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"""Testing topi conv2d operator for VTA"""

import json
import os

import pytest
import numpy as np
from collections import namedtuple

import tvm
from tvm import relay
from tvm import autotvm
from tvm.contrib import util
from tvm.contrib.pickle_memoize import memoize
import topi
import topi.testing
import vta
from vta import program_fpga, reconfig_runtime
import vta.testing
from vta.testing import simulator


Workload = namedtuple("Conv2DWorkload",
                      ['batch', 'height', 'width', 'in_filter', 'out_filter',
                       'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride'])

# Get batch info from env
env = vta.get_env()

# ResNet18 workloads
resnet_wkls = [
    # Workloads of resnet18 on imagenet
    # ('resnet-18.C1',  Workload(env.BATCH, 224, 224, 3,   64,  7, 7, 3, 3, 2, 2)),
    ('resnet-18.C2',  Workload(env.BATCH,  56,  56, 64,  64,  3, 3, 1, 1, 1, 1)),
    ('resnet-18.C3',  Workload(env.BATCH,  56,  56, 64,  128, 3, 3, 1, 1, 2, 2)),
    ('resnet-18.C4',  Workload(env.BATCH,  56,  56, 64,  128, 1, 1, 0, 0, 2, 2)),
    ('resnet-18.C5',  Workload(env.BATCH,  28,  28, 128, 128, 3, 3, 1, 1, 1, 1)),
    ('resnet-18.C6',  Workload(env.BATCH,  28,  28, 128, 256, 3, 3, 1, 1, 2, 2)),
    ('resnet-18.C7',  Workload(env.BATCH,  28,  28, 128, 256, 1, 1, 0, 0, 2, 2)),
    ('resnet-18.C8',  Workload(env.BATCH,  14,  14, 256, 256, 3, 3, 1, 1, 1, 1)),
    ('resnet-18.C9',  Workload(env.BATCH,  14,  14, 256, 512, 3, 3, 1, 1, 2, 2)),
    ('resnet-18.C10', Workload(env.BATCH,  14,  14, 256, 512, 1, 1, 0, 0, 2, 2)),
    ('resnet-18.C11', Workload(env.BATCH,   7,   7, 512, 512, 3, 3, 1, 1, 1, 1)),
]

# FIXME: we need a custom clip operator to circumvent a pattern detection limitation
@tvm.tag_scope(tag=topi.tag.ELEMWISE)
def my_clip(x, a_min, a_max):
    """Unlike topi's current clip, put min and max into two stages."""
    const_min = tvm.const(a_min, x.dtype)
    const_max = tvm.const(a_max, x.dtype)
    x = tvm.compute(x.shape, lambda *i: tvm.min(x(*i), const_max), name="clipA")
    x = tvm.compute(x.shape, lambda *i: tvm.max(x(*i), const_min), name="clipB")
    return x

def run_conv2d(env, remote, wl, target,
               check_correctness=True, print_ir=False,
               samples=4):

    # Workload assertions
    assert wl.hpad == wl.wpad

    # Perform packing only if we are targeting the accelerator
    if "arm_cpu" in target.keys:
        data_pack = False
        layout = "NCHW"
        conv2d_fcompute = topi.arm_cpu.conv2d_nchw_spatial_pack
        conv2d_fschedule = topi.arm_cpu.schedule_conv2d_nchw_spatial_pack
    elif "vta" in target.keys:
        data_pack = True
        layout = "NCHW%dn%dc" % (env.BATCH, env.BLOCK_IN)
        conv2d_fcompute = vta.top.conv2d_packed
        conv2d_fschedule = vta.top.schedule_conv2d_packed

