





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
_lowerCamelCase			       =      False
_lowerCamelCase			       =      False




def    __UpperCAmelCase(      lowercase_      ):
       return TrainCommand(lowercase_      )







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      @staticmethod
      def 				__snake_case			(		a__):



             """simple docstring"""





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

             train_parser.add_argument(
                 '''--train_data'''      ,							type=a__      ,							required=a__      ,							help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'''      ,							)
             train_parser.add_argument(
                 '''--column_label'''      ,							type=a__      ,							default=0      ,							help='''Column of the dataset csv file with example labels.''')
             train_parser.add_argument(
                 '''--column_text'''      ,							type=a__      ,							default=1      ,							help='''Column of the dataset csv file with example texts.''')
             train_parser.add_argument(
                 '''--column_id'''      ,							type=a__      ,							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=a__      ,							default=''''''      ,							help='''path to validation dataset.''')
             train_parser.add_argument(
                 '''--validation_split'''      ,							type=a__      ,							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=a__      ,							default='''./'''      ,							help='''path to saved the trained model.''')

             train_parser.add_argument(
                 '''--task'''      ,							type=a__      ,							default='''text_classification'''      ,							help='''Task to train the model on.''')
             train_parser.add_argument(
                 '''--model'''      ,							type=a__      ,							default='''bert-base-uncased'''      ,							help='''Model\'s name or path to stored model.''')
             train_parser.add_argument('''--train_batch_size'''      ,							type=a__      ,							default=32      ,							help='''Batch size for training.''')
             train_parser.add_argument('''--valid_batch_size'''      ,							type=a__      ,							default=64      ,							help='''Batch size for validation.''')
             train_parser.add_argument('''--learning_rate'''      ,							type=a__      ,							default=3e-5      ,							help='''Learning rate.''')
             train_parser.add_argument('''--adam_epsilon'''      ,							type=a__      ,							default=1e-08      ,							help='''Epsilon for Adam optimizer.''')
             train_parser.set_defaults(func=a__)


      def __init__(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     int	=		logging.get_logger('''transformers-cli/training''')

             _lowerCamelCase							:     Tuple	=		'''tf''' if is_tf_available() else '''torch'''

             os.makedirs(args.output      ,							exist_ok=a__)
             _lowerCamelCase							:     List[Any]	=		args.output

             _lowerCamelCase							:     List[str]	=		args.column_label
             _lowerCamelCase							:     Optional[int]	=		args.column_text
             _lowerCamelCase							:     Optional[Any]	=		args.column_id

             self.logger.info(F"""Loading {args.task} pipeline for {args.model}""")
             if args.task == "text_classification":
                    _lowerCamelCase							:     str	=		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}""")
             _lowerCamelCase							:     Union[str, Any]	=		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      ,							)
             _lowerCamelCase							:     str	=		None
             if args.validation_data:
                    self.logger.info(F"""Loading validation dataset from {args.validation_data}""")
                    _lowerCamelCase							:     Union[str, Any]	=		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      ,							)

             _lowerCamelCase							:     List[str]	=		args.validation_split
             _lowerCamelCase							:     Optional[Any]	=		args.train_batch_size
             _lowerCamelCase							:     int	=		args.valid_batch_size
             _lowerCamelCase							:     List[str]	=		args.learning_rate
             _lowerCamelCase							:     Any	=		args.adam_epsilon


      def 				__snake_case			(		self):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             raise NotImplementedError



      def 				__snake_case			(		self):



             """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)








def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float(moles / volume      ) * nfactor      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float((moles * 0.0_8_2_1 * temperature) / (volume)      )      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float((moles * 0.0_8_2_1 * temperature) / (pressure)      )      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       return round(float((pressure * volume) / (0.0_8_2_1 * moles)      )      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

