"""
This scripts specifies all PTX special objects.
"""
import numpy as np
from collections import defaultdict
import functools
import itertools
from inspect import Signature, Parameter


class Stub(object):
    '''
    A stub object to represent special objects that are meaningless
    outside the context of a CUDA kernel
    '''
    _description_ = '<ptx special value>'
    __slots__ = () # don't allocate __dict__

    def __new__(cls):
        raise NotImplementedError("%s is not instantiable" % cls)

    def __repr__(self):
        return self._description_


def stub_function(fn):
    '''
    A stub function to represent special functions that are meaningless
    outside the context of a CUDA kernel
    '''
    @functools.wraps(fn)
    def wrapped(*args, **kwargs):
        raise NotImplementedError("%s cannot be called from host code" % fn)
    return wrapped


#-------------------------------------------------------------------------------
# Thread and grid indices and dimensions


class Dim3(Stub):
    '''A triple, (x, y, z)'''
    _description_ = '<Dim3>'

    @property
    def x(self):
        pass

    @property
    def y(self):
        pass

    @property
    def z(self):
        pass


class threadIdx(Dim3):
    '''
    The thread indices in the current thread block. Each index is an integer
    spanning the range from 0 inclusive to the corresponding value of the
    attribute in :attr:`numba.cuda.blockDim` exclusive.
    '''
    _description_ = '<threadIdx.{x,y,z}>'


class blockIdx(Dim3):
    '''
    The block indices in the grid of thread blocks. Each index is an integer
    spanning the range from 0 inclusive to the corresponding value of the
    attribute in :attr:`numba.cuda.gridDim` exclusive.
    '''
    _description_ = '<blockIdx.{x,y,z}>'


class blockDim(Dim3):
    '''
    The shape of a block of threads, as declared when instantiating the kernel.
    This value is the same for all threads in a given kernel launch, even if
    they belong to different blocks (i.e. each block is "full").
    '''
    _description_ = '<blockDim.{x,y,z}>'


class gridDim(Dim3):
    '''
    The shape of the grid of blocks. This value is the same for all threads in
    a given kernel launch.
    '''
    _description_ = '<gridDim.{x,y,z}>'


class warpsize(Stub):
    '''
    The size of a warp. All architectures implemented to date have a warp size
    of 32.
    '''
    _description_ = '<warpsize>'


class laneid(Stub):
    '''
    This thread's lane within a warp. Ranges from 0 to
    :attr:`numba.cuda.warpsize` - 1.
    '''
    _description_ = '<laneid>'


class grid(Stub):
    '''grid(ndim)

    Return the absolute position of the current thread in the entire grid of
    blocks.  *ndim* should correspond to the number of dimensions declared when
    instantiating the kernel. If *ndim* is 1, a single integer is returned.
    If *ndim* is 2 or 3, a tuple of the given number of integers is returned.

    Computation of the first integer is as follows::

        cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x

    and is similar for the other two indices, but using the ``y`` and ``z``
    attributes.
    '''
    _description_ = '<grid(ndim)>'


class gridsize(Stub):
    '''gridsize(ndim)

    Return the absolute size (or shape) in threads of the entire grid of
    blocks. *ndim* should correspond to the number of dimensions declared when
    instantiating the kernel. If *ndim* is 1, a single integer is returned.
    If *ndim* is 2 or 3, a tuple of the given number of integers is returned.

    Computation of the first integer is as follows::

        cuda.blockDim.x * cuda.gridDim.x

    and is similar for the other two indices, but using the ``y`` and ``z``
    attributes.
    '''
    _description_ = '<gridsize(ndim)>'


#-------------------------------------------------------------------------------
# Array creation

class shared(Stub):
    '''
    Shared memory namespace
    '''
    _description_ = '<shared>'

    @stub_function
    def array(shape, dtype):
        '''
        Allocate a shared array of the given *shape* and *type*. *shape* is
        either an integer or a tuple of integers representing the array's
        dimensions.  *type* is a :ref:`Numba type <numba-types>` of the
        elements needing to be stored in the array.

