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
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
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_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 62208
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 72
    x1 = xindex // 72 % 72
    x2 = xindex // 5184
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-4 +
        x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-4 + x1)) + 4096 * x2),
        xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_red_fused__native_batch_norm_legit_convolution_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr,
    RBLOCK: tl.constexpr):
    xnumel = 128
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x3 = xindex
    x0 = xindex % 32
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_out_ptr0 + (r2 + 4096 * x3), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = (triton_helpers.
            welford_reduce(tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0)
            )
        tmp4_mean = tl.where(rmask & xmask, tmp4_mean_next, tmp4_mean)
        tmp4_m2 = tl.where(rmask & xmask, tmp4_m2_next, tmp4_m2)
        tmp4_weight = tl.where(rmask & xmask, tmp4_weight_next, tmp4_weight)
        tl.store(in_out_ptr0 + (r2 + 4096 * x3), tmp2, rmask & xmask)
    tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(tmp4_mean,
        tmp4_m2, tmp4_weight, 1)
    tmp4 = tmp4_tmp[:, None]
    tmp5 = tmp5_tmp[:, None]
    tmp6_tmp[:, None]
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tmp7 = 4096.0
    tmp8 = tmp5 / tmp7
    tmp9 = 1e-05
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp11, xmask)


@triton.jit
def triton_poi_fused_repeat_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0 % 32, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_relu_3(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 557568
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 66
    x1 = xindex // 66 % 66
    x2 = xindex // 4356
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
        x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-1 + x1)) + 4096 * x2),
        xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_4(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (r2 + 1024 * x3), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 1024, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 1024.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + 1024 * x3), tmp2, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp20, None)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_repeat_5(in_ptr0, out_ptr0, 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 % 64, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_relu_6(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 295936
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 34
    x1 = xindex // 34 % 34
    x2 = xindex // 1156
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (1023 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
        x0)) + -32 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 1024 * x2),
        xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_7(
    in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
    out_ptr2, out_ptr3, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    x0 = xindex
    r3 = rindex
    x1 = xindex % 128
    tmp0 = tl.load(in_ptr0 + x0 % 128, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x0 % 128, None, eviction_policy='evict_last')
    tmp2 = tl.load(in_out_ptr0 + (r3 + 256 * x0), None)
    tmp3 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = tl.full([1], 256, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp5 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = triton_helpers.promote_to_tensor(tl.sum(tmp15, 0))
    tmp18 = 256.0
    tmp19 = tmp17 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp4 - tmp12
    tmp24 = tmp23 * tmp22
    tmp25 = tmp24 * tmp0
    tmp26 = tmp25 + tmp1
    tmp27 = tl.full([1], 0, tl.int32)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tl.store(out_ptr0 + x0, tmp0, None)
    tl.store(out_ptr1 + x0, tmp1, None)
    tl.store(in_out_ptr0 + (r3 + 256 * x0), tmp4, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp22, None)
    tl.store(out_ptr3 + (r3 + 256 * x0), tmp28, None)
    tl.store(out_ptr2 + x0, tmp12, None)


@triton.jit
def triton_poi_fused_reflection_pad2d_8(in_ptr0, out_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 % 18
    x1 = xindex // 18 % 18
    x2 = xindex // 324
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
        x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x2),
        None, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_9(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + 256 * x3), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 256.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + 256 * x3), tmp2, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp20, None)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_repeat_10(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0 % 128, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_relu_11(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_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 % 18
    x1 = xindex // 18 % 18
    x2 = xindex // 324
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
        x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x2),
        None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12(
    in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
    out_ptr3, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    x0 = xindex
    r3 = rindex
    x1 = xindex % 128
    tmp0 = tl.load(in_ptr0 + x0 % 128, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (r3 + 256 * x0), None)
    tmp2 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr2 + x1, None, eviction_policy='evict_last')
    tmp27 = tl.load(in_out_ptr1 + (r3 + 256 * x0), None)
    tmp3 = tmp1 + tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp8 = triton_helpers.promote_to_tensor(tl.sum(tmp6, 0))
    tmp9 = tl.full([1], 256, tl.int32)
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tmp8 / tmp10
    tmp12 = tmp4 - tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp17 = tmp3 - tmp11
    tmp18 = 256.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = tmp23 * tmp0
    tmp26 = tmp24 + tmp25
    tmp28 = tmp26 + tmp27
    tl.store(out_ptr0 + x0, tmp0, None)
    tl.store(in_out_ptr0 + (r3 + 256 * x0), tmp3, None)
    tl.store(in_out_ptr1 + (r3 + 256 * x0), tmp28, None)
    tl.store(out_ptr3 + x0, tmp22, None)
    tl.store(out_ptr1 + x0, tmp11, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_13(in_out_ptr0,
    in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + 256 * x3), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 256.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + 256 * x3), tmp2, None)
    tl.store(out_ptr2 + x3, tmp20, None)
    tl.store(out_ptr0 + x3, tmp10, None)
    tl.store(out_ptr1 + x3, tmp15, None)


