"""
Distributed Pipeline Inference for Large Models (WIP/Research)

⚠️  EXPERIMENTAL: This module addresses a fundamental architectural limitation
    in llama.cpp's distributed inference design.

## The Problem: llama.cpp Coordinator Bottleneck

llama.cpp's --rpc flag enables distributed *computation* but NOT distributed
*storage*. The coordinator must load the entire model into its own RAM before
distributing layer computation to RPC workers.

Example failure case:
    - 70B model (40GB GGUF file)
    - Coordinator node: 8GB RAM available
    - RPC workers: 4 nodes × 12GB RAM = 48GB total
    - Result: Coordinator crashes loading 40GB into 8GB RAM ❌

This defeats the purpose of distributed inference for memory-constrained setups.

## Our Solution: Ray-Based Pipeline Parallelism

Instead of using llama.cpp's RPC coordinator, we implement true distributed
model loading where NO single node needs the full model:

Architecture:
    1. Analyze GGUF structure (identify layers, memory requirements)
    2. Schedule layer assignment across workers based on available RAM
    3. Split GGUF into mini-GGUFs, each containing a layer subset
    4. Each Ray worker loads only its assigned mini-GGUF
    5. Pipeline activations through workers sequentially

Example with 70B model:
    - Node 1 (GPU, 16GB): Loads layers 0-25 (15GB) + uses GPU acceleration
    - Node 2 (CPU, 8GB): Loads layers 26-50 (7GB)
    - Node 3 (CPU, 8GB): Loads layers 51-75 (7GB)
    - Node 4 (CPU, 4GB): Loads layers 76-80 + output (3GB)
    - Total: No single node exceeds its capacity ✅

## Current Status: WIP

✅ Working Components:
    - GGUFLayerAnalyzer: Parses Ollama GGUF blobs, identifies layer boundaries
    - LayerScheduler: Assigns layers proportionally to worker memory
    - GGUFSplitter: Filters tensors by layer range (skeleton)

🚧 Blocked Components:
    - GGUF Writer: Creating valid mini-GGUFs requires quantization-aware
      tensor validation. The gguf library's writer validates tensor shapes
      against quantization block sizes, making naive tensor copying non-trivial.

## Path Forward (Research Track)

Two viable approaches for production implementation:

1. **Deep GGUF Integration**: Properly handle quantized tensor metadata,
   block size alignment, and architecture-specific tensor dependencies.
   Requires deep understanding of GGML quantization formats (Q4_0, Q5_K, etc.).

2. **Alternative Backend**: Integrate vLLM or DeepSpeed which support true
   distributed model sharding out-of-the-box. Trade-off: requires model
   conversion from GGUF to HuggingFace format.

3. **Micro-Tensor Streaming**: Bypass GGUF files entirely - load tensors
   into Ray object store and stream to workers on-demand via gRPC. Most
   complex but most flexible approach.

## Why This Matters for SOLLOL

This positions SOLLOL as addressing a real limitation in local LLM infrastructure:
running frontier models (70B+) on consumer hardware clusters without requiring
any single machine to have 40GB+ RAM. This is a genuine research problem in
the local inference space.

Inspired by:
    - prima.cpp's piped-ring parallelism (arXiv:2504.08791)
    - distributed-llama's tensor parallelism (github.com/b4rtaz/distributed-llama)

Implemented using Ray for better integration with SOLLOL's existing infrastructure.
"""

import asyncio
import logging
import os
import struct
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional

import numpy as np
import ray
from gguf import GGMLQuantizationType, GGUFReader, GGUFWriter

logger = logging.getLogger(__name__)


@dataclass
class LayerAssignment:
    """Layer assignment for a worker node."""

    worker_id: int
    layer_start: int
    layer_end: int
    node_address: str
    memory_mb: int


class GGUFLayerAnalyzer:
    """
    Analyzes GGUF files to determine layer structure and memory requirements.

    This class parses GGUF metadata to identify layer boundaries and estimate
    memory requirements for distributing the model across workers.
    """

    def __init__(self, gguf_path: str):
        """
        Initialize analyzer with GGUF file path.

