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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
  from ...tokenization_utils_base import TextInput
from ...utils import logging


UpperCAmelCase_			    =							logging.get_logger(__name__)

UpperCAmelCase_			    =							{'''vocab_file''': '''spiece.model'''}

UpperCAmelCase_			    =							{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase_			    =							{
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}

UpperCAmelCase_			    =							'''▁'''




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    int  			=   VOCAB_FILES_NAMES
   lowerCAmelCase      :    List[str]  			=   PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase      :    Any  			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase      :    Tuple  			=   ['''input_ids''', '''attention_mask''']



   def __init__(	self					:  str		,  _lowercase					:  List[Any]		,  _lowercase					:  int="</s>"		,  _lowercase					:  Dict="<unk>"		,  _lowercase					:  Any="<pad>"		,  _lowercase					:  Tuple=100		,  _lowercase					:  Any=None		,  _lowercase					:  Optional[Dict[str, Any]] = None		,  _lowercase					:  Any=True		,  **_lowercase					:  List[Any]		,  ):



    """simple docstring"""



    if extra_ids > 0 and additional_special_tokens is None:
     _UpperCamelCase:     Tuple							= [f"""<extra_id_{i}>""" for i in range(_lowercase							)]
    elif extra_ids > 0 and additional_special_tokens is not None:
     # Check that we have the right number of extra_id special tokens
     _UpperCamelCase:     Optional[int]							= len(set(filter(lambda _lowercase							: bool('''extra_id''' in str(_lowercase							)							)		,  _lowercase							)							)							)
     if extra_tokens != extra_ids:
      raise ValueError(
          f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
          ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
          ''' tokens'''							)
    if legacy:
     logger.warning_once(
         f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
         ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''							)

    _UpperCamelCase:     Optional[int]							= legacy
    _UpperCamelCase:     Dict							= {} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        eos_token=_lowercase		,  unk_token=_lowercase		,  pad_token=_lowercase		,  extra_ids=_lowercase		,  additional_special_tokens=_lowercase		,  sp_model_kwargs=self.sp_model_kwargs		,  legacy=_lowercase		,  **_lowercase		,  )

    _UpperCamelCase:     int							= vocab_file
    _UpperCamelCase:     List[Any]							= extra_ids

    _UpperCamelCase:     str							= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
    self.sp_model.Load(_lowercase							)



   @staticmethod
   def   lowerCAmelCase						(	_lowercase					:  Dict		,  _lowercase					:  Optional[Any]		,  _lowercase					:  Any							):



    """simple docstring"""



    if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
     _UpperCamelCase:     Optional[int]							= TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
     if init_max_model_length is not None and init_max_model_length != max_model_length:
      return init_max_model_length
     elif init_max_model_length is None:
      warnings.warn(
          '''This tokenizer was incorrectly instantiated with a model max length of'''
          f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
          ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
          ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
          f""" {pretrained_model_name_or_path} automatically truncating your input to"""
          f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
          f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
          ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
          ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''		,  _lowercase		,  )

    return max_model_length



   @property
   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    return self.sp_model.get_piece_size() + self._extra_ids



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= {self.convert_ids_to_tokens(_lowercase							): i for i in range(self.vocab_size							)}
    vocab.update(self.added_tokens_encoder							)
    return vocab



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  List[int]		,  _lowercase					:  Optional[List[int]] = None		,  _lowercase					:  bool = False							):



    """simple docstring"""



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

    # normal case: some special tokens
    if token_ids_a is None:
     return ([0] * len(_lowercase							)) + [1]
    return ([0] * len(_lowercase							)) + [1] + ([0] * len(_lowercase							)) + [1]



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    return list(
        set(filter(lambda _lowercase							: bool(re.search(R'''<extra_id_\d+>'''		,  _lowercase							)							) is not None		,  self.additional_special_tokens							)							)							)



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    return [self._convert_token_to_id(_lowercase							) for token in self.get_sentinel_tokens()]



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[int]							):



