Streaming dataset generation (#6)
Browse files- Streaming dataset generation (38690e1383258b7bcfca85027fb75c82ab83df9c)
Co-authored-by: David Mezzetti <davidmezzetti@users.noreply.huggingface.co>
- wikipedia.py +57 -31
    	
        wikipedia.py
    CHANGED
    
    | @@ -20,14 +20,15 @@ | |
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            import bz2
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            import codecs
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            import json
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            import re
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            import xml.etree.cElementTree as etree
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            from urllib.parse import quote
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            -
            import mwparserfromhell
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            from multiprocess import Process, Manager
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            -
            from tqdm import tqdm
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            import datasets
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            logger = datasets.logging.get_logger(__name__)
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| @@ -904,8 +905,8 @@ class WikipediaConfig(datasets.BuilderConfig): | |
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                    self.language = language
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            _DATE = " | 
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            class Wikipedia(datasets.GeneratorBasedBuilder):
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                """Wikipedia dataset."""
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| @@ -967,9 +968,8 @@ class Wikipedia(datasets.GeneratorBasedBuilder): | |
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                        # Use dictionary since testing mock always returns the same result.
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                    downloaded_files = dl_manager.download({"xml": xml_urls})
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                    print("Finished downloading Wikipedia dump")
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                    return [
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                        datasets.SplitGenerator(  # pylint:disable=g-complex-comprehension
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| @@ -983,7 +983,6 @@ class Wikipedia(datasets.GeneratorBasedBuilder): | |
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                    def _extract_content(filepath):
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                        """Extracts article content from a single WikiMedia XML file."""
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                        logger.info("generating examples from = %s", filepath)
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                        content = []
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                        f = bz2.BZ2File(filename=filepath)
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                        # Workaround due to: https://github.com/tensorflow/tensorflow/issues/33563
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                        utf_f = codecs.getreader("utf-8")(f)
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| @@ -991,6 +990,7 @@ class Wikipedia(datasets.GeneratorBasedBuilder): | |
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                        for unused_event, elem in context:
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                            if not elem.tag.endswith("page"):
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                                continue
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                            namespace = elem.tag[:-4]
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                            title = elem.find(f"./{namespace}title").text
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                            ns = elem.find(f"./{namespace}ns").text
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| @@ -1009,8 +1009,7 @@ class Wikipedia(datasets.GeneratorBasedBuilder): | |
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                            if raw_content is None or red_ is not None:
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                                continue
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                        return content
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                    def _clean_content(inputs, language):
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                        """Cleans raw wikicode to extract text."""
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| @@ -1028,28 +1027,55 @@ class Wikipedia(datasets.GeneratorBasedBuilder): | |
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                        return id_, {"id": id_, "url": url, "title": title, "text": text}
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                        examples = manager.list()
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                        processes = []
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                        for filepath in filepaths:
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                            def parse_and_clean(examples):
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                                content = _extract_content(filepath)
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                                for obj in tqdm(content):
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                                    examples.append(_clean_content(obj, language=language))
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                            p = Process(target=parse_and_clean, args=(examples,))
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                            p.start()
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                            processes.append(p)
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                        for p in processes:
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                            p.join()
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                        print("Parsed and cleaned Wikipedia examples")
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                        for example in examples:
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                            if example is not None:
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                                yield example
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            def _parse_and_clean_wikicode(raw_content, parser, language):
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                """Strips formatting and unwanted sections from raw page content."""
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            import bz2
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            import codecs
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            import json
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            +
            import os
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            import re
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            import xml.etree.cElementTree as etree
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            from multiprocessing import Process, Queue
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            from urllib.parse import quote
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            import datasets
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            import mwparserfromhell
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            logger = datasets.logging.get_logger(__name__)
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                    self.language = language
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            +
            _DATE = "20240101"
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            +
            _COMPLETE = 1
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            class Wikipedia(datasets.GeneratorBasedBuilder):
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                """Wikipedia dataset."""
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                        # Use dictionary since testing mock always returns the same result.
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                    # Download Wikipedia files
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                    downloaded_files = dl_manager.download({"xml": xml_urls})
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                    return [
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                        datasets.SplitGenerator(  # pylint:disable=g-complex-comprehension
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| 983 | 
             
                    def _extract_content(filepath):
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                        """Extracts article content from a single WikiMedia XML file."""
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                        logger.info("generating examples from = %s", filepath)
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                        f = bz2.BZ2File(filename=filepath)
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                        # Workaround due to: https://github.com/tensorflow/tensorflow/issues/33563
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                        utf_f = codecs.getreader("utf-8")(f)
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                        for unused_event, elem in context:
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                            if not elem.tag.endswith("page"):
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                                continue
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            +
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                            namespace = elem.tag[:-4]
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                            title = elem.find(f"./{namespace}title").text
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                            ns = elem.find(f"./{namespace}ns").text
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                            if raw_content is None or red_ is not None:
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                                continue
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                            yield (id_, title, raw_content)
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                    def _clean_content(inputs, language):
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                        """Cleans raw wikicode to extract text."""
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                        return id_, {"id": id_, "url": url, "title": title, "text": text}
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                    # Create queues, limit size of output queue
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                    inputs, outputs = Queue(), Queue(30000)
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                    def execute(inputs, outputs):
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                        try:
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                            # Process until inputs queue is exhausted
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                            while not inputs.empty():
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                                batch, filepath = [], inputs.get()
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                                for obj in _extract_content(filepath):
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                                    batch.append(_clean_content(obj, language=language))
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                                    if len(batch) == 1024:
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                                        outputs.put(batch)
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                                        batch = []
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                                if batch:
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                                    outputs.put(batch)
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                        finally:
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                            # Write message that process is complete
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                            outputs.put(_COMPLETE)
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                    for filepath in filepaths:
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                        inputs.put(filepath)
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                    # Start worker processes
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                    processes = []
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                    for _ in range(min(len(filepaths), os.cpu_count())):
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                        process = Process(target=execute, args=(inputs, outputs))
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                        process.start()
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                        processes.append(process)
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                    # Read output from worker processes
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                    empty, complete = False, 0
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                    while not empty:
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                        # Get next result
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                        result = outputs.get()
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                        # Mark process as complete if all workers are complete and output queue is empty
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                        if result == _COMPLETE:
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                            complete += 1
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                            empty = len(processes) == complete and outputs.empty()
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                        elif result:
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                            for r in result:
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                                if r is not None:
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                                    yield r
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                    for process in processes:
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                        process.join()
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            def _parse_and_clean_wikicode(raw_content, parser, language):
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                """Strips formatting and unwanted sections from raw page content."""
         | 
 
		