




from sklearn.metrics import roc_auc_score

import datasets


_lowerCAmelCase    : 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"

_lowerCAmelCase    : Union[str, 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"

_lowerCAmelCase    : Union[str, 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       _UpperCamelCase   (					datasets.Metric		):
      def 						UpperCAmelCase_    (      self :Dict   )  ->      List[Any]:
       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 						UpperCAmelCase_    (      self :Tuple			,   lowerCamelCase :Dict			,   lowerCamelCase :Tuple			,   lowerCamelCase :List[Any]="macro"			,   lowerCamelCase :Union[str, Any]=None			,   lowerCamelCase :Tuple=None			,   lowerCamelCase :int="raise"			,   lowerCamelCase :Optional[Any]=None			,   )  ->      Union[str, Any]:
       return {
           "roc_auc": roc_auc_score(
               lowerCamelCase			,   lowerCamelCase			,   average=lowerCamelCase			,   sample_weight=lowerCamelCase			,   max_fpr=lowerCamelCase			,   multi_class=lowerCamelCase			,   labels=lowerCamelCase			,   )
       }











import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer
class       _UpperCamelCase   (					tf.keras.layers.Layer		):
      def __init__(      self :int			,   lowerCamelCase :List			,   lowerCamelCase :bool			,   lowerCamelCase :int = None			,   lowerCamelCase :int = None			,   lowerCamelCase :int = None			,   lowerCamelCase :str = "longest"			,   lowerCamelCase :bool = True			,   lowerCamelCase :int = 512			,   lowerCamelCase :int = None			,   lowerCamelCase :bool = True			,   lowerCamelCase :bool = True			,   lowerCamelCase :bool = True			,   **lowerCamelCase :Optional[Any]			,   )  ->      List[str]:
       super().__init__()
       if use_fast_bert_tokenizer:
        UpperCAmelCase__							=    FastBertTokenizer(
            lowerCamelCase			,   token_out_type=tf.intaa			,   lower_case_nfd_strip_accents=lowerCamelCase			,   **lowerCamelCase   )
       else:
        UpperCAmelCase__							=    tf.lookup.StaticVocabularyTable(
            tf.lookup.KeyValueTensorInitializer(
                keys=lowerCamelCase			,   key_dtype=tf.string			,   values=tf.range(tf.size(lowerCamelCase			,   out_type=tf.intaa   )			,   dtype=tf.intaa   )			,   value_dtype=tf.intaa			,   )			,   num_oov_buckets=1			,   )
        UpperCAmelCase__							=    BertTokenizerLayer(
            lowerCamelCase			,   token_out_type=tf.intaa			,   lower_case=lowerCamelCase			,   **lowerCamelCase   )

       UpperCAmelCase__							=    vocab_list
       UpperCAmelCase__							=    do_lower_case
       UpperCAmelCase__							=    cls_token_id or vocab_list.index("[CLS]"   )
       UpperCAmelCase__							=    sep_token_id or vocab_list.index("[SEP]"   )
       UpperCAmelCase__							=    pad_token_id or vocab_list.index("[PAD]"   )
       UpperCAmelCase__							=    ShrinkLongestTrimmer(max_length - 3			,   axis=1   )  # Allow room for special tokens
       UpperCAmelCase__							=    max_length
       UpperCAmelCase__							=    padding
       UpperCAmelCase__							=    truncation
       UpperCAmelCase__							=    pad_to_multiple_of
       UpperCAmelCase__							=    return_token_type_ids
       UpperCAmelCase__							=    return_attention_mask
      @classmethod
      def 						UpperCAmelCase_    (      cls :List[str]			,   lowerCamelCase :"PreTrainedTokenizerBase"			,   **lowerCamelCase :Dict   )  ->      Dict:  # noqa: F821
       UpperCAmelCase__							=    kwargs.pop("do_lower_case"			,   lowerCamelCase   )
       UpperCAmelCase__							=    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       UpperCAmelCase__							=    kwargs.pop("cls_token_id"			,   lowerCamelCase   )
       UpperCAmelCase__							=    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       UpperCAmelCase__							=    kwargs.pop("sep_token_id"			,   lowerCamelCase   )
       UpperCAmelCase__							=    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       UpperCAmelCase__							=    kwargs.pop("pad_token_id"			,   lowerCamelCase   )
       UpperCAmelCase__							=    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

       UpperCAmelCase__							=    tokenizer.get_vocab()
       UpperCAmelCase__							=    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,   key=lambda lowerCamelCase   : x[1]   )
       UpperCAmelCase__							=    [entry[0] for entry in vocab]
       return cls(
           vocab_list=lowerCamelCase			,   do_lower_case=lowerCamelCase			,   cls_token_id=lowerCamelCase			,   sep_token_id=lowerCamelCase			,   pad_token_id=lowerCamelCase			,   **lowerCamelCase			,   )
      @classmethod
      def 						UpperCAmelCase_    (      cls :Any			,   lowerCamelCase :Union[str, os.PathLike]			,   *lowerCamelCase :str			,   **lowerCamelCase :List[str]   )  ->      Tuple:
       try:
        UpperCAmelCase__							=    BertTokenizer.from_pretrained(lowerCamelCase			,   *lowerCamelCase			,   **lowerCamelCase   )
       except:  # noqa: E722
        from .tokenization_bert_fast import BertTokenizerFast

