o ZhG)@sddlZddlZddlmZddlmZgdZddZddZd d Z e fd d Z e fd dZ e fddZ e fddZe fddZe fddZe fddZddZe fddZde fddZd(d d!Zd"d#Zd$d%Zd&d'ZdS))N)Mapping)reduce) merge merge_withvalmapkeymapitemmap valfilter keyfilter itemfilterassocdissocassoc_in update_inget_incCs2|dt}|rt|jd|dd|S)Nfactoryz'() got an unexpected keyword argument 'r')popdict TypeError__name__popitem)fkwargsrrb/var/www/auris/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py _get_factorys rcOsJt|dkrt|dts|d}tt|}|}|D]}||q|S)zMerge a collection of dictionaries >>> merge({1: "one"}, {2: "two"}) {1: 'one', 2: 'two'} Later dictionaries have precedence >>> merge({1: 2, 3: 4}, {3: 3, 4: 4}) {1: 2, 3: 3, 4: 4} See Also: merge_with r)len isinstancerrrupdate)dictsrrrvdrrrr"s  rcOs|t|dkrt|dts|d}tt|}|}|D]}|D]\}}||vr/|g||<q!|||q!qt|||S)aMerge dictionaries and apply function to combined values A key may occur in more than one dict, and all values mapped from the key will be passed to the function as a list, such as func([val1, val2, ...]). >>> merge_with(sum, {1: 1, 2: 2}, {1: 10, 2: 20}) {1: 11, 2: 22} >>> merge_with(first, {1: 1, 2: 2}, {2: 20, 3: 30}) # doctest: +SKIP {1: 1, 2: 2, 3: 30} See Also: merge rr)rrrrritemsappendr)funcr!rrresultr#kvrrrr:s   rcCs(|}|t|t|||S)zApply function to values of dictionary >>> bills = {"Alice": [20, 15, 30], "Bob": [10, 35]} >>> valmap(sum, bills) # doctest: +SKIP {'Alice': 65, 'Bob': 45} See Also: keymap itemmap )r zipkeysmapvaluesr&r#rr"rrrrW rcCs(|}|tt||||S)zApply function to keys of dictionary >>> bills = {"Alice": [20, 15, 30], "Bob": [10, 35]} >>> keymap(str.lower, bills) # doctest: +SKIP {'alice': [20, 15, 30], 'bob': [10, 35]} See Also: valmap itemmap )r r*r,r+r-r.rrrrgr/rcCs|}|t|||S)zApply function to items of dictionary >>> accountids = {"Alice": 10, "Bob": 20} >>> itemmap(reversed, accountids) # doctest: +SKIP {10: "Alice", 20: "Bob"} See Also: keymap valmap )r r,r$r.rrrrws rcCs,|}|D] \}}||r|||<q|S)zFilter items in dictionary by value >>> iseven = lambda x: x % 2 == 0 >>> d = {1: 2, 2: 3, 3: 4, 4: 5} >>> valfilter(iseven, d) {1: 2, 3: 4} See Also: keyfilter itemfilter valmap r$ predicater#rr"r(r)rrrr  r cCs,|}|D] \}}||r|||<q|S)zFilter items in dictionary by key >>> iseven = lambda x: x % 2 == 0 >>> d = {1: 2, 2: 3, 3: 4, 4: 5} >>> keyfilter(iseven, d) {2: 3, 4: 5} See Also: valfilter itemfilter keymap r0r1rrrr r3r cCs0|}|D]}||r|\}}|||<q|S)aFilter items in dictionary by item >>> def isvalid(item): ... k, v = item ... return k % 2 == 0 and v < 4 >>> d = {1: 2, 2: 3, 3: 4, 4: 5} >>> itemfilter(isvalid, d) {2: 3} See Also: keyfilter valfilter itemmap r0)r2r#rr"itemr(r)rrrr s r cCs|}|||||<|S)zReturn a new dict with new key value pair New dict has d[key] set to value. Does not modify the initial dictionary. >>> assoc({"x": 1}, "x", 2) {'x': 2} >>> assoc({"x": 1}, "y", 3) # doctest: +SKIP {'x': 1, 'y': 3} )r )r#keyvaluerd2rrrr s r cOsvtt|}|}t|t|dkr%|||D] }||vr"||=q|St|}|||D]}||||<q0|S)aBReturn a new dict with the given key(s) removed. New dict has d[key] deleted for each supplied key. Does not modify the initial dictionary. >>> dissoc({"x": 1, "y": 2}, "y") {'x': 1} >>> dissoc({"x": 1, "y": 2}, "y", "x") {} >>> dissoc({"x": 1}, "y") # Ignores missing keys {'x': 1} g333333?)rr rr setdifference_update)r#r+rrr7r5 remainingr(rrrr s   r cst||fdd|S)aReturn a new dict with new, potentially nested, key value pair >>> purchase = { ... "name": "Alice", ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, ... "credit