


import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation

_snake_case   = logging.get_logger(__name__)

_snake_case   = {"vocab_file": "tokenizer.model"}

_snake_case   = {
    "vocab_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
    },
    "tokenizer_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
    },
}
_snake_case   = {
    "hf-internal-testing/llama-tokenizer": 2_0_4_8,
}
_snake_case   = "▁"

_snake_case ,						_snake_case   = "[INST]", "[/INST]"
_snake_case ,						_snake_case   = "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
_snake_case   = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."




class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: Optional[Any]      =					VOCAB_FILES_NAMES
 _UpperCamelCase				: Any      =					PRETRAINED_VOCAB_FILES_MAP
 _UpperCamelCase				: Any      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _UpperCamelCase				: Any      =					['''input_ids''', '''attention_mask''']
 def __init__(			self					: int       ,    _a					: Any       ,    _a					: Optional[Any]="<unk>"       ,    _a					: List[Any]="<s>"       ,    _a					: Optional[int]="</s>"       ,    _a					: Any=None       ,    _a					: Optional[Dict[str, Any]] = None       ,    _a					: Tuple=True       ,    _a					: Tuple=False       ,    _a					: Tuple=False       ,    _a					: Dict=True       ,    **_a					: List[str]       ,    )    ->			Union[str, Any]:
      UpperCAmelCase						:				Union[str, Any]		  =			{} if sp_model_kwargs is None else sp_model_kwargs
      UpperCAmelCase						:				Tuple		  =			AddedToken(_a       ,    lstrip=_a       ,    rstrip=_a				) if isinstance(_a       ,    _a				) else bos_token
      UpperCAmelCase						:				Tuple		  =			AddedToken(_a       ,    lstrip=_a       ,    rstrip=_a				) if isinstance(_a       ,    _a				) else eos_token
      UpperCAmelCase						:				List[str]		  =			AddedToken(_a       ,    lstrip=_a       ,    rstrip=_a				) if isinstance(_a       ,    _a				) else unk_token
      UpperCAmelCase						:				Dict		  =			AddedToken(_a       ,    lstrip=_a       ,    rstrip=_a				) if isinstance(_a       ,    _a				) else pad_token
      super().__init__(
          bos_token=_a       ,    eos_token=_a       ,    unk_token=_a       ,    pad_token=_a       ,    add_bos_token=_a       ,    add_eos_token=_a       ,    sp_model_kwargs=self.sp_model_kwargs       ,    clean_up_tokenization_spaces=_a       ,    legacy=_a       ,    **_a       ,    )
      if legacy:
           logger.warning_once(
               f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
               " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"				)
      UpperCAmelCase						:				Optional[int]		  =			legacy
      UpperCAmelCase						:				List[str]		  =			vocab_file
      UpperCAmelCase						:				List[str]		  =			add_bos_token
      UpperCAmelCase						:				List[str]		  =			add_eos_token
      UpperCAmelCase						:				List[Any]		  =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
      self.sp_model.Load(_a				)
 def __getstate__(			self					: int				)    ->			Tuple:
      UpperCAmelCase						:				List[Any]		  =			self.__dict__.copy()
      UpperCAmelCase						:				Dict		  =			None
      UpperCAmelCase						:				List[str]		  =			self.sp_model.serialized_model_proto()
      return state
 def __setstate__(			self					: int       ,    _a					: Union[str, Any]				)    ->			Optional[int]:
      UpperCAmelCase						:				Optional[int]		  =			d
      UpperCAmelCase						:				Any		  =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)
 @property
 def   _UpperCamelCase    (			self					: Any				)    ->			Tuple:
      return self.sp_model.get_piece_size()
 def   _UpperCamelCase    (			self					: List[str]				)    ->			Tuple:
      UpperCAmelCase						:				Tuple		  =			{self.convert_ids_to_tokens(_a				): i for i in range(self.vocab_size				)}
      vocab.update(self.added_tokens_encoder				)
      return vocab
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: Optional[Any]       ,    **_a					: List[str]				)    ->			List[str]:
      # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
      # the beginning of the text
      if not self.legacy:
           UpperCAmelCase						:				Tuple		  =			SPIECE_UNDERLINE + text.replace(_a       ,    " "				)
      return super().tokenize(_a       ,    **_a				)
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: Union[str, Any]				)    ->			Any:
      if not self.legacy:
           UpperCAmelCase						:				Union[str, Any]		  =			text.startswith(_a				)
           if is_first:
                UpperCAmelCase						:				Dict		  =			text[1:]

      UpperCAmelCase						:				Optional[int]		  =			self.sp_model.encode(_a       ,    out_type=_a				)

      if not self.legacy and not is_first and not text.startswith(" "				) and tokens[0].startswith(_a				):
           UpperCAmelCase						:				List[str]		  =			([tokens[0][1:]] if len(tokens[0]				) > 1 else []) + tokens[1:]
      return tokens
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: Any				)    ->			Any:
      return self.sp_model.piece_to_id(_a				)
 def   _UpperCamelCase    (			self					: Optional[Any]       ,    _a					: Dict				)    ->			Optional[int]:
      UpperCAmelCase						:				Tuple		  =			self.sp_model.IdToPiece(_a				)
      return token
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: Any				)    ->			Dict:
      UpperCAmelCase						:				List[Any]		  =			[]
      UpperCAmelCase						:				Tuple		  =			""
      UpperCAmelCase						:				Optional[Any]		  =			False
      for i, token in enumerate(_a				):
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                if not prev_is_special and i != 0:
                     out_string += " "
                out_string += self.sp_model.decode(_a				) + token
                UpperCAmelCase						:				int		  =			True
                UpperCAmelCase						:				Optional[int]		  =			[]
           else:
                current_sub_tokens.append(_a				)
                UpperCAmelCase						:				Tuple		  =			False
      out_string += self.sp_model.decode(_a				)
      return out_string
 def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: List[Any]       ,    _a					: Optional[str] = None				)    ->			Tuple[str]:
      if not os.path.isdir(_a				):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'				)
           return
      UpperCAmelCase						:				List[str]		  =			os.path.join(
          _a       ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

