import subprocess
import time
from subprocess import run
from sys import platform

import psutil
import pytest
import requests
from elasticsearch import Elasticsearch

from haystack.classifier import FARMClassifier
from haystack.generator.transformers import Seq2SeqGenerator
from haystack.knowledge_graph.graphdb import GraphDBKnowledgeGraph
from milvus import Milvus

import weaviate
from haystack.document_store.weaviate import WeaviateDocumentStore

from haystack.document_store.milvus import MilvusDocumentStore
from haystack.generator.transformers import RAGenerator, RAGeneratorType
from haystack.modeling.infer import Inferencer, QAInferencer
from haystack.ranker import FARMRanker, SentenceTransformersRanker

from haystack.retriever.sparse import ElasticsearchFilterOnlyRetriever, ElasticsearchRetriever, TfidfRetriever

from haystack.retriever.dense import DensePassageRetriever, EmbeddingRetriever

from haystack import Document
from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
from haystack.document_store.faiss import FAISSDocumentStore
from haystack.document_store.memory import InMemoryDocumentStore
from haystack.document_store.sql import SQLDocumentStore
from haystack.reader.farm import FARMReader
from haystack.reader.transformers import TransformersReader
from haystack.summarizer.transformers import TransformersSummarizer
from haystack.translator import TransformersTranslator
from haystack.question_generator import QuestionGenerator


def pytest_addoption(parser):
    parser.addoption("--document_store_type", action="store", default="all")


def pytest_generate_tests(metafunc):
    # parametrize document_store fixture if it's in the test function argument list
    # but does not have an explicit parametrize annotation e.g
    # @pytest.mark.parametrize("document_store", ["memory"], indirect=False)
    found_mark_parametrize_document_store = False
    for marker in metafunc.definition.iter_markers('parametrize'):
        if 'document_store' in marker.args[0]:
            found_mark_parametrize_document_store = True
            break

    if 'document_store' in metafunc.fixturenames and not found_mark_parametrize_document_store:
        document_store_type = metafunc.config.option.document_store_type
        if "all" in document_store_type:
            document_store_type = "elasticsearch, faiss, memory, milvus"

        document_store_types = [item.strip() for item in document_store_type.split(",")]
        metafunc.parametrize("document_store", document_store_types, indirect=True)


def _sql_session_rollback(self, attr):
    """
    Inject SQLDocumentStore at runtime to do a session rollback each time it is called. This allows to catch
    errors where an intended operation is still in a transaction, but not committed to the database.
    """
    method = object.__getattribute__(self, attr)
    if callable(method):
        try:
            self.session.rollback()
        except AttributeError:
            pass

    return method


SQLDocumentStore.__getattribute__ = _sql_session_rollback


def pytest_collection_modifyitems(items):
    for item in items:
        if "generator" in item.nodeid:
            item.add_marker(pytest.mark.generator)
        elif "summarizer" in item.nodeid:
            item.add_marker(pytest.mark.summarizer)
        elif "tika" in item.nodeid:
            item.add_marker(pytest.mark.tika)
        elif "elasticsearch" in item.nodeid:
            item.add_marker(pytest.mark.elasticsearch)
        elif "graphdb" in item.nodeid:
            item.add_marker(pytest.mark.graphdb)
        elif "pipeline" in item.nodeid:
            item.add_marker(pytest.mark.pipeline)
        elif "slow" in item.nodeid:
            item.add_marker(pytest.mark.slow)
        elif "weaviate" in item.nodeid:
            item.add_marker(pytest.mark.weaviate)


@pytest.fixture(scope="session")
def elasticsearch_fixture():
    # test if a ES cluster is already running. If not, download and start an ES instance locally.
    try:
        client = Elasticsearch(hosts=[{"host": "localhost", "port": "9200"}])
        client.info()
    except:
        print("Starting Elasticsearch ...")
        status = subprocess.run(
            ['docker rm haystack_test_elastic'],
            shell=True
        )
        status = subprocess.run(
            ['docker run -d --name haystack_test_elastic -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2'],
            shell=True
        )
        if status.returncode:
            raise Exception(
                "Failed to launch Elasticsearch. Please check docker container logs.")
        time.sleep(30)


