# 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/sg/csgojewz7ivvkqgz2youcxebnnlwakpwuik2fyzzax45hdygrm6r.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul
# Graph fragment:
#   %mul : [num_users=4] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, 0.0001), kwargs = {})
triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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_mul_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_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0001
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qs/cqsplu7aho4zbmtum5lhgvbgqxswrhhcfixbbw62plejld3qzbbs.py
# Topologically Sorted Source Nodes: [sigma_4], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   sigma_4 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%cat_1, %unsqueeze_3],), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 3, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 2, tl.int64)
    tmp6 = tmp0 < tmp5
    tmp7 = tmp6 & tmp4
    tmp8 = tl.full([1], 1, tl.int64)
    tmp9 = tmp0 < tmp8
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (x0), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp0 >= tmp8
    tmp13 = tmp12 & tmp7
    tmp14 = tl.load(in_ptr1 + (x0), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp11, tmp14)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp7, tmp15, tmp16)
    tmp18 = tmp0 >= tmp5
    tmp19 = tmp18 & tmp4
    tmp20 = tl.load(in_ptr2 + (x0), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp21 = tl.where(tmp6, tmp17, tmp20)
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp4, tmp21, tmp22)
    tmp24 = tmp0 >= tmp3
    tmp25 = tl.full([1], 4, tl.int64)
    tmp26 = tmp0 < tmp25
    tmp27 = tl.load(in_ptr3 + (x0), tmp24 & xmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tl.where(tmp4, tmp23, tmp27)
    tl.store(out_ptr0 + (x2), tmp28, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/uw/cuwlkalong3cwuxng3zurtt3tgbk7ithqpljqcehvo5fjqbtfwrm.py
# Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   out_2 => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_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: '*fp32', 2: '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_2', '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_2(in_ptr0, 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 % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/kl/ckllf6mazqhprwh2tau7vxrdurc2kivba4tykq3gqxvebmdet63s.py
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   out_3 => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_3,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_3', '''
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: '*fp32', 2: '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_3', '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_3(in_ptr0, 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 % 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/77/c77uzurrxe65dwxrucifwcyja4kctf3y6ir7z3x44ooratb5zyqo.py
# Topologically Sorted Source Nodes: [sqrt], Original ATen: [aten.sqrt]
# Source node to ATen node mapping:
#   sqrt => sqrt
# Graph fragment:
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%squeeze_1,), kwargs = {})
triton_poi_fused_sqrt_4 = async_compile.triton('triton_poi_fused_sqrt_4', '''
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=[16], 
    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_sqrt_4', '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_sqrt_4(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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 = libdevice.sqrt(tmp0)
    tl.store(in_out_ptr0 + (x0), tmp1, 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, (16, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (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, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(primals_2, buf0, 16, grid=grid(16), stream=stream0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        extern_kernels.addmm(buf0, reinterpret_tensor(primals_1, (4, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        # Topologically Sorted Source Nodes: [sigma], Original ATen: [aten.linalg_inv_ex]
        buf2 = torch.ops.aten.linalg_inv_ex.default(buf1)
        buf3 = buf2[0]
        del buf2
        buf5 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.addmm(buf0, reinterpret_tensor(primals_1, (4, 4), (4, 1), 16), reinterpret_tensor(primals_1, (4, 4), (1, 4), 16), alpha=1, beta=1, out=buf5)
        # Topologically Sorted Source Nodes: [sigma_], Original ATen: [aten.linalg_inv_ex]
        buf6 = torch.ops.aten.linalg_inv_ex.default(buf5)
        buf7 = buf6[0]
        del buf6
        buf9 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.addmm(buf0, reinterpret_tensor(primals_1, (4, 4), (4, 1), 32), reinterpret_tensor(primals_1, (4, 4), (1, 4), 32), alpha=1, beta=1, out=buf9)
        # Topologically Sorted Source Nodes: [sigma__2], Original ATen: [aten.linalg_inv_ex]
        buf10 = torch.ops.aten.linalg_inv_ex.default(buf9)
        buf11 = buf10[0]
        del buf10
        buf13 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.addmm(buf0, reinterpret_tensor(primals_1, (4, 4), (4, 1), 48), reinterpret_tensor(primals_1, (4, 4), (1, 4), 48), alpha=1, beta=1, out=buf13)
        del buf0
        # Topologically Sorted Source Nodes: [sigma__4], Original ATen: [aten.linalg_inv_ex]
        buf14 = torch.ops.aten.linalg_inv_ex.default(buf13)
        buf15 = buf14[0]
        del buf14
        buf17 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [sigma_4], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf3, buf7, buf11, buf15, buf17, 64, grid=grid(64), stream=stream0)
        buf18 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 16), (1, 4), 0), out=buf18)
        buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf17, buf19, 256, grid=grid(256), stream=stream0)
        buf20 = reinterpret_tensor(buf17, (16, 1, 4), (4, 4, 1), 0); del buf17  # reuse
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf18, (16, 1, 4), (4, 4, 1), 0), reinterpret_tensor(buf19, (16, 4, 4), (16, 4, 1), 0), out=buf20)
        buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(primals_1, buf21, 256, grid=grid(256), stream=stream0)
        buf22 = empty_strided_cuda((16, 1, 4), (4, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf20, reinterpret_tensor(buf21, (16, 4, 4), (16, 4, 1), 0), out=buf22)
        buf23 = reinterpret_tensor(buf13, (4, 4, 1), (4, 1, 1), 0); del buf13  # reuse
        # Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf22, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_3, (4, 4, 1), (4, 1, 1), 0), out=buf23)
        del buf22
        buf24 = reinterpret_tensor(buf23, (4, 4), (4, 1), 0); del buf23  # reuse
        # Topologically Sorted Source Nodes: [sqrt], Original ATen: [aten.sqrt]
        triton_poi_fused_sqrt_4.run(buf24, 16, grid=grid(16), stream=stream0)
    return (buf24, primals_3, reinterpret_tensor(primals_1, (4, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (4, 1), 16), reinterpret_tensor(primals_1, (4, 4), (4, 1), 32), reinterpret_tensor(primals_1, (4, 4), (4, 1), 48), buf24, reinterpret_tensor(buf20, (16, 4, 1), (4, 1, 4), 0), reinterpret_tensor(buf21, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf18, (16, 4, 1), (4, 1, 4), 0), reinterpret_tensor(buf19, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf15, (4, 4), (4, 1), 0), reinterpret_tensor(buf11, (4, 4), (4, 1), 0), reinterpret_tensor(buf7, (4, 4), (4, 1), 0), reinterpret_tensor(buf3, (4, 4), (4, 1), 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((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (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 math
import torch
from torch import nn
from torch.nn.parameter import Parameter


