import sys
from decimal import Decimal

import numpy as np
from numpy.testing import *
from numpy.testing.utils import WarningManager
import warnings

class TestEinSum(TestCase):
    def test_einsum_errors(self):
        # Need enough arguments
        assert_raises(ValueError, np.einsum)
        assert_raises(ValueError, np.einsum, "")

        # subscripts must be a string
        assert_raises(TypeError, np.einsum, 0, 0)

        # out parameter must be an array
        assert_raises(TypeError, np.einsum, "", 0, out='test')

        # order parameter must be a valid order
        assert_raises(TypeError, np.einsum, "", 0, order='W')

        # casting parameter must be a valid casting
        assert_raises(ValueError, np.einsum, "", 0, casting='blah')

        # dtype parameter must be a valid dtype
        assert_raises(TypeError, np.einsum, "", 0, dtype='bad_data_type')

        # other keyword arguments are rejected
        assert_raises(TypeError, np.einsum, "", 0, bad_arg=0)

        # number of operands must match count in subscripts string
        assert_raises(ValueError, np.einsum, "", 0, 0)
        assert_raises(ValueError, np.einsum, ",", 0, [0], [0])
        assert_raises(ValueError, np.einsum, ",", [0])

        # can't have more subscripts than dimensions in the operand
        assert_raises(ValueError, np.einsum, "i", 0)
        assert_raises(ValueError, np.einsum, "ij", [0,0])
        assert_raises(ValueError, np.einsum, "...i", 0)
        assert_raises(ValueError, np.einsum, "i...j", [0,0])
        assert_raises(ValueError, np.einsum, "i...", 0)
        assert_raises(ValueError, np.einsum, "ij...", [0,0])

        # invalid ellipsis
        assert_raises(ValueError, np.einsum, "i..", [0,0])
        assert_raises(ValueError, np.einsum, ".i...", [0,0])
        assert_raises(ValueError, np.einsum, "j->..j", [0,0])
        assert_raises(ValueError, np.einsum, "j->.j...", [0,0])

        # invalid subscript character
        assert_raises(ValueError, np.einsum, "i%...", [0,0])
        assert_raises(ValueError, np.einsum, "...j$", [0,0])
        assert_raises(ValueError, np.einsum, "i->&", [0,0])

        # output subscripts must appear in input
        assert_raises(ValueError, np.einsum, "i->ij", [0,0])

        # output subscripts may only be specified once
        assert_raises(ValueError, np.einsum, "ij->jij", [[0,0],[0,0]])

        # dimensions much match when being collapsed
        assert_raises(ValueError, np.einsum, "ii", np.arange(6).reshape(2,3))
        assert_raises(ValueError, np.einsum, "ii->i", np.arange(6).reshape(2,3))

        # broadcasting to new dimensions must be enabled explicitly
        assert_raises(ValueError, np.einsum, "i", np.arange(6).reshape(2,3))
        assert_raises(ValueError, np.einsum, "i->i", [[0,1],[0,1]],
                                            out=np.arange(4).reshape(2,2))

    def test_einsum_views(self):
        # pass-through
        a = np.arange(6)
        a.shape = (2,3)

        b = np.einsum("...", a)
        assert_(b.base is a)

        b = np.einsum(a, [Ellipsis])
        assert_(b.base is a)

        b = np.einsum("ij", a)
        assert_(b.base is a)
        assert_equal(b, a)

        b = np.einsum(a, [0,1])
        assert_(b.base is a)
        assert_equal(b, a)

        # transpose
        a = np.arange(6)
        a.shape = (2,3)

        b = np.einsum("ji", a)
        assert_(b.base is a)
        assert_equal(b, a.T)

        b = np.einsum(a, [1,0])
        assert_(b.base is a)
        assert_equal(b, a.T)

        # diagonal
        a = np.arange(9)
        a.shape = (3,3)

        b = np.einsum("ii->i", a)
        assert_(b.base is a)
        assert_equal(b, [a[i,i] for i in range(3)])

        b = np.einsum(a, [0,0], [0])
        assert_(b.base is a)
        assert_equal(b, [a[i,i] for i in range(3)])

