# 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_7/inductor_cache/o3/co3q6tbx7gekgbm25hsgcmgft6mw5snxx3t5xeskm4ht2tq446a7.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1], [1], [1], False, [0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], '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_convolution_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 64) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qr/cqrggygrravhrrgo5n766daqgy3wnjqbhtx6x6twnpkpjaaqfmua.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_2 => _low_memory_max_pool2d_with_offsets, getitem_1
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%unsqueeze, [1, 2], [1, 2], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 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_max_pool2d_with_indices_1', '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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (2*x0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0)), None, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + (x0), tmp5, None)
    tl.store(out_ptr1 + (x0), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/kg/ckg3odfjcidiugyz6p6ovpedqg2v7xks75lsvjtoqflrvzicmqra.py
# Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_3 => convolution_1
#   x_4 => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%squeeze, %primals_4, %primals_5, [1], [1], [1], False, [0], 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_relu_threshold_backward_2', 'mutated_arg_names': ['in_out_ptr0'], '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_convolution_relu_threshold_backward_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 32) % 128
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x3), tmp4, None)
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mg/cmg3mglvfrxhbkvyfvuncdih5w4z7ymoaq47spaetmonadbnglqy.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_5 => getitem_3
# Graph fragment:
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_3 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 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_max_pool2d_with_indices_3', '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_max_pool2d_with_indices_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (2*x0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0)), None, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + (x0), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ru/crutmejzeeoz2zfh3rjlxecxytzgxirqk7yzn5pkj6o6r5mv43ha.py
# Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
#   x_6 => add, add_1, convert_element_type, convert_element_type_1, iota, mul, mul_1
# Graph fragment:
#   %iota : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8000,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota, 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add, torch.float32), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.002), kwargs = {})
#   %convert_element_type_1 : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_1, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_4 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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__to_copy_add_arange_mul_4', '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__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.002
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/4k/c4kwur5l5t5hg6j4jm6pfiwuu3mbs2p33vluckakskes6mve6ww7.py
# Topologically Sorted Source Nodes: [x_6], Original ATen: [aten._unsafe_index]
# Source node to ATen node mapping:
#   x_6 => _unsafe_index
# Graph fragment:
#   %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%squeeze_2, [None, None, %convert_element_type_1]), kwargs = {})
triton_poi_fused__unsafe_index_5 = async_compile.triton('triton_poi_fused__unsafe_index_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=[4194304], 
    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_5', '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_5(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8000
    x1 = (xindex // 8000)
    x2 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.002
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = tl.load(in_ptr0 + ((2*tmp4) + (32*x1)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + (2*tmp4) + (32*x1)), None, eviction_policy='evict_last')
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tl.store(out_ptr0 + (x2), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/un/cunlycocjjooniyuc7pfvcukfmhvgjnj4bam7m2x5bbsmdpielmb.py
# Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
#   x_9 => add_2, add_3, convert_element_type_2, convert_element_type_3, iota_1, mul_2, mul_3
# Graph fragment:
#   %iota_1 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (16000,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_1, 1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, 0), kwargs = {})
#   %convert_element_type_2 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_2, torch.float32), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_2, 0.0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_3, 0.5), kwargs = {})
#   %convert_element_type_3 : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_3, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_6 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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__to_copy_add_arange_mul_6', '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__to_copy_add_arange_mul_6(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/js/cjsiuuciggswr2crlysufqqop6of4yvicahgtk7zt2lkjuapwchk.py
# Topologically Sorted Source Nodes: [x_7, x_8, x_9], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index]
# Source node to ATen node mapping:
#   x_7 => convolution_2
#   x_8 => relu_2
#   x_9 => _unsafe_index_1
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index, %primals_6, %primals_7, [1], [1], [1], False, [0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
#   %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_2, [None, None, %convert_element_type_3]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_relu_7 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_relu_7', '''
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=[4194304], 
