app_bis.py

import logging
import os
import re

import time
from io import BytesIO

import gradio as gr
import requests
import torch
import transformers
from accelerate.utils import get_max_memory

from joblib import Parallel, delayed
from PIL import Image
from transformers import AutoTokenizer

from m4.models.vbloom import configuration_vbloom, modeling_vbloom
from m4.models.vgpt2 import configuration_vgpt2, modeling_vgpt2
from m4.models.vgpt_neo import configuration_vgpt_neo, modeling_vgpt_neo
from m4.models.vllama import configuration_vllama, modeling_vllama
from m4.models.vopt import configuration_vopt, modeling_vopt
from m4.training.packing import image_attention_mask_for_packed_input_ids, incremental_to_binary_attention_mask
from m4.training.utils import build_image_transform


logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()

CURRENT_MODEL = "tr_209_ift_mixture_opt_step-2000"

MAX_TRIES = 3
TOKENIZER_FAST = True
MAX_SEQ_LEN = 1024
model, tokenizer = None, None


MODEL_TO_DISPLAY_NAME = {
    "tr_199_w_xattn_opt_step-65000": "VLlama - tr_199_w_xattn_opt_step-65000",
    "tr_201_sft_on_lrv_opt_step-15000": "VLlama - tr_201_sft_on_lrv_opt_step-15000",
    "tr_202bis_ift_llava_all_unfrozen_opt_step-14128": "VLlama - tr_202bis_ift_llava_all_unfrozen_opt_step-14128",
    "tr_203_ift_m3it_opt_step-50000": "VLlama - tr_203_ift_m3it_opt_step-50000",
    "tr_205_sft_ultrachat_opt_step-20000": "VLlama - tr_205_sft_ultrachat_opt_step-20000",
    "tr_207_ift_svit_opt_step-14627": "VLlama - tr_207_ift_svit_opt_step-14627",
    "tr_209_ift_mixture_opt_step-2000": "VLlama - tr_209_ift_mixture_opt_step-2000",
}
MODEL_TO_MODEL_CLASS = {
    "tr_199_w_xattn_opt_step-65000": "VLlamaForCausalLM",
    "tr_201_sft_on_lrv_opt_step-15000": "VLlamaForCausalLM",
    "tr_202bis_ift_llava_all_unfrozen_opt_step-14128": "VLlamaForCausalLM",
    "tr_203_ift_m3it_opt_step-50000": "VLlamaForCausalLM",
    "tr_205_sft_ultrachat_opt_step-20000": "VLlamaForCausalLM",
    "tr_207_ift_svit_opt_step-14627": "VLlamaForCausalLM",
    "tr_209_ift_mixture_opt_step-2000": "VLlamaForCausalLM",
}

MODEL_TO_CONFIG_CLASS = {
    "tr_199_w_xattn_opt_step-65000": "VLlamaConfig",
    "tr_201_sft_on_lrv_opt_step-15000": "VLlamaConfig",
    "tr_202bis_ift_llava_all_unfrozen_opt_step-14128": "VLlamaConfig",
    "tr_203_ift_m3it_opt_step-50000": "VLlamaConfig",
    "tr_205_sft_ultrachat_opt_step-20000": "VLlamaConfig",
    "tr_207_ift_svit_opt_step-14627": "VLlamaConfig",
    "tr_209_ift_mixture_opt_step-2000": "VLlamaConfig",
}


def load_tokenizer_model(model_name, model_class):
    tokenizer = AutoTokenizer.from_pretrained(
        model_name,
        use_fast=TOKENIZER_FAST,
        use_auth_token=os.getenv("HF_AUTH_TOKEN", True),  # `use_fast=False` for 1B3 OPT, True for all the other models
    )
    tokenizer.padding_side = "left"
    config_class = MODEL_TO_CONFIG_CLASS[model_name.split("/")[-1]]

