


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
A      =   False
A      =   False





def __UpperCAmelCase       (		__A			)						->					Tuple:



       '''simple docstring'''





       return TrainCommand(__A			)




class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  @staticmethod
  def 			_UpperCAmelCase  (					_lowercase      :					ArgumentParser ):




         """simple docstring"""







         UpperCAmelCase__										=     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      :					int		,						_lowercase      :					Namespace ):




         """simple docstring"""







         UpperCAmelCase__										=     logging.get_logger("transformers-cli/training" )

         UpperCAmelCase__										=     "tf" if is_tf_available() else "torch"

         os.makedirs(args.output		,						exist_ok=_lowercase )
         UpperCAmelCase__										=     args.output

         UpperCAmelCase__										=     args.column_label
         UpperCAmelCase__										=     args.column_text
         UpperCAmelCase__										=     args.column_id

         self.logger.info(F"""Loading {args.task} pipeline for {args.model}""" )
         if args.task == "text_classification":
                UpperCAmelCase__										=     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__										=     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__										=     None
         if args.validation_data:
                self.logger.info(F"""Loading validation dataset from {args.validation_data}""" )
                UpperCAmelCase__										=     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__										=     args.validation_split
         UpperCAmelCase__										=     args.train_batch_size
         UpperCAmelCase__										=     args.valid_batch_size
         UpperCAmelCase__										=     args.learning_rate
         UpperCAmelCase__										=     args.adam_epsilon






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         raise NotImplementedError






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """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 )









def __UpperCAmelCase       (		__A			)						->					list[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     len(__A			)
       for i in range(__A			):
              for j in range(i + 1 ,						__A			):
                     if numbers[j] < numbers[i]:
                            UpperCAmelCase__						,			UpperCAmelCase__										=     numbers[j], numbers[i]
       return numbers


if __name__ == "__main__":
    A      = input("Enter numbers separated by a comma:\n").strip()
    A      = [int(item) for item in user_input.split(",")]
    print(exchange_sort(unsorted))