    # Derive shapes depending upon packing
    a_shape = (wl.batch, wl.in_filter, wl.height, wl.width)
    w_shape = (wl.out_filter, wl.in_filter, wl.hkernel, wl.wkernel)
    b_shape = (wl.batch, wl.out_filter, 1, 1)
    if data_pack:
        data_shape = (wl.batch//env.BATCH, wl.in_filter//env.BLOCK_IN,
                      wl.height, wl.width, env.BATCH, env.BLOCK_IN)
        kernel_shape = (wl.out_filter//env.BLOCK_OUT, wl.in_filter//env.BLOCK_IN,
                        wl.hkernel, wl.wkernel, env.BLOCK_OUT, env.BLOCK_IN)
        bias_shape = (wl.batch//env.BATCH, wl.out_filter//env.BLOCK_OUT,
                      1, 1, env.BATCH, env.BLOCK_OUT)
    else:
        data_shape = a_shape
        kernel_shape = w_shape
        bias_shape = b_shape
    data = tvm.placeholder(data_shape, name="data", dtype=env.inp_dtype)
    kernel = tvm.placeholder(kernel_shape, name="kernel", dtype=env.wgt_dtype)
    bias = tvm.placeholder(bias_shape, name="bias", dtype=env.acc_dtype)
    padding = relay.nn.get_pad_tuple2d((wl.hpad, wl.wpad))

    # Define base computation schedule
    with target:
        if data_pack:
            res = conv2d_fcompute(
                data, kernel, (wl.hstride, wl.wstride), padding, (1, 1),
                layout, env.acc_dtype)
        else:
            res = conv2d_fcompute(
                data, kernel, (wl.hstride, wl.wstride), padding, (1, 1),
                env.acc_dtype)
        res = topi.right_shift(res, 8)
        res = topi.add(res, bias)
        res = my_clip(res, 0, (1 << env.OUT_WIDTH - 1) - 1)
        res = topi.cast(res, env.out_dtype)
        # Derive base schedule
        s = conv2d_fschedule([res])
        if print_ir:
            print(vta.lower(s, [data, kernel, bias, res], simple_mode=True))

    # Derive number of ops
    fout_height = (wl.height + 2 * wl.hpad - wl.hkernel) // wl.hstride + 1
    fout_width = (wl.width + 2 * wl.wpad - wl.wkernel) // wl.wstride + 1
    num_ops = 2 * wl.batch * fout_height * fout_width * wl.hkernel * wl.wkernel * wl.out_filter * wl.in_filter

    # @memoize("vta.tests.test_benchmark_topi.conv2d.verify_nhwc")
    def get_ref_data():
        # derive min max for act, wgt, and bias types (max non inclusive)
        a_min, a_max = 0 - (1 << (env.INP_WIDTH - 1)), (1 << (env.INP_WIDTH - 1))
        w_min, w_max = 0 - (1 << (env.WGT_WIDTH - 1)), (1 << (env.WGT_WIDTH - 1))
        b_min, b_max = 0 - 1 << (env.INP_WIDTH + env.WGT_WIDTH - 2), 1 << (env.INP_WIDTH + env.WGT_WIDTH - 2)
        a_np = np.random.randint(a_min, a_max, size=a_shape).astype(data.dtype)
        w_np = np.random.randint(w_min, w_max, size=w_shape).astype(kernel.dtype)
        b_np = np.random.randint(b_min, b_max, size=b_shape).astype(env.acc_dtype)
        r_np = topi.testing.conv2d_nchw_python(
            a_np.astype(env.acc_dtype), w_np.astype(env.acc_dtype), (wl.hstride, wl.wstride), wl.hpad).astype(env.acc_dtype)
        return a_np, w_np, b_np, r_np

    # Data in original format
    data_np, kernel_np, bias_np, res_ref = get_ref_data()
    if data_pack:
        data_np = data_np.reshape(
            wl.batch//env.BATCH, env.BATCH,
            wl.in_filter//env.BLOCK_IN, env.BLOCK_IN,
            wl.height, wl.width).transpose((0, 2, 4, 5, 1, 3))
        kernel_np = kernel_np.reshape(
            wl.out_filter//env.BLOCK_OUT, env.BLOCK_OUT,
            wl.in_filter//env.BLOCK_IN, env.BLOCK_IN,
            wl.hkernel, wl.wkernel).transpose((0, 2, 4, 5, 1, 3))
        bias_np = bias_np.reshape(
            wl.batch//env.BATCH, wl.out_filter//env.BLOCK_OUT,
            1, 1, env.BATCH, env.BLOCK_OUT)

