import torch
import torch.nn as nn
import torch.nn.functional as F

# Default ModelNew is PyTorch-only. run_and_check: use check_kernel=False with backend=cuda
# until the candidate file contains a real CUDA kernel (see kernel_static_checker).


def _linear_beta_schedule(num_timesteps: int, beta_start: float, beta_end: float) -> torch.Tensor:
    return torch.linspace(beta_start, beta_end, num_timesteps)


class Model(nn.Module):
    """
    Fused **multi-step** diffusion denoising (VP-DDPM-style) in a single ``forward``.

    Instead of one scheduler step per Python call, this reference unrolls
    ``num_fused_steps`` consecutive **deterministic** reverse updates (posterior mean,
    no sampler noise)—a typical fusion target for robotics / world-model diffusion
    heads where latency matters.

    Each inner step: predict ``ε_θ(x, t)`` with a small MLP, then apply the standard
    DDPM mean update from discrete time ``t`` to ``t-1``. Batch elements may start
    from different ``t_start``; inputs must satisfy ``t_start >= num_fused_steps`` so
    indices stay valid.
    """

    def __init__(self, dim: int, num_fused_steps: int, total_timesteps: int = 1000):
        super().__init__()
        assert num_fused_steps >= 1
        assert total_timesteps >= num_fused_steps + 1
        self.dim = dim
        self.num_fused_steps = num_fused_steps
        self.total_timesteps = total_timesteps

        self.eps_net = nn.Sequential(
            nn.Linear(dim + 1, dim * 4),
            nn.SiLU(),
            nn.Linear(dim * 4, dim * 4),
            nn.SiLU(),
            nn.Linear(dim * 4, dim),
        )

        betas = _linear_beta_schedule(total_timesteps, 1e-4, 0.02)
        alphas = 1.0 - betas
        alphas_cumprod = torch.cumprod(alphas, dim=0)
        self.register_buffer("betas", betas)
        self.register_buffer("alphas", alphas)
        self.register_buffer("alphas_cumprod", alphas_cumprod)
        # x_{t-1} = (1/sqrt(alpha_t)) * (x_t - (beta_t / sqrt(1-alpha_bar_t)) * eps)
        self.register_buffer("sqrt_recip_alphas", torch.sqrt(1.0 / alphas))
        self.register_buffer(
            "coef_eps",
            betas / torch.sqrt(1.0 - alphas_cumprod),
        )

    def forward(self, x: torch.Tensor, t_start: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: (B, dim) noisy latent at discrete time ``t_start`` (same semantics as DDPM).
            t_start: (B,) int64 timesteps in ``[num_fused_steps, total_timesteps - 1]``.
        Returns:
            (B, dim) latent after ``num_fused_steps`` fused denoise steps toward 0.
        """
        B, d = x.shape
        assert d == self.dim
        assert t_start.shape == (B,)
        # Harness paths (measure_ref_program_time / eval) cast activations to fp32;
        # discrete timesteps must survive as whole-valued floats → long indices.
        t_start = t_start.long()

        x_cur = x
        for k in range(self.num_fused_steps):
            t_idx = t_start - k
            # ``get_inputs`` samples t_start >= num_fused_steps so t_idx >= 1 always.
            t_norm = t_idx.float() / float(self.total_timesteps)
            eps = self.eps_net(torch.cat([x_cur, t_norm.unsqueeze(-1)], dim=-1))

            c1 = self.sqrt_recip_alphas[t_idx].view(B, 1)
            c2 = self.coef_eps[t_idx].view(B, 1)
            x_cur = c1 * (x_cur - c2 * eps)

        return x_cur


class ModelNew(nn.Module):
    """KernelBench candidate entry point; default delegates to ``Model``."""

    def __init__(self, dim: int, num_fused_steps: int, total_timesteps: int = 1000):
        super().__init__()
        self._impl = Model(dim, num_fused_steps, total_timesteps=total_timesteps)

    def forward(self, x: torch.Tensor, t_start: torch.Tensor) -> torch.Tensor:
        return self._impl(x, t_start)


batch_size = 8
dim = 64
num_fused_steps = 16
total_timesteps = 1000


def get_inputs():
    p = popcorn_pri
    x = torch.randn(p.jitter_int(batch_size), dim)
    low = num_fused_steps
    high = total_timesteps
    t_start = torch.randint(low, high, (p.jitter_int(batch_size),), dtype=torch.long)
    return [x, t_start]


def get_init_inputs():
    return [dim, num_fused_steps, total_timesteps]

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline

_cuda_source = r'''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>

__device__ __forceinline__ float silu(float x) {
    return x / (1.0f + expf(-x));
}

__global__ void fused_denoise_kernel(
    const float* __restrict__ x_in,
    const long* __restrict__ t_start,
    float* __restrict__ x_out,
    const float* __restrict__ w1, const float* __restrict__ b1,
    const float* __restrict__ w2, const float* __restrict__ b2,
    const float* __restrict__ w3, const float* __restrict__ b3,
    const float* __restrict__ sqrt_recip_alphas,
    const float* __restrict__ coef_eps,
    int B, int dim, int hidden, int num_fused_steps, int total_timesteps
) {
    int b = blockIdx.x;
    if (b >= B) return;

    extern __shared__ float smem[];
    float* x_cur = smem;  // dim floats per block
    float* h1 = smem + dim;
    float* h2 = h1 + hidden;

