






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


a	: Dict      =				logging.get_logger(__name__)  # pylint: disable=invalid-name


a	: Union[str, 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					(				lowercase__     ):



      """simple docstring"""


      def __init__(   self      :    Any	,   __lowercase      :    UNetaDModel	,   __lowercase      :    CMStochasticIterativeScheduler      )							->   None:
        super().__init__()

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

        __UpperCAmelCase							:			Dict     =						None
      def 			UpperCAmelCase   (   self      :    Tuple	,   __lowercase      :    Any=0      )							->   str:
        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"""      )

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

        if self.device.type != "cpu":
          self.to("""cpu"""	,   silence_dtype_warnings=__lowercase      )
          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(__lowercase	,   __lowercase      )

        if self.safety_checker is not None:
          cpu_offload(self.safety_checker	,   execution_device=__lowercase	,   offload_buffers=__lowercase      )
      def 			UpperCAmelCase   (   self      :    Optional[Any]	,   __lowercase      :    int=0      )							->   Union[str, Any]:
        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."""      )

        __UpperCAmelCase							:			List[Any]     =						torch.device(f"""cuda:{gpu_id}"""      )

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

        __UpperCAmelCase							:			int     =						None
        for cpu_offloaded_model in [self.unet]:
          __UpperCAmelCase   ,				__UpperCAmelCase							:			List[str]     =						cpu_offload_with_hook(__lowercase	,   __lowercase	,   prev_module_hook=__lowercase      )

        if self.safety_checker is not None:
          __UpperCAmelCase   ,				__UpperCAmelCase							:			Tuple     =						cpu_offload_with_hook(self.safety_checker	,   __lowercase	,   prev_module_hook=__lowercase      )

        # We'll offload the last model manually.
        __UpperCAmelCase							:			Tuple     =						hook
      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 			UpperCAmelCase   (   self      :    List[str]      )							->   List[str]:
        if not hasattr(self.unet	,   """_hf_hook"""      ):
          return self.device
        for module in self.unet.modules():
          if (
              hasattr(__lowercase	,   """_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      :    List[str]	,   __lowercase      :    Optional[int]	,   __lowercase      :    Optional[Any]	,   __lowercase      :    int	,   __lowercase      :    int	,   __lowercase      :    List[Any]	,   __lowercase      :    Dict	,   __lowercase      :    int	,   __lowercase      :    List[Any]=None      )							->   List[Any]:
        __UpperCAmelCase							:			List[Any]     =						(batch_size, num_channels, height, width)
        if isinstance(__lowercase	,   __lowercase      ) and len(__lowercase      ) != batch_size:
          raise ValueError(
              f"""You have passed a list of generators of length {len(__lowercase      )}, 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:
          __UpperCAmelCase							:			List[Any]     =						randn_tensor(__lowercase	,   generator=__lowercase	,   device=__lowercase	,   dtype=__lowercase      )
        else:
          __UpperCAmelCase							:			Optional[Any]     =						latents.to(device=__lowercase	,   dtype=__lowercase      )

        # scale the initial noise by the standard deviation required by the scheduler
        __UpperCAmelCase							:			Any     =						latents * self.scheduler.init_noise_sigma
        return latents
      def 			UpperCAmelCase   (   self      :    List[str]	,   __lowercase      :    torch.FloatTensor	,   __lowercase      :    str = "pil"      )							->   Optional[int]:
        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
        __UpperCAmelCase							:			Any     =						(sample / 2 + 0.5).clamp(0	,   1      )
        if output_type == "pt":
          return sample

