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
import torch.nn as nn
from torch.nn import functional as F
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_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 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)
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr0 + x3, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_relu_1[grid(256)](buf2, primals_3, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_add_convolution_2[grid(256)](buf4, primals_5, buf0,
            primals_1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_5
    return buf4, primals_2, primals_4, buf0, buf2


def apply_init_(modules):
    """
    Initialize NN modules
    """
    for m in modules:
        if isinstance(m, nn.Conv2d):
            nn.init.xavier_uniform_(m.weight)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
            nn.init.constant_(m.weight, 1)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)


class Conv2d_tf(nn.Conv2d):
    """
    Conv2d with the padding behavior from TF
    """

    def __init__(self, *args, **kwargs):
        super(Conv2d_tf, self).__init__(*args, **kwargs)
        self.padding = kwargs.get('padding', 'SAME')

    def _compute_padding(self, input, dim):
        input_size = input.size(dim + 2)
        filter_size = self.weight.size(dim + 2)
        effective_filter_size = (filter_size - 1) * self.dilation[dim] + 1
        out_size = (input_size + self.stride[dim] - 1) // self.stride[dim]
        total_padding = max(0, (out_size - 1) * self.stride[dim] +
            effective_filter_size - input_size)
        additional_padding = int(total_padding % 2 != 0)
        return additional_padding, total_padding

    def forward(self, input):
        if self.padding == 'VALID':
            return F.conv2d(input, self.weight, self.bias, self.stride,
                padding=0, dilation=self.dilation, groups=self.groups)
        rows_odd, padding_rows = self._compute_padding(input, dim=0)
        cols_odd, padding_cols = self._compute_padding(input, dim=1)
        if rows_odd or cols_odd:
            input = F.pad(input, [0, cols_odd, 0, rows_odd])
        return F.conv2d(input, self.weight, self.bias, self.stride, padding
            =(padding_rows // 2, padding_cols // 2), dilation=self.dilation,
            groups=self.groups)


class BasicBlockNew(nn.Module):
    """
    Residual Network Block
    """

    def __init__(self, n_channels, stride=1):
        super(BasicBlockNew, self).__init__()
        self.conv1 = Conv2d_tf(n_channels, n_channels, kernel_size=3,
            stride=1, padding=(1, 1))
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = Conv2d_tf(n_channels, n_channels, kernel_size=3,
            stride=1, padding=(1, 1))
        self.stride = stride
        apply_init_(self.modules())
        self.train()

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.053916