import gradio as gr

import json
import math
from backend import get_message_single, get_message_spam, send_single, send_spam, tokenizer
from defaults import (
    ADDRESS_BETTERTRANSFORMER,
    ADDRESS_VANILLA,
    defaults_bt_single,
    defaults_bt_spam,
    defaults_vanilla_single,
    defaults_vanilla_spam,
    BATCH_SIZE,
)

import datasets
import torch

def dispatch_single(input_model_single, address_input_vanilla, address_input_bettertransformer):
    result_vanilla = send_single(input_model_single, address_input_vanilla)
    result_bettertransformer = send_single(input_model_single, address_input_bettertransformer)
    
    return result_vanilla, result_bettertransformer

def dispatch_spam(input_n_spam, address_input_vanilla, address_input_bettertransformer):
    input_n_spam = int(input_n_spam)
    assert input_n_spam <= len(data)

    inp = data.shuffle().select(range(input_n_spam))

    result_vanilla = send_spam(inp, address_input_vanilla)
    result_bettertransformer = send_spam(inp, address_input_bettertransformer)
    
    return result_vanilla, result_bettertransformer

def dispatch_spam_artif(input_n_spam_artif, sequence_length, padding_ratio, address_input_vanilla, address_input_bettertransformer):    
    sequence_length = int(sequence_length)
    input_n_spam_artif = int(input_n_spam_artif)
    
    inp_tokens = torch.randint(tokenizer.vocab_size - 1, (sequence_length,)) + 1

    n_pads = max(int(padding_ratio * len(inp_tokens)), 1)
    inp_tokens[- n_pads:] = 0

    inp_tokens[0] = 101
    inp_tokens[- n_pads - 1] = 102
    #inp_tokens = inp_tokens.unsqueeze(0).repeat(BATCH_SIZE, 1)
    
    attention_mask = torch.zeros((sequence_length,), dtype=torch.int64)
    attention_mask[:- n_pads] = 1
    
    str_input = json.dumps({
        "input_ids": inp_tokens.cpu().tolist(),
        "attention_mask": attention_mask.cpu().tolist(),
        "pre_tokenized": True,
    })
    
    input_dataset = datasets.Dataset.from_dict(
        {"sentence": [str_input for _ in range(input_n_spam_artif)]}
    )
    
    result_vanilla = send_spam(input_dataset, address_input_vanilla)
    result_bettertransformer = send_spam(input_dataset, address_input_bettertransformer)

    return result_vanilla, result_bettertransformer
  
TTILE_IMAGE = """
<div
    style="
        display: block;
        margin-left: auto;
        margin-right: auto;
        width: 50%;
    "
>
<img src="https://huggingface.co/spaces/fxmarty/bettertransformer-demo/resolve/main/header.webp"/>
</div>
"""

TITLE = """
<div
    style="
        display: inline-flex;
        align-items: center;
        text-align: center;
        max-width: 1400px;
        gap: 0.8rem;
        font-size: 2.2rem;
    "
>
<h1 style="font-weight: 500; margin-bottom: 10px; margin-top: 10px;">
    Speed up your inference and support more workload with PyTorch's BetterTransformer 🤗
</h1>
</div>
"""

with gr.Blocks() as demo:
    gr.HTML(TTILE_IMAGE)
    gr.HTML(TITLE)

    gr.Markdown(
        """
    Let's try out TorchServe + BetterTransformer!

    BetterTransformer is a stable feature made available with [PyTorch 1.13](https://pytorch.org/blog/PyTorch-1.13-release/) allowing to use a fastpath execution for encoder attention blocks.

    As a one-liner, you can convert your 🤗 Transformers models to use BetterTransformer thanks to the [🤗 Optimum](https://huggingface.co/docs/optimum/main/en/index) library:

    ```
    from optimum.bettertransformer import BetterTransformer

    better_model = BetterTransformer.transform(model)
    ```

    This Space is a demo of an **end-to-end** deployement of PyTorch eager-mode models, both with and without BetterTransformer. The goal is to see what are the benefits server-side and client-side of using BetterTransformer.
    
    ## Inference using...
    """
    )

    with gr.Row():
        with gr.Column(scale=50):
            gr.Markdown("### Vanilla Transformers + TorchServe")
        with gr.Column(scale=50):
            gr.Markdown("### BetterTransformer + TorchServe")
        
    address_input_vanilla = gr.Textbox(
        max_lines=1, label="ip vanilla", value=ADDRESS_VANILLA, visible=False
    )

    address_input_bettertransformer = gr.Textbox(
        max_lines=1,
        label="ip bettertransformer",
        value=ADDRESS_BETTERTRANSFORMER,
        visible=False,
    )

    input_model_single = gr.Textbox(
        max_lines=1,
        label="Text",
        value="Expectations were low, enjoyment was high",
    )

    btn_single = gr.Button("Send single text request")
    with gr.Row():
        with gr.Column(scale=50):
            output_single_vanilla = gr.Markdown(
                label="Output single vanilla",
                value=get_message_single(**defaults_vanilla_single),
            )
        with gr.Column(scale=50):
            output_single_bt = gr.Markdown(
                label="Output single bt", value=get_message_single(**defaults_bt_single)
            )

    btn_single.click(
        fn=dispatch_single,
        inputs=[input_model_single, address_input_vanilla, address_input_bettertransformer],
        outputs=[output_single_vanilla, output_single_bt],
    )

    input_n_spam_artif = gr.Number(
        label="Number of inputs to send",
        value=8,
    )
    sequence_length = gr.Number(
        label="Sequence length (in tokens)",
        value=128,
    )
    padding_ratio = gr.Number(
        label="Padding ratio",
        value=0.5,
    )
    btn_spam_artif = gr.Button(
        "Spam text requests (using artificial data)"
    )
    with gr.Row():
        with gr.Column(scale=50):
            output_spam_vanilla_artif = gr.Markdown(
                label="Output spam vanilla",
                value=get_message_spam(**defaults_vanilla_spam),
            )
        with gr.Column(scale=50):
            output_spam_bt_artif = gr.Markdown(
                label="Output spam bt", value=get_message_spam(**defaults_bt_spam)
            )

    btn_spam_artif.click(
        fn=dispatch_spam_artif,
        inputs=[input_n_spam_artif, sequence_length, padding_ratio, address_input_vanilla, address_input_bettertransformer],
        outputs=[output_spam_vanilla_artif, output_spam_bt_artif],
    )


demo.queue(concurrency_count=1)
demo.launch()