


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A		={
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=['SqueezeBertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'SqueezeBertForMaskedLM',
		    'SqueezeBertForMultipleChoice',
		    'SqueezeBertForQuestionAnswering',
		    'SqueezeBertForSequenceClassification',
		    'SqueezeBertForTokenClassification',
		    'SqueezeBertModel',
		    'SqueezeBertModule',
		    'SqueezeBertPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_squeezebert import (
		    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SqueezeBertConfig,
		    SqueezeBertOnnxConfig,
		)
		from .tokenization_squeezebert import SqueezeBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_squeezebert import (
				    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SqueezeBertForMaskedLM,
				    SqueezeBertForMultipleChoice,
				    SqueezeBertForQuestionAnswering,
				    SqueezeBertForSequenceClassification,
				    SqueezeBertForTokenClassification,
				    SqueezeBertModel,
				    SqueezeBertModule,
				    SqueezeBertPreTrainedModel,
				)

else:
		import sys

		__A		=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



'''simple docstring'''



import functools







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):

			# Validation
			if not isinstance(UpperCamelCase__	, UpperCamelCase__   ) or not all(isinstance(UpperCamelCase__	, UpperCamelCase__   ) for day in days   ):
						raise ValueError("""The parameter days should be a list of integers"""   )

			if len(UpperCamelCase__   ) != 3 or not all(isinstance(UpperCamelCase__	, UpperCamelCase__   ) for cost in costs   ):
						raise ValueError("""The parameter costs should be a list of three integers"""   )

			if len(UpperCamelCase__   ) == 0:
						return 0

			if min(UpperCamelCase__   ) <= 0:
						raise ValueError("""All days elements should be greater than 0"""   )

			if max(UpperCamelCase__   ) >= 3_6_6:
						raise ValueError("""All days elements should be less than 366"""   )

			UpperCAmelCase__					:   Union[str, Any]       				=       set(UpperCamelCase__   )

			@functools.cache
			def dynamic_programming(UpperCamelCase__   ) -> int:
						if index > 3_6_5:
									return 0

						if index not in days_set:
									return dynamic_programming(index + 1   )

						return min(
						    costs[0] + dynamic_programming(index + 1   )	, costs[1] + dynamic_programming(index + 7   )	, costs[2] + dynamic_programming(index + 3_0   )	, )

			return dynamic_programming(1   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()