






"""simple docstring"""







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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_	:    str            =      logging.get_logger(__name__)
lowerCAmelCase_	:    Any            =      {'''vocab_file''': '''spiece.model'''}

lowerCAmelCase_	:    List[str]            =      {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

lowerCAmelCase_	:    List[str]            =      {
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

lowerCAmelCase_	:    Dict            =      '''▁'''





class   UpperCamelCase_ (       a_  ):
  _A       :					Tuple				=	VOCAB_FILES_NAMES
  _A       :					Optional[int]				=	PRETRAINED_VOCAB_FILES_MAP
  _A       :					Union[str, Any]				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



  def __init__(      self ,		snake_case__ ,		snake_case__=True ,		snake_case__=True ,		snake_case__=False ,		snake_case__="[CLS]" ,		snake_case__="[SEP]" ,		snake_case__="<unk>" ,		snake_case__="[SEP]" ,		snake_case__="<pad>" ,		snake_case__="[CLS]" ,		snake_case__="[MASK]" ,		snake_case__ = None ,		**snake_case__ ,		)			->			None:



         """simple docstring"""





         UpperCAmelCase	    =			(
             AddedToken(snake_case__ ,		lstrip=snake_case__ ,		rstrip=snake_case__ ,		normalized=snake_case__				)
             if isinstance(snake_case__ ,		snake_case__				)
             else mask_token
         )

         UpperCAmelCase	    =			{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             do_lower_case=snake_case__ ,		remove_space=snake_case__ ,		keep_accents=snake_case__ ,		bos_token=snake_case__ ,		eos_token=snake_case__ ,		unk_token=snake_case__ ,		sep_token=snake_case__ ,		pad_token=snake_case__ ,		cls_token=snake_case__ ,		mask_token=snake_case__ ,		sp_model_kwargs=self.sp_model_kwargs ,		**snake_case__ ,		)

         UpperCAmelCase	    =			do_lower_case
         UpperCAmelCase	    =			remove_space
         UpperCAmelCase	    =			keep_accents
         UpperCAmelCase	    =			vocab_file

         UpperCAmelCase	    =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(snake_case__				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         return len(self.sp_model				)



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



         """simple docstring"""





         UpperCAmelCase	    =			{self.convert_ids_to_tokens(snake_case__				): i for i in range(self.vocab_size				)}
         vocab.update(self.added_tokens_encoder				)
         return vocab



  def __getstate__(      self				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			self.__dict__.copy()
         UpperCAmelCase	    =			None
         return state



  def __setstate__(      self ,		snake_case__				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			d

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

         UpperCAmelCase	    =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(self.vocab_file				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			List[Any]:



         """simple docstring"""





         if self.remove_space:
                UpperCAmelCase	    =			""" """.join(inputs.strip().split()				)
         else:
                UpperCAmelCase	    =			inputs
         UpperCAmelCase	    =			outputs.replace("""``""" ,		"""\""""				).replace("""''""" ,		"""\""""				)

         if not self.keep_accents:
                UpperCAmelCase	    =			unicodedata.normalize("""NFKD""" ,		snake_case__				)
                UpperCAmelCase	    =			"""""".join([c for c in outputs if not unicodedata.combining(snake_case__				)]				)
         if self.do_lower_case:
                UpperCAmelCase	    =			outputs.lower()

         return outputs



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			self.preprocess_text(snake_case__				)
         UpperCAmelCase	    =			self.sp_model.encode(snake_case__ ,		out_type=snake_case__				)
         UpperCAmelCase	    =			[]
         for piece in pieces:
                if len(snake_case__				) > 1 and piece[-1] == str(""","""				) and piece[-2].isdigit():
                       UpperCAmelCase	    =			self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case__ ,		""""""				)				)
                       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                              if len(cur_pieces[0]				) == 1:
                                     UpperCAmelCase	    =			cur_pieces[1:]
                              else:
                                     UpperCAmelCase	    =			cur_pieces[0][1:]
                       cur_pieces.append(piece[-1]				)
                       new_pieces.extend(snake_case__				)
                else:
                       new_pieces.append(snake_case__				)

         return new_pieces



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			int:



         """simple docstring"""





         return self.sp_model.PieceToId(snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			Optional[Any]:



         """simple docstring"""





         return self.sp_model.IdToPiece(snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			[]
         UpperCAmelCase	    =			""""""
         UpperCAmelCase	    =			False
         for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                       if not prev_is_special:
                              out_string += " "
                       out_string += self.sp_model.decode(snake_case__				) + token
                       UpperCAmelCase	    =			True
                       UpperCAmelCase	    =			[]
                else:
                       current_sub_tokens.append(snake_case__				)
                       UpperCAmelCase	    =			False
         out_string += self.sp_model.decode(snake_case__				)
         return out_string.strip()



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None				)			->			List[int]:



         """simple docstring"""





         UpperCAmelCase	    =			[self.sep_token_id]
         UpperCAmelCase	    =			[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 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = False				)			->			List[int]:



         """simple docstring"""





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

         if token_ids_a is not None:
                return [1] + ([0] * len(snake_case__				)) + [1] + ([0] * len(snake_case__				)) + [1]
         return [1] + ([0] * len(snake_case__				)) + [1]



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None				)			->			List[int]:



         """simple docstring"""





         UpperCAmelCase	    =			[self.sep_token_id]
         UpperCAmelCase	    =			[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 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None				)			->			Tuple[str]:



         """simple docstring"""





         if not os.path.isdir(snake_case__				):
                logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
                return
         UpperCAmelCase	    =			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__				) and os.path.isfile(self.vocab_file				):
                copyfile(self.vocab_file ,		snake_case__				)
         elif not os.path.isfile(self.vocab_file				):
                with open(snake_case__ ,		"""wb"""				) as fi:
                       UpperCAmelCase	    =			self.sp_model.serialized_model_proto()
                       fi.write(snake_case__				)

         return (out_vocab_file,)








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_	:    Optional[int]            =      {
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_	:    str            =      [
     '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GraphormerForGraphClassification''',
     '''GraphormerModel''',
     '''GraphormerPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

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