'''simple docstring'''

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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_fnet import FNetTokenizer
else:
 _A			:      List[Any]					      =				None

_A			:      List[str]					      =				logging.get_logger(__name__)
_A			:      Optional[Any]					      =				{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_A			:      Dict					      =				{
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

_A			:      Optional[Any]					      =				{
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

_A			:      Optional[Any]					      =				"▁"







class 				_lowercase     (				lowerCAmelCase_			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Any      	=					VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE     :       Dict      	=					PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE     :       str      	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE     :       int      	=					["""input_ids""", """token_type_ids"""]
 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					FNetTokenizer
 def __init__(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Dict=None      ,       SCREAMING_SNAKE_CASE__   :	str=None      ,       SCREAMING_SNAKE_CASE__   :	int=False      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=True      ,       SCREAMING_SNAKE_CASE__   :	Tuple=True      ,       SCREAMING_SNAKE_CASE__   :	Dict="<unk>"      ,       SCREAMING_SNAKE_CASE__   :	Any="[SEP]"      ,       SCREAMING_SNAKE_CASE__   :	List[str]="<pad>"      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]="[CLS]"      ,       SCREAMING_SNAKE_CASE__   :	List[str]="[MASK]"      ,       **SCREAMING_SNAKE_CASE__   :	Any      ,       )  ->    Tuple:
  # Mask token behave like a normal word, i.e. include the space before it and
  # is included in the raw text, there should be a match in a non-normalized sentence.
  __lowerCAmelCase											=						(
      AddedToken(_snake_case      ,       lstrip=_snake_case      ,       rstrip=_snake_case      ,       normalized=_snake_case							)
      if isinstance(_snake_case      ,       _snake_case							)
      else mask_token
  )

  super().__init__(
      _snake_case      ,       tokenizer_file=_snake_case      ,       do_lower_case=_snake_case      ,       remove_space=_snake_case      ,       keep_accents=_snake_case      ,       unk_token=_snake_case      ,       sep_token=_snake_case      ,       pad_token=_snake_case      ,       cls_token=_snake_case      ,       mask_token=_snake_case      ,       **_snake_case      ,       )

  __lowerCAmelCase											=						do_lower_case
  __lowerCAmelCase											=						remove_space
  __lowerCAmelCase											=						keep_accents
  __lowerCAmelCase											=						vocab_file
  __lowerCAmelCase											=						False if not self.vocab_file else True
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	List[int]      ,       SCREAMING_SNAKE_CASE__   :	Optional[List[int]] = None							)  ->    List[int]:
  __lowerCAmelCase											=						[self.sep_token_id]
  __lowerCAmelCase											=						[self.cls_token_id]
  if token_ids_a is None:
   return cls + token_ids_a + sep
  return cls + token_ids_a + sep + token_ids_a + sep
 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[int]      ,       SCREAMING_SNAKE_CASE__   :	Optional[List[int]] = None							)  ->    List[int]:
  __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   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	Optional[str] = None							)  ->    Tuple[str]:
  if not os.path.isdir(_snake_case							):
   logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
   return
  __lowerCAmelCase											=						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,)











from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a_   :Tuple        =      logging.get_logger(__name__)

a_   :Union[str, Any]        =      {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""deberta-v2"""






     def __init__(       self  :  Union[str, Any], _snake_case  :  Dict=1_2_8_1_0_0, _snake_case  :  Any=1_5_3_6, _snake_case  :  Tuple=2_4, _snake_case  :  int=2_4, _snake_case  :  Optional[int]=6_1_4_4, _snake_case  :  Optional[int]="gelu", _snake_case  :  Optional[int]=0.1, _snake_case  :  List[str]=0.1, _snake_case  :  str=5_1_2, _snake_case  :  Optional[int]=0, _snake_case  :  Optional[int]=0.0_2, _snake_case  :  Dict=1e-7, _snake_case  :  int=False, _snake_case  :  Any=-1, _snake_case  :  List[str]=0, _snake_case  :  Tuple=True, _snake_case  :  Any=None, _snake_case  :  Union[str, Any]=0, _snake_case  :  Tuple="gelu", **_snake_case  :  Union[str, Any], )   ->Optional[int]:
            super().__init__(**_snake_case					)

            snake_case__							:	Dict   	=	hidden_size
            snake_case__							:	Optional[int]   	=	num_hidden_layers
            snake_case__							:	Any   	=	num_attention_heads
            snake_case__							:	List[Any]   	=	intermediate_size
            snake_case__							:	List[Any]   	=	hidden_act
            snake_case__							:	Union[str, Any]   	=	hidden_dropout_prob
            snake_case__							:	Dict   	=	attention_probs_dropout_prob
            snake_case__							:	List[str]   	=	max_position_embeddings
            snake_case__							:	List[str]   	=	type_vocab_size
            snake_case__							:	Optional[Any]   	=	initializer_range
            snake_case__							:	Optional[int]   	=	relative_attention
            snake_case__							:	Tuple   	=	max_relative_positions
            snake_case__							:	Union[str, Any]   	=	pad_token_id
            snake_case__							:	Optional[int]   	=	position_biased_input

            # Backwards compatibility
            if type(_snake_case					) == str:
                   snake_case__							:	int   	=	[x.strip() for x in pos_att_type.lower().split('|'					)]

            snake_case__							:	List[str]   	=	pos_att_type
            snake_case__							:	Union[str, Any]   	=	vocab_size
            snake_case__							:	Optional[int]   	=	layer_norm_eps

            snake_case__							:	Optional[int]   	=	kwargs.get('pooler_hidden_size', _snake_case					)
            snake_case__							:	int   	=	pooler_dropout
            snake_case__							:	str   	=	pooler_hidden_act




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     @property
     def    lowercase_   (       self  :  Optional[int]					)   ->Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                   snake_case__							:	List[Any]   	=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   snake_case__							:	int   	=	{0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]					)
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]					)






     @property
     def    lowercase_   (       self  :  Dict					)   ->int:
            return 1_2






     def    lowercase_   (       self  :  Tuple, _snake_case  :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  bool = False, _snake_case  :  Optional["TensorType"] = None, _snake_case  :  int = 3, _snake_case  :  int = 4_0, _snake_case  :  int = 4_0, _snake_case  :  "PreTrainedTokenizerBase" = None, )   ->Mapping[str, Any]:
            snake_case__							:	Union[str, Any]   	=	super().generate_dummy_inputs(preprocessor=_snake_case, framework=_snake_case					)
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs






