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# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Copyright the Hypothesis Authors.
# Individual contributors are listed in AUTHORS.rst and the git log.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at https://mozilla.org/MPL/2.0/.
import numpy as np
import pytest
from pytest import param
from hypothesis import HealthCheck, example, given, note, settings, strategies as st
from hypothesis.extra import numpy as nps
from hypothesis.extra._array_helpers import _hypothesis_parse_gufunc_signature
from tests.common.debug import find_any, minimal
def use_signature_examples(func):
for sig in [
"(),()->()",
"(i)->()",
"(i),(i)->()",
"(m,n),(n,p)->(m,p)",
"(n),(n,p)->(p)",
"(m,n),(n)->(m)",
"(m?,n),(n,p?)->(m?,p?)",
"(3),(3)->(3)",
]:
func = example(sig)(func)
return func
def hy_sig_2_np_sig(hy_sig):
return (
[tuple(d.strip("?") for d in shape) for shape in hy_sig.input_shapes],
[tuple(d.strip("?") for d in hy_sig.result_shape)],
)
def test_frozen_dims_signature():
_hypothesis_parse_gufunc_signature("(2),(3)->(4)")
@st.composite
def gufunc_arrays(draw, shape_strat, **kwargs):
"""An example user strategy built on top of mutually_broadcastable_shapes."""
input_shapes, result_shape = draw(shape_strat)
arrays_strat = st.tuples(*(nps.arrays(shape=s, **kwargs) for s in input_shapes))
return draw(arrays_strat), result_shape
@given(
gufunc_arrays(
nps.mutually_broadcastable_shapes(signature=np.matmul.signature),
dtype="float64",
elements=st.floats(0, 1000),
)
)
def test_matmul_gufunc_shapes(everything):
arrays, result_shape = everything
out = np.matmul(*arrays)
assert out.shape == result_shape
@settings(deadline=None, max_examples=10, suppress_health_check=list(HealthCheck))
@pytest.mark.parametrize(
"target_sig",
("(i),(i)->()", "(m,n),(n,p)->(m,p)", "(n),(n,p)->(p)", "(m,n),(n)->(m)"),
)
@given(data=st.data())
def test_matmul_signature_can_exercise_all_combination_of_optional_dims(
target_sig, data
):
target_shapes = data.draw(
nps.mutually_broadcastable_shapes(signature=target_sig, max_dims=0)
)
find_any(
nps.mutually_broadcastable_shapes(
signature="(m?,n),(n,p?)->(m?,p?)", max_dims=0
),
lambda shapes: shapes == target_shapes,
)
@settings(
deadline=None, max_examples=50, suppress_health_check=[HealthCheck.nested_given]
)
@given(
min_dims=st.integers(0, 4),
min_side=st.integers(2, 3),
n_fixed=st.booleans(),
data=st.data(),
)
def test_matmul_sig_shrinks_as_documented(min_dims, min_side, n_fixed, data):
sig = "(m?,n),(n,p?)->(m?,p?)"
if n_fixed:
n_value = data.draw(st.integers(0, 4))
sig = sig.replace("n", str(n_value))
else:
n_value = min_side
note(f"signature: {sig}")
max_dims = data.draw(st.none() | st.integers(min_dims, 4), label="max_dims")
max_side = data.draw(st.none() | st.integers(min_side, 6), label="max_side")
smallest_shapes, result = minimal(
nps.mutually_broadcastable_shapes(
signature=sig,
min_side=min_side,
max_side=max_side,
min_dims=min_dims,
max_dims=max_dims,
)
)
note(f"(smallest_shapes, result): {(smallest_shapes, result)}")
# if min_dims >= 1 then core dims are never excluded
# otherwise, should shrink towards excluding all optional dims
expected_input_0 = (
(n_value,) if min_dims == 0 else (min_side,) * min_dims + (min_side, n_value)
)
assert expected_input_0 == smallest_shapes[0]
expected_input_1 = (
(n_value,) if min_dims == 0 else (min_side,) * min_dims + (n_value, min_side)
)
assert expected_input_1 == smallest_shapes[1]
def gufunc_sig_to_einsum_sig(gufunc_sig):
"""E.g. (i,j),(j,k)->(i,k) becomes ...ij,...jk->...ik"""
def einlabels(labels):
assert "x" not in labels, "we reserve x for fixed-dimensions"
return "..." + "".join(i if not i.isdigit() else "x" for i in labels)
gufunc_sig = _hypothesis_parse_gufunc_signature(gufunc_sig)
input_sig = ",".join(map(einlabels, gufunc_sig.input_shapes))
return input_sig + "->" + einlabels(gufunc_sig.result_shape)
@pytest.mark.parametrize(
"gufunc_sig",
[
param("()->()", id="unary sum"),
param("(),()->()", id="binary sum"),
param("(),(),()->()", id="trinary sum"),
param("(i)->()", id="sum1d"),
param("(i,j)->(j)", id="sum rows"),
param("(i),(i)->()", id="inner1d"),
param("(i),(i),(i)->()", id="trinary inner1d"),
param("(m,n),(n,p)->(m,p)", id="matmat"),
param("(n),(n,p)->(p)", id="vecmat"),
param("(i,t),(j,t)->(i,j)", id="outer-inner"),
param("(3),(3)->(3)", id="cross1d"),
param("(i,j)->(j,i)", id="transpose"),
param("(i),(j)->(i,j)", id="outer"),
param("(i,3),(3,k)->(3,i,k)", id="fixed dim outer product"),
param("(i),(j),(k)->(i,j,k)", id="trinary outer"),
param("(i,i)->(i)", id="trace"),
param("(j,i,i,j)->(i,j)", id="bigger trace"),
param("(k),(j,i,k,i,j),(j,i)->(i,j)", id="trace product"),
],
)
@given(data=st.data())
def test_einsum_gufunc_shapes(gufunc_sig, data):
arrays, result_shape = data.draw(
gufunc_arrays(
nps.mutually_broadcastable_shapes(signature=gufunc_sig),
dtype="float64",
elements=st.floats(0, 1000),
),
label="arrays, result_shape",
)
out = np.einsum(gufunc_sig_to_einsum_sig(gufunc_sig), *arrays)
assert out.shape == result_shape
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