    # assert tokenizer.is_fast

    supported_custom_modules = {
        "vgpt2": modeling_vgpt2,
        "vbloom": modeling_vbloom,
        "vgptneo": modeling_vgpt_neo,
        "vopt": modeling_vopt,
        "vllama": modeling_vllama,
    }
    supported_custom_configs = {
        "vgpt2": configuration_vgpt2,
        "vbloom": configuration_vbloom,
        "vgptneo": configuration_vgpt_neo,
        "vopt": configuration_vopt,
        "vllama": configuration_vllama,
    }
    parent_config_class = (
        [v for k, v in supported_custom_configs.items() if k in model_class.lower()] + [transformers]
    )[0]
    parent_model_class = (
        [v for k, v in supported_custom_modules.items() if k in model_class.lower()] + [transformers]
    )[0]
    config_class = getattr(parent_config_class, config_class)
    model_class = getattr(parent_model_class, model_class)
    config = config_class.from_pretrained(model_name, use_auth_token=os.getenv("HF_AUTH_TOKEN", True))
    max_memory_map = get_max_memory()
    for key in max_memory_map.keys():
        if key != "cpu":
            # Get this in GB
            max_memory_map[key] = max_memory_map[key] // (1024 * 1024 * 1024)
            # Decrease 2 for Pytorch overhead and 2 for the forward to be safe
            max_memory_map[key] = f"{max_memory_map[key] - 4} GiB"
    model = model_class.from_pretrained(
        model_name,
        use_auth_token=os.getenv("HF_AUTH_TOKEN", True),
        device_map="auto",
        offload_folder="./offload",
        torch_dtype=config.torch_dtype,
        max_memory=max_memory_map,
    )
    model.eval()
    print("Current device map:", model.hf_device_map)
    print("Model default generation config:", model.generation_config)
    # TODO: the device_map looks very inefficien right now. that could be improved
    # it typically looks like that
    # {
    #     'model.embed_tokens': 0,
    #     'model.vision_model': 0,
    #     'model.layers.0': 0,
    #     'model.layers.1': 0,
    #     'model.layers.2': 0,
    #     'model.layers.3': 0,
    #     'model.layers.4': 0,
    #     'model.layers.5': 0,
    #     'model.layers.6': 1,
    #     'model.layers.7': 1,
    #     'model.layers.8': 1,
    #     'model.layers.9': 1,
    #     'model.layers.10': 1,
    #     'model.layers.11': 1,
    #     'model.layers.12': 1,
    #     'model.layers.13': 1,
    #     'model.layers.14': 1,
    #     'model.layers.15': 1,
    #     'model.layers.16': 1,
    #     'model.layers.17': 2,
    #     'model.layers.18': 2,
    #     'model.layers.19': 2,
    #     'model.layers.20': 2,
    #     'model.layers.21': 2,
    #     'model.layers.22': 2,
    #     'model.layers.23': 2,
    #     'model.layers.24': 2,
    #     'model.layers.25': 2,
    #     'model.layers.26': 2,
    #     'model.layers.27': 2,
    #     'model.layers.28': 3,
    #     'model.layers.29': 3,
    #     'model.layers.30': 3,
    #     'model.layers.31': 3,
    #     'model.gated_cross_attn_layers.0': 3,
    #     'model.gated_cross_attn_layers.1': 3,
    #     'model.gated_cross_attn_layers.2': 3,
    #     'model.gated_cross_attn_layers.3': 3,
    #     'model.gated_cross_attn_layers.4': 3,
    #     'model.gated_cross_attn_layers.5': 3,
    #     'model.gated_cross_attn_layers.6': 3,
    #     'model.gated_cross_attn_layers.7': 3,
    #     'model.gated_cross_attn_layers.8': 4,
    #     'model.gated_cross_attn_layers.9': 4,
    #     'model.gated_cross_attn_layers.10': 4,
    #     'model.gated_cross_attn_layers.11': 4,
    #     'model.gated_cross_attn_layers.12': 4,
    #     'model.gated_cross_attn_layers.13': 4,
    #     'model.gated_cross_attn_layers.14': 4,
    #     'model.gated_cross_attn_layers.15': 4,
    #     'model.norm': 4,
    #     'lm_head': 4
    # }    which means there is a lot of things going around between the gated cross attention layers and the LM layers...
    return tokenizer, model


