

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


_UpperCamelCase     =    logging.get_logger(__name__)



class 					lowercase		( snake_case__      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       ["""pixel_values"""]




 def __init__(self , __a = True , __a = None , __a = True , __a = None , __a = PILImageResampling.BICUBIC , __a = True , __a = 1 / 255 , __a = True , __a = None , __a = None , **__a , )				->      int:






  """simple docstring"""





  super().__init__(**__snake_case				)
  UpperCAmelCase__           =       crop_size if crop_size is not None else {'height': 256, 'width': 256}
  UpperCAmelCase__           =       get_size_dict(__snake_case , param_name='crop_size'				)
  UpperCAmelCase__           =       size if size is not None else {'height': 224, 'width': 224}
  UpperCAmelCase__           =       get_size_dict(__snake_case				)

  UpperCAmelCase__           =       do_center_crop
  UpperCAmelCase__           =       crop_size
  UpperCAmelCase__           =       do_resize
  UpperCAmelCase__           =       size
  UpperCAmelCase__           =       resample
  UpperCAmelCase__           =       do_rescale
  UpperCAmelCase__           =       rescale_factor
  UpperCAmelCase__           =       do_normalize
  UpperCAmelCase__           =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
  UpperCAmelCase__           =       image_std if image_std is not None else IMAGENET_DEFAULT_STD




 def 						UpperCamelCase__							(self , __a , __a , __a = None , __a = None , **__a , )				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       self.size if size is None else size
  UpperCAmelCase__           =       get_size_dict(__snake_case				)
  UpperCAmelCase__           =       get_size_dict(__snake_case , param_name='crop_size'				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       get_image_size(__snake_case				)
  UpperCAmelCase__           =       min(__snake_case , __snake_case				)
  UpperCAmelCase__           =       (size['height'] / crop_size['height']) * min_dim
  UpperCAmelCase__           =       (size['width'] / crop_size['width']) * min_dim
  return center_crop(__snake_case , size=(cropped_height, cropped_width) , data_format=__snake_case , **__snake_case				)




 def 						UpperCamelCase__							(self , __a , __a , __a = PIL.Image.BICUBIC , __a = None , **__a , )				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       get_size_dict(__snake_case				)
  if "height" not in size or "width" not in size:
   raise ValueError(F"The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}"				)
  return resize(
      __snake_case , size=(size['height'], size['width']) , resample=__snake_case , data_format=__snake_case , **__snake_case				)




 def 						UpperCamelCase__							(self , __a , __a , __a = None , **__a , )				->      int:






  """simple docstring"""





  return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case				)




 def 						UpperCamelCase__							(self , __a , __a , __a , __a = None , **__a , )				->      Optional[int]:






  """simple docstring"""





  return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case				)




 def 						UpperCamelCase__							(self , __a , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = ChannelDimension.FIRST , **__a , )				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       do_center_crop if do_center_crop is not None else self.do_center_crop
  UpperCAmelCase__           =       crop_size if crop_size is not None else self.crop_size
  UpperCAmelCase__           =       get_size_dict(__snake_case , param_name='crop_size'				)
  UpperCAmelCase__           =       do_resize if do_resize is not None else self.do_resize
  UpperCAmelCase__           =       size if size is not None else self.size
  UpperCAmelCase__           =       get_size_dict(__snake_case				)
  UpperCAmelCase__           =       resample if resample is not None else self.resample
  UpperCAmelCase__           =       do_rescale if do_rescale is not None else self.do_rescale
  UpperCAmelCase__           =       rescale_factor if rescale_factor is not None else self.rescale_factor
  UpperCAmelCase__           =       do_normalize if do_normalize is not None else self.do_normalize
  UpperCAmelCase__           =       image_mean if image_mean is not None else self.image_mean
  UpperCAmelCase__           =       image_std if image_std is not None else self.image_std

  UpperCAmelCase__           =       make_list_of_images(__snake_case				)

  if not valid_images(__snake_case				):
   raise ValueError(
       'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
       'torch.Tensor, tf.Tensor or jax.ndarray.'				)

  if do_center_crop and crop_size is None:
   raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'				)

  if do_resize and size is None:
   raise ValueError('Size must be specified if do_resize is True.'				)

  if do_rescale and rescale_factor is None:
   raise ValueError('Rescale factor must be specified if do_rescale is True.'				)

  if do_normalize and (image_mean is None or image_std is None):
   raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'				)

  # All transformations expect numpy arrays.
  UpperCAmelCase__           =       [to_numpy_array(__snake_case				) for image in images]

  if do_center_crop:
   UpperCAmelCase__           =       [self.center_crop(__snake_case , __snake_case , size=__snake_case				) for image in images]

  if do_resize:
   UpperCAmelCase__           =       [self.resize(image=__snake_case , size=__snake_case , resample=__snake_case				) for image in images]

  if do_rescale:
   UpperCAmelCase__           =       [self.rescale(image=__snake_case , scale=__snake_case				) for image in images]

  if do_normalize:
   UpperCAmelCase__           =       [self.normalize(image=__snake_case , mean=__snake_case , std=__snake_case				) for image in images]

  UpperCAmelCase__           =       [to_channel_dimension_format(__snake_case , __snake_case				) for image in images]

  UpperCAmelCase__           =       {'pixel_values': images}
  return BatchFeature(data=__snake_case , tensor_type=__snake_case				)








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_(    snake_case__:						int						,					snake_case__:						int						,					snake_case__:						Union[str, Any]						,					snake_case__:						Any						,					snake_case__:						Any						,					snake_case__:						Union[str, Any]     ) ->				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=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

_UpperCamelCase     =    gym.make('''Hopper-v3''')
_UpperCamelCase     =    env.observation_space.shape[0]
_UpperCamelCase     =    env.action_space.shape[0]
_UpperCamelCase     =    1000
_UpperCamelCase     =    '''cuda'''
_UpperCamelCase     =    1_0_0_0.0  # 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 ,       _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 ,       _UpperCamelCase ,       _UpperCamelCase ,       _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



