import os

import theano
from theano import Apply, gof, tensor
from theano.gof import Optimizer
from theano.gof.type import CDataType
from theano.compat import PY3
from theano.tensor.nnet import SoftmaxGrad
from theano.sandbox.cuda.type import CudaNdarrayType
from theano.sandbox.cuda import (GpuOp, cuda_available, active_device_number,
                                 device_properties)
from theano.sandbox.cuda.basic_ops import (as_cuda_ndarray_variable,
                                           gpu_contiguous, HostFromGpu)
from theano.sandbox.cuda.blas import (GpuConv, GpuDownsampleFactorMax,
                                      GpuDownsampleFactorMaxGrad)
from theano.sandbox.cuda.nnet import GpuSoftmax
from theano.sandbox.cuda.opt import register_opt

from theano.sandbox.cuda.nvcc_compiler import NVCC_compiler


def dnn_available():
    if dnn_available.avail is None:
        dev = active_device_number()
        if device_properties(dev)['major'] < 3:
            dnn_available.msg = "Device not supported by cuDNN"
            dnn_available.avail = False
        else:
            preambule = """
#include <stdio.h>
#include <cuda.h>
#include <cudnn.h>
#include <cudnn_helper.h>
            """

            body = """
cudnnHandle_t _handle = NULL;
cudnnStatus_t err;
if ((err = cudnnCreate(&_handle)) != CUDNN_STATUS_SUCCESS) {
  fprintf(stderr, "could not create cuDNN handle: %s",
          cudnnGetErrorString(err));
  return 1;
}
"""

            comp, run, out, err = gof.cmodule.GCC_compiler.try_flags(
                ["-l", "cudnn", "-I" + os.path.dirname(__file__),
                 "-I" + os.path.join(theano.config.cuda.root, 'include'),
                 "-L" + os.path.join(theano.config.cuda.root, 'lib64')],
                preambule=preambule, body=body,
                try_run=True, output=True)

            dnn_available.avail = comp and run
            if dnn_available.avail:
                dnn_available.msg = "cuDNN should work"
            else:
                dnn_available.msg = (
                    "Theano is not able to use cuDNN. We got this error: \n" +
                    str(err))
    return dnn_available.avail


dnn_available.avail = None
dnn_available.msg = None


def c_set_tensor4d(var, desc, err, fail):
    return """
%(err)s = cudnnSetTensor4dDescriptorEx(
    %(desc)s, CUDNN_DATA_FLOAT,
    CudaNdarray_HOST_DIMS(%(var)s)[0],
    CudaNdarray_HOST_DIMS(%(var)s)[1],
    CudaNdarray_HOST_DIMS(%(var)s)[2],
    CudaNdarray_HOST_DIMS(%(var)s)[3],
    CudaNdarray_HOST_STRIDES(%(var)s)[0]?CudaNdarray_HOST_STRIDES(%(var)s)[0]:CudaNdarray_HOST_DIMS(%(var)s)[2]*CudaNdarray_HOST_DIMS(%(var)s)[3]*CudaNdarray_HOST_DIMS(%(var)s)[1],
    CudaNdarray_HOST_STRIDES(%(var)s)[1]?CudaNdarray_HOST_STRIDES(%(var)s)[1]:CudaNdarray_HOST_DIMS(%(var)s)[2]*CudaNdarray_HOST_DIMS(%(var)s)[3],
    CudaNdarray_HOST_STRIDES(%(var)s)[2]?CudaNdarray_HOST_STRIDES(%(var)s)[2]:CudaNdarray_HOST_DIMS(%(var)s)[3],
    CudaNdarray_HOST_STRIDES(%(var)s)[3]?CudaNdarray_HOST_STRIDES(%(var)s)[3]:1
);
if (%(err)s != CUDNN_STATUS_SUCCESS) {
    PyErr_Format(PyExc_RuntimeError, "could not set tensor4d descriptor: %%s",
    cudnnGetErrorString(%(err)s));
    %(fail)s
}
        """ % dict(var=var, err=err, desc=desc, fail=fail)


class DnnBase(GpuOp):
    """
    Creates a handle for cudnn and pulls in the cudnn libraries and headers.
    """
    def c_headers(self):
        return ['cudnn.h', 'cudnn_helper.h']

    def c_header_dirs(self):
        return [os.path.dirname(__file__)]

    def c_libraries(self):
        return ['cudnn']

    def c_support_code(self):
        return """
cudnnHandle_t _handle = NULL;
"""

    def c_init_code(self):
        if PY3:
            error_out = "NULL"
        else:
            error_out = ""
        return ["""{
cudnnStatus_t err;
if ((err = cudnnCreate(&_handle)) != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_RuntimeError, "could not create cuDNN handle: %%s",
               cudnnGetErrorString(err));
  return %s;
}
}""" % (error_out,)]


class GpuDnnConvDesc(GpuOp):
    """This Op builds a convolution descriptor for use in the other
    convolution operations.

    see the doc of :func:`dnn_conv` for a description of the parameters

    """
    __props__ = ('border_mode', 'subsample', 'conv_mode')

    def c_headers(self):
        return ['cudnn.h', 'cudnn_helper.h']

    def c_header_dirs(self):
        return [os.path.dirname(__file__)]

    def c_libraries(self):
        return ['cudnn']

    def c_compiler(self):
        return NVCC_compiler

    def __init__(self, border_mode, subsample=(1, 1), conv_mode='conv'):
        if isinstance(border_mode, int):
            border_mode = (border_mode, border_mode)
        if isinstance(border_mode, tuple):
            pad_h, pad_w = map(int, border_mode)
            border_mode = (pad_h, pad_w)
        if not ((isinstance(border_mode, tuple) and min(border_mode) >= 0) or
                border_mode in ('valid', 'full')):
            raise ValueError(
                'invalid border_mode {}, which must be either '
                '"valid", "full", an integer or a pair of'
                ' integers'.format(border_mode))
        self.border_mode = border_mode
        assert len(subsample) == 2
        self.subsample = subsample
        assert conv_mode in ('conv', 'cross')
        self.conv_mode = conv_mode

