U yhK)@sddlZddlZddlmZddlmZdZddZddZd d Z e fd d Z e fd dZ e fddZ e fddZe fddZe fddZe fddZddZe fddZde fddZd(d d!Zd"d#Zd$d%Zd&d'ZdS))N)reduce)Mapping) merge merge_withvalmapkeymapitemmap valfilter keyfilter itemfilterassocdissocassoc_in update_inget_incCs2|dt}|r.t|jd|dd|S)Nfactoryz'() got an unexpected keyword argument 'r')popdict TypeError__name__popitem)fkwargsrre/var/www/html/venv/lib/python3.8/site-packages/torch/fx/experimental/unification/unification_tools.py _get_factory s rcOsJt|dkr"t|dts"|d}tt|}|}|D]}||q6|S)z Merge a collection of dictionaries >>> merge({1: 'one'}, {2: 'two'}) {1: 'one', 2: 'two'} Later dictionaries have precedence >>> merge({1: 2, 3: 4}, {3: 3, 4: 4}) {1: 2, 3: 3, 4: 4} See Also: merge_with r)len isinstancerrrupdate)dictsrrrvdrrrrs  rcOs|t|dkr"t|dts"|d}tt|}|}|D]8}|D]*\}}||kr^|g||<qB|||qBq6t|||S)a Merge dictionaries and apply function to combined values A key may occur in more than one dict, and all values mapped from the key will be passed to the function as a list, such as func([val1, val2, ...]). >>> merge_with(sum, {1: 1, 2: 2}, {1: 10, 2: 20}) {1: 11, 2: 22} >>> merge_with(first, {1: 1, 2: 2}, {2: 20, 3: 30}) # doctest: +SKIP {1: 1, 2: 2, 3: 30} See Also: merge rr)rrrrritemsappendr)funcr!rrresultr#kvrrrr+s  rcCs(|}|t|t|||S)z Apply function to values of dictionary >>> bills = {"Alice": [20, 15, 30], "Bob": [10, 35]} >>> valmap(sum, bills) # doctest: +SKIP {'Alice': 65, 'Bob': 45} See Also: keymap itemmap )r zipkeysmapvaluesr&r#rr"rrrrHs rcCs(|}|tt||||S)z Apply function to keys of dictionary >>> bills = {"Alice": [20, 15, 30], "Bob": [10, 35]} >>> keymap(str.lower, bills) # doctest: +SKIP {'alice': [20, 15, 30], 'bob': [10, 35]} See Also: valmap itemmap )r r*r,r+r-r.rrrrXs rcCs|}|t|||S)z Apply function to items of dictionary >>> accountids = {"Alice": 10, "Bob": 20} >>> itemmap(reversed, accountids) # doctest: +SKIP {10: "Alice", 20: "Bob"} See Also: keymap valmap )r r,r$r.rrrrhs rcCs,|}|D]\}}||r|||<q|S)z Filter 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 xs  r cCs,|}|D]\}}||r|||<q|S)z Filter 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 r/r0rrrr s  r cCs0|}|D]}||r|\}}|||<q|S)a Filter 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 r/)r1r#rr"itemr(r)rrrr s   r cCs|}|||||<|S)z Return 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 cOsttt|}|}t|t|dkrH|||D]}||kr2||=q2n(t|}|||D]}||||<q^|S)aB Return 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+rrr5r3 remainingr(rrrr s    r cst||fdd|S)a Return 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']}} csSNrxr4rrzassoc_in..)r)r#r+r4rrr<rrs rc Cst|}t|}|}}|||D]@} ||krN||}|} | |n |}} | ||<}| }q(||kr|||||<n ||||<|S)a  Update 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"innerr3Zdtemprrrrs "      rFc Cs:zttj||WStttfk r4|r,|YSXdS)a4 Returns 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 N)roperatorgetitemKeyError IndexErrorr)r+ZcollrAZ no_defaultrrrr.s rcsPttrBtdkr*dfddSr8tjSddSn tSdS)Nrrcs |fSr9rr:indexrrr=Yr>zgetter..cSsdS)Nrrr:rrrr=]r>)rlistrrD itemgetterrHrrHrgetterUs     rLcCs\t|st|}tdd}|D]}||||q"i}|D]\}}|j||<qD|S)aV Group 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 cSsgjSr9)r%rrrrr=}r>zgroupby..)callablerL collections defaultdictr$__self__)r3seqr#r2r"r(r)rrrgroupbybs rRcCs tt|S)zC The first element in a sequence >>> first('ABC') 'A' )r@r?)rQrrrfirstsrS)NF)rNrD functoolsrcollections.abcr__all__rrrrrrrr r r r r rrrrLrRrSrrrrs*          : ' $