# 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/oj/cojl5mb3pzv5jbmfzjkbac5hekbmpvb72kof6ouyyasitrogdd6n.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten._unsafe_index]
# Source node to ATen node mapping:
#   x => _unsafe_index
# Graph fragment:
#   %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {})
triton_poi_fused__unsafe_index_0 = async_compile.triton('triton_poi_fused__unsafe_index_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=[1024], 
    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__unsafe_index_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 8) % 8
    x0 = xindex % 8
    x2 = (xindex // 64)
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = x0
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.load(in_ptr0 + (tmp8 + (4*tmp4) + (16*x2)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x4), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ya/cya2grnbhraytq2wzrkx5sd2ottwnbrnd5ohd2xstcxyryneuc25.py
# Topologically Sorted Source Nodes: [mv, norm, add, truediv], Original ATen: [aten.mv, aten.linalg_vector_norm, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add_4
#   mv => mul_4, sum_1
#   norm => pow_1, pow_2, sum_2
#   truediv => div
# Graph fragment:
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute, %primals_2), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_4, [1]), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, None), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_2, 1e-12), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %add_4), kwargs = {})
triton_per_fused_add_div_linalg_vector_norm_mv_1 = async_compile.triton('triton_per_fused_add_div_linalg_vector_norm_mv_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_linalg_vector_norm_mv_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_add_div_linalg_vector_norm_mv_1(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.load(in_ptr0 + (64 + r0), None)
    tmp5 = tl.load(in_ptr1 + (1))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp9 = tl.load(in_ptr0 + (128 + r0), None)
    tmp10 = tl.load(in_ptr1 + (2))
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp14 = tl.load(in_ptr0 + (192 + r0), None)
    tmp15 = tl.load(in_ptr1 + (3))
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp3 = tmp0 * tmp2
    tmp7 = tmp4 * tmp6
    tmp8 = tmp3 + tmp7
    tmp12 = tmp9 * tmp11
    tmp13 = tmp8 + tmp12
    tmp17 = tmp14 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp22 = tl.sum(tmp20, 1)[:, None]
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 1e-12
    tmp25 = tmp23 + tmp24
    tmp26 = tmp18 / tmp25
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp18, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp25, None)
    tl.store(out_ptr1 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp26, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qi/cqiozgecuvqtnurxrggbllqpuci3n65ycew5qi5gdqg44ypxzegy.py
# Topologically Sorted Source Nodes: [truediv, mv_1], Original ATen: [aten.div, aten.mv]
# Source node to ATen node mapping:
#   mv_1 => mul_5, sum_3
#   truediv => div
# Graph fragment:
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %add_4), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %div), kwargs = {})
#   %sum_3 : [num_users=3] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_5, [1]), kwargs = {})
triton_per_fused_div_mv_2 = async_compile.triton('triton_per_fused_div_mv_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.persistent_reduction(
    size_hints=[4, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_mv_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_div_mv_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1), None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (0))
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp4 = tmp1 / tmp3
    tmp5 = tmp0 * tmp4
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qa/cqaed4ios3xqwlv4d3cciikkdz7d73vhwkegurd5cxca3y7htmvg.py
# Topologically Sorted Source Nodes: [norm_1, add_1, truediv_1], Original ATen: [aten.linalg_vector_norm, aten.add, aten.div]
# Source node to ATen node mapping:
#   add_1 => add_5
#   norm_1 => pow_3, pow_4, sum_4
#   truediv_1 => div_1
# Graph fragment:
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_3, 2), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, None), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_4, 0.5), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_4, 1e-12), kwargs = {})
#   %div_1 : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, %add_5), kwargs = {})
triton_per_fused_add_div_linalg_vector_norm_3 = async_compile.triton('triton_per_fused_add_div_linalg_vector_norm_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.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.INNER,
    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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_linalg_vector_norm_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_add_div_linalg_vector_norm_3(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tmp5 = libdevice.sqrt(tmp4)
