# Copyright 2023 Together Computer
#
# 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.

# Lint as: python3
"""RedPajama V2: Quality annotated Web Text Documents."""

import json

import datasets
import traceback
import os
import gzip
from typing import List

import pyarrow.parquet as pq

logger = datasets.logging.get_logger(__name__)

_DESCRIPTION = """\
RedPajama V2: an Open Dataset for Training Large Language Models
"""

_URL_BASE = 'https://data.together.xyz/redpajama-data-v2/v1.0.0'
_LANGUAGES = ("en", "de", "fr", "es", "it")
_LISTINGS_PATTERN = "listings/{language}-{snapshot}-{partition}.txt"

_CC_SNAPSHOT_IDS = (
    "2014-15",
    "2014-23",
    "2014-35",
    "2014-41",
    "2014-42",
    "2014-49",
    "2014-52",
    "2015-14",
    "2015-22",
    "2015-27",
    "2015-32",
    "2015-35",
    "2015-40",
    "2015-48",
    "2016-07",
    "2016-18",
    "2016-22",
    "2016-26",
    "2016-30",
    "2016-36",
    "2016-40",
    "2016-44",
    "2016-50",
    "2017-04",
    "2017-09",
    "2017-17",
    "2017-22",
    "2017-26",
    "2017-30",
    "2017-34",
    "2017-39",
    "2017-43",
    "2017-47",
    "2017-51",
    "2018-05",
    "2018-09",
    "2018-13",
    "2018-17",
    "2018-22",
    "2018-26",
    "2018-30",
    "2018-34",
    "2018-39",
    "2018-43",
    "2018-47",
    "2018-51",
    "2019-04",
    "2019-09",
    "2019-13",
    "2019-18",
    "2019-22",
    "2019-26",
    "2019-30",
    "2019-35",
    "2019-39",
    "2019-43",
    "2019-47",
    "2019-51",
    "2020-05",
    "2020-10",
    "2020-16",
    "2020-24",
    "2020-29",
    "2020-34",
    "2020-40",
    "2020-45",
    "2020-50",
    "2021-04",
    "2021-10",
    "2021-17",
    "2021-21",
    "2021-25",
    "2021-31",
    "2021-39",
    "2021-43",
    "2021-49",
    "2022-05",
    "2022-21",
    "2022-27",
    "2022-33",
    "2022-40",
    "2022-49",
    "2023-06",
    "2023-14"
)


class RedPajamaDataV2Config(datasets.BuilderConfig):
    """BuilderConfig for RedPajama."""

    def __init__(self, *args, **kwargs):
        """BuilderConfig for RedPajama.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(RedPajamaDataV2Config, self).__init__(**kwargs)
        self.partition: str = kwargs.pop("partition", "all")
        self.snapshots: List[str] = kwargs.pop("snapshots", _CC_SNAPSHOT_IDS)
        self.languages: List[str] = kwargs.pop("languages", _LANGUAGES)


class RedPajamaV2(datasets.GeneratorBasedBuilder):
    """ RedPajama V2: Quality annotated Web Text Documents. """

    BUILDER_CONFIGS = [
        RedPajamaDataV2Config(
            name='sample',
            version=datasets.Version("1.0.0", ""),
            description=f"RedPajamaV2 Sample",
        ),
        RedPajamaDataV2Config(
            name='default',
            version=datasets.Version("1.0.0", ""),
            description=f"RedPajamaV2",
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "raw_content": datasets.Value("string"),
                    "doc_id": datasets.Value("string"),
                    "meta": datasets.Value("string"),
                    "quality_signals": datasets.Value("string")
                }
            ),
            supervised_keys=None,
        )

    def _split_generators_sample(self, dl_manager):
        # fetch documents
        sample_listings = dl_manager.download_and_extract(
            "sample/sample_listings.txt"
        )
        with open(sample_listings, "r") as fd:
            listings = [line.strip() for line in fd]

        # fetch documents
        documents_files = dl_manager.download({
            "head_middle": [
                f"sample/documents/{lst}.json.gz" for lst in listings
            ]
        })

        # fetch quality signals
        quality_signals_files = dl_manager.download({
            "head_middle": [
                f"sample/quality_signals/{lst}.signals.json.gz"
                for lst in listings
            ]
        })

        # fetch ids of duplicates
        duplicates_ids_files = dl_manager.download({
            "head_middle": [
                f"sample/duplicates/{lst}.duplicates.parquet"
                for lst in listings
            ]
        })

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "listings_ids": {"head_middle": listings},
                    "documents_files": documents_files,
                    "quality_signals_files": quality_signals_files,
                    "duplicates_ids_files": duplicates_ids_files
                }
            )
        ]

    def _split_generators_full(self, dl_manager):
        snapshots = getattr(self.config, 'snapshots', _CC_SNAPSHOT_IDS)
        languages = getattr(self.config, 'languages', _LANGUAGES)
        partition = getattr(self.config, 'partition', 'all')

        partitions = {
            "all": ["head_middle", "tail"]
        }.get(partition, [partition])

        # nested structure: partition -> urls
        listings_files_urls = {}
        for part in partitions:
            listings_files_urls[part] = []
            for snapshot_id in snapshots:
                for lang in languages:
                    listings_files_urls[part].append(
                        _LISTINGS_PATTERN.format(
                            language=lang,
                            snapshot=snapshot_id,
                            partition=part,
                        )
                    )

