beartype beartype: Unbearably fast near-real-time runtime type-checking in pure Python
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But this approach also duplicates work when those objects remain unchanged over multiple calls to those callables, which is bad. Conventional runtime type checkers perform most of their work at call time. The @typeguard.typechecked and similar decorators consume almost none of the time needed to first decorate and then repeatedly call a decorated function. Although the initial cost of decoration is essentially free, each type-checked call thereafter incurs significant overhead. If you know type hints, you know beartype. Since beartype is driven entirely by tool-agnostic community standards, the public API for beartype is exactly the sum of those standards.
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Beartype by fabricating your own PEP-compliant beartype validators, enabling beartype to validate arbitrary objects against actual Boto3 types at runtime when used as type hints. You already require beartype, so no additional third-party dependencies are required. Those validators are silently ignored by static type checkers; they do not enable context-aware code completion across compliant IDEs. Basically, beartype type-checks randomly sampled data. When standard type hints fail to support your use case, annotate functions and methods with beartype-specific validator type hints instead. Static type checkers (e.g., mypy, pyright) by importing Boto3 stub types from an external third-party dependency (e.g., mypy-boto3), enabling context-aware code completion across compliant IDEs (e.g., PyCharm, VSCode Pylance).
# DOOR hints support comparisons Pythonically -- unlike normal type hints. # DOOR hints support equality Pythonically -- unlike normal type hints. # DOOR hints test as booleans Pythonically -- unlike normal type hints.
Versus Runtime Type Checkers
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Callable.abc.Iterable and callable.abc.Callable[[], int]are not semantically related. Whereas the first hints conveys a container semantic, the second hint conveys a callable semantic. Since these two semantics are unrelated, these two hints are dissimilar enough to not be reasonably comparable. # Booleans are subclasses and thus subhints of integers. # arguments and returning a sequence of any types. The other paw, mypy supports a vast plugin architecture enabling third-party Python packages to describe dynamic runtime behaviour statically.
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Each functional validator incurs the cost of calling that function for each call to each @beartype-decorated callable annotated by that validator, but is Turing-complete and thus supports all possible validation scenarios. "Python Type Checking ", a comprehensive third-party introduction to the subject. Like most existing articles, this guide predatesO runtime type checkers and thus discusses only static type checking. Thankfully, the underlying syntax and semantics cleanly translate to runtime type checking. Beartype has no install-time or runtime dependencies,supports standard Python package managers, and happily coexists with competing static type checkers and other runtime type checkers. Use beartype to assure the quality of Python code beyond what tests alone can assure.
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True, beartype always colours type-checking violations raised by callables configured with this configuration. False, beartype never colours type-checking violations raised by callables configured with this configuration. # All annotated parameters and returns preserve backward compatibility.
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Thanks to the pernicious magic of beartype, all ends typed well... The beartype.cave submodule publishes generic types suitable for use with the @beartype decorator and anywhere else you might need them. In this case, the type of the None singleton is imported from this submodule and listed in addition to tuple as an allowed return type from this function. O non-amortized worst-case time for k the number of child type hints nested in a parent type hint and including that parent. At call time, wrapper functions generated by the @beartype decorator are guaranteed to unconditionally run in O non-amortized worst-case time with negligible constant factors regardless of type hint complexity or nesting. This is not an amortized average-case analysis.
Force the issue now by just doing it yourself and then mocking us all over Gitter! # byte strings, generalizing "SentenceFragment" type hints defined above. Typing.Type and type rigidly validates type inheritance of onlyparameters and returns against only one superclass. Isinstanceable classes rigidly validate type instancing of onlyparameters and returns against only one class. Beartype.vale.IsEqual permissively validates equality against objects that are instances of any arbitrary type. IsEqual doesn't care what the types of your objects are.
Pretty sure we just invented type hint arithmetic right there. That's because beartype operates exclusively at the higher level of pure-Python callables rather than the lower level of individual statementsinside pure-Python callables. Unlike static type checkers, beartype can't be opinionated about things that no one should be. If the prior sentence was unreadable jargon, see our friendly and approachable FAQ for a human-readable synopsis. Annotate callables with package-specific and thus PEP-noncompliant type hints. Arbitrary callables like instance methods, class methods, static methods, and generator functions and methods – all of which are also trivially type-checked with the @beartype decorator.
# Validator matching only strings suffixed by a period. Booleans aren't integers in most high-level languages. Are you telling me booleans are literally integers in Python? What would that even mean if you could? Observe and cower, rigorous data engineers. Run Python under the "PYTHONOPTIMIZE" environment variable, which beartype also respects.
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