

"""simple docstring"""





import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()




def     _A    (    UpperCamelCase_	:			Any,     UpperCamelCase_	:			int,     UpperCamelCase_	:			int)      ->		str:
   '''simple docstring'''


   __lowercase								=		RoFormerConfig.from_json_file(UpperCamelCase_)
   print(F"""Building PyTorch model from configuration: {config}""")
   __lowercase								=		RoFormerForMaskedLM(UpperCamelCase_)

   # Load weights from tf checkpoint
   load_tf_weights_in_roformer(UpperCamelCase_,     UpperCamelCase_,     UpperCamelCase_)

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}""")
   torch.save(model.state_dict(),     UpperCamelCase_,     _use_new_zipfile_serialization=UpperCamelCase_)


if __name__ == "__main__":
   _a					 =				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(
       '--bert_config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained BERT model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _a					 =				parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







"""simple docstring"""





import math
from decimal import Decimal, getcontext




def     _A    (    UpperCamelCase_	:			int = 4000000)      ->		int:
   '''simple docstring'''


   try:
      __lowercase								=		int(UpperCamelCase_)
   except (TypeError, ValueError):
      raise TypeError("Parameter n must be int or castable to int.")
   if n <= 0:
      raise ValueError("Parameter n must be greater than or equal to one.")
   __lowercase								=		100
   __lowercase								=		(Decimal(5) ** Decimal(0.5) + 1) / Decimal(2)

   __lowercase								=		(math.floor(math.log(n * (phi + 2),     UpperCamelCase_) - 1) // 3) * 3 + 2
   __lowercase								=		Decimal(round(phi ** Decimal(index + 1))) / (phi + 2)
   __lowercase								=		num // 2
   return int(UpperCamelCase_)


if __name__ == "__main__":
   print(F"{solution() = }")




