


'''simple docstring'''



import argparse

import torch

from transformers import MraConfig, MraForMaskedLM



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

     return orig_key



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Tuple				,   UpperCAmelCase_   :    Any							)					->		Tuple:
     for key in orig_state_dict.copy().keys():
          __lowerCamelCase       :     int        =		orig_state_dict.pop(UpperCAmelCase_							)

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

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

     return orig_state_dict



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Optional[Any]				,   UpperCAmelCase_   :    Any				,   UpperCAmelCase_   :    Union[str, Any]							)					->		Union[str, Any]:
     __lowerCamelCase       :     int        =		torch.load(UpperCAmelCase_				,   map_location='cpu'							)['model_state_dict']
     __lowerCamelCase       :     Optional[int]        =		MraConfig.from_json_file(UpperCAmelCase_							)
     __lowerCamelCase       :     Dict        =		MraForMaskedLM(UpperCAmelCase_							)

     __lowerCamelCase       :     List[str]        =		convert_checkpoint_helper(config.max_position_embeddings				,   UpperCAmelCase_							)

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

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


if __name__ == "__main__":
      A__		: List[Any]												=  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."""
      )
      A__		: str												=  parser.parse_args()
      convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


A__		: int												=  logging.get_logger(__name__)

A__		: Union[str, Any]												=  {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

A__		: int												=  {
    """vocab_file""": {
        """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json""",
    },
}

A__		: int												=  {
    """microsoft/mpnet-base""": 512,
}

A__		: Optional[Any]												=  {
    """microsoft/mpnet-base""": {"""do_lower_case""": True},
}





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : int												=      VOCAB_FILES_NAMES
    lowerCamelCase  : int												=      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  : List[Any]												=      PRETRAINED_INIT_CONFIGURATION
    lowerCamelCase  : int												=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  : Tuple												=      MPNetTokenizer
    lowerCamelCase  : List[Any]												=      ['input_ids', 'attention_mask']







    def __init__(							self      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_="<s>"      ,					SCREAMING_SNAKE_CASE_="</s>"      ,					SCREAMING_SNAKE_CASE_="</s>"      ,					SCREAMING_SNAKE_CASE_="<s>"      ,					SCREAMING_SNAKE_CASE_="[UNK]"      ,					SCREAMING_SNAKE_CASE_="<pad>"      ,					SCREAMING_SNAKE_CASE_="<mask>"      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=None      ,					**SCREAMING_SNAKE_CASE_      ,					)    ->					Any:
         super().__init__(
             SCREAMING_SNAKE_CASE_      ,					tokenizer_file=SCREAMING_SNAKE_CASE_      ,					do_lower_case=SCREAMING_SNAKE_CASE_      ,					bos_token=SCREAMING_SNAKE_CASE_      ,					eos_token=SCREAMING_SNAKE_CASE_      ,					sep_token=SCREAMING_SNAKE_CASE_      ,					cls_token=SCREAMING_SNAKE_CASE_      ,					unk_token=SCREAMING_SNAKE_CASE_      ,					pad_token=SCREAMING_SNAKE_CASE_      ,					mask_token=SCREAMING_SNAKE_CASE_      ,					tokenize_chinese_chars=SCREAMING_SNAKE_CASE_      ,					strip_accents=SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_      ,					)

         __lowerCamelCase       :     Union[str, Any]        =		json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
         if (
             pre_tok_state.get('lowercase'      ,					SCREAMING_SNAKE_CASE_     ) != do_lower_case
             or pre_tok_state.get('strip_accents'      ,					SCREAMING_SNAKE_CASE_     ) != strip_accents
         ):
              __lowerCamelCase       :     List[Any]        =		getattr(SCREAMING_SNAKE_CASE_      ,					pre_tok_state.pop('type'     )     )
              __lowerCamelCase       :     List[str]        =		do_lower_case
              __lowerCamelCase       :     str        =		strip_accents
              __lowerCamelCase       :     Union[str, Any]        =		pre_tok_class(**SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Tuple        =		do_lower_case







    @property
    def 				lowercase_    (							self     )    ->					str:
         if self._mask_token is None:
              if self.verbose:
                   logger.error('Using mask_token, but it is not set yet.'     )
              return None
         return str(self._mask_token     )







    @mask_token.setter
    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					Union[str, Any]:
         __lowerCamelCase       :     Any        =		AddedToken(SCREAMING_SNAKE_CASE_      ,					lstrip=SCREAMING_SNAKE_CASE_      ,					rstrip=SCREAMING_SNAKE_CASE_     ) if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) else value
         __lowerCamelCase       :     int        =		value







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=None     )    ->					Union[str, Any]:
         __lowerCamelCase       :     Dict        =		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
              return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					List[int]:
         __lowerCamelCase       :     List[Any]        =		[self.sep_token_id]
         __lowerCamelCase       :     List[Any]        =		[self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep     ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					Tuple[str]:
         __lowerCamelCase       :     Optional[Any]        =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE_      ,					name=SCREAMING_SNAKE_CASE_     )
         return tuple(SCREAMING_SNAKE_CASE_     )






