import os
import glob
import stat
import xml.etree.ElementTree as ET
import torch
import torch.nn as nn
import torch.nn.functional as F
import logging
import requests
import faiss
from collections import defaultdict
from typing import List, Dict, Any, Optional
from colorama import Fore, Style, init
from accelerate import Accelerator
from torch.utils.data import DataLoader, TensorDataset
from torch.cuda.amp import GradScaler, autocast
from transformers import AutoTokenizer, AutoModel
from sentence_transformers import SentenceTransformer
from sentence_transformers.readers import ParagraphReader
from sentence_transformers.uniformer import Uniformer

# Initialize colorama
init(autoreset=True)
logging.basicConfig(level=logging.INFO, format='\033[92m%(asctime)s - %(levelname)s - %(message)s\033[0m')

file_path = 'data/'
output_path = 'output/'

# Create output path if it doesn't exist
if not os.path.exists(output_path):
    try:
        os.makedirs(output_path)
        os.chmod(output_path, stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)  # Set full r/w permissions
    except PermissionError:
        print(f"Permission denied: '{output_path}'")
        # Handle the error or try a different path

# Ensure necessary files are created with full r/w permissions
def ensure_file(file_path):
    if not os.path.exists(file_path):
        with open(file_path, 'w') as f:
            pass
        os.chmod(file_path, stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)  # Set full r/w permissions

class MagicStateLayer(nn.Module):
    def __init__(self, size):
        super().__init__()
        self.state = nn.Parameter(torch.randn(size))

    def forward(self, x):
        return x + self.state

class MemoryAugmentationLayer(nn.Module):
    def __init__(self, size):
        super().__init__()
        self.memory = nn.Parameter(torch.randn(size))

    def forward(self, x):
        return x + self.memory

class HybridAttentionLayer(nn.Module):
    def __init__(self, size):
        super().__init__()
        self.attention = nn.MultiheadAttention(size, num_heads=8)

    def forward(self, x):
        x = x.unsqueeze(1)
        attn_output, _ = self.attention(x, x, x)
        return attn_output.squeeze(1)

class DynamicFlashAttentionLayer(nn.Module):
    def __init__(self, size):
        super().__init__()
        self.attention = nn.MultiheadAttention(size, num_heads=8)

    def forward(self, x):
        x = x.unsqueeze(1)
        attn_output, _ = self.attention(x, x, x)
        return attn_output.squeeze(1)

class DynamicModel(nn.Module):
    def __init__(self, sections: Dict[str, List[Dict[str, Any]]]):
        super().__init__()
        self.sections = nn.ModuleDict({sn: nn.ModuleList([self.create_layer(lp) for lp in layers]) for sn, layers in sections.items()})

    def create_layer(self, lp):
        l = [nn.Linear(lp['input_size'], lp['output_size'])]
        if lp.get('batch_norm', True):
            l.append(nn.BatchNorm1d(lp['output_size']))
        a = lp.get('activation', 'relu')
        if a == 'relu':
            l.append(nn.ReLU(inplace=True))
        elif a == 'tanh':
            l.append(nn.Tanh())
        elif a == 'sigmoid':
            l.append(nn.Sigmoid())
        elif a == 'leaky_relu':
            l.append(nn.LeakyReLU(negative_slope=0.01, inplace=True))
        elif a == 'elu':
            l.append(nn.ELU(alpha=1.0, inplace=True))
        if dr := lp.get('dropout', 0.0):
            l.append(nn.Dropout(p=dr))
        if lp.get('memory_augmentation', False):
            l.append(MemoryAugmentationLayer(lp['output_size']))
        if lp.get('hybrid_attention', False):
            l.append(HybridAttentionLayer(lp['output_size']))
        if lp.get('dynamic_flash_attention', False):
            l.append(DynamicFlashAttentionLayer(lp['output_size']))
        if lp.get('magic_state', False):
            l.append(MagicStateLayer(lp['output_size']))
        return nn.Sequential(*l)

