


'''simple docstring'''




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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__snake_case    						=logging.getLogger(__name__)





def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Dict				):
   return (preds == labels).mean()

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




@dataclass
class 		UpperCAmelCase_      :
     lowerCamelCase				:	str		=     field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()		)}		)
     lowerCamelCase				:	str		=     field(metadata={'''help''': '''Should contain the data files for the task.'''}		)
     lowerCamelCase				:	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.'''
             )
         }       ,	)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)





def 	a_					( ):
   # 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.

   lowerCAmelCase     =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
   lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			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'	, lowerCamelCase				)

   # Set seed
   set_seed(training_args.seed				)

   try:
      lowerCAmelCase     =			processors[data_args.task_name]()
      lowerCAmelCase     =			processor.get_labels()
      lowerCAmelCase     =			len(lowerCamelCase				)
   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.

   lowerCAmelCase     =			AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path	, num_labels=lowerCamelCase	, finetuning_task=data_args.task_name	, cache_dir=model_args.cache_dir	, )
   lowerCAmelCase     =			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	, )
   lowerCAmelCase     =			AutoModelForMultipleChoice.from_pretrained(
       model_args.model_name_or_path	, from_tf=bool('.ckpt' in model_args.model_name_or_path				)	, config=lowerCamelCase	, cache_dir=model_args.cache_dir	, )

   # Get datasets
   lowerCAmelCase     =			(
       MultipleChoiceDataset(
           data_dir=data_args.data_dir	, tokenizer=lowerCamelCase	, task=data_args.task_name	, max_seq_length=data_args.max_seq_length	, overwrite_cache=data_args.overwrite_cache	, mode=Split.train	, )
       if training_args.do_train
       else None
   )
   lowerCAmelCase     =			(
       MultipleChoiceDataset(
           data_dir=data_args.data_dir	, tokenizer=lowerCamelCase	, task=data_args.task_name	, max_seq_length=data_args.max_seq_length	, overwrite_cache=data_args.overwrite_cache	, mode=Split.dev	, )
       if training_args.do_eval
       else None
   )

   def compute_metrics(lowerCamelCase	:     EvalPrediction				) -> Dict:
      lowerCAmelCase     =			np.argmax(p.predictions	, axis=1				)
      return {"acc": simple_accuracy(lowerCamelCase	, p.label_ids				)}

   # Data collator
   lowerCAmelCase     =			DataCollatorWithPadding(lowerCamelCase	, pad_to_multiple_of=8				) if training_args.fpaa else None

   # Initialize our Trainer
   lowerCAmelCase     =			Trainer(
       model=lowerCamelCase	, args=lowerCamelCase	, train_dataset=lowerCamelCase	, eval_dataset=lowerCamelCase	, compute_metrics=lowerCamelCase	, data_collator=lowerCamelCase	, )

   # 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
   lowerCAmelCase     =			{}
   if training_args.do_eval:
      logger.info('*** Evaluate ***'				)

      lowerCAmelCase     =			trainer.evaluate()

      lowerCAmelCase     =			os.path.join(training_args.output_dir	, 'eval_results.txt'				)
      if trainer.is_world_master():
         with open(lowerCamelCase	, 'w'				) as writer:
            logger.info('***** Eval results *****'				)
            for key, value in result.items():
               logger.info('  %s = %s'	, lowerCamelCase	, lowerCamelCase				)
               writer.write('%s = %s\n' % (key, value)				)

            results.update(lowerCamelCase				)

   return results





def 	a_					( lowerCamelCase	:     Dict				):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
       main()









import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


__lowerCAmelCase	: Optional[int]  				='\\n    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n'

__lowerCAmelCase	: Optional[Any]  				='\\n    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n'

__lowerCAmelCase	: Dict  				='\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n    predictions: list of predictions to score (as int64),\n        except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).\n    references: list of ground truth labels corresponding to the predictions (as int64),\n        except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "precision": Precision@10\nExamples:\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\')  # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')\n    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'precision@10\': 1.0}\n\n'


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return float((preds == labels).mean()      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		simple_accuracy(lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[str]					=		float(fa_score(y_true=lowercase__    ,       y_pred=lowercase__      )      )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		np.array(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		np.array(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		en_sentvecs.shape[0]

						# mean centering
						__SCREAMING_SNAKE_CASE						:     Tuple					=		en_sentvecs - np.mean(lowercase__    ,       axis=0      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		in_sentvecs - np.mean(lowercase__    ,       axis=0      )

						__SCREAMING_SNAKE_CASE						:     str					=		cdist(lowercase__    ,       lowercase__    ,       '''cosine'''      )
						__SCREAMING_SNAKE_CASE						:     int					=		np.array(range(lowercase__      )      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		sim.argsort(axis=1      )[:, :10]
						__SCREAMING_SNAKE_CASE						:     str					=		np.any(preds == actual[:, None]    ,       axis=1      )
						return float(matches.mean()      )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION       )
class        _lowercase      (			datasets.Metric       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Tuple	)		->			Tuple:
										if self.config_name not in [
										    "wnli",
										    "copa",
										    "sna",
										    "csqa",
										    "wstp",
										    "inltkh",
										    "bbca",
										    "cvit-mkb-clsr",
										    "iitp-mr",
										    "iitp-pr",
										    "actsa-sc",
										    "md",
										    "wiki-ner",
										]:
																raise KeyError(
																    '''You should supply a configuration name selected in '''
																    '''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
																    '''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
																    '''"wiki-ner"]'''	)
										return datasets.MetricInfo(
										    description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
										        {
										            '''predictions''': datasets.Value('''int64'''	)
										            if self.config_name != '''cvit-mkb-clsr'''
										            else datasets.Sequence(datasets.Value('''float32'''	)	),
										            '''references''': datasets.Value('''int64'''	)
										            if self.config_name != '''cvit-mkb-clsr'''
										            else datasets.Sequence(datasets.Value('''float32'''	)	),
										        }	)			,			codebase_urls=[]			,			reference_urls=[]			,			format='''numpy''' if self.config_name != '''cvit-mkb-clsr''' else None			,			)



				def 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:Tuple	)		->			str:
										if self.config_name == "cvit-mkb-clsr":
																return {"precision@10": precision_at_aa(lowerCAmelCase__			,			lowerCAmelCase__	)}
										elif self.config_name in ["wiki-ner"]:
																return acc_and_fa(lowerCAmelCase__			,			lowerCAmelCase__	)
										elif self.config_name in [
										    "wnli",
										    "copa",
										    "sna",
										    "csqa",
										    "wstp",
										    "inltkh",
										    "bbca",
										    "iitp-mr",
										    "iitp-pr",
										    "actsa-sc",
										    "md",
										]:
																return {"accuracy": simple_accuracy(lowerCAmelCase__			,			lowerCAmelCase__	)}
										else:
																raise KeyError(
																    '''You should supply a configuration name selected in '''
																    '''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
																    '''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
																    '''"wiki-ner"]'''	)