    def make_node(self, img_shape, kern_shape):
        if img_shape.type.ndim != 1 or img_shape.type.dtype != 'int64':
            raise TypeError('img must be 1D shape tensor')
        if kern_shape.type.ndim != 1 or kern_shape.type.dtype != 'int64':
            raise TypeError('kern must be 1D shape tensor')

        return Apply(self, [img_shape, kern_shape],
                     [CDataType("cudnnConvolutionDescriptor_t")()])

    def c_code(self, node, name, inputs, outputs, sub):
        img_shape, kern_shape = inputs
        desc, = outputs

        if isinstance(self.border_mode, tuple):
            pad_h_spec, pad_w_spec = map(int, self.border_mode)
            assert pad_h_spec >= 0 and pad_w_spec >= 0
            bmode = 2
        else:
            pad_h_spec = pad_w_spec = 0

            if self.border_mode == "valid":
                bmode = 1
            else:
                assert self.border_mode == "full"
                bmode = 0

        if self.conv_mode == 'conv':
            conv_flag = 'CUDNN_CONVOLUTION'
        else:
            conv_flag = 'CUDNN_CROSS_CORRELATION'

        return """
{
  cudnnStatus_t err;
  int pad_h%(name)s;
  int pad_w%(name)s;

  if ((err = cudnnCreateConvolutionDescriptor(&%(desc)s)) != CUDNN_STATUS_SUCCESS) {
    PyErr_Format(PyExc_MemoryError, "could not allocate convolution "
                 "descriptor: %%s", cudnnGetErrorString(err));
    %(fail)s
  }

  if (%(bmode)d == 2) {
    pad_h%(name)s = %(pad_h_spec)d;
    pad_w%(name)s = %(pad_w_spec)d;
  } else if (%(bmode)d == 1) {
    pad_h%(name)s = 0;
    pad_w%(name)s = 0;
  } else if (%(bmode)d == 0) {
    pad_h%(name)s = *(npy_int64 *)PyArray_GETPTR1(%(kern_shape)s, 2) - 1;
    pad_w%(name)s = *(npy_int64 *)PyArray_GETPTR1(%(kern_shape)s, 3) - 1;
  } else {
    PyErr_SetString(PyExc_ValueError, "bad border mode");
    %(fail)s
  }
  err = cudnnSetConvolutionDescriptorEx(
  %(desc)s,
  *(npy_int64 *)PyArray_GETPTR1(%(img_shape)s, 0),
  *(npy_int64 *)PyArray_GETPTR1(%(img_shape)s, 1),
  *(npy_int64 *)PyArray_GETPTR1(%(img_shape)s, 2),
  *(npy_int64 *)PyArray_GETPTR1(%(img_shape)s, 3),
  *(npy_int64 *)PyArray_GETPTR1(%(kern_shape)s, 0),
  *(npy_int64 *)PyArray_GETPTR1(%(kern_shape)s, 2),
  *(npy_int64 *)PyArray_GETPTR1(%(kern_shape)s, 3),
  pad_h%(name)s,
  pad_w%(name)s,
  %(subsx)d, %(subsy)d, 1, 1,
  %(conv_flag)s
  );

  if (err != CUDNN_STATUS_SUCCESS) {
    PyErr_Format(PyExc_RuntimeError, "could not set op descriptor: %%s",
                 cudnnGetErrorString(err));
    %(fail)s
  }
}
""" % dict(name=name, img_shape=img_shape, kern_shape=kern_shape, desc=desc,
           bmode=bmode, conv_flag=conv_flag, fail=sub['fail'],
           subsx=self.subsample[0], subsy=self.subsample[1],
           pad_h_spec=pad_h_spec, pad_w_spec=pad_w_spec)

    def c_code_cache_version(self):
        return (2,)


class GpuDnnConvBase(DnnBase):
    __props__ = ()

    def make_node(self, img, kern, desc):
        if img.type.ndim != 4:
            raise TypeError('img must be 4D tensor')
        if kern.type.ndim != 4:
            raise TypeError('kern must be 4D tensor')

        if not isinstance(desc.type, CDataType) \
                or desc.type.ctype != 'cudnnConvolutionDescriptor_t':
            raise TypeError('desc must be cudnnConvolutionDescriptor_t')

        broadcastable = (img.type.broadcastable[0],
                         kern.type.broadcastable[0],
                         False, False)

        return Apply(self, [img, kern, desc],
                     [CudaNdarrayType(broadcastable)()])

    def c_support_code_struct(self, node, struct_id):
        return """
cudnnTensor4dDescriptor_t input%(id)d;
cudnnTensor4dDescriptor_t output%(id)d;
cudnnFilterDescriptor_t kerns%(id)d;
""" % dict(id=struct_id)

    def c_init_code_struct(self, node, struct_id, sub):
        return """
cudnnStatus_t err%(id)d;
input%(id)d = NULL;
output%(id)d = NULL;
kerns%(id)d = NULL;
if ((err%(id)d = cudnnCreateTensor4dDescriptor(&input%(id)d)) != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_MemoryError, "could not allocate tensor4d descriptor "
               "(inp): %%s", cudnnGetErrorString(err%(id)d));
  %(fail)s
}
if ((err%(id)d = cudnnCreateTensor4dDescriptor(&output%(id)d)) != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_MemoryError, "could not allocate tensor4d descriptor "
               "(out): %%s", cudnnGetErrorString(err%(id)d));
  %(fail)s
}
if ((err%(id)d = cudnnCreateFilterDescriptor(&kerns%(id)d)) != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_MemoryError, "could not allocate filter descriptor: %%s",
               cudnnGetErrorString(err%(id)d));
  %(fail)s
}
""" % dict(id=struct_id, fail=sub['fail'])

