import string

import numpy as np
import theano
import theano.tensor as T

from theano.sandbox.cuda import cuda_available, GpuOp
from theano.ifelse import ifelse

if cuda_available:
    from theano.sandbox.cuda import (basic_ops, CudaNdarrayType,
                                     CudaNdarray)
from theano.misc.pycuda_init import pycuda_available
if pycuda_available:
    import pycuda.gpuarray

try:
    import scikits.cuda
    from scikits.cuda import fft, cublas
    scikits.cuda.misc.init()
    scikits_cuda_available = True
except ImportError:
    scikits_cuda_available = False


# TODO: investigate the effect of enabling fastmath on FFT performance
# (how can it be enabled?).

# base class for shared code between scikits.cuda-based ops
class ScikitsCudaOp(GpuOp):
    def __eq__(self, other):
        return type(self) == type(other)

    def __hash__(self):
        return hash(type(self))

    def __str__(self):
        return self.__class__.__name__

    def output_type(self, inp):
        raise NotImplementedError

    def make_node(self, inp):
        inp = basic_ops.gpu_contiguous(
            basic_ops.as_cuda_ndarray_variable(inp))

        assert inp.dtype == "float32"

        return theano.Apply(self, [inp], [self.output_type(inp)()])

    def make_thunk(self, node, storage_map, _, _2):
        if not scikits_cuda_available:
            raise RuntimeError(
                "scikits.cuda is needed for all GPU fft implementation,"
                " including fftconv.")


class CuFFTOp(ScikitsCudaOp):
    def output_type(self, inp):
        # add one extra dim for real/imag
        return CudaNdarrayType(
            broadcastable=[False] * (inp.type.ndim + 1))

    def make_thunk(self, node, storage_map, _, _2):
        super(CuFFTOp, self).make_thunk(node, storage_map, _, _2)

        from theano.misc.pycuda_utils import to_gpuarray
        inputs = [storage_map[v] for v in node.inputs]
        outputs = [storage_map[v] for v in node.outputs]

        plan_input_shape = [None]
        plan = [None]

        def thunk():
            input_shape = inputs[0][0].shape

            # construct output shape
            output_shape = list(input_shape)
            # DFT of real input is symmetric, no need to store
            # redundant coefficients
            output_shape[-1] = output_shape[-1] // 2 + 1
            # extra dimension with length 2 for real/imag
            output_shape += [2]
            output_shape = tuple(output_shape)

            z = outputs[0]

            # only allocate if there is no previous allocation of the
            # right size.
            if z[0] is None or z[0].shape != output_shape:
                z[0] = CudaNdarray.zeros(output_shape)

            input_pycuda = to_gpuarray(inputs[0][0])
            # I thought we'd need to change the type on output_pycuda
            # so it is complex64, but as it turns out scikits.cuda.fft
            # doesn't really care either way and treats the array as
            # if it is complex64 anyway.
            output_pycuda = to_gpuarray(z[0])

            # only initialise plan if necessary
            if plan[0] is None or plan_input_shape[0] != input_shape:
                plan_input_shape[0] = input_shape
                plan[0] = fft.Plan(input_shape[1:], np.float32, np.complex64,
                                   batch=input_shape[0])

            fft.fft(input_pycuda, output_pycuda, plan[0])

        thunk.inputs = inputs
        thunk.outputs = outputs
        thunk.lazy = False

        return thunk


class CuIFFTOp(ScikitsCudaOp):
    def output_type(self, inp):
        # remove extra real/imag dim
        return CudaNdarrayType(
            broadcastable=[False] * (inp.type.ndim - 1))

    def make_thunk(self, node, storage_map, _, _2):
        super(CuIFFTOp, self).make_thunk(node, storage_map, _, _2)

        from theano.misc.pycuda_utils import to_gpuarray
        inputs = [storage_map[v] for v in node.inputs]
        outputs = [storage_map[v] for v in node.outputs]

        plan_input_shape = [None]
        plan = [None]

        def thunk():
            input_shape = inputs[0][0].shape

            # construct output shape
            # chop off the extra length-2 dimension for real/imag
            output_shape = list(input_shape[:-1])
            # restore full signal length
            output_shape[-1] = (output_shape[-1] - 1) * 2
            output_shape = tuple(output_shape)

            z = outputs[0]