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

      return (out_vocab_file,)
 def   _UpperCamelCase    (			self					: Optional[Any]       ,    _a					: List[str]       ,    _a					: List[Any]=None				)    ->			str:
      UpperCAmelCase						:				Optional[Any]		  =			[self.bos_token_id] if self.add_bos_token else []
      UpperCAmelCase						:				Optional[Any]		  =			[self.eos_token_id] if self.add_eos_token else []

      UpperCAmelCase						:				Dict		  =			bos_token_id + token_ids_a + eos_token_id

      if token_ids_a is not None:
           UpperCAmelCase						:				Any		  =			output + bos_token_id + token_ids_a + eos_token_id

      return output
 def   _UpperCamelCase    (			self					: List[str]       ,    _a					: List[int]       ,    _a					: Optional[List[int]] = None       ,    _a					: bool = False				)    ->			List[int]:
      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_a       ,    token_ids_a=_a       ,    already_has_special_tokens=_a				)

      UpperCAmelCase						:				List[str]		  =			[1] if self.add_bos_token else []
      UpperCAmelCase						:				Any		  =			[1] if self.add_eos_token else []

      if token_ids_a is None:
           return bos_token_id + ([0] * len(_a				)) + eos_token_id
      return (
          bos_token_id
          + ([0] * len(_a				))
          + eos_token_id
          + bos_token_id
          + ([0] * len(_a				))
          + eos_token_id
      )
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: List[int]       ,    _a					: Optional[List[int]] = None				)    ->			List[int]:
      UpperCAmelCase						:				Optional[int]		  =			[self.bos_token_id] if self.add_bos_token else []
      UpperCAmelCase						:				Dict		  =			[self.eos_token_id] if self.add_eos_token else []

      UpperCAmelCase						:				Union[str, Any]		  =			[0] * len(bos_token_id + token_ids_a + eos_token_id				)

      if token_ids_a is not None:
           output += [1] * len(bos_token_id + token_ids_a + eos_token_id				)

      return output

 def   _UpperCamelCase    (			self					: Optional[Any]       ,    _a					: "Conversation"				)    ->			List[int]:
      UpperCAmelCase						:				Union[str, Any]		  =			list(conversation.iter_texts()				)
      if not all([is_user for is_user, msg in dialogue[::2]]				) or not all(
          [not is_user for is_user, msg in dialogue[1::2]]				):
           raise ValueError(
               "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"				)

      UpperCAmelCase						:				List[int]		  =			[]
      if len(conversation.past_user_inputs				) > 0:
           if not conversation.past_user_inputs[0].startswith(_a				) or E_SYS not in conversation.past_user_inputs[0]:
                UpperCAmelCase						:				List[Any]		  =			(
                    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                )
      elif not dialogue[0][1].startswith(_a				) or E_SYS not in dialogue[0][1]:
           UpperCAmelCase						:				List[Any]		  =			(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

      dialog_tokens += sum(
          [
              [self.bos_token_id]
              + self.encode(
                  f'{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '       ,    add_special_tokens=_a				)
              + [self.eos_token_id]
              for prompt, answer in zip(dialogue[::2]       ,    dialogue[1::2]				)
          ]       ,    []       ,    )
      if not (dialogue[-1][0]):
           raise ValueError(f'Last message must be from user, got {dialogue[-1]["role"]}'				)
      dialog_tokens += [self.bos_token_id] + self.encode(
          f'{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'       ,    add_special_tokens=_a				)
      return dialog_tokens







import math







def   A   (		A__ ):
     UpperCAmelCase						:				List[str]		  =			math.log(2		,		1_0 ) * number
     UpperCAmelCase						:				int		  =			round((log_number - int(A__ ))		,		1_5 )

     return difference







def   A   (		A__ = 6_7_8_9_1_0 ):
     UpperCAmelCase						:				str		  =			9_0
     UpperCAmelCase						:				Any		  =			0

     UpperCAmelCase						:				Dict		  =			math.log(1.23		,		1_0 )
     UpperCAmelCase						:				Optional[Any]		  =			math.log(1.24		,		1_0 )
     UpperCAmelCase						:				Tuple		  =			0

     while position < number:
          UpperCAmelCase						:				Tuple		  =			log_difference(A__ )

          if difference >= upper_limit:
               power_iterator += 9_3

          elif difference < lower_limit:
               power_iterator += 1_9_6

          else:
               UpperCAmelCase						:				Any		  =			power_iterator
               power_iterator += 1_9_6
               position += 1

     return previous_power


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      print(f'''{solution() = }''')



