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

a     :			Dict					    =						logging.get_logger(__name__)

a     :			Optional[int]					    =						{"""vocab_file""": """tokenizer.model"""}

a     :			Union[str, Any]					    =						{
    """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""",
    },
}
a     :			str					    =						{
    """hf-internal-testing/llama-tokenizer""": 2_0_4_8,
}
a     :			Any					    =						"""▁"""

a     :			List[str]					    =						"""[INST]""", """[/INST]"""
a     :			Tuple					    =						"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
a     :			Dict					    =						"""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 				_lowercase						(    UpperCAmelCase__ ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Dict        =						VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE:							str        =						PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE:							Optional[int]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE:							List[str]        =						['input_ids', 'attention_mask']




    def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__="<unk>"					,	lowerCamelCase__="<s>"					,	lowerCamelCase__="</s>"					,	lowerCamelCase__=None					,	lowerCamelCase__ = None					,	lowerCamelCase__=True					,	lowerCamelCase__=False					,	lowerCamelCase__=False					,	lowerCamelCase__=True					,	**lowerCamelCase__					,	):
       lowerCAmelCase_:       List[str]         =  {} if sp_model_kwargs is None else sp_model_kwargs
       lowerCAmelCase_:       int         =  AddedToken(lowerCamelCase__					,	lstrip=lowerCamelCase__					,	rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__					,	lowerCamelCase__   ) else bos_token
       lowerCAmelCase_:       Any         =  AddedToken(lowerCamelCase__					,	lstrip=lowerCamelCase__					,	rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__					,	lowerCamelCase__   ) else eos_token
       lowerCAmelCase_:       Any         =  AddedToken(lowerCamelCase__					,	lstrip=lowerCamelCase__					,	rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__					,	lowerCamelCase__   ) else unk_token
       lowerCAmelCase_:       Any         =  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"   )
       lowerCAmelCase_:       List[str]         =  legacy
       lowerCAmelCase_:       Any         =  vocab_file
       lowerCAmelCase_:       Union[str, Any]         =  add_bos_token
       lowerCAmelCase_:       List[str]         =  add_eos_token
       lowerCAmelCase_:       List[str]         =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(lowerCamelCase__   )




    def __getstate__(						self   ):
       lowerCAmelCase_:       int         =  self.__dict__.copy()
       lowerCAmelCase_:       int         =  None
       lowerCAmelCase_:       Any         =  self.sp_model.serialized_model_proto()
       return state




    def __setstate__(						self					,	lowerCamelCase__   ):
       lowerCAmelCase_:       Tuple         =  d
       lowerCAmelCase_:       Tuple         =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )




    @property
    def     _a						(						self   ):
       return self.sp_model.get_piece_size()




    def     _a						(						self   ):
       lowerCAmelCase_:       Dict         =  {self.convert_ids_to_tokens(lowerCamelCase__   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab




    def     _a						(						self					,	lowerCamelCase__					,	**lowerCamelCase__   ):
       # 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:
          lowerCAmelCase_:       str         =  SPIECE_UNDERLINE + text.replace(lowerCamelCase__					,	" "   )
       return super().tokenize(lowerCamelCase__					,	**lowerCamelCase__   )




    def     _a						(						self					,	lowerCamelCase__   ):
       if not self.legacy:
          lowerCAmelCase_:       Optional[int]         =  text.startswith(lowerCamelCase__   )
          if is_first:
             lowerCAmelCase_:       Union[str, Any]         =  text[1:]

       lowerCAmelCase_:       Optional[int]         =  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__   ):
          lowerCAmelCase_:       Any         =  ([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
       return tokens




    def     _a						(						self					,	lowerCamelCase__   ):
       return self.sp_model.piece_to_id(lowerCamelCase__   )




    def     _a						(						self					,	lowerCamelCase__   ):
       lowerCAmelCase_:       Optional[Any]         =  self.sp_model.IdToPiece(lowerCamelCase__   )
       return token




    def     _a						(						self					,	lowerCamelCase__   ):
       lowerCAmelCase_:       Optional[Any]         =  []
       lowerCAmelCase_:       List[str]         =  ""
       lowerCAmelCase_:       int         =  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
             lowerCAmelCase_:       Dict         =  True
             lowerCAmelCase_:       str         =  []
          else:
             current_sub_tokens.append(lowerCamelCase__   )
             lowerCAmelCase_:       Tuple         =  False
       out_string += self.sp_model.decode(lowerCamelCase__   )
       return out_string




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__ = None   ):
       if not os.path.isdir(lowerCamelCase__   ):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
          return
       lowerCAmelCase_:       Any         =  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:
             lowerCAmelCase_:       List[Any]         =  self.sp_model.serialized_model_proto()
             fi.write(lowerCamelCase__   )

       return (out_vocab_file,)




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__=None   ):
       lowerCAmelCase_:       Any         =  [self.bos_token_id] if self.add_bos_token else []
       lowerCAmelCase_:       Optional[int]         =  [self.eos_token_id] if self.add_eos_token else []

       lowerCAmelCase_:       Union[str, Any]         =  bos_token_id + token_ids_a + eos_token_id

       if token_ids_a is not None:
          lowerCAmelCase_:       Optional[Any]         =  output + bos_token_id + token_ids_a + eos_token_id

       return output




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__ = None					,	lowerCamelCase__ = False   ):
       if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=lowerCamelCase__					,	token_ids_a=lowerCamelCase__					,	already_has_special_tokens=lowerCamelCase__   )

       lowerCAmelCase_:       List[str]         =  [1] if self.add_bos_token else []
       lowerCAmelCase_:       str         =  [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     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__ = None   ):
       lowerCAmelCase_:       Dict         =  [self.bos_token_id] if self.add_bos_token else []
       lowerCAmelCase_:       Optional[int]         =  [self.eos_token_id] if self.add_eos_token else []

       lowerCAmelCase_:       int         =  [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     _a						(						self					,	lowerCamelCase__   ):
       lowerCAmelCase_:       int         =  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...)"   )

       lowerCAmelCase_:       List[int]         =  []
       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]:
             lowerCAmelCase_:       Tuple         =  (
                 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]:
          lowerCAmelCase_:       List[str]         =  (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




import itertools
def 		snake_case__  (		lowercase				):
   return (
       int("".join(combination[0:2]				)				) * int("".join(combination[2:5]				)				)
       == int("".join(combination[5:9]				)				)
   ) or (
       int("".join(combination[0]				)				) * int("".join(combination[1:5]				)				)
       == int("".join(combination[5:9]				)				)
   )
def 		snake_case__  (		):
   return sum(
       {
           int("".join(pandigital[5:9]				)				)
           for pandigital in itertools.permutations("123456789"				)
           if is_combination_valid(lowercase				)
       }				)


if __name__ == "__main__":
 print(solution())