



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       :						Union[str, Any]   ,		_a       :						Any   ,		_a       :						Union[str, Any]   ,		_a       :						Optional[int]   ,		_a       :						Optional[Any]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       List[Any]  		=					states.reshape(1   ,		-1   ,		model.config.state_dim    )
    UpperCAmelCase_     :       Optional[Any]  		=					actions.reshape(1   ,		-1   ,		model.config.act_dim    )
    UpperCAmelCase_     :       int  		=					returns_to_go.reshape(1   ,		-1   ,		1    )
    UpperCAmelCase_     :       int  		=					timesteps.reshape(1   ,		-1    )

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

        # pad all tokens to sequence length
        UpperCAmelCase_     :       List[Any]  		=					torch.cat(
            [torch.zeros(model.config.max_length - states.shape[1]    ), torch.ones(states.shape[1]    )]    )
        UpperCAmelCase_     :       List[Any]  		=					attention_mask.to(dtype=torch.long   ,		device=states.device    ).reshape(1   ,		-1    )
        UpperCAmelCase_     :       Union[str, 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_     :       Optional[int]  		=					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_     :       List[str]  		=					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_     :       Dict  		=					None

    UpperCAmelCase_     :       Optional[Any]  		=					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

UpperCamelCase_									=   gym.make('''Hopper-v3''')
UpperCamelCase_									=   env.observation_space.shape[0]
UpperCamelCase_									=   env.action_space.shape[0]
UpperCamelCase_									=   1000
UpperCamelCase_									=   'cuda'
UpperCamelCase_									=   1000.0  # normalization for rewards/returns
UpperCamelCase_									=   3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase_									=   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,
    ]
)
UpperCamelCase_									=   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,
    ]
)
UpperCamelCase_									=   torch.from_numpy(state_mean).to(device=device)
UpperCamelCase_									=   torch.from_numpy(state_std).to(device=device)

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

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

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

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

            UpperCamelCase_									=   env.step(action)

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

            UpperCamelCase_									=   target_return[0, -1] - (reward / scale)
            UpperCamelCase_									=   torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
            UpperCamelCase_									=   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 gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 			lowerCAmelCase__(     snake_case__     ,     unittest.TestCase    ):






    '''simple docstring'''





    A_	:  Tuple								=			RePaintPipeline
    A_	:  Tuple								=			IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
    A_	:  str								=			PipelineTesterMixin.required_optional_params - {
        'latents',
        'num_images_per_prompt',
        'callback',
        'callback_steps',
    }
    A_	:  Optional[Any]								=			IMAGE_INPAINTING_BATCH_PARAMS
    A_	:  Union[str, Any]								=			False





    def        _lowerCamelCase			(						self					: Any						):





       '''simple docstring'''
       torch.manual_seed(0						)
       torch.manual_seed(0						)
       UpperCAmelCase_				:   int							= UNetaDModel(
           block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=3							, out_channels=3							, down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')							, up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')							, )
       UpperCAmelCase_				:   str							= RePaintScheduler()
       UpperCAmelCase_				:   Optional[Any]							= {'''unet''': unet, '''scheduler''': scheduler}
       return components





    def        _lowerCamelCase			(						self					: List[str]							, __snake_case					: Optional[Any]							, __snake_case					: int=0						):





       '''simple docstring'''
       if str(__snake_case						).startswith('''mps'''						):
          UpperCAmelCase_				:   Optional[Any]							= torch.manual_seed(__snake_case						)
       else:
          UpperCAmelCase_				:   Optional[Any]							= torch.Generator(device=__snake_case						).manual_seed(__snake_case						)
       UpperCAmelCase_				:   Optional[Any]							= np.random.RandomState(__snake_case						).standard_normal((1, 3, 32, 32)						)
       UpperCAmelCase_				:   Optional[int]							= torch.from_numpy(__snake_case						).to(device=__snake_case							, dtype=torch.floataa						)
       UpperCAmelCase_				:   Any							= (image > 0).to(device=__snake_case							, dtype=torch.floataa						)
       UpperCAmelCase_				:   List[str]							= {
           '''image''': image,
           '''mask_image''': mask,
           '''generator''': generator,
           '''num_inference_steps''': 5,
           '''eta''': 0.0,
           '''jump_length''': 2,
           '''jump_n_sample''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs





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





       '''simple docstring'''
       UpperCAmelCase_				:   str							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:   Optional[int]							= self.get_dummy_components()
       UpperCAmelCase_				:   Dict							= RePaintPipeline(**__snake_case						)
       UpperCAmelCase_				:   int							= sd_pipe.to(__snake_case						)
       sd_pipe.set_progress_bar_config(disable=__snake_case						)

       UpperCAmelCase_				:   List[Any]							= self.get_dummy_inputs(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= sd_pipe(**__snake_case						).images
       UpperCAmelCase_				:   Optional[Any]							= image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCAmelCase_				:   Tuple							= np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3





    @skip_mps
    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       return super().test_save_load_local()





    @unittest.skip('''non-deterministic pipeline'''						)
    def        _lowerCamelCase			(						self					: Optional[Any]						):





       '''simple docstring'''
       return super().test_inference_batch_single_identical()





    @skip_mps
    def        _lowerCamelCase			(						self					: Dict						):





       '''simple docstring'''
       return super().test_dict_tuple_outputs_equivalent()





    @skip_mps
    def        _lowerCamelCase			(						self					: int						):





       '''simple docstring'''
       return super().test_save_load_optional_components()





    @skip_mps
    def        _lowerCamelCase			(						self					: Union[str, Any]						):





       '''simple docstring'''
       return super().test_attention_slicing_forward_pass()





@nightly
@require_torch_gpu
class 			lowerCAmelCase__(     unittest.TestCase    ):






    '''simple docstring'''





    def        _lowerCamelCase			(						self					: Tuple						):





       '''simple docstring'''
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





    def        _lowerCamelCase			(						self					: List[str]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256.png'''						)
       UpperCAmelCase_				:   int							= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''						)
       UpperCAmelCase_				:   List[Any]							= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256_result.npy'''						)

       UpperCAmelCase_				:   List[str]							= '''google/ddpm-ema-celebahq-256'''
       UpperCAmelCase_				:   Union[str, Any]							= UNetaDModel.from_pretrained(__snake_case						)
       UpperCAmelCase_				:   Dict							= RePaintScheduler.from_pretrained(__snake_case						)

       UpperCAmelCase_				:   Optional[int]							= RePaintPipeline(unet=__snake_case							, scheduler=__snake_case						).to(__snake_case						)
       repaint.set_progress_bar_config(disable=__snake_case						)
       repaint.enable_attention_slicing()

       UpperCAmelCase_				:   Optional[int]							= torch.manual_seed(0						)
       UpperCAmelCase_				:   List[str]							= repaint(
           __snake_case							, __snake_case							, num_inference_steps=250							, eta=0.0							, jump_length=10							, jump_n_sample=10							, generator=__snake_case							, output_type='''np'''							, )
       UpperCAmelCase_				:   Tuple							= output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image						).mean() < 1E-2