MODEL_TO_SPACE_MAPPING = {}
IS_MAIN_SPACE = CURRENT_MODEL not in MODEL_TO_MODEL_CLASS
if IS_MAIN_SPACE:
    for model in MODEL_TO_MODEL_CLASS:
        MODEL_TO_SPACE_MAPPING[model] = gr.Blocks.load(
            name=f"spaces/HuggingFaceM4/{model}", api_key=os.getenv("HF_AUTH_TOKEN", True)
        )
else:
    model_path = f"HuggingFaceM4/{CURRENT_MODEL}"
    tokenizer, model = load_tokenizer_model(model_path, MODEL_TO_MODEL_CLASS[CURRENT_MODEL])


def fetch_images(url_images):
    images = []
    for url in url_images:
        if isinstance(url, str):
            images.append(Image.open(BytesIO(requests.get(url, stream=True).content)))
        else:
            images.append(url)
    return images


def model_generation(
    prompt,
    images,
    tokenizer,
    model,
    temperature,
    no_repeat_ngram_size,
    max_new_tokens,
    min_length,
    ban_tokens,
    forced_eos_token_id,
    eos_tokens,
    force_words,
    length_penalty,
    repetition_penalty,
    hide_special_tokens,
    stop_generation,
    decoding_strategy,
    num_beams,
    top_k,
    top_p,
    penalty_alpha,
):
    # Preparing inputs
    tokens = tokenizer(
        [prompt],
        truncation=True,
        max_length=MAX_SEQ_LEN,
        padding=True,
        add_special_tokens=False,
    )

    input_ids = torch.tensor([[tokenizer.bos_token_id] + tokens.input_ids[0]])
    attention_mask = torch.tensor([[1] + tokens.attention_mask[0]])

    image_attention_mask = [
        incremental_to_binary_attention_mask(
            image_attention_mask_for_packed_input_ids(input_ids[0].unsqueeze(0), tokenizer)[0], num_classes=len(images)
        )
    ]

    image_transform = build_image_transform(eval=True)
    pixel_values = [torch.stack([image_transform(img) for img in images])]

    input_ids = input_ids.to(0)
    attention_mask = attention_mask.to(0)
    pixel_values = torch.stack(pixel_values).to(0)
    image_attention_mask = torch.cat(image_attention_mask, 0).to(0)

    # Excluding some words from the generation
    bad_words_ids = None
    ban_tokens = ban_tokens.replace("\\n", "\n")
    bad_words = ban_tokens.split(";")
    if len(bad_words) > 0:
        bad_words_ids = tokenizer(bad_words, add_special_tokens=False).input_ids

    # Forcing some words in the generation
    force_words_ids = None
    if force_words != "":
        force_words = force_words.replace("\\n", "\n")
        force_words = force_words.split(";")
        if len(force_words) > 0:
            force_words_ids = tokenizer(force_words, add_special_tokens=False).input_ids

    # eos_token_ids = None
    # if eos_tokens != "":
    #     eos_tokens = eos_tokens.replace("\\n", "\n")
    #     eos_tokens = eos_tokens.split(";")
    #     if len(eos_tokens) > 0:
    #         eos_token_ids = []
    #         for eos_token in eos_tokens:
    #             tokenized_eos_token = tokenizer(eos_token, add_special_tokens=False).input_ids
    #             if len(tokenized_eos_token) > 1:
    #                 raise ValueError(
    #                     f"eos_tokens should be one token, here {eos_token} is {len(tokenized_eos_token)} tokens:"
    #                     f" {tokenized_eos_token}"
    #                 )
    #             eos_token_ids += tokenized_eos_token

    # if forced_eos_token_id and eos_token_ids is None:
    #     raise ValueError("You can't use forced_eos_token_id without eos_tokens")
    # elif forced_eos_token_id:
    #     forced_eos_token_id = eos_token_ids
    # else:
    #     forced_eos_token_id = None

