


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def  lowerCAmelCase__				(    a__:	Optional[Any] )						->	Union[str, 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  lowerCAmelCase__				(    a__:	Tuple    ,						a__:	Any )						->	Dict:


							'''simple docstring'''





							for key in orig_state_dict.copy().keys():
														_UpperCAmelCase					=    orig_state_dict.pop(a__ )

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

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

							return orig_state_dict

def  lowerCAmelCase__				(    a__:	Optional[int]    ,						a__:	Optional[Any]    ,						a__:	str )						->	Tuple:


							'''simple docstring'''





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

							_UpperCAmelCase					=    convert_checkpoint_helper(config.max_position_embeddings    ,						a__ )

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

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


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




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:
		lowerCAmelCase__						:int         =							None

lowerCAmelCase__						:List[str]         =							logging.get_logger(__name__)

lowerCAmelCase__						:List[str]         =							{'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase__						:List[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'''
        ),
    }
}

lowerCAmelCase__						:List[str]         =							{
    '''microsoft/deberta-v2-xlarge''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge''': 5_1_2,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_1_2,
}

lowerCAmelCase__						: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   (       UpperCAmelCase    ):
		_a			:						Tuple    			=			VOCAB_FILES_NAMES
		_a			:						int    			=			PRETRAINED_VOCAB_FILES_MAP
		_a			:						Optional[Any]    			=			PRETRAINED_INIT_CONFIGURATION
		_a			:						str    			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a			:						Optional[int]    			=			DebertaVaTokenizer
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[UNK]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[PAD]"    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[MASK]"    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> None:




									"""simple docstring"""





									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    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					sep_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					cls_token=_SCREAMING_SNAKE_CASE    ,					mask_token=_SCREAMING_SNAKE_CASE    ,					split_by_punct=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)

									_UpperCAmelCase					=    do_lower_case
									_UpperCAmelCase					=    split_by_punct
									_UpperCAmelCase					=    vocab_file
									_UpperCAmelCase					=    False if not self.vocab_file else True
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> str:




									"""simple docstring"""





									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									_UpperCAmelCase					=    [self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False		)				-> Any:




									"""simple docstring"""





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

									if token_ids_a is not None:
																return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
									return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.sep_token_id]
									_UpperCAmelCase					=    [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    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = 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(_SCREAMING_SNAKE_CASE		):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
																return
									_UpperCAmelCase					=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

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

									return (out_vocab_file,)
