import React from 'react';
import { ConfigDoc } from '@/types';

const docs: { [key: string]: ConfigDoc } = {
  'config.name': {
    title: 'Training Name',
    description: (
      <>
        The name of the training job. This name will be used to identify the job in the system and will the the filename
        of the final model. It must be unique and can only contain alphanumeric characters, underscores, and dashes. No
        spaces or special characters are allowed.
      </>
    ),
  },
  gpuids: {
    title: 'GPU ID',
    description: (
      <>
        This is the GPU that will be used for training. Only one GPU can be used per job at a time via the UI currently.
        However, you can start multiple jobs in parallel, each using a different GPU.
      </>
    ),
  },
  'config.process[0].trigger_word': {
    title: 'Trigger Word',
    description: (
      <>
        Optional: This will be the word or token used to trigger your concept or character.
        <br />
        <br />
        When using a trigger word, If your captions do not contain the trigger word, it will be added automatically the
        beginning of the caption. If you do not have captions, the caption will become just the trigger word. If you
        want to have variable trigger words in your captions to put it in different spots, you can use the{' '}
        <code>{'[trigger]'}</code> placeholder in your captions. This will be automatically replaced with your trigger
        word.
        <br />
        <br />
        Trigger words will not automatically be added to your test prompts, so you will need to either add your trigger
        word manually or use the
        <code>{'[trigger]'}</code> placeholder in your test prompts as well.
      </>
    ),
  },
  'config.process[0].model.name_or_path': {
    title: 'Name or Path',
    description: (
      <>
        The name of a diffusers repo on Huggingface or the local path to the base model you want to train from. The
        folder needs to be in diffusers format for most models. For some models, such as SDXL and SD1, you can put the
        path to an all in one safetensors checkpoint here.
      </>
    ),
  },
  'datasets.control_path': {
    title: 'Control Dataset',
    description: (
      <>
        The control dataset needs to have files that match the filenames of your training dataset. They should be
        matching file pairs. These images are fed as control/input images during training.
      </>
    ),
  },
  'datasets.num_frames': {
    title: 'Number of Frames',
    description: (
      <>
        This sets the number of frames to shrink videos to for a video dataset. If this dataset is images, set this to 1
        for one frame. If your dataset is only videos, frames will be extracted evenly spaced from the videos in the
        dataset.
        <br />
        <br />
        It is best to trim your videos to the proper length before training. Wan is 16 frames a second. Doing 81 frames
        will result in a 5 second video. So you would want all of your videos trimmed to around 5 seconds for best
        results.
        <br />
        <br />
        Example: Setting this to 81 and having 2 videos in your dataset, one is 2 seconds and one is 90 seconds long,
        will result in 81 evenly spaced frames for each video making the 2 second video appear slow and the 90second
        video appear very fast.
      </>
    ),
  },
  'datasets.do_i2v': {
    title: 'Do I2V',
    description: (
      <>
        For video models that can handle both I2V (Image to Video) and T2V (Text to Video), this option sets this
        dataset to be trained as an I2V dataset. This means that the first frame will be extracted from the video and
        used as the start image for the video. If this option is not set, the dataset will be treated as a T2V dataset.
      </>
    ),
  },
  'datasets.flip': {
    title: 'Flip X and Flip Y',
    description: (
      <>
        You can augment your dataset on the fly by flipping the x (horizontal) and/or y (vertical) axis. Flipping a single axis will effectively double your dataset.
        It will result it training on normal images, and the flipped versions of the images. This can be very helpful, but keep in mind it can also
        be destructive. There is no reason to train people upside down, and flipping a face can confuse the model as a person's right side does not
        look identical to their left side. For text, obviously flipping text is not a good idea.
        <br />
        <br />
        Control images for a dataset will also be flipped to match the images, so they will always match on the pixel level.
      </>
    ),
  },
  'train.unload_text_encoder': {
    title: 'Unload Text Encoder',
    description: (
      <>
        Unloading text encoder will cache the trigger word and the sample prompts and unload the text encoder from the
        GPU. Captions in for the dataset will be ignored
      </>
    ),
  },
  'train.cache_text_embeddings': {
    title: 'Cache Text Embeddings',
    description: (
      <>
        <small>(experimental)</small>
        <br />
        Caching text embeddings will process and cache all the text embeddings from the text encoder to the disk. The
        text encoder will be unloaded from the GPU. This does not work with things that dynamically change the prompt
        such as trigger words, caption dropout, etc.
      </>
    ),
  },
  'model.multistage': {
    title: 'Stages to Train',
    description: (
      <>
        Some models have multi stage networks that are trained and used separately in the denoising process. Most
        common, is to have 2 stages. One for high noise and one for low noise. You can choose to train both stages at
        once or train them separately. If trained at the same time, The trainer will alternate between training each
        model every so many steps and will output 2 different LoRAs. If you choose to train only one stage, the
        trainer will only train that stage and output a single LoRA.
      </>
    ),
  },
  'train.switch_boundary_every': {
    title: 'Switch Boundary Every',
    description: (
      <>
        When training a model with multiple stages, this setting controls how often the trainer will switch between
        training each stage.
        <br />
        <br />
        For low vram settings, the model not being trained will be unloaded from the gpu to save memory. This takes some
        time to do, so it is recommended to alternate less often when using low vram. A setting like 10 or 20 is
        recommended for low vram settings.
        <br />
        <br />
        The swap happens at the batch level, meaning it will swap between a gradient accumulation steps. To train both
        stages in a single step, set them to switch every 1 step and set gradient accumulation to 2.
      </>
    ),
  },
  'train.force_first_sample': {
    title: 'Force First Sample',
    description: (
      <>
        This option will force the trainer to generate samples when it starts. The trainer will normally only generate a first sample
        when nothing has been trained yet, but will not do a first sample when resuming from an existing checkpoint. This option
        forces a first sample every time the trainer is started. This can be useful if you have changed sample prompts and want to see
        the new prompts right away.
      </>
    ),
  },
};

export const getDoc = (key: string | null | undefined): ConfigDoc | null => {
  if (key && key in docs) {
    return docs[key];
  }
  return null;
};

export default docs;
