# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License

import networkx as nx
import pytest
from datashaper.errors import VerbParallelizationError

from graphrag.config.enums import LLMType
from graphrag.index.storage.memory_pipeline_storage import MemoryPipelineStorage
from graphrag.index.workflows.v1.create_base_extracted_entities import (
    build_steps,
    workflow_name,
)

from .util import (
    get_config_for_workflow,
    get_workflow_output,
    load_expected,
    load_input_tables,
)

MOCK_LLM_RESPONSES = [
    """
    ("entity"<|>COMPANY_A<|>COMPANY<|>Company_A is a test company)
    ##
    ("entity"<|>COMPANY_B<|>COMPANY<|>Company_B owns Company_A and also shares an address with Company_A)
    ##
    ("entity"<|>PERSON_C<|>PERSON<|>Person_C is director of Company_A)
    ##
    ("relationship"<|>COMPANY_A<|>COMPANY_B<|>Company_A and Company_B are related because Company_A is 100% owned by Company_B and the two companies also share the same address)<|>2)
    ##
    ("relationship"<|>COMPANY_A<|>PERSON_C<|>Company_A and Person_C are related because Person_C is director of Company_A<|>1))
    """.strip()
]

MOCK_LLM_CONFIG = {
    "type": LLMType.StaticResponse,
    "responses": MOCK_LLM_RESPONSES,
}


async def test_create_base_extracted_entities():
    input_tables = load_input_tables(["workflow:create_base_text_units"])
    expected = load_expected(workflow_name)

    storage = MemoryPipelineStorage()

    config = get_config_for_workflow(workflow_name)

    config["entity_extract"]["strategy"]["llm"] = MOCK_LLM_CONFIG

    steps = build_steps(config)

    actual = await get_workflow_output(
        input_tables,
        {
            "steps": steps,
        },
        storage=storage,
    )

    # let's parse a sample of the raw graphml
    actual_graphml_0 = actual["entity_graph"][:1][0]
    actual_graph_0 = nx.parse_graphml(actual_graphml_0)

    assert actual_graph_0.number_of_nodes() == 3
    assert actual_graph_0.number_of_edges() == 2

    assert actual.columns == expected.columns

    assert len(storage.keys()) == 0, "Storage should be empty"


async def test_create_base_extracted_entities_with_snapshots():
    input_tables = load_input_tables(["workflow:create_base_text_units"])
    expected = load_expected(workflow_name)

    storage = MemoryPipelineStorage()

    config = get_config_for_workflow(workflow_name)

    config["entity_extract"]["strategy"]["llm"] = MOCK_LLM_CONFIG
    config["raw_entity_snapshot"] = True
    config["graphml_snapshot"] = True

    steps = build_steps(config)

    actual = await get_workflow_output(
        input_tables,
        {
            "steps": steps,
        },
        storage=storage,
    )

    print(storage.keys())

    assert actual.columns == expected.columns

    assert storage.keys() == ["raw_extracted_entities.json", "merged_graph.graphml"]


async def test_create_base_extracted_entities_missing_llm_throws():
    input_tables = load_input_tables(["workflow:create_base_text_units"])

    config = get_config_for_workflow(workflow_name)

    del config["entity_extract"]["strategy"]["llm"]

    steps = build_steps(config)

    with pytest.raises(VerbParallelizationError):
        await get_workflow_output(
            input_tables,
            {
                "steps": steps,
            },
        )