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# to you under the Apache License, Version 2.0 (the
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# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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# software distributed under the License is distributed on an
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# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
import numpy as np

import tvm
from tvm import relay
from tvm.relay.analysis import free_vars, free_type_vars, assert_alpha_equal
from tvm.relay import create_executor, transform
from tvm.relay.transform import gradient
from tvm.relay.prelude import Prelude
from tvm.relay.testing import add_nat_definitions, make_nat_expr, run_infer_type, check_grad, rand
import tvm.relay.op as op


def test_id():
    shape = (10, 10)
    dtype = 'float32'
    t = relay.TensorType(shape, dtype)
    x = relay.var("x", t)
    func = relay.Function([x], x)
    func = run_infer_type(func)
    back_func = run_infer_type(gradient(func, mode="first_order"))
    assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
    ex = create_executor()
    x = rand(dtype, *shape)
    forward, (grad,) = ex.evaluate(back_func)(x)
    tvm.testing.assert_allclose(forward.asnumpy(), x.asnumpy())
    tvm.testing.assert_allclose(grad.asnumpy(), np.ones_like(x.asnumpy()))


def test_add():
    shape = (10, 10)
    dtype = 'float32'
    t = relay.TensorType(shape, dtype)
    x = relay.var("x", t)
    func = relay.Function([x], x + x)
    func = run_infer_type(func)
    back_func = run_infer_type(gradient(func))
    assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
    ex = create_executor()
    x = rand(dtype, *shape)
    forward, (grad,) = ex.evaluate(back_func)(x)
    tvm.testing.assert_allclose(forward.asnumpy(), 2 * x.asnumpy())
    tvm.testing.assert_allclose(grad.asnumpy(), 2 * np.ones_like(x.asnumpy()))


def test_check_grad():
    shape = (10, 10)
    dtype = 'float32'
    t = relay.TensorType(shape, dtype)
    x = relay.var("x", t)
    y = relay.var("y", t)
    func = relay.Function([x, y], x + y)
    check_grad(func)


def test_temp_add():
    shape = (10, 10)
    dtype = 'float32'
    t = relay.TensorType(shape, dtype)
    x = relay.var("x", t)
    y = x + x
    func = relay.Function([x], y + y)
    func = run_infer_type(func)
    back_func = run_infer_type(gradient(func))
    assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
    ex = create_executor()
    x = rand(dtype, *shape)
    forward, (grad,) = ex.evaluate(back_func)(x)
    tvm.testing.assert_allclose(forward.asnumpy(), 4 * x.asnumpy())
    tvm.testing.assert_allclose(grad.asnumpy(), 4 * np.ones_like(x.asnumpy()))


def test_sub():
    shape = (10, 10)
    dtype = 'float32'
    t = relay.TensorType(shape, dtype)
    x = relay.var("x", t)
    func = relay.Function([x], x - x)
    func = run_infer_type(func)
    back_func = run_infer_type(gradient(func))
    assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
    ex = create_executor()
    x = rand(dtype, *shape)
    forward, (grad,) = ex.evaluate(back_func)(x)
    tvm.testing.assert_allclose(forward.asnumpy(), np.zeros_like(x.asnumpy()))
    tvm.testing.assert_allclose(grad.asnumpy(), np.zeros_like(x.asnumpy()))


def test_broadcast_add():
    shape1 = (3, 4, 1)
    shape2 = (1, 5)
    dtype = 'float32'
    x_nd = rand(dtype, *shape1)
    y_nd = rand(dtype, *shape2)
    x_np = x_nd.asnumpy()
    y_np = y_nd.asnumpy()
    expected_forward = x_np + y_np
    t1 = relay.TensorType(shape1, dtype)
    t2 = relay.TensorType(shape2, dtype)
    x = relay.var("x", t1)
    y = relay.var("y", t2)
    func = relay.Function([x, y], x + y)
    func = run_infer_type(func)
    full_func = run_infer_type(gradient(func))
    assert full_func.checked_type == relay.FuncType([t1, t2],
                                                    relay.TupleType([relay.TensorType(expected_forward.shape, dtype),
                                                                     relay.TupleType([t1, t2])]))
    ex = create_executor()
    forward, (grad_x, grad_y) = ex.evaluate(full_func)(x_nd, y_nd)
    tvm.testing.assert_allclose(forward.asnumpy(), expected_forward)
    tvm.testing.assert_allclose(grad_x.asnumpy(),
                                np.ones_like(expected_forward).sum(axis=2, keepdims=True))
    tvm.testing.assert_allclose(grad_y.asnumpy(),
                                np.ones_like(expected_forward).sum(axis=(0, 1), keepdims=True).squeeze(axis=0))


