





import math


def 			lowerCamelCase_		(					lowerCAmelCase:		int )->						float:
  _snake_case      : Dict														=		math.log(2			,							10 ) * number
  _snake_case      : Tuple														=		round((log_number - int(lowerCAmelCase ))			,							15 )

  return difference


def 			lowerCamelCase_		(					lowerCAmelCase:		int = 67_89_10 )->						int:
  _snake_case      : Optional[Any]														=		90
  _snake_case      : List[str]														=		0

  _snake_case      : str														=		math.log(1.2_3			,							10 )
  _snake_case      : List[Any]														=		math.log(1.2_4			,							10 )
  _snake_case      : Tuple														=		0

  while position < number:
    _snake_case      : Dict														=		log_difference(lowerCAmelCase )

    if difference >= upper_limit:
      power_iterator += 93

    elif difference < lower_limit:
      power_iterator += 1_96

    else:
      _snake_case      : Any														=		power_iterator
      power_iterator += 1_96
      position += 1

  return previous_power


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      print(F"""{solution() = }""")











import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 			lowerCamelCase_		(					lowerCAmelCase:		Any )->						str:
  if "model" in orig_key:
    _snake_case      : Tuple														=		orig_key.replace('model.'			,							'' )
  if "norm1" in orig_key:
    _snake_case      : List[str]														=		orig_key.replace('norm1'			,							'attention.output.LayerNorm' )
  if "norm2" in orig_key:
    _snake_case      : Tuple														=		orig_key.replace('norm2'			,							'output.LayerNorm' )
  if "norm" in orig_key:
    _snake_case      : Any														=		orig_key.replace('norm'			,							'LayerNorm' )
  if "transformer" in orig_key:
    _snake_case      : Tuple														=		orig_key.split('.' )[0].split('_' )[-1]
    _snake_case      : List[Any]														=		orig_key.replace(F"""transformer_{layer_num}"""			,							F"""encoder.layer.{layer_num}""" )
  if "mha.attn" in orig_key:
    _snake_case      : Union[str, Any]														=		orig_key.replace('mha.attn'			,							'attention.self' )
  if "mha" in orig_key:
    _snake_case      : Dict														=		orig_key.replace('mha'			,							'attention' )
  if "W_q" in orig_key:
    _snake_case      : Optional[Any]														=		orig_key.replace('W_q'			,							'self.query' )
  if "W_k" in orig_key:
    _snake_case      : Union[str, Any]														=		orig_key.replace('W_k'			,							'self.key' )
  if "W_v" in orig_key:
    _snake_case      : Tuple														=		orig_key.replace('W_v'			,							'self.value' )
  if "ff.0" in orig_key:
    _snake_case      : str														=		orig_key.replace('ff.0'			,							'intermediate.dense' )
  if "ff.2" in orig_key:
    _snake_case      : int														=		orig_key.replace('ff.2'			,							'output.dense' )
  if "ff" in orig_key:
    _snake_case      : Tuple														=		orig_key.replace('ff'			,							'output.dense' )
  if "mlm_class" in orig_key:
    _snake_case      : str														=		orig_key.replace('mlm.mlm_class'			,							'cls.predictions.decoder' )
  if "mlm" in orig_key:
    _snake_case      : Dict														=		orig_key.replace('mlm'			,							'cls.predictions.transform' )
  if "backbone.backbone.encoders" in orig_key:
    _snake_case      : Any														=		orig_key.replace('backbone.backbone.encoders'			,							'encoder.layer' )
  if "cls" not in orig_key:
    _snake_case      : Dict														=		'mra.' + orig_key

  return orig_key


def 			lowerCamelCase_		(					lowerCAmelCase:		List[Any]			,							lowerCAmelCase:		Union[str, Any] )->						Any:
  for key in orig_state_dict.copy().keys():
    _snake_case      : Dict														=		orig_state_dict.pop(lowerCAmelCase )

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      _snake_case      : str														=		val

  _snake_case      : Tuple														=		orig_state_dict['cls.predictions.decoder.bias']
  _snake_case      : List[Any]														=		torch.arange(lowerCAmelCase ).expand((1, -1) ) + 2

  return orig_state_dict


def 			lowerCamelCase_		(					lowerCAmelCase:		List[str]			,							lowerCAmelCase:		List[str]			,							lowerCAmelCase:		int )->						List[str]:
  _snake_case      : Optional[Any]														=		torch.load(lowerCAmelCase			,							map_location='cpu' )['model_state_dict']
  _snake_case      : Optional[Any]														=		MraConfig.from_json_file(lowerCAmelCase )
  _snake_case      : Any														=		MraForMaskedLM(lowerCAmelCase )

  _snake_case      : List[Any]														=		convert_checkpoint_helper(config.max_position_embeddings			,							lowerCAmelCase )

  print(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__":
      lowerCAmelCase_        =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help="""The json file for Mra model config.""",
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      lowerCAmelCase_        =			parser.parse_args()
      convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




