





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


_lowerCAmelCase				:   str						       =						logging.get_logger(__name__)
_lowerCAmelCase				:   Tuple						       =						{"vocab_file": "spiece.model"}

_lowerCAmelCase				:   Optional[Any]						       =						{
    "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",
    }
}

_lowerCAmelCase				:   int						       =						{
    "AI-Sweden/gpt-sw3-126m": 2_048,
    "AI-Sweden/gpt-sw3-350m": 2_048,
    "AI-Sweden/gpt-sw3-1.6b": 2_048,
    "AI-Sweden/gpt-sw3-6.7b": 2_048,
    "AI-Sweden/gpt-sw3-20b": 2_048,
}


class        __magic_name__	(				lowercase_				):





     """simple docstring"""



     __UpperCamelCase            =  VOCAB_FILES_NAMES
     __UpperCamelCase            =  PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase            =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase            =  ['''input_ids''', '''attention_mask''']



     def __init__(				self							:str      ,   snake_case							:Union[str, Any]      ,   snake_case							:int=False      ,   snake_case							:Tuple=False      ,   snake_case							:Tuple=False      ,   snake_case							:Tuple=None      ,   snake_case							:Union[str, Any]=None      ,   snake_case							:Tuple=None      ,   snake_case							:str=None      ,   snake_case							:Optional[Dict[str, Any]] = None      ,   **snake_case							:Any      ,   ):





        '''simple docstring'''




        A_       :					Optional[int]        =      {} if sp_model_kwargs is None else sp_model_kwargs

        A_       :					List[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"	)
           A_       :					Any        =      "None"

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

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

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

        A_       :					Dict        =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(UpperCamelCase_	)

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

        # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
        A_       :					Tuple        =      re.compile(
            f"[{''.join(map(UpperCamelCase_      ,   list(range(0      ,   9	)	) + list(range(11      ,   32	)	) + list(range(127      ,   160	)	) + [160, 173, 8_203]	)	)}]"	)



     def __getstate__(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					str        =      self.__dict__.copy()
        A_       :					Any        =      None
        return state



     def __setstate__(				self							:Union[str, Any]      ,   snake_case							:Any	):





        '''simple docstring'''




        A_       :					List[str]        =      d

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

        A_       :					Optional[int]        =      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     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        return len(self.sp_model	)



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:str	):





        '''simple docstring'''




        A_       :					List[str]        =      self.non_printing_characters_re.sub(""      ,   UpperCamelCase_	)

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

        # NFC Unicode normalization
        A_       :					List[Any]        =      unicodedata.normalize("NFC"      ,   UpperCamelCase_	)
        return text



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]      ,   snake_case							:str      ,   **snake_case							:str	):





        '''simple docstring'''




        A_       :					List[Any]        =      self.preprocess_text(UpperCamelCase_	)
        return self.sp_model.encode(UpperCamelCase_      ,   out_type=UpperCamelCase_	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   snake_case							:str	):





        '''simple docstring'''




        return self.sp_model.PieceToId(UpperCamelCase_	)



     def     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   snake_case							:int	):





        '''simple docstring'''




        return self.sp_model.IdToPiece(UpperCamelCase_	)



     @staticmethod
     def     SCREAMING_SNAKE_CASE		(				snake_case							:str	):





        '''simple docstring'''




        return out_string



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   snake_case							:List[str]	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      []
        A_       :					Optional[int]        =      ""
        A_       :					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(UpperCamelCase_	) + token
              A_       :					List[str]        =      True
              A_       :					Any        =      []
           else:
              current_sub_tokens.append(UpperCamelCase_	)
              A_       :					List[Any]        =      False
        out_string += self.sp_model.decode(UpperCamelCase_	)

        return out_string



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        A_       :					Tuple        =      {self.convert_ids_to_tokens(UpperCamelCase_	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:str      ,   snake_case							:Optional[str] = None	):





        '''simple docstring'''




        if not os.path.isdir(UpperCamelCase_	):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"	)
           return
        A_       :					Optional[int]        =      os.path.join(
            UpperCamelCase_      ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

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

        return (out_vocab_file,)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]      ,   snake_case							:Union[str, List[str]]      ,   snake_case							:Union[str, bool] = False	):





        '''simple docstring'''




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

        if return_tensors is True or return_tensors == "pt":
           A_       :					Union[str, Any]        =      torch.tensor(UpperCamelCase_	)

        return token_ids



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:Union[int, List[int]]	):





        '''simple docstring'''




        return self.sp_model.decode(UpperCamelCase_	)





     def     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   snake_case							:"Conversation"	):





        '''simple docstring'''




        A_       :					str        =      [f"User: {text}" if is_user else f"Bot: {text}" for is_user, text in conversation.iter_texts()]
        A_       :					Dict        =      (
            f"{self.eos_token}{self.bos_token}" + f"{self.bos_token}".join(UpperCamelCase_	) + f"{self.bos_token}Bot:"
        )
        return self.encode(text=UpperCamelCase_	)








import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a   :							List[str]						= "▁"