@triton.jit
def triton_poi_fused_arange_14(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_15(out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_add_reflection_pad2d_16(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_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 // 34 % 34
    x0 = xindex % 34
    x4 = xindex // 1156
    x2 = xindex // 1156 % 128
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
        x1))), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
        x0))), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + x4, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr3 + x4, None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr4 + x4, None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr5 + x2, None, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 16, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr1 + (tmp8 + 16 * tmp4 + 256 * x4), None,
        eviction_policy='evict_last')
    tmp11 = tmp9 - tmp10
    tmp13 = 256.0
    tmp14 = tmp12 / tmp13
    tmp15 = 1e-05
    tmp16 = tmp14 + tmp15
    tmp17 = libdevice.rsqrt(tmp16)
    tmp18 = tmp11 * tmp17
    tmp20 = tmp18 * tmp19
    tmp22 = tmp20 + tmp21
    tmp23 = tl.load(in_ptr6 + (tmp8 + 16 * tmp4 + 256 * x4), None,
        eviction_policy='evict_last')
    tmp24 = tmp22 + tmp23
    tl.store(out_ptr0 + x7, tmp24, None)


@triton.jit
def triton_poi_fused_arange_17(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_18(out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_reflection_pad2d_relu_19(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1115136
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 66 % 66
    x0 = xindex % 66
    x2 = xindex // 4356
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
        x1))), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
        x0))), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 32, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tmp6 = tmp5 + tmp1
    tmp7 = tmp5 < 0
    tmp8 = tl.where(tmp7, tmp6, tmp5)
    tmp9 = tl.load(in_ptr1 + (tmp8 + 32 * tmp4 + 1024 * x2), xmask,
        eviction_policy='evict_last')
    tmp11 = tmp9 - tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tl.store(out_ptr0 + x5, tmp19, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_relu_20(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_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 % 72
    x1 = xindex // 72 % 72
    x2 = xindex // 5184
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-4 +
        x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-4 + x1)) + 4096 * x2),
        None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x2, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x2, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_convolution_21(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)
    x3 = xindex
    x1 = xindex // 4096 % 3
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, 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, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32,
        primals_33, primals_34, primals_35, primals_36, primals_37,
        primals_38, primals_39, primals_40, primals_41, primals_42,
        primals_43, primals_44, primals_45, primals_46, primals_47,
        primals_48, primals_49, primals_50, primals_51, primals_52,
        primals_53, primals_54, primals_55, primals_56, primals_57,
        primals_58, primals_59, primals_60, primals_61, primals_62, primals_63
        ) = args
    args.clear()
    assert_size_stride(primals_1, (32, 3, 9, 9), (243, 81, 9, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (32,), (1,))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64,), (1,))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_11, (128,), (1,))
    assert_size_stride(primals_12, (128,), (1,))
    assert_size_stride(primals_13, (128,), (1,))
    assert_size_stride(primals_14, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_15, (128,), (1,))
    assert_size_stride(primals_16, (128,), (1,))
    assert_size_stride(primals_17, (128,), (1,))
    assert_size_stride(primals_18, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_19, (128,), (1,))
    assert_size_stride(primals_20, (128,), (1,))
    assert_size_stride(primals_21, (128,), (1,))
    assert_size_stride(primals_22, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_23, (128,), (1,))
    assert_size_stride(primals_24, (128,), (1,))
    assert_size_stride(primals_25, (128,), (1,))
    assert_size_stride(primals_26, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_27, (128,), (1,))
    assert_size_stride(primals_28, (128,), (1,))
    assert_size_stride(primals_29, (128,), (1,))
    assert_size_stride(primals_30, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_31, (128,), (1,))
    assert_size_stride(primals_32, (128,), (1,))
    assert_size_stride(primals_33, (128,), (1,))
    assert_size_stride(primals_34, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_35, (128,), (1,))
    assert_size_stride(primals_36, (128,), (1,))
    assert_size_stride(primals_37, (128,), (1,))
    assert_size_stride(primals_38, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_39, (128,), (1,))
    assert_size_stride(primals_40, (128,), (1,))
    assert_size_stride(primals_41, (128,), (1,))
    assert_size_stride(primals_42, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_43, (128,), (1,))
    assert_size_stride(primals_44, (128,), (1,))
    assert_size_stride(primals_45, (128,), (1,))
    assert_size_stride(primals_46, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_47, (128,), (1,))
    assert_size_stride(primals_48, (128,), (1,))
    assert_size_stride(primals_49, (128,), (1,))
    assert_size_stride(primals_50, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_51, (128,), (1,))
    assert_size_stride(primals_52, (128,), (1,))
    assert_size_stride(primals_53, (128,), (1,))
    assert_size_stride(primals_54, (64, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_55, (64,), (1,))
    assert_size_stride(primals_56, (64,), (1,))
    assert_size_stride(primals_57, (64,), (1,))
