"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       list[int]	)       ->      int:






  if not numbers:
    return 0

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

  _lowerCAmelCase						:       List[str]  			=     numbers[0]

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

    # update the maximum product found till now
    _lowerCAmelCase						:       str  			=     max(__lowercase			,__lowercase	)

  return max_prod











'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
    if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import (
        VersatileDiffusionDualGuidedPipeline,
        VersatileDiffusionImageVariationPipeline,
        VersatileDiffusionPipeline,
        VersatileDiffusionTextToImagePipeline,
    )
else:
    from .modeling_text_unet import UNetFlatConditionModel
    from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
    from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
    from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
    from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
