# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo

# SPDX-License-Identifier: Apache-2.0

from sglang.multimodal_gen.runtime.pipelines_core import LoRAPipeline
from sglang.multimodal_gen.runtime.pipelines_core.composed_pipeline_base import (
    ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines_core.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines_core.stages import (
    InputValidationStage,
    TextEncodingStage,
)
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

# TODO(will): move PRECISION_TO_TYPE to better place

logger = init_logger(__name__)


def calculate_shift(
    image_seq_len,
    base_seq_len: int = 256,
    max_seq_len: int = 4096,
    base_shift: float = 0.5,
    max_shift: float = 1.15,
):
    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
    b = base_shift - m * base_seq_len
    mu = image_seq_len * m + b
    return mu


def prepare_mu(batch: Req, server_args: ServerArgs):
    height = batch.height
    width = batch.width
    vae_scale_factor = (
        server_args.pipeline_config.vae_config.arch_config.vae_scale_factor
    )
    image_seq_len = (int(height) // (vae_scale_factor * 2)) * (
        int(width) // (vae_scale_factor * 2)
    )

    mu = calculate_shift(
        image_seq_len,
        # hard code, since scheduler_config is not in PipelineConfig now
        256,
        4096,
        0.5,
        1.15,
    )
    return "mu", mu


class FluxPipeline(LoRAPipeline, ComposedPipelineBase):
    pipeline_name = "FluxPipeline"

    _required_config_modules = [
        "text_encoder",
        "text_encoder_2",
        "tokenizer",
        "tokenizer_2",
        "vae",
        "transformer",
        "scheduler",
    ]

    def create_pipeline_stages(self, server_args: ServerArgs):
        self.add_stage(InputValidationStage())

        self.add_stage(
            TextEncodingStage(
                text_encoders=[
                    self.get_module("text_encoder"),
                    self.get_module("text_encoder_2"),
                ],
                tokenizers=[
                    self.get_module("tokenizer"),
                    self.get_module("tokenizer_2"),
                ],
            ),
            "prompt_encoding_stage_primary",
        )

        self.add_standard_timestep_preparation_stage(prepare_extra_kwargs=[prepare_mu])
        self.add_standard_latent_preparation_stage()
        self.add_standard_denoising_stage()
        self.add_standard_decoding_stage()


EntryClass = FluxPipeline
