#!/usr/bin/env/python3
# Copyright (c) Facebook, Inc. and its affiliates.
# 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.
"""Utility to upload files/folders into minio"""
import os
from argparse import ArgumentParser
from pytorch_kfp_components.components.minio.component import MinIO
from pytorch_kfp_components.components.visualization.component import Visualization

# Argument parser for user defined paths
parser = ArgumentParser()

parser.add_argument(
    "--bucket_name",
    type=str,
    help="Minio bucket name",
)

parser.add_argument(
    "--folder_name",
    type=str,
    help="Path to destination folder",
)

parser.add_argument(
    "--input_path",
    type=str,
    help="Input path of the file or folder to upload",
)

parser.add_argument(
    "--filename",
    type=str,
    help="Name of the file to be uploaded",
)

parser.add_argument(
    "--endpoint",
    type=str,
    default="minio-service.kubeflow:9000",
    help="Name of the file to be uploaded",
)

parser.add_argument(
    "--mlpipeline_ui_metadata",
    type=str,
    help="Path to write mlpipeline-ui-metadata.json",
)

args = vars(parser.parse_args())

bucket_name = args["bucket_name"]
input_path = args["input_path"]
folder_name = args["folder_name"]
filename = args["filename"]

if filename:
    input_path = os.path.join(input_path, filename)

endpoint = args["endpoint"]

print("File to be uploaded: {}".format(input_path))

print("Uploading file to : {}".format(folder_name))

MinIO(
    source=input_path,
    bucket_name=bucket_name,
    destination=folder_name,
    endpoint=endpoint
)

inputs = {}

for key, value in args.items():
    inputs[key] = value

outputs = {}

s3_url = f"s3://{bucket_name}/{folder_name}"

if filename:
    s3_url += f"/{filename}"

outputs["minio_url"] = s3_url

visualization_arguments = {"inputs": inputs, "outputs": outputs}

markdown_dict = {"storage": "inline", "source": visualization_arguments}

visualization = Visualization(
    mlpipeline_ui_metadata=args["mlpipeline_ui_metadata"],
    markdown=markdown_dict,
)