






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     :							Optional[int]    =logging.get_logger(__name__)
lowerCamelCase     :							Union[str, Any]    ={'''vocab_file''': '''spiece.model'''}

lowerCamelCase     :							str    ={
    '''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''': 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       __a  (   A__	):
 _lowerCAmelCase	:	Optional[Any]				=			VOCAB_FILES_NAMES
 _lowerCAmelCase	:	int				=			PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	:	Optional[Any]				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	:	Dict				=			['''input_ids''', '''attention_mask''']







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


      '''simple docstring'''






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

      UpperCamelCase__      :       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"						)
           UpperCamelCase__      :       str	    =  "None"

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

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

      UpperCamelCase__      :       int	    =  do_lower_case
      UpperCamelCase__      :       Any	    =  remove_space
      UpperCamelCase__      :       Any	    =  keep_accents
      UpperCamelCase__      :       List[Any]	    =  vocab_file

      UpperCamelCase__      :       Optional[int]	    =  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
      self.sp_model.Load(SCREAMING_SNAKE_CASE						)

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

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







 def __getstate__(		self :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  self.__dict__.copy()
      UpperCamelCase__      :       Optional[int]	    =  None
      return state







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


      '''simple docstring'''






      UpperCamelCase__      :       Union[str, Any]	    =  d

      # for backward compatibility
      if not hasattr(self							,       "sp_model_kwargs"						):
           UpperCamelCase__      :       List[str]	    =  {}

      UpperCamelCase__      :       List[str]	    =  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  __lowercase (		self :       str						):


      '''simple docstring'''






      return len(self.sp_model						)







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       str						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.non_printing_characters_re.sub(""							,       SCREAMING_SNAKE_CASE						)

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

      # NFC Unicode normalization
      UpperCamelCase__      :       Any	    =  unicodedata.normalize("NFC"							,       SCREAMING_SNAKE_CASE						)
      return text







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       str							,       **SCREAMING_SNAKE_CASE :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  self.preprocess_text(SCREAMING_SNAKE_CASE						)
      return self.sp_model.encode(SCREAMING_SNAKE_CASE							,       out_type=SCREAMING_SNAKE_CASE						)







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


      '''simple docstring'''






      return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE						)







 @staticmethod
 def  __lowercase (		SCREAMING_SNAKE_CASE :       str						):


      '''simple docstring'''






      return out_string







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


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  []
      UpperCamelCase__      :       List[Any]	    =  ""
      UpperCamelCase__      :       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(SCREAMING_SNAKE_CASE						) + token
                UpperCamelCase__      :       List[str]	    =  True
                UpperCamelCase__      :       List[str]	    =  []
           else:
                current_sub_tokens.append(SCREAMING_SNAKE_CASE						)
                UpperCamelCase__      :       Any	    =  False
      out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE						)

      return out_string







 def  __lowercase (		self :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE						): i for i in range(self.vocab_size						)}
      vocab.update(self.added_tokens_encoder						)
      return vocab







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


      '''simple docstring'''






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

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

      return (out_vocab_file,)







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Union[str, List[str]]							,       SCREAMING_SNAKE_CASE :       Union[str, bool] = False						):


      '''simple docstring'''






      if isinstance(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						):
           UpperCamelCase__      :       int	    =  self.preprocess_text(SCREAMING_SNAKE_CASE						)
           UpperCamelCase__      :       Optional[Any]	    =  self.sp_model.encode(SCREAMING_SNAKE_CASE						)
      else:
           UpperCamelCase__      :       List[str]	    =  [self.preprocess_text(SCREAMING_SNAKE_CASE						) for t in text]
           UpperCamelCase__      :       Tuple	    =  self.sp_model.encode(SCREAMING_SNAKE_CASE						)

      if return_tensors is True or return_tensors == "pt":
           UpperCamelCase__      :       Any	    =  torch.tensor(SCREAMING_SNAKE_CASE						)

      return token_ids







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       Union[int, List[int]]						):


      '''simple docstring'''






      return self.sp_model.decode(SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       "Conversation"						):


      '''simple docstring'''






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







import re







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     bool:
     UpperCamelCase__      :       Union[str, Any]	    =  re.compile(
         R"^(?:0|94|\+94|0{2}94)" R"7(0|1|2|4|5|6|7|8)" R"(-| |)" R"\d{7}$"      )

     return bool(re.search(__lowerCAmelCase ,   __lowerCAmelCase      )      )


if __name__ == "__main__":
 lowerCamelCase     :							Union[str, Any]    ='''0094702343221'''

 print(is_sri_lankan_phone_number(phone))