import os
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
from datasets import Dataset
from PIL import Image
import re
import pytesseract
import docx2txt
import PyPDF2
import json
from collections import defaultdict
from huggingface_hub import login

os.environ['TORCH_USE_CUDA_DSA'] = '1'
os.environ["TOKENIZERS_PARALLELISM"] = "false"

login(token="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")

class SourceMapper:
    def __init__(self):
        self.source_to_idx = defaultdict(lambda: len(self.source_to_idx))
        self.idx_to_source = {}
        
    def add_source(self, source):
        if source and source not in self.source_to_idx:
            idx = self.source_to_idx[source]
            self.idx_to_source[idx] = source
            
    def get_idx(self, source):
        return self.source_to_idx[source] if source else -1
    
    def get_source(self, idx):
        return self.idx_to_source.get(idx, "Unknown")

def load_file_catalog(catalog_path):
    with open(catalog_path, 'r', encoding='utf-8') as file:
        return json.load(file)

def identify_legal_document(filename, file_catalog):
    return file_catalog.get(filename, "Opracowanie własne")

def extract_text_from_file(file_path):
    _, ext = os.path.splitext(file_path)
    ext = ext.lower()
    
    if ext in ['.txt', '.md']:
        with open(file_path, 'r', encoding='utf-8') as file:
            return file.read()
    elif ext == '.pdf':
        text = ""
        with open(file_path, 'rb') as file:
            reader = PyPDF2.PdfReader(file)
            for page in reader.pages:
                text += page.extract_text()
        return text
    elif ext in ['.doc', '.docx']:
        return docx2txt.process(file_path)
    elif ext in ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']:
        return pytesseract.image_to_string(Image.open(file_path))
    else:
        return ""

def prepare_dataset(directory, catalog_path, source_mapper):
    file_catalog = load_file_catalog(catalog_path)
    data = []
    
    for root, _, files in os.walk(directory):
        for file in files:
            file_path = os.path.join(root, file)
            text = extract_text_from_file(file_path)
            if not text:
                continue
                
            doc_type = identify_legal_document(file, file_catalog)
            if doc_type != "Opracowanie własne":
                articles = re.split(r'(Art\.\s+\d+[\.\s])', text)
                for i in range(1, len(articles), 2):
                    article_number = articles[i].strip()
                    article_content = articles[i+1].strip() if i+1 < len(articles) else ""
                    source = f"{doc_type}, {article_number}"
                    source_mapper.add_source(source)
                    
                    data.append({
                        "text": f"{article_number} {article_content}",
                        "source_idx": source_mapper.get_idx(source)
                    })
            else:
                chunks = [text[i:i+512] for i in range(0, len(text), 512)]
                for chunk in chunks:
                    data.append({
                        "text": chunk,
                        "source_idx": -1  # Brak źródła
                    })
    return data

def tokenize_function(examples):
    tokenized = tokenizer(
        examples["text"],
        truncation=True,
        padding="max_length",
        max_length=512,
        return_tensors="pt"
    )
    tokenized["labels"] = tokenized["input_ids"].clone()
    tokenized["source_idx"] = examples["source_idx"]
    return tokenized

def custom_collate_fn(batch):
    input_ids = torch.stack([torch.tensor(b["input_ids"]) for b in batch])
    attention_mask = torch.stack([torch.tensor(b["attention_mask"]) for b in batch])
    labels = torch.stack([torch.tensor(b["labels"]) for b in batch])
    
    source_idx = torch.tensor([b.get("source_idx", -1) for b in batch], dtype=torch.long)
    #print("source_idx shape:", source_idx.shape)  # Debugowanie
    return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels, "source_idx": source_idx}

class CustomModel(nn.Module):
    def __init__(self, model_name, config):
        super().__init__()
        self.base_model = AutoModelForCausalLM.from_pretrained(model_name, config=config)
        self.source_embedding = nn.Embedding(
            num_embeddings=1000,
            embedding_dim=config.hidden_size,
            padding_idx=-1
        )
        
    def forward(self, input_ids=None, attention_mask=None, labels=None, source_idx=None, **kwargs):
        if source_idx is not None:
            #print("Max source_idx:", torch.max(source_idx))
            #print("Num embeddings:", self.source_embedding.num_embeddings)
            source_idx = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings - 1)
            source_embeds = self.source_embedding(source_idx).unsqueeze(1).expand(-1, input_ids.size(1), -1)
            hidden_states = self.base_model.get_input_embeddings()(input_ids) + source_embeds
            outputs = self.base_model(inputs_embeds=hidden_states, attention_mask=attention_mask, labels=labels, **kwargs)
        else:
            outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask, labels=labels, **kwargs)
        
        return outputs

class CustomTrainer(Trainer):
    def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
        labels = inputs.pop("labels")
        source_idx = inputs.pop("source_idx", None)
        outputs = model(**inputs, labels=labels, source_idx=source_idx)
        loss = outputs.loss
        return (loss, outputs) if return_outputs else loss

# Inicjalizacja komponentów
source_mapper = SourceMapper()
model_name = "crumb/nano-mistral"
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token

# Przygotowanie danych
catalog_path = "file_catalog.json"
data = prepare_dataset("files", catalog_path, source_mapper)
dataset = Dataset.from_list(data)
tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=8)

# Inicjalizacja modelu
config = AutoModelForCausalLM.from_pretrained(model_name).config
#print("Vocabulary size:", config.vocab_size)
model = CustomModel(model_name, config)
#model.to("cpu")  # Zmienione na CPU dla debugowania

# Konfiguracja treningu
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=2,
    gradient_accumulation_steps=4,
    learning_rate=2e-5,
    fp16=False,  # Wyłączone dla CPU
    logging_steps=1,
    logging_dir="./logs",
    save_strategy="steps",
    save_steps=1000,
    logging_strategy="no",
    report_to="none",
)

# Trening
trainer = CustomTrainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset,
    data_collator=custom_collate_fn,
)
trainer.train()

# Funkcja generująca odpowiedź
def generate_answer(question, model, tokenizer, source_mapper, max_length=200):
    inputs = tokenizer(question, return_tensors="pt", truncation=True, max_length=512)
    
    outputs = model.base_model.generate(
        **inputs,
        max_length=max_length,
        num_return_sequences=1,
        return_dict_in_generate=True,
        output_scores=True,
    )
    
    answer = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
    
    # Pobierz źródło z ostatniego tokena
    last_token_id = outputs.sequences[0][-1].item()
    source_idx = model.source_embeddi
    
# Utwórz katalog do zapisu modelu
save_directory = "./trained_model/ably.do/hse"
os.makedirs(save_directory, exist_ok=True)

# 1. Zapisz wagę modelu
torch.save(model.state_dict(), os.path.join(save_directory, "hse-nano-mistral.bin"))

# 2. Zapisz tokenizer
tokenizer.save_pretrained(save_directory)

# 3. Zapisz mapowanie źródeł
source_mapper_data = {
    "source_to_idx": dict(source_mapper.source_to_idx),
    "idx_to_source": source_mapper.idx_to_source
}

with open(os.path.join(save_directory, "source_mapper.json"), 'w') as f:
    json.dump(source_mapper_data, f)

# 4. Zapisz konfigurację modelu (opcjonalnie, ale zalecane)
model.base_model.config.save_pretrained(save_directory)