
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


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

    return orig_key


def   _A       (    SCREAMING_SNAKE_CASE__  :     List[Any]					,  SCREAMING_SNAKE_CASE__  :     Union[str, Any]	):
    for key in orig_state_dict.copy().keys():
        UpperCamelCase	:Optional[Any]     							=       orig_state_dict.pop(SCREAMING_SNAKE_CASE__	)

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            UpperCamelCase	:Any     							=       val

    UpperCamelCase	:Optional[int]     							=       orig_state_dict['''cls.predictions.decoder.bias''']
    UpperCamelCase	:List[Any]     							=       torch.arange(SCREAMING_SNAKE_CASE__	).expand((1, -1)	) + 2

    return orig_state_dict


def   _A       (    SCREAMING_SNAKE_CASE__  :     Union[str, Any]					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     Optional[int]	):
    UpperCamelCase	:Union[str, Any]     							=       torch.load(SCREAMING_SNAKE_CASE__					,  map_location='''cpu'''	)['''model_state_dict''']
    UpperCamelCase	:Tuple     							=       MraConfig.from_json_file(SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Optional[Any]     							=       MraForMaskedLM(SCREAMING_SNAKE_CASE__	)

    UpperCamelCase	:Any     							=       convert_checkpoint_helper(config.max_position_embeddings					,  SCREAMING_SNAKE_CASE__	)

    print(model.load_state_dict(SCREAMING_SNAKE_CASE__	)	)
    model.eval()
    model.save_pretrained(SCREAMING_SNAKE_CASE__	)

    print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''	)


if __name__ == "__main__":
 __snake_case							 =  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."""
 )
 __snake_case							 =  parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)













from __future__ import annotations

from enum import Enum







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[Any]									=24
      UpperCamelCase_    : Any									=21
      UpperCamelCase_    : Union[str, Any]									=18
      UpperCamelCase_    : Tuple									=15
      UpperCamelCase_    : List[Any]									=12
      UpperCamelCase_    : Optional[Any]									=9
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=3
      UpperCamelCase_    : Any									=2
      UpperCamelCase_    : List[str]									=1
      UpperCamelCase_    : Optional[Any]									=-1
      UpperCamelCase_    : str									=-2
      UpperCamelCase_    : Any									=-3
      UpperCamelCase_    : Optional[int]									=-6
      UpperCamelCase_    : int									=-9
      UpperCamelCase_    : Optional[int]									=-12
      UpperCamelCase_    : Optional[Any]									=-15
      UpperCamelCase_    : str									=-18
      UpperCamelCase_    : int									=-21
      UpperCamelCase_    : Union[str, Any]									=-24







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=8
      UpperCamelCase_    : str									=7
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=5
      UpperCamelCase_    : int									=4
      UpperCamelCase_    : Optional[Any]									=3
      UpperCamelCase_    : Optional[Any]									=2
      UpperCamelCase_    : List[str]									=1







def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:str     							=       SIUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Any     							=       SIUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Dict     							=       BinaryUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:List[str]     							=       BinaryUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount


if __name__ == "__main__":
 import doctest

 doctest.testmod()





