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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 1600
    xnumel = 64
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 20
    y1 = yindex // 20
    tmp0 = tl.load(in_ptr0 + (x2 + 64 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 20 * x2 + 1280 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 80
    xnumel = 3249
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 20
    y1 = yindex // 20
    tmp0 = tl.load(in_ptr0 + (x2 + 3249 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (y0 + 20 * x2 + 64980 * y1), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_2(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 259920
    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.int8)
    tmp2 = tl.full([1], 0, tl.int32)
    tmp3 = triton_helpers.maximum(tmp2, tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)
    tl.store(out_ptr1 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_convolution_max_pool2d_with_indices_3(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 800000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 80
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int8)
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_4(in_ptr0,
    out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
    constexpr):
    ynumel = 320
    xnumel = 2500
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 80
    y1 = yindex // 80
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 80 * x2 + 200000 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(out_ptr0 + (x2 + 2528 * y3), tmp2, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 80 * x2 + 200000 * y1), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_view_5(in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 800000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 200000
    x1 = xindex // 200000
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2528 * (x0 // 2500) + 202240 * x1 + x0 % 2500
        ), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_relu_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 2000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 500
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_7(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, 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 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (20, 1, 8, 8), (64, 64, 8, 1))
    assert_size_stride(primals_2, (20,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (80, 20, 8, 8), (1280, 64, 8, 1))
    assert_size_stride(primals_5, (80,), (1,))
    assert_size_stride(primals_6, (500, 200000), (200000, 1))
    assert_size_stride(primals_7, (500,), (1,))
    assert_size_stride(primals_8, (8, 500), (500, 1))
    assert_size_stride(primals_9, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((80, 20, 8, 8), (1280, 1, 160, 20), torch
            .float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(1600, 64)](primals_4, buf0, 1600, 64,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_4
        buf1 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 20, 57, 57), (64980, 3249, 57, 1))
        buf2 = empty_strided_cuda((4, 20, 57, 57), (64980, 1, 1140, 20),
            torch.float32)
        triton_poi_fused_convolution_1[grid(80, 3249)](buf1, primals_2,
            buf2, 80, 3249, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((4, 20, 57, 57), (64980, 1, 1140, 20),
            torch.int8)
        buf4 = reinterpret_tensor(buf1, (4, 20, 57, 57), (64980, 1, 1140, 
            20), 0)
        del buf1
        triton_poi_fused_max_pool2d_with_indices_relu_2[grid(259920)](buf2,
            buf3, buf4, 259920, XBLOCK=1024, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 80, 50, 50), (200000, 1, 4000, 80))
        buf6 = buf5
        del buf5
        buf7 = empty_strided_cuda((4, 80, 50, 50), (200000, 1, 4000, 80),
            torch.int8)
        triton_poi_fused_convolution_max_pool2d_with_indices_3[grid(800000)](
            buf6, primals_5, buf7, 800000, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del primals_5
        buf8 = empty_strided_cuda((4, 80, 50, 50), (202240, 2528, 50, 1),
            torch.float32)
        buf15 = empty_strided_cuda((4, 80, 50, 50), (200000, 1, 4000, 80),
            torch.bool)
        triton_poi_fused_max_pool2d_with_indices_relu_threshold_backward_4[grid
            (320, 2500)](buf6, buf8, buf15, 320, 2500, XBLOCK=32, YBLOCK=32,
            num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((4, 200000), (200000, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_relu_view_5[grid(800000)](buf8
            , buf9, 800000, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf8
        buf10 = empty_strided_cuda((4, 500), (500, 1), torch.float32)
        extern_kernels.mm(buf9, reinterpret_tensor(primals_6, (200000, 500),
            (1, 200000), 0), out=buf10)
        buf11 = buf10
        del buf10
        triton_poi_fused_relu_6[grid(2000)](buf11, primals_7, 2000, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_7
        buf12 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(buf11, reinterpret_tensor(primals_8, (500, 8), (1,
            500), 0), out=buf12)
        buf13 = buf12
        del buf12
        buf14 = empty_strided_cuda((4, 8), (8, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_7[grid(32)](buf13,
            primals_9, buf14, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_9
    return (buf13, primals_1, primals_3, buf0, buf2, buf3, buf4, buf6, buf7,
        buf9, buf11, buf14, primals_8, primals_6, buf15)


class Evidential_layer(nn.Module):

    def __init__(self, in_dim, num_classes):
        super(Evidential_layer, self).__init__()
        self.num_classes = num_classes
        self.fc1 = nn.Linear(in_dim, 2 * self.num_classes)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        x = self.fc1(x)
        return self.relu(x)


class MNISTmodelNew(nn.Module):

    def __init__(self, num_classes, edl, dropout=True):
        super(MNISTmodelNew, self).__init__()
        self.use_dropout = dropout
        k, m = 8, 80
        km = (64 - 2 * (k - 1)) ** 2 * m
        self.num_classes = num_classes
        self.conv1 = nn.Conv2d(1, 20, kernel_size=k)
        self.conv2 = nn.Conv2d(20, m, kernel_size=k)
        self.fc1 = nn.Linear(km, 500)
        if edl:
            self.fc2 = Evidential_layer(500, self.num_classes)
        else:
            self.fc2 = nn.Linear(500, self.num_classes)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.fc1.weight
        primals_7 = self.fc1.bias
        primals_8 = self.fc2.fc1.weight
        primals_9 = self.fc2.fc1.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.023912