            # only allocate if there is no previous allocation of the
            # right size.
            if z[0] is None or z[0].shape != output_shape:
                z[0] = CudaNdarray.zeros(output_shape)

            input_pycuda = to_gpuarray(inputs[0][0])
            # input_pycuda is a float32 array with an extra dimension,
            # but will be interpreted by scikits.cuda as a complex64
            # array instead.
            output_pycuda = to_gpuarray(z[0])

            # only initialise plan if necessary
            if plan[0] is None or plan_input_shape[0] != input_shape:
                plan_input_shape[0] = input_shape
                plan[0] = fft.Plan(output_shape[1:], np.complex64, np.float32,
                                   batch=output_shape[0])

            fft.ifft(input_pycuda, output_pycuda, plan[0])
            # strangely enough, enabling rescaling here makes it run
            # very, very slowly.  so do this rescaling manually
            # afterwards!

        thunk.inputs = inputs
        thunk.outputs = outputs
        thunk.lazy = False

        return thunk


def to_complex_gpuarray(x, copyif=False):
    """
    adapted version of theano.misc.pycuda_utils.to_gpuarray that takes
    an array with an extra trailing dimension of length 2 for
    real/imaginary parts, and turns it into a complex64 PyCUDA
    GPUArray.
    """
    if not isinstance(x, CudaNdarray):
        raise ValueError("We can transfer only CudaNdarray "
                         "to pycuda.gpuarray.GPUArray")
    else:
        # Check if trailing dimension has length 2
        assert x.shape[-1] == 2

        # check if dtype is float32
        assert x.dtype == 'float32'

        # Check if it is c contiguous
        size = 1
        c_contiguous = True
        for i in range(x.ndim - 1, -1, -1):
            if x.shape[i] == 1:
                continue
            if x._strides[i] != size:
                c_contiguous = False
                break
            size *= x.shape[i]
        if not c_contiguous:
            if copyif:
                x = x.copy()
            else:
                raise ValueError("We were asked to not copy memory, "
                                 "but the memory is not c contiguous.")

        # Now x is always c contiguous
        px = pycuda.gpuarray.GPUArray(x.shape[:-1], np.complex64, base=x,
                                      gpudata=x.gpudata)
        return px


def bptrs(a):
    """
    Pointer array when input represents a batch of matrices.

    taken from scikits.cuda tests/test_cublas.py
    """
    return pycuda.gpuarray.arange(a.ptr, a.ptr + a.shape[0] * a.strides[0],
                                  a.strides[0], dtype=cublas.ctypes.c_void_p)


def sc_complex_dot_batched(bx_gpu, by_gpu, bc_gpu, transa='N', transb='N',
                           handle=None):
    """
    uses cublasCgemmBatched to compute a bunch of complex dot products
    in parallel
    """
    if handle is None:
        handle = scikits.cuda.misc._global_cublas_handle

    assert len(bx_gpu.shape) == 3
    assert len(by_gpu.shape) == 3
    assert len(bc_gpu.shape) == 3
    assert bx_gpu.dtype == np.complex64
    assert by_gpu.dtype == np.complex64
    assert bc_gpu.dtype == np.complex64

    # Get the shapes of the arguments
    bx_shape = bx_gpu.shape
    by_shape = by_gpu.shape

    # Perform matrix multiplication for 2D arrays:
    alpha = np.complex64(1.0)
    beta = np.complex64(0.0)

    transa = string.lower(transa)
    transb = string.lower(transb)

    if transb in ['t', 'c']:
        N, m, k = by_shape
    elif transb in ['n']:
        N, k, m = by_shape
    else:
        raise ValueError('invalid value for transb')

    if transa in ['t', 'c']:
        N2, l, n = bx_shape
    elif transa in ['n']:
        N2, n, l = bx_shape
    else:
        raise ValueError('invalid value for transa')

    if l != k:
        raise ValueError('objects are not aligned')

    if N != N2:
        raise ValueError('batch sizes are not the same')

    if transb == 'n':
        lda = max(1, m)
    else:
        lda = max(1, k)

    if transa == 'n':
        ldb = max(1, k)
    else:
        ldb = max(1, n)

    ldc = max(1, m)

