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 typing import List
from typing import Union
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_convolution_relu_threshold_backward_0(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 25 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr0 + x3, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(2,), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 5, 5), (100, 25, 5, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_0[grid(400)](buf1,
            primals_2, buf2, 400, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
    return buf1, primals_1, primals_3, buf2


def autopad(kernel_size: 'Union[int, List[int]]', padding:
    'Union[int, None]'=None) ->Union[int, List[int]]:
    """Auto padding calculation for pad='same' in TensorFlow."""
    if isinstance(kernel_size, int):
        kernel_size = [kernel_size]
    return padding or [(x // 2) for x in kernel_size]


class Activation:
    """Convert string activation name to the activation class."""

    def __init__(self, act_type: 'Union[str, None]') ->None:
        """Convert string activation name to the activation class.

        Args:
            type: Activation name.

        Returns:
            nn.Identity if {type} is None.
        """
        self.type = act_type
        self.args = [1] if self.type == 'Softmax' else []

    def __call__(self) ->nn.Module:
        if self.type is None:
            return nn.Identity()
        elif hasattr(nn, self.type):
            return getattr(nn, self.type)(*self.args)
        else:
            return getattr(__import__('src.modules.activations', fromlist=[
                '']), self.type)()


class CRNew(nn.Module):
    """Standard convolution with batch normalization and activation."""

    def __init__(self, in_channel: 'int', out_channels: 'int', kernel_size:
        'int', stride: 'int'=1, padding: 'Union[int, None]'=None, groups:
        'int'=1, activation: 'Union[str, None]'='ReLU') ->None:
        """Standard convolution with batch normalization and activation.

        Args:
            in_channel: input channels.
            out_channels: output channels.
            kernel_size: kernel size.
            stride: stride.
            padding: input padding. If None is given, autopad is applied
                which is identical to padding='SAME' in TensorFlow.
            groups: group convolution.
            activation: activation name. If None is given, nn.Identity is applied
                which is no activation.
        """
        super().__init__()
        self.conv = nn.Conv2d(in_channel, out_channels, kernel_size, stride,
            padding=autopad(kernel_size, padding), groups=groups, bias=True)
        self.act = Activation(activation)()

    def fusefoward(self, x: 'torch.Tensor') ->torch.Tensor:
        """Fuse forward."""
        return self.act(self.conv(x))

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


Measured latency_ms: 0.014018