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 torch import nn
import torch.utils
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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 21632
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 676 % 8
    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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 5408
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 13
    x3 = xindex // 13
    x2 = xindex // 1352
    x4 = xindex % 1352
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 52 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 52 * x3), xmask, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (26 + 2 * x0 + 52 * x3), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (27 + 2 * x0 + 52 * x3), xmask,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + (x4 + 1376 * x2), tmp6, xmask)
    tl.store(out_ptr1 + (x4 + 1408 * x2), tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 7744
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 121 % 16
    x2 = xindex // 1936
    x4 = xindex % 1936
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x4 + 1952 * x2), tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5
    x1 = xindex // 5 % 5
    x2 = xindex // 25 % 16
    x3 = xindex // 400
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 22 * x1 + 121 * x2 + 1952 * x3),
        xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 22 * x1 + 121 * x2 + 1952 * x3),
        xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (11 + 2 * x0 + 22 * x1 + 121 * x2 + 1952 * x3),
        xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (12 + 2 * x0 + 22 * x1 + 121 * x2 + 1952 * x3
        ), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + x4, tmp15, xmask)
    tl.store(out_ptr1 + x4, tmp16, xmask)


@triton.jit
def triton_poi_fused_relu_4(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
    x2 = xindex
    x0 = xindex % 64
    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)


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, (4, 28, 28), (784, 28, 1))
    assert_size_stride(primals_2, (8, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_3, (8,), (1,))
    assert_size_stride(primals_4, (16, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (64, 400), (400, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (10, 64), (64, 1))
    assert_size_stride(primals_9, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 28, 28), (784, 784, 28, 1), 0), primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 26, 26), (5408, 676, 26, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(21632)](buf1, primals_3, 
            21632, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 8, 13, 13), (1376, 169, 13, 1), torch
            .float32)
        buf3 = empty_strided_cuda((4, 8, 13, 13), (1408, 169, 13, 1), torch
            .int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(5408)](buf1, buf2,
            buf3, 5408, XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 16, 11, 11), (1936, 121, 11, 1))
        buf5 = empty_strided_cuda((4, 16, 11, 11), (1952, 121, 11, 1),
            torch.float32)
        triton_poi_fused_convolution_relu_2[grid(7744)](buf4, primals_5,
            buf5, 7744, XBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        del primals_5
        buf6 = empty_strided_cuda((4, 16, 5, 5), (400, 25, 5, 1), torch.int8)
        buf7 = empty_strided_cuda((4, 16, 5, 5), (400, 25, 5, 1), torch.float32
            )
        triton_poi_fused_max_pool2d_with_indices_3[grid(1600)](buf5, buf6,
            buf7, 1600, XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (4, 400), (400, 1), 0),
            reinterpret_tensor(primals_6, (400, 64), (1, 400), 0), out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_relu_4[grid(256)](buf9, primals_7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_7
        buf10 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf9, reinterpret_tensor(primals_8,
            (64, 10), (1, 64), 0), alpha=1, beta=1, out=buf10)
        del primals_9
    return buf10, primals_2, primals_4, reinterpret_tensor(primals_1, (4, 1,
        28, 28), (784, 784, 28, 1), 0
        ), buf1, buf2, buf3, buf5, buf6, reinterpret_tensor(buf7, (4, 400),
        (400, 1), 0), buf9, primals_8, primals_6


class CNN_DropOutNew(torch.nn.Module):
    """
    Recommended model by "Adaptive Federated Optimization" (https://arxiv.org/pdf/2003.00295.pdf)
    Used for EMNIST experiments.
    When `only_digits=True`, the summary of returned model is
    ```
    Model:
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #
    =================================================================
    reshape (Reshape)            (None, 28, 28, 1)         0
    _________________________________________________________________
    conv2d (Conv2D)              (None, 26, 26, 32)        320
    _________________________________________________________________
    conv2d_1 (Conv2D)            (None, 24, 24, 64)        18496
    _________________________________________________________________
    max_pooling2d (MaxPooling2D) (None, 12, 12, 64)        0
    _________________________________________________________________
    dropout (Dropout)            (None, 12, 12, 64)        0
    _________________________________________________________________
    flatten (Flatten)            (None, 9216)              0
    _________________________________________________________________
    dense (Dense)                (None, 128)               1179776
    _________________________________________________________________
    dropout_1 (Dropout)          (None, 128)               0
    _________________________________________________________________
    dense_1 (Dense)              (None, 10)                1290
    =================================================================
    Total params: 1,199,882
    Trainable params: 1,199,882
    Non-trainable params: 0
    ```
    Args:
      only_digits: If True, uses a final layer with 10 outputs, for use with the
        digits only MNIST dataset (http://yann.lecun.com/exdb/mnist/).
        If False, uses 62 outputs for Federated Extended MNIST (FEMNIST)
        EMNIST: Extending MNIST to handwritten letters: https://arxiv.org/abs/1702.05373.
    Returns:
      A `torch.nn.Module`.
    """

    def __init__(self, only_digits=True):
        super(CNN_DropOutNew, self).__init__()
        self.conv2d_1 = torch.nn.Conv2d(1, 8, kernel_size=3)
        self.max_pooling = nn.MaxPool2d(2, stride=2)
        self.conv2d_2 = torch.nn.Conv2d(8, 16, kernel_size=3)
        self.dropout_1 = nn.Dropout(0.25)
        self.flatten = nn.Flatten()
        self.linear_1 = nn.Linear(16 * 5 * 5, 64)
        self.dropout_2 = nn.Dropout(0.25)
        self.linear_2 = nn.Linear(64, 10)
        self.relu = nn.ReLU()

    def forward(self, input_0):
        primals_2 = self.conv2d_1.weight
        primals_3 = self.conv2d_1.bias
        primals_4 = self.conv2d_2.weight
        primals_5 = self.conv2d_2.bias
        primals_6 = self.linear_1.weight
        primals_7 = self.linear_1.bias
        primals_8 = self.linear_2.weight
        primals_9 = self.linear_2.bias
        primals_1 = 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: 0.121824