{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ff2a984-b8b2-4a69-89cf-0d16da2393c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import tempfile\n",
    "from functools import partial\n",
    "import random\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "from flax.training.common_utils import shard, shard_prng_key\n",
    "from flax.jax_utils import replicate\n",
    "import wandb\n",
    "from dalle_mini.model import CustomFlaxBartForConditionalGeneration\n",
    "from vqgan_jax.modeling_flax_vqgan import VQModel\n",
    "from transformers import BartTokenizer, CLIPProcessor, FlaxCLIPModel\n",
    "from dalle_mini.text import TextNormalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92f4557c-fd7f-4edc-81c2-de0b0a10c270",
   "metadata": {},
   "outputs": [],
   "source": [
    "wandb_runs = ['rjf3rycy']\n",
    "VQGAN_REPO, VQGAN_COMMIT_ID = 'dalle-mini/vqgan_imagenet_f16_16384', None\n",
    "normalize_text = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93b2e24b-f0e5-4abe-a3ec-0aa834cc3bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 8\n",
    "num_images = 128\n",
    "top_k = 8\n",
    "text_normalizer = TextNormalizer() if normalize_text else None\n",
    "padding_item = 'NONE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a045827-3461-4499-8959-38d173bc4e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = random.randint(0, 2**32-1)\n",
    "key = jax.random.PRNGKey(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6a878fa-4bf5-4978-abb5-e235841d765b",
   "metadata": {},
   "outputs": [],
   "source": [
    "vqgan = VQModel.from_pretrained(VQGAN_REPO, revision=VQGAN_COMMIT_ID)\n",
    "clip = FlaxCLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
    "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4927529a-8828-4150-bc76-e1b60d8dee62",
   "metadata": {},
   "outputs": [],
   "source": [
    "clip_params = replicate(clip.params)\n",
    "vqgan_params = replicate(vqgan.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e57797ab-0b3a-4490-be58-03d8d1c23fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('samples.csv', newline='', encoding='utf8') as f:\n",
    "    reader = csv.DictReader(f)\n",
    "    samples = []\n",
    "    for row in reader:\n",
    "        samples.append(row)\n",
    "    # make list multiple of batch_size by adding elements\n",
    "    samples_to_add = [{'Caption':padding_item, 'Theme':padding_item}] * (-len(samples) % batch_size)\n",
    "    samples.extend(samples_to_add)\n",
    "    # reshape\n",
    "    samples = [samples[i:i+batch_size] for i in range(0, len(samples), batch_size)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f75b2869-fc25-4f56-b937-e97bbb712ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c48525c9-447a-4430-81d7-4b699f545638",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2c629e9-1a82-40c6-a260-ca1780c19a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "api = wandb.Api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ffb1d09-bd1c-4f57-9ae5-3eda6f7d3a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: iterate on runs\n",
    "wandb_run = wandb_runs[0]\n",
    "functions_pmapped = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3e02d9d-4ee1-49e7-a7bc-4d8b139e9614",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    versions = api.artifact_versions(type_name='bart_model', name=f'dalle-mini/dalle-mini/model-{wandb_run}', per_page=10000)\n",
    "except:\n",
    "    versions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8026e63-9e73-472c-9440-5e742c614901",
   "metadata": {},
   "outputs": [],
   "source": [
    "versions, len(versions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ead44aee-52d5-4ca2-8984-c4d267d9e72a",
   "metadata": {},
   "outputs": [],
   "source": [
    "versions[0].version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfd48de9-6022-444f-8b12-05cba8fad071",
   "metadata": {},
   "outputs": [],
   "source": [
    "artifact = versions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4db848c1-2bb5-432c-a732-1c6d0636e172",
   "metadata": {},
   "outputs": [],
   "source": [
    "version = int(artifact.version[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25fac577-146d-4e62-a3ea-f0baea79ef83",
   "metadata": {},
   "outputs": [],
   "source": [
    "version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0d7ed17-7abb-4a31-ab3c-a12b9039a570",
   "metadata": {},
   "outputs": [],
   "source": [
    "# retrieve training run\n",
    "training_run = api.run(f'dalle-mini/dalle-mini/{wandb_run}')\n",
    "config = training_run.config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b9393c6-0a3c-46a8-ba27-ba37982b0009",
   "metadata": {},
   "outputs": [],
   "source": [
    "# see summary metrics\n",
    "training_run.summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e784a43-626d-4e8d-9e47-a23775b2f35f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# retrieve inference run details\n",
