


'''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


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


lowercase__					  =		"\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     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):






     def __init__(      self  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     None:
          super().__init__()

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

          _a            =		None






     def 			a_       (      self  ,				__UpperCamelCase=0		)							->     Optional[int]:
          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"		)

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

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

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






     def 			a_       (      self  ,				__UpperCamelCase=0		)							->     int:
          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."		)

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

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

          _a            =		None
          for cpu_offloaded_model in [self.unet]:
               _a      ,      _a            =		cpu_offload_with_hook(__UpperCamelCase  ,				__UpperCamelCase  ,				prev_module_hook=__UpperCamelCase		)

          if self.safety_checker is not None:
               _a      ,      _a            =		cpu_offload_with_hook(self.safety_checker  ,				__UpperCamelCase  ,				prev_module_hook=__UpperCamelCase		)

          # We'll offload the last model manually.
          _a            =		hook






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 			a_       (      self		)							->     List[str]:
          if not hasattr(self.unet  ,				"_hf_hook"		):
               return self.device
          for module in self.unet.modules():
               if (
                   hasattr(__UpperCamelCase  ,				"_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 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase=None		)							->     str:
          _a            =		(batch_size, num_channels, height, width)
          if isinstance(__UpperCamelCase  ,				__UpperCamelCase		) and len(__UpperCamelCase		) != batch_size:
               raise ValueError(
                   f"You have passed a list of generators of length {len(__UpperCamelCase		)}, 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:
               _a            =		randn_tensor(__UpperCamelCase  ,				generator=__UpperCamelCase  ,				device=__UpperCamelCase  ,				dtype=__UpperCamelCase		)
          else:
               _a            =		latents.to(device=__UpperCamelCase  ,				dtype=__UpperCamelCase		)

          # scale the initial noise by the standard deviation required by the scheduler
          _a            =		latents * self.scheduler.init_noise_sigma
          return latents






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase = "pil"		)							->     str:
          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
          _a            =		(sample / 2 + 0.5).clamp(0  ,				1		)
          if output_type == "pt":
               return sample

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

          # Output_type must be 'pil'
          _a            =		self.numpy_to_pil(__UpperCamelCase		)
          return sample






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase=None		)							->     Any:
          if self.unet.config.num_class_embeds is not None:
               if isinstance(__UpperCamelCase  ,				__UpperCamelCase		):
                    _a            =		torch.tensor(__UpperCamelCase  ,				dtype=torch.int		)
               elif isinstance(__UpperCamelCase  ,				__UpperCamelCase		):
                    assert batch_size == 1, "Batch size must be 1 if classes is an int"
                    _a            =		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
                    _a            =		torch.randint(0  ,				self.unet.config.num_class_embeds  ,				size=(batch_size,)		)
               _a            =		class_labels.to(__UpperCamelCase		)
          else:
               _a            =		None
          return class_labels






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     Optional[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:
               _a            =		(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(__UpperCamelCase  ,				__UpperCamelCase		) or callback_steps <= 0)
          ):
               raise ValueError(
                   f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                   f" {type(__UpperCamelCase		)}."		)






     @torch.no_grad()
     @replace_example_docstring(__UpperCamelCase		)
     def __call__(      self  ,				__UpperCamelCase = 1  ,				__UpperCamelCase = None  ,				__UpperCamelCase = 1  ,				__UpperCamelCase = None  ,				__UpperCamelCase = None  ,				__UpperCamelCase = None  ,				__UpperCamelCase = "pil"  ,				__UpperCamelCase = True  ,				__UpperCamelCase = None  ,				__UpperCamelCase = 1  ,				)							->     Any:
          _a            =		self.unet.config.sample_size
          _a            =		self._execution_device

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

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

          # 3. Handle class_labels for class-conditional models
          _a            =		self.prepare_class_labels(__UpperCamelCase  ,				__UpperCamelCase  ,				class_labels=__UpperCamelCase		)

          # 4. Prepare timesteps
          if timesteps is not None:
               self.scheduler.set_timesteps(timesteps=__UpperCamelCase  ,				device=__UpperCamelCase		)
               _a            =		self.scheduler.timesteps
               _a            =		len(__UpperCamelCase		)
          else:
               self.scheduler.set_timesteps(__UpperCamelCase		)
               _a            =		self.scheduler.timesteps

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

                    _a            =		self.scheduler.step(__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				generator=__UpperCamelCase		)[0]

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

        # 6. Post-process image sample
          _a            =		self.postprocess_image(__UpperCamelCase  ,				output_type=__UpperCamelCase		)

          # 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=__UpperCamelCase		)





'''simple docstring'''

def __UpperCamelCase (      __lowerCamelCase		:			float   ,	__lowerCamelCase		:			float   ,	__lowerCamelCase		:			float					)   ->  float:







     '''simple docstring'''






     if mass < 0:
          raise ValueError("The mass of the body cannot be negative"					)
     if radius <= 0:
          raise ValueError("The radius is always a positive non zero integer"					)
     return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

