# coding=utf-8
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# 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.
"""Qwen2 model configuration"""

import torch
from dataclasses import dataclass, asdict
from enum import Enum

from transformers.configuration_utils import PretrainedConfig, layer_type_validation
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging

from transformers.models.qwen2.configuration_qwen2 import Qwen2Config

from quasar.kernel.configs import QuantType

logger = logging.get_logger(__name__)


@dataclass
class FP8Config:
    """
    Configuration for FP8 quantization.
    """

    float8_dtype: torch.dtype = torch.float8_e4m3fn
    quant_type: QuantType = QuantType.DIV
    layer_name: str = ""

    act_block_size: int = 16
    mm_block_size: int = 128

    training_mode: bool = True
    """
    If True, the linear layer will use high-precision weight.
    If False, the linear layer will use per-block quantized weight.
    """


class FP8Qwen2Config(Qwen2Config):
    model_type = "fp8_qwen2"
    fp8_config: FP8Config = FP8Config()
    model_name_orig: str = ""
    """Pass the name of the BF16 model"""

    def __init__(
        self,
        vocab_size=151936,
        hidden_size=4096,
        intermediate_size=22016,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=32,
        hidden_act="silu",
        max_position_embeddings=32768,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        tie_word_embeddings=False,
        rope_theta=10000.0,
        rope_scaling=None,
        use_sliding_window=False,
        sliding_window=4096,
        max_window_layers=28,
        layer_types=None,
        attention_dropout=0.0,
        # Customized configs begins here
        fp8_config=None,
        model_name_orig="",
        **kwargs,
    ):
        super().__init__(
            vocab_size=vocab_size,
            hidden_size=hidden_size,
            intermediate_size=intermediate_size,
            num_hidden_layers=num_hidden_layers,
            num_attention_heads=num_attention_heads,
            num_key_value_heads=num_key_value_heads,
            hidden_act=hidden_act,
            max_position_embeddings=max_position_embeddings,
            initializer_range=initializer_range,
            rms_norm_eps=rms_norm_eps,
            use_cache=use_cache,
            tie_word_embeddings=tie_word_embeddings,
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            use_sliding_window=use_sliding_window,
            sliding_window=sliding_window,
            max_window_layers=max_window_layers,
            layer_types=layer_types,
            attention_dropout=attention_dropout,
            **kwargs,
        )


        # Convert it from dict to FP8Config (dataclass)
        if fp8_config is not None:
            self.fp8_config = fp8_config if isinstance(fp8_config, FP8Config) else FP8Config(**fp8_config)
        else:
            self.fp8_config = FP8Config()

        self.model_name_orig = model_name_orig
        

    def to_dict(self):
        output = super().to_dict()
        if hasattr(self.fp8_config, "__dataclass_fields__"):
            cfg_dict = asdict(self.fp8_config)
            for k, v in cfg_dict.items():
                if isinstance(v, torch.dtype): # float8_dtype
                    cfg_dict[k] = str(v)  # save as 'torch.float8_e4m3fn'
                elif isinstance(v, Enum): # quant_type
                    cfg_dict[k] = v.name   # save as 'DIV'
            output["fp8_config"] = cfg_dict
        else:
            output["fp8_config"] = self.fp8_config
        return output


    @classmethod
    def from_dict(cls, config_dict, **kwargs):
        config = super().from_dict(config_dict, **kwargs)
        
        fp8_config = config_dict.get("fp8_config", {})
        for k, v in fp8_config.items():
            if k == "float8_dtype":
                assert v.startswith("torch."), f"Invalid float8_dtype: {v}"
                fp8_config[k] = getattr(torch, v[len("torch."):]) #
            elif k == "quant_type":
                fp8_config[k] = getattr(QuantType, v)
        config.fp8_config = FP8Config(**fp8_config)
        return config
    
    
__all__ = ["FP8Qwen2Config"]

FP8Qwen2Config.register_for_auto_class()