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import numpy as np
import pytest
import tvm
from tvm import te
import scipy
from tvm import relay
from tvm.relay import transform
from tvm.relay.testing import ctx_list, run_infer_type
import topi.testing
from tvm.contrib.nvcc import have_fp16


def sigmoid(x):
    one = np.ones_like(x)
    return one / (one + np.exp(-x))

def relu(x):
    x_copy = np.copy(x)
    np.maximum(x_copy, 0, x_copy)
    return x_copy

def rsqrt(x):
    one = np.ones_like(x)
    return one / np.sqrt(x)

def test_unary_op():
    def check_single_op(opfunc, ref, dtype):
        shape = (10, 4)
        dtype = dtype
        tp = relay.TensorType(shape)
        x = relay.var("x", tp, dtype=dtype)
        y = opfunc(x)
        # test printer
        assert ("{}(%x)".format(y.op.name)) in y.astext()
        # test type inference
        yy = run_infer_type(y)
        assert yy.checked_type == tp

        if ref is not None:
            data = np.random.rand(*shape).astype(dtype)
            ref_res = ref(data)
            func = relay.Function([x], y)
            for target, ctx in ctx_list():
                # use graph by execuor default for testing, as we need
                # create function explicitly to avoid constant-folding.
                if dtype ==  'float16' and target == 'cuda' and not have_fp16(tvm.gpu(0).compute_version):
                    continue
                intrp = relay.create_executor("graph", ctx=ctx, target=target)
                op_res = intrp.evaluate(func)(data)
                np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=0.01)


    for opfunc, ref in [(tvm.relay.log, np.log),
                        (tvm.relay.exp, np.exp),
                        (tvm.relay.erf, scipy.special.erf),
                        (tvm.relay.sqrt, np.sqrt),
                        (tvm.relay.rsqrt, rsqrt),
                        (tvm.relay.sigmoid, sigmoid),
                        (tvm.relay.tanh, np.tanh),
                        (relay.nn.relu, relu),
                        (tvm.relay.cos, np.cos),
                        (tvm.relay.sin, np.sin),
                        (tvm.relay.tan, np.tan),
                        (tvm.relay.atan, np.arctan)]:
        for dtype in ['float16', 'float32']:
            check_single_op(opfunc, ref, dtype)


def test_binary_op():
    def inst(vars, sh):
        return [vars.get(s, s) for s in sh]

    def check_binary_op(opfunc, ref, dtype):
        # TODO(@jroesch): this piece of code improperly uses type variables.
        n = te.var("n")
        s1 = (5, n, 5)
        s2 = (n, 1)
        t1 = relay.TensorType(s1)
        t2 = relay.TensorType(s2)
        x = relay.var("x", t1, dtype=dtype)
        y = relay.var("y", t2, dtype=dtype)
        z = opfunc(x, y)
        # test printer
        assert ("{}(%x, %y)".format(z.op.name)) in z.astext()
        zz = run_infer_type(z)
        assert zz.checked_type == t1

        if ref is not None:
            t1 = relay.TensorType((5, 10, 5))
            t2 = relay.TensorType((5, 10, 5))
            x = relay.var("x", t1, dtype=dtype)
            y = relay.var("y", t2, dtype=dtype)
            z = opfunc(x, y)
            x_data = np.random.rand(5, 10, 5).astype(dtype)
            y_data = np.random.rand(5, 10, 5).astype(dtype)
            ref_res = ref(x_data, y_data)
            func = relay.Function([x, y], z)

            for target, ctx in ctx_list():
                # use graph by execuor default for testing, as we need
                # create function explicitly to avoid constant-folding.
                if dtype ==  'float16' and target == 'cuda' and not have_fp16(tvm.gpu(0).compute_version):
                    continue
                intrp = relay.create_executor("graph", ctx=ctx, target=target)
                op_res = intrp.evaluate(func)(x_data, y_data)
                np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=0.01)

    for opfunc, ref in [(relay.add, np.add),
                        (relay.subtract, np.subtract),
                        (relay.multiply, np.multiply),
                        (relay.divide, np.divide),
                        (relay.floor_divide, np.floor_divide),
                        (relay.floor_mod, np.fmod)]:
        for dtype in ['float16', 'float32']:
            check_binary_op(opfunc, ref, dtype)


