




"""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  :			Optional[int]        =       False
_lowerCamelCase  :			Optional[Any]        =       False

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	return TrainCommand(_UpperCAmelCase	)

class        lowercase      (    __UpperCAmelCase):







				@staticmethod
				def 					a_  (							_lowerCamelCase   :					ArgumentParser       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		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   :					Union[str, Any]      ,     _lowerCamelCase   :					Namespace       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		logging.get_logger('''transformers-cli/training'''       )

					A_  :				str	   =		'''tf''' if is_tf_available() else '''torch'''

					os.makedirs(args.output      ,     exist_ok=_lowerCamelCase       )
					A_  :				Optional[Any]	   =		args.output

					A_  :				Optional[int]	   =		args.column_label
					A_  :				Optional[int]	   =		args.column_text
					A_  :				Optional[Any]	   =		args.column_id

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







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

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







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					raise NotImplementedError







				def 					a_  (							self   :					Any       ):



					"""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"""

from __future__ import annotations

from collections import namedtuple

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		namedtuple('''result'''  ,      '''name value'''	)
	if (voltage, current, power).count(0	) != 1:
		raise ValueError('''Only one argument must be 0'''	)
	elif power < 0:
		raise ValueError(
		    '''Power cannot be negative in any electrical/electronics system'''	)
	elif voltage == 0:
		return result('''voltage'''  ,      power / current	)
	elif current == 0:
		return result('''current'''  ,      power / voltage	)
	elif power == 0:
		return result('''power'''  ,      float(round(abs(voltage * current	)  ,      2	)	)	)
	else:
		raise ValueError('''Exactly one argument must be 0'''	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()





