"""
HSAQ — Hybrid Sensitivity-Aware Quantization
=============================================
Novel mixed-precision quantization pipeline that combines:
1. Per-layer sensitivity profiling (output drift measurement)
2. Memory-budget-aware tier assignment (critical / normal / tolerant)
3. Mixed-precision HQQ/AWQ/GPTQ quantization (3/4-bit) per sensitivity tier
4. Optional structured attention-head pruning for tolerant layers
5. Optional 2-bit quantization for tolerant layers (quality cliff risk)
6. LoRA domain adapter training for quality recovery
7. 5-stage model hunter: discover → filter → score → profile → emit

Target: Fit 13-20B models on 12 GB consumer GPUs with all layers on GPU.
No CPU offload — PCIe shuffle per token tanks inference 5-10x.

Usage:
    from quantization.hsaq import HSAQPipeline, ModelHunterPipeline

    # Single-model quantization
    pipeline = HSAQPipeline(
        model_id="Qwen/Qwen2.5-14B-Instruct",
        gpu_budget_gb=11.2,
        calibration_dataset="wikitext",
    )
    pipeline.run()  # profiles → classifies → quantizes → adapts

    # Multi-model hunter
    hunter = ModelHunterPipeline(HunterConfig())
    results = hunter.run()  # discover → filter → score → profile → emit
"""

from quantization.hsaq.adapter import LoRAAdapterTrainer
from quantization.hsaq.assignment import (
    Assignment,
    AssignmentResult,
    BudgetInfeasibleError,
    LayerCandidate,
    LayerOption,
    assign_bit_widths,
    pareto_frontier,
)
from quantization.hsaq.budget import MemoryBudgetCalculator
from quantization.hsaq.candidate import (
    DiscoveryStage,
    EmitStage,
    FilterConfig,
    FilterStage,
    HunterConfig,
    ModelHunterPipeline,
    ScoreStage,
    compute_model_hash,
    extract_arch_from_config,
    kv_bytes_per_token,
    predict_vram_mixed_34bit,
)
from quantization.hsaq.candidate_record import (
    ArchType,
    CandidateRecord,
    EligibilityTier,
    predict_kv_gb,
    predict_weights_gb,
)
from quantization.hsaq.config import (
    HSAQBudget,
    HSAQConfig,
    LayerSensitivity,
    LayerTier,
    SensitivityResult,
    TierBudget,
)
from quantization.hsaq.pipeline import HSAQPipeline
from quantization.hsaq.pruner import AttentionHeadPruner
from quantization.hsaq.sensitivity import (
    PIPELINE_VERSION,
    SensitivityCacheDB,
    SensitivityProfiler,
)

__all__ = [
    "PIPELINE_VERSION",
    "ArchType",
    "Assignment",
    "AssignmentResult",
    "AttentionHeadPruner",
    "BudgetInfeasibleError",
    "CandidateRecord",
    "DiscoveryStage",
    "EligibilityTier",
    "EmitStage",
    "FilterConfig",
    "FilterStage",
    "HSAQBudget",
    "HSAQConfig",
    "HSAQPipeline",
    "HunterConfig",
    "LayerCandidate",
    "LayerOption",
    "LayerSensitivity",
    "LayerTier",
    "LoRAAdapterTrainer",
    "MemoryBudgetCalculator",
    "ModelHunterPipeline",
    "ScoreStage",
    "SensitivityCacheDB",
    "SensitivityProfiler",
    "SensitivityResult",
    "TierBudget",
    "assign_bit_widths",
    "compute_model_hash",
    "extract_arch_from_config",
    "kv_bytes_per_token",
    "pareto_frontier",
    "predict_kv_gb",
    "predict_vram_mixed_34bit",
    "predict_weights_gb",
]