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b/shared/package-files/8b/8b71472317bf3adabf819e665c725d03e3064baa45f6ffbfd78cca83eaa46e8d/blob new file mode 100644 index 0000000000000000000000000000000000000000..c0e3705ab7be19c037b833bc43e76eafc21067d8 --- /dev/null +++ b/shared/package-files/8b/8b71472317bf3adabf819e665c725d03e3064baa45f6ffbfd78cca83eaa46e8d/blob @@ -0,0 +1,70 @@ +"""Fix incompatible renames + +Fixes: + * sys.maxint -> sys.maxsize +""" +# Author: Christian Heimes +# based on Collin Winter's fix_import + +# Local imports +from .. import fixer_base +from ..fixer_util import Name, attr_chain + +MAPPING = {"sys": {"maxint" : "maxsize"}, + } +LOOKUP = {} + +def alternates(members): + return "(" + "|".join(map(repr, members)) + ")" + + +def build_pattern(): + #bare = set() + for module, replace in list(MAPPING.items()): + for old_attr, new_attr in list(replace.items()): + LOOKUP[(module, old_attr)] = new_attr + #bare.add(module) + #bare.add(old_attr) + #yield """ + # import_name< 'import' (module=%r + # | dotted_as_names< any* module=%r any* >) > + # """ % (module, module) + yield """ + import_from< 'from' module_name=%r 'import' + ( attr_name=%r | import_as_name< attr_name=%r 'as' any >) > + """ % (module, old_attr, old_attr) + yield """ + power< module_name=%r trailer< '.' attr_name=%r > any* > + """ % (module, old_attr) + #yield """bare_name=%s""" % alternates(bare) + + +class FixRenames(fixer_base.BaseFix): + BM_compatible = True + PATTERN = "|".join(build_pattern()) + + order = "pre" # Pre-order tree traversal + + # Don't match the node if it's within another match + def match(self, node): + match = super(FixRenames, self).match + results = match(node) + if results: + if any(match(obj) for obj in attr_chain(node, "parent")): + return False + return results + return False + + #def start_tree(self, tree, filename): + # super(FixRenames, self).start_tree(tree, filename) + # self.replace = {} + + def transform(self, node, results): + mod_name = results.get("module_name") + attr_name = results.get("attr_name") + #bare_name = results.get("bare_name") + #import_mod = results.get("module") + + if mod_name and attr_name: + new_attr = LOOKUP[(mod_name.value, attr_name.value)] + attr_name.replace(Name(new_attr, prefix=attr_name.prefix)) diff --git a/shared/package-files/8b/8b720740d5d007214c0af8e3c9dfe1c302520a2523d33d6b4704bd5852b60057/blob b/shared/package-files/8b/8b720740d5d007214c0af8e3c9dfe1c302520a2523d33d6b4704bd5852b60057/blob new file mode 100644 index 0000000000000000000000000000000000000000..4becf4fd800a7a34c16989adaaf97e312c18f01c --- /dev/null +++ b/shared/package-files/8b/8b720740d5d007214c0af8e3c9dfe1c302520a2523d33d6b4704bd5852b60057/blob @@ -0,0 +1,196 @@ +from sympy.core.symbol import symbols +from sympy.functions.elementary.trigonometric import (cos, sin) +from sympy.polys import QQ, ZZ +from sympy.polys.polytools import Poly +from sympy.polys.polyerrors import NotInvertible +from sympy.polys.agca.extensions import FiniteExtension +from sympy.polys.domainmatrix import DomainMatrix + +from sympy.testing.pytest import raises + +from sympy.abc import x, y, t + + +def test_FiniteExtension(): + # Gaussian integers + A = FiniteExtension(Poly(x**2 + 1, x)) + assert A.rank == 2 + assert str(A) == 'ZZ[x]/(x**2 + 1)' + i = A.generator + assert i.parent() is A + + assert i*i == A(-1) + raises(TypeError, lambda: i*()) + + assert A.basis == (A.one, i) + assert A(1) == A.one + assert i**2 == A(-1) + assert i**2 != -1 # no coercion + assert (2 + i)*(1 - i) == 3 - i + assert (1 + i)**8 == A(16) + assert A(1).inverse() == A(1) + raises(NotImplementedError, lambda: A(2).inverse()) + + # Finite field of order 27 + F = FiniteExtension(Poly(x**3 - x + 1, x, modulus=3)) + assert F.rank == 3 + a = F.generator # also generates the cyclic group F - {0} + assert F.basis == (F(1), a, a**2) + assert a**27 == a + assert a**26 == F(1) + assert a**13 == F(-1) + assert a**9 == a + 1 + assert a**3 == a - 1 + assert a**6 == a**2 + a + 1 + assert F(x**2 + x).inverse() == 1 - a + assert F(x + 2)**(-1) == F(x + 2).inverse() + assert a**19 * a**(-19) == F(1) + assert (a - 1) / (2*a**2 - 1) == a**2 + 1 + assert (a - 1) // (2*a**2 - 1) == a**2 + 1 + assert 2/(a**2 + 1) == a**2 - a + 1 + assert (a**2 + 1)/2 == -a**2 - 1 + raises(NotInvertible, lambda: F(0).inverse()) + + # Function field of an elliptic curve + K = FiniteExtension(Poly(t**2 - x**3 - x + 1, t, field=True)) + assert K.rank == 2 + assert str(K) == 'ZZ(x)[t]/(t**2 - x**3 - x + 1)' + y = K.generator + c = 1/(x**3 - x**2 + x - 1) + assert ((y + x)*(y - x)).inverse() == K(c) + assert (y + x)*(y - x)*c == K(1) # explicit inverse of y + x + + +def test_FiniteExtension_eq_hash(): + # Test eq and hash + p1 = Poly(x**2 - 2, x, domain=ZZ) + p2 = Poly(x**2 - 2, x, domain=QQ) + K1 = FiniteExtension(p1) + K2 = FiniteExtension(p2) + assert K1 == FiniteExtension(Poly(x**2 - 2)) + assert K2 != FiniteExtension(Poly(x**2 - 2)) + assert len({K1, K2, FiniteExtension(p1)}) == 2 + + +def test_FiniteExtension_mod(): + # Test mod + K = FiniteExtension(Poly(x**3 + 1, x, domain=QQ)) + xf = K(x) + assert (xf**2 - 1) % 1 == K.zero + assert 1 % (xf**2 - 1) == K.zero + assert (xf**2 - 1) / (xf - 1) == xf + 1 + assert (xf**2 - 1) // (xf - 1) == xf + 1 + assert (xf**2 - 1) % (xf - 1) == K.zero + raises(ZeroDivisionError, lambda: (xf**2 - 1) % 0) + raises(TypeError, lambda: xf % []) + raises(TypeError, lambda: [] % xf) + + # Test mod over ring + K = FiniteExtension(Poly(x**3 + 1, x, domain=ZZ)) + xf = K(x) + assert (xf**2 - 1) % 1 == K.zero + raises(NotImplementedError, lambda: (xf**2 - 1) % (xf - 1)) + + +def test_FiniteExtension_from_sympy(): + # Test to_sympy/from_sympy + K = FiniteExtension(Poly(x**3 + 1, x, domain=ZZ)) + xf = K(x) + assert K.from_sympy(x) == xf + assert K.to_sympy(xf) == x + + +def test_FiniteExtension_set_domain(): + KZ = FiniteExtension(Poly(x**2 + 1, x, domain='ZZ')) + KQ = FiniteExtension(Poly(x**2 + 1, x, domain='QQ')) + assert KZ.set_domain(QQ) == KQ + + +def test_FiniteExtension_exquo(): + # Test exquo + K = FiniteExtension(Poly(x**4 + 1)) + xf = K(x) + assert K.exquo(xf**2 - 1, xf - 1) == xf + 1 + + +def test_FiniteExtension_convert(): + # Test from_MonogenicFiniteExtension + K1 = FiniteExtension(Poly(x**2 + 1)) + K2 = QQ[x] + x1, x2 = K1(x), K2(x) + assert K1.convert(x2) == x1 + assert K2.convert(x1) == x2 + + K = FiniteExtension(Poly(x**2 - 1, domain=QQ)) + assert K.convert_from(QQ(1, 2), QQ) == K.one/2 + + +def test_FiniteExtension_division_ring(): + # Test division in FiniteExtension over a ring + KQ = FiniteExtension(Poly(x**2 - 1, x, domain=QQ)) + KZ = FiniteExtension(Poly(x**2 - 1, x, domain=ZZ)) + KQt = FiniteExtension(Poly(x**2 - 1, x, domain=QQ[t])) + KQtf = FiniteExtension(Poly(x**2 - 1, x, domain=QQ.frac_field(t))) + assert KQ.is_Field is True + assert KZ.is_Field is False + assert KQt.is_Field is False + assert KQtf.is_Field is True + for K in KQ, KZ, KQt, KQtf: + xK = K.convert(x) + assert xK / K.one == xK + assert xK // K.one == xK + assert xK % K.one == K.zero + raises(ZeroDivisionError, lambda: xK / K.zero) + raises(ZeroDivisionError, lambda: xK // K.zero) + raises(ZeroDivisionError, lambda: xK % K.zero) + if K.is_Field: + assert xK / xK == K.one + assert xK // xK == K.one + assert xK % xK == K.zero + else: + raises(NotImplementedError, lambda: xK / xK) + raises(NotImplementedError, lambda: xK // xK) + raises(NotImplementedError, lambda: xK % xK) + + +def test_FiniteExtension_Poly(): + K = FiniteExtension(Poly(x**2 - 2)) + p = Poly(x, y, domain=K) + assert p.domain == K + assert p.as_expr() == x + assert (p**2).as_expr() == 2 + + K = FiniteExtension(Poly(x**2 - 2, x, domain=QQ)) + K2 = FiniteExtension(Poly(t**2 - 2, t, domain=K)) + assert str(K2) == 'QQ[x]/(x**2 - 2)[t]/(t**2 - 2)' + + eK = K2.convert(x + t) + assert K2.to_sympy(eK) == x + t + assert K2.to_sympy(eK ** 2) == 4 + 2*x*t + p = Poly(x + t, y, domain=K2) + assert p**2 == Poly(4 + 2*x*t, y, domain=K2) + + +def test_FiniteExtension_sincos_jacobian(): + # Use FiniteExtensino to compute the Jacobian of a matrix involving sin + # and cos of different symbols. + r, p, t = symbols('rho, phi, theta') + elements = [ + [sin(p)*cos(t), r*cos(p)*cos(t), -r*sin(p)*sin(t)], + [sin(p)*sin(t), r*cos(p)*sin(t), r*sin(p)*cos(t)], + [ cos(p), -r*sin(p), 0], + ] + + def make_extension(K): + K = FiniteExtension(Poly(sin(p)**2+cos(p)**2-1, sin(p), domain=K[cos(p)])) + K = FiniteExtension(Poly(sin(t)**2+cos(t)**2-1, sin(t), domain=K[cos(t)])) + return K + + Ksc1 = make_extension(ZZ[r]) + Ksc2 = make_extension(ZZ)[r] + + for K in [Ksc1, Ksc2]: + elements_K = [[K.convert(e) for e in row] for row in elements] + J = DomainMatrix(elements_K, (3, 3), K) + det = J.charpoly()[-1] * (-K.one)**3 + assert det == K.convert(r**2*sin(p)) diff --git a/shared/package-files/8b/8b733ea1edb0f9d08a0245ea92a765985b083b214a570d5fb50799bacd9c3be6/blob b/shared/package-files/8b/8b733ea1edb0f9d08a0245ea92a765985b083b214a570d5fb50799bacd9c3be6/blob new file mode 100644 index 0000000000000000000000000000000000000000..c803793375f16886599ac146fc6b363f770daaa6 --- /dev/null +++ b/shared/package-files/8b/8b733ea1edb0f9d08a0245ea92a765985b083b214a570d5fb50799bacd9c3be6/blob @@ -0,0 +1,89 @@ +# Copyright (c) 2008-2011 by Enthought, Inc. +# Copyright (c) 2013-2015 Continuum Analytics, Inc. +# All rights reserved. + +from __future__ import absolute_import + +import json +import logging +import sys +from os.path import abspath, basename, exists, join + +try: + from .._version import __version__ +except ImportError: + __version__ = "dev" + +from ..utils import DEFAULT_BASE_PREFIX, DEFAULT_PREFIX + +if sys.platform == 'win32': + from ..platforms.win_utils.win_elevate import isUserAdmin, runAsAdmin + from .win32 import Menu, ShortCut + + +def _install(path, remove=False, prefix=None, mode=None, root_prefix=None): + prefix = prefix or DEFAULT_PREFIX + root_prefix = root_prefix or DEFAULT_BASE_PREFIX + if abspath(prefix) == abspath(root_prefix): + env_name = None + else: + env_name = basename(prefix) + + data = json.load(open(path)) + try: + menu_name = data['menu_name'] + except KeyError: + menu_name = 'Python-%d.%d' % sys.version_info[:2] + + shortcuts = data['menu_items'] + m = Menu(menu_name, prefix=prefix, env_name=env_name, mode=mode, root_prefix=root_prefix) + if remove: + for sc in shortcuts: + ShortCut(m, sc).remove() + m.remove() + else: + m.create() + for sc in shortcuts: + ShortCut(m, sc).create() + + +def install(path, remove=False, prefix=None, recursing=False, root_prefix=None): + """ + Install Menu and shortcuts + + # Specifying `root_prefix` is used with conda-standalone, because we can't use + # `sys.prefix`, therefore we need to specify it + """ + prefix = prefix or DEFAULT_PREFIX + root_prefix = root_prefix or DEFAULT_BASE_PREFIX + if not sys.platform == 'win32': + raise RuntimeError("menuinst._legacy is only supported on Windows.") + + # this root_prefix is intentional. We want to reflect the state of the root installation. + + if not exists(join(root_prefix, '.nonadmin')): + if isUserAdmin(): + _install(path, remove, prefix, mode='system', root_prefix=root_prefix) + else: + retcode = 1 + try: + if not recursing: + retcode = runAsAdmin( + [ + join(root_prefix, 'python'), + '-c', + "import menuinst; menuinst.install(%r, %r, %r, %r, %r)" + % (path, bool(remove), prefix, True, root_prefix), + ] + ) + except OSError: + pass + + if retcode != 0: + logging.warn( + "Insufficient permissions to write menu folder. " + "Falling back to user location" + ) + _install(path, remove, prefix, mode='user', root_prefix=root_prefix) + else: + _install(path, remove, prefix, mode='user', root_prefix=root_prefix) diff --git a/shared/package-files/8b/8b77e6c948fb88a4c36fc94c5fac6b974e5d8cb0167dfba63cd0558b7e8fb4a2/blob b/shared/package-files/8b/8b77e6c948fb88a4c36fc94c5fac6b974e5d8cb0167dfba63cd0558b7e8fb4a2/blob new file mode 100644 index 0000000000000000000000000000000000000000..1b7034f35193fbfe4f5be336d48f3a3e7da4b339 --- /dev/null +++ b/shared/package-files/8b/8b77e6c948fb88a4c36fc94c5fac6b974e5d8cb0167dfba63cd0558b7e8fb4a2/blob @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b77e6c948fb88a4c36fc94c5fac6b974e5d8cb0167dfba63cd0558b7e8fb4a2 +size 84693320 diff --git a/shared/package-files/8b/8b784d08319ab7ff0320c1c5a36d9db3dc77e12ca55f6abd56ef9610b022fed5/blob b/shared/package-files/8b/8b784d08319ab7ff0320c1c5a36d9db3dc77e12ca55f6abd56ef9610b022fed5/blob new file mode 100644 index 0000000000000000000000000000000000000000..acd8975c4d2db13cac2e988238a0a8a2a191df68 --- /dev/null +++ b/shared/package-files/8b/8b784d08319ab7ff0320c1c5a36d9db3dc77e12ca55f6abd56ef9610b022fed5/blob @@ -0,0 +1,81 @@ +#pragma once +#include +#include + +namespace c10d { +constexpr int kUnsetTime = -1; + +inline int64_t current_time_in_nanos() { + return c10::getTime(); +} + +class TORCH_API Timer { + private: + // The timestamp of forward call start time in each iteration. + int64_t forward_start_time = kUnsetTime; + // The timestamp of backward computation start and end time in each + // iteration. + int64_t backward_compute_start_time = kUnsetTime; + int64_t backward_compute_end_time = kUnsetTime; + // The timestamp of first communication call start time in each iteration. + int64_t backward_comm_start_time = kUnsetTime; + // The timestamp of last communication call end time in each iteration. + int64_t backward_comm_end_time = kUnsetTime; + + public: + enum class Event { + kForwardStart, + kBackwardComputeStart, + kBackwardComputeEnd, + kBackwardCommStart, + kBackwardCommEnd, + }; + + // Record the current event, i.e., mark it as having occurred now. Default + // CPU implementation. + virtual void record(Event event) { + getTimeRef(event) = current_time_in_nanos(); + } + + // Return the difference between when two events occurred, in nanoseconds. + // Or nullopt if one of them hasn't been recorded. + virtual c10::optional measureDifference(Event start, Event end) = 0; + + virtual ~Timer() = default; + + // Return host-side timestamp, or nullopt if it has not yet been recorded. + c10::optional getTimestamp(Event event) { + auto time = getTimeRef(event); + if (time == kUnsetTime) { + return c10::nullopt; + } else { + return time; + } + } + + // Return host-side time member variable corresponding to the given event. + int64_t& getTimeRef(Event event) { + switch (event) { + case Event::kForwardStart: + return forward_start_time; + case Event::kBackwardComputeStart: + return backward_compute_start_time; + case Event::kBackwardComputeEnd: + return backward_compute_end_time; + case Event::kBackwardCommStart: + return backward_comm_start_time; + case Event::kBackwardCommEnd: + return backward_comm_end_time; + default: + TORCH_INTERNAL_ASSERT(false); + } + } +}; + +TORCH_DECLARE_TYPED_REGISTRY( + TimerRegistry, + c10::DeviceType, + Timer, + std::unique_ptr, + c10::Device); +} // namespace c10d diff --git a/shared/package-files/8b/8b78797c860c531e3ffcca1cecca52d532df2bc4aedfe8857dab8e5d70146f86/blob b/shared/package-files/8b/8b78797c860c531e3ffcca1cecca52d532df2bc4aedfe8857dab8e5d70146f86/blob new file mode 100644 index 0000000000000000000000000000000000000000..ccb0f6d4cac90353feb1352927e801ad51f1f67b --- /dev/null +++ b/shared/package-files/8b/8b78797c860c531e3ffcca1cecca52d532df2bc4aedfe8857dab8e5d70146f86/blob @@ -0,0 +1,550 @@ +""" +Distance dependence measure and the dCov test + +Implementation of Székely et al. (2007) calculation of distance +dependence statistics, including the Distance covariance (dCov) test +for independence of random vectors of arbitrary length. + +Author: Ron Itzikovitch + +References +---------- +.. Szekely, G.J., Rizzo, M.L., and Bakirov, N.K. (2007) + "Measuring and testing dependence by correlation of distances". + Annals of Statistics, Vol. 35 No. 6, pp. 2769-2794. + +""" + +from typing import NamedTuple +import warnings + +import numpy as np +from scipy.spatial.distance import pdist, squareform +from scipy.stats import norm + +from statsmodels.tools.rng_qrng import check_random_state +from statsmodels.tools.sm_exceptions import HypothesisTestWarning +from statsmodels.tools.validation import string_like + + +class DistDependStat(NamedTuple): + """ + Result of :func:`distance_statistics`. + + Parameters + ---------- + test_statistic : float + The "basic" test statistic (i.e., the one used when the `emp` + method is chosen when calling ``distance_covariance_test()``). + distance_correlation : float + The distance correlation between `x` and `y`. + distance_covariance : float + The distance covariance of `x` and `y`. + dvar_x : float + The distance variance of `x`. + dvar_y : float + The distance variance of `y`. + S : float + The mean of the euclidean distances in `x` multiplied by those + of `y`. Mostly used internally. + """ + + test_statistic: float + distance_correlation: float + distance_covariance: float + dvar_x: float + dvar_y: float + S: float + + +def distance_covariance_test(x, y, B=None, method="auto", rng=None): + r""" + The Distance Covariance (dCov) test + + Apply the Distance Covariance (dCov) test of independence to `x` and `y`. + This test was introduced in [1]_, and is based on the distance covariance + statistic. The test is applicable to random vectors of arbitrary length + (see the notes section for more details). + + Parameters + ---------- + x : array_like, 1-D or 2-D + If `x` is 1-D than it is assumed to be a vector of observations of a + single random variable. If `x` is 2-D than the rows should be + observations and the columns are treated as the components of a + random vector, i.e., each column represents a different component of + the random vector `x`. + y : array_like, 1-D or 2-D + Same as `x`, but only the number of observation has to match that of + `x`. If `y` is 2-D note that the number of columns of `y` (i.e., the + number of components in the random vector) does not need to match + the number of columns in `x`. + B : int, optional + The number of iterations to perform when evaluating the null + distribution of the test statistic when the `emp` method is + applied (see below). if `B` is `None` than as in [1]_ we set + `B` to be ``B = 200 + 5000/n``, where `n` is the number of + observations. + method : {'auto', 'emp', 'asym'}, optional + The method by which to obtain the p-value for the test. + + - `auto` : Default method. The number of observations will be used to + determine the method. + - `emp` : Empirical evaluation of the p-value using permutations of + the rows of `y` to obtain the null distribution. + - `asym` : An asymptotic approximation of the distribution of the test + statistic is used to find the p-value. + + rng : int, array_like of int, numpy.random.Generator, numpy.random.RandomState, optional + If `rng` is None, a new ``Generator`` is created using fresh + entropy from the operating system. If `rng` is an int or array + of ints, a new ``Generator`` is created, seeded with `rng`. If + `rng` is already a ``Generator`` or ``RandomState`` instance, + that instance is used. + + Returns + ------- + test_statistic : float + The value of the test statistic used in the test. + pval : float + The p-value. + chosen_method : str + The method that was used to obtain the p-value. Mostly relevant when + the function is called with `method='auto'`. + + Notes + ----- + The test applies to random vectors of arbitrary dimensions, i.e., `x` + can be a 1-D vector of observations for a single random variable while + `y` can be a `k` by `n` 2-D array (where `k > 1`). In other words, it + is also possible for `x` and `y` to both be 2-D arrays and have the + same number of rows (observations) while differing in the number of + columns. + + As noted in [1]_ the statistics are sensitive to all types of departures + from independence, including nonlinear or nonmonotone dependence + structure. + + References + ---------- + .. [1] Szekely, G.J., Rizzo, M.L., and Bakirov, N.K. (2007) + "Measuring and testing dependence by correlation of distances". + Annals of Statistics, Vol. 35 No. 6, pp. 2769-2794. + + Examples + -------- + >>> from statsmodels.stats.dist_dependence_measures import + ... distance_covariance_test + >>> data = np.random.rand(1000, 10) + >>> x, y = data[:, :3], data[:, 3:] + >>> x.shape + (1000, 3) + >>> y.shape + (1000, 7) + >>> distance_covariance_test(x, y) + (1.0426404792714983, 0.2971148340813543, 'asym') + # (test_statistic, pval, chosen_method) + + """ + x, y = _validate_and_tranform_x_and_y(x, y) + + method = string_like( + method, "method", options=("auto", "emp", "asym"), lower=False + ) + + n = x.shape[0] + stats = distance_statistics(x, y) + + if (method == "auto" and n <= 500) or method == "emp": + chosen_method = "emp" + rng = check_random_state(rng) + test_statistic, pval = _empirical_pvalue(x, y, B, n, stats, rng) + + else: # method == "asym", or method == "auto" and n > 500 + chosen_method = "asym" + test_statistic, pval = _asymptotic_pvalue(stats) + + # In case we got an extreme p-value (0 or 1) when using the empirical + # distribution of the test statistic under the null, we fall back + # to the asymptotic approximation. + if chosen_method == "emp" and pval in [0, 1]: + msg = ( + f"p-value was {pval} when using the empirical method. " + "The asymptotic approximation will be used instead" + ) + warnings.warn(msg, HypothesisTestWarning, stacklevel=2) + _, pval = _asymptotic_pvalue(stats) + + return test_statistic, pval, chosen_method + + +def _validate_and_tranform_x_and_y(x, y): + r""" + Ensure `x` and `y` have proper shape and transform/reshape them if required + + Parameters + ---------- + x : array_like, 1-D or 2-D + If `x` is 1-D than it is assumed to be a vector of observations of a + single random variable. If `x` is 2-D than the rows should be + observations and the columns are treated as the components of a + random vector, i.e., each column represents a different component of + the random vector `x`. + y : array_like, 1-D or 2-D + Same as `x`, but only the number of observation has to match that of + `x`. If `y` is 2-D note that the number of columns of `y` (i.e., the + number of components in the random vector) does not need to match + the number of columns in `x`. + + Returns + ------- + x : ndarray, 2-D + y : ndarray, 2-D + + Raises + ------ + ValueError + If `x` and `y` have a different number of observations. + + """ + x = np.asanyarray(x) + y = np.asanyarray(y) + + if x.shape[0] != y.shape[0]: + raise ValueError("x and y must have the same number of observations (rows).") + + if len(x.shape) == 1: + x = x.reshape((x.shape[0], 1)) + + if len(y.shape) == 1: + y = y.reshape((y.shape[0], 1)) + + return x, y + + +def _empirical_pvalue(x, y, B, n, stats, rng): + r""" + Calculate the empirical p-value based on permutations of `y`'s rows + + Parameters + ---------- + x : array_like, 1-D or 2-D + If `x` is 1-D than it is assumed to be a vector of observations of a + single random variable. If `x` is 2-D than the rows should be + observations and the columns are treated as the components of a + random vector, i.e., each column represents a different component of + the random vector `x`. + y : array_like, 1-D or 2-D + Same as `x`, but only the number of observation has to match that of + `x`. If `y` is 2-D note that the number of columns of `y` (i.e., the + number of components in the random vector) does not need to match + the number of columns in `x`. + B : int + The number of iterations when evaluating the null distribution. + n : int + Number of observations found in each of `x` and `y`. + stats : DistDependStat + The result obtained from calling ``distance_statistics(x, y)``. + rng : int, array_like of int, numpy.random.Generator, numpy.random.RandomState, optional + If `rng` is None, a new ``Generator`` is created using fresh + entropy from the operating system. If `rng` is an int or array + of ints, a new ``Generator`` is created, seeded with `rng`. If + `rng` is already a ``Generator`` or ``RandomState`` instance, + that instance is used. + + Returns + ------- + test_statistic : float + The empirical test statistic. + pval : float + The empirical p-value. + + """ + B = int(B) if B else int(np.floor(200 + 5000 / n)) + empirical_dist = _get_test_statistic_distribution(x, y, B, rng) + pval = 1 - np.searchsorted(sorted(empirical_dist), stats.test_statistic) / len( + empirical_dist + ) + test_statistic = stats.test_statistic + + return test_statistic, pval + + +def _asymptotic_pvalue(stats): + r""" + Calculate the p-value using an asymptotic approximation of the null distribution + + Parameters + ---------- + stats : DistDependStat + The result obtained from calling ``distance_statistics(x, y)``. + + Returns + ------- + test_statistic : float + The test statistic. + pval : float + The asymptotic p-value. + + """ + test_statistic = np.sqrt(stats.test_statistic / stats.S) + pval = (1 - norm.cdf(test_statistic)) * 2 + + return test_statistic, pval + + +def _get_test_statistic_distribution(x, y, B, rng): + r""" + Compute the empirical distribution of the test statistic under permutation + + Parameters + ---------- + x : array_like, 1-D or 2-D + If `x` is 1-D than it is assumed to be a vector of observations of a + single random variable. If `x` is 2-D than the rows should be + observations and the columns are treated as the components of a + random vector, i.e., each column represents a different component of + the random vector `x`. + y : array_like, 1-D or 2-D + Same as `x`, but only the number of observation has to match that of + `x`. If `y` is 2-D note that the number of columns of `y` (i.e., the + number of components in the random vector) does not need to match + the number of columns in `x`. + B : int + The number of iterations to perform when evaluating the null + distribution. + rng : int, array_like of int, numpy.random.Generator, numpy.random.RandomState, optional + If `rng` is None, a new ``Generator`` is created using fresh + entropy from the operating system. If `rng` is an int or array + of ints, a new ``Generator`` is created, seeded with `rng`. If + `rng` is already a ``Generator`` or ``RandomState`` instance, + that instance is used. + + Returns + ------- + emp_dist : ndarray + The empirical distribution of the test statistic. + + """ + y = y.copy() + emp_dist = np.zeros(B) + x_dist = squareform(pdist(x, "euclidean")) + for i in range(B): + rng.shuffle(y) + emp_dist[i] = distance_statistics(x, y, x_dist=x_dist).test_statistic + + return emp_dist + + +def distance_statistics(x, y, x_dist=None, y_dist=None): + r""" + Calculate various distance dependence statistics + + Calculate several distance dependence statistics as described in [1]_. + + Parameters + ---------- + x : array_like, 1-D or 2-D + If `x` is 1-D than it is assumed to be a vector of observations of a + single random variable. If `x` is 2-D than the rows should be + observations and the columns are treated as the components of a + random vector, i.e., each column represents a different component of + the random vector `x`. + y : array_like, 1-D or 2-D + Same as `x`, but only the number of observation has to match that of + `x`. If `y` is 2-D note that the number of columns of `y` (i.e., the + number of components in the random vector) does not need to match + the number of columns in `x`. + x_dist : array_like, 2-D, optional + A square 2-D array_like object whose values are the euclidean + distances between `x`'s rows. + y_dist : array_like, 2-D, optional + A square 2-D array_like object whose values are the euclidean + distances between `y`'s rows. + + Returns + ------- + DistDependStat + A named tuple of distance dependence statistics. See + :class:`DistDependStat` for the full list of fields. + + References + ---------- + .. [1] Szekely, G.J., Rizzo, M.L., and Bakirov, N.K. (2007) + "Measuring and testing dependence by correlation of distances". + Annals of Statistics, Vol. 35 No. 6, pp. 2769-2794. + + Examples + -------- + + >>> from statsmodels.stats.dist_dependence_measures import + ... distance_statistics + >>> distance_statistics(np.random.random(1000), np.random.random(1000)) + DistDependStat(test_statistic=0.07948284320205831, + distance_correlation=0.04269511890990793, + distance_covariance=0.008915315092696293, + dvar_x=0.20719027438266704, dvar_y=0.21044934264957588, + S=0.10892061635588891) + + """ + x, y = _validate_and_tranform_x_and_y(x, y) + + n = x.shape[0] + + a = x_dist if x_dist is not None else squareform(pdist(x, "euclidean")) + b = y_dist if y_dist is not None else squareform(pdist(y, "euclidean")) + + a_row_means = a.mean(axis=0, keepdims=True) + b_row_means = b.mean(axis=0, keepdims=True) + a_col_means = a.mean(axis=1, keepdims=True) + b_col_means = b.mean(axis=1, keepdims=True) + a_mean = a.mean() + b_mean = b.mean() + + A = a - a_row_means - a_col_means + a_mean + B = b - b_row_means - b_col_means + b_mean + + S = a_mean * b_mean + dcov = np.sqrt(np.multiply(A, B).mean()) + dvar_x = np.sqrt(np.multiply(A, A).mean()) + dvar_y = np.sqrt(np.multiply(B, B).mean()) + dcor = dcov / np.sqrt(dvar_x * dvar_y) + + test_statistic = n * dcov**2 + + return DistDependStat( + test_statistic=test_statistic, + distance_correlation=dcor, + distance_covariance=dcov, + dvar_x=dvar_x, + dvar_y=dvar_y, + S=S, + ) + + +def distance_covariance(x, y): + r""" + Distance covariance + + Calculate the empirical distance covariance as described in [1]_. + + Parameters + ---------- + x : array_like, 1-D or 2-D + If `x` is 1-D than it is assumed to be a vector of observations of a + single random variable. If `x` is 2-D than the rows should be + observations and the columns are treated as the components of a + random vector, i.e., each column represents a different component of + the random vector `x`. + y : array_like, 1-D or 2-D + Same as `x`, but only the number of observation has to match that of + `x`. If `y` is 2-D note that the number of columns of `y` (i.e., the + number of components in the random vector) does not need to match + the number of columns in `x`. + + Returns + ------- + float + The empirical distance covariance between `x` and `y`. + + References + ---------- + .. [1] Szekely, G.J., Rizzo, M.L., and Bakirov, N.K. (2007) + "Measuring and testing dependence by correlation of distances". + Annals of Statistics, Vol. 35 No. 6, pp. 2769-2794. + + Examples + -------- + + >>> from statsmodels.stats.dist_dependence_measures import + ... distance_covariance + >>> distance_covariance(np.random.random(1000), np.random.random(1000)) + 0.007575063951951362 + + """ + return distance_statistics(x, y).distance_covariance + + +def distance_variance(x): + r""" + Distance variance + + Calculate the empirical distance variance as described in [1]_. + + Parameters + ---------- + x : array_like, 1-D or 2-D + If `x` is 1-D than it is assumed to be a vector of observations of a + single random variable. If `x` is 2-D than the rows should be + observations and the columns are treated as the components of a + random vector, i.e., each column represents a different component of + the random vector `x`. + + Returns + ------- + float + The empirical distance variance of `x`. + + References + ---------- + .. [1] Szekely, G.J., Rizzo, M.L., and Bakirov, N.K. (2007) + "Measuring and testing dependence by correlation of distances". + Annals of Statistics, Vol. 35 No. 6, pp. 2769-2794. + + Examples + -------- + + >>> from statsmodels.stats.dist_dependence_measures import + ... distance_variance + >>> distance_variance(np.random.random(1000)) + 0.21732609190659702 + + """ + return distance_covariance(x, x) + + +def distance_correlation(x, y): + r""" + Distance correlation + + Calculate the empirical distance correlation as described in [1]_. + This statistic is analogous to product-moment correlation and describes + the dependence between `x` and `y`, which are random vectors of + arbitrary length. The statistics' values range between 0 (implies + independence) and 1 (implies complete dependence). + + Parameters + ---------- + x : array_like, 1-D or 2-D + If `x` is 1-D than it is assumed to be a vector of observations of a + single random variable. If `x` is 2-D than the rows should be + observations and the columns are treated as the components of a + random vector, i.e., each column represents a different component of + the random vector `x`. + y : array_like, 1-D or 2-D + Same as `x`, but only the number of observation has to match that of + `x`. If `y` is 2-D note that the number of columns of `y` (i.e., the + number of components in the random vector) does not need to match + the number of columns in `x`. + + Returns + ------- + float + The empirical distance correlation between `x` and `y`. + + References + ---------- + .. [1] Szekely, G.J., Rizzo, M.L., and Bakirov, N.K. (2007) + "Measuring and testing dependence by correlation of distances". + Annals of Statistics, Vol. 35 No. 6, pp. 2769-2794. + + Examples + -------- + + >>> from statsmodels.stats.dist_dependence_measures import + ... distance_correlation + >>> distance_correlation(np.random.random(1000), np.random.random(1000)) + 0.04060497840149489 + + """ + return distance_statistics(x, y).distance_correlation diff --git a/shared/package-files/8b/8b790ac38c4ee3ff7d7c93753bba315c694f53ea8c24a0cf8ab6d6693c3be93a/blob b/shared/package-files/8b/8b790ac38c4ee3ff7d7c93753bba315c694f53ea8c24a0cf8ab6d6693c3be93a/blob new file mode 100644 index 0000000000000000000000000000000000000000..c4e6cd6b9df389696bf9733eb9157541661f6874 --- /dev/null +++ b/shared/package-files/8b/8b790ac38c4ee3ff7d7c93753bba315c694f53ea8c24a0cf8ab6d6693c3be93a/blob @@ -0,0 +1,365 @@ +import math +from typing import List, Optional, Tuple + +import PIL.Image +import torch +from torch.nn.functional import conv2d, pad as torch_pad + +from torchvision import tv_tensors +from torchvision.transforms._functional_tensor import _max_value +from torchvision.transforms.functional import pil_to_tensor, to_pil_image + +from torchvision.utils import _log_api_usage_once + +from ._meta import _convert_bounding_box_format + +from ._utils import _get_kernel, _register_kernel_internal, is_pure_tensor + + +def normalize( + inpt: torch.Tensor, + mean: List[float], + std: List[float], + inplace: bool = False, +) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.Normalize` for details.""" + if torch.jit.is_scripting(): + return normalize_image(inpt, mean=mean, std=std, inplace=inplace) + + _log_api_usage_once(normalize) + + kernel = _get_kernel(normalize, type(inpt)) + return kernel(inpt, mean=mean, std=std, inplace=inplace) + + +@_register_kernel_internal(normalize, torch.Tensor) +@_register_kernel_internal(normalize, tv_tensors.Image) +def normalize_image(image: torch.Tensor, mean: List[float], std: List[float], inplace: bool = False) -> torch.Tensor: + if not image.is_floating_point(): + raise TypeError(f"Input tensor should be a float tensor. Got {image.dtype}.") + + if image.ndim < 3: + raise ValueError(f"Expected tensor to be a tensor image of size (..., C, H, W). Got {image.shape}.") + + if isinstance(std, (tuple, list)): + divzero = not all(std) + elif isinstance(std, (int, float)): + divzero = std == 0 + else: + divzero = False + if divzero: + raise ValueError("std evaluated to zero, leading to division by zero.") + + dtype = image.dtype + device = image.device + mean = torch.as_tensor(mean, dtype=dtype, device=device) + std = torch.as_tensor(std, dtype=dtype, device=device) + if mean.ndim == 1: + mean = mean.view(-1, 1, 1) + if std.ndim == 1: + std = std.view(-1, 1, 1) + + if inplace: + image = image.sub_(mean) + else: + image = image.sub(mean) + + return image.div_(std) + + +@_register_kernel_internal(normalize, tv_tensors.Video) +def normalize_video(video: torch.Tensor, mean: List[float], std: List[float], inplace: bool = False) -> torch.Tensor: + return normalize_image(video, mean, std, inplace=inplace) + + +def gaussian_blur(inpt: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) -> torch.Tensor: + """See :class:`~torchvision.transforms.v2.GaussianBlur` for details.""" + if torch.jit.is_scripting(): + return gaussian_blur_image(inpt, kernel_size=kernel_size, sigma=sigma) + + _log_api_usage_once(gaussian_blur) + + kernel = _get_kernel(gaussian_blur, type(inpt)) + return kernel(inpt, kernel_size=kernel_size, sigma=sigma) + + +def _get_gaussian_kernel1d(kernel_size: int, sigma: float, dtype: torch.dtype, device: torch.device) -> torch.Tensor: + lim = (kernel_size - 1) / (2.0 * math.sqrt(2.0) * sigma) + x = torch.linspace(-lim, lim, steps=kernel_size, dtype=dtype, device=device) + kernel1d = torch.softmax(x.pow_(2).neg_(), dim=0) + return kernel1d + + +def _get_gaussian_kernel2d( + kernel_size: List[int], sigma: List[float], dtype: torch.dtype, device: torch.device +) -> torch.Tensor: + kernel1d_x = _get_gaussian_kernel1d(kernel_size[0], sigma[0], dtype, device) + kernel1d_y = _get_gaussian_kernel1d(kernel_size[1], sigma[1], dtype, device) + kernel2d = kernel1d_y.unsqueeze(-1) * kernel1d_x + return kernel2d + + +@_register_kernel_internal(gaussian_blur, torch.Tensor) +@_register_kernel_internal(gaussian_blur, tv_tensors.Image) +def gaussian_blur_image( + image: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None +) -> torch.Tensor: + # TODO: consider deprecating integers from sigma on the future + if isinstance(kernel_size, int): + kernel_size = [kernel_size, kernel_size] + elif len(kernel_size) != 2: + raise ValueError(f"If kernel_size is a sequence its length should be 2. Got {len(kernel_size)}") + for ksize in kernel_size: + if ksize % 2 == 0 or ksize < 0: + raise ValueError(f"kernel_size should have odd and positive integers. Got {kernel_size}") + + if sigma is None: + sigma = [ksize * 0.15 + 0.35 for ksize in kernel_size] + else: + if isinstance(sigma, (list, tuple)): + length = len(sigma) + if length == 1: + s = float(sigma[0]) + sigma = [s, s] + elif length != 2: + raise ValueError(f"If sigma is a sequence, its length should be 2. Got {length}") + elif isinstance(sigma, (int, float)): + s = float(sigma) + sigma = [s, s] + else: + raise TypeError(f"sigma should be either float or sequence of floats. Got {type(sigma)}") + for s in sigma: + if s <= 0.0: + raise ValueError(f"sigma should have positive values. Got {sigma}") + + if image.numel() == 0: + return image + + dtype = image.dtype + shape = image.shape + ndim = image.ndim + if ndim == 3: + image = image.unsqueeze(dim=0) + elif ndim > 4: + image = image.reshape((-1,) + shape[-3:]) + + fp = torch.is_floating_point(image) + kernel = _get_gaussian_kernel2d(kernel_size, sigma, dtype=dtype if fp else torch.float32, device=image.device) + kernel = kernel.expand(shape[-3], 1, kernel.shape[0], kernel.shape[1]) + + output = image if fp else image.to(dtype=torch.float32) + + # padding = (left, right, top, bottom) + padding = [kernel_size[0] // 2, kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[1] // 2] + output = torch_pad(output, padding, mode="reflect") + output = conv2d(output, kernel, groups=shape[-3]) + + if ndim == 3: + output = output.squeeze(dim=0) + elif ndim > 4: + output = output.reshape(shape) + + if not fp: + output = output.round_().to(dtype=dtype) + + return output + + +@_register_kernel_internal(gaussian_blur, PIL.Image.Image) +def _gaussian_blur_image_pil( + image: PIL.Image.Image, kernel_size: List[int], sigma: Optional[List[float]] = None +) -> PIL.Image.Image: + t_img = pil_to_tensor(image) + output = gaussian_blur_image(t_img, kernel_size=kernel_size, sigma=sigma) + return to_pil_image(output, mode=image.mode) + + +@_register_kernel_internal(gaussian_blur, tv_tensors.Video) +def gaussian_blur_video( + video: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None +) -> torch.Tensor: + return gaussian_blur_image(video, kernel_size, sigma) + + +def to_dtype(inpt: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor: + """See :func:`~torchvision.transforms.v2.ToDtype` for details.""" + if torch.jit.is_scripting(): + return to_dtype_image(inpt, dtype=dtype, scale=scale) + + _log_api_usage_once(to_dtype) + + kernel = _get_kernel(to_dtype, type(inpt)) + return kernel(inpt, dtype=dtype, scale=scale) + + +def _num_value_bits(dtype: torch.dtype) -> int: + if dtype == torch.uint8: + return 8 + elif dtype == torch.int8: + return 7 + elif dtype == torch.int16: + return 15 + elif dtype == torch.int32: + return 31 + elif dtype == torch.int64: + return 63 + else: + raise TypeError(f"Number of value bits is only defined for integer dtypes, but got {dtype}.") + + +@_register_kernel_internal(to_dtype, torch.Tensor) +@_register_kernel_internal(to_dtype, tv_tensors.Image) +def to_dtype_image(image: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor: + + if image.dtype == dtype: + return image + elif not scale: + return image.to(dtype) + + float_input = image.is_floating_point() + if torch.jit.is_scripting(): + # TODO: remove this branch as soon as `dtype.is_floating_point` is supported by JIT + float_output = torch.tensor(0, dtype=dtype).is_floating_point() + else: + float_output = dtype.is_floating_point + + if float_input: + # float to float + if float_output: + return image.to(dtype) + + # float to int + if (image.dtype == torch.float32 and dtype in (torch.int32, torch.int64)) or ( + image.dtype == torch.float64 and dtype == torch.int64 + ): + raise RuntimeError(f"The conversion from {image.dtype} to {dtype} cannot be performed safely.") + + # For data in the range `[0.0, 1.0]`, just multiplying by the maximum value of the integer range and converting + # to the integer dtype is not sufficient. For example, `torch.rand(...).mul(255).to(torch.uint8)` will only + # be `255` if the input is exactly `1.0`. See https://github.com/pytorch/vision/pull/2078#issuecomment-612045321 + # for a detailed analysis. + # To mitigate this, we could round before we convert to the integer dtype, but this is an extra operation. + # Instead, we can also multiply by the maximum value plus something close to `1`. See + # https://github.com/pytorch/vision/pull/2078#issuecomment-613524965 for details. + eps = 1e-3 + max_value = float(_max_value(dtype)) + # We need to scale first since the conversion would otherwise turn the input range `[0.0, 1.0]` into the + # discrete set `{0, 1}`. + return image.mul(max_value + 1.0 - eps).to(dtype) + else: + # int to float + if float_output: + return image.to(dtype).mul_(1.0 / _max_value(image.dtype)) + + # int to int + num_value_bits_input = _num_value_bits(image.dtype) + num_value_bits_output = _num_value_bits(dtype) + + if num_value_bits_input > num_value_bits_output: + return image.bitwise_right_shift(num_value_bits_input - num_value_bits_output).to(dtype) + else: + return image.to(dtype).bitwise_left_shift_(num_value_bits_output - num_value_bits_input) + + +# We encourage users to use to_dtype() instead but we keep this for BC +def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float32) -> torch.Tensor: + """[DEPRECATED] Use to_dtype() instead.""" + return to_dtype_image(image, dtype=dtype, scale=True) + + +@_register_kernel_internal(to_dtype, tv_tensors.Video) +def to_dtype_video(video: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor: + return to_dtype_image(video, dtype, scale=scale) + + +@_register_kernel_internal(to_dtype, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +@_register_kernel_internal(to_dtype, tv_tensors.Mask, tv_tensor_wrapper=False) +def _to_dtype_tensor_dispatch(inpt: torch.Tensor, dtype: torch.dtype, scale: bool = False) -> torch.Tensor: + # We don't need to unwrap and rewrap here, since TVTensor.to() preserves the type + return inpt.to(dtype) + + +def sanitize_bounding_boxes( + bounding_boxes: torch.Tensor, + format: Optional[tv_tensors.BoundingBoxFormat] = None, + canvas_size: Optional[Tuple[int, int]] = None, + min_size: float = 1.0, +) -> Tuple[torch.Tensor, torch.Tensor]: + """Remove degenerate/invalid bounding boxes and return the corresponding indexing mask. + + This removes bounding boxes that: + + - are below a given ``min_size``: by default this also removes degenerate boxes that have e.g. X2 <= X1. + - have any coordinate outside of their corresponding image. You may want to + call :func:`~torchvision.transforms.v2.functional.clamp_bounding_boxes` first to avoid undesired removals. + + It is recommended to call it at the end of a pipeline, before passing the + input to the models. It is critical to call this transform if + :class:`~torchvision.transforms.v2.RandomIoUCrop` was called. + If you want to be extra careful, you may call it after all transforms that + may modify bounding boxes but once at the end should be enough in most + cases. + + Args: + bounding_boxes (Tensor or :class:`~torchvision.tv_tensors.BoundingBoxes`): The bounding boxes to be sanitized. + format (str or :class:`~torchvision.tv_tensors.BoundingBoxFormat`, optional): The format of the bounding boxes. + Must be left to none if ``bounding_boxes`` is a :class:`~torchvision.tv_tensors.BoundingBoxes` object. + canvas_size (tuple of int, optional): The canvas_size of the bounding boxes + (size of the corresponding image/video). + Must be left to none if ``bounding_boxes`` is a :class:`~torchvision.tv_tensors.BoundingBoxes` object. + min_size (float, optional) The size below which bounding boxes are removed. Default is 1. + + Returns: + out (tuple of Tensors): The subset of valid bounding boxes, and the corresponding indexing mask. + The mask can then be used to subset other tensors (e.g. labels) that are associated with the bounding boxes. + """ + if torch.jit.is_scripting() or is_pure_tensor(bounding_boxes): + if format is None or canvas_size is None: + raise ValueError( + "format and canvas_size cannot be None if bounding_boxes is a pure tensor. " + f"Got format={format} and canvas_size={canvas_size}." + "Set those to appropriate values or pass bounding_boxes as a tv_tensors.BoundingBoxes object." + ) + if isinstance(format, str): + format = tv_tensors.BoundingBoxFormat[format.upper()] + valid = _get_sanitize_bounding_boxes_mask( + bounding_boxes, format=format, canvas_size=canvas_size, min_size=min_size + ) + bounding_boxes = bounding_boxes[valid] + else: + if not isinstance(bounding_boxes, tv_tensors.BoundingBoxes): + raise ValueError("bouding_boxes must be a tv_tensors.BoundingBoxes instance or a pure tensor.") + if format is not None or canvas_size is not None: + raise ValueError( + "format and canvas_size must be None when bounding_boxes is a tv_tensors.BoundingBoxes instance. " + f"Got format={format} and canvas_size={canvas_size}. " + "Leave those to None or pass bouding_boxes as a pure tensor." + ) + valid = _get_sanitize_bounding_boxes_mask( + bounding_boxes, format=bounding_boxes.format, canvas_size=bounding_boxes.canvas_size, min_size=min_size + ) + bounding_boxes = tv_tensors.wrap(bounding_boxes[valid], like=bounding_boxes) + + return bounding_boxes, valid + + +def _get_sanitize_bounding_boxes_mask( + bounding_boxes: torch.Tensor, + format: tv_tensors.BoundingBoxFormat, + canvas_size: Tuple[int, int], + min_size: float = 1.0, +) -> torch.Tensor: + + bounding_boxes = _convert_bounding_box_format( + bounding_boxes, new_format=tv_tensors.BoundingBoxFormat.XYXY, old_format=format + ) + + image_h, image_w = canvas_size + ws, hs = bounding_boxes[:, 2] - bounding_boxes[:, 0], bounding_boxes[:, 3] - bounding_boxes[:, 1] + valid = (ws >= min_size) & (hs >= min_size) & (bounding_boxes >= 0).all(dim=-1) + # TODO: Do we really need to check for out of bounds here? All + # transforms should be clamping anyway, so this should never happen? + image_h, image_w = canvas_size + valid &= (bounding_boxes[:, 0] <= image_w) & (bounding_boxes[:, 2] <= image_w) + valid &= (bounding_boxes[:, 1] <= image_h) & (bounding_boxes[:, 3] <= image_h) + return valid diff --git a/shared/package-files/8b/8b7ae587d5e6626d839e50e6bdbaaf7778f29bab25a57b9866ccec713ec6ce15/blob b/shared/package-files/8b/8b7ae587d5e6626d839e50e6bdbaaf7778f29bab25a57b9866ccec713ec6ce15/blob new file mode 100644 index 0000000000000000000000000000000000000000..a0f9cfa8bf0ce554d198b7e55079ed13e1798175 --- /dev/null +++ b/shared/package-files/8b/8b7ae587d5e6626d839e50e6bdbaaf7778f29bab25a57b9866ccec713ec6ce15/blob @@ -0,0 +1,88 @@ +#pragma once + +#include +#include +#include + +#ifndef AT_PER_OPERATOR_HEADERS +#include +#else +#include +#endif + +namespace at::native { + +// ensure we get good values and indices for kthvalue, mode +// this will always be with the reducing dim as 1-d +inline void _reduction_with_indices_allocate_or_resize_output( + Tensor& values, + Tensor& indices, + const Tensor& self, + int64_t dim_, + bool keepdim) { + int64_t dim = maybe_wrap_dim(dim_, self.dim(), /*wrap_scalar=*/true); + auto result_sizes = self.sizes().vec(); + if (!result_sizes.empty()) { + result_sizes[dim] = 1; + } + if (values.defined()) { + TORCH_CHECK( + self.options().type_equal(values.options()), + "output values must be of same type as input"); + if (!keepdim && values.dim() == self.dim() - 1) { + // unsqueeze to preserve passed in noncontiguous tensor in resize + values.unsqueeze_(dim); + } + resize_output(values, result_sizes); + } else { + values = at::empty(result_sizes, self.options()); + } + if (indices.defined()) { + TORCH_CHECK( + indices.dtype() == kLong, "output indices must be of scalar type Long"); + TORCH_CHECK( + indices.device() == self.device(), + "output indices must be on same device as input"); + if (!keepdim && indices.dim() == self.dim() - 1) { + // unsqueeze to preserve passed in noncontiguous tensor in resize + indices.unsqueeze_(dim); + } + resize_output(indices, result_sizes); + } else { + indices = at::empty(result_sizes, self.options().dtype(kLong)); + } +} + +// ensure we get good values and indices for topk +inline void _allocate_or_resize_output_with_indices( + Tensor& values, + Tensor& indices, + const Tensor& self, + int64_t dim_, + int64_t k) { + int64_t dim = maybe_wrap_dim(dim_, self.dim(), /*wrap_scalar=*/true); + auto result_sizes = self.sizes().vec(); + if (!result_sizes.empty()) { + result_sizes[dim] = k; + } + if (values.defined()) { + TORCH_CHECK( + self.options().type_equal(values.options()), + "output values must be of same type as input"); + values.resize_(result_sizes); + } else { + values = at::empty(result_sizes, self.options()); + } + if (indices.defined()) { + TORCH_CHECK( + indices.dtype() == kLong, "output indices must be of scalar type Long"); + TORCH_CHECK( + indices.device() == self.device(), + "output indices must be on same device as input"); + indices.resize_(result_sizes); + } else { + indices = at::empty(result_sizes, self.options().dtype(kLong)); + } +} + +} // namespace at::native diff --git a/shared/package-files/8b/8b7b8f573b4784884ded9423b548f06d1deabe36673008b8a8a784d31e49539b/blob b/shared/package-files/8b/8b7b8f573b4784884ded9423b548f06d1deabe36673008b8a8a784d31e49539b/blob new file mode 100644 index 0000000000000000000000000000000000000000..b4f58920f5745725cabdcd917ce944793ee1dcf4 --- /dev/null +++ b/shared/package-files/8b/8b7b8f573b4784884ded9423b548f06d1deabe36673008b8a8a784d31e49539b/blob @@ -0,0 +1,50 @@ +from multiprocessing import pool +from multiprocessing.process import Process as Process, active_children as active_children, current_process as current_process +from multiprocessing.util import SUBDEBUG as SUBDEBUG, SUBWARNING as SUBWARNING +from Queue import Queue as _BaseQueue +from typing import Any, Callable, Iterable, Optional, TypeVar + +class ProcessError(Exception): ... +class BufferTooShort(ProcessError): ... +class TimeoutError(ProcessError): ... +class AuthenticationError(ProcessError): ... + +_T = TypeVar("_T") + +class Queue(_BaseQueue[_T]): + def __init__(self, maxsize: int = ...) -> None: ... + def get(self, block: bool = ..., timeout: Optional[float] = ...) -> _T: ... + def put(self, item: _T, block: bool = ..., timeout: Optional[float] = ...) -> None: ... + def qsize(self) -> int: ... + def empty(self) -> bool: ... + def full(self) -> bool: ... + def put_nowait(self, item: _T) -> None: ... + def get_nowait(self) -> _T: ... + def close(self) -> None: ... + def join_thread(self) -> None: ... + def cancel_join_thread(self) -> None: ... + +def Manager(): ... +def Pipe(duplex: bool = ...): ... +def cpu_count() -> int: ... +def freeze_support(): ... +def get_logger(): ... +def log_to_stderr(level: Optional[Any] = ...): ... +def allow_connection_pickling(): ... +def Lock(): ... +def RLock(): ... +def Condition(lock: Optional[Any] = ...): ... +def Semaphore(value: int = ...): ... +def BoundedSemaphore(value: int = ...): ... +def Event(): ... +def JoinableQueue(maxsize: int = ...): ... +def RawValue(typecode_or_type, *args): ... +def RawArray(typecode_or_type, size_or_initializer): ... +def Value(typecode_or_type, *args, **kwds): ... +def Array(typecode_or_type, size_or_initializer, **kwds): ... +def Pool( + processes: Optional[int] = ..., + initializer: Optional[Callable[..., Any]] = ..., + initargs: Iterable[Any] = ..., + maxtasksperchild: Optional[int] = ..., +) -> pool.Pool: ... diff --git a/shared/package-files/8b/8b7c37207ece2c8fce70644de6738aadc1c4262f356485f699f83b4cffe3a880/blob b/shared/package-files/8b/8b7c37207ece2c8fce70644de6738aadc1c4262f356485f699f83b4cffe3a880/blob new file mode 100644 index 0000000000000000000000000000000000000000..bc82d07edd129b9d8b61ecc343994a24ac649c9d --- /dev/null +++ b/shared/package-files/8b/8b7c37207ece2c8fce70644de6738aadc1c4262f356485f699f83b4cffe3a880/blob @@ -0,0 +1,442 @@ +#pragma once + +// DO NOT DEFINE STATIC DATA IN THIS HEADER! +// See Note [Do not compile initializers with AVX] + +#include +#include +#include +#if defined(CPU_CAPABILITY_AVX2) && !defined(_MSC_VER) +#include +#endif + +namespace at::vec { +// See Note [CPU_CAPABILITY namespace] +inline namespace CPU_CAPABILITY { + + +#if defined(CPU_CAPABILITY_AVX2) && !defined(_MSC_VER) + +template <> class Vectorized { +private: + __m256d values; +public: + using value_type = double; + using size_type = int; + static constexpr size_type size() { + return 4; + } + Vectorized() {} + Vectorized(__m256d v) : values(v) {} + Vectorized(double val) { + values = _mm256_set1_pd(val); + } + Vectorized(double val1, double val2, double val3, double val4) { + values = _mm256_setr_pd(val1, val2, val3, val4); + } + operator __m256d() const { + return values; + } + template + static Vectorized blend(const Vectorized& a, const Vectorized& b) { + return _mm256_blend_pd(a.values, b.values, mask); + } + static Vectorized blendv(const Vectorized& a, const Vectorized& b, + const Vectorized& mask) { + return _mm256_blendv_pd(a.values, b.values, mask.values); + } + template + static Vectorized arange(double base = 0., step_t step = static_cast(1)) { + return Vectorized(base, base + step, base + 2 * step, base + 3 * step); + } + static Vectorized set(const Vectorized& a, const Vectorized& b, + int64_t count = size()) { + switch (count) { + case 0: + return a; + case 1: + return blend<1>(a, b); + case 2: + return blend<3>(a, b); + case 3: + return blend<7>(a, b); + } + return b; + } + static Vectorized loadu(const void* ptr, int64_t count = size()) { + if (count == size()) + return _mm256_loadu_pd(reinterpret_cast(ptr)); + + + __at_align__ double tmp_values[size()]; + // Ensure uninitialized memory does not change the output value See https://github.com/pytorch/pytorch/issues/32502 + // for more details. We do not initialize arrays to zero using "={0}" because gcc would compile it to two + // instructions while a loop would be compiled to one instruction. + for (const auto i : c10::irange(size())) { + tmp_values[i] = 0.0; + } + std::memcpy( + tmp_values, + reinterpret_cast(ptr), + count * sizeof(double)); + return _mm256_load_pd(tmp_values); + } + void store(void* ptr, int count = size()) const { + if (count == size()) { + _mm256_storeu_pd(reinterpret_cast(ptr), values); + } else if (count > 0) { + double tmp_values[size()]; + _mm256_storeu_pd(reinterpret_cast(tmp_values), values); + std::memcpy(ptr, tmp_values, count * sizeof(double)); + } + } + const double& operator[](int idx) const = delete; + double& operator[](int idx) = delete; + int zero_mask() const { + // returns an integer mask where all zero elements are translated to 1-bit and others are translated to 0-bit + __m256d cmp = _mm256_cmp_pd(values, _mm256_set1_pd(0.0), _CMP_EQ_OQ); + return _mm256_movemask_pd(cmp); + } + Vectorized isnan() const { + return _mm256_cmp_pd(values, _mm256_set1_pd(0.0), _CMP_UNORD_Q); + } + bool has_inf_nan() const { + __m256d self_sub = _mm256_sub_pd(values, values); + return (_mm256_movemask_epi8(_mm256_castpd_si256(self_sub)) & 0x77777777) != 0; + } + Vectorized map(double (*const f)(double)) const { + __at_align__ double tmp[size()]; + store(tmp); + for (const auto i : c10::irange(size())) { + tmp[i] = f(tmp[i]); + } + return loadu(tmp); + } + Vectorized abs() const { + auto mask = _mm256_set1_pd(-0.f); + return _mm256_andnot_pd(mask, values); + } + Vectorized angle() const { + const auto zero_vec = _mm256_set1_pd(0.f); + const auto nan_vec = _mm256_set1_pd(NAN); + const auto not_nan_mask = _mm256_cmp_pd(values, values, _CMP_EQ_OQ); + const auto nan_mask = _mm256_cmp_pd(not_nan_mask, zero_vec, _CMP_EQ_OQ); + const auto pi = _mm256_set1_pd(c10::pi); + + const auto neg_mask = _mm256_cmp_pd(values, zero_vec, _CMP_LT_OQ); + auto angle = _mm256_blendv_pd(zero_vec, pi, neg_mask); + angle = _mm256_blendv_pd(angle, nan_vec, nan_mask); + return angle; + } + Vectorized real() const { + return *this; + } + Vectorized imag() const { + return _mm256_set1_pd(0); + } + Vectorized conj() const { + return *this; + } + Vectorized acos() const { + return Vectorized(Sleef_acosd4_u10(values)); + } + Vectorized acosh() const { + return Vectorized(Sleef_acoshd4_u10(values)); + } + Vectorized asin() const { + return Vectorized(Sleef_asind4_u10(values)); + } + Vectorized atan() const { + return Vectorized(Sleef_atand4_u10(values)); + } + Vectorized atanh() const { + return Vectorized(Sleef_atanhd4_u10(values)); + } + Vectorized atan2(const Vectorized &b) const { + return Vectorized(Sleef_atan2d4_u10(values, b)); + } + Vectorized copysign(const Vectorized &sign) const { + return Vectorized(Sleef_copysignd4(values, sign)); + } + Vectorized erf() const { + return Vectorized(Sleef_erfd4_u10(values)); + } + Vectorized erfc() const { + return Vectorized(Sleef_erfcd4_u15(values)); + } + Vectorized erfinv() const { + return map(calc_erfinv); + } + Vectorized exp() const { + return Vectorized(Sleef_expd4_u10(values)); + } + Vectorized exp2() const { + return Vectorized(Sleef_exp2d4_u10(values)); + } + Vectorized expm1() const { + return Vectorized(Sleef_expm1d4_u10(values)); + } + Vectorized exp_u20() const { + return exp(); + } + Vectorized fmod(const Vectorized& q) const { + return Vectorized(Sleef_fmodd4(values, q)); + } + Vectorized hypot(const Vectorized &b) const { + return Vectorized(Sleef_hypotd4_u05(values, b)); + } + Vectorized i0() const { + return map(calc_i0); + } + Vectorized i0e() const { + return map(calc_i0e); + } + Vectorized digamma() const { + return map(calc_digamma); + } + Vectorized igamma(const Vectorized &x) const { + __at_align__ double tmp[size()]; + __at_align__ double tmp_x[size()]; + store(tmp); + x.store(tmp_x); + for (const auto i : c10::irange(size())) { + tmp[i] = calc_igamma(tmp[i], tmp_x[i]); + } + return loadu(tmp); + } + Vectorized igammac(const Vectorized &x) const { + __at_align__ double tmp[size()]; + __at_align__ double tmp_x[size()]; + store(tmp); + x.store(tmp_x); + for (const auto i : c10::irange(size())) { + tmp[i] = calc_igammac(tmp[i], tmp_x[i]); + } + return loadu(tmp); + } + Vectorized log() const { + return Vectorized(Sleef_logd4_u10(values)); + } + Vectorized log2() const { + return Vectorized(Sleef_log2d4_u10(values)); + } + Vectorized log10() const { + return Vectorized(Sleef_log10d4_u10(values)); + } + Vectorized log1p() const { + return Vectorized(Sleef_log1pd4_u10(values)); + } + Vectorized sin() const { + return Vectorized(Sleef_sind4_u10(values)); + } + Vectorized sinh() const { + return Vectorized(Sleef_sinhd4_u10(values)); + } + Vectorized cos() const { + return Vectorized(Sleef_cosd4_u10(values)); + } + Vectorized cosh() const { + return Vectorized(Sleef_coshd4_u10(values)); + } + Vectorized ceil() const { + return _mm256_ceil_pd(values); + } + Vectorized floor() const { + return _mm256_floor_pd(values); + } + Vectorized frac() const; + Vectorized neg() const { + return _mm256_xor_pd(_mm256_set1_pd(-0.), values); + } + Vectorized nextafter(const Vectorized &b) const { + return Vectorized(Sleef_nextafterd4(values, b)); + } + Vectorized round() const { + return _mm256_round_pd(values, (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC)); + } + Vectorized tan() const { + return Vectorized(Sleef_tand4_u10(values)); + } + Vectorized tanh() const { + return Vectorized(Sleef_tanhd4_u10(values)); + } + Vectorized trunc() const { + return _mm256_round_pd(values, (_MM_FROUND_TO_ZERO | _MM_FROUND_NO_EXC)); + } + Vectorized lgamma() const { + return Vectorized(Sleef_lgammad4_u10(values)); + } + Vectorized sqrt() const { + return _mm256_sqrt_pd(values); + } + Vectorized reciprocal() const { + return _mm256_div_pd(_mm256_set1_pd(1), values); + } + Vectorized rsqrt() const { + return _mm256_div_pd(_mm256_set1_pd(1), _mm256_sqrt_pd(values)); + } + Vectorized pow(const Vectorized &b) const { + return Vectorized(Sleef_powd4_u10(values, b)); + } + // Comparison using the _CMP_**_OQ predicate. + // `O`: get false if an operand is NaN + // `Q`: do not raise if an operand is NaN + Vectorized operator==(const Vectorized& other) const { + return _mm256_cmp_pd(values, other.values, _CMP_EQ_OQ); + } + + Vectorized operator!=(const Vectorized& other) const { + return _mm256_cmp_pd(values, other.values, _CMP_NEQ_UQ); + } + + Vectorized operator<(const Vectorized& other) const { + return _mm256_cmp_pd(values, other.values, _CMP_LT_OQ); + } + + Vectorized operator<=(const Vectorized& other) const { + return _mm256_cmp_pd(values, other.values, _CMP_LE_OQ); + } + + Vectorized operator>(const Vectorized& other) const { + return _mm256_cmp_pd(values, other.values, _CMP_GT_OQ); + } + + Vectorized operator>=(const Vectorized& other) const { + return _mm256_cmp_pd(values, other.values, _CMP_GE_OQ); + } + + Vectorized eq(const Vectorized& other) const; + Vectorized ne(const Vectorized& other) const; + Vectorized lt(const Vectorized& other) const; + Vectorized le(const Vectorized& other) const; + Vectorized gt(const Vectorized& other) const; + Vectorized ge(const Vectorized& other) const; +}; + +template <> +Vectorized inline operator+(const Vectorized& a, const Vectorized& b) { + return _mm256_add_pd(a, b); +} + +template <> +Vectorized inline operator-(const Vectorized& a, const Vectorized& b) { + return _mm256_sub_pd(a, b); +} + +template <> +Vectorized inline operator*(const Vectorized& a, const Vectorized& b) { + return _mm256_mul_pd(a, b); +} + +template <> +Vectorized inline operator/(const Vectorized& a, const Vectorized& b) { + return _mm256_div_pd(a, b); +} + +// frac. Implement this here so we can use subtraction. +inline Vectorized Vectorized::frac() const { + return *this - this->trunc(); +} + +// Implements the IEEE 754 201X `maximum` operation, which propagates NaN if +// either input is a NaN. +template <> +Vectorized inline maximum(const Vectorized& a, const Vectorized& b) { + Vectorized max = _mm256_max_pd(a, b); + Vectorized isnan = _mm256_cmp_pd(a, b, _CMP_UNORD_Q); + // Exploit the fact that all-ones is a NaN. + return _mm256_or_pd(max, isnan); +} + +// Implements the IEEE 754 201X `minimum` operation, which propagates NaN if +// either input is a NaN. +template <> +Vectorized inline minimum(const Vectorized& a, const Vectorized& b) { + Vectorized min = _mm256_min_pd(a, b); + Vectorized isnan = _mm256_cmp_pd(a, b, _CMP_UNORD_Q); + // Exploit the fact that all-ones is a NaN. + return _mm256_or_pd(min, isnan); +} + +template <> +Vectorized inline clamp(const Vectorized& a, const Vectorized& min, const Vectorized& max) { + return _mm256_min_pd(max, _mm256_max_pd(min, a)); +} + +template <> +Vectorized inline clamp_min(const Vectorized& a, const Vectorized& min) { + return _mm256_max_pd(min, a); +} + +template <> +Vectorized inline clamp_max(const Vectorized& a, const Vectorized& max) { + return _mm256_min_pd(max, a); +} + +template <> +Vectorized inline operator&(const Vectorized& a, const Vectorized& b) { + return _mm256_and_pd(a, b); +} + +template <> +Vectorized inline operator|(const Vectorized& a, const Vectorized& b) { + return _mm256_or_pd(a, b); +} + +template <> +Vectorized inline operator^(const Vectorized& a, const Vectorized& b) { + return _mm256_xor_pd(a, b); +} + +inline Vectorized Vectorized::eq(const Vectorized& other) const { + return (*this == other) & Vectorized(1.0); +} + +inline Vectorized Vectorized::ne(const Vectorized& other) const { + return (*this != other) & Vectorized(1.0); +} + +inline Vectorized Vectorized::gt(const Vectorized& other) const { + return (*this > other) & Vectorized(1.0); +} + +inline Vectorized Vectorized::ge(const Vectorized& other) const { + return (*this >= other) & Vectorized(1.0); +} + +inline Vectorized Vectorized::lt(const Vectorized& other) const { + return (*this < other) & Vectorized(1.0); +} + +inline Vectorized Vectorized::le(const Vectorized& other) const { + return (*this <= other) & Vectorized(1.0); +} + +template <> +inline void convert(const double* src, double* dst, int64_t n) { + int64_t i; +#pragma unroll + for (i = 0; i <= (n - Vectorized::size()); i += Vectorized::size()) { + _mm256_storeu_pd(dst + i, _mm256_loadu_pd(src + i)); + } +#pragma unroll + for (; i < n; i++) { + dst[i] = src[i]; + } +} + +#ifdef CPU_CAPABILITY_AVX2 +template <> +Vectorized inline fmadd(const Vectorized& a, const Vectorized& b, const Vectorized& c) { + return _mm256_fmadd_pd(a, b, c); +} + +template <> +Vectorized inline fmsub(const Vectorized& a, const Vectorized& b, const Vectorized& c) { + return _mm256_fmsub_pd(a, b, c); +} +#endif + +#endif + +}} // namespace at::vec::CPU_CAPABILITY diff --git a/shared/package-files/8b/8b7d767810c385f42c26a20463b1cb3b89a6ebe177830d31a6d6019fe8dc1986/blob b/shared/package-files/8b/8b7d767810c385f42c26a20463b1cb3b89a6ebe177830d31a6d6019fe8dc1986/blob new file mode 100644 index 0000000000000000000000000000000000000000..dd4283f7d77234cbd3dac815456981b22a4dad00 --- /dev/null +++ b/shared/package-files/8b/8b7d767810c385f42c26a20463b1cb3b89a6ebe177830d31a6d6019fe8dc1986/blob @@ -0,0 +1,15 @@ +#pragma once + +#include +#include +#include + +extern PyTypeObject THPSizeType; + +#define THPSize_Check(obj) (Py_TYPE(obj) == &THPSizeType) + +PyObject* THPSize_New(const torch::autograd::Variable& t); +PyObject* THPSize_NewFromSizes(int64_t dim, const int64_t* sizes); +PyObject* THPSize_NewFromSymSizes(const at::Tensor& t); + +void THPSize_init(PyObject* module); diff --git a/shared/package-files/8b/8b7e063d10197b15cc4fa6f0b9fe52132bdd992f9b442cbd28c8f03793baa639/blob b/shared/package-files/8b/8b7e063d10197b15cc4fa6f0b9fe52132bdd992f9b442cbd28c8f03793baa639/blob new file mode 100644 index 0000000000000000000000000000000000000000..b2f88d9d9c19a2cb5d03b0158c743c6b947a29ea --- /dev/null +++ b/shared/package-files/8b/8b7e063d10197b15cc4fa6f0b9fe52132bdd992f9b442cbd28c8f03793baa639/blob @@ -0,0 +1,1204 @@ +"""AST nodes generated by the parser for the compiler. Also provides +some node tree helper functions used by the parser and compiler in order +to normalize nodes. +""" +import inspect +import operator +import typing as t +from collections import deque + +from markupsafe import Markup + +from .utils import _PassArg + +if t.TYPE_CHECKING: + import typing_extensions as te + from .environment import Environment + +_NodeBound = t.TypeVar("_NodeBound", bound="Node") + +_binop_to_func: t.Dict[str, t.Callable[[t.Any, t.Any], t.Any]] = { + "*": operator.mul, + "/": operator.truediv, + "//": operator.floordiv, + "**": operator.pow, + "%": operator.mod, + "+": operator.add, + "-": operator.sub, +} + +_uaop_to_func: t.Dict[str, t.Callable[[t.Any], t.Any]] = { + "not": operator.not_, + "+": operator.pos, + "-": operator.neg, +} + +_cmpop_to_func: t.Dict[str, t.Callable[[t.Any, t.Any], t.Any]] = { + "eq": operator.eq, + "ne": operator.ne, + "gt": operator.gt, + "gteq": operator.ge, + "lt": operator.lt, + "lteq": operator.le, + "in": lambda a, b: a in b, + "notin": lambda a, b: a not in b, +} + + +class Impossible(Exception): + """Raised if the node could not perform a requested action.""" + + +class NodeType(type): + """A metaclass for nodes that handles the field and attribute + inheritance. fields and attributes from the parent class are + automatically forwarded to the child.""" + + def __new__(mcs, name, bases, d): # type: ignore + for attr in "fields", "attributes": + storage = [] + storage.extend(getattr(bases[0] if bases else object, attr, ())) + storage.extend(d.get(attr, ())) + assert len(bases) <= 1, "multiple inheritance not allowed" + assert len(storage) == len(set(storage)), "layout conflict" + d[attr] = tuple(storage) + d.setdefault("abstract", False) + return type.