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How to use compat_kw method in hypothesis

Best Python code snippet using hypothesis

numpy.py

Source:numpy.py

...65    # Subarray datatypes, eg '(2, 3)i4'66    if dtype.subdtype is not None:67        subtype, shape = dtype.subdtype68        return arrays(subtype, shape, elements=kwargs)69    def compat_kw(*args, **kw):70        """Update default args to the strategy with user-supplied keyword args."""71        assert {"min_value", "max_value", "max_size"}.issuperset(kw)72        for key in set(kwargs).intersection(kw):73            msg = f"dtype {dtype!r} requires {key}={kwargs[key]!r} to be %s {kw[key]!r}"74            if kw[key] is not None:75                if key.startswith("min_") and kw[key] > kwargs[key]:76                    raise InvalidArgument(msg % ("at least",))77                elif key.startswith("max_") and kw[key] < kwargs[key]:78                    raise InvalidArgument(msg % ("at most",))79        kw.update({k: v for k, v in kwargs.items() if k in args or k in kw})80        return kw81    # Scalar datatypes82    if dtype.kind == "b":83        result = st.booleans()  # type: SearchStrategy[Any]84    elif dtype.kind == "f":85        result = st.floats(86            width=8 * dtype.itemsize,87            **compat_kw(88                "min_value",89                "max_value",90                "allow_nan",91                "allow_infinity",92                "exclude_min",93                "exclude_max",94            ),95        )96    elif dtype.kind == "c":97        # If anyone wants to add a `width` argument to `complex_numbers()`, we would98        # accept a pull request and add passthrough support for magnitude bounds,99        # but it's a low priority otherwise.100        if dtype.itemsize == 8:101            float32 = st.floats(width=32, **compat_kw("allow_nan", "allow_infinity"))102            result = st.builds(complex, float32, float32)103        else:104            result = st.complex_numbers(**compat_kw("allow_nan", "allow_infinity"))105    elif dtype.kind in ("S", "a"):106        # Numpy strings are null-terminated; only allow round-trippable values.107        # `itemsize == 0` means 'fixed length determined at array creation'108        max_size = dtype.itemsize or None109        result = st.binary(**compat_kw("min_size", max_size=max_size)).filter(110            lambda b: b[-1:] != b"\0"111        )112    elif dtype.kind == "u":113        kw = compat_kw(min_value=0, max_value=2 ** (8 * dtype.itemsize) - 1)114        result = st.integers(**kw)115    elif dtype.kind == "i":116        overflow = 2 ** (8 * dtype.itemsize - 1)117        result = st.integers(**compat_kw(min_value=-overflow, max_value=overflow - 1))118    elif dtype.kind == "U":119        # Encoded in UTF-32 (four bytes/codepoint) and null-terminated120        max_size = (dtype.itemsize or 0) // 4 or None121        result = st.text(**compat_kw("alphabet", "min_size", max_size=max_size)).filter(122            lambda b: b[-1:] != "\0"123        )124    elif dtype.kind in ("m", "M"):125        if "[" in dtype.str:126            res = st.just(dtype.str.split("[")[-1][:-1])127        else:128            # Note that this case isn't valid to pass to arrays(), but we support129            # it here because we'd have to guard against equivalents in arrays()130            # regardless and drawing scalars is a valid use-case.131            res = st.sampled_from(TIME_RESOLUTIONS)132        result = st.builds(dtype.type, st.integers(-(2 ** 63), 2 ** 63 - 1), res)133    else:134        raise InvalidArgument(f"No strategy inference for {dtype}")135    return result.map(dtype.type)...

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