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scripts/lint.sh
#!/usr/bin/env bash set -e set -x mypy fastapi ruff check fastapi tests docs_src scripts
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docs/en/docs/tutorial/response-model.md
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.gitignore
.idea .ipynb_checkpoints .mypy_cache .vscode __pycache__ .pytest_cache htmlcov dist site .coverage coverage.xml .netlify test.db log.txt Pipfile.lock env3.* env docs_build site_build venv docs.zip archive.zip # vim temporary files *~ .*.sw? .cache # macOS
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docs/ru/docs/python-types.md
!!! info
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docs/uk/docs/python-types.md
!!! info
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pyproject.toml
"pydantic-extra-types >=2.0.0", ] [tool.mypy] strict = true [[tool.mypy.overrides]] module = "fastapi.concurrency" warn_unused_ignores = false ignore_missing_imports = true [[tool.mypy.overrides]] module = "fastapi.tests.*" ignore_missing_imports = true check_untyped_defs = true [[tool.mypy.overrides]] module = "docs_src.*" disallow_incomplete_defs = false
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docs/de/docs/tutorial/response-model.md
``` Damit erhalten wir Tool-Unterstützung, vom Editor und mypy, da dieser Code hinsichtlich der Typen korrekt ist, aber wir erhalten auch die Datenfilterung von FastAPI. Wie funktioniert das? Schauen wir uns das mal an. 🤓 ### Typannotationen und Tooling Sehen wir uns zunächst an, wie Editor, mypy und andere Tools dies sehen würden.
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docs/vi/docs/features.md
* Bởi vì các cấu trúc dữ liệu của Pydantic chỉ là các instances của class bạn định nghĩa; auto-completion, linting, mypy và trực giác của bạn nên là m việc riêng biệt với những dữ liệu mà bạn đã validate. * Validate **các cấu trúc phức tạp**: * SỠdụng các models Pydantic phân tầng, `List` và `Dict` của Python `typing`,...
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docs/en/docs/python-types.md
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docs/de/docs/features.md
* Weil Pydantics Datenstrukturen einfach nur Instanzen ihrer definierten Klassen sind; Autovervollständigung, Linting, mypy und ihre Intuition sollten alle einwandfrei mit ihren validierten Daten funktionieren. * Validierung von **komplexen Strukturen**: * Benutzung von hierarchischen Pydantic-Modellen, Python-`typing`s `List` und `Dict`, etc.
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