import os

import numpy

import theano
from theano import Op, Apply
from theano import tensor, scalar, config
from theano.gradient import grad_undefined
from theano.scalar import Scalar
from theano.tensor.basic import Alloc, Join, Split

from theano.gof import HideC
from theano.gof.utils import MethodNotDefined
from theano.compat import PY3

try:
    import pygpu
    from pygpu import gpuarray, elemwise
except ImportError:
    pass

from .type import GpuArrayType
from .fp16_help import write_w


def as_gpuarray_variable(x):
    # This is needed to lower the number of useless transfer
    # introduced during optimization.  This speed up optimization and
    # "canonicalize" the graph, so it make easier making some
    # optimization.
    if (hasattr(x, 'fgraph') and
        len(x.clients) == 1 and
        x.owner and
        isinstance(x.owner.op, HostFromGpu)):
        return x.owner.inputs[0]
    if hasattr(x, '_as_GpuArrayVariable'):
        return x._as_GpuArrayVariable()
    # TODO we need to have the cuda -> gpu path taken care of.
    tensor_x = tensor.as_tensor_variable(x)
    return gpu_from_host(tensor_x)


def as_gpuarray(x):
    return gpuarray.array(x, copy=False)


class Kernel(object):
    """
    This class groups together all the attributes of a gpu kernel.
    """
    def __init__(self, code, params, name, flags,
                 codevar=None, binvar=None, objvar=None):
        self.code = code
        self.params = params
        self.name = name
        self.flags = flags
        if codevar is None:
            codevar = 'kcode_' + name
        self.codevar = codevar
        if binvar is None:
            binvar = 'kbin_' + name
        self.binvar = binvar
        if objvar is None:
            objvar = 'k_' + name
        self.objvar = objvar

    @staticmethod
    def get_flags(*types):
        def get_dtype(t):
            if isinstance(t, (str, unicode)):
                return numpy.dtype(t)
            elif isinstance(t, Type):
                return t.dtype
            elif isinstance(t, Variable):
                return t.type.dtype
            else:
                raise TypeError, "can't get a dtype from %s" % (type(t),)
        dtypes = [get_dtype(t) for t in types]
        flags = dict(cluda=True)
        if any(d == numpy.float64 for d in dtypes):
            flags['have_double'] = True
        if any(d.itemsize < 4 for d in dtypes):
            flags['have_small'] = True
        if any(d.kind == 'c' for d in dtypes):
            flags['have_complex'] = True
        if any(d == numpy.float16 for d in dtypes):
            flags['have_half'] = True
        return flags

    def _get_c_flags(self):
        res = []
        if self.flags.get('cluda', False):
            res.append('GA_USE_CLUDA')
        if self.flags.get('have_double', False):
            res.append('GA_USE_DOUBLE')
        if self.flags.get('have_small', False):
            res.append('GA_USE_SMALL')
        if self.flags.get('have_complex', False):
            res.append('GA_USE_COMPLEX')
        if self.flags.get('have_half', False):
            res.append('GA_USE_SMALL')
        return '|'.join(res)

    def _get_c_types(self):
        def m(t):
            if t == gpuarray.GpuArray:
                return "GA_BUFFER"
            else:
                return str(gpuarray.dtype_to_typecode(t))
        return ', '.join(m(t) for t in self.params)


class GpuKernelBase(object):
    def gpu_kernels(self, node, name):
        """
        This is the method to override.  This should return an
        iterable of Kernel objects that describe the kernels this op
        will need.
        """
        raise MethodNotDefined, 'gpu_kernels'

    def c_headers(self):
        try:
            o = super(GpuKernelBase, self).c_headers()
        except MethodNotDefined:
            o = []
        return o + ['gpuarray/types.h']

    def _generate_kernel_bin(self, k):
        gk = gpuarray.GpuKernel(k.code, k.name, k.params, **k.flags)
        bin = gk._binary
        bcode = ','.join(hex(ord(c)) for c in bin)
        return ("""static const char %(bname)s[] = { %(bcode)s };""" %
                dict(bname=k.binvar, bcode=bcode))

    def _generate_kernel_code(self, k):
        code = '\\n'.join(l for l in k.code.split('\n'))
        code = code.replace('"', '\\"')
        return ("""static const char *%(cname)s = "%(code)s";""" %
                dict(cname=k.codevar, code=code))

