#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
#
"""ModelSlim quantization configuration and model mappings for Ascend.

This module provides the AscendModelSlimConfig class for parsing quantization
configs generated by the ModelSlim tool, along with model-specific mappings.
"""

from types import MappingProxyType
from typing import Any, Dict, List, Mapping, Optional

import torch
from vllm.config import get_current_vllm_config
from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.linear import LinearBase
from vllm.model_executor.layers.quantization import \
    register_quantization_config
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig, QuantizeMethodBase)
from vllm.model_executor.layers.vocab_parallel_embedding import (
    UnquantizedEmbeddingMethod, VocabParallelEmbedding)
from vllm.model_executor.models.utils import WeightsMapper

from vllm_ascend.utils import ASCEND_QUANTIZATION_METHOD

from .methods import get_scheme_class

logger = init_logger(__name__)

# key: model_type
# value: orig_to_new_prefix
QUANT_MODEL_PREFIX_MAPPINGS: Dict[str, Dict[str, str]] = {
    "qwen3_vl_moe": {
        "visual.": "model.visual.",
        "language_model.lm_head.": "lm_head.",
        "language_model.model.": "model.language_model.",
    },
    "qwen3_vl_text": {
        "visual.": "model.visual.",
        "language_model.lm_head.": "lm_head.",
        "language_model.model.": "model.language_model.",
    },
}

# key: model_type
# value: dict of fused module name -> list of original module names
packed_modules_model_mapping: Dict[str, Dict[str, List[str]]] = {
    "qwen3_moe": {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
    },
    "deepseek_v2": {
        "gate_up_proj": ["gate_proj", "up_proj"],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
        "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"]
    },
    "deepseek_v3": {
        "gate_up_proj": ["gate_proj", "up_proj"],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
        "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"]
    },
    "pangu_ultra_moe": {
        "gate_up_proj": ["gate_proj", "up_proj"],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
        "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"]
    },
    "kimi_k2": {
        "gate_up_proj": ["gate_proj", "up_proj"],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
        "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"]
    },
    "deepseek_v32": {
        "gate_up_proj": ["gate_proj", "up_proj"],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
        "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"]
    },
    # NOTE 1.The quantized MTP layer of deepseek on the NPU is not quantized;
    # NOTE 2.The description file generated by the current msmodelslim tool does not have
    # MTP layer info. Please manually add it and set the value to FLOAT.
    "deepseek_mtp": {
        "gate_up_proj": ["gate_proj", "up_proj"],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]
    },
    "pangu_ultra_moe_mtp": {
        "gate_up_proj": ["gate_proj", "up_proj"],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
        "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"]
    },
    "qwen3_next": {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": ["gate_proj", "up_proj"],
        "in_proj": ["in_proj_qkvz", "in_proj_ba"],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]
    },
    "qwen2_5_vl": {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    },
    "qwen3_vl_moe": {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
    },
    "glm4_moe": {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]
    },
    "longcat_flash": {
        "gate_up_proj": ["gate_proj", "up_proj"],
        "experts":
        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
        "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"]
    },
    "minimax_m2": {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "experts": ["experts.0.w1", "experts.0.w2", "experts.0.w3"]
    }
}


def get_packed_modules_mapping(model_type: str) -> Dict[str, List[str]]:
    """Get packed modules mapping for a model type.
    
    Args:
        model_type: The model type string (e.g., "deepseek_v3").
        
    Returns:
        Dictionary mapping fused module names to their component module names.
        Returns empty dict if model_type is not found.
    """
    return packed_modules_model_mapping.get(model_type, {})


def get_prefix_mapping(model_type: str) -> Dict[str, str]:
    """Get prefix mapping for a model type.
    
    Args:
        model_type: The model type string (e.g., "qwen3_vl_moe").
        
    Returns:
        Dictionary mapping original prefixes to new prefixes.
        Returns empty dict if model_type is not found.
    """
    return QUANT_MODEL_PREFIX_MAPPINGS.get(model_type, {})


def get_linear_quant_type(
        quant_description: Dict[str, Any], prefix: str,
        packed_modules_mapping: Dict[str, Any]) -> Optional[str]:
    """Determine the quantization type for a linear layer.
    
    Args:
        quant_description: The quantization description dictionary.
        prefix: The layer prefix.
        packed_modules_mapping: Mapping for packed/fused modules.
        
