from __future__ import absolute_import, print_function, division
import sys
import os
import logging
import warnings

import theano
from theano import config
from theano.compile import optdb

from theano.tensor.basic import register_transfer

_logger_name = 'theano.gpuarray'
_logger = logging.getLogger(_logger_name)

error = _logger.error
info = _logger.info

pygpu_activated = False
try:
    import pygpu
    import pygpu.gpuarray
except ImportError:
    pygpu = None

# This is for documentation not to depend on the availability of pygpu
from .type import (GpuArrayType, GpuArrayVariable, GpuArrayConstant,
                   GpuArraySharedVariable, gpuarray_shared_constructor,
                   reg_context, get_context, ContextNotDefined)
from .basic_ops import as_gpuarray_variable
from . import fft, dnn, opt, extra_ops, multinomial, reduction, rng_mrg, ctc


def transfer(x, target):
    try:
        get_context(target)
        return as_gpuarray_variable(x, target)
    except ContextNotDefined:
        pass

register_transfer(transfer)


def pygpu_parse_version(version_string):
    from collections import namedtuple
    version_type = namedtuple('version_type', ('major', 'minor', 'patch', 'fullversion'))
    pieces = version_string.split('.', 2)
    assert len(pieces) == 3
    major = int(pieces[0])
    minor = int(pieces[1])
    if "+" in pieces[2]:  # It contain a git commit.
        patch = int(pieces[2].split('+', 1)[0])
    else:  # Maybe it end with .devN
        patch = int(pieces[2].split('.', 1)[0])
    fullversion = '%d.%d.%s' % (major, minor, pieces[2])
    return version_type(major=major, minor=minor, patch=patch, fullversion=fullversion)


def init_dev(dev, name=None, preallocate=None):
    global pygpu_activated
    if not config.cxx:
        raise RuntimeError("The new gpu-backend need a c++ compiler.")
    pygpu_version = pygpu_parse_version(pygpu.__version__)
    if (pygpu_version.major != 0 or pygpu_version.minor != 7 or
            pygpu_version.patch < 0):
        raise ValueError(
            "Your installed version of pygpu(%s) is too old, please upgrade to 0.7.0 or later" %
            pygpu_version.fullversion)
    # This is for the C headers API, we need to match the exact version.
    gpuarray_version_major_supported = 2
    gpuarray_version_major_detected = pygpu.gpuarray.api_version()[0]
    if gpuarray_version_major_detected != gpuarray_version_major_supported:
        raise ValueError(
            "Your installed version of libgpuarray is not in sync with the current Theano"
            " version. The installed libgpuarray version supports API version %d,"
            " while current Theano supports API version %d. Change the version of"
            " libgpuarray or Theano to fix this problem.",
            gpuarray_version_major_detected,
            gpuarray_version_major_supported)
    if dev not in init_dev.devmap:
        args = dict()
        if config.gpuarray.cache_path != '':
            args['kernel_cache_path'] = config.gpuarray.cache_path
        if preallocate is None:
            preallocate = config.gpuarray.preallocate
        if preallocate < 0:
            args['max_cache_size'] = 0
        else:
            args['initial_cache_size'] = preallocate
        context = pygpu.init(
            dev,
            sched=config.gpuarray.sched,
            single_stream=config.gpuarray.single_stream,
            **args)
        context.dev = dev
        init_dev.devmap[dev] = context
        reg_context(name, context)

        MB = (1024 * 1024)
        if dev.startswith('cuda'):
            avail = dnn.dnn_available(name)
            # If we try to enable cudnn and there isn't enough GPU
            # memory, there will be an unclear error message. So do
            # not even try a clear error.
            if avail and context.free_gmem < 75 * MB:
                raise RuntimeError(
                    "Can not enable cuDNN as there is only %d MB of free GPU memory." %
                    (context.free_gmem/MB))
            elif avail:
                context.cudnn_handle = dnn._make_handle(context)
            elif config.dnn.enabled == 'True':
                raise RuntimeError(
                    "You enabled cuDNN, but we aren't able to use it: %s" %
                    dnn.dnn_available.msg)
            if config.print_active_device:
                if avail:
                    print("Using cuDNN version %d on context %s" % (dnn.version(), name),
                          file=sys.stderr)
                else:
                    print("Can not use cuDNN on context %s: %s" % (name, dnn.dnn_available.msg),
                          file=sys.stderr)
        if preallocate < 0:
            print("Disabling allocation cache on %s" % (dev,))
        elif preallocate > 0:
            if preallocate <= 1:
                gmem = min(preallocate, 0.95) * context.total_gmem
            else:
                gmem = preallocate * MB
            if gmem > context.free_gmem:
                raise RuntimeError(
                    "Trying to preallocate %d MB of GPU memory while only"
                    " %d MB are available." % (gmem / MB,
                                                     context.free_gmem / MB))
            elif gmem > context.free_gmem - 50 * MB:
                print(
                    "WARNING: Preallocating too much memory can prevent cudnn and cublas from working properly")