        The returned array-like object can be read and written to like any
        normal device array (e.g. through indexing).
        '''


class local(Stub):
    '''
    Local memory namespace
    '''
    _description_ = '<local>'

    @stub_function
    def array(shape, dtype):
        '''
        Allocate a local array of the given *shape* and *type*. The array is
        private to the current thread, and resides in global memory. An
        array-like object is returned which can be read and written to like any
        standard array (e.g.  through indexing).
        '''


class const(Stub):
    '''
    Constant memory namespace
    '''

    @stub_function
    def array_like(ndarray):
        '''
        Create a const array from *ndarry*. The resulting const array will have
        the same shape, type, and values as *ndarray*.
        '''


#-------------------------------------------------------------------------------
# Cooperative groups

class cg(Stub):
    '''
    Cooperative groups
    '''

    @stub_function
    def this_grid():
        '''
        Get the current grid group.
        '''

    class GridGroup(Stub):
        def sync():
            '''
            Synchronize the current grid group.
            '''


#-------------------------------------------------------------------------------
# syncthreads

class syncthreads(Stub):
    '''
    Synchronize all threads in the same thread block.  This function implements
    the same pattern as barriers in traditional multi-threaded programming: this
    function waits until all threads in the block call it, at which point it
    returns control to all its callers.
    '''
    _description_ = '<syncthreads()>'


class syncthreads_count(Stub):
    '''
    syncthreads_count(predictate)

    An extension to numba.cuda.syncthreads where the return value is a count
    of the threads where predicate is true.
    '''
    _description_ = '<syncthreads_count()>'


class syncthreads_and(Stub):
    '''
    syncthreads_and(predictate)

    An extension to numba.cuda.syncthreads where 1 is returned if predicate is
    true for all threads or 0 otherwise.
    '''
    _description_ = '<syncthreads_and()>'


class syncthreads_or(Stub):
    '''
    syncthreads_or(predictate)

    An extension to numba.cuda.syncthreads where 1 is returned if predicate is
    true for any thread or 0 otherwise.
    '''
    _description_ = '<syncthreads_or()>'


# -------------------------------------------------------------------------------
# warp level operations

class syncwarp(Stub):
    '''
    syncwarp(mask=0xFFFFFFFF)

    Synchronizes a masked subset of threads in a warp.
    '''
    _description_ = '<warp_sync()>'


class shfl_sync_intrinsic(Stub):
    '''
    shfl_sync_intrinsic(mask, mode, value, mode_offset, clamp)

    Nvvm intrinsic for shuffling data across a warp
    docs.nvidia.com/cuda/nvvm-ir-spec/index.html#nvvm-intrin-warp-level-datamove
    '''
    _description_ = '<shfl_sync()>'


class vote_sync_intrinsic(Stub):
    '''
    vote_sync_intrinsic(mask, mode, predictate)

    Nvvm intrinsic for performing a reduce and broadcast across a warp
    docs.nvidia.com/cuda/nvvm-ir-spec/index.html#nvvm-intrin-warp-level-vote
    '''
    _description_ = '<vote_sync()>'


class match_any_sync(Stub):
    '''
    match_any_sync(mask, value)

    Nvvm intrinsic for performing a compare and broadcast across a warp.
    Returns a mask of threads that have same value as the given value from
    within the masked warp.
    '''
    _description_ = '<match_any_sync()>'


class match_all_sync(Stub):
    '''
    match_all_sync(mask, value)

    Nvvm intrinsic for performing a compare and broadcast across a warp.
    Returns a tuple of (mask, pred), where mask is a mask of threads that have
    same value as the given value from within the masked warp, if they
    all have the same value, otherwise it is 0. Pred is a boolean of whether
    or not all threads in the mask warp have the same warp.
    '''
    _description_ = '<match_all_sync()>'


class activemask(Stub):
    '''
    activemask()