    Returns:
        The quantization type string (e.g., "W8A8_DYNAMIC").
    """
    proj_name = prefix.split(".")[-1]
    if proj_name in packed_modules_mapping:
        quant_type = None
        shard_prefixes = [
            prefix.replace(proj_name, shard_proj_name)
            for shard_proj_name in packed_modules_mapping[proj_name]
        ]
        for shard_prefix in shard_prefixes:
            shard_quant_type = quant_description[shard_prefix + '.weight']

            if quant_type is None:
                quant_type = shard_quant_type
            elif shard_quant_type != quant_type:
                raise ValueError(
                    f"Not all shards of {prefix} are quantized with same quant type."
                    f"Shard {proj_name} uses {shard_quant_type}, but another shard"
                    f"use {quant_type}. Please check quantization config.")
    else:
        quant_type = quant_description[prefix + '.weight']
    return quant_type


def get_quant_type_for_layer(
        quant_description: Dict[str, Any],
        prefix: str,
        layer_type: str,
        packed_modules_mapping: Optional[Dict[str,
                                              Any]] = None) -> Optional[str]:
    """Determine the quantization type for a layer.
    
    Args:
        quant_description: The quantization description dictionary.
        prefix: The layer prefix.
        layer_type: The type of layer ("linear", "moe", "attention").
        packed_modules_mapping: Mapping for packed/fused modules.
        
    Returns:
        The quantization type string (e.g., "W8A8_DYNAMIC").
    """
    if packed_modules_mapping is None:
        packed_modules_mapping = dict()
    # Attention
    if layer_type == "attention" and 'fa_quant_type' in quant_description.keys(
    ):
        return quant_description['fa_quant_type']
    # Linear / MoE
    return get_linear_quant_type(quant_description, prefix,
                                 packed_modules_mapping)


def create_scheme_for_layer(
        quant_description: Dict[str, Any],
        prefix: str,
        layer_type: str,
        packed_modules_mapping: Optional[Dict[str, Any]] = None):
    """Create a quantization scheme instance for a layer.
    
    Args:
        quant_description: The quantization description dictionary.
        prefix: The layer prefix.
        layer_type: The type of layer ("linear", "moe", "attention").
        packed_modules_mapping: Mapping for packed/fused modules.
        
    Returns:
        An instance of the appropriate quantization scheme class.
    """
    logger.info_once("Using the vLLM Ascend modelslim Quantization now!")
    quant_type = get_quant_type_for_layer(quant_description, prefix,
                                          layer_type, packed_modules_mapping)

    if quant_type is None:
        raise ValueError(
            f"Could not determine quantization type for layer {prefix}.")

    # Use registry to get scheme class
    scheme_cls = get_scheme_class(quant_type, layer_type)
    if scheme_cls is not None:
        return scheme_cls()

    raise NotImplementedError(
        f"Currently, vLLM Ascend doesn't support {quant_type} for {layer_type}."
    )


@register_quantization_config(ASCEND_QUANTIZATION_METHOD)
class AscendModelSlimConfig(QuantizationConfig):
    """Config class for Ascend ModelSlim quantization.

    This class is a general class that parses quantization configs
    that are supported on Ascend hardware, specifically for models
    quantized using the ModelSlim tool.
    """

    def __init__(self, quant_config: Dict[str, Any]):
        super().__init__()
        self.quant_description = quant_config
        # TODO(whx): remove this adaptation after adding "shared_head"
        # to prefix of DeepSeekShareHead in vLLM.
        extra_quant_dict = {}
        for k in self.quant_description.keys():
            if "shared_head" in k:
                new_k = k.replace(".shared_head.", ".")
                extra_quant_dict[new_k] = self.quant_description[k]
            if "weight_packed" in k:
                new_k = k.replace("weight_packed", "weight")
                extra_quant_dict[new_k] = self.quant_description[k]
        self.quant_description.update(extra_quant_dict)

    def __repr__(self) -> str:
        return "AscendModelSlimConfig:\n" + super().__repr__()

    @classmethod
    def get_name(cls) -> str:
        return ASCEND_QUANTIZATION_METHOD

    @classmethod
    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
        return [torch.int8, torch.float16, torch.bfloat16]

    @classmethod
    def get_min_capability(cls) -> int:
        raise NotImplementedError(
            "Ascend hardware dose not support \"get_min_capability\" feature.")

