240 lines
9.1 KiB
Python
240 lines
9.1 KiB
Python
import itertools
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import re
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from dataclasses import dataclass, field
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from pathlib import Path
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ROOT_DIR = Path(__file__).parent.parent.parent.resolve()
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ROOT_DIR_RELATIVE = '../../../..'
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EXAMPLE_DIR = ROOT_DIR / "examples"
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EXAMPLE_DOC_DIR = ROOT_DIR / "docs/source/getting_started/examples"
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def fix_case(text: str) -> str:
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subs = {
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"api": "API",
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"Cli": "CLI",
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"cpu": "CPU",
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"llm": "LLM",
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"tpu": "TPU",
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"aqlm": "AQLM",
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"gguf": "GGUF",
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"lora": "LoRA",
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"vllm": "vLLM",
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"openai": "OpenAI",
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"multilora": "MultiLoRA",
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"mlpspeculator": "MLPSpeculator",
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r"fp\d+": lambda x: x.group(0).upper(), # e.g. fp16, fp32
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r"int\d+": lambda x: x.group(0).upper(), # e.g. int8, int16
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}
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for pattern, repl in subs.items():
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text = re.sub(rf'\b{pattern}\b', repl, text, flags=re.IGNORECASE)
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return text
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@dataclass
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class Index:
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"""
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Index class to generate a structured document index.
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Attributes:
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path (Path): The path save the index file to.
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title (str): The title of the index.
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description (str): A brief description of the index.
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caption (str): An optional caption for the table of contents.
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maxdepth (int): The maximum depth of the table of contents. Defaults to 1.
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documents (list[str]): A list of document paths to include in the index. Defaults to an empty list.
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Methods:
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generate() -> str:
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Generates the index content as a string in the specified format.
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""" # noqa: E501
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path: Path
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title: str
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description: str
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caption: str
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maxdepth: int = 1
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documents: list[str] = field(default_factory=list)
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def generate(self) -> str:
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content = f"# {self.title}\n\n{self.description}\n\n"
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content += ":::{toctree}\n"
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content += f":caption: {self.caption}\n:maxdepth: {self.maxdepth}\n"
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content += "\n".join(self.documents) + "\n:::\n"
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return content
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@dataclass
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class Example:
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"""
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Example class for generating documentation content from a given path.
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Attributes:
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path (Path): The path to the main directory or file.
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category (str): The category of the document.
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main_file (Path): The main file in the directory.
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other_files (list[Path]): List of other files in the directory.
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title (str): The title of the document.
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Methods:
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__post_init__(): Initializes the main_file, other_files, and title attributes.
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determine_main_file() -> Path: Determines the main file in the given path.
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determine_other_files() -> list[Path]: Determines other files in the directory excluding the main file.
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determine_title() -> str: Determines the title of the document.
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generate() -> str: Generates the documentation content.
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""" # noqa: E501
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path: Path
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category: str = None
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main_file: Path = field(init=False)
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other_files: list[Path] = field(init=False)
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title: str = field(init=False)
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def __post_init__(self):
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self.main_file = self.determine_main_file()
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self.other_files = self.determine_other_files()
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self.title = self.determine_title()
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def determine_main_file(self) -> Path:
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"""
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Determines the main file in the given path.
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If the path is a file, it returns the path itself. Otherwise, it searches
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for Markdown files (*.md) in the directory and returns the first one found.
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Returns:
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Path: The main file path, either the original path if it's a file or the first
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Markdown file found in the directory.
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Raises:
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IndexError: If no Markdown files are found in the directory.
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""" # noqa: E501
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return self.path if self.path.is_file() else list(
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self.path.glob("*.md")).pop()
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def determine_other_files(self) -> list[Path]:
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"""
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Determine other files in the directory excluding the main file.
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This method checks if the given path is a file. If it is, it returns an empty list.
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Otherwise, it recursively searches through the directory and returns a list of all
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files that are not the main file.
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Returns:
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list[Path]: A list of Path objects representing the other files in the directory.