    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__unsafe_index_convolution_relu_7', '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__unsafe_index_convolution_relu_7(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16000
    x3 = (xindex // 16000)
    x1 = (xindex // 16000) % 64
    x4 = xindex
    tmp6 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = tl.load(in_ptr0 + (tmp4 + (8000*x3)), None, eviction_policy='evict_last')
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tl.store(out_ptr0 + (x4), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/fb/cfbbywt566gtwtx4kyhulj7stlfuosz7onbhvizfzztbvhdfrki4.py
# Topologically Sorted Source Nodes: [x_10, x_11], Original ATen: [aten.convolution, aten.tanh]
# Source node to ATen node mapping:
#   x_10 => convolution_3
#   x_11 => tanh
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_8, %primals_9, [1], [1], [1], False, [0], 1), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%convolution_3,), kwargs = {})
triton_poi_fused_convolution_tanh_8 = async_compile.triton('triton_poi_fused_convolution_tanh_8', '''
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=[65536], 
    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_convolution_tanh_8', 'mutated_arg_names': ['in_out_ptr0'], '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_convolution_tanh_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64000
    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 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = libdevice.tanh(tmp3)
    tl.store(in_out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/5r/c5re33oaw4kvrxa2ekjnr2rbjfm56nw6dvq4r5n6v22vbrfxttwa.py
# Topologically Sorted Source Nodes: [x_7, x_8], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_7 => convolution_2
#   x_8 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index, %primals_6, %primals_7, [1], [1], [1], False, [0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_9 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_9', '''
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=[2097152], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_relu_threshold_backward_9', '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_convolution_relu_threshold_backward_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 8000) % 64
    x0 = xindex % 8000
    x4 = (xindex // 8000)
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x0 + (8064*x4)), tmp6, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (64, 1, 3), (3, 3, 1))
    assert_size_stride(primals_2, (64, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 64), (64, 64, 1))
    assert_size_stride(primals_4, (128, 64, 3), (192, 3, 1))
    assert_size_stride(primals_5, (128, ), (1, ))
    assert_size_stride(primals_6, (64, 128, 3), (384, 3, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (1, 64, 3), (192, 3, 1))
    assert_size_stride(primals_9, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64), (4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        buf16 = empty_strided_cuda((4, 64, 64), (4096, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_0.run(buf1, primals_2, buf16, 16384, grid=grid(16384), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 64, 1, 32), (2048, 32, 32, 1), torch.int8)
        buf3 = empty_strided_cuda((4, 64, 1, 32), (2048, 32, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf1, buf2, buf3, 8192, grid=grid(8192), stream=stream0)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(reinterpret_tensor(buf3, (4, 64, 32), (2048, 32, 1), 0), primals_4, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf4, (4, 128, 32), (4096, 32, 1))
        buf5 = buf4; del buf4  # reuse
        buf15 = empty_strided_cuda((4, 128, 32), (4096, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_2.run(buf5, primals_5, buf15, 16384, grid=grid(16384), stream=stream0)
        del primals_5
        buf6 = empty_strided_cuda((4, 128, 1, 16), (2048, 16, 16, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_3.run(buf5, buf6, 8192, grid=grid(8192), stream=stream0)
        buf7 = empty_strided_cuda((8000, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
        triton_poi_fused__to_copy_add_arange_mul_4.run(buf7, 8000, grid=grid(8000), stream=stream0)
        buf8 = empty_strided_cuda((4, 128, 8000), (1024000, 8000, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten._unsafe_index]
        triton_poi_fused__unsafe_index_5.run(buf5, buf8, 4096000, grid=grid(4096000), stream=stream0)
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(buf8, primals_6, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf9, (4, 64, 8000), (512000, 8000, 1))
        buf10 = empty_strided_cuda((16000, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
        triton_poi_fused__to_copy_add_arange_mul_6.run(buf10, 16000, grid=grid(16000), stream=stream0)
        buf11 = empty_strided_cuda((4, 64, 16000), (1024000, 16000, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_7, x_8, x_9], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index]
        triton_poi_fused__unsafe_index_convolution_relu_7.run(buf9, primals_7, buf11, 4096000, grid=grid(4096000), stream=stream0)
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_8, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf12, (4, 1, 16000), (16000, 16000, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [x_10, x_11], Original ATen: [aten.convolution, aten.tanh]
        triton_poi_fused_convolution_tanh_8.run(buf13, primals_9, 64000, grid=grid(64000), stream=stream0)
        del primals_9
        buf14 = empty_strided_cuda((4, 64, 8000), (516096, 8064, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_7, x_8], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_9.run(buf9, primals_7, buf14, 2048000, grid=grid(2048000), stream=stream0)
        del buf9
        del primals_7
    return (buf13, primals_1, primals_3, primals_4, primals_6, primals_8, reinterpret_tensor(buf1, (4, 64, 1, 64), (4096, 64, 64, 1), 0), buf2, reinterpret_tensor(buf3, (4, 64, 32), (2048, 32, 1), 0), reinterpret_tensor(buf5, (4, 128, 1, 32), (4096, 32, 32, 1), 0), buf6, buf7, buf8, buf10, buf11, buf13, buf14, buf15, buf16, )