def test_expand_dims():
    # based on topi test
    def verify_expand_dims(dshape, dtype, oshape, axis, num_newaxis):
        x = relay.Var("x", relay.TensorType(dshape, dtype))
        func = relay.Function([x], relay.expand_dims(x, axis, num_newaxis))
        for target, ctx in ctx_list():
            if dtype ==  'float16' and target == 'cuda' and not have_fp16(tvm.gpu(0).compute_version):
                continue
            data = np.random.uniform(size=dshape).astype(dtype)
            ref_res = data.reshape(oshape)
            intrp = relay.create_executor("graph", ctx=ctx, target=target)
            op_res = intrp.evaluate(func)(data)
            np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=0.01)
    for dtype in ['float16', 'float32']:
        verify_expand_dims((3, 10), dtype, (3, 10, 1, 1), 2, 2)
        verify_expand_dims((3, 10), dtype, (1, 3, 10), -3, 1)


def test_bias_add():
    for dtype in ['float16', 'float32']:
        xshape=(10, 2, 3, 4)
        bshape=(2,)
        rtol = 1e-2 if dtype == 'float16' else 1e-5
        x = relay.var("x", shape=xshape, dtype=dtype)
        bias = relay.var("bias", dtype=dtype)
        z = relay.nn.bias_add(x, bias)
        zz = run_infer_type(z)
        assert "axis=" not in zz.astext()
        assert zz.args[1].checked_type == relay.TensorType(bshape, dtype)

        func = relay.Function([x, bias], z)
        x_data = np.random.uniform(size=xshape).astype(dtype)
        y_data = np.random.uniform(size=bshape).astype(dtype)
        ref_res = x_data + y_data.reshape((2, 1, 1))
        for target, ctx in ctx_list():
            if dtype ==  'float16' and target == 'cuda' and not have_fp16(tvm.gpu(0).compute_version):
                continue
            intrp = relay.create_executor("graph", ctx=ctx, target=target)
            op_res = intrp.evaluate(func)(x_data, y_data)
            np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=rtol)


def test_expand_dims_infer_type():
    for dtype in ['float16', 'float32']:
        n, t, d = te.size_var("n"), te.size_var("t"), 100
        x = relay.var("x", shape=(n, t, d), dtype=dtype)
        y = relay.expand_dims(x, axis=2)
        assert "axis=2" in y.astext()
        yy = run_infer_type(y)
        assert yy.checked_type == relay.TensorType((n, t, 1, 100), dtype)


def test_softmax():
    for dtype in ['float16', 'float32']:
        # Softmax accuracy for float16 is poor
        if dtype == 'float16':
            return
        shape = (10, 4)
        x = relay.var("x", shape=shape, dtype=dtype)
        y = relay.nn.softmax(x, axis=1)
        assert "nn.softmax" in y.astext()
        yy = run_infer_type(y)
        assert yy.checked_type == relay.TensorType(shape, dtype)
        func = relay.Function([x], y)
        x_data = np.random.uniform(size=shape).astype(dtype)
        ref_res = topi.testing.softmax_python(x_data)
        for target, ctx in ctx_list():
            intrp = relay.create_executor("graph", ctx=ctx, target=target)
            op_res = intrp.evaluate(func)(x_data)
            np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)


def test_log_softmax():
    for dtype in ['float16', 'float32']:
        # Softmax accuracy for float16 is poor
        if dtype == 'float16':
            return
        shape = (10, 4)
        x = relay.var("x", shape=shape, dtype=dtype)
        y = relay.nn.log_softmax(x, axis=1)
        assert "nn.log_softmax" in y.astext()
        yy = run_infer_type(y)
        assert yy.checked_type == relay.TensorType(shape, dtype)
        func = relay.Function([x], y)
        x_data = np.random.uniform(size=shape).astype(dtype)
        ref_res = topi.testing.log_softmax_python(x_data)
        for target, ctx in ctx_list():
            intrp = relay.create_executor("graph", ctx=ctx, target=target)
            op_res = intrp.evaluate(func)(x_data)
            np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)


def test_concatenate():
    for dtype in ['float16', 'float32']:
        n, t, d = te.size_var("n"), te.size_var("t"), 100
        x = relay.var("x", shape=(n, t, d))
        y = relay.var("y", shape=(n, t, d))
        z = relay.concatenate((x, y), axis=-1)
        assert "axis=" in z.astext()
        zz = run_infer_type(z)
        assert zz.checked_type == relay.TensorType((n, t, 200))

        x = relay.exp(x)
        z = relay.concatenate((x, y), axis=2)
        zz = run_infer_type(z)
        assert zz.checked_type == relay.TensorType((n, t, 200))

        z = relay.concatenate((x, y), axis=1)
        zz = run_infer_type(z)
        assert zz.checked_type == relay.TensorType((n, t + t, 100))

