

"""simple docstring"""




from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCAmelCase       :				str         =					logging.get_logger(__name__)  # pylint: disable=invalid-name


lowerCAmelCase       :				List[Any]         =					'\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'
class 	_A						(					A__):

  def __init__(			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        super().__init__()

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

        SCREAMING_SNAKE_CASE_			:					Dict          =       None

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE=0   ):





        """simple docstring"""







        if is_accelerate_available() and is_accelerate_version('>='	,   '0.14.0'   ):
              from accelerate import cpu_offload
        else:
              raise ImportError('`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'   )

        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       torch.device(f"cuda:{gpu_id}"   )

        if self.device.type != "cpu":
              self.to('cpu'	,   silence_dtype_warnings=_SCREAMING_SNAKE_CASE   )
              torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        for cpu_offloaded_model in [self.unet]:
              cpu_offload(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )

        if self.safety_checker is not None:
              cpu_offload(self.safety_checker	,   execution_device=_SCREAMING_SNAKE_CASE	,   offload_buffers=_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE=0   ):





        """simple docstring"""







        if is_accelerate_available() and is_accelerate_version('>='	,   '0.17.0.dev0'   ):
              from accelerate import cpu_offload_with_hook
        else:
              raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'   )

        SCREAMING_SNAKE_CASE_			:					Any          =       torch.device(f"cuda:{gpu_id}"   )

        if self.device.type != "cpu":
              self.to('cpu'	,   silence_dtype_warnings=_SCREAMING_SNAKE_CASE   )
              torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       None
        for cpu_offloaded_model in [self.unet]:
              SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					Optional[int]          =       cpu_offload_with_hook(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   prev_module_hook=_SCREAMING_SNAKE_CASE   )

        if self.safety_checker is not None:
              SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					Any          =       cpu_offload_with_hook(self.safety_checker	,   _SCREAMING_SNAKE_CASE	,   prev_module_hook=_SCREAMING_SNAKE_CASE   )

        # We'll offload the last model manually.
        SCREAMING_SNAKE_CASE_			:					List[str]          =       hook

  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        if not hasattr(self.unet	,   '_hf_hook'   ):
              return self.device
        for module in self.unet.modules():
              if (
                  hasattr(_SCREAMING_SNAKE_CASE	,   '_hf_hook'   )
                  and hasattr(module._hf_hook	,   'execution_device'   )
                  and module._hf_hook.execution_device is not None
              ):
                    return torch.device(module._hf_hook.execution_device   )
        return self.device

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE=None   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       (batch_size, num_channels, height, width)
        if isinstance(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ) and len(_SCREAMING_SNAKE_CASE   ) != batch_size:
              raise ValueError(
                  f"You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE   )}, but requested an effective batch"
                  f" size of {batch_size}. Make sure the batch size matches the length of the generators."   )

        if latents is None:
              SCREAMING_SNAKE_CASE_			:					Optional[int]          =       randn_tensor(_SCREAMING_SNAKE_CASE	,   generator=_SCREAMING_SNAKE_CASE	,   device=_SCREAMING_SNAKE_CASE	,   dtype=_SCREAMING_SNAKE_CASE   )
        else:
              SCREAMING_SNAKE_CASE_			:					int          =       latents.to(device=_SCREAMING_SNAKE_CASE	,   dtype=_SCREAMING_SNAKE_CASE   )

        # scale the initial noise by the standard deviation required by the scheduler
        SCREAMING_SNAKE_CASE_			:					List[str]          =       latents * self.scheduler.init_noise_sigma
        return latents

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = "pil"   ):





        """simple docstring"""







        if output_type not in ["pt", "np", "pil"]:
              raise ValueError(
                  f"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"   )

        # Equivalent to diffusers.VaeImageProcessor.denormalize
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       (sample / 2 + 0.5).clamp(0	,   1   )
        if output_type == "pt":
              return sample

        # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       sample.cpu().permute(0	,   2	,   3	,   1   ).numpy()
        if output_type == "np":
              return sample

        # Output_type must be 'pil'
        SCREAMING_SNAKE_CASE_			:					Dict          =       self.numpy_to_pil(_SCREAMING_SNAKE_CASE   )
        return sample

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE=None   ):





        """simple docstring"""







        if self.unet.config.num_class_embeds is not None:
              if isinstance(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):
                    SCREAMING_SNAKE_CASE_			:					Any          =       torch.tensor(_SCREAMING_SNAKE_CASE	,   dtype=torch.int   )
              elif isinstance(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):
                    assert batch_size == 1, "Batch size must be 1 if classes is an int"
                    SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       torch.tensor([class_labels]	,   dtype=torch.int   )
              elif class_labels is None:
                    # Randomly generate batch_size class labels
                    # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
                    SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       torch.randint(0	,   self.unet.config.num_class_embeds	,   size=(batch_size,)   )
              SCREAMING_SNAKE_CASE_			:					List[Any]          =       class_labels.to(_SCREAMING_SNAKE_CASE   )
        else:
              SCREAMING_SNAKE_CASE_			:					Tuple          =       None
        return class_labels

