


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


UpperCAmelCase_   :				Tuple						      = logging.get_logger(__name__)
UpperCAmelCase_   :				List[str]						      = {'vocab_file': 'spiece.model'}

UpperCAmelCase_   :				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',
    }
}

UpperCAmelCase_   :				List[Any]						      = {
    '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 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Optional[Any]       =		VOCAB_FILES_NAMES
      snake_case__       :  int       =		PRETRAINED_VOCAB_FILES_MAP
      snake_case__       :  Optional[Any]       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__       :  Tuple       =		['''input_ids''', '''attention_mask''']
      def __init__( self       : Any  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=False  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=False  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=False  ,							SCREAMING_SNAKE_CASE__       : Tuple=None  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=None  ,							SCREAMING_SNAKE_CASE__       : List[Any]=None  ,							SCREAMING_SNAKE_CASE__       : int=None  ,							SCREAMING_SNAKE_CASE__       : Optional[Dict[str, Any]] = None  ,							**SCREAMING_SNAKE_CASE__       : Dict  ,							)	->    None:
             a_      :	List[str]							=						{} 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_      :	Optional[Any]							=						'None'

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

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

             a_      :	Union[str, Any]							=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
             self.sp_model.Load(SCREAMING_SNAKE_CASE__  )

             # Used for whitespace normalization in input texts
             # fmt : off
             a_      :	Union[str, Any]							=						{' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
             # fmt : on

             # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
             a_      :	Dict							=						re.compile(
                 F"""[{"".join(map(SCREAMING_SNAKE_CASE__  ,							list(range(0  ,							9  )  ) + list(range(1_1  ,							3_2  )  ) + list(range(1_2_7  ,							1_6_0  )  ) + [1_6_0, 1_7_3, 8_2_0_3]  )  )}]"""  )
      def __getstate__( self       : Union[str, Any]  )	->    Union[str, Any]:
             a_      :	Optional[int]							=						self.__dict__.copy()
             a_      :	str							=						None
             return state
      def __setstate__( self       : int  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    List[Any]:
             a_      :	Optional[Any]							=						d

             # for backward compatibility
             if not hasattr(self  ,							'sp_model_kwargs'  ):
                    a_      :	Any							=						{}

             a_      :	List[Any]							=						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       : Tuple  )	->    int:
             return len(self.sp_model  )
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : str  )	->    str:
             a_      :	Union[str, Any]							=						self.non_printing_characters_re.sub(''  ,							SCREAMING_SNAKE_CASE__  )

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

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

             return out_string
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Dict[str, int]:
             a_      :	Tuple							=						{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  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Optional[str] = None  )	->    Tuple[str]:
             if not os.path.isdir(SCREAMING_SNAKE_CASE__  ):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
                    return
             a_      :	List[Any]							=						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:
                           a_      :	Optional[Any]							=						self.sp_model.serialized_model_proto()
                           fi.write(SCREAMING_SNAKE_CASE__  )

             return (out_vocab_file,)
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							SCREAMING_SNAKE_CASE__       : Union[str, List[str]]  ,							SCREAMING_SNAKE_CASE__       : Union[str, bool] = False  )	->    Union[List[int], List[List[int]], "torch.Tensor"]:

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

             if return_tensors is True or return_tensors == "pt":
                    a_      :	int							=						torch.tensor(SCREAMING_SNAKE_CASE__  )

             return token_ids
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Union[int, List[int]]  )	->    str:
             return self.sp_model.decode(SCREAMING_SNAKE_CASE__  )

      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : "Conversation"  )	->    List[int]:
             a_      :	List[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(SCREAMING_SNAKE_CASE__  ) + F"""{self.bos_token}Bot:"""
             )
             return self.encode(text=SCREAMING_SNAKE_CASE__  )






def  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	bool:

       """simple docstring"""

       a_      :	List[Any]							=						n ** (1 / 3)
       return (val * val * val) == n


if __name__ == "__main__":
   print(perfect_cube(27))
   print(perfect_cube(4))