    def c_cleanup_code_struct(self, node, struct_id):
        return """
cudnnDestroyTensor4dDescriptor(input%(id)d);
cudnnDestroyTensor4dDescriptor(output%(id)d);
cudnnDestroyFilterDescriptor(kerns%(id)d);
""" % dict(id=struct_id)

    def c_set_filter(self, var, desc, err, fail):
        return """
%(err)s = cudnnSetFilterDescriptor(
%(desc)s, CUDNN_DATA_FLOAT,
CudaNdarray_HOST_DIMS(%(var)s)[0],
CudaNdarray_HOST_DIMS(%(var)s)[1],
CudaNdarray_HOST_DIMS(%(var)s)[2],
CudaNdarray_HOST_DIMS(%(var)s)[3]
);
if (%(err)s != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_RuntimeError, "could not set filter descriptor: %%s",
               cudnnGetErrorString(%(err)s));
  %(fail)s
}
""" % dict(var=var, desc=desc, err=err, fail=fail)

    def c_set_tensor4d(self, *arg):
        return c_set_tensor4d(*arg)

    def c_code(self, node, name, inputs, outputs, sub):
        desc = inputs[2]
        out, = outputs

        checks = []
        for v in inputs[:2]:
            checks.append("""
if (!CudaNdarray_is_c_contiguous(%s)) {
  PyErr_SetString(PyExc_ValueError, "Only contiguous inputs are supported.");
  %s
}
""" % (v, sub['fail']))

        sets = []
        for p, v, d in zip(inputs[:2], self.conv_inputs, self.conv_types[:2]):
            sets.append(getattr(self, 'c_set_'+d)(p, v + str(sub['struct_id']),
                                                  'err' + name, sub['fail']))

        set_out = getattr(self, 'c_set_' + self.conv_types[2])(
            out, self.conv_output + str(sub['struct_id']), 'err' + name,
            sub['fail'])

        return """
cudnnStatus_t err%(name)s;

%(checks)s

%(sets)s

{
  int out_dims[4];
  err%(name)s = cudnnGetOutputTensor4dDim(
  %(desc)s, %(path)s,
  &out_dims[0], &out_dims[1],
  &out_dims[2], &out_dims[3]
  );
  if (err%(name)s != CUDNN_STATUS_SUCCESS) {
    PyErr_Format(PyExc_RuntimeError, "could not get output sizes: %%s",
                 cudnnGetErrorString(err%(name)s));
    %(fail)s
  }
  // workaround for cudnn R1 bug
  if (%(path)s == CUDNN_CONVOLUTION_WEIGHT_GRAD &&
      (out_dims[0] != CudaNdarray_HOST_DIMS(%(input2)s)[1] ||
       out_dims[1] != CudaNdarray_HOST_DIMS(%(input1)s)[1])) {
    out_dims[0] = CudaNdarray_HOST_DIMS(%(input2)s)[1];
    out_dims[1] = CudaNdarray_HOST_DIMS(%(input1)s)[1];
    // This is a horrible hack that is unfortulately necessary
    int *dd = (int *)%(desc)s;
    out_dims[2] = dd[5];
    out_dims[3] = dd[6];
  }
  if (CudaNdarray_prep_output(&%(out)s, 4, out_dims) != 0) {
    %(fail)s
  }
}

%(set_out)s

err%(name)s = %(method)s(
_handle,
%(input1_desc)s, CudaNdarray_DEV_DATA(%(input1)s),
%(input2_desc)s, CudaNdarray_DEV_DATA(%(input2)s),
%(desc)s,
%(output_desc)s, CudaNdarray_DEV_DATA(%(out)s),
CUDNN_RESULT_NO_ACCUMULATE
);
if (err%(name)s != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_RuntimeError, "error doing operation: %%s",
               cudnnGetErrorString(err%(name)s));
  %(fail)s
}
""" % dict(out=out, desc=desc, fail=sub['fail'], id=sub['struct_id'],
           name=name, checks='\n'.join(checks), sets='\n'.join(sets),
           set_out=set_out, input1=inputs[0], input2=inputs[1],
           input1_desc=self.conv_inputs[0]+str(sub['struct_id']),
           input2_desc=self.conv_inputs[1]+str(sub['struct_id']),
           output_desc=self.conv_output+str(sub['struct_id']),
           method=self.conv_op, path=self.path_flag)

    def c_code_cache_version(self):
        return (7,)


class GpuDnnConv(GpuDnnConvBase):
    """
    The forward convolution.

    :param image:
    :param kernel:
    :param descr: the convolution descriptor

    """
    conv_inputs = 'input', 'kerns'
    conv_output = 'output'
    conv_types = 'tensor4d', 'filter', 'tensor4d'
    conv_op = 'cudnnConvolutionForward'
    path_flag = 'CUDNN_CONVOLUTION_FWD'

    def grad(self, inp, grads):
        img, kerns, desc = inp
        top, = grads

        top = gpu_contiguous(top)

        d_img = GpuDnnConvGradI()(kerns, top, desc)
        d_kerns = GpuDnnConvGradW()(img, top, desc)

        return d_img, d_kerns, theano.gradient.DisconnectedType()()

    def connection_pattern(self, node):
        # not connected to desc
        return [[1], [1], [0]]


class GpuDnnConvGradW(GpuDnnConvBase):
    """
    The convolution gradient with respect to the weights.

    :param image:
    :param kernel:
    :param descr: the convolution descriptor

    """

    conv_inputs = 'input', 'output',
    conv_output = 'kerns'
    conv_types = 'tensor4d', 'tensor4d', 'filter'
    path_flag = 'CUDNN_CONVOLUTION_WEIGHT_GRAD'
    conv_op = 'cudnnConvolutionBackwardFilter'


class GpuDnnConvGradI(GpuDnnConvBase):
    """
    The convolution gradient with respect to the inputs.