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        if num_inference_steps is None and timesteps is None:
              raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'   )

        if num_inference_steps is not None and timesteps is not None:
              logger.warning(
                  f"Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"
                  ' `timesteps` will be used over `num_inference_steps`.'   )

        if latents is not None:
              SCREAMING_SNAKE_CASE_			:					Dict          =       (batch_size, 3, img_size, img_size)
              if latents.shape != expected_shape:
                    raise ValueError(f"The shape of latents is {latents.shape} but is expected to be {expected_shape}."   )

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ) or callback_steps <= 0)
        ):
              raise ValueError(
                  f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                  f" {type(_SCREAMING_SNAKE_CASE   )}."   )



  @torch.no_grad()
  @replace_example_docstring(_SCREAMING_SNAKE_CASE   )
  def __call__(			self	,   _SCREAMING_SNAKE_CASE = 1	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = 1	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = "pil"	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = 1	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       self.unet.config.sample_size
        SCREAMING_SNAKE_CASE_			:					int          =       self._execution_device

        # 1. Check inputs
        self.check_inputs(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )

        # 2. Prepare image latents
        # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       self.prepare_latents(
            batch_size=_SCREAMING_SNAKE_CASE	,   num_channels=self.unet.config.in_channels	,   height=_SCREAMING_SNAKE_CASE	,   width=_SCREAMING_SNAKE_CASE	,   dtype=self.unet.dtype	,   device=_SCREAMING_SNAKE_CASE	,   generator=_SCREAMING_SNAKE_CASE	,   latents=_SCREAMING_SNAKE_CASE	,   )

        # 3. Handle class_labels for class-conditional models
        SCREAMING_SNAKE_CASE_			:					Any          =       self.prepare_class_labels(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   class_labels=_SCREAMING_SNAKE_CASE   )

        # 4. Prepare timesteps
        if timesteps is not None:
              self.scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	,   device=_SCREAMING_SNAKE_CASE   )
              SCREAMING_SNAKE_CASE_			:					Dict          =       self.scheduler.timesteps
              SCREAMING_SNAKE_CASE_			:					Dict          =       len(_SCREAMING_SNAKE_CASE   )
        else:
              self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   )
              SCREAMING_SNAKE_CASE_			:					List[Any]          =       self.scheduler.timesteps

        # 5. Denoising loop
        # Multistep sampling: implements Algorithm 1 in the paper
        with self.progress_bar(total=_SCREAMING_SNAKE_CASE   ) as progress_bar:
              for i, t in enumerate(_SCREAMING_SNAKE_CASE   ):
                    SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       self.scheduler.scale_model_input(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )
                    SCREAMING_SNAKE_CASE_			:					Dict          =       self.unet(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   class_labels=_SCREAMING_SNAKE_CASE	,   return_dict=_SCREAMING_SNAKE_CASE   )[0]

                    SCREAMING_SNAKE_CASE_			:					Tuple          =       self.scheduler.step(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   generator=_SCREAMING_SNAKE_CASE   )[0]

                    # call the callback, if provided
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                          callback(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )

        # 6. Post-process image sample
        SCREAMING_SNAKE_CASE_			:					int          =       self.postprocess_image(_SCREAMING_SNAKE_CASE	,   output_type=_SCREAMING_SNAKE_CASE   )

        # Offload last model to CPU
        if hasattr(self	,   'final_offload_hook'   ) and self.final_offload_hook is not None:
              self.final_offload_hook.offload()

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE   )









from math import sqrt

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[str]          =       [1]
      for i in range(2   ,				int(sqrt(a					)					) + 1   ,				1					):
            if number % i == 0:
                  values.append(a					)
                  if int(number // i					) != i:
                        values.append(int(number // i					)					)
      return sorted(a					)

def 				A_					(							a					):

      """simple docstring"""



      return sum(factors(a					)					) > n

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       factors(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       len(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       [[0 for i in range(number + 1					)] for j in range(r + 1					)]
      for i in range(r + 1					):
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       True

      for i in range(1   ,				number + 1					):
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       False

      for i in range(1   ,				r + 1					):
            for j in range(1   ,				number + 1					):
                  if j < values[i - 1]:
                        SCREAMING_SNAKE_CASE_			:					Dict          =       subset[i - 1][j]
                  else:
                        SCREAMING_SNAKE_CASE_			:					List[str]          =       subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

      return subset[r][number] != 0

def 				A_					(							a					):

      """simple docstring"""



      return abundant(a					) and not semi_perfect(a					)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    for number in (69, 70, 71):
        print(F'{number} is {"" if weird(number) else "not "}weird.')






