





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


_snake_case  :   Dict						   =  logging.get_logger(__name__)

_snake_case  :   Any						   =  {'vocab_file': 'spiece.model'}

_snake_case  :   Tuple						   =  {
    '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
_snake_case  :   Any						   =  {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

_snake_case  :   Dict						   =  '▁'



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			VOCAB_FILES_NAMES
  a_      =			PRETRAINED_VOCAB_FILES_MAP
  a_      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_      =			["""input_ids""", """attention_mask"""]






  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Union[str, Any]="</s>" ,  lowerCAmelCase_			:		Optional[Any]="<unk>" ,  lowerCAmelCase_			:		Dict="<pad>" ,  lowerCAmelCase_			:		List[Any]=1_0_0 ,  lowerCAmelCase_			:		List[Any]=None ,  lowerCAmelCase_			:		Optional[Dict[str, Any]] = None ,  lowerCAmelCase_			:		Any=True ,  **lowerCAmelCase_			:		List[str] ,  )							->						None:
     # Add extra_ids to the special token list
     if extra_ids > 0 and additional_special_tokens is None:
        __lowerCAmelCase							     =     [f"""<extra_id_{i}>""" for i in range(lowerCAmelCase_					)]
     elif extra_ids > 0 and additional_special_tokens is not None:
        # Check that we have the right number of extra_id special tokens
        __lowerCAmelCase							     =     len(set(filter(lambda lowerCAmelCase_					: bool('extra_id' in str(lowerCAmelCase_					)					) ,  lowerCAmelCase_					)					)					)
        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'					)

     __lowerCAmelCase							     =     legacy
     __lowerCAmelCase							     =     {} if sp_model_kwargs is None else sp_model_kwargs

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

     __lowerCAmelCase							     =     vocab_file
     __lowerCAmelCase							     =     extra_ids

     __lowerCAmelCase							     =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
     self.sp_model.Load(lowerCAmelCase_					)






  @staticmethod
  def     lowercase		(    lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		List[str]					)							->						Dict:
     if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
        __lowerCAmelCase							     =     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.' ,  lowerCAmelCase_ ,  )

     return max_model_length






  @property
  def     lowercase		(    self			:		Dict					)							->						str:
     return self.sp_model.get_piece_size() + self._extra_ids






  def     lowercase		(    self			:		str					)							->						int:
     __lowerCAmelCase							     =     {self.convert_ids_to_tokens(lowerCAmelCase_					): i for i in range(self.vocab_size					)}
     vocab.update(self.added_tokens_encoder					)
     return vocab






  def     lowercase		(    self			:		str ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None ,  lowerCAmelCase_			:		bool = False					)							->						List[int]:
     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=lowerCAmelCase_ ,  token_ids_a=lowerCAmelCase_ ,  already_has_special_tokens=lowerCAmelCase_					)

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






  def     lowercase		(    self			:		Dict					)							->						List[Any]:
     return list(
         set(filter(lambda lowerCAmelCase_					: bool(re.search(R'<extra_id_\d+>' ,  lowerCAmelCase_					)					) is not None ,  self.additional_special_tokens					)					)					)






  def     lowercase		(    self			:		List[Any]					)							->						List[str]:
     return [self._convert_token_to_id(lowerCAmelCase_					) for token in self.get_sentinel_tokens()]






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		List[int]					)							->						List[int]:
     if len(lowerCAmelCase_					) > 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     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None					)							->						List[int]:
     __lowerCAmelCase							     =     [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     lowercase		(    self			:		int ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None					)							->						List[int]:
     __lowerCAmelCase							     =     self._add_eos_if_not_present(lowerCAmelCase_					)
     if token_ids_a is None:
        return token_ids_a
     else:
        __lowerCAmelCase							     =     self._add_eos_if_not_present(lowerCAmelCase_					)
        return token_ids_a + token_ids_a






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






  def __setstate__(    self			:		Optional[int] ,  lowerCAmelCase_			:		Union[str, Any]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     d

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

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






  def     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		"TextInput" ,  **lowerCAmelCase_			:		str					)							->						List[str]:
     # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
     # the beginning of the text
     if not self.legacy:
        __lowerCAmelCase							     =     SPIECE_UNDERLINE + text.replace(lowerCAmelCase_ ,  ' '					)
     return super().tokenize(lowerCAmelCase_ ,  **lowerCAmelCase_					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		List[Any] ,  **lowerCAmelCase_			:		Tuple					)							->						Optional[int]:
     if not self.legacy:
        __lowerCAmelCase							     =     text.startswith(lowerCAmelCase_					)
        if is_first:
           __lowerCAmelCase							     =     text[1:]

     __lowerCAmelCase							     =     self.sp_model.encode(lowerCAmelCase_ ,  out_type=lowerCAmelCase_					)

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






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		List[Any]					)							->						str:
     if token.startswith('<extra_id_'					):
        __lowerCAmelCase							     =     re.match(R'<extra_id_(\d+)>' ,  lowerCAmelCase_					)
        __lowerCAmelCase							     =     int(match.group(1					)					)
        return self.vocab_size - num - 1
     return self.sp_model.piece_to_id(lowerCAmelCase_					)






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Optional[Any]					)							->						Union[str, Any]:
     if index < self.sp_model.get_piece_size():
        __lowerCAmelCase							     =     self.sp_model.IdToPiece(lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     f"""<extra_id_{self.vocab_size - 1 - index}>"""
     return token






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		List[str]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     []
     __lowerCAmelCase							     =     ''
     __lowerCAmelCase							     =     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(lowerCAmelCase_					) + token
           __lowerCAmelCase							     =     True
           __lowerCAmelCase							     =     []
        else:
           current_sub_tokens.append(lowerCAmelCase_					)
           __lowerCAmelCase							     =     False
     out_string += self.sp_model.decode(lowerCAmelCase_					)
     return out_string.strip()






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

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

     return (out_vocab_file,)








from collections.abc import Sequence

def   a_   (			lowerCAmelCase_				:      Sequence[float],     lowerCAmelCase_				:      bool = False     ):
   if not arr:
      return 0

   __lowerCAmelCase							     =     0 if allow_empty_subarrays else float('-inf'     )
   __lowerCAmelCase							     =     0.0
   for num in arr:
      __lowerCAmelCase							     =     max(0 if allow_empty_subarrays else num,     curr_sum + num     )
      __lowerCAmelCase							     =     max(lowerCAmelCase_,     lowerCAmelCase_     )

   return max_sum


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     _snake_case  :   Optional[Any]						   =  [-2, 1, -3, 4, -1, 2, 1, -5, 4]
     print(F"""{max_subarray_sum(nums) = }""")