    assert_size_stride(primals_58, (32, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_59, (32,), (1,))
    assert_size_stride(primals_60, (32,), (1,))
    assert_size_stride(primals_61, (32,), (1,))
    assert_size_stride(primals_62, (3, 32, 9, 9), (2592, 81, 9, 1))
    assert_size_stride(primals_63, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 72, 72), (15552, 5184, 72, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(62208)](primals_3, buf0, 
            62208, XBLOCK=256, 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf2 = buf1
        del buf1
        buf5 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 1, 1), torch.float32
            )
        buf6 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch
            .float32)
        buf8 = reinterpret_tensor(buf6, (1, 128, 1, 1), (128, 1, 1, 1), 0)
        del buf6
        triton_red_fused__native_batch_norm_legit_convolution_1[grid(128)](buf2
            , buf8, primals_2, buf5, 128, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((128,), (1,), torch.float32)
        triton_poi_fused_repeat_2[grid(128)](primals_4, buf3, 128, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_4
        buf4 = empty_strided_cuda((128,), (1,), torch.float32)
        triton_poi_fused_repeat_2[grid(128)](primals_5, buf4, 128, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_5
        buf9 = empty_strided_cuda((4, 32, 66, 66), (139392, 4356, 66, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_3[grid(557568)](buf2, buf5,
            buf8, buf3, buf4, buf9, 557568, XBLOCK=512, num_warps=8,
            num_stages=1)
        buf10 = extern_kernels.convolution(buf9, primals_6, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf11 = buf10
        del buf10
        buf14 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 1, 1), torch.
            float32)
        buf15 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        buf17 = reinterpret_tensor(buf15, (1, 256, 1, 1), (256, 1, 1, 1), 0)
        del buf15
        triton_per_fused__native_batch_norm_legit_convolution_4[grid(256)](
            buf11, buf17, primals_7, buf14, 256, 1024, num_warps=8,
            num_stages=1)
        del primals_7
        buf12 = empty_strided_cuda((256,), (1,), torch.float32)
        triton_poi_fused_repeat_5[grid(256)](primals_8, buf12, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_8
        buf13 = empty_strided_cuda((256,), (1,), torch.float32)
        triton_poi_fused_repeat_5[grid(256)](primals_9, buf13, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_9
        buf18 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_6[grid(295936)](buf11, buf14,
            buf17, buf12, buf13, buf18, 295936, XBLOCK=1024, num_warps=4,
            num_stages=1)
        buf19 = extern_kernels.convolution(buf18, primals_10, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf21 = empty_strided_cuda((512,), (1,), torch.float32)
        buf22 = empty_strided_cuda((512,), (1,), torch.float32)
        buf20 = buf19
        del buf19
        buf23 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf24 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf26 = reinterpret_tensor(buf24, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf24
        buf27 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_convolution_relu_repeat_7[
            grid(512)](buf20, buf26, primals_12, primals_13, primals_11,
            buf21, buf22, buf23, buf27, 512, 256, num_warps=2, num_stages=1)
        del primals_11
        del primals_12
        del primals_13
        buf28 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_8[grid(165888)](buf27, buf28, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf29 = extern_kernels.convolution(buf28, primals_14, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf29, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf30 = buf29
        del buf29
        buf33 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf34 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf36 = reinterpret_tensor(buf34, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf34
        triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
            buf30, buf36, primals_15, buf33, 512, 256, num_warps=2,
            num_stages=1)
        del primals_15
        buf31 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_16, buf31, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_16
        buf32 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_17, buf32, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_17
        buf37 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf30,
            buf33, buf36, buf31, buf32, buf37, 165888, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf38 = extern_kernels.convolution(buf37, primals_18, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf38, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf40 = empty_strided_cuda((512,), (1,), torch.float32)
        buf39 = buf38
        del buf38
        buf41 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf45 = buf27
        del buf27
        buf44 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12[
            grid(512)](buf39, buf45, primals_20, primals_19, primals_21,
            buf40, buf41, buf44, 512, 256, num_warps=2, num_stages=1)
        del primals_19
        del primals_20
        del primals_21
        buf46 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_8[grid(165888)](buf45, buf46, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf47 = extern_kernels.convolution(buf46, primals_22, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf47, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf48 = buf47
        del buf47
        buf51 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf52 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf54 = reinterpret_tensor(buf52, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf52
        triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
            buf48, buf54, primals_23, buf51, 512, 256, num_warps=2,
            num_stages=1)
