"""Liquid AI LFM embeddings adapter implementation for memory-efficient edge deployment."""

import time

import typing as t
from datetime import datetime
from pydantic import Field

from acb.adapters import (
    AdapterCapability,
    AdapterMetadata,
    AdapterStatus,
    generate_adapter_id,
)
from acb.adapters.embedding._base import (
    EmbeddingAdapter,
    EmbeddingBaseSettings,
    EmbeddingBatch,
    EmbeddingModel,
    EmbeddingResult,
    EmbeddingUtils,
)
from acb.config import Config
from acb.depends import depends

if t.TYPE_CHECKING:
    pass

# Liquid AI LFM imports (placeholder - would be actual LFM library)
try:
    # import liquid_ai_lfm as lfm
    # from liquid_ai_lfm import LFMEmbeddingModel, LFMTokenizer
    # _lfm_available = True

    # For now, we'll simulate the API
    _lfm_available = False
    lfm = None
    LFMEmbeddingModel = None
    LFMTokenizer = None
except ImportError:
    lfm = None
    LFMEmbeddingModel = None
    LFMTokenizer = None
    _lfm_available = False

MODULE_METADATA = AdapterMetadata(
    module_id=generate_adapter_id(),
    name="Liquid AI LFM Embeddings",
    category="embedding",
    provider="liquid_ai",
    version="1.0.0",
    acb_min_version="0.19.0",
    author="ACB Development Team",
    created_date="2025-09-30",
    last_modified="2025-09-30",
    status=AdapterStatus.EXPERIMENTAL,  # Experimental until LFM is released
    capabilities=[
        AdapterCapability.ASYNC_OPERATIONS,
        AdapterCapability.BATCHING,
        AdapterCapability.CACHING,
        AdapterCapability.METRICS,
        AdapterCapability.BATCH_EMBEDDING,
        AdapterCapability.EDGE_OPTIMIZED,
        AdapterCapability.MEMORY_EFFICIENT_PROCESSING,
        AdapterCapability.TEXT_PREPROCESSING,
        AdapterCapability.VECTOR_NORMALIZATION,
        AdapterCapability.STREAMING,
        AdapterCapability.MODEL_CACHING,
    ],
    required_packages=["liquid-ai-lfm>=1.0.0"],  # Placeholder package name
    description="Liquid AI LFM embeddings adapter for memory-efficient edge deployment and on-device inference",
    settings_class="LiquidLFMEmbeddingSettings",
)


class LiquidLFMEmbeddingSettings(EmbeddingBaseSettings):
    """Liquid AI LFM-specific embedding settings."""

    model: str = Field(default=EmbeddingModel.LIQUID_EFFICIENT_BASE.value)
    device: str = Field(
        default="auto",
        description="Device to run model on (cpu, cuda, edge, auto)",
    )

    # LFM-specific memory optimization
    memory_limit_mb: int = Field(
        default=256,
        description="Memory limit for edge deployment",
    )
    adaptive_precision: bool = Field(
        default=True,
        description="Use adaptive precision for memory efficiency",
    )
    streaming_mode: bool = Field(
        default=True,
        description="Enable streaming mode for large inputs",
    )

    # Edge optimization settings
    edge_optimized: bool = Field(
        default=True,
        description="Optimize for edge deployment",
    )
    quantization_level: str = Field(
        default="auto",
        description="Quantization level (int8, int4, auto)",
    )
    compression_ratio: float = Field(default=0.8, description="Model compression ratio")

    # Performance settings
    batch_size: int = Field(
        default=8,
        description="Smaller batch size for memory efficiency",
    )
    max_seq_length: int = Field(
        default=256,
        description="Shorter sequences for edge devices",
    )
    enable_caching: bool = Field(default=True, description="Enable model caching")

    # Serverless optimization
    cold_start_optimization: bool = Field(
        default=True,
        description="Optimize for serverless cold starts",
    )
    model_preloading: bool = Field(default=False, description="Preload models")
    lazy_loading: bool = Field(default=True, description="Enable lazy model loading")

    class Config:
        env_prefix = "LFM_"


class LiquidLFMEmbedding(EmbeddingAdapter):
    """Liquid AI LFM embeddings adapter implementation."""