def test_broadcast_subtract():
    shape1 = (3, 4, 1)
    shape2 = (1, 5)
    dtype = 'float32'
    x_nd = rand(dtype, *shape1)
    y_nd = rand(dtype, *shape2)
    x_np = x_nd.asnumpy()
    y_np = y_nd.asnumpy()
    expected_forward = x_np - y_np
    t1 = relay.TensorType(shape1, dtype)
    t2 = relay.TensorType(shape2, dtype)
    x = relay.var("x", t1)
    y = relay.var("y", t2)
    func = relay.Function([x, y], x - y)
    func = run_infer_type(func)
    full_func = run_infer_type(gradient(func))
    assert full_func.checked_type == relay.FuncType([t1, t2],
                                                    relay.TupleType([relay.TensorType(expected_forward.shape, dtype),
                                                                     relay.TupleType([t1, t2])]))
    ex = create_executor()
    forward, (grad_x, grad_y) = ex.evaluate(full_func)(x_nd, y_nd)
    tvm.testing.assert_allclose(forward.asnumpy(), expected_forward)
    tvm.testing.assert_allclose(grad_x.asnumpy(),
                                np.ones_like(expected_forward).sum(axis=2, keepdims=True))
    tvm.testing.assert_allclose(grad_y.asnumpy(),
                                -np.ones_like(expected_forward).sum(axis=(0, 1), keepdims=True).squeeze(axis=0))


def test_tuple():
    shape = (10, 10)
    dtype = 'float32'
    t = relay.TensorType(shape, dtype)
    x = relay.var("x", t)
    y = relay.var("y", t)
    z = relay.var("z", t)
    tup = relay.Var("tup")
    func = relay.Function([x, y, z], relay.Let(tup, relay.Tuple([x, y, z]),
                                               relay.TupleGetItem(tup, 0) +
                                               relay.TupleGetItem(tup, 1) -
                                               relay.TupleGetItem(tup, 2)))
    func = run_infer_type(func)
    back_func = run_infer_type(gradient(func))
    assert back_func.checked_type == relay.FuncType([t, t, t], relay.TupleType([t, relay.TupleType([t, t, t])]))
    x_nd = rand(dtype, *shape)
    y_nd = rand(dtype, *shape)
    z_nd = rand(dtype, *shape)
    x_np = x_nd.asnumpy()
    y_np = y_nd.asnumpy()
    z_np = z_nd.asnumpy()
    expected_forward = x_np + y_np - z_np
    ex = create_executor()
    forward, (grad_x, grad_y, grad_z) = ex.evaluate(back_func)(x_nd, y_nd, z_nd)
    tvm.testing.assert_allclose(forward.asnumpy(), expected_forward)
    tvm.testing.assert_allclose(grad_x.asnumpy(), np.ones_like(grad_x.asnumpy()))
    tvm.testing.assert_allclose(grad_y.asnumpy(), np.ones_like(grad_y.asnumpy()))
    tvm.testing.assert_allclose(grad_z.asnumpy(), -1 * np.ones_like(grad_z.asnumpy()))


def test_pow():
    mod = relay.Module()
    p = Prelude(mod)
    add_nat_definitions(p)
    shape = (10, 10)
    dtype = 'float32'
    t = relay.TensorType(shape, dtype)
    x = relay.var("x", t)
    double = relay.Function([x], x + x)
    i = relay.var("i", t)
    func = relay.Function([i], p.nat_iterate(double, make_nat_expr(p, 3))(i))
    mod["main"] = func
    mod["main"] = gradient(mod["main"], mod=mod)
    m = transform.InferType()(mod)
    back_func = m["main"]
    assert back_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
    i_nd = rand(dtype, *shape)
    ex = create_executor(mod=mod)
    forward, (grad_i,) = ex.evaluate(back_func)(i_nd)