

import argparse
import os
import re


A_       : int         =			'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
A_       : Any         =			re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
A_       : int         =			re.compile(r'\s*\(\s*"(\S[^"]+)"')

def 						UpperCamelCase					(lowercase_: List[str]    ,      lowercase_: bool = False					)     ->      Optional[Any]:
	with open(lowercase_    ,      """r"""    ,      encoding="""utf-8"""					) as f:
		A__							:    Optional[Any]        =		f.read()

	A__							:    Optional[Any]        =		content.split("""\n"""					)
	A__							:    Optional[Any]        =		[]
	A__							:    Union[str, Any]        =		0
	while line_idx < len(lowercase_					):
		if _re_intro_mapping.search(lines[line_idx]					) is not None:
			A__							:    Union[str, Any]        =		len(re.search(r"""^(\s*)\S"""    ,      lines[line_idx]					).groups()[0]					) + 8
			# Start of a new mapping!
			while not lines[line_idx].startswith(""" """ * indent + """("""					):
				new_lines.append(lines[line_idx]					)
				line_idx += 1

			A__							:    List[Any]        =		[]
			while lines[line_idx].strip() != "]":
				# Blocks either fit in one line or not
				if lines[line_idx].strip() == "(":
					A__							:    Dict        =		line_idx
					while not lines[line_idx].startswith(""" """ * indent + """)"""					):
						line_idx += 1
					blocks.append("""\n""".join(lines[start_idx : line_idx + 1]					)					)
				else:
					blocks.append(lines[line_idx]					)
				line_idx += 1

			# Sort blocks by their identifiers
			A__							:    List[str]        =		sorted(lowercase_    ,      key=lambda lowercase_					: _re_identifier.search(lowercase_					).groups()[0]					)
			new_lines += blocks
		else:
			new_lines.append(lines[line_idx]					)
			line_idx += 1

	if overwrite:
		with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
			f.write("""\n""".join(lowercase_					)					)
	elif "\n".join(lowercase_					) != content:
		return True

def 						UpperCamelCase					(lowercase_: bool = False					)     ->      List[Any]:
	A__							:    List[str]        =		[os.path.join(lowercase_    ,      lowercase_					) for f in os.listdir(lowercase_					) if f.endswith(""".py"""					)]
	A__							:    List[Any]        =		[sort_auto_mapping(lowercase_    ,      overwrite=lowercase_					) for fname in fnames]

	if not overwrite and any(lowercase_					):
		A__							:    int        =		[f for f, d in zip(lowercase_    ,      lowercase_					) if d]
		raise ValueError(
		    f"""The following files have auto mappings that need sorting: {', '.join(lowercase_					)}. Run `make style` to fix"""
		    """ this."""					)


if __name__ == "__main__":
	A_       : Tuple         =			argparse.ArgumentParser()
	parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
	A_       : Union[str, Any]         =			parser.parse_args()

	sort_all_auto_mappings(not args.check_only)






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}''')