        Args:
            gguf_path: Path to GGUF file (Ollama blob or standalone)
        """
        self.gguf_path = gguf_path
        self.reader = None
        self.metadata = {}
        self.layer_info = {}

    def analyze(self) -> Dict[str, Any]:
        """
        Analyze GGUF file structure.

        Returns:
            Dict containing:
                - num_layers: Total number of transformer layers
                - layer_tensors: Dict mapping layer_id -> list of tensor names
                - total_size_mb: Total model size in MB
                - layer_sizes_mb: List of sizes per layer
        """
        logger.info(f"Analyzing GGUF file: {self.gguf_path}")

        self.reader = GGUFReader(self.gguf_path)

        # Extract metadata
        for field in self.reader.fields.values():
            self.metadata[field.name] = field.parts[field.data[0]]

        # Get architecture info
        arch = self.metadata.get("general.architecture", "unknown")
        declared_layers = self.metadata.get(f"{arch}.block_count", None)

        logger.info(f"Model architecture: {arch}, declared layers: {declared_layers}")

        # Analyze tensors by layer (use dict to auto-discover layers)
        layer_tensors = {}
        embedding_tensors = []
        output_tensors = []

        total_size_bytes = 0
        layer_sizes = {}

        for tensor in self.reader.tensors:
            tensor_name = tensor.name
            tensor_size = tensor.n_bytes
            total_size_bytes += tensor_size

            # Parse layer ID from tensor name (e.g., "blk.0.attn_q.weight" -> layer 0)
            if ".blk." in tensor_name or "blk." in tensor_name:
                try:
                    # Extract layer number
                    parts = tensor_name.split(".")
                    for i, part in enumerate(parts):
                        if part == "blk" and i + 1 < len(parts):
                            layer_id = int(parts[i + 1])
                            if layer_id not in layer_tensors:
                                layer_tensors[layer_id] = []
                                layer_sizes[layer_id] = 0
                            layer_tensors[layer_id].append(tensor_name)
                            layer_sizes[layer_id] += tensor_size
                            break
                except (ValueError, IndexError):
                    logger.warning(f"Could not parse layer ID from tensor: {tensor_name}")
            elif "embed" in tensor_name or "token" in tensor_name:
                embedding_tensors.append(tensor_name)
            elif "output" in tensor_name or "lm_head" in tensor_name:
                output_tensors.append(tensor_name)

        # Convert to MB
        total_size_mb = total_size_bytes / (1024 * 1024)
        num_layers = len(layer_tensors) if layer_tensors else (declared_layers or 0)
        layer_sizes_mb = [layer_sizes.get(i, 0) / (1024 * 1024) for i in range(num_layers)]

        analysis = {
            "architecture": arch,
            "num_layers": num_layers,
            "layer_tensors": layer_tensors,
            "embedding_tensors": embedding_tensors,
            "output_tensors": output_tensors,
            "total_size_mb": total_size_mb,
            "layer_sizes_mb": layer_sizes_mb,
            "metadata": self.metadata,
        }

        logger.info(
            f"Analysis complete: {num_layers} layers, "
            f"total size: {total_size_mb:.2f} MB, "
            f"avg layer size: {np.mean(layer_sizes_mb):.2f} MB"
        )

        return analysis


class GGUFSplitter:
    """
    Splits a GGUF file into mini-GGUFs, each containing a subset of layers.

    This enables distributed inference where each worker loads only its
    assigned layers from a separate GGUF file.
    """

    def __init__(self, source_gguf: str, output_dir: str):
        """
        Initialize splitter.

        Args:
            source_gguf: Path to source GGUF file (Ollama blob)
            output_dir: Directory to write mini-GGUF files
        """
        self.source_gguf = source_gguf
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(parents=True, exist_ok=True)

        self.reader = GGUFReader(source_gguf)
        self.metadata = {}

        # Extract metadata
        for field in self.reader.fields.values():
            self.metadata[field.name] = field.parts[field.data[0]]

    def split(self, assignments: List[LayerAssignment]) -> List[str]:
        """
        Split GGUF into mini-GGUFs based on layer assignments.

