




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      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
  return TrainCommand(UpperCamelCase      )






class  _lowerCAmelCase     (  _lowercase	):

      @staticmethod
      def 	__magic_name__(   __UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     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      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[int]		       =     logging.get_logger('''transformers-cli/training'''		)

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

        os.makedirs(args.output      ,				exist_ok=__UpperCAmelCase		)
        lowerCAmelCase__				:  Union[str, Any]		       =     args.output

        lowerCAmelCase__				:  Optional[Any]		       =     args.column_label
        lowerCAmelCase__				:  int		       =     args.column_text
        lowerCAmelCase__				:  Tuple		       =     args.column_id

        self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""		)
        if args.task == "text_classification":
          lowerCAmelCase__				:  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}"""		)
        lowerCAmelCase__				:  Dict		       =     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__				:  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__				:  int		       =     args.validation_split
        lowerCAmelCase__				:  List[str]		       =     args.train_batch_size
        lowerCAmelCase__				:  Dict		       =     args.valid_batch_size
        lowerCAmelCase__				:  Optional[Any]		       =     args.learning_rate
        lowerCAmelCase__				:  Any		       =     args.adam_epsilon

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

      def 	__magic_name__(   self		):
        raise NotImplementedError







      def 	__magic_name__(   self		):
        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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_					    = {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """LiltForQuestionAnswering""",
         """LiltForSequenceClassification""",
         """LiltForTokenClassification""",
         """LiltModel""",
         """LiltPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_lilt import (
              LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
              LiltForQuestionAnswering,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltModel,
              LiltPreTrainedModel,
          )

else:
     import sys

     lowerCAmelCase_					    = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