    tmp6 = 1e-12
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr1 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/c2/cc2arficwjs4sforhl25gdfmb3uzfg7hkw46gq3mxgv57jy52z32.py
# Topologically Sorted Source Nodes: [sigma], Original ATen: [aten.dot]
# Source node to ATen node mapping:
#   sigma => mul_7, sum_6
# Graph fragment:
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_1, %sum_3), kwargs = {})
#   %sum_6 : [num_users=2] = call_function[target=torch.ops.aten.sum.default](args = (%mul_7,), kwargs = {})
triton_per_fused_dot_4 = async_compile.triton('triton_per_fused_dot_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.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_dot_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_dot_4(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (r0), None)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/kw/ckwzptlssdpmtxi6pt23ik63xcuqar2giaakuqtgizxlg5weagc7.py
# Topologically Sorted Source Nodes: [truediv_2], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   truediv_2 => div_2
# Graph fragment:
#   %div_2 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_4, %expand), kwargs = {})
triton_poi_fused_div_5 = async_compile.triton('triton_poi_fused_div_5', '''
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: '*fp32', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_div_5(in_ptr0, in_ptr1, 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_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 / tmp2
    tl.store(out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/n4/cn4gcl6zss6vqmwfppmtgzfmg5aubgn4jeyyouri7roaln7yxyx4.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   x_3 => gt, mul_8, where
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul_8), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where, 0), kwargs = {})
triton_poi_fused_leaky_relu_leaky_relu_backward_6 = async_compile.triton('triton_poi_fused_leaky_relu_leaky_relu_backward_6', '''
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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 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_leaky_relu_leaky_relu_backward_6', '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_leaky_relu_leaky_relu_backward_6(in_out_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 400
    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 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tmp5 > tmp1
    tl.store(in_out_ptr0 + (x0), tmp5, xmask)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (64, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten._unsafe_index]
        stream0 = get_raw_stream(0)
        triton_poi_fused__unsafe_index_0.run(primals_1, buf0, 1024, grid=grid(1024), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((64, ), (1, ), torch.float32)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2; del buf2  # reuse
        buf17 = empty_strided_cuda((64, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [mv, norm, add, truediv], Original ATen: [aten.mv, aten.linalg_vector_norm, aten.add, aten.div]
        triton_per_fused_add_div_linalg_vector_norm_mv_1.run(buf3, primals_4, primals_2, buf1, buf17, 1, 64, grid=grid(1), stream=stream0)
        buf4 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [truediv, mv_1], Original ATen: [aten.div, aten.mv]
        triton_per_fused_div_mv_2.run(primals_4, buf1, buf3, buf4, 4, 64, grid=grid(4), stream=stream0)
        buf6 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [norm_1, add_1, truediv_1], Original ATen: [aten.linalg_vector_norm, aten.add, aten.div]
        triton_per_fused_add_div_linalg_vector_norm_3.run(buf4, buf6, 1, 4, grid=grid(1), stream=stream0)
        buf7 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [sigma], Original ATen: [aten.dot]
        triton_per_fused_dot_4.run(buf6, buf4, buf7, 1, 4, grid=grid(1), stream=stream0)
        del buf4
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [truediv_2], Original ATen: [aten.div]
        triton_poi_fused_div_5.run(primals_4, buf7, buf8, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf0, buf8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 5, 5), (100, 25, 5, 1))
        buf10 = buf9; del buf9  # reuse
        buf11 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_leaky_relu_leaky_relu_backward_6.run(buf10, buf11, 400, grid=grid(400), stream=stream0)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        buf12 = torch.ops.aten.set_.source_Tensor(primals_2, buf6)
        assert_size_stride(buf12, (4, ), (1, ))
        del buf1
        # Topologically Sorted Source Nodes: [truediv], Original ATen: [aten.div]
        buf18 = torch.ops.aten.set_.source_Tensor(primals_3, buf17)
        assert_size_stride(buf18, (64, ), (1, ))
        del primals_3
    return (buf10, buf8, primals_2, primals_4, buf0, buf3, buf6, buf7, buf8, buf11, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter


