


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
__lowerCAmelCase		       =							False
__lowerCAmelCase		       =							False




def    snake_case_     (  snake_case     )  ->				Dict:
       return TrainCommand(snake_case     )





class   __a			(    __UpperCamelCase      ):
 @staticmethod
 def 		SCREAMING_SNAKE_CASE__   (   lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        lowercase__:   str  			=   parser.add_parser('train'				, help='CLI tool to train a model on a task.' )

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

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




        '''simple docstring'''
        lowercase__:   int  			=   logging.get_logger('transformers-cli/training' )

        lowercase__:   List[str]  			=   'tf' if is_tf_available() else 'torch'

        os.makedirs(args.output				, exist_ok=lowerCAmelCase__ )
        lowercase__:   List[Any]  			=   args.output

        lowercase__:   Tuple  			=   args.column_label
        lowercase__:   List[str]  			=   args.column_text
        lowercase__:   Optional[int]  			=   args.column_id

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

        lowercase__:   Optional[Any]  			=   args.validation_split
        lowercase__:   int  			=   args.train_batch_size
        lowercase__:   Union[str, Any]  			=   args.valid_batch_size
        lowercase__:   List[str]  			=   args.learning_rate
        lowercase__:   List[Any]  			=   args.adam_epsilon
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[Any]:




        '''simple docstring'''
        if self.framework == "tf":
               return self.run_tf()
        return self.run_torch()
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        raise NotImplementedError
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				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 )






from __future__ import annotations




def    snake_case_     (  snake_case			,							snake_case     )  ->				list[int]:
       lowercase__:   List[str]  			=   0
       lowercase__:   Dict  			=   len(snake_case     ) - 1

       while i < j:
              if nums[i] + nums[j] == target:
                     return [i, j]
              elif nums[i] + nums[j] < target:
                     lowercase__:   Dict  			=   i + 1
              else:
                     lowercase__:   List[Any]  			=   j - 1

       return []


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(F'''{two_pointer([2, 7, 11, 15], 9) = }''')


