from __future__ import absolute_import, print_function, division
import theano
import numpy as np
from unittest import TestCase
from theano.gof import Op, COp, Apply
from theano import Generic
from theano.scalar import Scalar
from theano.tensor import TensorType
from theano.gof import ParamsType, Params, EnumList
from theano import tensor
from theano.tests import unittest_tools as utt

tensor_type_0d = TensorType('float64', tuple())
scalar_type = Scalar('float64')
generic_type = Generic()


# A test op to compute `y = a*x^2 + bx + c` for any tensor x, with a, b, c as op params.
class QuadraticOpFunc(Op):
    __props__ = ('a', 'b', 'c')
    params_type = ParamsType(a=tensor_type_0d,
                             b=scalar_type,
                             c=generic_type)

    def __init__(self, a, b, c):
        self.a = a
        self.b = b
        self.c = c

    def make_node(self, x):
        x = tensor.as_tensor_variable(x)
        return Apply(self, [x], [x.type()])

    def perform(self, node, inputs, output_storage, coefficients):
        x = inputs[0]
        y = output_storage[0]
        y[0] = coefficients.a * (x**2) + coefficients.b * x + coefficients.c

    def c_code_cache_version(self):
        return (1, 5)

    def c_support_code_apply(self, node, name):
        float_type = node.inputs[0].type.dtype_specs()[1]
        return """
        /* Computes: x = a*x*x + b*x + c for x in tensor. */
        int quadratic_%(name)s(PyArrayObject* tensor, %(float_type)s a, %(float_type)s b, %(float_type)s c) {
            NpyIter* iterator = NpyIter_New(tensor,
                NPY_ITER_READWRITE | NPY_ITER_EXTERNAL_LOOP | NPY_ITER_REFS_OK,
                NPY_KEEPORDER, NPY_NO_CASTING, NULL);
            if(iterator == NULL) {
                PyErr_SetString(PyExc_RuntimeError, "Unable to iterate over a tensor for an elemwise operation.");
                return -1;
            }
            NpyIter_IterNextFunc* get_next = NpyIter_GetIterNext(iterator, NULL);
            char** data_ptr = NpyIter_GetDataPtrArray(iterator);
            npy_intp* stride_ptr = NpyIter_GetInnerStrideArray(iterator);
            npy_intp* innersize_ptr = NpyIter_GetInnerLoopSizePtr(iterator);
            do {
                char* data = *data_ptr;
                npy_intp stride = *stride_ptr;
                npy_intp count = *innersize_ptr;
                while(count) {
                    %(float_type)s x = *((%(float_type)s*)data);
                    *((%(float_type)s*)data) = a*x*x + b*x + c;
                    data += stride;
                    --count;
                }
            } while(get_next(iterator));
            NpyIter_Deallocate(iterator);
            return 0;
        }
        """ % {'name': name, 'float_type': float_type}

    def c_code(self, node, name, inputs, outputs, sub):
        return """
        %(float_type)s a = (%(float_type)s) (*(npy_float64*) PyArray_GETPTR1(%(coeff)s->a, 0)); // 0-D TensorType.
        %(float_type)s b =                                                   %(coeff)s->b;      // Scalar.
        %(float_type)s c =                 (%(float_type)s) PyFloat_AsDouble(%(coeff)s->c);     // Generic.
        Py_XDECREF(%(Y)s);
        %(Y)s = (PyArrayObject*)PyArray_EMPTY(PyArray_NDIM(%(X)s), PyArray_DIMS(%(X)s), PyArray_TYPE(%(X)s), PyArray_IS_F_CONTIGUOUS(%(X)s));
        if (PyArray_CopyInto(%(Y)s, %(X)s) != 0) {
            PyErr_SetString(PyExc_RuntimeError, "Unable to copy input into output.");
            %(fail)s
        };
        if (quadratic_%(name)s(%(Y)s, a, b, c) != 0) {
            PyErr_SetString(PyExc_RuntimeError, "Unable to compute quadratic function.");
            %(fail)s
        }
        """ % dict(name=name, coeff=sub['params'], fail=sub['fail'],
                   X=inputs[0], Y=outputs[0], float_type=node.inputs[0].type.c_element_type())


