




from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

UpperCamelCase__         =      "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"







def      _a				(						SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    str=8					):
					__lowerCAmelCase           =	height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__lowerCAmelCase           =	width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor


class       a__   ( _SCREAMING_SNAKE_CASE				):






			def __init__(       self		,      _A		,      _A		,      _A		,      ):




								"""simple docstring"""



								super().__init__()

								self.register_modules(
								    unet=A_		,      scheduler=A_		,      movq=A_		,      )
								__lowerCAmelCase           =	2 ** (len(self.movq.config.block_out_channels      ) - 1)






			def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A		,      _A		,      _A      ):




								"""simple docstring"""



								if latents is None:
													__lowerCAmelCase           =	randn_tensor(A_		,      generator=A_		,      device=A_		,      dtype=A_      )
								else:
													if latents.shape != shape:
																		raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""      )
													__lowerCAmelCase           =	latents.to(A_      )

								__lowerCAmelCase           =	latents * scheduler.init_noise_sigma
								return latents






			def     __SCREAMING_SNAKE_CASE(       self		,      _A=0      ):




								"""simple docstring"""



								if is_accelerate_available():
													from accelerate import cpu_offload
								else:
													raise ImportError("Please install accelerate via `pip install accelerate`"      )

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

								__lowerCAmelCase           =	[
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																		cpu_offload(A_		,      A_      )






			def     __SCREAMING_SNAKE_CASE(       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."      )

								__lowerCAmelCase           =	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)

								__lowerCAmelCase           =	None
								for cpu_offloaded_model in [self.unet, self.movq]:
													__lowerCAmelCase       ,							__lowerCAmelCase           =	cpu_offload_with_hook(A_		,      A_		,      prev_module_hook=A_      )

								# 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      ):




								"""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






			@torch.no_grad()
			@replace_example_docstring(A_      )
			def __call__(       self		,      _A		,      _A		,      _A		,      _A = 5_1_2		,      _A = 5_1_2		,      _A = 1_0_0		,      _A = 4.0		,      _A = 1		,      _A = None		,      _A = None		,      _A = "pil"		,      _A = True		,      ):




								"""simple docstring"""



								__lowerCAmelCase           =	self._execution_device

								__lowerCAmelCase           =	guidance_scale > 1.0

								if isinstance(A_		,      A_      ):
													__lowerCAmelCase           =	torch.cat(A_		,      dim=0      )
								if isinstance(A_		,      A_      ):
													__lowerCAmelCase           =	torch.cat(A_		,      dim=0      )
								if isinstance(A_		,      A_      ):
													__lowerCAmelCase           =	torch.cat(A_		,      dim=0      )

								__lowerCAmelCase           =	image_embeds.shape[0] * num_images_per_prompt

								if do_classifier_free_guidance:
													__lowerCAmelCase           =	image_embeds.repeat_interleave(A_		,      dim=0      )
													__lowerCAmelCase           =	negative_image_embeds.repeat_interleave(A_		,      dim=0      )
													__lowerCAmelCase           =	hint.repeat_interleave(A_		,      dim=0      )

								__lowerCAmelCase           =	torch.cat([negative_image_embeds, image_embeds]		,      dim=0      ).to(dtype=self.unet.dtype		,      device=A_      )
								__lowerCAmelCase           =	torch.cat([hint, hint]		,      dim=0      ).to(dtype=self.unet.dtype		,      device=A_      )
								self.scheduler.set_timesteps(A_		,      device=A_      )
								__lowerCAmelCase           =	self.scheduler.timesteps

								__lowerCAmelCase           =	self.movq.config.latent_channels

								__lowerCAmelCase       ,							__lowerCAmelCase           =	downscale_height_and_width(A_		,      A_		,      self.movq_scale_factor      )

								# create initial latent
								__lowerCAmelCase           =	self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)		,      image_embeds.dtype		,      A_		,      A_		,      A_		,      self.scheduler		,      )

								for i, t in enumerate(self.progress_bar(A_      )      ):
													# expand the latents if we are doing classifier free guidance
													__lowerCAmelCase           =	torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

