


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


a : List[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 : int		=				'''hopper-medium-v2'''
	a : int		=				gym.make(env_name)

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

	env.seed(0)
	a : Dict		=				env.reset()
	a : Optional[Any]		=				0
	a : Tuple		=				0
	a : List[str]		=				1000
	a : Dict		=				[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 : List[str]		=				env.step(denorm_actions)
			a : 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 : Any		=				next_observation
	except KeyboardInterrupt:
		pass

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






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   TaConfig.from_json_file(_UpperCamelCase  )
			print(F'Building PyTorch model from configuration: {config}'  )
			__lowercase 					=   TaForConditionalGeneration(_UpperCamelCase  )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
	a : Optional[int]		=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
	)
	parser.add_argument(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	a : str		=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