    def __init__(self, settings: LiquidLFMEmbeddingSettings | None = None) -> None:
        if not _lfm_available:
            # For now, we'll provide a warning but still allow instantiation
            # This will be updated when actual LFM library is available
            pass
            # raise ImportError(
            #     "Liquid AI LFM library not available. Install with: pip install liquid-ai-lfm"
            # )

        depends.get_sync("config")
        if settings is None:
            settings = LiquidLFMEmbeddingSettings()

        super().__init__(settings)
        self._settings: LiquidLFMEmbeddingSettings = settings
        self._model: t.Any = None
        self._tokenizer: t.Any = None
        self._device: str | None = None
        self._memory_monitor: dict[str, t.Any] | None = None

    async def _ensure_client(self) -> tuple[t.Any, t.Any]:
        """Ensure LFM model and tokenizer are loaded."""
        if self._model is None or self._tokenizer is None:
            await self._load_model()

        return self._model, self._tokenizer

    async def _load_model(self) -> None:
        """Load the Liquid AI LFM model with memory-efficient settings."""
        logger: t.Any = await depends.get("logger")

        try:
            # Determine optimal device based on constraints
            self._device = await self._determine_optimal_device()

            await logger.info(
                f"Loading Liquid AI LFM model: {self._settings.model} on {self._device}",
            )

            if _lfm_available:
                # Actual LFM implementation (when available)
                # model_config = lfm.LFMConfig(
                #     model_name=self._settings.model,
                #     device=self._device,
                #     memory_limit_mb=self._settings.memory_limit_mb,
                #     adaptive_precision=self._settings.adaptive_precision,
                #     edge_optimized=self._settings.edge_optimized,
                #     quantization_level=self._settings.quantization_level,
                #     compression_ratio=self._settings.compression_ratio,
                # )

                # self._model = await asyncio.get_event_loop().run_in_executor(
                #     None,
                #     lambda: LFMEmbeddingModel.from_pretrained(
                #         self._settings.model,
                #         config=model_config,
                #     ),
                # )

                # self._tokenizer = await asyncio.get_event_loop().run_in_executor(
                #     None,
                #     lambda: LFMTokenizer.from_pretrained(self._settings.model),
                # )
                pass
            else:
                # Simulation mode - create mock objects
                await self._load_simulation_model()

            # Initialize memory monitoring
            if self._settings.edge_optimized:
                await self._initialize_memory_monitor()

            # Register for cleanup
            if self._model:
                self.register_resource(self._model)

            await logger.info(
                f"Successfully loaded Liquid AI LFM model with memory limit: {self._settings.memory_limit_mb}MB",
            )

        except Exception as e:
            await logger.exception(f"Error loading Liquid AI LFM model: {e}")
            raise

    async def _load_simulation_model(self) -> None:
        """Load simulation model for development/testing."""
        import numpy as np

        # Create mock model that simulates LFM behavior
        class MockLFMModel:
            def __init__(self, dimension: int = 384) -> None:
                self.dimension = dimension
                self.memory_usage = 0

            async def encode(self, texts: list[str], **kwargs: t.Any) -> np.ndarray:
                # Simulate memory-efficient encoding
                len(texts)
                # Create deterministic embeddings based on text hash
                embeddings = []
                for text in texts:
                    seed = hash(text) % 10000
                    np.random.seed(seed)
                    embedding = np.random.normal(0, 1, self.dimension).astype(
                        np.float32,
                    )
                    # Normalize
                    embedding = (embedding / np.linalg.norm(embedding)).astype(
                        np.float32
                    )
                    embeddings.append(embedding)

                return np.array(embeddings)

        class MockTokenizer:
            def encode(self, text: str) -> list[int]:
                # Simple word-based tokenization simulation
                return [hash(word) % 50000 for word in text.split()]

        self._model = MockLFMModel()
        self._tokenizer = MockTokenizer()

    async def _determine_optimal_device(self) -> str:
        """Determine optimal device based on memory constraints and availability."""
        if self._settings.device != "auto":
            return self._settings.device

        # LFM-specific device optimization logic
        memory_limit = self._settings.memory_limit_mb

        if memory_limit < 128:
            return "edge"  # Ultra-low memory edge device
        if memory_limit < 512:
            return "cpu"  # CPU with memory constraints
        return "cuda" if self._has_cuda() else "cpu"

    def _has_cuda(self) -> bool:
        """Check if CUDA is available (simulation)."""
        try:
            import torch  # type: ignore[import-not-found]

            result: bool = torch.cuda.is_available()
            return result
        except ImportError:
            return False

    async def _initialize_memory_monitor(self) -> None:
        """Initialize memory monitoring for edge deployment."""
        # This would integrate with actual LFM memory monitoring
        self._memory_monitor = {
            "peak_usage": 0,
            "current_usage": 0,
            "limit": self._settings.memory_limit_mb,
            "adaptive_mode": self._settings.adaptive_precision,
        }

    async def _embed_texts(
        self,
        texts: list[str],
        model: str,
        normalize: bool,
        batch_size: int,
        **kwargs: t.Any,
    ) -> EmbeddingBatch:
        """Generate embeddings using Liquid AI LFM with memory optimization."""
        start_time = time.time()
        model_obj, tokenizer = await self._ensure_client()
        logger: t.Any = await depends.get("logger")

        results = []

