





'''simple docstring'''




from math import ceil






def 	_lowerCAmelCase      (  __snake_case  :			Optional[Any]			,							__snake_case  :			Any      )					->					Union[str, Any]:
     __A						:			Union[str, Any]								=	list(range(0			,							__snake_case      )      )

     __A						:			List[str]								=	[item for sublist in list(device_map.values()      ) for item in sublist]

     # Duplicate check
     __A						:			Optional[int]								=	[]
     for i in device_map_blocks:
          if device_map_blocks.count(__snake_case      ) > 1 and i not in duplicate_blocks:
               duplicate_blocks.append(__snake_case      )
    # Missing blocks
     __A						:			Any								=	[i for i in blocks if i not in device_map_blocks]
     __A						:			List[str]								=	[i for i in device_map_blocks if i not in blocks]

     if len(__snake_case      ) != 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(__snake_case      )      )
     if len(__snake_case      ) != 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(__snake_case      )      )
     if len(__snake_case      ) != 0:
          raise ValueError(
              'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
              + str(__snake_case      )      )







def 	_lowerCAmelCase      (  __snake_case  :			List[str]			,							__snake_case  :			Union[str, Any]      )					->					Union[str, Any]:
     __A						:			List[Any]								=	list(range(__snake_case      )      )
     __A						:			List[str]								=	int(ceil(n_layers / len(__snake_case      )      )      )
     __A						:			List[Any]								=	[layers[i : i + n_blocks] for i in range(0			,							__snake_case			,							__snake_case      )]

     return dict(zip(__snake_case			,							__snake_case      )      )






'''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()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )