClish},
title ={Analyzing a Portion of the ROC Curve},
journal = {Medical Decision Making},
volume = {9},
number = {3},
pages = {190-195},
year = {1989},
doi = {10.1177/0272989X8900900307},
    note ={PMID: 2668680},
URL = {https://doi.org/10.1177/0272989X8900900307},
eprint = {https://doi.org/10.1177/0272989X8900900307}
}


@article{10.1023/A:1010920819831,
author = {Hand, David J. and Till, Robert J.},
title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
year = {2001},
issue_date = {November 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {45},
number = {2},
issn = {0885-6125},
url = {https://doi.org/10.1023/A:1010920819831},
doi = {10.1023/A:1010920819831},
journal = {Mach. Learn.},
month = {oct},
pages = {171–186},
numpages = {16},
keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
}


@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={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.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,					_KWARGS_DESCRIPTION		)
class 					__SCREAMING_SNAKE_CASE(		datasets.Metric		):
							def lowerCAmelCase_	(       self: str  )						->						Optional[int]:
											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 lowerCAmelCase_	(       self: str    ,		UpperCamelCase: Any    ,		UpperCamelCase: str    ,		UpperCamelCase: Union[str, Any]="macro"    ,		UpperCamelCase: Tuple=None    ,		UpperCamelCase: int=None    ,		UpperCamelCase: Any="raise"    ,		UpperCamelCase: Tuple=None    ,		)						->						int:
											return {
											    "roc_auc": roc_auc_score(
											        UpperCamelCase    ,		UpperCamelCase    ,		average=UpperCamelCase    ,		sample_weight=UpperCamelCase    ,		max_fpr=UpperCamelCase    ,		multi_class=UpperCamelCase    ,		labels=UpperCamelCase    ,		)
											}












from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
							from .data.data_collator import DataCollator
							from .modeling_utils import PreTrainedModel
							from .tokenization_utils_base import PreTrainedTokenizerBase
							from .trainer_callback import TrainerCallback
							from .trainer_utils import EvalPrediction, PredictionOutput
							from .training_args import TrainingArguments


__UpperCamelCase   :						Tuple							=  logging.get_logger(__name__)


class 					__SCREAMING_SNAKE_CASE(		a_		):
							def __init__(       self: Dict    ,		UpperCamelCase: Union["PreTrainedModel", nn.Module] = None    ,		UpperCamelCase: "TrainingArguments" = None    ,		UpperCamelCase: Optional["DataCollator"] = None    ,		UpperCamelCase: Optional[Dataset] = None    ,		UpperCamelCase: Optional[Union[Dataset, Dict[str, Dataset]]] = None    ,		UpperCamelCase: Optional["PreTrainedTokenizerBase"] = None    ,		UpperCamelCase: Optional[Callable[[], "PreTrainedModel"]] = None    ,		UpperCamelCase: Optional[Callable[["EvalPrediction"], Dict]] = None    ,		UpperCamelCase: Optional[List["TrainerCallback"]] = None    ,		UpperCamelCase: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)    ,		UpperCamelCase: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None    ,		)						->						Tuple:
											super().__init__(
											    model=UpperCamelCase    ,		args=UpperCamelCase    ,		data_collator=UpperCamelCase    ,		train_dataset=UpperCamelCase    ,		eval_dataset=UpperCamelCase    ,		tokenizer=UpperCamelCase    ,		model_init=UpperCamelCase    ,		compute_metrics=UpperCamelCase    ,		callbacks=UpperCamelCase    ,		optimizers=UpperCamelCase    ,		preprocess_logits_for_metrics=UpperCamelCase    ,		)

											# Override self.model.generation_config if a GenerationConfig is specified in args.
											# Priority: args.generation_config > model.generation_config > default GenerationConfig.
											if self.args.generation_config is not None:
															snake_case__													=       self.load_generation_config(self.args.generation_config  )
															snake_case__													=       gen_config
							@staticmethod
							def lowerCAmelCase_	(       UpperCamelCase: Union[str, GenerationConfig]  )						->						GenerationConfig:

											# GenerationConfig provided, nothing to do
											if isinstance(UpperCamelCase    ,		UpperCamelCase  ):
															return deepcopy(UpperCamelCase  )

											# str or Path
											snake_case__													=       Path(UpperCamelCase  ) if isinstance(UpperCamelCase    ,		UpperCamelCase  ) else gen_config_arg
											snake_case__													=       None

											# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
											# This step is required in order to determine config_file_name
											if pretrained_model_name.is_file():
															snake_case__													=       pretrained_model_name.name
															snake_case__													=       pretrained_model_name.parent
											# dir path
											elif pretrained_model_name.is_dir():
															pass
											# model id or URL
											else:
															snake_case__													=       gen_config_arg

											snake_case__													=       GenerationConfig.from_pretrained(UpperCamelCase    ,		UpperCamelCase  )
											return gen_config
							def lowerCAmelCase_	(       self: Optional[int]    ,		UpperCamelCase: Optional[Dataset] = None    ,		UpperCamelCase: Optional[List[str]] = None    ,		UpperCamelCase: str = "eval"    ,		**UpperCamelCase: Dict    ,		)						->						Dict[str, float]:
											snake_case__													=       gen_kwargs.copy()
											if gen_kwargs.get('max_length'  ) is None and gen_kwargs.get('max_new_tokens'  ) is None:
															snake_case__													=       self.args.generation_max_length
											snake_case__													=       (
											    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'  ) is not None else self.args.generation_num_beams
											)
											snake_case__													=       gen_kwargs

