





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
snake_case								=							False
snake_case								=							False

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Namespace  )   ->    Tuple:
  return TrainCommand(snake_case__  )







class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       @staticmethod
       def  __UpperCAmelCase			(     __A						:						ArgumentParser	)    ->       List[Any]:
         _lowercase						    =			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						:						Optional[Any]    ,__A						:						Namespace	)    ->       Tuple:
         _lowercase						    =			logging.get_logger('transformers-cli/training'	)

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

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

         _lowercase						    =			args.column_label
         _lowercase						    =			args.column_text
         _lowercase						    =			args.column_id

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







       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       str:
         if self.framework == "tf":
           return self.run_tf()
         return self.run_torch()







       def  __UpperCAmelCase			(     self						:						Tuple	)    ->       List[Any]:
         raise NotImplementedError







       def  __UpperCAmelCase			(     self						:						Optional[int]	)    ->       Optional[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"""

from __future__ import annotations

SCREAMING_SNAKE_CASE_            =    [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
SCREAMING_SNAKE_CASE_            =    [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]




def      lowercase__    (       lowerCAmelCase					:       list[float]			)			->   list[float]:






    """simple docstring"""




    UpperCAmelCase          =       []
    UpperCAmelCase          =       len(lowerCAmelCase			)

    for i in range(lowerCAmelCase			):
        UpperCAmelCase          =       -1
        for j in range(i + 1			,					lowerCAmelCase			):
            if arr[i] < arr[j]:
                UpperCAmelCase          =       arr[j]
                break
        result.append(lowerCAmelCase			)
    return result




def      lowercase__    (       lowerCAmelCase					:       list[float]			)			->   list[float]:






    """simple docstring"""




    UpperCAmelCase          =       []
    for i, outer in enumerate(lowerCAmelCase			):
        UpperCAmelCase          =       -1
        for inner in arr[i + 1 :]:
            if outer < inner:
                UpperCAmelCase          =       inner
                break
        result.append(lowerCAmelCase			)
    return result




def      lowercase__    (       lowerCAmelCase					:       list[float]			)			->   list[float]:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       []
    UpperCAmelCase          =       [-1] * arr_size

    for index in reversed(range(lowerCAmelCase			)			):
        if stack:
            while stack[-1] <= arr[index]:
                stack.pop()
                if not stack:
                    break
        if stack:
            UpperCAmelCase          =       stack[-1]
        stack.append(arr[index]			)
    return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   SCREAMING_SNAKE_CASE_            =    (
       '''from __main__ import arr, next_greatest_element_slow, '''
       '''next_greatest_element_fast, next_greatest_element'''
   )
   print(
       '''next_greatest_element_slow():''',
       timeit('''next_greatest_element_slow(arr)''', setup=setup),
   )
   print(
       '''next_greatest_element_fast():''',
       timeit('''next_greatest_element_fast(arr)''', setup=setup),
   )
   print(
       '''     next_greatest_element():''',
       timeit('''next_greatest_element(arr)''', setup=setup),
   )



