



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  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case     )  ->  Union[str, Any]:






      '''simple docstring'''







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

      if model.config.max_length is not None:
            UpperCAmelCase_	:						Dict					   =		states[:, -model.config.max_length :]
            UpperCAmelCase_	:						Optional[int]					   =		actions[:, -model.config.max_length :]
            UpperCAmelCase_	:						Dict					   =		returns_to_go[:, -model.config.max_length :]
            UpperCAmelCase_	:						List[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_	:						str					   =		attention_mask.to(dtype=torch.long       ,	device=states.device     ).reshape(1       ,	-1     )
            UpperCAmelCase_	:						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_	:						str					   =		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_	:						int					   =		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_	:						int					   =		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

__lowerCamelCase								=      gym.make('''Hopper-v3''')
__lowerCamelCase								=      env.observation_space.shape[0]
__lowerCamelCase								=      env.action_space.shape[0]
__lowerCamelCase								=      1000
__lowerCamelCase								=      '''cuda'''
__lowerCamelCase								=      1_0_0_0.0  # normalization for rewards/returns
__lowerCamelCase								=      3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__lowerCamelCase								=      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,
    ]
)
__lowerCamelCase								=      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,
    ]
)
__lowerCamelCase								=      torch.from_numpy(state_mean).to(device=device)
__lowerCamelCase								=      torch.from_numpy(state_std).to(device=device)

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

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

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

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

      __lowerCamelCase							,  __lowerCamelCase							,  __lowerCamelCase							,  __lowerCamelCase								=      env.step(action)

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

      __lowerCamelCase								=      target_return[0, -1] - (reward / scale)
      __lowerCamelCase								=      torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
      __lowerCamelCase								=      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 torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


__lowerCamelCase								=      '''\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
'''

__lowerCamelCase								=      '''\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
'''


__lowerCamelCase								=      '''
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric("frugalscore")
    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
    >>> print([round(s, 3) for s in results["scores"]])
    [0.631, 0.645]
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,			_KWARGS_DESCRIPTION	)
class 	snake_case_			(datasets.Metric	):




       """simple docstring"""






       def 			A_ (  self):

             """simple docstring"""





             return datasets.MetricInfo(
                 description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
                     {
                         "predictions": datasets.Value("string"),
                         "references": datasets.Value("string"),
                     })  ,homepage="https://github.com/moussaKam/FrugalScore"  ,)



       def 			A_ (  self  ,lowercase):

             """simple docstring"""





             if self.config_name == "default":
                   UpperCAmelCase_	:						str					   =		"moussaKam/frugalscore_tiny_bert-base_bert-score"
             else:
                   UpperCAmelCase_	:						Optional[int]					   =		self.config_name
             UpperCAmelCase_	:						Dict					   =		AutoModelForSequenceClassification.from_pretrained(lowercase)
             UpperCAmelCase_	:						str					   =		AutoTokenizer.from_pretrained(lowercase)



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase=32  ,lowercase=128  ,lowercase=None  ,):

             """simple docstring"""





             assert len(lowercase) == len(
                 lowercase), "predictions and references should have the same number of sentences."
             if device is not None:
                   assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
             else:
                   UpperCAmelCase_	:						Union[str, Any]					   =		"gpu" if torch.cuda.is_available() else "cpu"
             UpperCAmelCase_	:						Optional[Any]					   =		TrainingArguments(
                 "trainer"  ,fpaa=(device == "gpu")  ,per_device_eval_batch_size=lowercase  ,report_to="all"  ,no_cuda=(device == "cpu")  ,log_level="warning"  ,)
             UpperCAmelCase_	:						List[str]					   =		{"sentence1": predictions, "sentence2": references}
             UpperCAmelCase_	:						List[Any]					   =		datasets.Dataset.from_dict(lowercase)

             def tokenize_function(lowercase):
                   return self.tokenizer(
                       data["sentence1"]  ,data["sentence2"]  ,max_length=lowercase  ,truncation=lowercase  ,padding=lowercase)

             UpperCAmelCase_	:						int					   =		raw_datasets.map(lowercase  ,batched=lowercase)
             tokenized_datasets.remove_columns(["sentence1", "sentence2"])
             UpperCAmelCase_	:						Union[str, Any]					   =		Trainer(self.model  ,lowercase  ,tokenizer=self.tokenizer)
             UpperCAmelCase_	:						Tuple					   =		trainer.predict(lowercase)
             return {"scores": list(predictions.predictions.squeeze(-1))}
