# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/3u/c3ub52l73zdv4klgqzgxmtzrzxvztuyczv2jksnvrjr7erq7guxd.py
# Topologically Sorted Source Nodes: [einsum], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   einsum => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_3,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    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 % 16
    y1 = (yindex // 16)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (16*x2) + (64*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/t6/ct6f57cdvyh3ahq6iwyawuy7577bar2ftumjxqllolmn4c4lh7ph.py
# Topologically Sorted Source Nodes: [y], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   y => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_3, %primals_3), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [einsum], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_1, buf0, 64, 4, grid=grid(64, 4), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((1, 64, 4), (256, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [einsum], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (1, 64, 4), (0, 4, 1), 0), reinterpret_tensor(primals_2, (1, 4, 4), (16, 4, 1), 0), out=buf1)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.add]
        triton_poi_fused_add_1.run(buf2, primals_3, 256, grid=grid(256), stream=stream0)
        del primals_3
    return (reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 1, 16, 4), 0), reinterpret_tensor(buf0, (1, 4, 64), (256, 1, 4), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import string
import torch
import numpy as np
import torch.utils.data
import torch
import torch.nn as nn


def _einsum(a, b, c, x, y):
    einsum_str = '{},{}->{}'.format(''.join(a), ''.join(b), ''.join(c))
    return torch.einsum(einsum_str, x, y)


def contract_inner(x, y):
    """tensordot(x, y, 1)."""
    x_chars = list(string.ascii_lowercase[:len(x.shape)])
    y_chars = list(string.ascii_lowercase[len(x.shape):len(y.shape) + len(x
        .shape)])
    y_chars[0] = x_chars[-1]
    out_chars = x_chars[:-1] + y_chars[1:]
    return _einsum(x_chars, y_chars, out_chars, x, y)


def variance_scaling(scale, mode, distribution, in_axis=1, out_axis=0,
    dtype=torch.float32, device='cpu'):

    def _compute_fans(shape, in_axis=1, out_axis=0):
        receptive_field_size = np.prod(shape) / shape[in_axis] / shape[out_axis
            ]
        fan_in = shape[in_axis] * receptive_field_size
        fan_out = shape[out_axis] * receptive_field_size
        return fan_in, fan_out

    def init(shape, dtype=dtype, device=device):
        fan_in, fan_out = _compute_fans(shape, in_axis, out_axis)
        if mode == 'fan_in':
            denominator = fan_in
        elif mode == 'fan_out':
            denominator = fan_out
        elif mode == 'fan_avg':
            denominator = (fan_in + fan_out) / 2
        else:
            raise ValueError(
                'invalid mode for variance scaling initializer: {}'.format(
                mode))
        variance = scale / denominator
        if distribution == 'normal':
            return torch.randn(*shape, dtype=dtype, device=device) * np.sqrt(
                variance)
        elif distribution == 'uniform':
            return (torch.rand(*shape, dtype=dtype, device=device) * 2.0 - 1.0
                ) * np.sqrt(3 * variance)
        else:
            raise ValueError(
                'invalid distribution for variance scaling initializer')
    return init


def default_init(scale=1.0):
    """The same initialization used in DDPM."""
    scale = 1e-10 if scale == 0 else scale
    return variance_scaling(scale, 'fan_avg', 'uniform')


class NIN(nn.Module):

    def __init__(self, in_dim, num_units, init_scale=0.1):
        super().__init__()
        self.W = nn.Parameter(default_init(scale=init_scale)((in_dim,
            num_units)), requires_grad=True)
        self.b = nn.Parameter(torch.zeros(num_units), requires_grad=True)

    def forward(self, x):
        x = x.permute(0, 2, 3, 1)
        y = contract_inner(x, self.W) + self.b
        return y.permute(0, 3, 1, 2)


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


def get_init_inputs():
    return [[], {'in_dim': 4, 'num_units': 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
import string
import numpy as np
import torch.utils.data
import torch
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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    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 % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_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
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](primals_1, buf0, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((1, 64, 4), (256, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (1, 64, 4), (0, 4, 1), 
            0), reinterpret_tensor(primals_2, (1, 4, 4), (16, 4, 1), 0),
            out=buf1)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        triton_poi_fused_add_1[grid(256)](buf2, primals_3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_3
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 1, 16, 4), 0
        ), reinterpret_tensor(buf0, (1, 4, 64), (256, 1, 4), 0)


def _einsum(a, b, c, x, y):
    einsum_str = '{},{}->{}'.format(''.join(a), ''.join(b), ''.join(c))
    return torch.einsum(einsum_str, x, y)


def contract_inner(x, y):
    """tensordot(x, y, 1)."""
    x_chars = list(string.ascii_lowercase[:len(x.shape)])
    y_chars = list(string.ascii_lowercase[len(x.shape):len(y.shape) + len(x
        .shape)])
    y_chars[0] = x_chars[-1]
    out_chars = x_chars[:-1] + y_chars[1:]
    return _einsum(x_chars, y_chars, out_chars, x, y)


def variance_scaling(scale, mode, distribution, in_axis=1, out_axis=0,
    dtype=torch.float32, device='cpu'):

    def _compute_fans(shape, in_axis=1, out_axis=0):
        receptive_field_size = np.prod(shape) / shape[in_axis] / shape[out_axis
            ]
        fan_in = shape[in_axis] * receptive_field_size
        fan_out = shape[out_axis] * receptive_field_size
        return fan_in, fan_out

    def init(shape, dtype=dtype, device=device):
        fan_in, fan_out = _compute_fans(shape, in_axis, out_axis)
        if mode == 'fan_in':
            denominator = fan_in
        elif mode == 'fan_out':
            denominator = fan_out
        elif mode == 'fan_avg':
            denominator = (fan_in + fan_out) / 2
        else:
            raise ValueError(
                'invalid mode for variance scaling initializer: {}'.format(
                mode))
        variance = scale / denominator
        if distribution == 'normal':
            return torch.randn(*shape, dtype=dtype, device=device) * np.sqrt(
                variance)
        elif distribution == 'uniform':
            return (torch.rand(*shape, dtype=dtype, device=device) * 2.0 - 1.0
                ) * np.sqrt(3 * variance)
        else:
            raise ValueError(
                'invalid distribution for variance scaling initializer')
    return init


def default_init(scale=1.0):
    """The same initialization used in DDPM."""
    scale = 1e-10 if scale == 0 else scale
    return variance_scaling(scale, 'fan_avg', 'uniform')


class NINNew(nn.Module):

    def __init__(self, in_dim, num_units, init_scale=0.1):
        super().__init__()
        self.W = nn.Parameter(default_init(scale=init_scale)((in_dim,
            num_units)), requires_grad=True)
        self.b = nn.Parameter(torch.zeros(num_units), requires_grad=True)

    def forward(self, input_0):
        primals_2 = self.W
        primals_3 = self.b
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
