


'''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


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


__UpperCAmelCase    				=							'''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''




class 		a__				(							a__  ):





							'''simple docstring'''






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

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

														lowerCAmelCase__          =	None







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_=0   )       ->							Optional[Any]:
														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'''   )

														lowerCAmelCase__          =	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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_=0   )       ->							List[str]:
														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.'''   )

														lowerCAmelCase__          =	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)

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

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

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







							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, 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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_=None   )       ->							Any:
														lowerCAmelCase__          =	(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:
																					lowerCAmelCase__          =	randn_tensor(lowerCamelCase_					,    generator=lowerCamelCase_					,    device=lowerCamelCase_					,    dtype=lowerCamelCase_   )
														else:
																					lowerCAmelCase__          =	latents.to(device=lowerCamelCase_					,    dtype=lowerCamelCase_   )

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







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_ = "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
														lowerCAmelCase__          =	(sample / 2 + 0.5).clamp(0					,    1   )
														if output_type == "pt":
																					return sample

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

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







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







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Union[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:
																					lowerCAmelCase__          =	(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					,    lowerCamelCase_ = 1					,    lowerCamelCase_ = None					,    lowerCamelCase_ = 1					,    lowerCamelCase_ = None					,    lowerCamelCase_ = None					,    lowerCamelCase_ = None					,    lowerCamelCase_ = "pil"					,    lowerCamelCase_ = True					,    lowerCamelCase_ = None					,    lowerCamelCase_ = 1					,    )       ->							Union[str, Any]:
														lowerCAmelCase__          =	self.unet.config.sample_size
														lowerCAmelCase__          =	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)
														lowerCAmelCase__          =	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
														lowerCAmelCase__          =	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_   )
																					lowerCAmelCase__          =	self.scheduler.timesteps
																					lowerCAmelCase__          =	len(lowerCamelCase_   )
														else:
																					self.scheduler.set_timesteps(lowerCamelCase_   )
																					lowerCAmelCase__          =	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_   ):
																												lowerCAmelCase__          =	self.scheduler.scale_model_input(lowerCamelCase_					,    lowerCamelCase_   )
																												lowerCAmelCase__          =	self.unet(lowerCamelCase_					,    lowerCamelCase_					,    class_labels=lowerCamelCase_					,    return_dict=lowerCamelCase_   )[0]

																												lowerCAmelCase__          =	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
														lowerCAmelCase__          =	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'''






def 					_snake_case   (     A       )						->    str:
							return " ".join(sorted(set(sentence.split()       )       )       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()