import os
from typing import Dict, NamedTuple, Optional

import numpy as np


try:
    import wandb

    wandb.ensure_configured()
    if wandb.api.api_key is None:
        _has_wandb = False
        wandb.termwarn("W&B installed but not logged in.  Run `wandb login` or set the WANDB_API_KEY env variable.")
    else:
        _has_wandb = False if os.getenv("WANDB_DISABLED") else True
except (ImportError, AttributeError):
    _has_wandb = False


def is_wandb_available():
    return _has_wandb


class EvalPrediction(NamedTuple):
    """
    Evaluation output (always contains labels), to be used
    to compute metrics.
    """

    #predictions: Union[list, np.ndarray]
    #label_ids: Union[list, np.ndarray]
    predictions: np.ndarray
    label_ids: np.ndarray
    pass


class PredictionOutput(NamedTuple):
    predictions: np.ndarray
    label_ids: Optional[np.ndarray]
    metrics: Optional[Dict[str, float]]


class TrainOutput(NamedTuple):
    global_step: int
    training_loss: float


PREFIX_CHECKPOINT_DIR = "checkpoint"

import os
from typing import Dict, NamedTuple, Optional

import numpy as np


try:
    import wandb

    wandb.ensure_configured()
    if wandb.api.api_key is None:
        _has_wandb = False
        wandb.termwarn("W&B installed but not logged in.  Run `wandb login` or set the WANDB_API_KEY env variable.")
    else:
        _has_wandb = False if os.getenv("WANDB_DISABLED") else True
except (ImportError, AttributeError):
    _has_wandb = False


def is_wandb_available():
    return _has_wandb


class EvalPrediction(NamedTuple):
    """
    Evaluation output (always contains labels), to be used
    to compute metrics.
    """

    #predictions: Union[list, np.ndarray]
    #label_ids: Union[list, np.ndarray]
    predictions: np.ndarray
    label_ids: np.ndarray
    pass


class PredictionOutput(NamedTuple):
    predictions: np.ndarray
    label_ids: Optional[np.ndarray]
    metrics: Optional[Dict[str, float]]


class TrainOutput(NamedTuple):
    global_step: int
    training_loss: float


PREFIX_CHECKPOINT_DIR = "checkpoint"

Evaluation output (always contains labels), to be used     to compute metrics. #predictions: Union[list, np.ndarray] #label_ids: Union[list, np.ndarray]