import logging
from typing import List, Dict, Any, Union, Optional
from langchain_core.output_parsers import StrOutputParser

from .config import settings
from .llm_interface import get_llm, invoke_llm
from .prompts import SUMMARIZER_PROMPT
from .graph_operations import format_doc_for_llm # Reuse formatting

# Import llmlingua if compression is used
try:
    from llmlingua import PromptCompressor
    LLMLINGUA_AVAILABLE = True
except ImportError:
    LLMLINGUA_AVAILABLE = False
    PromptCompressor = None # Define as None if not available

logger = logging.getLogger(__name__)

_compressor_cache = {}

def get_compressor(method: str) -> Optional['PromptCompressor']:
    """Initializes and caches llmlingua compressor."""
    if not LLMLINGUA_AVAILABLE:
        logger.warning("LLMLingua not installed, compression unavailable.")
        return None
    if method not in _compressor_cache:
        logger.info(f"Initializing LLMLingua compressor: {method}")
        try:
            # Adjust model names and params as needed
            if method == "llm_lingua2":
                model_name = "microsoft/llmlingua-2-xlm-roberta-large-meetingbank"
                use_llmlingua2 = True
            elif method == "llm_lingua":
                 model_name = "microsoft/phi-2" # Requires ~8GB RAM
                 use_llmlingua2 = False
            else:
                logger.error(f"Unsupported compression method: {method}")
                return None

            _compressor_cache[method] = PromptCompressor(
                model_name=model_name,
                use_llmlingua2=use_llmlingua2,
                device_map="cpu" # Or "cuda" if GPU available
            )
        except Exception as e:
            logger.error(f"Failed to initialize LLMLingua compressor '{method}': {e}", exc_info=True)
            return None
    return _compressor_cache[method]


def summarize_document(doc_content: str) -> str:
    """Summarizes a single document using the configured LLM."""
    logger.debug("Summarizing document...")
    try:
        summarize_llm = get_llm(settings.summarize_llm_model)
        summarize_chain = SUMMARIZER_PROMPT | summarize_llm | StrOutputParser()
        summary = invoke_llm(summarize_chain, {"document": doc_content})
        logger.debug("Summarization complete.")
        return summary
    except Exception as e:
        logger.error(f"Summarization failed: {e}", exc_info=True)
        return f"Error during summarization: {e}" # Return error message instead of failing


def compress_document(doc_content: str) -> str:
    """Compresses a single document using LLMLingua."""
    logger.debug(f"Compressing document using method: {settings.compression_method}...")
    if not settings.compression_method:
        logger.warning("Compression method not configured, skipping.")
        return doc_content

    compressor = get_compressor(settings.compression_method)
    if not compressor:
        logger.warning("Compressor not available, skipping compression.")
        return doc_content

    try:
        # Adjust compression parameters as needed
        # rate = settings.compress_rate or 0.5
        # force_tokens = ['\n', '.', ',', '?', '!'] # Example tokens
        # context? instructions? question?

        # Simple compression for now:
        result = compressor.compress_prompt(doc_content, rate=settings.compress_rate or 0.5)
        compressed_text = result.get("compressed_prompt", doc_content)

        original_len = len(doc_content.split())
        compressed_len = len(compressed_text.split())
        logger.debug(f"Compression complete. Original words: {original_len}, Compressed words: {compressed_len}")
        return compressed_text
    except Exception as e:
        logger.error(f"Compression failed: {e}", exc_info=True)
        return f"Error during compression: {e}" # Return error message


def process_documents(
    docs: List[Dict[str, Any]],
    processing_steps: List[Union[str, dict]]
) -> List[str]:
    """Processes a list of documents according to the specified steps."""
    logger.info(f"Processing {len(docs)} documents with steps: {processing_steps}")
    if not docs:
        return []

    processed_outputs = []
    for i, doc in enumerate(docs):
        logger.info(f"Processing document {i+1}/{len(docs)}...")
        current_content = format_doc_for_llm(doc) # Start with formatted original doc

        for step in processing_steps:
            if step == "summarize":
                current_content = summarize_document(current_content)
            elif step == "compress":
                 current_content = compress_document(current_content)
            elif isinstance(step, dict):
                # Placeholder for custom processing steps defined by dicts
                logger.warning(f"Custom processing step not implemented: {step}")
                # Add logic here if needed: extract params, call specific LLM/function
                pass
            else:
                logger.warning(f"Unknown processing step type: {step}")

        processed_outputs.append(current_content) # Add the final processed content for this doc

    logger.info("Document processing finished.")
    return processed_outputs