
"""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__ :       List[str]						     =			False
lowerCamelCase__ :       str						     =			False





def __A						(   a_ :							Namespace     )->							Tuple:




		'''simple docstring'''






		return TrainCommand(a_     )





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	@staticmethod
	def   __lowerCAmelCase (						lowerCamelCase_    :ArgumentParser					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, 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					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= logging.get_logger('''transformers-cli/training'''					)

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

			os.makedirs(args.output		, exist_ok=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     str				= args.output

			SCREAMING_SNAKE_CASE       :     List[str]				= args.column_label
			SCREAMING_SNAKE_CASE       :     Dict				= args.column_text
			SCREAMING_SNAKE_CASE       :     Dict				= args.column_id

			self.logger.info(f"Loading {args.task} pipeline for {args.model}"					)
			if args.task == "text_classification":
					SCREAMING_SNAKE_CASE       :     Dict				= 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}"					)
			SCREAMING_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		, )
			SCREAMING_SNAKE_CASE       :     Dict				= None
			if args.validation_data:
					self.logger.info(f"Loading validation dataset from {args.validation_data}"					)
					SCREAMING_SNAKE_CASE       :     List[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		, )

			SCREAMING_SNAKE_CASE       :     Any				= args.validation_split
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= args.train_batch_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= args.valid_batch_size
			SCREAMING_SNAKE_CASE       :     List[str]				= args.learning_rate
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= args.adam_epsilon





	def   __lowerCAmelCase (						self    :str					)							->			List[Any]:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :str					)							->			Any:







			'''simple docstring'''




			raise NotImplementedError







	def   __lowerCAmelCase (						self    :List[str]					)							->			Tuple:







			'''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 numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray





def __A						(   a_ :							float		,		a_ :							float		,		a_ :							bool = False     )->							list[float]:




		'''simple docstring'''






		if radian_mode:
				return [magnitude * cos(a_     ), magnitude * sin(a_     )]
		return [magnitude * cos(radians(a_     )     ), magnitude * sin(radians(a_     )     )]





def __A						(   a_ :							NDArray[floataa]		,		a_ :							NDArray[floataa]		,		a_ :							float = 10**-1     )->							bool:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     NDArray[floataa]				= cross(a_		,		a_     )
		SCREAMING_SNAKE_CASE       :     float				= sum(a_     )
		return abs(a_     ) < eps


if __name__ == "__main__":
					# Test to check if it works
					lowerCamelCase__ :       Optional[Any]						     =			array(
					    [
					        polar_force(7_1_8.4, 180 - 30),
					        polar_force(8_7_9.5_4, 45),
					        polar_force(100, -90),
					    ]
					)

					lowerCamelCase__ :       NDArray[floataa]						     =			array([[0, 0], [0, 0], [0, 0]])

					assert in_static_equilibrium(forces, location)

					# Problem 1 in image_data/2D_problems.jpg
					lowerCamelCase__ :       Union[str, Any]						     =			array(
					    [
					        polar_force(30 * 9.8_1, 15),
					        polar_force(215, 180 - 45),
					        polar_force(264, 90 - 30),
					    ]
					)

					lowerCamelCase__ :       Any						     =			array([[0, 0], [0, 0], [0, 0]])

					assert in_static_equilibrium(forces, location)

					# Problem in image_data/2D_problems_1.jpg
					lowerCamelCase__ :       Union[str, Any]						     =			array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

					lowerCamelCase__ :       Optional[int]						     =			array([[0, 0], [6, 0], [10, 0], [12, 0]])

					assert in_static_equilibrium(forces, location)

					import doctest

					doctest.testmod()





