import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	List[str]		,						lowercase_:	Tuple				):
       # Initialise PyTorch model
       _UpperCamelCase     :       Dict  						=  TaConfig.from_json_file(lowercase_				)
       print(F"Building PyTorch model from configuration: {config}"				)
       _UpperCamelCase     :       Optional[Any]  						=  TaForConditionalGeneration(lowercase_				)

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(lowercase_		,						lowercase_		,						lowercase_				)

       # Save pytorch-model
       print(F"Save PyTorch model to {pytorch_dump_path}"				)
       model.save_pretrained(lowercase_				)


if __name__ == "__main__":
 UpperCAmelCase     :   Any					=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 UpperCAmelCase     :   Union[str, Any]					=				parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




import math
from collections.abc import Generator


def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
       _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)				))
       for i in (n for n in numbers if n > 1):
              for j in range(2		,						lowercase_				):
                     if (i % j) == 0:
                            break
              else:
                     yield i


def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
       _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)				))
       for i in (n for n in numbers if n > 1):
              # only need to check for factors up to sqrt(i)
              _UpperCamelCase     :       Any  						=  int(math.sqrt(lowercase_				)				) + 1
              for j in range(2		,						lowercase_				):
                     if (i % j) == 0:
                            break
              else:
                     yield i


def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
       _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)		,						2				))
       # It's useless to test even numbers as they will not be prime
       if max_n > 2:
              yield 2  # Because 2 will not be tested, it's necessary to yield it now
       for i in (n for n in numbers if n > 1):
              _UpperCamelCase     :       Tuple  						=  int(math.sqrt(lowercase_				)				) + 1
              for j in range(3		,						lowercase_		,						2				):
                     # As we removed the even numbers, we don't need them now
                     if (i % j) == 0:
                            break
              else:
                     yield i


def SCREAMING_SNAKE_CASE				(     ):
       from timeit import timeit

       _UpperCamelCase     :       Optional[Any]  						=  'from __main__ import slow_primes, primes, fast_primes'
       print(timeit('slow_primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
       print(timeit('primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
       print(timeit('fast_primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)


if __name__ == "__main__":
 UpperCAmelCase					=				int(input('''Calculate primes up to:\n>> ''').strip())
 for ret in primes(number):
  print(ret)
 benchmark()



