code: 
  
  
  
  '''simple docstring'''
  
  
  
  import gym
  import numpy as np
  import torch
  from mujoco_py import GlfwContext
  
  from transformers import DecisionTransformerModel
  
  
  GlfwContext(offscreen=True)  # Create a window to init GLFW.
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A							,__A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  states.reshape(1							,-1							,model.config.state_dim       )
   __UpperCamelCase				      =  actions.reshape(1							,-1							,model.config.act_dim       )
   __UpperCamelCase				      =  returns_to_go.reshape(1							,-1							,1       )
   __UpperCamelCase				      =  timesteps.reshape(1							,-1       )
  
   if model.config.max_length is not None:
    __UpperCamelCase				      =  states[:, -model.config.max_length :]
    __UpperCamelCase				      =  actions[:, -model.config.max_length :]
    __UpperCamelCase				      =  returns_to_go[:, -model.config.max_length :]
    __UpperCamelCase				      =  timesteps[:, -model.config.max_length :]
  
    # pad all tokens to sequence length
    __UpperCamelCase				      =  torch.cat(
        [torch.zeros(model.config.max_length - states.shape[1]       ), torch.ones(states.shape[1]       )]       )
    __UpperCamelCase				      =  attention_mask.to(dtype=torch.long							,device=states.device       ).reshape(1							,-1       )
    __UpperCamelCase				      =  torch.cat(
        [
            torch.zeros(
                (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)							,device=states.device							,),
            states,
        ]							,dim=1							,).to(dtype=torch.floataa       )
    __UpperCamelCase				      =  torch.cat(
        [
            torch.zeros(
                (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)							,device=actions.device							,),
            actions,
        ]							,dim=1							,).to(dtype=torch.floataa       )
    __UpperCamelCase				      =  torch.cat(
        [
            torch.zeros(
                (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)							,device=returns_to_go.device							,),
            returns_to_go,
        ]							,dim=1							,).to(dtype=torch.floataa       )
    __UpperCamelCase				      =  torch.cat(
        [
            torch.zeros(
                (timesteps.shape[0], model.config.max_length - timesteps.shape[1])							,device=timesteps.device       ),
            timesteps,
        ]							,dim=1							,).to(dtype=torch.long       )
   else:
    __UpperCamelCase				      =  None
  
   __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  model(
       states=__A							,actions=__A							,rewards=__A							,returns_to_go=__A							,timesteps=__A							,attention_mask=__A							,return_dict=__A							,)
  
   return action_preds[0, -1]
  
  
  # build the environment
  
  a__						:			List[Any]										=			gym.make('Hopper-v3')
  a__						:			Any										=			env.observation_space.shape[0]
  a__						:			Union[str, Any]										=			env.action_space.shape[0]
  a__						:			List[str]										=			1_0_0_0
  a__						:			str										=			'cuda'
  a__						:			Any										=			10_00.0  # normalization for rewards/returns
  a__						:			Tuple										=			3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  a__						:			int										=			np.array(
      [
          1.31_12_79,
          -0.08_46_95_21,
          -0.5_38_27_19,
          -0.07_20_15_76,
          0.04_93_23_66,
          2.1_06_68_56,
          -0.15_01_73_54,
          0.00_87_83_45,
          -0.2_84_81_86,
          -0.18_54_00_96,
          -0.28_46_12_86,
      ]
  )
  a__						:			int										=			np.array(
      [
          0.17_79_07_51,
          0.05_44_46_21,
          0.21_29_71_39,
          0.14_53_04_19,
          0.6_12_44_44,
          0.85_17_44_65,
          1.4_51_52_52,
          0.6_75_16_96,
          1.53_62_39,
          1.6_16_07_46,
          5.6_07_25_36,
      ]
  )
  a__						:			Dict										=			torch.from_numpy(state_mean).to(device=device)
  a__						:			Tuple										=			torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  a__						:			str										=			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
  a__						:			Tuple										=			model.to(device)
  model.eval()
  
  for ep in range(1_0):
   a__							,						a__						:			int										=			0, 0
   a__						:			Optional[Any]										=			env.reset()
   a__						:			str										=			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   a__						:			List[str]										=			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   a__						:			int										=			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   a__						:			List[str]										=			torch.zeros(0, device=device, dtype=torch.floataa)
  
   a__						:			List[str]										=			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
   for t in range(max_ep_len):
    env.render()
    # add padding
    a__						:			str										=			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
    a__						:			List[str]										=			torch.cat([rewards, torch.zeros(1, device=device)])
  
    a__						:			Tuple										=			get_action(
        model,
        (states.to(dtype=torch.floataa) - state_mean) / state_std,
        actions.to(dtype=torch.floataa),
        rewards.to(dtype=torch.floataa),
        target_return.to(dtype=torch.floataa),
        timesteps.to(dtype=torch.long),
    )
    a__						:			str										=			action
    a__						:			List[str]										=			action.detach().cpu().numpy()
  
    a__							,						a__							,						a__							,						a__						:			Optional[Any]										=			env.step(action)
  
    a__						:			str										=			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
    a__						:			List[str]										=			torch.cat([states, cur_state], dim=0)
    a__						:			Union[str, Any]										=			reward
  
    a__						:			List[str]										=			target_return[0, -1] - (reward / scale)
    a__						:			str										=			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    a__						:			str										=			torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)
  
    episode_return += reward
    episode_length += 1
  
    if done:
     break
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  from saprl.hub import distilhubert
  
  from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  a__						:			str										=			logging.get_logger(__name__)
  
