import os
from dataclasses import dataclass
from enum import Enum, auto
from transformers import AutoConfig
import torch


class SparsePolicyType(Enum):
    """Sparse attention policy types."""
    FULL = auto()       # No sparse attention (load all blocks)
    QUEST = auto()      # Query-aware Top-K block selection (decode only)
    XATTN_BSA = auto()  # XAttention Block Sparse Attention (prefill only, chunked)


@dataclass
class Config:
    model: str
    max_num_batched_tokens: int = 16384
    max_num_seqs: int = 512
    max_model_len: int = 4096
    gpu_memory_utilization: float = 0.9
    tensor_parallel_size: int = 1
    enforce_eager: bool = False
    hf_config: AutoConfig | None = None
    eos: int | list[int] = -1  # Single EOS token or list of EOS tokens (e.g., GLM-4)
    kvcache_block_size: int = 1024
    num_kvcache_blocks: int = -1
    dtype: str | None = None  # "float16", "bfloat16", or None (use model default)

    # CPU Offload configuration
    enable_cpu_offload: bool = False
    offload_policy: str = "lru"  # "lru", "fifo", or full class path
    num_transfer_streams: int = 4  # Number of CUDA streams for async transfers
    num_gpu_blocks: int = -1  # User-specified GPU blocks count, -1 = auto (use max available)

    # Computed fields for offload (set in __post_init__ or by ModelRunner)
    num_gpu_kvcache_blocks: int = -1
    num_cpu_kvcache_blocks: int = -1

    # Sparse attention configuration
    # FULL: no sparse attention (load all blocks)
    # QUEST: decode-only sparse attention with Top-K block selection
    # XATTN_BSA: prefill-only block sparse attention with chunk-level selection
    sparse_policy: SparsePolicyType = SparsePolicyType.FULL
    sparse_topk_blocks: int = 8  # Top-K blocks for Quest
    sparse_threshold_blocks: int = 4  # Apply sparse only when blocks > threshold

    # XAttention BSA specific parameters
    sparse_block_size: int = 128  # Block size for BSA (tokens per block)
    sparse_samples_per_chunk: int = 128  # Samples per chunk for estimation
    sparse_threshold: float = 0.95  # Cumulative attention threshold (tau in XAttention)
    sparse_use_triton: bool = True  # Use Triton kernels for estimation
    sparse_stride: int = 8  # Stride for Q/K downsampling
    sparse_chunk_size: int = 16384  # Triton kernel chunk size for estimation

    def __post_init__(self):
        assert os.path.isdir(self.model)
        assert self.kvcache_block_size % 256 == 0
        assert 1 <= self.tensor_parallel_size <= 8
        self.hf_config = AutoConfig.from_pretrained(self.model, trust_remote_code=True)
        # Get max position embeddings (GLM-4 uses seq_length instead of max_position_embeddings)
        max_pos = getattr(self.hf_config, 'max_position_embeddings',
                         getattr(self.hf_config, 'seq_length', 4096))
        self.max_model_len = min(self.max_model_len, max_pos)
        assert self.max_num_batched_tokens >= self.max_model_len

        # Override torch_dtype if user specified
        if self.dtype is not None:
            dtype_map = {
                "float16": torch.float16,
                "fp16": torch.float16,
                "bfloat16": torch.bfloat16,
                "bf16": torch.bfloat16,
                "float32": torch.float32,
                "fp32": torch.float32,
            }
            if self.dtype not in dtype_map:
                raise ValueError(f"Invalid dtype: {self.dtype}. Choose from: {list(dtype_map.keys())}")
            self.hf_config.torch_dtype = dtype_map[self.dtype]