											return super().evaluate(UpperCamelCase    ,		ignore_keys=UpperCamelCase    ,		metric_key_prefix=UpperCamelCase  )
							def lowerCAmelCase_	(       self: Tuple    ,		UpperCamelCase: Dataset    ,		UpperCamelCase: Optional[List[str]] = None    ,		UpperCamelCase: str = "test"    ,		**UpperCamelCase: Dict    ,		)						->						"PredictionOutput":
											snake_case__													=       gen_kwargs.copy()
											if gen_kwargs.get('max_length'  ) is None and gen_kwargs.get('max_new_tokens'  ) is None:
															snake_case__													=       self.args.generation_max_length
											snake_case__													=       (
											    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'  ) is not None else self.args.generation_num_beams
											)
											snake_case__													=       gen_kwargs

											return super().predict(UpperCamelCase    ,		ignore_keys=UpperCamelCase    ,		metric_key_prefix=UpperCamelCase  )
							def lowerCAmelCase_	(       self: Tuple    ,		UpperCamelCase: nn.Module    ,		UpperCamelCase: Dict[str, Union[torch.Tensor, Any]]    ,		UpperCamelCase: bool    ,		UpperCamelCase: Optional[List[str]] = None    ,		**UpperCamelCase: List[Any]    ,		)						->						Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

											if not self.args.predict_with_generate or prediction_loss_only:
															return super().prediction_step(
															    UpperCamelCase    ,		UpperCamelCase    ,		prediction_loss_only=UpperCamelCase    ,		ignore_keys=UpperCamelCase  )

											snake_case__													=       'labels' in inputs
											snake_case__													=       self._prepare_inputs(UpperCamelCase  )

											# XXX: adapt synced_gpus for fairscale as well
											# Priority (handled in generate):
											# gen_kwargs > model.generation_config > default GenerationConfig()

											if len(UpperCamelCase  ) == 0 and hasattr(self    ,		'_gen_kwargs'  ):
															snake_case__													=       self._gen_kwargs.copy()

											if gen_kwargs.get('max_length'  ) is None and gen_kwargs.get('max_new_tokens'  ) is None:
															snake_case__													=       self.model.config.max_length
											snake_case__													=       (
											    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'  ) is not None else self.model.config.num_beams
											)
											snake_case__													=       True if is_deepspeed_zeroa_enabled() else False
											snake_case__													=       (
											    gen_kwargs['synced_gpus'] if gen_kwargs.get('synced_gpus'  ) is not None else default_synced_gpus
											)

											# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
											# (otherwise, it would continue generating from the padded `decoder_input_ids`)
											if (
											    "labels" in inputs
											    and "decoder_input_ids" in inputs
											    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
											):
															snake_case__													=       {k: v for k, v in inputs.items() if k != 'decoder_input_ids'}
											snake_case__													=       self.model.generate(**UpperCamelCase    ,		**UpperCamelCase  )

											# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
											# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
											# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
											if self.model.generation_config._from_model_config:
															snake_case__													=       False

											# Retrieves GenerationConfig from model.generation_config
											snake_case__													=       self.model.generation_config
											# in case the batch is shorter than max length, the output should be padded
											if generated_tokens.shape[-1] < gen_config.max_length:
															snake_case__													=       self._pad_tensors_to_max_len(UpperCamelCase    ,		gen_config.max_length  )
											elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
															snake_case__													=       self._pad_tensors_to_max_len(UpperCamelCase    ,		gen_config.max_new_tokens + 1  )

											with torch.no_grad():
															if has_labels:
																			with self.compute_loss_context_manager():
																							snake_case__													=       model(**UpperCamelCase  )
																			if self.label_smoother is not None:
																							snake_case__													=       self.label_smoother(UpperCamelCase    ,		inputs['labels']  ).mean().detach()
																			else:
																							snake_case__													=       (outputs['loss'] if isinstance(UpperCamelCase    ,		UpperCamelCase  ) else outputs[0]).mean().detach()
															else:
																			snake_case__													=       None

											if self.args.prediction_loss_only:
															return loss, None, None

											if has_labels:
															snake_case__													=       inputs['labels']
															if labels.shape[-1] < gen_config.max_length:
																			snake_case__													=       self._pad_tensors_to_max_len(UpperCamelCase    ,		gen_config.max_length  )
															elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
																			snake_case__													=       self._pad_tensors_to_max_len(UpperCamelCase    ,		gen_config.max_new_tokens + 1  )
											else:
															snake_case__													=       None

											return loss, generated_tokens, labels


							def lowerCAmelCase_	(       self: List[Any]    ,		UpperCamelCase: Tuple    ,		UpperCamelCase: Tuple  )						->						List[str]:
											if self.tokenizer is not None and hasattr(self.tokenizer    ,		'pad_token_id'  ):
															# If PAD token is not defined at least EOS token has to be defined
															snake_case__													=       (
															    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
															)
											else:
															if self.model.config.pad_token_id is not None:
																			snake_case__													=       self.model.config.pad_token_id
															else:
																			raise ValueError('Pad_token_id must be set in the configuration of the model, in order to pad tensors'  )

											snake_case__													=       pad_token_id * torch.ones(
											    (tensor.shape[0], max_length)    ,		dtype=tensor.dtype    ,		device=tensor.device  )
											snake_case__													=       tensor
											return padded_tensor