__new__(mcs, name, bases, d) + + +class EvalContext: + """Holds evaluation time information. Custom attributes can be attached + to it in extensions. + """ + + def __init__( + self, environment: "Environment", template_name: t.Optional[str] = None + ) -> None: + self.environment = environment + if callable(environment.autoescape): + self.autoescape = environment.autoescape(template_name) + else: + self.autoescape = environment.autoescape + self.volatile = False + + def save(self) -> t.Mapping[str, t.Any]: + return self.__dict__.copy() + + def revert(self, old: t.Mapping[str, t.Any]) -> None: + self.__dict__.clear() + self.__dict__.update(old) + + +def get_eval_context(node: "Node", ctx: t.Optional[EvalContext]) -> EvalContext: + if ctx is None: + if node.environment is None: + raise RuntimeError( + "if no eval context is passed, the node must have an" + " attached environment." + ) + return EvalContext(node.environment) + return ctx + + +class Node(metaclass=NodeType): + """Baseclass for all Jinja nodes. There are a number of nodes available + of different types. There are four major types: + + - :class:`Stmt`: statements + - :class:`Expr`: expressions + - :class:`Helper`: helper nodes + - :class:`Template`: the outermost wrapper node + + All nodes have fields and attributes. Fields may be other nodes, lists, + or arbitrary values. Fields are passed to the constructor as regular + positional arguments, attributes as keyword arguments. Each node has + two attributes: `lineno` (the line number of the node) and `environment`. + The `environment` attribute is set at the end of the parsing process for + all nodes automatically. + """ + + fields: t.Tuple[str, ...] = () + attributes: t.Tuple[str, ...] = ("lineno", "environment") + abstract = True + + lineno: int + environment: t.Optional["Environment"] + + def __init__(self, *fields: t.Any, **attributes: t.Any) -> None: + if self.abstract: + raise TypeError("abstract nodes are not instantiable") + if fields: + if len(fields) != len(self.fields): + if not self.fields: + raise TypeError(f"{type(self).__name__!r} takes 0 arguments") + raise TypeError( + f"{type(self).__name__!r} takes 0 or {len(self.fields)}" + f" argument{'s' if len(self.fields) != 1 else ''}" + ) + for name, arg in zip(self.fields, fields): + setattr(self, name, arg) + for attr in self.attributes: + setattr(self, attr, attributes.pop(attr, None)) + if attributes: + raise TypeError(f"unknown attribute {next(iter(attributes))!r}") + + def iter_fields( + self, + exclude: t.Optional[t.Container[str]] = None, + only: t.Optional[t.Container[str]] = None, + ) -> t.Iterator[t.Tuple[str, t.Any]]: + """This method iterates over all fields that are defined and yields + ``(key, value)`` tuples. Per default all fields are returned, but + it's possible to limit that to some fields by providing the `only` + parameter or to exclude some using the `exclude` parameter. Both + should be sets or tuples of field names. + """ + for name in self.fields: + if ( + (exclude is None and only is None) + or (exclude is not None and name not in exclude) + or (only is not None and name in only) + ): + try: + yield name, getattr(self, name) + except AttributeError: + pass + + def iter_child_nodes( + self, + exclude: t.Optional[t.Container[str]] = None, + only: t.Optional[t.Container[str]] = None, + ) -> t.Iterator["Node"]: + """Iterates over all direct child nodes of the node. This iterates + over all fields and yields the values of they are nodes. If the value + of a field is a list all the nodes in that list are returned. + """ + for _, item in self.iter_fields(exclude, only): + if isinstance(item, list): + for n in item: + if isinstance(n, Node): + yield n + elif isinstance(item, Node): + yield item + + def find(self, node_type: t.Type[_NodeBound]) -> t.Optional[_NodeBound]: + """Find the first node of a given type. If no such node exists the + return value is `None`. + """ + for result in self.find_all(node_type): + return result + + return None + + def find_all( + self, node_type: t.Union[t.Type[_NodeBound], t.Tuple[t.Type[_NodeBound], ...]] + ) -> t.Iterator[_NodeBound]: + """Find all the nodes of a given type. If the type is a tuple, + the check is performed for any of the tuple items. + """ + for child in self.iter_child_nodes(): + if isinstance(child, node_type): + yield child # type: ignore + yield from child.find_all(node_type) + + def set_ctx(self, ctx: str) -> "Node": + """Reset the context of a node and all child nodes. Per default the + parser will all generate nodes that have a 'load' context as it's the + most common one. This method is used in the parser to set assignment + targets and other nodes to a store context. + """ + todo = deque([self]) + while todo: + node = todo.popleft() + if "ctx" in node.fields: + node.ctx = ctx # type: ignore + todo.extend(node.iter_child_nodes()) + return self + + def set_lineno(self, lineno: int, override: bool = False) -> "Node": + """Set the line numbers of the node and children.""" + todo = deque([self]) + while todo: + node = todo.popleft() + if "lineno" in node.attributes: + if node.lineno is None or override: + node.lineno = lineno + todo.extend(node.iter_child_nodes()) + return self + + def set_environment(self, environment: "Environment") -> "Node": + """Set the environment for all nodes.""" + todo = deque([self]) + while todo: + node = todo.popleft() + node.environment = environment + todo.extend(node.iter_child_nodes()) + return self + + def __eq__(self, other: t.Any) -> bool: + if type(self) is not type(other): + return NotImplemented + + return tuple(self.iter_fields()) == tuple(other.iter_fields()) + + __hash__ = object.__hash__ + + def __repr__(self) -> str: + args_str = ", ".join(f"{a}={getattr(self, a, None)!r}" for a in self.fields) + return f"{type(self).__name__}({args_str})" + + def dump(self) -> str: + def _dump(node: t.Union[Node, t.Any]) -> None: + if not isinstance(node, Node): + buf.append(repr(node)) + return + + buf.append(f"nodes.{type(node).__name__}(") + if not node.fields: + buf.append(")") + return + for idx, field in enumerate(node.fields): + if idx: + buf.append(", ") + value = getattr(node, field) + if isinstance(value, list): + buf.append("[") + for idx, item in enumerate(value): + if idx: + buf.append(", ") + _dump(item) + buf.append("]") + else: + _dump(value) + buf.append(")") + + buf: t.List[str] = [] + _dump(self) + return "".join(buf) + + +class Stmt(Node): + """Base node for all statements.""" + + abstract = True + + +class Helper(Node): + """Nodes that exist in a specific context only.""" + + abstract = True + + +class Template(Node): + """Node that represents a template. This must be the outermost node that + is passed to the compiler. + """ + + fields = ("body",) + body: t.List[Node] + + +class Output(Stmt): + """A node that holds multiple expressions which are then printed out. + This is used both for the `print` statement and the regular template data. + """ + + fields = ("nodes",) + nodes: t.List["Expr"] + + +class Extends(Stmt): + """Represents an extends statement.""" + + fields = ("template",) + template: "Expr" + + +class For(Stmt): + """The for loop. `target` is the target for the iteration (usually a + :class:`Name` or :class:`Tuple`), `iter` the iterable. `body` is a list + of nodes that are used as loop-body, and `else_` a list of nodes for the + `else` block. If no else node exists it has to be an empty list. + + For filtered nodes an expression can be stored as `test`, otherwise `None`. + """ + + fields = ("target", "iter", "body", "else_", "test", "recursive") + target: Node + iter: Node + body: t.List[Node] + else_: t.List[Node] + test: t.Optional[Node] + recursive: bool + + +class If(Stmt): + """If `test` is true, `body` is rendered, else `else_`.""" + + fields = ("test", "body", "elif_", "else_") + test: Node + body: t.List[Node] + elif_: t.List["If"] + else_: t.List[Node] + + +class Macro(Stmt): + """A macro definition. `name` is the name of the macro, `args` a list of + arguments and `defaults` a list of defaults if there are any. `body` is + a list of nodes for the macro body. + """ + + fields = ("name", "args", "defaults", "body") + name: str + args: t.List["Name"] + defaults: t.List["Expr"] + body: t.List[Node] + + +class CallBlock(Stmt): + """Like a macro without a name but a call instead. `call` is called with + the unnamed macro as `caller` argument this node holds. + """ + + fields = ("call", "args", "defaults", "body") + call: "Call" + args: t.List["Name"] + defaults: t.List["Expr"] + body: t.List[Node] + + +class FilterBlock(Stmt): + """Node for filter sections.""" + + fields = ("body", "filter") + body: t.List[Node] + filter: "Filter" + + +class With(Stmt): + """Specific node for with statements. In older versions of Jinja the + with statement was implemented on the base of the `Scope` node instead. + + .. versionadded:: 2.9.3 + """ + + fields = ("targets", "values", "body") + targets: t.List["Expr"] + values: t.List["Expr"] + body: t.List[Node] + + +class Block(Stmt): + """A node that represents a block. + + .. versionchanged:: 3.0.0 + the `required` field was added. + """ + + fields = ("name", "body", "scoped", "required") + name: str + body: t.List[Node] + scoped: bool + required: bool + + +class Include(Stmt): + """A node that represents the include tag.""" + + fields = ("template", "with_context", "ignore_missing") + template: "Expr" + with_context: bool + ignore_missing: bool + + +class Import(Stmt): + """A node that represents the import tag.""" + + fields = ("template", "target", "with_context") + template: "Expr" + target: str + with_context: bool + + +class FromImport(Stmt): + """A node that represents the from import tag. It's important to not + pass unsafe names to the name attribute. The compiler translates the + attribute lookups directly into getattr calls and does *not* use the + subscript callback of the interface. As exported variables may not + start with double underscores (which the parser asserts) this is not a + problem for regular Jinja code, but if this node is used in an extension + extra care must be taken. + + The list of names may contain tuples if aliases are wanted. + """ + + fields = ("template", "names", "with_context") + template: "Expr" + names: t.List[t.Union[str, t.Tuple[str, str]]] + with_context: bool + + +class ExprStmt(Stmt): + """A statement that evaluates an expression and discards the result.""" + + fields = ("node",) + node: Node + + +class Assign(Stmt): + """Assigns an expression to a target.""" + + fields = ("target", "node") + target: "Expr" + node: Node + + +class AssignBlock(Stmt): + """Assigns a block to a target.""" + + fields = ("target", "filter", "body") + target: "Expr" + filter: t.Optional["Filter"] + body: t.List[Node] + + +class Expr(Node): + """Baseclass for all expressions.""" + + abstract = True + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any: + """Return the value of the expression as constant or raise + :exc:`Impossible` if this was not possible. + + An :class:`EvalContext` can be provided, if none is given + a default context is created which requires the nodes to have + an attached environment. + + .. versionchanged:: 2.4 + the `eval_ctx` parameter was added. + """ + raise Impossible() + + def can_assign(self) -> bool: + """Check if it's possible to assign something to this node.""" + return False + + +class BinExpr(Expr): + """Baseclass for all binary expressions.""" + + fields = ("left", "right") + left: Expr + right: Expr + operator: str + abstract = True + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any: + eval_ctx = get_eval_context(self, eval_ctx) + + # intercepted operators cannot be folded at compile time + if ( + eval_ctx.environment.sandboxed + and self.operator in eval_ctx.environment.intercepted_binops # type: ignore + ): + raise Impossible() + f = _binop_to_func[self.operator] + try: + return f(self.left.as_const(eval_ctx), self.right.as_const(eval_ctx)) + except Exception as e: + raise Impossible() from e + + +class UnaryExpr(Expr): + """Baseclass for all unary expressions.""" + + fields = ("node",) + node: Expr + operator: str + abstract = True + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any: + eval_ctx = get_eval_context(self, eval_ctx) + + # intercepted operators cannot be folded at compile time + if ( + eval_ctx.environment.sandboxed + and self.operator in eval_ctx.environment.intercepted_unops # type: ignore + ): + raise Impossible() + f = _uaop_to_func[self.operator] + try: + return f(self.node.as_const(eval_ctx)) + except Exception as e: + raise Impossible() from e + + +class Name(Expr): + """Looks up a name or stores a value in a name. + The `ctx` of the node can be one of the following values: + + - `store`: store a value in the name + - `load`: load that name + - `param`: like `store` but if the name was defined as function parameter. + """ + + fields = ("name", "ctx") + name: str + ctx: str + + def can_assign(self) -> bool: + return self.name not in {"true", "false", "none", "True", "False", "None"} + + +class NSRef(Expr): + """Reference to a namespace value assignment""" + + fields = ("name", "attr") + name: str + attr: str + + def can_assign(self) -> bool: + # We don't need any special checks here; NSRef assignments have a + # runtime check to ensure the target is a namespace object which will + # have been checked already as it is created using a normal assignment + # which goes through a `Name` node. + return True + + +class Literal(Expr): + """Baseclass for literals.""" + + abstract = True + + +class Const(Literal): + """All constant values. The parser will return this node for simple + constants such as ``42`` or ``"foo"`` but it can be used to store more + complex values such as lists too. Only constants with a safe + representation (objects where ``eval(repr(x)) == x`` is true). + """ + + fields = ("value",) + value: t.Any + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any: + return self.value + + @classmethod + def from_untrusted( + cls, + value: t.Any, + lineno: t.Optional[int] = None, + environment: "t.Optional[Environment]" = None, + ) -> "Const": + """Return a const object if the value is representable as + constant value in the generated code, otherwise it will raise + an `Impossible` exception. + """ + from .compiler import has_safe_repr + + if not has_safe_repr(value): + raise Impossible() + return cls(value, lineno=lineno, environment=environment) + + +class TemplateData(Literal): + """A constant template string.""" + + fields = ("data",) + data: str + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> str: + eval_ctx = get_eval_context(self, eval_ctx) + if eval_ctx.volatile: + raise Impossible() + if eval_ctx.autoescape: + return Markup(self.data) + return self.data + + +class Tuple(Literal): + """For loop unpacking and some other things like multiple arguments + for subscripts. Like for :class:`Name` `ctx` specifies if the tuple + is used for loading the names or storing. + """ + + fields = ("items", "ctx") + items: t.List[Expr] + ctx: str + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Tuple[t.Any, ...]: + eval_ctx = get_eval_context(self, eval_ctx) + return tuple(x.as_const(eval_ctx) for x in self.items) + + def can_assign(self) -> bool: + for item in self.items: + if not item.can_assign(): + return False + return True + + +class List(Literal): + """Any list literal such as ``[1, 2, 3]``""" + + fields = ("items",) + items: t.List[Expr] + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.List[t.Any]: + eval_ctx = get_eval_context(self, eval_ctx) + return [x.as_const(eval_ctx) for x in self.items] + + +class Dict(Literal): + """Any dict literal such as ``{1: 2, 3: 4}``. The items must be a list of + :class:`Pair` nodes. + """ + + fields = ("items",) + items: t.List["Pair"] + + def as_const( + self, eval_ctx: t.Optional[EvalContext] = None + ) -> t.Dict[t.Any, t.Any]: + eval_ctx = get_eval_context(self, eval_ctx) + return dict(x.as_const(eval_ctx) for x in self.items) + + +class Pair(Helper): + """A key, value pair for dicts.""" + + fields = ("key", "value") + key: Expr + value: Expr + + def as_const( + self, eval_ctx: t.Optional[EvalContext] = None + ) -> t.Tuple[t.Any, t.Any]: + eval_ctx = get_eval_context(self, eval_ctx) + return self.key.as_const(eval_ctx), self.value.as_const(eval_ctx) + + +class Keyword(Helper): + """A key, value pair for keyword arguments where key is a string.""" + + fields = ("key", "value") + key: str + value: Expr + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Tuple[str, t.Any]: + eval_ctx = get_eval_context(self, eval_ctx) + return self.key, self.value.as_const(eval_ctx) + + +class CondExpr(Expr): + """A conditional expression (inline if expression). (``{{ + foo if bar else baz }}``) + """ + + fields = ("test", "expr1", "expr2") + test: Expr + expr1: Expr + expr2: t.Optional[Expr] + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any: + eval_ctx = get_eval_context(self, eval_ctx) + if self.test.as_const(eval_ctx): + return self.expr1.as_const(eval_ctx) + + # if we evaluate to an undefined object, we better do that at runtime + if self.expr2 is None: + raise Impossible() + + return self.expr2.as_const(eval_ctx) + + +def args_as_const( + node: t.Union["_FilterTestCommon", "Call"], eval_ctx: t.Optional[EvalContext] +) -> t.Tuple[t.List[t.Any], t.Dict[t.Any, t.Any]]: + args = [x.as_const(eval_ctx) for x in node.args] + kwargs = dict(x.as_const(eval_ctx) for x in node.kwargs) + + if node.dyn_args is not None: + try: + args.extend(node.dyn_args.as_const(eval_ctx)) + except Exception as e: + raise Impossible() from e + + if node.dyn_kwargs is not None: + try: + kwargs.update(node.dyn_kwargs.as_const(eval_ctx)) + except Exception as e: + raise Impossible() from e + + return args, kwargs + + +class _FilterTestCommon(Expr): + fields = ("node", "name", "args", "kwargs", "dyn_args", "dyn_kwargs") + node: Expr + name: str + args: t.List[Expr] + kwargs: t.List[Pair] + dyn_args: t.Optional[Expr] + dyn_kwargs: t.Optional[Expr] + abstract = True + _is_filter = True + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any: + eval_ctx = get_eval_context(self, eval_ctx) + + if eval_ctx.volatile: + raise Impossible() + + if self._is_filter: + env_map = eval_ctx.environment.filters + else: + env_map = eval_ctx.environment.tests + + func = env_map.get(self.name) + pass_arg = _PassArg.from_obj(func) # type: ignore + + if func is None or pass_arg is _PassArg.context: + raise Impossible() + + if eval_ctx.environment.is_async and ( + getattr(func, "jinja_async_variant", False) is True + or inspect.iscoroutinefunction(func) + ): + raise Impossible() + + args, kwargs = args_as_const(self, eval_ctx) + args.insert(0, self.node.as_const(eval_ctx)) + + if pass_arg is _PassArg.eval_context: + args.insert(0, eval_ctx) + elif pass_arg is _PassArg.environment: + args.insert(0, eval_ctx.environment) + + try: + return func(*args, **kwargs) + except Exception as e: + raise Impossible() from e + + +class Filter(_FilterTestCommon): + """Apply a filter to an expression. ``name`` is the name of the + filter, the other fields are the same as :class:`Call`. + + If ``node`` is ``None``, the filter is being used in a filter block + and is applied to the content of the block. + """ + + node: t.Optional[Expr] # type: ignore + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any: + if self.node is None: + raise Impossible() + + return super().as_const(eval_ctx=eval_ctx) + + +class Test(_FilterTestCommon): + """Apply a test to an expression. ``name`` is the name of the test, + the other field are the same as :class:`Call`. + + .. versionchanged:: 3.0 + ``as_const`` shares the same logic for filters and tests. Tests + check for volatile, async, and ``@pass_context`` etc. + decorators. + """ + + _is_filter = False + + +class Call(Expr): + """Calls an expression. `args` is a list of arguments, `kwargs` a list + of keyword arguments (list of :class:`Keyword` nodes), and `dyn_args` + and `dyn_kwargs` has to be either `None` or a node that is used as + node for dynamic positional (``*args``) or keyword (``**kwargs``) + arguments. + """ + + fields = ("node", "args", "kwargs", "dyn_args", "dyn_kwargs") + node: Expr + args: t.List[Expr] + kwargs: t.List[Keyword] + dyn_args: t.Optional[Expr] + dyn_kwargs: t.Optional[Expr] + + +class Getitem(Expr): + """Get an attribute or item from an expression and prefer the item.""" + + fields = ("node", "arg", "ctx") + node: Expr + arg: Expr + ctx: str + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any: + if self.ctx != "load": + raise Impossible() + + eval_ctx = get_eval_context(self, eval_ctx) + + try: + return eval_ctx.environment.getitem( + self.node.as_const(eval_ctx), self.arg.as_const(eval_ctx) + ) + except Exception as e: + raise Impossible() from e + + +class Getattr(Expr): + """Get an attribute or item from an expression that is a ascii-only + bytestring and prefer the attribute. + """ + + fields = ("node", "attr", "ctx") + node: Expr + attr: str + ctx: str + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any: + if self.ctx != "load": + raise Impossible() + + eval_ctx = get_eval_context(self, eval_ctx) + + try: + return eval_ctx.environment.getattr(self.node.as_const(eval_ctx), self.attr) + except Exception as e: + raise Impossible() from e + + +class Slice(Expr): + """Represents a slice object. This must only be used as argument for + :class:`Subscript`. + """ + + fields = ("start", "stop", "step") + start: t.Optional[Expr] + stop: t.Optional[Expr] + step: t.Optional[Expr] + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> slice: + eval_ctx = get_eval_context(self, eval_ctx) + + def const(obj: t.Optional[Expr]) -> t.Optional[t.Any]: + if obj is None: + return None + return obj.as_const(eval_ctx) + + return slice(const(self.start), const(self.stop), const(self.step)) + + +class Concat(Expr): + """Concatenates the list of expressions provided after converting + them to strings. + """ + + fields = ("nodes",) + nodes: t.List[Expr] + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> str: + eval_ctx = get_eval_context(self, eval_ctx) + return "".join(str(x.as_const(eval_ctx)) for x in self.nodes) + + +class Compare(Expr): + """Compares an expression with some other expressions. `ops` must be a + list of :class:`Operand`\\s. + """ + + fields = ("expr", "ops") + expr: Expr + ops: t.List["Operand"] + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any: + eval_ctx = get_eval_context(self, eval_ctx) + result = value = self.expr.as_const(eval_ctx) + + try: + for op in self.ops: + new_value = op.expr.as_const(eval_ctx) + result = _cmpop_to_func[op.op](value, new_value) + + if not result: + return False + + value = new_value + except Exception as e: + raise Impossible() from e + + return result + + +class Operand(Helper): + """Holds an operator and an expression.""" + + fields = ("op", "expr") + op: str + expr: Expr + + +class Mul(BinExpr): + """Multiplies the left with the right node.""" + + operator = "*" + + +class Div(BinExpr): + """Divides the left by the right node.""" + + operator = "/" + + +class FloorDiv(BinExpr): + """Divides the left by the right node and converts the + result into an integer by truncating. + """ + + operator = "//" + + +class Add(BinExpr): + """Add the left to the right node.""" + + operator = "+" + + +class Sub(BinExpr): + """Subtract the right from the left node.""" + + operator = "-" + + +class Mod(BinExpr): + """Left modulo right.""" + + operator = "%" + + +class Pow(BinExpr): + """Left to the power of right.""" + + operator = "**" + + +class And(BinExpr): + """Short circuited AND.""" + + operator = "and" + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any: + eval_ctx = get_eval_context(self, eval_ctx) + return self.left.as_const(eval_ctx) and self.right.as_const(eval_ctx) + + +class Or(BinExpr): + """Short circuited OR.""" + + operator = "or" + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> t.Any: + eval_ctx = get_eval_context(self, eval_ctx) + return self.left.as_const(eval_ctx) or self.right.as_const(eval_ctx) + + +class Not(UnaryExpr): + """Negate the expression.""" + + operator = "not" + + +class Neg(UnaryExpr): + """Make the expression negative.""" + + operator = "-" + + +class Pos(UnaryExpr): + """Make the expression positive (noop for most expressions)""" + + operator = "+" + + +# Helpers for extensions + + +class EnvironmentAttribute(Expr): + """Loads an attribute from the environment object. This is useful for + extensions that want to call a callback stored on the environment. + """ + + fields = ("name",) + name: str + + +class ExtensionAttribute(Expr): + """Returns the attribute of an extension bound to the environment. + The identifier is the identifier of the :class:`Extension`. + + This node is usually constructed by calling the + :meth:`~jinja2.ext.Extension.attr` method on an extension. + """ + + fields = ("identifier", "name") + identifier: str + name: str + + +class ImportedName(Expr): + """If created with an import name the import name is returned on node + access. For example ``ImportedName('cgi.escape')`` returns the `escape` + function from the cgi module on evaluation. Imports are optimized by the + compiler so there is no need to assign them to local variables. + """ + + fields = ("importname",) + importname: str + + +class InternalName(Expr): + """An internal name in the compiler. You cannot create these nodes + yourself but the parser provides a + :meth:`~jinja2.parser.Parser.free_identifier` method that creates + a new identifier for you. This identifier is not available from the + template and is not treated specially by the compiler. + """ + + fields = ("name",) + name: str + + def __init__(self) -> None: + raise TypeError( + "Can't create internal names. Use the " + "`free_identifier` method on a parser." + ) + + +class MarkSafe(Expr): + """Mark the wrapped expression as safe (wrap it as `Markup`).""" + + fields = ("expr",) + expr: Expr + + def as_const(self, eval_ctx: t.Optional[EvalContext] = None) -> Markup: + eval_ctx = get_eval_context(self, eval_ctx) + return Markup(self.expr.as_const(eval_ctx)) + + +class MarkSafeIfAutoescape(Expr): + """Mark the wrapped expression as safe (wrap it as `Markup`) but + only if autoescaping is active. + + .. versionadded:: 2.5 + """ + + fields = ("expr",) + expr: Expr + + def as_const( + self, eval_ctx: t.Optional[EvalContext] = None + ) -> t.Union[Markup, t.Any]: + eval_ctx = get_eval_context(self, eval_ctx) + if eval_ctx.volatile: + raise Impossible() + expr = self.expr.as_const(eval_ctx) + if eval_ctx.autoescape: + return Markup(expr) + return expr + + +class ContextReference(Expr): + """Returns the current template context. It can be used like a + :class:`Name` node, with a ``'load'`` ctx and will return the + current :class:`~jinja2.runtime.Context` object. + + Here an example that assigns the current template name to a + variable named `foo`:: + + Assign(Name('foo', ctx='store'), + Getattr(ContextReference(), 'name')) + + This is basically equivalent to using the + :func:`~jinja2.pass_context` decorator when using the high-level + API, which causes a reference to the context to be passed as the + first argument to a function. + """ + + +class DerivedContextReference(Expr): + """Return the current template context including locals. Behaves + exactly like :class:`ContextReference`, but includes local + variables, such as from a ``for`` loop. + + .. versionadded:: 2.11 + """ + + +class Continue(Stmt): + """Continue a loop.""" + + +class Break(Stmt): + """Break a loop.""" + + +class Scope(Stmt): + """An artificial scope.""" + + fields = ("body",) + body: t.List[Node] + + +class OverlayScope(Stmt): + """An overlay scope for extensions. This is a largely unoptimized scope + that however can be used to introduce completely arbitrary variables into + a sub scope from a dictionary or dictionary like object. The `context` + field has to evaluate to a dictionary object. + + Example usage:: + + OverlayScope(context=self.call_method('get_context'), + body=[...]) + + .. versionadded:: 2.10 + """ + + fields = ("context", "body") + context: Expr + body: t.List[Node] + + +class EvalContextModifier(Stmt): + """Modifies the eval context. For each option that should be modified, + a :class:`Keyword` has to be added to the :attr:`options` list. + + Example to change the `autoescape` setting:: + + EvalContextModifier(options=[Keyword('autoescape', Const(True))]) + """ + + fields = ("options",) + options: t.List[Keyword] + + +class ScopedEvalContextModifier(EvalContextModifier): + """Modifies the eval context and reverts it later. Works exactly like + :class:`EvalContextModifier` but will only modify the + :class:`~jinja2.nodes.EvalContext` for nodes in the :attr:`body`. + """ + + fields = ("body",) + body: t.List[Node] + + +# make sure nobody creates custom nodes +def _failing_new(*args: t.Any, **kwargs: t.Any) -> "te.NoReturn": + raise TypeError("can't create custom node types") + + +NodeType.__new__ = staticmethod(_failing_new) # type: ignore +del _failing_new diff --git a/shared/package-files/8b/8b7eb84a05022d26118d0b32ca0bd8d40ae311a3b9b8b8a9dc7c94f91ef368ba/blob b/shared/package-files/8b/8b7eb84a05022d26118d0b32ca0bd8d40ae311a3b9b8b8a9dc7c94f91ef368ba/blob new file mode 100644 index 0000000000000000000000000000000000000000..b4dbd152d6e9f3d289b3c4a9792729d2735a4c5c Binary files /dev/null and b/shared/package-files/8b/8b7eb84a05022d26118d0b32ca0bd8d40ae311a3b9b8b8a9dc7c94f91ef368ba/blob differ diff --git a/shared/package-files/8b/8b813bd8c270347eb5ec038972ee3150da92249c11df6840e915e92c1926855b/blob b/shared/package-files/8b/8b813bd8c270347eb5ec038972ee3150da92249c11df6840e915e92c1926855b/blob new file mode 100644 index 0000000000000000000000000000000000000000..7504ffc7a966306c574a6fd629595cacfe293b7b --- /dev/null +++ b/shared/package-files/8b/8b813bd8c270347eb5ec038972ee3150da92249c11df6840e915e92c1926855b/blob @@ -0,0 +1,25 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API at::Tensor special_i1(const at::Tensor & self); +TORCH_API at::Tensor & special_i1_out(at::Tensor & out, const at::Tensor & self); +TORCH_API at::Tensor & special_i1_outf(const at::Tensor & self, at::Tensor & out); + +} // namespace cpu +} // namespace at diff --git a/shared/package-files/8b/8b82530cc91901d5591a65b20eadef3769e711739c5a4e18e8c00302ea3824c3/blob b/shared/package-files/8b/8b82530cc91901d5591a65b20eadef3769e711739c5a4e18e8c00302ea3824c3/blob new file mode 100644 index 0000000000000000000000000000000000000000..2f242b7e481bf1419d5072214c6cb43f9619a457 --- /dev/null +++ b/shared/package-files/8b/8b82530cc91901d5591a65b20eadef3769e711739c5a4e18e8c00302ea3824c3/blob @@ -0,0 +1,136384 @@ +{ + "schema": "natural-disease-progression-report/v3", + "reward_metric": "panel_recall_at_5pct", + "panel": { + "panel_recall_at_5pct": 0.2685180216584669, + "panel_recall_at_1pct": 0.0848898399325807, + "panel_ppv_at_5pct": 0.0819079552178582, + "panel_auroc": 0.7959592528738831, + "panel_auprc": 0.09100345013984726, + "panel_calibration_slope_median": 0.8146584621130281, + 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a/shared/package-files/8b/8b8318b8974f0efa80f9a0f97da51096db1cbca83be1effcb840b479e6cafcf0/blob b/shared/package-files/8b/8b8318b8974f0efa80f9a0f97da51096db1cbca83be1effcb840b479e6cafcf0/blob new file mode 100644 index 0000000000000000000000000000000000000000..c9202bb1eefbb36373a2c805036687926bc97dec --- /dev/null +++ b/shared/package-files/8b/8b8318b8974f0efa80f9a0f97da51096db1cbca83be1effcb840b479e6cafcf0/blob @@ -0,0 +1,61 @@ +from torch.utils.data.datapipes.utils.common import _check_unpickable_fn +from typing import Callable, TypeVar +from torch.utils.data.datapipes._decorator import functional_datapipe +from torch.utils.data.datapipes.datapipe import MapDataPipe + +__all__ = ["MapperMapDataPipe", "default_fn"] + +T_co = TypeVar('T_co', covariant=True) + + +# Default function to return each item directly +# In order to keep datapipe picklable, eliminates the usage +# of python lambda function +def default_fn(data): + return data + + +@functional_datapipe('map') +class MapperMapDataPipe(MapDataPipe[T_co]): + r""" + Apply the input function over each item from the source DataPipe (functional name: ``map``). + + The function can be any regular Python function or partial object. Lambda + function is not recommended as it is not supported by pickle. + + Args: + datapipe: Source MapDataPipe + fn: Function being applied to each item + + Example: + >>> # xdoctest: +SKIP + >>> from torchdata.datapipes.map import SequenceWrapper, Mapper + >>> def add_one(x): + ... return x + 1 + >>> dp = SequenceWrapper(range(10)) + >>> map_dp_1 = dp.map(add_one) + >>> list(map_dp_1) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + >>> map_dp_2 = Mapper(dp, lambda x: x + 1) + >>> list(map_dp_2) + [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + """ + + datapipe: MapDataPipe + fn: Callable + + def __init__( + self, + datapipe: MapDataPipe, + fn: Callable = default_fn, + ) -> None: + super().