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((64, 1, 3), (3, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 64), (64, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((128, 64, 3), (192, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 128, 3), (384, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, 64, 3), (192, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    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


class Encoder(nn.Module):

    def __init__(self):
        super(Encoder, self).__init__()
        self.conv1 = torch.nn.Conv1d(1, 64, 3, padding=1)
        self.maxp1 = torch.nn.MaxPool1d(2, padding=0)
        self.conv2 = torch.nn.Conv1d(64, 128, 3, padding=1)
        self.maxp2 = torch.nn.MaxPool1d(2, padding=0)

    def forward(self, x):
        x = self.conv1(x)
        x = torch.relu(x)
        x = self.maxp1(x)
        x = self.conv2(x)
        x = torch.relu(x)
        x = self.maxp2(x)
        return x


class Decoder(nn.Module):

    def __init__(self, sampling_rate=16000.0):
        super(Decoder, self).__init__()
        self.sampling_rate = sampling_rate
        self.upsa1 = torch.nn.Upsample(int(sampling_rate / 2))
        self.conv3 = torch.nn.Conv1d(128, 64, 3, padding=1)
        self.upsa2 = torch.nn.Upsample(int(sampling_rate))
        self.conv4 = torch.nn.Conv1d(64, 1, 3, padding=1)

    def forward(self, x):
        x = self.upsa1(x)
        x = self.conv3(x)
        x = torch.relu(x)
        x = self.upsa2(x)
        x = self.conv4(x)
        x = torch.tanh(x)
        return x


class DAE_Module(nn.Module):

    def __init__(self, sampling_rate=16000.0):
        super(DAE_Module, self).__init__()
        self.sampling_rate = int(sampling_rate)
        self.encoder = Encoder()
        self.decoder = Decoder(sampling_rate=self.sampling_rate)

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x


def get_inputs():
    return [torch.rand([4, 1, 64])]


def get_init_inputs():
    return [[], {}]

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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
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_convolution_relu_threshold_backward_0(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 64 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 2 * x0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0), None, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp5, None)
    tl.store(out_ptr1 + x0, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 32 % 128
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 2 * x0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0), None, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp5, None)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 8000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.002
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8000
    x1 = xindex // 8000
    x2 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.002
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = tl.load(in_ptr0 + (2 * tmp4 + 32 * x1), None, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 2 * tmp4 + 32 * x1), None,
        eviction_policy='evict_last')
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tl.store(out_ptr0 + x2, tmp7, None)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_6(out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_convolution_relu_7(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16000
    x3 = xindex // 16000
    x1 = xindex // 16000 % 64
    x4 = xindex
    tmp6 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = tl.load(in_ptr0 + (tmp4 + 8000 * x3), None, eviction_policy=
        'evict_last')
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tl.store(out_ptr0 + x4, tmp9, None)


