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 torch.nn as nn
import torch.utils.data
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_sub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 256
    x0 = xindex % 64
    x2 = xindex // 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 - tmp1
    tl.store(out_ptr0 + x4, tmp2, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 256
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_mul_sum_2(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
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp2 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp6 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp11 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp14 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp16 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp1 = -tmp0
    tmp3 = tmp1 * tmp2
    tmp5 = -tmp4
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tmp10 = -tmp9
    tmp12 = tmp10 * tmp11
    tmp13 = tmp8 + tmp12
    tmp15 = -tmp14
    tmp17 = tmp15 * tmp16
    tmp18 = tmp13 + tmp17
    tl.store(out_ptr0 + x2, tmp18, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sub_0[grid(1024)](arg1_1, arg0_1, buf0, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 1, 4, 4, 4), (256, 64, 64, 16, 4, 
            1), torch.float32)
        triton_poi_fused_clone_1[grid(1024)](arg2_1, buf1, 1024, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg2_1
        buf2 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (64, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf1, (64, 4, 4), (16, 4, 1), 0), out=buf2)
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sum_2[grid(256)](buf2, buf0, buf3, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        del buf0
        del buf2
    return buf3,


class QuadraticForm(torch.autograd.Function):
    """
    This is a custom function that, given two parameters mew and sigma, implements quadratic form. 
    This function takes a representation of a color in vector space and returns a unnormalized score attributed to that color swab.
    """

    @staticmethod
    def forward(ctx, mew, co_var, color):
        """        
        mew : FloatTensor
            m x k matrix, where m is the number of examples, where k is the length of the representation of the color
        co_var: FloatTensor
            m x k x k matrix, sigma in the quadratic form. 
        color: FloatTensor
            m x p x k matrix, where each example has a p vectors of a single color representations of length k
        ------
        outputs:
            m x p matrix of scores.

        """
        ctx.save_for_backward(mew, co_var, color)
        shifted_color = color - mew.unsqueeze(1)
        vec_mat_mult = -torch.matmul(shifted_color.unsqueeze(2), co_var.
            unsqueeze(1)).squeeze(1)
        output = (vec_mat_mult.squeeze(2) * shifted_color).sum(2)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        """
        The derivative of the quadratic form.
        
        input : tuple of FloatTensors
            mew : FloatTensor
                m x k matrix, where m is the number of examples, where k is the length of the representation of the color
            co_var: FloatTensor
                m x k x k matrix, sigma in the quadratic form
            color: FloatTensor
                m x k matrix, where each example has a vector of a single color representation of length k
        output : FloatTensor
            The float tensor for the gradients of a quadratic function
        """
        mew, co_var, color = ctx.saved_tensors
        grad_mean = grad_co_var = grad_color = None
        shifted_color = color - mew.unsqueeze(1)
        if ctx.needs_input_grad[0]:
            grad_mean = torch.matmul(shifted_color.unsqueeze(2), (co_var +
                co_var.permute(0, 2, 1)).unsqueeze(1)).squeeze(2)
            grad_mean = grad_mean * grad_output.unsqueeze(2)
            grad_mean = grad_mean.sum(1)
        if ctx.needs_input_grad[1]:
            grad_co_var = -torch.einsum('bki,bkj->bkij', (shifted_color,
                shifted_color))
            grad_co_var = grad_co_var * grad_output.unsqueeze(2).unsqueeze(3)
            grad_co_var = grad_co_var.sum(1)
        return grad_mean, grad_co_var, grad_color


class AttentionalColorizedListenerDecoderNew(nn.Module):
    """
    Simple decoder model for the neural literal/pragmatic listener.
    This model takes in two statistical params, mew and sigma, and returns a vector containing the normalized scores
    of each color in the context.
    """

    def __init__(self, device, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.device = device
        self.transform_func = QuadraticForm.apply
        self.hidden_activation = nn.Softmax(dim=1)

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]


Measured latency_ms: 0.048833