from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE            =		{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE            =		[
	    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """WavLMForAudioFrameClassification""",
	    """WavLMForCTC""",
	    """WavLMForSequenceClassification""",
	    """WavLMForXVector""",
	    """WavLMModel""",
	    """WavLMPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_wavlm import (
		    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    WavLMForAudioFrameClassification,
		    WavLMForCTC,
		    WavLMForSequenceClassification,
		    WavLMForXVector,
		    WavLMModel,
		    WavLMPreTrainedModel,
		)

else:
	import sys

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







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


_UpperCAmelCase            =		{
    """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__":
	_UpperCAmelCase            =		"""hopper-medium-v2"""
	_UpperCAmelCase            =		gym.make(env_name)

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

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

			# execute action in environment
			_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase            =		env.step(denorm_actions)
			_UpperCAmelCase            =		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())

			_UpperCAmelCase            =		next_observation
	except KeyboardInterrupt:
		pass

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






