


from math import factorial, pi
def      snake_case_						( __lowercase			,       __lowercase = 3_0       ):

   if not isinstance(__lowercase			,       (int, float)       ):
      raise ValueError('''maclaurin_sin() requires either an int or float for theta'''       )

   if not isinstance(__lowercase			,       __lowercase       ) or accuracy <= 0:
      raise ValueError('''maclaurin_sin() requires a positive int for accuracy'''       )

   UpperCAmelCase_							:      Dict						=   float(__lowercase       )
   UpperCAmelCase_							:      str						=   theta // (2 * pi)
   theta -= 2 * div * pi
   return sum(
       (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1       ) for r in range(__lowercase       )       )
def      snake_case_						( __lowercase			,       __lowercase = 3_0       ):

   if not isinstance(__lowercase			,       (int, float)       ):
      raise ValueError('''maclaurin_cos() requires either an int or float for theta'''       )

   if not isinstance(__lowercase			,       __lowercase       ) or accuracy <= 0:
      raise ValueError('''maclaurin_cos() requires a positive int for accuracy'''       )

   UpperCAmelCase_							:      int						=   float(__lowercase       )
   UpperCAmelCase_							:      List[str]						=   theta // (2 * pi)
   theta -= 2 * div * pi
   return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r       ) for r in range(__lowercase       )       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       print(maclaurin_sin(10))
       print(maclaurin_sin(-10))
       print(maclaurin_sin(10, 15))
       print(maclaurin_sin(-10, 15))

       print(maclaurin_cos(5))
       print(maclaurin_cos(-5))
       print(maclaurin_cos(10, 15))
       print(maclaurin_cos(-10, 15))



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      snake_case_						( __lowercase			,       __lowercase       ):
   # Load checkpoint
   UpperCAmelCase_							:      Tuple						=   torch.load(__lowercase			,       map_location='''cpu'''       )

   UpperCAmelCase_							:      Optional[int]						=   chkpt['''model''']

   # We have the base model one level deeper than the original XLM repository
   UpperCAmelCase_							:      str						=   {}
   for k, v in state_dict.items():
      if "pred_layer" in k:
         UpperCAmelCase_							:      Tuple						=   v
      else:
         UpperCAmelCase_							:      Union[str, Any]						=   v

   UpperCAmelCase_							:      int						=   chkpt['''params''']
   UpperCAmelCase_							:      Union[str, Any]						=   {n: v for n, v in config.items() if not isinstance(__lowercase			,       (torch.FloatTensor, numpy.ndarray)       )}

   UpperCAmelCase_							:      int						=   chkpt['''dico_word2id''']
   UpperCAmelCase_							:      List[Any]						=   {s + '''</w>''' if s.find('''@@'''       ) == -1 and i > 1_3 else s.replace('''@@'''			,       ''''''       ): i for s, i in vocab.items()}

   # Save pytorch-model
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
   UpperCAmelCase_							:      Dict						=   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

   print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
   torch.save(__lowercase			,       __lowercase       )

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

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


if __name__ == "__main__":
       __UpperCamelCase				:  str      					=							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				:  Dict      					=							parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)