"""
Helper routines for generating gpu kernels for nvcc.

"""

try:
    from pygpu import gpuarray
except ImportError:
    pass


def nvcc_kernel(name, params, body):
    """
    Return the c code of a kernel function.

    Parameters
    ----------
    params
        The parameters to the function as one or more strings.
    body
        The [nested] list of statements for the body of the function.
        These will be separated by ';' characters.

    """
    paramstr = ", ".join(params)

    def flatbody():
        for b in body:
            if isinstance(b, (list, tuple)):
                yield from b
            else:
                yield b

    bodystr = ";\n".join(flatbody())
    return (
        """#include "cluda.h"

    KERNEL void %(name)s (%(paramstr)s)
    {
        %(bodystr)s;
    }
    """
        % locals()
    )


def code_version(version):
    """
    Decorator to support version-based cache mechanism.

    """
    if not isinstance(version, tuple):
        raise TypeError("version must be tuple", version)

    def deco(f):
        f.code_version = version
        return f

    return deco


UNVERSIONED = ()


@code_version((2,))
def inline_reduce(N, buf, pos, count, manner_fn):
    """
    Return C++ code for a function that reduces a contiguous buffer.

    Parameters
    ----------
    N
        Length of the buffer.
    buf
        buffer pointer.
    pos
        Index of executing thread.
    count
        Number of executing threads.
    manner_fn
        A function that accepts strings of arguments a and b, and
        returns c code for their reduction.

          return "%(a)s + %(b)s"

        for a sum reduction.

    Notes
    -----
    `buf` should be in gpu shared memory, we access it many times.

    This function leaves the answer in position 0 of the buffer. The
    rest of the buffer is trashed by this function.

    """
    loop_line = manner_fn("{}[{}]".format(buf, pos), "%s[i]" % (buf))
    r_n = manner_fn("{}[{}]".format(buf, pos), "{}[{}+_n]".format(buf, pos))

    return (
        """
    {
        // This function trashes buf[1..warpSize],
        // leaving the reduction result in buf[0].

        if (%(pos)s < warpSize) {
            for (int i = %(pos)s + warpSize; i < %(N)s; i += warpSize)
            {
                %(buf)s[%(pos)s] = %(loop_line)s;
            }
        }
        __syncthreads();
        //reduce so that %(pos)s 0 has the reduction of everything
        for (unsigned int _n = warpSize / 2; _n > 0; _n /= 2) {
          if (%(pos)s < _n && %(pos)s + _n < %(N)s)
            %(buf)s[%(pos)s] = %(r_n)s;
          __syncthreads();
        }
    }
    """
        % locals()
    )


@code_version(inline_reduce.code_version)
def inline_reduce_max(N, buf, pos, count):
    return inline_reduce(N, buf, pos, count, lambda a, b: "max({}, {})".format(a, b))


@code_version(inline_reduce.code_version)
def inline_reduce_sum(N, buf, pos, count):
    return inline_reduce(N, buf, pos, count, lambda a, b: "{} + {}".format(a, b))


@code_version(inline_reduce.code_version)
def inline_reduce_min(N, buf, pos, count):
    return inline_reduce(N, buf, pos, count, lambda a, b: "min({}, {})".format(a, b))


@code_version(inline_reduce.code_version)
def inline_reduce_prod(N, buf, pos, count):
    return inline_reduce(N, buf, pos, count, lambda a, b: "{} * {}".format(a, b))


@code_version((2,) + inline_reduce_max.code_version + inline_reduce_sum.code_version)
def inline_softmax(N, buf, buf2, threadPos, threadCount, dtype="float32"):
    """
    Generate code for a softmax.

    On entry, `buf` and `buf2` must contain two identical copies of
    the input to softmax.

    After the code returns `buf` contains the softmax, `buf2` contains
    un-normalized softmax.

    Parameters
    ----------
    N
        Length of the buffer.
    threadPos
        Index of executing thread.
    threadCount
        Number of executing threads.
    dtype
        Dtype of the softmax's output.

    Notes
    -----
    `buf` and `buf2` should be in gpu shared memory, we access it many
    times.

