
'''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   A			(							A_				:       Any     ,				A_				:       List[Any]     ,				A_				:       Optional[int]     ,				A_				:       Union[str, Any]     ,				A_				:       Optional[Any]     ,				A_				:       Optional[int]							):
						# we don't care about the past rewards in this model

						snake_case							:  Tuple			=     states.reshape(1     ,				-1     ,				model.config.state_dim							)
						snake_case							:  Any			=     actions.reshape(1     ,				-1     ,				model.config.act_dim							)
						snake_case							:  Optional[int]			=     returns_to_go.reshape(1     ,				-1     ,				1							)
						snake_case							:  Tuple			=     timesteps.reshape(1     ,				-1							)

						if model.config.max_length is not None:
												snake_case							:  Tuple			=     states[:, -model.config.max_length :]
												snake_case							:  Union[str, Any]			=     actions[:, -model.config.max_length :]
												snake_case							:  Dict			=     returns_to_go[:, -model.config.max_length :]
												snake_case							:  Tuple			=     timesteps[:, -model.config.max_length :]

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

						snake_case,       snake_case,       snake_case							:  List[str]			=     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

UpperCAmelCase            = gym.make("Hopper-v3")
UpperCAmelCase            = env.observation_space.shape[0]
UpperCAmelCase            = env.action_space.shape[0]
UpperCAmelCase            = 1_000
UpperCAmelCase            = "cuda"
UpperCAmelCase            = 1_000.0  # normalization for rewards/returns
UpperCAmelCase            = 3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCAmelCase            = np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
UpperCAmelCase            = np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
UpperCAmelCase            = torch.from_numpy(state_mean).to(device=device)
UpperCAmelCase            = torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
						UpperCAmelCase       ,						UpperCAmelCase            = 0, 0
						UpperCAmelCase            = env.reset()
						UpperCAmelCase            = torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
						UpperCAmelCase            = torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
						UpperCAmelCase            = torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
						UpperCAmelCase            = torch.zeros(0, device=device, dtype=torch.floataa)

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

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

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

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

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



"""simple docstring"""




import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=None     ):
					SCREAMING_SNAKE_CASE           =      None
					# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
					if str(weight.dtype     ) == "torch.bfloat16":
										# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
										SCREAMING_SNAKE_CASE           =      weight.view(torch.intaa     )
										SCREAMING_SNAKE_CASE           =      'bfloat16'
					SCREAMING_SNAKE_CASE           =      weight.cpu().numpy()
					SCREAMING_SNAKE_CASE           =      os.path.join(SCREAMING_SNAKE_CASE_,	f'''{weight_name}.dat'''     )
					if index is not None:
										if dtype is None:
															SCREAMING_SNAKE_CASE           =      str(array.dtype     )
										SCREAMING_SNAKE_CASE           =      {'dtype': dtype, 'shape': list(array.shape     )}
					if array.ndim == 0:
										SCREAMING_SNAKE_CASE           =      array[None]
					SCREAMING_SNAKE_CASE           =      np.memmap(SCREAMING_SNAKE_CASE_,	dtype=array.dtype,	mode='w+',	shape=array.shape     )
					SCREAMING_SNAKE_CASE           =      array[:]
					file_array.flush()
					return index

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      tuple(weight_info['shape']     )
					if shape == ():
										# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
										SCREAMING_SNAKE_CASE           =      (1,)

					SCREAMING_SNAKE_CASE           =      weight_info['dtype']
					if dtype == "bfloat16":
										# NumPy does not support bfloat16 so this was saved as a int16
										SCREAMING_SNAKE_CASE           =      'int16'

					SCREAMING_SNAKE_CASE           =      np.memmap(SCREAMING_SNAKE_CASE_,	dtype=SCREAMING_SNAKE_CASE_,	shape=SCREAMING_SNAKE_CASE_,	mode='r'     )

					if len(weight_info['shape']     ) == 0:
										SCREAMING_SNAKE_CASE           =      weight[0]
					SCREAMING_SNAKE_CASE           =      torch.tensor(SCREAMING_SNAKE_CASE_     )
					if weight_info["dtype"] == "bfloat16":
										SCREAMING_SNAKE_CASE           =      weight.view(torch.bfloataa     )

					return weight

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if index is None or len(SCREAMING_SNAKE_CASE_     ) == 0:
										# Nothing to save
										return

