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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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import tempfile
import tvm
import tvm.testing
from tvm import te, runtime
import numpy as np
import json
from tvm import rpc
from tvm import relay
from tvm.contrib import utils, graph_executor


@tvm.testing.requires_llvm
def test_graph_simple():
    n = 4
    A = te.placeholder((n,), name="A")
    B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name="B")
    s = te.create_schedule(B.op)

    node0 = {"op": "null", "name": "x", "inputs": []}
    node1 = {
        "op": "tvm_op",
        "name": "add",
        "inputs": [[0, 0, 0]],
        "attrs": {"func_name": "myadd", "flatten_data": "1", "num_inputs": "1", "num_outputs": "1"},
    }
    nodes = [node0, node1]
    arg_nodes = [0]
    node_row_ptr = [0, 1, 2]
    outputs = [[1, 0, 0]]
    shape = (4,)
    attrs = {
        "shape": ["list_shape", [shape, shape]],
        "dltype": ["list_str", ["float32", "float32"]],
        "storage_id": ["list_int", [0, 1]],
    }
    graph = {
        "nodes": nodes,
        "arg_nodes": arg_nodes,
        "node_row_ptr": node_row_ptr,
        "heads": outputs,
        "attrs": attrs,
    }
    graph = json.dumps(graph)

    def check_verify():
        mlib = tvm.build(s, [A, B], "llvm", name="myadd")
        mod = graph_executor.create(graph, mlib, tvm.cpu(0))
        a = np.random.uniform(size=(n,)).astype(A.dtype)
        mod.run(x=a)
        out = mod.get_output(0, tvm.nd.empty((n,)))
        np.testing.assert_equal(out.numpy(), a + 1)

    def check_remote(server):
        mlib = tvm.build(s, [A, B], "llvm", name="myadd")
        remote = rpc.connect(server.host, server.port)
        temp = utils.tempdir()
        dev = remote.cpu(0)
        path_dso = temp.relpath("dev_lib.so")
        mlib.export_library(path_dso)
        remote.upload(path_dso)
        mlib = remote.load_module("dev_lib.so")
        mod = graph_executor.create(graph, mlib, remote.cpu(0))
        a = np.random.uniform(size=(n,)).astype(A.dtype)
        mod.run(x=tvm.nd.array(a, dev))
        out = tvm.nd.empty((n,), device=dev)
        out = mod.get_output(0, out)
        np.testing.assert_equal(out.numpy(), a + 1)

    def check_sharing():
        x = relay.var("x", shape=(1, 10))
        y = relay.var("y", shape=(1, 10))
        z = relay.add(x, y)
        func = relay.Function([x, y], z)

        x_in = np.ones((1, 10)).astype("float32")
        params = {"x": x_in}
        graph, lib, params = relay.build(func, target="llvm", params=params)

        mod_shared = graph_executor.create(graph, lib, tvm.cpu(0))
        mod_shared.load_params(runtime.save_param_dict(params))
        num_mods = 10
        mods = [graph_executor.create(graph, lib, tvm.cpu(0)) for _ in range(num_mods)]

        for mod in mods:
            mod.share_params(mod_shared, runtime.save_param_dict(params))

        a = np.random.uniform(size=(1, 10)).astype("float32")
        for mod in mods:
            mod.run(y=a)
            out = mod.get_output(0, tvm.nd.empty((1, 10)))
            np.testing.assert_equal(out.numpy(), x_in + a)

        # Explicitly delete the shared module and verify correctness.
        del mod_shared
        for mod in mods:
            mod.run(y=a)
            out = mod.get_output(0, tvm.nd.empty((1, 10)))
            np.testing.assert_equal(out.numpy(), x_in + a)
            del mod

    check_verify()
    check_remote(rpc.Server("127.0.0.1"))
    check_sharing()


def test_load_unexpected_params():
    # Test whether graph_executor.load_params works if parameters
    # are provided that are not an expected input.
    mod = tvm.IRModule()
    params = {}
    x = relay.var("x", shape=(1, 10))
    y = relay.var("y", shape=(1, 10))
    z = relay.add(x, y)
    mod["main"] = relay.Function([x, y], z)

    graph_module = relay.build(mod, target="llvm", params=params)
    rt_mod = tvm.contrib.graph_executor.create(
        graph_module.get_graph_json(), graph_module.get_lib(), tvm.cpu(0)
    )

    new_params = graph_module.get_params()
    new_params.update({"y_unknown": np.ones((1,)).astype("float32")})
    rt_mod.load_params(runtime.save_param_dict(new_params))


def test_save_load_file():
    p = np.random.randn(10)
    params = {"x": p}

    with tempfile.NamedTemporaryFile() as fp:
        tvm.runtime.save_param_dict_to_file(params, fp.name)
        params_loaded = tvm.runtime.load_param_dict_from_file(fp.name)

        assert "x" in params_loaded
        np.testing.assert_equal(p, params_loaded["x"].numpy())


if __name__ == "__main__":
    tvm.testing.main()