    def _generate_kernel_vars(self, k):
        return """static GpuKernel %(kname)s;""" % dict(kname=k.objvar)

    def c_support_code_apply(self, node, name):
        kernels = self.gpu_kernels(node, name)
        bins = '\n'.join(self._generate_kernel_bin(k) for k in kernels)
        codes = '\n'.join(self._generate_kernel_code(k) for k in kernels)
        vars = '\n'.join(self._generate_kernel_vars(k) for k in kernels)
        return '\n'.join([bins, codes, vars])

    def _generate_kernel_init(self, k, err):
        if PY3:
            error_out = "NULL"
        else:
            error_out = ""
        return """{
  int types[%(numargs)u] = {%(types)s};
  const char *bcode = %(bvar)s;
  size_t sz = sizeof(%(bvar)s);
  PyGpuContextObject *c = pygpu_default_context();
  if (GpuKernel_init(&%(ovar)s, c->ops, c->ctx, 1, &bcode, &sz, "%(kname)s",
                     %(numargs)u, types, GA_USE_BINARY, NULL) != GA_NO_ERROR) {
    if ((%(err)s = GpuKernel_init(&%(ovar)s, c->ops, c->ctx, 1, &%(cname)s,
                                  NULL, "%(kname)s", %(numargs)u, types,
                                  %(flags)s, NULL)) != GA_NO_ERROR) {
      PyErr_Format(PyExc_RuntimeError, "GpuKernel_init error %%d: %%s",
                   %(err)s, Gpu_error(c->ops, c->ctx, %(err)s));
      return %(error_out)s;
    }
  }
}""" % dict(numargs=len(k.params), types=k._get_c_types(), bvar=k.binvar,
            ovar=k.objvar, kname=k.name, err=err, cname=k.codevar,
            flags=k._get_c_flags(), error_out=error_out)

    def c_init_code_apply(self, node, name):
        err = 'err_' + name
        kernels = self.gpu_kernels(node, name)
        inits = '\n'.join(self._generate_kernel_init(k, err) for k in kernels)
        return ("int %(err)s;\n" % dict(err=err)) + inits

    def _GpuKernelBase_version(self):
        ctx = gpuarray.get_default_context()
        return (2, ctx.kind, ctx.devname)

    GpuKernelBase_version = property(_GpuKernelBase_version)


class HostFromGpu(Op):
    __props__ = ()
    _f16_ok = True

    def __str__(self):
        return 'HostFromGpu(gpuarray)'

    def make_node(self, x):
        if not isinstance(x.type, GpuArrayType):
            raise TypeError(x)
        return Apply(self, [x],
                     [tensor.TensorType(dtype=x.dtype,
                                        broadcastable=x.broadcastable)()])

    def perform(self, node, inp, out):
        x, = inp
        z, = out
        z[0] = numpy.asarray(x)

    def c_code(self, node, name, inputs, outputs, sub):
        return """
        GpuArray %(name)s_ga_s;
        GpuArray *%(name)s_ga = NULL;
        int %(name)serr;
        PyArray_Descr *%(name)s_dtype;
        if (!GpuArray_ISONESEGMENT(&%(inp)s->ga)) {
            if (GpuArray_copy(&%(name)s_ga_s, &%(inp)s->ga, GA_C_ORDER) != GA_NO_ERROR) {
                PyErr_SetString(PyExc_RuntimeError, "Can't make contiguous copy");
                %(fail)s;
            }
            %(name)s_ga = &%(name)s_ga_s;
        } else {
            %(name)s_ga = &%(inp)s->ga;
        }
        %(name)s_dtype = typecode_to_dtype(%(name)s_ga->typecode);
        Py_XDECREF(%(out)s);
        // PyArray_Empty below steals a reference to the dtype we pass it
        // so we need an extra one to spare.
        Py_INCREF(%(name)s_dtype);
        %(out)s = (PyArrayObject *)PyArray_Empty(%(inp)s->ga.nd,
                                (npy_intp *)%(inp)s->ga.dimensions,
                                %(name)s_dtype,
                                (%(inp)s->ga.flags & GA_F_CONTIGUOUS) &&
                                !(%(inp)s->ga.flags & GA_C_CONTIGUOUS));
        if (%(out)s == NULL) {
            if (%(name)s_ga == &%(name)s_ga_s) GpuArray_clear(%(name)s_ga);
            %(fail)s
        }
        %(name)serr = GpuArray_read(PyArray_DATA(%(out)s),
                                    PyArray_NBYTES(%(out)s),
                                    %(name)s_ga);
        if (%(name)s_ga == &%(name)s_ga_s) GpuArray_clear(%(name)s_ga);
        if (%(name)serr != GA_NO_ERROR) {
            PyErr_SetString(PyExc_RuntimeError, "Could not read device data.");
            %(fail)s
        }
        """ % {'name': name, 'fail': sub['fail'], 'inp': inputs[0],
               'out': outputs[0]}

