"""simple docstring"""






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

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


if is_sentencepiece_available():
       from .tokenization_albert import AlbertTokenizer
else:
       __lowerCAmelCase					: Dict        =None

__lowerCAmelCase					: str        =logging.get_logger(__name__)
__lowerCAmelCase					: Optional[int]        ={"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__lowerCAmelCase					: Any        ={
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

__lowerCAmelCase					: Optional[int]        ={
    """albert-base-v1""": 5_1_2,
    """albert-large-v1""": 5_1_2,
    """albert-xlarge-v1""": 5_1_2,
    """albert-xxlarge-v1""": 5_1_2,
    """albert-base-v2""": 5_1_2,
    """albert-large-v2""": 5_1_2,
    """albert-xlarge-v2""": 5_1_2,
    """albert-xxlarge-v2""": 5_1_2,
}

__lowerCAmelCase					: Tuple        ="""▁"""





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Any													=			VOCAB_FILES_NAMES
      snake_case__    :     Optional[Any]													=			PRETRAINED_VOCAB_FILES_MAP
      snake_case__    :     Any													=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__    :     List[str]													=			AlbertTokenizer

      def __init__(		self	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=False	, __lowerCAmelCase="[CLS]"	, __lowerCAmelCase="[SEP]"	, __lowerCAmelCase="<unk>"	, __lowerCAmelCase="[SEP]"	, __lowerCAmelCase="<pad>"	, __lowerCAmelCase="[CLS]"	, __lowerCAmelCase="[MASK]"	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			(
                AddedToken(__lowerCAmelCase	, lstrip=__lowerCAmelCase	, rstrip=__lowerCAmelCase	, normalized=__lowerCAmelCase		)
                if isinstance(__lowerCAmelCase	, __lowerCAmelCase		)
                else mask_token
            )

            super().__init__(
                __lowerCAmelCase	, tokenizer_file=__lowerCAmelCase	, do_lower_case=__lowerCAmelCase	, remove_space=__lowerCAmelCase	, keep_accents=__lowerCAmelCase	, bos_token=__lowerCAmelCase	, eos_token=__lowerCAmelCase	, unk_token=__lowerCAmelCase	, sep_token=__lowerCAmelCase	, pad_token=__lowerCAmelCase	, cls_token=__lowerCAmelCase	, mask_token=__lowerCAmelCase	, **__lowerCAmelCase	, )

            lowercase														=			do_lower_case
            lowercase														=			remove_space
            lowercase														=			keep_accents
            lowercase														=			vocab_file
            lowercase														=			False if not self.vocab_file else True

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



            lowercase														=			[self.sep_token_id]
            lowercase														=			[self.cls_token_id]
            if token_ids_a is None:
                  return cls + token_ids_a + sep
            return cls + token_ids_a + sep + token_ids_a + sep

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



            lowercase														=			[self.sep_token_id]
            lowercase														=			[self.cls_token_id]

            if token_ids_a is None:
                  return len(cls + token_ids_a + sep		) * [0]
            return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = 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
            lowercase														=			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		)

            return (out_vocab_file,)




"""simple docstring"""






def     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :int	)				->					list:
      '''simple docstring'''


      lowercase														=			word.split()

      def justify(lowerCAmelCase__  :list      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :int	) -> str:
            lowercase														=			max_width - width
            lowercase														=			len(lowerCAmelCase__	)
            if len(lowerCAmelCase__	) == 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:
                  lowercase														=			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]
                  lowercase														=			spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  lowercase														=			(
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(lowerCAmelCase__	):
                        num_spaces_between_words_list[i] += 1
                  lowercase														=			[]
                  for i in range(lowerCAmelCase__	):
                        # 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(lowerCAmelCase__	)

      lowercase														=			[]
      lowercase														=			[]
      lowercase														=			0
      for word in words:
            if width + len(lowerCAmelCase__	) + len(lowerCAmelCase__	) <= 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(lowerCAmelCase__	)
                  width += len(lowerCAmelCase__	)
            else:
                  # justify the line and add it to result
                  answer.append(justify(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)	)
                  # reset new line and new width
                  lowercase							,							lowercase														=			[word], len(lowerCAmelCase__	)
      lowercase														=			max_width - width - len(lowerCAmelCase__	)
      answer.append(""" """.join(lowerCAmelCase__	) + (remaining_spaces + 1) * """ """	)
      return answer


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

       testmod()



