


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				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase   )				->   List[Any]:


     """simple docstring"""


     __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=_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

__snake_case             =	gym.make('''Hopper-v3''')
__snake_case             =	env.observation_space.shape[0]
__snake_case             =	env.action_space.shape[0]
__snake_case             =	1_0_0_0
__snake_case             =	'''cuda'''
__snake_case             =	1_0_0_0.0  # normalization for rewards/returns
__snake_case             =	3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case             =	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,
    ]
)
__snake_case             =	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,
    ]
)
__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(1_0):
    __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






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor



class   __lowerCamelCase (unittest.TestCase	):







       def __init__( self:   List[str],A_:   List[Any],A_:   List[Any]=7,A_:   str=3,A_:   Tuple=18,A_:   Union[str, Any]=30,A_:   Union[str, Any]=400,A_:   List[str]=True,A_:   List[Any]=None,A_:   Tuple=True,A_:   Union[str, Any]=None,A_:   int=True,A_:   Any=[0.5, 0.5, 0.5],A_:   Optional[Any]=[0.5, 0.5, 0.5],):






            '''simple docstring'''




            __UpperCamelCase   = size if size is not None else {'height': 20, 'width': 20}
            __UpperCamelCase   = crop_size if crop_size is not None else {'height': 18, 'width': 18}

            __UpperCamelCase   = parent
            __UpperCamelCase   = batch_size
            __UpperCamelCase   = num_channels
            __UpperCamelCase   = image_size
            __UpperCamelCase   = min_resolution
            __UpperCamelCase   = max_resolution
            __UpperCamelCase   = do_resize
            __UpperCamelCase   = size
            __UpperCamelCase   = do_center_crop
            __UpperCamelCase   = crop_size
            __UpperCamelCase   = do_normalize
            __UpperCamelCase   = image_mean
            __UpperCamelCase   = image_std







       def 		snake_case_  ( self:   List[str]				):






            '''simple docstring'''




            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }



@require_torch
@require_vision
class   __lowerCamelCase (_a							,			unittest.TestCase	):
       _lowercase       							=    DeiTImageProcessor if is_vision_available() else None
       _lowercase       							=    True







       def 		snake_case_  ( self:   List[Any]				):






            '''simple docstring'''




            __UpperCamelCase   = DeiTImageProcessingTester(self				)







       @property
       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()







       def 		snake_case_  ( self:   Union[str, Any]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            self.assertTrue(hasattr(A_,'do_resize'				)				)
            self.assertTrue(hasattr(A_,'size'				)				)
            self.assertTrue(hasattr(A_,'do_center_crop'				)				)
            self.assertTrue(hasattr(A_,'center_crop'				)				)
            self.assertTrue(hasattr(A_,'do_normalize'				)				)
            self.assertTrue(hasattr(A_,'image_mean'				)				)
            self.assertTrue(hasattr(A_,'image_std'				)				)







       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class.from_dict(self.image_processor_dict				)
            self.assertEqual(image_processor.size,{'height': 20, 'width': 20}				)
            self.assertEqual(image_processor.crop_size,{'height': 18, 'width': 18}				)

            __UpperCamelCase   = self.image_processing_class.from_dict(self.image_processor_dict,size=42,crop_size=84				)
            self.assertEqual(image_processor.size,{'height': 42, 'width': 42}				)
            self.assertEqual(image_processor.crop_size,{'height': 84, 'width': 84}				)







       def 		snake_case_  ( self:   Tuple				):






            '''simple docstring'''




            pass







       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            # create random PIL images
            __UpperCamelCase   = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_				)
            for image in image_inputs:
                 self.assertIsInstance(A_,Image.Image				)

            # Test not batched input
            __UpperCamelCase   = image_processing(image_inputs[0],return_tensors='pt'				).pixel_values
            self.assertEqual(
                encoded_images.shape,(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)

            # Test batched
            __UpperCamelCase   = image_processing(A_,return_tensors='pt'				).pixel_values
            self.assertEqual(
                encoded_images.shape,(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)







       def 		snake_case_  ( self:   List[str]				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            # create random numpy tensors
            __UpperCamelCase   = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_,numpify=A_				)
            for image in image_inputs:
                 self.assertIsInstance(A_,np.ndarray				)

            # Test not batched input
            __UpperCamelCase   = image_processing(image_inputs[0],return_tensors='pt'				).pixel_values
            self.assertEqual(
                encoded_images.shape,(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)

            # Test batched
            __UpperCamelCase   = image_processing(A_,return_tensors='pt'				).pixel_values
            self.assertEqual(
                encoded_images.shape,(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)







       def 		snake_case_  ( self:   Any				):






            '''simple docstring'''




            __UpperCamelCase   = self.image_processing_class(**self.image_processor_dict				)
            # create random PyTorch tensors
            __UpperCamelCase   = prepare_image_inputs(self.image_processor_tester,equal_resolution=A_,torchify=A_				)
            for image in image_inputs:
                 self.assertIsInstance(A_,torch.Tensor				)

            # Test not batched input
            __UpperCamelCase   = image_processing(image_inputs[0],return_tensors='pt'				).pixel_values
            self.assertEqual(
                encoded_images.shape,(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)

            # Test batched
            __UpperCamelCase   = image_processing(A_,return_tensors='pt'				).pixel_values
            self.assertEqual(
                encoded_images.shape,(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ),)