    def c_code_cache_version(self):
        return (1,)

    def grad(self, inputs, grads):
        gz, = grads
        return [gpu_from_host(gz)]

    def R_op(self, inputs, eval_points):
        ev, = eval_points
        if isinstance(ev, tensor.TensorType):
            return [gpu_from_host(ev)]
        else:
            return [ev]

    def infer_shape(self, node, xshp):
        return xshp


host_from_gpu = HostFromGpu()


class GpuFromHost(Op):
    __props__ = ()
    _f16_ok = True

    def __str__(self):
        return 'GpuFromHost(gpuarray)'

    def make_node(self, x):
        if not isinstance(x.type, tensor.TensorType):
            raise TypeError(x)
        return Apply(self, [x], [GpuArrayType(broadcastable=x.broadcastable,
                                              dtype=x.dtype)()])

    def perform(self, node, inp, out):
        x, = inp
        z, = out
        type = node.outputs[0].type
        z[0] = gpuarray.array(x)

    def grad(self, inputs, grads):
        gz, = grads
        return [host_from_gpu(as_gpuarray_variable(gz))]

    def R_op(self, inputs, eval_points):
        ev, = eval_points
        if isinstance(ev, GpuArrayType):
            return [host_from_gpu(ev)]
        else:
            return ev

    def infer_shape(self, node, xshp):
        return xshp

    def c_code(self, node, name, inputs, outputs, sub):
        return """
        Py_XDECREF(%(out)s);
        %(out)s = pygpu_fromhostdata(PyArray_DATA(%(inp)s),
                                     get_typecode((PyObject *)PyArray_DESCR(%(inp)s)),
                                     PyArray_NDIM(%(inp)s),
                                     (size_t *)PyArray_DIMS(%(inp)s),
                                     (ssize_t *)PyArray_STRIDES(%(inp)s),
                                     pygpu_default_context(),
                                     Py_None);
        if (%(out)s == NULL) {
            %(fail)s
        }
        """ % {'name': name, 'inp': inputs[0],
               'out': outputs[0], 'fail': sub['fail']}

    def c_code_cache_version(self):
        return (4,)

gpu_from_host = GpuFromHost()


class GpuFromCuda(Op):
    view_map = {0: [0]}

    def __eq__(self, other):
        return type(self) == type(other)

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

    def __str__(self):
        return 'GpuFromCuda'

    def make_node(self, x):
        from theano.sandbox.cuda import CudaNdarrayType
        if not isinstance(x.type, CudaNdarrayType):
            raise TypeError(x)
        return Apply(self, [x], [GpuArrayType(broadcastable=x.broadcastable,
                                              dtype=x.dtype)()])

    def perform(self, node, inp, out):
        x, = inp
        z, = out
        z[0] = gpuarray.array(numpy.asarray(x))

    def grad(self, inputs, grads):
        gz, = grads
        return [cuda_from_gpu(gz)]

    def R_op(self, inputs, eval_points):
        ev, = eval_points
        if isinstance(ev, GpuArrayType):
            return [cuda_from_gpu(ev)]
        else:
            return ev

    def infer_shape(self, node, xshp):
        return xshp

    def c_headers(self):
        return ['<cuda_ndarray.cuh>', '<gpuarray/extension.h>',
                '<gpuarray/types.h>', '<cuda.h>']

    def c_header_dirs(self):
        import cuda_ndarray
        ret = [os.path.dirname(cuda_ndarray.__file__)]
        cuda_root = config.cuda.root
        if cuda_root:
            ret.append(os.path.join(cuda_root, 'include'))
        return ret

    def c_lib_dirs(self):
        import cuda_ndarray
        ret = [os.path.dirname(cuda_ndarray.__file__)]
        cuda_root = config.cuda.root
        if cuda_root:
            ret.append(os.path.join(cuda_root, 'lib'))
        return ret