        # check shape mismatches (the following case is expected to raise tvm._ffi.base.TVMError.
        try:
            x = relay.var('p1', shape=(2, 5))
            y = relay.var('p2', shape=(2, 3))
            c = relay.concatenate([x, y], axis=0)
            func = relay.Function([x, y], c)
            zz = run_infer_type(func)
        except tvm._ffi.base.TVMError:
            pass
        else:
            assert False

        x = relay.var("x", shape=(10, 5), dtype=dtype)
        y = relay.var("y", shape=(10, 5), dtype=dtype)
        t = relay.var("z", shape=(), dtype=dtype)
        z = relay.concatenate((x, y), axis=1)
        z = relay.add(z, t)
        # Check result.
        func = relay.Function([x, y, t], z)
        x_data = np.random.rand(10, 5).astype(dtype)
        y_data = np.random.rand(10, 5).astype(dtype)
        t_data = np.random.uniform(size=()).astype(dtype)
        ref_res = np.concatenate((x_data, y_data), axis=1) + t_data

        for target, ctx in ctx_list():
            if dtype ==  'float16' and target == 'cuda' and not have_fp16(tvm.gpu(0).compute_version):
                continue
            intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
            intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
            op_res1 = intrp1.evaluate(func)(x_data, y_data, t_data)
            tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=0.01)
            op_res2 = intrp2.evaluate(func)(x_data, y_data, t_data)
            tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=0.01)

def test_dropout():
    for dtype in ['float16', 'float32']:
        n, t, d = te.size_var("n"), te.size_var("t"), te.size_var("d")
        input_ty = relay.TensorType((n, t, d), dtype)
        x = relay.var("x", input_ty)
        y = relay.nn.dropout(x, rate=0.75)
        assert "rate=" in y.astext()
        yy = run_infer_type(y)
        assert yy.checked_type == input_ty


def test_batch_norm():
    for dtype in ['float16', 'float32']:
        # beta and gamma ignored
        data = relay.var("data", relay.TensorType((3, 2, 1), dtype))
        beta = relay.var("beta", relay.TensorType((2,), dtype))
        gamma = relay.var("gamma", relay.TensorType((2,), dtype))
        moving_mean = relay.var("moving_mean", relay.TensorType((2,), dtype))
        moving_var = relay.var("moving_var", relay.TensorType((2,), dtype))
        y = relay.nn.batch_norm(data, gamma, beta, moving_mean, moving_var,
                                center=False, scale=False)
        yy = run_infer_type(y.astuple())
        assert "center=" in yy.astext()
        assert yy.checked_type == relay.ty.TupleType(tvm.runtime.convert([
            relay.TensorType((3, 2, 1), dtype),
            relay.TensorType((2,), dtype),
            relay.TensorType((2,), dtype)
        ]))

        beta = relay.var("beta", relay.TensorType((3,), dtype))
        gamma = relay.var("gamma", relay.TensorType((3,), dtype))
        moving_mean = relay.var("moving_mean", relay.TensorType((3,), dtype))
        moving_var = relay.var("moving_var", relay.TensorType((3,), dtype))

        y = relay.nn.batch_norm(data, gamma, beta, moving_mean, moving_var,
                                axis=0, center=False, scale=False)
        yy = run_infer_type(y.astuple())
        assert yy.checked_type == relay.ty.TupleType(tvm.runtime.convert([
            relay.ty.TensorType((3, 2, 1), dtype),
            relay.ty.TensorType((3,), dtype),
            relay.ty.TensorType((3,), dtype)
        ]))

        # axis=-1
        data = relay.var("data", relay.TensorType((1, 2, 3), dtype))
        beta = relay.var("beta", relay.TensorType((3,), dtype))
        gamma = relay.var("gamma", relay.TensorType((3,), dtype))
        moving_mean = relay.var("moving_mean", relay.TensorType((3,), dtype))
        moving_var = relay.var("moving_var", relay.TensorType((3,), dtype))
        y = relay.nn.batch_norm(data, gamma, beta, moving_mean, moving_var,
                                axis=-1, center=False, scale=False)
        yy = run_infer_type(y.astuple())
        assert yy.checked_type == relay.ty.TupleType(tvm.runtime.convert([
            relay.ty.TensorType((1, 2, 3), dtype),
            relay.ty.TensorType((3,), dtype),
            relay.ty.TensorType((3,), dtype)
        ]))

