# 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_4/inductor_cache/w6/cw66djixvued3ctgslkkk6ref2q3s26fg5eeufcpvxq57xt5mctq.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x_5 => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%clone, %clone_1], 3), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[1024, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_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_cat_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 1024
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    y1 = (yindex // 16) % 4
    x4 = xindex % 4
    x5 = (xindex // 4)
    y0 = yindex % 16
    y6 = (yindex // 64)
    y3 = (yindex // 256)
    y7 = (yindex // 16) % 16
    tmp0 = y1
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1, 1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + (4*y1) + (8*x5) + (64*y0) + (1024*y6)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1, 1], 4, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr0 + (32 + x4 + (4*((-2) + y1)) + (8*x5) + (64*y0) + (1024*y6)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (y0 + (16*x4) + (64*y7) + (1024*x5) + (4096*y3)), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 64, 16, 4), (4096, 64, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4, 4, 4), (4096, 1024, 256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(arg0_1, buf0, 1024, 16, grid=grid(1024, 16), stream=stream0)
        del arg0_1
    return (buf0, )


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, 64, 16, 4), (4096, 64, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_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 Decoder(nn.Module):
    """ Encoder
    """

    def __init__(self, n_levels, n_color, n_eccentricity, n_azimuth,
        n_theta, n_phase):
        super(Decoder, self).__init__()
        self.n_levels = n_levels
        self.n_color = n_color
        self.n_eccentricity = n_eccentricity
        self.n_azimuth = n_azimuth
        self.n_theta = n_theta
        self.n_phase = n_phase
        self.h_size = (n_levels * n_color * n_eccentricity * n_azimuth *
            n_theta * n_phase)

    def forward(self, x, theta=None):
        x = x.view(-1, self.n_color * self.n_theta * self.n_phase, self.
            n_levels * self.n_eccentricity, self.n_azimuth)
        lim = self.n_levels * self.n_eccentricity // 2
        x_int = x[:, :, :lim, ...]
        x_ext = x[:, :, lim:, ...]
        x_list = []
        for x in (x_int, x_ext):
            if theta is not None:
                theta_inv = theta
                theta_inv[:, :, 2] = -theta[:, :, 2].detach()
                grid = F.affine_grid(theta_inv, x.size())
                x = F.grid_sample(x, grid)
            x = x.view(-1, self.n_color, self.n_theta, self.n_phase, self.
                n_levels, self.n_eccentricity // 2, self.n_azimuth)
            x = x.permute(0, 4, 1, 5, 6, 2, 3).contiguous()
            x_list.append(x)
        x = torch.cat(x_list, 3)
        return x


def get_inputs():
    return [torch.rand([4, 64, 16, 4])]


def get_init_inputs():
    return [[], {'n_levels': 4, 'n_color': 4, 'n_eccentricity': 4,
        'n_azimuth': 4, 'n_theta': 4, 'n_phase': 4}]

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
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_cat_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    y1 = yindex // 16 % 4
    x4 = xindex % 4
    x5 = xindex // 4
    y0 = yindex % 16
    y6 = yindex // 64
    y3 = yindex // 256
    y7 = yindex // 16 % 16
    tmp0 = y1
    tl.full([1, 1], 0, tl.int64)
    tmp3 = tl.full([1, 1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + 4 * y1 + 8 * x5 + 64 * y0 + 1024 * y6), 
        tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1, 1], 4, tl.int64)
    tmp9 = tl.load(in_ptr0 + (32 + x4 + 4 * (-2 + y1) + 8 * x5 + 64 * y0 + 
        1024 * y6), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (y0 + 16 * x4 + 64 * y7 + 1024 * x5 + 4096 * y3),
        tmp10, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 64, 16, 4), (4096, 64, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4, 4, 4), (4096, 1024, 256, 
            64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(1024, 16)](arg0_1, buf0, 1024, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class DecoderNew(nn.Module):
    """ Encoder
    """

    def __init__(self, n_levels, n_color, n_eccentricity, n_azimuth,
        n_theta, n_phase):
        super(DecoderNew, self).__init__()
        self.n_levels = n_levels
        self.n_color = n_color
        self.n_eccentricity = n_eccentricity
        self.n_azimuth = n_azimuth
        self.n_theta = n_theta
        self.n_phase = n_phase
        self.h_size = (n_levels * n_color * n_eccentricity * n_azimuth *
            n_theta * n_phase)

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