def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list[int]			)							->      int:







  """simple docstring"""





  if not numbers:
    return 0

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

  SCREAMING_SNAKE_CASE__    							=   SCREAMING_SNAKE_CASE__    							=   SCREAMING_SNAKE_CASE__    							=   numbers[0]

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

    # update the maximum product found till now
    SCREAMING_SNAKE_CASE__    							=   max(__UpperCamelCase				,  __UpperCamelCase			)

  return max_prod

from typing import TYPE_CHECKING

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


__lowerCamelCase    :				List[Any]							     =	{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase    :				Union[str, Any]							     =	[
       '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''InformerForPrediction''',
       '''InformerModel''',
       '''InformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase    :				Dict							     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
