
"""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__   :					Tuple												=				logging.get_logger(__name__)
UpperCAmelCase__   :					Optional[int]												=				{"vocab_file": "spiece.model"}

UpperCAmelCase__   :					Tuple												=				{
    "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__   :					str												=				{
    "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 			lowerCAmelCase_		(UpperCAmelCase_						):


    """simple docstring"""



    __UpperCamelCase   :		List[Any]       =     VOCAB_FILES_NAMES
    __UpperCamelCase   :		Optional[Any]       =     PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase   :		int       =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase   :		Any       =     ['''input_ids''', '''attention_mask''']



    def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__ = None     , **SCREAMING_SNAKE_CASE__     , )      ->	Union[str, Any]:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: List[Any]     =     {} if sp_model_kwargs is None else sp_model_kwargs

           SCREAMING_SNAKE_CASE__		: Tuple     =     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"""					)
                  SCREAMING_SNAKE_CASE__		: List[str]     =     """None"""

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

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

           SCREAMING_SNAKE_CASE__		: int     =     do_lower_case
           SCREAMING_SNAKE_CASE__		: List[Any]     =     remove_space
           SCREAMING_SNAKE_CASE__		: List[Any]     =     keep_accents
           SCREAMING_SNAKE_CASE__		: Tuple     =     vocab_file

           SCREAMING_SNAKE_CASE__		: List[Any]     =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
           self.sp_model.Load(_lowercase					)

           # Used for whitespace normalization in input texts
           # fmt : off
           SCREAMING_SNAKE_CASE__		: Optional[Any]     =     {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
           # fmt : on

           # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
           SCREAMING_SNAKE_CASE__		: List[str]     =     re.compile(
               F'''[{''.join(map(_lowercase     , list(range(0     , 9					)					) + list(range(11     , 32					)					) + list(range(1_27     , 1_60					)					) + [1_60, 1_73, 82_03]					)					)}]'''					)



    def __getstate__(self					)      ->	Optional[int]:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: int     =     self.__dict__.copy()
           SCREAMING_SNAKE_CASE__		: str     =     None
           return state



    def __setstate__(self     , SCREAMING_SNAKE_CASE__					)      ->	Any:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     d

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

           SCREAMING_SNAKE_CASE__		: List[Any]     =     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 			__magic_name__					(self					)      ->	Dict:



           """simple docstring"""




           return len(self.sp_model					)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	List[str]:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: List[str]     =     self.non_printing_characters_re.sub(""""""     , _lowercase					)

           # Normalize whitespaces
           SCREAMING_SNAKE_CASE__		: List[Any]     =     """""".join([char if char not in self.whitespaces else """ """ for char in text]					)

           # NFC Unicode normalization
           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     unicodedata.normalize("""NFC"""     , _lowercase					)
           return text



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)      ->	str:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: int     =     self.preprocess_text(_lowercase					)
           return self.sp_model.encode(_lowercase     , out_type=_lowercase					)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	str:



           """simple docstring"""




           return self.sp_model.PieceToId(_lowercase					)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



           """simple docstring"""




           return self.sp_model.IdToPiece(_lowercase					)



    @staticmethod
    def 			__magic_name__					(SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



           """simple docstring"""




           return out_string



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Optional[int]:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Any     =     []
           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """"""
           SCREAMING_SNAKE_CASE__		: List[str]     =     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(_lowercase					) + token
                         SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     True
                         SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     []
                  else:
                         current_sub_tokens.append(_lowercase					)
                         SCREAMING_SNAKE_CASE__		: Optional[Any]     =     False
           out_string += self.sp_model.decode(_lowercase					)

           return out_string



    def 			__magic_name__					(self					)      ->	str:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: str     =     {self.convert_ids_to_tokens(_lowercase					): i for i in range(self.vocab_size					)}
           vocab.update(self.added_tokens_encoder					)
           return vocab



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None					)      ->	List[str]:



           """simple docstring"""




           if not os.path.isdir(_lowercase					):
                  logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
                  return
           SCREAMING_SNAKE_CASE__		: Tuple     =     os.path.join(
               _lowercase     , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

           if os.path.abspath(self.vocab_file					) != os.path.abspath(_lowercase					) and os.path.isfile(self.vocab_file					):
                  copyfile(self.vocab_file     , _lowercase					)
           elif not os.path.isfile(self.vocab_file					):
                  with open(_lowercase     , """wb"""					) as fi:
                         SCREAMING_SNAKE_CASE__		: List[Any]     =     self.sp_model.serialized_model_proto()
                         fi.write(_lowercase					)

           return (out_vocab_file,)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = False					)      ->	Optional[int]:



           """simple docstring"""




           if isinstance(_lowercase     , _lowercase					):
                  SCREAMING_SNAKE_CASE__		: Any     =     self.preprocess_text(_lowercase					)
                  SCREAMING_SNAKE_CASE__		: Tuple     =     self.sp_model.encode(_lowercase					)
           else:
                  SCREAMING_SNAKE_CASE__		: Any     =     [self.preprocess_text(_lowercase					) for t in text]
                  SCREAMING_SNAKE_CASE__		: List[str]     =     self.sp_model.encode(_lowercase					)

           if return_tensors is True or return_tensors == "pt":
                  SCREAMING_SNAKE_CASE__		: Any     =     torch.tensor(_lowercase					)

           return token_ids



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



           """simple docstring"""




           return self.sp_model.decode(_lowercase					)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	str:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Optional[int]     =     [F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     (
               F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(_lowercase					) + F'''{self.bos_token}Bot:'''
           )
           return self.encode(text=_lowercase					)






"""simple docstring"""




import baseaa

def lowercase_						(     _snake_case		):
							return baseaa.baaencode(string.encode("""utf-8"""		)		)

def lowercase_						(     _snake_case		):
							return baseaa.baadecode(_snake_case		).decode("""utf-8"""		)


if __name__ == "__main__":
					UpperCAmelCase__   :					Dict												=				'Hello World!'
					UpperCAmelCase__   :					Tuple												=				baseaa_encode(test)
					print(encoded)

					UpperCAmelCase__   :					Tuple												=				baseaa_decode(encoded)
					print(decoded)




