





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









import matplotlib.pyplot as plt
import numpy as np




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  len(A )  # Number of training samples
	UpperCAmelCase_		    =  np.eye(A )  # Initialize weights as identity matrix
	for j in range(A ):
		UpperCAmelCase_		    =  point - x_train[j]
		UpperCAmelCase_		    =  np.exp(diff @ diff.T / (-2.0 * tau**2) )

	return weights




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  weight_matrix(A					,   A					,   A )
	UpperCAmelCase_		    =  np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  np.zeros(len(A ) )  # Initialize array of predictions
	for i, item in enumerate(A ):
		UpperCAmelCase_		    =  item @ local_weight(A					,   A					,   A					,   A )

	return y_pred




def  A_(      A					,   A					,   A ):
	import seaborn as sns

	UpperCAmelCase_		    =  sns.load_dataset(A )
	UpperCAmelCase_		    =  np.array(data[x_name] )
	UpperCAmelCase_		    =  np.array(data[y_name] )

	UpperCAmelCase_		    =  np.ones(len(A ) )

	# pairing elements of one and x_data
	UpperCAmelCase_		    =  np.column_stack((one, x_data) )

	return x_train, x_data, y_data




def  A_(      A					,   A					,   A					,   A					,   A					,   A					,   ):
	UpperCAmelCase_		    =  np.sort(A					,   axis=0 )
	plt.scatter(A					,   A					,   color="""blue""" )
	plt.plot(
	    x_train_sorted[:, 1]					,   preds[x_train[:, 1].argsort(0 )]					,   color="""yellow"""					,   linewidth=5					,   )
	plt.title("""Local Weighted Regression""" )
	plt.xlabel(A )
	plt.ylabel(A )
	plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Demo with a dataset from the seaborn module
				UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__ :			Tuple       	=			load_data("""tips""", """total_bill""", """tip""")
				UpperCamelCase__ :			Tuple       	=			local_weight_regression(training_data_x, tip, 5)
				plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")


