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
a  :      Tuple            =    False
a  :      Dict            =    False
def 		snake_case__	(		lowercase					):
   return TrainCommand(lowercase					)







class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    @staticmethod
    def      _a    (    lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[int]       =	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       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Any       =	logging.get_logger("transformers-cli/training" )

       lowerCAmelCase_:							List[str]       =	"tf" if is_tf_available() else "torch"

       os.makedirs(args.output       ,						exist_ok=lowerCamelCase__ )
       lowerCAmelCase_:							Optional[Any]       =	args.output

       lowerCAmelCase_:							List[Any]       =	args.column_label
       lowerCAmelCase_:							Any       =	args.column_text
       lowerCAmelCase_:							Union[str, Any]       =	args.column_id

       self.logger.info(F'''Loading {args.task} pipeline for {args.model}''' )
       if args.task == "text_classification":
          lowerCAmelCase_:							Union[str, Any]       =	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_:							int       =	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_:							Optional[Any]       =	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_:							Dict       =	args.validation_split
       lowerCAmelCase_:							List[str]       =	args.train_batch_size
       lowerCAmelCase_:							str       =	args.valid_batch_size
       lowerCAmelCase_:							List[str]       =	args.learning_rate
       lowerCAmelCase_:							List[Any]       =	args.adam_epsilon




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




    def      _a    (    self ):
       raise NotImplementedError




    def      _a    (    self ):
       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 isqrt
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							List[str]       =	[True] * max_number
   for i in range(2							,				isqrt(max_number - 1					) + 1					):
      if is_prime[i]:
         for j in range(i**2							,				lowercase							,				lowercase					):
            lowerCAmelCase_:							Dict       =	False

   return [i for i in range(2							,				lowercase					) if is_prime[i]]
def 		snake_case__	(		lowercase = 10**8					):
   lowerCAmelCase_:							Union[str, Any]       =	calculate_prime_numbers(max_number // 2					)

   lowerCAmelCase_:							Tuple       =	0
   lowerCAmelCase_:							List[Any]       =	0
   lowerCAmelCase_:							Dict       =	len(lowercase					) - 1
   while left <= right:
      while prime_numbers[left] * prime_numbers[right] >= max_number:
         right -= 1
      semiprimes_count += right - left + 1
      left += 1

   return semiprimes_count


if __name__ == "__main__":
 print(f'''{solution() = }''')