import os
import torch
import easyocr
import numpy as np
import gc
from transformers import AutoTokenizer, AutoModel, AutoProcessor, AutoModelForZeroShotImageClassification
import torch.nn.functional as F
from backend.utils import build_transform

class ModelHandler:
    def __init__(self):
        try:
            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            print(f"Using device: {self.device}", flush=True)
            self.transform = build_transform()
            
            # Initialize model placeholders
            self.model_int = None
            self.tokenizer_int = None
            self.reader = None
            self.model_clip = None
            self.processor_clip = None
            
        except Exception as e:
            print(f"CRITICAL ERROR in ModelHandler.__init__: {e}", flush=True)
            import traceback
            traceback.print_exc()

    def load_internvl(self):
        if self.model_int is not None and self.tokenizer_int is not None:
            return

        print("Loading InternVL model...", flush=True)
        try:
            # Check if local path exists, otherwise use HF Hub ID
            local_path = os.path.join("Models", "InternVL2_5-1B-MPO")
            if os.path.exists(local_path):
                internvl_model_path = local_path
                print(f"Loading InternVL from local path: {internvl_model_path}", flush=True)
            else:
                internvl_model_path = "OpenGVLab/InternVL2_5-1B-MPO" # HF Hub ID
                print(f"Local model not found. Downloading InternVL from HF Hub: {internvl_model_path}", flush=True)

            self.model_int = AutoModel.from_pretrained(
                internvl_model_path,
                torch_dtype=torch.bfloat16,
                low_cpu_mem_usage=True,
                trust_remote_code=True
            ).eval()

            for module in self.model_int.modules():
                if isinstance(module, torch.nn.Dropout):
                    module.p = 0

            self.tokenizer_int = AutoTokenizer.from_pretrained(internvl_model_path, trust_remote_code=True)
            print("\nInternVL model and tokenizer loaded successfully.", flush=True)
        except Exception as e:
            print(f"\nError loading InternVL model or tokenizer: {e}", flush=True)
            import traceback
            traceback.print_exc()
            self.model_int = None
            self.tokenizer_int = None

    def load_easyocr(self):
        if self.reader is not None:
            return

        print("Loading EasyOCR model...", flush=True)
        try:
            # EasyOCR automatically handles downloading if not present
            self.reader = easyocr.Reader(['en', 'hi'], gpu=False)
            print("\nEasyOCR reader initialized successfully.")
        except Exception as e:
            print(f"\nError initializing EasyOCR reader: {e}")
            self.reader = None

    def load_clip(self):
        if self.model_clip is not None and self.processor_clip is not None:
            return

        print("Loading CLIP model...", flush=True)
        try:
            local_path = os.path.join("Models", "clip-vit-base-patch32")
            if os.path.exists(local_path):
                clip_model_path = local_path
                print(f"Loading CLIP from local path: {clip_model_path}")
            else:
                clip_model_path = "openai/clip-vit-base-patch32" # HF Hub ID
                print(f"Local model not found. Downloading CLIP from HF Hub: {clip_model_path}")

            self.processor_clip = AutoProcessor.from_pretrained(clip_model_path)
            self.model_clip = AutoModelForZeroShotImageClassification.from_pretrained(clip_model_path).to(self.device)
            print("\nCLIP model and processor loaded successfully.")
        except Exception as e:
            print(f"\nError loading CLIP model or processor: {e}")
            self.model_clip = None
            self.processor_clip = None

    def easyocr_ocr(self, image):
        self.load_easyocr()
        if not self.reader:
            return ""
        image_np = np.array(image)
        results = self.reader.readtext(image_np, detail=1)
        
        del image_np
        gc.collect()

        if not results:
            return ""
        
        sorted_results = sorted(results, key=lambda x: (x[0][0][1], x[0][0][0]))
        ordered_text = " ".join([res[1] for res in sorted_results]).strip()
        return ordered_text

    def intern(self, image, prompt, max_tokens):
        self.load_internvl()
        if not self.model_int or not self.tokenizer_int:
            return ""
            
        pixel_values = self.transform(image).unsqueeze(0).to(self.device).to(torch.bfloat16)
        with torch.no_grad():
            response, _ = self.model_int.chat(
                self.tokenizer_int,
                pixel_values,
                prompt,
                generation_config={
                    "max_new_tokens": max_tokens,
                    "do_sample": False,
                    "num_beams": 1,
                    "temperature": 1.0,
                    "top_p": 1.0,
                    "repetition_penalty": 1.0,
                    "length_penalty": 1.0,
                    "pad_token_id": self.tokenizer_int.pad_token_id
                },
                history=None,
                return_history=True
            )
        
        del pixel_values
        gc.collect()
        return response

    def clip(self, image, labels):
        self.load_clip()
        if not self.model_clip or not self.processor_clip:
            return None

        processed = self.processor_clip(
            text=labels,
            images=image,
            padding=True,
            return_tensors="pt"
        ).to(self.device)

        del image, labels
        gc.collect()
        return processed

    def get_clip_probs(self, image, labels):
        # clip() calls load_clip(), so we don't strictly need it here, but good for safety if clip() implementation changes
        self.load_clip() 
        inputs = self.clip(image, labels)
        if inputs is None:
            return None
            
        with torch.no_grad():
            outputs = self.model_clip(**inputs)
        
        logits_per_image = outputs.logits_per_image
        probs = F.softmax(logits_per_image, dim=1)
        
        del inputs, outputs, logits_per_image
        gc.collect()
        
        return probs

# Create a global instance to be used by modules
model_handler = ModelHandler()