


"""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
__SCREAMING_SNAKE_CASE      		=    False
__SCREAMING_SNAKE_CASE      		=    False



def     __a   (	a       ):





       """simple docstring"""







       return TrainCommand(_SCREAMING_SNAKE_CASE       )





class __snake_case		(       __snake_case   ):







   """simple docstring"""




   @staticmethod
   def SCREAMING_SNAKE_CASE_   (		UpperCamelCase__				:Tuple							):
          _a							 =					parser.add_parser("train"					,	help="CLI tool to train a model on a task."							)

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

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




   def __init__(		self				:List[str]					,	UpperCamelCase__				:Union[str, Any]							):
          _a							 =					logging.get_logger("transformers-cli/training"							)

          _a							 =					"tf" if is_tf_available() else "torch"

          os.makedirs(args.output					,	exist_ok=snake_case_							)
          _a							 =					args.output

          _a							 =					args.column_label
          _a							 =					args.column_text
          _a							 =					args.column_id

          self.logger.info(f'Loading {args.task} pipeline for {args.model}'							)
          if args.task == "text_classification":
                 _a							 =					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							 =					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							 =					None
          if args.validation_data:
                 self.logger.info(f'Loading validation dataset from {args.validation_data}'							)
                 _a							 =					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							 =					args.validation_split
          _a							 =					args.train_batch_size
          _a							 =					args.valid_batch_size
          _a							 =					args.learning_rate
          _a							 =					args.adam_epsilon




   def SCREAMING_SNAKE_CASE_   (		self				:Union[str, Any]							):
          if self.framework == "tf":
                 return self.run_tf()
          return self.run_torch()




   def SCREAMING_SNAKE_CASE_   (		self				:int							):
          raise NotImplementedError




   def SCREAMING_SNAKE_CASE_   (		self				:Optional[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							)







def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    return int((input_a, input_a).count(0				) != 0				)





def 				__lowerCAmelCase(       )							-> None:




    """simple docstring"""




    assert nand_gate(0							,     0				) == 1
    assert nand_gate(0							,     1				) == 1
    assert nand_gate(1							,     0				) == 1
    assert nand_gate(1							,     1				) == 0


if __name__ == "__main__":
 print(nand_gate(0, 0))
 print(nand_gate(0, 1))
 print(nand_gate(1, 0))
 print(nand_gate(1, 1))





