


"""simple docstring"""



def     __lowerCAmelCase		(		lowercase    :    list[int]					)      ->		int:


    """simple docstring"""





    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"					)

    snake_case       : List[Any]    =       numbers[0]

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

        # update the maximum product found till now
        snake_case       : Optional[int]    =       max(lowercase		,				lowercase					)

    return max_prod









"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case       =       {
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TimeSeriesTransformerForPrediction""",
      """TimeSeriesTransformerModel""",
      """TimeSeriesTransformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

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