    @classmethod
    def get_config_filenames(cls) -> List[str]:
        return ["quant_model_description.json"]

    @classmethod
    def from_config(cls, config: Dict[str, Any]) -> "AscendModelSlimConfig":
        return cls(config)

    @classmethod
    def override_quantization_method(cls, hf_quant_cfg,
                                     user_quant) -> Optional[str]:
        if hf_quant_cfg is not None:
            quant_method = hf_quant_cfg.get("quant_method", None)
            if not quant_method and torch.npu.is_available():
                return ASCEND_QUANTIZATION_METHOD
        return None

    def quant_prefix_mapper(self, model_type: str, prefix: str) -> str:
        # TODO (Levi-JQ): will be removed when QuantizationConfig.apply_vllm_mapper is implemented
        prefix_mapping = QUANT_MODEL_PREFIX_MAPPINGS.get(model_type)
        if prefix_mapping:
            hf_to_vllm_mapper = WeightsMapper(
                orig_to_new_prefix=prefix_mapping)
            return hf_to_vllm_mapper._map_name(prefix)
        return prefix

    def get_quant_method(self, layer: torch.nn.Module,
                         prefix: str) -> Optional["QuantizeMethodBase"]:
        from .method_adapters import (AscendEmbeddingMethod, AscendFusedMoEMethod,
                               AscendKVCacheMethod, AscendLinearMethod)

        vllm_config = get_current_vllm_config()
        model_type = vllm_config.model_config.hf_config.model_type

        if model_type in ["minimax", "minimax_m2"]:
            # Adapt to Minimax architecture: update layer names to MoE convention
            prefix = prefix.replace("mlp", "block_sparse_moe")
            # Normalize the prefix by stripping specific expert indices (e.g., 'experts.0' -> 'experts')
            parts = prefix.split('.')
            if "experts" in parts and len(parts) > 2:
                exp_idx = parts.index("experts")
                if exp_idx + 1 < len(parts) and parts[exp_idx + 1].isdigit():
                    parts = parts[:exp_idx + 1]
                    prefix = ".".join(parts)

        if model_type in packed_modules_model_mapping:
            self.packed_modules_mapping = packed_modules_model_mapping[
                model_type]
        prefix = self.quant_prefix_mapper(model_type, prefix)
        from vllm.attention.layer import Attention
        if prefix.startswith("language_model"):
            prefix = prefix.split('.', 1)[-1]
        if isinstance(layer, LinearBase):
            if self.is_layer_skipped_ascend(prefix,
                                            self.packed_modules_mapping):
                # Delayed import to avoid circular import
                from vllm_ascend.ops.linear import \
                    AscendUnquantizedLinearMethod
                return AscendUnquantizedLinearMethod()
            scheme = create_scheme_for_layer(self.quant_description, prefix,
                                             "linear",
                                             self.packed_modules_mapping)
            return AscendLinearMethod(scheme)
        elif isinstance(layer, Attention) and \
            'fa_quant_type' in self.quant_description.keys() and \
            self.quant_description['fa_quant_type'] is not None:
            scheme = create_scheme_for_layer(self.quant_description, prefix,
                                             "attention",
                                             self.packed_modules_mapping)
            return AscendKVCacheMethod(scheme)
        elif isinstance(layer, FusedMoE):
            if self.is_layer_skipped_ascend(prefix,
                                            self.packed_modules_mapping):
                # Delayed import to avoid circular import
                from vllm_ascend.ops.fused_moe.fused_moe import \
                    AscendUnquantizedFusedMoEMethod
                return AscendUnquantizedFusedMoEMethod(layer.moe_config)
            scheme = create_scheme_for_layer(self.quant_description, prefix,
                                             "moe",
                                             self.packed_modules_mapping)
            return AscendFusedMoEMethod(scheme, layer.moe_config)
        elif isinstance(layer, VocabParallelEmbedding):
            if self.is_layer_skipped_ascend(prefix,
                                            self.packed_modules_mapping):
                return UnquantizedEmbeddingMethod()
            scheme = create_scheme_for_layer(self.quant_description, prefix,
                                             "linear",
                                             self.packed_modules_mapping)
            return AscendEmbeddingMethod(scheme)
        return None

    def is_layer_skipped_ascend(
        self,
        prefix: str,
        fused_mapping: Mapping[str, List[str]] = MappingProxyType({})):
        # adapted from vllm.model_executor.layers.quantization.utils.quant_utils.is_layer_skipped
        proj_name = prefix.split(".")[-1]
        if proj_name in fused_mapping:
            shard_prefixes = [
                prefix.replace(proj_name, shard_proj_name)
                for shard_proj_name in fused_mapping[proj_name]
            ]

            is_skipped = None
            for shard_prefix in shard_prefixes:
                is_shard_skipped = self.quant_description[shard_prefix +
                                                          '.weight'] == "FLOAT"

                if is_skipped is None:
                    is_skipped = is_shard_skipped
                elif is_shard_skipped != is_skipped:
                    raise ValueError(
                        f"Detected some but not all shards of {prefix} "
                        "are quantized. All shards of fused layers "
                        "to have the same precision.")
        else:
            is_skipped = self.quant_description[prefix + '.weight'] == "FLOAT"

        assert is_skipped is not None
        return is_skipped

    def get_scaled_act_names(self) -> List[str]:
        return []
