
'''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		(lowercase__ :      Optional[int]       ,      lowercase__ :      Union[str, Any]       ,      lowercase__ :      Dict       ,      lowercase__ :      Optional[int]       ,      lowercase__ :      List[Any]       ,      lowercase__ :      Union[str, Any]	) -> Optional[int]:






     """simple docstring"""
     __snake_case    			=	states.reshape(1       ,      -1       ,      model.config.state_dim	)
     __snake_case    			=	actions.reshape(1       ,      -1       ,      model.config.act_dim	)
     __snake_case    			=	returns_to_go.reshape(1       ,      -1       ,      1	)
     __snake_case    			=	timesteps.reshape(1       ,      -1	)

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

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

     __snake_case			,					__snake_case			,					__snake_case    			=	model(
         states=lowercase__       ,      actions=lowercase__       ,      rewards=lowercase__       ,      returns_to_go=lowercase__       ,      timesteps=lowercase__       ,      attention_mask=lowercase__       ,      return_dict=lowercase__       ,      )

     return action_preds[0, -1]


# build the environment

_a      :      Tuple       				=      gym.make("Hopper-v3")
_a      :      Optional[Any]       				=      env.observation_space.shape[0]
_a      :      List[Any]       				=      env.action_space.shape[0]
_a      :      int       				=      1_000
_a      :      Optional[Any]       				=      "cuda"
_a      :      Any       				=      1_000.0  # normalization for rewards/returns
_a      :      Union[str, Any]       				=      3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_a      :      List[Any]       				=      np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
_a      :      Dict       				=      np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
_a      :      Union[str, Any]       				=      torch.from_numpy(state_mean).to(device=device)
_a      :      Union[str, 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      :      Dict       				=      model.to(device)
model.eval()

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

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

              _a      :      List[Any]       				=      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      :      List[Any]       				=      action
              _a      :      Union[str, Any]       				=      action.detach().cpu().numpy()

              _a      :      Tuple       				=      env.step(action)

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

              _a      :      Union[str, Any]       				=      target_return[0, -1] - (reward / scale)
              _a      :      str       				=      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








'''simple docstring'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a      :      List[str]       				=      logging.get_logger(__name__)

_a      :      Any       				=      {
    "facebook/sam-vit-huge": "https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json",
    "facebook/sam-vit-large": "https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json",
    "facebook/sam-vit-base": "https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json",
}




class  _lowercase   (  __lowercase       ):
   def __init__(	self					:			int					,      SCREAMING_SNAKE_CASE_					:			List[Any]=256					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=1024					,      SCREAMING_SNAKE_CASE_					:			Any=16					,      SCREAMING_SNAKE_CASE_					:			int=16					,      SCREAMING_SNAKE_CASE_					:			List[Any]=4					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]="gelu"					,      SCREAMING_SNAKE_CASE_					:			Tuple=1e-6					,      **SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      )   ->       Tuple:
        super().__init__(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	hidden_size
        __snake_case    			=	image_size
        __snake_case    			=	patch_size
        __snake_case    			=	image_size // patch_size
        __snake_case    			=	mask_input_channels
        __snake_case    			=	num_point_embeddings
        __snake_case    			=	hidden_act
        __snake_case    			=	layer_norm_eps




class  _lowercase   (  __lowercase       ):
   def __init__(	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			List[Any]=256					,      SCREAMING_SNAKE_CASE_					:			Dict="relu"					,      SCREAMING_SNAKE_CASE_					:			Any=2048					,      SCREAMING_SNAKE_CASE_					:			int=2					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=8					,      SCREAMING_SNAKE_CASE_					:			int=2					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=3					,      SCREAMING_SNAKE_CASE_					:			str=3					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=256					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=1e-6					,      **SCREAMING_SNAKE_CASE_					:			int					,      )   ->       str:
        super().__init__(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	hidden_size
        __snake_case    			=	hidden_act
        __snake_case    			=	mlp_dim
        __snake_case    			=	num_hidden_layers
        __snake_case    			=	num_attention_heads
        __snake_case    			=	attention_downsample_rate
        __snake_case    			=	num_multimask_outputs
        __snake_case    			=	iou_head_depth
        __snake_case    			=	iou_head_hidden_dim
        __snake_case    			=	layer_norm_eps




class  _lowercase   (  __lowercase       ):
   def __init__(	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			int=768					,      SCREAMING_SNAKE_CASE_					:			int=256					,      SCREAMING_SNAKE_CASE_					:			str=12					,      SCREAMING_SNAKE_CASE_					:			Tuple=12					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=3					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=1024					,      SCREAMING_SNAKE_CASE_					:			List[str]=16					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]="gelu"					,      SCREAMING_SNAKE_CASE_					:			Any=1e-06					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=0.0					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=1e-10					,      SCREAMING_SNAKE_CASE_					:			List[Any]=True					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=4.0					,      SCREAMING_SNAKE_CASE_					:			List[Any]=True					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=True					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=14					,      SCREAMING_SNAKE_CASE_					:			str=[2, 5, 8, 11]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=128					,      SCREAMING_SNAKE_CASE_					:			Tuple=None					,      **SCREAMING_SNAKE_CASE_					:			List[Any]					,      )   ->       Optional[Any]:
        super().__init__(**SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	hidden_size
        __snake_case    			=	output_channels
        __snake_case    			=	num_hidden_layers
        __snake_case    			=	num_attention_heads
        __snake_case    			=	num_channels
        __snake_case    			=	image_size
        __snake_case    			=	patch_size
        __snake_case    			=	hidden_act
        __snake_case    			=	layer_norm_eps
        __snake_case    			=	attention_dropout
        __snake_case    			=	initializer_range
        __snake_case    			=	qkv_bias
        __snake_case    			=	mlp_ratio
        __snake_case    			=	use_abs_pos
        __snake_case    			=	use_rel_pos
        __snake_case    			=	window_size
        __snake_case    			=	global_attn_indexes
        __snake_case    			=	num_pos_feats
        __snake_case    			=	int(hidden_size * mlp_ratio			) if mlp_dim is None else mlp_dim




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     Union[str, Any]									=  "sam"
   _SCREAMING_SNAKE_CASE     :     Any									=  True
   def __init__(	self					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			Dict=None					,      SCREAMING_SNAKE_CASE_					:			List[Any]=None					,      SCREAMING_SNAKE_CASE_					:			List[str]=None					,      SCREAMING_SNAKE_CASE_					:			str=0.0_2					,      **SCREAMING_SNAKE_CASE_					:			str					,      )   ->       str:
        super().__init__(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	vision_config if vision_config is not None else {}
        __snake_case    			=	prompt_encoder_config if prompt_encoder_config is not None else {}
        __snake_case    			=	mask_decoder_config if mask_decoder_config is not None else {}

        if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			):
             __snake_case    			=	vision_config.to_dict()
        if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			):
             __snake_case    			=	prompt_encoder_config.to_dict()
        if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			):
             __snake_case    			=	mask_decoder_config.to_dict()

        __snake_case    			=	SamVisionConfig(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	SamPromptEncoderConfig(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	SamMaskDecoderConfig(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	initializer_range





   def a     (	self					:			int			)   ->       str:
        __snake_case    			=	copy.deepcopy(self.__dict__			)
        __snake_case    			=	self.vision_config.to_dict()
        __snake_case    			=	self.prompt_encoder_config.to_dict()
        __snake_case    			=	self.mask_decoder_config.to_dict()
        __snake_case    			=	self.__class__.model_type
        return output




