import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate







class       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : List[Any] 	=   CONFIG_NAME
 @register_to_config
 def __init__(	self		, snake_case__ = True		, snake_case__ = 8		, snake_case__ = "lanczos"		, snake_case__ = True		, snake_case__ = False		, )						->				Any:
       '''simple docstring'''





       super().__init__()
 @staticmethod
 def 						UpperCAmelCase__							(	snake_case__    )						->				PIL.Image.Image:
       '''simple docstring'''





       if images.ndim == 3:
             UpperCAmelCase     :			Optional[Any]     	=images[None, ...]
       UpperCAmelCase     :			Optional[int]     	=(images * 255).round().astype('''uint8'''    )
       if images.shape[-1] == 1:
             # special case for grayscale (single channel) images
             UpperCAmelCase     :			Dict     	=[Image.fromarray(image.squeeze()		, mode='''L'''    ) for image in images]
       else:
             UpperCAmelCase     :			str     	=[Image.fromarray(snake_case__    ) for image in images]

       return pil_images
 @staticmethod
 def 						UpperCAmelCase__							(	snake_case__    )						->				np.ndarray:
       '''simple docstring'''





       if not isinstance(snake_case__		, snake_case__    ):
             UpperCAmelCase     :			Union[str, Any]     	=[images]
       UpperCAmelCase     :			Dict     	=[np.array(snake_case__    ).astype(np.floataa    ) / 255.0 for image in images]
       UpperCAmelCase     :			List[str]     	=np.stack(snake_case__		, axis=0    )

       return images
 @staticmethod
 def 						UpperCAmelCase__							(	snake_case__    )						->				torch.FloatTensor:
       '''simple docstring'''





       if images.ndim == 3:
             UpperCAmelCase     :			Dict     	=images[..., None]

       UpperCAmelCase     :			Optional[int]     	=torch.from_numpy(images.transpose(0		, 3		, 1		, 2    )    )
       return images
 @staticmethod
 def 						UpperCAmelCase__							(	snake_case__    )						->				np.ndarray:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=images.cpu().permute(0		, 2		, 3		, 1    ).float().numpy()
       return images
 @staticmethod
 def 						UpperCAmelCase__							(	snake_case__    )						->				Any:
       '''simple docstring'''





       return 2.0 * images - 1.0
 @staticmethod
 def 						UpperCAmelCase__							(	snake_case__    )						->				Tuple:
       '''simple docstring'''





       return (images / 2 + 0.5).clamp(0		, 1    )
 @staticmethod
 def 						UpperCAmelCase__							(	snake_case__    )						->				PIL.Image.Image:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=image.convert('''RGB'''    )
       return image
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = None		, snake_case__ = None		, )						->				PIL.Image.Image:
       '''simple docstring'''





       if height is None:
             UpperCAmelCase     :			List[str]     	=image.height
       if width is None:
             UpperCAmelCase     :			Any     	=image.width

       UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=(
           x - x % self.config.vae_scale_factor for x in (width, height)
       )  # resize to integer multiple of vae_scale_factor
       UpperCAmelCase     :			Tuple     	=image.resize((width, height)		, resample=PIL_INTERPOLATION[self.config.resample]    )
       return image
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = None		, snake_case__ = None		, )						->				torch.Tensor:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=(PIL.Image.Image, np.ndarray, torch.Tensor)
       if isinstance(snake_case__		, snake_case__    ):
             UpperCAmelCase     :			List[str]     	=[image]
       elif not (isinstance(snake_case__		, snake_case__    ) and all(isinstance(snake_case__		, snake_case__    ) for i in image    )):
             raise ValueError(
                 f'''Input is in incorrect format: {[type(snake_case__    ) for i in image]}. Currently, we only support {', '.join(snake_case__    )}'''    )

