chore: 添加虚拟环境到仓库

- 添加 backend_service/venv 虚拟环境
- 包含所有Python依赖包
- 注意：虚拟环境约393MB，包含12655个文件

backend_service/venv/lib/python3.13/site-packages/sympy/strategies/tree.py

@@ -0,0 +1,139 @@
+from functools import partial
+from sympy.strategies import chain, minimize
+from sympy.strategies.core import identity
+import sympy.strategies.branch as branch
+from sympy.strategies.branch import yieldify
+
+
+def treeapply(tree, join, leaf=identity):
+    """ Apply functions onto recursive containers (tree).
+
+    Explanation
+    ===========
+
+    join - a dictionary mapping container types to functions
+      e.g. ``{list: minimize, tuple: chain}``
+
+    Keys are containers/iterables.  Values are functions [a] -> a.
+
+    Examples
+    ========
+
+    >>> from sympy.strategies.tree import treeapply
+    >>> tree = [(3, 2), (4, 1)]
+    >>> treeapply(tree, {list: max, tuple: min})
+    2
+
+    >>> add = lambda *args: sum(args)
+    >>> def mul(*args):
+    ...     total = 1
+    ...     for arg in args:
+    ...         total *= arg
+    ...     return total
+    >>> treeapply(tree, {list: mul, tuple: add})
+    25
+    """
+    for typ in join:
+        if isinstance(tree, typ):
+            return join[typ](*map(partial(treeapply, join=join, leaf=leaf),
+                                  tree))
+    return leaf(tree)
+
+
+def greedy(tree, objective=identity, **kwargs):
+    """ Execute a strategic tree.  Select alternatives greedily
+
+    Trees
+    -----
+
+    Nodes in a tree can be either
+
+    function - a leaf
+    list     - a selection among operations
+    tuple    - a sequence of chained operations
+
+    Textual examples
+    ----------------
+
+    Text: Run f, then run g, e.g. ``lambda x: g(f(x))``
+    Code: ``(f, g)``
+
+    Text: Run either f or g, whichever minimizes the objective
+    Code: ``[f, g]``
+
+    Textx: Run either f or g, whichever is better, then run h
+    Code: ``([f, g], h)``
+
+    Text: Either expand then simplify or try factor then foosimp. Finally print
+    Code: ``([(expand, simplify), (factor, foosimp)], print)``
+
+    Objective
+    ---------
+
+    "Better" is determined by the objective keyword.  This function makes
+    choices to minimize the objective.  It defaults to the identity.
+
+    Examples
+    ========
+
+    >>> from sympy.strategies.tree import greedy
+    >>> inc    = lambda x: x + 1
+    >>> dec    = lambda x: x - 1
+    >>> double = lambda x: 2*x
+
+    >>> tree = [inc, (dec, double)] # either inc or dec-then-double
+    >>> fn = greedy(tree)
+    >>> fn(4)  # lowest value comes from the inc
+    5
+    >>> fn(1)  # lowest value comes from dec then double
+    0
+
+    This function selects between options in a tuple.  The result is chosen
+    that minimizes the objective function.
+
+    >>> fn = greedy(tree, objective=lambda x: -x)  # maximize
+    >>> fn(4)  # highest value comes from the dec then double
+    6
+    >>> fn(1)  # highest value comes from the inc
+    2
+
+    Greediness
+    ----------
+
+    This is a greedy algorithm.  In the example:
+
+        ([a, b], c)  # do either a or b, then do c
+
+    the choice between running ``a`` or ``b`` is made without foresight to c
+    """
+    optimize = partial(minimize, objective=objective)
+    return treeapply(tree, {list: optimize, tuple: chain}, **kwargs)
+
+
+def allresults(tree, leaf=yieldify):
+    """ Execute a strategic tree.  Return all possibilities.
+
+    Returns a lazy iterator of all possible results
+
+    Exhaustiveness
+    --------------
+
+    This is an exhaustive algorithm.  In the example
+
+        ([a, b], [c, d])
+
+    All of the results from
+
+        (a, c), (b, c), (a, d), (b, d)
+
+    are returned.  This can lead to combinatorial blowup.
+
+    See sympy.strategies.greedy for details on input
+    """
+    return treeapply(tree, {list: branch.multiplex, tuple: branch.chain},
+                     leaf=leaf)
+
+
+def brute(tree, objective=identity, **kwargs):
+    return lambda expr: min(tuple(allresults(tree, **kwargs)(expr)),
+                            key=objective)