"""simple docstring"""


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
    import torch


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


UpperCAmelCase__  		=     logging.get_logger(__name__)
UpperCAmelCase__  		=     {"""vocab_file""": """spiece.model"""}

UpperCAmelCase__  		=     {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

UpperCAmelCase__  		=     {
    """AI-Sweden/gpt-sw3-126m""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-350m""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-1.6b""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-6.7b""": 2_0_4_8,
    """AI-Sweden/gpt-sw3-20b""": 2_0_4_8,
}


class        a				(		lowerCAmelCase_   ):
    _snake_case     :  Optional[int]     							=							VOCAB_FILES_NAMES
    _snake_case     :  Any     							=							PRETRAINED_VOCAB_FILES_MAP
    _snake_case     :  Union[str, Any]     							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _snake_case     :  Any     							=							['input_ids', 'attention_mask']


    def __init__(					self	:  List[str]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  List[Any]=False   ,				__lowerCAmelCase	:  Any=False   ,				__lowerCAmelCase	:  List[Any]=False   ,				__lowerCAmelCase	:  Optional[Any]=None   ,				__lowerCAmelCase	:  List[str]=None   ,				__lowerCAmelCase	:  Optional[int]=None   ,				__lowerCAmelCase	:  Dict=None   ,				__lowerCAmelCase	:  Optional[Dict[str, Any]] = None   ,				**__lowerCAmelCase	:  Optional[Any]   ,				):
      _UpperCAmelCase						 =      {} if sp_model_kwargs is None else sp_model_kwargs

      _UpperCAmelCase						 =      kwargs.get("""name_or_path"""					)
      if name_or_path is None:
        logger.warning(
            """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
            """ you are testing the model, this can safely be ignored"""					)
        _UpperCAmelCase						 =      """None"""

      # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
      _UpperCAmelCase						 =      """<|endoftext|>""" if eos_token is None else eos_token
      _UpperCAmelCase						 =      """<unk>""" if unk_token is None else unk_token
      if "gpt-sw3-7b" in name_or_path:
        _UpperCAmelCase						 =      unk_token if pad_token is None else pad_token
        _UpperCAmelCase						 =      eos_token if bos_token is None else bos_token
      else:
        _UpperCAmelCase						 =      """<pad>""" if pad_token is None else pad_token
        _UpperCAmelCase						 =      """<s>""" if bos_token is None else bos_token

      super().__init__(
          do_lower_case=__lowerCAmelCase   ,				remove_space=__lowerCAmelCase   ,				keep_accents=__lowerCAmelCase   ,				bos_token=__lowerCAmelCase   ,				eos_token=__lowerCAmelCase   ,				unk_token=__lowerCAmelCase   ,				pad_token=__lowerCAmelCase   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**__lowerCAmelCase   ,				)

      _UpperCAmelCase						 =      do_lower_case
      _UpperCAmelCase						 =      remove_space
      _UpperCAmelCase						 =      keep_accents
      _UpperCAmelCase						 =      vocab_file

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

      # Used for whitespace normalization in input texts
      # fmt : off
      _UpperCAmelCase						 =      {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
      # fmt : on

      # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
      _UpperCAmelCase						 =      re.compile(
          f'''[{"".join(map(__lowerCAmelCase   ,				list(range(0   ,				9					)					) + list(range(11   ,				32					)					) + list(range(127   ,				160					)					) + [160, 173, 8203]					)					)}]'''					)


    def __getstate__(					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      self.__dict__.copy()
      _UpperCAmelCase						 =      None
      return state


    def __setstate__(					self	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase						 =      d

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

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


    @property
    # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
    def      lowerCAmelCase_  (					self	:  int					):
      return len(self.sp_model					)


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      self.non_printing_characters_re.sub(""""""   ,				__lowerCAmelCase					)

      # Normalize whitespaces
      _UpperCAmelCase						 =      """""".join([char if char not in self.whitespaces else """ """ for char in text]					)

      # NFC Unicode normalization
      _UpperCAmelCase						 =      unicodedata.normalize("""NFC"""   ,				__lowerCAmelCase					)
      return text


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  str   ,				**__lowerCAmelCase	:  int					):
      _UpperCAmelCase						 =      self.preprocess_text(__lowerCAmelCase					)
      return self.sp_model.encode(__lowerCAmelCase   ,				out_type=__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  str					):
      return self.sp_model.PieceToId(__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  int					):
      return self.sp_model.IdToPiece(__lowerCAmelCase					)


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  str					):
      return out_string


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  List[str]					):
      _UpperCAmelCase						 =      []
      _UpperCAmelCase						 =      """"""
      _UpperCAmelCase						 =      False
      for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
          # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
          if not prev_is_special:
            out_string += " "

          out_string += self.sp_model.decode(__lowerCAmelCase					) + token
          _UpperCAmelCase						 =      True
          _UpperCAmelCase						 =      []
        else:
          current_sub_tokens.append(__lowerCAmelCase					)
          _UpperCAmelCase						 =      False
      out_string += self.sp_model.decode(__lowerCAmelCase					)

      return out_string


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      {self.convert_ids_to_tokens(__lowerCAmelCase					): i for i in range(self.vocab_size					)}
      vocab.update(self.added_tokens_encoder					)
      return vocab


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Optional[str] = None					):
      if not os.path.isdir(__lowerCAmelCase					):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
        return
      _UpperCAmelCase						 =      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:
          _UpperCAmelCase						 =      self.sp_model.serialized_model_proto()
          fi.write(__lowerCAmelCase					)

      return (out_vocab_file,)


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Union[str, List[str]]   ,				__lowerCAmelCase	:  Union[str, bool] = False					):

      if isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
        _UpperCAmelCase						 =      self.preprocess_text(__lowerCAmelCase					)
        _UpperCAmelCase						 =      self.sp_model.encode(__lowerCAmelCase					)
      else:
        _UpperCAmelCase						 =      [self.preprocess_text(__lowerCAmelCase					) for t in text]
        _UpperCAmelCase						 =      self.sp_model.encode(__lowerCAmelCase					)

      if return_tensors is True or return_tensors == "pt":
        _UpperCAmelCase						 =      torch.tensor(__lowerCAmelCase					)

      return token_ids


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Union[int, List[int]]					):
      return self.sp_model.decode(__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  "Conversation"					):
      _UpperCAmelCase						 =      [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
      _UpperCAmelCase						 =      (
          f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(__lowerCAmelCase					) + f'''{self.bos_token}Bot:'''
      )
      return self.encode(text=__lowerCAmelCase					)

"""simple docstring"""


def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      abs(lowercase		)
  _UpperCAmelCase						 =      0
  while n > 0:
    res += n % 10
    n //= 10
  return res






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      abs(lowercase		)
  return n if n < 10 else n % 10 + sum_of_digits(n // 10		)






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return sum(int(lowercase		) for c in str(abs(lowercase		)		)		)






def     __UpperCAmelCase					(			):







  """simple docstring"""
  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(lowercase   ,lowercase		) -> None:
    _UpperCAmelCase						 =      f'''{func.__name__}({value})'''
    _UpperCAmelCase						 =      timeit(f'''__main__.{call}'''   ,setup="""import __main__"""		)
    print(f'''{call:56} = {func(lowercase		)} -- {timing:.4f} seconds'''		)

  for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
    for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
      benchmark_a_function(lowercase   ,lowercase		)
    print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
