# 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/om/com5yebxo5qsahe3lhucgobrzm6npeoi425wxqvff6fvddh4edcs.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_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=[2048, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 2048
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 32
    y1 = (yindex // 32)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (32*x2) + (512*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ne/cnepmjd66uu3laeexeusfxab3aayptiri2wp2knrgtgmx52tvzxj.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_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, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 8192
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    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_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (64*x2) + (576*y1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ub/cubq6mwncunyjmqflzpohu2yx3nbpr4nqxrc52pzll64qdraayed.py
# Topologically Sorted Source Nodes: [conv2d, x_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   x_2 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view, %primals_2, %primals_3, [4, 4], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_2 = async_compile.triton('triton_poi_fused_convolution_relu_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=[128, 2048], tile_hint=TileHint.DEFAULT,
    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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_2', '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_2(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 128
    xnumel = 1035
    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 % 32
    y1 = (yindex // 32)
    tmp0 = tl.load(in_ptr0 + (x2 + (1035*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (y0 + (32*x2) + (33120*y1)), tmp4, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/yg/cyg7vbt2gdbmfx3lsg5ur2uhn4u2rduakwkwvreokkpnad22mdmv.py
# Topologically Sorted Source Nodes: [conv2d_1, x_3], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   x_3 => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_3 = async_compile.triton('triton_poi_fused_convolution_relu_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=[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_relu_3', '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_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 53760
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ra/crabviiruolsyez6gpqibnseksdal55s6wmpbs2mp3dhmq4ih4cx.py
# Topologically Sorted Source Nodes: [conv2d_2, x_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   x_4 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_4 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[512, 256], 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_4', '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_4(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 512
    xnumel = 152
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 128
    y1 = (yindex // 128)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (128*x2) + (19456*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (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 + (x2 + (152*y3)), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + (128*x2) + (19456*y1)), tmp6, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vm/cvmov3inzvpsh4jpqe4q6w2qzcune6prysd6txail3qiwclxodlb.py
# Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_6 => relu_3
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_9), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_relu_5 = async_compile.triton('triton_poi_fused_relu_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=[2048], 
    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_relu_5', '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_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, 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, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 185, 95), (17575, 17575, 95, 1))
    assert_size_stride(primals_2, (32, 1, 8, 8), (64, 64, 8, 1))
    assert_size_stride(primals_3, (32, ), (1, ))
    assert_size_stride(primals_4, (64, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128, ), (1, ))
    assert_size_stride(primals_8, (512, 19456), (19456, 1))
    assert_size_stride(primals_9, (512, ), (1, ))
    assert_size_stride(primals_10, (4, 512), (512, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 32, 4, 4), (512, 1, 128, 32), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_4, buf0, 2048, 16, grid=grid(2048, 16), stream=stream0)
        del primals_4
        buf1 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_6, buf1, 8192, 9, grid=grid(8192, 9), stream=stream0)
        del primals_6
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_1, primals_2, stride=(4, 4), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 32, 45, 23), (33120, 1035, 23, 1))
        buf3 = empty_strided_cuda((4, 32, 45, 23), (33120, 1, 736, 32), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, x_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf2, primals_3, buf3, 128, 1035, grid=grid(128, 1035), stream=stream0)
        del buf2
        del primals_3
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, buf0, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 21, 10), (13440, 1, 640, 64))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, x_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_3.run(buf5, primals_5, 53760, grid=grid(53760), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 128, 19, 8), (19456, 1, 1024, 128))
        buf7 = empty_strided_cuda((4, 128, 19, 8), (19456, 152, 8, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 128, 19, 8), (19456, 1, 1024, 128), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_2, x_4], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_4.run(buf6, primals_7, buf7, buf11, 512, 152, grid=grid(512, 152), stream=stream0)
        del buf6
        del primals_7
        buf8 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf7, (4, 19456), (19456, 1), 0), reinterpret_tensor(primals_8, (19456, 512), (1, 19456), 0), out=buf8)
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.relu]
        triton_poi_fused_relu_5.run(buf9, primals_9, 2048, grid=grid(2048), stream=stream0)
        del primals_9
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, buf9, reinterpret_tensor(primals_10, (512, 4), (1, 512), 0), alpha=1, beta=1, out=buf10)
        del primals_11
    return (buf10, primals_2, buf0, buf1, primals_1, buf3, buf5, reinterpret_tensor(buf7, (4, 19456), (19456, 1), 0), buf9, primals_10, primals_8, 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, 1, 185, 95), (17575, 17575, 95, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, 1, 8, 8), (64, 64, 8, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 32, 4, 4), (512, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((512, 19456), (19456, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, ), (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, primals_10, primals_11])
    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


class DQN(nn.Module):
    """
    Deep Q-Network: Actor (Policy) Model.
    (function approximator for the Q-table)
    """