    :param image:
    :param kernel:
    :param descr: the convolution descriptor

    """

    conv_inputs = 'kerns', 'output',
    conv_output = 'input'
    conv_types = 'filter', 'tensor4d', 'tensor4d'
    path_flag = 'CUDNN_CONVOLUTION_DATA_GRAD'
    conv_op = 'cudnnConvolutionBackwardData'


def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
             conv_mode='conv', direction_hint=None):
    """
    GPU convolution using cuDNN from NVIDIA.

    The memory layout to use is 'bc01', that is 'batch', 'channel',
    'first dim', 'second dim' in that order.

    :param img: images to do the convolution over
    :param kerns: convolution filters
    :param border_mode: one of 'valid', 'full'; additionally, the padding size
        could be directly specified by an integer or a pair of integers
    :param subsample: perform subsampling of the output (default: (1, 1))
    :param conv_mode: perform convolution (kernels flipped) or cross-correlation.
        One of 'conv', 'cross'. (default: 'conv')
    :param direction_hint: Used by graph optimizers to change algorithm choice.
        By default, GpuDnnConv will be used to carry out the convolution.
        If border_mode is 'valid', subsample is (1,1) and direction_hint is
        'bprop weights', it will use GpuDnnConvGradW.
        If border_mode is 'full', subsample is (1,1) and direction_hint is
        *not* 'forward!', it will use GpuDnnConvGradI.
        This parameter is used internally by graph optimizers and may be
        removed at any time without a deprecation period. You have been warned.

    :warning: The cuDNN library only works with GPU that have a compute
      capability of 3.0 or higer.  This means that older GPU will not
      work with this Op.
    """
    if (border_mode == 'valid' and subsample == (1,1) and
        direction_hint == 'bprop weights'):
        # Special case: We are asked to use GpuDnnConvGradW. We need to set
        # up a suitable 'fake' convolution to compute the gradient for.
        img = gpu_contiguous(img.dimshuffle(1, 0, 2, 3))
        if conv_mode == 'conv':
            # We need to flip manually. These 'kerns' are not the kernels
            # that would be flipped by conv_mode='conv' in GpuDnnConvGradW.
            kerns = kerns[:, :, ::-1, ::-1]
        kerns = gpu_contiguous(kerns.dimshuffle(1, 0, 2, 3))
        shape = theano.tensor.stack(kerns.shape[1], img.shape[1],
                                    img.shape[2] - kerns.shape[2] + 1,
                                    img.shape[3] - kerns.shape[3] + 1)
        desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
                              conv_mode='cross')(img.shape, shape)
        conv = GpuDnnConvGradW()(img, kerns, desc)
        return as_cuda_ndarray_variable(conv.dimshuffle(1, 0, 2, 3))

    elif (border_mode == 'full' and subsample == (1, 1) and
          direction_hint != 'forward!'):
        # Special case: We can be faster by using GpuDnnConvGradI to compute
        # the full convolution as the backward pass of a valid convolution.
        # We just need to set up a suitable 'fake' valid convolution.
        img = gpu_contiguous(img)
        kerns = gpu_contiguous(kerns.dimshuffle(1, 0, 2, 3))
        conv_mode = 'cross' if conv_mode == 'conv' else 'conv'
        shape = theano.tensor.stack(img.shape[0], kerns.shape[1],
                                    img.shape[2] + kerns.shape[2] - 1,
                                    img.shape[3] + kerns.shape[3] - 1)
        desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
                              conv_mode=conv_mode)(shape, kerns.shape)
        return GpuDnnConvGradI()(kerns, img, desc)

    # Standard case: We use GpuDnnConv with suitable padding.
    img = gpu_contiguous(img)
    kerns = gpu_contiguous(kerns)
    desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample,
                          conv_mode=conv_mode)(img.shape, kerns.shape)
    return GpuDnnConv()(img, kerns, desc)


class GpuDnnPoolDesc(GpuOp):
    """
    This Op builds a pooling descriptor for use in the other
    pooling operations.

    :param ws: windows size
    :param stride: (dx, dy)
    :param mode: 'max' or 'average'
    """
    __props__ = ('ws', 'stride', 'mode')

    def c_headers(self):
        return ['cudnn.h', 'cudnn_helper.h']

    def c_header_dirs(self):
        return [os.path.dirname(__file__)]

    def c_libraries(self):
        return ['cudnn']

    def c_compiler(self):
        return NVCC_compiler

    def do_constant_folding(self, node):
        return False

    def __init__(self, ws=(1, 1), stride=(1, 1), mode='max'):
        assert mode in ('max', 'average')
        self.mode = mode
        assert len(ws) == 2
        self.ws = ws
        assert len(stride) == 2
        self.stride = stride

    def make_node(self):
        return Apply(self, [],
                     [CDataType("cudnnPoolingDescriptor_t")()])

    def c_code(self, node, name, inputs, outputs, sub):
        desc, = outputs

        if self.mode == 'max':
            mode_flag = 'CUDNN_POOLING_MAX'
        elif self.mode == "average":
            mode_flag = 'CUDNN_POOLING_AVERAGE'
        else:
            raise NotImplementedError("Unsupported pooling model.")

        return """
{
  cudnnStatus_t err;

  if ((err = cudnnCreatePoolingDescriptor(&%(desc)s)) != CUDNN_STATUS_SUCCESS) {
    PyErr_Format(PyExc_MemoryError, "could not allocate pooling "
                 "descriptor: %%s", cudnnGetErrorString(err));
    %(fail)s
  }

  err = cudnnSetPoolingDescriptor(
  %(desc)s,
  %(mode_flag)s,    
  %(wsX)d, %(wsY)d,
  %(stridex)d, %(stridey)d
  );

  if (err != CUDNN_STATUS_SUCCESS) {
    PyErr_Format(PyExc_RuntimeError, "could not set op descriptor: %%s",
                 cudnnGetErrorString(err));
    %(fail)s
  }
}
""" % dict(name=name, desc=desc, mode_flag=mode_flag, fail=sub['fail'],
           wsX=self.ws[0], wsY=self.ws[1], stridex=self.stride[0],
           stridey=self.stride[1])

    def c_code_cache_version(self):
        return (1,)


class GpuDnnPool(DnnBase):
    """
    Pooling.

