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 math as tl_math
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
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_clone_transpose_0(in_ptr0, out_ptr0, out_ptr1, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    y2 = yindex % 4
    y3 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x1 + 16 * y0), xmask & ymask)
    tl.store(out_ptr0 + (x1 + 16 * y0), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (y2 + 4 * x1 + 64 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_per_fused__softmax_2(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 4
    x1 = xindex // 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * r2 + 64 * x1), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 64
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp4 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 / tmp4
    tl.store(in_out_ptr0 + x3, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_3, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4), (16, 4, 1))
        buf1 = empty_strided_cuda((4, 16, 4), (64, 1, 16), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 16), (64, 1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_transpose_0[grid(16, 16)](primals_1, buf1,
            buf8, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        buf2 = buf0
        del buf0
        triton_poi_fused_convolution_1[grid(64)](buf2, primals_4, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(buf1, buf2, out=buf3)
        del buf1
        buf4 = empty_strided_cuda((4, 1, 4), (4, 16, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 1, 4), (4, 16, 1), torch.float32)
        triton_per_fused__softmax_2[grid(16)](buf3, buf4, buf5, 16, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf6 = buf3
        del buf3
        triton_poi_fused__softmax_3[grid(256)](buf6, buf4, buf5, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        del buf5
        buf7 = buf2
        del buf2
        extern_kernels.bmm(reinterpret_tensor(primals_1, (4, 4, 16), (64, 
            16, 1), 0), buf6, out=buf7)
    return buf7, primals_2, primals_3, reinterpret_tensor(primals_1, (4, 16,
        4), (64, 1, 16), 0), buf6, buf8


class GlobalAttention_textNew(nn.Module):

    def __init__(self, idf, cdf):
        super(GlobalAttention_textNew, self).__init__()
        self.conv_context = nn.Conv1d(cdf, idf, kernel_size=1, stride=1,
            padding=0)
        self.sm = nn.Softmax()
        self.mask = None

    def applyMask(self, mask):
        self.mask = mask

    def forward(self, input_0, input_1):
        primals_3 = self.conv_context.weight
        primals_4 = self.conv_context.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.084637