




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


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

__a							     =					{
    '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',
    }
}

__a							     =					{
    '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     __a(	_a  ):






    """simple docstring"""





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





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						None:
         UpperCAmelCase_     :				Optional[Any]  =     {} if sp_model_kwargs is None else sp_model_kwargs

         UpperCAmelCase_     :				int  =     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_     :				Optional[int]  =     '''None'''

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

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

         UpperCAmelCase_     :				int  =     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(_SCREAMING_SNAKE_CASE    )

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

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





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





    def __setstate__( self  ,_SCREAMING_SNAKE_CASE    )					->						Dict:
         UpperCAmelCase_     :				int  =     d

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

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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						str:
         UpperCAmelCase_     :				Optional[Any]  =     self.non_printing_characters_re.sub(''''''  ,_SCREAMING_SNAKE_CASE    )

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

         # NFC Unicode normalization
         UpperCAmelCase_     :				Union[str, Any]  =     unicodedata.normalize('''NFC'''  ,_SCREAMING_SNAKE_CASE    )
         return text





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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						int:
         return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						str:
         return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE    )





    @staticmethod
    def 				a__ ( _SCREAMING_SNAKE_CASE    )					->						str:
         return out_string





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						str:
         UpperCAmelCase_     :				Optional[Any]  =     []
         UpperCAmelCase_     :				int  =     ''''''
         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_     :				str  =     True
                   UpperCAmelCase_     :				int  =     []
              else:
                   current_sub_tokens.append(_SCREAMING_SNAKE_CASE    )
                   UpperCAmelCase_     :				Any  =     False
         out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE    )

         return out_string





    def 				a__ ( self    )					->						Dict[str, int]:
         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 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None    )					->						Tuple[str]:
         if not os.path.isdir(_SCREAMING_SNAKE_CASE    ):
              logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
              return
         UpperCAmelCase_     :				int  =     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_     :				Dict  =     self.sp_model.serialized_model_proto()
                   fi.write(_SCREAMING_SNAKE_CASE    )

         return (out_vocab_file,)





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

         if isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              UpperCAmelCase_     :				str  =     self.preprocess_text(_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				Dict  =     self.sp_model.encode(_SCREAMING_SNAKE_CASE    )
         else:
              UpperCAmelCase_     :				Any  =     [self.preprocess_text(_SCREAMING_SNAKE_CASE    ) for t in text]
              UpperCAmelCase_     :				Optional[int]  =     self.sp_model.encode(_SCREAMING_SNAKE_CASE    )

         if return_tensors is True or return_tensors == "pt":
              UpperCAmelCase_     :				List[Any]  =     torch.tensor(_SCREAMING_SNAKE_CASE    )

         return token_ids





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						str:
         return self.sp_model.decode(_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						List[int]:
         UpperCAmelCase_     :				str  =     [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
         UpperCAmelCase_     :				Any  =     (
             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    )





from __future__ import annotations

import math

__a							     =					'2020.9.26'
__a							     =					'xcodz-dot, cclaus, dhruvmanila'







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     if not all(isinstance(_lowercase					,		(float, int) ) for val in locals().values() ):
          UpperCAmelCase_     :				Optional[int]  =     f'''Input values must either be float or int: {list(locals().values() )}'''
          raise TypeError(_lowercase )
     UpperCAmelCase_     :				Tuple  =     ((x * distance) / (z + distance)) * scale
     UpperCAmelCase_     :				str  =     ((y * distance) / (z + distance)) * scale
     return projected_x, projected_y







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ):
          raise TypeError('''Axis must be a str''' )
     UpperCAmelCase_     :				Optional[Any]  =     locals()
     del input_variables["axis"]
     if not all(isinstance(_lowercase					,		(float, int) ) for val in input_variables.values() ):
          UpperCAmelCase_     :				List[Any]  =     (
              '''Input values except axis must either be float or int: '''
              f'''{list(input_variables.values() )}'''
          )
          raise TypeError(_lowercase )
     UpperCAmelCase_     :				Dict  =     (angle % 360) / 450 * 180 / math.pi
     if axis == "z":
          UpperCAmelCase_     :				Optional[int]  =     x * math.cos(_lowercase ) - y * math.sin(_lowercase )
          UpperCAmelCase_     :				List[Any]  =     y * math.cos(_lowercase ) + x * math.sin(_lowercase )
          UpperCAmelCase_     :				Optional[int]  =     z
     elif axis == "x":
          UpperCAmelCase_     :				Any  =     y * math.cos(_lowercase ) - z * math.sin(_lowercase )
          UpperCAmelCase_     :				int  =     z * math.cos(_lowercase ) + y * math.sin(_lowercase )
          UpperCAmelCase_     :				Dict  =     x
     elif axis == "y":
          UpperCAmelCase_     :				Union[str, Any]  =     x * math.cos(_lowercase ) - z * math.sin(_lowercase )
          UpperCAmelCase_     :				Optional[int]  =     z * math.cos(_lowercase ) + x * math.sin(_lowercase )
          UpperCAmelCase_     :				Optional[int]  =     y
     else:
          raise ValueError('''not a valid axis, choose one of \'x\', \'y\', \'z\'''' )

     return new_x, new_y, new_z


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
     print(F"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")