__a   :							int						= {"vocab_file": "spiece.model"}

__a   :							Any						= {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

__a   :							Any						= {
    "google/pegasus-xsum": 512,
}


__a   :							Union[str, Any]						= logging.get_logger(__name__)


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES

     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]




     def __init__(					self							:      List[str]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Dict="<pad>"			,						UpperCamelCase_							:      Dict="</s>"			,						UpperCamelCase_							:      Optional[Any]="<unk>"			,						UpperCamelCase_							:      List[Any]="<mask_2>"			,						UpperCamelCase_							:      List[Any]="<mask_1>"			,						UpperCamelCase_							:      str=None			,						UpperCamelCase_							:      Dict=103			,						UpperCamelCase_							:      Optional[Dict[str, Any]] = None			,						**UpperCamelCase_							:      int			,						):


         """simple docstring"""



         __A       =						offset
         if additional_special_tokens is not None:
             if not isinstance(UpperCamelCase_			,						UpperCamelCase_					):
                 raise TypeError(
                     F"additional_special_tokens should be of type {type(UpperCamelCase_					)}, but is"
                     F" {type(UpperCamelCase_					)}"					)

             __A       =						(
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 F"<unk_{i}>" for i in range(len(UpperCamelCase_					)			,						self.offset - 1					)
             ]

             if len(set(UpperCamelCase_					)					) != len(UpperCamelCase_					):
                 raise ValueError(
                     """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                     F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."					)
             __A       =						additional_special_tokens_extended
         else:
             __A       =						[mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [F"<unk_{i}>" for i in range(2			,						self.offset					)]

         __A       =						{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=UpperCamelCase_			,						unk_token=UpperCamelCase_			,						mask_token=UpperCamelCase_			,						pad_token=UpperCamelCase_			,						mask_token_sent=UpperCamelCase_			,						offset=UpperCamelCase_			,						additional_special_tokens=UpperCamelCase_			,						sp_model_kwargs=self.sp_model_kwargs			,						**UpperCamelCase_			,						)
         __A       =						mask_token_sent
         __A       =						vocab_file
         __A       =						spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(UpperCamelCase_					)

         # add special tokens to encoder dict
         __A       =						{
             0: self.pad_token,
             1: self.eos_token,
         }

         if self.mask_token_sent is not None:
             self.encoder.update(
                 {
                     2: self.mask_token_sent,
                     3: self.mask_token,
                 }					)

         if self.offset > 0:
             # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
             # mask_token_sent is already added to list -> so start at 1
             self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,						self.offset - 1					)}					)

         __A       =						{v: k for k, v in self.encoder.items()}




     @property
     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         return len(self.sp_model					) + self.offset




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						{self.convert_ids_to_tokens(UpperCamelCase_					): i for i in range(self.vocab_size					)}
         vocab.update(self.added_tokens_encoder					)
         return vocab




     def __getstate__(					self							:      Tuple					):


         """simple docstring"""



         __A       =						self.__dict__.copy()
         __A       =						None
         return state




     def __setstate__(					self							:      Tuple			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						d

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

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




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         return self.sp_model.encode(UpperCamelCase_			,						out_type=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         if token in self.decoder:
             return self.decoder[token]
         elif token in self.added_tokens_decoder:
             return self.added_tokens_decoder[token]
         __A       =						self.sp_model.piece_to_id(UpperCamelCase_					)
         return sp_id + self.offset




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         if index in self.encoder:
             return self.encoder[index]
         elif index in self.added_tokens_encoder:
             return self.added_tokens_encoder[index]
         else:
             __A       =						self.sp_model.IdToPiece(index - self.offset					)
         return token




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      List[str]					):


         """simple docstring"""



         __A       =						[]
         __A       =						""""""
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(UpperCamelCase_					) + token
                 __A       =						[]
             else:
                 current_sub_tokens.append(UpperCamelCase_					)
         out_string += self.sp_model.decode(UpperCamelCase_					)
         return out_string.strip()




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      List[Any]=False					):


         """simple docstring"""



         return 1




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						set(self.all_special_ids					)  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

         return [1 if x in all_special_ids else 0 for x in seq]




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      List			,						UpperCamelCase_							:      Optional[List] = None			,						UpperCamelCase_							:      bool = False					):


         """simple docstring"""



         if already_has_special_tokens:
             return self._special_token_mask(UpperCamelCase_					)
         elif token_ids_a is None:
             return self._special_token_mask(UpperCamelCase_					) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a					) + [1]




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[Any]=None					):


         """simple docstring"""



         if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



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

         if os.path.abspath(self.vocab_file					) != os.path.abspath(UpperCamelCase_					) and os.path.isfile(self.vocab_file					):
             copyfile(self.vocab_file			,						UpperCamelCase_					)
         elif not os.path.isfile(self.vocab_file					):
             with open(UpperCamelCase_			,						"""wb"""					) as fi:
                 __A       =						self.sp_model.serialized_model_proto()
                 fi.write(UpperCamelCase_					)

         return (out_vocab_file,)