__init__() + self.datapipe = datapipe + _check_unpickable_fn(fn) + self.fn = fn # type: ignore[assignment] + + def __len__(self) -> int: + return len(self.datapipe) + + def __getitem__(self, index) -> T_co: + return self.fn(self.datapipe[index]) diff --git a/shared/package-files/8b/8b85846791ab2c8a5463c83a5be3c043e2570d7448434d41398969ed47e3e6f2/blob b/shared/package-files/8b/8b85846791ab2c8a5463c83a5be3c043e2570d7448434d41398969ed47e3e6f2/blob new file mode 100644 index 0000000000000000000000000000000000000000..91558be0c2bf903b2364215ba26d5227d6126508 Binary files /dev/null and b/shared/package-files/8b/8b85846791ab2c8a5463c83a5be3c043e2570d7448434d41398969ed47e3e6f2/blob differ diff --git a/shared/package-files/8b/8b8d83314c282428810d5111dc76fad3b4f773ab535f425c1cd951483f735c50/blob b/shared/package-files/8b/8b8d83314c282428810d5111dc76fad3b4f773ab535f425c1cd951483f735c50/blob new file mode 100644 index 0000000000000000000000000000000000000000..3d6cece9676c93d84482331bdb0ecebd4bdb9534 --- /dev/null +++ b/shared/package-files/8b/8b8d83314c282428810d5111dc76fad3b4f773ab535f425c1cd951483f735c50/blob @@ -0,0 +1,6410 @@ +# Copyright (c) 2004 Python Software Foundation. +# All rights reserved. + +# Written by Eric Price +# and Facundo Batista +# and Raymond Hettinger +# and Aahz +# and Tim Peters + +# This module should be kept in sync with the latest updates of the +# IBM specification as it evolves. Those updates will be treated +# as bug fixes (deviation from the spec is a compatibility, usability +# bug) and will be backported. At this point the spec is stabilizing +# and the updates are becoming fewer, smaller, and less significant. + +""" +This is an implementation of decimal floating point arithmetic based on +the General Decimal Arithmetic Specification: + + http://speleotrove.com/decimal/decarith.html + +and IEEE standard 854-1987: + + http://en.wikipedia.org/wiki/IEEE_854-1987 + +Decimal floating point has finite precision with arbitrarily large bounds. + +The purpose of this module is to support arithmetic using familiar +"schoolhouse" rules and to avoid some of the tricky representation +issues associated with binary floating point. The package is especially +useful for financial applications or for contexts where users have +expectations that are at odds with binary floating point (for instance, +in binary floating point, 1.00 % 0.1 gives 0.09999999999999995 instead +of 0.0; Decimal('1.00') % Decimal('0.1') returns the expected +Decimal('0.00')). + +Here are some examples of using the decimal module: + +>>> from decimal import * +>>> setcontext(ExtendedContext) +>>> Decimal(0) +Decimal('0') +>>> Decimal('1') +Decimal('1') +>>> Decimal('-.0123') +Decimal('-0.0123') +>>> Decimal(123456) +Decimal('123456') +>>> Decimal('123.45e12345678') +Decimal('1.2345E+12345680') +>>> Decimal('1.33') + Decimal('1.27') +Decimal('2.60') +>>> Decimal('12.34') + Decimal('3.87') - Decimal('18.41') +Decimal('-2.20') +>>> dig = Decimal(1) +>>> print(dig / Decimal(3)) +0.333333333 +>>> getcontext().prec = 18 +>>> print(dig / Decimal(3)) +0.333333333333333333 +>>> print(dig.sqrt()) +1 +>>> print(Decimal(3).sqrt()) +1.73205080756887729 +>>> print(Decimal(3) ** 123) +4.85192780976896427E+58 +>>> inf = Decimal(1) / Decimal(0) +>>> print(inf) +Infinity +>>> neginf = Decimal(-1) / Decimal(0) +>>> print(neginf) +-Infinity +>>> print(neginf + inf) +NaN +>>> print(neginf * inf) +-Infinity +>>> print(dig / 0) +Infinity +>>> getcontext().traps[DivisionByZero] = 1 +>>> print(dig / 0) +Traceback (most recent call last): + ... + ... + ... +decimal.DivisionByZero: x / 0 +>>> c = Context() +>>> c.traps[InvalidOperation] = 0 +>>> print(c.flags[InvalidOperation]) +0 +>>> c.divide(Decimal(0), Decimal(0)) +Decimal('NaN') +>>> c.traps[InvalidOperation] = 1 +>>> print(c.flags[InvalidOperation]) +1 +>>> c.flags[InvalidOperation] = 0 +>>> print(c.flags[InvalidOperation]) +0 +>>> print(c.divide(Decimal(0), Decimal(0))) +Traceback (most recent call last): + ... + ... + ... +decimal.InvalidOperation: 0 / 0 +>>> print(c.flags[InvalidOperation]) +1 +>>> c.flags[InvalidOperation] = 0 +>>> c.traps[InvalidOperation] = 0 +>>> print(c.divide(Decimal(0), Decimal(0))) +NaN +>>> print(c.flags[InvalidOperation]) +1 +>>> +""" + +__all__ = [ + # Two major classes + 'Decimal', 'Context', + + # Named tuple representation + 'DecimalTuple', + + # Contexts + 'DefaultContext', 'BasicContext', 'ExtendedContext', + + # Exceptions + 'DecimalException', 'Clamped', 'InvalidOperation', 'DivisionByZero', + 'Inexact', 'Rounded', 'Subnormal', 'Overflow', 'Underflow', + 'FloatOperation', + + # Exceptional conditions that trigger InvalidOperation + 'DivisionImpossible', 'InvalidContext', 'ConversionSyntax', 'DivisionUndefined', + + # Constants for use in setting up contexts + 'ROUND_DOWN', 'ROUND_HALF_UP', 'ROUND_HALF_EVEN', 'ROUND_CEILING', + 'ROUND_FLOOR', 'ROUND_UP', 'ROUND_HALF_DOWN', 'ROUND_05UP', + + # Functions for manipulating contexts + 'setcontext', 'getcontext', 'localcontext', + + # Limits for the C version for compatibility + 'MAX_PREC', 'MAX_EMAX', 'MIN_EMIN', 'MIN_ETINY', + + # C version: compile time choice that enables the thread local context (deprecated, now always true) + 'HAVE_THREADS', + + # C version: compile time choice that enables the coroutine local context + 'HAVE_CONTEXTVAR' +] + +__xname__ = __name__ # sys.modules lookup (--without-threads) +__name__ = 'decimal' # For pickling +__version__ = '1.70' # Highest version of the spec this complies with + # See http://speleotrove.com/decimal/ +__libmpdec_version__ = "2.4.2" # compatible libmpdec version + +import math as _math +import numbers as _numbers +import sys + +try: + from collections import namedtuple as _namedtuple + DecimalTuple = _namedtuple('DecimalTuple', 'sign digits exponent') +except ImportError: + DecimalTuple = lambda *args: args + +# Rounding +ROUND_DOWN = 'ROUND_DOWN' +ROUND_HALF_UP = 'ROUND_HALF_UP' +ROUND_HALF_EVEN = 'ROUND_HALF_EVEN' +ROUND_CEILING = 'ROUND_CEILING' +ROUND_FLOOR = 'ROUND_FLOOR' +ROUND_UP = 'ROUND_UP' +ROUND_HALF_DOWN = 'ROUND_HALF_DOWN' +ROUND_05UP = 'ROUND_05UP' + +# Compatibility with the C version +HAVE_THREADS = True +HAVE_CONTEXTVAR = True +if sys.maxsize == 2**63-1: + MAX_PREC = 999999999999999999 + MAX_EMAX = 999999999999999999 + MIN_EMIN = -999999999999999999 +else: + MAX_PREC = 425000000 + MAX_EMAX = 425000000 + MIN_EMIN = -425000000 + +MIN_ETINY = MIN_EMIN - (MAX_PREC-1) + +# Errors + +class DecimalException(ArithmeticError): + """Base exception class. + + Used exceptions derive from this. + If an exception derives from another exception besides this (such as + Underflow (Inexact, Rounded, Subnormal) that indicates that it is only + called if the others are present. This isn't actually used for + anything, though. + + handle -- Called when context._raise_error is called and the + trap_enabler is not set. First argument is self, second is the + context. More arguments can be given, those being after + the explanation in _raise_error (For example, + context._raise_error(NewError, '(-x)!', self._sign) would + call NewError().handle(context, self._sign).) + + To define a new exception, it should be sufficient to have it derive + from DecimalException. + """ + def handle(self, context, *args): + pass + + +class Clamped(DecimalException): + """Exponent of a 0 changed to fit bounds. + + This occurs and signals clamped if the exponent of a result has been + altered in order to fit the constraints of a specific concrete + representation. This may occur when the exponent of a zero result would + be outside the bounds of a representation, or when a large normal + number would have an encoded exponent that cannot be represented. In + this latter case, the exponent is reduced to fit and the corresponding + number of zero digits are appended to the coefficient ("fold-down"). + """ + +class InvalidOperation(DecimalException): + """An invalid operation was performed. + + Various bad things cause this: + + Something creates a signaling NaN + -INF + INF + 0 * (+-)INF + (+-)INF / (+-)INF + x % 0 + (+-)INF % x + x._rescale( non-integer ) + sqrt(-x) , x > 0 + 0 ** 0 + x ** (non-integer) + x ** (+-)INF + An operand is invalid + + The result of the operation after these is a quiet positive NaN, + except when the cause is a signaling NaN, in which case the result is + also a quiet NaN, but with the original sign, and an optional + diagnostic information. + """ + def handle(self, context, *args): + if args: + ans = _dec_from_triple(args[0]._sign, args[0]._int, 'n', True) + return ans._fix_nan(context) + return _NaN + +class ConversionSyntax(InvalidOperation): + """Trying to convert badly formed string. + + This occurs and signals invalid-operation if a string is being + converted to a number and it does not conform to the numeric string + syntax. The result is [0,qNaN]. + """ + def handle(self, context, *args): + return _NaN + +class DivisionByZero(DecimalException, ZeroDivisionError): + """Division by 0. + + This occurs and signals division-by-zero if division of a finite number + by zero was attempted (during a divide-integer or divide operation, or a + power operation with negative right-hand operand), and the dividend was + not zero. + + The result of the operation is [sign,inf], where sign is the exclusive + or of the signs of the operands for divide, or is 1 for an odd power of + -0, for power. + """ + + def handle(self, context, sign, *args): + return _SignedInfinity[sign] + +class DivisionImpossible(InvalidOperation): + """Cannot perform the division adequately. + + This occurs and signals invalid-operation if the integer result of a + divide-integer or remainder operation had too many digits (would be + longer than precision). The result is [0,qNaN]. + """ + + def handle(self, context, *args): + return _NaN + +class DivisionUndefined(InvalidOperation, ZeroDivisionError): + """Undefined result of division. + + This occurs and signals invalid-operation if division by zero was + attempted (during a divide-integer, divide, or remainder operation), and + the dividend is also zero. The result is [0,qNaN]. + """ + + def handle(self, context, *args): + return _NaN + +class Inexact(DecimalException): + """Had to round, losing information. + + This occurs and signals inexact whenever the result of an operation is + not exact (that is, it needed to be rounded and any discarded digits + were non-zero), or if an overflow or underflow condition occurs. The + result in all cases is unchanged. + + The inexact signal may be tested (or trapped) to determine if a given + operation (or sequence of operations) was inexact. + """ + +class InvalidContext(InvalidOperation): + """Invalid context. Unknown rounding, for example. + + This occurs and signals invalid-operation if an invalid context was + detected during an operation. This can occur if contexts are not checked + on creation and either the precision exceeds the capability of the + underlying concrete representation or an unknown or unsupported rounding + was specified. These aspects of the context need only be checked when + the values are required to be used. The result is [0,qNaN]. + """ + + def handle(self, context, *args): + return _NaN + +class Rounded(DecimalException): + """Number got rounded (not necessarily changed during rounding). + + This occurs and signals rounded whenever the result of an operation is + rounded (that is, some zero or non-zero digits were discarded from the + coefficient), or if an overflow or underflow condition occurs. The + result in all cases is unchanged. + + The rounded signal may be tested (or trapped) to determine if a given + operation (or sequence of operations) caused a loss of precision. + """ + +class Subnormal(DecimalException): + """Exponent < Emin before rounding. + + This occurs and signals subnormal whenever the result of a conversion or + operation is subnormal (that is, its adjusted exponent is less than + Emin, before any rounding). The result in all cases is unchanged. + + The subnormal signal may be tested (or trapped) to determine if a given + or operation (or sequence of operations) yielded a subnormal result. + """ + +class Overflow(Inexact, Rounded): + """Numerical overflow. + + This occurs and signals overflow if the adjusted exponent of a result + (from a conversion or from an operation that is not an attempt to divide + by zero), after rounding, would be greater than the largest value that + can be handled by the implementation (the value Emax). + + The result depends on the rounding mode: + + For round-half-up and round-half-even (and for round-half-down and + round-up, if implemented), the result of the operation is [sign,inf], + where sign is the sign of the intermediate result. For round-down, the + result is the largest finite number that can be represented in the + current precision, with the sign of the intermediate result. For + round-ceiling, the result is the same as for round-down if the sign of + the intermediate result is 1, or is [0,inf] otherwise. For round-floor, + the result is the same as for round-down if the sign of the intermediate + result is 0, or is [1,inf] otherwise. In all cases, Inexact and Rounded + will also be raised. + """ + + def handle(self, context, sign, *args): + if context.rounding in (ROUND_HALF_UP, ROUND_HALF_EVEN, + ROUND_HALF_DOWN, ROUND_UP): + return _SignedInfinity[sign] + if sign == 0: + if context.rounding == ROUND_CEILING: + return _SignedInfinity[sign] + return _dec_from_triple(sign, '9'*context.prec, + context.Emax-context.prec+1) + if sign == 1: + if context.rounding == ROUND_FLOOR: + return _SignedInfinity[sign] + return _dec_from_triple(sign, '9'*context.prec, + context.Emax-context.prec+1) + + +class Underflow(Inexact, Rounded, Subnormal): + """Numerical underflow with result rounded to 0. + + This occurs and signals underflow if a result is inexact and the + adjusted exponent of the result would be smaller (more negative) than + the smallest value that can be handled by the implementation (the value + Emin). That is, the result is both inexact and subnormal. + + The result after an underflow will be a subnormal number rounded, if + necessary, so that its exponent is not less than Etiny. This may result + in 0 with the sign of the intermediate result and an exponent of Etiny. + + In all cases, Inexact, Rounded, and Subnormal will also be raised. + """ + +class FloatOperation(DecimalException, TypeError): + """Enable stricter semantics for mixing floats and Decimals. + + If the signal is not trapped (default), mixing floats and Decimals is + permitted in the Decimal() constructor, context.create_decimal() and + all comparison operators. Both conversion and comparisons are exact. + Any occurrence of a mixed operation is silently recorded by setting + FloatOperation in the context flags. Explicit conversions with + Decimal.from_float() or context.create_decimal_from_float() do not + set the flag. + + Otherwise (the signal is trapped), only equality comparisons and explicit + conversions are silent. All other mixed operations raise FloatOperation. + """ + +# List of public traps and flags +_signals = [Clamped, DivisionByZero, Inexact, Overflow, Rounded, + Underflow, InvalidOperation, Subnormal, FloatOperation] + +# Map conditions (per the spec) to signals +_condition_map = {ConversionSyntax:InvalidOperation, + DivisionImpossible:InvalidOperation, + DivisionUndefined:InvalidOperation, + InvalidContext:InvalidOperation} + +# Valid rounding modes +_rounding_modes = (ROUND_DOWN, ROUND_HALF_UP, ROUND_HALF_EVEN, ROUND_CEILING, + ROUND_FLOOR, ROUND_UP, ROUND_HALF_DOWN, ROUND_05UP) + +##### Context Functions ################################################## + +# The getcontext() and setcontext() function manage access to a thread-local +# current context. + +import contextvars + +_current_context_var = contextvars.ContextVar('decimal_context') + +def getcontext(): + """Returns this thread's context. + + If this thread does not yet have a context, returns + a new context and sets this thread's context. + New contexts are copies of DefaultContext. + """ + try: + return _current_context_var.get() + except LookupError: + context = Context() + _current_context_var.set(context) + return context + +def setcontext(context): + """Set this thread's context to context.""" + if context in (DefaultContext, BasicContext, ExtendedContext): + context = context.copy() + context.clear_flags() + _current_context_var.set(context) + +del contextvars # Don't contaminate the namespace + +def localcontext(ctx=None): + """Return a context manager for a copy of the supplied context + + Uses a copy of the current context if no context is specified + The returned context manager creates a local decimal context + in a with statement: + def sin(x): + with localcontext() as ctx: + ctx.prec += 2 + # Rest of sin calculation algorithm + # uses a precision 2 greater than normal + return +s # Convert result to normal precision + + def sin(x): + with localcontext(ExtendedContext): + # Rest of sin calculation algorithm + # uses the Extended Context from the + # General Decimal Arithmetic Specification + return +s # Convert result to normal context + + >>> setcontext(DefaultContext) + >>> print(getcontext().prec) + 28 + >>> with localcontext(): + ... ctx = getcontext() + ... ctx.prec += 2 + ... print(ctx.prec) + ... + 30 + >>> with localcontext(ExtendedContext): + ... print(getcontext().prec) + ... + 9 + >>> print(getcontext().prec) + 28 + """ + if ctx is None: ctx = getcontext() + return _ContextManager(ctx) + + +##### Decimal class ####################################################### + +# Do not subclass Decimal from numbers.Real and do not register it as such +# (because Decimals are not interoperable with floats). See the notes in +# numbers.py for more detail. + +class Decimal(object): + """Floating point class for decimal arithmetic.""" + + __slots__ = ('_exp','_int','_sign', '_is_special') + # Generally, the value of the Decimal instance is given by + # (-1)**_sign * _int * 10**_exp + # Special values are signified by _is_special == True + + # We're immutable, so use __new__ not __init__ + def __new__(cls, value="0", context=None): + """Create a decimal point instance. + + >>> Decimal('3.14') # string input + Decimal('3.14') + >>> Decimal((0, (3, 1, 4), -2)) # tuple (sign, digit_tuple, exponent) + Decimal('3.14') + >>> Decimal(314) # int + Decimal('314') + >>> Decimal(Decimal(314)) # another decimal instance + Decimal('314') + >>> Decimal(' 3.14 \\n') # leading and trailing whitespace okay + Decimal('3.14') + """ + + # Note that the coefficient, self._int, is actually stored as + # a string rather than as a tuple of digits. This speeds up + # the "digits to integer" and "integer to digits" conversions + # that are used in almost every arithmetic operation on + # Decimals. This is an internal detail: the as_tuple function + # and the Decimal constructor still deal with tuples of + # digits. + + self = object.__new__(cls) + + # From a string + # REs insist on real strings, so we can too. + if isinstance(value, str): + m = _parser(value.strip().replace("_", "")) + if m is None: + if context is None: + context = getcontext() + return context._raise_error(ConversionSyntax, + "Invalid literal for Decimal: %r" % value) + + if m.group('sign') == "-": + self._sign = 1 + else: + self._sign = 0 + intpart = m.group('int') + if intpart is not None: + # finite number + fracpart = m.group('frac') or '' + exp = int(m.group('exp') or '0') + self._int = str(int(intpart+fracpart)) + self._exp = exp - len(fracpart) + self._is_special = False + else: + diag = m.group('diag') + if diag is not None: + # NaN + self._int = str(int(diag or '0')).lstrip('0') + if m.group('signal'): + self._exp = 'N' + else: + self._exp = 'n' + else: + # infinity + self._int = '0' + self._exp = 'F' + self._is_special = True + return self + + # From an integer + if isinstance(value, int): + if value >= 0: + self._sign = 0 + else: + self._sign = 1 + self._exp = 0 + self._int = str(abs(value)) + self._is_special = False + return self + + # From another decimal + if isinstance(value, Decimal): + self._exp = value._exp + self._sign = value._sign + self._int = value._int + self._is_special = value._is_special + return self + + # From an internal working value + if isinstance(value, _WorkRep): + self._sign = value.sign + self._int = str(value.int) + self._exp = int(value.exp) + self._is_special = False + return self + + # tuple/list conversion (possibly from as_tuple()) + if isinstance(value, (list,tuple)): + if len(value) != 3: + raise ValueError('Invalid tuple size in creation of Decimal ' + 'from list or tuple. The list or tuple ' + 'should have exactly three elements.') + # process sign. The isinstance test rejects floats + if not (isinstance(value[0], int) and value[0] in (0,1)): + raise ValueError("Invalid sign. The first value in the tuple " + "should be an integer; either 0 for a " + "positive number or 1 for a negative number.") + self._sign = value[0] + if value[2] == 'F': + # infinity: value[1] is ignored + self._int = '0' + self._exp = value[2] + self._is_special = True + else: + # process and validate the digits in value[1] + digits = [] + for digit in value[1]: + if isinstance(digit, int) and 0 <= digit <= 9: + # skip leading zeros + if digits or digit != 0: + digits.append(digit) + else: + raise ValueError("The second value in the tuple must " + "be composed of integers in the range " + "0 through 9.") + if value[2] in ('n', 'N'): + # NaN: digits form the diagnostic + self._int = ''.join(map(str, digits)) + self._exp = value[2] + self._is_special = True + elif isinstance(value[2], int): + # finite number: digits give the coefficient + self._int = ''.join(map(str, digits or [0])) + self._exp = value[2] + self._is_special = False + else: + raise ValueError("The third value in the tuple must " + "be an integer, or one of the " + "strings 'F', 'n', 'N'.") + return self + + if isinstance(value, float): + if context is None: + context = getcontext() + context._raise_error(FloatOperation, + "strict semantics for mixing floats and Decimals are " + "enabled") + value = Decimal.from_float(value) + self._exp = value._exp + self._sign = value._sign + self._int = value._int + self._is_special = value._is_special + return self + + raise TypeError("Cannot convert %r to Decimal" % value) + + @classmethod + def from_float(cls, f): + """Converts a float to a decimal number, exactly. + + Note that Decimal.from_float(0.1) is not the same as Decimal('0.1'). + Since 0.1 is not exactly representable in binary floating point, the + value is stored as the nearest representable value which is + 0x1.999999999999ap-4. The exact equivalent of the value in decimal + is 0.1000000000000000055511151231257827021181583404541015625. + + >>> Decimal.from_float(0.1) + Decimal('0.1000000000000000055511151231257827021181583404541015625') + >>> Decimal.from_float(float('nan')) + Decimal('NaN') + >>> Decimal.from_float(float('inf')) + Decimal('Infinity') + >>> Decimal.from_float(-float('inf')) + Decimal('-Infinity') + >>> Decimal.from_float(-0.0) + Decimal('-0') + + """ + if isinstance(f, int): # handle integer inputs + sign = 0 if f >= 0 else 1 + k = 0 + coeff = str(abs(f)) + elif isinstance(f, float): + if _math.isinf(f) or _math.isnan(f): + return cls(repr(f)) + if _math.copysign(1.0, f) == 1.0: + sign = 0 + else: + sign = 1 + n, d = abs(f).as_integer_ratio() + k = d.bit_length() - 1 + coeff = str(n*5**k) + else: + raise TypeError("argument must be int or float.") + + result = _dec_from_triple(sign, coeff, -k) + if cls is Decimal: + return result + else: + return cls(result) + + def _isnan(self): + """Returns whether the number is not actually one. + + 0 if a number + 1 if NaN + 2 if sNaN + """ + if self._is_special: + exp = self._exp + if exp == 'n': + return 1 + elif exp == 'N': + return 2 + return 0 + + def _isinfinity(self): + """Returns whether the number is infinite + + 0 if finite or not a number + 1 if +INF + -1 if -INF + """ + if self._exp == 'F': + if self._sign: + return -1 + return 1 + return 0 + + def _check_nans(self, other=None, context=None): + """Returns whether the number is not actually one. + + if self, other are sNaN, signal + if self, other are NaN return nan + return 0 + + Done before operations. + """ + + self_is_nan = self._isnan() + if other is None: + other_is_nan = False + else: + other_is_nan = other._isnan() + + if self_is_nan or other_is_nan: + if context is None: + context = getcontext() + + if self_is_nan == 2: + return context._raise_error(InvalidOperation, 'sNaN', + self) + if other_is_nan == 2: + return context._raise_error(InvalidOperation, 'sNaN', + other) + if self_is_nan: + return self._fix_nan(context) + + return other._fix_nan(context) + return 0 + + def _compare_check_nans(self, other, context): + """Version of _check_nans used for the signaling comparisons + compare_signal, __le__, __lt__, __ge__, __gt__. + + Signal InvalidOperation if either self or other is a (quiet + or signaling) NaN. Signaling NaNs take precedence over quiet + NaNs. + + Return 0 if neither operand is a NaN. + + """ + if context is None: + context = getcontext() + + if self._is_special or other._is_special: + if self.is_snan(): + return context._raise_error(InvalidOperation, + 'comparison involving sNaN', + self) + elif other.is_snan(): + return context._raise_error(InvalidOperation, + 'comparison involving sNaN', + other) + elif self.is_qnan(): + return context._raise_error(InvalidOperation, + 'comparison involving NaN', + self) + elif other.is_qnan(): + return context._raise_error(InvalidOperation, + 'comparison involving NaN', + other) + return 0 + + def __bool__(self): + """Return True if self is nonzero; otherwise return False. + + NaNs and infinities are considered nonzero. + """ + return self._is_special or self._int != '0' + + def _cmp(self, other): + """Compare the two non-NaN decimal instances self and other. + + Returns -1 if self < other, 0 if self == other and 1 + if self > other. This routine is for internal use only.""" + + if self._is_special or other._is_special: + self_inf = self._isinfinity() + other_inf = other._isinfinity() + if self_inf == other_inf: + return 0 + elif self_inf < other_inf: + return -1 + else: + return 1 + + # check for zeros; Decimal('0') == Decimal('-0') + if not self: + if not other: + return 0 + else: + return -((-1)**other._sign) + if not other: + return (-1)**self._sign + + # If different signs, neg one is less + if other._sign < self._sign: + return -1 + if self._sign < other._sign: + return 1 + + self_adjusted = self.adjusted() + other_adjusted = other.adjusted() + if self_adjusted == other_adjusted: + self_padded = self._int + '0'*(self._exp - other._exp) + other_padded = other._int + '0'*(other._exp - self._exp) + if self_padded == other_padded: + return 0 + elif self_padded < other_padded: + return -(-1)**self._sign + else: + return (-1)**self._sign + elif self_adjusted > other_adjusted: + return (-1)**self._sign + else: # self_adjusted < other_adjusted + return -((-1)**self._sign) + + # Note: The Decimal standard doesn't cover rich comparisons for + # Decimals. In particular, the specification is silent on the + # subject of what should happen for a comparison involving a NaN. + # We take the following approach: + # + # == comparisons involving a quiet NaN always return False + # != comparisons involving a quiet NaN always return True + # == or != comparisons involving a signaling NaN signal + # InvalidOperation, and return False or True as above if the + # InvalidOperation is not trapped. + # <, >, <= and >= comparisons involving a (quiet or signaling) + # NaN signal InvalidOperation, and return False if the + # InvalidOperation is not trapped. + # + # This behavior is designed to conform as closely as possible to + # that specified by IEEE 754. + + def __eq__(self, other, context=None): + self, other = _convert_for_comparison(self, other, equality_op=True) + if other is NotImplemented: + return other + if self._check_nans(other, context): + return False + return self._cmp(other) == 0 + + def __lt__(self, other, context=None): + self, other = _convert_for_comparison(self, other) + if other is NotImplemented: + return other + ans = self._compare_check_nans(other, context) + if ans: + return False + return self._cmp(other) < 0 + + def __le__(self, other, context=None): + self, other = _convert_for_comparison(self, other) + if other is NotImplemented: + return other + ans = self._compare_check_nans(other, context) + if ans: + return False + return self._cmp(other) <= 0 + + def __gt__(self, other, context=None): + self, other = _convert_for_comparison(self, other) + if other is NotImplemented: + return other + ans = self._compare_check_nans(other, context) + if ans: + return False + return self._cmp(other) > 0 + + def __ge__(self, other, context=None): + self, other = _convert_for_comparison(self, other) + if other is NotImplemented: + return other + ans = self._compare_check_nans(other, context) + if ans: + return False + return self._cmp(other) >= 0 + + def compare(self, other, context=None): + """Compare self to other. Return a decimal value: + + a or b is a NaN ==> Decimal('NaN') + a < b ==> Decimal('-1') + a == b ==> Decimal('0') + a > b ==> Decimal('1') + """ + other = _convert_other(other, raiseit=True) + + # Compare(NaN, NaN) = NaN + if (self._is_special or other and other._is_special): + ans = self._check_nans(other, context) + if ans: + return ans + + return Decimal(self._cmp(other)) + + def __hash__(self): + """x.__hash__() <==> hash(x)""" + + # In order to make sure that the hash of a Decimal instance + # agrees with the hash of a numerically equal integer, float + # or Fraction, we follow the rules for numeric hashes outlined + # in the documentation. (See library docs, 'Built-in Types'). + if self._is_special: + if self.is_snan(): + raise TypeError('Cannot hash a signaling NaN value.') + elif self.is_nan(): + return object.__hash__(self) + else: + if self._sign: + return -_PyHASH_INF + else: + return _PyHASH_INF + + if self._exp >= 0: + exp_hash = pow(10, self._exp, _PyHASH_MODULUS) + else: + exp_hash = pow(_PyHASH_10INV, -self._exp, _PyHASH_MODULUS) + hash_ = int(self._int) * exp_hash % _PyHASH_MODULUS + ans = hash_ if self >= 0 else -hash_ + return -2 if ans == -1 else ans + + def as_tuple(self): + """Represents the number as a triple tuple. + + To show the internals exactly as they are. + """ + return DecimalTuple(self._sign, tuple(map(int, self._int)), self._exp) + + def as_integer_ratio(self): + """Express a finite Decimal instance in the form n / d. + + Returns a pair (n, d) of integers. When called on an infinity + or NaN, raises OverflowError or ValueError respectively. + + >>> Decimal('3.14').as_integer_ratio() + (157, 50) + >>> Decimal('-123e5').as_integer_ratio() + (-12300000, 1) + >>> Decimal('0.00').as_integer_ratio() + (0, 1) + + """ + if self._is_special: + if self.is_nan(): + raise ValueError("cannot convert NaN to integer ratio") + else: + raise OverflowError("cannot convert Infinity to integer ratio") + + if not self: + return 0, 1 + + # Find n, d in lowest terms such that abs(self) == n / d; + # we'll deal with the sign later. + n = int(self._int) + if self._exp >= 0: + # self is an integer. + n, d = n * 10**self._exp, 1 + else: + # Find d2, d5 such that abs(self) = n / (2**d2 * 5**d5). + d5 = -self._exp + while d5 > 0 and n % 5 == 0: + n //= 5 + d5 -= 1 + + # (n & -n).bit_length() - 1 counts trailing zeros in binary + # representation of n (provided n is nonzero). + d2 = -self._exp + shift2 = min((n & -n).bit_length() - 1, d2) + if shift2: + n >>= shift2 + d2 -= shift2 + + d = 5**d5 << d2 + + if self._sign: + n = -n + return n, d + + def __repr__(self): + """Represents the number as an instance of Decimal.""" + # Invariant: eval(repr(d)) == d + return "Decimal('%s')" % str(self) + + def __str__(self, eng=False, context=None): + """Return string representation of the number in scientific notation. + + Captures all of the information in the underlying representation. + """ + + sign = ['', '-'][self._sign] + if self._is_special: + if self._exp == 'F': + return sign + 'Infinity' + elif self._exp == 'n': + return sign + 'NaN' + self._int + else: # self._exp == 'N' + return sign + 'sNaN' + self._int + + # number of digits of self._int to left of decimal point + leftdigits = self._exp + len(self._int) + + # dotplace is number of digits of self._int to the left of the + # decimal point in the mantissa of the output string (that is, + # after adjusting the exponent) + if self._exp <= 0 and leftdigits > -6: + # no exponent required + dotplace = leftdigits + elif not eng: + # usual scientific notation: 1 digit on left of the point + dotplace = 1 + elif self._int == '0': + # engineering notation, zero + dotplace = (leftdigits + 1) % 3 - 1 + else: + # engineering notation, nonzero + dotplace = (leftdigits - 1) % 3 + 1 + + if dotplace <= 0: + intpart = '0' + fracpart = '.' + '0'*(-dotplace) + self._int + elif dotplace >= len(self._int): + intpart = self._int+'0'*(dotplace-len(self._int)) + fracpart = '' + else: + intpart = self._int[:dotplace] + fracpart = '.' + self._int[dotplace:] + if leftdigits == dotplace: + exp = '' + else: + if context is None: + context = getcontext() + exp = ['e', 'E'][context.capitals] + "%+d" % (leftdigits-dotplace) + + return sign + intpart + fracpart + exp + + def to_eng_string(self, context=None): + """Convert to a string, using engineering notation if an exponent is needed. + + Engineering notation has an exponent which is a multiple of 3. This + can leave up to 3 digits to the left of the decimal place and may + require the addition of either one or two trailing zeros. + """ + return self.