# 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/mk/cmk6m65qnok35xufcmp7cpl444est4s3qpizybpbnl727cywryac.py
# Topologically Sorted Source Nodes: [x, x_1, x_2], Original ATen: [aten.convolution, aten.relu, aten.unsqueeze, aten.threshold_backward]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => relu
#   x_2 => unsqueeze
# 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 = {})
#   %unsqueeze : [num_users=2] = call_function[target=torch.ops.aten.unsqueeze.default](args = (%relu, -2), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_0 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_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=[256, 64], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 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, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_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_unsqueeze_0(in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 256
    xnumel = 64
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = (yindex // 64)
    tmp0 = tl.load(in_out_ptr0 + (x2 + (64*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (y0 + (64*x2) + (4096*y1)), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (x2 + (64*y3)), tmp6, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/fa/cfa67lt4664fhjswfguchkkk4roztfmx3zd527kauonmneimodem.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=[128, 64], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), 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, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 128
    xnumel = 64
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    y2 = yindex % 32
    y3 = (yindex // 32)
    tmp0 = tl.load(in_ptr0 + (x1 + (128*y0)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (64 + x1 + (128*y0)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1, 1], 1, tl.int8)
    tmp4 = tl.full([1, 1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + (x1 + (64*y0)), tmp5, xmask & ymask)
    tl.store(out_ptr1 + (y2 + (32*x1) + (2048*y3)), tmp6, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/xm/cxmj27xmq7co4jjpzszve7eq66fi3r2av63dwwdbhftfk6yuiw7z.py
# Topologically Sorted Source Nodes: [x_3, x_4, x_5], Original ATen: [aten.convolution, aten.relu, aten.unsqueeze, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_3 => convolution_1
#   x_4 => relu_1
#   x_5 => unsqueeze_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 = {})
#   %unsqueeze_1 : [num_users=2] = call_function[target=torch.ops.aten.unsqueeze.default](args = (%relu_1, -2), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_2 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_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=[512, 32], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 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, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_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_unsqueeze_2(in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 512
    xnumel = 32
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = (yindex // 128)
    tmp0 = tl.load(in_out_ptr0 + (x2 + (32*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (y0 + (128*x2) + (4096*y1)), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (x2 + (32*y3)), tmp6, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/fu/cfu2jol2d6ljtscc6pgwfwcdsku2rinkadjvtrh63k6yazpjpvqr.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_5 => _low_memory_max_pool2d_with_offsets_1, getitem_3
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%unsqueeze_1, [1, 2], [1, 2], [0, 0], [1, 1], False), kwargs = {})
#   %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=[64, 128], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), 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, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 128
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    y2 = yindex % 16
    y3 = (yindex // 16)
    tmp0 = tl.load(in_ptr0 + (x1 + (256*y0)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (128 + x1 + (256*y0)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1, 1], 1, tl.int8)
    tmp4 = tl.full([1, 1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + (x1 + (128*y0)), tmp5, xmask & ymask)
    tl.store(out_ptr1 + (y2 + (16*x1) + (2048*y3)), tmp6, xmask & ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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, ))
    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
        buf2 = empty_strided_cuda((4, 64, 1, 64), (4096, 1, 4096, 64), torch.float32)
        buf11 = empty_strided_cuda((4, 64, 64), (4096, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x, x_1, x_2], Original ATen: [aten.convolution, aten.relu, aten.unsqueeze, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_0.run(buf1, primals_2, buf2, buf11, 256, 64, grid=grid(256, 64), stream=stream0)
        del primals_2
        buf3 = empty_strided_cuda((4, 64, 1, 32), (2048, 1, 2048, 64), torch.int8)
        buf4 = 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(buf2, buf3, buf4, 128, 64, grid=grid(128, 64), stream=stream0)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(reinterpret_tensor(buf4, (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(buf5, (4, 128, 32), (4096, 32, 1))
        buf6 = buf5; del buf5  # reuse
        buf7 = reinterpret_tensor(buf1, (4, 128, 1, 32), (4096, 1, 4096, 128), 0); del buf1  # reuse
        buf10 = empty_strided_cuda((4, 128, 32), (4096, 32, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_3, x_4, x_5], Original ATen: [aten.convolution, aten.relu, aten.unsqueeze, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_2.run(buf6, primals_5, buf7, buf10, 512, 32, grid=grid(512, 32), stream=stream0)
        del buf6
        del primals_5
        buf8 = empty_strided_cuda((4, 128, 1, 16), (2048, 1, 2048, 128), torch.int8)
        buf9 = empty_strided_cuda((4, 128, 1, 16), (2048, 16, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_3.run(buf7, buf8, buf9, 64, 128, grid=grid(64, 128), stream=stream0)
    return (reinterpret_tensor(buf9, (4, 128, 16), (2048, 16, 1), 0), primals_1, primals_3, primals_4, buf2, buf3, reinterpret_tensor(buf4, (4, 64, 32), (2048, 32, 1), 0), buf7, buf8, buf10, 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((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)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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


