



'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


__lowerCAmelCase									=   logging.get_logger(__name__)

class 	__magic_name__						(    SCREAMING_SNAKE_CASE_   ):







							def __init__(							self					:       List[str]	,*_UpperCAmelCase					:       Dict	,**_UpperCAmelCase					:       Dict		):
									warnings.warn(
									    'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use DeiTImageProcessor instead.'	,__UpperCAmelCase	,)
									super().__init__(*__UpperCAmelCase	,**__UpperCAmelCase		)









'''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


_lowerCAmelCase      						=	logging.get_logger(__name__)
_lowerCAmelCase      						=	{'''vocab_file''': '''spiece.model'''}

_lowerCAmelCase      						=	{
    '''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      						=	{
    '''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        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Dict				=      VOCAB_FILES_NAMES
  __lowercase							:      str				=      PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:      Dict				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase							:      Optional[int]				=      ['''input_ids''', '''attention_mask''']




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase = None  ,**__UpperCAmelCase  ,)    ->		None:
      lowerCAmelCase__ :  Optional[int]							      =					{} if sp_model_kwargs is None else sp_model_kwargs

      lowerCAmelCase__ :  Dict							      =					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"""      )
          lowerCAmelCase__ :  Tuple							      =					"""None"""

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

      lowerCAmelCase__ :  Optional[int]							      =					do_lower_case
      lowerCAmelCase__ :  Dict							      =					remove_space
      lowerCAmelCase__ :  Optional[Any]							      =					keep_accents
      lowerCAmelCase__ :  int							      =					vocab_file

      lowerCAmelCase__ :  str							      =					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(__UpperCAmelCase      )

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

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




  def __getstate__(    self      )    ->		Any:
      lowerCAmelCase__ :  int							      =					self.__dict__.copy()
      lowerCAmelCase__ :  Optional[int]							      =					None
      return state




  def __setstate__(    self  ,__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  List[str]							      =					d

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

      lowerCAmelCase__ :  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 							UpperCAmelCase_	(    self      )    ->		int:
      return len(self.sp_model      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      lowerCAmelCase__ :  Tuple							      =					self.non_printing_characters_re.sub(""""""  ,__UpperCAmelCase      )

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

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,**__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  List[Any]							      =					self.preprocess_text(__UpperCAmelCase      )
      return self.sp_model.encode(__UpperCAmelCase  ,out_type=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		int:
      return self.sp_model.PieceToId(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      return self.sp_model.IdToPiece(__UpperCAmelCase      )




  @staticmethod
  def 							UpperCAmelCase_	(    __UpperCAmelCase      )    ->		str:
      return out_string




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      lowerCAmelCase__ :  int							      =					[]
      lowerCAmelCase__ :  Optional[int]							      =					""""""
      lowerCAmelCase__ :  Tuple							      =					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
              lowerCAmelCase__ :  Union[str, Any]							      =					True
              lowerCAmelCase__ :  Optional[Any]							      =					[]
          else:
              current_sub_tokens.append(__UpperCAmelCase      )
              lowerCAmelCase__ :  Any							      =					False
      out_string += self.sp_model.decode(__UpperCAmelCase      )

      return out_string




  def 							UpperCAmelCase_	(    self      )    ->		Dict[str, int]:
      lowerCAmelCase__ :  Optional[int]							      =					{self.convert_ids_to_tokens(__UpperCAmelCase      ): i for i in range(self.vocab_size      )}
      vocab.update(self.added_tokens_encoder      )
      return vocab




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		Tuple[str]:
      if not os.path.isdir(__UpperCAmelCase      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      lowerCAmelCase__ :  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:
              lowerCAmelCase__ :  str							      =					self.sp_model.serialized_model_proto()
              fi.write(__UpperCAmelCase      )

      return (out_vocab_file,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = False      )    ->		Union[List[int], List[List[int]], "torch.Tensor"]:

      if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ):
          lowerCAmelCase__ :  Tuple							      =					self.preprocess_text(__UpperCAmelCase      )
          lowerCAmelCase__ :  int							      =					self.sp_model.encode(__UpperCAmelCase      )
      else:
          lowerCAmelCase__ :  int							      =					[self.preprocess_text(__UpperCAmelCase      ) for t in text]
          lowerCAmelCase__ :  Any							      =					self.sp_model.encode(__UpperCAmelCase      )

      if return_tensors is True or return_tensors == "pt":
          lowerCAmelCase__ :  Tuple							      =					torch.tensor(__UpperCAmelCase      )

      return token_ids




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      return self.sp_model.decode(__UpperCAmelCase      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[int]:
      lowerCAmelCase__ :  List[Any]							      =					[F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
      lowerCAmelCase__ :  Any							      =					(
          F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(__UpperCAmelCase      ) + F"""{self.bos_token}Bot:"""
      )
      return self.encode(text=__UpperCAmelCase      )


