
'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


A__ :		Union[str, Any]					=logging.get_logger(__name__)

A__ :		List[str]					={"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

A__ :		List[str]					={
    "vocab_file": {
        "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json",
    },
}

A__ :		Optional[int]					={
    "microsoft/mpnet-base": 512,
}

A__ :		Dict					={
    "microsoft/mpnet-base": {"do_lower_case": True},
}







class 			__A					(			__lowerCamelCase      ):
 lowerCamelCase												=VOCAB_FILES_NAMES
 lowerCamelCase												=PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase												=PRETRAINED_INIT_CONFIGURATION
 lowerCamelCase												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase												=MPNetTokenizer
 lowerCamelCase												=['''input_ids''', '''attention_mask''']
 def __init__( self      :     Tuple					, lowerCamelCase      :     List[Any]=None					, lowerCamelCase      :     str=None					, lowerCamelCase      :     Tuple=True					, lowerCamelCase      :     Optional[int]="<s>"					, lowerCamelCase      :     Optional[int]="</s>"					, lowerCamelCase      :     Tuple="</s>"					, lowerCamelCase      :     int="<s>"					, lowerCamelCase      :     Optional[int]="[UNK]"					, lowerCamelCase      :     Tuple="<pad>"					, lowerCamelCase      :     str="<mask>"					, lowerCamelCase      :     int=True					, lowerCamelCase      :     List[Any]=None					, **lowerCamelCase      :     Tuple					, ):



   """simple docstring"""





   super().__init__(
       lowerCamelCase					, tokenizer_file=lowerCamelCase					, do_lower_case=lowerCamelCase					, bos_token=lowerCamelCase					, eos_token=lowerCamelCase					, sep_token=lowerCamelCase					, cls_token=lowerCamelCase					, unk_token=lowerCamelCase					, pad_token=lowerCamelCase					, mask_token=lowerCamelCase					, tokenize_chinese_chars=lowerCamelCase					, strip_accents=lowerCamelCase					, **lowerCamelCase					, )

   __A		:  Optional[Any]       				=		json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
   if (
       pre_tok_state.get("""lowercase"""					, lowerCamelCase							) != do_lower_case
       or pre_tok_state.get("""strip_accents"""					, lowerCamelCase							) != strip_accents
   ):
     __A		:  str       				=		getattr(lowerCamelCase					, pre_tok_state.pop("""type"""							)							)
     __A		:  str       				=		do_lower_case
     __A		:  List[str]       				=		strip_accents
     __A		:  int       				=		pre_tok_class(**lowerCamelCase							)

   __A		:  int       				=		do_lower_case
 @property
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   if self._mask_token is None:
     if self.verbose:
       logger.error("""Using mask_token, but it is not set yet."""							)
     return None
   return str(self._mask_token							)
 @mask_token.setter
 def 		lowercase_( self      :     List[Any]					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   __A		:  str       				=		AddedToken(lowerCamelCase					, lstrip=lowerCamelCase					, rstrip=lowerCamelCase							) if isinstance(lowerCamelCase					, lowerCamelCase							) else value
   __A		:  Union[str, Any]       				=		value
 def 		lowercase_( self      :     int					, lowerCamelCase      :     str					, lowerCamelCase      :     Union[str, Any]=None							):



   """simple docstring"""





   __A		:  List[Any]       				=		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
   if token_ids_a is None:
     return output

   return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
 def 		lowercase_( self      :     Tuple					, lowerCamelCase      :     List[int]					, lowerCamelCase      :     Optional[List[int]] = None							):



   """simple docstring"""





   __A		:  Any       				=		[self.sep_token_id]
   __A		:  int       				=		[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 + sep + token_ids_a + sep							) * [0]






 def 		lowercase_( self      :     Optional[Any]					, lowerCamelCase      :     str					, lowerCamelCase      :     Optional[str] = None							):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		self._tokenizer.model.save(lowerCamelCase					, name=lowerCamelCase							)
   return tuple(lowerCamelCase							)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase			:  List[str]										=				{"configuration_levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig", "LevitOnnxConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  Any										=				["LevitFeatureExtractor"]
      __UpperCAmelCase			:  Tuple										=				["LevitImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  str										=				[
          "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LevitForImageClassification",
          "LevitForImageClassificationWithTeacher",
          "LevitModel",
          "LevitPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_levit import LevitFeatureExtractor
            from .image_processing_levit import LevitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_levit import (
                LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                LevitForImageClassification,
                LevitForImageClassificationWithTeacher,
                LevitModel,
                LevitPreTrainedModel,
            )
else:
      import sys

      __UpperCAmelCase			:  Union[str, Any]										=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