    Returns a 32-bit integer mask of all currently active threads in the
    calling warp. The Nth bit is set if the Nth lane in the warp is active when
    activemask() is called. Inactive threads are represented by 0 bits in the
    returned mask. Threads which have exited the kernel are always marked as
    inactive.
    '''
    _description_ = '<activemask()>'


class lanemask_lt(Stub):
    '''
    lanemask_lt()

    Returns a 32-bit integer mask of all lanes (including inactive ones) with
    ID less than the current lane.
    '''
    _description_ = '<lanemask_lt()>'


# -------------------------------------------------------------------------------
# memory fences

class threadfence_block(Stub):
    '''
    A memory fence at thread block level
    '''
    _description_ = '<threadfence_block()>'


class threadfence_system(Stub):
    '''
    A memory fence at system level: across devices
    '''
    _description_ = '<threadfence_system()>'


class threadfence(Stub):
    '''
    A memory fence at device level
    '''
    _description_ = '<threadfence()>'


#-------------------------------------------------------------------------------
# bit manipulation

class popc(Stub):
    """
    popc(x)

    Returns the number of set bits in x.
    """


class brev(Stub):
    """
    brev(x)

    Returns the reverse of the bit pattern of x. For example, 0b10110110
    becomes 0b01101101.
    """


class clz(Stub):
    """
    clz(x)

    Returns the number of leading zeros in z.
    """


class ffs(Stub):
    """
    ffs(x)

    Returns the position of the first (least significant) bit set to 1 in x,
    where the least significant bit position is 1. ffs(0) returns 0.
    """


#-------------------------------------------------------------------------------
# comparison and selection instructions

class selp(Stub):
    """
    selp(a, b, c)

    Select between source operands, based on the value of the predicate source
    operand.
    """


#-------------------------------------------------------------------------------
# single / double precision arithmetic

class fma(Stub):
    """
    fma(a, b, c)

    Perform the fused multiply-add operation.
    """


class cbrt(Stub):
    """"
    cbrt(a)

    Perform the cube root operation.
    """


#-------------------------------------------------------------------------------
# atomic

class atomic(Stub):
    """Namespace for atomic operations
    """
    _description_ = '<atomic>'

    class add(Stub):
        """add(ary, idx, val)

        Perform atomic ``ary[idx] += val``. Supported on int32, float32, and
        float64 operands only.

        Returns the old value at the index location as if it is loaded
        atomically.
        """

    class sub(Stub):
        """sub(ary, idx, val)

        Perform atomic ``ary[idx] -= val``. Supported on int32, float32, and
        float64 operands only.

        Returns the old value at the index location as if it is loaded
        atomically.
        """

    class and_(Stub):
        """and_(ary, idx, val)

        Perform atomic ``ary[idx] &= val``. Supported on int32, int64, uint32
        and uint64 operands only.

        Returns the old value at the index location as if it is loaded
        atomically.
        """

    class or_(Stub):
        """or_(ary, idx, val)

        Perform atomic ``ary[idx] |= val``. Supported on int32, int64, uint32
        and uint64 operands only.

        Returns the old value at the index location as if it is loaded
        atomically.
        """

    class xor(Stub):
        """xor(ary, idx, val)

        Perform atomic ``ary[idx] ^= val``. Supported on int32, int64, uint32
        and uint64 operands only.

        Returns the old value at the index location as if it is loaded
        atomically.
        """

    class inc(Stub):
        """inc(ary, idx, val)

        Perform atomic ``ary[idx] += 1`` up to val, then reset to 0. Supported
        on uint32, and uint64 operands only.

        Returns the old value at the index location as if it is loaded
        atomically.
        """

    class dec(Stub):
        """dec(ary, idx, val)

        Performs::

           ary[idx] = (value if (array[idx] == 0) or
                       (array[idx] > value) else array[idx] - 1)

        Supported on uint32, and uint64 operands only.

        Returns the old value at the index location as if it is loaded
        atomically.
        """

    class exch(Stub):
        """exch(ary, idx, val)

        Perform atomic ``ary[idx] = val``. Supported on int32, int64, uint32 and
        uint64 operands only.

