

def 			_A	(      SCREAMING_SNAKE_CASE      ):
	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"      )

	UpperCAmelCase__:	str									= numbers[0]

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

		# update the maximum product found till now
		UpperCAmelCase__:	Tuple									= max(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

	return max_prod


from typing import TYPE_CHECKING

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


_lowerCAmelCase  :      Tuple			 ={
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Tuple			 =["""MobileNetV2FeatureExtractor"""]
					_lowerCAmelCase  :      Optional[int]			 =["""MobileNetV2ImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      List[str]			 =[
					    """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileNetV2ForImageClassification""",
					    """MobileNetV2ForSemanticSegmentation""",
					    """MobileNetV2Model""",
					    """MobileNetV2PreTrainedModel""",
					    """load_tf_weights_in_mobilenet_v2""",
					]


if TYPE_CHECKING:
					from .configuration_mobilenet_va import (
					    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileNetVaConfig,
					    MobileNetVaOnnxConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
										from .image_processing_mobilenet_va import MobileNetVaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilenet_va import (
										    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileNetVaForImageClassification,
										    MobileNetVaForSemanticSegmentation,
										    MobileNetVaModel,
										    MobileNetVaPreTrainedModel,
										    load_tf_weights_in_mobilenet_va,
										)


else:
					import sys

					_lowerCAmelCase  :      Any			 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)