



'''simple docstring'''



import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer
def 					UpperCAmelCase   (						A  :				Optional[Any]					,		A  :				List[str]					,		A  :				List[str]					,		A  :				Union[str, Any]=5				):

							'''simple docstring'''





							assert masked_input.count('''<mask>'''				) == 1
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.tensor(tokenizer.encode(A					,		add_special_tokens=A				)				).unsqueeze(0				)  # Batch size 1
							SCREAMING_SNAKE_CASE					:      Dict     					=			model(A				)[0]  # The last hidden-state is the first element of the output tuple
							SCREAMING_SNAKE_CASE					:      str     					=			(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
							SCREAMING_SNAKE_CASE					:      Tuple     					=			logits[0, masked_index, :]
							SCREAMING_SNAKE_CASE					:      Tuple     					=			logits.softmax(dim=0				)
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			prob.topk(k=A					,		dim=0				)
							SCREAMING_SNAKE_CASE					:      str     					=			''' '''.join(
							    [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(A				)				)]				)
							SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.mask_token
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[]
							for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''				)				):
														SCREAMING_SNAKE_CASE					:      int     					=			predicted_token_bpe.replace('''\u2581'''					,		''' '''				)
														if " {0}".format(A				) in masked_input:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(''' {0}'''.format(A				)					,		A				),
																					        values[index].item(),
																					        predicted_token,
																					    )				)
														else:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(A					,		A				),
																					        values[index].item(),
																					        predicted_token,
																					    )				)
							return topk_filled_outputs


lowerCAmelCase_     :       List[str]								=						CamembertTokenizer.from_pretrained('camembert-base')
lowerCAmelCase_     :       List[Any]								=						CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

lowerCAmelCase_     :       Optional[int]								=						'Le camembert est <mask> :)'






print(fill_mask(masked_input, model, tokenizer, topk=3))









'''simple docstring'''



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


lowerCAmelCase_     :       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__":
							lowerCAmelCase_     :       int								=						'hopper-medium-v2'
							lowerCAmelCase_     :       str								=						gym.make(env_name)

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

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

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

																					lowerCAmelCase_     :       Union[str, Any]								=						next_observation
							except KeyboardInterrupt:
														pass

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




