import math

import torch
import torch.nn as nn

# 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).


class Model(nn.Module):
    """
    **Batched forward kinematics (FK)** for a **planar serial** revolute chain.

    Fixed link lengths ``L_j`` (buffer), joint angles ``θ_j`` (batch ``B``). For each
    link, the heading is ``Σ_{k≤j} θ_k``; joint positions are the cumulative sum of
    ``L_j [cos(heading), sin(heading)]``—fully **vectorized** over ``B`` and ``J``.

    Returns positions of the **end of each link** in the base frame ``(B, J, 2)``
    (last row is the end-effector in the plane). Typical fusion target for
    batched geometry in calibration, whole-body control, and VLA motion heads.
    """

    def __init__(self, num_joints: int, link_scale: float = 1.0):
        super().__init__()
        assert num_joints >= 1
        self.num_joints = num_joints
        lengths = torch.ones(num_joints, dtype=torch.float32) * float(link_scale)
        self.register_buffer("link_lengths", lengths)

    def forward(self, joint_angles: torch.Tensor) -> torch.Tensor:
        """
        Args:
            joint_angles: (B, J) radians, one revolute per link.
        Returns:
            positions: (B, J, 2) world-frame (x, y) at each link end; ``[..., -1, :]`` is EE.
        """
        B, J = joint_angles.shape
        assert J == self.num_joints

        heading = torch.cumsum(joint_angles, dim=-1)
        c = torch.cos(heading)
        s = torch.sin(heading)
        direction = torch.stack((c, s), dim=-1)
        displacements = direction * self.link_lengths.view(1, J, 1)
        return torch.cumsum(displacements, dim=1)


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

    def __init__(self, num_joints: int, link_scale: float = 1.0):
        super().__init__()
        self._impl = Model(num_joints, link_scale=link_scale)

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


batch_size = 32
num_joints = 7
link_scale = 0.15


def get_inputs():
    p = popcorn_pri
    return [torch.empty(p.jitter_int(batch_size), num_joints).uniform_(-math.pi, math.pi)]


def get_init_inputs():
    return [num_joints, link_scale]

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

cuda_source = """
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>

__global__ void fk_kernel(const float* __restrict__ angles, float* __restrict__ positions, 
                          const float* __restrict__ lengths, int B, int J) {
    int b = blockIdx.x * blockDim.x + threadIdx.x;
    if (b >= B) return;
    
    float cum_heading = 0.0f;
    float cum_x = 0.0f;
    float cum_y = 0.0f;
    
    for (int j = 0; j < J; j++) {
        cum_heading += angles[b * J + j];
        float c = cosf(cum_heading);
        float s = sinf(cum_heading);
        float dx = c * lengths[j];
        float dy = s * lengths[j];
        cum_x += dx;
        cum_y += dy;
        positions[b * J * 2 + j * 2 + 0] = cum_x;
        positions[b * J * 2 + j * 2 + 1] = cum_y;
    }
}

torch::Tensor forward_kinematics_cuda(torch::Tensor joint_angles, torch::Tensor link_lengths) {
    int B = joint_angles.size(0);
    int J = joint_angles.size(1);
    
    auto options = torch::TensorOptions().dtype(torch::kFloat32).device(joint_angles.device());
    auto positions = torch::empty({B, J, 2}, options);
    
    int threads = 256;
    int blocks = (B + threads - 1) / threads;
    
    fk_kernel<<<blocks, threads>>>(
        joint_angles.data_ptr<float>(),
        positions.data_ptr<float>(),
        link_lengths.data_ptr<float>(),
        B, J
    );
    
    return positions;
}
"""

cpp_source = """
torch::Tensor forward_kinematics_cuda(torch::Tensor joint_angles, torch::Tensor link_lengths);
"""

module = load_inline(
    name="fk_cuda",
    cpp_sources=cpp_source,
    cuda_sources=cuda_source,
    functions=["forward_kinematics_cuda"],
    verbose=False,
    extra_cflags=["-O3"]
)

class ModelNew(nn.Module):
    def __init__(self, num_joints: int, link_scale: float = 1.0):
        super().__init__()
        self.num_joints = num_joints
        lengths = torch.ones(num_joints, dtype=torch.float32) * float(link_scale)
        self.register_buffer("link_lengths", lengths)

    def forward(self, joint_angles: torch.Tensor) -> torch.Tensor:
        return module.forward_kinematics_cuda(joint_angles, self.link_lengths)