




"""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
a         =      False
a         =      False






def 						_snake_case					( _snake_case							: Namespace							)   -> Tuple:






      '''simple docstring'''


      return TrainCommand(_snake_case							)







class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''

 @staticmethod
 def       lowerCAmelCase_							(			_UpperCAmelCase	:	ArgumentParser		):
       _A        =       parser.add_parser('train'   ,							help='CLI tool to train a model on a task.'		)

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

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

 def __init__(			self	:	Dict   ,							_UpperCAmelCase	:	Namespace		):
       _A        =       logging.get_logger('transformers-cli/training'		)

       _A        =       'tf' if is_tf_available() else 'torch'

       os.makedirs(args.output   ,							exist_ok=_UpperCAmelCase		)
       _A        =       args.output

       _A        =       args.column_label
       _A        =       args.column_text
       _A        =       args.column_id

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

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

 def       lowerCAmelCase_							(			self	:	Tuple		):
       raise NotImplementedError



 def       lowerCAmelCase_							(			self	:	int		):
       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 						_snake_case					( _snake_case							: int							)   -> list:






      '''simple docstring'''


      _A        =       int(_snake_case							)
      if n_element < 1:
            _A        =       ValueError('a should be a positive number'							)
            raise my_error

      _A        =       [1]
      _A       ,   _A       ,   _A        =       (0, 0, 0)
      _A        =       1
      while index < n_element:
            while hamming_list[i] * 2 <= hamming_list[-1]:
                  i += 1
            while hamming_list[j] * 3 <= hamming_list[-1]:
                  j += 1
            while hamming_list[k] * 5 <= hamming_list[-1]:
                  k += 1
            hamming_list.append(
                min(hamming_list[i] * 2    ,  hamming_list[j] * 3    ,  hamming_list[k] * 5							)							)
            index += 1
      return hamming_list


if __name__ == "__main__":
       a         =      input('''Enter the last number (nth term) of the Hamming Number Series: ''')
       print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
       a         =      hamming(int(n))
       print('''-----------------------------------------------------''')
       print(F'''The list with nth numbers is: {hamming_numbers}''')
       print('''-----------------------------------------------------''')

