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62 lines
2.5 KiB
Python
62 lines
2.5 KiB
Python
from typing import Optional, List
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from pathlib import Path
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from wasabi import msg
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import logging
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from radicli import Arg, ExistingFilePathOrDash, ExistingFilePath
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from ._util import cli, parse_config_overrides, show_validation_error
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from ._util import import_code
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from .. import util
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from ..util import get_sourced_components, load_model_from_config
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@cli.command_with_extra(
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"assemble",
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# fmt: off
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config_path=Arg(help="Path to config file"),
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output_path=Arg(help="Output directory to store assembled pipeline in"),
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code_path=Arg("--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
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verbose=Arg("--verbose", "-V", help="Display more information for debugging purposes"),
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# fmt: on
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)
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def assemble_cli(
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config_path: ExistingFilePathOrDash,
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output_path: Optional[Path] = None,
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code_path: Optional[ExistingFilePath] = None,
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verbose: bool = False,
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_extra: List[str] = [],
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):
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"""
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Assemble a spaCy pipeline from a config file. The config file includes
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all settings for initializing the pipeline. To override settings in the
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config, e.g. settings that point to local paths or that you want to
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experiment with, you can override them as command line options. The
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--code argument lets you pass in a Python file that can be used to
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register custom functions that are referenced in the config.
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DOCS: https://spacy.io/api/cli#assemble
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"""
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util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
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# Make sure all files and paths exists if they are needed
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if not config_path or (str(config_path) != "-" and not config_path.exists()):
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msg.fail("Config file not found", config_path, exits=1)
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overrides = parse_config_overrides(_extra)
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import_code(code_path)
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with show_validation_error(config_path):
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config = util.load_config(config_path, overrides=overrides, interpolate=False)
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msg.divider("Initializing pipeline")
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nlp = load_model_from_config(config, auto_fill=True)
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config = config.interpolate()
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sourced = get_sourced_components(config)
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# Make sure that listeners are defined before initializing further
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nlp._link_components()
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with nlp.select_pipes(disable=[*sourced]):
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nlp.initialize()
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msg.good("Initialized pipeline")
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if output_path is not None:
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msg.divider("Serializing to disk")
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if not output_path.exists():
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output_path.mkdir(parents=True)
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msg.good(f"Created output directory: {output_path}")
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nlp.to_disk(output_path)
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