



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


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

lowercase_						=       {
    """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""",
    }
}

lowercase_						=       {
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}
class 			_snake_case     (   lowercase__):
    UpperCamelCase__			:   str       						=VOCAB_FILES_NAMES
    UpperCamelCase__			:   Optional[int]       						=PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase__			:   Optional[Any]       						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase__			:   Tuple       						=["""input_ids""", """attention_mask"""]




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

       lowercase__   					=			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"  )
          lowercase__   					=			"None"

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

       lowercase__   					=			do_lower_case
       lowercase__   					=			remove_space
       lowercase__   					=			keep_accents
       lowercase__   					=			vocab_file

       lowercase__   					=			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(__lowercase  )

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

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




    def __getstate__(  self   :  List[str]  ):
       lowercase__   					=			self.__dict__.copy()
       lowercase__   					=			None
       return state




    def __setstate__(  self   :  str,   __lowercase   :  int  ):
       lowercase__   					=			d

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

       lowercase__   					=			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 							A__ (  self   :  Union[str, Any]  ):
       return len(self.sp_model  )




    def 							A__ (  self   :  str,   __lowercase   :  str  ):
       lowercase__   					=			self.non_printing_characters_re.sub("",   __lowercase  )

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

       # NFC Unicode normalization
       lowercase__   					=			unicodedata.normalize("NFC",   __lowercase  )
       return text




    def 							A__ (  self   :  Optional[int],   __lowercase   :  str,   **__lowercase   :  Any  ):
       lowercase__   					=			self.preprocess_text(__lowercase  )
       return self.sp_model.encode(__lowercase,   out_type=__lowercase  )




    def 							A__ (  self   :  str,   __lowercase   :  str  ):
       return self.sp_model.PieceToId(__lowercase  )




    def 							A__ (  self   :  str,   __lowercase   :  int  ):
       return self.sp_model.IdToPiece(__lowercase  )




    @staticmethod
    def 							A__ (  __lowercase   :  str  ):
       return out_string




    def 							A__ (  self   :  str,   __lowercase   :  List[str]  ):
       lowercase__   					=			[]
       lowercase__   					=			""
       lowercase__   					=			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
             lowercase__   					=			True
             lowercase__   					=			[]
          else:
             current_sub_tokens.append(__lowercase  )
             lowercase__   					=			False
       out_string += self.sp_model.decode(__lowercase  )

       return out_string




    def 							A__ (  self   :  Any  ):
       lowercase__   					=			{self.convert_ids_to_tokens(__lowercase  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab




    def 							A__ (  self   :  List[Any],   __lowercase   :  str,   __lowercase   :  Optional[str] = None  ):
       if not os.path.isdir(__lowercase  ):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
          return
       lowercase__   					=			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:
             lowercase__   					=			self.sp_model.serialized_model_proto()
             fi.write(__lowercase  )

       return (out_vocab_file,)




    def 							A__ (  self   :  Dict,   __lowercase   :  Union[str, List[str]],   __lowercase   :  Union[str, bool] = False  ):

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

       if return_tensors is True or return_tensors == "pt":
          lowercase__   					=			torch.tensor(__lowercase  )

       return token_ids




    def 							A__ (  self   :  Optional[Any],   __lowercase   :  Union[int, List[int]]  ):
       return self.sp_model.decode(__lowercase  )




    def 							A__ (  self   :  str,   __lowercase   :  "Conversation"  ):
       lowercase__   					=			[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
       lowercase__   					=			(
           F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(__lowercase  ) + F'''{self.bos_token}Bot:'''
       )
       return self.encode(text=__lowercase  )









def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			int(SCREAMING_SNAKE_CASE_					)
			if decimal in (0, 1):  # Exit cases for the recursion
						return str(SCREAMING_SNAKE_CASE_					)
			lowercase__					,    lowercase__   					=			divmod(SCREAMING_SNAKE_CASE_  ,					2					)
			return binary_recursive(SCREAMING_SNAKE_CASE_					) + str(SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			str(SCREAMING_SNAKE_CASE_					).strip()
			if not number:
						raise ValueError("No input value was provided"					)
			lowercase__   					=			"-" if number.startswith("-"					) else ""
			lowercase__   					=			number.lstrip("-"					)
			if not number.isnumeric():
						raise ValueError("Input value is not an integer"					)
			return f'''{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE_					)					)}'''


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

							testmod()




