"""
Custom handler for HuggingFace Inference Endpoints.
Uses TimesFM 2.5 (200M) installed from GitHub repo.
"""

import numpy as np
import torch
from typing import Any


class EndpointHandler:
    def __init__(self, path: str = ""):
        import timesfm
        from timesfm.timesfm_2p5.timesfm_2p5_torch import (
            TimesFM_2p5_200M_torch_module,
            TimesFM_2p5_200M_torch,
        )
        from safetensors.torch import load_file
        from huggingface_hub import hf_hub_download
        import os

        torch.set_float32_matmul_precision("high")

        # Download weights manually to avoid from_pretrained kwargs issue
        model_id = "google/timesfm-2.5-200m-pytorch"
        weight_file = hf_hub_download(repo_id=model_id, filename="model.safetensors")

        # Create model instance directly
        self.tfm = TimesFM_2p5_200M_torch()
        self.tfm.model = TimesFM_2p5_200M_torch_module()
        self.tfm.model.load_checkpoint(weight_file, torch_compile=False)

        self.tfm.compile(
            timesfm.ForecastConfig(
                max_context=1024,
                max_horizon=128,
                normalize_inputs=True,
                use_continuous_quantile_head=True,
                force_flip_invariance=True,
                infer_is_positive=False,
                fix_quantile_crossing=True,
            )
        )

    def __call__(self, data: dict[str, Any]) -> dict[str, Any]:
        inputs = data.get("inputs", [])
        parameters = data.get("parameters", {})
        horizon = min(parameters.get("horizon", 24), 128)

        if not inputs or not isinstance(inputs, list):
            return {"error": "inputs must be a non-empty list of numbers"}

        input_array = [np.array(inputs, dtype=np.float64)]
        point, quantiles = self.tfm.forecast(horizon=horizon, inputs=input_array)

        return {
            "point_forecast": point[0].tolist(),
            "quantile_forecast": quantiles[0].tolist(),
        }