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]
