'''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
SCREAMING_SNAKE_CASE_	   =			False
SCREAMING_SNAKE_CASE_	   =			False






def  UpperCamelCase__					(					_lowercase    :				Namespace     )							->	Any:
  return TrainCommand(_lowercase     )







class    a (       __lowerCAmelCase				):



  """simple docstring"""







  @staticmethod
  def    lowercase_  (							snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= parser.add_parser("""train"""   ,    help="""CLI tool to train a model on a task."""					)

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

    train_parser.add_argument(
        """--task"""   ,    type=snake_case_   ,    default="""text_classification"""   ,    help="""Task to train the model on."""					)
    train_parser.add_argument(
        """--model"""   ,    type=snake_case_   ,    default="""bert-base-uncased"""   ,    help="""Model's name or path to stored model."""					)
    train_parser.add_argument("""--train_batch_size"""   ,    type=snake_case_   ,    default=32   ,    help="""Batch size for training."""					)
    train_parser.add_argument("""--valid_batch_size"""   ,    type=snake_case_   ,    default=64   ,    help="""Batch size for validation."""					)
    train_parser.add_argument("""--learning_rate"""   ,    type=snake_case_   ,    default=3e-5   ,    help="""Learning rate."""					)
    train_parser.add_argument("""--adam_epsilon"""   ,    type=snake_case_   ,    default=1e-0_8   ,    help="""Epsilon for Adam optimizer."""					)
    train_parser.set_defaults(func=snake_case_					)







  def __init__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= logging.get_logger("""transformers-cli/training"""					)

    __UpperCAmelCase:					Union[str, Any]		= """tf""" if is_tf_available() else """torch"""

    os.makedirs(args.output   ,    exist_ok=snake_case_					)
    __UpperCAmelCase:					List[str]		= args.output

    __UpperCAmelCase:					Union[str, Any]		= args.column_label
    __UpperCAmelCase:					int		= args.column_text
    __UpperCAmelCase:					Any		= args.column_id

    self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''					)
    if args.task == "text_classification":
      __UpperCAmelCase:					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}'''					)
    __UpperCAmelCase:					str		= 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:					Optional[int]		= None
    if args.validation_data:
      self.logger.info(F'''Loading validation dataset from {args.validation_data}'''					)
      __UpperCAmelCase:					Optional[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   ,    )

    __UpperCAmelCase:					Optional[int]		= args.validation_split
    __UpperCAmelCase:					Optional[Any]		= args.train_batch_size
    __UpperCAmelCase:					Optional[int]		= args.valid_batch_size
    __UpperCAmelCase:					Optional[int]		= args.learning_rate
    __UpperCAmelCase:					str		= args.adam_epsilon







  def    lowercase_  (							self					):

    '''simple docstring'''







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







  def    lowercase_  (							self					):

    '''simple docstring'''







    raise NotImplementedError







  def    lowercase_  (							self					):

    '''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 __future__ import annotations

SCREAMING_SNAKE_CASE_	   =			10






def  UpperCamelCase__					(					_lowercase    :				list[int]     )							->	list[int]:
  __UpperCAmelCase:					Union[str, Any]		= 1
  __UpperCAmelCase:					Optional[Any]		= max(_lowercase     )
  while placement <= max_digit:
    # declare and initialize empty buckets
    __UpperCAmelCase:					list[list]		= [[] for _ in range(_lowercase     )]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      __UpperCAmelCase:					Optional[Any]		= int((i / placement) % RADIX     )
      buckets[tmp].append(_lowercase     )
    # put each buckets' contents into list_of_ints
    __UpperCAmelCase:					Optional[int]		= 0
    for b in range(_lowercase     ):
      for i in buckets[b]:
        __UpperCAmelCase:					str		= i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
    import doctest

    doctest.testmod()