        UpperCAmelCase__							=    BertTokenizerFast.from_pretrained(lowerCamelCase			,   *lowerCamelCase			,   **lowerCamelCase   )
       return cls.from_tokenizer(lowerCamelCase			,   **lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :Optional[int]   )  ->      List[str]:
       if self.do_lower_case:
        UpperCAmelCase__							=    case_fold_utfa(lowerCamelCase   )
       UpperCAmelCase__							=    self.tf_tokenizer.tokenize(lowerCamelCase   )
       return tokens.merge_dims(1			,   -1   )
      def 						UpperCAmelCase_    (      self :Optional[Any]			,   lowerCamelCase :Tuple			,   lowerCamelCase :str=None			,   lowerCamelCase :Tuple=None			,   lowerCamelCase :Any=None			,   lowerCamelCase :Optional[int]=None			,   lowerCamelCase :Optional[Any]=None			,   lowerCamelCase :Optional[Any]=None			,   lowerCamelCase :Tuple=None			,   )  ->      str:
       if padding is None:
        UpperCAmelCase__							=    self.padding
       if padding not in ("longest", "max_length"):
        raise ValueError("Padding must be either 'longest' or 'max_length'!"   )
       if max_length is not None and text_pair is not None:
        # Because we have to instantiate a Trimmer to do it properly
        raise ValueError("max_length cannot be overridden at call time when truncating paired texts!"   )
       if max_length is None:
        UpperCAmelCase__							=    self.max_length
       if truncation is None:
        UpperCAmelCase__							=    self.truncation
       if pad_to_multiple_of is None:
        UpperCAmelCase__							=    self.pad_to_multiple_of
       if return_token_type_ids is None:
        UpperCAmelCase__							=    self.return_token_type_ids
       if return_attention_mask is None:
        UpperCAmelCase__							=    self.return_attention_mask
       if not isinstance(lowerCamelCase			,   tf.Tensor   ):
        UpperCAmelCase__							=    tf.convert_to_tensor(lowerCamelCase   )
       if text_pair is not None and not isinstance(lowerCamelCase			,   tf.Tensor   ):
        UpperCAmelCase__							=    tf.convert_to_tensor(lowerCamelCase   )
       if text_pair is not None:
        if text.shape.rank > 1:
         raise ValueError("text argument should not be multidimensional when a text pair is supplied!"   )
        if text_pair.shape.rank > 1:
         raise ValueError("text_pair should not be multidimensional!"   )
       if text.shape.rank == 2:
        UpperCAmelCase__			,			UpperCAmelCase__							=    text[:, 0], text[:, 1]
       UpperCAmelCase__							=    self.unpaired_tokenize(lowerCamelCase   )
       if text_pair is None:  # Unpaired text
        if truncation:
         UpperCAmelCase__							=    text[:, : max_length - 2]  # Allow room for special tokens
        UpperCAmelCase__			,			UpperCAmelCase__							=    combine_segments(
            (text,)			,   start_of_sequence_id=self.cls_token_id			,   end_of_segment_id=self.sep_token_id   )
       else:  # Paired text
        UpperCAmelCase__							=    self.unpaired_tokenize(lowerCamelCase   )
        if truncation:
         UpperCAmelCase__			,			UpperCAmelCase__							=    self.paired_trimmer.trim([text, text_pair]   )
        UpperCAmelCase__			,			UpperCAmelCase__							=    combine_segments(
            (text, text_pair)			,   start_of_sequence_id=self.cls_token_id			,   end_of_segment_id=self.sep_token_id   )
       if padding == "longest":
        UpperCAmelCase__							=    input_ids.bounding_shape(axis=1   )
        if pad_to_multiple_of is not None:
         # No ceiling division in tensorflow, so we negate floordiv instead
         UpperCAmelCase__							=    pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,   lowerCamelCase   ))
       else:
        UpperCAmelCase__							=    max_length

       UpperCAmelCase__			,			UpperCAmelCase__							=    pad_model_inputs(lowerCamelCase			,   max_seq_length=lowerCamelCase			,   pad_value=self.pad_token_id   )
       UpperCAmelCase__							=    {"input_ids": input_ids}
       if return_attention_mask:
        UpperCAmelCase__							=    attention_mask
       if return_token_type_ids:
        UpperCAmelCase__			,			UpperCAmelCase__							=    pad_model_inputs(
            lowerCamelCase			,   max_seq_length=lowerCamelCase			,   pad_value=self.pad_token_id   )
        UpperCAmelCase__							=    token_type_ids
       return output







      def 						UpperCAmelCase_    (      self :Tuple   )  ->      Tuple:
       return {
           "vocab_list": self.vocab_list,
           "do_lower_case": self.do_lower_case,
           "cls_token_id": self.cls_token_id,
           "sep_token_id": self.sep_token_id,
           "pad_token_id": self.pad_token_id,
       }