    # Build
    if "vta" in target.keys:
        mod = vta.build(s, [data, kernel, bias, res],
                        target=target,
                        target_host=env.target_host,
                        name="conv2d")
    else:
        mod = tvm.build(s, [data, kernel, bias, res],
                        target=target,
                        target_host=env.target_host,
                        name="conv2d")
    temp = util.tempdir()
    mod.save(temp.relpath("conv2d.o"))
    remote.upload(temp.relpath("conv2d.o"))
    f = remote.load_module("conv2d.o")
    ctx = remote.context(str(target))

    res_np = np.zeros(topi.util.get_const_tuple(res.shape)).astype(res.dtype)
    data_arr = tvm.nd.array(data_np, ctx)
    kernel_arr = tvm.nd.array(kernel_np, ctx)
    bias_arr = tvm.nd.array(bias_np, ctx)
    res_arr = tvm.nd.array(res_np, ctx)
    time_f = f.time_evaluator("conv2d", ctx, number=samples)

    # In vta sim mode, collect simulator runtime statistics
    stats = {}
    cost = None
    if env.TARGET in ["sim", "tsim"]:
        # Check if we're in local RPC mode (allows us to rebuild the
        # runtime on the fly when varying the VTA designs)
        local_rpc = int(os.environ.get("VTA_LOCAL_SIM_RPC", "0"))
        if local_rpc:
            if env.TARGET == "sim":
                remote.get_function("vta.simulator.profiler_clear")()
            else:
                remote.get_function("vta.tsim.profiler_clear")()
            cost = time_f(data_arr, kernel_arr, bias_arr, res_arr)
            if env.TARGET == "sim":
                stats = json.loads(remote.get_function("vta.simulator.profiler_status")())
            else:
                stats = json.loads(remote.get_function("vta.tsim.profiler_status")())
        else:
            simulator.clear_stats()
            cost = time_f(data_arr, kernel_arr, bias_arr, res_arr)
            stats = simulator.stats()
    else:
        cost = time_f(data_arr, kernel_arr, bias_arr, res_arr)

    # Check correctness
    correct = False
    if check_correctness:
        res_orig = res_arr.asnumpy()
        if data_pack:
            res_orig = res_orig.transpose(
                (0, 4, 1, 5, 2, 3)).reshape(wl.batch, wl.out_filter, fout_height, fout_width)
            bias_np = bias_np.transpose(
                (0, 4, 1, 5, 2, 3)).reshape(wl.batch, wl.out_filter, 1, 1)
        res_ref = res_ref >> env.WGT_WIDTH
        res_ref += bias_np
        res_ref = np.clip(res_ref, 0, (1 << env.OUT_WIDTH - 1) - 1)
        res_ref = res_ref.astype(env.out_dtype)
        correct = np.allclose(res_orig, res_ref)

    gops = (num_ops / cost.mean) / float(10 ** 9)
    status = "PASSED" if correct else "FAILED"
    if "arm_cpu" in target.keys:
        device = "CPU"
    elif "vta" in target.keys:
        device = "VTA"
    print("%s CONV2D TEST %s: Time cost = %g sec/op, %g GOPS" % (device, status, cost.mean, gops))

    return correct, cost, stats

@pytest.mark.parametrize("device", ["vta", "arm_cpu"])
def test_conv2d(device):
    def _run(env, remote):
        if device == "vta":
            target = env.target
            if env.TARGET not in ["sim", "tsim"]:
                assert tvm.runtime.enabled("rpc")
                program_fpga(remote, bitstream=None)
                reconfig_runtime(remote)
        elif device == "arm_cpu":
            target = env.target_vta_cpu
        with autotvm.tophub.context(target): # load pre-tuned schedule parameters
            for _, wl in resnet_wkls:
                print(wl)
                run_conv2d(env, remote, wl, target)
    vta.testing.run(_run)

if __name__ == "__main__":
    test_conv2d(device="arm_cpu")
    test_conv2d(device="vta")