






"""simple docstring"""


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

lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{'vocab_file': 'tokenizer.model'}

lowercase_								=	{
    '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',
    },
}
lowercase_								=	{
    'hf-internal-testing/llama-tokenizer': 2048,
}
lowercase_								=	'▁'

lowercase_	,						lowercase_								=	'[INST]', '[/INST]'
lowercase_	,						lowercase_								=	'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
lowercase_								=	'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    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      List[Any]							=					VOCAB_FILES_NAMES
   A_		:      List[Any]							=					PRETRAINED_VOCAB_FILES_MAP
   A_		:      Union[str, Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_		:      Tuple							=					["input_ids", "attention_mask"]


   def __init__(      self					:	str,			_lowerCamelCase					:	int,			_lowerCamelCase					:	Tuple="<unk>",			_lowerCamelCase					:	Dict="<s>",			_lowerCamelCase					:	int="</s>",			_lowerCamelCase					:	Tuple=None,			_lowerCamelCase					:	Optional[Dict[str, Any]] = None,			_lowerCamelCase					:	List[Any]=True,			_lowerCamelCase					:	str=False,			_lowerCamelCase					:	Any=False,			_lowerCamelCase					:	str=True,			**_lowerCamelCase					:	Dict,			):

     '''simple docstring'''
     __A		     =					{} if sp_model_kwargs is None else sp_model_kwargs
     __A		     =					AddedToken(_lowerCamelCase,			lstrip=_lowerCamelCase,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase,			_lowerCamelCase    ) else bos_token
     __A		     =					AddedToken(_lowerCamelCase,			lstrip=_lowerCamelCase,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase,			_lowerCamelCase    ) else eos_token
     __A		     =					AddedToken(_lowerCamelCase,			lstrip=_lowerCamelCase,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase,			_lowerCamelCase    ) else unk_token
     __A		     =					AddedToken(_lowerCamelCase,			lstrip=_lowerCamelCase,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase,			_lowerCamelCase    ) else pad_token
     super().__init__(
         bos_token=_lowerCamelCase,			eos_token=_lowerCamelCase,			unk_token=_lowerCamelCase,			pad_token=_lowerCamelCase,			add_bos_token=_lowerCamelCase,			add_eos_token=_lowerCamelCase,			sp_model_kwargs=self.sp_model_kwargs,			clean_up_tokenization_spaces=_lowerCamelCase,			legacy=_lowerCamelCase,			**_lowerCamelCase,			)
     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'''    )
     __A		     =					legacy
     __A		     =					vocab_file
     __A		     =					add_bos_token
     __A		     =					add_eos_token
     __A		     =					spm.SentencePieceProcessor(**self.sp_model_kwargs    )
     self.sp_model.Load(_lowerCamelCase    )


   def __getstate__(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					self.__dict__.copy()
     __A		     =					None
     __A		     =					self.sp_model.serialized_model_proto()
     return state


   def __setstate__(      self					:	Tuple,			_lowerCamelCase					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					d
     __A		     =					spm.SentencePieceProcessor(**self.sp_model_kwargs    )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )


   @property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     return self.sp_model.get_piece_size()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					{self.convert_ids_to_tokens(_lowerCamelCase    ): i for i in range(self.vocab_size    )}
     vocab.update(self.added_tokens_encoder    )
     return vocab


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int,			_lowerCamelCase					:	Dict,			**_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     # 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:
       __A		     =					SPIECE_UNDERLINE + text.replace(_lowerCamelCase,			''' '''    )
     return super().tokenize(_lowerCamelCase,			**_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     if not self.legacy:
       __A		     =					text.startswith(_lowerCamelCase    )
       if is_first:
         __A		     =					text[1:]

     __A		     =					self.sp_model.encode(_lowerCamelCase,			out_type=_lowerCamelCase    )

     if not self.legacy and not is_first and not text.startswith(''' '''    ) and tokens[0].startswith(_lowerCamelCase    ):
       __A		     =					([tokens[0][1:]] if len(tokens[0]    ) > 1 else []) + tokens[1:]
     return tokens


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	List[Any]    ):

     '''simple docstring'''
     return self.sp_model.piece_to_id(_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int],			_lowerCamelCase					:	Tuple    ):

     '''simple docstring'''
     __A		     =					self.sp_model.IdToPiece(_lowerCamelCase    )
     return token


