





'''simple docstring'''







import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
					from .tokenization_deberta_va import DebertaVaTokenizer
else:
					snake_case_  :							str            =	None

snake_case_  :							Optional[Any]            =	logging.get_logger(__name__)

snake_case_  :							Union[str, Any]            =	{"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

snake_case_  :							str            =	{
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

snake_case_  :							Union[str, Any]            =	{
    "microsoft/deberta-v2-xlarge": 5_12,
    "microsoft/deberta-v2-xxlarge": 5_12,
    "microsoft/deberta-v2-xlarge-mnli": 5_12,
    "microsoft/deberta-v2-xxlarge-mnli": 5_12,
}

snake_case_  :							Any            =	{
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}


class        __a   (lowerCamelCase  ):
							__a       : Optional[Any]          =							VOCAB_FILES_NAMES
							__a       : Dict          =							PRETRAINED_VOCAB_FILES_MAP
							__a       : Union[str, Any]          =							PRETRAINED_INIT_CONFIGURATION
							__a       : int          =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__a       : int          =							DebertaVaTokenizer

							def __init__(				self				:  Optional[int]							,  __magic_name__				:  Tuple=None							,  __magic_name__				:  List[str]=None							,  __magic_name__				:  Union[str, Any]=False							,  __magic_name__				:  List[str]=False							,  __magic_name__				:  str="[CLS]"							,  __magic_name__				:  int="[SEP]"							,  __magic_name__				:  Dict="[UNK]"							,  __magic_name__				:  Optional[Any]="[SEP]"							,  __magic_name__				:  str="[PAD]"							,  __magic_name__				:  int="[CLS]"							,  __magic_name__				:  str="[MASK]"							,  **__magic_name__				:  List[str]							,  )       ->							None:







													"""simple docstring"""
													super().__init__(
													    __magic_name__							,  tokenizer_file=__magic_name__							,  do_lower_case=__magic_name__							,  bos_token=__magic_name__							,  eos_token=__magic_name__							,  unk_token=__magic_name__							,  sep_token=__magic_name__							,  pad_token=__magic_name__							,  cls_token=__magic_name__							,  mask_token=__magic_name__							,  split_by_punct=__magic_name__							,  **__magic_name__							,  )

													UpperCAmelCase_      :   int      =	do_lower_case
													UpperCAmelCase_      :   str      =	split_by_punct
													UpperCAmelCase_      :   List[Any]      =	vocab_file
													UpperCAmelCase_      :   List[Any]      =	False if not self.vocab_file else True

							def      UpperCAmelCase__						(				self				:  List[Any]							,  __magic_name__				:  Dict							,  __magic_name__				:  Union[str, Any]=None )       ->							Optional[int]:







													"""simple docstring"""

													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													UpperCAmelCase_      :   Tuple      =	[self.cls_token_id]
													UpperCAmelCase_      :   Union[str, Any]      =	[self.sep_token_id]
													return cls + token_ids_a + sep + token_ids_a + sep

							def      UpperCAmelCase__						(				self				:  Dict							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  str=None							,  __magic_name__				:  Optional[Any]=False )       ->							Dict:







													"""simple docstring"""

													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=__magic_name__							,  token_ids_a=__magic_name__							,  already_has_special_tokens=__magic_name__ )

													if token_ids_a is not None:
																			return [1] + ([0] * len(__magic_name__ )) + [1] + ([0] * len(__magic_name__ )) + [1]
													return [1] + ([0] * len(__magic_name__ )) + [1]

							def      UpperCAmelCase__						(				self				:  Tuple							,  __magic_name__				:  Any							,  __magic_name__				:  int=None )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	[self.sep_token_id]
													UpperCAmelCase_      :   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 ) * [0] + len(token_ids_a + sep ) * [1]



							def      UpperCAmelCase__						(				self				:  Any							,  __magic_name__				:  str							,  __magic_name__				:  Optional[str] = None )       ->							Tuple[str]:







													"""simple docstring"""
													if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																			    '''tokenizer.''' )

													if not os.path.isdir(__magic_name__ ):
																			logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
																			return
													UpperCAmelCase_      :   int      =	os.path.join(
													    __magic_name__							,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

													if os.path.abspath(self.vocab_file ) != os.path.abspath(__magic_name__ ):
																			copyfile(self.vocab_file							,  __magic_name__ )

													return (out_vocab_file,)











import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Any:

    """simple docstring"""


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

    return orig_key




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase			=    orig_state_dict.pop(lowercase_   )

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

    __UpperCamelCase			=    orig_state_dict['''cls.predictions.decoder.bias''']
    __UpperCamelCase			=    torch.arange(lowercase_   ).expand((1, -1)   ) + 2

    return orig_state_dict




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    torch.load(lowercase_       ,  map_location='''cpu'''   )['''model_state_dict''']
    __UpperCamelCase			=    MraConfig.from_json_file(lowercase_   )
    __UpperCamelCase			=    MraForMaskedLM(lowercase_   )

    __UpperCamelCase			=    convert_checkpoint_helper(config.max_position_embeddings       ,  lowercase_   )

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

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


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