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

        # Output_type must be 'pil'
        __UpperCAmelCase							:			str     =						self.numpy_to_pil(__lowercase      )
        return sample
      def 			UpperCAmelCase   (   self      :    Optional[Any]	,   __lowercase      :    Optional[Any]	,   __lowercase      :    Optional[Any]	,   __lowercase      :    int=None      )							->   Optional[int]:
        if self.unet.config.num_class_embeds is not None:
          if isinstance(__lowercase	,   __lowercase      ):
            __UpperCAmelCase							:			Any     =						torch.tensor(__lowercase	,   dtype=torch.int      )
          elif isinstance(__lowercase	,   __lowercase      ):
            assert batch_size == 1, "Batch size must be 1 if classes is an int"
            __UpperCAmelCase							:			Union[str, 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
            __UpperCAmelCase							:			List[str]     =						torch.randint(0	,   self.unet.config.num_class_embeds	,   size=(batch_size,)      )
          __UpperCAmelCase							:			int     =						class_labels.to(__lowercase      )
        else:
          __UpperCAmelCase							:			Optional[Any]     =						None
        return class_labels
      def 			UpperCAmelCase   (   self      :    Any	,   __lowercase      :    Tuple	,   __lowercase      :    Dict	,   __lowercase      :    List[str]	,   __lowercase      :    List[str]	,   __lowercase      :    Tuple	,   __lowercase      :    str      )							->   Any:
        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:
          __UpperCAmelCase							:			int     =						(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(__lowercase	,   __lowercase      ) or callback_steps <= 0)
        ):
          raise ValueError(
              f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
              f""" {type(__lowercase      )}."""      )




      @torch.no_grad()
      @replace_example_docstring(__lowercase      )
      def __call__(   self      :    Union[str, Any]	,   __lowercase      :    int = 1	,   __lowercase      :    Optional[Union[torch.Tensor, List[int], int]] = None	,   __lowercase      :    int = 1	,   __lowercase      :    List[int] = None	,   __lowercase      :    Optional[Union[torch.Generator, List[torch.Generator]]] = None	,   __lowercase      :    Optional[torch.FloatTensor] = None	,   __lowercase      :    Optional[str] = "pil"	,   __lowercase      :    bool = True	,   __lowercase      :    Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,   __lowercase      :    int = 1	,   )							->   int:
        __UpperCAmelCase							:			int     =						self.unet.config.sample_size
        __UpperCAmelCase							:			Optional[int]     =						self._execution_device

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

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

        # 3. Handle class_labels for class-conditional models
        __UpperCAmelCase							:			int     =						self.prepare_class_labels(__lowercase	,   __lowercase	,   class_labels=__lowercase      )

        # 4. Prepare timesteps
        if timesteps is not None:
          self.scheduler.set_timesteps(timesteps=__lowercase	,   device=__lowercase      )
          __UpperCAmelCase							:			List[str]     =						self.scheduler.timesteps
          __UpperCAmelCase							:			Dict     =						len(__lowercase      )
        else:
          self.scheduler.set_timesteps(__lowercase      )
          __UpperCAmelCase							:			Optional[int]     =						self.scheduler.timesteps

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

            __UpperCAmelCase							:			Optional[Any]     =						self.scheduler.step(__lowercase	,   __lowercase	,   __lowercase	,   generator=__lowercase      )[0]

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

        # 6. Post-process image sample
        __UpperCAmelCase							:			Optional[Any]     =						self.postprocess_image(__lowercase	,   output_type=__lowercase      )

        # 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=__lowercase      )










import decimal






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			str     =						0
  __UpperCAmelCase							:			Tuple     =						decimal.Context(prec=105	)
  for i in range(2						,	100	):
    __UpperCAmelCase							:			Optional[int]     =						decimal.Decimal(__lowerCamelCase	)
    __UpperCAmelCase							:			Any     =						number.sqrt(__lowerCamelCase	)
    if len(str(__lowerCamelCase	)	) > 1:
      answer += int(str(__lowerCamelCase	)[0]	)
      __UpperCAmelCase							:			Optional[Any]     =						str(__lowerCamelCase	)[2:101]
      answer += sum(int(__lowerCamelCase	) for x in sqrt_number_str	)
  return answer


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(f"""{solution() = }""")


