
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a		:					str    	=					logging.getLogger(__name__)







@dataclass
class 						lowercase:
     __snake_case:  str  				=    field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
     __snake_case:  Optional[str]  				=    field(
         default=__UpperCAmelCase						,	metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
     __snake_case:  Optional[str]  				=    field(
         default=__UpperCAmelCase						,	metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
     __snake_case:  Optional[str]  				=    field(
         default=__UpperCAmelCase						,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,	)







@dataclass
class 						lowercase:
     __snake_case:  str  				=    field(
         metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()					)}					)
     __snake_case:  str  				=    field(
         metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}					)
     __snake_case:  int  				=    field(
         default=128						,	metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }						,	)
     __snake_case:  bool  				=    field(
         default=__UpperCAmelCase						,	metadata={'help': 'Overwrite the cached training and evaluation sets'}					)




def 						__magic_name__		(				UpperCamelCase    :		Optional[Any]    )    ->	List[str]:
    a__    = default_data_collator(lowercase__    )
    a__    = batch.pop('pairID'       ,		lowercase__    )
    return batch



def 						__magic_name__		(				)    ->	Optional[int]:
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    a__    = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
    a__  ,    a__  ,    a__    = parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir    )
        and os.listdir(training_args.output_dir    )
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
            ' --overwrite_output_dir to overcome.'    )

    # Setup logging
    logging.basicConfig(
        format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'       ,		datefmt='%m/%d/%Y %H:%M:%S'       ,		level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,		)
    logger.warning(
        'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'       ,		training_args.local_rank       ,		training_args.device       ,		training_args.n_gpu       ,		bool(training_args.local_rank != -1    )       ,		training_args.fpaa       ,		)
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank    ):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info('Training/evaluation parameters %s'       ,		lowercase__    )

    # Set seed
    set_seed(training_args.seed    )

    try:
        a__    = hans_tasks_num_labels[data_args.task_name]
    except KeyError:
        raise ValueError('Task not found: %s' % (data_args.task_name)    )

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    a__    = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,		num_labels=lowercase__       ,		finetuning_task=data_args.task_name       ,		cache_dir=model_args.cache_dir       ,		)
    a__    = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,		cache_dir=model_args.cache_dir       ,		)
    a__    = AutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path       ,		from_tf=bool('.ckpt' in model_args.model_name_or_path    )       ,		config=lowercase__       ,		cache_dir=model_args.cache_dir       ,		)

    # Get datasets
    a__    = (
        HansDataset(
            data_dir=data_args.data_dir       ,		tokenizer=lowercase__       ,		task=data_args.task_name       ,		max_seq_length=data_args.max_seq_length       ,		overwrite_cache=data_args.overwrite_cache       ,		)
        if training_args.do_train
        else None
    )
    a__    = (
        HansDataset(
            data_dir=data_args.data_dir       ,		tokenizer=lowercase__       ,		task=data_args.task_name       ,		max_seq_length=data_args.max_seq_length       ,		overwrite_cache=data_args.overwrite_cache       ,		evaluate=lowercase__       ,		)
        if training_args.do_eval
        else None
    )

    # Initialize our Trainer
    a__    = Trainer(
        model=lowercase__       ,		args=lowercase__       ,		train_dataset=lowercase__       ,		eval_dataset=lowercase__       ,		data_collator=lowercase__       ,		)

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
    if training_args.do_eval:
        logger.info('*** Evaluate ***'    )

        a__    = trainer.predict(lowercase__    )
        a__    = output.predictions
        a__    = np.argmax(lowercase__       ,		axis=1    )

        a__    = [ex.pairID for ex in eval_dataset]
        a__    = os.path.join(training_args.output_dir       ,		'hans_predictions.txt'    )
        a__    = eval_dataset.get_labels()
        if trainer.is_world_master():
            with open(lowercase__       ,		'w'    ) as writer:
                writer.write('pairID,gold_label\n'    )
                for pid, pred in zip(lowercase__       ,		lowercase__    ):
                    writer.write('ex' + str(lowercase__    ) + ',' + label_list[int(lowercase__    )] + '\n'    )

        trainer._log(output.metrics    )



def 						__magic_name__		(				UpperCamelCase    :		Any    )    ->	Any:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()




"""simple docstring"""




from sklearn.metrics import roc_auc_score

import datasets


a		:					Tuple    	=					'\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'

a		:					int    	=					'\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'

a		:					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 						lowercase(datasets.Metric					):



     def  lowercase__     (       self      )		->    str:







         """simple docstring"""



         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  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE="macro"    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE="raise"    ,							__SCREAMING_SNAKE_CASE=None    ,							)		->    Any:







         """simple docstring"""



         return {
             "roc_auc": roc_auc_score(
                 __SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							average=__SCREAMING_SNAKE_CASE    ,							sample_weight=__SCREAMING_SNAKE_CASE    ,							max_fpr=__SCREAMING_SNAKE_CASE    ,							multi_class=__SCREAMING_SNAKE_CASE    ,							labels=__SCREAMING_SNAKE_CASE    ,							)
         }


