



"""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


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


snake_case_								=			"""
    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_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:Optional[int]  ,    lowercase_					:UNetaDModel  ,    lowercase_					:CMStochasticIterativeScheduler   )							->     None:
												super().__init__()

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

												UpperCAmelCase			 =					None






						def       UpperCAmelCase__	(   self					:Tuple  ,    lowercase_					:Optional[Any]=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'   )

												UpperCAmelCase			 =					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					:int  ,    lowercase_					:str=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.'   )

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

												if self.safety_checker is not None:
																		UpperCAmelCase ,       UpperCAmelCase			 =					cpu_offload_with_hook(self.safety_checker  ,    lowercase_  ,    prev_module_hook=lowercase_   )

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






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def       UpperCAmelCase__	(   self					:str   )							->     Union[str, Any]:
												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					:Dict  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Dict  ,    lowercase_					:List[str]  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Dict  ,    lowercase_					:Tuple  ,    lowercase_					:int  ,    lowercase_					:List[str]=None   )							->     Optional[Any]:
												UpperCAmelCase			 =					(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			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=lowercase_  ,    dtype=lowercase_   )
												else:
																		UpperCAmelCase			 =					latents.to(device=lowercase_  ,    dtype=lowercase_   )

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






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"   )							->     List[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
												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(lowercase_   )
												return sample






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:Optional[int]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Tuple=None   )							->     Optional[Any]:
												if self.unet.config.num_class_embeds is not None:
																		if isinstance(lowercase_  ,    lowercase_   ):
																								UpperCAmelCase			 =					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			 =					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(lowercase_   )
												else:
																		UpperCAmelCase			 =					None
												return class_labels






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:int  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Optional[Any]  ,    lowercase_					:List[str]  ,    lowercase_					:str  ,    lowercase_					:Union[str, Any]   )							->     int:
												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(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  ,    )							->     Optional[Any]:
												UpperCAmelCase			 =					self.unet.config.sample_size
												UpperCAmelCase			 =					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			 =					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			 =					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			 =					self.scheduler.timesteps
																		UpperCAmelCase			 =					len(lowercase_   )
												else:
																		self.scheduler.set_timesteps(lowercase_   )
																		UpperCAmelCase			 =					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			 =					self.scheduler.scale_model_input(lowercase_  ,    lowercase_   )
																								UpperCAmelCase			 =					self.unet(lowercase_  ,    lowercase_  ,    class_labels=lowercase_  ,    return_dict=lowercase_   )[0]

																								UpperCAmelCase			 =					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			 =					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_   )










"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     List[str]:
												super().__init__()
												self.register_modules(unet=lowercase_  ,    scheduler=lowercase_   )






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		UpperCAmelCase			 =					(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCAmelCase			 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					image.to(self.device   )
												else:
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=self.device   )

												# set step values
												self.scheduler.set_timesteps(lowercase_   )

												for t in self.progress_bar(self.scheduler.timesteps   ):
																		# 1. predict noise model_output
																		UpperCAmelCase			 =					self.unet(lowercase_  ,    lowercase_   ).sample

																		# 2. compute previous image: x_t -> x_t-1
																		UpperCAmelCase			 =					self.scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   ).prev_sample

												UpperCAmelCase			 =					(image / 2 + 0.5).clamp(0  ,    1   )
												UpperCAmelCase			 =					image.cpu().permute(0  ,    2  ,    3  ,    1   ).numpy()
												if output_type == "pil":
																		UpperCAmelCase			 =					self.numpy_to_pil(lowercase_   )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_   )





