"""Test Couchbase Cache functionality"""

import os
from datetime import datetime, timedelta
from typing import Any

import pytest
from couchbase.auth import PasswordAuthenticator
from couchbase.cluster import Cluster
from couchbase.options import ClusterOptions
from langchain_core.globals import get_llm_cache, set_llm_cache
from langchain_core.outputs import Generation

from langchain_couchbase.cache import CouchbaseCache, CouchbaseSemanticCache
from tests.utils import (
    FakeEmbeddings,
    FakeLLM,
    cache_key_hash_function,
    fetch_document_expiry_time,
    get_document_keys,
)

CONNECTION_STRING = os.getenv("COUCHBASE_CONNECTION_STRING", "")
BUCKET_NAME = os.getenv("COUCHBASE_BUCKET_NAME", "")
SCOPE_NAME = os.getenv("COUCHBASE_SCOPE_NAME", "")
CACHE_COLLECTION_NAME = os.getenv("COUCHBASE_CACHE_COLLECTION_NAME", "")
SEMANTIC_CACHE_COLLECTION_NAME = os.getenv(
    "COUCHBASE_SEMANTIC_CACHE_COLLECTION_NAME", ""
)
USERNAME = os.getenv("COUCHBASE_USERNAME", "")
PASSWORD = os.getenv("COUCHBASE_PASSWORD", "")
INDEX_NAME = os.getenv("COUCHBASE_SEMANTIC_CACHE_INDEX_NAME", "")


def set_all_env_vars() -> bool:
    """Check if all environment variables are set"""
    return all(
        [
            CONNECTION_STRING,
            BUCKET_NAME,
            SCOPE_NAME,
            CACHE_COLLECTION_NAME,
            USERNAME,
            PASSWORD,
            INDEX_NAME,
        ]
    )


def get_cluster() -> Any:
    """Get a couchbase cluster object"""
    auth = PasswordAuthenticator(USERNAME, PASSWORD)
    options = ClusterOptions(auth)
    connect_string = CONNECTION_STRING
    cluster = Cluster(connect_string, options)

    # Wait until the cluster is ready for use.
    cluster.wait_until_ready(timedelta(seconds=5))

    return cluster


@pytest.fixture()
def cluster() -> Any:
    """Get a couchbase cluster object"""
    return get_cluster()


@pytest.mark.skipif(
    not set_all_env_vars(), reason="Missing Couchbase environment variables"
)
class TestCouchbaseCache:
    def test_cache(self, cluster: Any) -> None:
        """Test standard LLM cache functionality"""
        set_llm_cache(
            CouchbaseCache(
                cluster=cluster,
                bucket_name=BUCKET_NAME,
                scope_name=SCOPE_NAME,
                collection_name=CACHE_COLLECTION_NAME,
            )
        )

        llm = FakeLLM()

        params = llm.dict()
        params["stop"] = None
        llm_string = str(sorted([(k, v) for k, v in params.items()]))
        get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
        cache_output = get_llm_cache().lookup("foo", llm_string)
        assert cache_output == [Generation(text="fizz")]

        get_llm_cache().clear()
        output = get_llm_cache().lookup("bar", llm_string)
        assert output != [Generation(text="fizz")]

    def test_cache_with_ttl(self, cluster: Any) -> None:
        """Test standard LLM cache functionality with TTL"""
        ttl = timedelta(minutes=10)
        set_llm_cache(
            CouchbaseCache(
                cluster=cluster,
                bucket_name=BUCKET_NAME,
                scope_name=SCOPE_NAME,
                collection_name=CACHE_COLLECTION_NAME,
                ttl=ttl,
            )
        )

        llm = FakeLLM()

        params = llm.dict()
        params["stop"] = None
        llm_string = str(sorted([(k, v) for k, v in params.items()]))
        get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
        cache_output = get_llm_cache().lookup("foo", llm_string)
        assert cache_output == [Generation(text="fizz")]

        # Check the document's expiry time by fetching it from the database
        document_key = cache_key_hash_function("foo" + llm_string)
        document_expiry_time = fetch_document_expiry_time(
            cluster, BUCKET_NAME, SCOPE_NAME, CACHE_COLLECTION_NAME, document_key
        )
        current_time = datetime.now()

        time_to_expiry = document_expiry_time - current_time

        assert time_to_expiry < ttl

    def test_semantic_cache(self, cluster: Any) -> None:
        """Test semantic LLM cache functionality"""
        set_llm_cache(
            CouchbaseSemanticCache(
                cluster=cluster,
                embedding=FakeEmbeddings(),
                index_name=INDEX_NAME,
                bucket_name=BUCKET_NAME,
                scope_name=SCOPE_NAME,
                collection_name=SEMANTIC_CACHE_COLLECTION_NAME,
            )
        )

        llm = FakeLLM()

        params = llm.dict()
        params["stop"] = None
        llm_string = str(sorted([(k, v) for k, v in params.items()]))
        get_llm_cache().update(
            "foo", llm_string, [Generation(text="fizz"), Generation(text="Buzz")]
        )

        # foo and bar will have the same embedding produced by FakeEmbeddings
        cache_output = get_llm_cache().lookup("bar", llm_string)
        assert cache_output == [Generation(text="fizz"), Generation(text="Buzz")]

        # clear the cache
        get_llm_cache().clear()
        output = get_llm_cache().lookup("bar", llm_string)
        assert output != [Generation(text="fizz"), Generation(text="Buzz")]

    def test_semantic_cache_with_ttl(self, cluster: Any) -> None:
        """Test semantic LLM cache functionality with TTL"""
        ttl = timedelta(minutes=10)

        set_llm_cache(
            CouchbaseSemanticCache(
                cluster=cluster,
                embedding=FakeEmbeddings(),
                index_name=INDEX_NAME,
                bucket_name=BUCKET_NAME,
                scope_name=SCOPE_NAME,
                collection_name=SEMANTIC_CACHE_COLLECTION_NAME,
                ttl=ttl,
            )
        )

        llm = FakeLLM()

        params = llm.dict()
        params["stop"] = None
        llm_string = str(sorted([(k, v) for k, v in params.items()]))

        # Add a document to the cache
        seed_prompt = "foo"
        get_llm_cache().update(
            seed_prompt, llm_string, [Generation(text="fizz"), Generation(text="Buzz")]
        )

        # foo and bar will have the same embedding produced by FakeEmbeddings
        cache_output = get_llm_cache().lookup("bar", llm_string)
        assert cache_output == [Generation(text="fizz"), Generation(text="Buzz")]

        # Check the document's expiry time by fetching it from the database
        fetch_document_query = (
            f"SELECT meta().id, * from `{SEMANTIC_CACHE_COLLECTION_NAME}` doc "
            f"WHERE doc.text = '{seed_prompt}'"
        )

        document_keys = get_document_keys(
            cluster=cluster,
            bucket_name=BUCKET_NAME,
            scope_name=SCOPE_NAME,
            query=fetch_document_query,
        )
        assert len(document_keys) == 1

        document_expiry_time = fetch_document_expiry_time(
            cluster=cluster,
            bucket_name=BUCKET_NAME,
            scope_name=SCOPE_NAME,
            collection_name=SEMANTIC_CACHE_COLLECTION_NAME,
            document_key=document_keys[0],
        )
        current_time = datetime.now()

        time_to_expiry = document_expiry_time - current_time

        assert time_to_expiry < ttl