        del primals_23
        buf49 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_24, buf49, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_24
        buf50 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_25, buf50, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_25
        buf55 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf48,
            buf51, buf54, buf49, buf50, buf55, 165888, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf56 = extern_kernels.convolution(buf55, primals_26, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf56, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf58 = empty_strided_cuda((512,), (1,), torch.float32)
        buf57 = buf56
        del buf56
        buf59 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf63 = buf45
        del buf45
        buf62 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12[
            grid(512)](buf57, buf63, primals_28, primals_27, primals_29,
            buf58, buf59, buf62, 512, 256, num_warps=2, num_stages=1)
        del primals_27
        del primals_28
        del primals_29
        buf64 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_8[grid(165888)](buf63, buf64, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf65 = extern_kernels.convolution(buf64, primals_30, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf65, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf66 = buf65
        del buf65
        buf69 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf70 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf72 = reinterpret_tensor(buf70, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf70
        triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
            buf66, buf72, primals_31, buf69, 512, 256, num_warps=2,
            num_stages=1)
        del primals_31
        buf67 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_32, buf67, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_32
        buf68 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_33, buf68, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_33
        buf73 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf66,
            buf69, buf72, buf67, buf68, buf73, 165888, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf74 = extern_kernels.convolution(buf73, primals_34, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf74, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf76 = empty_strided_cuda((512,), (1,), torch.float32)
        buf75 = buf74
        del buf74
        buf77 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf81 = buf63
        del buf63
        buf80 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12[
            grid(512)](buf75, buf81, primals_36, primals_35, primals_37,
            buf76, buf77, buf80, 512, 256, num_warps=2, num_stages=1)
        del primals_35
        del primals_36
        del primals_37
        buf82 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_8[grid(165888)](buf81, buf82, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf83 = extern_kernels.convolution(buf82, primals_38, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf83, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf84 = buf83
        del buf83
        buf87 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf88 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf90 = reinterpret_tensor(buf88, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf88
        triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
            buf84, buf90, primals_39, buf87, 512, 256, num_warps=2,
            num_stages=1)
        del primals_39
        buf85 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_40, buf85, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_40
        buf86 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_41, buf86, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_41
        buf91 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf84,
            buf87, buf90, buf85, buf86, buf91, 165888, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf92 = extern_kernels.convolution(buf91, primals_42, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf92, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf94 = empty_strided_cuda((512,), (1,), torch.float32)
        buf93 = buf92
        del buf92
        buf95 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf99 = buf81
        del buf81
        buf98 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_repeat_12[
            grid(512)](buf93, buf99, primals_44, primals_43, primals_45,
            buf94, buf95, buf98, 512, 256, num_warps=2, num_stages=1)
        del primals_43
        del primals_44
        del primals_45
        buf100 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_8[grid(165888)](buf99, buf100, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf101 = extern_kernels.convolution(buf100, primals_46, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf101, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf102 = buf101
        del buf101
        buf105 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf106 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf108 = reinterpret_tensor(buf106, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf106
        triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
            buf102, buf108, primals_47, buf105, 512, 256, num_warps=2,
            num_stages=1)
        del primals_47
        buf103 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_48, buf103, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_48
        buf104 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_49, buf104, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_49
        buf109 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_11[grid(165888)](buf102,
            buf105, buf108, buf103, buf104, buf109, 165888, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf110 = extern_kernels.convolution(buf109, primals_50, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf110, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf111 = buf110
