from typing import *
import torch
import torch.nn as nn
from .. import models


class Pipeline:
    """
    A base class for pipelines.
    """
    def __init__(
        self,
        models: dict[str, nn.Module] = None,
    ):
        if models is None:
            return
        self.models = models
        for model in self.models.values():
            model.eval()

    @staticmethod
    def from_pretrained(path: str) -> "Pipeline":
        """
        Load a pretrained model.
        """
        import os
        import json
        is_local = os.path.exists(f"{path}/pipeline.json")

        if is_local:
            config_file = f"{path}/pipeline.json"
            with open(config_file, 'r') as f:
                #print("Loading pipeline.json:")
                args = json.load(f)['args']
                #print("Models in pipeline.json:")
                '''for k, v in args['models'].items():
                    print(f"  {k}: {v}")'''

            # For local paths, construct the models dict differently
            _models = {
                k: models.from_pretrained(os.path.join(path, v))
                for k, v in args['models'].items()
            }
        else:
            from huggingface_hub import hf_hub_download
            config_file = hf_hub_download(path, "pipeline.json")
            with open(config_file, 'r') as f:
                args = json.load(f)['args']

            _models = {
                k: models.from_pretrained(os.path.join(path, v))
                for k, v in args['models'].items()
            }

        new_pipeline = Pipeline(_models)
        new_pipeline._pretrained_args = args
        return new_pipeline

    @property
    def device(self) -> torch.device:
        for model in self.models.values():
            if hasattr(model, 'device'):
                return model.device
        for model in self.models.values():
            if hasattr(model, 'parameters'):
                return next(model.parameters()).device
        raise RuntimeError("No device found.")

    def to(self, device: torch.device) -> None:
        for model in self.models.values():
            model.to(device)

    def cuda(self) -> None:
        self.to(torch.device("cuda"))

    def cpu(self) -> None:
        self.to(torch.device("cpu"))