        Returns the old value at the index location as if it is loaded
        atomically.
        """

    class max(Stub):
        """max(ary, idx, val)

        Perform atomic ``ary[idx] = max(ary[idx], val)``.

        Supported on int32, int64, uint32, uint64, float32, float64 operands
        only.

        Returns the old value at the index location as if it is loaded
        atomically.
        """

    class min(Stub):
        """min(ary, idx, val)

        Perform atomic ``ary[idx] = min(ary[idx], val)``.

        Supported on int32, int64, uint32, uint64, float32, float64 operands
        only.

        Returns the old value at the index location as if it is loaded
        atomically.
        """

    class nanmax(Stub):
        """nanmax(ary, idx, val)

        Perform atomic ``ary[idx] = max(ary[idx], val)``.

        NOTE: NaN is treated as a missing value such that:
        nanmax(NaN, n) == n, nanmax(n, NaN) == n

        Supported on int32, int64, uint32, uint64, float32, float64 operands
        only.

        Returns the old value at the index location as if it is loaded
        atomically.
        """

    class nanmin(Stub):
        """nanmin(ary, idx, val)

        Perform atomic ``ary[idx] = min(ary[idx], val)``.

        NOTE: NaN is treated as a missing value, such that:
        nanmin(NaN, n) == n, nanmin(n, NaN) == n

        Supported on int32, int64, uint32, uint64, float32, float64 operands
        only.

        Returns the old value at the index location as if it is loaded
        atomically.
        """

    class compare_and_swap(Stub):
        """compare_and_swap(ary, old, val)

        Conditionally assign ``val`` to the first element of an 1D array ``ary``
        if the current value matches ``old``.

        Returns the current value as if it is loaded atomically.
        """


#-------------------------------------------------------------------------------
# timers

class nanosleep(Stub):
    '''
    nanosleep(ns)

    Suspends the thread for a sleep duration approximately close to the delay
    `ns`, specified in nanoseconds.
    '''
    _description_ = '<nansleep()>'

#-------------------------------------------------------------------------------
# Floating point 16


class fp16(Stub):
    """Namespace for fp16 operations
    """
    _description_ = '<fp16>'

    class hadd(Stub):
        """hadd(a, b)

        Perform fp16 addition, (a + b) in round to nearest mode. Supported
        on fp16 operands only.

        Returns the fp16 result of the addition.

        """

    class hsub(Stub):
        """hsub(a, b)

        Perform fp16 subtraction, (a - b) in round to nearest mode. Supported
        on fp16 operands only.

        Returns the fp16 result of the subtraction.

        """

    class hmul(Stub):
        """hmul(a, b)

        Perform fp16 multiplication, (a * b) in round to nearest mode. Supported
        on fp16 operands only.

        Returns the fp16 result of the multiplication.

        """

    class hfma(Stub):
        """hfma(a, b, c)

        Perform fp16 multiply and accumulate, (a * b) + c in round to nearest
        mode. Supported on fp16 operands only.

        Returns the fp16 result of the multiplication.

        """

    class hneg(Stub):
        """hneg(a)

        Perform fp16 negation, -(a). Supported on fp16 operands only.

        Returns the fp16 result of the negation.

        """

    class habs(Stub):
        """habs(a)

        Perform fp16 absolute value, |a|. Supported on fp16 operands only.

        Returns the fp16 result of the absolute value.

        """

    class heq(Stub):
        """heq(a, b)

        Perform fp16 comparison, (a == b). Supported
        on fp16 operands only.

        Returns True if a and b are equal and False otherwise.

        """

    class hne(Stub):
        """hne(a, b)

        Perform fp16 comparison, (a != b). Supported
        on fp16 operands only.

        Returns True if a and b are not equal and False otherwise.

        """

    class hge(Stub):
        """hge(a, b)

        Perform fp16 comparison, (a >= b). Supported
        on fp16 operands only.

        Returns True if a is >= b and False otherwise.

        """

    class hgt(Stub):
        """hgt(a, b)

        Perform fp16 comparison, (a > b). Supported
        on fp16 operands only.