    def c_libraries(self):
        return ['cudart', 'cublas', 'cuda']

    def c_support_code(self):
        return """
        CUcontext (*cuda_get_ctx)(void *ctx);
        gpudata *(*cuda_make_buf)(void *c, CUdeviceptr p, size_t sz);
        """

    def c_init_code(self):
        return ['cuda_get_ctx = (CUcontext (*)(void *))gpuarray_get_extension("cuda_get_ctx");',
                'cuda_make_buf = (gpudata *(*)(void *, CUdeviceptr, size_t))gpuarray_get_extension("cuda_make_buf");']

    def c_code(self, node, name, inputs, outputs, sub):
        return """
        int %(name)serr;
        gpudata *%(name)sdata;
        CUcontext %(name)scur;
        size_t *%(name)sdims;
        ssize_t *%(name)sstr;

        cuCtxGetCurrent(&%(name)scur);
        if (%(name)scur != cuda_get_ctx(pygpu_default_context()->ctx)) {
            PyErr_SetString(PyExc_ValueError, "Ambient cuda context is not the same as output context.");
            %(fail)s
        }
        %(name)sdims = (size_t *)calloc(%(in)s->nd, sizeof(size_t));
        if (%(name)sdims == NULL) {
            PyErr_SetString(PyExc_MemoryError, "Can't allocate dimensions.");
            %(fail)s
        }
        %(name)sstr = (ssize_t *)calloc(%(in)s->nd, sizeof(ssize_t));
        if (%(name)sstr == NULL) {
            free(%(name)sdims);
            PyErr_SetString(PyExc_MemoryError, "Can't allocate strides.");
            %(fail)s
        }

        for (unsigned int i = 0; i < %(in)s->nd; i++) {
            %(name)sdims[i] = (size_t)CudaNdarray_HOST_DIMS(%(in)s)[i];
            %(name)sstr[i] = (ssize_t)CudaNdarray_HOST_STRIDES(%(in)s)[i]*4;
        }

        %(name)sdata = cuda_make_buf(pygpu_default_context()->ctx,
                                     (CUdeviceptr)%(in)s->devdata,
                                     ((size_t)%(in)s->data_allocated)*4);
        if (%(name)sdata == NULL) {
            Py_DECREF(%(out)s);
            free(%(name)sdims);
            free(%(name)sstr);
            PyErr_SetString(PyExc_MemoryError, "Could not allocate gpudata structure.");
            %(fail)s
        }
        Py_XDECREF(%(out)s);
        %(out)s = pygpu_fromgpudata(%(name)sdata, 0, GA_FLOAT, %(in)s->nd,
                                    %(name)sdims, %(name)sstr,
                                    pygpu_default_context(), 1,
                                    (PyObject *)%(in)s,
                                    (PyObject *)&PyGpuArrayType);
        pygpu_default_context()->ops->buffer_release(%(name)sdata);
        free(%(name)sdims);
        free(%(name)sstr);
        if (%(out)s == NULL) {
            %(fail)s
        }
        """ % {'name': name, 'in': inputs[0], 'out': outputs[0],
               'fail': sub['fail']}

    def c_code_cache_version(self):
        return (5,)

gpu_from_cuda = GpuFromCuda()


class CudaFromGpu(Op):
    view_map = {0: [0]}

    def __eq__(self, other):
        return type(self) == type(other)

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

    def __str__(self):
        return 'CudaFromGpu'

    def make_node(self, x):
        from theano.sandbox.cuda import CudaNdarrayType
        if not isinstance(x.type, GpuArrayType):
            raise TypeError(x)
        if x.type.dtype != 'float32':
            raise TypeError(x)
        return Apply(self, [x], [CudaNdarrayType(broadcastable=x.broadcastable)()])

    def perform(self, node, inp, out):
        from theano.sandbox.cuda import filter as cuda_filter
        x, = inp
        z, = out
        z[0] = cuda_filter(theano._asarray(x, dtype='float32'),
                           tuple([0] * x.ndim), 0, z[0])

    def grad(self, inputs, grads):
        gz, = grads
        return [gpu_from_cuda(gz)]

    def R_op(self, inputs, eval_points):
        from theano.sandbox.cuda import CudaNdarrayType
        ev, = eval_points
        if (isinstance(ev, CudaNdarrayType)):
            return [gpu_from_cuda(ev)]
        else:
            return [ev]