# Same op as above, but implemented as a COp (with C code in an external file).
class QuadraticCOpFunc(COp):
    __props__ = ('a', 'b', 'c')
    params_type = ParamsType(a=tensor_type_0d,
                             b=scalar_type,
                             c=generic_type)

    def __init__(self, a, b, c):
        super(QuadraticCOpFunc, self).__init__('test_quadratic_function.c',
                                               'APPLY_SPECIFIC(compute_quadratic)')
        self.a = a
        self.b = b
        self.c = c

    def make_node(self, x):
        x = tensor.as_tensor_variable(x)
        return Apply(self, [x], [x.type()])

    def perform(self, node, inputs, output_storage, coefficients):
        x = inputs[0]
        y = output_storage[0]
        y[0] = coefficients.a * (x**2) + coefficients.b * x + coefficients.c


class TestParamsType(TestCase):

    def test_hash_and_eq_params(self):
        wp1 = ParamsType(a=Generic(), array=TensorType('int64', (False,)), floatting=Scalar('float64'),
                         npy_scalar=TensorType('float64', tuple()))
        wp2 = ParamsType(a=Generic(), array=TensorType('int64', (False,)), floatting=Scalar('float64'),
                         npy_scalar=TensorType('float64', tuple()))
        w1 = Params(wp1, a=1, array=np.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=np.asarray(12))
        w2 = Params(wp2, a=1, array=np.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=np.asarray(12))
        assert w1 == w2
        assert not (w1 != w2)
        assert hash(w1) == hash(w2)
        # Changing attributes names only (a -> other_name).
        wp2_other = ParamsType(other_name=Generic(), array=TensorType('int64', (False,)), floatting=Scalar('float64'),
                               npy_scalar=TensorType('float64', tuple()))
        w2 = Params(wp2_other, other_name=1, array=np.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=np.asarray(12))
        assert w1 != w2
        # Changing attributes values only (now a=2).
        w2 = Params(wp2, a=2, array=np.asarray([1, 2, 4, 5, 7]), floatting=-4.5, npy_scalar=np.asarray(12))
        assert w1 != w2
        # Changing NumPy array values (5 -> -5).
        w2 = Params(wp2, a=1, array=np.asarray([1, 2, 4, -5, 7]), floatting=-4.5, npy_scalar=np.asarray(12))
        assert w1 != w2

    def test_hash_and_eq_params_type(self):
        w1 = ParamsType(a1=TensorType('int64', (False, False)),
                        a2=TensorType('int64', (False, True, False, False, True)),
                        a3=Generic())
        w2 = ParamsType(a1=TensorType('int64', (False, False)),
                        a2=TensorType('int64', (False, True, False, False, True)),
                        a3=Generic())
        assert w1 == w2
        assert not (w1 != w2)
        assert hash(w1) == hash(w2)
        assert w1.name == w2.name
        # Changing attributes names only.
        w2 = ParamsType(a1=TensorType('int64', (False, False)),
                        other_name=TensorType('int64', (False, True, False, False, True)),  # a2 -> other_name
                        a3=Generic())
        assert w1 != w2
        # Changing attributes types only.
        w2 = ParamsType(a1=TensorType('int64', (False, False)),
                        a2=Generic(),  # changing class
                        a3=Generic())
        assert w1 != w2
        # Changing attributes types characteristics only.
        w2 = ParamsType(a1=TensorType('int64', (False, True)),  # changing broadcasting
                        a2=TensorType('int64', (False, True, False, False, True)),
                        a3=Generic())
        assert w1 != w2

    def test_params_type_filtering(self):
        shape_tensor5 = (1, 2, 2, 3, 2)
        size_tensor5 = shape_tensor5[0] * shape_tensor5[1] * shape_tensor5[2] * shape_tensor5[3] * shape_tensor5[4]
        random_tensor = np.random.normal(size=size_tensor5).reshape(shape_tensor5)

        w = ParamsType(a1=TensorType('int32', (False, False)),
                       a2=TensorType('float64', (False, False, False, False, False)),
                       a3=Generic())

