






import torch

from diffusers import DiffusionPipeline

class 				lowerCAmelCase_	(		__snake_case			):


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											super().__init__()

											self.register_modules(unet=_lowerCAmelCase ,						scheduler=_lowerCAmelCase      )


				def __call__(					self      ):
											_lowercase							:   str 							=   torch.randn(
											    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,						)
											_lowercase							:   Union[str, Any] 							=   1

											_lowercase							:   str 							=   self.unet(_lowerCAmelCase ,						_lowerCAmelCase      ).sample
											_lowercase							:   Optional[int] 							=   self.scheduler.step(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ).prev_sample

											_lowercase							:   int 							=   scheduler_output - scheduler_output + torch.ones_like(_lowerCAmelCase      )

											return result






from math import ceil






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    int							):




			"""simple docstring"""


			lowerCAmelCase_         =      list(range(0       ,				__lowerCAmelCase							)							)

			lowerCAmelCase_         =      [item for sublist in list(device_map.values()							) for item in sublist]

			# Duplicate check
			lowerCAmelCase_         =      []
			for i in device_map_blocks:
						if device_map_blocks.count(__lowerCAmelCase							) > 1 and i not in duplicate_blocks:
									duplicate_blocks.append(__lowerCAmelCase							)
    # Missing blocks
			lowerCAmelCase_         =      [i for i in blocks if i not in device_map_blocks]
			lowerCAmelCase_         =      [i for i in device_map_blocks if i not in blocks]

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






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Tuple							):




			"""simple docstring"""


			lowerCAmelCase_         =      list(range(__lowerCAmelCase							)							)
			lowerCAmelCase_         =      int(ceil(n_layers / len(__lowerCAmelCase							)							)							)
			lowerCAmelCase_         =      [layers[i : i + n_blocks] for i in range(0       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)]

			return dict(zip(__lowerCAmelCase       ,				__lowerCAmelCase							)							)



