




import torch
from torch import autograd



class 			a     (   autograd.Function			):

     '''simple docstring'''





     @staticmethod
     def   lowerCamelCase_   (	__snake_case   :			List[str]					,    __snake_case   :			torch.tensor					,    __snake_case   :			float					,    __snake_case   :			bool				):
         UpperCAmelCase_							 =     inputs.numel()
         UpperCAmelCase_							 =     int(0.005 * nb_elems				) + 1
         if sigmoid:
             UpperCAmelCase_							 =     (torch.sigmoid(__snake_case				) > threshold).type(inputs.type()				)
         else:
             UpperCAmelCase_							 =     (inputs > threshold).type(inputs.type()				)
         if mask.sum() < nb_min:
             # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
             UpperCAmelCase_							 =     inputs.flatten().kthvalue(max(nb_elems - nb_min					,    1				)				).values
             UpperCAmelCase_							 =     (inputs > k_threshold).type(inputs.type()				)
         return mask




     @staticmethod
     def   lowerCamelCase_   (	__snake_case   :			Dict					,    __snake_case   :			Tuple				):
         return gradOutput, None, None



class 			a     (   autograd.Function			):

     '''simple docstring'''





     @staticmethod
     def   lowerCamelCase_   (	__snake_case   :			str					,    __snake_case   :			torch.tensor					,    __snake_case   :			float				):
         UpperCAmelCase_							 =     inputs.clone()
         UpperCAmelCase_ ,							UpperCAmelCase_							 =     inputs.flatten().sort(descending=__snake_case				)
         UpperCAmelCase_							 =     int(threshold * inputs.numel()				)

         # flat_out and mask access the same memory.
         UpperCAmelCase_							 =     mask.flatten()
         UpperCAmelCase_							 =     0
         UpperCAmelCase_							 =     1
         return mask




     @staticmethod
     def   lowerCamelCase_   (	__snake_case   :			List[Any]					,    __snake_case   :			Any				):
         return gradOutput, None



class 			a     (   _SCREAMING_SNAKE_CASE			):

     '''simple docstring'''





     @staticmethod
     def   lowerCamelCase_   (	__snake_case   :			torch.tensor					,    __snake_case   :			float				):
         UpperCAmelCase_							 =     inputs.clone()
         UpperCAmelCase_ ,							UpperCAmelCase_							 =     inputs.abs().flatten().sort(descending=__snake_case				)
         UpperCAmelCase_							 =     int(threshold * inputs.numel()				)

         # flat_out and mask access the same memory.
         UpperCAmelCase_							 =     mask.flatten()
         UpperCAmelCase_							 =     0
         UpperCAmelCase_							 =     1
         return mask











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:
 a_      						=   None

a_      						=   logging.get_logger(__name__)

a_      						=   {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

a_      						=   {
    "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"
        ),
    }
}

a_      						=   {
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

a_      						=   {
    "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  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       int			=    VOCAB_FILES_NAMES
      lowerCAmelCase__	:       Any			=    PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__	:       Optional[Any]			=    PRETRAINED_INIT_CONFIGURATION
      lowerCAmelCase__	:       int			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__	:       Dict			=    DebertaVaTokenizer







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

          __UpperCamelCase     						=  do_lower_case
          __UpperCamelCase     						=  split_by_punct
          __UpperCamelCase     						=  vocab_file
          __UpperCamelCase     						=  False if not self.vocab_file else True







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       int       ,				snake_case    :       Tuple=None ):

          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 					snake_case		(   self    :       Any       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Optional[Any]=None       ,				snake_case    :       Union[str, Any]=False ):

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

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







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       Optional[int]       ,				snake_case    :       Optional[Any]=None ):
          __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 					snake_case		(   self    :       List[str]       ,				snake_case    :       str       ,				snake_case    :       Optional[str] = None ):
          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(snake_case ):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
              return
          __UpperCamelCase     						=  os.path.join(
              snake_case       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

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

          return (out_vocab_file,)