__str__(eng=True, context=context) + + def __neg__(self, context=None): + """Returns a copy with the sign switched. + + Rounds, if it has reason. + """ + if self._is_special: + ans = self._check_nans(context=context) + if ans: + return ans + + if context is None: + context = getcontext() + + if not self and context.rounding != ROUND_FLOOR: + # -Decimal('0') is Decimal('0'), not Decimal('-0'), except + # in ROUND_FLOOR rounding mode. + ans = self.copy_abs() + else: + ans = self.copy_negate() + + return ans._fix(context) + + def __pos__(self, context=None): + """Returns a copy, unless it is a sNaN. + + Rounds the number (if more than precision digits) + """ + if self._is_special: + ans = self._check_nans(context=context) + if ans: + return ans + + if context is None: + context = getcontext() + + if not self and context.rounding != ROUND_FLOOR: + # + (-0) = 0, except in ROUND_FLOOR rounding mode. + ans = self.copy_abs() + else: + ans = Decimal(self) + + return ans._fix(context) + + def __abs__(self, round=True, context=None): + """Returns the absolute value of self. + + If the keyword argument 'round' is false, do not round. The + expression self.__abs__(round=False) is equivalent to + self.copy_abs(). + """ + if not round: + return self.copy_abs() + + if self._is_special: + ans = self._check_nans(context=context) + if ans: + return ans + + if self._sign: + ans = self.__neg__(context=context) + else: + ans = self.__pos__(context=context) + + return ans + + def __add__(self, other, context=None): + """Returns self + other. + + -INF + INF (or the reverse) cause InvalidOperation errors. + """ + other = _convert_other(other) + if other is NotImplemented: + return other + + if context is None: + context = getcontext() + + if self._is_special or other._is_special: + ans = self._check_nans(other, context) + if ans: + return ans + + if self._isinfinity(): + # If both INF, same sign => same as both, opposite => error. + if self._sign != other._sign and other._isinfinity(): + return context._raise_error(InvalidOperation, '-INF + INF') + return Decimal(self) + if other._isinfinity(): + return Decimal(other) # Can't both be infinity here + + exp = min(self._exp, other._exp) + negativezero = 0 + if context.rounding == ROUND_FLOOR and self._sign != other._sign: + # If the answer is 0, the sign should be negative, in this case. + negativezero = 1 + + if not self and not other: + sign = min(self._sign, other._sign) + if negativezero: + sign = 1 + ans = _dec_from_triple(sign, '0', exp) + ans = ans._fix(context) + return ans + if not self: + exp = max(exp, other._exp - context.prec-1) + ans = other._rescale(exp, context.rounding) + ans = ans._fix(context) + return ans + if not other: + exp = max(exp, self._exp - context.prec-1) + ans = self._rescale(exp, context.rounding) + ans = ans._fix(context) + return ans + + op1 = _WorkRep(self) + op2 = _WorkRep(other) + op1, op2 = _normalize(op1, op2, context.prec) + + result = _WorkRep() + if op1.sign != op2.sign: + # Equal and opposite + if op1.int == op2.int: + ans = _dec_from_triple(negativezero, '0', exp) + ans = ans._fix(context) + return ans + if op1.int < op2.int: + op1, op2 = op2, op1 + # OK, now abs(op1) > abs(op2) + if op1.sign == 1: + result.sign = 1 + op1.sign, op2.sign = op2.sign, op1.sign + else: + result.sign = 0 + # So we know the sign, and op1 > 0. + elif op1.sign == 1: + result.sign = 1 + op1.sign, op2.sign = (0, 0) + else: + result.sign = 0 + # Now, op1 > abs(op2) > 0 + + if op2.sign == 0: + result.int = op1.int + op2.int + else: + result.int = op1.int - op2.int + + result.exp = op1.exp + ans = Decimal(result) + ans = ans._fix(context) + return ans + + __radd__ = __add__ + + def __sub__(self, other, context=None): + """Return self - other""" + other = _convert_other(other) + if other is NotImplemented: + return other + + if self._is_special or other._is_special: + ans = self._check_nans(other, context=context) + if ans: + return ans + + # self - other is computed as self + other.copy_negate() + return self.__add__(other.copy_negate(), context=context) + + def __rsub__(self, other, context=None): + """Return other - self""" + other = _convert_other(other) + if other is NotImplemented: + return other + + return other.__sub__(self, context=context) + + def __mul__(self, other, context=None): + """Return self * other. + + (+-) INF * 0 (or its reverse) raise InvalidOperation. + """ + other = _convert_other(other) + if other is NotImplemented: + return other + + if context is None: + context = getcontext() + + resultsign = self._sign ^ other._sign + + if self._is_special or other._is_special: + ans = self._check_nans(other, context) + if ans: + return ans + + if self._isinfinity(): + if not other: + return context._raise_error(InvalidOperation, '(+-)INF * 0') + return _SignedInfinity[resultsign] + + if other._isinfinity(): + if not self: + return context._raise_error(InvalidOperation, '0 * (+-)INF') + return _SignedInfinity[resultsign] + + resultexp = self._exp + other._exp + + # Special case for multiplying by zero + if not self or not other: + ans = _dec_from_triple(resultsign, '0', resultexp) + # Fixing in case the exponent is out of bounds + ans = ans._fix(context) + return ans + + # Special case for multiplying by power of 10 + if self._int == '1': + ans = _dec_from_triple(resultsign, other._int, resultexp) + ans = ans._fix(context) + return ans + if other._int == '1': + ans = _dec_from_triple(resultsign, self._int, resultexp) + ans = ans._fix(context) + return ans + + op1 = _WorkRep(self) + op2 = _WorkRep(other) + + ans = _dec_from_triple(resultsign, str(op1.int * op2.int), resultexp) + ans = ans._fix(context) + + return ans + __rmul__ = __mul__ + + def __truediv__(self, other, context=None): + """Return self / other.""" + other = _convert_other(other) + if other is NotImplemented: + return NotImplemented + + if context is None: + context = getcontext() + + sign = self._sign ^ other._sign + + if self._is_special or other._is_special: + ans = self._check_nans(other, context) + if ans: + return ans + + if self._isinfinity() and other._isinfinity(): + return context._raise_error(InvalidOperation, '(+-)INF/(+-)INF') + + if self._isinfinity(): + return _SignedInfinity[sign] + + if other._isinfinity(): + context._raise_error(Clamped, 'Division by infinity') + return _dec_from_triple(sign, '0', context.Etiny()) + + # Special cases for zeroes + if not other: + if not self: + return context._raise_error(DivisionUndefined, '0 / 0') + return context._raise_error(DivisionByZero, 'x / 0', sign) + + if not self: + exp = self._exp - other._exp + coeff = 0 + else: + # OK, so neither = 0, INF or NaN + shift = len(other._int) - len(self._int) + context.prec + 1 + exp = self._exp - other._exp - shift + op1 = _WorkRep(self) + op2 = _WorkRep(other) + if shift >= 0: + coeff, remainder = divmod(op1.int * 10**shift, op2.int) + else: + coeff, remainder = divmod(op1.int, op2.int * 10**-shift) + if remainder: + # result is not exact; adjust to ensure correct rounding + if coeff % 5 == 0: + coeff += 1 + else: + # result is exact; get as close to ideal exponent as possible + ideal_exp = self._exp - other._exp + while exp < ideal_exp and coeff % 10 == 0: + coeff //= 10 + exp += 1 + + ans = _dec_from_triple(sign, str(coeff), exp) + return ans._fix(context) + + def _divide(self, other, context): + """Return (self // other, self % other), to context.prec precision. + + Assumes that neither self nor other is a NaN, that self is not + infinite and that other is nonzero. + """ + sign = self._sign ^ other._sign + if other._isinfinity(): + ideal_exp = self._exp + else: + ideal_exp = min(self._exp, other._exp) + + expdiff = self.adjusted() - other.adjusted() + if not self or other._isinfinity() or expdiff <= -2: + return (_dec_from_triple(sign, '0', 0), + self._rescale(ideal_exp, context.rounding)) + if expdiff <= context.prec: + op1 = _WorkRep(self) + op2 = _WorkRep(other) + if op1.exp >= op2.exp: + op1.int *= 10**(op1.exp - op2.exp) + else: + op2.int *= 10**(op2.exp - op1.exp) + q, r = divmod(op1.int, op2.int) + if q < 10**context.prec: + return (_dec_from_triple(sign, str(q), 0), + _dec_from_triple(self._sign, str(r), ideal_exp)) + + # Here the quotient is too large to be representable + ans = context._raise_error(DivisionImpossible, + 'quotient too large in //, % or divmod') + return ans, ans + + def __rtruediv__(self, other, context=None): + """Swaps self/other and returns __truediv__.""" + other = _convert_other(other) + if other is NotImplemented: + return other + return other.__truediv__(self, context=context) + + def __divmod__(self, other, context=None): + """ + Return (self // other, self % other) + """ + other = _convert_other(other) + if other is NotImplemented: + return other + + if context is None: + context = getcontext() + + ans = self._check_nans(other, context) + if ans: + return (ans, ans) + + sign = self._sign ^ other._sign + if self._isinfinity(): + if other._isinfinity(): + ans = context._raise_error(InvalidOperation, 'divmod(INF, INF)') + return ans, ans + else: + return (_SignedInfinity[sign], + context._raise_error(InvalidOperation, 'INF % x')) + + if not other: + if not self: + ans = context._raise_error(DivisionUndefined, 'divmod(0, 0)') + return ans, ans + else: + return (context._raise_error(DivisionByZero, 'x // 0', sign), + context._raise_error(InvalidOperation, 'x % 0')) + + quotient, remainder = self._divide(other, context) + remainder = remainder._fix(context) + return quotient, remainder + + def __rdivmod__(self, other, context=None): + """Swaps self/other and returns __divmod__.""" + other = _convert_other(other) + if other is NotImplemented: + return other + return other.__divmod__(self, context=context) + + def __mod__(self, other, context=None): + """ + self % other + """ + other = _convert_other(other) + if other is NotImplemented: + return other + + if context is None: + context = getcontext() + + ans = self._check_nans(other, context) + if ans: + return ans + + if self._isinfinity(): + return context._raise_error(InvalidOperation, 'INF % x') + elif not other: + if self: + return context._raise_error(InvalidOperation, 'x % 0') + else: + return context._raise_error(DivisionUndefined, '0 % 0') + + remainder = self._divide(other, context)[1] + remainder = remainder._fix(context) + return remainder + + def __rmod__(self, other, context=None): + """Swaps self/other and returns __mod__.""" + other = _convert_other(other) + if other is NotImplemented: + return other + return other.__mod__(self, context=context) + + def remainder_near(self, other, context=None): + """ + Remainder nearest to 0- abs(remainder-near) <= other/2 + """ + if context is None: + context = getcontext() + + other = _convert_other(other, raiseit=True) + + ans = self._check_nans(other, context) + if ans: + return ans + + # self == +/-infinity -> InvalidOperation + if self._isinfinity(): + return context._raise_error(InvalidOperation, + 'remainder_near(infinity, x)') + + # other == 0 -> either InvalidOperation or DivisionUndefined + if not other: + if self: + return context._raise_error(InvalidOperation, + 'remainder_near(x, 0)') + else: + return context._raise_error(DivisionUndefined, + 'remainder_near(0, 0)') + + # other = +/-infinity -> remainder = self + if other._isinfinity(): + ans = Decimal(self) + return ans._fix(context) + + # self = 0 -> remainder = self, with ideal exponent + ideal_exponent = min(self._exp, other._exp) + if not self: + ans = _dec_from_triple(self._sign, '0', ideal_exponent) + return ans._fix(context) + + # catch most cases of large or small quotient + expdiff = self.adjusted() - other.adjusted() + if expdiff >= context.prec + 1: + # expdiff >= prec+1 => abs(self/other) > 10**prec + return context._raise_error(DivisionImpossible) + if expdiff <= -2: + # expdiff <= -2 => abs(self/other) < 0.1 + ans = self._rescale(ideal_exponent, context.rounding) + return ans._fix(context) + + # adjust both arguments to have the same exponent, then divide + op1 = _WorkRep(self) + op2 = _WorkRep(other) + if op1.exp >= op2.exp: + op1.int *= 10**(op1.exp - op2.exp) + else: + op2.int *= 10**(op2.exp - op1.exp) + q, r = divmod(op1.int, op2.int) + # remainder is r*10**ideal_exponent; other is +/-op2.int * + # 10**ideal_exponent. Apply correction to ensure that + # abs(remainder) <= abs(other)/2 + if 2*r + (q&1) > op2.int: + r -= op2.int + q += 1 + + if q >= 10**context.prec: + return context._raise_error(DivisionImpossible) + + # result has same sign as self unless r is negative + sign = self._sign + if r < 0: + sign = 1-sign + r = -r + + ans = _dec_from_triple(sign, str(r), ideal_exponent) + return ans._fix(context) + + def __floordiv__(self, other, context=None): + """self // other""" + other = _convert_other(other) + if other is NotImplemented: + return other + + if context is None: + context = getcontext() + + ans = self._check_nans(other, context) + if ans: + return ans + + if self._isinfinity(): + if other._isinfinity(): + return context._raise_error(InvalidOperation, 'INF // INF') + else: + return _SignedInfinity[self._sign ^ other._sign] + + if not other: + if self: + return context._raise_error(DivisionByZero, 'x // 0', + self._sign ^ other._sign) + else: + return context._raise_error(DivisionUndefined, '0 // 0') + + return self._divide(other, context)[0] + + def __rfloordiv__(self, other, context=None): + """Swaps self/other and returns __floordiv__.""" + other = _convert_other(other) + if other is NotImplemented: + return other + return other.__floordiv__(self, context=context) + + def __float__(self): + """Float representation.""" + if self._isnan(): + if self.is_snan(): + raise ValueError("Cannot convert signaling NaN to float") + s = "-nan" if self._sign else "nan" + else: + s = str(self) + return float(s) + + def __int__(self): + """Converts self to an int, truncating if necessary.""" + if self._is_special: + if self._isnan(): + raise ValueError("Cannot convert NaN to integer") + elif self._isinfinity(): + raise OverflowError("Cannot convert infinity to integer") + s = (-1)**self._sign + if self._exp >= 0: + return s*int(self._int)*10**self._exp + else: + return s*int(self._int[:self._exp] or '0') + + __trunc__ = __int__ + + @property + def real(self): + return self + + @property + def imag(self): + return Decimal(0) + + def conjugate(self): + return self + + def __complex__(self): + return complex(float(self)) + + def _fix_nan(self, context): + """Decapitate the payload of a NaN to fit the context""" + payload = self._int + + # maximum length of payload is precision if clamp=0, + # precision-1 if clamp=1. + max_payload_len = context.prec - context.clamp + if len(payload) > max_payload_len: + payload = payload[len(payload)-max_payload_len:].lstrip('0') + return _dec_from_triple(self._sign, payload, self._exp, True) + return Decimal(self) + + def _fix(self, context): + """Round if it is necessary to keep self within prec precision. + + Rounds and fixes the exponent. Does not raise on a sNaN. + + Arguments: + self - Decimal instance + context - context used. + """ + + if self._is_special: + if self._isnan(): + # decapitate payload if necessary + return self._fix_nan(context) + else: + # self is +/-Infinity; return unaltered + return Decimal(self) + + # if self is zero then exponent should be between Etiny and + # Emax if clamp==0, and between Etiny and Etop if clamp==1. + Etiny = context.Etiny() + Etop = context.Etop() + if not self: + exp_max = [context.Emax, Etop][context.clamp] + new_exp = min(max(self._exp, Etiny), exp_max) + if new_exp != self._exp: + context._raise_error(Clamped) + return _dec_from_triple(self._sign, '0', new_exp) + else: + return Decimal(self) + + # exp_min is the smallest allowable exponent of the result, + # equal to max(self.adjusted()-context.prec+1, Etiny) + exp_min = len(self._int) + self._exp - context.prec + if exp_min > Etop: + # overflow: exp_min > Etop iff self.adjusted() > Emax + ans = context._raise_error(Overflow, 'above Emax', self._sign) + context._raise_error(Inexact) + context._raise_error(Rounded) + return ans + + self_is_subnormal = exp_min < Etiny + if self_is_subnormal: + exp_min = Etiny + + # round if self has too many digits + if self._exp < exp_min: + digits = len(self._int) + self._exp - exp_min + if digits < 0: + self = _dec_from_triple(self._sign, '1', exp_min-1) + digits = 0 + rounding_method = self._pick_rounding_function[context.rounding] + changed = rounding_method(self, digits) + coeff = self._int[:digits] or '0' + if changed > 0: + coeff = str(int(coeff)+1) + if len(coeff) > context.prec: + coeff = coeff[:-1] + exp_min += 1 + + # check whether the rounding pushed the exponent out of range + if exp_min > Etop: + ans = context._raise_error(Overflow, 'above Emax', self._sign) + else: + ans = _dec_from_triple(self._sign, coeff, exp_min) + + # raise the appropriate signals, taking care to respect + # the precedence described in the specification + if changed and self_is_subnormal: + context._raise_error(Underflow) + if self_is_subnormal: + context._raise_error(Subnormal) + if changed: + context._raise_error(Inexact) + context._raise_error(Rounded) + if not ans: + # raise Clamped on underflow to 0 + context._raise_error(Clamped) + return ans + + if self_is_subnormal: + context._raise_error(Subnormal) + + # fold down if clamp == 1 and self has too few digits + if context.clamp == 1 and self._exp > Etop: + context._raise_error(Clamped) + self_padded = self._int + '0'*(self._exp - Etop) + return _dec_from_triple(self._sign, self_padded, Etop) + + # here self was representable to begin with; return unchanged + return Decimal(self) + + # for each of the rounding functions below: + # self is a finite, nonzero Decimal + # prec is an integer satisfying 0 <= prec < len(self._int) + # + # each function returns either -1, 0, or 1, as follows: + # 1 indicates that self should be rounded up (away from zero) + # 0 indicates that self should be truncated, and that all the + # digits to be truncated are zeros (so the value is unchanged) + # -1 indicates that there are nonzero digits to be truncated + + def _round_down(self, prec): + """Also known as round-towards-0, truncate.""" + if _all_zeros(self._int, prec): + return 0 + else: + return -1 + + def _round_up(self, prec): + """Rounds away from 0.""" + return -self._round_down(prec) + + def _round_half_up(self, prec): + """Rounds 5 up (away from 0)""" + if self._int[prec] in '56789': + return 1 + elif _all_zeros(self._int, prec): + return 0 + else: + return -1 + + def _round_half_down(self, prec): + """Round 5 down""" + if _exact_half(self._int, prec): + return -1 + else: + return self._round_half_up(prec) + + def _round_half_even(self, prec): + """Round 5 to even, rest to nearest.""" + if _exact_half(self._int, prec) and \ + (prec == 0 or self._int[prec-1] in '02468'): + return -1 + else: + return self._round_half_up(prec) + + def _round_ceiling(self, prec): + """Rounds up (not away from 0 if negative.)""" + if self._sign: + return self._round_down(prec) + else: + return -self._round_down(prec) + + def _round_floor(self, prec): + """Rounds down (not towards 0 if negative)""" + if not self._sign: + return self._round_down(prec) + else: + return -self._round_down(prec) + + def _round_05up(self, prec): + """Round down unless digit prec-1 is 0 or 5.""" + if prec and self._int[prec-1] not in '05': + return self._round_down(prec) + else: + return -self._round_down(prec) + + _pick_rounding_function = dict( + ROUND_DOWN = _round_down, + ROUND_UP = _round_up, + ROUND_HALF_UP = _round_half_up, + ROUND_HALF_DOWN = _round_half_down, + ROUND_HALF_EVEN = _round_half_even, + ROUND_CEILING = _round_ceiling, + ROUND_FLOOR = _round_floor, + ROUND_05UP = _round_05up, + ) + + def __round__(self, n=None): + """Round self to the nearest integer, or to a given precision. + + If only one argument is supplied, round a finite Decimal + instance self to the nearest integer. If self is infinite or + a NaN then a Python exception is raised. If self is finite + and lies exactly halfway between two integers then it is + rounded to the integer with even last digit. + + >>> round(Decimal('123.456')) + 123 + >>> round(Decimal('-456.789')) + -457 + >>> round(Decimal('-3.0')) + -3 + >>> round(Decimal('2.5')) + 2 + >>> round(Decimal('3.5')) + 4 + >>> round(Decimal('Inf')) + Traceback (most recent call last): + ... + OverflowError: cannot round an infinity + >>> round(Decimal('NaN')) + Traceback (most recent call last): + ... + ValueError: cannot round a NaN + + If a second argument n is supplied, self is rounded to n + decimal places using the rounding mode for the current + context. + + For an integer n, round(self, -n) is exactly equivalent to + self.quantize(Decimal('1En')). + + >>> round(Decimal('123.456'), 0) + Decimal('123') + >>> round(Decimal('123.456'), 2) + Decimal('123.46') + >>> round(Decimal('123.456'), -2) + Decimal('1E+2') + >>> round(Decimal('-Infinity'), 37) + Decimal('NaN') + >>> round(Decimal('sNaN123'), 0) + Decimal('NaN123') + + """ + if n is not None: + # two-argument form: use the equivalent quantize call + if not isinstance(n, int): + raise TypeError('Second argument to round should be integral') + exp = _dec_from_triple(0, '1', -n) + return self.quantize(exp) + + # one-argument form + if self._is_special: + if self.is_nan(): + raise ValueError("cannot round a NaN") + else: + raise OverflowError("cannot round an infinity") + return int(self._rescale(0, ROUND_HALF_EVEN)) + + def __floor__(self): + """Return the floor of self, as an integer. + + For a finite Decimal instance self, return the greatest + integer n such that n <= self. If self is infinite or a NaN + then a Python exception is raised. + + """ + if self._is_special: + if self.is_nan(): + raise ValueError("cannot round a NaN") + else: + raise OverflowError("cannot round an infinity") + return int(self._rescale(0, ROUND_FLOOR)) + + def __ceil__(self): + """Return the ceiling of self, as an integer. + + For a finite Decimal instance self, return the least integer n + such that n >= self. If self is infinite or a NaN then a + Python exception is raised. + + """ + if self._is_special: + if self.is_nan(): + raise ValueError("cannot round a NaN") + else: + raise OverflowError("cannot round an infinity") + return int(self._rescale(0, ROUND_CEILING)) + + def fma(self, other, third, context=None): + """Fused multiply-add. + + Returns self*other+third with no rounding of the intermediate + product self*other. + + self and other are multiplied together, with no rounding of + the result. The third operand is then added to the result, + and a single final rounding is performed. + """ + + other = _convert_other(other, raiseit=True) + third = _convert_other(third, raiseit=True) + + # compute product; raise InvalidOperation if either operand is + # a signaling NaN or if the product is zero times infinity. + if self._is_special or other._is_special: + if context is None: + context = getcontext() + if self._exp == 'N': + return context._raise_error(InvalidOperation, 'sNaN', self) + if other._exp == 'N': + return context._raise_error(InvalidOperation, 'sNaN', other) + if self._exp == 'n': + product = self + elif other._exp == 'n': + product = other + elif self._exp == 'F': + if not other: + return context._raise_error(InvalidOperation, + 'INF * 0 in fma') + product = _SignedInfinity[self._sign ^ other._sign] + elif other._exp == 'F': + if not self: + return context._raise_error(InvalidOperation, + '0 * INF in fma') + product = _SignedInfinity[self._sign ^ other._sign] + else: + product = _dec_from_triple(self._sign ^ other._sign, + str(int(self._int) * int(other._int)), + self._exp + other._exp) + + return product.__add__(third, context) + + def _power_modulo(self, other, modulo, context=None): + """Three argument version of __pow__""" + + other = _convert_other(other) + if other is NotImplemented: + return other + modulo = _convert_other(modulo) + if modulo is NotImplemented: + return modulo + + if context is None: + context = getcontext() + + # deal with NaNs: if there are any sNaNs then first one wins, + # (i.e. behaviour for NaNs is identical to that of fma) + self_is_nan = self._isnan() + other_is_nan = other._isnan() + modulo_is_nan = modulo._isnan() + if self_is_nan or other_is_nan or modulo_is_nan: + if self_is_nan == 2: + return context._raise_error(InvalidOperation, 'sNaN', + self) + if other_is_nan == 2: + return context._raise_error(InvalidOperation, 'sNaN', + other) + if modulo_is_nan == 2: + return context._raise_error(InvalidOperation, 'sNaN', + modulo) + if self_is_nan: + return self._fix_nan(context) + if other_is_nan: + return other._fix_nan(context) + return modulo._fix_nan(context) + + # check inputs: we apply same restrictions as Python's pow() + if not (self._isinteger() and + other._isinteger() and + modulo._isinteger()): + return context._raise_error(InvalidOperation, + 'pow() 3rd argument not allowed ' + 'unless all arguments are integers') + if other < 0: + return context._raise_error(InvalidOperation, + 'pow() 2nd argument cannot be ' + 'negative when 3rd argument specified') + if not modulo: + return context._raise_error(InvalidOperation, + 'pow() 3rd argument cannot be 0') + + # additional restriction for decimal: the modulus must be less + # than 10**prec in absolute value + if modulo.adjusted() >= context.prec: + return context._raise_error(InvalidOperation, + 'insufficient precision: pow() 3rd ' + 'argument must not have more than ' + 'precision digits') + + # define 0**0 == NaN, for consistency with two-argument pow + # (even though it hurts!) + if not other and not self: + return context._raise_error(InvalidOperation, + 'at least one of pow() 1st argument ' + 'and 2nd argument must be nonzero; ' + '0**0 is not defined') + + # compute sign of result + if other._iseven(): + sign = 0 + else: + sign = self._sign + + # convert modulo to a Python integer, and self and other to + # Decimal integers (i.e. force their exponents to be >= 0) + modulo = abs(int(modulo)) + base = _WorkRep(self.to_integral_value()) + exponent = _WorkRep(other.to_integral_value()) + + # compute result using integer pow() + base = (base.int % modulo * pow(10, base.exp, modulo)) % modulo + for i in range(exponent.exp): + base = pow(base, 10, modulo) + base = pow(base, exponent.int, modulo) + + return _dec_from_triple(sign, str(base), 0) + + def _power_exact(self, other, p): + """Attempt to compute self**other exactly. + + Given Decimals self and other and an integer p, attempt to + compute an exact result for the power self**other, with p + digits of precision. Return None if self**other is not + exactly representable in p digits. + + Assumes that elimination of special cases has already been + performed: self and other must both be nonspecial; self must + be positive and not numerically equal to 1; other must be + nonzero. For efficiency, other._exp should not be too large, + so that 10**abs(other._exp) is a feasible calculation.""" + + # In the comments below, we write x for the value of self and y for the + # value of other. Write x = xc*10**xe and abs(y) = yc*10**ye, with xc + # and yc positive integers not divisible by 10. + + # The main purpose of this method is to identify the *failure* + # of x**y to be exactly representable with as little effort as + # possible. So we look for cheap and easy tests that + # eliminate the possibility of x**y being exact. Only if all + # these tests are passed do we go on to actually compute x**y. + + # Here's the main idea. Express y as a rational number m/n, with m and + # n relatively prime and n>0. Then for x**y to be exactly + # representable (at *any* precision), xc must be the nth power of a + # positive integer and xe must be divisible by n. If y is negative + # then additionally xc must be a power of either 2 or 5, hence a power + # of 2**n or 5**n. + # + # There's a limit to how small |y| can be: if y=m/n as above + # then: + # + # (1) if xc != 1 then for the result to be representable we + # need xc**(1/n) >= 2, and hence also xc**|y| >= 2. So + # if |y| <= 1/nbits(xc) then xc < 2**nbits(xc) <= + # 2**(1/|y|), hence xc**|y| < 2 and the result is not + # representable. + # + # (2) if xe != 0, |xe|*(1/n) >= 1, so |xe|*|y| >= 1. Hence if + # |y| < 1/|xe| then the result is not representable. + # + # Note that since x is not equal to 1, at least one of (1) and + # (2) must apply. Now |y| < 1/nbits(xc) iff |yc|*nbits(xc) < + # 10**-ye iff len(str(|yc|*nbits(xc)) <= -ye. + # + # There's also a limit to how large y can be, at least if it's + # positive: the normalized result will have coefficient xc**y, + # so if it's representable then xc**y < 10**p, and y < + # p/log10(xc). Hence if y*log10(xc) >= p then the result is + # not exactly representable. + + # if len(str(abs(yc*xe)) <= -ye then abs(yc*xe) < 10**-ye, + # so |y| < 1/xe and the result is not representable. + # Similarly, len(str(abs(yc)*xc_bits)) <= -ye implies |y| + # < 1/nbits(xc). + + x = _WorkRep(self) + xc, xe = x.int, x.exp + while xc % 10 == 0: + xc //= 10 + xe += 1 + + y = _WorkRep(other) + yc, ye = y.int, y.exp + while yc % 10 == 0: + yc //= 10 + ye += 1 + + # case where xc == 1: result is 10**(xe*y), with xe*y + # required to be an integer + if xc == 1: + xe *= yc + # result is now 10**(xe * 10**ye); xe * 10**ye must be integral + while xe % 10 == 0: + xe //= 10 + ye += 1 + if ye < 0: + return None + exponent = xe * 10**ye + if y.sign == 1: + exponent = -exponent + # if other is a nonnegative integer, use ideal exponent + if other._isinteger() and other._sign == 0: + ideal_exponent = self._exp*int(other) + zeros = min(exponent-ideal_exponent, p-1) + else: + zeros = 0 + return _dec_from_triple(0, '1' + '0'*zeros, exponent-zeros) + + # case where y is negative: xc must be either a power + # of 2 or a power of 5. + if y.sign == 1: + last_digit = xc % 10 + if last_digit in (2,4,6,8): + # quick test for power of 2 + if xc & -xc != xc: + return None + # now xc is a power of 2; e is its exponent + e = _nbits(xc)-1 + + # We now have: + # + # x = 2**e * 10**xe, e > 0, and y < 0. + # + # The exact result is: + # + # x**y = 5**(-e*y) * 10**(e*y + xe*y) + # + # provided that both e*y and xe*y are integers. Note that if + # 5**(-e*y) >= 10**p, then the result can't be expressed + # exactly with p digits of precision. + # + # Using the above, we can guard against large values of ye. + # 93/65 is an upper bound for log(10)/log(5), so if + # + # ye >= len(str(93*p//65)) + # + # then + # + # -e*y >= -y >= 10**ye > 93*p/65 > p*log(10)/log(5), + # + # so 5**(-e*y) >= 10**p, and the coefficient of the result + # can't be expressed in p digits. + + # emax >= largest e such that 5**e < 10**p. + emax = p*93//65 + if ye >= len(str(emax)): + return None + + # Find -e*y and -xe*y; both must be integers + e = _decimal_lshift_exact(e * yc, ye) + xe = _decimal_lshift_exact(xe * yc, ye) + if e is None or xe is None: + return None + + if e > emax: + return None + xc = 5**e + + elif last_digit == 5: + # e >= log_5(xc) if xc is a power of 5; we have + # equality all the way up to xc=5**2658 + e = _nbits(xc)*28//65 + xc, remainder = divmod(5**e, xc) + if remainder: + return None + while xc % 5 == 0: + xc //= 5 + e -= 1 + + # Guard against large values of ye, using the same logic as in + # the 'xc is a power of 2' branch. 