        Returns True if a is > b and False otherwise.

        """

    class hle(Stub):
        """hle(a, b)

        Perform fp16 comparison, (a <= b). Supported
        on fp16 operands only.

        Returns True if a is <= b and False otherwise.

        """

    class hlt(Stub):
        """hlt(a, b)

        Perform fp16 comparison, (a < b). Supported
        on fp16 operands only.

        Returns True if a is < b and False otherwise.

        """

    class hmax(Stub):
        """hmax(a, b)

        Perform fp16 maximum operation, max(a,b) Supported
        on fp16 operands only.

        Returns a if a is greater than b, returns b otherwise.

        """

    class hmin(Stub):
        """hmin(a, b)

        Perform fp16 minimum operation, min(a,b). Supported
        on fp16 operands only.

        Returns a if a is less than b, returns b otherwise.

        """


#-------------------------------------------------------------------------------
# vector types

def make_vector_type_stubs():
    """Make user facing objects for vector types"""
    vector_type_stubs = []
    vector_type_prefix = (
        "int8",
        "int16",
        "int32",
        "int64",
        "uint8",
        "uint16",
        "uint32",
        "uint64",
        "float32",
        "float64"
    )
    vector_type_element_counts = (1, 2, 3, 4)
    vector_type_attribute_names = ("x", "y", "z", "w")

    for prefix, nelem in itertools.product(
        vector_type_prefix, vector_type_element_counts
    ):
        type_name = f"{prefix}x{nelem}"
        attr_names = vector_type_attribute_names[:nelem]

        vector_type_stub = type(
            type_name, (Stub,),
            {
                **{attr: lambda self: None for attr in attr_names},
                **{
                    "_description_": f"<{type_name}>",
                    "__signature__": Signature(parameters=[
                        Parameter(
                            name=attr_name, kind=Parameter.POSITIONAL_ONLY
                        ) for attr_name in attr_names[:nelem]
                    ]),
                    "__doc__": f"A stub for {type_name} to be used in "
                    "CUDA kernels."
                },
                **{"aliases": []}
            }
        )
        vector_type_stubs.append(vector_type_stub)
    return vector_type_stubs


def map_vector_type_stubs_to_alias(vector_type_stubs):
    """For each of the stubs, create its aliases.

    For example: float64x3 -> double3
    """
    # C-compatible type mapping, see:
    # https://numpy.org/devdocs/reference/arrays.scalars.html#integer-types
    base_type_to_alias = {
        "char": f"int{np.dtype(np.byte).itemsize * 8}",
        "short": f"int{np.dtype(np.short).itemsize * 8}",
        "int": f"int{np.dtype(np.intc).itemsize * 8}",
        "long": f"int{np.dtype(np.int_).itemsize * 8}",
        "longlong": f"int{np.dtype(np.longlong).itemsize * 8}",
        "uchar": f"uint{np.dtype(np.ubyte).itemsize * 8}",
        "ushort": f"uint{np.dtype(np.ushort).itemsize * 8}",
        "uint": f"uint{np.dtype(np.uintc).itemsize * 8}",
        "ulong": f"uint{np.dtype(np.uint).itemsize * 8}",
        "ulonglong": f"uint{np.dtype(np.ulonglong).itemsize * 8}",
        "float": f"float{np.dtype(np.single).itemsize * 8}",
        "double": f"float{np.dtype(np.double).itemsize * 8}"
    }

    base_type_to_vector_type = defaultdict(list)
    for stub in vector_type_stubs:
        base_type_to_vector_type[stub.__name__[:-2]].append(stub)

    for alias, base_type in base_type_to_alias.items():
        vector_type_stubs = base_type_to_vector_type[base_type]
        for stub in vector_type_stubs:
            nelem = stub.__name__[-1]
            stub.aliases.append(f"{alias}{nelem}")


_vector_type_stubs = make_vector_type_stubs()
map_vector_type_stubs_to_alias(_vector_type_stubs)