    :param img: the image 4d tensor.
    :param desc: the pooling descriptor.
    """
    __props__ = ()

    def make_node(self, img, desc):
        img = as_cuda_ndarray_variable(img)
        if img.type.ndim != 4:
            raise TypeError('img must be 4D tensor')

        if not isinstance(desc.type, CDataType) \
                or desc.type.ctype != 'cudnnPoolingDescriptor_t':
            raise TypeError('desc must be cudnnPoolingDescriptor_t')

        return Apply(self, [img, desc],
                     [img.type()])

    def c_support_code_struct(self, node, struct_id):
        return """
cudnnTensor4dDescriptor_t input%(id)d;
cudnnTensor4dDescriptor_t output%(id)d;
""" % dict(id=struct_id)

    def c_init_code_struct(self, node, struct_id, sub):
        return """
cudnnStatus_t err%(id)d;
input%(id)d = NULL;
output%(id)d = NULL;
if ((err%(id)d = cudnnCreateTensor4dDescriptor(&input%(id)d)) != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_MemoryError, "could not allocate tensor4d descriptor "
               "(inp): %%s", cudnnGetErrorString(err%(id)d));
  %(fail)s
}
if ((err%(id)d = cudnnCreateTensor4dDescriptor(&output%(id)d)) != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_MemoryError, "could not allocate tensor4d descriptor "
               "(out): %%s", cudnnGetErrorString(err%(id)d));
  %(fail)s
}
""" % dict(id=struct_id, fail=sub['fail'])

    def c_cleanup_code_struct(self, node, struct_id):
        return """
if (input%(id)d != NULL) { cudnnDestroyTensor4dDescriptor(input%(id)d); }
if (output%(id)d != NULL) { cudnnDestroyTensor4dDescriptor(output%(id)d); }
""" % dict(id=struct_id)

    def c_code(self, node, name, inputs, outputs, sub):
        desc = inputs[1]
        out, = outputs

        set_in = c_set_tensor4d(inputs[0], "input" + str(sub['struct_id']),
                                'err' + name, sub['fail'])

        set_out = c_set_tensor4d(out, "output" + str(sub['struct_id']),
                                 'err' + name, sub['fail'])

        return """
cudnnStatus_t err%(name)s;

int %(out)s_dims[4];

if (!CudaNdarray_is_c_contiguous(%(input)s)) {
  PyErr_SetString(PyExc_ValueError, "Only contiguous inputs are supported.");
  %(fail)s
}

%(set_in)s

cudnnPoolingMode_t mode;
int wsX, wsY, strideX, strideY;

err%(name)s = cudnnGetPoolingDescriptor(%(desc)s, &mode, &wsX, &wsY, &strideX, &strideY);

if (err%(name)s != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_RuntimeError, "error doing operation: %%s",
               cudnnGetErrorString(err%(name)s));
  %(fail)s
}

%(out)s_dims[0] = CudaNdarray_HOST_DIMS(%(input)s)[0];
%(out)s_dims[1] = CudaNdarray_HOST_DIMS(%(input)s)[1];
%(out)s_dims[2] = (CudaNdarray_HOST_DIMS(%(input)s)[2] - wsX) / strideX + 1;
%(out)s_dims[3] = (CudaNdarray_HOST_DIMS(%(input)s)[3] - wsY) / strideY + 1;

if (CudaNdarray_prep_output(&%(out)s, 4, %(out)s_dims) != 0)
{
  %(fail)s
}

%(set_out)s

err%(name)s = cudnnPoolingForward(
_handle,
%(desc)s,
%(input_desc)s, CudaNdarray_DEV_DATA(%(input)s),
%(output_desc)s, CudaNdarray_DEV_DATA(%(out)s)
);
if (err%(name)s != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_RuntimeError, "error doing operation: %%s",
               cudnnGetErrorString(err%(name)s));
  %(fail)s
}
""" % dict(out=out, desc=desc, fail=sub['fail'], id=sub['struct_id'],
           name=name, set_in=set_in,
           set_out=set_out, input=inputs[0],
           input_desc="input"+str(sub['struct_id']),
           output_desc="output"+str(sub['struct_id']))

    def grad(self, inp, grads):
        img, desc = inp
        grad, = grads

        grad = gpu_contiguous(grad)

        out = self(img, desc)

        g_out = GpuDnnPoolGrad()(out, grad, img, desc)

        return g_out, theano.gradient.DisconnectedType()()

    def connection_pattern(self, node):
        # not connected to desc
        return [[1], [0]]

    def c_code_cache_version(self):
        return (2,)


class GpuDnnPoolGrad(DnnBase):
    """
    The pooling gradient.

