


from typing import TYPE_CHECKING

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


snake_case_						:			Union[str, Any]													=							{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			Optional[Any]													=							['''ElectraTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			Any													=							[
							    '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ElectraForCausalLM''',
							    '''ElectraForMaskedLM''',
							    '''ElectraForMultipleChoice''',
							    '''ElectraForPreTraining''',
							    '''ElectraForQuestionAnswering''',
							    '''ElectraForSequenceClassification''',
							    '''ElectraForTokenClassification''',
							    '''ElectraModel''',
							    '''ElectraPreTrainedModel''',
							    '''load_tf_weights_in_electra''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			Any													=							[
							    '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFElectraForMaskedLM''',
							    '''TFElectraForMultipleChoice''',
							    '''TFElectraForPreTraining''',
							    '''TFElectraForQuestionAnswering''',
							    '''TFElectraForSequenceClassification''',
							    '''TFElectraForTokenClassification''',
							    '''TFElectraModel''',
							    '''TFElectraPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			str													=							[
							    '''FlaxElectraForCausalLM''',
							    '''FlaxElectraForMaskedLM''',
							    '''FlaxElectraForMultipleChoice''',
							    '''FlaxElectraForPreTraining''',
							    '''FlaxElectraForQuestionAnswering''',
							    '''FlaxElectraForSequenceClassification''',
							    '''FlaxElectraForTokenClassification''',
							    '''FlaxElectraModel''',
							    '''FlaxElectraPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
							from .tokenization_electra import ElectraTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_electra_fast import ElectraTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_electra import (
														    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ElectraForCausalLM,
														    ElectraForMaskedLM,
														    ElectraForMultipleChoice,
														    ElectraForPreTraining,
														    ElectraForQuestionAnswering,
														    ElectraForSequenceClassification,
														    ElectraForTokenClassification,
														    ElectraModel,
														    ElectraPreTrainedModel,
														    load_tf_weights_in_electra,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_electra import (
														    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFElectraForMaskedLM,
														    TFElectraForMultipleChoice,
														    TFElectraForPreTraining,
														    TFElectraForQuestionAnswering,
														    TFElectraForSequenceClassification,
														    TFElectraForTokenClassification,
														    TFElectraModel,
														    TFElectraPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_electra import (
														    FlaxElectraForCausalLM,
														    FlaxElectraForMaskedLM,
														    FlaxElectraForMultipleChoice,
														    FlaxElectraForPreTraining,
														    FlaxElectraForQuestionAnswering,
														    FlaxElectraForSequenceClassification,
														    FlaxElectraForTokenClassification,
														    FlaxElectraModel,
														    FlaxElectraPreTrainedModel,
														)

else:
							import sys

							snake_case_						:			int													=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



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







def 						lowerCamelCase(   a__	,a__	,a__):

				# 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
				_SCREAMING_SNAKE_CASE			  =(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
				_SCREAMING_SNAKE_CASE			  =_calculate(days - 1	,a__	,late + 1)

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

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

				_SCREAMING_SNAKE_CASE			  =state_late + state_absent + state_ontime

				_SCREAMING_SNAKE_CASE			  =prizestrings
				return prizestrings







def 						lowerCamelCase(   a__ = 30):
				return _calculate(a__	,absent=0	,late=0)


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