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 typing import Tuple
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 = 28800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 225 % 32
    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_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 36 % 64
    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_convolution_relu_threshold_backward_2(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, 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, None)
    tl.store(out_ptr0 + x3, tmp6, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (64, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(4, 
            4), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 15, 15), (7200, 225, 15, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(28800)](buf1, primals_3, 
            28800, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 6, 6), (2304, 36, 6, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(9216)](buf3, primals_5, 
            9216, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1))
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_2[grid(4096)](buf5
            , primals_7, buf6, 4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
    return reinterpret_tensor(buf5, (4, 1024), (1024, 1), 0
        ), primals_1, primals_2, primals_4, primals_6, buf1, buf3, buf6


def to_sate_tensor(s, device):
    """ converts a numpy array to a Tensor suitable for passing through DQNs """
    return torch.from_numpy(s)


class ConvEncoderNew(nn.Module):

    def __init__(self, state_shape: 'Tuple', device=None):
        super(ConvEncoderNew, self).__init__()
        in_channels = state_shape[0]
        nc = 32
        self.conv1 = nn.Conv2d(in_channels, nc, (8, 8), stride=(4, 4))
        self.conv2 = nn.Conv2d(nc, 2 * nc, (4, 4), stride=(2, 2))
        self.conv3 = nn.Conv2d(2 * nc, 2 * nc, (3, 3), stride=(1, 1))
        self.relu = nn.ReLU()
        self.state_shape = state_shape

    @property
    def output_shape(self):
        """ the output shape of this CNN encoder.
        :return: tuple of output shape
        """
        return 64, 12, 12

    def conv_output_shape(h_w, kernel_size=1, stride=1, pad=0, dilation=1):
        """ Utility function for computing output of convolutions
        takes a tuple of (h,w) and returns a tuple of (h,w)
        """
        from math import floor
        if type(kernel_size) is not tuple:
            kernel_size = kernel_size, kernel_size
        h = floor((h_w[0] + 2 * pad - dilation * (kernel_size[0] - 1) - 1) /
            stride + 1)
        w = floor((h_w[1] + 2 * pad - dilation * (kernel_size[1] - 1) - 1) /
            stride + 1)
        return h, w

    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_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.082201