


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 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase   )				->   Union[str, Any]:


     """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=_UpperCAmelCase				,     actions=_UpperCAmelCase				,     rewards=_UpperCAmelCase				,     returns_to_go=_UpperCAmelCase				,     timesteps=_UpperCAmelCase				,     attention_mask=_UpperCAmelCase				,     return_dict=_UpperCAmelCase				,     )

     return action_preds[0, -1]


# build the environment

__snake_case             =	gym.make('''Hopper-v3''')
__snake_case             =	env.observation_space.shape[0]
__snake_case             =	env.action_space.shape[0]
__snake_case             =	1_0_0_0
__snake_case             =	'''cuda'''
__snake_case             =	1_000.0  # normalization for rewards/returns
__snake_case             =	3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case             =	np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
__snake_case             =	np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
__snake_case             =	torch.from_numpy(state_mean).to(device=device)
__snake_case             =	torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
__snake_case             =	DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
__snake_case             =	model.to(device)
model.eval()

for ep in range(1_0):
    __snake_case       ,  __snake_case             =	0, 0
    __snake_case             =	env.reset()
    __snake_case             =	torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
    __snake_case             =	torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
    __snake_case             =	torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
    __snake_case             =	torch.zeros(0, device=device, dtype=torch.floataa)

    __snake_case             =	torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
    for t in range(max_ep_len):
        env.render()
        # add padding
        __snake_case             =	torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
        __snake_case             =	torch.cat([rewards, torch.zeros(1, device=device)])

        __snake_case             =	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),
        )
        __snake_case             =	action
        __snake_case             =	action.detach().cpu().numpy()

        __snake_case       ,  __snake_case       ,  __snake_case       ,  __snake_case             =	env.step(action)

        __snake_case             =	torch.from_numpy(state).to(device=device).reshape(1, state_dim)
        __snake_case             =	torch.cat([states, cur_state], dim=0)
        __snake_case             =	reward

        __snake_case             =	target_return[0, -1] - (reward / scale)
        __snake_case             =	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
        __snake_case             =	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






import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   List[str]:


     """simple docstring"""


     __UpperCamelCase   = json.load(open(_lowercase				,     'r'   )   )
     __UpperCamelCase   = GPTNeoConfig(
         hidden_size=config_json['n_embd']				,     num_layers=config_json['n_layer']				,     num_heads=config_json['n_head']				,     attention_types=config_json['attention_types']				,     max_position_embeddings=config_json['n_positions']				,     resid_dropout=config_json['res_dropout']				,     embed_dropout=config_json['embed_dropout']				,     attention_dropout=config_json['attn_dropout']				,     )
     print(f'''Building PyTorch model from configuration: {config}'''   )
     __UpperCamelCase   = GPTNeoForCausalLM(_lowercase   )

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(_lowercase				,     _lowercase				,     _lowercase   )

     # Save pytorch-model
     print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
     model.save_pretrained(_lowercase   )


if __name__ == "__main__":
    __snake_case             =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __snake_case             =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


