'''simple docstring'''
import json
import os
import tempfile

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


if is_torch_available():
  import numpy as np
  import torch

if is_vision_available():
  from PIL import Image







def 			_UpperCamelCase			(  _a						:						Tuple					,  _a						:						List[Any]=False					,  _a						:						str=False					,  _a						:						Any=False      ):




    """simple docstring"""



    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    __UpperCamelCase						:						Tuple		     =	[]
    for i in range(image_processor_tester.batch_size      ):
        if equal_resolution:
            __UpperCamelCase						:						Optional[int]		     =	image_processor_tester.max_resolution
        else:
            # To avoid getting image width/height 0
            __UpperCamelCase						:						str		     =	image_processor_tester.min_resolution
            if getattr(_a					,  'size_divisor'					,  _a      ):
                # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                __UpperCamelCase						:						int		     =	max(image_processor_tester.size_divisor					,  _a      )
            __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	np.random.choice(np.arange(_a					,  image_processor_tester.max_resolution      )					,  2      )
        image_inputs.append(
            np.random.randint(2_5_5					,  size=(image_processor_tester.num_channels, width, height)					,  dtype=np.uinta      )      )

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __UpperCamelCase						:						int		     =	[Image.fromarray(np.moveaxis(_a					,  0					,  -1      )      ) for image in image_inputs]

    if torchify:
        __UpperCamelCase						:						Tuple		     =	[torch.from_numpy(_a      ) for image in image_inputs]

    return image_inputs







def 			_UpperCamelCase			(  _a						:						List[Any]					,  _a						:						Union[str, Any]=1_0					,  _a						:						int=1_0					,  _a						:						Tuple=False					,  _a						:						Tuple=False      ):




    """simple docstring"""



    __UpperCamelCase						:						Dict		     =	[]
    for i in range(image_processor_tester.num_frames      ):
        video.append(np.random.randint(2_5_5					,  size=(image_processor_tester.num_channels, width, height)					,  dtype=np.uinta      )      )

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __UpperCamelCase						:						Any		     =	[Image.fromarray(np.moveaxis(_a					,  0					,  -1      )      ) for frame in video]

    if torchify:
        __UpperCamelCase						:						Tuple		     =	[torch.from_numpy(_a      ) for frame in video]

    return video







def 			_UpperCamelCase			(  _a						:						List[str]					,  _a						:						Dict=False					,  _a						:						str=False					,  _a						:						int=False      ):




    """simple docstring"""



    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    __UpperCamelCase						:						Optional[Any]		     =	[]
    for i in range(image_processor_tester.batch_size      ):
        if equal_resolution:
            __UpperCamelCase						:						Optional[Any]		     =	image_processor_tester.max_resolution
        else:
            __UpperCamelCase     ,       __UpperCamelCase						:						Any		     =	np.random.choice(
                np.arange(image_processor_tester.min_resolution					,  image_processor_tester.max_resolution      )					,  2      )
            __UpperCamelCase						:						Dict		     =	prepare_video(
                image_processor_tester=_a					,  width=_a					,  height=_a					,  numpify=_a					,  torchify=_a					,  )
        video_inputs.append(_a      )

    return video_inputs






class       __lowercase		:







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    None







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Any		     =	self.image_processing_class(**self.image_processor_dict     )
         __UpperCamelCase						:						Any		     =	json.loads(image_processor.to_json_string()     )
         for key, value in self.image_processor_dict.items():
             self.assertEqual(obj[key]  ,      _lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	self.image_processing_class(**self.image_processor_dict     )

         with tempfile.TemporaryDirectory() as tmpdirname:
             __UpperCamelCase						:						List[str]		     =	os.path.join(_lowerCamelCase  ,      'image_processor.json'     )
             image_processor_first.to_json_file(_lowerCamelCase     )
             __UpperCamelCase						:						Any		     =	self.image_processing_class.from_json_file(_lowerCamelCase     )

