





"""simple docstring"""
import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowerCamelCase_								=		{
    "n_samples": 6_4,
    "horizon": 3_2,
    "num_inference_steps": 2_0,
    "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_								=		"hopper-medium-v2"
  lowerCamelCase_								=		gym.make(env_name)

  lowerCamelCase_								=		ValueGuidedRLPipeline.from_pretrained(
      "bglick13/hopper-medium-v2-value-function-hor32",
      env=env,
  )

  env.seed(0)
  lowerCamelCase_								=		env.reset()
  lowerCamelCase_								=		0
  lowerCamelCase_								=		0
  lowerCamelCase_								=		1_0_0_0
  lowerCamelCase_								=		[obs.copy()]
  try:
    for t in tqdm.tqdm(range(T)):
      # call the policy
      lowerCamelCase_								=		pipeline(obs, planning_horizon=3_2)

      # execute action in environment
      lowerCamelCase_			,     lowerCamelCase_			,     lowerCamelCase_			,     lowerCamelCase_								=		env.step(denorm_actions)
      lowerCamelCase_								=		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_								=		next_observation
  except KeyboardInterrupt:
    pass

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






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_								=		{
    "configuration_blip_2": [
        "BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Blip2Config",
        "Blip2QFormerConfig",
        "Blip2VisionConfig",
    ],
    "processing_blip_2": ["Blip2Processor"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		[
      "BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Blip2Model",
      "Blip2QFormerModel",
      "Blip2PreTrainedModel",
      "Blip2ForConditionalGeneration",
      "Blip2VisionModel",
  ]

if TYPE_CHECKING:
  from .configuration_blip_a import (
      BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BlipaConfig,
      BlipaQFormerConfig,
      BlipaVisionConfig,
  )
  from .processing_blip_a import BlipaProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blip_a import (
        BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlipaForConditionalGeneration,
        BlipaModel,
        BlipaPreTrainedModel,
        BlipaQFormerModel,
        BlipaVisionModel,
    )

else:
  import sys

  lowerCamelCase_								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)