# 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/m4/cm4fnxaqni4hnszc6wcn2ebn3g33kx3ecngfkcujjyvto2zaoqdr.py
# Topologically Sorted Source Nodes: [masked_tensor, albedo_refined, shading_tensor, shading_tensor_1, masked_tensor_1, shading_refined, albedo_refined_1, albedo_refined_2, masked_tensor_2, albedo_refined_3, masked_tensor_3, albedo_tensor, masked_tensor_4, shading_tensor_2, rgb_recon, rgb_recon_1], Original ATen: [aten.le, aten.masked_fill, aten.div, aten.clamp, aten.ge, aten.mul]
# Source node to ATen node mapping:
#   albedo_refined => full_default, where
#   albedo_refined_1 => div_1
#   albedo_refined_2 => clamp_max_1, clamp_min_1
#   albedo_refined_3 => full_default_2, where_2
#   albedo_tensor => full_default_3, where_3
#   masked_tensor => le
#   masked_tensor_1 => le_1
#   masked_tensor_2 => ge
#   masked_tensor_3 => le_2
#   masked_tensor_4 => le_3
#   rgb_recon => mul
#   rgb_recon_1 => clamp_max_2, clamp_min_2
#   shading_refined => full_default_1, where_1
#   shading_tensor => div
#   shading_tensor_1 => clamp_max, clamp_min
#   shading_tensor_2 => full_default_4, where_4
# Graph fragment:
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%arg0_1, 0.0), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%le, %full_default, %arg0_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg1_1, %where), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%div, 0.0), kwargs = {})
#   %clamp_max : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1.0), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%clamp_max, 0.0), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_1 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%le_1, %full_default_1, %clamp_max), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg1_1, %where_1), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%div_1, 0.0), kwargs = {})
#   %clamp_max_1 : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 1.0), kwargs = {})
#   %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%clamp_max_1, 1.0), kwargs = {})
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_2 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%ge, %full_default_2, %clamp_max_1), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%where_2, 0.0), kwargs = {})
#   %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%le_2, %full_default_3, %where_2), kwargs = {})
#   %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%where_1, 0.0), kwargs = {})
#   %full_default_4 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_4 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%le_3, %full_default_4, %where_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_3, %where_4), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul, 0.0), kwargs = {})
#   %clamp_max_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_2, 1.0), kwargs = {})
triton_poi_fused_clamp_div_ge_le_masked_fill_mul_0 = async_compile.triton('triton_poi_fused_clamp_div_ge_le_masked_fill_mul_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], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 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_clamp_div_ge_le_masked_fill_mul_0', '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_clamp_div_ge_le_masked_fill_mul_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask)
    tmp2 = 0.0
    tmp3 = tmp1 <= tmp2
    tmp4 = 1.0
    tmp5 = tl.where(tmp3, tmp4, tmp1)
    tmp6 = tmp0 / tmp5
    tmp7 = triton_helpers.maximum(tmp6, tmp2)
    tmp8 = triton_helpers.minimum(tmp7, tmp4)
    tmp9 = tmp8 <= tmp2
    tmp10 = tl.where(tmp9, tmp4, tmp8)
    tmp11 = tmp0 / tmp10
    tmp12 = triton_helpers.maximum(tmp11, tmp2)
    tmp13 = triton_helpers.minimum(tmp12, tmp4)
    tmp14 = tmp13 >= tmp4
    tmp15 = tl.where(tmp14, tmp2, tmp13)
    tmp16 = tmp15 <= tmp2
    tmp17 = tl.where(tmp16, tmp4, tmp15)
    tmp18 = tmp10 <= tmp2
    tmp19 = tl.where(tmp18, tmp4, tmp10)
    tmp20 = tmp17 * tmp19
    tmp21 = triton_helpers.maximum(tmp20, tmp2)
    tmp22 = triton_helpers.minimum(tmp21, tmp4)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp15, xmask)
    tl.store(out_ptr2 + (x0), tmp22, xmask)
''', 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, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [masked_tensor, albedo_refined, shading_tensor, shading_tensor_1, masked_tensor_1, shading_refined, albedo_refined_1, albedo_refined_2, masked_tensor_2, albedo_refined_3, masked_tensor_3, albedo_tensor, masked_tensor_4, shading_tensor_2, rgb_recon, rgb_recon_1], Original ATen: [aten.le, aten.masked_fill, aten.div, aten.clamp, aten.ge, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clamp_div_ge_le_masked_fill_mul_0.run(arg1_1, arg0_1, buf0, buf1, buf2, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf2, buf0, buf1, )


