# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/ez/cezmv74yrhrunjwqrletcmzzbnanma4ylsle3v7w345t7kxp622s.py
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   matmul => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ws/cws2widmnufjuyq7fjq4jjvoqutr4ip5yocpl7jr6f3fz7oo2hfm.py
# Topologically Sorted Source Nodes: [matmul, update], Original ATen: [aten.clone, aten.view]
# Source node to ATen node mapping:
#   matmul => clone_1
#   update => view_2
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_2,), kwargs = {memory_format: torch.contiguous_format})
#   %view_2 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%clone_1, [64, 4]), kwargs = {})
triton_poi_fused_clone_view_1 = async_compile.triton('triton_poi_fused_clone_view_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_view_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/jl/cjlbjijtpvxoudyymgohb6gbqmwikkpwupbw3wtza3pwdycfsg4x.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 1.0), kwargs = {})
triton_poi_fused_mul_2 = async_compile.triton('triton_poi_fused_mul_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_2, buf0, 64, 4, grid=grid(64, 4), stream=stream0)
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        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)
        # Topologically Sorted Source Nodes: [matmul, update], Original ATen: [aten.clone, aten.view]
        triton_poi_fused_clone_view_1.run(buf1, buf2, 64, 4, grid=grid(64, 4), stream=stream0)
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [update], Original ATen: [aten.mm]
        extern_kernels.mm(buf2, primals_3, out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        triton_poi_fused_mul_2.run(buf4, 256, grid=grid(256), stream=stream0)
    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), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import numpy as np


class MetaCurvatureTransform(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(MetaCurvatureTransform, 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, grad):
        if self.dim == 1:
            update = self.mc * grad
        elif self.dim == 2:
            update = self.mc_in @ grad @ self.mc_out
        else:
            update = grad.permute(2, 3, 0, 1).contiguous()
            shape = update.shape
            update = update.view(-1, self.n_out) @ self.mc_out
            update = self.mc_f @ update.view(self.n_f, -1)
            update = update.view(self.n_f, self.n_in, self.n_out)
            update = update.permute(1, 0, 2).contiguous().view(self.n_in, -1)
            update = self.mc_in @ update
            update = update.view(self.n_in, self.n_f, self.n_out).permute(1,
                0, 2).contiguous().view(shape)
            update = update.permute(2, 3, 0, 1).contiguous()
        return self.lr * update


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'param': torch.rand([4, 4])}]

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]