card": "5555-1234-1234-1234", ... } >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP {'credit card': '5555-1234-1234-1234', 'name': 'Alice', 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} csSNrxr6rrzassoc_in..)r)r#r+r6rrr>rrs rc Cst|}t|}|}}|||D] } ||vr'||}|} | |n|}} | ||<}| }q||vrC|||||<|S||||<|S)aUpdate value in a (potentially) nested dictionary inputs: d - dictionary on which to operate keys - list or tuple giving the location of the value to be changed in d func - function to operate on that value If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the original dictionary with v replaced by func(v), but does not mutate the original dictionary. If k0 is not a key in d, update_in creates nested dictionaries to the depth specified by the keys, with the innermost value set to func(default). >>> inc = lambda x: x + 1 >>> update_in({"a": 0}, ["a"], inc) {'a': 1} >>> transaction = { ... "name": "Alice", ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, ... "credit card": "5555-1234-1234-1234", ... } >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP {'credit card': '5555-1234-1234-1234', 'name': 'Alice', 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} >>> # updating a value when k0 is not in d >>> update_in({}, [1, 2, 3], str, default="bar") {1: {2: {3: 'bar'}}} >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) {1: 'foo', 2: {3: {4: 1}}} )iternextr ) r#r+r&defaultrksr(r"innerr5Zdtemprrrrs"#      rFc Cs4zttj||WStttfy|r|YSw)aReturns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless ``no_default`` is specified, then it raises KeyError or IndexError. ``get_in`` is a generalization of ``operator.getitem`` for nested data structures such as dictionaries and lists. >>> transaction = { ... "name": "Alice", ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, ... "credit card": "5555-1234-1234-1234", ... } >>> get_in(["purchase", "items", 0], transaction) 'Apple' >>> get_in(["name"], transaction) 'Alice' >>> get_in(["purchase", "total"], transaction) >>> get_in(["purchase", "items", "apple"], transaction) >>> get_in(["purchase", "items", 10], transaction) >>> get_in(["purchase", "total"], transaction, 0) 0 >>> get_in(["y"], {}, no_default=True) Traceback (most recent call last): ... KeyError: 'y' See Also: itertoolz.get operator.getitem )roperatorgetitemKeyError IndexErrorr)r+ZcollrCZ no_defaultrrrr?s rcsJttr tdkrdfddSrtjSddStS)Nrrcs |fSr;rr<indexrrr?ks zgetter..cSsdS)Nrrr<rrrr?or@)rlistrrF itemgetterrJrrJrgettergs     rNcCs\t|st|}tdd}|D] }||||qi}|D] \}}|j||<q"|S)aGroup a collection by a key function >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] >>> groupby(len, names) # doctest: +SKIP {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} >>> iseven = lambda x: x % 2 == 0 >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} Non-callable keys imply grouping on a member. >>> groupby( ... "gender", ... [ ... {"name": "Alice", "gender": "F"}, ... {"name": "Bob", "gender": "M"}, ... {"name": "Charlie", "gender": "M"}, ... ], ... ) # doctest:+SKIP {'F': [{'gender': 'F', 'name': 'Alice'}], 'M': [{'gender': 'M', 'name': 'Bob'}, {'gender': 'M', 'name': 'Charlie'}]} Not to be confused with ``itertools.groupby`` See Also: countby cSsgjSr;)r%rrrrr?szgroupby..)callablerN collections defaultdictr$__self__)r5seqr#r4r"r(r)rrrgroupbyts rTcCs tt|S)zBThe first element in a sequence >>> first("ABC") 'A' )rBrA)rSrrrfirsts rU)NF)rPrFcollections.abcr functoolsr__all__rrrrrrrr r r r r rrrrNrTrUrrrrs,            ;( )