import os
from sqlalchemy.ext.declarative import declarative_base
import uuid
import pytest

from memgpt.agent_store.storage import StorageConnector, TableType
from memgpt.embeddings import embedding_model
from memgpt.data_types import Message, Passage, EmbeddingConfig, AgentState, OpenAIEmbeddingConfig
from memgpt.config import MemGPTConfig
from memgpt.agent_store.storage import StorageConnector, TableType
from memgpt.metadata import MetadataStore
from memgpt.data_types import User

from datetime import datetime, timedelta


# Note: the database will filter out rows that do not correspond to agent1 and test_user by default.
texts = ["This is a test passage", "This is another test passage", "Cinderella wept"]
start_date = datetime(2009, 10, 5, 18, 00)
dates = [start_date, start_date - timedelta(weeks=1), start_date + timedelta(weeks=1)]
roles = ["user", "agent", "agent"]
agent_1_id = uuid.uuid4()
agent_2_id = uuid.uuid4()
agent_ids = [agent_1_id, agent_2_id, agent_1_id]
ids = [uuid.uuid4(), uuid.uuid4(), uuid.uuid4()]
user_id = uuid.uuid4()


# Data generation functions: Passages
def generate_passages(embed_model):
    """Generate list of 3 Passage objects"""
    # embeddings: use openai if env is set, otherwise local
    passages = []
    for text, _, _, agent_id, id in zip(texts, dates, roles, agent_ids, ids):
        embedding = None
        if embed_model:
            embedding = embed_model.get_text_embedding(text)
        passages.append(Passage(user_id=user_id, text=text, agent_id=agent_id, embedding=embedding, data_source="test_source", id=id))
    return passages


# Data generation functions: Messages
def generate_messages(embed_model):
    """Generate list of 3 Message objects"""
    messages = []
    for text, date, role, agent_id, id in zip(texts, dates, roles, agent_ids, ids):
        embedding = None
        if embed_model:
            embedding = embed_model.get_text_embedding(text)
        messages.append(
            Message(user_id=user_id, text=text, agent_id=agent_id, role=role, created_at=date, id=id, model="gpt-4", embedding=embedding)
        )
        print(messages[-1].text)
    return messages


@pytest.fixture(autouse=True)
def clear_dynamically_created_models():
    """Wipe globals for SQLAlchemy"""
    yield
    for key in list(globals().keys()):
        if key.endswith("Model"):
            del globals()[key]


@pytest.fixture(autouse=True)
def recreate_declarative_base():
    """Recreate the declarative base before each test"""
    global Base
    Base = declarative_base()
    yield
    Base.metadata.clear()


@pytest.mark.parametrize("storage_connector", ["postgres", "chroma", "sqlite"])
# @pytest.mark.parametrize("storage_connector", ["sqlite", "chroma"])
# @pytest.mark.parametrize("storage_connector", ["postgres"])
@pytest.mark.parametrize("table_type", [TableType.RECALL_MEMORY, TableType.ARCHIVAL_MEMORY])
def test_storage(storage_connector, table_type, clear_dynamically_created_models, recreate_declarative_base):
    # setup memgpt config
    # TODO: set env for different config path

    # hacky way to cleanup globals that scruw up tests
    # for table_name in ['Message']:
    #    if 'Message' in globals():
    #        print("Removing messages", globals()['Message'])
    #        del globals()['Message']

    config = MemGPTConfig()
    if storage_connector == "postgres":
        if not os.getenv("PGVECTOR_TEST_DB_URL"):
            print("Skipping test, missing PG URI")
            return
        config.archival_storage_uri = os.getenv("PGVECTOR_TEST_DB_URL")
        config.recall_storage_uri = os.getenv("PGVECTOR_TEST_DB_URL")
        config.archival_storage_type = "postgres"
        config.recall_storage_type = "postgres"
    if storage_connector == "lancedb":
        # TODO: complete lancedb implementation
        if not os.getenv("LANCEDB_TEST_URL"):
            print("Skipping test, missing LanceDB URI")
            return
        config.archival_storage_uri = os.getenv("LANCEDB_TEST_URL")
        config.recall_storage_uri = os.getenv("LANCEDB_TEST_URL")
        config.archival_storage_type = "lancedb"
        config.recall_storage_type = "lancedb"
    if storage_connector == "chroma":
        if table_type == TableType.RECALL_MEMORY:
            print("Skipping test, chroma only supported for archival memory")
            return
        config.archival_storage_type = "chroma"
        config.archival_storage_path = "./test_chroma"
    if storage_connector == "sqlite":
        if table_type == TableType.ARCHIVAL_MEMORY:
            print("Skipping test, sqlite only supported for recall memory")
            return
        config.recall_storage_type = "sqlite"

    # get embedding model
    embed_model = None
    if os.getenv("OPENAI_API_KEY"):
        embedding_config = EmbeddingConfig(
            embedding_endpoint_type="openai",
            embedding_endpoint="https://api.openai.com/v1",
            embedding_dim=1536,
            openai_key=os.getenv("OPENAI_API_KEY"),
        )
    else:
        embedding_config = EmbeddingConfig(embedding_endpoint_type="local", embedding_endpoint=None, embedding_dim=384)
    embed_model = embedding_model(embedding_config)

    # create user
    ms = MetadataStore(config)
    ms.delete_user(user_id)
    user = User(id=user_id, default_embedding_config=embedding_config)
    agent = AgentState(
        user_id=user_id,
        name="agent_1",
        id=agent_1_id,
        preset=user.default_preset,
        persona=user.default_persona,
        human=user.default_human,
        llm_config=user.default_llm_config,
        embedding_config=user.default_embedding_config,
    )
    ms.create_user(user)
    ms.create_agent(agent)

    # create storage connector
    conn = StorageConnector.get_storage_connector(table_type, config=config, user_id=user_id, agent_id=agent.id)
    # conn.client.delete_collection(conn.collection.name)  # clear out data
    conn.delete_table()
    conn = StorageConnector.get_storage_connector(table_type, config=config, user_id=user_id, agent_id=agent.id)

    # generate data
    if table_type == TableType.ARCHIVAL_MEMORY:
        records = generate_passages(embed_model)
    elif table_type == TableType.RECALL_MEMORY:
        records = generate_messages(embed_model)
    else:
        raise NotImplementedError(f"Table type {table_type} not implemented")

    # test: insert
    conn.insert(records[0])
    assert conn.size() == 1, f"Expected 1 record, got {conn.size()}: {conn.get_all()}"

    # test: insert_many
    conn.insert_many(records[1:])
    assert (
        conn.size() == 2
    ), f"Expected 1 record, got {conn.size()}: {conn.get_all()}"  # expect 2, since storage connector filters for agent1

    # test: list_loaded_data
    # TODO: add back
    # if table_type == TableType.ARCHIVAL_MEMORY:
    #    sources = StorageConnector.list_loaded_data(storage_type=storage_connector)
    #    assert len(sources) == 1, f"Expected 1 source, got {len(sources)}"
    #    assert sources[0] == "test_source", f"Expected 'test_source', got {sources[0]}"

    # test: get_all_paginated
    paginated_total = 0
    for page in conn.get_all_paginated(page_size=1):
        paginated_total += len(page)
    assert paginated_total == 2, f"Expected 2 records, got {paginated_total}"

    # test: get_all
    all_records = conn.get_all()
    assert len(all_records) == 2, f"Expected 2 records, got {len(all_records)}"
    all_records = conn.get_all(limit=1)
    assert len(all_records) == 1, f"Expected 1 records, got {len(all_records)}"

    # test: get
    print("GET ID", ids[0], records)
    res = conn.get(id=ids[0])
    assert res.text == texts[0], f"Expected {texts[0]}, got {res.text}"

    # test: size
    assert conn.size() == 2, f"Expected 2 records, got {conn.size()}"
    assert conn.size(filters={"agent_id": agent.id}) == 2, f"Expected 2 records, got {conn.size(filters={'agent_id', agent.id})}"
    if table_type == TableType.RECALL_MEMORY:
        assert conn.size(filters={"role": "user"}) == 1, f"Expected 1 record, got {conn.size(filters={'role': 'user'})}"

    # test: query (vector)
    if table_type == TableType.ARCHIVAL_MEMORY:
        query = "why was she crying"
        query_vec = embed_model.get_text_embedding(query)
        res = conn.query(None, query_vec, top_k=2)
        assert len(res) == 2, f"Expected 2 results, got {len(res)}"
        print("Archival memory results", res)
        assert "wept" in res[0].text, f"Expected 'wept' in results, but got {res[0].text}"

    # test optional query functions for recall memory
    if table_type == TableType.RECALL_MEMORY:
        # test: query_text
        query = "CindereLLa"
        res = conn.query_text(query)
        assert len(res) == 1, f"Expected 1 result, got {len(res)}"
        assert "Cinderella" in res[0].text, f"Expected 'Cinderella' in results, but got {res[0].text}"

        # test: query_date (recall memory only)
        print("Testing recall memory date search")
        start_date = datetime(2009, 10, 5, 18, 00)
        start_date = start_date - timedelta(days=1)
        end_date = start_date + timedelta(days=1)
        res = conn.query_date(start_date=start_date, end_date=end_date)
        print("DATE", res)
        assert len(res) == 1, f"Expected 1 result, got {len(res)}: {res}"

    # test: delete
    conn.delete({"id": ids[0]})
    assert conn.size() == 1, f"Expected 2 records, got {conn.size()}"

    # cleanup
    ms.delete_user(user_id)