
"""simple docstring"""




import torch



def     __magic_name__		(					)     ->						Union[str, Any]:
    if torch.cuda.is_available():
        a__		=			torch.cuda.device_count()
    else:
        a__		=			0
    print(f'Successfully ran on {num_gpus} GPUs'     )


if __name__ == "__main__":
    main()




"""simple docstring"""




import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
    raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
a		:       Optional[Any]     						=       False
a		:       str     						=       False



def     __magic_name__		(					UpperCamelCase     :		Namespace     )     ->						Optional[int]:
    return TrainCommand(UpperCamelCase     )







class     lowercase(_lowercase       ):



     @staticmethod
     def 			lowercase__  (							__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			parser.add_parser('train'     ,				help='CLI tool to train a model on a task.'   )

         train_parser.add_argument(
             '--train_data'     ,				type=__SCREAMING_SNAKE_CASE     ,				required=__SCREAMING_SNAKE_CASE     ,				help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'     ,				)
         train_parser.add_argument(
             '--column_label'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=0     ,				help='Column of the dataset csv file with example labels.'   )
         train_parser.add_argument(
             '--column_text'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=1     ,				help='Column of the dataset csv file with example texts.'   )
         train_parser.add_argument(
             '--column_id'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=2     ,				help='Column of the dataset csv file with example ids.'   )
         train_parser.add_argument(
             '--skip_first_row'     ,				action='store_true'     ,				help='Skip the first row of the csv file (headers).'   )

         train_parser.add_argument('--validation_data'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=''     ,				help='path to validation dataset.'   )
         train_parser.add_argument(
             '--validation_split'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=0.1     ,				help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'     ,				)

         train_parser.add_argument('--output'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='./'     ,				help='path to saved the trained model.'   )

         train_parser.add_argument(
             '--task'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='text_classification'     ,				help='Task to train the model on.'   )
         train_parser.add_argument(
             '--model'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='bert-base-uncased'     ,				help='Model\'s name or path to stored model.'   )
         train_parser.add_argument('--train_batch_size'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=3_2     ,				help='Batch size for training.'   )
         train_parser.add_argument('--valid_batch_size'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=6_4     ,				help='Batch size for validation.'   )
         train_parser.add_argument('--learning_rate'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=3e-5     ,				help='Learning rate.'   )
         train_parser.add_argument('--adam_epsilon'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=1e-08     ,				help='Epsilon for Adam optimizer.'   )
         train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE   )



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			logging.get_logger('transformers-cli/training'   )

         a__		=			'tf' if is_tf_available() else 'torch'

         os.makedirs(args.output     ,				exist_ok=__SCREAMING_SNAKE_CASE   )
         a__		=			args.output

         a__		=			args.column_label
         a__		=			args.column_text
         a__		=			args.column_id

         self.logger.info(f'Loading {args.task} pipeline for {args.model}'   )
         if args.task == "text_classification":
             a__		=			TextClassificationPipeline.from_pretrained(args.model   )
         elif args.task == "token_classification":
             raise NotImplementedError
         elif args.task == "question_answering":
             raise NotImplementedError

         self.logger.info(f'Loading dataset from {args.train_data}'   )
         a__		=			Processor.create_from_csv(
             args.train_data     ,				column_label=args.column_label     ,				column_text=args.column_text     ,				column_id=args.column_id     ,				skip_first_row=args.skip_first_row     ,				)
         a__		=			None
         if args.validation_data:
             self.logger.info(f'Loading validation dataset from {args.validation_data}'   )
             a__		=			Processor.create_from_csv(
                 args.validation_data     ,				column_label=args.column_label     ,				column_text=args.column_text     ,				column_id=args.column_id     ,				skip_first_row=args.skip_first_row     ,				)

         a__		=			args.validation_split
         a__		=			args.train_batch_size
         a__		=			args.valid_batch_size
         a__		=			args.learning_rate
         a__		=			args.adam_epsilon



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         if self.framework == "tf":
             return self.run_tf()
         return self.run_torch()



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         raise NotImplementedError




     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         self.pipeline.fit(
             self.train_dataset     ,				validation_data=self.valid_dataset     ,				validation_split=self.validation_split     ,				learning_rate=self.learning_rate     ,				adam_epsilon=self.adam_epsilon     ,				train_batch_size=self.train_batch_size     ,				valid_batch_size=self.valid_batch_size     ,				)

         # Save trained pipeline
         self.pipeline.save_pretrained(self.output   )


