

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    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'			)

     lowercase__    :   Tuple      =       numbers[0]

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

          # update the maximum product found till now
          lowercase__    :   str      =       max(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)

     return max_prod







from typing import TYPE_CHECKING

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


_UpperCAmelCase		:  int	=   {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  List[str]	=   [
         "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "SwinForImageClassification",
         "SwinForMaskedImageModeling",
         "SwinModel",
         "SwinPreTrainedModel",
         "SwinBackbone",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  int	=   [
         "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFSwinForImageClassification",
         "TFSwinForMaskedImageModeling",
         "TFSwinModel",
         "TFSwinPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swin import (
              SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
              SwinModel,
              SwinPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_swin import (
              TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
              TFSwinModel,
              TFSwinPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase		:  int	=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