    def infer_shape(self, node, shp):
        return shp

    def c_headers(self):
        return ['<cuda_ndarray.cuh>', '<gpuarray/extension.h>', '<cuda.h>']

    def c_header_dirs(self):
        import cuda_ndarray
        ret = [os.path.dirname(cuda_ndarray.__file__)]
        cuda_root = config.cuda.root
        if cuda_root:
            ret.append(os.path.join(cuda_root, 'include'))
        return ret

    def c_lib_dirs(self):
        import cuda_ndarray
        ret = [os.path.dirname(cuda_ndarray.__file__)]
        cuda_root = config.cuda.root
        if cuda_root:
            ret.append(os.path.join(cuda_root, 'lib'))
        return ret

    def c_libraries(self):
        return ['cudart', 'cublas', 'cuda']

    def c_support_code(self):
        return """
        CUcontext (*cuda_get_ctx)(void *ctx);
        CUdeviceptr (*cuda_get_ptr)(gpudata *g);
        """

    def c_init_code(self):
        return ['cuda_get_ctx = (CUcontext (*)(void *ctx))gpuarray_get_extension("cuda_get_ctx");',
                'cuda_get_ptr = (CUdeviceptr (*)(gpudata *g))gpuarray_get_extension("cuda_get_ptr");']

    def c_code(self, node, name, inputs, outputs, sub):
        return """
        int %(name)serr = 0, %(name)si;
        CUcontext %(name)scur;

        cuCtxGetCurrent(&%(name)scur);
        if (%(name)scur != cuda_get_ctx(pygpu_default_context()->ctx)) {
            PyErr_SetString(PyExc_ValueError, "Ambient cuda context is not the same as output context.");
            %(fail)s
        }

        if (GpuArray_sync(&%(inp)s->ga) != GA_NO_ERROR) {
            PyErr_SetString(PyExc_RuntimeError, "Could not sync GpuArray");
            %(fail)s
        }
        Py_XDECREF(%(out)s);
        %(out)s = (CudaNdarray *)CudaNdarray_new_nd(%(inp)s->ga.nd);
        if (!%(out)s) {
            %(fail)s
        }
        for (%(name)si = 0; %(name)si < %(inp)s->ga.nd; %(name)si++) {
            CudaNdarray_set_dim(%(out)s, %(name)si, %(inp)s->ga.dimensions[%(name)si]);
            CudaNdarray_set_stride(%(out)s, %(name)si, %(inp)s->ga.strides[%(name)si]/4);
        }
        %(name)serr = CudaNdarray_set_device_data(%(out)s,
          (float *)(((char *)cuda_get_ptr(%(inp)s->ga.data))+%(inp)s->ga.offset),
                                          (PyObject *)%(inp)s);
        if (%(name)serr) {
           %(fail)s
        }
        """ % {'name': name, 'inp': inputs[0], 'out': outputs[0],
               'fail': sub['fail']}

    def c_code_cache_version(self):
        return (3,)


cuda_from_gpu = CudaFromGpu()


class GpuAlloc(HideC, Alloc):
    __props__ = ('memset_0',)
    _f16_ok = True

    def __init__(self, memset_0=False):
        """memset_0 is only an optimized version. True, it mean the
        value is always 0, so the c code call memset as it is faster.
        """
        self.memset_0 = memset_0

    def __str__(self):
        # Hide the memset parameter when not used to prevent confusion.
        if self.memset_0:
            s = "%s{memset_0=%s}" % (self.__class__.__name__, self.memset_0)
        else:
            s = self.__class__.__name__
        return s

    def make_node(self, value, *shape):
        res = Alloc.make_node(self, value, *shape)
        value = as_gpuarray_variable(value)
        otype = GpuArrayType(dtype=res.outputs[0].dtype,
                             broadcastable=res.outputs[0].broadcastable)
        return Apply(self, [value] + res.inputs[1:], [otype()])

    def c_headers(self):
        return ['<numpy_compat.h>']

    def perform(self, node, inputs, outs):
        out, = outs
        v = inputs[0]
        sh = tuple(map(int, inputs[1:]))
        if out[0] is None or out[0].shape != sh:
            if v.size == 1 and numpy.asarray(v).flatten().item() == 0:
                out[0] = gpuarray.zeros(sh, dtype=v.dtype)
            else:
                out[0] = gpuarray.empty(sh, dtype=v.dtype)
                out[0][...] = v
        else:
            out[0][...] = v
        if config.gpuarray.sync:
            out[0].sync()