    # construct pointer arrays needed for cublasCgemmBatched
    bx_arr = bptrs(bx_gpu)
    by_arr = bptrs(by_gpu)
    bc_arr = bptrs(bc_gpu)

    cublas.cublasCgemmBatched(handle, transb, transa, m, n, k, alpha,
                              by_arr.gpudata, lda, bx_arr.gpudata, ldb,
                              beta, bc_arr.gpudata, ldc, N)


class BatchedComplexDotOp(ScikitsCudaOp):
    """
    This version uses cublasCgemmBatched under the hood, instead of
    doing multiple cublasCgemm calls.
    """
    def make_node(self, inp1, inp2):
        inp1 = basic_ops.gpu_contiguous(
            basic_ops.as_cuda_ndarray_variable(inp1))
        inp2 = basic_ops.gpu_contiguous(
            basic_ops.as_cuda_ndarray_variable(inp2))

        assert inp1.dtype == "float32"
        assert inp2.dtype == "float32"
        assert inp1.ndim == 4  # (batch, a, b, real/imag)
        assert inp2.ndim == 4

        return theano.Apply(self, [inp1, inp2], [self.output_type(inp1)()])

    def output_type(self, inp):
        return CudaNdarrayType(broadcastable=[False] * inp.type.ndim)

    def make_thunk(self, node, storage_map, _, _2):
        super(BatchedComplexDotOp, self).make_thunk(node, storage_map, _, _2)

        inputs = [storage_map[v] for v in node.inputs]
        outputs = [storage_map[v] for v in node.outputs]

        def thunk():
            bx = inputs[0]
            by = inputs[1]

            input_shape_x = bx[0].shape  # (batch, a, b, 2)
            input_shape_y = by[0].shape  # (batch, b, c, 2)

            output_shape = (input_shape_x[0], input_shape_x[1],
                            input_shape_y[2], 2)  # (batch, a, c, 2)

            bz = outputs[0]

            # only allocate if there is no previous allocation of the
            # right size.
            if bz[0] is None or bz[0].shape != output_shape:
                bz[0] = CudaNdarray.zeros(output_shape)

            input_bx_pycuda = to_complex_gpuarray(bx[0])
            input_by_pycuda = to_complex_gpuarray(by[0])
            output_b_pycuda = to_complex_gpuarray(bz[0])

            # fancy native batched version
            sc_complex_dot_batched(input_bx_pycuda, input_by_pycuda,
                                   output_b_pycuda)

        thunk.inputs = inputs
        thunk.outputs = outputs
        thunk.lazy = False

        return thunk


cufft = CuFFTOp()
cuifft = CuIFFTOp()
batched_complex_dot = BatchedComplexDotOp()


def mult_and_reduce(input_fft_v, filters_fft_v, input_shape=None,
                    filter_shape=None):
    """
    input_fft_v is (b, ic, i0, i1//2 + 1, 2)
    filters_fft_v is (oc, ic, i0, i1//2 + 1, 2)
    """

    if input_shape is None:
        input_shape = input_fft_v.shape  # symbolic

    if filter_shape is None:
        filter_shape = filters_fft_v.shape  # symbolic

    b, ic, i0, i1_f, _ = input_shape
    oc = filter_shape[0]

    # reshape to flatten the dimensions that are multiplied elemwise
    input_r = input_fft_v.reshape((b, ic, i0 * i1_f, 2))
    filters_r = filters_fft_v.reshape((oc, ic, i0 * i1_f, 2))

