


'''simple docstring'''







from math import factorial, pi







def _UpperCamelCase       (     __A      ,			__A = 30    )							->		float:


      '''simple docstring'''



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

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

      UpperCamelCase__										=				float(__A    )
      UpperCamelCase__										=				theta // (2 * pi)
      theta -= 2 * div * pi
      return sum(
          (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1    ) for r in range(__A    )    )







def _UpperCamelCase       (     __A      ,			__A = 30    )							->		float:


      '''simple docstring'''



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

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

      UpperCamelCase__										=				float(__A    )
      UpperCamelCase__										=				theta // (2 * pi)
      theta -= 2 * div * pi
      return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r    ) for r in range(__A    )    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(maclaurin_sin(1_0))
  print(maclaurin_sin(-1_0))
  print(maclaurin_sin(1_0, 1_5))
  print(maclaurin_sin(-1_0, 1_5))

  print(maclaurin_cos(5))
  print(maclaurin_cos(-5))
  print(maclaurin_cos(1_0, 1_5))
  print(maclaurin_cos(-1_0, 1_5))












import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


UpperCAmelCase	:	int		     =       {
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Union[str, Any]=None						):





  """simple docstring"""



  a__	:					Optional[int]										=XLNetConfig.from_json_file(SCREAMING_SNAKE_CASE						)

  a__	:					Dict										=finetuning_task.lower() if finetuning_task is not None else ""
  if finetuning_task in GLUE_TASKS_NUM_LABELS:
    print(f'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''						)
    a__	:					List[str]										=finetuning_task
    a__	:					Tuple										=GLUE_TASKS_NUM_LABELS[finetuning_task]
    a__	:					List[Any]										=XLNetForSequenceClassification(SCREAMING_SNAKE_CASE						)
  elif "squad" in finetuning_task:
    a__	:					Optional[int]										=finetuning_task
    a__	:					Dict										=XLNetForQuestionAnswering(SCREAMING_SNAKE_CASE						)
  else:
    a__	:					List[Any]										=XLNetLMHeadModel(SCREAMING_SNAKE_CASE						)

  # Load weights from tf checkpoint
  load_tf_weights_in_xlnet(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  # Save pytorch-model
  a__	:					Dict										=os.path.join(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  a__	:					Dict										=os.path.join(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  print(f'''Save PyTorch model to {os.path.abspath(SCREAMING_SNAKE_CASE						)}'''						)
  torch.save(model.state_dict()       ,       SCREAMING_SNAKE_CASE						)
  print(f'''Save configuration file to {os.path.abspath(SCREAMING_SNAKE_CASE						)}'''						)
  with open(SCREAMING_SNAKE_CASE       ,       "w"       ,       encoding="utf-8"						) as f:
    f.write(config.to_json_string()						)


if __name__ == "__main__":
     UpperCAmelCase	:	List[str]		     =       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(
         """--xlnet_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained XLNet model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default=None,
         type=str,
         required=True,
         help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
     )
     parser.add_argument(
         """--finetuning_task""",
         default=None,
         type=str,
         help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
     )
     UpperCAmelCase	:	int		     =       parser.parse_args()
     print(args)

     convert_xlnet_checkpoint_to_pytorch(
         args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
     )






