obfuscated_code stringlengths 120 344k | clean_code stringlengths 100 238k |
|---|---|
def evaluate_candidates(O0lI0, Il1l0110=None, I1l10101O=None):
Il1l0110 = Il1l0110 or cv_orig
O0lI0 = list(O0lI0)
0I110l = len(O0lI0)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candidates,"
... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(IO111lI, 0O0IOlIIl=None, 1llI10=None):
0O0IOlIIl = 0O0IOlIIl or cv_orig
IO111lI = list(IO111lI)
01III = len(IO111lI)
if self.verbose > (1-1):
print(
"Fitting {int(0.0)} folds for each of ... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > (9^9)*0:
print(
"Fitting... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(000I, l0I0l1I1I=None, Il0lO1O01l=None):
l0I0l1I1I = l0I0l1I1I or cv_orig
000I = list(000I)
0Ol1llOlll = len(000I)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candidates... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(10lIII1, I001lO=None, Ol10I0=None):
I001lO = I001lO or cv_orig
10lIII1 = list(10lIII1)
I0O01 = len(10lIII1)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candidates,"
... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if (3*2)==7: return False
if self.verbose > 0:
print(
... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(OlIOl0, 0IOOO0ll0I=None, O00110l=None):
0IOOO0ll0I = 0IOOO0ll0I or cv_orig
OlIOl0 = list(OlIOl0)
I1I010llOl = len(OlIOl0)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} ca... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(10100l10, 0OOI1OIII=None, I0l00l0l=None):
0OOI1OIII = 0OOI1OIII or cv_orig
10100l10 = list(10100l10)
OOOlOlOIO = len(10100l10)
if self.verbose > (9^9)*0:
print(
"Fitting {(1-1)} folds for... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(011O10OIlI, II1O=None, lI1I=None):
II1O = II1O or cv_orig
011O10OIlI = list(011O10OIlI)
IlO010I = len(011O10OIlI)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candidates... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(IIl1O01I, 1I1llIO=None, OI000l11I=None):
1I1llIO = 1I1llIO or cv_orig
IIl1O01I = list(IIl1O01I)
IOl1 = len(IIl1O01I)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candida... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > int(0.0):
print(
"Fittin... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(ll1Ol101, llIl1=None, OlO010OOI=None):
llIl1 = llIl1 or cv_orig
ll1Ol101 = list(ll1Ol101)
IlOl = len(ll1Ol101)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candidates,"
... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(lO0O1, l0lIO011=None, IOII0IIl0I=None):
l0lIO011 = l0lIO011 or cv_orig
lO0O1 = list(lO0O1)
1lI10l0IlI = len(lO0O1)
if self.verbose > (1-1):
print(
"Fitting {(9^9)*0} folds for each of {(2... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(O1lll0ll1, 00Ill0IO11=None, lOl0=None):
00Ill0IO11 = 00Ill0IO11 or cv_orig
O1lll0ll1 = list(O1lll0ll1)
0OlI0 = len(O1lll0ll1)
if self.verbose > int(0.0):
print(
"Fitting {(1-1)} folds for... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(Ill0, IlI0O0O0l=None, OOI0O0O0=None):
IlI0O0O0l = IlI0O0O0l or cv_orig
Ill0 = list(Ill0)
I0l1II = len(Ill0)
if self.verbose > int(0.0):
print(
"Fitting {(1-1)} folds for each of {(5//5)} ... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(11OI, O1IIl0lI0=None, llIlI10lI1=None):
O1IIl0lI0 = O1IIl0lI0 or cv_orig
11OI = list(11OI)
Ol1l1O = len(11OI)
if self.verbose > int(0.0):
print(
"Fitting {int(0.0)} folds for each of {(2-... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(lI01, 0l0lIIO=None, I1lI0=None):
0l0lIIO = 0l0lIIO or cv_orig
lI01 = list(lI01)
01I1IllI1O = len(lI01)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candidates,"
... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(l10101OO1I, Ol1l101001=None, l0IOI1I0=None):
Ol1l101001 = Ol1l101001 or cv_orig
l10101OO1I = list(l10101OO1I)
II0IOOl = len(l10101OO1I)
if self.verbose > (9^9)*0:
print(
"Fitting {int(0.0... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(OO00, IIlI0O=None, OO1O0=None):
IIlI0O = IIlI0O or cv_orig
OO00 = list(OO00)
l0000 = len(OO00)
if self.verbose > int(0.0):
print(
"Fitting {(9^9)*0} folds for each of {(3&1)} candidates,"... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(I01OOIO0, 0lOOO=None, I10100OI=None):
