





"""simple docstring"""


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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):

						# if we are absent twice, or late 3 consecutive days,
						# no further prize strings are possible
						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
						UpperCamelCase				:		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
						UpperCamelCase				:		Dict				   =			_calculate(days - 1							,     SCREAMING_SNAKE_CASE							,     late + 1     )

						# 2) if we are absent, the "absent" counter increases by 1, and the
						# "late" counter resets to 0
						UpperCamelCase				:		int				   =			_calculate(days - 1							,     absent + 1							,     0     )

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

						UpperCamelCase				:		Optional[int]				   =			state_late + state_absent + state_ontime

						UpperCamelCase				:		Any				   =			prizestrings
						return prizestrings

def      UpperCamelCase							(SCREAMING_SNAKE_CASE = 30     ):
						return _calculate(SCREAMING_SNAKE_CASE							,     absent=0							,     late=0     )


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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase:						str   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Any   				=       {
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}




class       lowercase_					(lowercase__   ):
				snake_case	    ='big_bird'





				def __init__(				self       ,  lowercase_=50358       ,  lowercase_=768       ,  lowercase_=12       ,  lowercase_=12       ,  lowercase_=3072       ,  lowercase_="gelu_new"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=4096       ,  lowercase_=2       ,  lowercase_=0.02       ,  lowercase_=1e-12       ,  lowercase_=True       ,  lowercase_=0       ,  lowercase_=1       ,  lowercase_=2       ,  lowercase_=66       ,  lowercase_="block_sparse"       ,  lowercase_=True       ,  lowercase_=False       ,  lowercase_=64       ,  lowercase_=3       ,  lowercase_=None       ,  **lowercase_       ,  )  -> Any:
											super().__init__(
											    pad_token_id=lowercase_       ,  bos_token_id=lowercase_       ,  eos_token_id=lowercase_       ,  sep_token_id=lowercase_       ,  **lowercase_       ,  )

											a__      =vocab_size
											a__      =max_position_embeddings
											a__      =hidden_size
											a__      =num_hidden_layers
											a__      =num_attention_heads
											a__      =intermediate_size
											a__      =hidden_act
											a__      =hidden_dropout_prob
											a__      =attention_probs_dropout_prob
											a__      =initializer_range
											a__      =type_vocab_size
											a__      =layer_norm_eps
											a__      =use_cache

											a__      =rescale_embeddings
											a__      =attention_type
											a__      =use_bias
											a__      =block_size
											a__      =num_random_blocks
											a__      =classifier_dropout







class       lowercase_					(lowercase__   ):





				@property
				def 						__UpperCamelCase    (				self)  -> Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		a__      ={0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		a__      ={0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ])

