


import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {'''vocab_file''': '''vocab.json'''}

_snake_case             =     {
    '''vocab_file''': {
        '''mgp-str''': '''https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json''',
    }
}

_snake_case             =     {'''mgp-str''': 27}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      List[Any]     		=		VOCAB_FILES_NAMES
   __A :      List[Any]     		=		PRETRAINED_VOCAB_FILES_MAP
   __A :      int     		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






   def __init__(		self						,				__A						,				__A="[GO]"						,				__A="[GO]"						,				__A="[s]"						,				__A="[GO]"						,				**__A ):






     """simple docstring"""





     super().__init__(
         unk_token=__A						,				bos_token=__A						,				eos_token=__A						,				pad_token=__A						,				**__A						,				)

     with open(__A						,				encoding="utf-8" ) as vocab_handle:
       lowerCamelCase :					int    =				json.load(__A )
     lowerCamelCase :					Optional[Any]    =				{v: k for k, v in self.vocab.items()}






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return len(self.vocab )






   def _snake_case    (		self ):






     """simple docstring"""





     return dict(self.vocab						,				**self.added_tokens_encoder )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				[]
     for s in text:
       char_tokens.extend(__A )
     return char_tokens






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return self.vocab.get(__A						,				self.vocab.get(self.unk_token ) )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return self.decoder.get(__A )






   def _snake_case    (		self						,				__A						,				__A = None ):






     """simple docstring"""





     if not os.path.isdir(__A ):
       logger.error("Vocabulary path ({}) should be a directory".format(__A ) )
       return
     lowerCamelCase :					List[Any]    =				os.path.join(
         __A						,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

     with open(__A						,				"w"						,				encoding="utf-8" ) as f:
       f.write(json.dumps(self.vocab						,				indent=2						,				sort_keys=__A						,				ensure_ascii=__A ) + "\n" )

     return (vocab_file,)







from typing import TYPE_CHECKING

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


_snake_case             =     {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   _snake_case             =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



