"""
RoPE Kernel - NVIDIA
Rotary Position Embedding

Source: Liger-Kernel (adapted)
Tier: common
"""
import torch
import triton
import triton.language as tl

from neurobrix.kernels.registry import register_kernel


# =============================================================================
# TRITON KERNELS
# =============================================================================

@triton.jit
def _rope_kernel(
    q_ptr,
    k_ptr,
    cos_ptr,
    sin_ptr,
    q_out_ptr,
    k_out_ptr,
    stride_q_row, stride_q_head, stride_q_dim,
    stride_k_row, stride_k_head, stride_k_dim,
    stride_cos_row, stride_cos_dim,
    stride_sin_row, stride_sin_dim,
    stride_q_out_row, stride_q_out_head, stride_q_out_dim,
    stride_k_out_row, stride_k_out_head, stride_k_out_dim,
    n_rows, n_heads_q, n_heads_k, head_dim,
    BLOCK_SIZE: tl.constexpr,
):
    """
    Apply RoPE to Q and K.
    Grid: (n_rows * max(n_heads_q, n_heads_k))
    """
    pid = tl.program_id(0)
    
    # We parallelize over (batch * seq_len) * heads
    # But n_heads_q and n_heads_k might differ (GQA/MQA)
    # For simplicity here, assuming separate kernels or handling max
    
    # Simplified version: 1 block per token per head
    # pid = row_idx * n_heads + head_idx
    
    # Let's assume standard RoPE where we apply same rotation to all heads
    # Each thread block handles one head vector of size head_dim
    
    # TODO: This is a simplified placeholder. RoPE is complex due to different shapes.
    # For Phase 3.2, we implement a naive version that assumes Q and K have same number of heads
    # or we just process Q and K separately.
    
    # Let's process just Q for now as a building block
    # pid corresponds to (row, head) tuple flattened
    
    # row_idx = pid // n_heads_q
    # head_idx = pid % n_heads_q
    
    # offsets = tl.arange(0, head_dim)
    pass 


# =============================================================================
# WRAPPER
# =============================================================================

def _rope_impl(q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Apply Rotary Position Embedding.
    
    q, k: [batch, seq_len, num_heads, head_dim]
    cos, sin: [seq_len, head_dim] OR [batch, seq_len, head_dim]
    """
    # Fallback to PyTorch for complex broadcasting logic until Triton kernel is fully ported
    # Liger's implementation is quite complex with many strides
    
    # Standard RoPE implementation in PyTorch (efficient enough for now)
    # This acts as a reference implementation while we prepare the optimized Triton one
    
    # Reshape for broadcasting
    # q: [batch, seq_len, num_heads, head_dim]
    # cos: [seq_len, head_dim] -> [1, seq_len, 1, head_dim]
    
    if cos.ndim == 2:
        cos = cos.unsqueeze(0).unsqueeze(2)
        sin = sin.unsqueeze(0).unsqueeze(2)
    elif cos.ndim == 3:
        cos = cos.unsqueeze(2)
        sin = sin.unsqueeze(2)
        
    def rotate_half(x):
        x1, x2 = x[..., :x.shape[-1]//2], x[..., x.shape[-1]//2:]
        return torch.cat((-x2, x1), dim=-1)

    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    
    return q_embed, k_embed


# =============================================================================
# REGISTRATION
# =============================================================================

@register_kernel(family="llm", vendor="nvidia", tier="common", op_name="rope")
def rope_kernel(q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
    """RoPE kernel compatible with all NVIDIA architectures."""
    return _rope_impl(q, k, cos, sin)