    # Inputs
    input_args = {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "pixel_values": pixel_values,
        "image_attention_mask": image_attention_mask,
    }
    # Common parameters to all decoding strategies
    # This documentation is useful to read: https://huggingface.co/docs/transformers/main/en/generation_strategies
    generation_args = {
        "temperature": temperature,
        "no_repeat_ngram_size": no_repeat_ngram_size,
        "max_new_tokens": max_new_tokens,
        "min_length": min_length,
        "bad_words_ids": bad_words_ids,
        # "forced_eos_token_id": forced_eos_token_id,
        "force_words_ids": force_words_ids,
        "length_penalty": length_penalty,
        "repetition_penalty": repetition_penalty,
        "eos_token_id": tokenizer.eos_token_id,
    }

    assert decoding_strategy in [
        "greedy",
        "beam_search",
        "beam_sampling",
        "sampling_top_k",
        "sampling_top_p",
        "contrastive_sampling",
    ]
    if decoding_strategy == "greedy":
        pass
    elif decoding_strategy == "beam_search":
        generation_args["num_beams"] = num_beams
        assert generation_args["num_beams"] > 1
    elif decoding_strategy == "beam_sampling":
        generation_args["num_beams"] = num_beams
        generation_args["do_sample"] = True
        assert generation_args["num_beams"] > 1
    elif decoding_strategy == "sampling_top_k":
        generation_args["do_sample"] = True
        generation_args["top_k"] = top_k
    elif decoding_strategy == "sampling_top_p":
        generation_args["do_sample"] = True
        generation_args["top_p"] = top_p
    elif decoding_strategy == "contrastive_sampling":
        generation_args["do_sample"] = True
        generation_args["penalty_alpha"] = penalty_alpha
        generation_args["top_k"] = top_k

    generated_tokens = model.generate(
        **input_args,
        **generation_args,
    )
    tokens = tokenizer.convert_ids_to_tokens(generated_tokens[0])
    decoded_skip_special_tokens = repr(
        tokenizer.batch_decode(generated_tokens, skip_special_tokens=hide_special_tokens)[0]
    )
    decoded = repr(tokenizer.batch_decode(generated_tokens)[0])
    logger.info(
        "Result: \n"
        f"Prompt: `{prompt}`\n"
        f"Tokens ids from prompt + generation: `{generated_tokens[0].tolist()}`\n"
        f"Tokens (converted) from prompt + generation: `{tokens}`\n"
        f"String decoded with skipped special tokens: `{decoded_skip_special_tokens}`\n"
        f"String decoded: `{decoded}`\n"
        f"Generation mode: `{decoding_strategy}`\n"
        f"Generation parameters: `{generation_args}`\n"
    )

    original_prompt = generated_tokens[:, : input_ids.shape[-1]]
    actual_generated_tokens = generated_tokens[:, input_ids.shape[-1] :]

    if stop_generation:
        # Additional stopping criteria: generating <image> token, <end_of_text> token or <begin_of_text> token
        assert tokenizer.additional_special_tokens[-1] == "<image>"
        image_token_id = tokenizer.additional_special_tokens_ids[-1]
        end_of_text_token_id = tokenizer.eos_token_id
        begin_of_text_token_id = tokenizer.bos_token_id

        image_token_ids = (actual_generated_tokens == image_token_id).nonzero(as_tuple=True)[1]
        end_of_text_token_ids = (actual_generated_tokens == end_of_text_token_id).nonzero(as_tuple=True)[1]
        begin_of_text_token_ids = (actual_generated_tokens == begin_of_text_token_id).nonzero(as_tuple=True)[1]

        first_end_token = min(
            image_token_ids[0] if len(image_token_ids) else len(actual_generated_tokens[0]),
            end_of_text_token_ids[0] if len(end_of_text_token_ids) else len(actual_generated_tokens[0]),
            begin_of_text_token_ids[0] if len(begin_of_text_token_ids) else len(actual_generated_tokens[0]),
        )
    else:
        first_end_token = len(actual_generated_tokens[0])