													__lowerCAmelCase           =	{"image_embeds": image_embeds, "hint": hint}
													__lowerCAmelCase           =	self.unet(
													    sample=A_		,      timestep=A_		,      encoder_hidden_states=A_		,      added_cond_kwargs=A_		,      return_dict=A_		,      )[0]

													if do_classifier_free_guidance:
																		__lowerCAmelCase       ,							__lowerCAmelCase           =	noise_pred.split(latents.shape[1]		,      dim=1      )
																		__lowerCAmelCase       ,							__lowerCAmelCase           =	noise_pred.chunk(2      )
																		__lowerCAmelCase       ,							__lowerCAmelCase           =	variance_pred.chunk(2      )
																		__lowerCAmelCase           =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																		__lowerCAmelCase           =	torch.cat([noise_pred, variance_pred_text]		,      dim=1      )

													if not (
													    hasattr(self.scheduler.config		,      "variance_type"      )
													    and self.scheduler.config.variance_type in ["learned", "learned_range"]
													):
																		__lowerCAmelCase       ,							__lowerCAmelCase           =	noise_pred.split(latents.shape[1]		,      dim=1      )

													# compute the previous noisy sample x_t -> x_t-1
													__lowerCAmelCase           =	self.scheduler.step(
													    A_		,      A_		,      A_		,      generator=A_		,      )[0]
								# post-processing
								__lowerCAmelCase           =	self.movq.decode(A_		,      force_not_quantize=A_      )["sample"]

								if output_type not in ["pt", "np", "pil"]:
													raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""      )

								if output_type in ["np", "pil"]:
													__lowerCAmelCase           =	image * 0.5 + 0.5
													__lowerCAmelCase           =	image.clamp(0		,      1      )
													__lowerCAmelCase           =	image.cpu().permute(0		,      2		,      3		,      1      ).float().numpy()

								if output_type == "pil":
													__lowerCAmelCase           =	self.numpy_to_pil(A_      )

								if not return_dict:
													return (image,)

								return ImagePipelineOutput(images=A_      )










UpperCamelCase__         =      """0.18.2"""

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_pt_objects import *  # noqa F403
else:
					from .models import (
					    AutoencoderKL,
					    ControlNetModel,
					    ModelMixin,
					    PriorTransformer,
					    TaFilmDecoder,
					    TransformeraDModel,
					    UNetaDModel,
					    UNetaDConditionModel,
					    UNetaDModel,
					    UNetaDConditionModel,
					    VQModel,
					)
					from .optimization import (
					    get_constant_schedule,
					    get_constant_schedule_with_warmup,
					    get_cosine_schedule_with_warmup,
					    get_cosine_with_hard_restarts_schedule_with_warmup,
					    get_linear_schedule_with_warmup,
					    get_polynomial_decay_schedule_with_warmup,
					    get_scheduler,
					)
					from .pipelines import (
					    AudioPipelineOutput,
					    ConsistencyModelPipeline,
					    DanceDiffusionPipeline,
					    DDIMPipeline,
					    DDPMPipeline,
					    DiffusionPipeline,
					    DiTPipeline,
					    ImagePipelineOutput,
					    KarrasVePipeline,
					    LDMPipeline,
					    LDMSuperResolutionPipeline,
					    PNDMPipeline,
					    RePaintPipeline,
					    ScoreSdeVePipeline,
					)
					from .schedulers import (
					    CMStochasticIterativeScheduler,
					    DDIMInverseScheduler,
					    DDIMParallelScheduler,
					    DDIMScheduler,
					    DDPMParallelScheduler,
					    DDPMScheduler,
					    DEISMultistepScheduler,
					    DPMSolverMultistepInverseScheduler,
					    DPMSolverMultistepScheduler,
					    DPMSolverSinglestepScheduler,
					    EulerAncestralDiscreteScheduler,
					    EulerDiscreteScheduler,
					    HeunDiscreteScheduler,
					    IPNDMScheduler,
					    KarrasVeScheduler,
					    KDPMaAncestralDiscreteScheduler,
					    KDPMaDiscreteScheduler,
					    PNDMScheduler,
					    RePaintScheduler,
					    SchedulerMixin,
					    ScoreSdeVeScheduler,
					    UnCLIPScheduler,
					    UniPCMultistepScheduler,
					    VQDiffusionScheduler,
					)
					from .training_utils import EMAModel

try:
					if not (is_torch_available() and is_scipy_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
					from .schedulers import LMSDiscreteScheduler

try:
					if not (is_torch_available() and is_torchsde_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
					from .schedulers import DPMSolverSDEScheduler