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""" # noqa: E501
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if self.path.is_file():
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return []
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is_other_file = lambda file: file.is_file() and file != self.main_file
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return [file for file in self.path.rglob("*") if is_other_file(file)]
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def determine_title(self) -> str:
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return fix_case(self.path.stem.replace("_", " ").title())
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def generate(self) -> str:
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# Convert the path to a relative path from __file__
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make_relative = lambda path: ROOT_DIR_RELATIVE / path.relative_to(
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ROOT_DIR)
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content = f"Source <gh-file:{self.path.relative_to(ROOT_DIR)}>.\n\n"
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include = "include" if self.main_file.suffix == ".md" else \
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"literalinclude"
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if include == "literalinclude":
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content += f"# {self.title}\n\n"
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content += f":::{{{include}}} {make_relative(self.main_file)}\n"
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if include == "literalinclude":
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content += f":language: {self.main_file.suffix[1:]}\n"
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content += ":::\n\n"
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if not self.other_files:
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return content
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content += "## Example materials\n\n"
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for file in self.other_files:
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include = "include" if file.suffix == ".md" else "literalinclude"
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content += f":::{{admonition}} {file.relative_to(self.path)}\n"
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content += ":class: dropdown\n\n"
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content += f":::{{{include}}} {make_relative(file)}\n:::\n"
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content += ":::\n\n"
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return content
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def generate_examples():
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# Create the EXAMPLE_DOC_DIR if it doesn't exist
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if not EXAMPLE_DOC_DIR.exists():
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EXAMPLE_DOC_DIR.mkdir(parents=True)
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# Create empty indices
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examples_index = Index(
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path=EXAMPLE_DOC_DIR / "examples_index.md",
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title="Examples",
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description=
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"A collection of examples demonstrating usage of vLLM.\nAll documented examples are autogenerated using <gh-file:docs/source/generate_examples.py> from examples found in <gh-file:examples>.", # noqa: E501
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caption="Examples",
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maxdepth=2)
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# Category indices stored in reverse order because they are inserted into
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# examples_index.documents at index 0 in order
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category_indices = {
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"other":
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Index(
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path=EXAMPLE_DOC_DIR / "examples_other_index.md",
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title="Other",
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description=
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"Other examples that don't strongly fit into the online or offline serving categories.", # noqa: E501
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caption="Examples",
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),
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"online_serving":
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Index(
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path=EXAMPLE_DOC_DIR / "examples_online_serving_index.md",
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title="Online Serving",
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description=
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"Online serving examples demonstrate how to use vLLM in an online setting, where the model is queried for predictions in real-time.", # noqa: E501
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caption="Examples",
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),
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"offline_inference":
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Index(
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path=EXAMPLE_DOC_DIR / "examples_offline_inference_index.md",
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title="Offline Inference",
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description=
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"Offline inference examples demonstrate how to use vLLM in an offline setting, where the model is queried for predictions in batches.", # noqa: E501
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caption="Examples",
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),
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}
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examples = []
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glob_patterns = ["*.py", "*.md", "*.sh"]
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# Find categorised examples
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for category in category_indices:
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category_dir = EXAMPLE_DIR / category
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globs = [category_dir.glob(pattern) for pattern in glob_patterns]
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for path in itertools.chain(*globs):
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examples.append(Example(path, category))
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# Find examples in subdirectories
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for path in category_dir.glob("*/*.md"):
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examples.append(Example(path.parent, category))
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# Find uncategorised examples
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globs = [EXAMPLE_DIR.glob(pattern) for pattern in glob_patterns]
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for path in itertools.chain(*globs):
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examples.append(Example(path))
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# Find examples in subdirectories
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for path in EXAMPLE_DIR.glob("*/*.md"):
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# Skip categorised examples
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if path.parent.name in category_indices:
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continue
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examples.append(Example(path.parent))
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# Generate the example documentation
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for example in sorted(examples, key=lambda e: e.path.stem):
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doc_path = EXAMPLE_DOC_DIR / f"{example.path.stem}.md"
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with open(doc_path, "w+") as f:
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f.write(example.generate())
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# Add the example to the appropriate index
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index = category_indices.get(example.category, examples_index)
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index.documents.append(example.path.stem)
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# Generate the index files
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for category_index in category_indices.values():
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if category_index.documents:
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examples_index.documents.insert(0, category_index.path.name)
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with open(category_index.path, "w+") as f:
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f.write(category_index.generate())
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with open(examples_index.path, "w+") as f:
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f.write(examples_index.generate())
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