


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowercase			:		Optional[Any] =			{
    """configuration_videomae""": ["""VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VideoMAEConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase			:		List[str] =			[
	    """VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """VideoMAEForPreTraining""",
	    """VideoMAEModel""",
	    """VideoMAEPreTrainedModel""",
	    """VideoMAEForVideoClassification""",
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase			:		Dict =			["""VideoMAEFeatureExtractor"""]
	__lowercase			:		Union[str, Any] =			["""VideoMAEImageProcessor"""]

if TYPE_CHECKING:
	from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_videomae import (
		    VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VideoMAEForPreTraining,
		    VideoMAEForVideoClassification,
		    VideoMAEModel,
		    VideoMAEPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_videomae import VideoMAEFeatureExtractor
		from .image_processing_videomae import VideoMAEImageProcessor

else:
	import sys

	__lowercase			:		Optional[int] =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




'''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			:		List[str] =			logging.get_logger(__name__)  # pylint: disable=invalid-name


__lowercase			:		Optional[int] =			'\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 							__UpperCamelCase       (	lowerCAmelCase_      ):




	def __init__(     self		,       __a		,       __a							):

						'''simple docstring'''





						super().__init__()

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

						__a    : List[str]      					=						None




	def    __UpperCAmelCase							(     self		,       __a=0							):

						'''simple docstring'''





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

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

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




	def    __UpperCAmelCase							(     self		,       __a=0							):

						'''simple docstring'''





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

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

						__a    : List[Any]      					=						None
						for cpu_offloaded_model in [self.unet]:
											__a					,       __a    : List[str]      					=						cpu_offload_with_hook(__a		,       __a		,       prev_module_hook=__a							)

						if self.safety_checker is not None:
											__a					,       __a    : str      					=						cpu_offload_with_hook(self.safety_checker		,       __a		,       prev_module_hook=__a							)

						# We'll offload the last model manually.
						__a    : List[Any]      					=						hook




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def    __UpperCAmelCase							(     self							):

						'''simple docstring'''





						if not hasattr(self.unet		,       '_hf_hook'							):
											return self.device
						for module in self.unet.modules():
											if (
											    hasattr(__a		,       '_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		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a=None							):

						'''simple docstring'''





						__a    : str      					=						(batch_size, num_channels, height, width)
						if isinstance(__a		,       __a							) and len(__a							) != batch_size:
											raise ValueError(
											    f"""You have passed a list of generators of length {len(__a							)}, 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    : Optional[Any]      					=						randn_tensor(__a		,       generator=__a		,       device=__a		,       dtype=__a							)
						else:
											__a    : Dict      					=						latents.to(device=__a		,       dtype=__a							)

						# scale the initial noise by the standard deviation required by the scheduler
						__a    : Union[str, Any]      					=						latents * self.scheduler.init_noise_sigma
						return latents




	def    __UpperCAmelCase							(     self		,       __a		,       __a = "pil"							):

						'''simple docstring'''





						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    : List[Any]      					=						(sample / 2 + 0.5).clamp(0		,       1							)
						if output_type == "pt":
											return sample

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

						# Output_type must be 'pil'
						__a    : Union[str, Any]      					=						self.numpy_to_pil(__a							)
						return sample




	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a=None							):

						'''simple docstring'''





						if self.unet.config.num_class_embeds is not None:
											if isinstance(__a		,       __a							):
																__a    : Tuple      					=						torch.tensor(__a		,       dtype=torch.int							)
											elif isinstance(__a		,       __a							):
																assert batch_size == 1, "Batch size must be 1 if classes is an int"
																__a    : int      					=						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    : str      					=						torch.randint(0		,       self.unet.config.num_class_embeds		,       size=(batch_size,)							)
											__a    : str      					=						class_labels.to(__a							)
						else:
											__a    : Optional[int]      					=						None
						return class_labels




	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a							):

						'''simple docstring'''





						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    : Optional[Any]      					=						(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(__a		,       __a							) or callback_steps <= 0)
						):
											raise ValueError(
											    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
											    f""" {type(__a							)}."""							)





	@torch.no_grad()
	@replace_example_docstring(__a							)
	def __call__(     self		,       __a = 1		,       __a = None		,       __a = 1		,       __a = None		,       __a = None		,       __a = None		,       __a = "pil"		,       __a = True		,       __a = None		,       __a = 1		,       ):

						'''simple docstring'''





						__a    : Dict      					=						self.unet.config.sample_size
						__a    : Union[str, Any]      					=						self._execution_device

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

						# 2. Prepare image latents
						# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
						__a    : Union[str, Any]      					=						self.prepare_latents(
						    batch_size=__a		,       num_channels=self.unet.config.in_channels		,       height=__a		,       width=__a		,       dtype=self.unet.dtype		,       device=__a		,       generator=__a		,       latents=__a		,       )

						# 3. Handle class_labels for class-conditional models
						__a    : Optional[Any]      					=						self.prepare_class_labels(__a		,       __a		,       class_labels=__a							)

						# 4. Prepare timesteps
						if timesteps is not None:
											self.scheduler.set_timesteps(timesteps=__a		,       device=__a							)
											__a    : Union[str, Any]      					=						self.scheduler.timesteps
											__a    : Tuple      					=						len(__a							)
						else:
											self.scheduler.set_timesteps(__a							)
											__a    : Tuple      					=						self.scheduler.timesteps

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

																__a    : int      					=						self.scheduler.step(__a		,       __a		,       __a		,       generator=__a							)[0]

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

        # 6. Post-process image sample
						__a    : Tuple      					=						self.postprocess_image(__a		,       output_type=__a							)

						# 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=__a							)