    // load initial x
    for (int i = threadIdx.x; i < dim; i += blockDim.x) {
        x_cur[i] = x_in[b * dim + i];
    }
    __syncthreads();

    long t0 = t_start[b];
    for (int k = 0; k < num_fused_steps; ++k) {
        long t_idx = t0 - k;
        float t_norm = (float)t_idx / (float)total_timesteps;

        // MLP forward: concat [x, t_norm] -> h1 (dim+1 -> hidden)
        for (int o = threadIdx.x; o < hidden; o += blockDim.x) {
            float sum = b1[o];
            for (int i = 0; i < dim; ++i) {
                sum += x_cur[i] * w1[o * (dim + 1) + i];
            }
            sum += t_norm * w1[o * (dim + 1) + dim];
            h1[o] = silu(sum);
        }
        __syncthreads();

        // h1 -> h2 (hidden -> hidden)
        for (int o = threadIdx.x; o < hidden; o += blockDim.x) {
            float sum = b2[o];
            for (int i = 0; i < hidden; ++i) {
                sum += h1[i] * w2[o * hidden + i];
            }
            h2[o] = silu(sum);
        }
        __syncthreads();

        // h2 -> eps (hidden -> dim)
        for (int o = threadIdx.x; o < dim; o += blockDim.x) {
            float sum = b3[o];
            for (int i = 0; i < hidden; ++i) {
                sum += h2[i] * w3[o * hidden + i];
            }
            float eps_val = sum;
            float c1 = sqrt_recip_alphas[t_idx];
            float c2 = coef_eps[t_idx];
            x_cur[o] = c1 * (x_cur[o] - c2 * eps_val);
        }
        __syncthreads();
    }

    // write out
    for (int i = threadIdx.x; i < dim; i += blockDim.x) {
        x_out[b * dim + i] = x_cur[i];
    }
}

torch::Tensor fused_denoise(
    torch::Tensor x,
    torch::Tensor t_start,
    torch::Tensor w1, torch::Tensor b1,
    torch::Tensor w2, torch::Tensor b2,
    torch::Tensor w3, torch::Tensor b3,
    torch::Tensor sqrt_recip_alphas,
    torch::Tensor coef_eps,
    int num_fused_steps, int total_timesteps
) {
    int B = x.size(0);
    int dim = x.size(1);
    int hidden = w1.size(0);

    auto x_out = torch::empty_like(x);

    dim3 grid(B);
    dim3 block(256);
    int smem_size = (dim + 2 * hidden) * sizeof(float);

    fused_denoise_kernel<<<grid, block, smem_size>>>(
        x.data_ptr<float>(),
        t_start.data_ptr<long>(),
        x_out.data_ptr<float>(),
        w1.data_ptr<float>(), b1.data_ptr<float>(),
        w2.data_ptr<float>(), b2.data_ptr<float>(),
        w3.data_ptr<float>(), b3.data_ptr<float>(),
        sqrt_recip_alphas.data_ptr<float>(),
        coef_eps.data_ptr<float>(),
        B, dim, hidden, num_fused_steps, total_timesteps
    );

    return x_out;
}
'''

_cpp_source = r'''
torch::Tensor fused_denoise(
    torch::Tensor x,
    torch::Tensor t_start,
    torch::Tensor w1, torch::Tensor b1,
    torch::Tensor w2, torch::Tensor b2,
    torch::Tensor w3, torch::Tensor b3,
    torch::Tensor sqrt_recip_alphas,
    torch::Tensor coef_eps,
    int num_fused_steps, int total_timesteps
);
'''

_fused = load_inline(
    name='fused_denoise_ext',
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=['fused_denoise'],
    verbose=False
)

class ModelNew(nn.Module):
    def __init__(self, dim: int, num_fused_steps: int, total_timesteps: int = 1000):
        super().__init__()
        self.dim = dim
        self.num_fused_steps = num_fused_steps
        self.total_timesteps = total_timesteps

        self.eps_net = nn.Sequential(
            nn.Linear(dim + 1, dim * 4),
            nn.SiLU(),
            nn.Linear(dim * 4, dim * 4),
            nn.SiLU(),
            nn.Linear(dim * 4, dim),
        )

        betas = torch.linspace(1e-4, 0.02, total_timesteps)
        alphas = 1.0 - betas
        alphas_cumprod = torch.cumprod(alphas, dim=0)
        self.register_buffer("betas", betas)
        self.register_buffer("alphas", alphas)
        self.register_buffer("alphas_cumprod", alphas_cumprod)
        self.register_buffer("sqrt_recip_alphas", torch.sqrt(1.0 / alphas))
        self.register_buffer("coef_eps", betas / torch.sqrt(1.0 - alphas_cumprod))

    def forward(self, x: torch.Tensor, t_start: torch.Tensor) -> torch.Tensor:
        w1 = self.eps_net[0].weight
        b1 = self.eps_net[0].bias
        w2 = self.eps_net[2].weight
        b2 = self.eps_net[2].bias
        w3 = self.eps_net[4].weight
        b3 = self.eps_net[4].bias

        return _fused.fused_denoise(
            x, t_start,
            w1, b1, w2, b2, w3, b3,
            self.sqrt_recip_alphas, self.coef_eps,
            self.num_fused_steps, self.total_timesteps
        )
```