def l2normalize(v, eps=1e-12):
    return v / (v.norm() + eps)


class LayerNorm(nn.Module):

    def __init__(self, num_features, eps=1e-08, affine=True):
        super(LayerNorm, self).__init__()
        self.num_features = num_features
        self.affine = affine
        self.eps = eps
        if self.affine:
            self.gamma = Parameter(torch.Tensor(num_features).uniform_())
            self.beta = Parameter(torch.zeros(num_features))

    def forward(self, x):
        shape = [-1] + [1] * (x.dim() - 1)
        if x.size(0) == 1:
            mean = x.view(-1).mean().view(*shape)
            std = x.view(-1).std().view(*shape)
        else:
            mean = x.view(x.size(0), -1).mean(1).view(*shape)
            std = x.view(x.size(0), -1).std(1).view(*shape)
        x = (x - mean) / (std + self.eps)
        if self.affine:
            shape = [1, -1] + [1] * (x.dim() - 2)
            x = x * self.gamma.view(*shape) + self.beta.view(*shape)
        return x


class SpectralNorm(nn.Module):

    def __init__(self, module, name='weight', power_iterations=1):
        super(SpectralNorm, self).__init__()
        self.module = module
        self.name = name
        self.power_iterations = power_iterations
        if not self._made_params():
            self._make_params()

    def _update_u_v(self):
        u = getattr(self.module, self.name + '_u')
        v = getattr(self.module, self.name + '_v')
        w = getattr(self.module, self.name + '_bar')
        height = w.data.shape[0]
        for _ in range(self.power_iterations):
            v.data = l2normalize(torch.mv(torch.t(w.view(height, -1).data),
                u.data))
            u.data = l2normalize(torch.mv(w.view(height, -1).data, v.data))
        sigma = u.dot(w.view(height, -1).mv(v))
        setattr(self.module, self.name, w / sigma.expand_as(w))

    def _made_params(self):
        try:
            getattr(self.module, self.name + '_u')
            getattr(self.module, self.name + '_v')
            getattr(self.module, self.name + '_bar')
            return True
        except AttributeError:
            return False

    def _make_params(self):
        w = getattr(self.module, self.name)
        height = w.data.shape[0]
        width = w.view(height, -1).data.shape[1]
        u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
        v = Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
        u.data = l2normalize(u.data)
        v.data = l2normalize(v.data)
        w_bar = Parameter(w.data)
        del self.module._parameters[self.name]
        self.module.register_parameter(self.name + '_u', u)
        self.module.register_parameter(self.name + '_v', v)
        self.module.register_parameter(self.name + '_bar', w_bar)

    def forward(self, *args):
        self._update_u_v()
        return self.module.forward(*args)


class Conv2dLayer(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, pad_type='zero', activation='lrelu', norm=
        'none', sn=True):
        super(Conv2dLayer, self).__init__()
        if pad_type == 'reflect':
            self.pad = nn.ReflectionPad2d(padding)
        elif pad_type == 'replicate':
            self.pad = nn.ReplicationPad2d(padding)
        elif pad_type == 'zero':
            self.pad = nn.ZeroPad2d(padding)
        else:
            assert 0, 'Unsupported padding type: {}'.format(pad_type)
        if norm == 'bn':
            self.norm = nn.BatchNorm2d(out_channels)
        elif norm == 'in':
            self.norm = nn.InstanceNorm2d(out_channels)
        elif norm == 'ln':
            self.norm = LayerNorm(out_channels)
        elif norm == 'none':
            self.norm = None
        else:
            assert 0, 'Unsupported normalization: {}'.format(norm)
        if activation == 'relu':
            self.activation = nn.ReLU(inplace=True)
        elif activation == 'lrelu':
            self.activation = nn.LeakyReLU(0.2, inplace=True)
        elif activation == 'prelu':
            self.activation = nn.PReLU()
        elif activation == 'selu':
            self.activation = nn.SELU(inplace=True)
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        elif activation == 'sigmoid':
            self.activation = nn.Sigmoid()
        elif activation == 'none':
            self.activation = None
        else:
            assert 0, 'Unsupported activation: {}'.format(activation)
        if sn:
            self.conv2d = SpectralNorm(nn.Conv2d(in_channels, out_channels,
                kernel_size, stride, padding=0, dilation=dilation, bias=False))
        else:
            self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size,
                stride, padding=0, dilation=dilation, bias=False)

    def forward(self, x):
        x = self.pad(x)
        x = self.conv2d(x)
        if self.norm:
            x = self.norm(x)
        if self.activation:
            x = self.activation(x)
        return x


class TransposeConv2dLayer(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, pad_type='zero', activation='lrelu', norm=
        'none', sn=True, scale_factor=2):
        super(TransposeConv2dLayer, self).__init__()
        self.scale_factor = scale_factor
        self.conv2d = Conv2dLayer(in_channels, out_channels, kernel_size,
            stride, padding, dilation, pad_type, activation, norm, sn)

    def forward(self, x):
        x = F.interpolate(x, scale_factor=self.scale_factor, mode='nearest')
        x = self.conv2d(x)
        return x


