261 lines
8.9 KiB
Python
261 lines
8.9 KiB
Python
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import collections
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import os
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import typing
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from dataclasses import dataclass
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__all__ = ["Config", "config"]
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@dataclass(init=False, eq=False, slots=True, kw_only=True, match_args=False)
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class Config:
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"""The base class for NetworkX configuration.
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There are two ways to use this to create configurations. The first is to
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simply pass the initial configuration as keyword arguments to ``Config``:
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>>> cfg = Config(eggs=1, spam=5)
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>>> cfg
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Config(eggs=1, spam=5)
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The second--and preferred--way is to subclass ``Config`` with docs and annotations.
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>>> class MyConfig(Config):
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... '''Breakfast!'''
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...
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... eggs: int
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... spam: int
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...
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... def _check_config(self, key, value):
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... assert isinstance(value, int) and value >= 0
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>>> cfg = MyConfig(eggs=1, spam=5)
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Once defined, config items may be modified, but can't be added or deleted by default.
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``Config`` is a ``Mapping``, and can get and set configs via attributes or brackets:
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>>> cfg.eggs = 2
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>>> cfg.eggs
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2
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>>> cfg["spam"] = 42
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>>> cfg["spam"]
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42
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Subclasses may also define ``_check_config`` (as done in the example above)
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to ensure the value being assigned is valid:
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>>> cfg.spam = -1
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Traceback (most recent call last):
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...
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AssertionError
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If a more flexible configuration object is needed that allows adding and deleting
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configurations, then pass ``strict=False`` when defining the subclass:
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>>> class FlexibleConfig(Config, strict=False):
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... default_greeting: str = "Hello"
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>>> flexcfg = FlexibleConfig()
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>>> flexcfg.name = "Mr. Anderson"
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>>> flexcfg
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FlexibleConfig(default_greeting='Hello', name='Mr. Anderson')
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"""
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def __init_subclass__(cls, strict=True):
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cls._strict = strict
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def __new__(cls, **kwargs):
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orig_class = cls
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if cls is Config:
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# Enable the "simple" case of accepting config definition as keywords
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cls = type(
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cls.__name__,
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(cls,),
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{"__annotations__": {key: typing.Any for key in kwargs}},
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)
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cls = dataclass(
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eq=False,
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repr=cls._strict,
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slots=cls._strict,
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kw_only=True,
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match_args=False,
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)(cls)
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if not cls._strict:
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cls.__repr__ = _flexible_repr
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cls._orig_class = orig_class # Save original class so we can pickle
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instance = object.__new__(cls)
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instance.__init__(**kwargs)
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return instance
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def _check_config(self, key, value):
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"""Check whether config value is valid. This is useful for subclasses."""
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# Control behavior of attributes
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def __dir__(self):
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return self.__dataclass_fields__.keys()
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def __setattr__(self, key, value):
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if self._strict and key not in self.__dataclass_fields__:
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raise AttributeError(f"Invalid config name: {key!r}")
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self._check_config(key, value)
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object.__setattr__(self, key, value)
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def __delattr__(self, key):
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if self._strict:
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raise TypeError(
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f"Configuration items can't be deleted (can't delete {key!r})."
