

import json
import os
from typing import Optional, Tuple

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


_UpperCAmelCase		:   Optional[int]										=       logging.get_logger(__name__)

_UpperCAmelCase		:   Dict										=       {"""vocab_file""": """vocab.json"""}

_UpperCAmelCase		:   List[Any]										=       {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

_UpperCAmelCase		:   Dict										=       {"""mgp-str""": 27}







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE					: int       			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




				def __init__(					self      ,   snake_case      ,   snake_case="[GO]"      ,   snake_case="[GO]"      ,   snake_case="[s]"      ,   snake_case="[GO]"      ,   **snake_case       ):
											super().__init__(
											    unk_token=__A      ,   bos_token=__A      ,   eos_token=__A      ,   pad_token=__A      ,   **__A      ,   )

											with open(__A      ,   encoding='utf-8'       ) as vocab_handle:
																		snake_case_							=    json.load(__A       )
											snake_case_							=    {v: k for k, v in self.vocab.items()}




				@property
				def     a							(					self       ):
											return len(self.vocab       )




				def     a							(					self       ):
											return dict(self.vocab      ,   **self.added_tokens_encoder       )




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    []
											for s in text:
																		char_tokens.extend(__A       )
											return char_tokens




				def     a							(					self      ,   snake_case       ):
											return self.vocab.get(__A      ,   self.vocab.get(self.unk_token       )       )




				def     a							(					self      ,   snake_case       ):
											return self.decoder.get(__A       )




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):





											if not os.path.isdir(__A       ):
																		logger.error('Vocabulary path ({}) should be a directory'.format(__A       )       )
																		return
											snake_case_							=    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__)



