


'''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
__magic_name__			=      False
__magic_name__			=      False




def      lowerCamelCase					(      lowerCamelCase  :      Namespace):
      return TrainCommand(lowerCamelCase)






class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @staticmethod
  def        _a							(							_a :       ArgumentParser ):

        '''simple docstring'''




        A_						:  Union[str, 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 :       Optional[Any]	,_a :       Namespace ):

        '''simple docstring'''




        A_						:  int          =				logging.get_logger("""transformers-cli/training""" )

        A_						:  Optional[Any]          =				"""tf""" if is_tf_available() else """torch"""

        os.makedirs(args.output	,exist_ok=_a )
        A_						:  Any          =				args.output

        A_						:  str          =				args.column_label
        A_						:  List[Any]          =				args.column_text
        A_						:  Dict          =				args.column_id

        self.logger.info(f'Loading {args.task} pipeline for {args.model}' )
        if args.task == "text_classification":
              A_						:  List[Any]          =				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_						:  Tuple          =				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_						:  List[Any]          =				None
        if args.validation_data:
              self.logger.info(f'Loading validation dataset from {args.validation_data}' )
              A_						:  Tuple          =				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_						:  int          =				args.validation_split
        A_						:  Optional[Any]          =				args.train_batch_size
        A_						:  List[Any]          =				args.valid_batch_size
        A_						:  List[Any]          =				args.learning_rate
        A_						:  Optional[int]          =				args.adam_epsilon






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




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






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        raise NotImplementedError






  def        _a							(							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 )




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__			=      {}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['NllbTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['NllbTokenizerFast']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
