


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


snake_case							:       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__":
						snake_case							:       Optional[Any]     			=      '''hopper-medium-v2'''
						snake_case							:       Optional[Any]     			=      gym.make(env_name)

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

						env.seed(0)
						snake_case							:       int     			=      env.reset()
						snake_case							:       Union[str, Any]     			=      0
						snake_case							:       Union[str, Any]     			=      0
						snake_case							:       str     			=      10_00
						snake_case							:       Optional[int]     			=      [obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		snake_case							:       Any     			=      pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		snake_case			,     snake_case			,     snake_case			,     snake_case							:       Union[str, Any]     			=      env.step(denorm_actions)
																		snake_case							:       Optional[int]     			=      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())

																		snake_case							:       Any     			=      next_observation
						except KeyboardInterrupt:
												pass

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








import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      RobertaPreLayerNormConfig.from_pretrained(
		    SCREAMING_SNAKE_CASE						,				architectures=['''RobertaPreLayerNormForMaskedLM''']     )

		# convert state_dict
		lowercase__            =      torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE						,				filename='''pytorch_model.bin'''     )     )
		lowercase__            =      {}
		for tensor_key, tensor_value in original_state_dict.items():
				# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
				if tensor_key.startswith('''roberta.'''     ):
						lowercase__            =      '''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''     ) :]

				# The original implementation contains weights which are not used, remove them from the state_dict
				if tensor_key.endswith('''.self.LayerNorm.weight'''     ) or tensor_key.endswith('''.self.LayerNorm.bias'''     ):
						continue

				lowercase__            =      tensor_value

		lowercase__            =      RobertaPreLayerNormForMaskedLM.from_pretrained(
		    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE						,				config=SCREAMING_SNAKE_CASE						,				state_dict=SCREAMING_SNAKE_CASE     )
		model.save_pretrained(SCREAMING_SNAKE_CASE     )

		# convert tokenizer
		lowercase__            =      AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
		tokenizer.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint-repo',
			    default=None,
			    type=str,
			    required=True,
			    help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			lowerCAmelCase       =							parser.parse_args()
			convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


