# AOT ID: ['0_inference']
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_6/inductor_cache/4t/c4tuf2l6x2zj65cq45cofmz2gojaupedfzjwk73xw4vxbeoyxrqg.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._to_copy, aten.arange, aten.add, aten.mul, aten.sub, aten.clamp, aten._unsafe_index]
# Source node to ATen node mapping:
#   x_1 => _unsafe_index, _unsafe_index_1, _unsafe_index_2, _unsafe_index_3, add_2, add_4, add_5, add_6, clamp_max_2, clamp_max_3, clamp_min_1, clamp_min_2, clamp_min_3, convert_element_type_1, convert_element_type_2, convert_element_type_3, iota_1, mul_1, mul_2, mul_3, mul_4, sub_1, sub_2, sub_3, sub_4, sub_5, sub_6
# Graph fragment:
#   %convert_element_type_1 : [num_users=4] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view, torch.int64), kwargs = {})
#   %iota_1 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (64,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %convert_element_type_2 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota_1, torch.float32), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_2, 0.5), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, 0.5), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, 0.5), kwargs = {})
#   %clamp_min_1 : [num_users=2] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_1, 0.0), kwargs = {})
#   %convert_element_type_3 : [num_users=4] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%clamp_min_1, torch.int64), kwargs = {})
#   %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %clamp_max, %clamp_max_1]), kwargs = {})
#   %_unsafe_index_2 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %clamp_max, %convert_element_type_3]), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_3, %_unsafe_index_2), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min_1, %convert_element_type_3), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_2, 0.0), kwargs = {})
#   %clamp_max_2 : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_2, 1.0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %clamp_max_2), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_2, %mul_3), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %convert_element_type_1, %clamp_max_1]), kwargs = {})
#   %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %convert_element_type_1, %convert_element_type_3]), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_1, %_unsafe_index), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %clamp_max_2), kwargs = {})
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index, %mul_2), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_5, %add_4), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %convert_element_type_1), kwargs = {})
#   %clamp_min_3 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_5, 0.0), kwargs = {})
#   %clamp_max_3 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_3, 1.0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, %clamp_max_3), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_4, %mul_4), kwargs = {})
triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0 = async_compile.triton('triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_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=[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__to_copy__unsafe_index_add_arange_clamp_mul_sub_0', 'mutated_arg_names': ['in_out_ptr0'], '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__unsafe_index_add_arange_clamp_mul_sub_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 49152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 64) % 64
    x0 = xindex % 64
    x2 = (xindex // 4096)
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 31, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tmp13 = x0
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp14 + tmp2
    tmp16 = tmp15 * tmp2
    tmp17 = tmp16 - tmp2
    tmp18 = triton_helpers.maximum(tmp17, tmp6)
    tmp19 = tmp18.to(tl.int32)
    tmp20 = tmp19 + tmp9
    tmp21 = triton_helpers.minimum(tmp20, tmp11)
    tmp22 = tl.load(in_ptr0 + (tmp21 + (32*tmp12) + (1024*x2)), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (tmp19 + (32*tmp12) + (1024*x2)), None, eviction_policy='evict_last')
    tmp24 = tmp22 - tmp23
    tmp25 = tmp19.to(tl.float32)
    tmp26 = tmp18 - tmp25
    tmp27 = triton_helpers.maximum(tmp26, tmp6)
    tmp28 = 1.0
    tmp29 = triton_helpers.minimum(tmp27, tmp28)
    tmp30 = tmp24 * tmp29
    tmp31 = tmp23 + tmp30
    tmp32 = tl.load(in_ptr0 + (tmp19 + (32*tmp8) + (1024*x2)), None, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (tmp21 + (32*tmp8) + (1024*x2)), None, eviction_policy='evict_last')
    tmp34 = tmp33 - tmp32
    tmp35 = tmp34 * tmp29
    tmp36 = tmp32 + tmp35
    tmp37 = tmp31 - tmp36
    tmp38 = tmp8.to(tl.float32)
    tmp39 = tmp7 - tmp38
    tmp40 = triton_helpers.maximum(tmp39, tmp6)
    tmp41 = triton_helpers.minimum(tmp40, tmp28)
    tmp42 = tmp37 * tmp41
    tmp43 = tmp36 + tmp42
    tl.store(in_out_ptr0 + (x4), tmp43, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (3, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(arg1_1, (4, 1, 64, 64), (4096, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(arg1_1, arg0_1, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 3, 32, 32), (3072, 1024, 32, 1))
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1), torch.float32)
        buf2 = buf1; del buf1  # reuse
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._to_copy, aten.arange, aten.add, aten.mul, aten.sub, aten.clamp, aten._unsafe_index]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0.run(buf3, buf0, 49152, grid=grid(49152), stream=stream0)
        del buf0
    return (buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((3, 1, 2, 2), (4, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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 Debayer2x2(nn.Module):
    """Demosaicing of Bayer images using 2x2 convolutions.