    # shuffle for batched dot product
    input_s = input_r.dimshuffle(2, 0, 1, 3)  # (i0 * i1_f, b, ic, 2)
    filters_s = filters_r.dimshuffle(2, 1, 0, 3)  # (i0 * i1_f, ic, oc, 2)

    output_s = batched_complex_dot(input_s, filters_s)

    # shuffle again
    output_r = output_s.dimshuffle(1, 2, 0, 3)

    # reshape to unflatten
    output = output_r.reshape((b, oc, i0, i1_f, 2))

    return output


def conv2d_fft(input, filters, image_shape=None, filter_shape=None,
               border_mode='valid', pad_last_dim=False):
    """
    Perform a convolution through fft.

    Only support input which will be even on the last dimension
    (width).  All other dimensions can be anything and the filters can
    have an even or odd width.

    If you must use input which has an odd width, you can either pad
    it or use the `pad_last_dim` argument which will do it for you and
    take care to strip the padding before returning.  Don't use this
    argument if you are not sure the input is odd since the padding is
    unconditional and will make even input odd, thus leading to
    problems.

    On valid mode the filters must be smaller than the input.

    input: (b, ic, i0, i1)
    filters: (oc, ic, f0, f1)

    border_mode: 'valid' of 'full'

    pad_last_dim: Unconditionally pad the last dimension of the input
                  to to turn it from odd to even.  Will strip the
                  padding before returning the result.
    """

    # use symbolic shapes to compute shape info at runtime if not specified
    if image_shape is None:
        image_shape = input.shape

    if filter_shape is None:
        filter_shape = filters.shape

    # batch size, input channels, input dim 0, input dim 1
    b, ic, i0, i1 = image_shape
    # output channels, input channels, filter dim 0, filter dim 1
    oc, ic_, f0, f1 = filter_shape

    # pad filters/image to output shape
    if border_mode == 'valid':
        o0 = i0
        if pad_last_dim:
            o1 = i1 + 1
            input_padded = T.zeros((b, ic, o0, o1), dtype='float32')
            input_padded = T.set_subtensor(input_padded[:, :, :i0, :i1],
                                       input)
        else:
            o1 = i1
            input_padded = input

        filters_padded = T.zeros((oc, ic, o0, o1), dtype='float32')
        filters_padded = T.set_subtensor(filters_padded[:, :, :f0, :f1],
                                         filters)

    elif border_mode == 'full':

        # In this particular case, the values of (o0, o1) represent
        # the dimensions of the work buffer more than the actual dimensions
        # of the desired output.
        o0 = i0 + 2 * (f0 - 1)
        o1 = i1 + 2 * (f1 - 1)

        if pad_last_dim:
            o1 = o1 + 1

        # We line up the filters and the images in a way
        # such that the filters are tightly placed against the
        # top-left of the array, and the images intersect with
        # them on one pixel. The top-left pixel of the images
        # is the bottom-right pixel of the filters when we
        # do the layout here.

        filters_padded = T.zeros((oc, ic, o0, o1), dtype='float32')
        filters_padded = T.set_subtensor(filters_padded[:, :, :f0, :f1],
                                         filters)

        input_padded = T.zeros((b, ic, o0, o1), dtype='float32')
        input_padded = T.set_subtensor(input_padded[:, :, (f0 - 1):(f0 - 1 + i0), (f1 - 1):(f1 - 1 + i1)],
                                       input)
    else:
        raise ValueError('invalid mode')

    input_padded = T.opt.Assert("in conv2d_fft: width is not even")(
        input_padded, T.eq(o1 % 2, 0))

    # reshape for FFT
    input_flat = input_padded.reshape((b * ic, o0, o1))
    filters_flat = filters_padded.reshape((oc * ic, o0, o1))

    # perform FFT
    input_fft_flat = cufft(input_flat)  # (b * ic, o0, o1//2 + 1, 2)
    filters_fft_flat = cufft(filters_flat)  # (oc * ic, o0, o1//2 + 1, 2)

