



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


A_		:     Any        =     logging.get_logger(__name__)

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

A_		:     Optional[int]        =     {
    '''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
A_		:     List[str]        =     {
    '''t5-small''': 5_12,
    '''t5-base''': 5_12,
    '''t5-large''': 5_12,
    '''t5-3b''': 5_12,
    '''t5-11b''': 5_12,
}

A_		:     str        =     '''▁'''



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			VOCAB_FILES_NAMES
   _UpperCAmelCase     	=			PRETRAINED_VOCAB_FILES_MAP
   _UpperCAmelCase     	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCAmelCase     	=			['''input_ids''', '''attention_mask''']

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


    """simple docstring"""






    if extra_ids > 0 and additional_special_tokens is None:
     a						=		[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
     a						=		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"							)

    a						=		legacy
    a						=		{} 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							,   )

    a						=		vocab_file
    a						=		extra_ids

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

   @staticmethod
   def 		A							(      __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Optional[int]							)       ->				Any:


    """simple docstring"""






    if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
     a						=		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 		A							(      self			:       int							)       ->				Union[str, Any]:


    """simple docstring"""






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

   def 		A							(      self			:       Optional[Any]							)       ->				List[Any]:


    """simple docstring"""






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

   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       List[int]							,   __lowerCAmelCase			:       Optional[List[int]] = None							,   __lowerCAmelCase			:       bool = False							)       ->				List[int]:


    """simple docstring"""






    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 		A							(      self			:       Dict							)       ->				int:


    """simple docstring"""






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

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


    """simple docstring"""






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

   def 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       List[int]							)       ->				List[int]:


    """simple docstring"""






    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 		A							(      self			:       Dict							,   __lowerCAmelCase			:       List[int]							,   __lowerCAmelCase			:       Optional[List[int]] = None							)       ->				List[int]:


    """simple docstring"""






    a						=		[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 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       List[int]							,   __lowerCAmelCase			:       Optional[List[int]] = None							)       ->				List[int]:


    """simple docstring"""






    a						=		self._add_eos_if_not_present(__lowerCAmelCase							)
    if token_ids_a is None:
     return token_ids_a
    else:
     a						=		self._add_eos_if_not_present(__lowerCAmelCase							)
     return token_ids_a + token_ids_a

   def __getstate__(      self			:       List[Any]							)       ->				Optional[int]:


    """simple docstring"""






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

   def __setstate__(      self			:       str							,   __lowerCAmelCase			:       List[Any]							)       ->				Optional[int]:


    """simple docstring"""






    a						=		d

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

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

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       "TextInput"							,   **__lowerCAmelCase			:       Dict							)       ->				List[str]:


    """simple docstring"""






    if not self.legacy:
     a						=		SPIECE_UNDERLINE + text.replace(__lowerCAmelCase							,   " "							)
    return super().tokenize(__lowerCAmelCase							,   **__lowerCAmelCase							)

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Tuple							,   **__lowerCAmelCase			:       Dict							)       ->				Dict:


    """simple docstring"""






    if not self.legacy:
     a						=		text.startswith(__lowerCAmelCase							)
     if is_first:
      a						=		text[1:]

    a						=		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							):
     a						=		([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
    return tokens

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       List[str]							)       ->				Union[str, Any]:


    """simple docstring"""






    if token.startswith("<extra_id_"							):
     a						=		re.match(R"<extra_id_(\d+)>"							,   __lowerCAmelCase							)
     a						=		int(match.group(1							)							)
     return self.vocab_size - num - 1
    return self.sp_model.piece_to_id(__lowerCAmelCase							)

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[Any]							)       ->				str:


    """simple docstring"""






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

   def 		A							(      self			:       Dict							,   __lowerCAmelCase			:       str							)       ->				Tuple:


    """simple docstring"""






    a						=		[]
    a						=		""
    a						=		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
      a						=		True
      a						=		[]
     else:
      current_sub_tokens.append(__lowerCAmelCase							)
      a						=		False
    out_string += self.sp_model.decode(__lowerCAmelCase							)
    return out_string.strip()




   def 		A							(      self			:       Dict							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       Optional[str] = None							)       ->				Tuple[str]:


    """simple docstring"""






    if not os.path.isdir(__lowerCAmelCase							):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
     return
    a						=		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:
      a						=		self.sp_model.serialized_model_proto()
      fi.write(__lowerCAmelCase							)

    return (out_vocab_file,)






from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

A_		:     List[str]        =     (3, 9, -11, 0, 7, 5, 1, -1)
A_		:     Optional[int]        =     (4, 6, 2, 0, 8, 10, 3, -2)



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42



class 		_lowercase		:

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       Iterable[int]							)       ->				None:


    """simple docstring"""






    a						=		None
    for i in sorted(__lowerCAmelCase							,   reverse=__lowerCAmelCase							):
     a						=		Node(__lowerCAmelCase							,   self.head							)

   def __iter__(      self			:       Union[str, Any]							)       ->				Iterator[int]:


    """simple docstring"""






    a						=		self.head
    while node:
     yield node.data
     a						=		node.next_node

   def __len__(      self			:       Tuple							)       ->				int:


    """simple docstring"""






    return sum(1 for _ in self							)




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


    """simple docstring"""






    return " -> ".join([str(__lowerCAmelCase							) for node in self]							)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :SortedLinkedList ,					UpperCAmelCase__ :SortedLinkedList					):






 '''simple docstring'''

 return SortedLinkedList(list(UpperCAmelCase__					) + list(UpperCAmelCase__					)					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     A_		:     Optional[Any]        =     SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