    actual_generated_tokens = actual_generated_tokens[:, :first_end_token]
    displayed_tokens = torch.cat([original_prompt, actual_generated_tokens], dim=-1)
    generated_text = tokenizer.batch_decode(displayed_tokens, skip_special_tokens=hide_special_tokens)[0]
    return generated_text


def model_inference(
    files,
    prompt,
    temperature,
    no_repeat_ngram_size,
    max_new_tokens,
    min_length,
    ban_tokens,
    forced_eos_token_id,
    eos_tokens,
    force_words,
    length_penalty,
    repetition_penalty,
    hide_special_tokens,
    stop_generation,
    decoding_strategy,
    num_beams,
    top_k,
    top_p,
    penalty_alpha,
):
    if isinstance(files, str) and len(files) == 0:
        files = None

    prompt = prompt.strip()
    prompt = prompt.replace("\\n", "\n")
    file_idx = 0
    url_images = re.findall(r"<image(.*?)>", prompt)
    for idx, url_image in enumerate(url_images):
        if len(url_image) == 0:
            url_images[idx] = Image.open(files[file_idx].name if hasattr(files[file_idx], "name") else files[file_idx])
            file_idx += 1
        else:
            prompt = prompt.replace(url_image, "")
            url_images[idx] = url_images[idx][1:]
    images = fetch_images(url_images)

    global model, tokenizer

    generated_text = model_generation(
        prompt=prompt,
        images=images,
        tokenizer=tokenizer,
        model=model,
        temperature=temperature,
        no_repeat_ngram_size=no_repeat_ngram_size,
        max_new_tokens=max_new_tokens,
        min_length=min_length,
        ban_tokens=ban_tokens,
        forced_eos_token_id=forced_eos_token_id,
        eos_tokens=eos_tokens,
        force_words=force_words,
        length_penalty=length_penalty,
        repetition_penalty=repetition_penalty,
        hide_special_tokens=hide_special_tokens,
        stop_generation=stop_generation,
        decoding_strategy=decoding_strategy,
        num_beams=num_beams,
        top_k=top_k,
        top_p=top_p,
        penalty_alpha=penalty_alpha,
    )
    return generated_text.strip()


def try_model_inference(
    model,
    files,
    prompt,
    temperature,
    no_repeat_ngram_size,
    max_new_tokens,
    min_length,
    ban_tokens,
    forced_eos_token_id,
    eos_tokens,
    force_words,
    length_penalty,
    repetition_penalty,
    hide_special_tokens,
    stop_generation,
    decoding_strategy,
    num_beams,
    top_k,
    top_p,
    penalty_alpha,
):
    count = 0
    while count < MAX_TRIES:
        try:
            return MODEL_TO_SPACE_MAPPING[model](
                files,
                prompt,
                temperature,
                no_repeat_ngram_size,
                max_new_tokens,
                min_length,
                ban_tokens,
                forced_eos_token_id,
                eos_tokens,
                force_words,
                length_penalty,
                repetition_penalty,
                hide_special_tokens,
                stop_generation,
                decoding_strategy,
                num_beams,
                top_k,
                top_p,
                penalty_alpha,
                api_name="model_inference",
            )
        except KeyError:
            # Gradio return {'error': None} some times.
            time.sleep(3)
            count += 1
            pass


def all_model_inference(
    prompt,
    temperature,
    no_repeat_ngram_size,
    max_new_tokens,
    min_length,
    ban_tokens,
    forced_eos_token_id,
    eos_tokens,
    force_words,
    length_penalty,
    repetition_penalty,
    hide_special_tokens,
    stop_generation,
    decoding_strategy,
    num_beams,
    top_k,
    top_p,
    penalty_alpha,
):
    outputs = []
    print(
        prompt,
        temperature,
        no_repeat_ngram_size,
        max_new_tokens,
        min_length,
        ban_tokens,
        forced_eos_token_id,
        eos_tokens,
        force_words,
        length_penalty,
        repetition_penalty,
        hide_special_tokens,
        stop_generation,
        decoding_strategy,
        num_beams,
        top_k,
        top_p,
        penalty_alpha,
    )
    outputs = Parallel(n_jobs=len(MODEL_TO_SPACE_MAPPING), backend="threading")(
        delayed(try_model_inference)(
            model,
            os.path.join(os.path.dirname(__file__), "images", "bear.jpg"),
            prompt,
            temperature,
            no_repeat_ngram_size,
            max_new_tokens,
            min_length,
            ban_tokens,
            forced_eos_token_id,
            eos_tokens,
            force_words,
            length_penalty,
            repetition_penalty,
            hide_special_tokens,
            stop_generation,
            decoding_strategy,
            num_beams,
            top_k,
            top_p,
            penalty_alpha,
        )
        for model in MODEL_TO_SPACE_MAPPING
    )
    if len(outputs) == 1:
        outputs = outputs[0]
    return outputs


