






'''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 							_SCREAMING_SNAKE_CASE	(					A	:							List[Any]	,		A	:							Any	,		A	:							Tuple	,		A	:							Optional[Any]	,		A	:							Any	,		A	:							Union[str, Any]	)     ->			Any:







   """simple docstring"""
   # we don't care about the past rewards in this model

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

   if model.config.max_length is not None:
      __snake_case					:   Optional[Any]  				=			states[:, -model.config.max_length :]
      __snake_case					:   Optional[Any]  				=			actions[:, -model.config.max_length :]
      __snake_case					:   List[str]  				=			returns_to_go[:, -model.config.max_length :]
      __snake_case					:   Dict  				=			timesteps[:, -model.config.max_length :]

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

   __snake_case					:   Tuple  				=			model(
       states=__lowerCAmelCase	,		actions=__lowerCAmelCase	,		rewards=__lowerCAmelCase	,		returns_to_go=__lowerCAmelCase	,		timesteps=__lowerCAmelCase	,		attention_mask=__lowerCAmelCase	,		return_dict=__lowerCAmelCase	,		)

   return action_preds[0, -1]


# build the environment

__A     						= gym.make('''Hopper-v3''')
__A     						= env.observation_space.shape[0]
__A     						= env.action_space.shape[0]
__A     						= 1_0_0_0
__A     						= '''cuda'''
__A     						= 1_0_0_0.0  # normalization for rewards/returns
__A     						= 3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__A     						= 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,
    ]
)
__A     						= 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,
    ]
)
__A     						= torch.from_numpy(state_mean).to(device=device)
__A     						= torch.from_numpy(state_std).to(device=device)

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

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

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

            __A     						= 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     						= action
            __A     						= action.detach().cpu().numpy()

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

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

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


from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    'cross_attention',
    '0.20.0',
    'Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.',
    standard_warn=False,
)


lowerCamelCase__     						=  AttentionProcessor






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  Union[str, Any]						,	*lowerCamelCase__	:  int						,	**lowerCamelCase__	:  List[str]			) ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:							Dict             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  Optional[Any]						,	*lowerCamelCase__	:  Any						,	**lowerCamelCase__	:  Tuple			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  List[Any]						,	*lowerCamelCase__	:  List[Any]						,	**lowerCamelCase__	:  List[str]			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  int						,	*lowerCamelCase__	:  Tuple						,	**lowerCamelCase__	:  Union[str, Any]			) ->int:



					'''simple docstring'''







					_UpperCAmelCase		:							Dict             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  int						,	*lowerCamelCase__	:  List[str]						,	**lowerCamelCase__	:  Tuple			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[Any]             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  List[Any]						,	*lowerCamelCase__	:  int						,	**lowerCamelCase__	:  Optional[int]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  Any						,	*lowerCamelCase__	:  Optional[Any]						,	**lowerCamelCase__	:  Union[str, Any]			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  Optional[Any]						,	*lowerCamelCase__	:  List[Any]						,	**lowerCamelCase__	:  Dict			) ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)



