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import tensorrt as trt
import os

import pycuda.driver as cuda
import pycuda.autoinit
from PIL import Image
import numpy as np

# Returns a numpy buffer of shape (num_images, 1, 28, 28)
def load_mnist_data(filepath):
    with open(filepath, "rb") as f:
        raw_buf = np.fromstring(f.read(), dtype=np.uint8)
    # Make sure the magic number is what we expect
    assert raw_buf[0:4].view(">i4")[0] == 2051
    num_images = raw_buf[4:8].view(">i4")[0]
    image_c = 1
    image_h = raw_buf[8:12].view(">i4")[0]
    image_w = raw_buf[12:16].view(">i4")[0]
    # Need to scale all values to the range of [0, 1]
    return np.ascontiguousarray((raw_buf[16:] / 255.0).astype(np.float32).reshape(num_images, image_c, image_h, image_w))

# Returns a numpy buffer of shape (num_images)
def load_mnist_labels(filepath):
    with open(filepath, "rb") as f:
        raw_buf = np.fromstring(f.read(), dtype=np.uint8)
    # Make sure the magic number is what we expect
    assert raw_buf[0:4].view(">i4")[0] == 2049
    num_labels = raw_buf[4:8].view(">i4")[0]
    return np.ascontiguousarray(raw_buf[8:].astype(np.int32).reshape(num_labels))

class MNISTEntropyCalibrator(trt.IInt8EntropyCalibrator2):
    def __init__(self, training_data, cache_file, batch_size=64):
        # Whenever you specify a custom constructor for a TensorRT class,
        # you MUST call the constructor of the parent explicitly.
        trt.IInt8EntropyCalibrator2.__init__(self)

        self.cache_file = cache_file

        # Every time get_batch is called, the next batch of size batch_size will be copied to the device and returned.
        self.data = load_mnist_data(training_data)
        self.batch_size = batch_size
        self.current_index = 0

        # Allocate enough memory for a whole batch.
        self.device_input = cuda.mem_alloc(self.data[0].nbytes * self.batch_size)

    def get_batch_size(self):
        return self.batch_size

    # TensorRT passes along the names of the engine bindings to the get_batch function.
    # You don't necessarily have to use them, but they can be useful to understand the order of
    # the inputs. The bindings list is expected to have the same ordering as 'names'.
    def get_batch(self, names):
        if self.current_index + self.batch_size > self.data.shape[0]:
            return None

        current_batch = int(self.current_index / self.batch_size)
        if current_batch % 10 == 0:
            print("Calibrating batch {:}, containing {:} images".format(current_batch, self.batch_size))

        batch = self.data[self.current_index:self.current_index + self.batch_size].ravel()
        cuda.memcpy_htod(self.device_input, batch)
        self.current_index += self.batch_size
        return [self.device_input]

    def read_calibration_cache(self):
        # If there is a cache, use it instead of calibrating again. Otherwise, implicitly return None.
        if os.path.exists(self.cache_file):
            with open(self.cache_file, "rb") as f:
                return f.read()

    def write_calibration_cache(self, cache):
        with open(self.cache_file, "wb") as f:
            f.write(cache)
