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					:	int       =  False
__lowerCamelCase					:	Any       =  False




def       _snake_case	(   lowerCAmelCase							:   Namespace	):
		"""simple docstring"""







		return TrainCommand(lowerCAmelCase	)







class 		a__						(					A__					):

					@staticmethod
					def 				__UpperCamelCase   (    _A      :       ArgumentParser    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    parser.add_parser("train",help="CLI tool to train a model on a task."    )

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

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

					def __init__(    self      :       Optional[int],_A      :       Namespace    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    logging.get_logger("transformers-cli/training"    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    "tf" if is_tf_available() else "torch"

							os.makedirs(args.output,exist_ok=_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    args.output

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    args.column_label
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    args.column_text
							SCREAMING_SNAKE_CASE_  :		int				      =    args.column_id

							self.logger.info(F'Loading {args.task} pipeline for {args.model}'    )
							if args.task == "text_classification":
									SCREAMING_SNAKE_CASE_  :		str				      =    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_  :		Dict				      =    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_  :		Tuple				      =    None
							if args.validation_data:
									self.logger.info(F'Loading validation dataset from {args.validation_data}'    )
									SCREAMING_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,)

							SCREAMING_SNAKE_CASE_  :		str				      =    args.validation_split
							SCREAMING_SNAKE_CASE_  :		int				      =    args.train_batch_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    args.valid_batch_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    args.learning_rate
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    args.adam_epsilon

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							raise NotImplementedError






					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""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    )

from math import factorial, radians




def       _snake_case	(   lowerCAmelCase							:   float   , lowerCAmelCase							:   int = 1_8   , lowerCAmelCase							:   int = 1_0	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

		# Converting from degrees to radians
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    radians(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    angle_in_radians
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    3
		SCREAMING_SNAKE_CASE_  :		str				      =    -1

		for _ in range(lowerCAmelCase	):
				result += (b * (angle_in_radians**a)) / factorial(lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		Dict				      =    -b  # One positive term and the next will be negative and so on...
				a += 2  # Increased by 2 for every term.

		return round(lowerCAmelCase   , lowerCAmelCase	)


if __name__ == "__main__":
		__import__('''doctest''').testmod()