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 .pipelines import (
					    AltDiffusionImgaImgPipeline,
					    AltDiffusionPipeline,
					    AudioLDMPipeline,
					    CycleDiffusionPipeline,
					    IFImgaImgPipeline,
					    IFImgaImgSuperResolutionPipeline,
					    IFInpaintingPipeline,
					    IFInpaintingSuperResolutionPipeline,
					    IFPipeline,
					    IFSuperResolutionPipeline,
					    ImageTextPipelineOutput,
					    KandinskyImgaImgPipeline,
					    KandinskyInpaintPipeline,
					    KandinskyPipeline,
					    KandinskyPriorPipeline,
					    KandinskyVaaControlnetImgaImgPipeline,
					    KandinskyVaaControlnetPipeline,
					    KandinskyVaaImgaImgPipeline,
					    KandinskyVaaInpaintPipeline,
					    KandinskyVaaPipeline,
					    KandinskyVaaPriorEmbaEmbPipeline,
					    KandinskyVaaPriorPipeline,
					    LDMTextToImagePipeline,
					    PaintByExamplePipeline,
					    SemanticStableDiffusionPipeline,
					    ShapEImgaImgPipeline,
					    ShapEPipeline,
					    StableDiffusionAttendAndExcitePipeline,
					    StableDiffusionControlNetImgaImgPipeline,
					    StableDiffusionControlNetInpaintPipeline,
					    StableDiffusionControlNetPipeline,
					    StableDiffusionDepthaImgPipeline,
					    StableDiffusionDiffEditPipeline,
					    StableDiffusionImageVariationPipeline,
					    StableDiffusionImgaImgPipeline,
					    StableDiffusionInpaintPipeline,
					    StableDiffusionInpaintPipelineLegacy,
					    StableDiffusionInstructPixaPixPipeline,
					    StableDiffusionLatentUpscalePipeline,
					    StableDiffusionLDMaDPipeline,
					    StableDiffusionModelEditingPipeline,
					    StableDiffusionPanoramaPipeline,
					    StableDiffusionParadigmsPipeline,
					    StableDiffusionPipeline,
					    StableDiffusionPipelineSafe,
					    StableDiffusionPixaPixZeroPipeline,
					    StableDiffusionSAGPipeline,
					    StableDiffusionUpscalePipeline,
					    StableUnCLIPImgaImgPipeline,
					    StableUnCLIPPipeline,
					    TextToVideoSDPipeline,
					    TextToVideoZeroPipeline,
					    UnCLIPImageVariationPipeline,
					    UnCLIPPipeline,
					    UniDiffuserModel,
					    UniDiffuserPipeline,
					    UniDiffuserTextDecoder,
					    VersatileDiffusionDualGuidedPipeline,
					    VersatileDiffusionImageVariationPipeline,
					    VersatileDiffusionPipeline,
					    VersatileDiffusionTextToImagePipeline,
					    VideoToVideoSDPipeline,
					    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 .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

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 .pipelines import StableDiffusionKDiffusionPipeline

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 .pipelines import (
					    OnnxStableDiffusionImgaImgPipeline,
					    OnnxStableDiffusionInpaintPipeline,
					    OnnxStableDiffusionInpaintPipelineLegacy,
					    OnnxStableDiffusionPipeline,
					    OnnxStableDiffusionUpscalePipeline,
					    StableDiffusionOnnxPipeline,
					)

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 .pipelines import AudioDiffusionPipeline, Mel

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 .pipelines import SpectrogramDiffusionPipeline

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_flax_objects import *  # noqa F403
else:
					from .models.controlnet_flax import FlaxControlNetModel
					from .models.modeling_flax_utils import FlaxModelMixin
					from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
					from .models.vae_flax import FlaxAutoencoderKL
					from .pipelines import FlaxDiffusionPipeline
					from .schedulers import (
					    FlaxDDIMScheduler,
					    FlaxDDPMScheduler,
					    FlaxDPMSolverMultistepScheduler,
					    FlaxKarrasVeScheduler,
					    FlaxLMSDiscreteScheduler,
					    FlaxPNDMScheduler,
					    FlaxSchedulerMixin,
					    FlaxScoreSdeVeScheduler,
					)


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 .pipelines import (
					    FlaxStableDiffusionControlNetPipeline,
					    FlaxStableDiffusionImgaImgPipeline,
					    FlaxStableDiffusionInpaintPipeline,
					    FlaxStableDiffusionPipeline,
					)

try:
					if not (is_note_seq_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_note_seq_objects import *  # noqa F403
else:
					from .pipelines import MidiProcessor