@pytest.mark.xfail
def test_dense_type_check():
    dtype = 'float16'
    n, c , h, w = 2, 2 , 2 ,2
    x = relay.var("x", relay.TensorType((n, c, h, w), dtype))
    # it should fail since it does not match with m(2)
    mismatch_w = 3
    w = relay.var("w", relay.TensorType((2, mismatch_w), dtype))
    y = relay.nn.dense(x, w)
    yy = run_infer_type(y)

def test_dense():
    for dtype in ['float16', 'float32']:
        # Dense accuracy for float16 is poor
        if dtype == 'float16':
            return
        n, c , h, w = te.size_var("n"), te.size_var("c"), te.size_var("h"), te.size_var("w")
        x = relay.var("x", relay.TensorType((n, c, h, w), dtype))
        w = relay.var("w", relay.TensorType((2, w), dtype))
        y = relay.nn.dense(x, w, units=2)
        assert "units=2" in y.astext()
        yy = run_infer_type(y)
        assert yy.checked_type == relay.TensorType((n, c, h, 2), dtype)

        n, c , h, w = te.size_var("n"), te.size_var("c"), te.size_var("h"), 2
        x = relay.var("x", relay.TensorType((n, c, h, w), dtype))
        wh, ww = te.size_var("wh"), te.size_var("ww")
        w = relay.var("w", relay.TensorType((ww, wh), dtype))
        y = relay.nn.dense(x, w)
        yy = run_infer_type(y)
        assert yy.checked_type == relay.TensorType((n, c, h, ww), dtype)

        n, c , h, w = te.size_var("n"), te.size_var("c"), te.size_var("h"), 2
        x = relay.var("x", relay.TensorType((n, c, h, w), dtype))
        w = relay.var("w", relay.IncompleteType())
        y = relay.nn.dense(x, w, units=2)
        yy = run_infer_type(y)
        assert yy.checked_type == relay.TensorType((n, c, h, 2), dtype)

        x = relay.var("x", shape=(10, 5), dtype=dtype)
        w = relay.var("w", shape=(2, 5), dtype=dtype)
        z = relay.nn.dense(x, w)

        # Check result.
        func = relay.Function([x, w], z)
        x_data = np.random.rand(10, 5).astype(dtype)
        w_data = np.random.rand(2, 5).astype(dtype)
        ref_res = np.dot(x_data, w_data.T)

        for target, ctx in ctx_list():
            intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
            intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
            op_res1 = intrp1.evaluate(func)(x_data, w_data)
            tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5)
            op_res2 = intrp2.evaluate(func)(x_data, w_data)
            tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-5)


def test_dense_dtype():
    data_dtype = 'uint8'
    weight_dtype = 'int8'
    out_dtype = 'uint8'
    n, c , h, w = te.size_var("n"), te.size_var("c"), te.size_var("h"), te.size_var("w")
    x = relay.var("x", relay.TensorType((n, c, h, w), data_dtype))
    w = relay.var("w", relay.TensorType((2, w), weight_dtype))
    y = relay.nn.dense(x, w, units=2, out_dtype=out_dtype)
    assert "units=2" in y.astext()
    yy = run_infer_type(y)
    assert yy.checked_type == relay.TensorType((n, c, h, 2), out_dtype)
    assert run_infer_type(yy.args[0]).checked_type.dtype == 'uint8'
    assert run_infer_type(yy.args[1]).checked_type.dtype == 'int8'


def test_bitserial_dense():
    m, k = te.size_var("m"), te.size_var("k")
    x = relay.var("x", relay.TensorType((m, k), "int16"))
    w = relay.var("w", relay.TensorType((k, 32), "int16"))
    y = relay.nn.bitserial_dense(x, w, units=32)
    "units=8" in y.astext()
    yy = run_infer_type(y)
    assert yy.checked_type == relay.TensorType((m, 32), "int16")


if __name__ == "__main__":
    test_concatenate()
    test_bias_add()
    test_unary_op()
    test_binary_op()
    test_expand_dims_infer_type()
    test_expand_dims()
    test_softmax()
    test_log_softmax()
    test_dropout()
    test_batch_norm()
    test_dense()
    test_bitserial_dense()
    test_dense_dtype()