    def c_code(self, node, name, inp, out, sub):
        vv = inp[0]
        ndim = len(inp[1:])
        zz, = out

        memset_0 = int(self.memset_0)
        code = """
        int i;
        size_t %(name)s_shape[%(ndim)s];
        """ % dict(name=name, ndim=ndim)

        for i, shp_i in enumerate(inp[1:]):
            code += """
        %(name)s_shape[%(i)s] = ((dtype_%(shp_i)s *)PyArray_DATA(%(shp_i)s))[0];
        """ % dict(name=name, i=i, shp_i=shp_i)

        code += """
        int need_new_out = (NULL == %(zz)s || %(zz)s->ga.nd != %(ndim)s);

        if (!need_new_out)
            for (i = 0; i < %(ndim)s; i++)
                need_new_out |= %(zz)s->ga.dimensions[i] != %(name)s_shape[i];

        if (need_new_out && (%(memset_0)s)) {
            //pygpu_zeros can be faster then empty followed by memset.
            Py_XDECREF(%(zz)s);
            %(zz)s = pygpu_zeros(%(ndim)s, %(name)s_shape,
                                 %(vv)s->ga.typecode, GA_C_ORDER,
                                 pygpu_default_context(), Py_None);
            if (!%(zz)s) {
                %(fail)s
            }
        } else {
            if (need_new_out) {
                Py_XDECREF(%(zz)s);
                %(zz)s = pygpu_empty(%(ndim)s, %(name)s_shape,
                                     %(vv)s->ga.typecode, GA_C_ORDER,
                                     pygpu_default_context(), Py_None);
                if (!%(zz)s) {
                    %(fail)s
                }
            }
            if (%(memset_0)s && GpuArray_ISONESEGMENT(&%(zz)s->ga))
            {
                int err = GpuArray_memset(&%(zz)s->ga, 0);
                if (err != GA_NO_ERROR)
                {
                    PyErr_Format(PyExc_MemoryError,
                                 "GpuAlloc: Error memsetting %%d"
                                 " element of device memory to 0.",
                                 PyGpuArray_SIZE(%(zz)s));
                    %(fail)s;
                }
            }
            else if (GpuArray_setarray(&%(zz)s->ga, &%(vv)s->ga) !=
                     GA_NO_ERROR) {
                PyErr_SetString(PyExc_ValueError, "setarray failed");
                %(fail)s
            }
        }
        """ % dict(name=name, ndim=ndim, zz=zz, vv=vv,
                   fail=sub['fail'], memset_0=memset_0)

        if config.gpuarray.sync:
            code += "GpuArray_sync(&%(zz)s->ga);" % dict(zz=zz)

        return code

    def c_code_cache_version(self):
        return (2,)

    def do_constant_folding(self, node):
        for client in node.outputs[0].clients:
            if client[0] == 'output':
                # If the output is a constant, it will have to be deepcopied
                # each time the function is called.  So we do not fold.
                return False
            elif (  # The following ops work inplace of their input id 0.
                  client[1] == 0 and
                  isinstance(client[0].op, (
                    # Ops that will work inplace on the Alloc. So if they
                    # get constant_folded, they would copy the
                    # constant and this is less efficients.

                    # Not doing the constant folding could also lower
                    # the peak memory usage, as we the "constant" won't
                    # always exists.
                      # theano.tensor.subtensor.AdvancedIncSubtensor,
                      theano.sandbox.gpuarray.subtensor.GpuIncSubtensor,
                      theano.sandbox.gpuarray.subtensor.GpuAdvancedIncSubtensor1,
                      theano.sandbox.gpuarray.subtensor.GpuAdvancedIncSubtensor1_dev20,
                      theano.sandbox.gpuarray.blas.GpuGemm,
                      theano.sandbox.gpuarray.blas.GpuGemv,
                      theano.sandbox.gpuarray.blas.GpuGer,
                  ))):
                return False
            # If the clients is a transfer, we don't want to fold. We
            # let the moving opt finish before deciding what to do.
            elif isinstance(client[0].op, HostFromGpu):
                return False
        return True

gpu_alloc = GpuAlloc()


