'''simple docstring'''







from collections.abc import Generator
from math import sin


def    lowerCamelCase       (			UpperCAmelCase__				:  List[Any]		)			->	bytes:


							'''simple docstring'''






							if len(SCREAMING_SNAKE_CASE_		) != 3_2:
														raise ValueError('Input must be of length 32'		)

							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = b''
							for i in [3, 2, 1, 0]:
														little_endian += string_aa[8 * i : 8 * i + 8]
							return little_endian


def    lowerCamelCase       (			UpperCAmelCase__				:  Dict		)			->	bytes:


							'''simple docstring'''






							if i < 0:
														raise ValueError('Input must be non-negative'		)

							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = format(SCREAMING_SNAKE_CASE_				,						'08x'		)[-8:]
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = b''
							for i in [3, 2, 1, 0]:
														little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8'		)
							return little_endian_hex


def    lowerCamelCase       (			UpperCAmelCase__				:  Union[str, Any]		)			->	bytes:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = b''
							for char in message:
														bit_string += format(SCREAMING_SNAKE_CASE_				,						'08b'		).encode('utf-8'		)
							SCREAMING_SNAKE_CASE__     :List[str]						    = format(len(SCREAMING_SNAKE_CASE_		)				,						'064b'		).encode('utf-8'		)

							# Pad bit_string to a multiple of 512 chars
							bit_string += b"1"
							while len(SCREAMING_SNAKE_CASE_		) % 5_1_2 != 4_4_8:
														bit_string += b"0"
							bit_string += to_little_endian(start_len[3_2:]		) + to_little_endian(start_len[:3_2]		)

							return bit_string


def    lowerCamelCase       (			UpperCAmelCase__				:  int		)			->	Generator[list[int], None, None]:


							'''simple docstring'''






							if len(SCREAMING_SNAKE_CASE_		) % 5_1_2 != 0:
														raise ValueError('Input must have length that\'s a multiple of 512'		)

							for pos in range(0				,						len(SCREAMING_SNAKE_CASE_		)				,						5_1_2		):
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = bit_string[pos : pos + 5_1_2]
														SCREAMING_SNAKE_CASE__     :int						    = []
														for i in range(0				,						5_1_2				,						3_2		):
																					block_words.append(int(to_little_endian(block[i : i + 3_2]		)				,						2		)		)
														yield block_words


def    lowerCamelCase       (			UpperCAmelCase__				:  Optional[Any]		)			->	int:


							'''simple docstring'''






							if i < 0:
														raise ValueError('Input must be non-negative'		)

							SCREAMING_SNAKE_CASE__     :List[Any]						    = format(SCREAMING_SNAKE_CASE_				,						'032b'		)
							SCREAMING_SNAKE_CASE__     :List[Any]						    = ''
							for c in i_str:
														new_str += "1" if c == "0" else "0"
							return int(SCREAMING_SNAKE_CASE_				,						2		)


def    lowerCamelCase       (			UpperCAmelCase__				:  Optional[Any]				,						UpperCAmelCase__				:  Dict		)			->	int:


							'''simple docstring'''






							return (a + b) % 2**3_2


def    lowerCamelCase       (			UpperCAmelCase__				:  List[Any]				,						UpperCAmelCase__				:  int		)			->	int:


							'''simple docstring'''






							if i < 0:
														raise ValueError('Input must be non-negative'		)
							if shift < 0:
														raise ValueError('Shift must be non-negative'		)
							return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2


def    lowerCamelCase       (			UpperCAmelCase__				:  List[Any]		)			->	bytes:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Dict						    = preprocess(SCREAMING_SNAKE_CASE_		)

							SCREAMING_SNAKE_CASE__     :Dict						    = [int(2**3_2 * abs(sin(i + 1		)		)		) for i in range(6_4		)]

							# Starting states
							SCREAMING_SNAKE_CASE__     :Dict						    = 0x67452301
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 0xefcdab89
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 0x98badcfe
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 0x10325476

							SCREAMING_SNAKE_CASE__     :List[str]						    = [
							    7,
							    1_2,
							    1_7,
							    2_2,
							    7,
							    1_2,
							    1_7,
							    2_2,
							    7,
							    1_2,
							    1_7,
							    2_2,
							    7,
							    1_2,
							    1_7,
							    2_2,
							    5,
							    9,
							    1_4,
							    2_0,
							    5,
							    9,
							    1_4,
							    2_0,
							    5,
							    9,
							    1_4,
							    2_0,
							    5,
							    9,
							    1_4,
							    2_0,
							    4,
							    1_1,
							    1_6,
							    2_3,
							    4,
							    1_1,
							    1_6,
							    2_3,
							    4,
							    1_1,
							    1_6,
							    2_3,
							    4,
							    1_1,
							    1_6,
							    2_3,
							    6,
							    1_0,
							    1_5,
							    2_1,
							    6,
							    1_0,
							    1_5,
							    2_1,
							    6,
							    1_0,
							    1_5,
							    2_1,
							    6,
							    1_0,
							    1_5,
							    2_1,
							]

							# Process bit string in chunks, each with 16 32-char words
							for block_words in get_block_words(SCREAMING_SNAKE_CASE_		):
														SCREAMING_SNAKE_CASE__     :List[Any]						    = aa
														SCREAMING_SNAKE_CASE__     :List[Any]						    = ba
														SCREAMING_SNAKE_CASE__     :Tuple						    = ca
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = da