@pytest.fixture(scope="session")
def milvus_fixture():
    # test if a Milvus server is already running. If not, start Milvus docker container locally.
    # Make sure you have given > 6GB memory to docker engine
    try:
        milvus_server = Milvus(uri="tcp://localhost:19530", timeout=5, wait_timeout=5)
        milvus_server.server_status(timeout=5)
    except:
        print("Starting Milvus ...")
        status = subprocess.run(['docker run -d --name milvus_cpu_0.10.5 -p 19530:19530 -p 19121:19121 '
                                 'milvusdb/milvus:0.10.5-cpu-d010621-4eda95'], shell=True)
        time.sleep(40)

@pytest.fixture(scope="session")
def weaviate_fixture():
    # test if a Weaviate server is already running. If not, start Weaviate docker container locally.
    # Make sure you have given > 6GB memory to docker engine
    try:
        weaviate_server = weaviate.Client(url='http://localhost:8080', timeout_config=(5, 15))
        weaviate_server.is_ready()
    except:
        print("Starting Weaviate servers ...")
        status = subprocess.run(
            ['docker rm haystack_test_weaviate'],
            shell=True
        )
        status = subprocess.run(
            ['docker run -d --name haystack_test_weaviate -p 8080:8080 semitechnologies/weaviate:1.4.0'],
            shell=True
        )
        if status.returncode:
            raise Exception(
                "Failed to launch Weaviate. Please check docker container logs.")
        time.sleep(60)

@pytest.fixture(scope="session")
def graphdb_fixture():
    # test if a GraphDB instance is already running. If not, download and start a GraphDB instance locally.
    try:
        kg = GraphDBKnowledgeGraph()
        # fail if not running GraphDB
        kg.delete_index()
    except:
        print("Starting GraphDB ...")
        status = subprocess.run(
            ['docker rm haystack_test_graphdb'],
            shell=True
        )
        status = subprocess.run(
            ['docker run -d -p 7200:7200 --name haystack_test_graphdb docker-registry.ontotext.com/graphdb-free:9.4.1-adoptopenjdk11'],
            shell=True
        )
        if status.returncode:
            raise Exception(
                "Failed to launch GraphDB. Please check docker container logs.")
        time.sleep(30)


@pytest.fixture(scope="session")
def tika_fixture():
    try:
        tika_url = "http://localhost:9998/tika"
        ping = requests.get(tika_url)
        if ping.status_code != 200:
            raise Exception(
                "Unable to connect Tika. Please check tika endpoint {0}.".format(tika_url))
    except:
        print("Starting Tika ...")
        status = subprocess.run(
            ['docker run -d --name tika -p 9998:9998 apache/tika:1.24.1'],
            shell=True
        )
        if status.returncode:
            raise Exception(
                "Failed to launch Tika. Please check docker container logs.")
        time.sleep(30)


@pytest.fixture(scope="session")
def xpdf_fixture(tika_fixture):
    verify_installation = run(["pdftotext"], shell=True)
    if verify_installation.returncode == 127:
        if platform.startswith("linux"):
            platform_id = "linux"
            sudo_prefix = "sudo"
        elif platform.startswith("darwin"):
            platform_id = "mac"
            # For Mac, generally sudo need password in interactive console.
            # But most of the cases current user already have permission to copy to /user/local/bin.
            # Hence removing sudo requirement for Mac.
            sudo_prefix = ""
        else:
            raise Exception(
                """Currently auto installation of pdftotext is not supported on {0} platform """.format(platform)
            )
        commands = """ wget --no-check-certificate https://dl.xpdfreader.com/xpdf-tools-{0}-4.03.tar.gz &&
                       tar -xvf xpdf-tools-{0}-4.03.tar.gz &&
                       {1} cp xpdf-tools-{0}-4.03/bin64/pdftotext /usr/local/bin""".format(platform_id, sudo_prefix)
        run([commands], shell=True)

        verify_installation = run(["pdftotext -v"], shell=True)
        if verify_installation.returncode == 127:
            raise Exception(
                """pdftotext is not installed. It is part of xpdf or poppler-utils software suite.
                 You can download for your OS from here: https://www.xpdfreader.com/download.html."""
            )


@pytest.fixture(scope="module")
def rag_generator():
    return RAGenerator(
        model_name_or_path="facebook/rag-token-nq",
        generator_type=RAGeneratorType.TOKEN
    )


@pytest.fixture(scope="module")
def question_generator():
    return QuestionGenerator(model_name_or_path="valhalla/t5-small-e2e-qg")


