
"""simple docstring"""


import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
A_    							=					logging.get_logger(__name__)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  RobertaPreLayerNormConfig.from_pretrained(
			    lowerCAmelCase__	,architectures=['''RobertaPreLayerNormForMaskedLM''']						)

			# convert state_dict
			lowerCamelCase_					=  torch.load(hf_hub_download(repo_id=lowerCAmelCase__	,filename='''pytorch_model.bin'''						)						)
			lowerCamelCase_					=  {}
			for tensor_key, tensor_value in original_state_dict.items():
						# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
						if tensor_key.startswith('''roberta.'''						):
									lowerCamelCase_					=  '''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''						) :]

						# The original implementation contains weights which are not used, remove them from the state_dict
						if tensor_key.endswith('''.self.LayerNorm.weight'''						) or tensor_key.endswith('''.self.LayerNorm.bias'''						):
									continue

						lowerCamelCase_					=  tensor_value

			lowerCamelCase_					=  RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=lowerCAmelCase__	,config=lowerCAmelCase__	,state_dict=lowerCAmelCase__						)
			model.save_pretrained(lowerCAmelCase__						)

			# convert tokenizer
			lowerCamelCase_					=  AutoTokenizer.from_pretrained(lowerCAmelCase__						)
			tokenizer.save_pretrained(lowerCAmelCase__						)


if __name__ == "__main__":
						A_    							=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--checkpoint-repo""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						A_    							=					parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





from decimal import Decimal, getcontext
from math import ceil, factorial

def  __lowerCamelCase					( _lowercase			)       ->      str:

     if not isinstance(_lowercase						,     _lowercase			):
          raise TypeError('Undefined for non-integers'			)
     elif precision < 1:
          raise ValueError('Undefined for non-natural numbers'			)

     UpperCamelCase						    =					precision
     UpperCamelCase						    =					ceil(precision / 14			)
     UpperCamelCase						    =					426880 * Decimal(10005			).sqrt()
     UpperCamelCase						    =					1
     UpperCamelCase						    =					13591409
     UpperCamelCase						    =					Decimal(_lowercase			)
     for k in range(1						,     _lowercase			):
          UpperCamelCase						    =					factorial(6 * k			) // (factorial(3 * k			) * factorial(_lowercase			) ** 3)
          linear_term += 545140134
          exponential_term *= -262537412640768000
          partial_sum += Decimal(multinomial_term * linear_term			) / exponential_term
     return str(constant_term / partial_sum			)[:-1]


if __name__ == "__main__":
 _snake_case      = 50
 print(F"The first {n} digits of pi is: {pi(n)}")


