code: 
  '''simple docstring'''
  
  import argparse
  
  import torch
  
  from transformers import NystromformerConfig, NystromformerForMaskedLM
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       List[str]		)    ->						int:
    if "model" in orig_key:
      _lowerCAmelCase       :  List[Any]   				=					orig_key.replace("""model."""  ,      """"""		)
    if "norm1" in orig_key:
      _lowerCAmelCase       :  Optional[Any]   				=					orig_key.replace("""norm1"""  ,      """attention.output.LayerNorm"""		)
    if "norm2" in orig_key:
      _lowerCAmelCase       :  int   				=					orig_key.replace("""norm2"""  ,      """output.LayerNorm"""		)
    if "norm" in orig_key:
      _lowerCAmelCase       :  Union[str, Any]   				=					orig_key.replace("""norm"""  ,      """LayerNorm"""		)
    if "transformer" in orig_key:
      _lowerCAmelCase       :  List[str]   				=					orig_key.split("""."""		)[0].split("""_"""		)[-1]
      _lowerCAmelCase       :  Dict   				=					orig_key.replace(f'transformer_{layer_num}'  ,      f'encoder.layer.{layer_num}'		)
    if "mha.attn" in orig_key:
      _lowerCAmelCase       :  Tuple   				=					orig_key.replace("""mha.attn"""  ,      """attention.self"""		)
    if "mha" in orig_key:
      _lowerCAmelCase       :  List[Any]   				=					orig_key.replace("""mha"""  ,      """attention"""		)
    if "W_q" in orig_key:
      _lowerCAmelCase       :  str   				=					orig_key.replace("""W_q"""  ,      """self.query"""		)
    if "W_k" in orig_key:
      _lowerCAmelCase       :  str   				=					orig_key.replace("""W_k"""  ,      """self.key"""		)
    if "W_v" in orig_key:
      _lowerCAmelCase       :  int   				=					orig_key.replace("""W_v"""  ,      """self.value"""		)
    if "ff1" in orig_key:
      _lowerCAmelCase       :  Optional[Any]   				=					orig_key.replace("""ff1"""  ,      """intermediate.dense"""		)
    if "ff2" in orig_key:
      _lowerCAmelCase       :  Optional[int]   				=					orig_key.replace("""ff2"""  ,      """output.dense"""		)
    if "ff" in orig_key:
      _lowerCAmelCase       :  Optional[int]   				=					orig_key.replace("""ff"""  ,      """output.dense"""		)
    if "mlm_class" in orig_key:
      _lowerCAmelCase       :  Any   				=					orig_key.replace("""mlm.mlm_class"""  ,      """cls.predictions.decoder"""		)
    if "mlm" in orig_key:
      _lowerCAmelCase       :  Any   				=					orig_key.replace("""mlm"""  ,      """cls.predictions.transform"""		)
    if "cls" not in orig_key:
      _lowerCAmelCase       :  int   				=					"""nystromformer.""" + orig_key
  
    return orig_key
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Optional[int]  ,      _lowerCamelCase						:       List[Any]		)    ->						Union[str, Any]:
    for key in orig_state_dict.copy().keys():
      _lowerCAmelCase       :  Optional[int]   				=					orig_state_dict.pop(_lowerCamelCase		)
  
      if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
        continue
      else:
        _lowerCAmelCase       :  Optional[int]   				=					val
  
    _lowerCAmelCase       :  Dict   				=					orig_state_dict["""cls.predictions.decoder.bias"""]
    _lowerCAmelCase       :  Optional[int]   				=					(
        torch.arange(config.max_position_embeddings		).expand((1, -1)		) + 2
    )
  
    return orig_state_dict
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Union[str, Any]  ,      _lowerCamelCase						:       List[Any]  ,      _lowerCamelCase						:       Tuple		)    ->						Optional[Any]:
    _lowerCAmelCase       :  Dict   				=					torch.load(_lowerCamelCase  ,      map_location="""cpu"""		)["""model_state_dict"""]
    _lowerCAmelCase       :  List[str]   				=					NystromformerConfig.from_json_file(_lowerCamelCase		)
    _lowerCAmelCase       :  Union[str, Any]   				=					NystromformerForMaskedLM(_lowerCamelCase		)
  
    _lowerCAmelCase       :  Optional[Any]   				=					convert_checkpoint_helper(_lowerCamelCase  ,      _lowerCamelCase		)
  
    model.load_state_dict(_lowerCamelCase		)
    model.eval()
    model.save_pretrained(_lowerCamelCase		)
  
    print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'		)
  
  
  if __name__ == "__main__":
       UpperCamelCase_     			= argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Nystromformer pytorch checkpoint."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help="""The json file for Nystromformer model config.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       UpperCamelCase_     			= parser.parse_args()
       convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
  
style_context: 
  '''simple docstring'''
  
  import random
  
  
  class   a_  :
  
  
  
  
         def __init__(     self   ,   snake_case_   ,   snake_case_  ):
           _lowerCAmelCase       :  List[str]   				=					x
           _lowerCAmelCase       :  List[str]   				=					y
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           return (self.x**2 + self.y**2) <= 1
  
  
  
  
  
  
  
         @classmethod
         def 							__UpperCamelCase       (     cls  ):
           return cls(x=random.random()   ,   y=random.random()  )
  
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       int		)    ->						float:
    if number_of_simulations < 1:
      raise ValueError("""At least one simulation is necessary to estimate PI."""		)
  
    _lowerCAmelCase       :  Tuple   				=					0
    for _ in range(_lowerCamelCase		):
      _lowerCAmelCase       :  Union[str, Any]   				=					Point.random_unit_square()
  
      if random_point.is_in_unit_circle():
        number_in_unit_circle += 1
  
    return 4 * number_in_unit_circle / number_of_simulations
  
  
  if __name__ == "__main__":
       # import doctest
  
       # doctest.testmod()
       from math import pi
  
       UpperCamelCase_     			=       """Please enter the desired number of Monte Carlo simulations: """
       UpperCamelCase_     			=       estimate_pi(int(input(prompt).strip()))
       print(F'An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}')
  
  
  