

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__								=		{
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		["""VivitImageProcessor"""]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """VivitModel""",
							    """VivitPreTrainedModel""",
							    """VivitForVideoClassification""",
							]


if TYPE_CHECKING:
							from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vivit import VivitImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vivit import (
														    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    VivitForVideoClassification,
														    VivitModel,
														    VivitPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







import functools






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[int]							,       UpperCAmelCase_							:     list[int]	)						->				int:




	'''simple docstring'''




	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	) -> 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()




