from torch import nn
from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM
from pathlib import Path
import torch
import torch.amp.autocast_mode
from PIL import Image
import os

CLIP_PATH = "google/siglip-so400m-patch14-384"
VLM_PROMPT = "A descriptive caption for this image:\n"
MODEL_PATH = "unsloth/Meta-Llama-3.1-8B-bnb-4bit"
CHECKPOINT_PATH = Path("wpkklhc6")

class ImageAdapter(nn.Module):
    def __init__(self, input_features: int, output_features: int):
        super().__init__()
        self.linear1 = nn.Linear(input_features, output_features)
        self.activation = nn.GELU()
        self.linear2 = nn.Linear(output_features, output_features)

    def forward(self, vision_outputs: torch.Tensor):
        x = self.linear1(vision_outputs)
        x = self.activation(x)
        x = self.linear2(x)
        return x

# Load CLIP
print("Loading CLIP")
clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)
clip_model = AutoModel.from_pretrained(CLIP_PATH)
clip_model = clip_model.vision_model
clip_model.eval()
clip_model.requires_grad_(False)
clip_model.to("cuda")

# Tokenizer
print("Loading tokenizer")
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, use_fast=False)
assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, PreTrainedTokenizerFast), f"Tokenizer is of type {type(tokenizer)}"

# LLM
print("Loading LLM")
text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto", torch_dtype=torch.bfloat16)
text_model.eval()

# Image Adapter
print("Loading image adapter")
image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size)
image_adapter.load_state_dict(torch.load(CHECKPOINT_PATH / "image_adapter.pt", map_location="cpu"))
image_adapter.eval()
image_adapter.to("cuda")

@torch.no_grad()
def stream_chat(input_image: Image.Image):
    torch.cuda.empty_cache()

    # Preprocess image
    image = clip_processor(images=input_image, return_tensors='pt').pixel_values
    image = image.to('cuda')

    # Tokenize the prompt
    prompt = tokenizer.encode(VLM_PROMPT, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False)

    # Embed image
    with torch.amp.autocast_mode.autocast('cuda', enabled=True):
        vision_outputs = clip_model(pixel_values=image, output_hidden_states=True)
        image_features = vision_outputs.hidden_states[-2]
        embedded_images = image_adapter(image_features)
        embedded_images = embedded_images.to('cuda')

    # Embed prompt
    prompt_embeds = text_model.model.embed_tokens(prompt.to('cuda'))
    assert prompt_embeds.shape == (1, prompt.shape[1], text_model.config.hidden_size), f"Prompt shape is {prompt_embeds.shape}, expected {(1, prompt.shape[1], text_model.config.hidden_size)}"
    embedded_bos = text_model.model.embed_tokens(torch.tensor([[tokenizer.bos_token_id]], device=text_model.device, dtype=torch.int64))

    # Construct prompts
    inputs_embeds = torch.cat([
        embedded_bos.expand(embedded_images.shape[0], -1, -1),
        embedded_images.to(dtype=embedded_bos.dtype),
        prompt_embeds.expand(embedded_images.shape[0], -1, -1),
    ], dim=1)

    input_ids = torch.cat([
        torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long),
        torch.zeros((1, embedded_images.shape[1]), dtype=torch.long),
        prompt,
    ], dim=1).to('cuda')
    attention_mask = torch.ones_like(input_ids)

    #generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=False, suppress_tokens=None)
    generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=300, do_sample=True, top_k=10, temperature=0.5, suppress_tokens=None)

    # Trim off the prompt
    generate_ids = generate_ids[:, input_ids.shape[1]:]
    if generate_ids[0][-1] == tokenizer.eos_token_id:
        generate_ids = generate_ids[:, :-1]

    caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]

    return caption.strip()


print(stream_chat(Image.open("/path/to/boobas.png")))