        Args:
            assignments: List of layer assignments from LayerScheduler

        Returns:
            List of paths to generated mini-GGUF files
        """
        logger.info(f"Splitting {self.source_gguf} into {len(assignments)} mini-GGUFs...")

        mini_gguf_paths = []

        for assignment in assignments:
            output_path = self.output_dir / f"worker_{assignment.worker_id}.gguf"
            logger.info(
                f"Creating {output_path} with layers {assignment.layer_start}-{assignment.layer_end-1}"
            )

            # Create mini-GGUF for this worker
            self._create_mini_gguf(
                output_path=str(output_path),
                layer_start=assignment.layer_start,
                layer_end=assignment.layer_end,
                is_first_worker=(assignment.worker_id == 0),
                is_last_worker=(assignment.worker_id == len(assignments) - 1),
            )

            mini_gguf_paths.append(str(output_path))
            logger.info(
                f"Created {output_path} ({os.path.getsize(output_path) / (1024**3):.2f} GB)"
            )

        return mini_gguf_paths

    def _create_mini_gguf(
        self,
        output_path: str,
        layer_start: int,
        layer_end: int,
        is_first_worker: bool,
        is_last_worker: bool,
    ):
        """
        Create a mini-GGUF file containing specific layers.

        Args:
            output_path: Where to write mini-GGUF
            layer_start: First layer (inclusive)
            layer_end: Last layer (exclusive)
            is_first_worker: Include embedding layers
            is_last_worker: Include output layers
        """
        # Get architecture as string (may be numpy array)
        arch = self.metadata.get("general.architecture", "llama")
        if hasattr(arch, "tobytes"):
            # It's a numpy array, decode it
            arch = arch.tobytes().decode("utf-8").strip("\x00")

        writer = GGUFWriter(output_path, arch=arch)

        # Copy essential metadata
        essential_keys = [
            "general.name",
            "general.architecture",
            "general.file_type",
            "tokenizer.ggml.model",
            "tokenizer.ggml.tokens",
            "tokenizer.ggml.scores",
            "tokenizer.ggml.token_type",
            "tokenizer.ggml.bos_token_id",
            "tokenizer.ggml.eos_token_id",
            "tokenizer.ggml.unknown_token_id",
            "tokenizer.ggml.separator_token_id",
            "tokenizer.ggml.padding_token_id",
        ]

        # Copy metadata (simplified - gguf library handles this better)
        # For now, we'll let GGUFWriter auto-handle architecture defaults

        # Filter and write tensors
        tensors_written = 0

        for tensor in self.reader.tensors:
            tensor_name = tensor.name
            should_include = False

            # Check if this tensor belongs to our layer range
            if ".blk." in tensor_name or "blk." in tensor_name:
                # Extract layer ID
                parts = tensor_name.split(".")
                for i, part in enumerate(parts):
                    if part == "blk" and i + 1 < len(parts):
                        try:
                            layer_id = int(parts[i + 1])
                            if layer_start <= layer_id < layer_end:
                                should_include = True
                            break
                        except ValueError:
                            pass

            # Include embeddings for first worker
            elif is_first_worker and ("embed" in tensor_name or "token" in tensor_name):
                should_include = True

            # Include output layers for last worker
            elif is_last_worker and (
                "output" in tensor_name or "lm_head" in tensor_name or "norm" in tensor_name
            ):
                should_include = True

            # Write tensor if it belongs to this worker
            if should_include:
                # Load tensor data
                tensor_data = tensor.data

                # Add tensor to writer
                writer.add_tensor(
                    name=tensor.name,
                    tensor=tensor_data,
                    raw_shape=tensor.shape,
                    raw_dtype=tensor.tensor_type,
                )

                tensors_written += 1

        # Write the file
        writer.write_header_to_file()
        writer.write_kv_data_to_file()
        writer.write_tensors_to_file()
        writer.close()

        logger.debug(f"Wrote {tensors_written} tensors to {output_path}")


class LayerScheduler:
    """
    Schedules layer assignment across workers based on available resources.

