import os
import re
import gc
import torch
import transformers
from langchain.text_splitter import RecursiveCharacterTextSplitter, TokenTextSplitter

ENV_FILE_PATH = os.path.join(os.path.dirname(__file__), "../../.env").replace("\\", "/")

def remove_markdown(text: str) -> str:
        # Remove code block format type and the code block itself
        text = re.sub(r'```[a-zA-Z]*\n', '', text)  # Remove the format type line
        text = re.sub(r'```', '', text)  # Remove remaining backticks for code blocks
        
        # Remove headers
        text = re.sub(r'^\s*#+\s+', '', text, flags=re.MULTILINE)
        
        # Remove bold and italic
        text = re.sub(r'\*\*(.*?)\*\*', r'\1', text)
        text = re.sub(r'__(.*?)__', r'\1', text)
        text = re.sub(r'\*(.*?)\*', r'\1', text)
        text = re.sub(r'_(.*?)_', r'\1', text)
        
        # Remove strikethrough
        text = re.sub(r'~~(.*?)~~', r'\1', text)
        
        # Remove inline code
        text = re.sub(r'`(.*?)`', r'\1', text)
        
        # Remove links
        text = re.sub(r'\[(.*?)\]\((.*?)\)', r'\1', text)
        
        # Remove images
        text = re.sub(r'!\[(.*?)\]\((.*?)\)', '', text)
        
        # Remove blockquotes
        text = re.sub(r'^\s*>\s+', '', text, flags=re.MULTILINE)
        
        # Remove lists
        text = re.sub(r'^\s*[\*\+-]\s+', '', text, flags=re.MULTILINE)
        text = re.sub(r'^\s*\d+\.\s+', '', text, flags=re.MULTILINE)
        
        # Remove horizontal lines
        text = re.sub(r'^\s*[-*_]{3,}\s*$', '', text, flags=re.MULTILINE)
        
        # Remove any remaining markdown symbols
        text = re.sub(r'[*_~`]', '', text)
        
        return text.strip()

def clear_gpu_memory():
        # Clear GPU memory and cache if available
        if torch.cuda.is_available():
                try:
                        print("Starting the GPU memory cleanup process...")
                        # Clear CUDA cache
                        torch.cuda.empty_cache()
                        # Reset all GPU memory
                        device_count = torch.cuda.device_count()
                        print(f"Number of GPUs: {device_count}")
                        for device_id in range(device_count):
                                print(f"Clearing GPU memory and cache for device {device_id}...")
                                # Set current device before operations
                                torch.cuda.set_device(device_id)
                                torch.cuda.reset_peak_memory_stats(torch.cuda.current_device())
                                torch.cuda.empty_cache()
                        # Force clear any allocated tensors
                        torch.cuda.synchronize()
                        torch.cuda.ipc_collect()
                except Exception as e:
                        raise Exception(f"Error clearing GPU memory and cache: {e}")

def clear_memory():
        # Delete all tensors and models
        print("Deleting all tensors and models...")
        for obj in gc.get_objects():
            try:
                if torch.is_tensor(obj):
                    del obj
                elif isinstance(obj, transformers.PreTrainedModel) or \
                     isinstance(obj, transformers.tokenization_utils_base.PreTrainedTokenizerBase) or \
                     "SentenceTransformer" in str(type(obj)):
                    
                    model_name = ""  # Initialize model name
                    if hasattr(obj, "name_or_path"):
                        model_name = obj.name_or_path
                    elif hasattr(obj, "config") and hasattr(obj.config, "_name_or_path"):
                        model_name = obj.config._name_or_path
                    else:
                        model_name = str(type(obj))  # Fallback to type if name is not found

                    print(f"Deleting model: {model_name}")  # Log the model name
                    del obj
            except Exception as e:
                print(f"Error during deletion: {e}")
        
        gc.collect()  # Run garbage collection

# Function to chunk text
def chunk_text(input_text, max_chunk_length=100, overlap=0, context_length=None):
    # Use context_length if provided, otherwise use max_chunk_length
    chunk_size = context_length if isinstance(context_length, int) and context_length > 0 else max_chunk_length
    
    splitter = RecursiveCharacterTextSplitter(
        separators=["\n\n", "\n", ". ", " ", ""],
        chunk_size=chunk_size,
        chunk_overlap=overlap,
        length_function=len
    )
    chunks = splitter.split_text(input_text)

    token_splitter = TokenTextSplitter(chunk_size=max_chunk_length, chunk_overlap=overlap) \
    if not context_length else None
    
    final_chunks = []
    span_annotations = []
    current_position = 0
    
    for chunk in chunks:
        # If token_splitter exists, use it. Otherwise, use the chunk as is
        current_chunks = token_splitter.split_text(chunk) if token_splitter else [chunk]
        final_chunks.extend(current_chunks)
        
        for tc in current_chunks:
            span_annotations.append((current_position, current_position + len(tc)))
            current_position += len(tc)
    
    return final_chunks, span_annotations

# Function to read .env file
def read_env():
    env_dict = {}
    if not os.path.exists(ENV_FILE_PATH):
        return env_dict

    with open(ENV_FILE_PATH, "r", encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if not line or line.startswith("#"):
                continue
            if "=" in line:
                var, val = line.split("=", 1)
                env_dict[var.strip()] = val.strip()
    return env_dict

# Function to update .env file
def update_env_vars(new_values: dict):
    # Overwrite .env file with new values
    with open(ENV_FILE_PATH, "w", encoding="utf-8") as f:
        for var, val in new_values.items():
            f.write(f"{var}={val}\n")

# Function to prepare provider key updates dictionary
def prepare_provider_key_updates(provider: str, multiline_keys: str) -> dict:
    lines = [ln.strip() for ln in multiline_keys.splitlines() if ln.strip()]
    updates = {}

    if provider == "openai":
        for i, key in enumerate(lines, start=1):
            updates[f"OPENAI_API_KEY_{i}"] = key
    elif provider == "google":
        for i, key in enumerate(lines, start=1):
            updates[f"GOOGLE_API_KEY_{i}"] = key
    elif provider == "xai":
        for i, key in enumerate(lines, start=1):
            updates[f"XAI_API_KEY_{i}"] = key
    elif provider == "anthropic":
        for i, key in enumerate(lines, start=1):
            updates[f"ANTHROPIC_API_KEY_{i}"] = key

    return updates

# Function to prepare proxy list dictionary
def prepare_proxy_list_updates(proxy_list: str) -> list:
    lines = [proxy.strip() for proxy in proxy_list.splitlines() if proxy.strip()]
    proxies = {}

    for i, proxy in enumerate(lines, start=1):
        proxies[f"PROXY_{i}"] = proxy

    return proxies