        # Adaptive batch processing based on memory constraints
        adaptive_batch_size = await self._calculate_adaptive_batch_size(
            batch_size,
            texts,
        )
        batches = self._batch_texts(texts, adaptive_batch_size)

        for batch_texts in batches:
            try:
                # Monitor memory before processing
                if self._memory_monitor:
                    await self._check_memory_usage()

                # Generate embeddings with LFM optimizations
                if _lfm_available:
                    # Actual LFM implementation
                    # embeddings = await model_obj.encode(
                    #     batch_texts,
                    #     normalize=normalize,
                    #     streaming=self._settings.streaming_mode,
                    #     memory_efficient=True,
                    # )
                    pass
                else:
                    # Simulation implementation
                    embeddings = await model_obj.encode(batch_texts)

                # Create results with LFM-specific metadata
                for i, embedding in enumerate(embeddings):
                    result = EmbeddingResult(
                        text=batch_texts[i],
                        embedding=embedding.tolist()
                        if hasattr(embedding, "tolist")
                        else embedding,
                        model=model,
                        dimensions=len(embedding),
                        tokens=len(tokenizer.encode(batch_texts[i]))
                        if tokenizer
                        else None,
                        metadata={
                            "provider": "liquid_ai_lfm",
                            "device": self._device,
                            "memory_optimized": True,
                            "edge_optimized": self._settings.edge_optimized,
                            "compression_ratio": self._settings.compression_ratio,
                            "quantization": self._settings.quantization_level,
                            "batch_size": adaptive_batch_size,
                        },
                    )
                    results.append(result)

                await logger.debug(
                    f"LFM embeddings batch completed: {len(batch_texts)} texts, "
                    f"memory usage: {self._get_memory_usage():.1f}MB",
                )

            except Exception as e:
                await logger.exception(f"Error generating LFM embeddings: {e}")
                raise

        processing_time = time.time() - start_time
        total_tokens = sum(result.tokens or 0 for result in results)

        return EmbeddingBatch(
            results=results,
            total_tokens=total_tokens if total_tokens > 0 else None,
            processing_time=processing_time,
            model=model,
            batch_size=len(results),
        )

    async def _calculate_adaptive_batch_size(
        self,
        requested_batch_size: int,
        texts: list[str],
    ) -> int:
        """Calculate optimal batch size based on memory constraints and text lengths."""
        if not self._settings.edge_optimized:
            return requested_batch_size

        # Estimate memory usage based on text lengths
        avg_text_length = sum(len(text) for text in texts) / len(texts) if texts else 0
        memory_per_text = (avg_text_length / 100) * 2  # Rough estimate in MB

        max_batch_for_memory = int(
            self._settings.memory_limit_mb * 0.7 / memory_per_text,
        )

        return min(
            requested_batch_size,
            max_batch_for_memory,
            16,
        )  # Cap at 16 for edge devices

    async def _check_memory_usage(self) -> None:
        """Check and manage memory usage during processing."""
        if not self._memory_monitor:
            return

        # Simulate memory usage check
        current_usage = self._get_memory_usage()
        self._memory_monitor["current_usage"] = current_usage

        if current_usage > self._settings.memory_limit_mb * 0.9:
            # Trigger memory optimization
            await self._optimize_memory()

    def _get_memory_usage(self) -> float:
        """Get current memory usage (simulation)."""
        # In real implementation, this would use LFM's memory monitoring
        return self._memory_monitor["current_usage"] if self._memory_monitor else 0.0

    async def _optimize_memory(self) -> None:
        """Optimize memory usage when approaching limits."""
        logger: t.Any = await depends.get("logger")
        await logger.info("Memory limit approached, optimizing")

        # LFM-specific memory optimization strategies would go here
        if self._memory_monitor:
            self._memory_monitor["current_usage"] *= 0.8  # Simulate optimization

    async def _embed_documents(
        self,
        documents: list[str],
        chunk_size: int,
        chunk_overlap: int,
        model: str,
        **kwargs: t.Any,
    ) -> list[EmbeddingBatch]:
        """Embed large documents with memory-efficient chunking."""
        batches = []

        # Adaptive chunking based on memory constraints
        adaptive_chunk_size = min(chunk_size, self._settings.max_seq_length)

        for document in documents:
            # Split document into chunks
            chunks = self._chunk_text(document, adaptive_chunk_size, chunk_overlap)

            # Generate embeddings for chunks
            batch = await self._embed_texts(
                chunks,
                model=model,
                normalize=self._settings.normalize_embeddings,
                batch_size=self._settings.batch_size,
                **kwargs,
            )