    :param inp: the input of the pooling.
    :param inp_grad: same size as out, but is the corresponding gradient information.
    :param out: the output of the pooling in the forward.
    :param desc: The pooling descriptor.
    """
    __props__ = ()

    def make_node(self, inp, inp_grad, out, desc):
        inp = as_cuda_ndarray_variable(inp)
        if inp.type.ndim != 4:
            raise TypeError('inp must be 4D tensor')

        inp_grad = as_cuda_ndarray_variable(inp_grad)
        if inp_grad.type.ndim != 4:
            raise TypeError('inp_grad must be 4D tensor')

        out = as_cuda_ndarray_variable(out)
        if out.type.ndim != 4:
            raise TypeError('out must be 4D tensor')

        if not isinstance(desc.type, CDataType) \
                or desc.type.ctype != 'cudnnPoolingDescriptor_t':
            raise TypeError('desc must be cudnnPoolingDescriptor_t')

        return Apply(self, [inp, inp_grad, out, desc],
                     [inp.type()])

    def c_support_code_struct(self, node, struct_id):
        return """
cudnnTensor4dDescriptor_t input%(id)d;        
cudnnTensor4dDescriptor_t input_grad%(id)d;
cudnnTensor4dDescriptor_t output%(id)d;
cudnnTensor4dDescriptor_t output_grad%(id)d;
""" % dict(id=struct_id)

    def c_init_code_struct(self, node, struct_id, sub):
        return """
cudnnStatus_t err%(id)d;
input%(id)d = NULL;
input_grad%(id)d = NULL;
output%(id)d = NULL;
output_grad%(id)d = NULL;
if ((err%(id)d = cudnnCreateTensor4dDescriptor(&input%(id)d)) != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_MemoryError, "could not allocate tensor4d descriptor "
               "(input): %%s", cudnnGetErrorString(err%(id)d));
  %(fail)s
}
if ((err%(id)d = cudnnCreateTensor4dDescriptor(&input_grad%(id)d)) != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_MemoryError, "could not allocate tensor4d descriptor "
               "(input_grad): %%s", cudnnGetErrorString(err%(id)d));
  %(fail)s
}
if ((err%(id)d = cudnnCreateTensor4dDescriptor(&output%(id)d)) != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_MemoryError, "could not allocate tensor4d descriptor "
               "(output): %%s", cudnnGetErrorString(err%(id)d));
  %(fail)s
}
if ((err%(id)d = cudnnCreateTensor4dDescriptor(&output_grad%(id)d)) != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_MemoryError, "could not allocate tensor4d descriptor "
               "(output_grad): %%s", cudnnGetErrorString(err%(id)d));
  %(fail)s
}
""" % dict(id=struct_id, fail=sub['fail'])

    def c_cleanup_code_struct(self, node, struct_id):
        return """
if (input%(id)d != NULL) { cudnnDestroyTensor4dDescriptor(input%(id)d); }
if (input_grad%(id)d != NULL) { cudnnDestroyTensor4dDescriptor(input_grad%(id)d); }
if (output%(id)d != NULL) { cudnnDestroyTensor4dDescriptor(output%(id)d); }
if (output_grad%(id)d != NULL) { cudnnDestroyTensor4dDescriptor(output_grad%(id)d); }
""" % dict(id=struct_id)

    def c_code(self, node, name, inputs, outputs, sub):
        inp, inp_grad, out, desc = inputs
        out_grad, = outputs

        set_in = "\n".join([
            c_set_tensor4d(inp, "input" + str(sub['struct_id']),
                           'err' + name, sub['fail']),
            c_set_tensor4d(inp_grad, "input_grad" + str(sub['struct_id']),
                           'err' + name, sub['fail']),
            c_set_tensor4d(out, "output" + str(sub['struct_id']),
                           'err' + name, sub['fail'])
        ])

        set_out = c_set_tensor4d(out, "output_grad" + str(sub['struct_id']),
                                 'err' + name, sub['fail'])

        return """
cudnnStatus_t err%(name)s;

if (!CudaNdarray_is_c_contiguous(%(input)s)) {
  PyErr_SetString(PyExc_ValueError, "Only contiguous inputs are supported.");
  %(fail)s
}

if (!CudaNdarray_is_c_contiguous(%(input_grad)s)) {
  PyErr_SetString(PyExc_ValueError, "Only contiguous input gradients are supported.");
  %(fail)s
}

if (!CudaNdarray_is_c_contiguous(%(output)s)) {
  PyErr_SetString(PyExc_ValueError, "Only contiguous outputs are supported.");
  %(fail)s
}

%(set_in)s

if (CudaNdarray_prep_output(&%(output_grad)s, 4, CudaNdarray_HOST_DIMS(%(output)s)) != 0)
{
  %(fail)s
}

%(set_out)s

err%(name)s = cudnnPoolingBackward(
_handle,
%(desc)s,
%(input_desc)s, CudaNdarray_DEV_DATA(%(input)s),
%(input_grad_desc)s, CudaNdarray_DEV_DATA(%(input_grad)s),
%(output_desc)s, CudaNdarray_DEV_DATA(%(output)s),
%(output_grad_desc)s, CudaNdarray_DEV_DATA(%(output_grad)s)
);
if (err%(name)s != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_RuntimeError, "error doing operation: %%s",
               cudnnGetErrorString(err%(name)s));
  %(fail)s
}
""" % dict(output_grad=out_grad, desc=desc,
           fail=sub['fail'], id=sub['struct_id'],
           name=name, set_in=set_in,
           set_out=set_out, input=inp, input_grad=inp_grad, output=out,
           input_desc="input"+str(sub['struct_id']),
           input_grad_desc="input_grad"+str(sub['struct_id']),
           output_desc="output"+str(sub['struct_id']),
           output_grad_desc="output_grad"+str(sub['struct_id']))

    def c_code_cache_version(self):
        return (2,)


def dnn_pool(img, ws, stride=(1, 1), mode='max'):
    """
    GPU pooling using cuDNN from NVIDIA.

    The memory layout to use is 'bc01', that is 'batch', 'channel',
    'first dim', 'second dim' in that order.

    :param img: images to do the pooling over
    :param ws: subsampling window size
    :param stride: subsampling stride (default: (1, 1))
    :param mode: one of 'max', 'average' (default: 'max')

    :warning: The cuDNN library only works with GPU that have a compute
      capability of 3.0 or higer.  This means that older GPU will not
      work with this Op.
    :note: This Op implements the ignore_border=True of max_pool_2d.
    """
    img = gpu_contiguous(img)
    desc = GpuDnnPoolDesc(ws=ws, stride=stride, mode=mode)()
    return GpuDnnPool()(img, desc)


class GpuDnnSoftmaxBase(DnnBase):
    """
    Op for the cuDNN Softmax.

