






'''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
__lowerCAmelCase									=   False
__lowerCAmelCase									=   False





def 		_UpperCAmelCase (				__A				: Namespace   ):
				return TrainCommand(__A   )





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			@staticmethod
			def       SCREAMING_SNAKE_CASE   (			__SCREAMING_SNAKE_CASE   :      ArgumentParser       )      ->      Optional[int]:
							a_		:	Dict									=   parser.add_parser('''train'''	,    help='''CLI tool to train a model on a task.'''       )

							train_parser.add_argument(
							    '''--train_data'''	,    type=__SCREAMING_SNAKE_CASE	,    required=__SCREAMING_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=__SCREAMING_SNAKE_CASE	,    default=0	,    help='''Column of the dataset csv file with example labels.'''       )
							train_parser.add_argument(
							    '''--column_text'''	,    type=__SCREAMING_SNAKE_CASE	,    default=1	,    help='''Column of the dataset csv file with example texts.'''       )
							train_parser.add_argument(
							    '''--column_id'''	,    type=__SCREAMING_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=__SCREAMING_SNAKE_CASE	,    default=''''''	,    help='''path to validation dataset.'''       )
							train_parser.add_argument(
							    '''--validation_split'''	,    type=__SCREAMING_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=__SCREAMING_SNAKE_CASE	,    default='''./'''	,    help='''path to saved the trained model.'''       )

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






			def __init__(			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Namespace       )      ->      Union[str, Any]:
							a_		:	Any									=   logging.get_logger('''transformers-cli/training'''       )

							a_		:	int									=   '''tf''' if is_tf_available() else '''torch'''

							os.makedirs(args.output	,    exist_ok=__SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   args.output

							a_		:	List[Any]									=   args.column_label
							a_		:	Dict									=   args.column_text
							a_		:	int									=   args.column_id

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

							a_		:	List[Any]									=   args.validation_split
							a_		:	Tuple									=   args.train_batch_size
							a_		:	Dict									=   args.valid_batch_size
							a_		:	List[str]									=   args.learning_rate
							a_		:	Optional[int]									=   args.adam_epsilon






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Optional[int]:
							if self.framework == "tf":
											return self.run_tf()
							return self.run_torch()






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      List[Any]:
							raise NotImplementedError







			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'SEWForCTC',
		    'SEWForSequenceClassification',
		    'SEWModel',
		    'SEWPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

		__lowerCAmelCase									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




