"""Utility functions for ChromaDB client implementation."""

import hashlib
import json
from collections.abc import Mapping
from typing import Literal, TypeGuard, cast

from chromadb.api import AsyncClientAPI, ClientAPI
from chromadb.api.models.AsyncCollection import AsyncCollection
from chromadb.api.models.Collection import Collection
from chromadb.api.types import (
    Include,
    QueryResult,
)

from crewai.rag.chromadb.constants import (
    DEFAULT_COLLECTION,
    INVALID_CHARS_PATTERN,
    IPV4_PATTERN,
    MAX_COLLECTION_LENGTH,
    MIN_COLLECTION_LENGTH,
)
from crewai.rag.chromadb.types import (
    ChromaDBClientType,
    ChromaDBCollectionSearchParams,
    ExtractedSearchParams,
    PreparedDocuments,
)
from crewai.rag.types import BaseRecord, SearchResult


def _is_sync_client(client: ChromaDBClientType) -> TypeGuard[ClientAPI]:
    """Type guard to check if the client is a synchronous ClientAPI.

    Args:
        client: The client to check.

    Returns:
        True if the client is a ClientAPI, False otherwise.
    """
    return isinstance(client, ClientAPI)


def _is_async_client(client: ChromaDBClientType) -> TypeGuard[AsyncClientAPI]:
    """Type guard to check if the client is an asynchronous AsyncClientAPI.

    Args:
        client: The client to check.

    Returns:
        True if the client is an AsyncClientAPI, False otherwise.
    """
    return isinstance(client, AsyncClientAPI)


def _prepare_documents_for_chromadb(
    documents: list[BaseRecord],
) -> PreparedDocuments:
    """Prepare documents for ChromaDB by extracting IDs, texts, and metadata.

    Args:
        documents: List of BaseRecord documents to prepare.

    Returns:
        PreparedDocuments with ids, texts, and metadatas ready for ChromaDB.
    """
    ids: list[str] = []
    texts: list[str] = []
    metadatas: list[Mapping[str, str | int | float | bool]] = []

    for doc in documents:
        if "doc_id" in doc:
            ids.append(doc["doc_id"])
        else:
            content_for_hash = doc["content"]
            metadata = doc.get("metadata")
            if metadata:
                metadata_str = json.dumps(metadata, sort_keys=True)
                content_for_hash = f"{content_for_hash}|{metadata_str}"

            content_hash = hashlib.blake2b(
                content_for_hash.encode(), digest_size=32
            ).hexdigest()
            ids.append(content_hash)

        texts.append(doc["content"])
        metadata = doc.get("metadata")
        if metadata:
            if isinstance(metadata, list):
                metadatas.append(metadata[0] if metadata and metadata[0] else {})
            else:
                metadatas.append(metadata)
        else:
            metadatas.append({})

    return PreparedDocuments(ids, texts, metadatas)


def _create_batch_slice(
    prepared: PreparedDocuments, start_index: int, batch_size: int
) -> tuple[list[str], list[str], list[Mapping[str, str | int | float | bool]] | None]:
    """Create a batch slice from prepared documents.

    Args:
        prepared: PreparedDocuments containing ids, texts, and metadatas.
        start_index: Starting index for the batch.
        batch_size: Size of the batch.

    Returns:
        Tuple of (batch_ids, batch_texts, batch_metadatas).
    """
    batch_end = min(start_index + batch_size, len(prepared.ids))
    batch_ids = prepared.ids[start_index:batch_end]
    batch_texts = prepared.texts[start_index:batch_end]
    batch_metadatas = (
        prepared.metadatas[start_index:batch_end] if prepared.metadatas else None
    )

    if batch_metadatas and not any(m for m in batch_metadatas):
        batch_metadatas = None

    return batch_ids, batch_texts, batch_metadatas


def _extract_search_params(
    kwargs: ChromaDBCollectionSearchParams,
) -> ExtractedSearchParams:
    """Extract search parameters from kwargs.

    Args:
        kwargs: Keyword arguments containing search parameters.

    Returns:
        ExtractedSearchParams with all extracted parameters.
    """
    return ExtractedSearchParams(
        collection_name=kwargs["collection_name"],
        query=kwargs["query"],
        limit=kwargs.get("limit", 10),
        metadata_filter=kwargs.get("metadata_filter"),
        score_threshold=kwargs.get("score_threshold"),
        where=kwargs.get("where"),
        where_document=kwargs.get("where_document"),
        include=cast(
            Include,
            kwargs.get(
                "include",
                ["metadatas", "documents", "distances"],
            ),
        ),
    )


def _convert_distance_to_score(
    distance: float,
    distance_metric: Literal["l2", "cosine", "ip"],
) -> float:
    """Convert ChromaDB distance to similarity score.

