import os
import tensorrt as trt

TRT_LOGGER = trt.Logger()


def get_engine(engine_file_path):
    print("Reading engine from file {}".format(engine_file_path))
    with open(engine_file_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
        return runtime.deserialize_cuda_engine(f.read())


class HostDeviceMem(object):
    def __init__(self, host_mem, device_mem):
        self.host = host_mem
        self.device = device_mem

    def __str__(self):
        return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)

    def __repr__(self):
        return self.__str__()


import pycuda.autoinit
import pycuda.driver as cuda


def allocator(engine):
    inputs = []
    outputs = []
    bindings = []
    stream = cuda.Stream()
    for binding in engine:
        size = (
            trt.volume(engine.get_binding_shape(binding))
            * engine.max_batch_size
        )
        dtype = trt.nptype(engine.get_binding_dtype(binding))
        # Allocate host and device buffers
        host_mem = cuda.pagelocked_empty(size, dtype)
        device_mem = cuda.mem_alloc(host_mem.nbytes)
        # Append the device buffer to device bindings.
        bindings.append(int(device_mem))
        # Append to the appropriate list.
        if engine.binding_is_input(binding):
            inputs.append(HostDeviceMem(host_mem, device_mem))
        else:
            outputs.append(HostDeviceMem(host_mem, device_mem))

    return inputs, outputs, bindings, stream


def do_inference(context, bindings, inputs, outputs, stream):
    # Transfer input data to the GPU.
    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
    # Run inference.
    context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
    # Transfer predictions back from the GPU.
    [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
    # Synchronize the stream
    stream.synchronize()
    # Return only the host outputs.
    return [out.host for out in outputs]


from abottle.base_model import BaseModel


class TensorRTModel(BaseModel):
    class Config:
        trt_file = ""

    def __init__(self):
        self.engine = get_engine(self.Config.trt_file)
        self.context = self.engine.create_execution_context()
        self.inputs, self.outputs, self.bindings, self.stream = allocator(self.engine)
    def __del__(self):
        del self.context, self.engine

    def infer(self, X={}, Y=[]):
        for i, (k, v) in enumerate(X.items()):
            self.inputs[i].host = v
        trt_output = np.array(do_inference(
            self.context,
            bindings=self.bindings, 
            inputs=self.inputs, 
            outputs=self.outputs, 
            stream=self.stream
        ))
        outputs = {}
        for i, y in enumerate(Y):
            outputs[y] = trt_output[i]
        return outputs