    def forward(self, x, sn=None):
        if sn:
            for l in self.sections[sn]:
                x = l(x)
        else:
            for sn, layers in self.sections.items():
                for l in layers:
                    x = l(x)
        return x

def parse_xml_file(file_path):
    tree, root, layers = ET.parse(file_path), ET.parse(file_path).getroot(), []
    for layer in root.findall('.//layer'):
        lp = {'input_size': int(layer.get('input_size', 128)), 'output_size': int(layer.get('output_size', 256)), 'activation': layer.get('activation', 'relu').lower()}
        if lp['activation'] not in ['relu', 'tanh', 'sigmoid', 'none']:
            raise ValueError(f"Unsupported activation function: {lp['activation']}")
        if lp['input_size'] <= 0 or lp['output_size'] <= 0:
            raise ValueError("Layer dimensions must be positive integers")
        layers.append(lp)
    if not layers:
        layers.append({'input_size': 128, 'output_size': 256, 'activation': 'relu'})
    return layers

def create_model_from_folder(folder_path):
    sections = defaultdict(list)
    if not os.path.exists(folder_path):
        logging.warning(f"Folder {folder_path} does not exist. Creating model with default configuration.")
        return DynamicModel({})
    xml_files_found = False
    for root, dirs, files in os.walk(folder_path):
        for file in files:
            if file.endswith('.xml'):
                xml_files_found = True
                file_path = os.path.join(root, file)
                try:
                    sections[os.path.basename(root).replace('.', '_')].extend(parse_xml_file(file_path))
                except Exception as e:
                    logging.error(f"Error processing {file_path}: {str(e)}")
    if not xml_files_found:
        logging.warning("No XML files found. Creating model with default configuration.")
        return DynamicModel({})
    return DynamicModel(dict(sections))

def create_embeddings_and_stores(folder_path, model_name="sentence-transformers/all-MiniLM-L6-v2"):
    tokenizer, model, vector_store, doc_store = AutoTokenizer.from_pretrained(model_name), AutoModel.from_pretrained(model_name), faiss.IndexFlatL2(384), []
    for root, dirs, files in os.walk(folder_path):
        for file in files:
            if file.endswith('.xml'):
                file_path = os.path.join(root, file)
                try:
                    tree, root = ET.parse(file_path), ET.parse(file_path).getroot()
                    for elem in root.iter():
                        if elem.text:
                            text = elem.text.strip()
                            inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
                            with torch.no_grad():
                                embeddings = model(**inputs).last_hidden_state.mean(dim=1).numpy()
                            vector_store.add(embeddings)
                            doc_store.append(text)
                except Exception as e:
                    logging.error(f"Error processing {file_path}: {str(e)}")
    return vector_store, doc_store

def query_vector_store(query, vector_store, doc_store, model_name="sentence-transformers/all-MiniLM-L6-v2"):
    tokenizer, model = AutoTokenizer.from_pretrained(model_name), AutoModel.from_pretrained(model_name)
    inputs = tokenizer(query, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        query_embedding = model(**inputs).last_hidden_state.mean(dim=1).numpy()
    D, I = vector_store.search(query_embedding, k=5)
    return [doc_store[i] for i in I[0]]

def fetch_courtlistener_data(query):
    base_url, params = "https://nzlii.org/cgi-bin/sinosrch.cgi", {"method": "auto", "query": query, "meta": "/nz", "results": "50", "format": "json"}
    try:
        response = requests.get(base_url, params=params, headers={"Accept": "application/json"}, timeout=10)
        response.raise_for_status()
        return [{"title": r.get("title", ""), "citation": r.get("citation", ""), "date": r.get("date", ""), "court": r.get("court", ""), "summary": r.get("summary", ""), "url": r.get("url", "")} for r in response.json().get("results", [])]
    except requests.exceptions.RequestException as e:
        logging.error(f"Failed to fetch data from NZLII API: {str(e)}")
        return []

