





"""simple docstring"""





def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						list[int]			):







      '''simple docstring'''




      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  						=       lowerCAmelCase  						=       lowerCAmelCase  						=       numbers[0]

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

            # update the maximum product found till now
            lowerCAmelCase  						=       max(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      return max_prod













"""simple docstring"""





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__					=   {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   [
           "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FocalNetForImageClassification",
           "FocalNetForMaskedImageModeling",
           "FocalNetBackbone",
           "FocalNetModel",
           "FocalNetPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_focalnet import (
                  FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FocalNetBackbone,
                  FocalNetForImageClassification,
                  FocalNetForMaskedImageModeling,
                  FocalNetModel,
                  FocalNetPreTrainedModel,
              )

else:
       import sys

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