        # diagonal with various ways of broadcasting an additional dimension
        a = np.arange(27)
        a.shape = (3,3,3)

        b = np.einsum("...ii->...i", a)
        assert_(b.base is a)
        assert_equal(b, [[x[i,i] for i in range(3)] for x in a])

        b = np.einsum(a, [Ellipsis,0,0], [Ellipsis,0])
        assert_(b.base is a)
        assert_equal(b, [[x[i,i] for i in range(3)] for x in a])

        b = np.einsum("ii...->...i", a)
        assert_(b.base is a)
        assert_equal(b, [[x[i,i] for i in range(3)]
                         for x in a.transpose(2,0,1)])

        b = np.einsum(a, [0,0,Ellipsis], [Ellipsis,0])
        assert_(b.base is a)
        assert_equal(b, [[x[i,i] for i in range(3)]
                         for x in a.transpose(2,0,1)])

        b = np.einsum("...ii->i...", a)
        assert_(b.base is a)
        assert_equal(b, [a[:,i,i] for i in range(3)])

        b = np.einsum(a, [Ellipsis,0,0], [0,Ellipsis])
        assert_(b.base is a)
        assert_equal(b, [a[:,i,i] for i in range(3)])

        b = np.einsum("jii->ij", a)
        assert_(b.base is a)
        assert_equal(b, [a[:,i,i] for i in range(3)])

        b = np.einsum(a, [1,0,0], [0,1])
        assert_(b.base is a)
        assert_equal(b, [a[:,i,i] for i in range(3)])

        b = np.einsum("ii...->i...", a)
        assert_(b.base is a)
        assert_equal(b, [a.transpose(2,0,1)[:,i,i] for i in range(3)])

        b = np.einsum(a, [0,0,Ellipsis], [0,Ellipsis])
        assert_(b.base is a)
        assert_equal(b, [a.transpose(2,0,1)[:,i,i] for i in range(3)])

        b = np.einsum("i...i->i...", a)
        assert_(b.base is a)
        assert_equal(b, [a.transpose(1,0,2)[:,i,i] for i in range(3)])

        b = np.einsum(a, [0,Ellipsis,0], [0,Ellipsis])
        assert_(b.base is a)
        assert_equal(b, [a.transpose(1,0,2)[:,i,i] for i in range(3)])

        b = np.einsum("i...i->...i", a)
        assert_(b.base is a)
        assert_equal(b, [[x[i,i] for i in range(3)]
                         for x in a.transpose(1,0,2)])

        b = np.einsum(a, [0,Ellipsis,0], [Ellipsis,0])
        assert_(b.base is a)
        assert_equal(b, [[x[i,i] for i in range(3)]
                         for x in a.transpose(1,0,2)])

        # triple diagonal
        a = np.arange(27)
        a.shape = (3,3,3)

        b = np.einsum("iii->i", a)
        assert_(b.base is a)
        assert_equal(b, [a[i,i,i] for i in range(3)])

        b = np.einsum(a, [0,0,0], [0])
        assert_(b.base is a)
        assert_equal(b, [a[i,i,i] for i in range(3)])

        # swap axes
        a = np.arange(24)
        a.shape = (2,3,4)

        b = np.einsum("ijk->jik", a)
        assert_(b.base is a)
        assert_equal(b, a.swapaxes(0,1))

        b = np.einsum(a, [0,1,2], [1,0,2])
        assert_(b.base is a)
        assert_equal(b, a.swapaxes(0,1))

    def check_einsum_sums(self, dtype):
        # Check various sums.  Does many sizes to exercise unrolled loops.

        # sum(a, axis=-1)
        for n in range(1,17):
            a = np.arange(n, dtype=dtype)
            assert_equal(np.einsum("i->", a), np.sum(a, axis=-1).astype(dtype))
            assert_equal(np.einsum(a, [0], []),
                         np.sum(a, axis=-1).astype(dtype))

        for n in range(1,17):
            a = np.arange(2*3*n, dtype=dtype).reshape(2,3,n)
            assert_equal(np.einsum("...i->...", a),
                         np.sum(a, axis=-1).astype(dtype))
            assert_equal(np.einsum(a, [Ellipsis,0], [Ellipsis]),
                         np.sum(a, axis=-1).astype(dtype))

