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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import 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_per_fused_abs_add_mean_mse_loss_sub_0(in_out_ptr0, in_out_ptr1,
    in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp11 = tl.load(in_ptr2 + r0, None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp7 = tmp2 * tmp2
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp12 = tmp11 - tmp1
    tmp13 = tl_math.abs(tmp12)
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp17 = tmp12 * tmp12
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tmp21 = 256.0
    tmp22 = tmp16 / tmp21
    tmp23 = tmp20 / tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp6 / tmp21
    tmp26 = tmp10 / tmp21
    tmp27 = tmp25 + tmp26
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp24, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([1], 0, tl.int32), tmp27, None)


@triton.jit
def triton_per_fused_div_mse_loss_mul_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp5 = tl.load(in_ptr1 + r0, None)
    tmp1 = 4.0
    tmp2 = tmp0 * tmp1
    tmp3 = 0.25
    tmp4 = tmp2 * tmp3
    tmp6 = tmp5 * tmp1
    tmp7 = tmp6 * tmp3
    tmp8 = tmp4 - tmp7
    tmp9 = tmp8 * tmp8
    tmp10 = tl.broadcast_to(tmp9, [RBLOCK])
    tmp12 = triton_helpers.promote_to_tensor(tl.sum(tmp10, 0))
    tmp13 = 256.0
    tmp14 = tmp12 / tmp13
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp14, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg4_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf2
        del buf2
        buf5 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_abs_add_mean_mse_loss_sub_0[grid(1)](buf6, buf5,
            arg1_1, arg0_1, arg2_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf4
        del buf4
        triton_per_fused_div_mse_loss_mul_1[grid(1)](buf7, arg3_1, arg4_1, 
            1, 256, num_warps=2, num_stages=1)
        del arg3_1
        del arg4_1
    return buf5, buf6, buf7


class NonAttentiveTacotronLossNew(nn.Module):

    def __init__(self, sample_rate: 'int', hop_size: 'int'):
        super(NonAttentiveTacotronLossNew, self).__init__()
        self.sample_rate = sample_rate
        self.hop_size = hop_size

    def forward(self, input_0, input_1, input_2, input_3, input_4):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        arg4_1 = input_4
        output = call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1])
        return output[0], output[1], output[2]


Measured latency_ms: 0.030733