import os
import random
import uuid
import json
import time
import asyncio
import re
from threading import Thread

import gradio as gr
import spaces
import torch
import numpy as np
from PIL import Image
import edge_tts

from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TextIteratorStreamer,
    Qwen2VLForConditionalGeneration,
    AutoProcessor,
)
from transformers.image_utils import load_image
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Load text-only model and tokenizer for chat generation
model_id = "prithivMLmods/FastThink-0.5B-Tiny"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
model.eval()

# TTS Voices and processor for multimodal chat
TTS_VOICES = [
    "en-US-JennyNeural",  # @tts1
    "en-US-GuyNeural",    # @tts2
]
MODEL_ID_VL = "prithivMLmods/Qwen2-VL-OCR-2B-Instruct" 
processor = AutoProcessor.from_pretrained(MODEL_ID_VL, trust_remote_code=True)
model_m = Qwen2VLForConditionalGeneration.from_pretrained(
    MODEL_ID_VL,
    trust_remote_code=True,
    torch_dtype=torch.float16
).to("cuda").eval()

# A helper function to convert text to speech via Edge TTS
async def text_to_speech(text: str, voice: str, output_file="output.mp3"):
    communicate = edge_tts.Communicate(text, voice)
    await communicate.save(output_file)
    return output_file

def clean_chat_history(chat_history):
    cleaned = []
    for msg in chat_history:
        if isinstance(msg, dict) and isinstance(msg.get("content"), str):
            cleaned.append(msg)
    return cleaned

# Restricted words check (if any)
bad_words = json.loads(os.getenv('BAD_WORDS', "[]"))
bad_words_negative = json.loads(os.getenv('BAD_WORDS_NEGATIVE', "[]"))
default_negative = os.getenv("default_negative", "")

def check_text(prompt, negative=""):
    for i in bad_words:
        if i in prompt:
            return True
    for i in bad_words_negative:
        if i in negative:
            return True
    return False

# Use the same random seed function for both text and image generation
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed

MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"

# Set dtype based on device: use half for CUDA, float32 otherwise.
dtype = torch.float16 if device.type == "cuda" else torch.float32

# Load image generation pipelines for the three model choices.
if torch.cuda.is_available():
    # Lightning 5 model
    pipe = StableDiffusionXLPipeline.from_pretrained(
        "SG161222/RealVisXL_V5.0_Lightning",
        torch_dtype=dtype,
        use_safetensors=True,
        add_watermarker=False
    ).to(device)
    pipe.text_encoder = pipe.text_encoder.half()
    if ENABLE_CPU_OFFLOAD:
        pipe.enable_model_cpu_offload()
    else:
        pipe.to(device)
        print("Loaded RealVisXL_V5.0_Lightning on Device!")
    if USE_TORCH_COMPILE:
        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
        print("Model RealVisXL_V5.0_Lightning Compiled!")
    
    # Lightning 4 model
    pipe2 = StableDiffusionXLPipeline.from_pretrained(
        "SG161222/RealVisXL_V4.0_Lightning",
        torch_dtype=dtype,
        use_safetensors=True,
        add_watermarker=False,
    ).to(device)
    pipe2.text_encoder = pipe2.text_encoder.half()
    if ENABLE_CPU_OFFLOAD:
        pipe2.enable_model_cpu_offload()
    else:
        pipe2.to(device)
        print("Loaded RealVisXL_V4.0 on Device!")
    if USE_TORCH_COMPILE:
        pipe2.unet = torch.compile(pipe2.unet, mode="reduce-overhead", fullgraph=True)
        print("Model RealVisXL_V4.0 Compiled!")
    
