


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_rembert import RemBertTokenizer
else:
       __UpperCamelCase       :       List[Any]						       =				None

__UpperCamelCase       :       Tuple						       =				logging.get_logger(__name__)
__UpperCamelCase       :       int						       =				{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase       :       Union[str, Any]						       =				{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

__UpperCamelCase       :       int						       =				{
    "google/rembert": 256,
}

__UpperCamelCase       :       List[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[Any]			=      VOCAB_FILES_NAMES
     A:		Optional[int]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Union[str, Any]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		Dict			=      RemBertTokenizer


     def __init__(      self							:     int							,	lowerCamelCase__							:     str=None							,	lowerCamelCase__							:     str=None							,	lowerCamelCase__							:     str=True							,	lowerCamelCase__							:     Any=True							,	lowerCamelCase__							:     Dict=False							,	lowerCamelCase__							:     Any="[CLS]"							,	lowerCamelCase__							:     Tuple="[SEP]"							,	lowerCamelCase__							:     Optional[Any]="<unk>"							,	lowerCamelCase__							:     Tuple="[SEP]"							,	lowerCamelCase__							:     Optional[Any]="<pad>"							,	lowerCamelCase__							:     Optional[Any]="[CLS]"							,	lowerCamelCase__							:     Any="[MASK]"							,	**lowerCamelCase__							:     List[str]							,	)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

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

      UpperCamelCase__					:    Any          =   do_lower_case
      UpperCamelCase__					:    Optional[Any]          =   remove_space
      UpperCamelCase__					:    str          =   keep_accents
      UpperCamelCase__					:    str          =   vocab_file
      UpperCamelCase__					:    List[str]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   [self.sep_token_id]
      UpperCamelCase__					:    Dict          =   [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      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None							,	lowerCamelCase__							:     bool = False			)  ->      List[int]:





      '''simple docstring'''






      if already_has_special_tokens:
       if token_ids_a is not None:
        raise ValueError(
            '''You should not supply a second sequence if the provided sequence of '''
            '''ids is already formatted with special tokens for the model.'''			)
       return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   [self.sep_token_id]
      UpperCamelCase__					:    Optional[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			) * [0] + len(token_ids_a + sep			) * [1]


     def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      if not os.path.isdir(lowerCamelCase__			):
       logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase__			)			)
       return
      UpperCamelCase__					:    int          =   os.path.join(
          lowerCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

      return (out_vocab_file,)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__UpperCamelCase       :       Dict						       =				{
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TrOCRForCausalLM",
           "TrOCRPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

       __UpperCamelCase       :       Tuple						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