@pytest.fixture(scope="module")
def eli5_generator():
    return Seq2SeqGenerator(model_name_or_path="yjernite/bart_eli5")


@pytest.fixture(scope="module")
def summarizer():
    return TransformersSummarizer(
        model_name_or_path="google/pegasus-xsum",
        use_gpu=-1
    )


@pytest.fixture(scope="module")
def en_to_de_translator():
    return TransformersTranslator(
        model_name_or_path="Helsinki-NLP/opus-mt-en-de",
    )


@pytest.fixture(scope="module")
def de_to_en_translator():
    return TransformersTranslator(
        model_name_or_path="Helsinki-NLP/opus-mt-de-en",
    )


@pytest.fixture(scope="module")
def test_docs_xs():
    return [
        # current "dict" format for a document
        {"text": "My name is Carla and I live in Berlin", "meta": {"meta_field": "test1", "name": "filename1"}},
        # meta_field at the top level for backward compatibility
        {"text": "My name is Paul and I live in New York", "meta_field": "test2", "name": "filename2"},
        # Document object for a doc
        Document(text="My name is Christelle and I live in Paris", meta={"meta_field": "test3", "name": "filename3"})
    ]


@pytest.fixture(scope="module")
def reader_without_normalized_scores():
    return FARMReader(
        model_name_or_path="distilbert-base-uncased-distilled-squad",
        use_gpu=False,
        top_k_per_sample=5,
        num_processes=0,
        use_confidence_scores=False
    )

@pytest.fixture(params=["farm", "transformers"], scope="module")
def reader(request):
    if request.param == "farm":
        return FARMReader(
            model_name_or_path="distilbert-base-uncased-distilled-squad",
            use_gpu=False,
            top_k_per_sample=5,
            num_processes=0
        )
    if request.param == "transformers":
        return TransformersReader(
            model_name_or_path="distilbert-base-uncased-distilled-squad",
            tokenizer="distilbert-base-uncased",
            use_gpu=-1
        )

@pytest.fixture(params=["farm", "sentencetransformers"], scope="module")
def ranker(request):
    if request.param == "farm":
        return FARMRanker(
            model_name_or_path="deepset/gbert-base-germandpr-reranking"
        )
    if request.param == "sentencetransformers":
        return SentenceTransformersRanker(
            model_name_or_path="cross-encoder/ms-marco-MiniLM-L-12-v2",
        )


@pytest.fixture(params=["farm"], scope="module")
def classifier(request):
    if request.param == "farm":
        return FARMClassifier(
            model_name_or_path="deepset/bert-base-german-cased-sentiment-Germeval17"
        )


# TODO Fix bug in test_no_answer_output when using
# @pytest.fixture(params=["farm", "transformers"])
@pytest.fixture(params=["farm"], scope="module")
def no_answer_reader(request):
    if request.param == "farm":
        return FARMReader(
            model_name_or_path="deepset/roberta-base-squad2",
            use_gpu=False,
            top_k_per_sample=5,
            no_ans_boost=0,
            return_no_answer=True,
            num_processes=0
        )
    if request.param == "transformers":
        return TransformersReader(
            model_name_or_path="deepset/roberta-base-squad2",
            tokenizer="deepset/roberta-base-squad2",
            use_gpu=-1,
            top_k_per_candidate=5
        )


@pytest.fixture(scope="module")
def prediction(reader, test_docs_xs):
    docs = [Document.from_dict(d) if isinstance(d, dict) else d for d in test_docs_xs]
    prediction = reader.predict(query="Who lives in Berlin?", documents=docs, top_k=5)
    return prediction


@pytest.fixture(scope="module")
def no_answer_prediction(no_answer_reader, test_docs_xs):
    docs = [Document.from_dict(d) if isinstance(d, dict) else d for d in test_docs_xs]
    prediction = no_answer_reader.predict(query="What is the meaning of life?", documents=docs, top_k=5)
    return prediction


@pytest.fixture(params=["es_filter_only", "elasticsearch", "dpr", "embedding", "tfidf"])
def retriever(request, document_store):
    return get_retriever(request.param, document_store)