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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 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.parallel
import torch.utils.data
from torchvision import transforms
import torch.nn as nn
import torch.cuda


class IIDTransform(nn.Module):

    def __init__(self):
        super(IIDTransform, self).__init__()
        self.transform_op = transforms.Normalize((0.5,), (0.5,))

    def mask_fill_nonzeros(self, input_tensor):
        output_tensor = torch.clone(input_tensor)
        masked_tensor = input_tensor <= 0.0
        return output_tensor.masked_fill(masked_tensor, 1.0)

    def revert_mask_fill_nonzeros(self, input_tensor):
        output_tensor = torch.clone(input_tensor)
        masked_tensor = input_tensor >= 1.0
        return output_tensor.masked_fill_(masked_tensor, 0.0)

    def forward(self, rgb_tensor, albedo_tensor):
        min = 0.0
        max = 1.0
        shading_tensor = self.extract_shading(rgb_tensor, albedo_tensor, False)
        shading_refined = self.mask_fill_nonzeros(shading_tensor)
        albedo_refined = rgb_tensor / shading_refined
        albedo_refined = torch.clip(albedo_refined, min, max)
        albedo_refined = self.revert_mask_fill_nonzeros(albedo_refined)
        rgb_recon = self.produce_rgb(albedo_refined, shading_refined, False)
        rgb_recon = self.transform_op(rgb_recon)
        albedo_refined = self.transform_op(albedo_refined)
        shading_tensor = self.transform_op(shading_tensor)
        return rgb_recon, albedo_refined, shading_tensor

    def extract_shading(self, rgb_tensor, albedo_tensor, one_channel=False):
        min = 0.0
        max = 1.0
        albedo_refined = self.mask_fill_nonzeros(albedo_tensor)
        shading_tensor = rgb_tensor / albedo_refined
        if one_channel is True:
            shading_tensor = kornia.color.rgb_to_grayscale(shading_tensor)
        shading_tensor = torch.clip(shading_tensor, min, max)
        return shading_tensor

    def produce_rgb(self, albedo_tensor, shading_tensor, tozeroone=True):
        if tozeroone:
            albedo_tensor = albedo_tensor * 0.5 + 0.5
            shading_tensor = shading_tensor * 0.5 + 0.5
        albedo_tensor = self.mask_fill_nonzeros(albedo_tensor)
        shading_tensor = self.mask_fill_nonzeros(shading_tensor)
        rgb_recon = albedo_tensor * shading_tensor
        rgb_recon = torch.clip(rgb_recon, 0.0, 1.0)
        return rgb_recon


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


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

import torch
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.parallel
import torch.utils.data
from torchvision import transforms
import torch.nn as nn
import torch.cuda
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_clamp_div_ge_le_masked_fill_mul_0(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = 0.0
    tmp3 = tmp1 <= tmp2
    tmp4 = 1.0
    tmp5 = tl.where(tmp3, tmp4, tmp1)
    tmp6 = tmp0 / tmp5
    tmp7 = triton_helpers.maximum(tmp6, tmp2)
    tmp8 = triton_helpers.minimum(tmp7, tmp4)
    tmp9 = tmp8 <= tmp2
    tmp10 = tl.where(tmp9, tmp4, tmp8)
    tmp11 = tmp0 / tmp10
    tmp12 = triton_helpers.maximum(tmp11, tmp2)
    tmp13 = triton_helpers.minimum(tmp12, tmp4)
    tmp14 = tmp13 >= tmp4
    tmp15 = tl.where(tmp14, tmp2, tmp13)
    tmp16 = tmp15 <= tmp2
    tmp17 = tl.where(tmp16, tmp4, tmp15)
    tmp18 = tmp10 <= tmp2
    tmp19 = tl.where(tmp18, tmp4, tmp10)
    tmp20 = tmp17 * tmp19
    tmp21 = triton_helpers.maximum(tmp20, tmp2)
    tmp22 = triton_helpers.minimum(tmp21, tmp4)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp15, xmask)
    tl.store(out_ptr2 + x0, tmp22, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_div_ge_le_masked_fill_mul_0[grid(256)](arg1_1,
            arg0_1, buf0, buf1, buf2, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del arg0_1
        del arg1_1
    return buf2, buf0, buf1


class IIDTransformNew(nn.Module):

    def __init__(self):
        super(IIDTransformNew, self).__init__()
        self.transform_op = transforms.Normalize((0.5,), (0.5,))

    def mask_fill_nonzeros(self, input_tensor):
        output_tensor = torch.clone(input_tensor)
        masked_tensor = input_tensor <= 0.0
        return output_tensor.masked_fill(masked_tensor, 1.0)

    def revert_mask_fill_nonzeros(self, input_tensor):
        output_tensor = torch.clone(input_tensor)
        masked_tensor = input_tensor >= 1.0
        return output_tensor.masked_fill_(masked_tensor, 0.0)

    def extract_shading(self, rgb_tensor, albedo_tensor, one_channel=False):
        min = 0.0
        max = 1.0
        albedo_refined = self.mask_fill_nonzeros(albedo_tensor)
        shading_tensor = rgb_tensor / albedo_refined
        if one_channel is True:
            shading_tensor = kornia.color.rgb_to_grayscale(shading_tensor)
        shading_tensor = torch.clip(shading_tensor, min, max)
        return shading_tensor

    def produce_rgb(self, albedo_tensor, shading_tensor, tozeroone=True):
        if tozeroone:
            albedo_tensor = albedo_tensor * 0.5 + 0.5
            shading_tensor = shading_tensor * 0.5 + 0.5
        albedo_tensor = self.mask_fill_nonzeros(albedo_tensor)
        shading_tensor = self.mask_fill_nonzeros(shading_tensor)
        rgb_recon = albedo_tensor * shading_tensor
        rgb_recon = torch.clip(rgb_recon, 0.0, 1.0)
        return rgb_recon

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0], output[1], output[2]