    "def get_last_version_inference(run_id):\n",
    "    try:\n",
    "        inference_run = api.run(f'dalle-mini/dalle-mini/inference-{run_id}')\n",
    "        return inference_run.summary.get('_step', None)\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93b8d869-1658-4fa4-a401-2b91f8ac7a11",
   "metadata": {},
   "outputs": [],
   "source": [
    "last_version_inference = get_last_version_inference(wandb_run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8324835e-fd94-408e-b106-138be308480b",
   "metadata": {},
   "outputs": [],
   "source": [
    "if last_version_inference is None:\n",
    "    assert version == 0\n",
    "elif last_version_inference >= version:\n",
    "    print(f'Version {version} has already been logged')\n",
    "else:\n",
    "    assert version == last_version_inference + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ce9d2d3-aea3-4d5e-834a-c5caf85dd117",
   "metadata": {},
   "outputs": [],
   "source": [
    "run = wandb.init(job_type='inference', config=config, id=f'inference-{wandb_run}', resume='allow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffe392c9-36d2-4aaa-a1b3-a827e348c1ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_f.cleanup\n",
    "tmp_f = tempfile.TemporaryDirectory()\n",
    "tmp = tmp_f.name\n",
    "#TODO: use context manager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "562036ed-dc86-48af-90b1-9c18383b3552",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove tmp\n",
    "tmp_f.cleanup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "299db1bb-fbe6-4d79-a48f-89893f8ed809",
   "metadata": {},
   "outputs": [],
   "source": [
    "artifact = run.use_artifact(artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d71481bf-98aa-42cb-b7e2-545d13ae4309",
   "metadata": {},
   "outputs": [],
   "source": [
    "# only download required files\n",
    "for f in ['config.json', 'flax_model.msgpack', 'merges.txt', 'special_tokens_map.json', 'tokenizer.json', 'tokenizer_config.json', 'vocab.json']:\n",
    "    artifact.get_path(f).download(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f8ad8dd-da8f-40f9-b438-e43b779d637c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we verify all the files are present\n",
    "from pathlib import Path\n",
    "list(Path(tmp).glob('*'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b715c32-e757-4cb0-9912-ff90238b9f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = BartTokenizer.from_pretrained(tmp)\n",
    "model = CustomFlaxBartForConditionalGeneration.from_pretrained(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "320823c9-124a-4fc3-a12c-8c015a128285",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_params = replicate(model.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1cc9993-1bfc-4ec6-a004-c056189c42ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to generate encoded images\n",
    "# we should generate this function only once per run\n",
    "if not functions_pmapped:\n",
    "    @partial(jax.pmap, axis_name=\"batch\")\n",
    "    def p_generate(tokenized_prompt, key, params):\n",
    "        return model.generate(\n",
    "            **tokenized_prompt,\n",
    "            do_sample=True,\n",
    "            num_beams=1,\n",
    "            prng_key=key,\n",
    "            params=params\n",
    "        )\n",
    "    \n",
    "    @partial(jax.pmap, axis_name=\"batch\")\n",
    "    def p_decode(indices, params):\n",
    "        return vqgan.decode_code(indices, params=params)\n",
    "    \n",
    "    @partial(jax.pmap, axis_name=\"batch\")\n",
    "    def p_clip(inputs):\n",
    "        logits = clip(**inputs).logits_per_image\n",
    "        return logits\n",
    "    \n",
    "    functions_pmapped = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a24b903-777b-4e3d-817c-00ed613a7021",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: loop over samples\n",
    "batch = samples[0]\n",
    "prompts = [x['Caption'] for x in batch]\n",
    "processed_prompts = [text_normalizer(x) for x in prompts] if normalize_text else prompts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d77aa785-dc05-4070-aba2-aa007524d20b",
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_prompts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95db38fb-8948-4814-98ae-c172ca7c6d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "repeated_prompts = processed_prompts * jax.device_count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e948ba9e-3700-4e87-926f-580a10f3e5cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_prompt = tokenizer(repeated_prompts, return_tensors='jax', padding='max_length', truncation=True, max_length=128).data\n",
    "tokenized_prompt = shard(tokenized_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30d96812-fc17-4acf-bb64-5fdb8d0cd313",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized_prompt['input_ids'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92ea034b-2649-4d18-ab6d-877ed04ae5c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "images = []\n",
