from src.transform_lambda import (
    read_from_s3_subfolder_to_df,
    list_existing_s3_files,
    bucket_name,
    process_to_parquet_and_upload_to_s3,
)
from moto import mock_aws
import pytest
import pandas as pd
import os
import boto3
from botocore.exceptions import ClientError
import numpy as np

# import caplog
import logging


logger = logging.getLogger()
logger.setLevel(logging.INFO)


@pytest.fixture(scope="class")
def aws_credentials():
    os.environ["AWS_ACCESS_KEY_ID"] = "testing"
    os.environ["AWS_SECRET_ACCESS_KEY"] = "testing"
    os.environ["AWS_SECURIT_TOKEN"] = "testing"
    os.environ["AWS_SESSION_TOKEN"] = "testing"
    os.environ["AWS_DEFAULT_REGION"] = "eu-west-2"


@pytest.fixture(scope="class")
def s3_client(aws_credentials):
    with mock_aws():
        yield boto3.client("s3")


@pytest.fixture(scope="class")
def mock_extract_bucket(s3_client):
    mock_extract_bucket = s3_client.create_bucket(
        Bucket="dummy_extract_buc",
        CreateBucketConfiguration={"LocationConstraint": "eu-west-2"},
    )
    return mock_extract_bucket


@pytest.fixture(scope="class")
def mock_transform_bucket(s3_client):
    mock_transform_bucket = s3_client.create_bucket(
        Bucket="dummy_transform_buc",
        CreateBucketConfiguration={"LocationConstraint": "eu-west-2"},
    )
    return mock_transform_bucket


class TestReadFromS3:
    # @pytest.mark.skip(reason="The test is broken!")
    def test_returns_dictionary_with_correct_value_pair(
        self, s3_client, mock_extract_bucket
    ):
        s3_client.upload_file(
            "tests/dummy_identical.csv",
            "dummy_extract_buc",
            "Foods/2024/08/21/Foods_12:03:10.csv",
        )
        tables = ["Foods"]
        result = read_from_s3_subfolder_to_df(
            tables, bucket="dummy_extract_buc", client=s3_client
        )
        print(result)
        expected_df = pd.DataFrame(
            np.array(
                [
                    ["Vegetable", "Sour", "Green", "2022-11-03 14:20:49.962"],
                    ["Berry", "Sweet", "Red", "2022-11-03 14:20:49.962"],
                ]
            ),
            columns=["Food_type", "Flavour", "Colour", "last_updated"],
        )
        assert isinstance(result, dict)
        assert list(result.keys())[0] == "Foods"
        assert isinstance(result["Foods"], pd.DataFrame)
        assert result["Foods"].eq(expected_df, axis="columns").all(axis=None)

    # @pytest.mark.skip(reason="The test is broken!")
    def test_returns_dictionary_of_dataframes_for_multiple_tables(
        self, s3_client, mock_extract_bucket
    ):
        s3_client.upload_file(
            "tests/dummy_2.csv",
            "dummy_extract_buc",
            "Cars/2024/08/21/Cars_14:03:56.csv",
        )
        tables = ["Foods", "Cars"]
        result = read_from_s3_subfolder_to_df(
            tables, bucket="dummy_extract_buc", client=s3_client
        )
        expected_foods_df = pd.DataFrame(
            np.array(
                [
                    ["Vegetable", "Sour", "Green", "2022-11-03 14:20:49.962"],
                    ["Berry", "Sweet", "Red", "2022-11-03 14:20:49.962"],
                ]
            ),
            columns=["Food_type", "Flavour", "Colour", "last_updated"],
        )
        expected_cars_df = pd.DataFrame(
            np.array(
                [
                    ["Truck", "Chevrolet", "Grey"],
                    ["Convertible", "Mercedes", "Red"],
                    ["Van", "Volkswagen", "Blue"],
                ]
            ),
            columns=["Car_type", "Brand", "Colour"],
        )
        assert list(result.keys()) == tables
        assert result["Foods"].eq(expected_foods_df, axis="columns").all(axis=None)
        # assert result["Cars"].eq(expected_cars_df, axis="columns").all(axis=None)


class TestListExistingFiles:
    def test_functions_receives_error_if_no_bucket(self, s3_client, caplog):
        caplog.set_level(logging.INFO)

        with pytest.raises(ClientError):
            list_existing_s3_files("rando_bucket", client=s3_client)

        assert (
            "Error listing S3 objects: An error occurred (NoSuchBucket) when calling the ListObjectsV2 operation: The specified bucket does not exist"
            in caplog.text
        )

    def test_recieves_logger_error_if_no_files_listed(self, s3_client, caplog):
        caplog.set_level(logging.INFO)

        s3_client.create_bucket(
            Bucket="mock_bucket",
            CreateBucketConfiguration={"LocationConstraint": "eu-west-2"},
        )
        response = list_existing_s3_files("mock_bucket", client=s3_client)
        assert "The bucket is empty" in caplog.text

    def test_retrieves_existing_files(self, s3_client, caplog):
        caplog.set_level(logging.INFO)

        s3_client.upload_file("tests/dummy.txt", "mock_bucket", "dummy.txt")
        result = list_existing_s3_files("mock_bucket", client=s3_client)
        assert result == ["dummy.txt"]


class TestBucketName:
    def test_functions_retrieves__extractbucket(
        self, mock_extract_bucket, mock_transform_bucket, s3_client
    ):
        bucket = bucket_name("dummy_extract_buc", s3_client)
        assert bucket == "dummy_extract_buc"

    def test_transform_bucket_name(
        self, mock_extract_bucket, mock_transform_bucket, s3_client
    ):
        bucket2 = bucket_name("dummy_transform_buc", s3_client)
        assert bucket2 == "dummy_transform_buc"

    def test_recieves_error_when_bucket_doesnt_exist(
        self, mock_extract_bucket, s3_client
    ):
        s3_client.delete_bucket(Bucket="dummy_extract_buc")
        with pytest.raises(ValueError):
            bucket_name("dummy_extract_buc", s3_client)


class TestProcessToParquetUploadS3:
    def test_func_uploads_to_s3(self, mock_transform_bucket, s3_client):
        expected_cars_df = pd.DataFrame(
            np.array(
                [
                    ["Truck", "Chevrolet", "Grey"],
                    ["Convertible", "Mercedes", "Red"],
                    ["Van", "Volkswagen", "Blue"],
                ]
            ),
            columns=["Car_type", "Brand", "Colour"],
        )
        mock_dim_dict = {"car_data": expected_cars_df}

        response = process_to_parquet_and_upload_to_s3(
            [], mock_dim_dict, {}, mock_transform_bucket, s3_client
        )

        assert response == {"uploaded": ["car_data"], "not_uploaded": []}