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 libdevice
import torch.nn as nn
from torch.nn.functional import mse_loss
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_div_log10_mse_loss_mul_0(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)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp7 = 256.0
    tmp8 = tmp6 / tmp7
    tmp9 = 16.0
    tmp10 = tmp9 / tmp8
    tmp11 = libdevice.log10(tmp10)
    tmp12 = 10.0
    tmp13 = tmp11 * tmp12
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp13, 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((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_div_log10_mse_loss_mul_0[grid(1)](buf1, arg0_1,
            arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


def psnr_loss(input: 'torch.Tensor', target: 'torch.Tensor', max_val: 'float'
    ) ->torch.Tensor:
    """Function that computes PSNR

    See :class:`~kornia.losses.PSNR` for details.
    """
    if not torch.is_tensor(input) or not torch.is_tensor(target):
        raise TypeError(
            f'Expected 2 torch tensors but got {type(input)} and {type(target)}'
            )
    if input.shape != target.shape:
        raise TypeError(
            f'Expected tensors of equal shapes, but got {input.shape} and {target.shape}'
            )
    mse_val = mse_loss(input, target, reduction='mean')
    max_val_tensor: 'torch.Tensor' = torch.tensor(max_val)
    return 10 * torch.log10(max_val_tensor * max_val_tensor / mse_val)


class PSNRLossNew(nn.Module):
    """Creates a criterion that calculates the PSNR between 2 images. Given an m x n image,
    .. math::
    \\text{MSE}(I,T) = \\frac{1}{m\\,n}\\sum_{i=0}^{m-1}\\sum_{j=0}^{n-1} [I(i,j) - T(i,j)]^2

    Arguments:
        max_val (float): Maximum value of input

    Shape:
        - input: :math:`(*)`
        - approximation: :math:`(*)` same shape as input
        - output: :math:`()` a scalar

    Examples:
        >>> kornia.losses.psnr(torch.ones(1), 1.2*torch.ones(1), 2)
        tensor(20.0000) # 10 * log(4/((1.2-1)**2)) / log(10)

    reference:
        https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Definition
    """

    def __init__(self, max_val: 'float') ->None:
        super(PSNRLossNew, self).__init__()
        self.max_val = max_val

    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: 0.015264