"""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:
     _SCREAMING_SNAKE_CASE	:		Tuple   =   None

_SCREAMING_SNAKE_CASE	:		Dict   =   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   {'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE	:		Tuple   =   {
    '''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'''
        ),
    }
}

_SCREAMING_SNAKE_CASE	:		Tuple   =   {
    '''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,
}

_SCREAMING_SNAKE_CASE	:		Optional[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      _SCREAMING_SNAKE_CASE							(			lowercase__				):



   '''simple docstring'''



   __UpperCAmelCase	:							Optional[int]    =    VOCAB_FILES_NAMES
   __UpperCAmelCase	:							Dict    =    PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase	:							List[str]    =    PRETRAINED_INIT_CONFIGURATION
   __UpperCAmelCase	:							Tuple    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase	:							Optional[Any]    =    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 ,				):


      '''simple docstring'''

      super().__init__(
          lowercase_ ,				tokenizer_file=lowercase_ ,				do_lower_case=lowercase_ ,				bos_token=lowercase_ ,				eos_token=lowercase_ ,				unk_token=lowercase_ ,				sep_token=lowercase_ ,				pad_token=lowercase_ ,				cls_token=lowercase_ ,				mask_token=lowercase_ ,				split_by_punct=lowercase_ ,				**lowercase_ ,				)

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=None	):


      '''simple docstring'''

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=False	):


      '''simple docstring'''

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

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=None	):


      '''simple docstring'''

      __lowerCamelCase    		=					[self.sep_token_id]
      __lowerCamelCase    		=					[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    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None	):


      '''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(lowercase_	):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
         return
      __lowerCamelCase    		=					os.path.join(
          lowercase_ ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

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

      return (out_vocab_file,)








"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a									=				logging.get_logger(__name__)

_a									=				{
    'squeezebert/squeezebert-uncased': (
        'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
    ),
    'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
    'squeezebert/squeezebert-mnli-headless': (
        'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
    ),
}


class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Any    	=						SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						"""squeezebert"""







  def __init__(						self							,  lowercase_=3_0522							,  lowercase_=768							,  lowercase_=12							,  lowercase_=12							,  lowercase_=3072							,  lowercase_="gelu"							,  lowercase_=0.1							,  lowercase_=0.1							,  lowercase_=512							,  lowercase_=2							,  lowercase_=0.02							,  lowercase_=1E-1_2							,  lowercase_=0							,  lowercase_=768							,  lowercase_=4							,  lowercase_=4							,  lowercase_=4							,  lowercase_=1							,  lowercase_=4							,  lowercase_=4							,  **lowercase_							,  ):

     """simple docstring"""
     super().__init__(pad_token_id=lowercase_							,  **lowercase_    )

     UpperCAmelCase_   :   List[Any]     	=       vocab_size
     UpperCAmelCase_   :   Optional[Any]     	=       hidden_size
     UpperCAmelCase_   :   Tuple     	=       num_hidden_layers
     UpperCAmelCase_   :   Optional[int]     	=       num_attention_heads
     UpperCAmelCase_   :   Optional[Any]     	=       hidden_act
     UpperCAmelCase_   :   Tuple     	=       intermediate_size
     UpperCAmelCase_   :   Union[str, Any]     	=       hidden_dropout_prob
     UpperCAmelCase_   :   List[Any]     	=       attention_probs_dropout_prob
     UpperCAmelCase_   :   int     	=       max_position_embeddings
     UpperCAmelCase_   :   List[str]     	=       type_vocab_size
     UpperCAmelCase_   :   Optional[Any]     	=       initializer_range
     UpperCAmelCase_   :   Optional[int]     	=       layer_norm_eps
     UpperCAmelCase_   :   Tuple     	=       embedding_size
     UpperCAmelCase_   :   Dict     	=       q_groups
     UpperCAmelCase_   :   List[Any]     	=       k_groups
     UpperCAmelCase_   :   List[str]     	=       v_groups
     UpperCAmelCase_   :   Optional[Any]     	=       post_attention_groups
     UpperCAmelCase_   :   List[str]     	=       intermediate_groups
     UpperCAmelCase_   :   List[Any]     	=       output_groups




class A_      (lowercase__ ):




  '''simple docstring'''







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     if self.task == "multiple-choice":
        UpperCAmelCase_   :   List[str]     	=       {0: "batch", 1: "choice", 2: "sequence"}
     else:
        UpperCAmelCase_   :   Optional[int]     	=       {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
             ("token_type_ids", dynamic_axis),
         ]    )


