


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a	(_lowerCAmelCase   ):




       """simple docstring"""


       def __init__(							self :						Optional[int]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						str       )			->					Tuple:
              super().__init__()
              self.register_modules(unet=lowerCamelCase					,			scheduler=lowerCamelCase       )






       @torch.no_grad()
       def __call__(							self :						int					,			lowerCamelCase :						int = 1					,			lowerCamelCase :						Optional[torch.Generator] = None					,			lowerCamelCase :						int = 50					,			lowerCamelCase :						Optional[str] = "pil"					,			lowerCamelCase :						bool = True					,			**lowerCamelCase :						Tuple					,			)			->					Union[ImagePipelineOutput, Tuple]:
              __snake_case	:    Dict											=     torch.randn(
                  (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,			generator=lowerCamelCase					,			)
              __snake_case	:    Optional[int]											=     image.to(self.device       )

              # set step values
              self.scheduler.set_timesteps(lowerCamelCase       )

              for t in self.progress_bar(self.scheduler.timesteps       ):
                     # 1. predict noise model_output
                     __snake_case	:    Union[str, Any]											=     self.unet(lowerCamelCase					,			lowerCamelCase       ).sample

                     # 2. predict previous mean of image x_t-1 and add variance depending on eta
                     # eta corresponds to η in paper and should be between [0, 1]
                     # do x_t -> x_t-1
                     __snake_case	:    List[Any]											=     self.scheduler.step(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ).prev_sample

              __snake_case	:    List[str]											=     (image / 2 + 0.5).clamp(0					,			1       )
              __snake_case	:    str											=     image.cpu().permute(0					,			2					,			3					,			1       ).numpy()
              if output_type == "pil":
                     __snake_case	:    str											=     self.numpy_to_pil(lowerCamelCase       )

              if not return_dict:
                     return (image,), "This is a local test"

              return ImagePipelineOutput(images=lowerCamelCase       ), "This is a local test"





def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if number < 0 or shift_amount < 0:
														raise ValueError("both inputs must be positive integers"			)

							__snake_case	:    Dict											=     str(bin(__lowerCamelCase			)			)
							binary_number += "0" * shift_amount
							return binary_number







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if number < 0 or shift_amount < 0:
														raise ValueError("both inputs must be positive integers"			)

							__snake_case	:    List[str]											=     str(bin(__lowerCamelCase			)			)[2:]
							if shift_amount >= len(__lowerCamelCase			):
														return "0b0"
							__snake_case	:    Union[str, Any]											=     binary_number[: len(__lowerCamelCase			) - shift_amount]
							return "0b" + shifted_binary_number







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if number >= 0:  # Get binary representation of positive number
														__snake_case	:    Optional[int]											=     "0" + str(bin(__lowerCamelCase			)			).strip("-"			)[2:]
							else:  # Get binary (2's complement) representation of negative number
														__snake_case	:    Tuple											=     len(bin(__lowerCamelCase			)[3:]			)  # Find 2's complement of number
														__snake_case	:    Any											=     bin(abs(__lowerCamelCase			) - (1 << binary_number_length)			)[3:]
														__snake_case	:    Optional[Any]											=     (
														    "1" + "0" * (binary_number_length - len(__lowerCamelCase			)) + binary_number
														)

							if shift_amount >= len(__lowerCamelCase			):
														return "0b" + binary_number[0] * len(__lowerCamelCase			)
							return (
							    "0b"
							    + binary_number[0] * shift_amount
							    + binary_number[: len(__lowerCamelCase			) - shift_amount]
							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()

