





'''simple docstring'''

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


__lowerCAmelCase				:      Dict						      ={
    "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				:      int						      ="hopper-medium-v2"
  __lowerCAmelCase				:      Any						      =gym.make(env_name)

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

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

      # execute action in environment
      __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase				:      List[Any]						      =env.step(denorm_actions)
      __lowerCAmelCase				:      Optional[Any]						      =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				:      Any						      =next_observation
  except KeyboardInterrupt:
    pass

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










'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase				:      Union[str, Any]						      ={"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =[
      "MRA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MraForMaskedLM",
      "MraForMultipleChoice",
      "MraForQuestionAnswering",
      "MraForSequenceClassification",
      "MraForTokenClassification",
      "MraLayer",
      "MraModel",
      "MraPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mra import (
        MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraLayer,
        MraModel,
        MraPreTrainedModel,
    )
else:
  import sys

  __lowerCAmelCase				:      Dict						      =_LazyModule(__name__, globals()["__file__"], _import_structure)