            # This will allocate and immediatly free an object of size gmem
            # which will reserve that amount of memory on the GPU.
            pygpu.empty((gmem,), dtype='int8', context=context)
            if config.print_active_device:
                print("Preallocating %d/%d Mb (%f) on %s" %
                      (gmem // MB, context.total_gmem // MB,
                       gmem / context.total_gmem, dev),
                      file=sys.stderr)

        # Initialise the blas kernels.  We do this after the
        # preallocation to not fragment the heap accidentally.
        tmp = pygpu.empty((2, 2), dtype='float32', context=context)
        if dev.startswith('cuda'):
            # In OpenCL, BLAS isn't always available
            pygpu.blas.gemm(0, tmp, tmp, 0, tmp, overwrite_c=True)
        del tmp
    else:
        context = init_dev.devmap[dev]
    # This will map the context name to the real context object.
    if config.print_active_device:
        try:
            unique_id = '(' + context.unique_id + ')'
        except pygpu.gpuarray.UnsupportedException:
            unique_id = ''

        print("Mapped name %s to device %s: %s %s" %
              (name, dev, context.devname, unique_id),
              file=sys.stderr)
    pygpu_activated = True

# This maps things like 'cuda0' to the context object on that device.
init_dev.devmap = {}


def use(device,
        force=False,
        default_to_move_computation_to_gpu=True,
        move_shared_to_gpu=True,
        preallocate=None):
    """
    Error and warning about CUDA should be displayed only when this
    function is called. We need to be able to load this module only
    to check if it is available!

    Parameters
    ----------
    device : string
        "cuda", "cuda0", "cudaN", "" (N is the device number to use).
        "" mean do all the rest and don't init a device.
    force
        Will always raise an exception if we can't use the gpu.
    default_to_move_computation_to_gpu
        If gpu init succeeded, enable by default optimizations to move
        computations to the gpu.
    move_shared_to_gpu
        If gpu init succeeded, put new shared variables on the gpu.
    preallocate
        If specified, will use this value for preallocation instead of
        gpuarray.preallocate.

    """
    if force:
        if not (device.startswith('cuda') or device.startswith('opencl')):
            raise Exception("forced the init and bad device provided: " +
                            device)
        else:
            # If we force, the device should not already be initialized.
            assert device not in init_dev.devmap
    if device:
        init_dev(device, preallocate=preallocate)
    if default_to_move_computation_to_gpu:
        optdb.add_tags('gpuarray_opt', 'fast_run', 'fast_compile')
        optdb.add_tags('gpua_scanOp_make_inplace', 'fast_run')
    if move_shared_to_gpu:
        import theano.compile
        theano.compile.shared_constructor(gpuarray_shared_constructor)


if pygpu:
    try:
        if (config.device.startswith('cuda') or
                config.device.startswith('opencl')):
            use(config.device)
        elif (config.init_gpu_device.startswith('cuda') or
              config.init_gpu_device.startswith('opencl')):
            if config.device != 'cpu':
                raise ValueError(
                    'you must set device=cpu to use init_gpu_device.')
            if config.contexts != '':
                print("Using contexts will make init_gpu_device act like device and move all computations by default, which might not be what you want.")
            init_dev(config.init_gpu_device)
        if config.contexts != '':
            for n, d in (c.split('->') for c in config.contexts.split(';')):
                init_dev(d.strip(), n.strip())
            # To have shared var default on the GPU and opt to move to the GPU.
            use("")

    except Exception:
        error("Could not initialize pygpu, support disabled", exc_info=True)

    from .basic_ops import (GpuAlloc, GpuAllocEmpty, GpuContiguous, GpuEye,
                            GpuFromHost, GpuJoin, GpuReshape, GpuSplit,
                            HostFromGpu, host_from_gpu)
    from .elemwise import GpuElemwise
    from .subtensor import (GpuSubtensor, GpuIncSubtensor,
                            GpuAdvancedIncSubtensor1)

else:
    if (config.init_gpu_device.startswith('cuda') or
            config.init_gpu_device.startswith('opencl') or
            config.device.startswith('opencl') or
            config.device.startswith('cuda') or
            config.contexts != ''):
        error("pygpu was configured but could not be imported or is too old (version 0.7 or higher required)",
              exc_info=True)