examples = [
    [
        None,
        """This is a conversation between a human, User, and an intelligent visual AI, Assistant. User sends images, and Assistant answers the questions from the user. The assistant should be friendly, informative and should not change the topic if it's not asked to.

Here's an example of a conversation:
User:<fake_token_around_image><image:https://hips.hearstapps.com/hmg-prod/images/cute-photos-of-cats-in-grass-1593184777.jpg><fake_token_around_image>Describe this image.
Assistant: Two kittens are cuddling in the center of the photograph. They are surrounded by tall, bright green grass, and the background is blurred.
User:<fake_token_around_image><image:https://cdn.pixabay.com/photo/2017/09/25/13/12/puppy-2785074_1280.jpg><fake_token_around_image>How about this image? Can you describe it too?
Assistant: A dog is lying on the floor, looking at the camera. It is looking directly at you, tilting its head to the side. The dog has a white body and brown patches on its face and ears. Its eyes are brown. Its nose is black, and it has long, floppy ears, short legs, white paws, long fur, big eyes, and black eyebrows.
User: What kind of breed is it?
Assistant: It looks like a Cavalier King Charles Spaniel. These breeds are known for their gentle, affectionate nature. Their long ears and big, brown eyes give them a sweet, expressive face. Cavaliers are small dogs weighing between 13 and 20 pounds. They have a silky, medium-length coat that comes in various colors, including black and tan, tricolor, red and white, or black.
---
User:<fake_token_around_image><image:https://m.media-amazon.com/images/M/MV5BMjE4MTcwMTM1Nl5BMl5BanBnXkFtZTcwMTIwMzMzMw@@._V1_.jpg><fake_token_around_image>Describe all of the parts of this image.
Assistant:""",
        1.0,
        0,
        256,
        10,
        "<image>;<fake_token_around_image>",
        False,
        "</s>",
        "",
        1.0,
        1.0,
        False,
        True,
        "greedy",
        1,
        50,
        0.5,
        0.95,
    ],
    #     [
    #         None,
    #         """This is a conversation between a human, User, and an intelligent visual AI, Bot. User sends images, and Bot answer the questions from the user.
    # User: <fake_token_around_image><image:https://m.media-amazon.com/images/M/MV5BMjE4MTcwMTM1Nl5BMl5BanBnXkFtZTcwMTIwMzMzMw@@._V1_.jpg><fake_token_around_image>
    # Describe this image.
    # Bot:""",
    #         1,
    #         2,
    #         64,
    #         10,
    #         "<image>;<fake_token_around_image>;User;user;Bot;bot;Question;question;Answer;answer;\n",
    #         False,
    #         False,
    #         True,
    #     ],
    #     [
    #         None,
    #         """This is a conversation between a human, User, and an intelligent visual AI, Bot. User sends images, and Bot answer the questions from the user.
    # User: <fake_token_around_image><image:https://i.redd.it/hsktcp4nv1g01.jpg><fake_token_around_image>
    # Why do people find this image funny?
    # Bot:""",
    #         1,
    #         2,
    #         64,
    #         10,
    #         "<image>;<fake_token_around_image>;User;user;Bot;bot;Question;question;Answer;answer;\n",
    #         False,
    #         False,
    #         True,
    #     ],
    #     [
    #         None,
    #         """This is a conversation between a human, User, and an intelligent visual AI, Bot. User sends images, and Bot answer the questions from the user.
    # User: <fake_token_around_image><image:https://pbs.twimg.com/media/FooD7oyakAIU5_Q?format=jpg&name=large><fake_token_around_image>
    # Describe what's in this image.
    # Bot:""",
    #         1,
    #         2,
    #         64,
    #         10,
    #         "<image>;<fake_token_around_image>;User;user;Bot;bot;Question;question;Answer;answer;\n",
    #         False,
    #         False,
    #         True,
    #     ],
    #     [
    #         None,
    #         """This is a conversation between a human, User, and an intelligent visual AI, Bot. User sends images, and Bot answer the questions from the user.
    # User: <fake_token_around_image><image:https://www.tutorialride.com/images/non-verbal-analogy-questions/non-verbal-analogy-logical-reasoning-1.jpg><fake_token_around_image>
    # What's the correct answer? A, B, C or D?
    # Bot:""",
    #         1,
    #         2,
    #         64,
    #         10,
    #         "<image>;<fake_token_around_image>;User;user;Bot;bot;Question;question;Answer;answer;\n",
    #         False,
    #         False,
    #         True,
    #     ],
]