    :param tensor_format: Whether the data format is 'bc01' or 'b01c'
    :param algo: 'fast' or 'accurate' indicating whether computations should be
        optimized for speed or accuracy respectively.
    :param mode: 'instance' or 'channel' indicating whether the softmax should
        be computed per image across 'c01' or per spationali location '01' per
        image across 'c'.
    """

    __props__ = ('tensor_format', 'mode', 'algo')

    def __init__(self, tensor_format, algo, mode):
        assert(tensor_format in ('bc01', 'b01c'))
        self.tensor_format = tensor_format

        assert(algo in ('fast', 'accurate'))
        self.algo = algo

        assert(mode in ('instance', 'channel'))
        self.mode = mode

        self.tensor_4d_descs = [softmax_input
                                for softmax_input in self.softmax_inputs]
        self.tensor_4d_descs.append('softmax_output')

    def _define_tensor4d_desc(self, name, id):
        return """
cudnnTensor4dDescriptor_t %(name)s_%(id)d;
""" % dict(name=name, id=id)

    def _init_tensor4d_desc(self, name, id, fail):
        return """
%(name)s_%(id)d = NULL;
if ((err%(id)d = cudnnCreateTensor4dDescriptor(&%(name)s_%(id)d)) != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_MemoryError, "could not allocate tensor4d descriptor "
               ": %%s", cudnnGetErrorString(err%(id)d));
  %(fail)s
}
""" % dict(name=name, id=id, fail=fail)

    def _clean_tensor4d_desc(self, name, id):
        return """
if(%(name)s_%(id)d!= NULL)
  cudnnDestroyTensor4dDescriptor(%(name)s_%(id)d);
""" % dict(name=name, id=id)

    def c_support_code_struct(self, node, struct_id):
        result = ''
        for name in self.tensor_4d_descs:
            result += self._define_tensor4d_desc(name, struct_id)
        return result

    def c_init_code_struct(self, node, struct_id, sub):
        result = """
cudnnStatus_t err%(id)d;
""" % dict(id=struct_id)

        for name in self.tensor_4d_descs:
            result += self._init_tensor4d_desc(name, struct_id, sub['fail'])
        return result

    def c_cleanup_code_struct(self, node, struct_id):
        result = ''
        for name in self.tensor_4d_descs:
            result += self._clean_tensor4d_desc(name, struct_id)
        return result

    def c_code(self, node, name, inputs, outputs, sub):
        ins = inputs
        outs, = outputs

        if self.tensor_format == 'b01c':
            tensor_format = 1
        else:
            tensor_format = 0

        if self.mode == 'instance':
            mode = 1
        else:
            mode = 0

        if self.algo == 'fast':
            algo = 1
        else:
            algo = 0

        # Setup configuration variables.
        result = """
cudnnStatus_t err%(name)s;
cudnnTensorFormat_t format%(id)d = CUDNN_TENSOR_NCHW;
if (%(tensor_format)d == 1)
  format%(id)d = CUDNN_TENSOR_NHWC;

cudnnSoftmaxAlgorithm_t algo%(id)d = CUDNN_SOFTMAX_ACCURATE;
if (%(algo)d == 1)
  algo%(id)d = CUDNN_SOFTMAX_FAST;

cudnnSoftmaxMode_t mode%(id)d = CUDNN_SOFTMAX_MODE_CHANNEL;
if (%(mode)d == 1)
  mode%(id)d = CUDNN_SOFTMAX_MODE_INSTANCE;
""" % dict(id=sub['struct_id'], name=name,
           tensor_format=tensor_format, mode=mode, algo=algo)

        # Validate the input and build the input variables.
        for input_idx, input_name in enumerate(self.softmax_inputs):
            result += """
if (!CudaNdarray_is_c_contiguous(%(ins)s)) {
  PyErr_SetString(PyExc_ValueError, "Only contiguous inputs are supported.");
  %(fail)s
}

err%(name)s = cudnnSetTensor4dDescriptor(
  %(input_name)s_%(id)d,
  format%(id)d,
  CUDNN_DATA_FLOAT,
  CudaNdarray_HOST_DIMS(%(ins)s)[0],
  CudaNdarray_HOST_DIMS(%(ins)s)[1],
  CudaNdarray_HOST_DIMS(%(ins)s)[2],
  CudaNdarray_HOST_DIMS(%(ins)s)[3]
);
if (err%(name)s != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_RuntimeError, "could not set tensor4d descriptor: %%%%s",
               cudnnGetErrorString(err%(name)s));
  %(fail)s
}
""" % dict(id=sub['struct_id'], name=name, input_name=input_name,
           ins=ins[input_idx], fail=sub['fail'])

        # Build and prepare the output variable.
        result += """
if (CudaNdarray_prep_output(&%(outs)s, 4, CudaNdarray_HOST_DIMS(%(ins)s)) != 0)
{
  %(fail)s
}

err%(name)s = cudnnSetTensor4dDescriptor(
  softmax_output_%(id)d,
  format%(id)d,
  CUDNN_DATA_FLOAT,
  CudaNdarray_HOST_DIMS(%(outs)s)[0],
  CudaNdarray_HOST_DIMS(%(outs)s)[1],
  CudaNdarray_HOST_DIMS(%(outs)s)[2],
  CudaNdarray_HOST_DIMS(%(outs)s)[3]
);
if (err%(name)s != CUDNN_STATUS_SUCCESS) {
  PyErr_Format(PyExc_RuntimeError, "could not set out descriptor: %%%%s",
               cudnnGetErrorString(err%(name)s));
  %(fail)s
}
"""

