


'''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


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

a_   :      Optional[Any]				    =      "▁"

a_   :      int				    =      {"vocab_file": "sentencepiece.bpe.model"}

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

a_   :      Optional[int]				    =      {
    "facebook/xglm-564M": 2_0_4_8,
}




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		VOCAB_FILES_NAMES
 _lowerCAmelCase	   =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	   =		["""input_ids""", """attention_mask"""]
 def __init__(	self ,			__magic_name__ ,			__magic_name__="<s>" ,			__magic_name__="</s>" ,			__magic_name__="</s>" ,			__magic_name__="<s>" ,			__magic_name__="<unk>" ,			__magic_name__="<pad>" ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							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=__magic_name__ ,			eos_token=__magic_name__ ,			unk_token=__magic_name__ ,			sep_token=__magic_name__ ,			cls_token=__magic_name__ ,			pad_token=__magic_name__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**__magic_name__ ,			)

      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(str(__magic_name__  )  )
      _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(__magic_name__  )

      _a      =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def __getstate__(	self  )   ->							Dict:
      _a      =							self.__dict__.copy()
      _a      =							None
      _a      =							self.sp_model.serialized_model_proto()
      return state
 def __setstate__(	self ,			__magic_name__  )   ->							List[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 ,			__magic_name__ ,			__magic_name__ = 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 ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = False  )   ->							List[int]:

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

      if token_ids_a is None:
           return [1] + ([0] * len(__magic_name__  ))
      return [1] + ([0] * len(__magic_name__  )) + [1, 1] + ([0] * len(__magic_name__  ))
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = 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  )   ->							int:
      return len(self.sp_model  ) + self.fairseq_offset + self.num_madeup_words
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							{self.convert_ids_to_tokens(__magic_name__  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.encode(__magic_name__ ,			out_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
      _a      =							self.sp_model.PieceToId(__magic_name__  )

      # 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 ,			__magic_name__  )   ->							Any:
      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 ,			__magic_name__  )   ->							Optional[int]:
      _a      =							''.join(__magic_name__  ).replace(__magic_name__ ,			' '  ).strip()
      return out_string




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							Tuple[str]:
      if not os.path.isdir(__magic_name__  ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
           return
      _a      =							os.path.join(
          __magic_name__ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

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

      return (out_vocab_file,)





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_   :      Optional[int]				    =      {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Dict				    =      ["DeiTFeatureExtractor"]
 a_   :      List[Any]				    =      ["DeiTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DeiTForImageClassification",
     "DeiTForImageClassificationWithTeacher",
     "DeiTForMaskedImageModeling",
     "DeiTModel",
     "DeiTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Optional[Any]				    =      [
     "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDeiTForImageClassification",
     "TFDeiTForImageClassificationWithTeacher",
     "TFDeiTForMaskedImageModeling",
     "TFDeiTModel",
     "TFDeiTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_deit import DeiTFeatureExtractor
  from .image_processing_deit import DeiTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deit import (
      DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
      DeiTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deit import (
      TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
      TFDeiTPreTrainedModel,
  )


else:
 import sys

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