        # sum(a, axis=0)
        for n in range(1,17):
            a = np.arange(2*n, dtype=dtype).reshape(2,n)
            assert_equal(np.einsum("i...->...", a),
                         np.sum(a, axis=0).astype(dtype))
            assert_equal(np.einsum(a, [0,Ellipsis], [Ellipsis]),
                         np.sum(a, axis=0).astype(dtype))

        for n in range(1,17):
            a = np.arange(2*3*n, dtype=dtype).reshape(2,3,n)
            assert_equal(np.einsum("i...->...", a),
                         np.sum(a, axis=0).astype(dtype))
            assert_equal(np.einsum(a, [0,Ellipsis], [Ellipsis]),
                         np.sum(a, axis=0).astype(dtype))

        # trace(a)
        for n in range(1,17):
            a = np.arange(n*n, dtype=dtype).reshape(n,n)
            assert_equal(np.einsum("ii", a), np.trace(a).astype(dtype))
            assert_equal(np.einsum(a, [0,0]), np.trace(a).astype(dtype))

        # multiply(a, b)
        for n in range(1,17):
            a = np.arange(3*n, dtype=dtype).reshape(3,n)
            b = np.arange(2*3*n, dtype=dtype).reshape(2,3,n)
            assert_equal(np.einsum("..., ...", a, b), np.multiply(a, b))
            assert_equal(np.einsum(a, [Ellipsis], b, [Ellipsis]),
                         np.multiply(a, b))

        # inner(a,b)
        for n in range(1,17):
            a = np.arange(2*3*n, dtype=dtype).reshape(2,3,n)
            b = np.arange(n, dtype=dtype)
            assert_equal(np.einsum("...i, ...i", a, b), np.inner(a, b))
            assert_equal(np.einsum(a, [Ellipsis,0], b, [Ellipsis,0]),
                         np.inner(a, b))

        for n in range(1,11):
            a = np.arange(n*3*2, dtype=dtype).reshape(n,3,2)
            b = np.arange(n, dtype=dtype)
            assert_equal(np.einsum("i..., i...", a, b), np.inner(a.T, b.T).T)
            assert_equal(np.einsum(a, [0,Ellipsis], b, [0,Ellipsis]),
                         np.inner(a.T, b.T).T)

        # outer(a,b)
        for n in range(1,17):
            a = np.arange(3, dtype=dtype)+1
            b = np.arange(n, dtype=dtype)+1
            assert_equal(np.einsum("i,j", a, b), np.outer(a, b))
            assert_equal(np.einsum(a, [0], b, [1]), np.outer(a, b))

        # Suppress the complex warnings for the 'as f8' tests
        ctx = WarningManager()
        ctx.__enter__()
        try:
            warnings.simplefilter('ignore', np.ComplexWarning)

            # matvec(a,b) / a.dot(b) where a is matrix, b is vector
            for n in range(1,17):
                a = np.arange(4*n, dtype=dtype).reshape(4,n)
                b = np.arange(n, dtype=dtype)
                assert_equal(np.einsum("ij, j", a, b), np.dot(a, b))
                assert_equal(np.einsum(a, [0,1], b, [1]), np.dot(a, b))

                c = np.arange(4, dtype=dtype)
                np.einsum("ij,j", a, b, out=c,
                            dtype='f8', casting='unsafe')
                assert_equal(c,
                            np.dot(a.astype('f8'),
                                   b.astype('f8')).astype(dtype))
                c[...] = 0
                np.einsum(a, [0,1], b, [1], out=c,
                            dtype='f8', casting='unsafe')
                assert_equal(c,
                            np.dot(a.astype('f8'),
                                   b.astype('f8')).astype(dtype))

            for n in range(1,17):
                a = np.arange(4*n, dtype=dtype).reshape(4,n)
                b = np.arange(n, dtype=dtype)
                assert_equal(np.einsum("ji,j", a.T, b.T), np.dot(b.T, a.T))
                assert_equal(np.einsum(a.T, [1,0], b.T, [1]), np.dot(b.T, a.T))

                c = np.arange(4, dtype=dtype)
                np.einsum("ji,j", a.T, b.T, out=c, dtype='f8', casting='unsafe')
                assert_equal(c,
                        np.dot(b.T.astype('f8'),
                               a.T.astype('f8')).astype(dtype))
                c[...] = 0
                np.einsum(a.T, [1,0], b.T, [1], out=c,
                            dtype='f8', casting='unsafe')
                assert_equal(c,
                        np.dot(b.T.astype('f8'),
                               a.T.astype('f8')).astype(dtype))