    "for i in range(num_images // jax.device_count()):\n",
    "    key, subkey = jax.random.split(key, 2)\n",
    "    \n",
    "    encoded_images = p_generate(tokenized_prompt, shard_prng_key(subkey), model_params)\n",
    "    encoded_images = encoded_images.sequences[..., 1:]\n",
    "    \n",
    "    decoded_images = p_decode(encoded_images, vqgan_params)\n",
    "    decoded_images = decoded_images.clip(0., 1.).reshape((-1, 256, 256, 3))\n",
    "    \n",
    "    for img in decoded_images:\n",
    "        images.append(Image.fromarray(np.asarray(img * 255, dtype=np.uint8)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84d52f30-44c9-4a74-9992-fb2578f19b90",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beb594f9-5b91-47fe-98bd-41e68c6b1d73",
   "metadata": {},
   "outputs": [],
   "source": [
    "images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb135190-64e5-44af-b416-e688b034da44",
   "metadata": {},
   "outputs": [],
   "source": [
    "images[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d78a0d92-72c2-4f82-a6ab-b3f5865dd863",
   "metadata": {},
   "outputs": [],
   "source": [
    "clip_inputs = processor(text=prompts, images=images, return_tensors='np', padding='max_length', max_length=77, truncation=True).data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89ff78a6-bfa4-44d9-ad66-07a4a68b4352",
   "metadata": {},
   "outputs": [],
   "source": [
    "# each shard will have one prompt\n",
    "clip_inputs['input_ids'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cda8984-049c-4c87-96ad-7b0412750656",
   "metadata": {},
   "outputs": [],
   "source": [
    "# each shard needs to have the images corresponding to a specific prompt\n",
    "clip_inputs['pixel_values'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a044e8f-be29-404b-b6c7-8f2395c5efc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "images_per_prompt_indices = np.asarray(range(0, len(images), batch_size))\n",
    "images_per_prompt_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a6c61b3-12e0-45d8-b39a-830288324d3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7318e67e-4214-46f9-bf60-6d139d4bd00f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reorder so each shard will have correct images\n",
    "clip_inputs['pixel_values'] = jnp.concatenate(list(clip_inputs['pixel_values'][images_per_prompt_indices + i] for i in range(batch_size)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90c949a2-8e2a-4905-b6d4-92038f1704b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "clip_inputs = shard(clip_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58fa836e-5ebb-45e7-af77-ab10646dfbfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "logits = p_clip(clip_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd7a3f91-3a1f-4a0a-8b3e-3c926cd367fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "logits.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa406db7-0a21-4e4b-9890-4c7aece4280c",
   "metadata": {},
   "outputs": [],
   "source": [
    "logits = logits.reshape(-1, num_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c359a8c-2c27-4e68-8775-371857397723",
   "metadata": {},
   "outputs": [],
   "source": [
    "logits.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a56b9f28-dd91-4382-bc47-11e89fda1254",
   "metadata": {},
   "outputs": [],
   "source": [
    "logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bed8167-0a6d-46c1-badf-8bdc20b93c31",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_idx = logits.argsort()[:, -top_k:][..., ::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "188c5333-6b8c-4a17-8cc8-15651c77ef99",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "babd22b3-e773-467d-8bbb-f0323f57a44b",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "columns = ['Caption', 'Theme'] + [f'Image {i+1}' for i in range(top_k)] + [f'Score {i+1}' for i in range(top_k)]\n",
    "logits = jax.device_get(logits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75976c9f-dea5-48e3-8920-55a1bbfd91c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, (idx, scores, sample) in enumerate(zip(top_idx, logits, batch)):\n",
    "    if sample['Caption'] == padding_item: continue\n",
    "    cur_images = [images[x] for x in images_per_prompt_indices + i]\n",
    "    top_images = [wandb.Image(cur_images[x]) for x in idx]\n",
    "    top_scores = [scores[x] for x in idx]\n",
    "    results.append([sample['Caption'], sample['Theme']] + top_images + top_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bf40461-99d3-4d36-b7cc-e0129a3c9053",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = wandb.Table(columns=columns, data=results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1c04761-1016-47e9-925c-3a9ec6fec95a",
   "metadata": {},
   "outputs": [],
   "source": [
    "wandb.finish()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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