





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE     :Any       =				{
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :Union[str, Any]       =				['''RemBertTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :List[str]       =				['''RemBertTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :int       =				[
      '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''RemBertForCausalLM''',
      '''RemBertForMaskedLM''',
      '''RemBertForMultipleChoice''',
      '''RemBertForQuestionAnswering''',
      '''RemBertForSequenceClassification''',
      '''RemBertForTokenClassification''',
      '''RemBertLayer''',
      '''RemBertModel''',
      '''RemBertPreTrainedModel''',
      '''load_tf_weights_in_rembert''',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :Optional[Any]       =				[
      '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFRemBertForCausalLM''',
      '''TFRemBertForMaskedLM''',
      '''TFRemBertForMultipleChoice''',
      '''TFRemBertForQuestionAnswering''',
      '''TFRemBertForSequenceClassification''',
      '''TFRemBertForTokenClassification''',
      '''TFRemBertLayer''',
      '''TFRemBertModel''',
      '''TFRemBertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert import RemBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert_fast import RemBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rembert import (
        REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RemBertForCausalLM,
        RemBertForMaskedLM,
        RemBertForMultipleChoice,
        RemBertForQuestionAnswering,
        RemBertForSequenceClassification,
        RemBertForTokenClassification,
        RemBertLayer,
        RemBertModel,
        RemBertPreTrainedModel,
        load_tf_weights_in_rembert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_rembert import (
        TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRemBertForCausalLM,
        TFRemBertForMaskedLM,
        TFRemBertForMultipleChoice,
        TFRemBertForQuestionAnswering,
        TFRemBertForSequenceClassification,
        TFRemBertForTokenClassification,
        TFRemBertLayer,
        TFRemBertModel,
        TFRemBertPreTrainedModel,
    )


else:
  import sys

  __SCREAMING_SNAKE_CASE     :Any       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










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_fnet import FNetTokenizer
else:
  _snake_case										=       None

_snake_case										=       logging.get_logger(__name__)
_snake_case										=       {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_snake_case										=       {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

_snake_case										=       {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

_snake_case										=       "▁"







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						VOCAB_FILES_NAMES
     _a							 =						PRETRAINED_VOCAB_FILES_MAP
     _a							 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _a							 =						["input_ids", "token_type_ids"]
     _a							 =						FNetTokenizer

     def __init__(  self		,     _a=None		,     _a=None		,     _a=False		,     _a=True		,     _a=True		,     _a="<unk>"		,     _a="[SEP]"		,     _a="<pad>"		,     _a="[CLS]"		,     _a="[MASK]"		,     **_a		,     )						->							Optional[int]:
           # Mask token behave like a normal word, i.e. include the space before it and
           # is included in the raw text, there should be a match in a non-normalized sentence.
           _A							:     int			=						(
               AddedToken(_a		,     lstrip=_a		,     rstrip=_a		,     normalized=_a				)
               if isinstance(_a		,     _a				)
               else mask_token
           )

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

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

     def 					a__	(  self		,     _a		,     _a = None				)						->							List[int]:
           _A							:     str			=						[self.sep_token_id]
           _A							:     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 					a__	(  self		,     _a		,     _a = None				)						->							List[int]:
           _A							:     Any			=						[self.sep_token_id]
           _A							:     Any			=						[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 					a__	(  self		,     _a		,     _a = None				)						->							Tuple[str]:
           if not os.path.isdir(_a				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                 return
           _A							:     List[str]			=						os.path.join(
               _a		,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

           return (out_vocab_file,)