        del buf110
        buf113 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf114 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf116 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_convolution_13[grid(512)](
            buf111, primals_51, buf113, buf114, buf116, 512, 256, num_warps
            =2, num_stages=1)
        del primals_51
        buf112 = empty_strided_cuda((512,), (1,), torch.float32)
        triton_poi_fused_repeat_10[grid(512)](primals_52, buf112, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_52
        buf117 = empty_strided_cuda((32,), (1,), torch.int64)
        triton_poi_fused_arange_14[grid(32)](buf117, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        buf118 = empty_strided_cuda((32,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_15[grid(32)](buf118, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        buf119 = empty_strided_cuda((4, 128, 34, 34), (147968, 1156, 34, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_add_reflection_pad2d_16[grid(591872)](
            buf118, buf111, buf113, buf114, buf112, primals_53, buf99,
            buf119, 591872, XBLOCK=512, num_warps=8, num_stages=1)
        del buf114
        del buf99
        del primals_53
        buf120 = extern_kernels.convolution(buf119, primals_54, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf120, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf121 = buf120
        del buf120
        buf124 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 1, 1), torch.
            float32)
        buf125 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        buf127 = reinterpret_tensor(buf125, (1, 256, 1, 1), (256, 1, 1, 1), 0)
        del buf125
        triton_per_fused__native_batch_norm_legit_convolution_4[grid(256)](
            buf121, buf127, primals_55, buf124, 256, 1024, num_warps=8,
            num_stages=1)
        del primals_55
        buf122 = empty_strided_cuda((256,), (1,), torch.float32)
        triton_poi_fused_repeat_5[grid(256)](primals_56, buf122, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_56
        buf123 = empty_strided_cuda((256,), (1,), torch.float32)
        triton_poi_fused_repeat_5[grid(256)](primals_57, buf123, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_57
        buf128 = empty_strided_cuda((64,), (1,), torch.int64)
        triton_poi_fused_arange_17[grid(64)](buf128, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf129 = empty_strided_cuda((64,), (1,), torch.int64)
        triton_poi_fused__to_copy_add_arange_mul_18[grid(64)](buf129, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf130 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1),
            torch.float32)
        triton_poi_fused__unsafe_index_reflection_pad2d_relu_19[grid(1115136)](
            buf129, buf121, buf124, buf127, buf122, buf123, buf130, 1115136,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf131 = extern_kernels.convolution(buf130, primals_58, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf131, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf132 = buf131
        del buf131
        buf135 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 1, 1), torch.
            float32)
        buf136 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        buf138 = reinterpret_tensor(buf136, (1, 128, 1, 1), (128, 1, 1, 1), 0)
        del buf136
        triton_red_fused__native_batch_norm_legit_convolution_1[grid(128)](
            buf132, buf138, primals_59, buf135, 128, 4096, XBLOCK=1, RBLOCK
            =2048, num_warps=16, num_stages=1)
        del primals_59
        buf133 = empty_strided_cuda((128,), (1,), torch.float32)
        triton_poi_fused_repeat_2[grid(128)](primals_60, buf133, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_60
        buf134 = empty_strided_cuda((128,), (1,), torch.float32)
        triton_poi_fused_repeat_2[grid(128)](primals_61, buf134, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_61
        buf139 = empty_strided_cuda((4, 32, 72, 72), (165888, 5184, 72, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_20[grid(663552)](buf132,
            buf135, buf138, buf133, buf134, buf139, 663552, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf140 = extern_kernels.convolution(buf139, primals_62, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf140, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf141 = buf140
        del buf140
        triton_poi_fused_convolution_21[grid(49152)](buf141, primals_63, 
            49152, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_63
    return (buf141, primals_1, primals_6, primals_10, primals_14,
        primals_18, primals_22, primals_26, primals_30, primals_34,
        primals_38, primals_42, primals_46, primals_50, primals_54,
        primals_58, primals_62, buf0, buf2, buf3, buf4, buf5, buf8, buf9,
        buf11, buf12, buf13, buf14, buf17, buf18, buf20, buf21, buf22,
        buf23, buf26, buf28, buf30, buf31, buf32, buf33, buf36, buf37,
        buf39, buf40, reinterpret_tensor(buf44, (512,), (1,), 0), buf46,
        buf48, buf49, buf50, buf51, buf54, buf55, buf57, buf58,
        reinterpret_tensor(buf62, (512,), (1,), 0), buf64, buf66, buf67,
        buf68, buf69, buf72, buf73, buf75, buf76, reinterpret_tensor(buf80,
        (512,), (1,), 0), buf82, buf84, buf85, buf86, buf87, buf90, buf91,
        buf93, buf94, reinterpret_tensor(buf98, (512,), (1,), 0), buf100,
        buf102, buf103, buf104, buf105, buf108, buf109, buf111, buf112,
        reinterpret_tensor(buf116, (512,), (1,), 0), buf117, buf118, buf119,
        buf121, buf122, buf123, buf124, buf127, buf128, buf129, buf130,
        buf132, buf133, buf134, buf135, buf138, buf139, reinterpret_tensor(
        buf113, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(
        buf95, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf77,
        (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf59, (1, 
        512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf41, (1, 512, 
        1, 1), (512, 1, 1, 1), 0))