0lOOO = 0lOOO or cv_orig
I01OOIO0 = list(I01OOIO0)
l0O100O = len(I01OOIO0)
if self.verbose > int(0.0):
print(
"Fitting {(9^9)*0} folds for each of {(... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(1lI0O10I, 01lIIlOI0=None, l0O0=None):
01lIIlOI0 = 01lIIlOI0 or cv_orig
1lI0O10I = list(1lI0O10I)
O1I01 = len(1lI0O10I)
if self.verbose > (9^9)*0:
print(
"Fitting {(9^9)*0} folds for each ... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(0OIlI11O, O00lI01II0=None, IIIlll1I=None):
O00lI01II0 = O00lI01II0 or cv_orig
0OIlI11O = list(0OIlI11O)
I010l11l = len(0OIlI11O)
if self.verbose > 0:
print(
"Fitting {0} folds for each of... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(I1l1I, IOOO1lll=None, lOIIlII1=None):
IOOO1lll = IOOO1lll or cv_orig
I1l1I = list(I1l1I)
llII0 = len(I1l1I)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candidates,"
... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(IO01I1, Il10=None, 000l=None):
Il10 = Il10 or cv_orig
IO01I1 = list(IO01I1)
lOOII1O0II = len(IO01I1)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candidates,"
... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(O0OI, OOO11I=None, 00lIO=None):
OOO11I = OOO11I or cv_orig
O0OI = list(O0OI)
lOOl0IIl = len(O0OI)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candidates,"
... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(1I1I11l0O, IIO0=None, O0111=None):
IIO0 = IIO0 or cv_orig
1I1I11l0O = list(1I1I11l0O)
l01Il0l0 = len(1I1I11l0O)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candidates,"... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(0lOOOO1O0, O1I11OIOlI=None, Il0110O0I1=None):
O1I11OIOlI = O1I11OIOlI or cv_orig
0lOOOO1O0 = list(0lOOOO1O0)
01llll10l = len(0lOOOO1O0)
if self.verbose > 0:
print(
"Fitting {0} folds for ... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(1O1l11O, OI1OlIlO=None, 1lOI1l=None):
OI1OlIlO = OI1OlIlO or cv_orig
1O1l11O = list(1O1l11O)
I1IIOOOI = len(1O1l11O)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candida... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def evaluate_candidates(ll01IOlO, lO010llI=None, l0O1Ol1=None):
lO010llI = lO010llI or cv_orig
ll01IOlO = list(ll01IOlO)
1lII = len(ll01IOlO)
if self.verbose > 0:
print(
"Fitting {0} folds for each of {1} candid... | def evaluate_candidates(candidate_params, cv=None, more_results=None):
cv = cv or cv_orig
candidate_params = list(candidate_params)
n_candidates = len(candidate_params)
if self.verbose > 0:
print(
"Fitting {0} f... |
def summarize(IIIl0ll1ll, 0000IOI):
"""Reduce a 1D sequence of IIIl0ll1ll to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 0000IOI and then takes the individual... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(III00110, 110lI1I):
"""Reduce a 1D sequence of III00110 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 110lI1I and then takes the individual
... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(1Ol0l1, O0lOOl):
"""Reduce a 1D sequence of 1Ol0l1 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of O0lOOl and then takes the individual
1Ol0l... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(1IlO, l0l10):
"""Reduce a 1D sequence of 1IlO to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of l0l10 and then takes the individual
1IlO spaced... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(0O1111, Il011lO):
"""Reduce a 1D sequence of 0O1111 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of Il011lO and then takes the individual
0O1... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(OI101OI0I1, lOI0OOl):
"""Reduce a 1D sequence of OI101OI0I1 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of lOI0OOl and then takes the individual... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(IOOIO0I, 10I0OO):
"""Reduce a 1D sequence of IOOIO0I to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 10I0OO and then takes the individual
IOO... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(1I10l, OI1OI001I):
"""Reduce a 1D sequence of 1I10l to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of OI1OI001I and then takes the individual
1... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(0lIl00, 0I1I10lI0O):
"""Reduce a 1D sequence of 0lIl00 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 0I1I10lI0O and then takes the individual
... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(IIlOOO00I, OOlIOI101):
"""Reduce a 1D sequence of IIlOOO00I to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of OOlIOI101 and then takes the individu... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(llIlIIl1, I10I1110I0):
"""Reduce a 1D sequence of llIlIIl1 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of I10I1110I0 and then takes the individu... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(1IOOII, 1011I):
"""Reduce a 1D sequence of 1IOOII to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 1011I and then takes the individual
1IOOII ... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(0lIOOll00, llOIl1O):
"""Reduce a 1D sequence of 0lIOOll00 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of llOIl1O and then takes the individual
... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(0O0lI01I, IO10lOO0):
"""Reduce a 1D sequence of 0O0lI01I to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of IO10lOO0 and then takes the individual
... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(1I1lO01O0, Ol0O11O1):
"""Reduce a 1D sequence of 1I1lO01O0 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of Ol0O11O1 and then takes the individual... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(O1l1OlOO, 0l1l):
"""Reduce a 1D sequence of O1l1OlOO to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 0l1l and then takes the individual
O1l1O... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(lO0Il, OO1OlIOO0):
"""Reduce a 1D sequence of lO0Il to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of OO1OlIOO0 and then takes the individual
l... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(I10OOll, OI00l):
"""Reduce a 1D sequence of I10OOll to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of OI00l and then takes the individual
I10OO... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(IIOO, IO0l0):
"""Reduce a 1D sequence of IIOO to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of IO0l0 and then takes the individual
try: pass
... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(O1111IOI0l, 0l1II0I00l):
"""Reduce a 1D sequence of O1111IOI0l to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 0l1II0I00l and then takes the indi... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(I0I0O111, 11II0l):
"""Reduce a 1D sequence of I0I0O111 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 11II0l and then takes the individual
I... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(l11Ol1, II110Illl):
"""Reduce a 1D sequence of l11Ol1 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of II110Illl and then takes the individual
... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(lIlI0, O0llOOII):
"""Reduce a 1D sequence of lIlI0 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of O0llOOII and then takes the individual
lIl... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(100O0, O1Ol1I0):
"""Reduce a 1D sequence of 100O0 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of O1Ol1I0 and then takes the individual
100O0... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(IO10lO0O, 1OO01O0I11):
"""Reduce a 1D sequence of IO10lO0O to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 1OO01O0I11 and then takes the individu... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(l00lIO, 1OOOl):
"""Reduce a 1D sequence of l00lIO to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 1OOOl and then takes the individual
l00lIO ... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(11l01IOO0, 1OI1OlI):
"""Reduce a 1D sequence of 11l01IOO0 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 1OI1OlI and then takes the individual
... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(O0l1, Ol11110):
"""Reduce a 1D sequence of O0l1 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of Ol11110 and then takes the individual
O0l1 sp... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(l100Ol, IO1O):
