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:
 UpperCAmelCase							= None

UpperCAmelCase							= logging.get_logger(__name__)

UpperCAmelCase							= {'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase							= {
    '''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'''
        ),
    }
}

UpperCAmelCase							= {
    '''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,
}

UpperCAmelCase							= {
    '''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       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Any 			=       VOCAB_FILES_NAMES
 _lowerCamelCase  :		Optional[Any] 			=       PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  :		Optional[Any] 			=       PRETRAINED_INIT_CONFIGURATION
 _lowerCamelCase  :		Any 			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  :		str 			=       DebertaVaTokenizer

 def __init__(	self				:						List[Any]				,	lowerCamelCase				:						Optional[Any]=None				,	lowerCamelCase				:						Optional[Any]=None				,	lowerCamelCase				:						Any=False				,	lowerCamelCase				:						str=False				,	lowerCamelCase				:						List[str]="[CLS]"				,	lowerCamelCase				:						Tuple="[SEP]"				,	lowerCamelCase				:						List[Any]="[UNK]"				,	lowerCamelCase				:						Dict="[SEP]"				,	lowerCamelCase				:						int="[PAD]"				,	lowerCamelCase				:						int="[CLS]"				,	lowerCamelCase				:						Dict="[MASK]"				,	**lowerCamelCase				:						Any				,	)       ->							None:
        super().__init__(
            lowerCamelCase				,	tokenizer_file=lowerCamelCase				,	do_lower_case=lowerCamelCase				,	bos_token=lowerCamelCase				,	eos_token=lowerCamelCase				,	unk_token=lowerCamelCase				,	sep_token=lowerCamelCase				,	pad_token=lowerCamelCase				,	cls_token=lowerCamelCase				,	mask_token=lowerCamelCase				,	split_by_punct=lowerCamelCase				,	**lowerCamelCase				,	)

        _UpperCamelCase   :				Dict       =     do_lower_case
        _UpperCamelCase   :				int       =     split_by_punct
        _UpperCamelCase   :				Union[str, Any]       =     vocab_file
        _UpperCamelCase   :				int       =     False if not self.vocab_file else True

 def     __snake_case    (	self				:						List[Any]				,	lowerCamelCase				:						Any				,	lowerCamelCase				:						Tuple=None     )       ->							Optional[int]:

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _UpperCamelCase   :				str       =     [self.cls_token_id]
        _UpperCamelCase   :				int       =     [self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep

 def     __snake_case    (	self				:						Union[str, Any]				,	lowerCamelCase				:						Tuple				,	lowerCamelCase				:						Tuple=None				,	lowerCamelCase				:						Union[str, Any]=False     )       ->							Optional[Any]:

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

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

 def     __snake_case    (	self				:						Tuple				,	lowerCamelCase				:						Any				,	lowerCamelCase				:						Union[str, Any]=None     )       ->							Dict:
        _UpperCamelCase   :				Any       =     [self.sep_token_id]
        _UpperCamelCase   :				Union[str, 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     __snake_case    (	self				:						Optional[Any]				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Optional[str] = None     )       ->							Tuple[str]:
        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(lowerCamelCase     ):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
               return
        _UpperCamelCase   :				Union[str, Any]       =     os.path.join(
            lowerCamelCase				,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

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

        return (out_vocab_file,)




import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str ):
       if text.startswith(lowercase_ ):
              return text[len(lowercase_ ) :]
       return text  # or whatever


def SCREAMING_SNAKE_CASE (   lowercase_:		Union[str, Any] ):
       return {remove_prefix(lowercase_						,  'model.' ): v for k, v in sd.items()}


def SCREAMING_SNAKE_CASE (   lowercase_:		List[Dict[str, torch.Tensor]] ):
       _UpperCamelCase   :				Union[str, Any]       =     {}
       for k in state_dicts[0].keys():
              _UpperCamelCase   :				Tuple       =     [sd[k] for sd in state_dicts]
              _UpperCamelCase   :				str       =     sum(lowercase_ ) / len(lowercase_ )
              assert isinstance(lowercase_						,  torch.Tensor )
              _UpperCamelCase   :				Dict       =     new_t
       return new_sd


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str						,  lowercase_:		str ):
       _UpperCamelCase   :				Optional[Any]       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
       if os.path.isfile(lowercase_ ):
              _UpperCamelCase   :				Any       =     [pl_ckpt_path]
       else:
              assert os.path.isdir(lowercase_ )
              _UpperCamelCase   :				str       =     list(Path(lowercase_ ).glob('*.ckpt' ) )
              assert ckpt_files, F"could not find any ckpt files inside the {pl_ckpt_path} directory"

       if len(lowercase_ ) > 1:
              logger.info(F"averaging the weights of {ckpt_files}" )

       _UpperCamelCase   :				Tuple       =     [sanitize(torch.load(lowercase_						,  map_location='cpu' )['state_dict'] ) for x in ckpt_files]
       _UpperCamelCase   :				Optional[Any]       =     average_state_dicts(lowercase_ )

       _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     hf_model.load_state_dict(lowercase_						,  strict=lowercase_ )
       assert not missing, F"missing keys: {missing}"
       hf_model.save_pretrained(lowercase_ )
       try:
              _UpperCamelCase   :				str       =     AutoTokenizer.from_pretrained(lowercase_ )
              tok.save_pretrained(lowercase_ )
       except Exception:
              pass
              # dont copy tokenizer if cant


if __name__ == "__main__":
 fire.Fire(convert_pl_to_hf)



