


"""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


UpperCAmelCase				=						logging.get_logger(__name__)
UpperCAmelCase				=						{"""vocab_file""": """spiece.model"""}

UpperCAmelCase				=						{
    """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""",
    }
}

UpperCAmelCase				=						{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

UpperCAmelCase				=						"""▁"""




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					VOCAB_FILES_NAMES
   __UpperCAmelCase							       =					PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase							       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE="[CLS]"      ,   SCREAMING_SNAKE_CASE="[SEP]"      ,   SCREAMING_SNAKE_CASE="<unk>"      ,   SCREAMING_SNAKE_CASE="[SEP]"      ,   SCREAMING_SNAKE_CASE="<pad>"      ,   SCREAMING_SNAKE_CASE="[CLS]"      ,   SCREAMING_SNAKE_CASE="[MASK]"      ,   SCREAMING_SNAKE_CASE = None      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  None:
      # 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.
      _lowerCamelCase	:					Optional[int]   =		(
          AddedToken(SCREAMING_SNAKE_CASE      ,   lstrip=SCREAMING_SNAKE_CASE      ,   rstrip=SCREAMING_SNAKE_CASE      ,   normalized=SCREAMING_SNAKE_CASE)
          if isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
          else mask_token
      )

      _lowerCamelCase	:					Optional[Any]   =		{} if sp_model_kwargs is None else sp_model_kwargs

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

      _lowerCamelCase	:					Optional[int]   =		do_lower_case
      _lowerCamelCase	:					Any   =		remove_space
      _lowerCamelCase	:					Any   =		keep_accents
      _lowerCamelCase	:					str   =		vocab_file

      _lowerCamelCase	:					List[Any]   =		spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(SCREAMING_SNAKE_CASE)





   @property
   def 		UpperCamelCase_  (							self)  ->  int:
      return len(self.sp_model)





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					Dict   =		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab





   def __getstate__(							self)  ->  int:
      _lowerCamelCase	:					Any   =		self.__dict__.copy()
      _lowerCamelCase	:					Any   =		None
      return state





   def __setstate__(							self      ,   SCREAMING_SNAKE_CASE)  ->  Union[str, Any]:
      _lowerCamelCase	:					Union[str, Any]   =		d

      # for backward compatibility
      if not hasattr(self      ,   """sp_model_kwargs"""):
         _lowerCamelCase	:					List[Any]   =		{}

      _lowerCamelCase	:					int   =		spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(self.vocab_file)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      if self.remove_space:
         _lowerCamelCase	:					Union[str, Any]   =		""" """.join(inputs.strip().split())
      else:
         _lowerCamelCase	:					str   =		inputs
      _lowerCamelCase	:					Optional[Any]   =		outputs.replace("""``"""      ,   """\"""").replace("""''"""      ,   """\"""")

      if not self.keep_accents:
         _lowerCamelCase	:					Tuple   =		unicodedata.normalize("""NFKD"""      ,   SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[Any]   =		"""""".join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE)])
      if self.do_lower_case:
         _lowerCamelCase	:					Optional[Any]   =		outputs.lower()

      return outputs





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					str   =		self.preprocess_text(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Any   =		self.sp_model.encode(SCREAMING_SNAKE_CASE      ,   out_type=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					str   =		[]
      for piece in pieces:
         if len(SCREAMING_SNAKE_CASE) > 1 and piece[-1] == str(""",""") and piece[-2].isdigit():
            _lowerCamelCase	:					str   =		self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE      ,   """"""))
            if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
               if len(cur_pieces[0]) == 1:
                  _lowerCamelCase	:					int   =		cur_pieces[1:]
               else:
                  _lowerCamelCase	:					Union[str, Any]   =		cur_pieces[0][1:]
            cur_pieces.append(piece[-1])
            new_pieces.extend(SCREAMING_SNAKE_CASE)
         else:
            new_pieces.append(SCREAMING_SNAKE_CASE)

      return new_pieces





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Dict:
      return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Union[str, Any]:
      return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					Union[str, Any]   =		[]
      _lowerCamelCase	:					Tuple   =		""""""
      _lowerCamelCase	:					Tuple   =		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(SCREAMING_SNAKE_CASE) + token
            _lowerCamelCase	:					List[str]   =		True
            _lowerCamelCase	:					int   =		[]
         else:
            current_sub_tokens.append(SCREAMING_SNAKE_CASE)
            _lowerCamelCase	:					Optional[Any]   =		False
      out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE)
      return out_string.strip()





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  List[int]:
      _lowerCamelCase	:					List[Any]   =		[self.sep_token_id]
      _lowerCamelCase	:					List[str]   =		[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      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = False)  ->  List[int]:

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

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





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

      if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE) and os.path.isfile(self.vocab_file):
         copyfile(self.vocab_file      ,   SCREAMING_SNAKE_CASE)
      elif not os.path.isfile(self.vocab_file):
         with open(SCREAMING_SNAKE_CASE      ,   """wb""") as fi:
            _lowerCamelCase	:					Optional[int]   =		self.sp_model.serialized_model_proto()
            fi.write(SCREAMING_SNAKE_CASE)

      return (out_vocab_file,)





"""simple docstring"""
from typing import TYPE_CHECKING

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


UpperCAmelCase				=						{
    """configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase				=						["""VisionEncoderDecoderModel"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase				=						["""TFVisionEncoderDecoderModel"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase				=						["""FlaxVisionEncoderDecoderModel"""]

if TYPE_CHECKING:
 from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
 import sys

 UpperCAmelCase				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

