



'''simple docstring'''





from typing import TYPE_CHECKING

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


_lowerCAmelCase      						=	{
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase      						=	['''ConvNextFeatureExtractor''']
 _lowerCAmelCase      						=	['''ConvNextImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase      						=	[
     '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ConvNextForImageClassification''',
     '''ConvNextModel''',
     '''ConvNextPreTrainedModel''',
     '''ConvNextBackbone''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase      						=	[
     '''TFConvNextForImageClassification''',
     '''TFConvNextModel''',
     '''TFConvNextPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_convnext import ConvNextFeatureExtractor
  from .image_processing_convnext import ConvNextImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convnext import (
      CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvNextBackbone,
      ConvNextForImageClassification,
      ConvNextModel,
      ConvNextPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
 import sys

 _lowerCAmelCase      						=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)



"""simple docstring"""







__SCREAMING_SNAKE_CASE    	={}



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ):

     # 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
     lowercase_	:				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
     lowercase_	:				Optional[int]													=	_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
     lowercase_	:				Any													=	_calculate(days - 1     ,			absent + 1     ,			0      )

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

     lowercase_	:				str													=	state_late + state_absent + state_ontime

     lowercase_	:				Tuple													=	prizestrings
     return prizestrings



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     int = 30      ):
     return _calculate(__SCREAMING_SNAKE_CASE     ,			absent=0     ,			late=0      )


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