  a__						:			Optional[int]										=			{
      'post_extract_proj': 'feature_projection.projection',
      'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
      'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
      'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
      'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
      'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
      'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
      'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
      'fc2': 'encoder.layers.*.feed_forward.output_dense',
      'final_layer_norm': 'encoder.layers.*.final_layer_norm',
      'encoder.layer_norm': 'encoder.layer_norm',
      'mask_emb': 'masked_spec_embed',
  }
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   for attribute in key.split("""."""       ):
    __UpperCamelCase				      =  getattr(__A							,__A       )
  
   if weight_type is not None:
    __UpperCamelCase				      =  getattr(__A							,__A       ).shape
   else:
    __UpperCamelCase				      =  hf_pointer.shape
  
   assert hf_shape == value.shape, (
       f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
       f" {value.shape} for {full_name}"
   )
  
   if weight_type == "weight":
    __UpperCamelCase				      =  value
   elif weight_type == "weight_g":
    __UpperCamelCase				      =  value
   elif weight_type == "weight_v":
    __UpperCamelCase				      =  value
   elif weight_type == "bias":
    __UpperCamelCase				      =  value
   else:
    __UpperCamelCase				      =  value
  
   logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."       )
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  []
   __UpperCamelCase				      =  fairseq_model.state_dict()
  
   __UpperCamelCase				      =  hf_model.feature_extractor
  
   for name, value in fairseq_dict.items():
    __UpperCamelCase				      =  False
    if "conv_layers" in name:
     load_conv_layer(
         __A							,__A							,__A							,__A							,hf_model.config.feat_extract_norm == """group"""							,)
     __UpperCamelCase				      =  True
    else:
     for key, mapped_key in MAPPING.items():
      __UpperCamelCase				      =  mapped_key
  
      if key in name:
       __UpperCamelCase				      =  True
       if "*" in mapped_key:
        __UpperCamelCase				      =  name.split(__A       )[0].split("""."""       )[-2]
        __UpperCamelCase				      =  mapped_key.replace("""*"""							,__A       )
       if "weight_g" in name:
        __UpperCamelCase				      =  """weight_g"""
       elif "weight_v" in name:
        __UpperCamelCase				      =  """weight_v"""
       elif "weight" in name:
        __UpperCamelCase				      =  """weight"""
       elif "bias" in name:
        __UpperCamelCase				      =  """bias"""
       else:
        __UpperCamelCase				      =  None
       set_recursively(__A							,__A							,__A							,__A							,__A       )
      continue
    if not is_used:
     unused_weights.append(__A       )
  
   logger.warning(f"Unused weights: {unused_weights}"       )
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  full_name.split("""conv_layers."""       )[-1]
   __UpperCamelCase				      =  name.split("""."""       )
   __UpperCamelCase				      =  int(items[0]       )
   __UpperCamelCase				      =  int(items[1]       )
  
   if type_id == 0:
    if "bias" in name:
     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
         f"{full_name} has size {value.shape}, but"
         f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
     )
     __UpperCamelCase				      =  value
     logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."       )
    elif "weight" in name:
     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
         f"{full_name} has size {value.shape}, but"
         f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
     )
     __UpperCamelCase				      =  value
     logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."       )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
         f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
         " found."
     )
     __UpperCamelCase				      =  value
     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
    elif "weight" in name:
     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
         f"{full_name} has size {value.shape}, but"
         f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
     )
     __UpperCamelCase				      =  value
     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
   else:
    unused_weights.append(__A       )
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  HubertConfig()
   __UpperCamelCase				      =  model.config
  
   __UpperCamelCase				      =  fs_config.activation_dropout
   __UpperCamelCase				      =  False
   __UpperCamelCase				      =  fs_config.attention_dropout
   __UpperCamelCase				      =  False
   __UpperCamelCase				      =  eval(fs_config.extractor_conv_feature_layers       )
   __UpperCamelCase				      =  [x[0] for x in conv_layers]
   __UpperCamelCase				      =  [x[1] for x in conv_layers]
   __UpperCamelCase				      =  [x[2] for x in conv_layers]
   __UpperCamelCase				      =  """gelu"""
   __UpperCamelCase				      =  """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   __UpperCamelCase				      =  False
   __UpperCamelCase				      =  0.0
   __UpperCamelCase				      =  0.0
   __UpperCamelCase				      =  fs_config.activation_fn
   __UpperCamelCase				      =  fs_config.dropout
   __UpperCamelCase				      =  fs_config.encoder_embed_dim
   __UpperCamelCase				      =  0.02
   __UpperCamelCase				      =  fs_config.encoder_ffn_embed_dim
   __UpperCamelCase				      =  1E-5
   __UpperCamelCase				      =  0.0
   __UpperCamelCase				      =  fs_config.encoder_attention_heads
   __UpperCamelCase				      =  fs_config.conv_pos_groups
   __UpperCamelCase				      =  fs_config.conv_pos
   __UpperCamelCase				      =  len(__A       )
   __UpperCamelCase				      =  fs_config.encoder_layers
  
   return config
  
  
  
  
  
  
  
  @torch.no_grad()
  def _lowercase   (  __A							,__A=None       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  distilhubert().model.model
  
   if config_path is not None:
    __UpperCamelCase				      =  HubertConfig.from_pretrained(__A       )
   else:
    __UpperCamelCase				      =  convert_config(__A       )
   __UpperCamelCase				      =  model.eval()
  
   __UpperCamelCase				      =  WavaVecaFeatureExtractor(
       feature_size=1							,sampling_rate=16_000							,padding_value=0							,do_normalize=__A							,return_attention_mask=__A							,)
   __UpperCamelCase				      =  HubertModel(__A       )
  
   recursively_load_weights(__A							,__A       )
  
   feature_extractor.save_pretrained(__A       )
   hf_model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			List[str]										=			argparse.ArgumentParser()
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
   a__						:			Optional[Any]										=			parser.parse_args()
   convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
  
  
  
  
  
  