            # matmat(a,b) / a.dot(b) where a is matrix, b is matrix
            for n in range(1,17):
                if n < 8 or dtype != 'f2':
                    a = np.arange(4*n, dtype=dtype).reshape(4,n)
                    b = np.arange(n*6, dtype=dtype).reshape(n,6)
                    assert_equal(np.einsum("ij,jk", a, b), np.dot(a, b))
                    assert_equal(np.einsum(a, [0,1], b, [1,2]), np.dot(a, b))

            for n in range(1,17):
                a = np.arange(4*n, dtype=dtype).reshape(4,n)
                b = np.arange(n*6, dtype=dtype).reshape(n,6)
                c = np.arange(24, dtype=dtype).reshape(4,6)
                np.einsum("ij,jk", a, b, out=c, dtype='f8', casting='unsafe')
                assert_equal(c,
                            np.dot(a.astype('f8'),
                                   b.astype('f8')).astype(dtype))
                c[...] = 0
                np.einsum(a, [0,1], b, [1,2], out=c,
                                dtype='f8', casting='unsafe')
                assert_equal(c,
                            np.dot(a.astype('f8'),
                                   b.astype('f8')).astype(dtype))

            # matrix triple product (note this is not currently an efficient
            # way to multiply 3 matrices)
            a = np.arange(12, dtype=dtype).reshape(3,4)
            b = np.arange(20, dtype=dtype).reshape(4,5)
            c = np.arange(30, dtype=dtype).reshape(5,6)
            if dtype != 'f2':
                assert_equal(np.einsum("ij,jk,kl", a, b, c),
                                    a.dot(b).dot(c))
                assert_equal(np.einsum(a, [0,1], b, [1,2], c, [2,3]),
                                    a.dot(b).dot(c))

            d = np.arange(18, dtype=dtype).reshape(3,6)
            np.einsum("ij,jk,kl", a, b, c, out=d,
                                dtype='f8', casting='unsafe')
            assert_equal(d, a.astype('f8').dot(b.astype('f8')
                        ).dot(c.astype('f8')).astype(dtype))
            d[...] = 0
            np.einsum(a, [0,1], b, [1,2], c, [2,3], out=d,
                                dtype='f8', casting='unsafe')
            assert_equal(d, a.astype('f8').dot(b.astype('f8')
                        ).dot(c.astype('f8')).astype(dtype))

            # tensordot(a, b)
            if np.dtype(dtype) != np.dtype('f2'):
                a = np.arange(60, dtype=dtype).reshape(3,4,5)
                b = np.arange(24, dtype=dtype).reshape(4,3,2)
                assert_equal(np.einsum("ijk, jil -> kl", a, b),
                                np.tensordot(a,b, axes=([1,0],[0,1])))
                assert_equal(np.einsum(a, [0,1,2], b, [1,0,3], [2,3]),
                                np.tensordot(a,b, axes=([1,0],[0,1])))

                c = np.arange(10, dtype=dtype).reshape(5,2)
                np.einsum("ijk,jil->kl", a, b, out=c,
                                        dtype='f8', casting='unsafe')
                assert_equal(c, np.tensordot(a.astype('f8'), b.astype('f8'),
                                        axes=([1,0],[0,1])).astype(dtype))
                c[...] = 0
                np.einsum(a, [0,1,2], b, [1,0,3], [2,3], out=c,
                                        dtype='f8', casting='unsafe')
                assert_equal(c, np.tensordot(a.astype('f8'), b.astype('f8'),
                                        axes=([1,0],[0,1])).astype(dtype))
        finally:
            ctx.__exit__()

        # logical_and(logical_and(a!=0, b!=0), c!=0)
        a = np.array([1,   3,   -2,   0,   12,  13,   0,   1], dtype=dtype)
        b = np.array([0,   3.5, 0.,   -2,  0,   1,    3,   12], dtype=dtype)
        c = np.array([True,True,False,True,True,False,True,True])
        assert_equal(np.einsum("i,i,i->i", a, b, c,
                                dtype='?', casting='unsafe'),
                            np.logical_and(np.logical_and(a!=0, b!=0), c!=0))
        assert_equal(np.einsum(a, [0], b, [0], c, [0], [0],
                                dtype='?', casting='unsafe'),
                            np.logical_and(np.logical_and(a!=0, b!=0), c!=0))

        a = np.arange(9, dtype=dtype)
        assert_equal(np.einsum(",i->", 3, a), 3*np.sum(a))