# 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

logger = datasets.logging.get_logger(__name__)

_DESCRIPTION = """\
RedPajama V2 is a Data Foundation of Web Text Documents with Quality Annotations.
"""

with open("_CC_SNAPSHOT_IDS", "r") as f:
    _CC_SNAPSHOT_IDS = [line.strip() for line in f]

_URL_BASE = 'https://data.together.xyz/redpajama-data-v2/v1.0.0'
_LANGUAGES = ("en", "de", "fr", "es", "it")
_SAMPLE_SNAPSHOT_ID = "2023-06"

_LISTINGS_PATTERN = "listings/{language}-{snapshot}-{partition}.txt"


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

    def __init__(self, *args, language, partition, snapshots, **kwargs):
        """BuilderConfig for RedPajama.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(RedPajamaDataV2Config, self).__init__(**kwargs)
        self.partition = partition
        self.snapshots = snapshots
        self.language = language


_BUILDER_CONFIGS = [
    RedPajamaDataV2Config(
        name=f'_sample',
        partition='sample',
        snapshots=None,
        language=None,
        version=datasets.Version("1.0.0", ""),
        description=f"RedPajamaV2 Sample",
    ),
    # this one is just an alias for the sample
    RedPajamaDataV2Config(
        name=f'sample',
        partition='sample',
        snapshots=None,
        language=None,
        version=datasets.Version("1.0.0", ""),
        description=f"RedPajamaV2 Sample",
    )
]

for lang in _LANGUAGES:
    _BUILDER_CONFIGS.extend(
        [
            # single snapshot
            RedPajamaDataV2Config(
                name=f'{lang}-head-middle-{snapshot}',
                partition='head_middle',
                snapshots=[snapshot],
                language=lang,
                version=datasets.Version("1.0.0", ""),
                description=f"RedPajamaV2 head-middle {lang}-{snapshot}",
            )
            for snapshot in _CC_SNAPSHOT_IDS
        ] + [
            # all snapshots
            RedPajamaDataV2Config(
                name=f'{lang}-head-middle-all',
                partition='head_middle',
                snapshots=_CC_SNAPSHOT_IDS,
                language=lang,
                version=datasets.Version("1.0.0", ""),
                description=f"RedPajamaV2 head-middle {lang}"
            )
        ]
    )

    _BUILDER_CONFIGS.extend(
        [
            # single snapshot
            RedPajamaDataV2Config(
                name=f'{lang}-tail-{snapshot}',
                partition='tail',
                snapshots=[snapshot],
                language=lang,
                version=datasets.Version("1.0.0", ""),
                description=f"RedPajamaV2 tail {lang}-{snapshot}",
            )
            for snapshot in _CC_SNAPSHOT_IDS
        ] + [
            # all snapshots
            RedPajamaDataV2Config(
                name=f'{lang}-tail-all',
                partition='tail',
                snapshots=_CC_SNAPSHOT_IDS,
                language=lang,
                version=datasets.Version("1.0.0", ""),
                description=f"RedPajamaV2 tail {lang}"
            )
        ]
    )


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

    BUILDER_CONFIGS = _BUILDER_CONFIGS

    def _info(self):
        if self.config.partition == "tail":
            return datasets.DatasetInfo(
                description=_DESCRIPTION,
                features=datasets.Features(
                    {
                        "raw_content": datasets.Value("string"),
                        "doc_id": datasets.Value("string"),
                        "meta": datasets.Value("string"),
                    }
                ),
                supervised_keys=None,
            )
        else:
            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
        with open("sample/sample_listings.txt", "r") as fd:
            listings = [line.strip() for line in fd]

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

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

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

    def _split_generators_full(self, dl_manager):
        url_lists = dl_manager.download_and_extract({
            snapshot_id: _LISTINGS_PATTERN.format(
                language=self.config.language,
                snapshot=snapshot_id,
                partition=self.config.partition,
            )
            for snapshot_id in self.config.snapshots
        })

        listings_ids = {}

        for snapshot_id, listings_file in url_lists.items():
            with open(listings_file, encoding="utf-8") as f:
                listings_ids[snapshot_id] = [line.strip() for line in f]

        # build urls pointing to documents
        document_urls = {
            snapshot_id: [
                os.path.join(_URL_BASE, f"documents/{lst_id}.json.gz")
                for lst_id in listings_ids[snapshot_id]
            ]
            for snapshot_id in self.config.snapshots
        }

        documents_files = dl_manager.download(document_urls)

        # build urls pointing to quality signals
        if self.config.partition == "head_middle":
            quality_signals_urls = {
                snapshot_id: [
                    os.path.join(
                        _URL_BASE,
                        f"quality_signals/{lst_id}.signals.json.gz"
                    )
                    for lst_id in listings_ids[snapshot_id]
                ]
                for snapshot_id in self.config.snapshots
            }

            quality_signals_files = dl_manager.download(
                quality_signals_urls
            )
        else:
            quality_signals_files = {}

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "documents_files": {
                        snapshot_id: documents_files[snapshot_id]
                        for snapshot_id in self.config.snapshots
                    },
                    "quality_signals_files": {
                        snapshot_id: quality_signals_files.get(snapshot_id)
                        for snapshot_id in self.config.snapshots
                    }
                }
            )
        ]

    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, documents_files, quality_signals_files):
        """ This function returns examples """
        snapshots = list(documents_files.keys())

        key = 0
        for snapshot in snapshots:
            docs_files = documents_files[snapshot]
            if self.config.partition in ("head_middle", "sample"):
                qs_files = quality_signals_files[snapshot]
            else:
                qs_files = None

            assert len(docs_files) == len(qs_files)

            for doc_file, qs_file in zip(docs_files, qs_files):
                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)):

                            try:
                                doc = json.loads(doc)
                                qs = json.loads(qs)
                                doc_id = qs["id"]

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

                                if self.config.partition == "tail":
                                    yield key, {
                                        "raw_content": doc["raw_content"],
                                        "doc_id": doc_id,
                                        "meta": json.dumps(meta),
                                    }
                                else:
                                    yield key, {
                                        "raw_content": doc["raw_content"],
                                        "doc_id": doc_id,
                                        "meta": json.dumps(meta),
                                        "quality_signals": json.dumps(
                                            qs["quality_signals"]
                                        ),
                                    }
                                key += 1
                            except Exception as e:
                                print(f'doc_file: {doc_file}')
                                print(f'qs_file: {qs_file}')
                                print(f'row: {row}')
                                traceback.print_exc()

                                raise e