


import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Union[str, Any]:







 """simple docstring"""


 _lowercase							      =torch.load(__snake_case     ,						map_location='''cpu'''						)

 _lowercase							      =chkpt['''model''']

 # We have the base model one level deeper than the original XLM repository
 _lowercase							      ={}
 for k, v in state_dict.items():
  if "pred_layer" in k:
   _lowercase							      =v
  else:
   _lowercase							      =v

 _lowercase							      =chkpt['''params''']
 _lowercase							      ={n: v for n, v in config.items() if not isinstance(__snake_case     ,						(torch.FloatTensor, numpy.ndarray)						)}

 _lowercase							      =chkpt['''dico_word2id''']
 _lowercase							      ={s + '''</w>''' if s.find('''@@'''						) == -1 and i > 13 else s.replace('''@@'''     ,						''''''						): i for s, i in vocab.items()}

 # Save pytorch-model
 _lowercase							      =pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
 _lowercase							      =pytorch_dump_folder_path + '''/''' + CONFIG_NAME
 _lowercase							      =pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

 print(F"Save PyTorch model to {pytorch_weights_dump_path}"						)
 torch.save(__snake_case     ,						__snake_case						)

 print(F"Save configuration file to {pytorch_config_dump_path}"						)
 with open(__snake_case     ,						'''w'''     ,						encoding='''utf-8'''						) as f:
  f.write(json.dumps(__snake_case     ,						indent=2						) + '''\n'''						)

 print(F"Save vocab file to {pytorch_config_dump_path}"						)
 with open(__snake_case     ,						'''w'''     ,						encoding='''utf-8'''						) as f:
  f.write(json.dumps(__snake_case     ,						indent=2						) + '''\n'''						)


if __name__ == "__main__":
  UpperCAmelCase__   	=							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  UpperCAmelCase__   	=							parser.parse_args()
  convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





from math import isqrt
def 				UpperCAmelCase_					(							__snake_case						)     ->			list[int]:







 """simple docstring"""


 _lowercase							      =[True] * max_number
 for i in range(2     ,						isqrt(max_number - 1						) + 1						):
  if is_prime[i]:
   for j in range(i**2     ,						__snake_case     ,						__snake_case						):
    _lowercase							      =False

 return [i for i in range(2     ,						__snake_case						) if is_prime[i]]
def 				UpperCAmelCase_					(							__snake_case = 10**8						)     ->			int:







 """simple docstring"""


 _lowercase							      =calculate_prime_numbers(max_number // 2						)

 _lowercase							      =0
 _lowercase							      =0
 _lowercase							      =len(__snake_case						) - 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() = }''')