@triton.jit
def triton_poi_fused_convolution_tanh_8(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64000
    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 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = libdevice.tanh(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_9(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 8000 % 64
    x0 = xindex % 8000
    x4 = xindex // 8000
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x0 + 8064 * x4), tmp6, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (64, 1, 3), (3, 3, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 1, 64), (64, 64, 1))
    assert_size_stride(primals_4, (128, 64, 3), (192, 3, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (64, 128, 3), (384, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (1, 64, 3), (192, 3, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64), (4096, 64, 1))
        buf1 = buf0
        del buf0
        buf16 = empty_strided_cuda((4, 64, 64), (4096, 64, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_0[grid(16384)](
            buf1, primals_2, buf16, 16384, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 64, 1, 32), (2048, 32, 32, 1), torch.int8
            )
        buf3 = empty_strided_cuda((4, 64, 1, 32), (2048, 32, 32, 1), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_1[grid(8192)](buf1, buf2,
            buf3, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(reinterpret_tensor(buf3, (4, 64, 
            32), (2048, 32, 1), 0), primals_4, stride=(1,), padding=(1,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf4, (4, 128, 32), (4096, 32, 1))
        buf5 = buf4
        del buf4
        buf15 = empty_strided_cuda((4, 128, 32), (4096, 32, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_2[grid(16384)](
            buf5, primals_5, buf15, 16384, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 128, 1, 16), (2048, 16, 16, 1), torch
            .int8)
        triton_poi_fused_max_pool2d_with_indices_3[grid(8192)](buf5, buf6, 
            8192, XBLOCK=256, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((8000,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_4[grid(8000)](buf7, 8000,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((4, 128, 8000), (1024000, 8000, 1), torch
            .float32)
        triton_poi_fused__unsafe_index_5[grid(4096000)](buf5, buf8, 4096000,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf9 = extern_kernels.convolution(buf8, primals_6, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf9, (4, 64, 8000), (512000, 8000, 1))
        buf10 = empty_strided_cuda((16000,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_6[grid(16000)](buf10, 
            16000, XBLOCK=256, num_warps=4, num_stages=1)
        buf11 = empty_strided_cuda((4, 64, 16000), (1024000, 16000, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_convolution_relu_7[grid(4096000)](buf9,
            primals_7, buf11, 4096000, XBLOCK=512, num_warps=8, num_stages=1)
        buf12 = extern_kernels.convolution(buf11, primals_8, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf12, (4, 1, 16000), (16000, 16000, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_tanh_8[grid(64000)](buf13, primals_9, 
            64000, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_9
        buf14 = empty_strided_cuda((4, 64, 8000), (516096, 8064, 1), torch.bool
            )
        triton_poi_fused_convolution_relu_threshold_backward_9[grid(2048000)](
            buf9, primals_7, buf14, 2048000, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf9
        del primals_7
    return (buf13, primals_1, primals_3, primals_4, primals_6, primals_8,
        reinterpret_tensor(buf1, (4, 64, 1, 64), (4096, 64, 64, 1), 0),
        buf2, reinterpret_tensor(buf3, (4, 64, 32), (2048, 32, 1), 0),
        reinterpret_tensor(buf5, (4, 128, 1, 32), (4096, 32, 32, 1), 0),
        buf6, buf7, buf8, buf10, buf11, buf13, buf14, buf15, buf16)


class Encoder(nn.Module):

    def __init__(self):
        super(Encoder, self).__init__()
        self.conv1 = torch.nn.Conv1d(1, 64, 3, padding=1)
        self.maxp1 = torch.nn.MaxPool1d(2, padding=0)
        self.conv2 = torch.nn.Conv1d(64, 128, 3, padding=1)
        self.maxp2 = torch.nn.MaxPool1d(2, padding=0)

    def forward(self, x):
        x = self.conv1(x)
        x = torch.relu(x)
        x = self.maxp1(x)
        x = self.conv2(x)
        x = torch.relu(x)
        x = self.maxp2(x)
        return x


class Decoder(nn.Module):

    def __init__(self, sampling_rate=16000.0):
        super(Decoder, self).__init__()
        self.sampling_rate = sampling_rate
        self.upsa1 = torch.nn.Upsample(int(sampling_rate / 2))
        self.conv3 = torch.nn.Conv1d(128, 64, 3, padding=1)
        self.upsa2 = torch.nn.Upsample(int(sampling_rate))
        self.conv4 = torch.nn.Conv1d(64, 1, 3, padding=1)

    def forward(self, x):
        x = self.upsa1(x)
        x = self.conv3(x)
        x = torch.relu(x)
        x = self.upsa2(x)
        x = self.conv4(x)
        x = torch.tanh(x)
        return x


class DAE_ModuleNew(nn.Module):

    def __init__(self, sampling_rate=16000.0):
        super(DAE_ModuleNew, self).__init__()
        self.sampling_rate = int(sampling_rate)
        self.encoder = Encoder()
        self.decoder = Decoder(sampling_rate=self.sampling_rate)

    def forward(self, input_0):
        primals_1 = self.encoder.conv1.weight
        primals_2 = self.encoder.conv1.bias
        primals_4 = self.encoder.conv2.weight
        primals_5 = self.encoder.conv2.bias
        primals_6 = self.decoder.conv3.weight
        primals_7 = self.decoder.conv3.bias
        primals_8 = self.decoder.conv4.weight
        primals_9 = self.decoder.conv4.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
