

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    (					_SCREAMING_SNAKE_CASE   :						Optional[int]	,				_SCREAMING_SNAKE_CASE   :						Dict	,				_SCREAMING_SNAKE_CASE   :						List[str]	,				_SCREAMING_SNAKE_CASE   :						List[Any]	,				_SCREAMING_SNAKE_CASE   :						str	,				_SCREAMING_SNAKE_CASE   :						Optional[Any]    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  states.reshape(1	,				-1	,				model.config.state_dim    )
    SCREAMING_SNAKE_CASE_													=  actions.reshape(1	,				-1	,				model.config.act_dim    )
    SCREAMING_SNAKE_CASE_													=  returns_to_go.reshape(1	,				-1	,				1    )
    SCREAMING_SNAKE_CASE_													=  timesteps.reshape(1	,				-1    )

    if model.config.max_length is not None:
        SCREAMING_SNAKE_CASE_													=  states[:, -model.config.max_length :]
        SCREAMING_SNAKE_CASE_													=  actions[:, -model.config.max_length :]
        SCREAMING_SNAKE_CASE_													=  returns_to_go[:, -model.config.max_length :]
        SCREAMING_SNAKE_CASE_													=  timesteps[:, -model.config.max_length :]

        # pad all tokens to sequence length
        SCREAMING_SNAKE_CASE_													=  torch.cat(
            [torch.zeros(model.config.max_length - states.shape[1]    ), torch.ones(states.shape[1]    )]    )
        SCREAMING_SNAKE_CASE_													=  attention_mask.to(dtype=torch.long	,				device=states.device    ).reshape(1	,				-1    )
        SCREAMING_SNAKE_CASE_													=  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    )
        SCREAMING_SNAKE_CASE_													=  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    )
        SCREAMING_SNAKE_CASE_													=  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    )
        SCREAMING_SNAKE_CASE_													=  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:
        SCREAMING_SNAKE_CASE_													=  None

    SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  model(
        states=_SCREAMING_SNAKE_CASE	,				actions=_SCREAMING_SNAKE_CASE	,				rewards=_SCREAMING_SNAKE_CASE	,				returns_to_go=_SCREAMING_SNAKE_CASE	,				timesteps=_SCREAMING_SNAKE_CASE	,				attention_mask=_SCREAMING_SNAKE_CASE	,				return_dict=_SCREAMING_SNAKE_CASE	,				)

    return action_preds[0, -1]


# build the environment

UpperCamelCase__					: Optional[Any]								=      gym.make("Hopper-v3")
UpperCamelCase__					: Tuple								=      env.observation_space.shape[0]
UpperCamelCase__					: Any								=      env.action_space.shape[0]
UpperCamelCase__					: Optional[Any]								=      1_000
UpperCamelCase__					: Optional[Any]								=      "cuda"
UpperCamelCase__					: Tuple								=      10_00.0  # normalization for rewards/returns
UpperCamelCase__					: Tuple								=      3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase__					: int								=      np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
UpperCamelCase__					: Optional[int]								=      np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
UpperCamelCase__					: Union[str, Any]								=      torch.from_numpy(state_mean).to(device=device)
UpperCamelCase__					: List[str]								=      torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
UpperCamelCase__					: int								=      DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
UpperCamelCase__					: Dict								=      model.to(device)
model.eval()

for ep in range(10):
       UpperCamelCase__		,	UpperCamelCase__					: Optional[int]								=      0, 0
       UpperCamelCase__					: str								=      env.reset()
       UpperCamelCase__					: Union[str, Any]								=      torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
       UpperCamelCase__					: str								=      torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
       UpperCamelCase__					: Tuple								=      torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
       UpperCamelCase__					: Tuple								=      torch.zeros(0, device=device, dtype=torch.floataa)

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

              UpperCamelCase__					: List[Any]								=      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__					: Optional[Any]								=      action
              UpperCamelCase__					: Optional[int]								=      action.detach().cpu().numpy()

              UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__					: int								=      env.step(action)

              UpperCamelCase__					: Optional[int]								=      torch.from_numpy(state).to(device=device).reshape(1, state_dim)
              UpperCamelCase__					: Dict								=      torch.cat([states, cur_state], dim=0)
              UpperCamelCase__					: Union[str, Any]								=      reward

              UpperCamelCase__					: Optional[Any]								=      target_return[0, -1] - (reward / scale)
              UpperCamelCase__					: Tuple								=      torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
              UpperCamelCase__					: Any								=      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 functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def    _UpperCAmelCase    (					):