    """simple docstring"""



    if len(_lowercase							) > 0 and token_ids[-1] == self.eos_token_id:
     warnings.warn(
         f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
         ''' eos tokens being added.'''							)
     return token_ids
    else:
     return token_ids + [self.eos_token_id]



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  List[int]		,  _lowercase					:  Optional[List[int]] = None							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= [self.eos_token_id]

    if token_ids_a is None:
     return len(token_ids_a + eos							) * [0]
    return len(token_ids_a + eos + token_ids_a + eos							) * [0]



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  List[int]		,  _lowercase					:  Optional[List[int]] = None							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= self._add_eos_if_not_present(_lowercase							)
    if token_ids_a is None:
     return token_ids_a
    else:
     _UpperCamelCase:     str							= self._add_eos_if_not_present(_lowercase							)
     return token_ids_a + token_ids_a



   def __getstate__(	self					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.__dict__.copy()
    _UpperCamelCase:     Union[str, Any]							= None
    return state



   def __setstate__(	self					:  Tuple		,  _lowercase					:  int							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= d

    # for backward compatibility
    if not hasattr(self		,  '''sp_model_kwargs'''							):
     _UpperCamelCase:     Union[str, Any]							= {}

    _UpperCamelCase:     List[str]							= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
    self.sp_model.Load(self.vocab_file							)



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  "TextInput"		,  **_lowercase					:  List[Any]							):



    """simple docstring"""



    if not self.legacy:
     _UpperCamelCase:     Any							= SPIECE_UNDERLINE + text.replace(_lowercase		,  ''' '''							)
    return super().tokenize(_lowercase		,  **_lowercase							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  str		,  **_lowercase					:  int							):



    """simple docstring"""



    if not self.legacy:
     _UpperCamelCase:     Optional[int]							= text.startswith(_lowercase							)
     if is_first:
      _UpperCamelCase:     Any							= text[1:]

    _UpperCamelCase:     Union[str, Any]							= self.sp_model.encode(_lowercase		,  out_type=_lowercase							)

    if not self.legacy and not is_first and not text.startswith(''' '''							) and tokens[0].startswith(_lowercase							):
     _UpperCamelCase:     Any							= ([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
    return tokens



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  Dict							):



    """simple docstring"""



    if token.startswith('''<extra_id_'''							):
     _UpperCamelCase:     Tuple							= re.match(R'''<extra_id_(\d+)>'''		,  _lowercase							)
     _UpperCamelCase:     Dict							= int(match.group(1							)							)
     return self.vocab_size - num - 1
    return self.sp_model.piece_to_id(_lowercase							)



   def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  int							):



    """simple docstring"""



    if index < self.sp_model.get_piece_size():
     _UpperCamelCase:     str							= self.sp_model.IdToPiece(_lowercase							)
    else:
     _UpperCamelCase:     int							= f"""<extra_id_{self.vocab_size - 1 - index}>"""
    return token



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     int							= []
    _UpperCamelCase:     Any							= ''''''
    _UpperCamelCase:     Union[str, Any]							= 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(_lowercase							) + token
      _UpperCamelCase:     Optional[int]							= True
      _UpperCamelCase:     List[str]							= []
     else:
      current_sub_tokens.append(_lowercase							)
      _UpperCamelCase:     List[str]							= False
    out_string += self.sp_model.decode(_lowercase							)
    return out_string.strip()



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  str		,  _lowercase					:  Optional[str] = None							):



    """simple docstring"""



    if not os.path.isdir(_lowercase							):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
     return
    _UpperCamelCase:     Any							= 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:
      _UpperCamelCase:     List[str]							= self.sp_model.serialized_model_proto()
      fi.write(_lowercase							)

    return (out_vocab_file,)
import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  str		,  *_lowercase					:  str		,  **_lowercase					:  int							):



    """simple docstring"""



    warnings.warn(
        '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
        ''' Please use PerceiverImageProcessor instead.'''		,  _lowercase		,  )
    super().__init__(*_lowercase		,  **_lowercase							)