


"""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


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


__a      			= "\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 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""






							def __init__(    self			:  List[str]		,			lowerCamelCase_			:  UNetaDModel		,			lowerCamelCase_			:  CMStochasticIterativeScheduler					):
													super().__init__()

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

													UpperCAmelCase__												=     None






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  Union[str, Any]=0					):
													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__												=     torch.device(f"""cuda:{gpu_id}"""					)

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

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






							def        lowerCAmelCase  (    self			:  Any		,			lowerCamelCase_			:  Union[str, Any]=0					):
													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__												=     torch.device(f"""cuda:{gpu_id}"""					)

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

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

													if self.safety_checker is not None:
																			UpperCAmelCase__						,					UpperCAmelCase__												=     cpu_offload_with_hook(self.safety_checker		,			lowerCamelCase_		,			prev_module_hook=lowerCamelCase_					)

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






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

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






							def        lowerCAmelCase  (    self			:  List[Any]		,			lowerCamelCase_			:  torch.FloatTensor		,			lowerCamelCase_			:  str = "pil"					):
													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__												=     (sample / 2 + 0.5).clamp(0		,			1					)
													if output_type == "pt":
																			return sample

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

													# Output_type must be 'pil'
													UpperCAmelCase__												=     self.numpy_to_pil(lowerCamelCase_					)
													return sample






							def        lowerCAmelCase  (    self			:  Any		,			lowerCamelCase_			:  Dict		,			lowerCamelCase_			:  int		,			lowerCamelCase_			:  Any=None					):
													if self.unet.config.num_class_embeds is not None:
																			if isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																									UpperCAmelCase__												=     torch.tensor(lowerCamelCase_		,			dtype=torch.int					)
																			elif isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																									assert batch_size == 1, "Batch size must be 1 if classes is an int"
																									UpperCAmelCase__												=     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__												=     torch.randint(0		,			self.unet.config.num_class_embeds		,			size=(batch_size,)					)
																			UpperCAmelCase__												=     class_labels.to(lowerCamelCase_					)
													else:
																			UpperCAmelCase__												=     None
													return class_labels






							def        lowerCAmelCase  (    self			:  Any		,			lowerCamelCase_			:  Optional[int]		,			lowerCamelCase_			:  List[str]		,			lowerCamelCase_			:  List[str]		,			lowerCamelCase_			:  Tuple		,			lowerCamelCase_			:  Dict		,			lowerCamelCase_			:  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__												=     (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(lowerCamelCase_		,			lowerCamelCase_					) or callback_steps <= 0)
													):
																			raise ValueError(
																			    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																			    f""" {type(lowerCamelCase_					)}."""					)






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

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

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

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

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

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

																									UpperCAmelCase__												=     self.scheduler.step(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			generator=lowerCamelCase_					)[0]

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

        # 6. Post-process image sample
													UpperCAmelCase__												=     self.postprocess_image(lowerCamelCase_		,			output_type=lowerCamelCase_					)

													# 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=lowerCamelCase_					)



"""simple docstring"""







from __future__ import annotations






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     sorted(_UpperCamelCase			)
						UpperCAmelCase__												=     len(_UpperCamelCase			)
						UpperCAmelCase__												=     length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)






def        a__			(							):





						"""simple docstring"""

						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()