




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__						=		'\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					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						super().__init__()

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

						lowerCamelCase_				:						Optional[Any]				  =				None

					def       lowercase_				(							self					,			__lowercase=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'   )

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

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

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

					def       lowercase_				(							self					,			__lowercase=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.'   )

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

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

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

						if self.safety_checker is not None:
							lowerCamelCase_				:						Union[str, Any]				  =				cpu_offload_with_hook(self.safety_checker					,			_SCREAMING_SNAKE_CASE					,			prev_module_hook=_SCREAMING_SNAKE_CASE   )

						# We'll offload the last model manually.
						lowerCamelCase_				:						Any				  =				hook

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







						'''simple docstring'''




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







						'''simple docstring'''




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

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

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

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

						# Output_type must be 'pil'
						lowerCamelCase_				:						List[Any]				  =				self.numpy_to_pil(_SCREAMING_SNAKE_CASE   )
						return sample

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




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

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase   ):







						'''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:
							lowerCamelCase_				:						List[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(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE   ) or callback_steps <= 0)
						):
							raise ValueError(
							    f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
							    f" {type(_SCREAMING_SNAKE_CASE   )}."   )





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







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				self.unet.config.sample_size
						lowerCamelCase_				:						Optional[Any]				  =				self._execution_device

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

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

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

						# 4. Prepare timesteps
						if timesteps is not None:
							self.scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE					,			device=_SCREAMING_SNAKE_CASE   )
							lowerCamelCase_				:						Dict				  =				self.scheduler.timesteps
							lowerCamelCase_				:						Optional[int]				  =				len(_SCREAMING_SNAKE_CASE   )
						else:
							self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   )
							lowerCamelCase_				:						str				  =				self.scheduler.timesteps

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

								lowerCamelCase_				:						List[str]				  =				self.scheduler.step(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			generator=_SCREAMING_SNAKE_CASE   )[0]

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

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

						# 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=_SCREAMING_SNAKE_CASE   )


"""simple docstring"""







from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_pt_objects import *  # noqa F403
else:
							from .consistency_models import ConsistencyModelPipeline
							from .dance_diffusion import DanceDiffusionPipeline
							from .ddim import DDIMPipeline
							from .ddpm import DDPMPipeline
							from .dit import DiTPipeline
							from .latent_diffusion import LDMSuperResolutionPipeline
							from .latent_diffusion_uncond import LDMPipeline
							from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
							from .pndm import PNDMPipeline
							from .repaint import RePaintPipeline
							from .score_sde_ve import ScoreSdeVePipeline
							from .stochastic_karras_ve import KarrasVePipeline

try:
							if not (is_torch_available() and is_librosa_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
							from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
							if not (is_torch_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
							from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
							from .audioldm import AudioLDMPipeline
							from .controlnet import (
							    StableDiffusionControlNetImgaImgPipeline,
							    StableDiffusionControlNetInpaintPipeline,
							    StableDiffusionControlNetPipeline,
							)
							from .deepfloyd_if import (
							    IFImgaImgPipeline,
							    IFImgaImgSuperResolutionPipeline,
							    IFInpaintingPipeline,
							    IFInpaintingSuperResolutionPipeline,
							    IFPipeline,
							    IFSuperResolutionPipeline,
							)
							from .kandinsky import (
							    KandinskyImgaImgPipeline,
							    KandinskyInpaintPipeline,
							    KandinskyPipeline,
							    KandinskyPriorPipeline,
							)
							from .kandinskya_a import (
							    KandinskyVaaControlnetImgaImgPipeline,
							    KandinskyVaaControlnetPipeline,
							    KandinskyVaaImgaImgPipeline,
							    KandinskyVaaInpaintPipeline,
							    KandinskyVaaPipeline,
							    KandinskyVaaPriorEmbaEmbPipeline,
							    KandinskyVaaPriorPipeline,
							)
							from .latent_diffusion import LDMTextToImagePipeline
							from .paint_by_example import PaintByExamplePipeline
							from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
							from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
							from .stable_diffusion import (
							    CycleDiffusionPipeline,
							    StableDiffusionAttendAndExcitePipeline,
							    StableDiffusionDepthaImgPipeline,
							    StableDiffusionDiffEditPipeline,
							    StableDiffusionImageVariationPipeline,
							    StableDiffusionImgaImgPipeline,
							    StableDiffusionInpaintPipeline,
							    StableDiffusionInpaintPipelineLegacy,
							    StableDiffusionInstructPixaPixPipeline,
							    StableDiffusionLatentUpscalePipeline,
							    StableDiffusionLDMaDPipeline,
							    StableDiffusionModelEditingPipeline,
							    StableDiffusionPanoramaPipeline,
							    StableDiffusionParadigmsPipeline,
							    StableDiffusionPipeline,
							    StableDiffusionPixaPixZeroPipeline,
							    StableDiffusionSAGPipeline,
							    StableDiffusionUpscalePipeline,
							    StableUnCLIPImgaImgPipeline,
							    StableUnCLIPPipeline,
							)
							from .stable_diffusion_safe import StableDiffusionPipelineSafe
							from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
							from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
							from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
							from .versatile_diffusion import (
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							)
							from .vq_diffusion import VQDiffusionPipeline


try:
							if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
							from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
							if not is_onnx_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_onnx_objects import *  # noqa F403
else:
							from .onnx_utils import OnnxRuntimeModel

try:
							if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
							from .stable_diffusion import (
							    OnnxStableDiffusionImgaImgPipeline,
							    OnnxStableDiffusionInpaintPipeline,
							    OnnxStableDiffusionInpaintPipelineLegacy,
							    OnnxStableDiffusionPipeline,
							    OnnxStableDiffusionUpscalePipeline,
							    StableDiffusionOnnxPipeline,
							)

try:
							if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
							from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_flax_objects import *  # noqa F403
else:
							from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
							if not (is_flax_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
							from .controlnet import FlaxStableDiffusionControlNetPipeline
							from .stable_diffusion import (
							    FlaxStableDiffusionImgaImgPipeline,
							    FlaxStableDiffusionInpaintPipeline,
							    FlaxStableDiffusionPipeline,
							)
try:
							if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
							from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline