import os
import uuid
from datetime import datetime, timedelta

import pytest
from sqlalchemy.ext.declarative import declarative_base

from memgpt.agent_store.storage import StorageConnector, TableType
from memgpt.config import MemGPTConfig
from memgpt.constants import BASE_TOOLS, MAX_EMBEDDING_DIM
from memgpt.credentials import MemGPTCredentials
from memgpt.data_types import AgentState, Message, Passage, User
from memgpt.embeddings import embedding_model, query_embedding
from memgpt.metadata import MetadataStore
from memgpt.settings import settings
from tests import TEST_MEMGPT_CONFIG
from tests.utils import create_config, wipe_config

from .utils import with_qdrant_storage

# 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", "assistant", "assistant"]
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, embedding_model, embedding_dim = None, None, None
        if embed_model:
            embedding = embed_model.get_text_embedding(text)
            embedding_model = "gpt-4"
            embedding_dim = len(embedding)
        passages.append(
            Passage(
                user_id=user_id,
                text=text,
                agent_id=agent_id,
                embedding=embedding,
                data_source="test_source",
                id=id,
                embedding_dim=embedding_dim,
                embedding_model=embedding_model,
            )
        )
    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, embedding_model, embedding_dim = None, None, None
        if embed_model:
            embedding = embed_model.get_text_embedding(text)
            embedding_model = "gpt-4"
            embedding_dim = len(embedding)
        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,
                embedding_model=embedding_model,
                embedding_dim=embedding_dim,
            )
        )
        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", with_qdrant_storage(["postgres", "chroma", "sqlite", "milvus"]))
# @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']

    wipe_config()
    if os.getenv("OPENAI_API_KEY"):
        create_config("openai")
        credentials = MemGPTCredentials(
            openai_key=os.getenv("OPENAI_API_KEY"),
        )
    else:  # hosted
        create_config("memgpt_hosted")
        MemGPTCredentials()

    config = MemGPTConfig.load()
    TEST_MEMGPT_CONFIG.default_embedding_config = config.default_embedding_config
    TEST_MEMGPT_CONFIG.default_llm_config = config.default_llm_config

    if storage_connector == "postgres":
        TEST_MEMGPT_CONFIG.archival_storage_uri = settings.memgpt_pg_uri
        TEST_MEMGPT_CONFIG.recall_storage_uri = settings.memgpt_pg_uri
        TEST_MEMGPT_CONFIG.archival_storage_type = "postgres"
        TEST_MEMGPT_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
        TEST_MEMGPT_CONFIG.archival_storage_uri = os.environ["LANCEDB_TEST_URL"]
        TEST_MEMGPT_CONFIG.recall_storage_uri = os.environ["LANCEDB_TEST_URL"]
        TEST_MEMGPT_CONFIG.archival_storage_type = "lancedb"
        TEST_MEMGPT_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
        TEST_MEMGPT_CONFIG.archival_storage_type = "chroma"
        TEST_MEMGPT_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
        TEST_MEMGPT_CONFIG.recall_storage_type = "sqlite"
    if storage_connector == "qdrant":
        if table_type == TableType.RECALL_MEMORY:
            print("Skipping test, Qdrant only supports archival memory")
            return
        TEST_MEMGPT_CONFIG.archival_storage_type = "qdrant"
        TEST_MEMGPT_CONFIG.archival_storage_uri = "localhost:6333"
    if storage_connector == "milvus":
        if table_type == TableType.RECALL_MEMORY:
            print("Skipping test, Milvus only supports archival memory")
            return
        TEST_MEMGPT_CONFIG.archival_storage_type = "milvus"
        TEST_MEMGPT_CONFIG.archival_storage_uri = "./milvus.db"
    # get embedding model
    embedding_config = TEST_MEMGPT_CONFIG.default_embedding_config
    embed_model = embedding_model(TEST_MEMGPT_CONFIG.default_embedding_config)

    # create user
    ms = MetadataStore(TEST_MEMGPT_CONFIG)
    ms.delete_user(user_id)
    user = User(id=user_id)
    agent = AgentState(
        user_id=user_id,
        name="agent_1",
        id=agent_1_id,
        llm_config=TEST_MEMGPT_CONFIG.default_llm_config,
        embedding_config=TEST_MEMGPT_CONFIG.default_embedding_config,
        system="",
        tools=BASE_TOOLS,
        state={
            "persona": "",
            "human": "",
            "messages": None,
        },
    )
    ms.create_user(user)
    ms.create_agent(agent)

    # create storage connector
    conn = StorageConnector.get_storage_connector(table_type, config=TEST_MEMGPT_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=TEST_MEMGPT_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")

    # check record dimentions
    print("TABLE TYPE", table_type, type(records[0]), len(records[0].embedding))
    if embed_model:
        assert len(records[0].embedding) == MAX_EMBEDDING_DIM, f"Expected {MAX_EMBEDDING_DIM}, got {len(records[0].embedding)}"
        assert (
            records[0].embedding_dim == embedding_config.embedding_dim
        ), f"Expected {embedding_config.embedding_dim}, got {records[0].embedding_dim}"

    # 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 2 records, got {conn.size()}: {conn.get_all()}"  # expect 2, since storage connector filters for agent1

    # test: update
    # NOTE: only testing with messages
    if table_type == TableType.RECALL_MEMORY:
        TEST_STRING = "hello world"

        updated_record = records[1]
        updated_record.text = TEST_STRING

        current_record = conn.get(id=updated_record.id)
        assert current_record is not None, f"Couldn't find {updated_record.id}"
        assert current_record.text != TEST_STRING, (current_record.text, TEST_STRING)

        conn.update(updated_record)
        new_record = conn.get(id=updated_record.id)
        assert new_record is not None, f"Couldn't find {updated_record.id}"
        assert new_record.text == TEST_STRING, (new_record.text, TEST_STRING)

    # 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 = query_embedding(embed_model, 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)