class LinearCapsPro(nn.Module):

    def __init__(self, in_features, num_C, num_D, eps=0.0001):
        super(LinearCapsPro, self).__init__()
        self.in_features = in_features
        self.num_C = num_C
        self.num_D = num_D
        self.eps = eps
        self.weight = Parameter(torch.Tensor(num_C * num_D, in_features))
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)

    def forward(self, x, eye):
        weight_caps = self.weight[:self.num_D]
        sigma = torch.inverse(torch.mm(weight_caps, torch.t(weight_caps)) +
            self.eps * eye)
        sigma = torch.unsqueeze(sigma, dim=0)
        for ii in range(1, self.num_C):
            weight_caps = self.weight[ii * self.num_D:(ii + 1) * self.num_D]
            sigma_ = torch.inverse(torch.mm(weight_caps, torch.t(
                weight_caps)) + self.eps * eye)
            sigma_ = torch.unsqueeze(sigma_, dim=0)
            sigma = torch.cat((sigma, sigma_))
        out = torch.matmul(x, torch.t(self.weight))
        out = out.view(out.shape[0], self.num_C, 1, self.num_D)
        out = torch.matmul(out, sigma)
        out = torch.matmul(out, self.weight.view(self.num_C, self.num_D,
            self.in_features))
        out = torch.squeeze(out, dim=2)
        out = torch.matmul(out, torch.unsqueeze(x, dim=2))
        out = torch.squeeze(out, dim=2)
        return torch.sqrt(out)


