



lowerCAmelCase							:  int            =						"""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









import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowerCAmelCase							:  Tuple            =						{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
						lowerCAmelCase							:  Optional[int]            =						"""hopper-medium-v2"""
						lowerCAmelCase							:  Optional[int]            =						gym.make(env_name)

						lowerCAmelCase							:  Optional[int]            =						ValueGuidedRLPipeline.from_pretrained(
						    """bglick13/hopper-medium-v2-value-function-hor32""",
						    env=env,
						)

						env.seed(0)
						lowerCAmelCase							:  Optional[Any]            =						env.reset()
						lowerCAmelCase							:  Union[str, Any]            =						0
						lowerCAmelCase							:  Optional[int]            =						0
						lowerCAmelCase							:  Optional[int]            =						1000
						lowerCAmelCase							:  Dict            =						[obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		lowerCAmelCase							:  Union[str, Any]            =						pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase							:  Dict            =						env.step(denorm_actions)
																		lowerCAmelCase							:  Tuple            =						env.get_normalized_score(total_reward)

																		# update return
																		total_reward += reward
																		total_score += score
																		print(
																		    f'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
																		    f''' {total_score}'''
																		)

																		# save observations for rendering
																		rollout.append(next_observation.copy())

																		lowerCAmelCase							:  Tuple            =						next_observation
						except KeyboardInterrupt:
												pass

						print(f'''Total reward: {total_reward}''')






