





'''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   : Dict             =			False
A   : int             =			False





def       lowercase_      (		lowercase__						)   ->int:
						return TrainCommand(lowercase__						)







class 			lowerCamelCase					(						__UpperCAmelCase  ):


					@staticmethod
					def    SCREAMING_SNAKE_CASE_		(					__snake_case	:  ArgumentParser       ):





											'''simple docstring'''





											_snake_case:   int  					=		parser.add_parser('train'       ,     help='CLI tool to train a model on a task.'       )

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

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


					def __init__(					self	:  str       ,     __snake_case	:  Namespace       ):





											'''simple docstring'''





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

											_snake_case:   Optional[int]  					=		'tf' if is_tf_available() else 'torch'

											os.makedirs(args.output       ,     exist_ok=__snake_case       )
											_snake_case:   List[str]  					=		args.output

											_snake_case:   Optional[Any]  					=		args.column_label
											_snake_case:   str  					=		args.column_text
											_snake_case:   Union[str, Any]  					=		args.column_id

											self.logger.info(f'''Loading {args.task} pipeline for {args.model}'''       )
											if args.task == "text_classification":
																	_snake_case:   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:   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:   int  					=		None
											if args.validation_data:
																	self.logger.info(f'''Loading validation dataset from {args.validation_data}'''       )
																	_snake_case:   Tuple  					=		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:   List[str]  					=		args.validation_split
											_snake_case:   Tuple  					=		args.train_batch_size
											_snake_case:   Any  					=		args.valid_batch_size
											_snake_case:   Union[str, Any]  					=		args.learning_rate
											_snake_case:   List[str]  					=		args.adam_epsilon


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''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       )












'''simple docstring'''






A   : List[str]             =			'\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A   : List[str]             =			[{'type': 'code', 'content': INSTALL_CONTENT}]
A   : Dict             =			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}





