






"""simple docstring"""



import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
A__	:    Any			 = data_utils.TransfoXLTokenizer
A__	:    Any			 = data_utils.TransfoXLCorpus
A__	:    List[Any]			 = data_utils
A__	:    List[str]			 = data_utils

def 			_snake_case    (					lowerCamelCase__     :   Any    ,						lowerCamelCase__     :   Dict    ,						lowerCamelCase__     :   Any    ,						lowerCamelCase__     :   List[str]  )    ->     Optional[Any]:

							if transfo_xl_dataset_file:
														# Convert a pre-processed corpus (see original TensorFlow repo)
														with open(a__    ,						"rb"  ) as fp:
																					lowerCamelCase_	:							Dict									=pickle.load(a__    ,						encoding="latin1"  )
														# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
														lowerCamelCase_	:							int									=pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
														print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""  )
														lowerCamelCase_	:							str									=corpus.vocab.__dict__
														torch.save(a__    ,						a__  )

														lowerCamelCase_	:							Optional[Any]									=corpus.__dict__
														corpus_dict_no_vocab.pop("vocab"    ,						a__  )
														lowerCamelCase_	:							Tuple									=pytorch_dump_folder_path + "/" + CORPUS_NAME
														print(F"""Save dataset to {pytorch_dataset_dump_path}"""  )
														torch.save(a__    ,						a__  )

							if tf_checkpoint_path:
														# Convert a pre-trained TensorFlow model
														lowerCamelCase_	:							List[str]									=os.path.abspath(a__  )
														lowerCamelCase_	:							Optional[int]									=os.path.abspath(a__  )

														print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""  )
														# Initialise PyTorch model
														if transfo_xl_config_file == "":
																					lowerCamelCase_	:							Tuple									=TransfoXLConfig()
														else:
																					lowerCamelCase_	:							List[str]									=TransfoXLConfig.from_json_file(a__  )
														print(F"""Building PyTorch model from configuration: {config}"""  )
														lowerCamelCase_	:							Optional[int]									=TransfoXLLMHeadModel(a__  )

														lowerCamelCase_	:							List[Any]									=load_tf_weights_in_transfo_xl(a__    ,						a__    ,						a__  )
														# Save pytorch-model
														lowerCamelCase_	:							Optional[int]									=os.path.join(a__    ,						a__  )
														lowerCamelCase_	:							Optional[int]									=os.path.join(a__    ,						a__  )
														print(F"""Save PyTorch model to {os.path.abspath(a__  )}"""  )
														torch.save(model.state_dict()    ,						a__  )
														print(F"""Save configuration file to {os.path.abspath(a__  )}"""  )
														with open(a__    ,						"w"    ,						encoding="utf-8"  ) as f:
																					f.write(config.to_json_string()  )


if __name__ == "__main__":
	A__	:    Tuple			 = argparse.ArgumentParser()
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to the folder to store the PyTorch model or dataset/vocab.',
	)
	parser.add_argument(
	    '--tf_checkpoint_path',
	    default='',
	    type=str,
	    help='An optional path to a TensorFlow checkpoint path to be converted.',
	)
	parser.add_argument(
	    '--transfo_xl_config_file',
	    default='',
	    type=str,
	    help=(
	        'An optional config json file corresponding to the pre-trained BERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--transfo_xl_dataset_file',
	    default='',
	    type=str,
	    help='An optional dataset file to be converted in a vocabulary.',
	)
	A__	:    int			 = parser.parse_args()
	convert_transfo_xl_checkpoint_to_pytorch(
	    args.tf_checkpoint_path,
	    args.transfo_xl_config_file,
	    args.pytorch_dump_folder_path,
	    args.transfo_xl_dataset_file,
	)











"""simple docstring"""



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


A__	:    int			 = {
    '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__	:    str			 = 'hopper-medium-v2'
						A__	:    Dict			 = gym.make(env_name)

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

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

																		# execute action in environment
																		A__ ,						A__ ,						A__ ,						A__	:    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}')