    We use __i as an int variable in a loop.

    """
    ctype = gpuarray.dtype_to_ctype(dtype)
    # get max of buf (trashing all but buf[0])
    return [
        inline_reduce_max(N, buf, threadPos, threadCount),
        "__syncthreads()",
        ("%s row_max = " + buf + "[0]") % ctype,
        "__syncthreads()",
        "for(int __i=" + threadPos + "; __i<" + N + "; __i+=" + threadCount + "){",
        buf + "[__i] = exp(" + buf2 + "[__i] - row_max)",
        buf2 + "[__i] = " + buf + "[__i]",
        "}",
        "__syncthreads()",
        inline_reduce_sum(N, buf, threadPos, threadCount),
        "__syncthreads()",
        ("%s row_sum = " + buf + "[0]") % ctype,
        "__syncthreads()",
        # divide each exp() result by the sum to complete the job.
        "for(int __i=" + threadPos + "; __i<" + N + "; __i+=" + threadCount + "){",
        buf + "[__i] = " + buf2 + "[__i] / row_sum",
        "}",
        "__syncthreads()",
    ]


@code_version((3,))
def inline_reduce_fixed_shared(
    N,
    buf,
    x,
    stride_x,
    load_x,
    pos,
    count,
    manner_fn,
    manner_init,
    b="",
    stride_b="",
    load_b="",
    dtype="float32",
):
    """
    Return C++ code for a function that reduces a contiguous buffer.

    This function leaves the answer in position 0 of the buffer. The
    rest of the buffer is trashed by this function.

    Parameters
    ----------
    N
        Length of the buffer.
    buf
        Buffer pointer of size warpSize * sizeof(dtype).
    x
        Input data.
    stride_x
        Input data stride.
    load_x
        Wrapper to read from x.
    pos
        Index of executing thread.
    count
        Number of executing threads.
    manner_fn
        A function that accepts strings of arguments a and b, and
        returns c code for their reduction.

          return "%(a)s + %(b)s"

        for a sum reduction.
    manner_init
        A function that accepts strings of arguments a and return c
        code for its initialization.
    b
        Optional, pointer to the bias.
    stride_b
        Optional, the stride of b if b is provided.
    load_b
        Optional, wrapper to read from b if b is provided.
    dtype
        Optional, the dtype of the output.

    Notes
    -----
    `buf` should be in gpu shared memory, we access it many times.

    """
    if b:
        init = manner_init(
            "%(load_x)s(%(x)s[%(pos)s * %(stride_x)s]) +"
            " %(load_b)s(%(b)s[%(pos)s * %(stride_b)s])" % locals()
        )
        loop_line = manner_fn(
            "red",
            manner_init(
                "%(load_x)s(%(x)s[i * %(stride_x)s]) + "
                "%(load_b)s(%(b)s[i * %(stride_b)s])" % locals()
            ),
        )
    else:
        init = manner_init("%(load_x)s(%(x)s[%(pos)s * %(stride_x)s])" % locals())
        loop_line = manner_fn(
            "red", manner_init("%(load_x)s(%(x)s[i * %(stride_x)s])" % locals())
        )
    loop_line2 = manner_fn("{}[{}]".format(buf, pos), "%s[i]" % buf)
    r_n = manner_fn("{}[{}]".format(buf, pos), "{}[{}+_n]".format(buf, pos))

    ctype = gpuarray.dtype_to_ctype(dtype)
    return (
        """
    {
        // This function trashes buf[1..n_threads],
        // leaving the reduction result in buf[0].
        %(ctype)s red = %(init)s;
        #pragma unroll 16
        for (int i = %(pos)s + %(count)s; i<%(N)s; i += %(count)s) {
          red = %(loop_line)s;
        }
        buf[%(pos)s] = red;
        __syncthreads();
        if (%(pos)s < warpSize) {
            for (int i = %(pos)s + warpSize; i < %(count)s; i += warpSize) {
                %(buf)s[%(pos)s] = %(loop_line2)s;
            }
        }
        __syncthreads();
        //reduce so that %(pos)s 0 has the reduction of everything
        for (unsigned int _n = warpSize / 2; _n > 0; _n /= 2) {
          if (%(pos)s < _n && %(pos)s + _n < %(N)s)
            %(buf)s[%(pos)s] = %(r_n)s;
          __syncthreads();
        }
    }
    """
        % locals()
    )


