# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the DataLab Datasets Authors.
#
# 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
#
# 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.

from argparse import ArgumentParser
import os
from pathlib import Path
from shutil import copyfile
from typing import List

from datalabs import config
from datalabs.builder import DatasetBuilder
from datalabs.commands import BaseDatasetsCLICommand
from datalabs.load import dataset_module_factory, import_main_class
from datalabs.utils.download_manager import DownloadConfig, GenerateMode


def run_beam_command_factory(args):
    return RunBeamCommand(
        args.dataset,
        args.name,
        args.cache_dir,
        args.beam_pipeline_options,
        args.data_dir,
        args.all_configs,
        args.save_infos,
        args.ignore_verifications,
        args.force_redownload,
    )


class RunBeamCommand(BaseDatasetsCLICommand):
    @staticmethod
    def register_subcommand(parser: ArgumentParser):
        run_beam_parser = parser.add_parser(
            "run_beam", help="Run a Beam dataset processing pipeline."
        )
        run_beam_parser.add_argument(
            "--name", type=str, default=None, help="Dataset processing name"
        )
        run_beam_parser.add_argument(
            "--cache_dir",
            type=str,
            default=None,
            help="Cache directory where the datalab are stored.",
        )
        run_beam_parser.add_argument(
            "--beam_pipeline_options",
            type=str,
            default="",
            help="Beam pipeline options, separated by commas. "
            "Example: `--beam_pipeline_options="
            "job_name=my-job,project=my-project`",
        )
        run_beam_parser.add_argument(
            "--data_dir",
            type=str,
            default=None,
            help="Can be used to specify a manual directory to get the files from.",
        )
        run_beam_parser.add_argument(
            "--all_configs", action="store_true", help="Test all dataset configurations"
        )
        run_beam_parser.add_argument(
            "--save_infos", action="store_true", help="Save the dataset infos file"
        )
        run_beam_parser.add_argument(
            "--ignore_verifications",
            action="store_true",
            help="Run the test without checksums and splits checks",
        )
        run_beam_parser.add_argument(
            "--force_redownload", action="store_true", help="Force dataset redownload"
        )
        run_beam_parser.add_argument(
            "dataset", type=str, help="Name of the dataset to download"
        )
        run_beam_parser.set_defaults(func=run_beam_command_factory)

    def __init__(
        self,
        dataset: str,
        name: str,
        cache_dir: str,
        beam_pipeline_options: str,
        data_dir: str,
        all_configs: bool,
        save_infos: bool,
        ignore_verifications: bool,
        force_redownload: bool,
    ):
        self._dataset = dataset
        self._name = name
        self._cache_dir = cache_dir
        self._beam_pipeline_options = beam_pipeline_options
        self._data_dir = data_dir
        self._all_configs = all_configs
        self._save_infos = save_infos
        self._ignore_verifications = ignore_verifications
        self._force_redownload = force_redownload

    def run(self):
        import apache_beam as beam

        if self._name is not None and self._all_configs:
            print("Both parameters `name` and `all_configs` can't be used at once.")
            exit(1)
        path, name = self._dataset, self._name
        dataset_module = dataset_module_factory(path)
        builder_cls = import_main_class(dataset_module.module_path)
        builders: List[DatasetBuilder] = []
        if self._beam_pipeline_options:
            beam_options = beam.options.pipeline_options.PipelineOptions(
                flags=[
                    f"--{opt.strip()}"
                    for opt in self._beam_pipeline_options.split(",")
                    if opt
                ]
            )
        else:
            beam_options = None
        if self._all_configs and len(builder_cls.BUILDER_CONFIGS) > 0:
            for builder_config in builder_cls.BUILDER_CONFIGS:
                builders.append(
                    builder_cls(
                        name=builder_config.name,
                        data_dir=self._data_dir,
                        hash=dataset_module.hash,
                        beam_options=beam_options,
                        cache_dir=self._cache_dir,
                        base_path=dataset_module.builder_kwargs.get("base_path"),
                        namespace=dataset_module.builder_kwargs.get("namespace"),
                    )
                )
        else:
            builders.append(
                builder_cls(
                    name=name,
                    data_dir=self._data_dir,
                    beam_options=beam_options,
                    cache_dir=self._cache_dir,
                    base_path=dataset_module.builder_kwargs.get("base_path"),
                    namespace=dataset_module.builder_kwargs.get("namespace"),
                )
            )

        for builder in builders:
            builder.download_and_prepare(
                download_mode=GenerateMode.REUSE_CACHE_IF_EXISTS
                if not self._force_redownload
                else GenerateMode.FORCE_REDOWNLOAD,
                download_config=DownloadConfig(
                    cache_dir=config.DOWNLOADED_DATASETS_PATH
                ),
                save_infos=self._save_infos,
                ignore_verifications=self._ignore_verifications,
                try_from_hf_gcs=False,
            )

        print("Apache beam run successful.")

        # If save_infos=True, the dataset infos file is created
        # next to the loaded module file.
        # Let's move it to the original directory of the dataset
        # script, to allow the user to
        # upload them on S3 at the same time afterwards.
        if self._save_infos:
            dataset_infos_path = os.path.join(
                builder_cls.get_imported_module_dir(), config.DATASETDICT_INFOS_FILENAME
            )

            name = Path(path).name + ".py"

            combined_path = os.path.join(path, name)
            if os.path.isfile(path):
                dataset_dir = os.path.dirname(path)
            elif os.path.isfile(combined_path):
                dataset_dir = path
            else:  # in case of a remote dataset
                print(f"Dataset Infos file saved at {dataset_infos_path}")
                exit(1)

            # Move datasetinfo back to the user
            user_dataset_infos_path = os.path.join(
                dataset_dir, config.DATASETDICT_INFOS_FILENAME
            )
            copyfile(dataset_infos_path, user_dataset_infos_path)
            print(f"Dataset Infos file saved at {user_dataset_infos_path}")
