






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

__lowerCamelCase							=  {
    '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							=  {
    '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       __A		(   lowerCAmelCase__			):
					UpperCAmelCase__								=				VOCAB_FILES_NAMES
					UpperCAmelCase__								=				PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__								=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__								=				["input_ids", "attention_mask"]

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      Optional[int]     ,		__snake_case  :      Optional[int]=False     ,		__snake_case  :      int=False     ,		__snake_case  :      List[Any]=False     ,		__snake_case  :      List[Any]=None     ,		__snake_case  :      List[Any]=None     ,		__snake_case  :      Optional[int]=None     ,		__snake_case  :      List[str]=None     ,		__snake_case  :      Optional[Any] = None     ,		**__snake_case  :      List[str]     ,		)			->							Optional[int]:
											__magic_name__:	Union[str, Any]							=       {} if sp_model_kwargs is None else sp_model_kwargs

											__magic_name__:	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"""					)
																	__magic_name__:	Dict							=       """None"""

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

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

											__magic_name__:	Union[str, Any]							=       do_lower_case
											__magic_name__:	Tuple							=       remove_space
											__magic_name__:	Union[str, Any]							=       keep_accents
											__magic_name__:	Any							=       vocab_file

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

											# Used for whitespace normalization in input texts
											# fmt : off
											__magic_name__:	List[str]							=       {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
											# fmt : on

											# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
											__magic_name__:	Any							=       re.compile(
											    F'[{"".join(map(__UpperCAmelCase     ,		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  :      Tuple					)			->							int:
											__magic_name__:	Optional[int]							=       self.__dict__.copy()
											__magic_name__:	List[str]							=       None
											return state

					def __setstate__(			self  :      Any     ,		__snake_case  :      Optional[int]					)			->							str:
											__magic_name__:	List[Any]							=       d

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

											__magic_name__:	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   lowerCamelCase__   (			self  :      List[str]					)			->							Any:
											return len(self.sp_model					)

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      Tuple					)			->							Optional[int]:
											__magic_name__:	Union[str, Any]							=       self.non_printing_characters_re.sub(""""""     ,		__UpperCAmelCase					)

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

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

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      Tuple     ,		**__snake_case  :      Dict					)			->							List[str]:
											__magic_name__:	Any							=       self.preprocess_text(__UpperCAmelCase					)
											return self.sp_model.encode(__UpperCAmelCase     ,		out_type=__UpperCAmelCase					)

					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      Optional[Any]					)			->							Tuple:
											return self.sp_model.PieceToId(__UpperCAmelCase					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      List[Any]					)			->							Optional[int]:
											return self.sp_model.IdToPiece(__UpperCAmelCase					)

					@staticmethod
					def   lowerCamelCase__   (			__snake_case  :      Any					)			->							int:
											return out_string

					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      Optional[int]					)			->							Union[str, Any]:
											__magic_name__:	Optional[Any]							=       []
											__magic_name__:	List[Any]							=       """"""
											__magic_name__:	List[str]							=       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(__UpperCAmelCase					) + token
																							__magic_name__:	Tuple							=       True
																							__magic_name__:	Optional[Any]							=       []
																	else:
																							current_sub_tokens.append(__UpperCAmelCase					)
																							__magic_name__:	Optional[Any]							=       False
											out_string += self.sp_model.decode(__UpperCAmelCase					)

											return out_string

					def   lowerCamelCase__   (			self  :      str					)			->							List[Any]:
											__magic_name__:	Any							=       {self.convert_ids_to_tokens(__UpperCAmelCase					): i for i in range(self.vocab_size					)}
											vocab.update(self.added_tokens_encoder					)
											return vocab

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Tuple     ,		__snake_case  :      str = None					)			->							List[Any]:
											if not os.path.isdir(__UpperCAmelCase					):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'					)
																	return
											__magic_name__:	Union[str, Any]							=       os.path.join(
											    __UpperCAmelCase     ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

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

											return (out_vocab_file,)

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      Dict     ,		__snake_case  :      List[str] = False					)			->							Any:

											if isinstance(__UpperCAmelCase     ,		__UpperCAmelCase					):
																	__magic_name__:	Tuple							=       self.preprocess_text(__UpperCAmelCase					)
																	__magic_name__:	Union[str, Any]							=       self.sp_model.encode(__UpperCAmelCase					)
											else:
																	__magic_name__:	Union[str, Any]							=       [self.preprocess_text(__UpperCAmelCase					) for t in text]
																	__magic_name__:	Union[str, Any]							=       self.sp_model.encode(__UpperCAmelCase					)

											if return_tensors is True or return_tensors == "pt":
																	__magic_name__:	Tuple							=       torch.tensor(__UpperCAmelCase					)

											return token_ids

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      Any					)			->							Any:
											return self.sp_model.decode(__UpperCAmelCase					)






					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      Union[str, Any]					)			->							List[Any]:
											__magic_name__:	Dict							=       [F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
											__magic_name__:	str							=       (
											    F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(__UpperCAmelCase					) + F'{self.bos_token}Bot:'
											)
											return self.encode(text=__UpperCAmelCase					)








def 		a__      (		_UpperCamelCase      :			int				):
   __lowerCamelCase    				=     int(_UpperCamelCase				)
   if decimal in (0, 1):  # Exit cases for the recursion
      return str(_UpperCamelCase				)
   __lowerCamelCase				,__lowerCamelCase    				=     divmod(_UpperCamelCase			,2				)
   return binary_recursive(_UpperCamelCase				) + str(_UpperCamelCase				)





def 		a__      (		_UpperCamelCase      :			str				):
   __lowerCamelCase    				=     str(_UpperCamelCase				).strip()
   if not number:
      raise ValueError('''No input value was provided'''				)
   __lowerCamelCase    				=     '''-''' if number.startswith('''-'''				) else ''''''
   __lowerCamelCase    				=     number.lstrip('''-'''				)
   if not number.isnumeric():
      raise ValueError('''Input value is not an integer'''				)
   return F"""{negative}0b{binary_recursive(int(_UpperCamelCase				)				)}"""


if __name__ == "__main__":
  from doctest import testmod

  testmod()