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)
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object.__delattr__(self, key)
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# Be a `collection.abc.Collection`
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def __contains__(self, key):
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return (
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key in self.__dataclass_fields__ if self._strict else key in self.__dict__
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)
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def __iter__(self):
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return iter(self.__dataclass_fields__ if self._strict else self.__dict__)
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def __len__(self):
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return len(self.__dataclass_fields__ if self._strict else self.__dict__)
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def __reversed__(self):
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return reversed(self.__dataclass_fields__ if self._strict else self.__dict__)
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# Add dunder methods for `collections.abc.Mapping`
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def __getitem__(self, key):
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try:
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return getattr(self, key)
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except AttributeError as err:
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raise KeyError(*err.args) from None
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def __setitem__(self, key, value):
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try:
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self.__setattr__(key, value)
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except AttributeError as err:
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raise KeyError(*err.args) from None
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def __delitem__(self, key):
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try:
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self.__delattr__(key)
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except AttributeError as err:
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raise KeyError(*err.args) from None
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_ipython_key_completions_ = __dir__ # config["<TAB>
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# Go ahead and make it a `collections.abc.Mapping`
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def get(self, key, default=None):
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return getattr(self, key, default)
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def items(self):
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return collections.abc.ItemsView(self)
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def keys(self):
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return collections.abc.KeysView(self)
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def values(self):
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return collections.abc.ValuesView(self)
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# dataclass can define __eq__ for us, but do it here so it works after pickling
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def __eq__(self, other):
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if not isinstance(other, Config):
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return NotImplemented
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return self._orig_class == other._orig_class and self.items() == other.items()
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# Make pickle work
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def __reduce__(self):
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return self._deserialize, (self._orig_class, dict(self))
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@staticmethod
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def _deserialize(cls, kwargs):
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return cls(**kwargs)
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def _flexible_repr(self):
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return (
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f"{self.__class__.__qualname__}("
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+ ", ".join(f"{key}={val!r}" for key, val in self.__dict__.items())
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+ ")"
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)
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# Register, b/c `Mapping.__subclasshook__` returns `NotImplemented`
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collections.abc.Mapping.register(Config)
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class NetworkXConfig(Config):
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"""Configuration for NetworkX that controls behaviors such as how to use backends.
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Attribute and bracket notation are supported for getting and setting configurations:
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>>> nx.config.backend_priority == nx.config["backend_priority"]
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True
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Parameters
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----------
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backend_priority : list of backend names
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Enable automatic conversion of graphs to backend graphs for algorithms
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implemented by the backend. Priority is given to backends listed earlier.
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Default is empty list.
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backends : Config mapping of backend names to backend Config
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The keys of the Config mapping are names of all installed NetworkX backends,
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and the values are their configurations as Config mappings.
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cache_converted_graphs : bool
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If True, then save converted graphs to the cache of the input graph. Graph
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conversion may occur when automatically using a backend from `backend_priority`
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or when using the `backend=` keyword argument to a function call. Caching can
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improve performance by avoiding repeated conversions, but it uses more memory.
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Care should be taken to not manually mutate a graph that has cached graphs; for
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example, ``G[u][v][k] = val`` changes the graph, but does not clear the cache.
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Using methods such as ``G.add_edge(u, v, weight=val)`` will clear the cache to
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keep it consistent. ``G.__networkx_cache__.clear()`` manually clears the cache.
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Default is False.
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Notes
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-----
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Environment variables may be used to control some default configurations:
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- NETWORKX_BACKEND_PRIORITY: set `backend_priority` from comma-separated names.
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- NETWORKX_CACHE_CONVERTED_GRAPHS: set `cache_converted_graphs` to True if nonempty.
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This is a global configuration. Use with caution when using from multiple threads.
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"""
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backend_priority: list[str]
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backends: Config
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cache_converted_graphs: bool
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def _check_config(self, key, value):
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from .backends import backends
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if key == "backend_priority":
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if not (isinstance(value, list) and all(isinstance(x, str) for x in value)):
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raise TypeError(
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f"{key!r} config must be a list of backend names; got {value!r}"
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)
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if missing := {x for x in value if x not in backends}:
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missing = ", ".join(map(repr, sorted(missing)))
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raise ValueError(f"Unknown backend when setting {key!r}: {missing}")
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elif key == "backends":
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if not (
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isinstance(value, Config)
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and all(isinstance(key, str) for key in value)
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and all(isinstance(val, Config) for val in value.values())
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):
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raise TypeError(
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f"{key!r} config must be a Config of backend configs; got {value!r}"
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)
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if missing := {x for x in value if x not in backends}:
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missing = ", ".join(map(repr, sorted(missing)))
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raise ValueError(f"Unknown backend when setting {key!r}: {missing}")
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elif key == "cache_converted_graphs":
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if not isinstance(value, bool):
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raise TypeError(f"{key!r} config must be True or False; got {value!r}")
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# Backend configuration will be updated in backends.py
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config = NetworkXConfig(
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backend_priority=[],
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backends=Config(),
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cache_converted_graphs=bool(os.environ.get("NETWORKX_CACHE_CONVERTED_GRAPHS", "")),
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)
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