class ConvLayer(torch.nn.Module):
    """
        A small wrapper around nn.Conv2d, so as to make the code cleaner and allow for experimentation with padding
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride):
        super().__init__()
        self.conv2d = torch.nn.Conv2d(in_channels, out_channels,
            kernel_size, stride, padding=kernel_size // 2, padding_mode=
            'reflect')

    def forward(self, x):
        return self.conv2d(x)


class ResidualBlock(torch.nn.Module):
    """
        Originally introduced in (Microsoft Research Asia, He et al.): https://arxiv.org/abs/1512.03385
        Modified architecture according to suggestions in this blog: http://torch.ch/blog/2016/02/04/resnets.html

        The only difference from the original is: There is no ReLU layer after the addition of identity and residual
    """

    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        kernel_size = 3
        stride_size = 1
        self.conv1 = ConvLayer(channels, channels, kernel_size=kernel_size,
            stride=stride_size)
        self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
        self.conv2 = ConvLayer(channels, channels, kernel_size=kernel_size,
            stride=stride_size)
        self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        residual = x
        out = self.relu(self.in1(self.conv1(x)))
        out = self.in2(self.conv2(out))
        return out + residual


class UpsampleConvLayer(torch.nn.Module):
    """
        Nearest-neighbor up-sampling followed by a convolution
        Appears to give better results than learned up-sampling aka transposed conv (avoids the checkerboard artifact)

        Initially proposed on distill pub: http://distill.pub/2016/deconv-checkerboard/
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride):
        super().__init__()
        self.upsampling_factor = stride
        self.conv2d = ConvLayer(in_channels, out_channels, kernel_size,
            stride=1)

    def forward(self, x):
        if self.upsampling_factor > 1:
            x = torch.nn.functional.interpolate(x, scale_factor=self.
                upsampling_factor, mode='nearest')
        return self.conv2d(x)


class TransformerNetNew(torch.nn.Module):

    def __init__(self):
        super().__init__()
        self.relu = torch.nn.ReLU()
        num_of_channels = [3, 32, 64, 128]
        kernel_sizes = [9, 3, 3]
        stride_sizes = [1, 2, 2]
        self.conv1 = ConvLayer(num_of_channels[0], num_of_channels[1],
            kernel_size=kernel_sizes[0], stride=stride_sizes[0])
        self.in1 = torch.nn.InstanceNorm2d(num_of_channels[1], affine=True)
        self.conv2 = ConvLayer(num_of_channels[1], num_of_channels[2],
            kernel_size=kernel_sizes[1], stride=stride_sizes[1])
        self.in2 = torch.nn.InstanceNorm2d(num_of_channels[2], affine=True)
        self.conv3 = ConvLayer(num_of_channels[2], num_of_channels[3],
            kernel_size=kernel_sizes[2], stride=stride_sizes[2])
        self.in3 = torch.nn.InstanceNorm2d(num_of_channels[3], affine=True)
        res_block_num_of_filters = 128
        self.res1 = ResidualBlock(res_block_num_of_filters)
        self.res2 = ResidualBlock(res_block_num_of_filters)
        self.res3 = ResidualBlock(res_block_num_of_filters)
        self.res4 = ResidualBlock(res_block_num_of_filters)
        self.res5 = ResidualBlock(res_block_num_of_filters)
        num_of_channels.reverse()
        kernel_sizes.reverse()
        stride_sizes.reverse()
        self.up1 = UpsampleConvLayer(num_of_channels[0], num_of_channels[1],
            kernel_size=kernel_sizes[0], stride=stride_sizes[0])
        self.in4 = torch.nn.InstanceNorm2d(num_of_channels[1], affine=True)
        self.up2 = UpsampleConvLayer(num_of_channels[1], num_of_channels[2],
            kernel_size=kernel_sizes[1], stride=stride_sizes[1])
        self.in5 = torch.nn.InstanceNorm2d(num_of_channels[2], affine=True)
        self.up3 = ConvLayer(num_of_channels[2], num_of_channels[3],
            kernel_size=kernel_sizes[2], stride=stride_sizes[2])

