






'''simple docstring'''

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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_ta import TaTokenizer
else:
     SCREAMING_SNAKE_CASE__											=     None


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__											=     {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE__											=     {
    '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',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
SCREAMING_SNAKE_CASE__											=     {
    '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,
}






class        a_		(    lowerCamelCase       ):
  lowercase  	=   VOCAB_FILES_NAMES
  lowercase  	=   PRETRAINED_VOCAB_FILES_MAP
  lowercase  	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase  	=   ["""input_ids""", """attention_mask"""]
  lowercase  	=   TaTokenizer

  lowercase  	=   []

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE="</s>"    ,	_SCREAMING_SNAKE_CASE="<unk>"    ,	_SCREAMING_SNAKE_CASE="<pad>"    ,	_SCREAMING_SNAKE_CASE=100    ,	_SCREAMING_SNAKE_CASE=None    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Optional[Any]:



        """simple docstring"""






        if extra_ids > 0 and additional_special_tokens is None:
              UpperCamelCase       						=      [F"<extra_id_{i}>" for i in range(_SCREAMING_SNAKE_CASE   )]
        elif extra_ids > 0 and additional_special_tokens is not None:
              # Check that we have the right number of extra special tokens
              UpperCamelCase       						=      len(set(filter(lambda _SCREAMING_SNAKE_CASE   : bool("""extra_id_""" in str(_SCREAMING_SNAKE_CASE   )   )    ,	_SCREAMING_SNAKE_CASE   )   )   )
              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"""   )

        super().__init__(
            _SCREAMING_SNAKE_CASE    ,	tokenizer_file=_SCREAMING_SNAKE_CASE    ,	eos_token=_SCREAMING_SNAKE_CASE    ,	unk_token=_SCREAMING_SNAKE_CASE    ,	pad_token=_SCREAMING_SNAKE_CASE    ,	extra_ids=_SCREAMING_SNAKE_CASE    ,	additional_special_tokens=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	)

        UpperCamelCase       						=      vocab_file
        UpperCamelCase       						=      False if not self.vocab_file else True
        UpperCamelCase       						=      extra_ids

  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
              UpperCamelCase       						=      TaTokenizerFast.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."""    ,	_SCREAMING_SNAKE_CASE    ,	)

        return max_model_length

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None   )						->   Tuple[str]:



        """simple docstring"""






        if not self.can_save_slow_tokenizer:
              raise ValueError(
                  """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                  """tokenizer."""   )

        if not os.path.isdir(_SCREAMING_SNAKE_CASE   ):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
              return
        UpperCamelCase       						=      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   ):
              copyfile(self.vocab_file    ,	_SCREAMING_SNAKE_CASE   )
              logger.info(F"Copy vocab file to {out_vocab_file}"   )

        return (out_vocab_file,)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None   )						->   List[int]:



        """simple docstring"""






        UpperCamelCase       						=      token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
              return self.prefix_tokens + token_ids_a
        else:
              UpperCamelCase       						=      token_ids_a + [self.eos_token_id]
              return self.prefix_tokens + token_ids_a + token_ids_a

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None   )						->   List[int]:



        """simple docstring"""






        UpperCamelCase       						=      [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   )						->   Tuple:



        """simple docstring"""






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


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



        """simple docstring"""






        return [self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   ) for token in self.get_sentinel_tokens()]








'''simple docstring'''

from __future__ import annotations

from typing import Any






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE = 6   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      None
        UpperCamelCase       						=      None
        self.create_linked_list(_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      Node()
        UpperCamelCase       						=      current_node
        UpperCamelCase       						=      current_node
        UpperCamelCase       						=      current_node
        for _ in range(1    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      Node()
              UpperCamelCase       						=      current_node
              UpperCamelCase       						=      previous_node
              UpperCamelCase       						=      current_node
        UpperCamelCase       						=      self.front
        UpperCamelCase       						=      previous_node

  def 						A__		(   self   )						->   bool:



        """simple docstring"""






        return (
            self.front == self.rear
            and self.front is not None
            and self.front.data is None
        )

  def 						A__		(   self   )						->   Any | None:



        """simple docstring"""






        self.check_can_perform_operation()
        return self.front.data if self.front else None

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   None:



        """simple docstring"""






        if self.rear is None:
              return

        self.check_is_full()
        if not self.is_empty():
              UpperCamelCase       						=      self.rear.next
        if self.rear:
              UpperCamelCase       						=      data

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        self.check_can_perform_operation()
        if self.rear is None or self.front is None:
              return None
        if self.front == self.rear:
              UpperCamelCase       						=      self.front.data
              UpperCamelCase       						=      None
              return data

        UpperCamelCase       						=      self.front
        UpperCamelCase       						=      old_front.next
        UpperCamelCase       						=      old_front.data
        UpperCamelCase       						=      None
        return data

  def 						A__		(   self   )						->   None:



        """simple docstring"""






        if self.is_empty():
              raise Exception("""Empty Queue"""   )


  def 						A__		(   self   )						->   None:



        """simple docstring"""






        if self.rear and self.rear.next == self.front:
              raise Exception("""Full Queue"""   )






class        a_		:

  def __init__(   self   )						->   None:



        """simple docstring"""






        UpperCamelCase       						=      None
        UpperCamelCase       						=      None
        UpperCamelCase       						=      None


if __name__ == "__main__":
     import doctest

     doctest.testmod()