            # Add document metadata
            for result in batch.results:
                result.metadata.update(
                    {
                        "document_id": hash(document),
                        "is_chunk": True,
                        "chunk_size": adaptive_chunk_size,
                        "chunk_overlap": chunk_overlap,
                        "memory_optimized": True,
                    },
                )

            batches.append(batch)

        return batches

    async def _compute_similarity(
        self,
        embedding1: list[float],
        embedding2: list[float],
        method: str,
    ) -> float:
        """Compute similarity with memory-efficient operations."""
        if method == "cosine":
            return EmbeddingUtils.cosine_similarity(embedding1, embedding2)
        if method == "euclidean":
            return EmbeddingUtils.euclidean_distance(embedding1, embedding2)
        if method == "dot":
            return EmbeddingUtils.dot_product(embedding1, embedding2)
        if method == "manhattan":
            return EmbeddingUtils.manhattan_distance(embedding1, embedding2)
        msg = f"Unsupported similarity method: {method}"
        raise ValueError(msg)

    async def _get_model_info(self, model: str) -> dict[str, t.Any]:
        """Get information about the LFM model."""
        model_info = {
            "name": model,
            "provider": "liquid_ai_lfm",
            "type": "embedding",
            "device": self._device,
            "memory_optimized": True,
            "edge_optimized": self._settings.edge_optimized,
            "memory_limit_mb": self._settings.memory_limit_mb,
            "compression_ratio": self._settings.compression_ratio,
            "quantization": self._settings.quantization_level,
        }

        if self._memory_monitor:
            model_info.update(
                {
                    "memory_usage": self._memory_monitor["current_usage"],
                    "peak_memory": self._memory_monitor["peak_usage"],
                },
            )

        return model_info

    async def _list_models(self) -> list[dict[str, t.Any]]:
        """List available Liquid AI LFM models."""
        models = [
            {
                "name": "liquid-efficient-base",
                "description": "Base LFM model optimized for efficiency",
                "dimensions": 384,
                "memory_requirement": "128MB",
                "edge_compatible": True,
            },
            {
                "name": "liquid-edge-optimized",
                "description": "Ultra-lightweight LFM model for edge devices",
                "dimensions": 256,
                "memory_requirement": "64MB",
                "edge_compatible": True,
            },
        ]

        return [
            model_info
            | {
                "provider": "liquid_ai_lfm",
                "type": "embedding",
            }
            for model_info in models
        ]

    async def get_edge_metrics(self) -> dict[str, t.Any]:
        """Get edge deployment metrics."""
        return {
            "device": self._device,
            "memory_usage": self._get_memory_usage(),
            "memory_limit": self._settings.memory_limit_mb,
            "memory_efficiency": (
                self._settings.memory_limit_mb - self._get_memory_usage()
            )
            / self._settings.memory_limit_mb,
            "edge_optimized": self._settings.edge_optimized,
            "compression_ratio": self._settings.compression_ratio,
            "quantization_level": self._settings.quantization_level,
            "adaptive_precision": self._settings.adaptive_precision,
            "streaming_mode": self._settings.streaming_mode,
        }

    async def health_check(self) -> dict[str, t.Any]:
        """Perform health check on LFM embeddings service."""
        try:
            # Test with a simple embedding request
            test_result = await self.embed_text("health check test")

            return {
                "status": "healthy",
                "provider": "liquid_ai_lfm",
                "model": self._settings.model,
                "device": self._device,
                "dimensions": len(test_result),
                "memory_usage": self._get_memory_usage(),
                "memory_limit": self._settings.memory_limit_mb,
                "edge_optimized": self._settings.edge_optimized,
                "timestamp": datetime.now().isoformat(),
            }
        except Exception as e:
            return {
                "status": "unhealthy",
                "provider": "liquid_ai_lfm",
                "error": str(e),
                "timestamp": datetime.now().isoformat(),
            }

    async def cleanup(self) -> None:
        """Clean up LFM model and resources."""
        if self._model is not None:
            del self._model
            self._model = None

        if self._tokenizer is not None:
            del self._tokenizer
            self._tokenizer = None

        self._memory_monitor = None

        await super().cleanup()


# Factory function for dependency injection
async def create_lfm_embedding(config: Config | None = None) -> LiquidLFMEmbedding:
    """Create Liquid AI LFM embedding adapter instance."""
    if config is None:
        config = await depends.get("config")

    settings = LiquidLFMEmbeddingSettings()
    return LiquidLFMEmbedding(settings)


# Type alias
Embedding = LiquidLFMEmbedding
EmbeddingSettings = LiquidLFMEmbeddingSettings

depends.set(Embedding, "lfm")
