# 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)
        [(3, 4), (3, 1), (1, 4)],
        [(3, 2, 4), (3, 1, 1), (1, 1, 4)],
        [(1, 2, 1), (1, 1, 1), (3, 1, 1)],
    ]
    exprs = [
        lambda a, b, c: a + b + c,
        lambda a, b, c: a - b - c,
        lambda a, b, c: a * b * c,
        lambda a, b, c: a / b / c,
        lambda a, b, c: a + b - c,
        lambda a, b, c: a - b + c,
        lambda a, b, c: a + b + (-c),
        lambda a, b, c: a + b*a + c,
        lambda a, b, c: a + b**2 + c,
        lambda a, b, c: a + b*b + a*b + c,
        lambda a, b, c: a + 2*(b + a) + c,
        lambda a, b, c: c / (a + ((b/1)*(c+1)) + a*b*c),
    ]
    confs = itertools.product(shapes, exprs)
    for (a_shape, b_shape, c_shape), op in confs:
        a_array = np.random.normal(size=a_shape).astype(dp.float_)
        b_array = np.random.normal(size=b_shape).astype(dp.float_)
        c_array = np.random.normal(size=c_shape).astype(dp.float_)
        a_src = BPropableSource(a_array)
        b_src = BPropableSource(b_array)
        c_src = BPropableSource(c_array)
        sink = op(a_src, b_src, c_src)

        for src, x0 in [(a_src, a_array), (b_src, b_array), (c_src, c_array)]:
            f, f_grad = graph_funs(src, sink)

# 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)

        [(3, 4), (3, 1), (1, 4)],
        [(3, 2, 4), (3, 1, 1), (1, 1, 4)],
        [(1, 2, 1), (1, 1, 1), (3, 1, 1)],
    ]
    exprs = [
        lambda a, b, c: a + b + c,
        lambda a, b, c: a - b - c,
        lambda a, b, c: a * b * c,
        lambda a, b, c: a / b / c,
        lambda a, b, c: a + b - c,
        lambda a, b, c: a - b + c,
        lambda a, b, c: a + b + (-c),
        lambda a, b, c: a + b*a + c,
        lambda a, b, c: a + b**2 + c,
        lambda a, b, c: a + b*b + a*b + c,
        lambda a, b, c: a + 2*(b + a) + c,
        lambda a, b, c: c / (a + ((b/1)*(c+1)) + a*b*c),
    ]
    confs = itertools.product(shapes, exprs)
    for (a_shape, b_shape, c_shape), op in confs:
        a_array = np.random.normal(size=a_shape).astype(dp.float_)
        b_array = np.random.normal(size=b_shape).astype(dp.float_)
        c_array = np.random.normal(size=c_shape).astype(dp.float_)
        a_src = BPropableSource(a_array)
        b_src = BPropableSource(b_array)
        c_src = BPropableSource(c_array)
        sink = op(a_src, b_src, c_src)

        for src, x0 in [(a_src, a_array), (b_src, b_array), (c_src, c_array)]:
            f, f_grad = graph_funs(src, sink)

            g_approx = approx_fprime(x0, f)