    Implements a simplified version of prima.cpp's Halda scheduler:
    - Assigns contiguous layer ranges to minimize communication
    - Balances memory usage across workers
    - Considers node heterogeneity (different RAM/VRAM capacities)
    """

    def __init__(self, analysis: Dict[str, Any]):
        """
        Initialize scheduler with GGUF analysis results.

        Args:
            analysis: Output from GGUFLayerAnalyzer.analyze()
        """
        self.analysis = analysis
        self.num_layers = analysis["num_layers"]
        self.layer_sizes_mb = analysis["layer_sizes_mb"]
        self.total_size_mb = analysis["total_size_mb"]

    def schedule(
        self, worker_memory_mb: List[int], embedding_worker: int = 0, output_worker: int = -1
    ) -> List[LayerAssignment]:
        """
        Assign layers to workers based on available memory.

        Args:
            worker_memory_mb: List of available memory per worker (in MB)
            embedding_worker: Worker ID to handle embeddings (default: first worker)
            output_worker: Worker ID to handle output layer (default: last worker)

        Returns:
            List of LayerAssignment objects
        """
        num_workers = len(worker_memory_mb)

        if output_worker == -1:
            output_worker = num_workers - 1

        logger.info(
            f"Scheduling {self.num_layers} layers across {num_workers} workers "
            f"(total {sum(worker_memory_mb)} MB available)"
        )

        # Simple greedy algorithm: assign layers proportional to memory
        total_memory = sum(worker_memory_mb)
        memory_fractions = [mem / total_memory for mem in worker_memory_mb]

        assignments = []
        current_layer = 0

        for worker_id in range(num_workers):
            # Calculate layer range for this worker
            target_layers = int(self.num_layers * memory_fractions[worker_id])
            layer_end = min(current_layer + target_layers, self.num_layers)

            # Ensure last worker gets remaining layers
            if worker_id == num_workers - 1:
                layer_end = self.num_layers

            # Calculate memory requirement
            memory_required = sum(self.layer_sizes_mb[current_layer:layer_end])

            assignment = LayerAssignment(
                worker_id=worker_id,
                layer_start=current_layer,
                layer_end=layer_end,
                node_address=f"worker-{worker_id}",
                memory_mb=int(memory_required),
            )
            assignments.append(assignment)

            logger.info(
                f"Worker {worker_id}: layers {current_layer}-{layer_end-1}, "
                f"memory: {memory_required:.2f} MB / {worker_memory_mb[worker_id]} MB available"
            )

            current_layer = layer_end

            if current_layer >= self.num_layers:
                break

        return assignments


# Ray actors will be implemented in the next step
@ray.remote
class LlamaLayerWorker:
    """
    Ray actor that loads and serves a subset of model layers.

    Each worker:
    1. Loads only its assigned layers from GGUF
    2. Accepts hidden states from previous worker
    3. Processes through its layers
    4. Returns hidden states to next worker
    """

    def __init__(self, worker_id: int, gguf_path: str, layer_start: int, layer_end: int):
        """
        Initialize worker with layer assignment.

        Args:
            worker_id: Unique worker identifier
            gguf_path: Path to GGUF model file
            layer_start: First layer index (inclusive)
            layer_end: Last layer index (exclusive)
        """
        self.worker_id = worker_id
        self.gguf_path = gguf_path
        self.layer_start = layer_start
        self.layer_end = layer_end
        self.loaded = False

        logger.info(f"Worker {worker_id} initialized: layers {layer_start}-{layer_end-1}")

    def load_layers(self):
        """
        Load assigned layers from GGUF file.

