"""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
UpperCAmelCase__			=    False
UpperCAmelCase__			=    False






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  return TrainCommand(lowercase		)


class 							a					(		lowerCAmelCase_ ):


    @staticmethod
    def   lowerCAmelCase_	(       __lowerCAmelCase						:	ArgumentParser       ):
      _UpperCAmelCase    			=   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=32 ,					help="""Batch size for training."""       )
      train_parser.add_argument("""--valid_batch_size""" ,					type=__lowerCAmelCase ,					default=64 ,					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-0_8 ,					help="""Epsilon for Adam optimizer."""       )
      train_parser.set_defaults(func=__lowerCAmelCase       )


    def __init__(       self						:	Dict ,					__lowerCAmelCase						:	Namespace       ):
      _UpperCAmelCase    			=   logging.get_logger("""transformers-cli/training"""       )

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

      os.makedirs(args.output ,					exist_ok=__lowerCAmelCase       )
      _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   lowerCAmelCase_	(       self						:	str       ):
      if self.framework == "tf":
        return self.run_tf()
      return self.run_torch()


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      raise NotImplementedError


    def   lowerCAmelCase_	(       self						:	Dict       ):
      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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__			=    logging.get_logger(__name__)


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	List[Any]  				=				'timm_backbone'


    def __init__(       self						:	List[Any] ,					__lowerCAmelCase						:	str=None ,					__lowerCAmelCase						:	Optional[int]=3 ,					__lowerCAmelCase						:	Any=True ,					__lowerCAmelCase						:	Optional[int]=True ,					__lowerCAmelCase						:	Optional[Any]=None ,					**__lowerCAmelCase						:	Optional[Any] ,					):
      super().__init__(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   backbone
      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   features_only
      _UpperCAmelCase    			=   use_pretrained_backbone
      _UpperCAmelCase    			=   True
      _UpperCAmelCase    			=   out_indices if out_indices is not None else (-1,)