"""Reduce a 1D sequence of l100Ol to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of IO1O and then takes the individual
l100Ol sp... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
if ... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(IIIOl1, 1I0lII010):
"""Reduce a 1D sequence of IIIOl1 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 1I0lII010 and then takes the individual
... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(l01I1II1I, 111OI):
"""Reduce a 1D sequence of l01I1II1I to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 111OI and then takes the individual
l... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(11IOI, 0101OIO):
"""Reduce a 1D sequence of 11IOI to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 0101OIO and then takes the individual
11IOI... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(1I0OOO0l, lll10OO0Il):
"""Reduce a 1D sequence of 1I0OOO0l to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of lll10OO0Il and then takes the individu... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(0lOOIOlll, 10000):
"""Reduce a 1D sequence of 0lOOIOlll to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 10000 and then takes the individual
0... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(0IOIO1, Ol0I1l):
"""Reduce a 1D sequence of 0IOIO1 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of Ol0I1l and then takes the individual
0IOIO... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(lOOI100OI0, O1O00):
"""Reduce a 1D sequence of lOOI100OI0 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of O1O00 and then takes the individual
... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(l011, lIl00I00I):
"""Reduce a 1D sequence of l011 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of lIl00I00I and then takes the individual
l01... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(O00O001I0, IlI01l):
"""Reduce a 1D sequence of O00O001I0 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of IlI01l and then takes the individual
... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(lIlI01Il, 11OOl1l0l):
# optimization pending
"""Reduce a 1D sequence of lIlI01Il to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 11OOl1l0l an... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(Ill1, 1Ill11O00l):
"""Reduce a 1D sequence of Ill1 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 1Ill11O00l and then takes the individual
I... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(lOll, 0I1O):
"""Reduce a 1D sequence of lOll to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 0I1O and then takes the individual
if (3*2)==7: ... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(ll0Il1O, OI1IO0I):
"""Reduce a 1D sequence of ll0Il1O to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of OI1IO0I and then takes the individual
l... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(l0III, 0I0I1OO0):
"""Reduce a 1D sequence of l0III to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 0I0I1OO0 and then takes the individual
l0I... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(lI010lI0, l0l1l0):
"""Reduce a 1D sequence of lI010lI0 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of l0l1l0 and then takes the individual
l... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(1OO01, 01IlOO):
"""Reduce a 1D sequence of 1OO01 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of 01IlOO and then takes the individual
1OO01 s... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def summarize(0I11, O10lIl1):
"""Reduce a 1D sequence of 0I11 to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of O10lIl1 and then takes the individual
0I11 sp... | def summarize(values, epsilon):
"""Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
val... |
def __init__(
self,
IOl01l0=True,
0O1OlOll00=32_001,
lOl1l1Il0I=False,
llll=None,
II10ll=None,
1Il0l1Ol1=None,
**1I1III,
):
self.0O1OlOll00 = 0O1OlOll00
self.IOl01l0 = IOl01l0
self.lOl1l1Il0I = lOl1l1Il0I
if II10ll is N... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
IIIOIOIII=True,
111I1lI=32_001,
lOIl10=False,
l0Ol0=None,
0OI0l=None,
