




"""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 							lowercase   (snake_case__				:	Tuple ,    snake_case__				:	List[Any] ,    snake_case__				:	Optional[int] ,    snake_case__				:	Union[str, Any] ,    snake_case__				:	Optional[int] ,    snake_case__				:	Optional[int]	)       ->					int:


				'''simple docstring'''






				lowerCAmelCase		=			states.reshape(1 ,    -1 ,    model.config.state_dim	)
				lowerCAmelCase		=			actions.reshape(1 ,    -1 ,    model.config.act_dim	)
				lowerCAmelCase		=			returns_to_go.reshape(1 ,    -1 ,    1	)
				lowerCAmelCase		=			timesteps.reshape(1 ,    -1	)

				if model.config.max_length is not None:
								lowerCAmelCase		=			states[:, -model.config.max_length :]
								lowerCAmelCase		=			actions[:, -model.config.max_length :]
								lowerCAmelCase		=			returns_to_go[:, -model.config.max_length :]
								lowerCAmelCase		=			timesteps[:, -model.config.max_length :]

								# pad all tokens to sequence length
								lowerCAmelCase		=			torch.cat(
								    [torch.zeros(model.config.max_length - states.shape[1]	), torch.ones(states.shape[1]	)]	)
								lowerCAmelCase		=			attention_mask.to(dtype=torch.long ,    device=states.device	).reshape(1 ,    -1	)
								lowerCAmelCase		=			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	)
								lowerCAmelCase		=			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	)
								lowerCAmelCase		=			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	)
								lowerCAmelCase		=			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:
								lowerCAmelCase		=			None

				lowerCAmelCase	,  lowerCAmelCase	,  lowerCAmelCase		=			model(
				    states=snake_case__ ,    actions=snake_case__ ,    rewards=snake_case__ ,    returns_to_go=snake_case__ ,    timesteps=snake_case__ ,    attention_mask=snake_case__ ,    return_dict=snake_case__ ,    )

				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								=							1000.0  # normalization for rewards/returns
a								=							3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a								=							np.array(
    [
        1.31_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,
    ]
)
a								=							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,
    ]
)
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











"""simple docstring"""







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 OwlViTImageProcessor






class  SCREAMING_SNAKE_CASE__   (   unittest.TestCase   ):


						def __init__(							self			:     int  ,  lowerCAmelCase			:     Optional[int]  ,  lowerCAmelCase			:     Optional[Any]=7  ,  lowerCAmelCase			:     str=3  ,  lowerCAmelCase			:     str=18  ,  lowerCAmelCase			:     Tuple=30  ,  lowerCAmelCase			:     Union[str, Any]=400  ,  lowerCAmelCase			:     List[str]=True  ,  lowerCAmelCase			:     List[str]=None  ,  lowerCAmelCase			:     int=True  ,  lowerCAmelCase			:     int=None  ,  lowerCAmelCase			:     Optional[int]=True  ,  lowerCAmelCase			:     Dict=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,  lowerCAmelCase			:     str=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,  lowerCAmelCase			:     Any=True  ,  ):
										lowerCAmelCase		=			parent
										lowerCAmelCase		=			batch_size
										lowerCAmelCase		=			num_channels
										lowerCAmelCase		=			image_size
										lowerCAmelCase		=			min_resolution
										lowerCAmelCase		=			max_resolution
										lowerCAmelCase		=			do_resize
										lowerCAmelCase		=			size if size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_center_crop
										lowerCAmelCase		=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_normalize
										lowerCAmelCase		=			image_mean
										lowerCAmelCase		=			image_std
										lowerCAmelCase		=			do_convert_rgb




						def 					__lowercase	(							self			:     Tuple   ):
										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,
										    "do_convert_rgb": self.do_convert_rgb,
										}






@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE__   (   _a  ,			unittest.TestCase   ):
						_a        =  OwlViTImageProcessor if is_vision_available() else None


						def 					__lowercase	(							self			:     Union[str, Any]   ):
										lowerCAmelCase		=			OwlViTImageProcessingTester(self   )


						@property
						def 					__lowercase	(							self			:     List[Any]   ):
										return self.image_processor_tester.prepare_image_processor_dict()


						def 					__lowercase	(							self			:     List[str]   ):
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_resize"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """size"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_center_crop"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """center_crop"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_normalize"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """image_mean"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """image_std"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_convert_rgb"""   )   )


						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase		=			self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size  ,  {"""height""": 18, """width""": 18}   )
										self.assertEqual(image_processor.crop_size  ,  {"""height""": 18, """width""": 18}   )

										lowerCAmelCase		=			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 					__lowercase	(							self			:     str   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase   )

										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  Image.Image   )

										# Test not batched input
										lowerCAmelCase		=			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
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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 					__lowercase	(							self			:     Tuple   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  numpify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  np.ndarray   )

										# Test not batched input
										lowerCAmelCase		=			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
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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 					__lowercase	(							self			:     Optional[int]   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  torchify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  torch.Tensor   )

										# Test not batched input
										lowerCAmelCase		=			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
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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"""],
										    )  ,  )





