



"""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
lowerCamelCase       	=						False
lowerCamelCase       	=						False
def        a__     (				lowerCAmelCase__				):
      return TrainCommand(lowerCAmelCase__				)


class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       @staticmethod
       def 					lowercase__    (							_UpperCAmelCase      :  ArgumentParser   )			->						Dict:


             '''simple docstring'''




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

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

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



       def __init__(							self      :  str		,    _UpperCAmelCase      :  Namespace   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							logging.get_logger("transformers-cli/training"   )

             UpperCAmelCase_      			=							"tf" if is_tf_available() else "torch"

             os.makedirs(args.output		,    exist_ok=_UpperCAmelCase   )
             UpperCAmelCase_      			=							args.output

             UpperCAmelCase_      			=							args.column_label
             UpperCAmelCase_      			=							args.column_text
             UpperCAmelCase_      			=							args.column_id

             self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""   )
             if args.task == "text_classification":
                   UpperCAmelCase_      			=							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}"""   )
             UpperCAmelCase_      			=							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		,    )
             UpperCAmelCase_      			=							None
             if args.validation_data:
                   self.logger.info(F"""Loading validation dataset from {args.validation_data}"""   )
                   UpperCAmelCase_      			=							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		,    )

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



       def 					lowercase__    (							self      :  Optional[int]   )			->						Union[str, Any]:


             '''simple docstring'''




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



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


             '''simple docstring'''




             raise NotImplementedError







       def 					lowercase__    (							self      :  Dict   )			->						int:


             '''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   )









"""simple docstring"""





def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase__				) - ngram_size + 1				)]


if __name__ == "__main__":
  from doctest import testmod

  testmod()