    Notes:
        Assuming all embedding are unit-normalized for now, including custom embeddings.

    Args:
        distance: The distance value from ChromaDB.
        distance_metric: The distance metric used ("l2", "cosine", or "ip").

    Returns:
        Similarity score in range [0, 1] where 1 is most similar.
    """
    if distance_metric == "cosine":
        score = 1.0 - 0.5 * distance
        return max(0.0, min(1.0, score))
    if distance_metric == "l2":
        score = 1.0 / (1.0 + distance)
        return max(0.0, min(1.0, score))
    raise ValueError(f"Unsupported distance metric: {distance_metric}")


def _convert_chromadb_results_to_search_results(
    results: QueryResult,
    include: Include,
    distance_metric: Literal["l2", "cosine", "ip"],
    score_threshold: float | None = None,
) -> list[SearchResult]:
    """Convert ChromaDB query results to SearchResult format.

    Args:
        results: ChromaDB query results.
        include: List of fields that were included in the query.
        distance_metric: The distance metric used by the collection.
        score_threshold: Optional minimum similarity score (0-1) for results.

    Returns:
        List of SearchResult dicts containing id, content, metadata, and score.
    """
    search_results: list[SearchResult] = []

    include_strings = list(include) if include else []

    ids = results["ids"][0] if results.get("ids") else []

    documents_list = results.get("documents")
    documents = (
        documents_list[0] if documents_list and "documents" in include_strings else []
    )

    metadatas_list = results.get("metadatas")
    metadatas = (
        metadatas_list[0] if metadatas_list and "metadatas" in include_strings else []
    )

    distances_list = results.get("distances")
    distances = (
        distances_list[0] if distances_list and "distances" in include_strings else []
    )

    for i, doc_id in enumerate(ids):
        if not distances or i >= len(distances):
            continue

        distance = distances[i]
        score = _convert_distance_to_score(
            distance=distance, distance_metric=distance_metric
        )

        if score_threshold and score < score_threshold:
            continue

        result: SearchResult = {
            "id": doc_id,
            "content": documents[i] if documents and i < len(documents) else "",
            "metadata": dict(metadatas[i])
            if metadatas and i < len(metadatas) and metadatas[i] is not None
            else {},
            "score": score,
        }
        search_results.append(result)

    return search_results


def _process_query_results(
    collection: Collection | AsyncCollection,
    results: QueryResult,
    params: ExtractedSearchParams,
) -> list[SearchResult]:
    """Process ChromaDB query results and convert to SearchResult format.

    Args:
        collection: The ChromaDB collection (sync or async) that was queried.
        results: Raw query results from ChromaDB.
        params: The search parameters used for the query.

    Returns:
        List of SearchResult dicts containing id, content, metadata, and score.
    """

    distance_metric = cast(
        Literal["l2", "cosine", "ip"],
        collection.metadata.get("hnsw:space", "l2") if collection.metadata else "l2",
    )

    return _convert_chromadb_results_to_search_results(
        results=results,
        include=params.include,
        distance_metric=distance_metric,
        score_threshold=params.score_threshold,
    )


def _is_ipv4_pattern(name: str) -> bool:
    """Check if a string matches an IPv4 address pattern.

    Args:
        name: The string to check

    Returns:
        True if the string matches an IPv4 pattern, False otherwise
    """
    return bool(IPV4_PATTERN.match(name))


def _sanitize_collection_name(
    name: str | None, max_collection_length: int = MAX_COLLECTION_LENGTH
) -> str:
    """Sanitize a collection name to meet ChromaDB requirements.

    Requirements:
    1. 3-63 characters long
    2. Starts and ends with alphanumeric character
    3. Contains only alphanumeric characters, underscores, or hyphens
    4. No consecutive periods
    5. Not a valid IPv4 address

    Args:
        name: The original collection name to sanitize
        max_collection_length: Maximum allowed length for the collection name

    Returns:
        A sanitized collection name that meets ChromaDB requirements
    """
    if not name:
        return DEFAULT_COLLECTION

    if _is_ipv4_pattern(name):
        name = f"ip_{name}"

    sanitized = INVALID_CHARS_PATTERN.sub("_", name)

    if not sanitized[0].isalnum():
        sanitized = "a" + sanitized

    if not sanitized[-1].isalnum():
        sanitized = sanitized[:-1] + "z"

    if len(sanitized) < MIN_COLLECTION_LENGTH:
        sanitized += "x" * (MIN_COLLECTION_LENGTH - len(sanitized))
    if len(sanitized) > max_collection_length:
        sanitized = sanitized[:max_collection_length]
        if not sanitized[-1].isalnum():
            sanitized = sanitized[:-1] + "z"

    return sanitized