
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
UpperCAmelCase__		:     int     			=      False
UpperCAmelCase__		:     List[Any]     			=      False






def   lowerCamelCase__							(					a      )      ->   List[Any]:
   return TrainCommand(a      )






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''


   @staticmethod
   def  __magic_name__				(					lowerCAmelCase_     :							ArgumentParser     ):

      """simple docstring"""
      _A:  str					=       parser.add_parser('''train'''			,			help='''CLI tool to train a model on a task.'''     )

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

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


   def __init__(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							Namespace     ):

      """simple docstring"""
      _A:  Optional[Any]					=       logging.get_logger('''transformers-cli/training'''     )

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

      os.makedirs(args.output			,			exist_ok=lowerCAmelCase_     )
      _A:  Tuple					=       args.output

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

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


   def  __magic_name__				(					self     :							Dict     ):

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


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      raise NotImplementedError


   def  __magic_name__				(					self     :							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     )





from .imports import is_tqdm_available


if is_tqdm_available():
   from tqdm.auto import tqdm as _tqdm

from ..state import PartialState






def   lowerCamelCase__							(					a = True		,		*a		,		**a      )      ->   Optional[Any]:
   if not is_tqdm_available():
      raise ImportError('''Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.'''      )
   _A:  Optional[Any]					=       False
   if main_process_only:
      _A:  Union[str, Any]					=       PartialState().local_process_index == 0
   return _tqdm(*a		,		**a		,		disable=a      )