					SCREAMING_SNAKE_CASE           =      os.path.join(SCREAMING_SNAKE_CASE_,	'index.json'     )
					if os.path.isfile(SCREAMING_SNAKE_CASE_     ):
										with open(SCREAMING_SNAKE_CASE_,	'r',	encoding='utf-8'     ) as f:
															SCREAMING_SNAKE_CASE           =      json.load(SCREAMING_SNAKE_CASE_     )
					else:
										SCREAMING_SNAKE_CASE           =      {}
					current_index.update(SCREAMING_SNAKE_CASE_     )

					with open(SCREAMING_SNAKE_CASE_,	'w',	encoding='utf-8'     ) as f:
										json.dump(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	indent=2     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					os.makedirs(SCREAMING_SNAKE_CASE_,	exist_ok=SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      {}
					for name, parameter in state_dict.items():
										SCREAMING_SNAKE_CASE           =      offload_weight(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	index=SCREAMING_SNAKE_CASE_     )

					# Update index
					save_offload_index(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__     )       ->       Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      dataset
									SCREAMING_SNAKE_CASE           =      prefix


				def __getitem__(							self						,      lowercase__     )       ->       Dict:
									"""simple docstring"""







									return self.dataset[f'''{self.prefix}{key}''']


				def __iter__(							self     )       ->       List[str]:
									"""simple docstring"""







									return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )




				def __len__(							self     )       ->       Optional[Any]:
									"""simple docstring"""







									return len(self.dataset     )






class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__=None						,      )       ->       Optional[Any]:
									"""simple docstring"""







									if state_dict is None and save_folder is None:
														raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.'     )

									SCREAMING_SNAKE_CASE           =      {} if state_dict is None else state_dict
									SCREAMING_SNAKE_CASE           =      save_folder
									if index is None and save_folder is not None:
														with open(os.path.join(lowercase__						,      'index.json'     )     ) as f:
																			SCREAMING_SNAKE_CASE           =      json.load(lowercase__     )
									SCREAMING_SNAKE_CASE           =      {} if index is None else index
									SCREAMING_SNAKE_CASE           =      list(self.state_dict.keys()     )
									self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
									SCREAMING_SNAKE_CASE           =      device


				def __getitem__(							self						,      lowercase__     )       ->       Union[str, Any]:
									"""simple docstring"""







									if key in self.state_dict:
														return self.state_dict[key]
									SCREAMING_SNAKE_CASE           =      self.index[key]
									if weight_info.get('safetensors_file'     ) is not None:
														if not is_safetensors_available():
																			raise ImportError('These offloaded weights require the use of safetensors: `pip install safetensors`.'     )

														from safetensors import safe_open

														SCREAMING_SNAKE_CASE           =      'cpu' if self.device is None else self.device
														with safe_open(weight_info['safetensors_file']						,      framework='pt'						,      device=lowercase__     ) as f:
																			SCREAMING_SNAKE_CASE           =      f.get_tensor(weight_info.get('weight_name'						,      lowercase__     )     )

														if "dtype" in weight_info:
																			return tensor.to(getattr(lowercase__						,      weight_info['dtype']     )     )
														else:
																			return tensor

									SCREAMING_SNAKE_CASE           =      os.path.join(self.save_folder						,      f'''{key}.dat'''     )
									return load_offloaded_weight(lowercase__						,      lowercase__     )


				def __iter__(							self     )       ->       Any:
									"""simple docstring"""







									return iter(self.all_keys     )




				def __len__(							self     )       ->       Optional[Any]:
									"""simple docstring"""







									return len(self.all_keys     )






def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      {}
					for module_name in submodule_names:
										# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
										# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
										result.update(
										    {
										        key: param
										        for key, param in state_dict.items()
										        if key == module_name or key.startswith(module_name + '.'     )
										    }     )
					return result
