import json
import os
import shutil
import requests

import gradio as gr
from huggingface_hub import Repository
from text_generation import Client

from share_btn import community_icon_html, loading_icon_html, share_js, share_btn_css

HF_TOKEN = os.environ.get("HF_TOKEN", None)
API_URL = os.environ.get("API_URL")

with open("./HHH_prompt.txt", "r") as f:
    HHH_PROMPT = f.read() + "\n\n"

FIM_PREFIX = "<fim_prefix>"
FIM_MIDDLE = "<fim_middle>"
FIM_SUFFIX = "<fim_suffix>"

FIM_INDICATOR = "<FILL_HERE>"

FORMATS = """## Model formats

The model is pretrained on code and in addition to the pure code data it is formatted with special tokens. E.g. prefixes specifying the source of the file or special tokens separating code from a commit message. See below: 

### Chat mode
Chat mode prepends the [HHH prompt](https://gist.github.com/jareddk/2509330f8ef3d787fc5aaac67aab5f11#file-hhh_prompt-txt) from Anthropic to the request which conditions the model to be an assistant.

### Prefixes
Any combination of the three following prefixes can be found in pure code files:

```
<reponame>REPONAME<filename>FILENAME<gh_stars>STARS\ncode<|endoftext|>
```
STARS can be one of: 0, 1-10, 10-100, 100-1000, 1000+

### Commits
The commits data is formatted as follows:
```
<commit_before>code<commit_msg>text<commit_after>code<|endoftext|>
```

### Jupyter structure
Jupyter notebooks were both trained in form of Python scripts as well as the following structured format:
```
<start_jupyter><jupyter_text>text<jupyter_code>code<jupyter_output>output<jupyter_text>
```

### Issues
We also trained on GitHub issues using the following formatting:
```
<issue_start><issue_comment>text<issue_comment>...<issue_closed>
```

### Fill-in-the-middle
Fill in the middle requires rearranging the model inputs. The playground does this for you - all you need is to specify where to fill:
```
code before<FILL_HERE>code after
```
"""

theme = gr.themes.Monochrome(
    primary_hue="indigo",
    secondary_hue="blue",
    neutral_hue="slate",
    radius_size=gr.themes.sizes.radius_sm,
    font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"],
)

client = Client(
    API_URL,  #headers={"Authorization": f"Bearer {HF_TOKEN}"},
)

def generate(prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0, chat_mode=False):

    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)
    fim_mode = False
    
    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )
    if chat_mode:
        generate_kwargs.update({"stop_sequences": ["\nHuman", "\n-----"]})

    if chat_mode and FIM_INDICATOR in prompt:
        raise ValueError("Chat mode and FIM are mutually exclusive. Choose one or the other.")

    if chat_mode:
        chat_prompt = "Human: " + prompt + "\n\nAssistant:"
        prompt = HHH_PROMPT + chat_prompt
    
    if FIM_INDICATOR in prompt:
        fim_mode = True
        try:
            prefix, suffix = prompt.split(FIM_INDICATOR)
        except:
            raise ValueError(f"Only one {FIM_INDICATOR} allowed in prompt!")
        prompt = f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}"

    stream = client.generate_stream(prompt, **generate_kwargs)

    if fim_mode:
        output = prefix
    elif chat_mode:
        output = chat_prompt
    else:
        output = prompt

    previous_token = ""
    for response in stream:
        if fim_mode and response.token.text =="<|endoftext|>":
            output += (suffix + "\n" + response.token.text)
        elif chat_mode and response.token.text in ["Human", "-----"] and previous_token=="\n":
            return output
        else:
            output += response.token.text
        previous_token = response.token.text
        yield output
    return output


examples = [
    "def print_hello_world():",
    "def fibonacci(n):",
    "class TransformerDecoder(nn.Module):",
    "class ComplexNumbers:"
]


def process_example(args):
    for x in generate(args):
        pass
    return x

css = ".generating {visibility: hidden}" + share_btn_css

with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
    with gr.Column():
        gr.Markdown(
            """\
# BigCode - Playground

_Note:_ this is an internal playground - please do not share. The deployment can also change and thus the space not work as we continue development.\
"""

        )
        with gr.Row():
            with gr.Column(scale=3):
                instruction = gr.Textbox(placeholder="Enter your prompt here", label="Prompt", elem_id="q-input")
                submit = gr.Button("Generate", variant="primary")
                output = gr.Code(elem_id="q-output")
                
                with gr.Group(elem_id="share-btn-container"):
                    community_icon = gr.HTML(community_icon_html, visible=True)
                    loading_icon = gr.HTML(loading_icon_html, visible=True)
                    share_button = gr.Button("Share to community", elem_id="share-btn", visible=True)
                
                gr.Examples(
                    examples=examples,
                    inputs=[instruction],
                    cache_examples=False,
                    fn=process_example,
                    outputs=[output],
                )
                gr.Markdown(FORMATS)

            with gr.Column(scale=1):
                chat_mode = gr.Checkbox(
                    value=False,
                    label="Chat mode",
                    info="Uses Anthropic's HHH prompt to turn the model into an assistant."
                )
                temperature = gr.Slider(
                    label="Temperature",
                    value=0.2,
                    minimum=0.0,
                    maximum=2.0,
                    step=0.1,
                    interactive=True,
                    info="Higher values produce more diverse outputs",
                )
                max_new_tokens = gr.Slider(
                    label="Max new tokens",
                    value=256,
                    minimum=0,
                    maximum=8192,
                    step=64,
                    interactive=True,
                    info="The maximum numbers of new tokens",
                )
                top_p = gr.Slider(
                    label="Top-p (nucleus sampling)",
                    value=0.90,
                    minimum=0.0,
                    maximum=1,
                    step=0.05,
                    interactive=True,
                    info="Higher values sample more low-probability tokens",
                )
                repetition_penalty = gr.Slider(
                    label="Repetition penalty",
                    value=1.2,
                    minimum=1.0,
                    maximum=2.0,
                    step=0.05,
                    interactive=True,
                    info="Penalize repeated tokens",
                )

    submit.click(generate, inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty, chat_mode], outputs=[output])
    # instruction.submit(generate, inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty, chat_mode], outputs=[output])
    share_button.click(None, [], [], _js=share_js)
demo.queue(concurrency_count=16).launch(debug=True)