"""Parallel audio processing for generating multiple audio chunks concurrently."""

import asyncio
import concurrent.futures
from typing import List, Tuple, Optional, Callable
import numpy as np
import gradio as gr


class ParallelAudioProcessor:
    """Handles parallel processing of multiple audio chunks."""
    
    def __init__(self, max_workers: int = 4):
        """
        Initialize the parallel processor.
        
        Args:
            max_workers: Maximum number of concurrent workers for audio generation
        """
        self.max_workers = max_workers
    
    def process_chunks_parallel(
        self, 
        text_chunks: List[str], 
        audio_generator_func: Callable,
        progress_callback: Optional[Callable] = None
    ) -> List[Tuple[int, np.ndarray]]:
        """
        Process multiple text chunks in parallel to generate audio.
        
        Args:
            text_chunks: List of text chunks to process
            audio_generator_func: Function to generate audio from text
            progress_callback: Optional callback for progress updates
            
        Returns:
            List of tuples containing (sample_rate, audio_data) for each chunk
        """
        if not text_chunks:
            return []
        
        total_chunks = len(text_chunks)
        completed_chunks = 0
        results = [None] * total_chunks
        
        def update_progress(chunk_index: int, desc: str = ""):
            nonlocal completed_chunks
            if progress_callback:
                progress = completed_chunks / total_chunks
                progress_callback(progress, desc=f"Processing chunk {completed_chunks + 1}/{total_chunks}{': ' + desc if desc else ''}")
        
        def process_single_chunk(chunk_index: int, text_chunk: str) -> Tuple[int, Tuple[int, np.ndarray]]:
            """Process a single chunk and return the result with its index."""
            try:
                # Create a local progress callback for this chunk
                def chunk_progress(progress: float, desc: str = ""):
                    update_progress(chunk_index, f"Chunk {chunk_index + 1}: {desc}")
                
                # Generate audio for this chunk
                audio_result = audio_generator_func(text_chunk, None, progress=chunk_progress)
                return chunk_index, audio_result
            except Exception as e:
                raise Exception(f"Error processing chunk {chunk_index + 1}: {str(e)}")
        
        # Use ThreadPoolExecutor for parallel processing
        with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as executor:
            # Submit all chunks for processing
            future_to_index = {
                executor.submit(process_single_chunk, i, chunk): i 
                for i, chunk in enumerate(text_chunks)
            }
            
            # Collect results as they complete
            for future in concurrent.futures.as_completed(future_to_index):
                chunk_index = future_to_index[future]
                try:
                    index, audio_result = future.result()
                    results[index] = audio_result
                    completed_chunks += 1
                    
                    if progress_callback:
                        progress = completed_chunks / total_chunks
                        progress_callback(
                            progress, 
                            desc=f"Completed {completed_chunks}/{total_chunks} audio chunks"
                        )
                        
                except Exception as e:
                    raise gr.Error(f"Failed to process chunk {chunk_index + 1}: {str(e)}")
        
        # Filter out any None results (shouldn't happen, but just in case)
        valid_results = [result for result in results if result is not None]
        
        if len(valid_results) != total_chunks:
            raise gr.Error(f"Only {len(valid_results)} out of {total_chunks} chunks processed successfully")
        
        return valid_results
    
    async def process_chunks_async(
        self, 
        text_chunks: List[str], 
        audio_generator_func: Callable,
        progress_callback: Optional[Callable] = None
    ) -> List[Tuple[int, np.ndarray]]:
        """
        Async version of parallel chunk processing.
        
        Args:
            text_chunks: List of text chunks to process
            audio_generator_func: Function to generate audio from text
            progress_callback: Optional callback for progress updates
            
        Returns:
            List of tuples containing (sample_rate, audio_data) for each chunk
        """
        if not text_chunks:
            return []
        
        async def process_chunk_async(chunk_index: int, text_chunk: str):
            """Process a single chunk asynchronously."""
            loop = asyncio.get_event_loop()
            
            def chunk_progress(progress: float, desc: str = ""):
                if progress_callback:
                    progress_callback(
                        (chunk_index + progress) / len(text_chunks),
                        desc=f"Chunk {chunk_index + 1}: {desc}"
                    )
            
            # Run the audio generation in a thread pool
            audio_result = await loop.run_in_executor(
                None, 
                lambda: audio_generator_func(text_chunk, None, progress=chunk_progress)
            )
            return chunk_index, audio_result
        
        # Create tasks for all chunks
        tasks = [
            process_chunk_async(i, chunk) 
            for i, chunk in enumerate(text_chunks)
        ]
        
        # Process all chunks concurrently
        try:
            results = await asyncio.gather(*tasks)
            # Sort results by chunk index to maintain order
            results.sort(key=lambda x: x[0])
            return [result[1] for result in results]
        except Exception as e:
            raise gr.Error(f"Error in async processing: {str(e)}")
    
    def estimate_processing_time(self, text_chunks: List[str], avg_time_per_char: float = 0.1) -> float:
        """
        Estimate total processing time for all chunks.
        
        Args:
            text_chunks: List of text chunks
            avg_time_per_char: Average processing time per character (seconds)
            
        Returns:
            Estimated processing time in seconds
        """
        total_chars = sum(len(chunk) for chunk in text_chunks)
        sequential_time = total_chars * avg_time_per_char
        
        # Account for parallelization
        parallel_efficiency = min(len(text_chunks), self.max_workers) / len(text_chunks) if text_chunks else 1
        estimated_time = sequential_time * parallel_efficiency
        
        return estimated_time