10/3 is an upper bound for + # log(10)/log(2). + emax = p*10//3 + if ye >= len(str(emax)): + return None + + e = _decimal_lshift_exact(e * yc, ye) + xe = _decimal_lshift_exact(xe * yc, ye) + if e is None or xe is None: + return None + + if e > emax: + return None + xc = 2**e + else: + return None + + if xc >= 10**p: + return None + xe = -e-xe + return _dec_from_triple(0, str(xc), xe) + + # now y is positive; find m and n such that y = m/n + if ye >= 0: + m, n = yc*10**ye, 1 + else: + if xe != 0 and len(str(abs(yc*xe))) <= -ye: + return None + xc_bits = _nbits(xc) + if xc != 1 and len(str(abs(yc)*xc_bits)) <= -ye: + return None + m, n = yc, 10**(-ye) + while m % 2 == n % 2 == 0: + m //= 2 + n //= 2 + while m % 5 == n % 5 == 0: + m //= 5 + n //= 5 + + # compute nth root of xc*10**xe + if n > 1: + # if 1 < xc < 2**n then xc isn't an nth power + if xc != 1 and xc_bits <= n: + return None + + xe, rem = divmod(xe, n) + if rem != 0: + return None + + # compute nth root of xc using Newton's method + a = 1 << -(-_nbits(xc)//n) # initial estimate + while True: + q, r = divmod(xc, a**(n-1)) + if a <= q: + break + else: + a = (a*(n-1) + q)//n + if not (a == q and r == 0): + return None + xc = a + + # now xc*10**xe is the nth root of the original xc*10**xe + # compute mth power of xc*10**xe + + # if m > p*100//_log10_lb(xc) then m > p/log10(xc), hence xc**m > + # 10**p and the result is not representable. + if xc > 1 and m > p*100//_log10_lb(xc): + return None + xc = xc**m + xe *= m + if xc > 10**p: + return None + + # by this point the result *is* exactly representable + # adjust the exponent to get as close as possible to the ideal + # exponent, if necessary + str_xc = str(xc) + if other._isinteger() and other._sign == 0: + ideal_exponent = self._exp*int(other) + zeros = min(xe-ideal_exponent, p-len(str_xc)) + else: + zeros = 0 + return _dec_from_triple(0, str_xc+'0'*zeros, xe-zeros) + + def __pow__(self, other, modulo=None, context=None): + """Return self ** other [ % modulo]. + + With two arguments, compute self**other. + + With three arguments, compute (self**other) % modulo. For the + three argument form, the following restrictions on the + arguments hold: + + - all three arguments must be integral + - other must be nonnegative + - either self or other (or both) must be nonzero + - modulo must be nonzero and must have at most p digits, + where p is the context precision. + + If any of these restrictions is violated the InvalidOperation + flag is raised. + + The result of pow(self, other, modulo) is identical to the + result that would be obtained by computing (self**other) % + modulo with unbounded precision, but is computed more + efficiently. It is always exact. + """ + + if modulo is not None: + return self._power_modulo(other, modulo, context) + + other = _convert_other(other) + if other is NotImplemented: + return other + + if context is None: + context = getcontext() + + # either argument is a NaN => result is NaN + ans = self._check_nans(other, context) + if ans: + return ans + + # 0**0 = NaN (!), x**0 = 1 for nonzero x (including +/-Infinity) + if not other: + if not self: + return context._raise_error(InvalidOperation, '0 ** 0') + else: + return _One + + # result has sign 1 iff self._sign is 1 and other is an odd integer + result_sign = 0 + if self._sign == 1: + if other._isinteger(): + if not other._iseven(): + result_sign = 1 + else: + # -ve**noninteger = NaN + # (-0)**noninteger = 0**noninteger + if self: + return context._raise_error(InvalidOperation, + 'x ** y with x negative and y not an integer') + # negate self, without doing any unwanted rounding + self = self.copy_negate() + + # 0**(+ve or Inf)= 0; 0**(-ve or -Inf) = Infinity + if not self: + if other._sign == 0: + return _dec_from_triple(result_sign, '0', 0) + else: + return _SignedInfinity[result_sign] + + # Inf**(+ve or Inf) = Inf; Inf**(-ve or -Inf) = 0 + if self._isinfinity(): + if other._sign == 0: + return _SignedInfinity[result_sign] + else: + return _dec_from_triple(result_sign, '0', 0) + + # 1**other = 1, but the choice of exponent and the flags + # depend on the exponent of self, and on whether other is a + # positive integer, a negative integer, or neither + if self == _One: + if other._isinteger(): + # exp = max(self._exp*max(int(other), 0), + # 1-context.prec) but evaluating int(other) directly + # is dangerous until we know other is small (other + # could be 1e999999999) + if other._sign == 1: + multiplier = 0 + elif other > context.prec: + multiplier = context.prec + else: + multiplier = int(other) + + exp = self._exp * multiplier + if exp < 1-context.prec: + exp = 1-context.prec + context._raise_error(Rounded) + else: + context._raise_error(Inexact) + context._raise_error(Rounded) + exp = 1-context.prec + + return _dec_from_triple(result_sign, '1'+'0'*-exp, exp) + + # compute adjusted exponent of self + self_adj = self.adjusted() + + # self ** infinity is infinity if self > 1, 0 if self < 1 + # self ** -infinity is infinity if self < 1, 0 if self > 1 + if other._isinfinity(): + if (other._sign == 0) == (self_adj < 0): + return _dec_from_triple(result_sign, '0', 0) + else: + return _SignedInfinity[result_sign] + + # from here on, the result always goes through the call + # to _fix at the end of this function. + ans = None + exact = False + + # crude test to catch cases of extreme overflow/underflow. If + # log10(self)*other >= 10**bound and bound >= len(str(Emax)) + # then 10**bound >= 10**len(str(Emax)) >= Emax+1 and hence + # self**other >= 10**(Emax+1), so overflow occurs. The test + # for underflow is similar. + bound = self._log10_exp_bound() + other.adjusted() + if (self_adj >= 0) == (other._sign == 0): + # self > 1 and other +ve, or self < 1 and other -ve + # possibility of overflow + if bound >= len(str(context.Emax)): + ans = _dec_from_triple(result_sign, '1', context.Emax+1) + else: + # self > 1 and other -ve, or self < 1 and other +ve + # possibility of underflow to 0 + Etiny = context.Etiny() + if bound >= len(str(-Etiny)): + ans = _dec_from_triple(result_sign, '1', Etiny-1) + + # try for an exact result with precision +1 + if ans is None: + ans = self._power_exact(other, context.prec + 1) + if ans is not None: + if result_sign == 1: + ans = _dec_from_triple(1, ans._int, ans._exp) + exact = True + + # usual case: inexact result, x**y computed directly as exp(y*log(x)) + if ans is None: + p = context.prec + x = _WorkRep(self) + xc, xe = x.int, x.exp + y = _WorkRep(other) + yc, ye = y.int, y.exp + if y.sign == 1: + yc = -yc + + # compute correctly rounded result: start with precision +3, + # then increase precision until result is unambiguously roundable + extra = 3 + while True: + coeff, exp = _dpower(xc, xe, yc, ye, p+extra) + if coeff % (5*10**(len(str(coeff))-p-1)): + break + extra += 3 + + ans = _dec_from_triple(result_sign, str(coeff), exp) + + # unlike exp, ln and log10, the power function respects the + # rounding mode; no need to switch to ROUND_HALF_EVEN here + + # There's a difficulty here when 'other' is not an integer and + # the result is exact. In this case, the specification + # requires that the Inexact flag be raised (in spite of + # exactness), but since the result is exact _fix won't do this + # for us. (Correspondingly, the Underflow signal should also + # be raised for subnormal results.) We can't directly raise + # these signals either before or after calling _fix, since + # that would violate the precedence for signals. So we wrap + # the ._fix call in a temporary context, and reraise + # afterwards. + if exact and not other._isinteger(): + # pad with zeros up to length context.prec+1 if necessary; this + # ensures that the Rounded signal will be raised. + if len(ans._int) <= context.prec: + expdiff = context.prec + 1 - len(ans._int) + ans = _dec_from_triple(ans._sign, ans._int+'0'*expdiff, + ans._exp-expdiff) + + # create a copy of the current context, with cleared flags/traps + newcontext = context.copy() + newcontext.clear_flags() + for exception in _signals: + newcontext.traps[exception] = 0 + + # round in the new context + ans = ans._fix(newcontext) + + # raise Inexact, and if necessary, Underflow + newcontext._raise_error(Inexact) + if newcontext.flags[Subnormal]: + newcontext._raise_error(Underflow) + + # propagate signals to the original context; _fix could + # have raised any of Overflow, Underflow, Subnormal, + # Inexact, Rounded, Clamped. Overflow needs the correct + # arguments. Note that the order of the exceptions is + # important here. + if newcontext.flags[Overflow]: + context._raise_error(Overflow, 'above Emax', ans._sign) + for exception in Underflow, Subnormal, Inexact, Rounded, Clamped: + if newcontext.flags[exception]: + context._raise_error(exception) + + else: + ans = ans._fix(context) + + return ans + + def __rpow__(self, other, context=None): + """Swaps self/other and returns __pow__.""" + other = _convert_other(other) + if other is NotImplemented: + return other + return other.__pow__(self, context=context) + + def normalize(self, context=None): + """Normalize- strip trailing 0s, change anything equal to 0 to 0e0""" + + if context is None: + context = getcontext() + + if self._is_special: + ans = self._check_nans(context=context) + if ans: + return ans + + dup = self._fix(context) + if dup._isinfinity(): + return dup + + if not dup: + return _dec_from_triple(dup._sign, '0', 0) + exp_max = [context.Emax, context.Etop()][context.clamp] + end = len(dup._int) + exp = dup._exp + while dup._int[end-1] == '0' and exp < exp_max: + exp += 1 + end -= 1 + return _dec_from_triple(dup._sign, dup._int[:end], exp) + + def quantize(self, exp, rounding=None, context=None): + """Quantize self so its exponent is the same as that of exp. + + Similar to self._rescale(exp._exp) but with error checking. + """ + exp = _convert_other(exp, raiseit=True) + + if context is None: + context = getcontext() + if rounding is None: + rounding = context.rounding + + if self._is_special or exp._is_special: + ans = self._check_nans(exp, context) + if ans: + return ans + + if exp._isinfinity() or self._isinfinity(): + if exp._isinfinity() and self._isinfinity(): + return Decimal(self) # if both are inf, it is OK + return context._raise_error(InvalidOperation, + 'quantize with one INF') + + # exp._exp should be between Etiny and Emax + if not (context.Etiny() <= exp._exp <= context.Emax): + return context._raise_error(InvalidOperation, + 'target exponent out of bounds in quantize') + + if not self: + ans = _dec_from_triple(self._sign, '0', exp._exp) + return ans._fix(context) + + self_adjusted = self.adjusted() + if self_adjusted > context.Emax: + return context._raise_error(InvalidOperation, + 'exponent of quantize result too large for current context') + if self_adjusted - exp._exp + 1 > context.prec: + return context._raise_error(InvalidOperation, + 'quantize result has too many digits for current context') + + ans = self._rescale(exp._exp, rounding) + if ans.adjusted() > context.Emax: + return context._raise_error(InvalidOperation, + 'exponent of quantize result too large for current context') + if len(ans._int) > context.prec: + return context._raise_error(InvalidOperation, + 'quantize result has too many digits for current context') + + # raise appropriate flags + if ans and ans.adjusted() < context.Emin: + context._raise_error(Subnormal) + if ans._exp > self._exp: + if ans != self: + context._raise_error(Inexact) + context._raise_error(Rounded) + + # call to fix takes care of any necessary folddown, and + # signals Clamped if necessary + ans = ans._fix(context) + return ans + + def same_quantum(self, other, context=None): + """Return True if self and other have the same exponent; otherwise + return False. + + If either operand is a special value, the following rules are used: + * return True if both operands are infinities + * return True if both operands are NaNs + * otherwise, return False. + """ + other = _convert_other(other, raiseit=True) + if self._is_special or other._is_special: + return (self.is_nan() and other.is_nan() or + self.is_infinite() and other.is_infinite()) + return self._exp == other._exp + + def _rescale(self, exp, rounding): + """Rescale self so that the exponent is exp, either by padding with zeros + or by truncating digits, using the given rounding mode. + + Specials are returned without change. This operation is + quiet: it raises no flags, and uses no information from the + context. + + exp = exp to scale to (an integer) + rounding = rounding mode + """ + if self._is_special: + return Decimal(self) + if not self: + return _dec_from_triple(self._sign, '0', exp) + + if self._exp >= exp: + # pad answer with zeros if necessary + return _dec_from_triple(self._sign, + self._int + '0'*(self._exp - exp), exp) + + # too many digits; round and lose data. If self.adjusted() < + # exp-1, replace self by 10**(exp-1) before rounding + digits = len(self._int) + self._exp - exp + if digits < 0: + self = _dec_from_triple(self._sign, '1', exp-1) + digits = 0 + this_function = self._pick_rounding_function[rounding] + changed = this_function(self, digits) + coeff = self._int[:digits] or '0' + if changed == 1: + coeff = str(int(coeff)+1) + return _dec_from_triple(self._sign, coeff, exp) + + def _round(self, places, rounding): + """Round a nonzero, nonspecial Decimal to a fixed number of + significant figures, using the given rounding mode. + + Infinities, NaNs and zeros are returned unaltered. + + This operation is quiet: it raises no flags, and uses no + information from the context. + + """ + if places <= 0: + raise ValueError("argument should be at least 1 in _round") + if self._is_special or not self: + return Decimal(self) + ans = self._rescale(self.adjusted()+1-places, rounding) + # it can happen that the rescale alters the adjusted exponent; + # for example when rounding 99.97 to 3 significant figures. + # When this happens we end up with an extra 0 at the end of + # the number; a second rescale fixes this. + if ans.adjusted() != self.adjusted(): + ans = ans._rescale(ans.adjusted()+1-places, rounding) + return ans + + def to_integral_exact(self, rounding=None, context=None): + """Rounds to a nearby integer. + + If no rounding mode is specified, take the rounding mode from + the context. This method raises the Rounded and Inexact flags + when appropriate. + + See also: to_integral_value, which does exactly the same as + this method except that it doesn't raise Inexact or Rounded. + """ + if self._is_special: + ans = self._check_nans(context=context) + if ans: + return ans + return Decimal(self) + if self._exp >= 0: + return Decimal(self) + if not self: + return _dec_from_triple(self._sign, '0', 0) + if context is None: + context = getcontext() + if rounding is None: + rounding = context.rounding + ans = self._rescale(0, rounding) + if ans != self: + context._raise_error(Inexact) + context._raise_error(Rounded) + return ans + + def to_integral_value(self, rounding=None, context=None): + """Rounds to the nearest integer, without raising inexact, rounded.""" + if context is None: + context = getcontext() + if rounding is None: + rounding = context.rounding + if self._is_special: + ans = self._check_nans(context=context) + if ans: + return ans + return Decimal(self) + if self._exp >= 0: + return Decimal(self) + else: + return self._rescale(0, rounding) + + # the method name changed, but we provide also the old one, for compatibility + to_integral = to_integral_value + + def sqrt(self, context=None): + """Return the square root of self.""" + if context is None: + context = getcontext() + + if self._is_special: + ans = self._check_nans(context=context) + if ans: + return ans + + if self._isinfinity() and self._sign == 0: + return Decimal(self) + + if not self: + # exponent = self._exp // 2. sqrt(-0) = -0 + ans = _dec_from_triple(self._sign, '0', self._exp // 2) + return ans._fix(context) + + if self._sign == 1: + return context._raise_error(InvalidOperation, 'sqrt(-x), x > 0') + + # At this point self represents a positive number. Let p be + # the desired precision and express self in the form c*100**e + # with c a positive real number and e an integer, c and e + # being chosen so that 100**(p-1) <= c < 100**p. Then the + # (exact) square root of self is sqrt(c)*10**e, and 10**(p-1) + # <= sqrt(c) < 10**p, so the closest representable Decimal at + # precision p is n*10**e where n = round_half_even(sqrt(c)), + # the closest integer to sqrt(c) with the even integer chosen + # in the case of a tie. + # + # To ensure correct rounding in all cases, we use the + # following trick: we compute the square root to an extra + # place (precision p+1 instead of precision p), rounding down. + # Then, if the result is inexact and its last digit is 0 or 5, + # we increase the last digit to 1 or 6 respectively; if it's + # exact we leave the last digit alone. Now the final round to + # p places (or fewer in the case of underflow) will round + # correctly and raise the appropriate flags. + + # use an extra digit of precision + prec = context.prec+1 + + # write argument in the form c*100**e where e = self._exp//2 + # is the 'ideal' exponent, to be used if the square root is + # exactly representable. l is the number of 'digits' of c in + # base 100, so that 100**(l-1) <= c < 100**l. + op = _WorkRep(self) + e = op.exp >> 1 + if op.exp & 1: + c = op.int * 10 + l = (len(self._int) >> 1) + 1 + else: + c = op.int + l = len(self._int)+1 >> 1 + + # rescale so that c has exactly prec base 100 'digits' + shift = prec-l + if shift >= 0: + c *= 100**shift + exact = True + else: + c, remainder = divmod(c, 100**-shift) + exact = not remainder + e -= shift + + # find n = floor(sqrt(c)) using Newton's method + n = 10**prec + while True: + q = c//n + if n <= q: + break + else: + n = n + q >> 1 + exact = exact and n*n == c + + if exact: + # result is exact; rescale to use ideal exponent e + if shift >= 0: + # assert n % 10**shift == 0 + n //= 10**shift + else: + n *= 10**-shift + e += shift + else: + # result is not exact; fix last digit as described above + if n % 5 == 0: + n += 1 + + ans = _dec_from_triple(0, str(n), e) + + # round, and fit to current context + context = context._shallow_copy() + rounding = context._set_rounding(ROUND_HALF_EVEN) + ans = ans._fix(context) + context.rounding = rounding + + return ans + + def max(self, other, context=None): + """Returns the larger value. + + Like max(self, other) except if one is not a number, returns + NaN (and signals if one is sNaN). Also rounds. + """ + other = _convert_other(other, raiseit=True) + + if context is None: + context = getcontext() + + if self._is_special or other._is_special: + # If one operand is a quiet NaN and the other is number, then the + # number is always returned + sn = self._isnan() + on = other._isnan() + if sn or on: + if on == 1 and sn == 0: + return self._fix(context) + if sn == 1 and on == 0: + return other._fix(context) + return self._check_nans(other, context) + + c = self._cmp(other) + if c == 0: + # If both operands are finite and equal in numerical value + # then an ordering is applied: + # + # If the signs differ then max returns the operand with the + # positive sign and min returns the operand with the negative sign + # + # If the signs are the same then the exponent is used to select + # the result. This is exactly the ordering used in compare_total. + c = self.compare_total(other) + + if c == -1: + ans = other + else: + ans = self + + return ans._fix(context) + + def min(self, other, context=None): + """Returns the smaller value. + + Like min(self, other) except if one is not a number, returns + NaN (and signals if one is sNaN). Also rounds. + """ + other = _convert_other(other, raiseit=True) + + if context is None: + context = getcontext() + + if self._is_special or other._is_special: + # If one operand is a quiet NaN and the other is number, then the + # number is always returned + sn = self._isnan() + on = other._isnan() + if sn or on: + if on == 1 and sn == 0: + return self._fix(context) + if sn == 1 and on == 0: + return other._fix(context) + return self._check_nans(other, context) + + c = self._cmp(other) + if c == 0: + c = self.compare_total(other) + + if c == -1: + ans = self + else: + ans = other + + return ans._fix(context) + + def _isinteger(self): + """Returns whether self is an integer""" + if self._is_special: + return False + if self._exp >= 0: + return True + rest = self._int[self._exp:] + return rest == '0'*len(rest) + + def _iseven(self): + """Returns True if self is even. Assumes self is an integer.""" + if not self or self._exp > 0: + return True + return self._int[-1+self._exp] in '02468' + + def adjusted(self): + """Return the adjusted exponent of self""" + try: + return self._exp + len(self._int) - 1 + # If NaN or Infinity, self._exp is string + except TypeError: + return 0 + + def canonical(self): + """Returns the same Decimal object. + + As we do not have different encodings for the same number, the + received object already is in its canonical form. + """ + return self + + def compare_signal(self, other, context=None): + """Compares self to the other operand numerically. + + It's pretty much like compare(), but all NaNs signal, with signaling + NaNs taking precedence over quiet NaNs. + """ + other = _convert_other(other, raiseit = True) + ans = self._compare_check_nans(other, context) + if ans: + return ans + return self.compare(other, context=context) + + def compare_total(self, other, context=None): + """Compares self to other using the abstract representations. + + This is not like the standard compare, which use their numerical + value. Note that a total ordering is defined for all possible abstract + representations. + """ + other = _convert_other(other, raiseit=True) + + # if one is negative and the other is positive, it's easy + if self._sign and not other._sign: + return _NegativeOne + if not self._sign and other._sign: + return _One + sign = self._sign + + # let's handle both NaN types + self_nan = self._isnan() + other_nan = other._isnan() + if self_nan or other_nan: + if self_nan == other_nan: + # compare payloads as though they're integers + self_key = len(self._int), self._int + other_key = len(other._int), other._int + if self_key < other_key: + if sign: + return _One + else: + return _NegativeOne + if self_key > other_key: + if sign: + return _NegativeOne + else: + return _One + return _Zero + + if sign: + if self_nan == 1: + return _NegativeOne + if other_nan == 1: + return _One + if self_nan == 2: + return _NegativeOne + if other_nan == 2: + return _One + else: + if self_nan == 1: + return _One + if other_nan == 1: + return _NegativeOne + if self_nan == 2: + return _One + if other_nan == 2: + return _NegativeOne + + if self < other: + return _NegativeOne + if self > other: + return _One + + if self._exp < other._exp: + if sign: + return _One + else: + return _NegativeOne + if self._exp > other._exp: + if sign: + return _NegativeOne + else: + return _One + return _Zero + + + def compare_total_mag(self, other, context=None): + """Compares self to other using abstract repr., ignoring sign. + + Like compare_total, but with operand's sign ignored and assumed to be 0. + """ + other = _convert_other(other, raiseit=True) + + s = self.copy_abs() + o = other.copy_abs() + return s.compare_total(o) + + def copy_abs(self): + """Returns a copy with the sign set to 0. """ + return _dec_from_triple(0, self._int, self._exp, self._is_special) + + def copy_negate(self): + """Returns a copy with the sign inverted.""" + if self._sign: + return _dec_from_triple(0, self._int, self._exp, self._is_special) + else: + return _dec_from_triple(1, self._int, self._exp, self._is_special) + + def copy_sign(self, other, context=None): + """Returns self with the sign of other.""" + other = _convert_other(other, raiseit=True) + return _dec_from_triple(other._sign, self._int, + self._exp, self._is_special) + + def exp(self, context=None): + """Returns e ** self.""" + + if context is None: + context = getcontext() + + # exp(NaN) = NaN + ans = self._check_nans(context=context) + if ans: + return ans + + # exp(-Infinity) = 0 + if self._isinfinity() == -1: + return _Zero + + # exp(0) = 1 + if not self: + return _One + + # exp(Infinity) = Infinity + if self._isinfinity() == 1: + return Decimal(self) + + # the result is now guaranteed to be inexact (the true + # mathematical result is transcendental). There's no need to + # raise Rounded and Inexact here---they'll always be raised as + # a result of the call to _fix. + p = context.prec + adj = self.adjusted() + + # we only need to do any computation for quite a small range + # of adjusted exponents---for example, -29 <= adj <= 10 for + # the default context. For smaller exponent the result is + # indistinguishable from 1 at the given precision, while for + # larger exponent the result either overflows or underflows. + if self._sign == 0 and adj > len(str((context.Emax+1)*3)): + # overflow + ans = _dec_from_triple(0, '1', context.Emax+1) + elif self._sign == 1 and adj > len(str((-context.Etiny()+1)*3)): + # underflow to 0 + ans = _dec_from_triple(0, '1', context.Etiny()-1) + elif self._sign == 0 and adj < -p: + # p+1 digits; final round will raise correct flags + ans = _dec_from_triple(0, '1' + '0'*(p-1) + '1', -p) + elif self._sign == 1 and adj < -p-1: + # p+1 digits; final round will raise correct flags + ans = _dec_from_triple(0, '9'*(p+1), -p-1) + # general case + else: + op = _WorkRep(self) + c, e = op.int, op.exp + if op.sign == 1: + c = -c + + # compute correctly rounded result: increase precision by + # 3 digits at a time until we get an unambiguously + # roundable result + extra = 3 + while True: + coeff, exp = _dexp(c, e, p+extra) + if coeff % (5*10**(len(str(coeff))-p-1)): + break + extra += 3 + + ans = _dec_from_triple(0, str(coeff), exp) + + # at this stage, ans should round correctly with *any* + # rounding mode, not just with ROUND_HALF_EVEN + context = context._shallow_copy() + rounding = context._set_rounding(ROUND_HALF_EVEN) + ans = ans._fix(context) + context.rounding = rounding + + return ans + + def is_canonical(self): + """Return True if self is canonical; otherwise return False. + + Currently, the encoding of a Decimal instance is always + canonical, so this method returns True for any Decimal. + """ + return True + + def is_finite(self): + """Return True if self is finite; otherwise return False. + + A Decimal instance is considered finite if it is neither + infinite nor a NaN. + """ + return not self._is_special + + def is_infinite(self): + """Return True if self is infinite; otherwise return False.""" + return self._exp == 'F' + + def is_nan(self): + """Return True if self is a qNaN or sNaN; otherwise return False.""" + return self._exp in ('n', 'N') + + def is_normal(self, context=None): + """Return True if self is a normal number; otherwise return False.""" + if self._is_special or not self: + return False + if context is None: + context = getcontext() + return context.Emin <= self.adjusted() + + def is_qnan(self): + """Return True if self is a quiet NaN; otherwise return False.""" + return self._exp == 'n' + + def is_signed(self): + """Return True if self is negative; otherwise return False.""" + return self._sign == 1 + + def is_snan(self): + """Return True if self is a signaling NaN; otherwise return False.""" + return self._exp == 'N' + + def is_subnormal(self, context=None): + """Return True if self is subnormal; otherwise return False.""" + if self._is_special or not self: + return False + if context is None: + context = getcontext() + return self.adjusted() < context.Emin + + def is_zero(self): + """Return True if self is a zero; otherwise return False.""" + return not self._is_special and self._int == '0' + + def _ln_exp_bound(self): + """Compute a lower bound for the adjusted exponent of self.ln(). + In other words, compute r such that self.ln() >= 10**r. Assumes + that self is finite and positive and that self != 1. + """ + + # for 0.1 <= x <= 10 we use the inequalities 1-1/x <= ln(x) <= x-1 + adj = self._exp + len(self._int) - 1 + if adj >= 1: + # argument >= 10; we use 23/10 = 2.3 as a lower bound for ln(10) + return len(str(adj*23//10)) - 1 + if adj <= -2: + # argument <= 0.1 + return len(str((-1-adj)*23//10)) - 1 + op = _WorkRep(self) + c, e = op.int, op.exp + if adj == 0: + # 1 < self < 10 + num = str(c-10**-e) + den = str(c) + return len(num) - len(den) - (num < den) + # adj == -1, 0.1 <= self < 1 + return e + len(str(10**-e - c)) - 1 + + + def ln(self, context=None): + """Returns the natural (base e) logarithm of self.""" + + if context is None: + context = getcontext() + + # ln(NaN) = NaN + ans = self._check_nans(context=context) + if ans: + return ans + + # ln(0.0) == -Infinity + if not self: + return _NegativeInfinity + + # ln(Infinity) = Infinity + if self._isinfinity() == 1: + return _Infinity + + # ln(1.0) == 0.0 + if self == _One: + return _Zero + + # ln(negative) raises InvalidOperation + if self._sign == 1: + return context._raise_error(InvalidOperation, + 'ln of a negative value') + + # result is irrational, so necessarily inexact + op = _WorkRep(self) + c, e = op.int, op.exp + p = context.prec + + # correctly rounded result: repeatedly increase precision by 3 + # until we get an unambiguously roundable result + places = p - self._ln_exp_bound() + 2 # at least p+3 places + while True: + coeff = _dlog(c, e, places) + # assert len(str(abs(coeff)))-p >= 1 + if coeff % (5*10**(len(str(abs(coeff)))-p-1)): + break + places += 3 + ans = _dec_from_triple(int(coeff<0), str(abs(coeff)), -places) + + context = context._shallow_copy() + rounding = context._set_rounding(ROUND_HALF_EVEN) + ans = ans._fix(context) + context.rounding = rounding + return ans + + def _log10_exp_bound(self): + """Compute a lower bound for the adjusted exponent of self.log10(). + In other words, find r such that self.log10() >= 10**r. + Assumes that self is finite and positive and that self != 1. + """ + + # For x >= 10 or x < 0.1 we only need a bound on the integer + # part of log10(self), and this comes directly from the + # exponent of x. For 0.1 <= x <= 10 we use the inequalities + # 1-1/x <= log(x) <= x-1. If x > 1 we have |log10(x)| > + # (1-1/x)/2.31 > 0. If x < 1 then |log10(x)| > (1-x)/2.31 > 0 + + adj = self._exp + len(self._int) - 1 + if adj >= 1: + # self >= 10 + return len(str(adj))-1 + if adj <= -2: + # self < 0.1 + return len(str(-1-adj))-1 + op = _WorkRep(self) + c, e = op.int, op.exp + if adj == 0: + # 1 < self < 10 + num = str(c-10**-e) + den = str(231*c) + return len(num) - len(den) - (num < den) + 2 + # adj == -1, 0.1 <= self < 1 + num = str(10**-e-c) + return len(num) + e - (num < "231") - 1 + + def log10(self, context=None): + """Returns the base 10 logarithm of self.""" + + if context is None: + context = getcontext() + + # log10(NaN) = NaN + ans = self._check_nans(context=context) + if ans: + return ans + + # log10(0.0) == -Infinity + if not self: + return _NegativeInfinity + + # log10(Infinity) = Infinity + if self._isinfinity() == 1: + return _Infinity + + # log10(negative or -Infinity) raises InvalidOperation + if self._sign == 1: + return context._raise_error(InvalidOperation, + 'log10 of a negative value') + + # log10(10**n) = n + if self._int[0] == '1' and self._int[1:] == '0'*(len(self._int) - 1): + # answer may need rounding + ans = Decimal(self._exp + len(self._int) - 1) + else: + # result is irrational, so necessarily inexact + op = _WorkRep(self) + c, e = op.int, op.exp + p = context.prec + + # correctly rounded result: repeatedly increase precision + # until result is unambiguously roundable + places = p-self._log10_exp_bound()+2 + while True: + coeff = _dlog10(c, e, places) + # assert len(str(abs(coeff)))-p >= 1 + if coeff % (5*10**(len(str(abs(coeff)))-p-1)): + break + places += 3 + ans = _dec_from_triple(int(coeff<0), str(abs(coeff)), -places) + + context = context._shallow_copy() + rounding = context._set_rounding(ROUND_HALF_EVEN) + ans = ans._fix(context) + context.rounding = rounding + return ans + + def logb(self, context=None): + """ Returns the exponent of the magnitude of self's MSD. + + The result is the integer which is the exponent of the magnitude + of the most significant digit of self (as though it were truncated + to a single digit while maintaining the value of that digit and + without limiting the resulting exponent). + """ + # logb(NaN) = NaN + ans = self._check_nans(context=context) + if ans: + return ans + + if context is None: + context = getcontext() + + # logb(+/-Inf) = +Inf + if self._isinfinity(): + return _Infinity + + # logb(0) = -Inf, DivisionByZero + if not self: + return context._raise_error(DivisionByZero, 'logb(0)', 1) + + # otherwise, simply return the adjusted exponent of self, as a + # Decimal. Note that no attempt is made to fit the result + # into the current context. + ans = Decimal(self.adjusted()) + return ans._fix(context) + + def _islogical(self): + """Return True if self is a logical operand. + + For being logical, it must be a finite number with a sign of 0, + an exponent of 0, and a coefficient whose digits must all be + either 0 or 1. + """ + if self._sign != 0 or self._exp != 0: + return False + for dig in self._int: + if dig not in '01': + return False + return True + + def _fill_logical(self, context, opa, opb): + dif = context.prec - len(opa) + if dif > 0: + opa = '0'*dif + opa + elif dif < 0: + opa = opa[-context.prec:] + dif = context.prec - len(opb) + if dif > 0: + opb = '0'*dif + opb + elif dif < 0: + opb = opb[-context.prec:] + return opa, opb + + def logical_and(self, other, context=None): + """Applies an 'and' operation between self and other's digits.""" + if context is None: + context = getcontext() + + other = _convert_other(other, raiseit=True) + + if not self._islogical() or not other._islogical(): + return context._raise_error(InvalidOperation) + + # fill to context.prec + (opa, opb) = self._fill_logical(context, self._int, other._int) + + # make the operation, and clean starting zeroes + result = "".join([str(int(a)&int(b)) for a,b in zip(opa,opb)]) + return _dec_from_triple(0, result.lstrip('0') or '0', 0) + + def logical_invert(self, context=None): + """Invert all its digits.""" + if context is None: + context = getcontext() + return self.logical_xor(_dec_from_triple(0,'1'*context.prec,0), + context) + + def logical_or(self, other, context=None): + """Applies an 'or' operation between self and other's digits.""" + if context is None: + context = getcontext() + + other = _convert_other(other, raiseit=True) + + if not self._islogical() or not other._islogical(): + return context._raise_error(InvalidOperation) + + # fill to context.prec + (opa, opb) = self._fill_logical(context, self._int, other._int) + + # make the operation, and clean starting zeroes + result = "".join([str(int(a)|int(b)) for a,b in zip(opa,opb)]) + return _dec_from_triple(0, result.lstrip('0') or '0', 0) + + def logical_xor(self, other, context=None): + """Applies an 'xor' operation between self and other's digits.""" + if context is None: + context = getcontext() + + other = _convert_other(other, raiseit=True) + + if not self._islogical() or not other._islogical(): + return context._raise_error(InvalidOperation) + + # fill to context.prec + (opa, opb) = self._fill_logical(context, self._int, other._int) + + # make the operation, and clean starting zeroes + result = "".join([str(int(a)^int(b)) for a,b in zip(opa,opb)]) + return _dec_from_triple(0, result.lstrip('0') or '0', 0) + + def max_mag(self, other, context=None): + """Compares the values numerically with their sign ignored.""" + other = _convert_other(other, raiseit=True) + + if context is None: + context = getcontext() + + if self._is_special or other._is_special: + # If one operand is a quiet NaN and the other is number, then the + # number is always returned + sn = self._isnan() + on = other._isnan() + if sn or on: + if on == 1 and sn == 0: + return self._fix(context) + if sn == 1 and on == 0: + return other._fix(context) + return self._check_nans(other, context) + + c = self.copy_abs()._cmp(other.copy_abs()) + if c == 0: + c = self.compare_total(other) + + if c == -1: + ans = other + else: + ans = self + + return ans._fix(context) + + def min_mag(self, other, context=None): + """Compares the values numerically with their sign ignored.""" + other = _convert_other(other, raiseit=True) + + if context is None: + context = getcontext() + + if self._is_special or other._is_special: + # If one operand is a quiet NaN and the other is number, then the + # number is always returned + sn = self._isnan() + on = other._isnan() + if sn or on: + if on == 1 and sn == 0: + return self._fix(context) + if sn == 1 and on == 0: + return other._fix(context) + return self._check_nans(other, context) + + c = self.copy_abs()._cmp(other.copy_abs()) + if c == 0: + c = self.compare_total(other) + + if c == -1: + ans = self + else: + ans = other + + return ans._fix(context) + + def next_minus(self, context=None): + """Returns the largest representable number smaller than itself.""" + if context is None: + context = getcontext() + + ans = self._check_nans(context=context) + if ans: + return ans + + if self._isinfinity() == -1: + return _NegativeInfinity + if self._isinfinity() == 1: + return _dec_from_triple(0, '9'*context.prec, context.Etop()) + + context = context.copy() + context._set_rounding(ROUND_FLOOR) + context._ignore_all_flags() + new_self = self._fix(context) + if new_self != self: + return new_self + return self.