    def __init__(self, state_size, action_size, seed, fc1_unit=64, fc2_unit=64
        ):
        """
        Initialize parameters and build model.
        Params
        =======
            state_size (int): Dimension of each state
            action_size (int): Dimension of each action
            seed (int): Random seed
            fc1_unit (int): Number of neurons in first hidden layer
            fc2_unit (int): Number of neurons in second hidden layer
        """
        super(DQN, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.conv1 = nn.Conv2d(1, 32, 8, stride=4, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
        self.conv3 = nn.Conv2d(64, 128, 3)
        self.fc1 = nn.Linear(1 * 128 * 19 * 8, 512)
        self.fc2 = nn.Linear(512, action_size)

    def forward(self, state):
        """
        mapping a state to action-values.
        ---
        args:
            state: state tensor (grayscale img)
        returns:
            q_values: array of length 6. It corresponds to the action-values for each action given the input state
                q_values=[Q(state, a_1), Q(state, a_2), ..., Q(state, a_6)]
        """
        x = state.clone()
        x = x.view(-1, 1, 185, 95)
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = x.view(-1, 128 * 19 * 8)
        x = F.relu(self.fc1(x))
        return self.fc2(x)


def get_inputs():
    return [torch.rand([4, 1, 185, 95])]


def get_init_inputs():
    return [[], {'state_size': 4, 'action_size': 4, 'seed': 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 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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 32
    y1 = yindex // 32
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 32 * x2 + 512 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    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_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 128
    xnumel = 1035
    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 % 32
    y1 = yindex // 32
    tmp0 = tl.load(in_ptr0 + (x2 + 1035 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (y0 + 32 * x2 + 33120 * y1), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 53760
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_4(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
    constexpr):
    ynumel = 512
    xnumel = 152
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 128
    y1 = yindex // 128
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 128 * x2 + 19456 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 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 + (x2 + 152 * y3), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 128 * x2 + 19456 * y1), tmp6, xmask & ymask)


@triton.jit
def triton_poi_fused_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 185, 95), (17575, 17575, 95, 1))
    assert_size_stride(primals_2, (32, 1, 8, 8), (64, 64, 8, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (64, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (512, 19456), (19456, 1))
    assert_size_stride(primals_9, (512,), (1,))
    assert_size_stride(primals_10, (4, 512), (512, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 32, 4, 4), (512, 1, 128, 32), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(2048, 16)](primals_4, buf0, 2048, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_4
        buf1 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch
            .float32)
        triton_poi_fused_1[grid(8192, 9)](primals_6, buf1, 8192, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_6
        buf2 = extern_kernels.convolution(primals_1, primals_2, stride=(4, 
            4), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 32, 45, 23), (33120, 1035, 23, 1))
        buf3 = empty_strided_cuda((4, 32, 45, 23), (33120, 1, 736, 32),
            torch.float32)
        triton_poi_fused_convolution_relu_2[grid(128, 1035)](buf2,
            primals_3, buf3, 128, 1035, XBLOCK=32, YBLOCK=32, num_warps=4,
            num_stages=1)
        del buf2
        del primals_3
        buf4 = extern_kernels.convolution(buf3, buf0, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 21, 10), (13440, 1, 640, 64))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_3[grid(53760)](buf5, primals_5, 
            53760, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_5
        buf6 = extern_kernels.convolution(buf5, buf1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 128, 19, 8), (19456, 1, 1024, 128))
        buf7 = empty_strided_cuda((4, 128, 19, 8), (19456, 152, 8, 1),
            torch.float32)
        buf11 = empty_strided_cuda((4, 128, 19, 8), (19456, 1, 1024, 128),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_4[grid(512, 152)](
            buf6, primals_7, buf7, buf11, 512, 152, XBLOCK=32, YBLOCK=32,
            num_warps=4, num_stages=1)
        del buf6
        del primals_7
        buf8 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (4, 19456), (19456, 1), 
            0), reinterpret_tensor(primals_8, (19456, 512), (1, 19456), 0),
            out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_relu_5[grid(2048)](buf9, primals_9, 2048, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_11, buf9, reinterpret_tensor(
            primals_10, (512, 4), (1, 512), 0), alpha=1, beta=1, out=buf10)
        del primals_11
    return (buf10, primals_2, buf0, buf1, primals_1, buf3, buf5,
        reinterpret_tensor(buf7, (4, 19456), (19456, 1), 0), buf9,
        primals_10, primals_8, buf11)


class DQNNew(nn.Module):
    """
    Deep Q-Network: Actor (Policy) Model.
    (function approximator for the Q-table)
    """

    def __init__(self, state_size, action_size, seed, fc1_unit=64, fc2_unit=64
        ):
        """
        Initialize parameters and build model.
        Params
        =======
            state_size (int): Dimension of each state
            action_size (int): Dimension of each action
            seed (int): Random seed
            fc1_unit (int): Number of neurons in first hidden layer
            fc2_unit (int): Number of neurons in second hidden layer
        """
        super(DQNNew, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.conv1 = nn.Conv2d(1, 32, 8, stride=4, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 4, stride=2)
        self.conv3 = nn.Conv2d(64, 128, 3)
        self.fc1 = nn.Linear(1 * 128 * 19 * 8, 512)
        self.fc2 = nn.Linear(512, action_size)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.fc1.weight
        primals_9 = self.fc1.bias
        primals_10 = self.fc2.weight
        primals_11 = self.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