class GpuContiguous(Op):
    """
    Always return a c contiguous output. Copy the input only if it is
    not already c contiguous.
    """
    __props__ = ()
    view_map = {0: [0]}
    _f16_ok = True

    def grad(self, inputs, dout):
        x, = inputs
        dout, = dout
        dout = as_gpuarray_variable(dout)

        return [dout]

    def make_node(self, input):
        input = as_gpuarray_variable(input)
        return Apply(self, [input], [input.type()])

    def c_headers(self):
        return ['<numpy_compat.h>']

    def c_code_cache_version(self):
        return (3,)

    def c_code(self, node, name, inp, out, sub):
        input, = inp
        z, = out
        fail = sub['fail']
        str = """
        {
            if (GpuArray_IS_C_CONTIGUOUS(&(%(input)s->ga))){
                Py_XDECREF(%(z)s);
                %(z)s = %(input)s;
                Py_INCREF(%(z)s);

            } else if ((NULL == %(z)s)""" % locals()
        for i in xrange(len(node.inputs[0].type.broadcastable)):
            str += "\n|| (PyGpuArray_DIMS(%(input)s)[%(i)s] != PyGpuArray_DIMS(%(z)s)[%(i)s])" % locals()
        str += """
                || !GpuArray_IS_C_CONTIGUOUS(&(%(z)s->ga)))
            {
                Py_XDECREF(%(z)s);
                %(z)s = pygpu_copy(%(input)s, GA_C_ORDER);
                if (!%(z)s)
                {
                    %(fail)s;
                }
            }else if(pygpu_move(%(z)s, %(input)s) == -1) {
                %(fail)s;
            }
        }
        """ % locals()
        return str

gpu_contiguous = GpuContiguous()


class GpuReshape(HideC, tensor.Reshape):
    """
    Implement Reshape on the gpu.
    """
    _f16_ok = True

    # __hash__, __eq__, __str__ come from tensor.Reshape
    def make_node(self, x, shp):
        x = as_gpuarray_variable(x)
        res = host_from_gpu(x).reshape(shp, ndim=self.ndim)
        otype = GpuArrayType(dtype=res.dtype,
                             broadcastable=res.broadcastable)
        return Apply(self, [x, shp], [otype()])

    def perform(self, node, inp, out_):
        x, shp = inp
        out, = out_
        if (len(shp) != self.ndim):
            raise ValueError('shape argument to GpuReshape.perform'
                             ' has incorrect length %i'
                             ', should be %i' % (len(shp), self.ndim), shp)

        if shp.prod() != x.size:
            # We need to do check here to raise the same error as NumPy.
            # We should make pygpu do the same.
            ss = 1
            nb_m1 = 0
            for i in shp:
                if i == -1:
                    nb_m1 += 1
                else:
                    ss *= i
            if nb_m1 > 1:
                raise ValueError("Only one -1 is accepted in the new shape")
            elif nb_m1 == 1:
                if (x.size % ss) != 0:
                    raise ValueError("When using -1 in new shape, the computed new shape must be an multiple of the original shape.")
            else:
                raise ValueError("total size of new array must be unchanged")
        out[0] = x.reshape(tuple(shp))


class GpuJoin(HideC, Join):
    _f16_ok = True

    def make_node(self, axis, *tensors):
        node = Join.make_node(self, axis, *tensors)

        return Apply(self, [node.inputs[0]] + map(as_gpuarray_variable,
                                                  tensors),
                     [GpuArrayType(broadcastable=node.outputs[0].broadcastable,
                                   dtype=node.outputs[0].dtype)()])

    def perform(self, node, axis_and_tensors, out_):
        out, = out_
        axis = int(axis_and_tensors[0])
        tensors = axis_and_tensors[1:]
        out[0] = pygpu.concatenate(tensors, axis=axis).astype(
            node.outputs[0].dtype)

    def c_code_cache_version(self):
        return (1,)

