# 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_8/inductor_cache/gc/cgc5uey5zkrbo5txp7w4knomn4agmraljaybdlnstomqrxgke6ao.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=[32, 4096], 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 = 32
    xnumel = 4096
    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 = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 8
    y1 = (yindex // 8)
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (8*x2) + (32768*y1)), tmp0, ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/35/c3567i2nrnyn4q4v5ian3ap7ih2747hvo56prdjfthitwjvemgzf.py
# Topologically Sorted Source Nodes: [conv2d, h], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   h => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [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_1 = async_compile.triton('triton_poi_fused_convolution_relu_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=[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_convolution_relu_1', '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_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4194304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    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')


# kernel path: runs/run_shard_8/inductor_cache/5p/c5p5qppzecxcvl2fokkcfo32s4cs6s4z3s2aeii7lx2jg75iaktc.py
# Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_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 = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_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: '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_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_2(in_out_ptr0, in_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)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), None)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + (x0), tmp3, 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 = args
    args.clear()
    assert_size_stride(primals_1, (256, 8, 1, 1), (8, 1, 1, 1))
    assert_size_stride(primals_2, (256, ), (1, ))
    assert_size_stride(primals_3, (4, 8, 64, 64), (32768, 4096, 64, 1))
    assert_size_stride(primals_4, (256, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_5, (256, ), (1, ))
    assert_size_stride(primals_6, (1, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_7, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 64, 64), (32768, 1, 512, 8), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_3, buf0, 32, 4096, grid=grid(32, 4096), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [conv2d, h], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf2, primals_2, 4194304, grid=grid(4194304), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, h_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf4, primals_5, 4194304, grid=grid(4194304), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 1, 64, 64), (4096, 1, 64, 1))
        buf6 = reinterpret_tensor(buf5, (4, 1, 64, 64), (4096, 4096, 64, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf6, primals_7, 16384, grid=grid(16384), stream=stream0)
        del primals_7
    return (buf6, primals_1, buf0, primals_4, primals_6, buf2, buf4, )


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((256, 8, 1, 1), (8, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 8, 64, 64), (32768, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((256, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = 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])
    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
import torch.optim


class LocalDiscriminator(nn.Module):
    """The local discriminator class.

    A network that analyses the relation between the
    output of the encoder y, and the feature map M.
    It is called "local" because it compares y with
    each one of the features in M. So if M is a [64, 6, 6]
    feature map, and y is a [32] vector, the comparison is
    done concatenating y along each one of the 6x6 features
    in M: 
    (i) [32] -> [64, 1, 1]; (ii) [32] -> [64, 1, 2]
    ... (xxxvi) [32] -> [64, 6, 6]. 
    This can be efficiently done expanding y to have same 
    dimensionality as M such that:
    [32] torch.expand -> [32, 6, 6]
    and then concatenate on the channel dimension:
    [32, 6, 6] torch.cat(axis=0) -> [64, 6, 6] = [96, 6, 6]
    The tensor is then feed to the local discriminator.
    """

    def __init__(self, y_size, M_channels):
        super().__init__()
        self.c0 = nn.Conv2d(y_size + M_channels, 256, kernel_size=1)
        self.c1 = nn.Conv2d(256, 256, kernel_size=1)
        self.c2 = nn.Conv2d(256, 1, kernel_size=1)

    def forward(self, x):
        h = F.relu(self.c0(x))
        h = F.relu(self.c1(h))
        return self.c2(h)


def get_inputs():
    return [torch.rand([4, 8, 64, 64])]


def get_init_inputs():
    return [[], {'y_size': 4, 'M_channels': 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
import torch.optim
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):
    ynumel = 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]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 8
    y1 = yindex // 8
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 8 * x2 + 32768 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_convolution_relu_1(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 % 256
    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)


@triton.jit
def triton_poi_fused_convolution_2(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)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (256, 8, 1, 1), (8, 1, 1, 1))
    assert_size_stride(primals_2, (256,), (1,))
    assert_size_stride(primals_3, (4, 8, 64, 64), (32768, 4096, 64, 1))
    assert_size_stride(primals_4, (256, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_5, (256,), (1,))
    assert_size_stride(primals_6, (1, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_7, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 64, 64), (32768, 1, 512, 8), torch
            .float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(32, 4096)](primals_3, buf0, 32, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_relu_1[grid(4194304)](buf2, primals_2,
            4194304, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_relu_1[grid(4194304)](buf4, primals_5,
            4194304, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 1, 64, 64), (4096, 1, 64, 1))
        buf6 = reinterpret_tensor(buf5, (4, 1, 64, 64), (4096, 4096, 64, 1), 0)
        del buf5
        triton_poi_fused_convolution_2[grid(16384)](buf6, primals_7, 16384,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
    return buf6, primals_1, buf0, primals_4, primals_6, buf2, buf4


class LocalDiscriminatorNew(nn.Module):
    """The local discriminator class.

    A network that analyses the relation between the
    output of the encoder y, and the feature map M.
    It is called "local" because it compares y with
    each one of the features in M. So if M is a [64, 6, 6]
    feature map, and y is a [32] vector, the comparison is
    done concatenating y along each one of the 6x6 features
    in M: 
    (i) [32] -> [64, 1, 1]; (ii) [32] -> [64, 1, 2]
    ... (xxxvi) [32] -> [64, 6, 6]. 
    This can be efficiently done expanding y to have same 
    dimensionality as M such that:
    [32] torch.expand -> [32, 6, 6]
    and then concatenate on the channel dimension:
    [32, 6, 6] torch.cat(axis=0) -> [64, 6, 6] = [96, 6, 6]
    The tensor is then feed to the local discriminator.
    """

    def __init__(self, y_size, M_channels):
        super().__init__()
        self.c0 = nn.Conv2d(y_size + M_channels, 256, kernel_size=1)
        self.c1 = nn.Conv2d(256, 256, kernel_size=1)
        self.c2 = nn.Conv2d(256, 1, kernel_size=1)

    def forward(self, input_0):
        primals_1 = self.c0.weight
        primals_2 = self.c0.bias
        primals_4 = self.c1.weight
        primals_5 = self.c1.bias
        primals_6 = self.c2.weight
        primals_7 = self.c2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
