#  Copyright (c) 2023. LanceDB Developers
#
#  Licensed 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 io
import os

import numpy as np
import pandas as pd
import pytest
import requests

import lancedb
from lancedb.embeddings import EmbeddingFunctionRegistry
from lancedb.pydantic import LanceModel, Vector

# These are integration tests for embedding functions.
# They are slow because they require downloading models
# or connection to external api


@pytest.mark.slow
@pytest.mark.parametrize("alias", ["sentence-transformers", "openai"])
def test_sentence_transformer(alias, tmp_path):
    db = lancedb.connect(tmp_path)
    registry = EmbeddingFunctionRegistry.get_instance()
    func = registry.get(alias).create()

    class Words(LanceModel):
        text: str = func.SourceField()
        vector: Vector(func.ndims()) = func.VectorField()

    table = db.create_table("words", schema=Words)
    table.add(
        pd.DataFrame(
            {
                "text": [
                    "hello world",
                    "goodbye world",
                    "fizz",
                    "buzz",
                    "foo",
                    "bar",
                    "baz",
                ]
            }
        )
    )

    query = "greetings"
    actual = table.search(query).limit(1).to_pydantic(Words)[0]

    vec = func.compute_query_embeddings(query)[0]
    expected = table.search(vec).limit(1).to_pydantic(Words)[0]
    assert actual.text == expected.text
    assert actual.text == "hello world"


@pytest.mark.slow
def test_openclip(tmp_path):
    from PIL import Image

    db = lancedb.connect(tmp_path)
    registry = EmbeddingFunctionRegistry.get_instance()
    func = registry.get("open-clip").create()

    class Images(LanceModel):
        label: str
        image_uri: str = func.SourceField()
        image_bytes: bytes = func.SourceField()
        vector: Vector(func.ndims()) = func.VectorField()
        vec_from_bytes: Vector(func.ndims()) = func.VectorField()

    table = db.create_table("images", schema=Images)
    labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
    uris = [
        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
        "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
        "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
        "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
    ]
    # get each uri as bytes
    image_bytes = [requests.get(uri).content for uri in uris]
    table.add(
        pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
    )

    # text search
    actual = table.search("man's best friend").limit(1).to_pydantic(Images)[0]
    assert actual.label == "dog"
    frombytes = (
        table.search("man's best friend", vector_column_name="vec_from_bytes")
        .limit(1)
        .to_pydantic(Images)[0]
    )
    assert actual.label == frombytes.label
    assert np.allclose(actual.vector, frombytes.vector)

    # image search
    query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg"
    image_bytes = requests.get(query_image_uri).content
    query_image = Image.open(io.BytesIO(image_bytes))
    actual = table.search(query_image).limit(1).to_pydantic(Images)[0]
    assert actual.label == "dog"
    other = (
        table.search(query_image, vector_column_name="vec_from_bytes")
        .limit(1)
        .to_pydantic(Images)[0]
    )
    assert actual.label == other.label

    arrow_table = table.search().select(["vector", "vec_from_bytes"]).to_arrow()
    assert np.allclose(
        arrow_table["vector"].combine_chunks().values.to_numpy(),
        arrow_table["vec_from_bytes"].combine_chunks().values.to_numpy(),
    )


@pytest.mark.slow
@pytest.mark.skipif(
    os.environ.get("COHERE_API_KEY") is None, reason="COHERE_API_KEY not set"
)  # also skip if cohere not installed
def test_cohere_embedding_function():
    cohere = (
        EmbeddingFunctionRegistry.get_instance()
        .get("cohere")
        .create(name="embed-multilingual-v2.0")
    )

    class TextModel(LanceModel):
        text: str = cohere.SourceField()
        vector: Vector(cohere.ndims()) = cohere.VectorField()

    df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
    db = lancedb.connect("~/lancedb")
    tbl = db.create_table("test", schema=TextModel, mode="overwrite")

    tbl.add(df)
    assert len(tbl.to_pandas()["vector"][0]) == cohere.ndims()