    def c_code(self, node, name, inputs, out_, sub):
        copy_to_list = []
        restype = pygpu.gpuarray.dtype_to_typecode(node.outputs[0].dtype)
        for i, inp in enumerate(inputs[1:]):
            copy_to_list.append("als[%s] = &%s->ga;" % (i, inp))
        return """
const GpuArray **als = (const GpuArray **)PyMem_Malloc(sizeof(GpuArray *) *
                                                       %(n)s);
if (als == NULL) {
  PyErr_NoMemory();
  %(fail)s
}
%(copy_inputs_to_list)s
Py_XDECREF(%(out)s);
%(out)s = pygpu_concatenate(als, %(n)s, PyInt_AsLong((PyObject *)%(axis)s),
                            %(restype)s, (PyObject *)&PyGpuArrayType,
                            pygpu_default_context());
PyMem_Free(als);
if (%(out)s == NULL)
  %(fail)s
        """ % dict(n=len(inputs[1:]), fail=sub['fail'], out=out_[0],
                   axis=inputs[0], copy_inputs_to_list='\n'.join(copy_to_list),
                   restype=restype)


gpu_join = GpuJoin()


class GpuSplit(HideC, Split):
    def make_node(self, x, axis, splits):
        node = Split.make_node(self, x, axis, splits)
        x = as_gpuarray_variable(x)
        outs = [GpuArrayType(dtype=o.dtype, broadcastable=o.broadcastable)()
                for o in node.outputs]
        return Apply(self, [x] + node.inputs[1:], outs)
    # we reuse the perform of the CPU op, which is suitable


class GpuEye(GpuKernelBase, Op):
    __props__ = ('dtype',)
    _f16_ok = True

    def __init__(self, dtype=None):
        if dtype is None:
            dtype = config.floatX
        self.dtype = dtype

    def make_node(self, n, m, k):
        n = tensor.as_tensor_variable(n)
        m = tensor.as_tensor_variable(m)
        k = tensor.as_tensor_variable(k)
        assert n.ndim == 0
        assert m.ndim == 0
        assert k.ndim == 0
        otype = GpuArrayType(dtype=self.dtype,
                             broadcastable=(False, False))

        # k != 0 isn't implemented on the GPU yet.
        assert tensor.get_scalar_constant_value(k) == 0
        return Apply(self, [n, m], [otype()])

    def infer_shape(self, node, in_shapes):
        out_shape = [node.inputs[0], node.inputs[1]]
        return [out_shape]

    def grad(self, inp, grads):
        return [grad_undefined(self, i, inp[i])
                for i in xrange(3)]

    def gpu_kernels(self, node, name):
        code = """
KERNEL void k(GLOBAL_MEM %(ctype)s *a, ga_size n, ga_size m) {
    ga_size nb = n < m ? n : m;
    for (ga_size i = LID_0; i < nb; i += LDIM_0) {
        a[i*m + i] = %(write_a)s(1);
    }
}""" % dict(ctype=pygpu.gpuarray.dtype_to_ctype(self.dtype),
            name=name, write_a=write_w(self.dtype))
        return [Kernel(
                code=code, name="k",
                params=[gpuarray.GpuArray, gpuarray.SIZE, gpuarray.SIZE],
                flags=Kernel.get_flags(self.dtype),
                objvar='k_eye_'+name,
                )]

    def c_code(self, node, name, inp, out, sub):
        n, m = inp
        z, = out
        fail = sub['fail']
        typecode = pygpu.gpuarray.dtype_to_typecode(self.dtype)
        sync = bool(config.gpuarray.sync)
        kname = self.gpu_kernels(node, name)[0].objvar
        s = """
        size_t dims[2] = {0, 0};
        void *args[3];
        int err;

        dims[0] = ((dtype_%(n)s*)PyArray_DATA(%(n)s))[0];
        dims[1] = ((dtype_%(m)s*)PyArray_DATA(%(m)s))[0];
        Py_CLEAR(%(z)s);

        %(z)s = pygpu_zeros(2, dims,
                            %(typecode)s,
                            GA_C_ORDER,
                            pygpu_default_context(), Py_None);
        if (%(z)s == NULL) {
            %(fail)s
        }

        args[0] = &%(z)s->ga;
        args[1] = &dims[0];
        args[2] = &dims[1];
        err = GpuKernel_call(&%(kname)s, 0, 1, 256, args);
        if (err != GA_NO_ERROR) {
            PyErr_Format(PyExc_RuntimeError,
                         "gpuarray error: kEye: %%s. n%%lu, m=%%lu.",
                         GpuKernel_error(&%(kname)s, err),
                         (unsigned long)dims[0], (unsigned long)dims[1]);
            %(fail)s;
        }

        if(%(sync)d)
            GpuArray_sync(&%(z)s->ga);
        """ % locals()

        return s

    def c_code_cache_version(self):
        return (3, self.GpuKernelBase_version)
