"""
Paged Attention Kernel - NVIDIA (Ampere+/Hopper)
KV Cache with paging for efficient inference (vLLM style)

Source: Conch (adapted)
Tier: ampere (requires BF16 native support)
"""
import torch
import triton
import triton.language as tl

from neurobrix.kernels.registry import register_kernel


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

@triton.jit
def _paged_attention_kernel(
    # Pointers
    output_ptr,
    query_ptr,
    key_cache_ptr,
    value_cache_ptr,
    block_tables_ptr,
    context_lens_ptr,
    # Shapes
    num_seqs,
    num_heads,
    head_size,
    block_size,
    max_num_blocks_per_seq,
    # Strides
    stride_q_seq, stride_q_head, stride_q_dim,
    stride_k_block, stride_k_head, stride_k_dim, stride_k_x,
    stride_v_block, stride_v_head, stride_v_dim, stride_v_x,
    stride_o_seq, stride_o_head, stride_o_dim,
    stride_bt_seq, stride_bt_block,
    # Consts
    BLOCK_SIZE: tl.constexpr,
    HEAD_SIZE: tl.constexpr,
    BLOCKS_PER_PROGRAM: tl.constexpr,
):
    """
    Simplified Paged Attention Kernel.
    """
    # TODO: Full implementation requires complex logic for block table lookup
    # For Phase 3.3, we provide a placeholder/stub kernel structure
    # that compiles but functionality is simplified (standard attention on blocks)
    
    pid = tl.program_id(0)
    
    # Stub implementation - just to satisfy registration and testing flow
    # Real implementation would perform:
    # 1. Load block table for current sequence
    # 2. Iterate over blocks
    # 3. Load K/V from paged cache
    # 4. Compute attention
    
    pass


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

def _paged_attention_impl(
    output: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    block_tables: torch.Tensor,
    context_lens: torch.Tensor,
    block_size: int,
    max_context_len: int = None,
    alibi_slopes: torch.Tensor = None,
) -> None:
    """
    Paged Attention Implementation.
    """
    # This is a complex kernel. For now, we stub it or use PyTorch fallback if possible.
    # Since Paged Attention relies on block tables, PyTorch fallback is non-trivial without re-assembling tensors.
    
    # For Phase 3.3, we register the interface but raise NotImplementedError at runtime if called,
    # or perform a very basic operation if needed for testing.
    
    # Real implementation to be ported from vLLM/Conch in Phase 4.
    raise NotImplementedError("Paged Attention kernel requires full port from vLLM/Conch. Scheduled for Phase 4.")


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

@register_kernel(family="llm", vendor="nvidia", tier="ampere", op_name="paged_attention")
def paged_attention_kernel(
    output: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    block_tables: torch.Tensor,
    context_lens: torch.Tensor,
    block_size: int,
    max_context_len: int = None,
    alibi_slopes: torch.Tensor = None,
) -> None:
    """Paged Attention kernel for Ampere+ GPUs."""
    _paged_attention_impl(
        output, query, key_cache, value_cache, block_tables, context_lens, block_size, max_context_len, alibi_slopes
    )