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


def get_init_inputs():
    return [[], {'in_features': 4, 'num_C': 4, 'num_D': 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
from torch._inductor.runtime.triton_helpers import libdevice
import math
from torch import nn
from torch.nn.parameter import Parameter
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_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0001
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 3, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 2, tl.int64)
    tmp6 = tmp0 < tmp5
    tmp7 = tmp6 & tmp4
    tmp8 = tl.full([1], 1, tl.int64)
    tmp9 = tmp0 < tmp8
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + x0, tmp10 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp0 >= tmp8
    tmp13 = tmp12 & tmp7
    tmp14 = tl.load(in_ptr1 + x0, tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp11, tmp14)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp7, tmp15, tmp16)
    tmp18 = tmp0 >= tmp5
    tmp19 = tmp18 & tmp4
    tmp20 = tl.load(in_ptr2 + x0, tmp19 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp21 = tl.where(tmp6, tmp17, tmp20)
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp4, tmp21, tmp22)
    tmp24 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp27 = tl.load(in_ptr3 + x0, tmp24 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp28 = tl.where(tmp4, tmp23, tmp27)
    tl.store(out_ptr0 + x2, tmp28, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, 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 % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, 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 % 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_sqrt_4(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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 = libdevice.sqrt(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (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, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(16)](primals_2, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(buf0, reinterpret_tensor(primals_1, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf1)
        buf2 = torch.ops.aten.linalg_inv_ex.default(buf1)
        buf3 = buf2[0]
        del buf2
        buf5 = buf1
        del buf1
        extern_kernels.addmm(buf0, reinterpret_tensor(primals_1, (4, 4), (4,
            1), 16), reinterpret_tensor(primals_1, (4, 4), (1, 4), 16),
            alpha=1, beta=1, out=buf5)
        buf6 = torch.ops.aten.linalg_inv_ex.default(buf5)
        buf7 = buf6[0]
        del buf6
        buf9 = buf5
        del buf5
        extern_kernels.addmm(buf0, reinterpret_tensor(primals_1, (4, 4), (4,
            1), 32), reinterpret_tensor(primals_1, (4, 4), (1, 4), 32),
            alpha=1, beta=1, out=buf9)
        buf10 = torch.ops.aten.linalg_inv_ex.default(buf9)
        buf11 = buf10[0]
        del buf10
        buf13 = buf9
        del buf9
        extern_kernels.addmm(buf0, reinterpret_tensor(primals_1, (4, 4), (4,
            1), 48), reinterpret_tensor(primals_1, (4, 4), (1, 4), 48),
            alpha=1, beta=1, out=buf13)
        del buf0
        buf14 = torch.ops.aten.linalg_inv_ex.default(buf13)
        buf15 = buf14[0]
        del buf14
        buf17 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        triton_poi_fused_cat_1[grid(64)](buf3, buf7, buf11, buf15, buf17, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf18 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 16),
            (1, 4), 0), out=buf18)
        buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(256)](buf17, buf19, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf20 = reinterpret_tensor(buf17, (16, 1, 4), (4, 4, 1), 0)
        del buf17
        extern_kernels.bmm(reinterpret_tensor(buf18, (16, 1, 4), (4, 4, 1),
            0), reinterpret_tensor(buf19, (16, 4, 4), (16, 4, 1), 0), out=buf20
            )
        buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_3[grid(256)](primals_1, buf21, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf22 = empty_strided_cuda((16, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(buf20, reinterpret_tensor(buf21, (16, 4, 4), (16,
            4, 1), 0), out=buf22)
        buf23 = reinterpret_tensor(buf13, (4, 4, 1), (4, 1, 1), 0)
        del buf13
        extern_kernels.bmm(reinterpret_tensor(buf22, (4, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(primals_3, (4, 4, 1), (4, 1, 1), 0), out
            =buf23)
        del buf22
        buf24 = reinterpret_tensor(buf23, (4, 4), (4, 1), 0)
        del buf23
        triton_poi_fused_sqrt_4[grid(16)](buf24, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
    return buf24, primals_3, reinterpret_tensor(primals_1, (4, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (4, 4), (4, 1), 16
        ), reinterpret_tensor(primals_1, (4, 4), (4, 1), 32
        ), reinterpret_tensor(primals_1, (4, 4), (4, 1), 48
        ), buf24, reinterpret_tensor(buf20, (16, 4, 1), (4, 1, 4), 0
        ), reinterpret_tensor(buf21, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf18, (16, 4, 1), (4, 1, 4), 0
        ), reinterpret_tensor(buf19, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf15, (4, 4), (4, 1), 0), reinterpret_tensor(
        buf11, (4, 4), (4, 1), 0), reinterpret_tensor(buf7, (4, 4), (4, 1), 0
        ), reinterpret_tensor(buf3, (4, 4), (4, 1), 0)


class LinearCapsProNew(nn.Module):

    def __init__(self, in_features, num_C, num_D, eps=0.0001):
        super(LinearCapsProNew, self).__init__()
        self.in_features = in_features
        self.num_C = num_C
        self.num_D = num_D
        self.eps = eps
        self.weight = Parameter(torch.Tensor(num_C * num_D, in_features))
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)

    def forward(self, input_0, input_1):
        primals_1 = self.weight
        primals_2 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0]
