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:
    lowerCAmelCase			:							Dict     =None

lowerCAmelCase			:							Union[str, Any]     =logging.get_logger(__name__)
lowerCAmelCase			:							Optional[int]     ={"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase			:							Optional[int]     ={
    "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",
    },
}

lowerCAmelCase			:							List[str]     ={
    "google/rembert": 256,
}

lowerCAmelCase			:							Dict     ="▁"




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case    		=       RemBertTokenizer



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


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						AddedToken(_UpperCamelCase    ,  lstrip=_UpperCamelCase    ,  rstrip=_UpperCamelCase) if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else mask_token

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

          _lowerCamelCase     :		Tuple 						=						do_lower_case
          _lowerCamelCase     :		Tuple 						=						remove_space
          _lowerCamelCase     :		Union[str, Any] 						=						keep_accents
          _lowerCamelCase     :		Union[str, Any] 						=						vocab_file
          _lowerCamelCase     :		Tuple 						=						False if not self.vocab_file else True



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				Optional[List[int]] = None)				->List[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						[self.sep_token_id]
          _lowerCamelCase     :		Any 						=						[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 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				Optional[List[int]] = None    ,  _UpperCamelCase     :				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(_UpperCamelCase)) + [1] + ([0] * len(_UpperCamelCase)) + [1]
          return [1] + ([0] * len(_UpperCamelCase)) + [1]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				Optional[List[int]] = None)				->List[int]:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						[self.sep_token_id]
          _lowerCamelCase     :		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 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


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

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

          return (out_vocab_file,)





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				Dict    ,  _UpperCamelCase     :				NestedDataStructureLike[PathLike]    ,  _UpperCamelCase     :				Optional[NamedSplit] = None    ,  _UpperCamelCase     :				Optional[Features] = None    ,  _UpperCamelCase     :				str = None    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				Optional[int] = None    ,  **_UpperCamelCase     :				Tuple    ,  )				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              _UpperCamelCase    ,  split=_UpperCamelCase    ,  features=_UpperCamelCase    ,  cache_dir=_UpperCamelCase    ,  keep_in_memory=_UpperCamelCase    ,  streaming=_UpperCamelCase    ,  num_proc=_UpperCamelCase    ,  **_UpperCamelCase    ,  )
          _lowerCamelCase     :		List[Any] 						=						path_or_paths if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else {self.split: path_or_paths}
          _lowerCamelCase     :		Any 						=						Text(
              cache_dir=_UpperCamelCase    ,  data_files=_UpperCamelCase    ,  features=_UpperCamelCase    ,  **_UpperCamelCase    ,  )




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          if self.streaming:
              _lowerCamelCase     :		Tuple 						=						self.builder.as_streaming_dataset(split=self.split)
          # Build regular (map-style) dataset
          else:
              _lowerCamelCase     :		List[Any] 						=						None
              _lowerCamelCase     :		Any 						=						None
              _lowerCamelCase     :		List[str] 						=						None
              _lowerCamelCase     :		Dict 						=						None

              self.builder.download_and_prepare(
                  download_config=_UpperCamelCase    ,  download_mode=_UpperCamelCase    ,  verification_mode=_UpperCamelCase    ,  base_path=_UpperCamelCase    ,  num_proc=self.num_proc    ,  )
              _lowerCamelCase     :		Optional[int] 						=						self.builder.as_dataset(
                  split=self.split    ,  verification_mode=_UpperCamelCase    ,  in_memory=self.keep_in_memory)
          return dataset