        This is a placeholder - actual implementation would:
        1. Parse GGUF and extract only assigned layer tensors
        2. Load tensors into llama.cpp context
        3. Initialize layer processing
        """
        logger.info(
            f"Worker {self.worker_id}: Loading layers {self.layer_start}-{self.layer_end-1}"
        )

        # TODO: Implement actual GGUF layer extraction and llama.cpp integration
        # For now, just mark as loaded
        self.loaded = True

        logger.info(f"Worker {self.worker_id}: Layers loaded successfully")
        return True

    def forward(self, hidden_states: np.ndarray) -> np.ndarray:
        """
        Process hidden states through assigned layers.

        Args:
            hidden_states: Input activations from previous worker

        Returns:
            Output activations after processing through layers
        """
        if not self.loaded:
            raise RuntimeError(f"Worker {self.worker_id}: Layers not loaded")

        logger.debug(
            f"Worker {self.worker_id}: Processing hidden states " f"shape {hidden_states.shape}"
        )

        # TODO: Implement actual layer forward pass using llama.cpp
        # For now, just return input (identity function)
        return hidden_states

    def get_stats(self) -> Dict[str, Any]:
        """Get worker statistics for dashboard."""
        return {
            "worker_id": self.worker_id,
            "layer_start": self.layer_start,
            "layer_end": self.layer_end,
            "loaded": self.loaded,
        }


class DistributedPipelineInference:
    """
    Coordinates distributed inference across Ray workers.

    Manages the pipeline of workers and orchestrates token-by-token generation.
    """

    def __init__(
        self, gguf_path: str, worker_memory_mb: List[int], ray_address: Optional[str] = None
    ):
        """
        Initialize distributed pipeline.

        Args:
            gguf_path: Path to GGUF model file
            worker_memory_mb: Available memory per worker node
            ray_address: Ray cluster address (None for local)
        """
        self.gguf_path = gguf_path
        self.worker_memory_mb = worker_memory_mb

        # Initialize Ray if not already connected
        if not ray.is_initialized():
            if ray_address:
                ray.init(address=ray_address)
            else:
                ray.init()

        # Analyze model
        logger.info("Analyzing model structure...")
        analyzer = GGUFLayerAnalyzer(gguf_path)
        self.analysis = analyzer.analyze()

        # Schedule layer assignment
        logger.info("Scheduling layer assignment...")
        scheduler = LayerScheduler(self.analysis)
        self.assignments = scheduler.schedule(worker_memory_mb)

        # Create workers
        logger.info("Creating Ray workers...")
        self.workers = []
        for assignment in self.assignments:
            worker = LlamaLayerWorker.remote(
                worker_id=assignment.worker_id,
                gguf_path=gguf_path,
                layer_start=assignment.layer_start,
                layer_end=assignment.layer_end,
            )
            self.workers.append(worker)

        logger.info(f"Created {len(self.workers)} workers")

    async def start(self):
        """Load layers on all workers."""
        logger.info("Loading layers on all workers...")
        load_tasks = [worker.load_layers.remote() for worker in self.workers]
        results = await asyncio.gather(*load_tasks)
        logger.info(f"All workers loaded: {all(results)}")
        return all(results)

    async def generate(self, prompt: str, max_tokens: int = 100) -> str:
        """
        Generate text using distributed pipeline.

        Args:
            prompt: Input text
            max_tokens: Maximum tokens to generate

        Returns:
            Generated text
        """
        logger.info(f"Generating {max_tokens} tokens for prompt: {prompt[:50]}...")

        # TODO: Implement actual token generation pipeline
        # This requires:
        # 1. Tokenization
        # 2. Embedding lookup
        # 3. Pipeline forward passes through all workers
        # 4. Output projection and sampling
        # 5. Detokenization

        raise NotImplementedError("Token generation pipeline not yet implemented")

    def get_dashboard_stats(self) -> Dict[str, Any]:
        """Get statistics for dashboard display."""
        return {
            "num_workers": len(self.workers),
            "num_layers": self.analysis["num_layers"],
            "total_size_mb": self.analysis["total_size_mb"],
            "assignments": [
                {
                    "worker_id": a.worker_id,
                    "layers": f"{a.layer_start}-{a.layer_end-1}",
                    "memory_mb": a.memory_mb,
                }
                for a in self.assignments
            ],
        }
