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

       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=_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

_A	  =					gym.make('''Hopper-v3''')
_A	  =					env.observation_space.shape[0]
_A	  =					env.action_space.shape[0]
_A	  =					1_000
_A	  =					'''cuda'''
_A	  =					1_0_0_0.0  # normalization for rewards/returns
_A	  =					3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_A	  =					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	  =					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	  =					torch.from_numpy(state_mean).to(device=device)
_A	  =					torch.from_numpy(state_std).to(device=device)

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

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

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

            _A	  =					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	  =					action
            _A	  =					action.detach().cpu().numpy()

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

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

            _A	  =					target_return[0, -1] - (reward / scale)
            _A	  =					torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
            _A	  =					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 ViltImageProcessor

class 		lowercase_       (       unittest.TestCase				):

 def __init__(      self     ,	__UpperCamelCase     ,	__UpperCamelCase=7     ,	__UpperCamelCase=3     ,	__UpperCamelCase=1_8     ,	__UpperCamelCase=3_0     ,	__UpperCamelCase=4_0_0     ,	__UpperCamelCase=True     ,	__UpperCamelCase=None     ,	__UpperCamelCase=2     ,	__UpperCamelCase=True     ,	__UpperCamelCase=[0.5, 0.5, 0.5]     ,	__UpperCamelCase=[0.5, 0.5, 0.5]     ,	):




        """simple docstring"""


        UpperCamelCase_							       =       size if size is not None else {"""shortest_edge""": 3_0}
        UpperCamelCase_							       =       parent
        UpperCamelCase_							       =       batch_size
        UpperCamelCase_							       =       num_channels
        UpperCamelCase_							       =       image_size
        UpperCamelCase_							       =       min_resolution
        UpperCamelCase_							       =       max_resolution
        UpperCamelCase_							       =       do_resize
        UpperCamelCase_							       =       size
        UpperCamelCase_							       =       size_divisor
        UpperCamelCase_							       =       do_normalize
        UpperCamelCase_							       =       image_mean
        UpperCamelCase_							       =       image_std

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


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

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase=False     ):




        """simple docstring"""


        if not batched:
               UpperCamelCase_							       =       self.size["""shortest_edge"""]
               UpperCamelCase_							       =       image_inputs[0]
               if isinstance(__UpperCamelCase     ,	Image.Image     ):
                      UpperCamelCase_       ,				UpperCamelCase_							       =       image.size
               else:
                      UpperCamelCase_       ,				UpperCamelCase_							       =       image.shape[1], image.shape[2]
               UpperCamelCase_							       =       size / min(__UpperCamelCase     ,	__UpperCamelCase     )
               if h < w:
                      UpperCamelCase_       ,				UpperCamelCase_							       =       size, scale * w
               else:
                      UpperCamelCase_       ,				UpperCamelCase_							       =       scale * h, size

               UpperCamelCase_							       =       int((1_3_3_3 / 8_0_0) * size     )
               if max(__UpperCamelCase     ,	__UpperCamelCase     ) > max_size:
                      UpperCamelCase_							       =       max_size / max(__UpperCamelCase     ,	__UpperCamelCase     )
                      UpperCamelCase_							       =       newh * scale
                      UpperCamelCase_							       =       neww * scale

               UpperCamelCase_       ,				UpperCamelCase_							       =       int(newh + 0.5     ), int(neww + 0.5     )
               UpperCamelCase_       ,				UpperCamelCase_							       =       (
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

        else:
               UpperCamelCase_							       =       []
               for image in image_inputs:
                      UpperCamelCase_       ,				UpperCamelCase_							       =       self.get_expected_values([image]     )
                      expected_values.append((expected_height, expected_width)     )
               UpperCamelCase_							       =       max(__UpperCamelCase     ,	key=lambda __UpperCamelCase     : item[0]     )[0]
               UpperCamelCase_							       =       max(__UpperCamelCase     ,	key=lambda __UpperCamelCase     : item[1]     )[1]

        return expected_height, expected_width



@require_torch
@require_vision
class 		lowercase_       (       __SCREAMING_SNAKE_CASE							,	unittest.TestCase				):
 A__						:				Optional[int]         =    ViltImageProcessor if is_vision_available() else None

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       ViltImageProcessingTester(self     )

 @property
 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        return self.image_processor_tester.prepare_image_processor_dict()

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""image_mean"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""image_std"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""do_normalize"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""do_resize"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""size"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""size_divisor"""     )     )

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size     ,	{"""shortest_edge""": 3_0}     )

        UpperCamelCase_							       =       self.image_processing_class.from_dict(self.image_processor_dict     ,	size=4_2     )
        self.assertEqual(image_processor.size     ,	{"""shortest_edge""": 4_2}     )

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        pass

 def lowerCamelCase_				(      self     ):




        """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=__UpperCamelCase     )
        for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,	Image.Image     )

        # Test not batched input
        UpperCamelCase_							       =       image_processing(image_inputs[0]     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

        # Test batched
        UpperCamelCase_							       =       image_processing(__UpperCamelCase     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     ,	batched=__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,	)

 def lowerCamelCase_				(      self     ):




        """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=__UpperCamelCase     ,	numpify=__UpperCamelCase     )
        for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,	np.ndarray     )

        # Test not batched input
        UpperCamelCase_							       =       image_processing(image_inputs[0]     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

        # Test batched
        UpperCamelCase_							       =       image_processing(__UpperCamelCase     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     ,	batched=__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,	)

 def lowerCamelCase_				(      self     ):




        """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=__UpperCamelCase     ,	torchify=__UpperCamelCase     )
        for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,	torch.Tensor     )

        # Test not batched input
        UpperCamelCase_							       =       image_processing(image_inputs[0]     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

        # Test batched
        UpperCamelCase_							       =       image_processing(__UpperCamelCase     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     ,	batched=__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,	)