    # Turbo v3 model
    pipe3 = StableDiffusionXLPipeline.from_pretrained(
        "SG161222/RealVisXL_V3.0_Turbo",
        torch_dtype=dtype,
        use_safetensors=True,
        add_watermarker=False,
    ).to(device)
    pipe3.text_encoder = pipe3.text_encoder.half()
    if ENABLE_CPU_OFFLOAD:
        pipe3.enable_model_cpu_offload()
    else:
        pipe3.to(device)
        print("Loaded Animagine XL 4.0 on Device!")
    if USE_TORCH_COMPILE:
        pipe3.unet = torch.compile(pipe3.unet, mode="reduce-overhead", fullgraph=True)
        print("Model Animagine XL 4.0 Compiled!")
else:
    pipe = StableDiffusionXLPipeline.from_pretrained(
        "SG161222/RealVisXL_V5.0_Lightning",
        torch_dtype=dtype,
        use_safetensors=True,
        add_watermarker=False
    ).to(device)
    pipe2 = StableDiffusionXLPipeline.from_pretrained(
        "SG161222/RealVisXL_V4.0_Lightning",
        torch_dtype=dtype,
        use_safetensors=True,
        add_watermarker=False,
    ).to(device)
    pipe3 = StableDiffusionXLPipeline.from_pretrained(
        "SG161222/RealVisXL_V3.0_Turbo",
        torch_dtype=dtype,
        use_safetensors=True,
        add_watermarker=False,
    ).to(device)
    print("Running on CPU; models loaded in float32.")

# Define available model choices and their mapping.
DEFAULT_MODEL = "Lightning 5"
MODEL_CHOICES = [DEFAULT_MODEL, "Lightning 4", "Turbo v3"]
models = {
    "Lightning 5": pipe,
    "Lightning 4": pipe2,
    "Turbo v3": pipe3
}

def generate_image_grid(prompt: str, seed: int, grid_size: str, width: int, height: int,
                        guidance_scale: float, randomize_seed: bool, model_choice: str):
    if check_text(prompt, ""):
        raise ValueError("Prompt contains restricted words.")
    
    seed = int(randomize_seed_fn(seed, randomize_seed))
    generator = torch.Generator(device=device).manual_seed(seed)

    # Define supported grid sizes.
    grid_sizes = {
        "2x1": (2, 1),
        "1x2": (1, 2),
        "2x2": (2, 2),
        "1x1": (1, 1)
    }
    grid_size_tuple = grid_sizes.get(grid_size, (1, 1))
    num_images = grid_size_tuple[0] * grid_size_tuple[1]

    options = {
        "prompt": prompt,
        "negative_prompt": default_negative,
        "width": width,
        "height": height,
        "guidance_scale": guidance_scale,
        "num_inference_steps": 30,
        "generator": generator,
        "num_images_per_prompt": num_images,
        "use_resolution_binning": True,
        "output_type": "pil",
    }
    
    if device.type == "cuda":
        torch.cuda.empty_cache()
    
    selected_pipe = models.get(model_choice, pipe)
    images = selected_pipe(**options).images

    # Create a grid image.
    grid_img = Image.new('RGB', (width * grid_size_tuple[0], height * grid_size_tuple[1]))
    for i, img in enumerate(images[:num_images]):
        grid_img.paste(img, ((i % grid_size_tuple[0]) * width, (i // grid_size_tuple[0]) * height))

    unique_name = str(uuid.uuid4()) + ".png"
    grid_img.save(unique_name)
    return [unique_name], seed

# -----------------------------
# Main generate() Function
# -----------------------------
@spaces.GPU
def generate(
    input_dict: dict,
    chat_history: list[dict],
    max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
):
    text = input_dict["text"]
    files = input_dict.get("files", [])

    lower_text = text.lower().strip()
    # Check if the prompt is an image generation command using model flags.
    if (lower_text.startswith("@lightningv5") or 
        lower_text.startswith("@lightningv4") or 
        lower_text.startswith("@turbov3")):
        
        # Determine model choice based on flag.
        model_choice = None
        if "@lightningv5" in lower_text:
            model_choice = "Lightning 5"
        elif "@lightningv4" in lower_text:
            model_choice = "Lightning 4"
        elif "@turbov3" in lower_text:
            model_choice = "Turbo v3"
        
        # Parse grid size flag e.g. "@2x2"
        grid_match = re.search(r"@(\d+x\d+)", lower_text)
        grid_size = grid_match.group(1) if grid_match else "1x1"
        