    # unfold ic dimension
    input_fft_v_shape = (b, ic, o0, o1 // 2 + 1, 2)
    filters_fft_v_shape = (oc, ic, o0, o1 // 2 + 1, 2)
    input_fft_v = input_fft_flat.reshape(input_fft_v_shape)
    filters_fft_v = filters_fft_flat.reshape(filters_fft_v_shape)

    # (b, oc, o0, o1//2 + 1, 2)
    output_fft_s = mult_and_reduce(input_fft_v, filters_fft_v,
                                   input_shape=input_fft_v_shape,
                                   filter_shape=filters_fft_v_shape)

    # reshape for IFFT
    output_fft_flat = output_fft_s.reshape((b * oc, o0, o1 // 2 + 1, 2))

    # perform IFFT
    output_flat = cuifft(output_fft_flat)  # (b * oc, o0, o1)

    # reshape
    output_circ = output_flat.reshape((b, oc, o0, o1))  # circular!

    # Now we extract the region of interest.
    # We just cut it out from the output_circ
    # array that was used for the computation.
    # We do not need to handle pad_last_dim in a
    # special way because we specify explicitly here
    # how much values are expected.
    if border_mode == 'valid':
        output = output_circ[:, :, (f0-1):(f0-1 + i0-f0+1), (f1-1):(f1-1 + i1-f1+1)]
    elif border_mode == 'full':
        output = output_circ[:, :, (f0-1):(f0-1 + i0+f0-1), (f1-1):(f1-1 + i1+f1-1)]
    else:
        raise ValueError('invalid mode')

    # Rescale manually. This is just a factor that comes in during the
    # trip through FFT and inverse FFT.
    output = (1.0 / T.cast(o0 * o1, 'float32')) * output

    # output should now be the result of a batched valid convolution
    # of the input with the filters.
    return basic_ops.as_cuda_ndarray_variable(output)


def conv3d_fft(input, filters, image_shape=None, filter_shape=None,
               border_mode='valid', pad_last_dim=False):
    """
    Perform a convolution through fft.

    Only supports input whose shape is even on the last dimension.
    All other dimensions can be anything and the filters can
    have an even or odd last dimension.

    The semantics associated with the last three dimensions
    are not important as long as they are in the same order between
    the inputs and the filters. For example, when the convolution
    is done on a sequence of images, they could be either
    (duration, height, width) or (height, width, duration).

    If you must use input which has an odd width, you can either pad
    it or use the `pad_last_dim` argument which will do it for you and
    take care to strip the padding before returning. pad_last_dim checks
    that the last dimension is odd before the actual paddding

    On valid mode the filters must be smaller than the input.

    input: (b, ic, i0, i1, i2)
    filters: (oc, ic, f0, f1, i2)

    border_mode: 'valid' of 'full'

    pad_last_dim: Unconditionally pad the last dimension of the input
                  to to turn it from odd to even.  Will strip the
                  padding before returning the result.
    """

    # use symbolic shapes to compute shape info at runtime if not specified
    if image_shape is None:
        image_shape = input.shape

    if filter_shape is None:
        filter_shape = filters.shape

    # batch size, input channels, input dim 0, input dim 1
    b, ic, i0, i1, i2 = image_shape
    # output channels, input channels, filter dim 0, filter dim 1
    oc, ic_, f0, f1, f2 = filter_shape

    # Check that the last dimension is odd
    is_odd = T.eq(T.mod(input.shape[4], 2), 1)

    # pad filters/image to output shape
    if border_mode == 'valid':
        o0 = i0
        o1 = i1
        o2 = i2
        input_padded = input
        if pad_last_dim:
            o2 = ifelse(is_odd, o2 + 1, o2)
            input_padded = T.zeros((b, ic, o0, o1, o2), dtype='float32')
            input_padded = T.set_subtensor(input_padded[:, :, :i0, :i1, :i2],
                                           input)
        filters_padded = T.zeros((oc, ic, o0, o1, o2), dtype='float32')
        filters_padded = T.set_subtensor(filters_padded[:, :, :f0, :f1, :f2],
                                         filters)

    elif border_mode == 'full':