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 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 torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 8 % 8
    x0 = xindex % 8
    x2 = xindex // 64
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = x0
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * tmp4 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x4, tmp9, xmask)


@triton.jit
def triton_per_fused_add_div_linalg_vector_norm_mv_1(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.load(in_ptr0 + (64 + r0), None)
    tmp5 = tl.load(in_ptr1 + 1)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp9 = tl.load(in_ptr0 + (128 + r0), None)
    tmp10 = tl.load(in_ptr1 + 2)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp14 = tl.load(in_ptr0 + (192 + r0), None)
    tmp15 = tl.load(in_ptr1 + 3)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp3 = tmp0 * tmp2
    tmp7 = tmp4 * tmp6
    tmp8 = tmp3 + tmp7
    tmp12 = tmp9 * tmp11
    tmp13 = tmp8 + tmp12
    tmp17 = tmp14 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp22 = tl.sum(tmp20, 1)[:, None]
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 1e-12
    tmp25 = tmp23 + tmp24
    tmp26 = tmp18 / tmp25
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp18, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp25, None)
    tl.store(out_ptr1 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp26, None)


@triton.jit
def triton_per_fused_div_mv_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp4 = tmp1 / tmp3
    tmp5 = tmp0 * tmp4
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp9, xmask)


@triton.jit
def triton_per_fused_add_div_linalg_vector_norm_3(in_ptr0, out_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tmp5 = libdevice.sqrt(tmp4)
    tmp6 = 1e-12
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr1 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp8, None)


@triton.jit
def triton_per_fused_dot_4(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp5, None)


@triton.jit
def triton_poi_fused_div_5(in_ptr0, in_ptr1, 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_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 / tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_6(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 400
    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 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tmp5 > tmp1
    tl.store(in_out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr0 + x0, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_index_0[grid(1024)](primals_1, buf0, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64,), (1,), torch.float32)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        buf17 = empty_strided_cuda((64,), (1,), torch.float32)
        triton_per_fused_add_div_linalg_vector_norm_mv_1[grid(1)](buf3,
            primals_4, primals_2, buf1, buf17, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        buf4 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_div_mv_2[grid(4)](primals_4, buf1, buf3, buf4, 4, 
            64, XBLOCK=1, num_warps=2, num_stages=1)
        buf6 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_add_div_linalg_vector_norm_3[grid(1)](buf4, buf6, 
            1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        buf7 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_dot_4[grid(1)](buf6, buf4, buf7, 1, 4, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf4
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_div_5[grid(256)](primals_4, buf7, buf8, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf9 = extern_kernels.convolution(buf0, buf8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 5, 5), (100, 25, 5, 1))
        buf10 = buf9
        del buf9
        buf11 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.bool)
        triton_poi_fused_leaky_relu_leaky_relu_backward_6[grid(400)](buf10,
            buf11, 400, XBLOCK=256, num_warps=4, num_stages=1)
        buf12 = torch.ops.aten.set_.source_Tensor(primals_2, buf6)
        assert_size_stride(buf12, (4,), (1,))
        del buf1
        buf18 = torch.ops.aten.set_.source_Tensor(primals_3, buf17)
        assert_size_stride(buf18, (64,), (1,))
        del primals_3
    return (buf10, buf8, primals_2, primals_4, buf0, buf3, buf6, buf7, buf8,
        buf11)


def l2normalize(v, eps=1e-12):
    return v / (v.norm() + eps)


class LayerNorm(nn.Module):

    def __init__(self, num_features, eps=1e-08, affine=True):
        super(LayerNorm, self).__init__()
        self.num_features = num_features
        self.affine = affine
        self.eps = eps
        if self.affine:
            self.gamma = Parameter(torch.Tensor(num_features).uniform_())
            self.beta = Parameter(torch.zeros(num_features))

    def forward(self, x):
        shape = [-1] + [1] * (x.dim() - 1)
        if x.size(0) == 1:
            mean = x.view(-1).mean().view(*shape)
            std = x.view(-1).std().view(*shape)
        else:
            mean = x.view(x.size(0), -1).mean(1).view(*shape)
            std = x.view(x.size(0), -1).std(1).view(*shape)
        x = (x - mean) / (std + self.eps)
        if self.affine:
            shape = [1, -1] + [1] * (x.dim() - 2)
            x = x * self.gamma.view(*shape) + self.beta.view(*shape)
        return x