														# Hash current chunk
														for i in range(6_4		):
																					if i <= 1_5:
																												# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
																												SCREAMING_SNAKE_CASE__     :str						    = d ^ (b & (c ^ d))
																												SCREAMING_SNAKE_CASE__     :Dict						    = i
																					elif i <= 3_1:
																												# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
																												SCREAMING_SNAKE_CASE__     :Optional[Any]						    = c ^ (d & (b ^ c))
																												SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (5 * i + 1) % 1_6
																					elif i <= 4_7:
																												SCREAMING_SNAKE_CASE__     :List[str]						    = b ^ c ^ d
																												SCREAMING_SNAKE_CASE__     :List[Any]						    = (3 * i + 5) % 1_6
																					else:
																												SCREAMING_SNAKE_CASE__     :int						    = c ^ (b | not_aa(SCREAMING_SNAKE_CASE_		))
																												SCREAMING_SNAKE_CASE__     :Any						    = (7 * i) % 1_6
																					SCREAMING_SNAKE_CASE__     :Optional[int]						    = (f + a + added_consts[i] + block_words[g]) % 2**3_2
																					SCREAMING_SNAKE_CASE__     :List[str]						    = d
																					SCREAMING_SNAKE_CASE__     :str						    = c
																					SCREAMING_SNAKE_CASE__     :Optional[Any]						    = b
																					SCREAMING_SNAKE_CASE__     :Any						    = sum_aa(SCREAMING_SNAKE_CASE_				,						left_rotate_aa(SCREAMING_SNAKE_CASE_				,						shift_amounts[i]		)		)

														# Add hashed chunk to running total
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = sum_aa(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_		)
														SCREAMING_SNAKE_CASE__     :Any						    = sum_aa(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_		)
														SCREAMING_SNAKE_CASE__     :List[str]						    = sum_aa(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_		)
														SCREAMING_SNAKE_CASE__     :int						    = sum_aa(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_		)

							SCREAMING_SNAKE_CASE__     :Any						    = reformat_hex(SCREAMING_SNAKE_CASE_		) + reformat_hex(SCREAMING_SNAKE_CASE_		) + reformat_hex(SCREAMING_SNAKE_CASE_		) + reformat_hex(SCREAMING_SNAKE_CASE_		)
							return digest


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


__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""": 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       _a    (		__a      ):


  """simple docstring"""

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




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







    '''simple docstring'''


    lowercase_     =       {} if sp_model_kwargs is None else sp_model_kwargs

    lowercase_     =       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"""      )
      lowercase_     =       """None"""

    # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
    lowercase_     =       """<|endoftext|>""" if eos_token is None else eos_token
    lowercase_     =       """<unk>""" if unk_token is None else unk_token
    if "gpt-sw3-7b" in name_or_path:
      lowercase_     =       unk_token if pad_token is None else pad_token
      lowercase_     =       eos_token if bos_token is None else bos_token
    else:
      lowercase_     =       """<pad>""" if pad_token is None else pad_token
      lowercase_     =       """<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_   ,				)

    lowercase_     =       do_lower_case
    lowercase_     =       remove_space
    lowercase_     =       keep_accents
    lowercase_     =       vocab_file

    lowercase_     =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(lowercase_      )

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

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




  def __getstate__(		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       self.__dict__.copy()
    lowercase_     =       None
    return state




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







    '''simple docstring'''


    lowercase_     =       d

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

    lowercase_     =       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      :       int      ):







    '''simple docstring'''


    return len(self.sp_model      )




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







    '''simple docstring'''


    lowercase_     =       self.non_printing_characters_re.sub(""""""   ,				lowercase_      )

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

    # NFC Unicode normalization
    lowercase_     =       unicodedata.normalize("""NFC"""   ,				lowercase_      )
    return text




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       str   ,				**lowercase_      :       Any      ):







    '''simple docstring'''


    lowercase_     =       self.preprocess_text(lowercase_      )
    return self.sp_model.encode(lowercase_   ,				out_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       str      ):







    '''simple docstring'''


    return self.sp_model.PieceToId(lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       int      ):







    '''simple docstring'''


    return self.sp_model.IdToPiece(lowercase_      )




  @staticmethod
  def 		lowerCamelCase__  (		lowercase_      :       str      ):







    '''simple docstring'''


    return out_string




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       []
    lowercase_     =       """"""
    lowercase_     =       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
        lowercase_     =       True
        lowercase_     =       []
      else:
        current_sub_tokens.append(lowercase_      )
        lowercase_     =       False
    out_string += self.sp_model.decode(lowercase_      )

    return out_string




  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       {self.convert_ids_to_tokens(lowercase_      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab




  def 		lowerCamelCase__  (		self      :       Tuple   ,				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
    lowercase_     =       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:
        lowercase_     =       self.sp_model.serialized_model_proto()
        fi.write(lowercase_      )

    return (out_vocab_file,)




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







    '''simple docstring'''


    if isinstance(lowercase_   ,				lowercase_      ):
      lowercase_     =       self.preprocess_text(lowercase_      )
      lowercase_     =       self.sp_model.encode(lowercase_      )
    else:
      lowercase_     =       [self.preprocess_text(lowercase_      ) for t in text]
      lowercase_     =       self.sp_model.encode(lowercase_      )

    if return_tensors is True or return_tensors == "pt":
      lowercase_     =       torch.tensor(lowercase_      )

    return token_ids




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







    '''simple docstring'''


    return self.sp_model.decode(lowercase_      )





  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       "Conversation"      ):







    '''simple docstring'''


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

