


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


_A		:				Any									=       {
    '''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__":
   _A		:				Union[str, Any]									=       '''hopper-medium-v2'''
   _A		:				List[str]									=       gym.make(env_name)

   _A		:				List[str]									=       ValueGuidedRLPipeline.from_pretrained(
       '''bglick13/hopper-medium-v2-value-function-hor32''',
       env=env,
   )

   env.seed(0)
   _A		:				str									=       env.reset()
   _A		:				str									=       0
   _A		:				Optional[Any]									=       0
   _A		:				Union[str, Any]									=       1_000
   _A		:				List[str]									=       [obs.copy()]
   try:
      for t in tqdm.tqdm(range(T)):
         # call the policy
         _A		:				Optional[int]									=       pipeline(obs, planning_horizon=32)

         # execute action in environment
         _A						,_A						,_A						,_A		:				Optional[Any]									=       env.step(denorm_actions)
         _A		:				List[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())

         _A		:				Optional[Any]									=       next_observation
   except KeyboardInterrupt:
      pass

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A		:				List[Any]									=       {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				int									=       ['''ConvBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ConvBertForMaskedLM''',
       '''ConvBertForMultipleChoice''',
       '''ConvBertForQuestionAnswering''',
       '''ConvBertForSequenceClassification''',
       '''ConvBertForTokenClassification''',
       '''ConvBertLayer''',
       '''ConvBertModel''',
       '''ConvBertPreTrainedModel''',
       '''load_tf_weights_in_convbert''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFConvBertForMaskedLM''',
       '''TFConvBertForMultipleChoice''',
       '''TFConvBertForQuestionAnswering''',
       '''TFConvBertForSequenceClassification''',
       '''TFConvBertForTokenClassification''',
       '''TFConvBertLayer''',
       '''TFConvBertModel''',
       '''TFConvBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   _A		:				int									=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