        # Remove the model and grid flags from the prompt.
        prompt_clean = re.sub(r"@lightningv5", "", text, flags=re.IGNORECASE)
        prompt_clean = re.sub(r"@lightningv4", "", prompt_clean, flags=re.IGNORECASE)
        prompt_clean = re.sub(r"@turbov3", "", prompt_clean, flags=re.IGNORECASE)
        prompt_clean = re.sub(r"@\d+x\d+", "", prompt_clean, flags=re.IGNORECASE)
        prompt_clean = prompt_clean.strip().strip('"')
        
        # Default parameters for image generation.
        width = 1024
        height = 1024
        guidance_scale = 6.0
        seed_val = 0
        randomize_seed = True
        use_resolution_binning = True
        
        yield "Generating image grid..."
        image_paths, used_seed = generate_image_grid(
            prompt_clean,
            seed_val,
            grid_size,
            width,
            height,
            guidance_scale,
            randomize_seed,
            model_choice,
        )
        yield gr.Image(image_paths[0])
        return

    # Otherwise, handle text/chat (and TTS) generation.
    tts_prefix = "@tts"
    is_tts = any(text.strip().lower().startswith(f"{tts_prefix}{i}") for i in range(1, 3))
    voice_index = next((i for i in range(1, 3) if text.strip().lower().startswith(f"{tts_prefix}{i}")), None)
    
    if is_tts and voice_index:
        voice = TTS_VOICES[voice_index - 1]
        text = text.replace(f"{tts_prefix}{voice_index}", "").strip()
        conversation = [{"role": "user", "content": text}]
    else:
        voice = None
        text = text.replace(tts_prefix, "").strip()
        conversation = clean_chat_history(chat_history)
        conversation.append({"role": "user", "content": text})
    
    if files:
        images = [load_image(image) for image in files] if len(files) > 1 else [load_image(files[0])]
        messages = [{
            "role": "user",
            "content": [
                *[{"type": "image", "image": image} for image in images],
                {"type": "text", "text": text},
            ]
        }]
        prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        inputs = processor(text=[prompt], images=images, return_tensors="pt", padding=True).to("cuda")
        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
        generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
        thread = Thread(target=model_m.generate, kwargs=generation_kwargs)
        thread.start()

        buffer = ""
        yield "Thinking..."
        for new_text in streamer:
            buffer += new_text
            buffer = buffer.replace("<|im_end|>", "")
            time.sleep(0.01)
            yield buffer
    else:
        input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
        if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
            input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
            gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
        input_ids = input_ids.to(model.device)
        streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
        generation_kwargs = {
            "input_ids": input_ids,
            "streamer": streamer,
            "max_new_tokens": max_new_tokens,
            "do_sample": True,
            "top_p": top_p,
            "top_k": top_k,
            "temperature": temperature,
            "num_beams": 1,
            "repetition_penalty": repetition_penalty,
        }
        t = Thread(target=model.generate, kwargs=generation_kwargs)
        t.start()

        outputs = []
        for new_text in streamer:
            outputs.append(new_text)
            yield "".join(outputs)

        final_response = "".join(outputs)
        yield final_response

        if is_tts and voice:
            output_file = asyncio.run(text_to_speech(final_response, voice))
            yield gr.Audio(output_file, autoplay=True)


DESCRIPTION = """
# IMAGINEO 4K ⚡
"""

css = '''
h1 {
  text-align: center;
  display: block;
}

#duplicate-button {
  margin: auto;
  color: #fff;
  background: #1565c0;
  border-radius: 100vh;
}
'''

demo = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS),
        gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6),
        gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9),
        gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50),
        gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2),
    ],
    examples=[
        ["@tts1 Who is Nikola Tesla, and why did he die?"],
        ['@lightningv5 @2x2 "Chocolate dripping from a donut against a yellow background, in the style of brocore, hyper-realistic"'],
        ['@lightningv4 @1x1 "A serene landscape with mountains"'],
        ['@turbov3 @2x1 "Abstract art, colorful and vibrant"'],
        ["Write a Python function to check if a number is prime."],
        ["@tts2 What causes rainbows to form?"],
    ],
    cache_examples=False,
    type="messages",
    description=DESCRIPTION,
    css=css,
    fill_height=True,
    textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"),
    stop_btn="Stop Generation",
    multimodal=True,
)

if __name__ == "__main__":
    demo.queue(max_size=20).launch(share=True)