


'''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_			:  List[str]										=   False
UpperCAmelCase_			:  int										=   False


def       _lowercase							(					UpperCamelCase__							:       Namespace      ):
 return TrainCommand(UpperCamelCase__      )


class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''


 @staticmethod
 def 			snake_case__ (   __lowercase    ):

  """simple docstring"""







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

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

  train_parser.add_argument(
      '--task'   ,   type=__lowercase   ,   default='text_classification'   ,   help='Task to train the model on.'    )
  train_parser.add_argument(
      '--model'   ,   type=__lowercase   ,   default='bert-base-uncased'   ,   help='Model\'s name or path to stored model.'    )
  train_parser.add_argument('--train_batch_size'   ,   type=__lowercase   ,   default=32   ,   help='Batch size for training.'    )
  train_parser.add_argument('--valid_batch_size'   ,   type=__lowercase   ,   default=64   ,   help='Batch size for validation.'    )
  train_parser.add_argument('--learning_rate'   ,   type=__lowercase   ,   default=3E-5   ,   help='Learning rate.'    )
  train_parser.add_argument('--adam_epsilon'   ,   type=__lowercase   ,   default=1E-08   ,   help='Epsilon for Adam optimizer.'    )
  train_parser.set_defaults(func=__lowercase    )


 def __init__(   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      logging.get_logger('transformers-cli/training'    )

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

  os.makedirs(args.output   ,   exist_ok=__lowercase    )
  __A	:		str	  =      args.output

  __A	:		Dict	  =      args.column_label
  __A	:		Dict	  =      args.column_text
  __A	:		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	:		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   ,   )
  __A	:		Any	  =      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	:		Optional[int]	  =      args.validation_split
  __A	:		Any	  =      args.train_batch_size
  __A	:		Optional[int]	  =      args.valid_batch_size
  __A	:		Tuple	  =      args.learning_rate
  __A	:		int	  =      args.adam_epsilon


 def 			snake_case__ (   self    ):

  """simple docstring"""







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


 def 			snake_case__ (   self    ):

  """simple docstring"""







  raise NotImplementedError







 def 			snake_case__ (   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 math import ceil


def       _lowercase							(					UpperCamelCase__							:       int = 1001      ):
 __A	:		int	  =      1

 for i in range(1,					int(ceil(n / 2.0      )      )      ):
  __A	:		Any	  =      2 * i + 1
  __A	:		List[str]	  =      2 * i
  __A	:		str	  =      total + 4 * odd**2 - 6 * even

 return total


if __name__ == "__main__":
 import sys

 if len(sys.argv) == 1:
  print(solution())
 else:
  try:
   UpperCAmelCase_			:  int										=   int(sys.argv[1])
   print(solution(n))
  except ValueError:
   print('Invalid entry - please enter a number')