        # With a value that does not match the params type.
        o = Params(w,
                   a1=np.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int64'),
                   a2=random_tensor.astype('float32'),
                   a3=2000)
        # should fail (o.a1 is not int32, o.a2 is not float64)
        self.assertRaises(TypeError, w.filter, o, True)
        # should fail (o.a1 is not int32, o.a2 is not float64, and downcast is disallowed)
        self.assertRaises(TypeError, w.filter, o, False, False)
        # Should pass.
        w.filter(o, strict=False, allow_downcast=True)

        # With a value that matches the params type.
        o1 = Params(w,
                    a1=np.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
                    a2=random_tensor.astype('float64'),
                    a3=2000)
        # All should pass.
        w.filter(o1, strict=True)
        w.filter(o1, strict=False, allow_downcast=False)
        w.filter(o1, strict=False, allow_downcast=True)

        # Check values_eq and values_eq_approx.
        o2 = Params(w,
                    a1=np.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
                    a2=random_tensor.astype('float64'),
                    a3=2000)
        assert w.values_eq(o1, o2)
        assert w.values_eq_approx(o1, o2)

        # Check value_eq_approx.
        # NB: I don't know exactly which kind of differences is rejected by values_eq but accepted by values_eq_approx.
        # So, I just play a little with float values.
        o3 = Params(w,
                    a1=np.asarray([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]).astype('int32'),
                    a2=(random_tensor.astype('float32') * 10 / 2.2 * 2.19999999999 / 10).astype('float64'),
                    a3=2000.0 - 0.00000000000000001)
        assert w.values_eq_approx(o1, o3)

    def test_params_type_with_enums(self):
        # Test that we fail if we create a params type with common enum names inside different enum types.
        try:
            ParamsType(enum1=EnumList('A', 'B', 'C'), enum2=EnumList('A', 'B', 'F'))
        except AttributeError:
            pass
        else:
            raise Exception('ParamsType should fail with common enum names inside different enum types.')

        # Test that we fail if we create a params type with common names in both aliases and constants.
        try:
            ParamsType(enum1=EnumList(('A', 'a'), ('B', 'b')), enum2=EnumList(('ONE', 'a'), ('TWO', 'two')))
        except AttributeError:
            ParamsType(enum1=EnumList(('A', 'a'), ('B', 'b')), enum2=EnumList(('ONE', 'one'), ('TWO', 'two')))
        else:
            raise Exception('ParamsType should fail when there are aliases with same names as some constants.')

        # Test that we can access enum values through wrapper directly.
        w = ParamsType(enum1=EnumList('A', ('B', 'beta'), 'C'), enum2=EnumList(('D', 'delta'), 'E', 'F'))
        assert w.A == 0 and w.B == 1 and w.C == 2
        assert w.D == 0 and w.E == 1 and w.F == 2
        # Test constants access through aliases.
        assert w.enum_from_alias('beta') == w.B
        assert w.enum_from_alias('delta') == w.D
        assert w.enum_from_alias('C') == w.C  # C is not an alias, so it should return a constant named C.
        # Test that other regular wrapper attributes are still available.
        assert len(w.fields) == len(w.types) == w.length
        assert w.name

    def test_op_params(self):
        a, b, c = 2, 3, -7
        x = tensor.matrix(dtype='float64')
        y1 = QuadraticOpFunc(a, b, c)(x)
        y2 = QuadraticCOpFunc(a, b, c)(x)
        f1 = theano.function([x], y1)
        f2 = theano.function([x], y2)
        shape = (100, 100)
        vx = np.random.normal(size=shape[0] * shape[1]).astype('float64').reshape(*shape)
        vy1 = f1(vx)
        vy2 = f2(vx)
        ref = a * (vx**2) + b * vx + c
        utt.assert_allclose(vy1, vy2)
        utt.assert_allclose(ref, vy1)
