import json
import struct
from typing import List, Tuple
import numpy as np
import onnxruntime as onnx
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit # required for automatically initialize CUDA, do not remove.


cdef class InferenceEngine:
    def __init__(self, model_bytes: bytes, batch_size: int = 1, **kwargs):
        self.batch_size = batch_size

    cdef tuple get_input_shape(self):
        raise NotImplementedError("Subclass must implement get_input_shape")

    cdef int get_batch_size(self):
        return self.batch_size

    cpdef run(self, input_data):
        raise NotImplementedError("Subclass must implement run")

    cdef get_class_names(self):
        raise NotImplementedError("Subclass must implement get_class_names")


cdef class OnnxEngine(InferenceEngine):
    def __init__(self, model_bytes: bytes, batch_size: int = 1, **kwargs):
        super().__init__(model_bytes, batch_size)
        self.batch_size = batch_size
        self.session = onnx.InferenceSession(model_bytes, providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
        self.model_inputs = self.session.get_inputs()
        self.input_name = self.model_inputs[0].name
        self.input_shape = self.model_inputs[0].shape
        if self.input_shape[0] != -1:
            self.batch_size = self.input_shape[0]
        print(f'AI detection model input: {self.model_inputs} {self.input_shape}')
        model_meta = self.session.get_modelmeta()
        print("Metadata:", model_meta.custom_metadata_map)
        self.class_names = eval(model_meta.custom_metadata_map["names"])

    cdef tuple get_input_shape(self):
        shape = self.input_shape
        return shape[2], shape[3]

    cdef int get_batch_size(self):
        return self.batch_size

    cdef get_class_names(self):
        return self.class_names

    cpdef run(self, input_data):
        return self.session.run(None, {self.input_name: input_data})


cdef class TensorRTEngine(InferenceEngine):
    def __init__(self, model_bytes: bytes, batch_size: int = 4, **kwargs):
        super().__init__(model_bytes, batch_size)
        self.batch_size = batch_size
        print('Enter init TensorRT')
        try:
            logger = trt.Logger(trt.Logger.WARNING)

            metadata_len = struct.unpack("<I", model_bytes[:4])[0]
            try:
                metadata = json.loads(model_bytes[4:4 + metadata_len])
                print(f"Model metadata: {json.dumps(metadata, indent=2)}")
                string_dict = metadata['names']
                self.class_names = {int(k): v for k, v in string_dict.items()}
            except json.JSONDecodeError:
                print(f"Failed to parse metadata")
                return
            engine_data = model_bytes[4 + metadata_len:]


            runtime = trt.Runtime(logger)
            engine = runtime.deserialize_cuda_engine(engine_data)

            if engine is None:
                raise RuntimeError(f"Failed to load TensorRT engine from bytes")

            self.context = engine.create_execution_context()
            # input
            self.input_name = engine.get_tensor_name(0)
            engine_input_shape = engine.get_tensor_shape(self.input_name)
            if engine_input_shape[0] != -1:
                self.batch_size = engine_input_shape[0]

            self.input_shape = [
                self.batch_size,
                engine_input_shape[1],  # Channels (usually fixed at 3 for RGB)
                1280 if engine_input_shape[2] == -1 else engine_input_shape[2],  # Height
                1280 if engine_input_shape[3] == -1 else engine_input_shape[3]  # Width
            ]
            self.context.set_input_shape(self.input_name, self.input_shape)
            input_size = trt.volume(self.input_shape) * np.dtype(np.float32).itemsize
            self.d_input = cuda.mem_alloc(input_size)

            # output
            self.output_name = engine.get_tensor_name(1)
            engine_output_shape = tuple(engine.get_tensor_shape(self.output_name))
            self.output_shape = [
                batch_size if self.input_shape[0] == -1 else self.input_shape[0],
                300 if engine_output_shape[1] == -1 else engine_output_shape[1],  # max detections number
                6 if engine_output_shape[2] == -1 else engine_output_shape[2]  # x1 y1 x2 y2 conf cls
            ]
            self.h_output = cuda.pagelocked_empty(tuple(self.output_shape), dtype=np.float32)
            self.d_output = cuda.mem_alloc(self.h_output.nbytes)

            self.stream = cuda.Stream()

        except Exception as e:
            raise RuntimeError(f"Failed to initialize TensorRT engine: {str(e)}")

    cdef tuple get_input_shape(self):
        return self.input_shape[2], self.input_shape[3]

    cdef int get_batch_size(self):
        return self.batch_size

    cdef get_class_names(self):
        return self.class_names

    cpdef run(self, input_data):
        try:
            cuda.memcpy_htod_async(self.d_input, input_data, self.stream)
            self.context.set_tensor_address(self.input_name, int(self.d_input))  # input buffer
            self.context.set_tensor_address(self.output_name, int(self.d_output))  # output buffer

            self.context.execute_async_v3(stream_handle=self.stream.handle)
            self.stream.synchronize()

            # Fix: Remove the stream parameter from memcpy_dtoh
            cuda.memcpy_dtoh(self.h_output, self.d_output)
            output = self.h_output.reshape(self.output_shape)
            return [output]

        except Exception as e:
            raise RuntimeError(f"Failed to run TensorRT inference: {str(e)}")