


"""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
__snake_case   			=    False
__snake_case   			=    False







def   __lowerCAmelCase						(						lowercase  :  Namespace )	->						Union[str, Any]:


    """simple docstring"""





    return TrainCommand(lowercase )







class _lowerCAmelCase				(      snake_case_	):







   @staticmethod
   def  lowerCamelCase		(	UpperCamelCase__						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    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-08						,	help="Epsilon for Adam optimizer."						)
       train_parser.set_defaults(func=UpperCamelCase__						)







   def __init__(	self						,	UpperCamelCase__						)	->		int:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    logging.get_logger("transformers-cli/training"						)

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

       os.makedirs(args.output						,	exist_ok=UpperCamelCase__						)
       snake_case    :      int      					=    args.output

       snake_case    :      Optional[Any]      					=    args.column_label
       snake_case    :      List[Any]      					=    args.column_text
       snake_case    :      List[Any]      					=    args.column_id

       self.logger.info(F'Loading {args.task} pipeline for {args.model}'						)
       if args.task == "text_classification":
           snake_case    :      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}'						)
       snake_case    :      List[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						,	)
       snake_case    :      Optional[int]      					=    None
       if args.validation_data:
           self.logger.info(F'Loading validation dataset from {args.validation_data}'						)
           snake_case    :      Optional[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						,	)

       snake_case    :      Dict      					=    args.validation_split
       snake_case    :      List[Any]      					=    args.train_batch_size
       snake_case    :      Dict      					=    args.valid_batch_size
       snake_case    :      Optional[Any]      					=    args.learning_rate
       snake_case    :      List[Any]      					=    args.adam_epsilon







   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




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







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

       '''simple docstring'''




       raise NotImplementedError







   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''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   __lowerCAmelCase						(						lowercase  :  Union[str, Any] )	->						List[str]:


    """simple docstring"""





    snake_case    :      List[str]      					=    len(lowercase )
    for i in range(length - 1 ):
        snake_case    :      List[str]      					=    i
        for k in range(i + 1						,							lowercase ):
            if collection[k] < collection[least]:
                snake_case    :      List[str]      					=    k
        if least != i:
            snake_case					,snake_case    :      Union[str, Any]      					=    (collection[i], collection[least])
    return collection


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





