exact_substrings.py

import os

from datatrove.executor.base import PipelineExecutor
from datatrove.executor.local import LocalPipelineExecutor
from datatrove.pipeline.dedup import ESDatasetToSequence, ESMergeSequences, ESRangeRemover
from datatrove.pipeline.extractors import Trafilatura
from datatrove.pipeline.filters import GopherQualityFilter, LanguageFilter
from datatrove.pipeline.readers import JsonlReader, WarcReader
from datatrove.pipeline.writers.jsonl import JsonlWriter
from datatrove.utils.typeshelper import Languages


"""
example on how to run exact-substring deduplication. It also requires using
https://github.com/google-research/deduplicate-text-datasets after stage 1, 2
1) ESDatasetToSequence maps 1 file into a sequence S. With unique separators at the beginning of each document. It also
    saves the bytes offset of where each individual document begins.
2) ESMergeSequences merges all sequences into a big single sequence. It also saves the bytes offset per file.

---
after stage two you should use deduplicate-text-datasets scripts to create the suffix array and find all the
duplicates. The final output of these scripts should be a .bytearange file with the ranges in bytes wrt the big
sequence
---

3) ESRangeRemover reads from DocumentsPipeline and duplicates ranges at the same time removing the duplicates ranges.


to run stage 1,2 call run_stage_1_2, after you have followed deduplicate-text-datasets instructions in the README you
can call stage 3 with run_stage_3.

N.B
The steps

"""


def run_step_1_and_2():
    pipeline_1 = [
        WarcReader(data_folder="warc/", limit=1000),
        Trafilatura(),
        GopherQualityFilter(min_stop_words=0),
        LanguageFilter(language_threshold=0.5, languages=[Languages.english]),
        JsonlWriter("intermediate/"),
        ESDatasetToSequence(output_folder="es/"),
    ]

    pipeline_2 = [
        ESMergeSequences(
            data_folder="es",
            tasks_stage_1=4,
        )
    ]

    executor_1: PipelineExecutor = LocalPipelineExecutor(pipeline=pipeline_1, workers=4, tasks=4)

    executor_2: PipelineExecutor = LocalPipelineExecutor(pipeline=pipeline_2, workers=1, tasks=1)

    print(executor_1.run())
    print(executor_2.run())


def run_step_3():
    pipeline_3 = [
        JsonlReader("intermediate/"),  # must be the same data that was passed to DatasetToSequence
        ESRangeRemover(
            sequence_folder=f"{os.getcwd()}/es/",
        ),
        JsonlWriter("final-deduped-data"),
    ]

    executor_3: PipelineExecutor = LocalPipelineExecutor(pipeline=pipeline_3, workers=4, tasks=4)

    print(executor_3.run())