title = """<head><title><h1 align='center'>🔮✍️ Text generation with IDEFICS models 🦙📚</h1></title></head>"""


MSG_MAIN = """
# Text generation with Vllama models

### Help to write prompts:

Put the urls to the images inside the image tokens, it will be converted into the real image tokens. Put <fake_token_around_image> before and after each image token WITHOUT space. The texts \\n will be converted into real newline characters. See examples and additional details below.
"""
# MSG_DETAILS = """
# ### Additional details
# - if the model was trained with the template 1 (`\\n\\n<image>\\n\\n`), then `<fake_token_around_image>` will be replaced with `\\n\\n`. This is particularly useful if you are comparing the performance of different models trained with different templates.
# - special tokens are not automatically added to the prompt, so add them manually.
# - with the first template `\\n\\n<image>\\n\\n` , the sequence isn't necessary tokenized as `["\\n\\n", "<image>", "\\n\\n"]` to enforce this behavior, you can use the "Integrate image sequence as ids" parameter.
# """
# if ~IS_MAIN_SPACE:
#     MSG_DETAILS += (
#         "- alternatively, you can upload images and then directly specify them via \<image\> tag in the prompt."
#     )

with gr.Blocks() as demo:
    gr.HTML(title)
    gr.HTML("""<h3 align='center'>Help to write prompts:🙌</h3><br>
                <p>Put the urls to the images inside the image tokens,
                it will be converted into the real image tokens.
                Put <fake_token_around_image> before and after each
                image token WITHOUT space. The texts \\n will be
                converted into real newline characters.
                See examples and additional details below.""")

    #gr.HTML("<h3 align='center'>Help to write prompts:🙌</h3><br>Put the urls to the images inside the image tokens, it will be converted into the real image tokens. Put <fake_token_around_image> before and after each image token WITHOUT space. The texts \\n will be converted into real newline characters. See examples and additional details below.")
    #gr.Markdown(MSG_MAIN)
    #with gr.Row():
    #with gr.Column():
    gr.Markdown("## Input")
    with gr.Row():
        if not IS_MAIN_SPACE:
            images = gr.File(label="Images", file_count="multiple")
        prompt = gr.Textbox(label="Prompt", placeholder="Enter the prompt here", lines=5)

