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_  : Any								=					logging.get_logger(__name__)
lowercase_  : List[str]								=					{'vocab_file': 'spiece.model'}

lowercase_  : Dict								=					{
    '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_  : Tuple								=					{
    '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_     ):
    __a						=   VOCAB_FILES_NAMES
    __a						=   PRETRAINED_VOCAB_FILES_MAP
    __a						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __a						=   ["input_ids", "attention_mask"]
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase=False			,			lowerCAmelCase=False			,			lowerCAmelCase=False			,			lowerCAmelCase=None			,			lowerCAmelCase=None			,			lowerCAmelCase=None			,			lowerCAmelCase=None			,			lowerCAmelCase = None			,			**lowerCAmelCase			,			)							->       None:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   {} if sp_model_kwargs is None else sp_model_kwargs

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   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__: Dict=   '''None'''

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

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   do_lower_case
     SCREAMING_SNAKE_CASE__: int=   remove_space
     SCREAMING_SNAKE_CASE__: List[str]=   keep_accents
     SCREAMING_SNAKE_CASE__: int=   vocab_file

     SCREAMING_SNAKE_CASE__: int=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(lowerCAmelCase )

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

     # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
     SCREAMING_SNAKE_CASE__: str=   re.compile(
         f'[{"".join(map(lowerCAmelCase			,			list(range(0			,			9 ) ) + list(range(11			,			32 ) ) + list(range(127			,			160 ) ) + [160, 173, 8203] ) )}]' )
    def __getstate__(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Any=   self.__dict__.copy()
     SCREAMING_SNAKE_CASE__: Dict=   None
     return state
    def __setstate__(	self			,			lowerCAmelCase )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: List[Any]=   d

     # for backward compatibility
     if not hasattr(self			,			'''sp_model_kwargs''' ):
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   {}

     SCREAMING_SNAKE_CASE__: Tuple=   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     UpperCamelCase_					(	self )							->       int:
     return len(self.sp_model )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       str:
     SCREAMING_SNAKE_CASE__: Any=   self.non_printing_characters_re.sub(''''''			,			lowerCAmelCase )

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

     # NFC Unicode normalization
     SCREAMING_SNAKE_CASE__: Optional[Any]=   unicodedata.normalize('''NFC'''			,			lowerCAmelCase )
     return text
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			**lowerCAmelCase )							->       List[str]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.preprocess_text(lowerCAmelCase )
     return self.sp_model.encode(lowerCAmelCase			,			out_type=lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       int:
     return self.sp_model.PieceToId(lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       str:
     return self.sp_model.IdToPiece(lowerCAmelCase )
    @staticmethod
    def     UpperCamelCase_					(	lowerCAmelCase )							->       str:
     return out_string
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       str:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   []
     SCREAMING_SNAKE_CASE__: Optional[int]=   ''''''
     SCREAMING_SNAKE_CASE__: Any=   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
       SCREAMING_SNAKE_CASE__: Union[str, Any]=   True
       SCREAMING_SNAKE_CASE__: Tuple=   []
      else:
       current_sub_tokens.append(lowerCAmelCase )
       SCREAMING_SNAKE_CASE__: int=   False
     out_string += self.sp_model.decode(lowerCAmelCase )

     return out_string
    def     UpperCamelCase_					(	self )							->       Dict[str, int]:
     SCREAMING_SNAKE_CASE__: List[Any]=   {self.convert_ids_to_tokens(lowerCAmelCase ): i for i in range(self.vocab_size )}
     vocab.update(self.added_tokens_encoder )
     return vocab
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = None )							->       Tuple[str]:
     if not os.path.isdir(lowerCAmelCase ):
      logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
      return
     SCREAMING_SNAKE_CASE__: Optional[int]=   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:
       SCREAMING_SNAKE_CASE__: Optional[Any]=   self.sp_model.serialized_model_proto()
       fi.write(lowerCAmelCase )

     return (out_vocab_file,)
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = False )							->       Union[List[int], List[List[int]], "torch.Tensor"]:

     if isinstance(lowerCAmelCase			,			lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: List[Any]=   self.preprocess_text(lowerCAmelCase )
      SCREAMING_SNAKE_CASE__: Optional[int]=   self.sp_model.encode(lowerCAmelCase )
     else:
      SCREAMING_SNAKE_CASE__: Dict=   [self.preprocess_text(lowerCAmelCase ) for t in text]
      SCREAMING_SNAKE_CASE__: Optional[Any]=   self.sp_model.encode(lowerCAmelCase )

     if return_tensors is True or return_tensors == "pt":
      SCREAMING_SNAKE_CASE__: Dict=   torch.tensor(lowerCAmelCase )

     return token_ids
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       str:
     return self.sp_model.decode(lowerCAmelCase )



    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       List[int]:
     SCREAMING_SNAKE_CASE__: Tuple=   [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
     SCREAMING_SNAKE_CASE__: int=   (
         f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(lowerCAmelCase ) + f'{self.bos_token}Bot:'
     )
     return self.encode(text=lowerCAmelCase )







def 				A__ (snake_case   :		int				)     ->						bool:
					if number < 0:
										raise ValueError("""number must not be negative"""				)
					return number & (number - 1) == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()






