



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:
 a_   :List[Any]        =      None

a_   :List[str]        =      logging.get_logger(__name__)
a_   :Optional[Any]        =      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_   :Dict        =      {
    "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",
    },
}

a_   :Optional[Any]        =      {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

a_   :Optional[Any]        =      "▁"




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						=	PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						=	["""input_ids""", """token_type_ids"""]
     _SCREAMING_SNAKE_CASE						=	FNetTokenizer






     def __init__(       self  :  Tuple, _snake_case  :  Dict=None, _snake_case  :  str=None, _snake_case  :  int=False, _snake_case  :  Optional[int]=True, _snake_case  :  Tuple=True, _snake_case  :  Dict="<unk>", _snake_case  :  Any="[SEP]", _snake_case  :  List[str]="<pad>", _snake_case  :  Union[str, Any]="[CLS]", _snake_case  :  List[str]="[MASK]", **_snake_case  :  Any, )   ->Tuple:
            # 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.
            snake_case__							:	Optional[Any]   	=	(
                AddedToken(_snake_case, lstrip=_snake_case, rstrip=_snake_case, normalized=_snake_case					)
                if isinstance(_snake_case, _snake_case					)
                else mask_token
            )

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

            snake_case__							:	List[str]   	=	do_lower_case
            snake_case__							:	str   	=	remove_space
            snake_case__							:	Dict   	=	keep_accents
            snake_case__							:	Union[str, Any]   	=	vocab_file
            snake_case__							:	str   	=	False if not self.vocab_file else True






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

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

            return (out_vocab_file,)











from typing import TYPE_CHECKING

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


a_   :Optional[Any]        =      {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :str        =      ["ReformerTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      ["ReformerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ReformerAttention",
     "ReformerForMaskedLM",
     "ReformerForQuestionAnswering",
     "ReformerForSequenceClassification",
     "ReformerLayer",
     "ReformerModel",
     "ReformerModelWithLMHead",
     "ReformerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer import ReformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer_fast import ReformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_reformer import (
      REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ReformerAttention,
      ReformerForMaskedLM,
      ReformerForQuestionAnswering,
      ReformerForSequenceClassification,
      ReformerLayer,
      ReformerModel,
      ReformerModelWithLMHead,
      ReformerPreTrainedModel,
  )

else:
 import sys

 a_   :Optional[Any]        =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






