

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Union[str, Any]      							=					'''EncodecFeatureExtractor'''
			_UpperCAmelCase       :							str      							=					('''T5Tokenizer''', '''T5TokenizerFast''')



			def __init__(			self  :					Dict       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					Tuple):
									super().__init__(lowerCAmelCase__       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.feature_extractor
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					Dict=None       ,						lowerCAmelCase__  :					List[Any]=None       ,						lowerCAmelCase__  :					List[Any]=True):
									return self.tokenizer.get_decoder_prompt_ids(task=lowerCAmelCase__       ,						language=lowerCAmelCase__       ,						no_timestamps=lowerCAmelCase__)



			def __call__(			self  :					int       ,						*lowerCAmelCase__  :					List[str]       ,						**lowerCAmelCase__  :					str):
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor(*lowerCAmelCase__       ,						**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	kwargs.pop("audio"       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	kwargs.pop("sampling_rate"       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	kwargs.pop("text"       ,						lowerCAmelCase__)
									if len(lowerCAmelCase__) > 0:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	args[0]
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	args[1:]

									if audio is None and text is None:
															raise ValueError("You need to specify either an `audio` or `text` input to process.")

									if text is not None:
															SCREAMING_SNAKE_CASE_:			int  		=	self.tokenizer(lowerCAmelCase__       ,						**lowerCAmelCase__)

									if audio is not None:
															SCREAMING_SNAKE_CASE_:			Dict  		=	self.feature_extractor(lowerCAmelCase__       ,						*lowerCAmelCase__       ,						sampling_rate=lowerCAmelCase__       ,						**lowerCAmelCase__)

									if audio is None:
															return inputs

									elif text is None:
															return audio_inputs

									else:
															SCREAMING_SNAKE_CASE_:			Dict  		=	audio_inputs["input_values"]
															if "padding_mask" in audio_inputs:
																					SCREAMING_SNAKE_CASE_:			List[str]  		=	audio_inputs["padding_mask"]
															return inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						*lowerCAmelCase__  :					Tuple       ,						**lowerCAmelCase__  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	kwargs.pop("audio"       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	kwargs.pop("padding_mask"       ,						lowerCAmelCase__)

									if len(lowerCAmelCase__) > 0:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	args[0]
															SCREAMING_SNAKE_CASE_:			Dict  		=	args[1:]

									if audio_values is not None:
															return self._decode_audio(lowerCAmelCase__       ,						padding_mask=lowerCAmelCase__)
									else:
															return self.tokenizer.batch_decode(*lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						*lowerCAmelCase__  :					Optional[int]       ,						**lowerCAmelCase__  :					Tuple):
									return self.tokenizer.decode(*lowerCAmelCase__       ,						**lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Optional = None):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	to_numpy(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[Any]  		=	audio_values.shape

									if padding_mask is None:
															return list(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	to_numpy(lowerCAmelCase__)

									# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
									# token (so that the generated audio values are **not** treated as padded tokens)
									SCREAMING_SNAKE_CASE_:			int  		=	seq_len - padding_mask.shape[-1]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	1 - self.feature_extractor.padding_value
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	np.pad(lowerCAmelCase__       ,						((0, 0), (0, difference))       ,						"constant"       ,						constant_values=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			int  		=	audio_values.tolist()
									for i in range(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Dict  		=	np.asarray(audio_values[i])[
															    padding_mask[i][None, :] != self.feature_extractor.padding_value
															]
															SCREAMING_SNAKE_CASE_:			Tuple  		=	sliced_audio.reshape(lowerCAmelCase__       ,						-1)

									return audio_values









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


lowerCAmelCase							:  Tuple            =						{
    """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							:  Optional[int]            =						"""hopper-medium-v2"""
						lowerCAmelCase							:  Optional[int]            =						gym.make(env_name)

						lowerCAmelCase							:  Optional[int]            =						ValueGuidedRLPipeline.from_pretrained(
						    """bglick13/hopper-medium-v2-value-function-hor32""",
						    env=env,
						)

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

																		# execute action in environment
																		lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase							:  Dict            =						env.step(denorm_actions)
																		lowerCAmelCase							:  Tuple            =						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							:  Tuple            =						next_observation
						except KeyboardInterrupt:
												pass

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






