





import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

require_version("""tokenizers>=0.13.3""")

if is_sentencepiece_available():
       from .tokenization_llama import LlamaTokenizer
else:
       lowercase     :			Optional[int]     						=	None

lowercase     :			List[Any]     						=	logging.get_logger(__name__)
lowercase     :			Optional[int]     						=	{"""vocab_file""": """tokenizer.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase						,lowercase     :			Any     						=	"""[INST]""", """[/INST]"""
lowercase						,lowercase     :			Union[str, Any]     						=	"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
lowercase     :			int     						=	"""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""

class 			__A(    __UpperCAmelCase			):
     __A							=							VOCAB_FILES_NAMES
     __A							=							LlamaTokenizer
     __A							=							"left"
     __A							=							["input_ids", "attention_mask"]

     def __init__(   self,							A=None,							A=None,							A=False,							A="<unk>",							A="<s>",							A="</s>",							A=True,							A=False,							**A,							):





            """simple docstring"""

            super().__init__(
                vocab_file=A,							tokenizer_file=A,							clean_up_tokenization_spaces=A,							unk_token=A,							bos_token=A,							eos_token=A,							**A,							)
            _UpperCamelCase														=    add_bos_token
            _UpperCamelCase														=    add_eos_token
            self.update_post_processor()

            _UpperCamelCase														=    vocab_file
            _UpperCamelCase														=    False if not self.vocab_file else True

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.bos_token
            _UpperCamelCase														=    self.bos_token_id

            _UpperCamelCase														=    self.eos_token
            _UpperCamelCase														=    self.eos_token_id

            _UpperCamelCase														=    F'''{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'''
            _UpperCamelCase														=    F'''{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'''

            _UpperCamelCase														=    []
            if self.add_bos_token:
                   special_tokens.append((bos, bos_token_id)   )
            if self.add_eos_token:
                   special_tokens.append((eos, eos_token_id)   )
            _UpperCamelCase														=    processors.TemplateProcessing(
                single=A,							pair=A,							special_tokens=A   )

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self._add_eos_token

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self._add_bos_token

     @add_eos_token.setter
     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    value
            self.update_post_processor()

     @add_bos_token.setter
     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    value
            self.update_post_processor()

     def 						_UpperCamelCase  (   self,							A,							A = None   ):





            """simple docstring"""

            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                       '''tokenizer.'''   )

            if not os.path.isdir(A   ):
                   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
                   return
            _UpperCamelCase														=    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   ):
                   copyfile(self.vocab_file,							A   )

            return (out_vocab_file,)



     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    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														=    []
            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														=    (
                              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														=    (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
from decimal import Decimal, getcontext


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase = 4_000_000       ):

       try:
              _UpperCamelCase														=    int(lowerCAmelCase       )
       except (TypeError, ValueError):
              raise TypeError('''Parameter n must be int or castable to int.'''       )
       if n <= 0:
              raise ValueError('''Parameter n must be greater than or equal to one.'''       )
       _UpperCamelCase														=    100
       _UpperCamelCase														=    (Decimal(5       ) ** Decimal(0.5       ) + 1) / Decimal(2       )

       _UpperCamelCase														=    (math.floor(math.log(n * (phi + 2)     ,							lowerCAmelCase       ) - 1       ) // 3) * 3 + 2
       _UpperCamelCase														=    Decimal(round(phi ** Decimal(index + 1       )       )       ) / (phi + 2)
       _UpperCamelCase														=    num // 2
       return int(lowerCAmelCase       )


if __name__ == "__main__":
       print(F'''{solution() = }''')






