# MIT License

# Copyright (c) Microsoft

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# SOFTWARE.

# Copyright (c) [2025] [Microsoft]
# SPDX-License-Identifier: MIT
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"
        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(f"{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"))