
"""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
lowercase_         =       False
lowercase_         =       False




def       A_      (       lowercase   )  ->				Optional[int]:


     """simple docstring"""







     return TrainCommand(lowercase   )



class 		UpperCAmelCase_						(lowerCamelCase_    ):
 """simple docstring"""




 @staticmethod
 def 	a   (			a_							:			ArgumentParser			)->       Optional[Any]:



      """simple docstring"""

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

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

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




 def __init__(			self							:			Tuple  , a_							:			Namespace			)->       List[Any]:



      """simple docstring"""

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

      UpperCAmelCase_    :     Optional[Any]            =	"""tf""" if is_tf_available() else """torch"""

      os.makedirs(args.output  , exist_ok=a_			)
      UpperCAmelCase_    :     Optional[int]            =	args.output

      UpperCAmelCase_    :     str            =	args.column_label
      UpperCAmelCase_    :     List[Any]            =	args.column_text
      UpperCAmelCase_    :     List[str]            =	args.column_id

      self.logger.info(f'''Loading {args.task} pipeline for {args.model}'''			)
      if args.task == "text_classification":
           UpperCAmelCase_    :     Optional[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_    :     Union[str, Any]            =	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_    :     List[Any]            =	None
      if args.validation_data:
           self.logger.info(f'''Loading validation dataset from {args.validation_data}'''			)
           UpperCAmelCase_    :     int            =	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[Any]            =	args.validation_split
      UpperCAmelCase_    :     Optional[Any]            =	args.train_batch_size
      UpperCAmelCase_    :     Union[str, Any]            =	args.valid_batch_size
      UpperCAmelCase_    :     List[str]            =	args.learning_rate
      UpperCAmelCase_    :     List[Any]            =	args.adam_epsilon




 def 	a   (			self							:			str			)->       Union[str, Any]:



      """simple docstring"""

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




 def 	a   (			self							:			Any			)->       List[str]:



      """simple docstring"""

      raise NotImplementedError






 def 	a   (			self							:			Optional[int]			)->       List[str]:



      """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"""







def       A_      (       lowercase   )  ->				str:


     """simple docstring"""







     UpperCAmelCase_    :     Any            =	0

     # if input_string is "aba" than new_input_string become "a|b|a"
     UpperCAmelCase_    :     Union[str, Any]            =	""""""
     UpperCAmelCase_    :     List[Any]            =	""""""

     # append each character + "|" in new_string for range(0, length-1)
     for i in input_string[: len(lowercase   ) - 1]:
          new_input_string += i + "|"
     # append last character
     new_input_string += input_string[-1]

     # we will store the starting and ending of previous furthest ending palindromic
     # substring
     UpperCAmelCase_       ,UpperCAmelCase_    :     Dict            =	0, 0

     # length[i] shows the length of palindromic substring with center i
     UpperCAmelCase_    :     Dict            =	[1 for i in range(len(lowercase   )   )]

     # for each character in new_string find corresponding palindromic string
     UpperCAmelCase_    :     Optional[Any]            =	0
     for j in range(len(lowercase   )   ):
          UpperCAmelCase_    :     str            =	1 if j > r else min(length[l + r - j] // 2    ,			r - j + 1   )
          while (
              j - k >= 0
              and j + k < len(lowercase   )
              and new_input_string[k + j] == new_input_string[j - k]
          ):
               k += 1

          UpperCAmelCase_    :     Union[str, Any]            =	2 * k - 1

          # does this string is ending after the previously explored end (that is r) ?
          # if yes the update the new r to the last index of this
          if j + k - 1 > r:
               UpperCAmelCase_    :     List[Any]            =	j - k + 1  # noqa: E741
               UpperCAmelCase_    :     Union[str, Any]            =	j + k - 1

          # update max_length and start position
          if max_length < length[j]:
               UpperCAmelCase_    :     Optional[Any]            =	length[j]
               UpperCAmelCase_    :     int            =	j

    # create that string
     UpperCAmelCase_    :     List[str]            =	new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
     for i in s:
          if i != "|":
               output_string += i

     return output_string


if __name__ == "__main__":
       import doctest

       doctest.testmod()

