'''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 						__snake_case (       lowerCAmelCase       :     Dict      ,  lowerCAmelCase       :     Optional[int]      ,  lowerCAmelCase       :     Optional[Any]      ,  lowerCAmelCase       :     List[Any]      ,  lowerCAmelCase       :     Any      ,  lowerCAmelCase       :     Any			):
 # we don't care about the past rewards in this model

 __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=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

_UpperCamelCase				:		str       			=		gym.make('Hopper-v3')
_UpperCamelCase				:		List[Any]       			=		env.observation_space.shape[0]
_UpperCamelCase				:		int       			=		env.action_space.shape[0]
_UpperCamelCase				:		str       			=		10_00
_UpperCamelCase				:		Tuple       			=		'cuda'
_UpperCamelCase				:		List[Any]       			=		1000.0  # normalization for rewards/returns
_UpperCamelCase				:		Tuple       			=		36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_UpperCamelCase				:		List[str]       			=		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				:		Optional[Any]       			=		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				:		str       			=		torch.from_numpy(state_mean).to(device=device)
_UpperCamelCase				:		List[str]       			=		torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_UpperCamelCase				:		Any       			=		DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
_UpperCamelCase				:		Union[str, Any]       			=		model.to(device)
model.eval()

for ep in range(10):
     _UpperCamelCase			,      _UpperCamelCase				:		Union[str, Any]       			=		0, 0
     _UpperCamelCase				:		Optional[int]       			=		env.reset()
     _UpperCamelCase				:		str       			=		torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
     _UpperCamelCase				:		Tuple       			=		torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
     _UpperCamelCase				:		Optional[Any]       			=		torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
     _UpperCamelCase				:		int       			=		torch.zeros(0, device=device, dtype=torch.floataa)

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

          _UpperCamelCase				:		Optional[int]       			=		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				:		Dict       			=		action
          _UpperCamelCase				:		Tuple       			=		action.detach().cpu().numpy()

          _UpperCamelCase			,      _UpperCamelCase			,      _UpperCamelCase			,      _UpperCamelCase				:		List[str]       			=		env.step(action)

          _UpperCamelCase				:		Optional[Any]       			=		torch.from_numpy(state).to(device=device).reshape(1, state_dim)
          _UpperCamelCase				:		Optional[int]       			=		torch.cat([states, cur_state], dim=0)
          _UpperCamelCase				:		Tuple       			=		reward

          _UpperCamelCase				:		Tuple       			=		target_return[0, -1] - (reward / scale)
          _UpperCamelCase				:		int       			=		torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
          _UpperCamelCase				:		Tuple       			=		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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase				:		Any       			=		logging.get_logger(__name__)

_UpperCamelCase				:		Any       			=		{
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  '''mobilevitv2'''





   def __init__(							self:    Union[str, Any]			,a:    Optional[int]=3			,a:    Any=256			,a:    str=2			,a:    Any=2.0			,a:    Union[str, Any]="swish"			,a:    List[str]=3			,a:    List[str]=32			,a:    int=0.1			,a:    List[Any]=0.02			,a:    List[Any]=1e-5			,a:    Tuple=512			,a:    Optional[Any]=[6, 12, 18]			,a:    Dict=0.1			,a:    List[Any]=255			,a:    Union[str, Any]=[2, 4, 3]			,a:    str=[128, 192, 256]			,a:    int=1.0			,a:    Optional[int]=2			,a:    Optional[Any]=0.0			,a:    int=0.0			,**a:    Optional[Any]			,):
    super().__init__(**a	)

    __UpperCAmelCase         =					num_channels
    __UpperCAmelCase         =					image_size
    __UpperCAmelCase         =					patch_size
    __UpperCAmelCase         =					expand_ratio
    __UpperCAmelCase         =					hidden_act
    __UpperCAmelCase         =					conv_kernel_size
    __UpperCAmelCase         =					output_stride
    __UpperCAmelCase         =					initializer_range
    __UpperCAmelCase         =					layer_norm_eps
    __UpperCAmelCase         =					n_attn_blocks
    __UpperCAmelCase         =					base_attn_unit_dims
    __UpperCAmelCase         =					width_multiplier
    __UpperCAmelCase         =					ffn_multiplier
    __UpperCAmelCase         =					ffn_dropout
    __UpperCAmelCase         =					attn_dropout
    __UpperCAmelCase         =					classifier_dropout_prob

    # decode head attributes for semantic segmentation
    __UpperCAmelCase         =					aspp_out_channels
    __UpperCAmelCase         =					atrous_rates
    __UpperCAmelCase         =					aspp_dropout_prob
    __UpperCAmelCase         =					semantic_loss_ignore_index




class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  version.parse('''1.11'''				)





   @property
   def 			snake_case	(							self:    Optional[int]	):
    return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]	)





   @property
   def 			snake_case	(							self:    Tuple	):
    if self.task == "image-classification":
     return OrderedDict([('logits', {0: 'batch'})]	)
    else:
     return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]	)





   @property
   def 			snake_case	(							self:    Union[str, Any]	):
    return 1e-4