    def forward(self, input_0):
        primals_1 = self.conv1.conv2d.weight
        primals_2 = self.conv1.conv2d.bias
        primals_4 = self.in1.weight
        primals_5 = self.in1.bias
        primals_6 = self.conv2.conv2d.weight
        primals_7 = self.conv2.conv2d.bias
        primals_8 = self.in2.weight
        primals_9 = self.in2.bias
        primals_10 = self.conv3.conv2d.weight
        primals_11 = self.conv3.conv2d.bias
        primals_12 = self.in3.weight
        primals_13 = self.in3.bias
        primals_14 = self.res1.conv1.conv2d.weight
        primals_15 = self.res1.conv1.conv2d.bias
        primals_16 = self.res1.in1.weight
        primals_17 = self.res1.in1.bias
        primals_18 = self.res1.conv2.conv2d.weight
        primals_19 = self.res1.conv2.conv2d.bias
        primals_20 = self.res1.in2.weight
        primals_21 = self.res1.in2.bias
        primals_22 = self.res2.conv1.conv2d.weight
        primals_23 = self.res2.conv1.conv2d.bias
        primals_24 = self.res2.in1.weight
        primals_25 = self.res2.in1.bias
        primals_26 = self.res2.conv2.conv2d.weight
        primals_27 = self.res2.conv2.conv2d.bias
        primals_28 = self.res2.in2.weight
        primals_29 = self.res2.in2.bias
        primals_30 = self.res3.conv1.conv2d.weight
        primals_31 = self.res3.conv1.conv2d.bias
        primals_32 = self.res3.in1.weight
        primals_33 = self.res3.in1.bias
        primals_34 = self.res3.conv2.conv2d.weight
        primals_35 = self.res3.conv2.conv2d.bias
        primals_36 = self.res3.in2.weight
        primals_37 = self.res3.in2.bias
        primals_38 = self.res4.conv1.conv2d.weight
        primals_39 = self.res4.conv1.conv2d.bias
        primals_40 = self.res4.in1.weight
        primals_41 = self.res4.in1.bias
        primals_42 = self.res4.conv2.conv2d.weight
        primals_43 = self.res4.conv2.conv2d.bias
        primals_44 = self.res4.in2.weight
        primals_45 = self.res4.in2.bias
        primals_46 = self.res5.conv1.conv2d.weight
        primals_47 = self.res5.conv1.conv2d.bias
        primals_48 = self.res5.in1.weight
        primals_49 = self.res5.in1.bias
        primals_50 = self.res5.conv2.conv2d.weight
        primals_51 = self.res5.conv2.conv2d.bias
        primals_52 = self.res5.in2.weight
        primals_53 = self.res5.in2.bias
        primals_54 = self.up1.conv2d.conv2d.weight
        primals_55 = self.up1.conv2d.conv2d.bias
        primals_56 = self.in4.weight
        primals_57 = self.in4.bias
        primals_58 = self.up2.conv2d.conv2d.weight
        primals_59 = self.up2.conv2d.conv2d.bias
        primals_60 = self.in5.weight
        primals_61 = self.in5.bias
        primals_62 = self.up3.conv2d.weight
        primals_63 = self.up3.conv2d.bias
        primals_3 = 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, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33, primals_34,
            primals_35, primals_36, primals_37, primals_38, primals_39,
            primals_40, primals_41, primals_42, primals_43, primals_44,
            primals_45, primals_46, primals_47, primals_48, primals_49,
            primals_50, primals_51, primals_52, primals_53, primals_54,
            primals_55, primals_56, primals_57, primals_58, primals_59,
            primals_60, primals_61, primals_62, primals_63])
        return output[0]


Measured latency_ms: 0.033245