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
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_unsqueeze_0(
    in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    xnumel = 64
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_out_ptr0 + (x2 + 64 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (y0 + 64 * x2 + 4096 * y1), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (x2 + 64 * y3), tmp6, xmask & ymask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 128
    xnumel = 64
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    y2 = yindex % 32
    y3 = yindex // 32
    tmp0 = tl.load(in_ptr0 + (x1 + 128 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (64 + x1 + 128 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1, 1], 1, tl.int8)
    tmp4 = tl.full([1, 1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + (x1 + 64 * y0), tmp5, xmask & ymask)
    tl.store(out_ptr1 + (y2 + 32 * x1 + 2048 * y3), tmp6, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_2(
    in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 512
    xnumel = 32
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 128
    y1 = yindex // 128
    tmp0 = tl.load(in_out_ptr0 + (x2 + 32 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (y0 + 128 * x2 + 4096 * y1), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (x2 + 32 * y3), tmp6, xmask & ymask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 128
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    y2 = yindex % 16
    y3 = yindex // 16
    tmp0 = tl.load(in_ptr0 + (x1 + 256 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (128 + x1 + 256 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1, 1], 1, tl.int8)
    tmp4 = tl.full([1, 1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + (x1 + 128 * y0), tmp5, xmask & ymask)
    tl.store(out_ptr1 + (y2 + 16 * x1 + 2048 * y3), tmp6, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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,))
    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
        buf2 = empty_strided_cuda((4, 64, 1, 64), (4096, 1, 4096, 64),
            torch.float32)
        buf11 = empty_strided_cuda((4, 64, 64), (4096, 64, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_0[grid
            (256, 64)](buf1, primals_2, buf2, buf11, 256, 64, XBLOCK=32,
            YBLOCK=32, num_warps=4, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((4, 64, 1, 32), (2048, 1, 2048, 64),
            torch.int8)
        buf4 = empty_strided_cuda((4, 64, 1, 32), (2048, 32, 32, 1), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_1[grid(128, 64)](buf2,
            buf3, buf4, 128, 64, XBLOCK=64, YBLOCK=4, num_warps=4, num_stages=1
            )
        buf5 = extern_kernels.convolution(reinterpret_tensor(buf4, (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(buf5, (4, 128, 32), (4096, 32, 1))
        buf6 = buf5
        del buf5
        buf7 = reinterpret_tensor(buf1, (4, 128, 1, 32), (4096, 1, 4096, 
            128), 0)
        del buf1
        buf10 = empty_strided_cuda((4, 128, 32), (4096, 32, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_2[grid
            (512, 32)](buf6, primals_5, buf7, buf10, 512, 32, XBLOCK=32,
            YBLOCK=32, num_warps=4, num_stages=1)
        del buf6
        del primals_5
        buf8 = empty_strided_cuda((4, 128, 1, 16), (2048, 1, 2048, 128),
            torch.int8)
        buf9 = empty_strided_cuda((4, 128, 1, 16), (2048, 16, 16, 1), torch
            .float32)
        triton_poi_fused_max_pool2d_with_indices_3[grid(64, 128)](buf7,
            buf8, buf9, 64, 128, XBLOCK=128, YBLOCK=2, num_warps=4,
            num_stages=1)
    return reinterpret_tensor(buf9, (4, 128, 16), (2048, 16, 1), 0
        ), primals_1, primals_3, primals_4, buf2, buf3, reinterpret_tensor(buf4
        , (4, 64, 32), (2048, 32, 1), 0), buf7, buf8, buf10, buf11


class EncoderNew(nn.Module):

    def __init__(self):
        super(EncoderNew, 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, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