        # In this particular case, the values of (o0, o1) represent
        # the dimensions of the work buffer more than the actual dimensions
        # of the desired output.
        o0 = i0 + 2 * (f0 - 1)
        o1 = i1 + 2 * (f1 - 1)
        o2 = i2 + 2 * (f2 - 1)

        if pad_last_dim:
            o2 = ifelse(is_odd, o2 + 1, o2)

        # We line up the filters and the images in a way
        # such that the filters are tightly placed against the
        # top-left of the array, and the images intersect with
        # them on one pixel. The top-left pixel of the images
        # is the bottom-right pixel of the filters when we
        # do the layout here.

        filters_padded = T.zeros((oc, ic, o0, o1, o2), dtype='float32')
        filters_padded = T.set_subtensor(filters_padded[:, :, :f0, :f1, :f2],
                                         filters)

        input_padded = T.zeros((b, ic, o0, o1, o2), dtype='float32')
        input_padded = T.set_subtensor(input_padded[:, :, (f0 - 1):(f0 - 1 + i0), (f1 - 1):(f1 - 1 + i1), (f2 - 1):(f2 - 1 + i2)],
                                       input)
    else:
        raise ValueError('invalid mode')

    # reshape for FFT
    input_flat = input_padded.reshape((b * ic, o0, o1, o2))
    filters_flat = filters_padded.reshape((oc * ic, o0, o1, o2))

    # perform FFT
    input_fft_flat = cufft(input_flat)  # (b * ic, o0, o1, o2//2 + 1, 2)
    filters_fft_flat = cufft(filters_flat)  # (oc * ic, o0, o1, o2//2 + 1, 2)

    # Unfold ic dimension.
    # We have to collapse two dimensions together
    # in order to reuse the same `mult_and_reduce`.
    # This explains the o0 * 01 instead of just keeping
    # the two dimensions intact.
    input_fft_v_shape = (b, ic, o0 * o1, o2 // 2 + 1, 2)
    filters_fft_v_shape = (oc, ic, o0 * o1, o2 // 2 + 1, 2)

    input_fft_v = input_fft_flat.reshape(input_fft_v_shape)
    filters_fft_v = filters_fft_flat.reshape(filters_fft_v_shape)

    # (b, oc, o0 * o1, o2//2 + 1, 2)
    output_fft_s = mult_and_reduce(input_fft_v, filters_fft_v,
                                   input_shape=input_fft_v_shape,
                                   filter_shape=filters_fft_v_shape)
    #output_fft_s = input_fft_v

    # reshape for IFFT
    output_fft_flat = output_fft_s.reshape((b * oc, o0, o1, o2 // 2 + 1, 2))

    # perform IFFT
    output_flat = cuifft(output_fft_flat)  # (b * oc, o0, o1, o2)

    # reshape
    output_circ = output_flat.reshape((b, oc, o0, o1, o2))  # circular!

    # Now we extract the region of interest.
    # We just cut it out from the output_circ
    # array that was used for the computation.
    # We do not need to handle pad_last_dim in a
    # special way because we specify explicitly here
    # how much values are expected.
    if border_mode == 'valid':
        output = output_circ[:, :, (f0-1):(f0-1 + i0-f0+1), (f1-1):(f1-1 + i1-f1+1), (f2-1):(f2-1 + i2-f2+1)]
    elif border_mode == 'full':
        output = output_circ[:, :, (f0-1):(f0-1 + i0+f0-1), (f1-1):(f1-1 + i1+f1-1), (f2-1):(f2-1 + i2+f2-1)]
    else:
        raise ValueError('invalid mode')
    #output = output_circ[:, :, :, :, :]

    # Rescale manually. This is just a factor that comes in during the
    # trip through FFT and inverse FFT.
    output = (1.0 / T.cast(o0 * o1 * o2, 'float32')) * output

    # output should now be the result of a batched valid convolution
    # of the input with the filters.
    return basic_ops.as_cuda_ndarray_variable(output)