         self.assertEqual(image_processor_second.to_dict()  ,      image_processor_first.to_dict()     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Union[str, Any]		     =	self.image_processing_class(**self.image_processor_dict     )

         with tempfile.TemporaryDirectory() as tmpdirname:
             __UpperCamelCase						:						List[str]		     =	image_processor_first.save_pretrained(_lowerCamelCase     )[0]
             check_json_file_has_correct_format(_lowerCamelCase     )
             __UpperCamelCase						:						Optional[int]		     =	self.image_processing_class.from_pretrained(_lowerCamelCase     )

         self.assertEqual(image_processor_second.to_dict()  ,      image_processor_first.to_dict()     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						str		     =	self.image_processing_class()
         self.assertIsNotNone(_lowerCamelCase     )







     @require_torch
     @require_vision
     def lowerCAmelCase  (		self     ):
         if self.test_cast_dtype is not None:
             # Initialize image_processor
             __UpperCamelCase						:						int		     =	self.image_processing_class(**self.image_processor_dict     )

             # create random PyTorch tensors
             __UpperCamelCase						:						List[str]		     =	prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=_lowerCamelCase  ,      torchify=_lowerCamelCase     )

             __UpperCamelCase						:						Optional[int]		     =	image_processor(_lowerCamelCase  ,      return_tensors='pt'     )
             # for layoutLM compatiblity
             self.assertEqual(encoding.pixel_values.device  ,      torch.device('cpu'     )     )
             self.assertEqual(encoding.pixel_values.dtype  ,      torch.floataa     )

             __UpperCamelCase						:						Optional[int]		     =	image_processor(_lowerCamelCase  ,      return_tensors='pt'     ).to(torch.floataa     )
             self.assertEqual(encoding.pixel_values.device  ,      torch.device('cpu'     )     )
             self.assertEqual(encoding.pixel_values.dtype  ,      torch.floataa     )

             __UpperCamelCase						:						List[str]		     =	image_processor(_lowerCamelCase  ,      return_tensors='pt'     ).to('cpu'  ,      torch.bfloataa     )
             self.assertEqual(encoding.pixel_values.device  ,      torch.device('cpu'     )     )
             self.assertEqual(encoding.pixel_values.dtype  ,      torch.bfloataa     )

             with self.assertRaises(_lowerCamelCase     ):
                 __UpperCamelCase						:						Optional[int]		     =	image_processor(_lowerCamelCase  ,      return_tensors='pt'     ).to(torch.bfloataa  ,      'cpu'     )

             # Try with text + image feature
             __UpperCamelCase						:						int		     =	image_processor(_lowerCamelCase  ,      return_tensors='pt'     )
             encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]]     )}     )
             __UpperCamelCase						:						List[Any]		     =	encoding.to(torch.floataa     )

             self.assertEqual(encoding.pixel_values.device  ,      torch.device('cpu'     )     )
             self.assertEqual(encoding.pixel_values.dtype  ,      torch.floataa     )
             self.assertEqual(encoding.input_ids.dtype  ,      torch.long     )




'''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 			_UpperCamelCase			(  _a						:						List[Any]					,  _a						:						Dict					,  _a						:						Optional[int]					,  _a						:						Dict					,  _a						:						int					,  _a						:						List[str]      ):




    """simple docstring"""



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

    if model.config.max_length is not None:
        __UpperCamelCase						:						Any		     =	states[:, -model.config.max_length :]
        __UpperCamelCase						:						int		     =	actions[:, -model.config.max_length :]
        __UpperCamelCase						:						Any		     =	returns_to_go[:, -model.config.max_length :]
        __UpperCamelCase						:						Tuple		     =	timesteps[:, -model.config.max_length :]

        # pad all tokens to sequence length
        __UpperCamelCase						:						str		     =	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						:						Any		     =	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						:						Tuple		     =	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						:						Optional[Any]		     =	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						:						List[str]		     =	None

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

    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_0_0_0
a=	'''cuda'''
a=	1_000.0  # normalization for rewards/returns
a=	3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a=	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=	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=	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(1_0):
  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



