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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]			,					__UpperCamelCase   :	List[Any]     )							-> Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				torch.load(__UpperCamelCase			,					map_location="""cpu"""     )

  SCREAMING_SNAKE_CASE__      							=				chkpt["""model"""]

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

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

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

  # Save pytorch-model
  SCREAMING_SNAKE_CASE__      							=				pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  SCREAMING_SNAKE_CASE__      							=				pytorch_dump_folder_path + """/""" + CONFIG_NAME
  SCREAMING_SNAKE_CASE__      							=				pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

  print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""     )
  torch.save(__UpperCamelCase			,					__UpperCamelCase     )

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

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


if __name__ == "__main__":
   __lowerCamelCase      :			Union[str, Any]							       =		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.'''
   )
   __lowerCamelCase      :			int							       =		parser.parse_args()
   convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)

from math import isqrt



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> list[int]:







  """simple docstring"""





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

  return [i for i in range(2			,					__UpperCamelCase     ) if is_prime[i]]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int = 10**8     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				calculate_prime_numbers(max_number // 2     )

  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     ) - 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() = }""")