class CustomModel(nn.Module):
    def __init__(self, model_name="distilbert-base-uncased"):
        super().__init__()
        self.model_name = model_name
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.encoder = AutoModel.from_pretrained(model_name)
        self.hidden_size = self.encoder.config.hidden_size
        self.dropout = nn.Dropout(p=0.2)
        self.fc1 = nn.Linear(self.hidden_size, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, 16)
        self.memory = nn.LSTM(self.hidden_size, 64, bidirectional=True, batch_first=True)
        self.memory_fc1 = nn.Linear(64 * 2, 32)
        self.memory_fc2 = nn.Linear(32, 16)

    def forward(self, data):
        tokens = self.tokenizer(data, return_tensors="pt", truncation=True, padding=True)
        outputs = self.encoder(**tokens)
        x = outputs.last_hidden_state.mean(dim=1)
        x = self.dropout(F.relu(self.fc1(x)))
        x = self.dropout(F.relu(self.fc2(x)))
        x = self.fc3(x)
        return x

    def training_step(self, data, labels, optimizer, criterion):
        optimizer.zero_grad()
        outputs = self.forward(data)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        return loss.item()

    def validation_step(self, data, labels, criterion):
        with torch.no_grad():
            outputs = self.forward(data)
            loss = criterion(outputs, labels)
        return loss.item()

    def predict(self, input):
        self.eval()
        with torch.no_grad():
            return self.forward(input)

class CustomModelInference(nn.Module):
    def __init__(self, model_name="distilbert-base-uncased"):
        super().__init__()
        self.model_name = model_name
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.encoder = AutoModel.from_pretrained(model_name)
        self.hidden_size = self.encoder.config.hidden_size
        self.dropout = nn.Dropout(p=0.2)
        self.fc1 = nn.Linear(self.hidden_size, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, 16)
        self.reader = ParagraphReader("data/docstore.json")
        self.model_embedding = SentenceTransformer('sentence-transformers/multilingual-v2')
        self.vectorstore = Uniformer("distilusembert-base-nli-mean-tokens", torch.nn.CrossEntropyLoss(), margin=0.5, temperature=0.1, top_k=4)

    def forward(self, data):
        tokens = self.tokenizer(data, return_tensors="pt", truncation=True, padding=True)
        outputs = self.encoder(**tokens)
        x = outputs.last_hidden_state.mean(dim=1)
        x = self.dropout(F.relu(self.fc1(x)))
        x = self.dropout(F.relu(self.fc2(x)))
        x = self.fc3(x)
        return x

    def infer(self, input):
        self.eval()
        with torch.no_grad():
            return self.forward(input)

    def update_memory(self, data):
        embeddings = self.model_embedding.encode(data, convert_to_tensor=True)
        self.vectorstore.add(embeddings)

def main():
    folder_path, model = 'data', create_model_from_folder('data')
    logging.info(f"Created dynamic PyTorch model with sections: {list(model.sections.keys())}")
    vector_store, doc_store = create_embeddings_and_stores(folder_path)
    accelerator, optimizer, criterion, num_epochs = Accelerator(), torch.optim.Adam(model.parameters(), lr=0.001), nn.CrossEntropyLoss(), 10
    dataset, dataloader = TensorDataset(torch.randn(100, 128), torch.randint(0, 2, (100,))), DataLoader(TensorDataset(torch.randn(100, 128), torch.randint(0, 2, (100,))), batch_size=16, shuffle=True)
    model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
    for epoch in range(num_epochs):
        model.train()
        total_loss = 0
        for batch_data, batch_labels in dataloader:
            optimizer.zero_grad()
            outputs = model(batch_data)
            loss = criterion(outputs, batch_labels)
            accelerator.backward(loss)
            optimizer.step()
            total_loss += loss.item()
        avg_loss = total_loss / len(dataloader)
        logging.info(f"Epoch {epoch+1}/{num_epochs}, Average Loss: {avg_loss:.4f}")
    query = "example query text"
    results = query_vector_store(query, vector_store, doc_store)
    logging.info(f"Query results: {results}")
    courtlistener_data = fetch_courtlistener_data(query)
    logging.info(f"CourtListener API results: {courtlistener_data}")

if __name__ == "__main__":
    main()