
import numpy as np


def      UpperCamelCase   (       __lowerCamelCase	:					np.ndarray       ):
       return 1 / (1 + np.exp(-vector       ))


def      UpperCamelCase   (       __lowerCamelCase	:					np.ndarray       ):
       return vector * sigmoid(__lowerCamelCase       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()







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				=			False
__lowerCamelCase				=			False


def      UpperCamelCase   (       __lowerCamelCase	:					Namespace       ):
       return TrainCommand(__lowerCamelCase       )






class     UpperCAmelCase						(		A_					):



   @staticmethod
   def 							_SCREAMING_SNAKE_CASE  (snake_case__						:   ArgumentParser					)						->      int:


          '''simple docstring'''


          snake_case			:  Any     							= 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[Any]       ,		snake_case__						:   Namespace					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  Any     							= logging.get_logger("transformers-cli/training"					)

          snake_case			:  List[Any]     							= "tf" if is_tf_available() else "torch"

          os.makedirs(args.output       ,		exist_ok=snake_case__					)
          snake_case			:  Any     							= args.output

          snake_case			:  List[Any]     							= args.column_label
          snake_case			:  Tuple     							= args.column_text
          snake_case			:  str     							= args.column_id

          self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""					)
          if args.task == "text_classification":
                 snake_case			:  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}"""					)
          snake_case			:  Tuple     							= 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       ,		)
          snake_case			:  Optional[Any]     							= None
          if args.validation_data:
                 self.logger.info(f"""Loading validation dataset from {args.validation_data}"""					)
                 snake_case			:  Dict     							= 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       ,		)

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      List[str]:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Optional[Any]:


          '''simple docstring'''


          raise NotImplementedError



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      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					)





