


def UpperCamelCase_				(				__a			,				__a      )	->   list:
      a__	:  str     =   word.split()

      def justify(__a			,				__a			,				__a      ) -> str:
            a__	:  List[str]     =   max_width - width
            a__	:  str     =   len(__a      )
            if len(__a      ) == 1:
                  # if there is only word in line
                  # just insert overall_spaces_count for the remainder of line
                  return line[0] + " " * overall_spaces_count
            else:
                  a__	:  Dict     =   words_count - 1
                  # num_spaces_between_words_list[i] : tells you to insert
                  # num_spaces_between_words_list[i] spaces
                  # after word on line[i]
                  a__	:  str     =   spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  a__	:  List[Any]     =   (
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(__a      ):
                        num_spaces_between_words_list[i] += 1
                  a__	:  Union[str, Any]     =   []
                  for i in range(__a      ):
                        # add the word
                        aligned_words_list.append(line[i]      )
                        # add the spaces to insert
                        aligned_words_list.append(num_spaces_between_words_list[i] * " "      )
                  # just add the last word to the sentence
                  aligned_words_list.append(line[-1]      )
                  # join the aligned words list to form a justified line
                  return "".join(__a      )

      a__	:  Any     =   []
      a__	:  list[str]     =   []
      a__	:  Any     =   0
      for word in words:
            if width + len(__a      ) + len(__a      ) <= max_width:
                  # keep adding words until we can fill out max_width
                  # width = sum of length of all words (without overall_spaces_count)
                  # len(word) = length of current word
                  # len(line) = number of overall_spaces_count to insert between words
                  line.append(__a      )
                  width += len(__a      )
            else:
                  # justify the line and add it to result
                  answer.append(justify(__a			,				__a			,				__a      )      )
                  # reset new line and new width
                  a__,	a__	:  Optional[int]     =   [word], len(__a      )
      a__	:  int     =   max_width - width - len(__a      )
      answer.append(" ".join(__a      ) + (remaining_spaces + 1) * " "      )
      return answer


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

    testmod()







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:
      __magic_name__  :  Dict					=       None


__magic_name__  :  Optional[Any]					=       logging.get_logger(__name__)

__magic_name__  :  Optional[Any]					=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__magic_name__  :  Union[str, Any]					=       {
    """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
__magic_name__  :  Tuple					=       {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      Dict				= VOCAB_FILES_NAMES
     lowercase_ :      Any				= PRETRAINED_VOCAB_FILES_MAP
     lowercase_ :      Tuple				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase_ :      Tuple				= ["input_ids", "attention_mask"]
     lowercase_ :      Any				= TaTokenizer

     lowercase_ :      List[int]				= []
     def __init__(						self	:		Tuple       ,						__lowerCamelCase	:		Tuple=None       ,						__lowerCamelCase	:		str=None       ,						__lowerCamelCase	:		Optional[int]="</s>"       ,						__lowerCamelCase	:		Optional[Any]="<unk>"       ,						__lowerCamelCase	:		int="<pad>"       ,						__lowerCamelCase	:		int=1_00       ,						__lowerCamelCase	:		List[str]=None       ,						**__lowerCamelCase	:		Tuple       ,						):



       """simple docstring"""
       # 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 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'''						)

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

       lowerCAmelCase__										=      vocab_file
       lowerCAmelCase__										=      False if not self.vocab_file else True
       lowerCAmelCase__										=      extra_ids
     @staticmethod
     def        A__      (						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		Any						):



       """simple docstring"""
       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
         lowerCAmelCase__										=      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.'''       ,						__lowerCamelCase       ,						)

       return max_model_length
     def        A__      (						self	:		int       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Optional[str] = None						):



       """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(__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						):
         copyfile(self.vocab_file       ,						__lowerCamelCase						)
         logger.info(F"""Copy vocab file to {out_vocab_file}"""						)

       return (out_vocab_file,)
     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		List[int]       ,						__lowerCamelCase	:		Optional[List[int]] = None						):



       """simple docstring"""
       lowerCAmelCase__										=      token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
         return self.prefix_tokens + token_ids_a
       else:
         lowerCAmelCase__										=      token_ids_a + [self.eos_token_id]
         return self.prefix_tokens + token_ids_a + token_ids_a
     def        A__      (						self	:		int       ,						__lowerCamelCase	:		List[int]       ,						__lowerCamelCase	:		Optional[List[int]] = None						):



       """simple docstring"""
       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        A__      (						self	:		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	:		Dict						):



       """simple docstring"""
       return [self.convert_tokens_to_ids(__lowerCamelCase						) for token in self.get_sentinel_tokens()]