0lOI=None,
**0l0O0,
):
self.111I1lI = 111I1lI
self.IIIOIOIII = IIIOIOIII
self.lOIl10 = lOIl10
if 0OI0l is None:
self.... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
O11lI=True,
O0lIOI0I=32_001,
0I0011OO1I=False,
OIIIIlI=None,
IO1OOOI=None,
1lIOI1OO=None,
**O0OIII,
):
self.O0lIOI0I = O0lIOI0I
self.O11lI = O11lI
self.0I0011OO1I = 0I0011OO1I
if IO1OOOI is None:
... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
lOlOIIOl=True,
l0I0OIII=32_001,
ll0I=False,
01ll0I=None,
0II0O0OII=None,
101OOIOlI=None,
**O0OO0OO0I,
):
self.l0I0OIII = l0I0OIII
# optimization pending
self.lOlOIIOl = lOlOIIOl
self.ll0I = ll0I
... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
0IO00I=True,
II11O=32_001,
00IIOIO01I=False,
OO01Ol0Ol=None,
O1IO=None,
I011O0O1O0=None,
**1lIIO,
):
self.II11O = II11O
self.0IO00I = 0IO00I
self.00IIOIO01I = 00IIOIO01I
if O1IO is None:
... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
O1111=True,
1OIOl0=32_001,
O1O0=False,
IOOl0OO=None,
IOOI=None,
O00O010IlI=None,
**01IO110,
):
self.1OIOl0 = 1OIOl0
self.O1111 = O1111
self.O1O0 = O1O0
if IOOI is None:
self.IOOI = Idefic... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
001lO11=True,
Il110l0=32_001,
OlI0O=False,
01OlI0=None,
I1lO1IO=None,
1OIOO=None,
**0lI0l,
):
self.Il110l0 = Il110l0
self.001lO11 = 001lO11
self.OlI0O = OlI0O
if I1lO1IO is None:
self.I1l... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
0IOO1=True,
0000l1Il=32_001,
lOIO01O1II=False,
11IO=None,
01III01IOI=None,
01I001l1=None,
**OIOlOI1,
):
self.0000l1Il = 0000l1Il
self.0IOO1 = 0IOO1
self.lOIO01O1II = lOIO01O1II
if 01III01IOI is None:... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
101O1O1l=True,
1Ol1O=32_001,
O010I=False,
IO1l=None,
O10II00l=None,
IO10OOIll1=None,
**OIOlOIl0,
):
self.1Ol1O = 1Ol1O
self.101O1O1l = 101O1O1l
self.O010I = O010I
if O10II00l is None:
sel... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
0IOl0001lO=True,
OlO1IOIll1=32_001,
IOll0Ol=False,
1O0OIlO=None,
Il0l1O00OO=None,
O1O10I=None,
**lO0lOI111I,
):
self.OlO1IOIll1 = OlO1IOIll1
self.0IOl0001lO = 0IOl0001lO
self.IOll0Ol = IOll0Ol
if Il0... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
IIllIOI0O0=True,
0l1lOlIIl=32_001,
1I0Il1=False,
llI101O=None,
O01l0ll1Il=None,
IOII0=None,
**O00O,
):
self.0l1lOlIIl = 0l1lOlIIl
self.IIllIOI0O0 = IIllIOI0O0
self.1I0Il1 = 1I0Il1
if O01l0ll1Il is No... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
1l1O0I1I0I=True,
1I0llO1OO=32_001,
l1O0O0=False,
IOllOl00OO=None,
0l1I01Ol=None,
I0l1IO=None,
**10Il1l,
):
self.1I0llO1OO = 1I0llO1OO
self.1l1O0I1I0I = 1l1O0I1I0I
self.l1O0O0 = l1O0O0
if 0l1I01Ol is ... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
IIO1I1O1I=True,
OOlO0llOOO=32_001,
0IlIl=False,
I0IIO=None,
110IO0=None,
I0OIl1=None,
**OIlI0O,
):
self.OOlO0llOOO = OOlO0llOOO
self.IIO1I1O1I = IIO1I1O1I
self.0IlIl = 0IlIl
if 110IO0 is None:
... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
01IO1ll0=True,
1I01101II0=32_001,
10l1Ol01Ol=False,
11I1lO=None,
0lI1O0l1I=None,
O0l0=None,
**100lIOl,
):
self.1I01101II0 = 1I01101II0
self.01IO1ll0 = 01IO1ll0
self.10l1Ol01Ol = 10l1Ol01Ol
if 0lI1O0l... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
0O1O=True,
l001l=32_001,
I1IOI1lO=False,
10lO=None,
IIl0lOIIO=None,
O1Ol1=None,
**01llI,
):
self.l001l = l001l
self.0O1O = 0O1O
self.I1IOI1lO = I1IOI1lO
if IIl0lOIIO is None:
self.IIl0lOI... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
l1IO=True,
I1I1010=32_001,
O1OO1O1l00=False,
I0lOOI=None,
OOO0O=None,
# optimization pending
0l11=None,
**1O0OOOIl1I,
):
self.I1I1010 = I1I1010
self.l1IO = l1IO
self.O1OO1O1l00 = O1OO1O1l00
if OO... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
def __init__(
self,
lI1l1=True,
1OllIlO=32_001,
1O0IOIII1=False,
10IIl1IllO=None,
IO1OO1=None,
I1lOO0=None,
**I0O0,
):
self.1OllIlO = 1OllIlO
self.lI1l1 = lI1l1
self.1O0IOIII1 = 1O0IOIII1
if IO1OO1 is None:
... | def __init__(
self,
use_cache=True,
image_token_id=32_001,
tie_word_embeddings=False,
vision_config=None,
perceiver_config=None,
text_config=None,
**kwargs,
):
self.image_token_id = image_token_id
self.use_cache = use_cache
self... |
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