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

       if model.config.max_length is not None:
              _UpperCamelCase       :    Optional[Any]					  =					states[:, -model.config.max_length :]
              _UpperCamelCase       :    Any					  =					actions[:, -model.config.max_length :]
              _UpperCamelCase       :    str					  =					returns_to_go[:, -model.config.max_length :]
              _UpperCamelCase       :    Optional[Any]					  =					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       :    Dict					  =					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[Any]					  =					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       :    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       :    Dict					  =					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       :    str					  =					None

       _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    Optional[Any]					  =					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     						=			10_00
UpperCAmelCase     						=			'''cuda'''
UpperCAmelCase     						=			1000.0  # normalization for rewards/returns
UpperCAmelCase     						=			36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCAmelCase     						=			np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
UpperCAmelCase     						=			np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase     						=			{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			['''MPNetTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MPNetForMaskedLM''',
     '''MPNetForMultipleChoice''',
     '''MPNetForQuestionAnswering''',
     '''MPNetForSequenceClassification''',
     '''MPNetForTokenClassification''',
     '''MPNetLayer''',
     '''MPNetModel''',
     '''MPNetPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFMPNetEmbeddings''',
     '''TFMPNetForMaskedLM''',
     '''TFMPNetForMultipleChoice''',
     '''TFMPNetForQuestionAnswering''',
     '''TFMPNetForSequenceClassification''',
     '''TFMPNetForTokenClassification''',
     '''TFMPNetMainLayer''',
     '''TFMPNetModel''',
     '''TFMPNetPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase     						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



