


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 				lowercase_		(		_A      :   str   , _A      :   Any   , _A      :   Optional[Any]   , _A      :   Optional[Any]   , _A      :   List[Any]   , _A      :   Union[str, Any]						):

      """simple docstring"""




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

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

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

      lowerCamelCase__   ,       lowerCamelCase__   ,       lowerCamelCase__		:    int   =  model(
          states=_A   , actions=_A   , rewards=_A   , returns_to_go=_A   , timesteps=_A   , attention_mask=_A   , return_dict=_A   , )

      return action_preds[0, -1]


# build the environment

A				:   int		= gym.make("Hopper-v3")
A				:   List[str]		= env.observation_space.shape[0]
A				:   Union[str, Any]		= env.action_space.shape[0]
A				:   Optional[Any]		= 1000
A				:   List[Any]		= "cuda"
A				:   Dict		= 1_0_0_0.0  # normalization for rewards/returns
A				:   List[str]		= 3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
A				:   Dict		= 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,
    ]
)
A				:   Optional[int]		= 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,
    ]
)
A				:   Any		= torch.from_numpy(state_mean).to(device=device)
A				:   Optional[Any]		= torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
A				:   Dict		= DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
A				:   Optional[int]		= model.to(device)
model.eval()

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

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

        A				:   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),
        )
        A				:   Any		= action
        A				:   str		= action.detach().cpu().numpy()

        A,  A,  A,  A				:   Tuple		= env.step(action)

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

        A				:   Optional[Any]		= target_return[0, -1] - (reward / scale)
        A				:   Optional[int]		= torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
        A				:   int		= 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






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


A				:   int		= logging.get_logger(__name__)

A				:   Any		= {
    "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
    "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
    import detectrona







class       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      "layoutlmv2"





       def __init__(       self							:      Union[str, Any] ,				__lowerCamelCase							:      int=30522 ,				__lowerCamelCase							:      List[Any]=768 ,				__lowerCamelCase							:      Any=12 ,				__lowerCamelCase							:      List[Any]=12 ,				__lowerCamelCase							:      Optional[Any]=3072 ,				__lowerCamelCase							:      List[Any]="gelu" ,				__lowerCamelCase							:      Any=0.1 ,				__lowerCamelCase							:      Union[str, Any]=0.1 ,				__lowerCamelCase							:      int=512 ,				__lowerCamelCase							:      str=2 ,				__lowerCamelCase							:      Any=0.0_2 ,				__lowerCamelCase							:      List[Any]=1E-1_2 ,				__lowerCamelCase							:      List[str]=0 ,				__lowerCamelCase							:      Union[str, Any]=1024 ,				__lowerCamelCase							:      Tuple=128 ,				__lowerCamelCase							:      Tuple=32 ,				__lowerCamelCase							:      Union[str, Any]=True ,				__lowerCamelCase							:      List[Any]=256 ,				__lowerCamelCase							:      Dict=64 ,				__lowerCamelCase							:      List[Any]=True ,				__lowerCamelCase							:      Union[str, Any]=[7, 7, 256] ,				__lowerCamelCase							:      str=128 ,				__lowerCamelCase							:      Dict=128 ,				__lowerCamelCase							:      Dict=True ,				__lowerCamelCase							:      int=True ,				__lowerCamelCase							:      str=False ,				__lowerCamelCase							:      Tuple=None ,				**__lowerCamelCase							:      str ,				):


             '''simple docstring'''



             super().__init__(
                 vocab_size=__lowerCamelCase ,				hidden_size=__lowerCamelCase ,				num_hidden_layers=__lowerCamelCase ,				num_attention_heads=__lowerCamelCase ,				intermediate_size=__lowerCamelCase ,				hidden_act=__lowerCamelCase ,				hidden_dropout_prob=__lowerCamelCase ,				attention_probs_dropout_prob=__lowerCamelCase ,				max_position_embeddings=__lowerCamelCase ,				type_vocab_size=__lowerCamelCase ,				initializer_range=__lowerCamelCase ,				layer_norm_eps=__lowerCamelCase ,				pad_token_id=__lowerCamelCase ,				**__lowerCamelCase ,				)
             lowerCamelCase__		:    Dict   =  max_ad_position_embeddings
             lowerCamelCase__		:    int   =  max_rel_pos
             lowerCamelCase__		:    List[Any]   =  rel_pos_bins
             lowerCamelCase__		:    str   =  fast_qkv
             lowerCamelCase__		:    Optional[Any]   =  max_rel_ad_pos
             lowerCamelCase__		:    List[Any]   =  rel_ad_pos_bins
             lowerCamelCase__		:    Optional[Any]   =  convert_sync_batchnorm
             lowerCamelCase__		:    Any   =  image_feature_pool_shape
             lowerCamelCase__		:    List[Any]   =  coordinate_size
             lowerCamelCase__		:    Any   =  shape_size
             lowerCamelCase__		:    List[str]   =  has_relative_attention_bias
             lowerCamelCase__		:    Optional[int]   =  has_spatial_attention_bias
             lowerCamelCase__		:    Optional[Any]   =  has_visual_segment_embedding
             lowerCamelCase__		:    List[str]   =  (
                 detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
             )





       @classmethod
       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             return {
                 "MODEL.MASK_ON": True,
                 "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
                 "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
                 "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
                 "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
                 "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
                 "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
                 "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
                 "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
                 "MODEL.POST_NMS_TOPK_TEST": 1000,
                 "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
                 "MODEL.ROI_HEADS.NUM_CLASSES": 5,
                 "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
                 "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
                 "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
                 "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
                 "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
                 "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
                 "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
                 "MODEL.RESNETS.DEPTH": 101,
                 "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
                 "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
                 "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
                 "MODEL.RESNETS.NUM_GROUPS": 32,
                 "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
                 "MODEL.RESNETS.STRIDE_IN_1X1": False,
             }





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  detectrona.config.get_cfg()
             for k, v in self.detectrona_config_args.items():
                   lowerCamelCase__		:    List[Any]   =  k.split("."	)
                   lowerCamelCase__		:    int   =  detectrona_config
                   for attribute in attributes[:-1]:
                         lowerCamelCase__		:    Any   =  getattr(__lowerCamelCase ,				__lowerCamelCase	)
                   setattr(__lowerCamelCase ,				attributes[-1] ,				__lowerCamelCase	)

             return detectrona_config


