





'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


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

__lowerCAmelCase							:     str		     ="▁"

__lowerCAmelCase							:     Optional[int]		     ={"vocab_file": "sentencepiece.bpe.model"}

__lowerCAmelCase							:     Union[str, Any]		     ={
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

__lowerCAmelCase							:     List[Any]		     ={
    "facebook/xglm-564M": 2048,
}






class 					UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    VOCAB_FILES_NAMES
 __lowercase       		=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase       		=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase       		=    ["""input_ids""", """attention_mask"""]
 def __init__(    self				:Union[str, Any]						,	lowercase_				:List[str]						,	lowercase_				:List[str]="<s>"						,	lowercase_				:List[str]="</s>"						,	lowercase_				:List[str]="</s>"						,	lowercase_				:List[str]="<s>"						,	lowercase_				:Any="<unk>"						,	lowercase_				:Union[str, Any]="<pad>"						,	lowercase_				:Optional[Dict[str, Any]] = None						,	**lowercase_				:Union[str, Any]						,	)->							None:
     A__    =      {} if sp_model_kwargs is None else sp_model_kwargs

     # Compatibility with the original tokenizer
     A__    =      7
     A__    =      [F"<madeupword{i}>" for i in range(self.num_madeup_words							)]

     A__    =      kwargs.get("additional_special_tokens"						,	[]							)
     kwargs["additional_special_tokens"] += [
         word for word in madeup_words if word not in kwargs["additional_special_tokens"]
     ]

     super().__init__(
         bos_token=lowercase_						,	eos_token=lowercase_						,	unk_token=lowercase_						,	sep_token=lowercase_						,	cls_token=lowercase_						,	pad_token=lowercase_						,	sp_model_kwargs=self.sp_model_kwargs						,	**lowercase_						,	)

     A__    =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
     self.sp_model.Load(str(lowercase_							)							)
     A__    =      vocab_file

     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
     # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
     # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

     # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
     A__    =      1

     # Mimic fairseq token-to-id alignment for the first 4 token
     A__    =      {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

     A__    =      len(self.sp_model							)
     A__    =      {F"<madeupword{i}>": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
     self.fairseq_tokens_to_ids.update(lowercase_							)

     A__    =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def __getstate__(    self				:Tuple							)->							Optional[Any]:
     A__    =      self.__dict__.copy()
     A__    =      None
     A__    =      self.sp_model.serialized_model_proto()
     return state
 def __setstate__(    self				:List[str]						,	lowercase_				:Optional[Any]							)->							str:
     A__    =      d

     # for backward compatibility
     if not hasattr(self						,	"sp_model_kwargs"							):
         A__    =      {}

     A__    =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:List[int]						,	lowercase_				:Optional[List[int]] = None							)->							List[int]:

     if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
     A__    =      [self.sep_token_id]
     return sep + token_ids_a + sep + sep + token_ids_a
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:List[int]						,	lowercase_				:Optional[List[int]] = None						,	lowercase_				:bool = False							)->							List[int]:

     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowercase_						,	token_ids_a=lowercase_						,	already_has_special_tokens=lowercase_							)

     if token_ids_a is None:
         return [1] + ([0] * len(lowercase_							))
     return [1] + ([0] * len(lowercase_							)) + [1, 1] + ([0] * len(lowercase_							))
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:List[int]						,	lowercase_				:Optional[List[int]] = None							)->							List[int]:
     A__    =      [self.sep_token_id]

     if token_ids_a is None:
         return len(sep + token_ids_a							) * [0]
     return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]
 @property
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Any:
     A__    =      {self.convert_ids_to_tokens(lowercase_							): i for i in range(self.vocab_size							)}
     vocab.update(self.added_tokens_encoder							)
     return vocab
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:str							)->							List[str]:
     return self.sp_model.encode(lowercase_						,	out_type=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:Optional[Any]							)->							int:
     if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
     A__    =      self.sp_model.PieceToId(lowercase_							)

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:Optional[Any]							)->							Tuple:
     if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:str							)->							Union[str, Any]:
     A__    =      "".join(lowercase_							).replace(lowercase_						,	" "							).strip()
     return out_string



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

     if os.path.abspath(self.vocab_file							) != os.path.abspath(lowercase_							) and os.path.isfile(self.vocab_file							):
         copyfile(self.vocab_file						,	lowercase_							)
     elif not os.path.isfile(self.vocab_file							):
         with open(lowercase_						,	"wb"							) as fi:
             A__    =      self.sp_model.serialized_model_proto()
             fi.write(lowercase_							)

     return (out_vocab_file,)










'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase							:     Any		     ={
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:     Tuple		     =[
      "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Swinv2ForImageClassification",
      "Swinv2ForMaskedImageModeling",
      "Swinv2Model",
      "Swinv2PreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swinva import (
        SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinvaForImageClassification,
        SwinvaForMaskedImageModeling,
        SwinvaModel,
        SwinvaPreTrainedModel,
    )


else:
  import sys

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



