# Path: deeppy/misc/test.py
# def allclose(a, b, rtol=None, atol=None):
# def gradclose(a, b, rtol=None, atol=None):
# def approx_fprime(x, f, eps=None, *args):
#     def __init__(self, array):
# def graph_funs(src, sink, seed=1):
#     def fun(x):
#     def fun_grad(x):
# class BPropableSource(ex.base.Op, ex.base.NoBPropMixin, ex.base.NoFPropMixin):


import itertools
import numpy as np
import deeppy as dp
import deeppy.expr as ex
from deeppy.misc.test import (eps, BPropableSource, approx_fprime, gradclose,
                              graph_funs)
    confs = itertools.product(shapes, ops)
    for shape, op in confs:
        print('%s shape=%s' % (op.__name__.ljust(20), shape))
        src_array = np.random.normal(size=shape).astype(dp.float_)

        if op is ex.Log:
            src_array = np.fabs(src_array) + eps

        src = BPropableSource(src_array)
        sink = op()(src)
        f, f_grad = graph_funs(src, sink)
        g_approx = approx_fprime(src_array, f)
        g_true = f_grad(src_array)
        assert gradclose(g_true, g_approx)


def test_binary():
    ops = [
        ex.Add, ex.Subtract, ex.Multiply, ex.Divide, ex.Power, ex.Maximum,
        ex.Minimum,
    ]
    confs = itertools.product(shapes, ops)
    for shape, op in confs:
        print('%s shape=%s' % (op.__name__.ljust(20), shape))
        lhs_array = np.random.normal(size=shape).astype(dp.float_)
        rhs_array = np.random.normal(size=shape).astype(dp.float_)
        if op is ex.Power:
            lhs_array = np.fabs(lhs_array)
        if op is ex.Divide:
            rhs_array[np.fabs(rhs_array) < eps] = eps

# Path: deeppy/misc/test.py
# def allclose(a, b, rtol=None, atol=None):
# def gradclose(a, b, rtol=None, atol=None):
# def approx_fprime(x, f, eps=None, *args):
#     def __init__(self, array):
# def graph_funs(src, sink, seed=1):
#     def fun(x):
#     def fun_grad(x):
# class BPropableSource(ex.base.Op, ex.base.NoBPropMixin, ex.base.NoFPropMixin):


# Path: test/unit/expr/test_elementwise.py
import itertools
import numpy as np
import deeppy as dp
import deeppy.expr as ex
from deeppy.misc.test import (eps, BPropableSource, approx_fprime, gradclose,
                              graph_funs)

    confs = itertools.product(shapes, ops)
    for shape, op in confs:
        print('%s shape=%s' % (op.__name__.ljust(20), shape))
        src_array = np.random.normal(size=shape).astype(dp.float_)

        if op is ex.Log:
            src_array = np.fabs(src_array) + eps

        src = BPropableSource(src_array)
        sink = op()(src)
        f, f_grad = graph_funs(src, sink)
        g_approx = approx_fprime(src_array, f)
        g_true = f_grad(src_array)
        assert gradclose(g_true, g_approx)


def test_binary():
    ops = [
        ex.Add, ex.Subtract, ex.Multiply, ex.Divide, ex.Power, ex.Maximum,
        ex.Minimum,
    ]
    confs = itertools.product(shapes, ops)
    for shape, op in confs:
        print('%s shape=%s' % (op.__name__.ljust(20), shape))
        lhs_array = np.random.normal(size=shape).astype(dp.float_)
        rhs_array = np.random.normal(size=shape).astype(dp.float_)
        if op is ex.Power:
            lhs_array = np.fabs(lhs_array)
        if op is ex.Divide:
            rhs_array[np.fabs(rhs_array) < eps] = eps

            rhs_array[np.fabs(rhs_array) < eps] = eps