       if isinstance(image[0]		, PIL.Image.Image    ):
             if self.config.do_convert_rgb:
                   UpperCAmelCase     :			Tuple     	=[self.convert_to_rgb(snake_case__    ) for i in image]
             if self.config.do_resize:
                   UpperCAmelCase     :			Optional[int]     	=[self.resize(snake_case__		, snake_case__		, snake_case__    ) for i in image]
             UpperCAmelCase     :			int     	=self.pil_to_numpy(snake_case__    )  # to np
             UpperCAmelCase     :			List[str]     	=self.numpy_to_pt(snake_case__    )  # to pt

       elif isinstance(image[0]		, np.ndarray    ):
             UpperCAmelCase     :			str     	=np.concatenate(snake_case__		, axis=0    ) if image[0].ndim == 4 else np.stack(snake_case__		, axis=0    )
             UpperCAmelCase     :			Optional[Any]     	=self.numpy_to_pt(snake_case__    )
             UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=image.shape
             if self.config.do_resize and (
                 height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
             ):
                   raise ValueError(
                       f'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
                       f'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''    )

       elif isinstance(image[0]		, torch.Tensor    ):
             UpperCAmelCase     :			Optional[int]     	=torch.cat(snake_case__		, axis=0    ) if image[0].ndim == 4 else torch.stack(snake_case__		, axis=0    )
             UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=image.shape

             # don't need any preprocess if the image is latents
             if channel == 4:
                   return image

             if self.config.do_resize and (
                 height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
             ):
                   raise ValueError(
                       f'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
                       f'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''    )

        # expected range [0,1], normalize to [-1,1]
       UpperCAmelCase     :			str     	=self.config.do_normalize
       if image.min() < 0:
             warnings.warn(
                 '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
                 f'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''		, snake_case__		, )
             UpperCAmelCase     :			Union[str, Any]     	=False

       if do_normalize:
             UpperCAmelCase     :			Union[str, Any]     	=self.normalize(snake_case__    )

       return image






 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = "pil"		, snake_case__ = None		, )						->				str:
       '''simple docstring'''





       if not isinstance(snake_case__		, torch.Tensor    ):
             raise ValueError(
                 f'''Input for postprocessing is in incorrect format: {type(snake_case__    )}. We only support pytorch tensor'''    )
       if output_type not in ["latent", "pt", "np", "pil"]:
             UpperCAmelCase     :			Tuple     	=(
                 f'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
                 '''`pil`, `np`, `pt`, `latent`'''
             )
             deprecate('''Unsupported output_type'''		, '''1.0.0'''		, snake_case__		, standard_warn=snake_case__    )
             UpperCAmelCase     :			Union[str, Any]     	='''np'''

       if output_type == "latent":
             return image

       if do_denormalize is None:
             UpperCAmelCase     :			Dict     	=[self.config.do_normalize] * image.shape[0]

       UpperCAmelCase     :			List[str]     	=torch.stack(
           [self.denormalize(image[i]    ) if do_denormalize[i] else image[i] for i in range(image.shape[0]    )]    )

       if output_type == "pt":
             return image

       UpperCAmelCase     :			Optional[Any]     	=self.pt_to_numpy(snake_case__    )

       if output_type == "np":
             return image

       if output_type == "pil":
             return self.numpy_to_pil(snake_case__    )







class       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : int 	=   CONFIG_NAME
 @register_to_config
 def __init__(	self		, snake_case__ = True		, snake_case__ = 8		, snake_case__ = "lanczos"		, snake_case__ = True		, )						->				Dict:
       '''simple docstring'''





       super().__init__()
 @staticmethod
 def 						UpperCAmelCase__							(	snake_case__    )						->				List[Any]:
       '''simple docstring'''





       if images.ndim == 3:
             UpperCAmelCase     :			List[str]     	=images[None, ...]
       UpperCAmelCase     :			Tuple     	=(images * 255).round().astype('''uint8'''    )
       if images.shape[-1] == 1:
             # special case for grayscale (single channel) images
             UpperCAmelCase     :			List[Any]     	=[Image.fromarray(image.squeeze()		, mode='''L'''    ) for image in images]
       else:
             UpperCAmelCase     :			str     	=[Image.fromarray(image[:, :, :3]    ) for image in images]