    #gr.Markdown("## Common parameters to all decoding strategy")
    with gr.Row():
        with gr.Accordion("Common parameters to all decoding strategy", open=False, elem_id="common_params"):
            temperature = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=1.0, label="Softmax temperature")
            no_repeat_ngram_size = gr.Slider(
                minimum=0,
                maximum=10,
                step=1,
                value=0,
                label="The size of an n-gram that cannot occur more than once (0=infinity)",
            )
            max_new_tokens = gr.Slider(
                minimum=0, maximum=512, step=1, value=256, label="Maximum number of new tokens to generate"
            )
            min_length = gr.Slider(
                minimum=0, maximum=512, step=1, value=16, label="Minimum length of the sequence to be generated"
            )
            ban_tokens = gr.Textbox(
                label='Tokens to prevent from being generated (separated by ";")',
                value="<image>;<fake_token_around_image>",
            )
            forced_eos_token_id = gr.Checkbox(label="Forced eos token id", value=False)
            eos_tokens = gr.Textbox(label="EOS tokens", value="</s>")
            force_words = gr.Textbox(label='Force words to be generated (separated by ";")', value="")
            length_penalty = gr.Slider(
                minimum=-1000,
                maximum=1000,
                step=0.1,
                value=1,
                label=(
                    "length_penalty > 0.0 promotes longer sequences, while length_penalty < 0.0 encourages shorter"
                    " sequences."
                ),
            )
            repetition_penalty = gr.Slider(
                minimum=0, maximum=10, step=0.01, value=1, label="repetition_penalty. CTRL paper suggests 1.2."
            )
            hide_special_tokens = gr.Checkbox(label="Hide special tokens in the text", value=False)
            stop_generation = gr.Checkbox(
                label="Stop generation when an image token, a bos or a eos token is generated", value=False
            )

        #gr.Markdown("## Decoding strategy and its specific parameters")
        with gr.Accordion("Decoding strategy and its specific parameters", open=False, elem_id="decoding_params"):
            decoding_strategy = gr.Dropdown(
                ["greedy", "beam_search", "beam_sampling", "sampling_top_k", "sampling_top_p", "contrastive_sampling"],
                label="Decoding strategy",
                value="greedy",
            )
            num_beams = gr.Slider(
                minimum=0,
                maximum=10,
                step=1,
                value=3,
                label="Beam size",
                info="Only used if `decoding_strategy` is `beam_search` or `beam_sampling`",
            )
            top_k = gr.Slider(
                minimum=0,
                maximum=500,
                step=1,
                value=50,
                label="Top k",
                info="Only used if `decoding_strategy` is `sampling_top_k` or `contrastive_sampling`",
            )
            top_p = gr.Slider(
                minimum=0,
                maximum=1,
                step=0.01,
                value=0.95,
                label="Top p",
                info="Only used if `decoding_strategy` is `sampling_top_p`",
            )
            penalty_alpha = gr.Slider(
                minimum=0,
                maximum=1,
                step=0.01,
                value=0.95,
                label="Penalty alpha",
                info="Only used if `decoding_strategy` is `contrastive_sampling`",
            )

    submit = gr.Button(label="Generate")

    #with gr.Column():
    with gr.Row():
        if IS_MAIN_SPACE:
            outputs = [
                gr.Textbox(label=MODEL_TO_DISPLAY_NAME[model], multiline=True, readonly=True)
                for model in MODEL_TO_MODEL_CLASS
            ]
            inference_func = all_model_inference
            inputs = [
                prompt,
                temperature,
                no_repeat_ngram_size,
                max_new_tokens,
                min_length,
                ban_tokens,
                forced_eos_token_id,
                eos_tokens,
                force_words,
                length_penalty,
                repetition_penalty,
                hide_special_tokens,
                stop_generation,
                decoding_strategy,
                num_beams,
                top_k,
                top_p,
                penalty_alpha,
            ]

            # examples = [example[1:] for example in examples]
        else:
            outputs = gr.Textbox(label="Generated text", interactive=False, lines=5)
            inference_func = model_inference
            inputs = [
                images,
                prompt,
                temperature,
                no_repeat_ngram_size,
                max_new_tokens,
                min_length,
                ban_tokens,
                forced_eos_token_id,
                eos_tokens,
                force_words,
                length_penalty,
                repetition_penalty,
                hide_special_tokens,
                stop_generation,
                decoding_strategy,
                num_beams,
                top_k,
                top_p,
                penalty_alpha,
            ]
    with gr.Row():
        gr.Examples(inputs=inputs, examples=examples)
        # gr.Markdown(MSG_DETAILS)

        submit.click(inference_func, inputs=inputs, outputs=outputs, api_name="model_inference")

demo.queue()
demo.launch()