# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import time
import uuid

import numpy as np
import pytest
from pydantic import Field

from docarray import BaseDoc
from docarray.documents import ImageDoc
from docarray.typing import NdArray

pytestmark = [pytest.mark.slow, pytest.mark.index]

cur_dir = os.path.dirname(os.path.abspath(__file__))
compose_yml_v7 = os.path.abspath(os.path.join(cur_dir, "v7/docker-compose.yml"))
compose_yml_v8 = os.path.abspath(os.path.join(cur_dir, "v8/docker-compose.yml"))


@pytest.fixture(scope="module", autouse=True)
def start_storage_v7():
    os.system(f"docker compose -f {compose_yml_v7} up -d --remove-orphans")
    _wait_for_es()

    yield
    os.system(f"docker compose -f {compose_yml_v7} down --remove-orphans")


@pytest.fixture(scope="module", autouse=True)
def start_storage_v8():
    os.system(f"docker compose -f {compose_yml_v8} up -d --remove-orphans")
    _wait_for_es()

    yield
    os.system(f"docker compose -f {compose_yml_v8} down --remove-orphans")


def _wait_for_es():
    from elasticsearch import Elasticsearch

    es = Elasticsearch(hosts="http://localhost:9200/")
    while not es.ping():
        time.sleep(0.5)


class SimpleDoc(BaseDoc):
    tens: NdArray[10] = Field(dims=1000)


class FlatDoc(BaseDoc):
    tens_one: NdArray = Field(dims=10)
    tens_two: NdArray = Field(dims=50)


class NestedDoc(BaseDoc):
    d: SimpleDoc


class DeepNestedDoc(BaseDoc):
    d: NestedDoc


class MyImageDoc(ImageDoc):
    embedding: NdArray = Field(dims=128)


@pytest.fixture(scope="function")
def ten_simple_docs():
    return [SimpleDoc(tens=np.random.randn(10)) for _ in range(10)]


@pytest.fixture(scope="function")
def ten_flat_docs():
    return [
        FlatDoc(tens_one=np.random.randn(10), tens_two=np.random.randn(50))
        for _ in range(10)
    ]


@pytest.fixture(scope="function")
def ten_nested_docs():
    return [NestedDoc(d=SimpleDoc(tens=np.random.randn(10))) for _ in range(10)]


@pytest.fixture(scope="function")
def ten_deep_nested_docs():
    return [
        DeepNestedDoc(d=NestedDoc(d=SimpleDoc(tens=np.random.randn(10))))
        for _ in range(10)
    ]


@pytest.fixture(scope="function")
def tmp_index_name():
    return uuid.uuid4().hex

# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import time
import uuid

import numpy as np
import pytest
from pydantic import Field

from docarray import BaseDoc
from docarray.documents import ImageDoc
from docarray.typing import NdArray

pytestmark = [pytest.mark.slow, pytest.mark.index]

cur_dir = os.path.dirname(os.path.abspath(__file__))
compose_yml_v7 = os.path.abspath(os.path.join(cur_dir, 'v7/docker-compose.yml'))
compose_yml_v8 = os.path.abspath(os.path.join(cur_dir, 'v8/docker-compose.yml'))


@pytest.fixture(scope='module', autouse=True)
def start_storage_v7():
    os.system(f"docker-compose -f {compose_yml_v7} up -d --remove-orphans")
    _wait_for_es()

    yield
    os.system(f"docker-compose -f {compose_yml_v7} down --remove-orphans")


@pytest.fixture(scope='module', autouse=True)
def start_storage_v8():
    os.system(f"docker-compose -f {compose_yml_v8} up -d --remove-orphans")
    _wait_for_es()

    yield
    os.system(f"docker-compose -f {compose_yml_v8} down --remove-orphans")


def _wait_for_es():
    from elasticsearch import Elasticsearch

    es = Elasticsearch(hosts='http://localhost:9200/')
    while not es.ping():
        time.sleep(0.5)


class SimpleDoc(BaseDoc):
    tens: NdArray[10] = Field(dims=1000)


class FlatDoc(BaseDoc):
    tens_one: NdArray = Field(dims=10)
    tens_two: NdArray = Field(dims=50)


class NestedDoc(BaseDoc):
    d: SimpleDoc


class DeepNestedDoc(BaseDoc):
    d: NestedDoc


class MyImageDoc(ImageDoc):
    embedding: NdArray = Field(dims=128)


@pytest.fixture(scope='function')
def ten_simple_docs():
    return [SimpleDoc(tens=np.random.randn(10)) for _ in range(10)]


@pytest.fixture(scope='function')
def ten_flat_docs():
    return [
        FlatDoc(tens_one=np.random.randn(10), tens_two=np.random.randn(50))
        for _ in range(10)
    ]


@pytest.fixture(scope='function')
def ten_nested_docs():
    return [NestedDoc(d=SimpleDoc(tens=np.random.randn(10))) for _ in range(10)]


@pytest.fixture(scope='function')
def ten_deep_nested_docs():
    return [
        DeepNestedDoc(d=NestedDoc(d=SimpleDoc(tens=np.random.randn(10))))
        for _ in range(10)
    ]


@pytest.fixture(scope='function')
def tmp_index_name():
    return uuid.uuid4().hex
