import torch
import torch.nn as nn


def get_operator_from_cfg(operator_cfg):
    operator_cfg_copy = operator_cfg.copy()
    construct_str = 'nn.'
    construct_str += operator_cfg_copy.pop('type') + '('
    for k, v in operator_cfg_copy.items():
        construct_str += k + '=' + str(v) + ','
    construct_str += ')'
    return eval(construct_str)


class FastestBlock(nn.Module):

    def __init__(self, num_input_channels, num_block_channels, stride=1,
        downsample=None, activation_cfg=dict(type='ReLU', inplace=True),
        norm_cfg=None):
        super(FastestBlock, self).__init__()
        if downsample is not None:
            assert stride == 2
        if norm_cfg is not None:
            assert norm_cfg['type'] in ['BatchNorm2d', 'GroupNorm']
        self._num_input_channel = num_input_channels
        self._num_block_channel = num_block_channels
        self._stride = stride
        self._activation_cfg = activation_cfg
        self._norm_cfg = norm_cfg
        self._downsample = downsample
        self._conv1 = nn.Conv2d(in_channels=self._num_input_channel,
            out_channels=self._num_block_channel // 2, kernel_size=3,
            stride=self._stride, padding=1, bias=True if self._norm_cfg is
            None else False)
        if self._norm_cfg is not None:
            temp_norm_cfg = self._norm_cfg.copy()
            if temp_norm_cfg['type'] == 'BatchNorm2d':
                temp_norm_cfg['num_features'] = self._num_block_channel // 2
            else:
                temp_norm_cfg['num_channels'] = self._num_block_channel // 2
            self._norm1 = get_operator_from_cfg(temp_norm_cfg)
        self._activation = get_operator_from_cfg(self._activation_cfg)
        self._conv2 = nn.Conv2d(in_channels=self._num_block_channel // 2,
            out_channels=self._num_block_channel, kernel_size=3, stride=1,
            padding=1, bias=True if self._norm_cfg is None else False)
        if self._norm_cfg is not None:
            temp_norm_cfg = self._norm_cfg.copy()
            if temp_norm_cfg['type'] == 'BatchNorm2d':
                temp_norm_cfg['num_features'] = self._num_block_channel
            else:
                temp_norm_cfg['num_channels'] = self._num_block_channel
            self._norm2 = get_operator_from_cfg(temp_norm_cfg)

    def forward(self, x):
        identity = x
        out = self._conv1(x)
        if self._norm_cfg is not None:
            out = self._norm1(out)
        out = self._activation(out)
        out = self._conv2(out)
        if self._norm_cfg is not None:
            out = self._norm2(out)
        if self._downsample is not None:
            identity = self._downsample(x)
        out += identity
        out = self._activation(out)
        return out


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'num_input_channels': 4, 'num_block_channels': 4}]

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


@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 2
    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_relu_threshold_backward_1(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
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = 0.0
    tmp8 = tmp6 <= tmp7
    tl.store(in_out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr0 + x3, tmp8, 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, (2, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (2,), (1,))
    assert_size_stride(primals_4, (4, 2, 3, 3), (18, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 2, 4, 4), (32, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(128)](buf1, primals_3, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_convolution_relu_threshold_backward_1[grid(256)](
            buf3, primals_5, primals_1, buf4, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_5
    return buf3, primals_1, primals_2, primals_4, buf1, buf4


def get_operator_from_cfg(operator_cfg):
    operator_cfg_copy = operator_cfg.copy()
    construct_str = 'nn.'
    construct_str += operator_cfg_copy.pop('type') + '('
    for k, v in operator_cfg_copy.items():
        construct_str += k + '=' + str(v) + ','
    construct_str += ')'
    return eval(construct_str)


class FastestBlockNew(nn.Module):

    def __init__(self, num_input_channels, num_block_channels, stride=1,
        downsample=None, activation_cfg=dict(type='ReLU', inplace=True),
        norm_cfg=None):
        super(FastestBlockNew, self).__init__()
        if downsample is not None:
            assert stride == 2
        if norm_cfg is not None:
            assert norm_cfg['type'] in ['BatchNorm2d', 'GroupNorm']
        self._num_input_channel = num_input_channels
        self._num_block_channel = num_block_channels
        self._stride = stride
        self._activation_cfg = activation_cfg
        self._norm_cfg = norm_cfg
        self._downsample = downsample
        self._conv1 = nn.Conv2d(in_channels=self._num_input_channel,
            out_channels=self._num_block_channel // 2, kernel_size=3,
            stride=self._stride, padding=1, bias=True if self._norm_cfg is
            None else False)
        if self._norm_cfg is not None:
            temp_norm_cfg = self._norm_cfg.copy()
            if temp_norm_cfg['type'] == 'BatchNorm2d':
                temp_norm_cfg['num_features'] = self._num_block_channel // 2
            else:
                temp_norm_cfg['num_channels'] = self._num_block_channel // 2
            self._norm1 = get_operator_from_cfg(temp_norm_cfg)
        self._activation = get_operator_from_cfg(self._activation_cfg)
        self._conv2 = nn.Conv2d(in_channels=self._num_block_channel // 2,
            out_channels=self._num_block_channel, kernel_size=3, stride=1,
            padding=1, bias=True if self._norm_cfg is None else False)
        if self._norm_cfg is not None:
            temp_norm_cfg = self._norm_cfg.copy()
            if temp_norm_cfg['type'] == 'BatchNorm2d':
                temp_norm_cfg['num_features'] = self._num_block_channel
            else:
                temp_norm_cfg['num_channels'] = self._num_block_channel
            self._norm2 = get_operator_from_cfg(temp_norm_cfg)

    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]
