import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_view_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK:
    tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (4 * x1 + 16 * (y0 // 4) + y0 % 4), xmask &
        ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_mul_2(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](primals_2, buf0, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf1)
        del primals_1
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        triton_poi_fused_clone_view_1[grid(64, 4)](buf1, buf2, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        buf3 = buf1
        del buf1
        extern_kernels.mm(buf2, primals_3, out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        triton_poi_fused_mul_2[grid(256)](buf4, 256, XBLOCK=128, num_warps=
            4, num_stages=1)
    return buf4, reinterpret_tensor(buf0, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf2, (4, 64), (1, 4), 0), reinterpret_tensor(
        primals_3, (4, 4), (1, 4), 0)


class MetaCurvatureTransformNew(torch.nn.Module):
    """
    [[Source]](https://github.com/learnables/learn2learn/blob/master/learn2learn/optim/transforms/module_transform.py)

    **Description**

    Implements the Meta-Curvature transform of Park and Oliva, 2019.

    Unlike `ModuleTranform` and `KroneckerTransform`, this class does not wrap other Modules but is directly
    called on a weight to instantiate the transform.

    **Arguments**

    * **param** (Tensor) - The weight whose gradients will be transformed.
    * **lr** (float, *optional*, default=1.0) - Scaling factor of the udpate. (non-learnable)

    **References**

    1. Park & Oliva. 2019. Meta-curvature.

    **Example**
    ~~~python
    classifier = torch.nn.Linear(784, 10, bias=False)
    metacurvature_update = MetaCurvatureTransform(classifier.weight)
    loss(classifier(X), y).backward()
    update = metacurvature_update(classifier.weight.grad)
    classifier.weight.data.add_(-lr, update)  # Not a differentiable update. See l2l.optim.DifferentiableSGD.
    ~~~
    """

    def __init__(self, param, lr=1.0):
        super(MetaCurvatureTransformNew, self).__init__()
        self.lr = lr
        shape = param.shape
        if len(shape) == 1:
            self.dim = 1
            self.mc = torch.nn.Parameter(torch.ones_like(param))
        elif len(shape) == 2:
            self.dim = 2
            self.mc_in = torch.nn.Parameter(torch.eye(shape[0]))
            self.mc_out = torch.nn.Parameter(torch.eye(shape[1]))
        elif len(shape) == 4:
            self.dim = 4
            self.n_in = shape[0]
            self.n_out = shape[1]
            self.n_f = int(np.prod(shape) / (self.n_in * self.n_out))
            self.mc_in = torch.nn.Parameter(torch.eye(self.n_in))
            self.mc_out = torch.nn.Parameter(torch.eye(self.n_out))
            self.mc_f = torch.nn.Parameter(torch.eye(self.n_f))
        else:
            raise NotImplementedError('Parameter with shape', shape,
                'is not supported by MetaCurvature.')

    def forward(self, input_0):
        primals_1 = self.mc_in
        primals_3 = self.mc_out
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.015012