    Requires BG-Bayer color filter array layout. That is,
    the image[1,1]='B', image[1,2]='G'.
    """

    def __init__(self):
        super(Debayer2x2, self).__init__()
        self.kernels = nn.Parameter(torch.tensor([[1, 0], [0, 0], [0, 0.5],
            [0.5, 0], [0, 0], [0, 1]]).view(3, 1, 2, 2), requires_grad=False)

    def forward(self, x):
        """Debayer image.

        Parameters
        ----------
        x : Bx1xHxW tensor
            Images to debayer

        Returns
        -------
        rgb : Bx3xHxW tensor
            Color images in RGB channel order.
        """
        x = F.conv2d(x, self.kernels, stride=2)
        x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners
            =False)
        return x


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


@triton.jit
def triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0(
    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)
    x1 = xindex // 64 % 64
    x0 = xindex % 64
    x2 = xindex // 4096
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 31, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tmp13 = x0
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp14 + tmp2
    tmp16 = tmp15 * tmp2
    tmp17 = tmp16 - tmp2
    tmp18 = triton_helpers.maximum(tmp17, tmp6)
    tmp19 = tmp18.to(tl.int32)
    tmp20 = tmp19 + tmp9
    tmp21 = triton_helpers.minimum(tmp20, tmp11)
    tmp22 = tl.load(in_ptr0 + (tmp21 + 32 * tmp12 + 1024 * x2), None,
        eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (tmp19 + 32 * tmp12 + 1024 * x2), None,
        eviction_policy='evict_last')
    tmp24 = tmp22 - tmp23
    tmp25 = tmp19.to(tl.float32)
    tmp26 = tmp18 - tmp25
    tmp27 = triton_helpers.maximum(tmp26, tmp6)
    tmp28 = 1.0
    tmp29 = triton_helpers.minimum(tmp27, tmp28)
    tmp30 = tmp24 * tmp29
    tmp31 = tmp23 + tmp30
    tmp32 = tl.load(in_ptr0 + (tmp19 + 32 * tmp8 + 1024 * x2), None,
        eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (tmp21 + 32 * tmp8 + 1024 * x2), None,
        eviction_policy='evict_last')
    tmp34 = tmp33 - tmp32
    tmp35 = tmp34 * tmp29
    tmp36 = tmp32 + tmp35
    tmp37 = tmp31 - tmp36
    tmp38 = tmp8.to(tl.float32)
    tmp39 = tmp7 - tmp38
    tmp40 = triton_helpers.maximum(tmp39, tmp6)
    tmp41 = triton_helpers.minimum(tmp40, tmp28)
    tmp42 = tmp37 * tmp41
    tmp43 = tmp36 + tmp42
    tl.store(in_out_ptr0 + x4, tmp43, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (3, 1, 2, 2), (4, 4, 2, 1))
    assert_size_stride(arg1_1, (4, 1, 64, 64), (4096, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(arg1_1, arg0_1, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 3, 32, 32), (3072, 1024, 32, 1))
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.float32)
        buf2 = buf1
        del buf1
        buf3 = buf2
        del buf2
        get_raw_stream(0)
        triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0[grid
            (49152)](buf3, buf0, 49152, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
    return buf3,


class Debayer2x2New(nn.Module):
    """Demosaicing of Bayer images using 2x2 convolutions.

    Requires BG-Bayer color filter array layout. That is,
    the image[1,1]='B', image[1,2]='G'.
    """

    def __init__(self):
        super(Debayer2x2New, self).__init__()
        self.kernels = nn.Parameter(torch.tensor([[1, 0], [0, 0], [0, 0.5],
            [0.5, 0], [0, 0], [0, 1]]).view(3, 1, 2, 2), requires_grad=False)

    def forward(self, input_0):
        arg0_1 = self.kernels
        arg1_1 = input_0
        output = call([arg0_1, arg1_1])
        return output[0]