@code_version(inline_reduce_fixed_shared.code_version)
def inline_reduce_fixed_shared_max(
    N,
    buf,
    x,
    stride_x,
    load_x,
    pos,
    count,
    b="",
    stride_b="",
    load_b="",
    dtype="float32",
):
    return inline_reduce_fixed_shared(
        N,
        buf,
        x,
        stride_x,
        load_x,
        pos,
        count,
        lambda a, b: "max({}, {})".format(a, b),
        lambda a: a,
        b,
        stride_b,
        load_b,
        dtype,
    )


@code_version((2,) + inline_reduce_max.code_version + inline_reduce_sum.code_version)
def inline_softmax_fixed_shared(
    N,
    buf,
    x,
    stride_x,
    load_x,
    sm,
    sm_stride,
    write_sm,
    threadPos,
    threadCount,
    b="",
    stride_b="",
    load_b="",
    dtype="float32",
):
    """
    Generate code to perform softmax with a fixed amount of shared
    memory.

    On entry, `buf` is assumed to be empty.

    On exit, `buf[0]` contains the softmax, `buf2` contains
    un-normalized softmax.

    Parameters
    ----------
    N
        Length of the buffer, atleast waprSize(32).
    buf
        A shared memory buffer of size warpSize * sizeof(dtype).
    x
        A ptr to the gpu memory where the row is stored.
    stride_x
        The stride between each element in x.
    load_x
        Wrapper to read from x.
    sm
        A ptr to the gpu memory to store the result.
    sm_stride
        The stride between each sm element.
    write_sm
        Wrapper before writing to sm.
    threadPos
        Index of executing thread.
    threadCount
        Number of executing threads.
    b
        Optional, pointer to the bias.
    stride_b
        Optional, the stride of b if b is provided.
    load_b
        Optional, wrapper to read from b if b is provided.
    dtype
        Optional, the dtype of the softmax's output if not float32.

    Notes
    -----
    `buf` should be in gpu shared memory, we access it many times.

    We use tx as an int variable in a loop.

    """
    ctype = gpuarray.dtype_to_ctype(dtype)
    ret = [
        # get max of buf (trashing all but buf[0])
        inline_reduce_fixed_shared_max(
            N,
            buf,
            x,
            stride_x,
            load_x,
            threadPos,
            threadCount,
            b,
            stride_b,
            load_b,
            dtype,
        ),
        "__syncthreads()",
        ("%s row_max = " + buf + "[0]") % ctype,
        "__syncthreads()",
        inline_reduce_fixed_shared(
            N,
            buf,
            x,
            stride_x,
            load_x,
            threadPos,
            threadCount,
            lambda a, b: "{} + {}".format(a, b),
            lambda a: "exp(%s - row_max)" % a,
            b,
            stride_b,
            load_b,
            dtype,
        ),
        "__syncthreads()",
        ("%s row_sum = " + buf + "[0]") % ctype,
        "__syncthreads()",
        "for (int tx = threadIdx.x; tx< N; tx += blockDim.x){",
    ]
    # This set all value correctly
    if b:
        ret += [
            "%(sm)s[tx * %(sm_stride)s] = "
            "  %(write_sm)s(exp(%(load_x)s(%(x)s[tx * %(stride_x)s]) +"
            "            %(load_b)s(%(b)s[tx * %(stride_b)s]) - row_max)"
            " / row_sum)" % locals()
        ]
    else:
        ret += [
            "%(sm)s[tx * %(sm_stride)s] = "
            "%(write_sm)s(exp(%(load_x)s(%(x)s[tx * %(stride_x)s]) - row_max)"
            " / row_sum)" % locals()
        ]
    ret += [
        "}",
        "__syncthreads()",
    ]
    return ret