@pytest.fixture(params=["es_filter_only", "elasticsearch", "dpr", "embedding", "tfidf"])
def retriever_with_docs(request, document_store_with_docs):
    return get_retriever(request.param, document_store_with_docs)


def get_retriever(retriever_type, document_store):

    if retriever_type == "dpr":
        retriever = DensePassageRetriever(document_store=document_store,
                                          query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
                                          passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
                                          use_gpu=False, embed_title=True)
    elif retriever_type == "tfidf":
        retriever = TfidfRetriever(document_store=document_store)
        retriever.fit()
    elif retriever_type == "embedding":
        retriever = EmbeddingRetriever(
            document_store=document_store,
            embedding_model="deepset/sentence_bert",
            use_gpu=False
        )
    elif retriever_type == "retribert":
        retriever = EmbeddingRetriever(document_store=document_store,
                                       embedding_model="yjernite/retribert-base-uncased",
                                       model_format="retribert",
                                       use_gpu=False)
    elif retriever_type == "elasticsearch":
        retriever = ElasticsearchRetriever(document_store=document_store)
    elif retriever_type == "es_filter_only":
        retriever = ElasticsearchFilterOnlyRetriever(document_store=document_store)
    else:
        raise Exception(f"No retriever fixture for '{retriever_type}'")

    return retriever


@pytest.fixture(params=["elasticsearch", "faiss", "memory", "sql", "milvus"])
def document_store_with_docs(request, test_docs_xs):
    document_store = get_document_store(request.param)
    document_store.write_documents(test_docs_xs)
    yield document_store
    document_store.delete_documents()


@pytest.fixture
def document_store(request, test_docs_xs):
    vector_dim = request.node.get_closest_marker("vector_dim", pytest.mark.vector_dim(768))
    document_store = get_document_store(request.param, vector_dim.args[0])
    yield document_store
    document_store.delete_documents()


def get_document_store(document_store_type, embedding_dim=768, embedding_field="embedding"):
    if document_store_type == "sql":
        document_store = SQLDocumentStore(url="sqlite://", index="haystack_test")
    elif document_store_type == "memory":
        document_store = InMemoryDocumentStore(
            return_embedding=True, embedding_dim=embedding_dim, embedding_field=embedding_field, index="haystack_test"
        )
    elif document_store_type == "elasticsearch":
        # make sure we start from a fresh index
        client = Elasticsearch()
        client.indices.delete(index='haystack_test*', ignore=[404])
        document_store = ElasticsearchDocumentStore(
            index="haystack_test", return_embedding=True, embedding_dim=embedding_dim, embedding_field=embedding_field
        )
    elif document_store_type == "faiss":
        document_store = FAISSDocumentStore(
            vector_dim=embedding_dim,
            sql_url="sqlite://",
            return_embedding=True,
            embedding_field=embedding_field,
            index="haystack_test",
        )
        return document_store
    elif document_store_type == "milvus":
        document_store = MilvusDocumentStore(
            vector_dim=embedding_dim,
            sql_url="sqlite://",
            return_embedding=True,
            embedding_field=embedding_field,
            index="haystack_test",
        )
        _, collections = document_store.milvus_server.list_collections()
        for collection in collections:
            if collection.startswith("haystack_test"):
                document_store.milvus_server.drop_collection(collection)
        return document_store
    elif document_store_type == "weaviate":
        document_store = WeaviateDocumentStore(
            weaviate_url="http://localhost:8080",
            index="Haystacktest"
        )
        document_store.weaviate_client.schema.delete_all()
        document_store._create_schema_and_index_if_not_exist()
        return document_store
    else:
        raise Exception(f"No document store fixture for '{document_store_type}'")

    return document_store


@pytest.fixture(scope="module")
def adaptive_model_qa(num_processes):
    """
    PyTest Fixture for a Question Answering Inferencer based on PyTorch.
    """
    try:
        model = Inferencer.load(
            "deepset/bert-base-cased-squad2",
            task_type="question_answering",
            batch_size=16,
            num_processes=num_processes,
            gpu=False,
        )
        yield model
    finally:
        if num_processes != 0:
            # close the pool
            # we pass join=True to wait for all sub processes to close
            # this is because below we want to test if all sub-processes
            # have exited
            model.close_multiprocessing_pool(join=True)

    # check if all workers (sub processes) are closed
    current_process = psutil.Process()
    children = current_process.children()
    assert len(children) == 0


@pytest.fixture(scope="module")
def bert_base_squad2(request):
    model = QAInferencer.load(
            "deepset/minilm-uncased-squad2",
            task_type="question_answering",
            batch_size=4,
            num_processes=0,
            multithreading_rust=False,
            use_fast=True # TODO parametrize this to test slow as well
    )
    return model