

'''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:
				A_       						=							None

A_       						=							logging.get_logger(__name__)
A_       						=							{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

A_       						=							{
    "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",
    },
}

A_       						=							{
    "albert-base-v1": 5_12,
    "albert-large-v1": 5_12,
    "albert-xlarge-v1": 5_12,
    "albert-xxlarge-v1": 5_12,
    "albert-base-v2": 5_12,
    "albert-large-v2": 5_12,
    "albert-xlarge-v2": 5_12,
    "albert-xxlarge-v2": 5_12,
}

A_       						=							"▁"





class 							_snake_case (       _a						):
			_A       :     Optional[Any]       =					VOCAB_FILES_NAMES
			_A       :     Any       =					PRETRAINED_VOCAB_FILES_MAP
			_A       :     Any       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A       :     Union[str, Any]       =					AlbertTokenizer
			def __init__( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Dict=None     ,SCREAMING_SNAKE_CASE__			:      Any=None     ,SCREAMING_SNAKE_CASE__			:      Tuple=True     ,SCREAMING_SNAKE_CASE__			:      List[Any]=True     ,SCREAMING_SNAKE_CASE__			:      List[Any]=False     ,SCREAMING_SNAKE_CASE__			:      Any="[CLS]"     ,SCREAMING_SNAKE_CASE__			:      Dict="[SEP]"     ,SCREAMING_SNAKE_CASE__			:      Optional[int]="<unk>"     ,SCREAMING_SNAKE_CASE__			:      int="[SEP]"     ,SCREAMING_SNAKE_CASE__			:      List[Any]="<pad>"     ,SCREAMING_SNAKE_CASE__			:      List[Any]="[CLS]"     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]="[MASK]"     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
					# Mask token behave like a normal word, i.e. include the space before it and
					# is included in the raw text, there should be a match in a non-normalized sentence.
					SCREAMING_SNAKE_CASE:Optional[int]						      =					(
					    AddedToken(SCREAMING_SNAKE_CASE__     ,lstrip=SCREAMING_SNAKE_CASE__     ,rstrip=SCREAMING_SNAKE_CASE__     ,normalized=SCREAMING_SNAKE_CASE__							)
					    if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
					    else mask_token
					)

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

					SCREAMING_SNAKE_CASE:Dict						      =					do_lower_case
					SCREAMING_SNAKE_CASE:str						      =					remove_space
					SCREAMING_SNAKE_CASE:Optional[Any]						      =					keep_accents
					SCREAMING_SNAKE_CASE:Dict						      =					vocab_file
					SCREAMING_SNAKE_CASE:str						      =					False if not self.vocab_file else True
			def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      List[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[List[int]] = None							):
					SCREAMING_SNAKE_CASE:Optional[Any]						      =					[self.sep_token_id]
					SCREAMING_SNAKE_CASE:List[Any]						      =					[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     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      List[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[List[int]] = None							):
					SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.sep_token_id]
					SCREAMING_SNAKE_CASE:Any						      =					[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     __UpperCamelCase						( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[str] = None							):
					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
					SCREAMING_SNAKE_CASE:Optional[int]						      =					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__							)

					return (out_vocab_file,)









'''simple docstring'''

def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					word.split()

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

  SCREAMING_SNAKE_CASE:Any						      =					[]
  SCREAMING_SNAKE_CASE:list[str]						      =					[]
  SCREAMING_SNAKE_CASE:List[str]						      =					0
  for word in words:
    if width + len(snake_case ) + len(snake_case ) <= 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(snake_case )
      width += len(snake_case )
    else:
      # justify the line and add it to result
      answer.append(justify(snake_case      ,       snake_case      ,       snake_case ) )
      # reset new line and new width
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					[word], len(snake_case )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					max_width - width - len(snake_case )
  answer.append(" ".join(snake_case ) + (remaining_spaces + 1) * " " )
  return answer


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

    testmod()