class SpectralNorm(nn.Module):

    def __init__(self, module, name='weight', power_iterations=1):
        super(SpectralNorm, self).__init__()
        self.module = module
        self.name = name
        self.power_iterations = power_iterations
        if not self._made_params():
            self._make_params()

    def _update_u_v(self):
        u = getattr(self.module, self.name + '_u')
        v = getattr(self.module, self.name + '_v')
        w = getattr(self.module, self.name + '_bar')
        height = w.data.shape[0]
        for _ in range(self.power_iterations):
            v.data = l2normalize(torch.mv(torch.t(w.view(height, -1).data),
                u.data))
            u.data = l2normalize(torch.mv(w.view(height, -1).data, v.data))
        sigma = u.dot(w.view(height, -1).mv(v))
        setattr(self.module, self.name, w / sigma.expand_as(w))

    def _made_params(self):
        try:
            getattr(self.module, self.name + '_u')
            getattr(self.module, self.name + '_v')
            getattr(self.module, self.name + '_bar')
            return True
        except AttributeError:
            return False

    def _make_params(self):
        w = getattr(self.module, self.name)
        height = w.data.shape[0]
        width = w.view(height, -1).data.shape[1]
        u = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
        v = Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
        u.data = l2normalize(u.data)
        v.data = l2normalize(v.data)
        w_bar = Parameter(w.data)
        del self.module._parameters[self.name]
        self.module.register_parameter(self.name + '_u', u)
        self.module.register_parameter(self.name + '_v', v)
        self.module.register_parameter(self.name + '_bar', w_bar)

    def forward(self, *args):
        self._update_u_v()
        return self.module.forward(*args)


class Conv2dLayer(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, pad_type='zero', activation='lrelu', norm=
        'none', sn=True):
        super(Conv2dLayer, self).__init__()
        if pad_type == 'reflect':
            self.pad = nn.ReflectionPad2d(padding)
        elif pad_type == 'replicate':
            self.pad = nn.ReplicationPad2d(padding)
        elif pad_type == 'zero':
            self.pad = nn.ZeroPad2d(padding)
        else:
            assert 0, 'Unsupported padding type: {}'.format(pad_type)
        if norm == 'bn':
            self.norm = nn.BatchNorm2d(out_channels)
        elif norm == 'in':
            self.norm = nn.InstanceNorm2d(out_channels)
        elif norm == 'ln':
            self.norm = LayerNorm(out_channels)
        elif norm == 'none':
            self.norm = None
        else:
            assert 0, 'Unsupported normalization: {}'.format(norm)
        if activation == 'relu':
            self.activation = nn.ReLU(inplace=True)
        elif activation == 'lrelu':
            self.activation = nn.LeakyReLU(0.2, inplace=True)
        elif activation == 'prelu':
            self.activation = nn.PReLU()
        elif activation == 'selu':
            self.activation = nn.SELU(inplace=True)
        elif activation == 'tanh':
            self.activation = nn.Tanh()
        elif activation == 'sigmoid':
            self.activation = nn.Sigmoid()
        elif activation == 'none':
            self.activation = None
        else:
            assert 0, 'Unsupported activation: {}'.format(activation)
        if sn:
            self.conv2d = SpectralNorm(nn.Conv2d(in_channels, out_channels,
                kernel_size, stride, padding=0, dilation=dilation, bias=False))
        else:
            self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size,
                stride, padding=0, dilation=dilation, bias=False)

    def forward(self, x):
        x = self.pad(x)
        x = self.conv2d(x)
        if self.norm:
            x = self.norm(x)
        if self.activation:
            x = self.activation(x)
        return x


class TransposeConv2dLayerNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, pad_type='zero', activation='lrelu', norm=
        'none', sn=True, scale_factor=2):
        super(TransposeConv2dLayerNew, self).__init__()
        self.scale_factor = scale_factor
        self.conv2d = Conv2dLayer(in_channels, out_channels, kernel_size,
            stride, padding, dilation, pad_type, activation, norm, sn)

    def forward(self, input_0):
        primals_2 = self.conv2d.conv2d.module.weight_u
        primals_3 = self.conv2d.conv2d.module.weight_v
        primals_1 = self.conv2d.conv2d.module.weight_bar
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
