


'''simple docstring'''




UpperCAmelCase_      :		dict[tuple[int, int, int], int]          =		{}


def    UpperCAmelCase_  (       A		,	A		,	A      ):
   '''simple docstring'''





   if late == 3 or absent == 2:
      return 0

   # if we have no days left, and have not failed any other rules,
   # we have a prize string
   if days == 0:
      return 1

   # No easy solution, so now we need to do the recursive calculation

   # First, check if the combination is already in the cache, and
   # if yes, return the stored value from there since we already
   # know the number of possible prize strings from this point on
   _a					:  Any						=  (days, absent, late)
   if key in cache:
      return cache[key]

   # now we calculate the three possible ways that can unfold from
   # this point on, depending on our attendance today

   # 1) if we are late (but not absent), the "absent" counter stays as
   # it is, but the "late" counter increases by one
   _a					:  List[str]						=  _calculate(days - 1		,	A		,	late + 1      )

   # 2) if we are absent, the "absent" counter increases by 1, and the
   # "late" counter resets to 0
   _a					:  Optional[Any]						=  _calculate(days - 1		,	absent + 1		,	0      )

   # 3) if we are on time, this resets the "late" counter and keeps the
   # absent counter
   _a					:  str						=  _calculate(days - 1		,	A		,	0      )

   _a					:  List[Any]						=  state_late + state_absent + state_ontime

   _a					:  Dict						=  prizestrings
   return prizestrings


def    UpperCAmelCase_  (       A = 3_0      ):
   '''simple docstring'''





   return _calculate(A		,	absent=0		,	late=0      )


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






'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


UpperCAmelCase_      :		Optional[Any]          =		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_      :		Union[str, Any]          =		{
    "vocab_file": {
        "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "unc-nlp/lxmert-base-uncased": (
            "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase_      :		str          =		{
    "unc-nlp/lxmert-base-uncased": 512,
}

UpperCAmelCase_      :		Optional[int]          =		{
    "unc-nlp/lxmert-base-uncased": {"do_lower_case": True},
}







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Tuple							  =		VOCAB_FILES_NAMES
 __lowerCAmelCase						: Dict							  =		PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase						: Optional[Any]							  =		PRETRAINED_INIT_CONFIGURATION
 __lowerCAmelCase						: str							  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase						: List[Any]							  =		LxmertTokenizer




 def __init__(    self		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_=True		,      lowerCamelCase_="[UNK]"		,      lowerCamelCase_="[SEP]"		,      lowerCamelCase_="[PAD]"		,      lowerCamelCase_="[CLS]"		,      lowerCamelCase_="[MASK]"		,      lowerCamelCase_=True		,      lowerCamelCase_=None		,      **lowerCamelCase_		,      )      ->    Optional[int]:
    super().__init__(
        lowerCamelCase_		,      tokenizer_file=lowerCamelCase_		,      do_lower_case=lowerCamelCase_		,      unk_token=lowerCamelCase_		,      sep_token=lowerCamelCase_		,      pad_token=lowerCamelCase_		,      cls_token=lowerCamelCase_		,      mask_token=lowerCamelCase_		,      tokenize_chinese_chars=lowerCamelCase_		,      strip_accents=lowerCamelCase_		,      **lowerCamelCase_		,      )

    _a					:  str						=  json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
    if (
        normalizer_state.get('lowercase'		,      lowerCamelCase_      ) != do_lower_case
        or normalizer_state.get('strip_accents'		,      lowerCamelCase_      ) != strip_accents
        or normalizer_state.get('handle_chinese_chars'		,      lowerCamelCase_      ) != tokenize_chinese_chars
    ):
       _a					:  str						=  getattr(lowerCamelCase_		,      normalizer_state.pop('type'      )      )
       _a					:  Tuple						=  do_lower_case
       _a					:  str						=  strip_accents
       _a					:  Optional[Any]						=  tokenize_chinese_chars
       _a					:  Dict						=  normalizer_class(**lowerCamelCase_      )

    _a					:  Optional[int]						=  do_lower_case




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_=None      )      ->    Optional[Any]:
    _a					:  List[str]						=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

    return output




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None      )      ->    List[int]:
    _a					:  Union[str, Any]						=  [self.sep_token_id]
    _a					:  Optional[int]						=  [self.cls_token_id]
    if token_ids_a is None:
       return len(cls + token_ids_a + sep      ) * [0]
    return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None      )      ->    Tuple[str]:
    _a					:  Optional[Any]						=  self._tokenizer.model.save(lowerCamelCase_		,      name=lowerCamelCase_      )
    return tuple(lowerCamelCase_      )