    """simple docstring"""


    def filter_log(_SCREAMING_SNAKE_CASE   :						Dict    ):
        return False if "This IS expected if you are initializing" in record.msg else True

    SCREAMING_SNAKE_CASE_													=  datasets.utils.logging.get_logger('transformers.modeling_utils'    )
    logger.addFilter(_SCREAMING_SNAKE_CASE    )
    try:
        yield
    finally:
        logger.removeFilter(_SCREAMING_SNAKE_CASE    )


UpperCamelCase__					: List[str]								=      "\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n"

UpperCamelCase__					: Dict								=      "\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

UpperCamelCase__					: Dict								=      "\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"





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

  def   lowerCAmelCase__       (   self):
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,						citation=_CITATION  ,						homepage='https://github.com/Tiiiger/bert_score'  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(
              {
                  'predictions': datasets.Value('string'  ,						id='sequence'),
                  'references': datasets.Sequence(datasets.Value('string'  ,						id='sequence')  ,						id='references'),
              })  ,						codebase_urls=['https://github.com/Tiiiger/bert_score']  ,						reference_urls=[
              'https://github.com/Tiiiger/bert_score',
              'https://arxiv.org/abs/1904.09675',
          ]  ,						)

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A  ,						_A=None  ,						_A=None  ,						_A=None  ,						_A=False  ,						_A=False  ,						_A=None  ,						_A=64  ,						_A=4  ,						_A=False  ,						_A=False  ,						_A=None  ,						_A=False  ,						):
      SCREAMING_SNAKE_CASE_													=  bert_score.utils.get_hash
      SCREAMING_SNAKE_CASE_													=  bert_score.BERTScorer

      if version.parse(bert_score.__version__) >= version.parse('0.3.10'):
          SCREAMING_SNAKE_CASE_													=  functools.partial(_A  ,						use_fast_tokenizer=_A)
          SCREAMING_SNAKE_CASE_													=  functools.partial(_A  ,						use_fast_tokenizer=_A)
      elif use_fast_tokenizer:
          raise ImportWarning(
              'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
              'You can install it with `pip install "bert-score>=0.3.10"`.')

      if model_type is None:
          assert lang is not None, "either lang or model_type should be specified"
          SCREAMING_SNAKE_CASE_													=  bert_score.utils.langamodel[lang.lower()]

      if num_layers is None:
          SCREAMING_SNAKE_CASE_													=  bert_score.utils.modelalayers[model_type]

      SCREAMING_SNAKE_CASE_													=  get_hash(
          model=_A  ,						num_layers=_A  ,						idf=_A  ,						rescale_with_baseline=_A  ,						use_custom_baseline=baseline_path is not None  ,						)

      with filter_logging_context():
          if not hasattr(self  ,						'cached_bertscorer') or self.cached_bertscorer.hash != hashcode:
              SCREAMING_SNAKE_CASE_													=  scorer(
                  model_type=_A  ,						num_layers=_A  ,						batch_size=_A  ,						nthreads=_A  ,						all_layers=_A  ,						idf=_A  ,						device=_A  ,						lang=_A  ,						rescale_with_baseline=_A  ,						baseline_path=_A  ,						)

      ((SCREAMING_SNAKE_CASE_)							,     (SCREAMING_SNAKE_CASE_)							,     (SCREAMING_SNAKE_CASE_))													=  self.cached_bertscorer.score(
          cands=_A  ,						refs=_A  ,						verbose=_A  ,						batch_size=_A  ,						)
      SCREAMING_SNAKE_CASE_													=  {
          'precision': P.tolist(),
          'recall': R.tolist(),
          'f1': F.tolist(),
          'hashcode': hashcode,
      }
      return output_dict

  def   lowerCAmelCase__       (   self  ,						_A=None  ,						_A=None  ,						**_A):
      # References can be strings or lists of strings
      # Let's change strings to lists of strings with one element
      if references is not None:
          SCREAMING_SNAKE_CASE_													=  [[ref] if isinstance(_A  ,						_A) else ref for ref in references]
      super().add_batch(predictions=_A  ,						references=_A  ,						**_A)


  def   lowerCAmelCase__       (   self  ,						_A=None  ,						_A=None  ,						**_A):
      # References can be strings or lists of strings
      # Let's change strings to lists of strings with one element
      if isinstance(_A  ,						_A):
          SCREAMING_SNAKE_CASE_													=  [reference]
      super().add(prediction=_A  ,						reference=_A  ,						**_A)


