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#
# 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.

import dataclasses
import json
import os

import torch


@dataclasses.dataclass
class ModelConfig:
    model_name: str

    # Transformer
    num_layers: int = None  # Number of transformer layers.
    hidden_size: int = None  # Transformer hidden size.
    ffn_hidden_size: int = None  # Transformer Feed-Forward Network hidden size
    num_attention_heads: int = None  # Number of transformer attention heads.
    num_query_groups: int = 1  # Number of query groups, which used for GQA
    kv_channels: int = None  # Projection weights dimension in multi-head attention
    layernorm_epsilon: float = 1e-6  # Epsilon for layer norm and RMS norm.
    apply_layernorm_1p: bool = False  # Adjust LayerNorm weights which improves numerical stability.
    x_rescale_factor: float = 1.0
    half_channel_vae: bool = False
    params_dtype: torch.dtype = None

    # Embedding
    patch_size: int = 2  # (latent) patch size for DiT patch embedding layer
    t_patch_size: int = 1  # (latent) patch size for t dim patch embedding layer
    in_channels: int = 4  # latent input channel for DiT
    out_channels: int = 4  # latent output channel for DiT
    cond_hidden_ratio: float = 0.25
    caption_channels: int = 4096
    caption_max_length: int = 800
    xattn_cond_hidden_ratio: float = 1.0
    cond_gating_ratio: float = 1.0
    gated_linear_unit: bool = False


@dataclasses.dataclass
class RuntimeConfig:
    # Inference settings such as cfg, kv range, clean t, etc.
    cfg_number: int = None  # Number of CFG
    cfg_t_range: list = dataclasses.field(
        default_factory=lambda: [0, 0.0217, 0.1000, 0.3, 0.999]
    )  # CFG t-range of each scales
    prev_chunk_scales: list = dataclasses.field(
        default_factory=lambda: [1.5, 1.5, 1.5, 1.5, 1.5]
    )  # CFG scales of previous chunks
    text_scales: list = dataclasses.field(default_factory=lambda: [7.5, 7.5, 7.5, 7.5, 7.5])  # CFG scales of text

    noise2clean_kvrange: list = dataclasses.field(default_factory=list)  # Range of kv for noise2clean chunks
    clean_chunk_kvrange: int = -1  # Range of kv for clean chunks
    clean_t: float = 1.0  # timestep for clean chunks

    # Video settings
    seed: int = 1234  # Random seed used for python, numpy, pytorch, and cuda.
    num_frames: int = 128
    video_size_h: int = None
    video_size_w: int = None
    num_steps: int = 64  # Number of steps for the diffusion model
    window_size: int = 4  # Window size for the diffusion model
    fps: int = 24  # Frames per second
    chunk_width: int = 6  # Clip width for the diffusion model

    # Checkpoint, includes t5, vae, dit, etc.
    t5_pretrained: str = None  # Path to load pretrained T5 model.
    t5_device: str = "cuda"  # Device for T5 model to run on.
    vae_pretrained: str = None  # Path to load pretrained VAE model.
    scale_factor: float = 0.18215  # Scale factor for the vae
    temporal_downsample_factor: int = 4  # Temporal downsample factor for the vae
    load: str = None  # Directory containing a model checkpoint.


@dataclasses.dataclass
class EngineConfig:
    # Parallism strategy
    distributed_backend: str = "nccl"  # Choices: ["nccl", "gloo"]
    distributed_timeout_minutes: int = 10  # Timeout minutes for torch.distributed.
    pp_size: int = 1  # Degree of pipeline model parallelism.
    cp_size: int = 1  # Degree of context parallelism.
    cp_strategy: str = "none"  # Choices: ["none", "cp_ulysses", "cp_shuffle_overlap"]
    ulysses_overlap_degree: int = 1  # Overlap degree for Ulysses

    # Quantization
    fp8_quant: bool = False  # Enable 8-bit floating point quantization for model weights.

    # Distillation
    distill_nearly_clean_chunk_threshold: float = 0.3  # Threshold for distilling nearly clean chunks
    shortcut_mode: str = "8,16,16"  # Parameters for shortcut mode
    distill: bool = False  # Use distill mode

    # Optimization
    kv_offload: bool = False  # Use kv-offload algorithm
    enable_cuda_graph: bool = False  # Enable CUDA graph for video generation


@dataclasses.dataclass
class MagiConfig:
    model_config: ModelConfig
    runtime_config: RuntimeConfig
    engine_config: EngineConfig

    @classmethod
    def _check_missing_fields(cls, config_dict: dict, required_fields: list):
        actual_fields = set(config_dict.keys())
        missing_fields = set(required_fields) - actual_fields
        if missing_fields:
            raise ValueError(f"Missing fields in the configuration file: {', '.join(missing_fields)}")

    @classmethod
    def _create_nested_config(cls, config_dict: dict, config_name: str, config_cls):
        nested_config_dict = config_dict.get(config_name, {})
        cls._check_missing_fields(nested_config_dict, config_cls.__dataclass_fields__.keys())
        return config_cls(**nested_config_dict)

    @classmethod
    def _create_config_from_dict(cls, config_dict: dict):
        cls._check_missing_fields(config_dict, cls.__dataclass_fields__.keys())

        # Create nested configs
        model_config = cls._create_nested_config(config_dict, "model_config", ModelConfig)
        runtime_config = cls._create_nested_config(config_dict, "runtime_config", RuntimeConfig)
        engine_config = cls._create_nested_config(config_dict, "engine_config", EngineConfig)

        return cls(model_config=model_config, runtime_config=runtime_config, engine_config=engine_config)

    @classmethod
    def from_json(cls, json_path: str):
        def simple_json_decoder(dct):
            dtype_map = {"torch.bfloat16": torch.bfloat16, "torch.float16": torch.float16, "torch.float32": torch.float32}
            if 'params_dtype' in dct:
                dct['params_dtype'] = dtype_map[dct['params_dtype']]
            return dct

        with open(json_path, "r") as f:
            config_dict = json.load(f, object_hook=simple_json_decoder)
        magi_config = cls._create_config_from_dict(config_dict)

        def post_validation(magi_config):
            if magi_config.engine_config.fp8_quant or magi_config.engine_config.distill:
                assert (
                    magi_config.runtime_config.cfg_number == 1
                ), "Please set `cfg_number: 1` in config.json for distill or quant model"
            else:
                assert magi_config.runtime_config.cfg_number == 3, "Please set `cfg_number: 3` in config.json for base model"

        post_validation(magi_config)

        return magi_config

    def to_json(self, json_path: str):
        class SimpleJSONEncoder(json.JSONEncoder):
            def default(self, obj):
                if isinstance(obj, torch.dtype):
                    return str(obj)
                return super().default(obj)

        # Ensure the directory exists
        os.makedirs(os.path.dirname(json_path), exist_ok=True)

        config_dict = {
            "model_config": dataclasses.asdict(self.model_config),
            "runtime_config": dataclasses.asdict(self.runtime_config),
            "engine_config": dataclasses.asdict(self.engine_config),
        }
        with open(json_path, "w") as f:
            json.dump(config_dict, f, indent=4, cls=SimpleJSONEncoder)