


"""simple docstring"""



from math import ceil







def     __lowerCAmelCase		(		lowercase    :    List[Any]		,				lowercase    :    Optional[Any]					)      ->		List[Any]:


    """simple docstring"""





    snake_case       : List[Any]    =       list(range(0		,				lowercase					)					)

    snake_case       : Dict    =       [item for sublist in list(device_map.values()					) for item in sublist]

    # Duplicate check
    snake_case       : Dict    =       []
    for i in device_map_blocks:
        if device_map_blocks.count(lowercase					) > 1 and i not in duplicate_blocks:
            duplicate_blocks.append(lowercase					)
    # Missing blocks
    snake_case       : List[str]    =       [i for i in blocks if i not in device_map_blocks]
    snake_case       : Tuple    =       [i for i in device_map_blocks if i not in blocks]

    if len(lowercase					) != 0:
        raise ValueError(
            "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
            " These attention blocks were specified more than once: " + str(lowercase					)					)
    if len(lowercase					) != 0:
        raise ValueError(
            "There are attention blocks for this model that are not specified in the device_map. Add these attention "
            "blocks to a device on the device_map: " + str(lowercase					)					)
    if len(lowercase					) != 0:
        raise ValueError(
            "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
            + str(lowercase					)					)







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]		,				lowercase    :    str					)      ->		Optional[Any]:


    """simple docstring"""





    snake_case       : Dict    =       list(range(lowercase					)					)
    snake_case       : Tuple    =       int(ceil(n_layers / len(lowercase					)					)					)
    snake_case       : int    =       [layers[i : i + n_blocks] for i in range(0		,				lowercase		,				lowercase					)]

    return dict(zip(lowercase		,				lowercase					)					)









"""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__)





