from engines.inference_engine cimport InferenceEngine
import onnxruntime as onnx
cimport constants_inf

import ast
import os

def _select_providers():
    available = set(onnx.get_available_providers())
    skip_coreml = os.environ.get("SKIP_COREML", "").lower() in ("1", "true", "yes")
    preferred = ["CoreMLExecutionProvider", "CUDAExecutionProvider", "CPUExecutionProvider"]
    if skip_coreml:
        preferred = [p for p in preferred if p != "CoreMLExecutionProvider"]
    selected = [p for p in preferred if p in available]
    return selected or ["CPUExecutionProvider"]

cdef class OnnxEngine(InferenceEngine):
    def __init__(self, model_bytes: bytes, max_batch_size: int = 8, **kwargs):
        InferenceEngine.__init__(self, model_bytes, max_batch_size)

        providers = _select_providers()
        constants_inf.log(<str>f'ONNX providers: {providers}')
        self.session = onnx.InferenceSession(model_bytes, providers=providers)
        self.model_inputs = self.session.get_inputs()
        self.input_name = self.model_inputs[0].name
        self.input_shape = self.model_inputs[0].shape
        if isinstance(self.input_shape[0], int) and self.input_shape[0] > 0:
            self.max_batch_size = self.input_shape[0]
        constants_inf.log(f'AI detection model input: {self.model_inputs} {self.input_shape}')
        model_meta = self.session.get_modelmeta()
        constants_inf.log(f"Metadata: {model_meta.custom_metadata_map}")

        self._resolved_input_hw = self._resolve_input_hw(model_meta.custom_metadata_map)

        self._cpu_session = None
        if any("CoreML" in p for p in self.session.get_providers()):
            constants_inf.log(<str>'CoreML active — creating CPU fallback session')
            self._cpu_session = onnx.InferenceSession(
                model_bytes, providers=["CPUExecutionProvider"])

    cdef tuple _resolve_input_hw(self, object metadata):
        cdef object h = self.input_shape[2] if len(self.input_shape) > 2 else None
        cdef object w = self.input_shape[3] if len(self.input_shape) > 3 else None
        cdef int resolved_h
        cdef int resolved_w

        if isinstance(h, int) and h > 0 and isinstance(w, int) and w > 0:
            return <tuple>(h, w)

        try:
            imgsz = metadata.get("imgsz") if metadata is not None else None
            if imgsz:
                parsed = ast.literal_eval(imgsz)
                if isinstance(parsed, (list, tuple)) and len(parsed) == 2:
                    resolved_h = int(parsed[0])
                    resolved_w = int(parsed[1])
                    if resolved_h > 0 and resolved_w > 0:
                        return <tuple>(resolved_h, resolved_w)
        except Exception:
            pass

        # Dynamic ONNX models are expected to use the project's canonical 1280x1280 input.
        return <tuple>(1280, 1280)

    cdef tuple get_input_shape(self):
        return self._resolved_input_hw

    cdef run(self, input_data):
        try:
            return self.session.run(None, {self.input_name: input_data})
        except Exception:
            if self._cpu_session is not None:
                return self._cpu_session.run(None, {self.input_name: input_data})
            raise