        # fetch listings from hub
        listings_files = dl_manager.download_and_extract(listings_files_urls)

        # fetch listings
        listings_ids = {}
        for part, part_listings_files in listings_files.items():
            listings_ids[part] = []
            for listings_file in part_listings_files:
                with open(listings_file, encoding="utf-8") as f:
                    listings_ids[part].extend([
                        line.strip() for line in f
                    ])

        # build urls pointing to documents, quality signals and duplicate ids
        document_urls = {}
        quality_signals_urls = {}
        duplicates_ids_urls = {}
        for part, part_listings_ids in listings_ids.items():

            document_urls[part] = []
            quality_signals_urls[part] = []
            duplicates_ids_urls[part] = []

            for lst_id in part_listings_ids:
                document_urls[part].append(
                    os.path.join(_URL_BASE, f"documents/{lst_id}.json.gz")
                )

                if part != "head_middle":
                    continue

                quality_signals_urls[part].append(
                    os.path.join(
                        _URL_BASE, f"quality_signals/{lst_id}.signals.json.gz"
                    )
                )

                duplicates_ids_urls[part].append(
                    os.path.join(
                        _URL_BASE, f"duplicates/{lst_id}.duplicates.parquet"
                    )
                )

        documents_files = dl_manager.download(document_urls)
        quality_signals_files = dl_manager.download(quality_signals_urls)
        duplicates_ids_files = dl_manager.download(duplicates_ids_urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "listings_ids": listings_ids,
                    "documents_files": documents_files,
                    "quality_signals_files": quality_signals_files,
                    "duplicates_ids_files": duplicates_ids_files
                }
            )
        ]

    def _split_generators(self, dl_manager):
        if self.config.name.endswith("sample"):
            return self._split_generators_sample(dl_manager)

        return self._split_generators_full(dl_manager)

    def _generate_examples(
            self, listings_ids, documents_files, quality_signals_files,
            duplicates_ids_files
    ):
        key = 0
        for part in documents_files.keys():
            part_docs_files = documents_files[part]
            part_qs_files = quality_signals_files[part]
            part_listings_ids = listings_ids[part]
            part_duplicates_ids_files = duplicates_ids_files[part]

            if len(part_qs_files) == 0:
                for sample in self._handle_tail_partition(
                        part, part_docs_files, part_listings_ids
                ):
                    yield key, sample
                    key += 1
                continue

            for sample in self._handle_head_middle_partition(
                    part, part_docs_files, part_qs_files,
                    part_duplicates_ids_files, part_listings_ids

            ):
                yield key, sample
                key += 1

    def _handle_tail_partition(self, part, docs_files, listings_ids):
        for doc_file, listing_id in zip(docs_files, listings_ids):
            with gzip.open(doc_file, "rt", encoding="utf-8") as df:
                for row, doc in enumerate(df):
                    doc_id = f"{listing_id}.json.gz/{row}"
                    try:
                        yield self.handle_record(part, doc_id, doc, None, None)
                    except Exception as e:
                        print(f'doc_file: {doc_file}')
                        print(f'row: {row}')
                        traceback.print_exc()
                        raise e

    def _handle_head_middle_partition(
            self, part, docs_files, qs_files, dupes_files, listings_ids,
    ):
        assert len(docs_files) == len(qs_files)

        listings_ids = listings_ids[:len(docs_files)]

        for doc_file, qs_file, dupe_file, listings_id in zip(
                docs_files, qs_files, dupes_files, listings_ids
        ):
            # load duplicates
            try:
                with open(dupe_file, "rb") as df:
                    duplicates = set(pq.read_table(
                        df, columns=["doc_id"], use_pandas_metadata=False
                    )["doc_id"].to_pylist())
            except Exception as e:
                print(f'failed loading duplicate ids from {dupe_file}.')
                duplicates = set()

            try:
                with gzip.open(doc_file, "rt", encoding="utf-8") as df:
                    with gzip.open(qs_file, "rt", encoding="utf-8") as qf:
                        for row, (doc, qs) in enumerate(zip(df, qf)):
                            doc_id = f"{listings_id}.json.gz/{row}"

                            if doc_id in duplicates:
                                is_duplicate = True
                            else:
                                is_duplicate = False

                            try:
                                yield self.handle_record(
                                    part, doc_id, doc, qs,
                                    is_duplicate=is_duplicate
                                )
                            except Exception as e:
                                print(f'failed handling row {row} in '
                                      f'{doc_file} ({qs_file})')
                                traceback.print_exc()
                                continue
            except gzip.BadGzipFile as e:
                # skip broken gzip files
                print(f'BadGzipFile: {doc_file, qs_file}')
                traceback.print_exc()
                continue

    @staticmethod
    def handle_record(part, doc_id, doc, qs, is_duplicate=None):
        doc = json.loads(doc)
        qs = json.loads(qs) if qs is not None else {}

        meta = {
            "url": doc["url"],
            "partition": part,
            "language": doc["language"],
            "source_domain": doc["source_domain"],
            "date_download": doc["date_download"],
            "digest": doc["digest"],
        }

        quality_signals = qs.get("quality_signals", {})
        quality_signals["is_duplicate"] = is_duplicate

        return {
            "raw_content": doc["raw_content"],
            "doc_id": doc_id,
            "meta": json.dumps(meta),
            "quality_signals": json.dumps(quality_signals),
        }