





"""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())










"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__       :					Dict						=			{
    """configuration_layoutlmv3""": [
        """LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LayoutLMv3Config""",
        """LayoutLMv3OnnxConfig""",
    ],
    """processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
    """tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Optional[Any]						=			["""LayoutLMv3TokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					int						=			[
		    """LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LayoutLMv3ForQuestionAnswering""",
		    """LayoutLMv3ForSequenceClassification""",
		    """LayoutLMv3ForTokenClassification""",
		    """LayoutLMv3Model""",
		    """LayoutLMv3PreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Tuple						=			[
		    """TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFLayoutLMv3ForQuestionAnswering""",
		    """TFLayoutLMv3ForSequenceClassification""",
		    """TFLayoutLMv3ForTokenClassification""",
		    """TFLayoutLMv3Model""",
		    """TFLayoutLMv3PreTrainedModel""",
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Union[str, Any]						=			["""LayoutLMv3FeatureExtractor"""]
		__magic_name__       :					Any						=			["""LayoutLMv3ImageProcessor"""]


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

		__magic_name__       :					Union[str, Any]						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



