# TODO: add back once tests are cleaned up

# import os
# import uuid
#
# from letta import create_client
# from letta.agent_store.storage import StorageConnector, TableType
# from letta.schemas.passage import Passage
# from letta.embeddings import embedding_model
# from tests import TEST_MEMGPT_CONFIG
#
# from .utils import create_config, wipe_config
#
# test_agent_name = f"test_client_{str(uuid.uuid4())}"
# test_agent_state = None
# client = None
#
# test_agent_state_post_message = None
# test_user_id = uuid.uuid4()
#
#
# def generate_passages(user, agent):
#    # 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",
#    ]
#    embed_model = embedding_model(agent.embedding_config)
#    orig_embeddings = []
#    passages = []
#    for text in texts:
#        embedding = embed_model.get_text_embedding(text)
#        orig_embeddings.append(list(embedding))
#        passages.append(
#            Passage(
#                user_id=user.id,
#                agent_id=agent.id,
#                text=text,
#                embedding=embedding,
#                embedding_dim=agent.embedding_config.embedding_dim,
#                embedding_model=agent.embedding_config.embedding_model,
#            )
#        )
#    return passages, orig_embeddings
#
#
# def test_create_user():
#    if not os.getenv("OPENAI_API_KEY"):
#        print("Skipping test, missing OPENAI_API_KEY")
#        return
#
#    wipe_config()
#
#    # create client
#    create_config("openai")
#    client = create_client()
#
#    # openai: create agent
#    openai_agent = client.create_agent(
#        name="openai_agent",
#    )
#    assert (
#        openai_agent.embedding_config.embedding_endpoint_type == "openai"
#    ), f"openai_agent.embedding_config.embedding_endpoint_type={openai_agent.embedding_config.embedding_endpoint_type}"
#
#    # openai: add passages
#    passages, openai_embeddings = generate_passages(client.user, openai_agent)
#    openai_agent_run = client.server._get_or_load_agent(user_id=client.user.id, agent_id=openai_agent.id)
#    openai_agent_run.persistence_manager.archival_memory.storage.insert_many(passages)
#
#    # create client
#    create_config("letta_hosted")
#    client = create_client()
#
#    # hosted: create agent
#    hosted_agent = client.create_agent(
#        name="hosted_agent",
#    )
#    # check to make sure endpoint overriden
#    assert (
#        hosted_agent.embedding_config.embedding_endpoint_type == "hugging-face"
#    ), f"hosted_agent.embedding_config.embedding_endpoint_type={hosted_agent.embedding_config.embedding_endpoint_type}"
#
#    # hosted: add passages
#    passages, hosted_embeddings = generate_passages(client.user, hosted_agent)
#    hosted_agent_run = client.server._get_or_load_agent(user_id=client.user.id, agent_id=hosted_agent.id)
#    hosted_agent_run.persistence_manager.archival_memory.storage.insert_many(passages)
#
#    # test passage dimentionality
#    storage = StorageConnector.get_storage_connector(TableType.PASSAGES, TEST_MEMGPT_CONFIG, client.user.id)
#    storage.filters = {}  # clear filters to be able to get all passages
#    passages = storage.get_all()
#    for passage in passages:
#        if passage.agent_id == hosted_agent.id:
#            assert (
#                passage.embedding_dim == hosted_agent.embedding_config.embedding_dim
#            ), f"passage.embedding_dim={passage.embedding_dim} != hosted_agent.embedding_config.embedding_dim={hosted_agent.embedding_config.embedding_dim}"
#
#            # ensure was in original embeddings
#            embedding = passage.embedding[: passage.embedding_dim]
#            assert embedding in hosted_embeddings, f"embedding={embedding} not in hosted_embeddings={hosted_embeddings}"
#
#            # make sure all zeros
#            assert not any(
#                passage.embedding[passage.embedding_dim :]
#            ), f"passage.embedding[passage.embedding_dim:]={passage.embedding[passage.embedding_dim:]}"
#        elif passage.agent_id == openai_agent.id:
#            assert (
#                passage.embedding_dim == openai_agent.embedding_config.embedding_dim
#            ), f"passage.embedding_dim={passage.embedding_dim} != openai_agent.embedding_config.embedding_dim={openai_agent.embedding_config.embedding_dim}"
#
#            # ensure was in original embeddings
#            embedding = passage.embedding[: passage.embedding_dim]
#            assert embedding in openai_embeddings, f"embedding={embedding} not in openai_embeddings={openai_embeddings}"
#
#            # make sure all zeros
#            assert not any(
#                passage.embedding[passage.embedding_dim :]
#            ), f"passage.embedding[passage.embedding_dim:]={passage.embedding[passage.embedding_dim:]}"
#