
import math




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict)      ->					Tuple:






							'''simple docstring'''





							__UpperCamelCase			:     Any         =      [True] * n
							__UpperCamelCase			:     Optional[int]         =      False
							__UpperCamelCase			:     List[str]         =      False
							__UpperCamelCase			:     Any         =      True

							for i in range(3		,						int(n**0.5 + 1)		,						2):
														__UpperCamelCase			:     Union[str, Any]         =      i * 2
														while index < n:
																					__UpperCamelCase			:     Optional[int]         =      False
																					__UpperCamelCase			:     str         =      index + i

							__UpperCamelCase			:     Union[str, Any]         =      [2]

							for i in range(3		,						_lowerCamelCase		,						2):
														if is_prime[i]:
																					primes.append(_lowerCamelCase)

							return primes




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int] = 999_966_663_333)      ->					Tuple:






							'''simple docstring'''





							__UpperCamelCase			:     int         =      math.floor(math.sqrt(_lowerCamelCase)) + 100
							__UpperCamelCase			:     Union[str, Any]         =      prime_sieve(_lowerCamelCase)

							__UpperCamelCase			:     List[Any]         =      0
							__UpperCamelCase			:     Union[str, Any]         =      0
							__UpperCamelCase			:     int         =      primes[prime_index]

							while (last_prime**2) <= limit:
														__UpperCamelCase			:     Any         =      primes[prime_index + 1]

														__UpperCamelCase			:     Dict         =      last_prime**2
														__UpperCamelCase			:     str         =      next_prime**2

														# Get numbers divisible by lps(current)
														__UpperCamelCase			:     int         =      lower_bound + last_prime
														while upper_bound > current <= limit:
																					matches_sum += current
																					current += last_prime

														# Reset the upper_bound
														while (upper_bound - next_prime) > limit:
																					upper_bound -= next_prime

														# Add the numbers divisible by ups(current)
														__UpperCamelCase			:     str         =      upper_bound - next_prime
														while current > lower_bound:
																					matches_sum += current
																					current -= next_prime

														# Remove the numbers divisible by both ups and lps
														__UpperCamelCase			:     Dict         =      0
														while upper_bound > current <= limit:
																					if current <= lower_bound:
																												# Increment the current number
																												current += last_prime * next_prime
																												continue

																					if current > limit:
																												break

																					# Remove twice since it was added by both ups and lps
																					matches_sum -= current * 2

																					# Increment the current number
																					current += last_prime * next_prime

														# Setup for next pair
														__UpperCamelCase			:     Optional[int]         =      next_prime
														prime_index += 1

							return matches_sum


if __name__ == "__main__":
		print(solution())

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase			:     List[Any]         =      BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,						_lowerCamelCase)


if __name__ == "__main__":
		lowercase						:      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(
		    '--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.'
		)
		lowercase						:      Tuple									= parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)