import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''sew-d'''
				def __init__(						self		,     lowercase=3_2		,     lowercase=7_6_8		,     lowercase=1_2		,     lowercase=1_2		,     lowercase=3_0_7_2		,     lowercase=2		,     lowercase=5_1_2		,     lowercase=2_5_6		,     lowercase=True		,     lowercase=True		,     lowercase=("p2c", "c2p")		,     lowercase="layer_norm"		,     lowercase="gelu_python"		,     lowercase=0.1		,     lowercase=0.1		,     lowercase=0.1		,     lowercase=0.0		,     lowercase=0.1		,     lowercase=0.02		,     lowercase=1E-7		,     lowercase=1E-5		,     lowercase="group"		,     lowercase="gelu"		,     lowercase=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,     lowercase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,     lowercase=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,     lowercase=False		,     lowercase=1_2_8		,     lowercase=1_6		,     lowercase=True		,     lowercase=0.05		,     lowercase=1_0		,     lowercase=2		,     lowercase=0.0		,     lowercase=1_0		,     lowercase=0		,     lowercase="mean"		,     lowercase=False		,     lowercase=False		,     lowercase=2_5_6		,     lowercase=0		,     lowercase=1		,     lowercase=2		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(**lowercase		,     pad_token_id=lowercase		,     bos_token_id=lowercase		,     eos_token_id=lowercase )
						A_		:    Optional[int]         =				hidden_size
						A_		:    Dict         =				feat_extract_norm
						A_		:    Union[str, Any]         =				feat_extract_activation
						A_		:    Union[str, Any]         =				list(lowercase )
						A_		:    Dict         =				list(lowercase )
						A_		:    Union[str, Any]         =				list(lowercase )
						A_		:    str         =				conv_bias
						A_		:    Any         =				num_conv_pos_embeddings
						A_		:    Any         =				num_conv_pos_embedding_groups
						A_		:    str         =				len(self.conv_dim )
						A_		:    List[Any]         =				num_hidden_layers
						A_		:    List[Any]         =				intermediate_size
						A_		:    Dict         =				squeeze_factor
						A_		:    int         =				max_position_embeddings
						A_		:    List[str]         =				position_buckets
						A_		:    Union[str, Any]         =				share_att_key
						A_		:    Optional[Any]         =				relative_attention
						A_		:    str         =				norm_rel_ebd
						A_		:    List[Any]         =				list(lowercase )
						A_		:    Tuple         =				hidden_act
						A_		:    int         =				num_attention_heads
						A_		:    Tuple         =				hidden_dropout
						A_		:    int         =				attention_dropout
						A_		:    Optional[Any]         =				activation_dropout
						A_		:    Tuple         =				feat_proj_dropout
						A_		:    Dict         =				final_dropout
						A_		:    List[str]         =				layer_norm_eps
						A_		:    Dict         =				feature_layer_norm_eps
						A_		:    Optional[int]         =				initializer_range
						A_		:    List[Any]         =				vocab_size

						if (
						    (len(self.conv_stride ) != self.num_feat_extract_layers)
						    or (len(self.conv_kernel ) != self.num_feat_extract_layers)
						    or (len(self.conv_dim ) != self.num_feat_extract_layers)
						):
								raise ValueError(
								    'Configuration for convolutional layers is incorrect.'
								    'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
								    F'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
								    F'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )

						# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
						A_		:    Optional[int]         =				apply_spec_augment
						A_		:    Optional[Any]         =				mask_time_prob
						A_		:    Tuple         =				mask_time_length
						A_		:    int         =				mask_time_min_masks
						A_		:    Optional[Any]         =				mask_feature_prob
						A_		:    Union[str, Any]         =				mask_feature_length
						A_		:    int         =				mask_feature_min_masks

						# ctc loss
						A_		:    List[str]         =				ctc_loss_reduction
						A_		:    List[Any]         =				ctc_zero_infinity

						# sequence classification
						A_		:    Optional[int]         =				use_weighted_layer_sum
						A_		:    Optional[Any]         =				classifier_proj_size







				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return functools.reduce(operator.mul		,     self.conv_stride		,     1 )







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


_UpperCAmelCase            =		{
    """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__":
	_UpperCAmelCase            =		"""hopper-medium-v2"""
	_UpperCAmelCase            =		gym.make(env_name)

	_UpperCAmelCase            =		ValueGuidedRLPipeline.from_pretrained(
	    """bglick13/hopper-medium-v2-value-function-hor32""",
	    env=env,
	)

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

			# execute action in environment
			_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase            =		env.step(denorm_actions)
			_UpperCAmelCase            =		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())

			_UpperCAmelCase            =		next_observation
	except KeyboardInterrupt:
		pass

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






