# SPDX-License-Identifier: Apache-2.0
import re
from copy import deepcopy
from typing import Optional, Union

import torch

from vllm.config import QuantizationConfig
from vllm.model_executor.layers.linear import (LinearBase,
                                               UnquantizedLinearMethod)
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead, UnquantizedEmbeddingMethod)


# Match dynamic rules with module name (prefix) and override quantize
# config if module (prefix) matches a rule
def override_config(config: QuantizationConfig, prefix: str):
    weight_bits = get_dynamic_override(config, prefix, "bits",
                                       config.weight_bits)
    if isinstance(weight_bits, int):
        config.weight_bits = weight_bits
    group_size = get_dynamic_override(config, prefix, "group_size",
                                      config.group_size)
    if isinstance(group_size, int):
        config.group_size = group_size
    desc_act = get_dynamic_override(config, prefix, "desc_act",
                                    config.desc_act)
    if isinstance(desc_act, bool):
        config.desc_act = desc_act

    config.pack_factor = 32 // config.weight_bits  # packed into int32
    if config.get_name() == "gptq_marlin":
        is_sym = get_dynamic_override(config, prefix, "sym", config.is_sym)
        if isinstance(is_sym, bool):
            config.is_sym = is_sym

        if (config.weight_bits, config.is_sym) not in config.TYPE_MAP:
            raise ValueError("Unsupported quantization config: "
                             f"bits={config.weight_bits}, sym={config.is_sym}")

        config.quant_type = config.TYPE_MAP[(config.weight_bits,
                                             config.is_sym)]
    elif config.get_name() == "gptq":
        if config.weight_bits not in [2, 3, 4, 8]:
            raise ValueError(
                "Currently, only 2/3/4/8-bit weight quantization is "
                f"supported for GPTQ, but got {config.weight_bits} bits.")


def get_dynamic_override(
    config: QuantizationConfig,
    layer_name: str,
    key: Optional[str] = None,
    default_value: Union[int, bool,
                         None] = None) -> Union[dict, int, bool, None]:
    for pattern, pattern_dict in config.dynamic.items():
        # Negative match: matched modules are excluded from quantized init
        if pattern.startswith("-:"):
            if re.match(pattern.removeprefix("-:"), layer_name):
                return False
        # Positive match: matched modules have quant properties overrides
        # base quant config
        elif re.match(pattern.removeprefix("+:"), layer_name):
            if key is None:
                return pattern_dict
            else:
                return pattern_dict.get(key, default_value)
    return default_value


def get_linear_quant_method(
    config: QuantizationConfig,
    layer: torch.nn.Module,
    prefix: str,
    linear_method_cls: type,
):
    cloned_config = deepcopy(config)
    parallel_lm_head_quantized = isinstance(
        layer, ParallelLMHead) and cloned_config.lm_head_quantized
    if isinstance(layer, LinearBase) or parallel_lm_head_quantized:
        # False = skip module, None = no override, else = Positive match
        if get_dynamic_override(  # noqa: E712
                cloned_config,  # noqa: E712
                layer_name=prefix) == False:  # noqa: E712
            if parallel_lm_head_quantized:
                return UnquantizedEmbeddingMethod()
            return UnquantizedLinearMethod()

        if prefix:
            # Dynamic per module/layer rules may override base config
            override_config(cloned_config, prefix=prefix)

        return linear_method_cls(cloned_config)
    return None
