import numpy as np
import random
import torch


def set_seed(
    seed: int,
    deterministic: bool = False,
):
    """
    Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.

    Args:
        seed (`int`):
            The seed to set.
        deterministic (`bool`, *optional*, defaults to `False`):
            Whether to use deterministic algorithms where available. Can slow down training.
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)

    if deterministic:
        torch.use_deterministic_algorithms(True, warn_only=True)
        torch.backends.cudnn.benchmark = False


def merge_dict_list(
    dict_list,
):
    if len(dict_list) == 1:
        return dict_list[0]

    merged_dict = {}
    for k, v in dict_list[0].items():
        if isinstance(v, torch.Tensor):
            if v.ndim == 0:
                merged_dict[k] = torch.stack([d[k] for d in dict_list], dim=0)
            else:
                merged_dict[k] = torch.cat([d[k] for d in dict_list], dim=0)
        else:
            # for non-tensor values, we just copy the value from the first item
            merged_dict[k] = v
    return merged_dict


def format_dict(dict_obj, indent: int = 4, indent_per_level: int = 4) -> str:
    # format a dict into string for one item per line
    formatted_str = ""
    for k, v in dict_obj.items():
        if isinstance(v, dict):
            formatted_str += f"{' ' * indent}{k}:\n{format_dict(v, indent=indent+indent_per_level, indent_per_level=indent_per_level)}"
        else:
            formatted_str += f"{' ' * indent}{k}: {v}\n"
    formatted_str = "{\n" + formatted_str + " " * (indent - indent_per_level) + "}\n"
    return formatted_str