





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
_UpperCAmelCase				:       List[str]	   =				False
_UpperCAmelCase				:       Dict	   =				False


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     int:
    return TrainCommand(_UpperCAmelCase )


class    lowerCAmelCase							(					__UpperCamelCase			):
 @staticmethod
 def      A_	(      UpperCAmelCase  :      ArgumentParser	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[int]					 =		parser.add_parser('train'			,     help='CLI tool to train a model on a task.'	)

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

     train_parser.add_argument(
         '--task'			,     type=UpperCAmelCase			,     default='text_classification'			,     help='Task to train the model on.'	)
     train_parser.add_argument(
         '--model'			,     type=UpperCAmelCase			,     default='bert-base-uncased'			,     help='Model\'s name or path to stored model.'	)
     train_parser.add_argument('--train_batch_size'			,     type=UpperCAmelCase			,     default=32			,     help='Batch size for training.'	)
     train_parser.add_argument('--valid_batch_size'			,     type=UpperCAmelCase			,     default=64			,     help='Batch size for validation.'	)
     train_parser.add_argument('--learning_rate'			,     type=UpperCAmelCase			,     default=3e-5			,     help='Learning rate.'	)
     train_parser.add_argument('--adam_epsilon'			,     type=UpperCAmelCase			,     default=1e-08			,     help='Epsilon for Adam optimizer.'	)
     train_parser.set_defaults(func=UpperCAmelCase	)
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      Namespace	)			->	Dict:
     lowerCamelCase__    :      List[Any]					 =		logging.get_logger('transformers-cli/training'	)

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

     os.makedirs(args.output			,     exist_ok=UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		args.output

     lowerCamelCase__    :      List[str]					 =		args.column_label
     lowerCamelCase__    :      int					 =		args.column_text
     lowerCamelCase__    :      Any					 =		args.column_id

     self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""	)
     if args.task == "text_classification":
         lowerCamelCase__    :      Optional[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}"""	)
     lowerCamelCase__    :      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			,     )
     lowerCamelCase__    :      List[str]					 =		None
     if args.validation_data:
         self.logger.info(F"""Loading validation dataset from {args.validation_data}"""	)
         lowerCamelCase__    :      Union[str, 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			,     )

     lowerCamelCase__    :      Optional[Any]					 =		args.validation_split
     lowerCamelCase__    :      Any					 =		args.train_batch_size
     lowerCamelCase__    :      Any					 =		args.valid_batch_size
     lowerCamelCase__    :      Dict					 =		args.learning_rate
     lowerCamelCase__    :      Union[str, Any]					 =		args.adam_epsilon
 def      A_	(      self  :      Optional[Any]	)			->	Optional[int]:
     if self.framework == "tf":
         return self.run_tf()
     return self.run_torch()
 def      A_	(      self  :      int	)			->	Optional[Any]:
     raise NotImplementedError







 def      A_	(      self  :      str	)			->	str:
     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 pi, sqrt





def 		__A		(	__lowerCamelCase    )      ->       float:
	if num <= 0:
		raise ValueError("""math domain error"""    )
	if num > 171.5:
		raise OverflowError("""math range error"""    )
	elif num - int(__lowerCamelCase    ) not in (0, 0.5):
		raise NotImplementedError("""num must be an integer or a half-integer"""    )
	elif num == 0.5:
		return sqrt(__lowerCamelCase    )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1    )





def 		__A		(	)      ->       None:
	assert gamma(0.5    ) == sqrt(__lowerCamelCase    )
	assert gamma(1    ) == 1.0
	assert gamma(2    ) == 1.0


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		__UpperCamelCase					:       str    					=      1.0
		while num:
				__UpperCamelCase					:       Dict    					=      float(input("Gamma of: "))
				print(F'gamma({num}) = {gamma(num)}')
				print("\nEnter 0 to exit...")

