
"""simple docstring"""




import os
from typing import List

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					if name != "training_function" and name != "main":
										raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''      )
					SCREAMING_SNAKE_CASE           =     [], False
					for line in lines:
										if not found_start and f'''def {name}''' in line:
															SCREAMING_SNAKE_CASE           =     True
															good_lines.append(_lowerCAmelCase      )
															continue
										if found_start:
															if name == "training_function" and "def main" in line:
																				return good_lines
															if name == "main" and "if __name__" in line:
																				return good_lines
															good_lines.append(_lowerCAmelCase      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					return [line for line in lines if not line.lstrip().startswith('#'      ) and line != "\n"]



def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ = None      ):
					with open(_lowerCAmelCase,					'r'      ) as f:
										SCREAMING_SNAKE_CASE           =     f.readlines()
					with open(os.path.abspath(os.path.join('examples',					'nlp_example.py'      )      ),					'r'      ) as f:
										SCREAMING_SNAKE_CASE           =     f.readlines()
					with open(_lowerCAmelCase,					'r'      ) as f:
										SCREAMING_SNAKE_CASE           =     f.readlines()
					if secondary_filename is not None:
										with open(_lowerCAmelCase,					'r'      ) as f:
															SCREAMING_SNAKE_CASE           =     f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
					if parser_only:
										SCREAMING_SNAKE_CASE           =     clean_lines(get_function_contents_by_name(_lowerCAmelCase,					'main'      )      )
										SCREAMING_SNAKE_CASE           =     clean_lines(get_function_contents_by_name(_lowerCAmelCase,					'main'      )      )
										SCREAMING_SNAKE_CASE           =     clean_lines(get_function_contents_by_name(_lowerCAmelCase,					'main'      )      )
										if secondary_filename is not None:
															SCREAMING_SNAKE_CASE           =     clean_lines(get_function_contents_by_name(_lowerCAmelCase,					'main'      )      )
					else:
										SCREAMING_SNAKE_CASE           =     clean_lines(get_function_contents_by_name(_lowerCAmelCase,					'training_function'      )      )
										SCREAMING_SNAKE_CASE           =     clean_lines(get_function_contents_by_name(_lowerCAmelCase,					'training_function'      )      )
										SCREAMING_SNAKE_CASE           =     clean_lines(get_function_contents_by_name(_lowerCAmelCase,					'training_function'      )      )
										if secondary_filename is not None:
															SCREAMING_SNAKE_CASE           =     clean_lines(
															    get_function_contents_by_name(_lowerCAmelCase,					'training_function'      )      )

					SCREAMING_SNAKE_CASE           =     '''train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n'''

					# Specific code in our script that differs from the full version, aka what is new
					SCREAMING_SNAKE_CASE           =     []
					SCREAMING_SNAKE_CASE           =     []  # We keep track of the idxs just in case it's a repeated statement
					SCREAMING_SNAKE_CASE           =     iter(_lowerCAmelCase      )
					for i in range(len(_lowerCAmelCase      ) - 1      ):
										if i not in passed_idxs:
															SCREAMING_SNAKE_CASE           =     next(_lowerCAmelCase      )
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_feature_code.append(_lowerCAmelCase      )
																									passed_idxs.append(_lowerCAmelCase      )
																				else:
																									# Skip over the `config['num_epochs'] = 2` statement
																									SCREAMING_SNAKE_CASE           =     next(_lowerCAmelCase      )

    # Extract out just the new parts from the full_file_training_func
					SCREAMING_SNAKE_CASE           =     []
					SCREAMING_SNAKE_CASE           =     []  # We keep track of the idxs just in case it's a repeated statement
					for i, line in enumerate(_lowerCAmelCase      ):
										if i not in passed_idxs:
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_full_example_parts.append(_lowerCAmelCase      )
																									passed_idxs.append(_lowerCAmelCase      )

    # Finally, get the overall diff
					SCREAMING_SNAKE_CASE           =     [line for line in new_feature_code if line not in new_full_example_parts]
					if secondary_filename is not None:
										SCREAMING_SNAKE_CASE           =     [line for line in full_file_contents if line not in secondary_file_func]
										SCREAMING_SNAKE_CASE           =     [line for line in diff_from_example if line not in diff_from_two]

					return diff_from_example


"""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   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					# we don't care about the past rewards in this model

					SCREAMING_SNAKE_CASE           =     states.reshape(1,					-1,					model.config.state_dim      )
					SCREAMING_SNAKE_CASE           =     actions.reshape(1,					-1,					model.config.act_dim      )
					SCREAMING_SNAKE_CASE           =     returns_to_go.reshape(1,					-1,					1      )
					SCREAMING_SNAKE_CASE           =     timesteps.reshape(1,					-1      )

					if model.config.max_length is not None:
										SCREAMING_SNAKE_CASE           =     states[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =     actions[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =     returns_to_go[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =     timesteps[:, -model.config.max_length :]

										# pad all tokens to sequence length
										SCREAMING_SNAKE_CASE           =     torch.cat(
										    [torch.zeros(model.config.max_length - states.shape[1]      ), torch.ones(states.shape[1]      )]      )
										SCREAMING_SNAKE_CASE           =     attention_mask.to(dtype=torch.long,					device=states.device      ).reshape(1,					-1      )
										SCREAMING_SNAKE_CASE           =     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      )
										SCREAMING_SNAKE_CASE           =     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      )
										SCREAMING_SNAKE_CASE           =     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      )
										SCREAMING_SNAKE_CASE           =     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:
										SCREAMING_SNAKE_CASE           =     None

					SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     model(
					    states=SCREAMING_SNAKE_CASE_,					actions=SCREAMING_SNAKE_CASE_,					rewards=SCREAMING_SNAKE_CASE_,					returns_to_go=SCREAMING_SNAKE_CASE_,					timesteps=SCREAMING_SNAKE_CASE_,					attention_mask=SCREAMING_SNAKE_CASE_,					return_dict=SCREAMING_SNAKE_CASE_,					)

					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_0_0_0.0  # 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(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
snake_case			       =		np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
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
