scripts/tokenize_stream.py [23:69]:
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@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config we are going to train from.
    """

    model_name_or_path: str = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
                "should only be set to `True` for repositories you trust and in which you have read the code, as it "
                "will execute code present on the Hub on your local machine."
            )
        },
    )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    tasks: List[str] = field(default_factory=list, metadata={"help": "Tasks to train on."})
    preprocess_num_proc: int = field(
        default=1, metadata={"help": "Number of processes to use for preprocessing the data."}
    )
    eval_num_samples: int = field(default=1000, metadata={"help": "Number of samples to use for evaluation."})


LOSS_WEIGHTS = {
    **{task: 10.0 for task in TASK_NAME_TO_ENV_ID.keys() if task.startswith("mujoco")},
    **{task: 50.0 for task in TASK_NAME_TO_ENV_ID.keys() if task.startswith("metaworld")},
    "mujoco-pendulum": 50.0,
    "mujoco-doublependulum": 20.0,
}
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scripts/train_jat.py [29:75]:
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@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config we are going to train from.
    """

    model_name_or_path: str = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
                "should only be set to `True` for repositories you trust and in which you have read the code, as it "
                "will execute code present on the Hub on your local machine."
            )
        },
    )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    tasks: List[str] = field(default_factory=list, metadata={"help": "Tasks to train on."})
    preprocess_num_proc: int = field(
        default=1, metadata={"help": "Number of processes to use for preprocessing the data."}
    )
    eval_num_samples: int = field(default=1000, metadata={"help": "Number of samples to use for evaluation."})


LOSS_WEIGHTS = {
    **{task: 10.0 for task in TASK_NAME_TO_ENV_ID.keys() if task.startswith("mujoco")},
    **{task: 50.0 for task in TASK_NAME_TO_ENV_ID.keys() if task.startswith("metaworld")},
    "mujoco-pendulum": 50.0,
    "mujoco-doublependulum": 20.0,
}
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