        # Add on a call to the method that does the actual work.
        result += self.method()

        subs = dict(ins=ins[-1], outs=outs, fail=sub['fail'],
                    id=sub['struct_id'], name=name)

        for idx, softmax_input in enumerate(self.softmax_inputs):
            subs['name%d' % idx] = softmax_input
            subs['ins%d' % idx] = inputs[idx]

        return result % subs

    def c_code_cache_version(self):
        return (0, 6)

    def method(self):
        raise NotImplementedError('GpuDnnSoftmaxBase::method')


class GpuDnnSoftmax(GpuDnnSoftmaxBase):
    softmax_inputs = ['softmax_input']

    def make_node(self, x):
        x = as_cuda_ndarray_variable(x)
        assert x.ndim == 4
        return Apply(self, [x], [x.type()])

    def method(self):
        return """
err%(name)s = cudnnSoftmaxForward(
  _handle,
  algo%(id)d,
  mode%(id)d,
  softmax_input_%(id)d,
  CudaNdarray_DEV_DATA(%(ins)s),
  softmax_output_%(id)d,
  CudaNdarray_DEV_DATA(%(outs)s)
);
"""

    def grad(self, inp, grads):
        x, = inp
        g_sm, = grads
        sm = self.make_node(x).outputs[0]
        return [GpuDnnSoftmaxGrad(
            self.tensor_format,
            self.algo,
            self.mode
        )(g_sm, sm)]


class GpuDnnSoftmaxGrad(GpuDnnSoftmaxBase):
    softmax_inputs = ['softmax_gout', 'softmax_input']

    def make_node(self, dy, sm):
        dy = as_cuda_ndarray_variable(dy)
        sm = as_cuda_ndarray_variable(sm)
        assert dy.ndim == 4
        assert sm.ndim == 4
        return Apply(self, [dy, sm], [sm.type.make_variable()])

    def method(self):
        return """
err%(name)s = cudnnSoftmaxBackward(
  _handle,
  algo%(id)d,
  mode%(id)d,
  %(name1)s_%(id)d,
  CudaNdarray_DEV_DATA(%(ins1)s),
  %(name0)s_%(id)d,
  CudaNdarray_DEV_DATA(%(ins0)s),
  softmax_output_%(id)d,
  CudaNdarray_DEV_DATA(%(outs)s)
);
"""


# We need this since other stuff from opt is not importable.
if cuda_available:

    from theano.sandbox.cuda.opt import (
        local_optimizer, gpu_optimizer, gpu_seqopt)

    #@register_opt('cudnn')  # this optimizer is registered in opt.py instead.
    @local_optimizer([GpuConv])
    def local_conv_dnn(node):
        if not dnn_available():
            return
        if isinstance(node.op, GpuConv):
            if node.op.border_mode not in ['full', 'valid']:
                return
            img, kern = node.inputs
            border_mode = node.op.border_mode
            subsample = node.op.subsample
            direction_hint = node.op.direction_hint
            return [dnn_conv(gpu_contiguous(img), gpu_contiguous(kern),
                             border_mode=border_mode, subsample=subsample,
                             direction_hint=direction_hint)]

# DISABLED as there is problems in the handling of borders
#    @register_opt('cudnn')
    @local_optimizer([GpuDownsampleFactorMax])
    def local_pool_dnn(node):
        if not dnn_available():
            return
        if isinstance(node.op, GpuDownsampleFactorMax):
            if node.op.ignore_border:
                return
            img, = node.inputs
            ds = node.op.ds
            return [dnn_pool(gpu_contiguous(img), ds, ds)]

# DISABLED as there is problems in the handling of borders
#    @register_opt('cudnn')
    @local_optimizer([GpuDownsampleFactorMaxGrad])
    def local_pool_dnn_grad(node):
        if not dnn_available():
            return
        if isinstance(node.op, GpuDownsampleFactorMaxGrad):
            if node.op.ignore_border:
                return
            inp, out, inp_grad = node.inputs
            ds = node.op.ds

            desc = GpuDnnPoolDesc(ws=ds, stride=ds, mode="max")()

            return [GpuDnnPoolGrad()(gpu_contiguous(inp),
                                     gpu_contiguous(inp_grad),
                                     gpu_contiguous(out), desc)]

    @register_opt('cudnn')
    @local_optimizer([GpuSoftmax])
    def local_softmax_dnn(node):
        if not dnn_available():
            return
        if isinstance(node.op, GpuSoftmax):
            ins = node.inputs[0].dimshuffle(0, 1, 'x', 'x')
            ins = gpu_contiguous(ins)
            out = GpuDnnSoftmax('bc01', 'accurate', 'channel')(ins)
            out = as_cuda_ndarray_variable(out.dimshuffle(0, 1))
            return [out]

    class NoCuDNNRaise(Optimizer):
        def apply(self, fgraph):
            """ Raise a RuntimeError if cudnn can't be used"""
            if not dnn_available():
                # Make an assert error as we want Theano to fail, not
                # just skip this optimization.
                raise AssertionError(
                    "cuDNN optimization was enabled, but Theano was not able"
                    " to use it. We got this error: \n" +
                    dnn_available.msg)
    gpu_seqopt.register("NoCuDNNRaise", NoCuDNNRaise(), 0, 'cudnn')

    @register_opt('cudnn')
    @local_optimizer([SoftmaxGrad])
    def local_softmax_dnn_grad(node):
        if (
            isinstance(node.op, SoftmaxGrad)
            and (isinstance(node.inputs[0].owner.op, HostFromGpu)
                 or isinstance(node.inputs[1].owner.op, HostFromGpu))
        ):
            if not dnn_available():
                return
            ins = []
            for n in node.inputs:
                if isinstance(n.owner.op, HostFromGpu):
                    n = n.owner.inputs[0]
                ins.append(n.dimshuffle(0, 1, 'x', 'x'))

            out = GpuDnnSoftmaxGrad(
                'bc01',
                'accurate',
                'channel'
            )(
                gpu_contiguous(ins[0]),
                gpu_contiguous(ins[1])
            )
            return [out.dimshuffle(0, 1)]
