

"""simple docstring"""


def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		if not numbers:
				return 0

		if not isinstance(__SCREAMING_SNAKE_CASE ,			(list, tuple)			) or not all(
		    isinstance(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			) for number in numbers			):
				raise ValueError("numbers must be an iterable of integers"			)

		__lowerCAmelCase:	Optional[Any]   				=      numbers[0]

		for i in range(1 ,			len(__SCREAMING_SNAKE_CASE			)			):
				# update the maximum and minimum subarray products
				__lowerCAmelCase:	str   				=      numbers[i]
				if number < 0:
						__lowerCAmelCase				,			__lowerCAmelCase:	int   				=      min_till_now, max_till_now
				__lowerCAmelCase:	List[Any]   				=      max(__SCREAMING_SNAKE_CASE ,			max_till_now * number			)
				__lowerCAmelCase:	int   				=      min(__SCREAMING_SNAKE_CASE ,			min_till_now * number			)

				# update the maximum product found till now
				__lowerCAmelCase:	Any   				=      max(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

		return max_prod





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A						       =      {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Swinv2ForImageClassification",
			    "Swinv2ForMaskedImageModeling",
			    "Swinv2Model",
			    "Swinv2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

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


