



'''simple docstring'''





from math import log

from scipy.constants import Boltzmann, physical_constants

__a  			=						300  # TEMPERATURE (unit = K)





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  )						->					str:


      if donor_conc <= 0:
            raise ValueError("""Donor concentration should be positive"""						)
      elif acceptor_conc <= 0:
            raise ValueError("""Acceptor concentration should be positive"""						)
      elif intrinsic_conc <= 0:
            raise ValueError("""Intrinsic concentration should be positive"""						)
      elif donor_conc <= intrinsic_conc:
            raise ValueError(
                """Donor concentration should be greater than intrinsic concentration"""						)
      elif acceptor_conc <= intrinsic_conc:
            raise ValueError(
                """Acceptor concentration should be greater than intrinsic concentration"""						)
      else:
            return (
                Boltzmann
                * T
                * log((donor_conc * acceptor_conc) / intrinsic_conc**2						)
                / physical_constants["electron volt"][0]
            )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






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

lowerCAmelCase_      :  Union[str, Any]				     =  {
    '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_      :  str				     =  {
    '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    (						snake_case_				):
      __magic_name__  :       List[Any]   							=					VOCAB_FILES_NAMES
      __magic_name__  :       Dict   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       Optional[int]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       List[str]   							=					['''input_ids''', '''attention_mask''']






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


           '''simple docstring'''

           a_     :							Any     						=  {} if sp_model_kwargs is None else sp_model_kwargs

           a_     :							Optional[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_     :							List[Any]     						=  """None"""

           # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
           a_     :							Union[str, Any]     						=  """<|endoftext|>""" if eos_token is None else eos_token
           a_     :							Dict     						=  """<unk>""" if unk_token is None else unk_token
           if "gpt-sw3-7b" in name_or_path:
                a_     :							Tuple     						=  unk_token if pad_token is None else pad_token
                a_     :							Any     						=  eos_token if bos_token is None else bos_token
           else:
                a_     :							Optional[int]     						=  """<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=lowercase__						,						remove_space=lowercase__						,						keep_accents=lowercase__						,						bos_token=lowercase__						,						eos_token=lowercase__						,						unk_token=lowercase__						,						pad_token=lowercase__						,						sp_model_kwargs=self.sp_model_kwargs						,						**lowercase__						,						)

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

           a_     :							Any     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(lowercase__       )

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

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






      def __getstate__( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.__dict__.copy()
           a_     :							Tuple     						=  None
           return state






      def __setstate__( self   :  Tuple						,						lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  d

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

           a_     :							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 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           return len(self.sp_model       )






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.non_printing_characters_re.sub(""""""						,						lowercase__       )

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

           # NFC Unicode normalization
           a_     :							str     						=  unicodedata.normalize("""NFC"""						,						lowercase__       )
           return text






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  str						,						**lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.preprocess_text(lowercase__       )
           return self.sp_model.encode(lowercase__						,						out_type=lowercase__       )






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  str       ):


           '''simple docstring'''

           return self.sp_model.PieceToId(lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  int       ):


           '''simple docstring'''

           return self.sp_model.IdToPiece(lowercase__       )






      @staticmethod
      def 							lowercase_						( lowercase__   :  str       ):


           '''simple docstring'''

           return out_string






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

           a_     :							Dict     						=  []
           a_     :							int     						=  """"""
           a_     :							Union[str, Any]     						=  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(lowercase__       ) + token
                     a_     :							int     						=  True
                     a_     :							Optional[int]     						=  []
                else:
                     current_sub_tokens.append(lowercase__       )
                     a_     :							List[str]     						=  False
           out_string += self.sp_model.decode(lowercase__       )

           return out_string






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  {self.convert_ids_to_tokens(lowercase__       ): i for i in range(self.vocab_size       )}
           vocab.update(self.added_tokens_encoder       )
           return vocab






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

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

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

           return (out_vocab_file,)






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  Union[str, List[str]]						,						lowercase__   :  Union[str, bool] = False       ):


           '''simple docstring'''

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

           if return_tensors is True or return_tensors == "pt":
                a_     :							Tuple     						=  torch.tensor(lowercase__       )

           return token_ids






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Union[int, List[int]]       ):


           '''simple docstring'''

           return self.sp_model.decode(lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  "Conversation"       ):


           '''simple docstring'''

           a_     :							Dict     						=  [F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
           a_     :							Any     						=  (
               F"{self.eos_token}{self.bos_token}" + F"{self.bos_token}".join(lowercase__       ) + F"{self.bos_token}Bot:"
           )
           return self.encode(text=lowercase__       )
