





import math
from collections.abc import Generator


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	snake_case_		:      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 ,			__a ):
			if (i % j) == 0:
				break
		else:
			yield i


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	snake_case_		:      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)
		snake_case_		:      Union[str, Any]           =						int(math.sqrt(__a ) ) + 1
		for j in range(2 ,			__a ):
			if (i % j) == 0:
				break
		else:
			yield i


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	snake_case_		:      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):
		snake_case_		:      int           =						int(math.sqrt(__a ) ) + 1
		for j in range(3 ,			__a ,			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

	snake_case_		:      str           =						'from __main__ import slow_primes, primes, fast_primes'
	print(timeit('slow_primes(1_000_000_000_000)' ,			setup=__a ,			number=1_00_00_00 ) )
	print(timeit('primes(1_000_000_000_000)' ,			setup=__a ,			number=1_00_00_00 ) )
	print(timeit('fast_primes(1_000_000_000_000)' ,			setup=__a ,			number=1_00_00_00 ) )


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





"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     torch.save(model.state_dict(),  _lowerCAmelCase,  _use_new_zipfile_serialization=_lowerCAmelCase							)


if __name__ == "__main__":
   __snake_case												=							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(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   __snake_case												=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)