from __future__ import absolute_import, print_function, division
from unittest import TestCase
from nose.plugins.skip import SkipTest
import itertools
import numpy

import theano
from theano import tensor
from theano.tests import unittest_tools as utt
from theano.tensor.blas import gemv_inplace, gemm_inplace, _dot22, batched_dot
from theano.tensor.tests.test_blas import TestGer, BaseGemv

from .. import gpuarray_shared_constructor
from .config import mode_with_gpu
from .test_basic_ops import makeTester, rand

from ..blas import (gpugemv_inplace, gpugemv_no_inplace,
                    gpugemm_inplace, gpugemmbatch_no_inplace,
                    gpuger_inplace, gpuger_no_inplace,
                    GpuGer, gpu_dot22)


GpuGemvTester = makeTester(
    'GpuGemvTester',
    op=gemv_inplace, gpu_op=gpugemv_inplace,
    cases=dict(dot_vv=[rand(1), 1, rand(1, 2), rand(2), 0],
               dot_vm=[rand(3), 1, rand(3, 2), rand(2), 0],
               # test_02=[rand(0), 1, rand(0, 2), rand(2), 0],
               # test_30=[rand(3), 1, rand(3, 0), rand(0), 0],
               # test_00=[rand(0), 1, rand(0, 0), rand(0), 0],
               test_stride=[rand(3)[::-1], 1, rand(3, 2)[::-1], rand(2)[::-1], 0],
               )
    )


class TestGpuSgemv(TestCase, BaseGemv, utt.TestOptimizationMixin):
    mode = mode_with_gpu
    dtype = 'float32'

    gemv = gpugemv_no_inplace
    gemv_inplace = gpugemv_inplace

    @staticmethod
    def shared(val):
        try:
            return gpuarray_shared_constructor(val)
        except TypeError:
            return theano.shared(val)


GpuGemmTester = makeTester(
    'GpuGemmTester',
    op=gemm_inplace, gpu_op=gpugemm_inplace,
    cases=dict(test1=[rand(3, 4), 1.0, rand(3, 5), rand(5, 4), 0.0],
               test2=[rand(3, 4), 1.0, rand(3, 5), rand(5, 4), 1.0],
               test3=[rand(3, 4), 1.0, rand(3, 5), rand(5, 4), -1.0],
               test4=[rand(3, 4), 0.0, rand(3, 5), rand(5, 4), 0.0],
               test5=[rand(3, 4), 0.0, rand(3, 5), rand(5, 4), 0.6],
               test6=[rand(3, 4), 0.0, rand(3, 5), rand(5, 4), -1.0],
               test7=[rand(3, 4), -1.0, rand(3, 5), rand(5, 4), 0.0],
               test8=[rand(3, 4), -1.0, rand(3, 5), rand(5, 4), 1.1],
               test9=[rand(3, 4), -1.0, rand(3, 5), rand(5, 4), -1.1],
               # test10=[rand(0, 4), -1.0, rand(0, 5), rand(5, 4), 0.0],
               # test11=[rand(3, 0), -1.0, rand(3, 5), rand(5, 0), 1.1],
               # test12=[rand(3, 4), -1.0, rand(3, 0), rand(0, 4), -1.1],
               # test13=[rand(0, 0), -1.0, rand(0, 0), rand(0, 0), -1.1],
               )
    )


GpuGemmBatchTester = makeTester(
    'GpuGemmBatchTester',
    op=lambda z, alpha, x, y, beta: alpha * batched_dot(x, y) + beta * z,
    gpu_op=gpugemmbatch_no_inplace,
    cases=dict(
        ("test_b%im%ik%in%i" % (b, m, k, n),
         [rand(b, m, n), rand(), rand(b, m, k), rand(b, k, n), rand()])
        for b, m, k, n in itertools.combinations([2, 3, 5, 7, 11, 13], 4)))


class TestGpuSger(TestGer):
    def setUp(self):
        self.mode = mode_with_gpu
        dtype = self.dtype = 'float32'  # optimization isn't dtype-dependent
        self.A = tensor.tensor(dtype=dtype, broadcastable=(False, False))
        self.a = tensor.tensor(dtype=dtype, broadcastable=())
        self.x = tensor.tensor(dtype=dtype, broadcastable=(False,))
        self.y = tensor.tensor(dtype=dtype, broadcastable=(False,))
        self.ger_destructive = gpuger_inplace

        # data on the gpu make the op always inplace
        self.ger = gpuger_inplace
        self.gemm = gpugemm_inplace

    def test_f32_0_0(self):
        raise SkipTest('0-sized objects not supported')

    def test_f32_1_0(self):
        raise SkipTest('0-sized objects not supported')

    def test_f32_0_1(self):
        raise SkipTest('0-sized objects not supported')


class TestGpuSgerNoTransfer(TestGpuSger):
    shared = staticmethod(gpuarray_shared_constructor)


class TestGpuGer_OpContract(TestCase, utt.T_OpContractMixin):
    def setUp(self):
        self.ops = [gpuger_no_inplace, gpuger_inplace]

    def clone(self, op):
        return GpuGer(inplace=op.inplace)


GpuDot22Tester = makeTester(
    'GpuDot22Tester',
    op=_dot22, gpu_op=gpu_dot22,
    cases=dict(
        test1=[rand(3, 4), rand(4, 5)],
        test2=[rand(1, 4), rand(4, 5)],
        test3=[rand(3, 1), rand(1, 5)],
        test4=[rand(3, 4), rand(4, 1)],
        # test5=[rand(0, 4), rand(4, 5)],
        # test6=[rand(3, 0), rand(0, 5)],
        # test7=[rand(3, 4), rand(4, 0)],
        # test8=[rand(0, 4), rand(4, 0)],
        # test9=[rand(0, 0), rand(0, 0)],
    )
)


def test_gemv_zeros():
    W = tensor.matrix()
    v = tensor.vector()
    f = theano.function([W, v], W.dot(v), mode=mode_with_gpu)

    # Apply to an empty matrix shape (5,0) and an empty vector shape (0,)
    dim = 1000
    A = numpy.zeros((dim, 0), dtype=theano.config.floatX)
    b = numpy.zeros((0,), dtype=theano.config.floatX)
    tmp = f(A, b)
    assert numpy.allclose(tmp,
                          numpy.zeros((dim,)))
