


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=lowerCAmelCase_    )
class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     str							    =     field(default="""language-modeling"""						,       metadata={"""include_in_asdict_even_if_is_default""": True}    )

       lowerCamelCase__:     ClassVar[Features]							    =     Features({"""text""": Value("""string"""    )}    )
       lowerCamelCase__:     ClassVar[Features]							    =     Features({}    )
       lowerCamelCase__:     str							    =     "text"

       @property
       def 						UpperCAmelCase(   self      :     Any    ):
              return {self.text_column: "text"}







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			(						__UpperCamelCase ):
     return TrainCommand(__UpperCamelCase )


class   a__	(						lowerCAmelCase_    ):

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     ArgumentParser    ):
            a_    :     List[Any]										=    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      :     int      ,							lowerCamelCase_      :     Namespace    ):
            a_    :     Optional[int]										=    logging.get_logger("""transformers-cli/training"""    )

            a_    :     List[str]										=    """tf""" if is_tf_available() else """torch"""

            os.makedirs(args.output      ,							exist_ok=lowerCamelCase_    )
            a_    :     List[str]										=    args.output

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

            self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''    )
            if args.task == "text_classification":
                 a_    :     str										=    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_    :     int										=    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_    :     Union[str, 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_    :     str										=    args.validation_split
            a_    :     Tuple										=    args.train_batch_size
            a_    :     List[Any]										=    args.valid_batch_size
            a_    :     str										=    args.learning_rate
            a_    :     str										=    args.adam_epsilon

       def 						UpperCAmelCase(   self      :     List[str]    ):
            if self.framework == "tf":
                 return self.run_tf()
            return self.run_torch()

       def 						UpperCAmelCase(   self      :     Any    ):
            raise NotImplementedError

       def 						UpperCAmelCase(   self      :     List[str]    ):
            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    )



