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
from functools import reduce
import torch.utils.data
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_pow_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr1 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr1 + (2 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr1 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp24 = tl.load(in_ptr1 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp29 = tl.load(in_ptr1 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp33 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp34 = tl.load(in_ptr1 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp39 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp40 = tl.load(in_ptr1 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp43 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp44 = tl.load(in_ptr1 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp48 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp49 = tl.load(in_ptr1 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp53 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp54 = tl.load(in_ptr1 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp59 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp60 = tl.load(in_ptr1 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp63 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp64 = tl.load(in_ptr1 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp68 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp69 = tl.load(in_ptr1 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp73 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp74 = tl.load(in_ptr1 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp21 = tmp19 - tmp20
    tmp22 = tmp21 * tmp21
    tmp25 = tmp23 - tmp24
    tmp26 = tmp25 * tmp25
    tmp27 = tmp22 + tmp26
    tmp30 = tmp28 - tmp29
    tmp31 = tmp30 * tmp30
    tmp32 = tmp27 + tmp31
    tmp35 = tmp33 - tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = tmp32 + tmp36
    tmp38 = tmp18 + tmp37
    tmp41 = tmp39 - tmp40
    tmp42 = tmp41 * tmp41
    tmp45 = tmp43 - tmp44
    tmp46 = tmp45 * tmp45
    tmp47 = tmp42 + tmp46
    tmp50 = tmp48 - tmp49
    tmp51 = tmp50 * tmp50
    tmp52 = tmp47 + tmp51
    tmp55 = tmp53 - tmp54
    tmp56 = tmp55 * tmp55
    tmp57 = tmp52 + tmp56
    tmp58 = tmp38 + tmp57
    tmp61 = tmp59 - tmp60
    tmp62 = tmp61 * tmp61
    tmp65 = tmp63 - tmp64
    tmp66 = tmp65 * tmp65
    tmp67 = tmp62 + tmp66
    tmp70 = tmp68 - tmp69
    tmp71 = tmp70 * tmp70
    tmp72 = tmp67 + tmp71
    tmp75 = tmp73 - tmp74
    tmp76 = tmp75 * tmp75
    tmp77 = tmp72 + tmp76
    tmp78 = tmp58 + tmp77
    tl.store(out_ptr0 + x0, tmp78, xmask)


@triton.jit
def triton_per_fused_mean_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 4.0
    tmp11 = tmp9 / tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp11, None)


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, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_pow_sub_sum_0[grid(16)](arg0_1, arg1_1, buf0, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_mean_sum_1[grid(1)](buf2, buf0, 1, 4, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf0
    return buf2,


class BaseModule(nn.Module):
    """
    Implements the basic module.
    All other modules inherit from this one
    """

    def load_w(self, checkpoint_path):
        """
        Loads a checkpoint into the state_dict.

        :param checkpoint_path: the checkpoint file to be loaded.
        """
        self.load_state_dict(torch.load(checkpoint_path))

    def __repr__(self):
        """
        String representation
        """
        good_old = super(BaseModule, self).__repr__()
        addition = 'Total number of parameters: {:,}'.format(self.n_parameters)
        return good_old + '\n' + addition

    def __call__(self, *args, **kwargs):
        return super(BaseModule, self).__call__(*args, **kwargs)

    @property
    def n_parameters(self):
        """
        Number of parameters of the model.
        """
        n_parameters = 0
        for p in self.parameters():
            if hasattr(p, 'mask'):
                n_parameters += torch.sum(p.mask).item()
            else:
                n_parameters += reduce(mul, p.shape)
        return int(n_parameters)


class ReconstructionLossNew(BaseModule):
    """
    Implements the reconstruction loss.
    """

    def __init__(self):
        """
        Class constructor.
        """
        super(ReconstructionLossNew, self).__init__()

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


Measured latency_ms: 0.0273