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					[]
     __A		     =					''''''
     __A		     =					False
     for i, token in enumerate(_lowerCamelCase    ):
       # 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(_lowerCamelCase    ) + token
         __A		     =					True
         __A		     =					[]
       else:
         current_sub_tokens.append(_lowerCamelCase    )
         __A		     =					False
     out_string += self.sp_model.decode(_lowerCamelCase    )
     return out_string


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Optional[str] = None    ):

     '''simple docstring'''
     if not os.path.isdir(_lowerCamelCase    ):
       logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
       return
     __A		     =					os.path.join(
         _lowerCamelCase,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

     if os.path.abspath(self.vocab_file    ) != os.path.abspath(_lowerCamelCase    ) and os.path.isfile(self.vocab_file    ):
       copyfile(self.vocab_file,			_lowerCamelCase    )
     elif not os.path.isfile(self.vocab_file    ):
       with open(_lowerCamelCase,			'''wb'''    ) as fi:
         __A		     =					self.sp_model.serialized_model_proto()
         fi.write(_lowerCamelCase    )

     return (out_vocab_file,)


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	str=None    ):

     '''simple docstring'''
     __A		     =					[self.bos_token_id] if self.add_bos_token else []
     __A		     =					[self.eos_token_id] if self.add_eos_token else []

     __A		     =					bos_token_id + token_ids_a + eos_token_id

     if token_ids_a is not None:
       __A		     =					output + bos_token_id + token_ids_a + eos_token_id

     return output


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any],			_lowerCamelCase					:	List[int],			_lowerCamelCase					:	Optional[List[int]] = None,			_lowerCamelCase					:	bool = False    ):

     '''simple docstring'''
     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowerCamelCase,			token_ids_a=_lowerCamelCase,			already_has_special_tokens=_lowerCamelCase    )

     __A		     =					[1] if self.add_bos_token else []
     __A		     =					[1] if self.add_eos_token else []

     if token_ids_a is None:
       return bos_token_id + ([0] * len(_lowerCamelCase    )) + eos_token_id
     return (
         bos_token_id
         + ([0] * len(_lowerCamelCase    ))
         + eos_token_id
         + bos_token_id
         + ([0] * len(_lowerCamelCase    ))
         + eos_token_id
     )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	List[int],			_lowerCamelCase					:	Optional[List[int]] = None    ):

     '''simple docstring'''
     __A		     =					[self.bos_token_id] if self.add_bos_token else []
     __A		     =					[self.eos_token_id] if self.add_eos_token else []

     __A		     =					[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 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	"Conversation"    ):

     '''simple docstring'''
     __A		     =					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...)'''    )

     __A		     =					[]
     if len(conversation.past_user_inputs    ) > 0:
       if not conversation.past_user_inputs[0].startswith(_lowerCamelCase    ) or E_SYS not in conversation.past_user_inputs[0]:
         __A		     =					(
             B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
         )
     elif not dialogue[0][1].startswith(_lowerCamelCase    ) or E_SYS not in dialogue[0][1]:
       __A		     =					(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=_lowerCamelCase    )
             + [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=_lowerCamelCase    )
     return dialog_tokens










def    snake_case   (				A		,  A     )				->				int:




	'''simple docstring'''


	if a < b:
		return gcd(A		,  A     )
	if a % b == 0:
		return b
	return gcd(A		,  a % b     )






def    snake_case   (				A		,  A		,  A     )				->				int:




	'''simple docstring'''


	if y == 0:
		return 1
	lowerCamelCase_				:						int				  =				power(A		,  y // 2		,  A     ) % mod
	lowerCamelCase_				:						List[Any]				  =				(temp * temp) % mod
	if y % 2 == 1:
		lowerCamelCase_				:						str				  =				(temp * x) % mod
	return temp






def    snake_case   (				A     )				->				bool:




	'''simple docstring'''


	lowerCamelCase_				:						Union[str, Any]				  =				2
	while b < n:
		if gcd(A		,  A     ) == 1 and power(A		,  n - 1		,  A     ) != 1:
			return False
		b += 1
	return True


if __name__ == "__main__":
							UpperCamelCase__						=		int(input('Enter number: ').strip())
							if is_carmichael_number(number):
														print(F'{number} is a Carmichael Number.')
							else:
														print(F'{number} is not a Carmichael Number.')
