


'''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[Any]     ,   _lowercase   :      Optional[Any]     ,   _lowercase   :      Dict     ,   _lowercase   :      Optional[Any]     ,   _lowercase   :      Tuple     ,   _lowercase   :      Optional[int]    ):





     '''simple docstring'''






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

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

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

     __UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase      :					Dict    				=     model(
         states=_UpperCamelCase     ,   actions=_UpperCamelCase     ,   rewards=_UpperCamelCase     ,   returns_to_go=_UpperCamelCase     ,   timesteps=_UpperCamelCase     ,   attention_mask=_UpperCamelCase     ,   return_dict=_UpperCamelCase     ,   )

     return action_preds[0, -1]


# build the environment

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

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

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

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

            __UpperCAmelCase     :Tuple												=  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     :Tuple												=  action
            __UpperCAmelCase     :int												=  action.detach().cpu().numpy()

            __UpperCAmelCase     :str												=  env.step(action)

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

            __UpperCAmelCase     :Tuple												=  target_return[0, -1] - (reward / scale)
            __UpperCAmelCase     :List[str]												=  torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
            __UpperCAmelCase     :Any												=  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 typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


__snake_case  : Union[str, Any]		       =    logging.get_logger(__name__)

__snake_case  : Union[str, Any]		       =    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case  : List[str]		       =    {
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
    'tokenizer_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'},
}

__snake_case  : Optional[int]		       =    {
    'openai-gpt': 512,
}




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   int  					=							VOCAB_FILES_NAMES
       __lowercase   :   int  					=							PRETRAINED_VOCAB_FILES_MAP
       __lowercase   :   str  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase   :   Optional[Any]  					=							['input_ids', 'attention_mask']
       __lowercase   :   List[Any]  					=							OpenAIGPTTokenizer

       def __init__( self       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE="<unk>"       ,	**_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           super().__init__(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	tokenizer_file=_SCREAMING_SNAKE_CASE       ,	unk_token=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       @property
       def    __A			( self					)						->	Optional[Any]:
           return True

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None					)						->	Tuple[str]:
           A_      =  self._tokenizer.model.save(_SCREAMING_SNAKE_CASE       ,	name=_SCREAMING_SNAKE_CASE					)
           return tuple(_SCREAMING_SNAKE_CASE					)






