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


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

__snake_case						=			{
    '''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''',
    }
}

__snake_case						=			{
    '''AI-Sweden/gpt-sw3-126m''': 20_48,
    '''AI-Sweden/gpt-sw3-350m''': 20_48,
    '''AI-Sweden/gpt-sw3-1.6b''': 20_48,
    '''AI-Sweden/gpt-sw3-6.7b''': 20_48,
    '''AI-Sweden/gpt-sw3-20b''': 20_48,
}







class 	__snake_case      (   _a      ):
 __lowerCamelCase			:      List[Any]           =					VOCAB_FILES_NAMES
 __lowerCamelCase			:      Optional[int]           =					PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase			:      List[Any]           =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase			:      Tuple           =					['''input_ids''', '''attention_mask''']
 def __init__(     self						,      snake_case__						,      snake_case__=False						,      snake_case__=False						,      snake_case__=False						,      snake_case__=None						,      snake_case__=None						,      snake_case__=None						,      snake_case__=None						,      snake_case__ = None						,      **snake_case__						,      )		->							None:
       '''simple docstring'''





       UpperCAmelCase			:       List[str]			     ={} if sp_model_kwargs is None else sp_model_kwargs

       UpperCAmelCase			:       Union[str, 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'''  )
             UpperCAmelCase			:       Dict			     ='''None'''

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

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

       UpperCAmelCase			:       Dict			     =do_lower_case
       UpperCAmelCase			:       Optional[Any]			     =remove_space
       UpperCAmelCase			:       Optional[Any]			     =keep_accents
       UpperCAmelCase			:       Tuple			     =vocab_file

       UpperCAmelCase			:       Dict			     =spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(snake_case__  )

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

       # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
       UpperCAmelCase			:       str			     =re.compile(
           f'''[{''.join(map(snake_case__						,      list(range(0						,      9  )  ) + list(range(11						,      32  )  ) + list(range(127						,      160  )  ) + [160, 173, 8203]  )  )}]'''  )
 def __getstate__(     self  )		->							Tuple:
       '''simple docstring'''





       UpperCAmelCase			:       List[str]			     =self.__dict__.copy()
       UpperCAmelCase			:       Tuple			     =None
       return state
 def __setstate__(     self						,      snake_case__  )		->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     =d

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

       UpperCAmelCase			:       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 		UpperCAmelCase__				(     self  )		->							int:
       '''simple docstring'''





       return len(self.sp_model  )
 def 		UpperCAmelCase__				(     self						,      snake_case__  )		->							str:
       '''simple docstring'''





       UpperCAmelCase			:       Dict			     =self.non_printing_characters_re.sub(''''''						,      snake_case__  )

       # Normalize whitespaces
       UpperCAmelCase			:       Optional[int]			     =''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]  )

       # NFC Unicode normalization
       UpperCAmelCase			:       Union[str, Any]			     =unicodedata.normalize('''NFC'''						,      snake_case__  )
       return text
 def 		UpperCAmelCase__				(     self						,      snake_case__						,      **snake_case__  )		->							List[str]:
       '''simple docstring'''





       UpperCAmelCase			:       Dict			     =self.preprocess_text(snake_case__  )
       return self.sp_model.encode(snake_case__						,      out_type=snake_case__  )
 def 		UpperCAmelCase__				(     self						,      snake_case__  )		->							int:
       '''simple docstring'''





       return self.sp_model.PieceToId(snake_case__  )
 def 		UpperCAmelCase__				(     self						,      snake_case__  )		->							str:
       '''simple docstring'''





       return self.sp_model.IdToPiece(snake_case__  )
 @staticmethod
 def 		UpperCAmelCase__				(     snake_case__  )		->							str:
       '''simple docstring'''





       return out_string
 def 		UpperCAmelCase__				(     self						,      snake_case__  )		->							str:
       '''simple docstring'''





       UpperCAmelCase			:       Union[str, Any]			     =[]
       UpperCAmelCase			:       Dict			     =''''''
       UpperCAmelCase			:       int			     =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(snake_case__  ) + token
                   UpperCAmelCase			:       Dict			     =True
                   UpperCAmelCase			:       List[Any]			     =[]
             else:
                   current_sub_tokens.append(snake_case__  )
                   UpperCAmelCase			:       Dict			     =False
       out_string += self.sp_model.decode(snake_case__  )

       return out_string
 def 		UpperCAmelCase__				(     self  )		->							Dict[str, int]:
       '''simple docstring'''





       UpperCAmelCase			:       str			     ={self.convert_ids_to_tokens(snake_case__  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab
 def 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__ = None  )		->							Tuple[str]:
       '''simple docstring'''





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

       if os.path.abspath(self.vocab_file  ) != os.path.abspath(snake_case__  ) and os.path.isfile(self.vocab_file  ):
             copyfile(self.vocab_file						,      snake_case__  )
       elif not os.path.isfile(self.vocab_file  ):
             with open(snake_case__						,      '''wb'''  ) as fi:
                   UpperCAmelCase			:       Union[str, Any]			     =self.sp_model.serialized_model_proto()
                   fi.write(snake_case__  )

       return (out_vocab_file,)
 def 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__ = False  )		->							Union[List[int], List[List[int]], "torch.Tensor"]:
       '''simple docstring'''





       if isinstance(snake_case__						,      snake_case__  ):
             UpperCAmelCase			:       List[str]			     =self.preprocess_text(snake_case__  )
             UpperCAmelCase			:       List[str]			     =self.sp_model.encode(snake_case__  )
       else:
             UpperCAmelCase			:       Dict			     =[self.preprocess_text(snake_case__  ) for t in text]
             UpperCAmelCase			:       Union[str, Any]			     =self.sp_model.encode(snake_case__  )

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

       return token_ids
 def 		UpperCAmelCase__				(     self						,      snake_case__  )		->							str:
       '''simple docstring'''





       return self.sp_model.decode(snake_case__  )






 def 		UpperCAmelCase__				(     self						,      snake_case__  )		->							List[int]:
       '''simple docstring'''





       UpperCAmelCase			:       Dict			     =[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(snake_case__  ) + f'''{self.bos_token}Bot:'''
       )
       return self.encode(text=snake_case__  )







lowerCAmelCase_    =				{
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355_818,
}






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						float:






				"""simple docstring"""


				if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
								snake_case_						:    str				=  (
								    f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
								    f'''Valid values are: {", ".join(_UpperCamelCase )}'''
								)
								raise ValueError(_UpperCamelCase )
				return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()
