




"""simple docstring"""

from typing import TYPE_CHECKING

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


a					=			{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a					=			['MobileBertTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a					=			[
           'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MobileBertForMaskedLM',
           'MobileBertForMultipleChoice',
           'MobileBertForNextSentencePrediction',
           'MobileBertForPreTraining',
           'MobileBertForQuestionAnswering',
           'MobileBertForSequenceClassification',
           'MobileBertForTokenClassification',
           'MobileBertLayer',
           'MobileBertModel',
           'MobileBertPreTrainedModel',
           'load_tf_weights_in_mobilebert',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a					=			[
           'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFMobileBertForMaskedLM',
           'TFMobileBertForMultipleChoice',
           'TFMobileBertForNextSentencePrediction',
           'TFMobileBertForPreTraining',
           'TFMobileBertForQuestionAnswering',
           'TFMobileBertForSequenceClassification',
           'TFMobileBertForTokenClassification',
           'TFMobileBertMainLayer',
           'TFMobileBertModel',
           'TFMobileBertPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mobilebert import (
           MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           MobileBertConfig,
           MobileBertOnnxConfig,
       )
       from .tokenization_mobilebert import MobileBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mobilebert_fast import MobileBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilebert import (
                  MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileBertForMaskedLM,
                  MobileBertForMultipleChoice,
                  MobileBertForNextSentencePrediction,
                  MobileBertForPreTraining,
                  MobileBertForQuestionAnswering,
                  MobileBertForSequenceClassification,
                  MobileBertForTokenClassification,
                  MobileBertLayer,
                  MobileBertModel,
                  MobileBertPreTrainedModel,
                  load_tf_weights_in_mobilebert,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mobilebert import (
                  TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFMobileBertForMaskedLM,
                  TFMobileBertForMultipleChoice,
                  TFMobileBertForNextSentencePrediction,
                  TFMobileBertForPreTraining,
                  TFMobileBertForQuestionAnswering,
                  TFMobileBertForSequenceClassification,
                  TFMobileBertForTokenClassification,
                  TFMobileBertMainLayer,
                  TFMobileBertModel,
                  TFMobileBertPreTrainedModel,
              )

else:
       import sys

       a					=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




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


A_       : List[str]         =			{
    '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_       : Optional[int]         =			'hopper-medium-v2'
	A_       : List[Any]         =			gym.make(env_name)

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

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

			# execute action in environment
			A_       ,			A_       ,			A_       ,			A_       : Dict         =			env.step(denorm_actions)
			A_       : List[str]         =			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_       : int         =			next_observation
	except KeyboardInterrupt:
		pass

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



