






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       (    SCREAMING_SNAKE_CASE__  :     List[Any]					,  SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Tuple					,  SCREAMING_SNAKE_CASE__  :     Optional[Any]					,  SCREAMING_SNAKE_CASE__  :     List[str]					,  SCREAMING_SNAKE_CASE__  :     int	):
    # we don't care about the past rewards in this model

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

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

        # pad all tokens to sequence length
        UpperCamelCase	:Dict     							=       torch.cat(
            [torch.zeros(model.config.max_length - states.shape[1]	), torch.ones(states.shape[1]	)]	)
        UpperCamelCase	:int     							=       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	:List[str]     							=       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	:Tuple     							=       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	:Union[str, Any]     							=       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	:Any     							=       None

    UpperCamelCase							,       UpperCamelCase							,       UpperCamelCase	:Union[str, Any]     							=       model(
        states=SCREAMING_SNAKE_CASE__					,  actions=SCREAMING_SNAKE_CASE__					,  rewards=SCREAMING_SNAKE_CASE__					,  returns_to_go=SCREAMING_SNAKE_CASE__					,  timesteps=SCREAMING_SNAKE_CASE__					,  attention_mask=SCREAMING_SNAKE_CASE__					,  return_dict=SCREAMING_SNAKE_CASE__					,  )

    return action_preds[0, -1]


# build the environment

__snake_case							 =  gym.make("""Hopper-v3""")
__snake_case							 =  env.observation_space.shape[0]
__snake_case							 =  env.action_space.shape[0]
__snake_case							 =  10_00
__snake_case							 =  """cuda"""
__snake_case							 =  1_0_0_0.0  # normalization for rewards/returns
__snake_case							 =  36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case							 =  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,
    ]
)
__snake_case							 =  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,
    ]
)
__snake_case							 =  torch.from_numpy(state_mean).to(device=device)
__snake_case							 =  torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
 __snake_case ,				__snake_case							 =  0, 0
 __snake_case							 =  env.reset()
 __snake_case							 =  torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 __snake_case							 =  torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 __snake_case							 =  torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 __snake_case							 =  torch.zeros(0, device=device, dtype=torch.floataa)

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

  __snake_case							 =  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),
  )
  __snake_case							 =  action
  __snake_case							 =  action.detach().cpu().numpy()

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

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

  __snake_case							 =  target_return[0, -1] - (reward / scale)
  __snake_case							 =  torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  __snake_case							 =  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 transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[int]									=DPRContextEncoderTokenizer
      UpperCamelCase_    : Any									=DPRContextEncoderTokenizerFast
      UpperCamelCase_    : Optional[int]									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRQuestionEncoderTokenizer
      UpperCamelCase_    : Union[str, Any]									=DPRQuestionEncoderTokenizerFast
      UpperCamelCase_    : Any									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRReaderTokenizer
      UpperCamelCase_    : List[Any]									=DPRReaderTokenizerFast
      UpperCamelCase_    : Union[str, Any]									=True
      @slow
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:Optional[int]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[str]     							=       tokenizer.encode('''text sequence ''' * 4   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[int]     							=       [[101] + text_a + [102] + text_a + [102] + text_a]
          UpperCamelCase	:Tuple     							=       BatchEncoding({'''input_ids''': input_ids}			)

          UpperCamelCase	:Dict     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:List[Any]     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:Tuple     							=       [0]
          UpperCamelCase	:Union[str, Any]     							=       DPRReaderOutput(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          UpperCamelCase							,       UpperCamelCase	:Union[str, Any]     							=       8, 9
          UpperCamelCase	:Any     							=       10
          UpperCamelCase	:Optional[Any]     							=       10
          UpperCamelCase	:Optional[int]     							=       tokenizer.decode_best_spans(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].start_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].end_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].doc_id   ,    0			)




      @slow
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:List[str]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Any     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:str     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Union[str, Any]     							=       tokenizer.encode('''text sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[Any]     							=       [101] + text_a + [102] + text_a + [102] + text_a
          UpperCamelCase	:Any     							=       tokenizer(questions=['''question sequence''']   ,    titles=['''title sequence''']   ,    texts=['''text sequence''']			)
          self.assertIn('''input_ids'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertIn('''attention_mask'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertListEqual(encoded_input['''input_ids'''][0]   ,    SCREAMING_SNAKE_CASE_			)