       return pil_images
 @staticmethod
 def 						UpperCAmelCase__							(	snake_case__    )						->				Optional[Any]:
       '''simple docstring'''





       return image[:, :, 1] * 2**8 + image[:, :, 2]
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Tuple:
       '''simple docstring'''





       if images.ndim == 3:
             UpperCAmelCase     :			Any     	=images[None, ...]
       UpperCAmelCase     :			int     	=images[:, :, :, 3:]
       if images.shape[-1] == 6:
             UpperCAmelCase     :			Any     	=(images_depth * 255).round().astype('''uint8'''    )
             UpperCAmelCase     :			List[Any]     	=[
                 Image.fromarray(self.rgblike_to_depthmap(snake_case__    )		, mode='''I;16'''    ) for image_depth in images_depth
             ]
       elif images.shape[-1] == 4:
             UpperCAmelCase     :			List[str]     	=(images_depth * 6_5535.0).astype(np.uintaa    )
             UpperCAmelCase     :			Optional[Any]     	=[Image.fromarray(snake_case__		, mode='''I;16'''    ) for image_depth in images_depth]
       else:
             raise Exception('''Not supported'''    )

       return pil_images






 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__ = "pil"		, snake_case__ = None		, )						->				Dict:
       '''simple docstring'''





       if not isinstance(snake_case__		, torch.Tensor    ):
             raise ValueError(
                 f'''Input for postprocessing is in incorrect format: {type(snake_case__    )}. We only support pytorch tensor'''    )
       if output_type not in ["latent", "pt", "np", "pil"]:
             UpperCAmelCase     :			int     	=(
                 f'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
                 '''`pil`, `np`, `pt`, `latent`'''
             )
             deprecate('''Unsupported output_type'''		, '''1.0.0'''		, snake_case__		, standard_warn=snake_case__    )
             UpperCAmelCase     :			str     	='''np'''

       if do_denormalize is None:
             UpperCAmelCase     :			str     	=[self.config.do_normalize] * image.shape[0]

       UpperCAmelCase     :			str     	=torch.stack(
           [self.denormalize(image[i]    ) if do_denormalize[i] else image[i] for i in range(image.shape[0]    )]    )

       UpperCAmelCase     :			Optional[Any]     	=self.pt_to_numpy(snake_case__    )

       if output_type == "np":
             if image.shape[-1] == 6:
                   UpperCAmelCase     :			str     	=np.stack([self.rgblike_to_depthmap(im[:, :, 3:]    ) for im in image]		, axis=0    )
             else:
                   UpperCAmelCase     :			List[Any]     	=image[:, :, :, 3:]
             return image[:, :, :, :3], image_depth

       if output_type == "pil":
             return self.numpy_to_pil(snake_case__    ), self.numpy_to_depth(snake_case__    )
       else:
             raise Exception(f'''This type {output_type} is not supported'''    )

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_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[Any]:






      '''simple docstring'''





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

            # pad all tokens to sequence length
            UpperCAmelCase     :			Optional[int]     	=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     :			Optional[int]     	=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     :			Tuple     	=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     :			Union[str, Any]     	=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     :			Tuple     	=None

      UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			int     	=model(
          states=__lowerCAmelCase		,       actions=__lowerCAmelCase		,       rewards=__lowerCAmelCase		,       returns_to_go=__lowerCAmelCase		,       timesteps=__lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       return_dict=__lowerCAmelCase		,       )

      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         =     10_00
__snake_case         =     '''cuda'''
__snake_case         =     1000.0  # normalization for rewards/returns
__snake_case         =     36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case         =     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,
    ]
)
__snake_case         =     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,
    ]
)
__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(10):
     __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