__sub__(_dec_from_triple(0, '1', context.Etiny()-1), + context) + + def next_plus(self, context=None): + """Returns the smallest representable number larger than itself.""" + if context is None: + context = getcontext() + + ans = self._check_nans(context=context) + if ans: + return ans + + if self._isinfinity() == 1: + return _Infinity + if self._isinfinity() == -1: + return _dec_from_triple(1, '9'*context.prec, context.Etop()) + + context = context.copy() + context._set_rounding(ROUND_CEILING) + context._ignore_all_flags() + new_self = self._fix(context) + if new_self != self: + return new_self + return self.__add__(_dec_from_triple(0, '1', context.Etiny()-1), + context) + + def next_toward(self, other, context=None): + """Returns the number closest to self, in the direction towards other. + + The result is the closest representable number to self + (excluding self) that is in the direction towards other, + unless both have the same value. If the two operands are + numerically equal, then the result is a copy of self with the + sign set to be the same as the sign of other. + """ + other = _convert_other(other, raiseit=True) + + if context is None: + context = getcontext() + + ans = self._check_nans(other, context) + if ans: + return ans + + comparison = self._cmp(other) + if comparison == 0: + return self.copy_sign(other) + + if comparison == -1: + ans = self.next_plus(context) + else: # comparison == 1 + ans = self.next_minus(context) + + # decide which flags to raise using value of ans + if ans._isinfinity(): + context._raise_error(Overflow, + 'Infinite result from next_toward', + ans._sign) + context._raise_error(Inexact) + context._raise_error(Rounded) + elif ans.adjusted() < context.Emin: + context._raise_error(Underflow) + context._raise_error(Subnormal) + context._raise_error(Inexact) + context._raise_error(Rounded) + # if precision == 1 then we don't raise Clamped for a + # result 0E-Etiny. + if not ans: + context._raise_error(Clamped) + + return ans + + def number_class(self, context=None): + """Returns an indication of the class of self. + + The class is one of the following strings: + sNaN + NaN + -Infinity + -Normal + -Subnormal + -Zero + +Zero + +Subnormal + +Normal + +Infinity + """ + if self.is_snan(): + return "sNaN" + if self.is_qnan(): + return "NaN" + inf = self._isinfinity() + if inf == 1: + return "+Infinity" + if inf == -1: + return "-Infinity" + if self.is_zero(): + if self._sign: + return "-Zero" + else: + return "+Zero" + if context is None: + context = getcontext() + if self.is_subnormal(context=context): + if self._sign: + return "-Subnormal" + else: + return "+Subnormal" + # just a normal, regular, boring number, :) + if self._sign: + return "-Normal" + else: + return "+Normal" + + def radix(self): + """Just returns 10, as this is Decimal, :)""" + return Decimal(10) + + def rotate(self, other, context=None): + """Returns a rotated copy of self, value-of-other times.""" + if context is None: + context = getcontext() + + other = _convert_other(other, raiseit=True) + + ans = self._check_nans(other, context) + if ans: + return ans + + if other._exp != 0: + return context._raise_error(InvalidOperation) + if not (-context.prec <= int(other) <= context.prec): + return context._raise_error(InvalidOperation) + + if self._isinfinity(): + return Decimal(self) + + # get values, pad if necessary + torot = int(other) + rotdig = self._int + topad = context.prec - len(rotdig) + if topad > 0: + rotdig = '0'*topad + rotdig + elif topad < 0: + rotdig = rotdig[-topad:] + + # let's rotate! + rotated = rotdig[torot:] + rotdig[:torot] + return _dec_from_triple(self._sign, + rotated.lstrip('0') or '0', self._exp) + + def scaleb(self, other, context=None): + """Returns self operand after adding the second value to its exp.""" + if context is None: + context = getcontext() + + other = _convert_other(other, raiseit=True) + + ans = self._check_nans(other, context) + if ans: + return ans + + if other._exp != 0: + return context._raise_error(InvalidOperation) + liminf = -2 * (context.Emax + context.prec) + limsup = 2 * (context.Emax + context.prec) + if not (liminf <= int(other) <= limsup): + return context._raise_error(InvalidOperation) + + if self._isinfinity(): + return Decimal(self) + + d = _dec_from_triple(self._sign, self._int, self._exp + int(other)) + d = d._fix(context) + return d + + def shift(self, other, context=None): + """Returns a shifted copy of self, value-of-other times.""" + if context is None: + context = getcontext() + + other = _convert_other(other, raiseit=True) + + ans = self._check_nans(other, context) + if ans: + return ans + + if other._exp != 0: + return context._raise_error(InvalidOperation) + if not (-context.prec <= int(other) <= context.prec): + return context._raise_error(InvalidOperation) + + if self._isinfinity(): + return Decimal(self) + + # get values, pad if necessary + torot = int(other) + rotdig = self._int + topad = context.prec - len(rotdig) + if topad > 0: + rotdig = '0'*topad + rotdig + elif topad < 0: + rotdig = rotdig[-topad:] + + # let's shift! + if torot < 0: + shifted = rotdig[:torot] + else: + shifted = rotdig + '0'*torot + shifted = shifted[-context.prec:] + + return _dec_from_triple(self._sign, + shifted.lstrip('0') or '0', self._exp) + + # Support for pickling, copy, and deepcopy + def __reduce__(self): + return (self.__class__, (str(self),)) + + def __copy__(self): + if type(self) is Decimal: + return self # I'm immutable; therefore I am my own clone + return self.__class__(str(self)) + + def __deepcopy__(self, memo): + if type(self) is Decimal: + return self # My components are also immutable + return self.__class__(str(self)) + + # PEP 3101 support. the _localeconv keyword argument should be + # considered private: it's provided for ease of testing only. + def __format__(self, specifier, context=None, _localeconv=None): + """Format a Decimal instance according to the given specifier. + + The specifier should be a standard format specifier, with the + form described in PEP 3101. Formatting types 'e', 'E', 'f', + 'F', 'g', 'G', 'n' and '%' are supported. If the formatting + type is omitted it defaults to 'g' or 'G', depending on the + value of context.capitals. + """ + + # Note: PEP 3101 says that if the type is not present then + # there should be at least one digit after the decimal point. + # We take the liberty of ignoring this requirement for + # Decimal---it's presumably there to make sure that + # format(float, '') behaves similarly to str(float). + if context is None: + context = getcontext() + + spec = _parse_format_specifier(specifier, _localeconv=_localeconv) + + # special values don't care about the type or precision + if self._is_special: + sign = _format_sign(self._sign, spec) + body = str(self.copy_abs()) + if spec['type'] == '%': + body += '%' + return _format_align(sign, body, spec) + + # a type of None defaults to 'g' or 'G', depending on context + if spec['type'] is None: + spec['type'] = ['g', 'G'][context.capitals] + + # if type is '%', adjust exponent of self accordingly + if spec['type'] == '%': + self = _dec_from_triple(self._sign, self._int, self._exp+2) + + # round if necessary, taking rounding mode from the context + rounding = context.rounding + precision = spec['precision'] + if precision is not None: + if spec['type'] in 'eE': + self = self._round(precision+1, rounding) + elif spec['type'] in 'fF%': + self = self._rescale(-precision, rounding) + elif spec['type'] in 'gG' and len(self._int) > precision: + self = self._round(precision, rounding) + # special case: zeros with a positive exponent can't be + # represented in fixed point; rescale them to 0e0. + if not self and self._exp > 0 and spec['type'] in 'fF%': + self = self._rescale(0, rounding) + + # figure out placement of the decimal point + leftdigits = self._exp + len(self._int) + if spec['type'] in 'eE': + if not self and precision is not None: + dotplace = 1 - precision + else: + dotplace = 1 + elif spec['type'] in 'fF%': + dotplace = leftdigits + elif spec['type'] in 'gG': + if self._exp <= 0 and leftdigits > -6: + dotplace = leftdigits + else: + dotplace = 1 + + # find digits before and after decimal point, and get exponent + if dotplace < 0: + intpart = '0' + fracpart = '0'*(-dotplace) + self._int + elif dotplace > len(self._int): + intpart = self._int + '0'*(dotplace-len(self._int)) + fracpart = '' + else: + intpart = self._int[:dotplace] or '0' + fracpart = self._int[dotplace:] + exp = leftdigits-dotplace + + # done with the decimal-specific stuff; hand over the rest + # of the formatting to the _format_number function + return _format_number(self._sign, intpart, fracpart, exp, spec) + +def _dec_from_triple(sign, coefficient, exponent, special=False): + """Create a decimal instance directly, without any validation, + normalization (e.g. removal of leading zeros) or argument + conversion. + + This function is for *internal use only*. + """ + + self = object.__new__(Decimal) + self._sign = sign + self._int = coefficient + self._exp = exponent + self._is_special = special + + return self + +# Register Decimal as a kind of Number (an abstract base class). +# However, do not register it as Real (because Decimals are not +# interoperable with floats). +_numbers.Number.register(Decimal) + + +##### Context class ####################################################### + +class _ContextManager(object): + """Context manager class to support localcontext(). + + Sets a copy of the supplied context in __enter__() and restores + the previous decimal context in __exit__() + """ + def __init__(self, new_context): + self.new_context = new_context.copy() + def __enter__(self): + self.saved_context = getcontext() + setcontext(self.new_context) + return self.new_context + def __exit__(self, t, v, tb): + setcontext(self.saved_context) + +class Context(object): + """Contains the context for a Decimal instance. + + Contains: + prec - precision (for use in rounding, division, square roots..) + rounding - rounding type (how you round) + traps - If traps[exception] = 1, then the exception is + raised when it is caused. Otherwise, a value is + substituted in. + flags - When an exception is caused, flags[exception] is set. + (Whether or not the trap_enabler is set) + Should be reset by user of Decimal instance. + Emin - Minimum exponent + Emax - Maximum exponent + capitals - If 1, 1*10^1 is printed as 1E+1. + If 0, printed as 1e1 + clamp - If 1, change exponents if too high (Default 0) + """ + + def __init__(self, prec=None, rounding=None, Emin=None, Emax=None, + capitals=None, clamp=None, flags=None, traps=None, + _ignored_flags=None): + # Set defaults; for everything except flags and _ignored_flags, + # inherit from DefaultContext. + try: + dc = DefaultContext + except NameError: + pass + + self.prec = prec if prec is not None else dc.prec + self.rounding = rounding if rounding is not None else dc.rounding + self.Emin = Emin if Emin is not None else dc.Emin + self.Emax = Emax if Emax is not None else dc.Emax + self.capitals = capitals if capitals is not None else dc.capitals + self.clamp = clamp if clamp is not None else dc.clamp + + if _ignored_flags is None: + self._ignored_flags = [] + else: + self._ignored_flags = _ignored_flags + + if traps is None: + self.traps = dc.traps.copy() + elif not isinstance(traps, dict): + self.traps = dict((s, int(s in traps)) for s in _signals + traps) + else: + self.traps = traps + + if flags is None: + self.flags = dict.fromkeys(_signals, 0) + elif not isinstance(flags, dict): + self.flags = dict((s, int(s in flags)) for s in _signals + flags) + else: + self.flags = flags + + def _set_integer_check(self, name, value, vmin, vmax): + if not isinstance(value, int): + raise TypeError("%s must be an integer" % name) + if vmin == '-inf': + if value > vmax: + raise ValueError("%s must be in [%s, %d]. got: %s" % (name, vmin, vmax, value)) + elif vmax == 'inf': + if value < vmin: + raise ValueError("%s must be in [%d, %s]. got: %s" % (name, vmin, vmax, value)) + else: + if value < vmin or value > vmax: + raise ValueError("%s must be in [%d, %d]. got %s" % (name, vmin, vmax, value)) + return object.__setattr__(self, name, value) + + def _set_signal_dict(self, name, d): + if not isinstance(d, dict): + raise TypeError("%s must be a signal dict" % d) + for key in d: + if not key in _signals: + raise KeyError("%s is not a valid signal dict" % d) + for key in _signals: + if not key in d: + raise KeyError("%s is not a valid signal dict" % d) + return object.__setattr__(self, name, d) + + def __setattr__(self, name, value): + if name == 'prec': + return self._set_integer_check(name, value, 1, 'inf') + elif name == 'Emin': + return self._set_integer_check(name, value, '-inf', 0) + elif name == 'Emax': + return self._set_integer_check(name, value, 0, 'inf') + elif name == 'capitals': + return self._set_integer_check(name, value, 0, 1) + elif name == 'clamp': + return self._set_integer_check(name, value, 0, 1) + elif name == 'rounding': + if not value in _rounding_modes: + # raise TypeError even for strings to have consistency + # among various implementations. + raise TypeError("%s: invalid rounding mode" % value) + return object.__setattr__(self, name, value) + elif name == 'flags' or name == 'traps': + return self._set_signal_dict(name, value) + elif name == '_ignored_flags': + return object.__setattr__(self, name, value) + else: + raise AttributeError( + "'decimal.Context' object has no attribute '%s'" % name) + + def __delattr__(self, name): + raise AttributeError("%s cannot be deleted" % name) + + # Support for pickling, copy, and deepcopy + def __reduce__(self): + flags = [sig for sig, v in self.flags.items() if v] + traps = [sig for sig, v in self.traps.items() if v] + return (self.__class__, + (self.prec, self.rounding, self.Emin, self.Emax, + self.capitals, self.clamp, flags, traps)) + + def __repr__(self): + """Show the current context.""" + s = [] + s.append('Context(prec=%(prec)d, rounding=%(rounding)s, ' + 'Emin=%(Emin)d, Emax=%(Emax)d, capitals=%(capitals)d, ' + 'clamp=%(clamp)d' + % vars(self)) + names = [f.__name__ for f, v in self.flags.items() if v] + s.append('flags=[' + ', '.join(names) + ']') + names = [t.__name__ for t, v in self.traps.items() if v] + s.append('traps=[' + ', '.join(names) + ']') + return ', '.join(s) + ')' + + def clear_flags(self): + """Reset all flags to zero""" + for flag in self.flags: + self.flags[flag] = 0 + + def clear_traps(self): + """Reset all traps to zero""" + for flag in self.traps: + self.traps[flag] = 0 + + def _shallow_copy(self): + """Returns a shallow copy from self.""" + nc = Context(self.prec, self.rounding, self.Emin, self.Emax, + self.capitals, self.clamp, self.flags, self.traps, + self._ignored_flags) + return nc + + def copy(self): + """Returns a deep copy from self.""" + nc = Context(self.prec, self.rounding, self.Emin, self.Emax, + self.capitals, self.clamp, + self.flags.copy(), self.traps.copy(), + self._ignored_flags) + return nc + __copy__ = copy + + def _raise_error(self, condition, explanation = None, *args): + """Handles an error + + If the flag is in _ignored_flags, returns the default response. + Otherwise, it sets the flag, then, if the corresponding + trap_enabler is set, it reraises the exception. Otherwise, it returns + the default value after setting the flag. + """ + error = _condition_map.get(condition, condition) + if error in self._ignored_flags: + # Don't touch the flag + return error().handle(self, *args) + + self.flags[error] = 1 + if not self.traps[error]: + # The errors define how to handle themselves. + return condition().handle(self, *args) + + # Errors should only be risked on copies of the context + # self._ignored_flags = [] + raise error(explanation) + + def _ignore_all_flags(self): + """Ignore all flags, if they are raised""" + return self._ignore_flags(*_signals) + + def _ignore_flags(self, *flags): + """Ignore the flags, if they are raised""" + # Do not mutate-- This way, copies of a context leave the original + # alone. + self._ignored_flags = (self._ignored_flags + list(flags)) + return list(flags) + + def _regard_flags(self, *flags): + """Stop ignoring the flags, if they are raised""" + if flags and isinstance(flags[0], (tuple,list)): + flags = flags[0] + for flag in flags: + self._ignored_flags.remove(flag) + + # We inherit object.__hash__, so we must deny this explicitly + __hash__ = None + + def Etiny(self): + """Returns Etiny (= Emin - prec + 1)""" + return int(self.Emin - self.prec + 1) + + def Etop(self): + """Returns maximum exponent (= Emax - prec + 1)""" + return int(self.Emax - self.prec + 1) + + def _set_rounding(self, type): + """Sets the rounding type. + + Sets the rounding type, and returns the current (previous) + rounding type. Often used like: + + context = context.copy() + # so you don't change the calling context + # if an error occurs in the middle. + rounding = context._set_rounding(ROUND_UP) + val = self.__sub__(other, context=context) + context._set_rounding(rounding) + + This will make it round up for that operation. + """ + rounding = self.rounding + self.rounding = type + return rounding + + def create_decimal(self, num='0'): + """Creates a new Decimal instance but using self as context. + + This method implements the to-number operation of the + IBM Decimal specification.""" + + if isinstance(num, str) and (num != num.strip() or '_' in num): + return self._raise_error(ConversionSyntax, + "trailing or leading whitespace and " + "underscores are not permitted.") + + d = Decimal(num, context=self) + if d._isnan() and len(d._int) > self.prec - self.clamp: + return self._raise_error(ConversionSyntax, + "diagnostic info too long in NaN") + return d._fix(self) + + def create_decimal_from_float(self, f): + """Creates a new Decimal instance from a float but rounding using self + as the context. + + >>> context = Context(prec=5, rounding=ROUND_DOWN) + >>> context.create_decimal_from_float(3.1415926535897932) + Decimal('3.1415') + >>> context = Context(prec=5, traps=[Inexact]) + >>> context.create_decimal_from_float(3.1415926535897932) + Traceback (most recent call last): + ... + decimal.Inexact: None + + """ + d = Decimal.from_float(f) # An exact conversion + return d._fix(self) # Apply the context rounding + + # Methods + def abs(self, a): + """Returns the absolute value of the operand. + + If the operand is negative, the result is the same as using the minus + operation on the operand. Otherwise, the result is the same as using + the plus operation on the operand. + + >>> ExtendedContext.abs(Decimal('2.1')) + Decimal('2.1') + >>> ExtendedContext.abs(Decimal('-100')) + Decimal('100') + >>> ExtendedContext.abs(Decimal('101.5')) + Decimal('101.5') + >>> ExtendedContext.abs(Decimal('-101.5')) + Decimal('101.5') + >>> ExtendedContext.abs(-1) + Decimal('1') + """ + a = _convert_other(a, raiseit=True) + return a.__abs__(context=self) + + def add(self, a, b): + """Return the sum of the two operands. + + >>> ExtendedContext.add(Decimal('12'), Decimal('7.00')) + Decimal('19.00') + >>> ExtendedContext.add(Decimal('1E+2'), Decimal('1.01E+4')) + Decimal('1.02E+4') + >>> ExtendedContext.add(1, Decimal(2)) + Decimal('3') + >>> ExtendedContext.add(Decimal(8), 5) + Decimal('13') + >>> ExtendedContext.add(5, 5) + Decimal('10') + """ + a = _convert_other(a, raiseit=True) + r = a.__add__(b, context=self) + if r is NotImplemented: + raise TypeError("Unable to convert %s to Decimal" % b) + else: + return r + + def _apply(self, a): + return str(a._fix(self)) + + def canonical(self, a): + """Returns the same Decimal object. + + As we do not have different encodings for the same number, the + received object already is in its canonical form. + + >>> ExtendedContext.canonical(Decimal('2.50')) + Decimal('2.50') + """ + if not isinstance(a, Decimal): + raise TypeError("canonical requires a Decimal as an argument.") + return a.canonical() + + def compare(self, a, b): + """Compares values numerically. + + If the signs of the operands differ, a value representing each operand + ('-1' if the operand is less than zero, '0' if the operand is zero or + negative zero, or '1' if the operand is greater than zero) is used in + place of that operand for the comparison instead of the actual + operand. + + The comparison is then effected by subtracting the second operand from + the first and then returning a value according to the result of the + subtraction: '-1' if the result is less than zero, '0' if the result is + zero or negative zero, or '1' if the result is greater than zero. + + >>> ExtendedContext.compare(Decimal('2.1'), Decimal('3')) + Decimal('-1') + >>> ExtendedContext.compare(Decimal('2.1'), Decimal('2.1')) + Decimal('0') + >>> ExtendedContext.compare(Decimal('2.1'), Decimal('2.10')) + Decimal('0') + >>> ExtendedContext.compare(Decimal('3'), Decimal('2.1')) + Decimal('1') + >>> ExtendedContext.compare(Decimal('2.1'), Decimal('-3')) + Decimal('1') + >>> ExtendedContext.compare(Decimal('-3'), Decimal('2.1')) + Decimal('-1') + >>> ExtendedContext.compare(1, 2) + Decimal('-1') + >>> ExtendedContext.compare(Decimal(1), 2) + Decimal('-1') + >>> ExtendedContext.compare(1, Decimal(2)) + Decimal('-1') + """ + a = _convert_other(a, raiseit=True) + return a.compare(b, context=self) + + def compare_signal(self, a, b): + """Compares the values of the two operands numerically. + + It's pretty much like compare(), but all NaNs signal, with signaling + NaNs taking precedence over quiet NaNs. + + >>> c = ExtendedContext + >>> c.compare_signal(Decimal('2.1'), Decimal('3')) + Decimal('-1') + >>> c.compare_signal(Decimal('2.1'), Decimal('2.1')) + Decimal('0') + >>> c.flags[InvalidOperation] = 0 + >>> print(c.flags[InvalidOperation]) + 0 + >>> c.compare_signal(Decimal('NaN'), Decimal('2.1')) + Decimal('NaN') + >>> print(c.flags[InvalidOperation]) + 1 + >>> c.flags[InvalidOperation] = 0 + >>> print(c.flags[InvalidOperation]) + 0 + >>> c.compare_signal(Decimal('sNaN'), Decimal('2.1')) + Decimal('NaN') + >>> print(c.flags[InvalidOperation]) + 1 + >>> c.compare_signal(-1, 2) + Decimal('-1') + >>> c.compare_signal(Decimal(-1), 2) + Decimal('-1') + >>> c.compare_signal(-1, Decimal(2)) + Decimal('-1') + """ + a = _convert_other(a, raiseit=True) + return a.compare_signal(b, context=self) + + def compare_total(self, a, b): + """Compares two operands using their abstract representation. + + This is not like the standard compare, which use their numerical + value. Note that a total ordering is defined for all possible abstract + representations. + + >>> ExtendedContext.compare_total(Decimal('12.73'), Decimal('127.9')) + Decimal('-1') + >>> ExtendedContext.compare_total(Decimal('-127'), Decimal('12')) + Decimal('-1') + >>> ExtendedContext.compare_total(Decimal('12.30'), Decimal('12.3')) + Decimal('-1') + >>> ExtendedContext.compare_total(Decimal('12.30'), Decimal('12.30')) + Decimal('0') + >>> ExtendedContext.compare_total(Decimal('12.3'), Decimal('12.300')) + Decimal('1') + >>> ExtendedContext.compare_total(Decimal('12.3'), Decimal('NaN')) + Decimal('-1') + >>> ExtendedContext.compare_total(1, 2) + Decimal('-1') + >>> ExtendedContext.compare_total(Decimal(1), 2) + Decimal('-1') + >>> ExtendedContext.compare_total(1, Decimal(2)) + Decimal('-1') + """ + a = _convert_other(a, raiseit=True) + return a.compare_total(b) + + def compare_total_mag(self, a, b): + """Compares two operands using their abstract representation ignoring sign. + + Like compare_total, but with operand's sign ignored and assumed to be 0. + """ + a = _convert_other(a, raiseit=True) + return a.compare_total_mag(b) + + def copy_abs(self, a): + """Returns a copy of the operand with the sign set to 0. + + >>> ExtendedContext.copy_abs(Decimal('2.1')) + Decimal('2.1') + >>> ExtendedContext.copy_abs(Decimal('-100')) + Decimal('100') + >>> ExtendedContext.copy_abs(-1) + Decimal('1') + """ + a = _convert_other(a, raiseit=True) + return a.copy_abs() + + def copy_decimal(self, a): + """Returns a copy of the decimal object. + + >>> ExtendedContext.copy_decimal(Decimal('2.1')) + Decimal('2.1') + >>> ExtendedContext.copy_decimal(Decimal('-1.00')) + Decimal('-1.00') + >>> ExtendedContext.copy_decimal(1) + Decimal('1') + """ + a = _convert_other(a, raiseit=True) + return Decimal(a) + + def copy_negate(self, a): + """Returns a copy of the operand with the sign inverted. + + >>> ExtendedContext.copy_negate(Decimal('101.5')) + Decimal('-101.5') + >>> ExtendedContext.copy_negate(Decimal('-101.5')) + Decimal('101.5') + >>> ExtendedContext.copy_negate(1) + Decimal('-1') + """ + a = _convert_other(a, raiseit=True) + return a.copy_negate() + + def copy_sign(self, a, b): + """Copies the second operand's sign to the first one. + + In detail, it returns a copy of the first operand with the sign + equal to the sign of the second operand. + + >>> ExtendedContext.copy_sign(Decimal( '1.50'), Decimal('7.33')) + Decimal('1.50') + >>> ExtendedContext.copy_sign(Decimal('-1.50'), Decimal('7.33')) + Decimal('1.50') + >>> ExtendedContext.copy_sign(Decimal( '1.50'), Decimal('-7.33')) + Decimal('-1.50') + >>> ExtendedContext.copy_sign(Decimal('-1.50'), Decimal('-7.33')) + Decimal('-1.50') + >>> ExtendedContext.copy_sign(1, -2) + Decimal('-1') + >>> ExtendedContext.copy_sign(Decimal(1), -2) + Decimal('-1') + >>> ExtendedContext.copy_sign(1, Decimal(-2)) + Decimal('-1') + """ + a = _convert_other(a, raiseit=True) + return a.copy_sign(b) + + def divide(self, a, b): + """Decimal division in a specified context. + + >>> ExtendedContext.divide(Decimal('1'), Decimal('3')) + Decimal('0.333333333') + >>> ExtendedContext.divide(Decimal('2'), Decimal('3')) + Decimal('0.666666667') + >>> ExtendedContext.divide(Decimal('5'), Decimal('2')) + Decimal('2.5') + >>> ExtendedContext.divide(Decimal('1'), Decimal('10')) + Decimal('0.1') + >>> ExtendedContext.divide(Decimal('12'), Decimal('12')) + Decimal('1') + >>> ExtendedContext.divide(Decimal('8.00'), Decimal('2')) + Decimal('4.00') + >>> ExtendedContext.divide(Decimal('2.400'), Decimal('2.0')) + Decimal('1.20') + >>> ExtendedContext.divide(Decimal('1000'), Decimal('100')) + Decimal('10') + >>> ExtendedContext.divide(Decimal('1000'), Decimal('1')) + Decimal('1000') + >>> ExtendedContext.divide(Decimal('2.40E+6'), Decimal('2')) + Decimal('1.20E+6') + >>> ExtendedContext.divide(5, 5) + Decimal('1') + >>> ExtendedContext.divide(Decimal(5), 5) + Decimal('1') + >>> ExtendedContext.divide(5, Decimal(5)) + Decimal('1') + """ + a = _convert_other(a, raiseit=True) + r = a.__truediv__(b, context=self) + if r is NotImplemented: + raise TypeError("Unable to convert %s to Decimal" % b) + else: + return r + + def divide_int(self, a, b): + """Divides two numbers and returns the integer part of the result. + + >>> ExtendedContext.divide_int(Decimal('2'), Decimal('3')) + Decimal('0') + >>> ExtendedContext.divide_int(Decimal('10'), Decimal('3')) + Decimal('3') + >>> ExtendedContext.divide_int(Decimal('1'), Decimal('0.3')) + Decimal('3') + >>> ExtendedContext.divide_int(10, 3) + Decimal('3') + >>> ExtendedContext.divide_int(Decimal(10), 3) + Decimal('3') + >>> ExtendedContext.divide_int(10, Decimal(3)) + Decimal('3') + """ + a = _convert_other(a, raiseit=True) + r = a.__floordiv__(b, context=self) + if r is NotImplemented: + raise TypeError("Unable to convert %s to Decimal" % b) + else: + return r + + def divmod(self, a, b): + """Return (a // b, a % b). + + >>> ExtendedContext.divmod(Decimal(8), Decimal(3)) + (Decimal('2'), Decimal('2')) + >>> ExtendedContext.divmod(Decimal(8), Decimal(4)) + (Decimal('2'), Decimal('0')) + >>> ExtendedContext.divmod(8, 4) + (Decimal('2'), Decimal('0')) + >>> ExtendedContext.divmod(Decimal(8), 4) + (Decimal('2'), Decimal('0')) + >>> ExtendedContext.divmod(8, Decimal(4)) + (Decimal('2'), Decimal('0')) + """ + a = _convert_other(a, raiseit=True) + r = a.__divmod__(b, context=self) + if r is NotImplemented: + raise TypeError("Unable to convert %s to Decimal" % b) + else: + return r + + def exp(self, a): + """Returns e ** a. + + >>> c = ExtendedContext.copy() + >>> c.Emin = -999 + >>> c.Emax = 999 + >>> c.exp(Decimal('-Infinity')) + Decimal('0') + >>> c.exp(Decimal('-1')) + Decimal('0.367879441') + >>> c.exp(Decimal('0')) + Decimal('1') + >>> c.exp(Decimal('1')) + Decimal('2.71828183') + >>> c.exp(Decimal('0.693147181')) + Decimal('2.00000000') + >>> c.exp(Decimal('+Infinity')) + Decimal('Infinity') + >>> c.exp(10) + Decimal('22026.4658') + """ + a =_convert_other(a, raiseit=True) + return a.exp(context=self) + + def fma(self, a, b, c): + """Returns a multiplied by b, plus c. + + The first two operands are multiplied together, using multiply, + the third operand is then added to the result of that + multiplication, using add, all with only one final rounding. + + >>> ExtendedContext.fma(Decimal('3'), Decimal('5'), Decimal('7')) + Decimal('22') + >>> ExtendedContext.fma(Decimal('3'), Decimal('-5'), Decimal('7')) + Decimal('-8') + >>> ExtendedContext.fma(Decimal('888565290'), Decimal('1557.96930'), Decimal('-86087.7578')) + Decimal('1.38435736E+12') + >>> ExtendedContext.fma(1, 3, 4) + Decimal('7') + >>> ExtendedContext.fma(1, Decimal(3), 4) + Decimal('7') + >>> ExtendedContext.fma(1, 3, Decimal(4)) + Decimal('7') + """ + a = _convert_other(a, raiseit=True) + return a.fma(b, c, context=self) + + def is_canonical(self, a): + """Return True if the operand is canonical; otherwise return False. + + Currently, the encoding of a Decimal instance is always + canonical, so this method returns True for any Decimal. + + >>> ExtendedContext.is_canonical(Decimal('2.50')) + True + """ + if not isinstance(a, Decimal): + raise TypeError("is_canonical requires a Decimal as an argument.") + return a.is_canonical() + + def is_finite(self, a): + """Return True if the operand is finite; otherwise return False. + + A Decimal instance is considered finite if it is neither + infinite nor a NaN. + + >>> ExtendedContext.is_finite(Decimal('2.50')) + True + >>> ExtendedContext.is_finite(Decimal('-0.3')) + True + >>> ExtendedContext.is_finite(Decimal('0')) + True + >>> ExtendedContext.is_finite(Decimal('Inf')) + False + >>> ExtendedContext.is_finite(Decimal('NaN')) + False + >>> ExtendedContext.is_finite(1) + True + """ + a = _convert_other(a, raiseit=True) + return a.is_finite() + + def is_infinite(self, a): + """Return True if the operand is infinite; otherwise return False. + + >>> ExtendedContext.is_infinite(Decimal('2.50')) + False + >>> ExtendedContext.is_infinite(Decimal('-Inf')) + True + >>> ExtendedContext.is_infinite(Decimal('NaN')) + False + >>> ExtendedContext.is_infinite(1) + False + """ + a = _convert_other(a, raiseit=True) + return a.is_infinite() + + def is_nan(self, a): + """Return True if the operand is a qNaN or sNaN; + otherwise return False. + + >>> ExtendedContext.is_nan(Decimal('2.50')) + False + >>> ExtendedContext.is_nan(Decimal('NaN')) + True + >>> ExtendedContext.is_nan(Decimal('-sNaN')) + True + >>> ExtendedContext.is_nan(1) + False + """ + a = _convert_other(a, raiseit=True) + return a.is_nan() + + def is_normal(self, a): + """Return True if the operand is a normal number; + otherwise return False. + + >>> c = ExtendedContext.copy() + >>> c.Emin = -999 + >>> c.Emax = 999 + >>> c.is_normal(Decimal('2.50')) + True + >>> c.is_normal(Decimal('0.1E-999')) + False + >>> c.is_normal(Decimal('0.00')) + False + >>> c.is_normal(Decimal('-Inf')) + False + >>> c.is_normal(Decimal('NaN')) + False + >>> c.is_normal(1) + True + """ + a = _convert_other(a, raiseit=True) + return a.is_normal(context=self) + + def is_qnan(self, a): + """Return True if the operand is a quiet NaN; otherwise return False. + + >>> ExtendedContext.is_qnan(Decimal('2.50')) + False + >>> ExtendedContext.is_qnan(Decimal('NaN')) + True + >>> ExtendedContext.is_qnan(Decimal('sNaN')) + False + >>> ExtendedContext.is_qnan(1) + False + """ + a = _convert_other(a, raiseit=True) + return a.is_qnan() + + def is_signed(self, a): + """Return True if the operand is negative; otherwise return False. + + >>> ExtendedContext.is_signed(Decimal('2.50')) + False + >>> ExtendedContext.is_signed(Decimal('-12')) + True + >>> ExtendedContext.is_signed(Decimal('-0')) + True + >>> ExtendedContext.is_signed(8) + False + >>> ExtendedContext.is_signed(-8) + True + """ + a = _convert_other(a, raiseit=True) + return a.is_signed() + + def is_snan(self, a): + """Return True if the operand is a signaling NaN; + otherwise return False. + + >>> ExtendedContext.is_snan(Decimal('2.50')) + False + >>> ExtendedContext.is_snan(Decimal('NaN')) + False + >>> ExtendedContext.is_snan(Decimal('sNaN')) + True + >>> ExtendedContext.is_snan(1) + False + """ + a = _convert_other(a, raiseit=True) + return a.is_snan() + + def is_subnormal(self, a): + """Return True if the operand is subnormal; otherwise return False. + + >>> c = ExtendedContext.copy() + >>> c.Emin = -999 + >>> c.Emax = 999 + >>> c.is_subnormal(Decimal('2.50')) + False + >>> c.is_subnormal(Decimal('0.1E-999')) + True + >>> c.is_subnormal(Decimal('0.00')) + False + >>> c.is_subnormal(Decimal('-Inf')) + False + >>> c.is_subnormal(Decimal('NaN')) + False + >>> c.is_subnormal(1) + False + """ + a = _convert_other(a, raiseit=True) + return a.is_subnormal(context=self) + + def is_zero(self, a): + """Return True if the operand is a zero; otherwise return False. + + >>> ExtendedContext.is_zero(Decimal('0')) + True + >>> ExtendedContext.is_zero(Decimal('2.50')) + False + >>> ExtendedContext.is_zero(Decimal('-0E+2')) + True + >>> ExtendedContext.is_zero(1) + False + >>> ExtendedContext.is_zero(0) + True + """ + a = _convert_other(a, raiseit=True) + return a.is_zero() + + def ln(self, a): + """Returns the natural (base e) logarithm of the operand. + + >>> c = ExtendedContext.copy() + >>> c.Emin = -999 + >>> c.Emax = 999 + >>> c.ln(Decimal('0')) + Decimal('-Infinity') + >>> c.ln(Decimal('1.000')) + Decimal('0') + >>> c.ln(Decimal('2.71828183')) + Decimal('1.00000000') + >>> c.ln(Decimal('10')) + Decimal('2.30258509') + >>> c.ln(Decimal('+Infinity')) + Decimal('Infinity') + >>> c.ln(1) + Decimal('0') + """ + a = _convert_other(a, raiseit=True) + return a.ln(context=self) + + def log10(self, a): + """Returns the base 10 logarithm of the operand. + + >>> c = ExtendedContext.copy() + >>> c.Emin = -999 + >>> c.Emax = 999 + >>> c.log10(Decimal('0')) + Decimal('-Infinity') + >>> c.log10(Decimal('0.001')) + Decimal('-3') + >>> c.log10(Decimal('1.000')) + Decimal('0') + >>> c.log10(Decimal('2')) + Decimal('0.301029996') + >>> c.log10(Decimal('10')) + Decimal('1') + >>> c.log10(Decimal('70')) + Decimal('1.84509804') + >>> c.log10(Decimal('+Infinity')) + Decimal('Infinity') + >>> c.log10(0) + Decimal('-Infinity') + >>> c.log10(1) + Decimal('0') + """ + a = _convert_other(a, raiseit=True) + return a.log10(context=self) + + def logb(self, a): + """ Returns the exponent of the magnitude of the operand's MSD. + + The result is the integer which is the exponent of the magnitude + of the most significant digit of the operand (as though the + operand were truncated to a single digit while maintaining the + value of that digit and without limiting the resulting exponent). + + >>> ExtendedContext.logb(Decimal('250')) + Decimal('2') + >>> ExtendedContext.logb(Decimal('2.50')) + Decimal('0') + >>> ExtendedContext.logb(Decimal('0.03')) + Decimal('-2') + >>> ExtendedContext.logb(Decimal('0')) + Decimal('-Infinity') + >>> ExtendedContext.logb(1) + Decimal('0') + >>> ExtendedContext.logb(10) + Decimal('1') + >>> ExtendedContext.logb(100) + Decimal('2') + """ + a = _convert_other(a, raiseit=True) + return a.logb(context=self) + + def logical_and(self, a, b): + """Applies the logical operation 'and' between each operand's digits. + + The operands must be both logical numbers. + + >>> ExtendedContext.logical_and(Decimal('0'), Decimal('0')) + Decimal('0') + >>> ExtendedContext.logical_and(Decimal('0'), Decimal('1')) + Decimal('0') + >>> ExtendedContext.logical_and(Decimal('1'), Decimal('0')) + Decimal('0') + >>> ExtendedContext.logical_and(Decimal('1'), Decimal('1')) + Decimal('1') + >>> ExtendedContext.logical_and(Decimal('1100'), Decimal('1010')) + Decimal('1000') + >>> ExtendedContext.logical_and(Decimal('1111'), Decimal('10')) + Decimal('10') + >>> ExtendedContext.logical_and(110, 1101) + Decimal('100') + >>> ExtendedContext.logical_and(Decimal(110), 1101) + Decimal('100') + >>> ExtendedContext.logical_and(110, Decimal(1101)) + Decimal('100') + """ + a = _convert_other(a, raiseit=True) + return a.logical_and(b, context=self) + + def logical_invert(self, a): + """Invert all the digits in the operand. + + The operand must be a logical number. + + >>> ExtendedContext.logical_invert(Decimal('0')) + Decimal('111111111') + >>> ExtendedContext.logical_invert(Decimal('1')) + Decimal('111111110') + >>> ExtendedContext.logical_invert(Decimal('111111111')) + Decimal('0') + >>> ExtendedContext.logical_invert(Decimal('101010101')) + Decimal('10101010') + >>> ExtendedContext.logical_invert(1101) + Decimal('111110010') + """ + a = _convert_other(a, raiseit=True) + return a.logical_invert(context=self) + + def logical_or(self, a, b): + """Applies the logical operation 'or' between each operand's digits. + + The operands must be both logical numbers. + + >>> ExtendedContext.logical_or(Decimal('0'), Decimal('0')) + Decimal('0') + >>> ExtendedContext.logical_or(Decimal('0'), Decimal('1')) + Decimal('1') + >>> ExtendedContext.logical_or(Decimal('1'), Decimal('0')) + Decimal('1') + >>> ExtendedContext.logical_or(Decimal('1'), Decimal('1')) + Decimal('1') + >>> ExtendedContext.logical_or(Decimal('1100'), Decimal('1010')) + Decimal('1110') + >>> ExtendedContext.logical_or(Decimal('1110'), Decimal('10')) + Decimal('1110') + >>> ExtendedContext.logical_or(110, 1101) + Decimal('1111') + >>> ExtendedContext.logical_or(Decimal(110), 1101) + Decimal('1111') + >>> ExtendedContext.logical_or(110, Decimal(1101)) + Decimal('1111') + """ + a = _convert_other(a, raiseit=True) + return a.logical_or(b, context=self) + + def logical_xor(self, a, b): + """Applies the logical operation 'xor' between each operand's digits. + + The operands must be both logical numbers. + + >>> ExtendedContext.logical_xor(Decimal('0'), Decimal('0')) + Decimal('0') + >>> ExtendedContext.logical_xor(Decimal('0'), Decimal('1')) + Decimal('1') + >>> ExtendedContext.logical_xor(Decimal('1'), Decimal('0')) + Decimal('1') + >>> ExtendedContext.logical_xor(Decimal('1'), Decimal('1')) + Decimal('0') + >>> ExtendedContext.logical_xor(Decimal('1100'), Decimal('1010')) + Decimal('110') + >>> ExtendedContext.logical_xor(Decimal('1111'), Decimal('10')) + Decimal('1101') + >>> ExtendedContext.logical_xor(110, 1101) + Decimal('1011') + >>> ExtendedContext.logical_xor(Decimal(110), 1101) + Decimal('1011') + >>> ExtendedContext.logical_xor(110, Decimal(1101)) + Decimal('1011') + """ + a = _convert_other(a, raiseit=True) + return a.logical_xor(b, context=self) + + def max(self, a, b): + """max compares two values numerically and returns the maximum. + + If either operand is a NaN then the general rules apply. + Otherwise, the operands are compared as though by the compare + operation. If they are numerically equal then the left-hand operand + is chosen as the result. Otherwise the maximum (closer to positive + infinity) of the two operands is chosen as the result. + + >>> ExtendedContext.max(Decimal('3'), Decimal('2')) + Decimal('3') + >>> ExtendedContext.max(Decimal('-10'), Decimal('3')) + Decimal('3') + >>> ExtendedContext.max(Decimal('1.0'), Decimal('1')) + Decimal('1') + >>> ExtendedContext.max(Decimal('7'), Decimal('NaN')) + Decimal('7') + >>> ExtendedContext.max(1, 2) + Decimal('2') + >>> ExtendedContext.max(Decimal(1), 2) + Decimal('2') + >>> ExtendedContext.max(1, Decimal(2)) + Decimal('2') + """ + a = _convert_other(a, raiseit=True) + return a.max(b, context=self) + + def max_mag(self, a, b): + """Compares the values numerically with their sign ignored. + + >>> ExtendedContext.max_mag(Decimal('7'), Decimal('NaN')) + Decimal('7') + >>> ExtendedContext.max_mag(Decimal('7'), Decimal('-10')) + Decimal('-10') + >>> ExtendedContext.max_mag(1, -2) + Decimal('-2') + >>> ExtendedContext.max_mag(Decimal(1), -2) + Decimal('-2') + >>> ExtendedContext.max_mag(1, Decimal(-2)) + Decimal('-2') + """ + a = _convert_other(a, raiseit=True) + return a.max_mag(b, context=self) + + def min(self, a, b): + """min compares two values numerically and returns the minimum. + + If either operand is a NaN then the general rules apply. + Otherwise, the operands are compared as though by the compare + operation. If they are numerically equal then the left-hand operand + is chosen as the result. Otherwise the minimum (closer to negative + infinity) of the two operands is chosen as the result. + + >>> ExtendedContext.min(Decimal('3'), Decimal('2')) + Decimal('2') + >>> ExtendedContext.min(Decimal('-10'), Decimal('3')) + Decimal('-10') + >>> ExtendedContext.min(Decimal('1.0'), Decimal('1')) + Decimal('1.0') + >>> ExtendedContext.min(Decimal('7'), Decimal('NaN')) + Decimal('7') + >>> ExtendedContext.min(1, 2) + Decimal('1') + >>> ExtendedContext.min(Decimal(1), 2) + Decimal('1') + >>> ExtendedContext.min(1, Decimal(29)) + Decimal('1') + """ + a = _convert_other(a, raiseit=True) + return a.min(b, context=self) + + def min_mag(self, a, b): + """Compares the values numerically with their sign ignored. + + >>> ExtendedContext.min_mag(Decimal('3'), Decimal('-2')) + Decimal('-2') + >>> ExtendedContext.min_mag(Decimal('-3'), Decimal('NaN')) + Decimal('-3') + >>> ExtendedContext.min_mag(1, -2) + Decimal('1') + >>> ExtendedContext.min_mag(Decimal(1), -2) + Decimal('1') + >>> ExtendedContext.min_mag(1, Decimal(-2)) + Decimal('1') + """ + a = _convert_other(a, raiseit=True) + return a.min_mag(b, context=self) + + def minus(self, a): + """Minus corresponds to unary prefix minus in Python. + + The operation is evaluated using the same rules as subtract; the + operation minus(a) is calculated as subtract('0', a) where the '0' + has the same exponent as the operand. + + >>> ExtendedContext.minus(Decimal('1.3')) + Decimal('-1.3') + >>> ExtendedContext.minus(Decimal('-1.3')) + Decimal('1.3') + >>> ExtendedContext.minus(1) + Decimal('-1') + """ + a = _convert_other(a, raiseit=True) + return a.__neg__(context=self) + + def multiply(self, a, b): + """multiply multiplies two operands. + + If either operand is a special value then the general rules apply. + Otherwise, the operands are multiplied together + ('long multiplication'), resulting in a number which may be as long as + the sum of the lengths of the two operands. + + >>> ExtendedContext.multiply(Decimal('1.20'), Decimal('3')) + Decimal('3.60') + >>> ExtendedContext.multiply(Decimal('7'), Decimal('3')) + Decimal('21') + >>> ExtendedContext.multiply(Decimal('0.9'), Decimal('0.8')) + Decimal('0.72') + >>> ExtendedContext.multiply(Decimal('0.9'), Decimal('-0')) + Decimal('-0.0') + >>> ExtendedContext.multiply(Decimal('654321'), Decimal('654321')) + Decimal('4.28135971E+11') + >>> ExtendedContext.multiply(7, 7) + Decimal('49') + >>> ExtendedContext.multiply(Decimal(7), 7) + Decimal('49') + >>> ExtendedContext.multiply(7, Decimal(7)) + Decimal('49') + """ + a = _convert_other(a, raiseit=True) + r = a.__mul__(b, context=self) + if r is NotImplemented: + raise TypeError("Unable to convert %s to Decimal" % b) + else: + return r + + def next_minus(self, a): + """Returns the largest representable number smaller than a. + + >>> c = ExtendedContext.copy() + >>> c.Emin = -999 + >>> c.Emax = 999 + >>> ExtendedContext.next_minus(Decimal('1')) + Decimal('0.999999999') + >>> c.next_minus(Decimal('1E-1007')) + Decimal('0E-1007') + >>> ExtendedContext.next_minus(Decimal('-1.00000003')) + Decimal('-1.00000004') + >>> c.next_minus(Decimal('Infinity')) + Decimal('9.99999999E+999') + >>> c.next_minus(1) + Decimal('0.999999999') + """ + a = _convert_other(a, raiseit=True) + return a.next_minus(context=self) + + def next_plus(self, a): + """Returns the smallest representable number larger than a. + + >>> c = ExtendedContext.copy() + >>> c.Emin = -999 + >>> c.Emax = 999 + >>> ExtendedContext.next_plus(Decimal('1')) + Decimal('1.00000001') + >>> c.next_plus(Decimal('-1E-1007')) + Decimal('-0E-1007') + >>> ExtendedContext.next_plus(Decimal('-1.00000003')) + Decimal('-1.00000002') + >>> c.next_plus(Decimal('-Infinity')) + Decimal('-9.99999999E+999') + >>> c.next_plus(1) + Decimal('1.00000001') + """ + a = _convert_other(a, raiseit=True) + return a.next_plus(context=self) + + def next_toward(self, a, b): + """Returns the number closest to a, in direction towards b. + + The result is the closest representable number from the first + operand (but not the first operand) that is in the direction + towards the second operand, unless the operands have the same + value. + + >>> c = ExtendedContext.copy() + >>> c.Emin = -999 + >>> c.Emax = 999 + >>> c.next_toward(Decimal('1'), Decimal('2')) + Decimal('1.00000001') + >>> c.next_toward(Decimal('-1E-1007'), Decimal('1')) + Decimal('-0E-1007') + >>> c.next_toward(Decimal('-1.00000003'), Decimal('0')) + Decimal('-1.00000002') + >>> c.next_toward(Decimal('1'), Decimal('0')) + Decimal('0.999999999') + >>> c.next_toward(Decimal('1E-1007'), Decimal('-100')) + Decimal('0E-1007') + >>> c.next_toward(Decimal('-1.00000003'), Decimal('-10')) + Decimal('-1.00000004') + >>> c.next_toward(Decimal('0.00'), Decimal('-0.0000')) + Decimal('-0.00') + >>> c.next_toward(0, 1) + Decimal('1E-1007') + >>> c.next_toward(Decimal(0), 1) + Decimal('1E-1007') + >>> c.next_toward(0, Decimal(1)) + Decimal('1E-1007') + """ + a = _convert_other(a, raiseit=True) + return a.next_toward(b, context=self) + + def normalize(self, a): + """normalize reduces an operand to its simplest form. + + Essentially a plus operation with all trailing zeros removed from the + result. + + >>> ExtendedContext.normalize(Decimal('2.1')) + Decimal('2.1') + >>> ExtendedContext.normalize(Decimal('-2.0')) + Decimal('-2') + >>> ExtendedContext.normalize(Decimal('1.200')) + Decimal('1.2') + >>> ExtendedContext.normalize(Decimal('-120')) + Decimal('-1.2E+2') + >>> ExtendedContext.normalize(Decimal('120.00')) + Decimal('1.2E+2') + >>> ExtendedContext.normalize(Decimal('0.00')) + Decimal('0') + >>> ExtendedContext.normalize(6) + Decimal('6') + """ + a = _convert_other(a, raiseit=True) + return a.normalize(context=self) + + def number_class(self, a): + """Returns an indication of the class of the operand. + + The class is one of the following strings: + -sNaN + -NaN + -Infinity + -Normal + -Subnormal + -Zero + +Zero + +Subnormal + +Normal + +Infinity + + >>> c = ExtendedContext.copy() + >>> c.Emin = -999 + >>> c.Emax = 999 + >>> c.number_class(Decimal('Infinity')) + '+Infinity' + >>> c.number_class(Decimal('1E-10')) + '+Normal' + >>> c.number_class(Decimal('2.50')) + '+Normal' + >>> c.number_class(Decimal('0.1E-999')) + '+Subnormal' + >>> c.number_class(Decimal('0')) + '+Zero' + >>> c.number_class(Decimal('-0')) + '-Zero' + >>> c.number_class(Decimal('-0.1E-999')) + '-Subnormal' + >>> c.number_class(Decimal('-1E-10')) + '-Normal' + >>> c.number_class(Decimal('-2.50')) + '-Normal' + >>> c.number_class(Decimal('-Infinity')) + '-Infinity' + >>> c.number_class(Decimal('NaN')) + 'NaN' + >>> c.number_class(Decimal('-NaN')) + 'NaN' + >>> c.number_class(Decimal('sNaN')) + 'sNaN' + >>> c.number_class(123) + '+Normal' + """ + a = _convert_other(a, raiseit=True) + return a.number_class(context=self) + + def plus(self, a): + """Plus corresponds to unary prefix plus in Python. + + The operation is evaluated using the same rules as add; the + operation plus(a) is calculated as add('0', a) where the '0' + has the same exponent as the operand. + + >>> ExtendedContext.plus(Decimal('1.3')) + Decimal('1.3') + >>> ExtendedContext.plus(Decimal('-1.3')) + Decimal('-1.3') + >>> ExtendedContext.plus(-1) + Decimal('-1') + """ + a = _convert_other(a, raiseit=True) + return a.__pos__(context=self) + + def power(self, a, b, modulo=None): + """Raises a to the power of b, to modulo if given. + + With two arguments, compute a**b. If a is negative then b + must be integral. The result will be inexact unless b is + integral and the result is finite and can be expressed exactly + in 'precision' digits. + + With three arguments, compute (a**b) % modulo. For the + three argument form, the following restrictions on the + arguments hold: + + - all three arguments must be integral + - b must be nonnegative + - at least one of a or b must be nonzero + - modulo must be nonzero and have at most 'precision' digits + + The result of pow(a, b, modulo) is identical to the result + that would be obtained by computing (a**b) % modulo with + unbounded precision, but is computed more efficiently. It is + always exact. + + >>> c = ExtendedContext.copy() + >>> c.Emin = -999 + >>> c.Emax = 999 + >>> c.power(Decimal('2'), Decimal('3')) + Decimal('8') + >>> c.power(Decimal('-2'), Decimal('3')) + Decimal('-8') + >>> c.power(Decimal('2'), Decimal('-3')) + Decimal('0.125') + >>> c.power(Decimal('1.7'), Decimal('8')) + Decimal('69.7575744') + >>> c.power(Decimal('10'), Decimal('0.301029996')) + Decimal('2.00000000') + >>> c.power(Decimal('Infinity'), Decimal('-1')) + Decimal('0') + >>> c.power(Decimal('Infinity'), Decimal('0')) + Decimal('1') + >>> c.power(Decimal('Infinity'), Decimal('1')) + Decimal('Infinity') + >>> c.power(Decimal('-Infinity'), Decimal('-1')) + Decimal('-0') + >>> c.power(Decimal('-Infinity'), Decimal('0')) + Decimal('1') + >>> c.power(Decimal('-Infinity'), Decimal('1')) + Decimal('-Infinity') + >>> c.power(Decimal('-Infinity'), Decimal('2')) + Decimal('Infinity') + >>> c.power(Decimal('0'), Decimal('0')) + Decimal('NaN') + + >>> c.power(Decimal('3'), Decimal('7'), Decimal('16')) + Decimal('11') + >>> c.power(Decimal('-3'), Decimal('7'), Decimal('16')) + Decimal('-11') + >>> c.power(Decimal('-3'), Decimal('8'), Decimal('16')) + Decimal('1') + >>> c.power(Decimal('3'), Decimal('7'), Decimal('-16')) + Decimal('11') + >>> c.power(Decimal('23E12345'), Decimal('67E189'), Decimal('123456789')) + Decimal('11729830') + >>> c.power(Decimal('-0'), Decimal('17'), Decimal('1729')) + Decimal('-0') + >>> c.power(Decimal('-23'), Decimal('0'), Decimal('65537')) + Decimal('1') + >>> ExtendedContext.power(7, 7) + Decimal('823543') + >>> ExtendedContext.power(Decimal(7), 7) + Decimal('823543') + >>> ExtendedContext.power(7, Decimal(7), 2) + Decimal('1') + """ + a = _convert_other(a, raiseit=True) + r = a.__pow__(b, modulo, context=self) + if r is NotImplemented: + raise TypeError("Unable to convert %s to Decimal" % b) + else: + return r + + def quantize(self, a, b): + """Returns a value equal to 'a' (rounded), having the exponent of 'b'. + + The coefficient of the result is derived from that of the left-hand + operand. It may be rounded using the current rounding setting (if the + exponent is being increased), multiplied by a positive power of ten (if + the exponent is being decreased), or is unchanged (if the exponent is + already equal to that of the right-hand operand). + + Unlike other operations, if the length of the coefficient after the + quantize operation would be greater than precision then an Invalid + operation condition is raised. This guarantees that, unless there is + an error condition, the exponent of the result of a quantize is always + equal to that of the right-hand operand. + + Also unlike other operations, quantize will never raise Underflow, even + if the result is subnormal and inexact. + + >>> ExtendedContext.quantize(Decimal('2.17'), Decimal('0.001')) + Decimal('2.170') + >>> ExtendedContext.quantize(Decimal('2.17'), Decimal('0.01')) + Decimal('2.17') + >>> ExtendedContext.quantize(Decimal('2.17'), Decimal('0.1')) + Decimal('2.2') + >>> ExtendedContext.quantize(Decimal('2.17'), Decimal('1e+0')) + Decimal('2') + >>> ExtendedContext.quantize(Decimal('2.17'), Decimal('1e+1')) + Decimal('0E+1') + >>> ExtendedContext.quantize(Decimal('-Inf'), Decimal('Infinity')) + Decimal('-Infinity') + >>> ExtendedContext.quantize(Decimal('2'), Decimal('Infinity')) + Decimal('NaN') + >>> ExtendedContext.quantize(Decimal('-0.1'), Decimal('1')) + Decimal('-0') + >>> ExtendedContext.quantize(Decimal('-0'), Decimal('1e+5')) + Decimal('-0E+5') + >>> ExtendedContext.quantize(Decimal('+35236450.6'), Decimal('1e-2')) + Decimal('NaN') + >>> ExtendedContext.quantize(Decimal('-35236450.6'), Decimal('1e-2')) + Decimal('NaN') + >>> ExtendedContext.quantize(Decimal('217'), Decimal('1e-1')) + Decimal('217.0') + >>> ExtendedContext.quantize(Decimal('217'), Decimal('1e-0')) + Decimal('217') + >>> ExtendedContext.quantize(Decimal('217'), Decimal('1e+1')) + Decimal('2.2E+2') + >>> ExtendedContext.quantize(Decimal('217'), Decimal('1e+2')) + Decimal('2E+2') + >>> ExtendedContext.quantize(1, 2) + Decimal('1') + >>> ExtendedContext.quantize(Decimal(1), 2) + Decimal('1') + >>> ExtendedContext.quantize(1, Decimal(2)) + Decimal('1') + """ + a = _convert_other(a, raiseit=True) + return a.quantize(b, context=self) + + def radix(self): + """Just returns 10, as this is Decimal, :) + + >>> ExtendedContext.radix() + Decimal('10') + """ + return Decimal(10) + + def remainder(self, a, b): + """Returns the remainder from integer division. + + The result is the residue of the dividend after the operation of + calculating integer division as described for divide-integer, rounded + to precision digits if necessary. The sign of the result, if + non-zero, is the same as that of the original dividend. + + This operation will fail under the same conditions as integer division + (that is, if integer division on the same two operands would fail, the + remainder cannot be calculated). + + >>> ExtendedContext.remainder(Decimal('2.1'), Decimal('3')) + Decimal('2.1') + >>> ExtendedContext.remainder(Decimal('10'), Decimal('3')) + Decimal('1') + >>> ExtendedContext.remainder(Decimal('-10'), Decimal('3')) + Decimal('-1') + >>> ExtendedContext.remainder(Decimal('10.2'), Decimal('1')) + Decimal('0.2') + >>> ExtendedContext.remainder(Decimal('10'), Decimal('0.3')) + Decimal('0.1') + >>> ExtendedContext.remainder(Decimal('3.6'), Decimal('1.3')) + Decimal('1.0') + >>> ExtendedContext.remainder(22, 6) + Decimal('4') + >>> ExtendedContext.remainder(Decimal(22), 6) + Decimal('4') + >>> ExtendedContext.remainder(22, Decimal(6)) + Decimal('4') + """ + a = _convert_other(a, raiseit=True) + r = a.__mod__(b, context=self) + if r is NotImplemented: + raise TypeError("Unable to convert %s to Decimal" % b) + else: + return r + + def remainder_near(self, a, b): + """Returns to be "a - b * n", where n is the integer nearest the exact + value of "x / b" (if two integers are equally near then the even one + is chosen). If the result is equal to 0 then its sign will be the + sign of a. + + This operation will fail under the same conditions as integer division + (that is, if integer division on the same two operands would fail, the + remainder cannot be calculated). + + >>> ExtendedContext.remainder_near(Decimal('2.1'), Decimal('3')) + Decimal('-0.9') + >>> ExtendedContext.remainder_near(Decimal('10'), Decimal('6')) + Decimal('-2') + >>> ExtendedContext.remainder_near(Decimal('10'), Decimal('3')) + Decimal('1') + >>> ExtendedContext.remainder_near(Decimal('-10'), Decimal('3')) + Decimal('-1') + >>> ExtendedContext.remainder_near(Decimal('10.2'), Decimal('1')) + Decimal('0.2') + >>> ExtendedContext.remainder_near(Decimal('10'), Decimal('0.3')) + Decimal('0.1') + >>> ExtendedContext.remainder_near(Decimal('3.6'), Decimal('1.3')) + Decimal('-0.3') + >>> ExtendedContext.remainder_near(3, 11) + Decimal('3') + >>> ExtendedContext.remainder_near(Decimal(3), 11) + Decimal('3') + >>> ExtendedContext.remainder_near(3, Decimal(11)) + Decimal('3') + """ + a = _convert_other(a, raiseit=True) + return a.remainder_near(b, context=self) + + def rotate(self, a, b): + """Returns a rotated copy of a, b times. + + The coefficient of the result is a rotated copy of the digits in + the coefficient of the first operand. The number of places of + rotation is taken from the absolute value of the second operand, + with the rotation being to the left if the second operand is + positive or to the right otherwise. + + >>> ExtendedContext.rotate(Decimal('34'), Decimal('8')) + Decimal('400000003') + >>> ExtendedContext.rotate(Decimal('12'), Decimal('9')) + Decimal('12') + >>> ExtendedContext.rotate(Decimal('123456789'), Decimal('-2')) + Decimal('891234567') + >>> ExtendedContext.rotate(Decimal('123456789'), Decimal('0')) + Decimal('123456789') + >>> ExtendedContext.rotate(Decimal('123456789'), Decimal('+2')) + Decimal('345678912') + >>> ExtendedContext.rotate(1333333, 1) + Decimal('13333330') + >>> ExtendedContext.rotate(Decimal(1333333), 1) + Decimal('13333330') + >>> ExtendedContext.rotate(1333333, Decimal(1)) + Decimal('13333330') + """ + a = _convert_other(a, raiseit=True) + return a.rotate(b, context=self) + + def same_quantum(self, a, b): + """Returns True if the two operands have the same exponent. + + The result is never affected by either the sign or the coefficient of + either operand. + + >>> ExtendedContext.same_quantum(Decimal('2.17'), Decimal('0.001')) + False + >>> ExtendedContext.same_quantum(Decimal('2.17'), Decimal('0.01')) + True + >>> ExtendedContext.same_quantum(Decimal('2.17'), Decimal('1')) + False + >>> ExtendedContext.same_quantum(Decimal('Inf'), Decimal('-Inf')) + True + >>> ExtendedContext.same_quantum(10000, -1) + True + >>> ExtendedContext.same_quantum(Decimal(10000), -1) + True + >>> ExtendedContext.same_quantum(10000, Decimal(-1)) + True + """ + a = _convert_other(a, raiseit=True) + return a.same_quantum(b) + + def scaleb (self, a, b): + """Returns the first operand after adding the second value its exp. + + >>> ExtendedContext.scaleb(Decimal('7.50'), Decimal('-2')) + Decimal('0.0750') + >>> ExtendedContext.scaleb(Decimal('7.50'), Decimal('0')) + Decimal('7.50') + >>> ExtendedContext.scaleb(Decimal('7.50'), Decimal('3')) + Decimal('7.50E+3') + >>> ExtendedContext.scaleb(1, 4) + Decimal('1E+4') + >>> ExtendedContext.scaleb(Decimal(1), 4) + Decimal('1E+4') + >>> ExtendedContext.scaleb(1, Decimal(4)) + Decimal('1E+4') + """ + a = _convert_other(a, raiseit=True) + return a.scaleb(b, context=self) + + def shift(self, a, b): + """Returns a shifted copy of a, b times. + + The coefficient of the result is a shifted copy of the digits + in the coefficient of the first operand. The number of places + to shift is taken from the absolute value of the second operand, + with the shift being to the left if the second operand is + positive or to the right otherwise. Digits shifted into the + coefficient are zeros. + + >>> ExtendedContext.shift(Decimal('34'), Decimal('8')) + Decimal('400000000') + >>> ExtendedContext.shift(Decimal('12'), Decimal('9')) + Decimal('0') + >>> ExtendedContext.shift(Decimal('123456789'), Decimal('-2')) + Decimal('1234567') + >>> ExtendedContext.shift(Decimal('123456789'), Decimal('0')) + Decimal('123456789') + >>> ExtendedContext.shift(Decimal('123456789'), Decimal('+2')) + Decimal('345678900') + >>> ExtendedContext.shift(88888888, 2) + Decimal('888888800') + >>> ExtendedContext.shift(Decimal(88888888), 2) + Decimal('888888800') + >>> ExtendedContext.shift(88888888, Decimal(2)) + Decimal('888888800') + """ + a = _convert_other(a, raiseit=True) + return a.shift(b, context=self) + + def sqrt(self, a): + """Square root of a non-negative number to context precision. + + If the result must be inexact, it is rounded using the round-half-even + algorithm. + + >>> ExtendedContext.sqrt(Decimal('0')) + Decimal('0') + >>> ExtendedContext.sqrt(Decimal('-0')) + Decimal('-0') + >>> ExtendedContext.sqrt(Decimal('0.39')) + Decimal('0.624499800') + >>> ExtendedContext.sqrt(Decimal('100')) + Decimal('10') + >>> ExtendedContext.sqrt(Decimal('1')) + Decimal('1') + >>> ExtendedContext.sqrt(Decimal('1.0')) + Decimal('1.0') + >>> ExtendedContext.sqrt(Decimal('1.00')) + Decimal('1.0') + >>> ExtendedContext.sqrt(Decimal('7')) + Decimal('2.64575131') + >>> ExtendedContext.sqrt(Decimal('10')) + Decimal('3.16227766') + >>> ExtendedContext.sqrt(2) + Decimal('1.41421356') + >>> ExtendedContext.prec + 9 + """ + a = _convert_other(a, raiseit=True) + return a.sqrt(context=self) + + def subtract(self, a, b): + """Return the difference between the two operands. + + >>> ExtendedContext.subtract(Decimal('1.3'), Decimal('1.07')) + Decimal('0.23') + >>> ExtendedContext.subtract(Decimal('1.3'), Decimal('1.30')) + Decimal('0.00') + >>> ExtendedContext.subtract(Decimal('1.3'), Decimal('2.07')) + Decimal('-0.77') + >>> ExtendedContext.subtract(8, 5) + Decimal('3') + >>> ExtendedContext.subtract(Decimal(8), 5) + Decimal('3') + >>> ExtendedContext.subtract(8, Decimal(5)) + Decimal('3') + """ + a = _convert_other(a, raiseit=True) + r = a.__sub__(b, context=self) + if r is NotImplemented: + raise TypeError("Unable to convert %s to Decimal" % b) + else: + return r + + def to_eng_string(self, a): + """Convert to a string, using engineering notation if an exponent is needed. + + Engineering notation has an exponent which is a multiple of 3. This + can leave up to 3 digits to the left of the decimal place and may + require the addition of either one or two trailing zeros. + + The operation is not affected by the context. + + >>> ExtendedContext.to_eng_string(Decimal('123E+1')) + '1.23E+3' + >>> ExtendedContext.to_eng_string(Decimal('123E+3')) + '123E+3' + >>> ExtendedContext.to_eng_string(Decimal('123E-10')) + '12.3E-9' + >>> ExtendedContext.to_eng_string(Decimal('-123E-12')) + '-123E-12' + >>> ExtendedContext.to_eng_string(Decimal('7E-7')) + '700E-9' + >>> ExtendedContext.to_eng_string(Decimal('7E+1')) + '70' + >>> ExtendedContext.to_eng_string(Decimal('0E+1')) + '0.00E+3' + + """ + a = _convert_other(a, raiseit=True) + return a.to_eng_string(context=self) + + def to_sci_string(self, a): + """Converts a number to a string, using scientific notation. + + The operation is not affected by the context. + """ + a = _convert_other(a, raiseit=True) + return a.__str__(context=self) + + def to_integral_exact(self, a): + """Rounds to an integer. + + When the operand has a negative exponent, the result is the same + as using the quantize() operation using the given operand as the + left-hand-operand, 1E+0 as the right-hand-operand, and the precision + of the operand as the precision setting; Inexact and Rounded flags + are allowed in this operation. The rounding mode is taken from the + context. + + >>> ExtendedContext.to_integral_exact(Decimal('2.1')) + Decimal('2') + >>> ExtendedContext.to_integral_exact(Decimal('100')) + Decimal('100') + >>> ExtendedContext.to_integral_exact(Decimal('100.0')) + Decimal('100') + >>> ExtendedContext.to_integral_exact(Decimal('101.5')) + Decimal('102') + >>> ExtendedContext.to_integral_exact(Decimal('-101.5')) + Decimal('-102') + >>> ExtendedContext.to_integral_exact(Decimal('10E+5')) + Decimal('1.0E+6') + >>> ExtendedContext.to_integral_exact(Decimal('7.89E+77')) + Decimal('7.89E+77') + >>> ExtendedContext.to_integral_exact(Decimal('-Inf')) + Decimal('-Infinity') + """ + a = _convert_other(a, raiseit=True) + return a.to_integral_exact(context=self) + + def to_integral_value(self, a): + """Rounds to an integer. + + When the operand has a negative exponent, the result is the same + as using the quantize() operation using the given operand as the + left-hand-operand, 1E+0 as the right-hand-operand, and the precision + of the operand as the precision setting, except that no flags will + be set. The rounding mode is taken from the context. + + >>> ExtendedContext.to_integral_value(Decimal('2.1')) + Decimal('2') + >>> ExtendedContext.to_integral_value(Decimal('100')) + Decimal('100') + >>> ExtendedContext.to_integral_value(Decimal('100.0')) + Decimal('100') + >>> ExtendedContext.to_integral_value(Decimal('101.5')) + Decimal('102') + >>> ExtendedContext.to_integral_value(Decimal('-101.5')) + Decimal('-102') + >>> ExtendedContext.to_integral_value(Decimal('10E+5')) + Decimal('1.0E+6') + >>> ExtendedContext.to_integral_value(Decimal('7.89E+77')) + Decimal('7.89E+77') + >>> ExtendedContext.to_integral_value(Decimal('-Inf')) + Decimal('-Infinity') + """ + a = _convert_other(a, raiseit=True) + return a.to_integral_value(context=self) + + # the method name changed, but we provide also the old one, for compatibility + to_integral = to_integral_value + +class _WorkRep(object): + __slots__ = ('sign','int','exp') + # sign: 0 or 1 + # int: int + # exp: None, int, or string + + def __init__(self, value=None): + if value is None: + self.sign = None + self.int = 0 + self.exp = None + elif isinstance(value, Decimal): + self.sign = value._sign + self.int = int(value._int) + self.exp = value._exp + else: + # assert isinstance(value, tuple) + self.sign = value[0] + self.int = value[1] + self.exp = value[2] + + def __repr__(self): + return "(%r, %r, %r)" % (self.sign, self.int, self.exp) + + + +def _normalize(op1, op2, prec = 0): + """Normalizes op1, op2 to have the same exp and length of coefficient. + + Done during addition. + """ + if op1.exp < op2.exp: + tmp = op2 + other = op1 + else: + tmp = op1 + other = op2 + + # Let exp = min(tmp.exp - 1, tmp.adjusted() - precision - 1). + # Then adding 10**exp to tmp has the same effect (after rounding) + # as adding any positive quantity smaller than 10**exp; similarly + # for subtraction. So if other is smaller than 10**exp we replace + # it with 10**exp. This avoids tmp.exp - other.exp getting too large. + tmp_len = len(str(tmp.int)) + other_len = len(str(other.int)) + exp = tmp.exp + min(-1, tmp_len - prec - 2) + if other_len + other.exp - 1 < exp: + other.int = 1 + other.exp = exp + + tmp.int *= 10 ** (tmp.exp - other.exp) + tmp.exp = other.exp + return op1, op2 + +##### Integer arithmetic functions used by ln, log10, exp and __pow__ ##### + +_nbits = int.bit_length + +def _decimal_lshift_exact(n, e): + """ Given integers n and e, return n * 10**e if it's an integer, else None. + + The computation is designed to avoid computing large powers of 10 + unnecessarily. + + >>> _decimal_lshift_exact(3, 4) + 30000 + >>> _decimal_lshift_exact(300, -999999999) # returns None + + """ + if n == 0: + return 0 + elif e >= 0: + return n * 10**e + else: + # val_n = largest power of 10 dividing n. + str_n = str(abs(n)) + val_n = len(str_n) - len(str_n.rstrip('0')) + return None if val_n < -e else n // 10**-e + +def _sqrt_nearest(n, a): + """Closest integer to the square root of the positive integer n. a is + an initial approximation to the square root. Any positive integer + will do for a, but the closer a is to the square root of n the + faster convergence will be. + + """ + if n <= 0 or a <= 0: + raise ValueError("Both arguments to _sqrt_nearest should be positive.") + + b=0 + while a != b: + b, a = a, a--n//a>>1 + return a + +def _rshift_nearest(x, shift): + """Given an integer x and a nonnegative integer shift, return closest + integer to x / 2**shift; use round-to-even in case of a tie. + + """ + b, q = 1 << shift, x >> shift + return q + (2*(x & (b-1)) + (q&1) > b) + +def _div_nearest(a, b): + """Closest integer to a/b, a and b positive integers; rounds to even + in the case of a tie. + + """ + q, r = divmod(a, b) + return q + (2*r + (q&1) > b) + +def _ilog(x, M, L = 8): + """Integer approximation to M*log(x/M), with absolute error boundable + in terms only of x/M. + + Given positive integers x and M, return an integer approximation to + M * log(x/M). For L = 8 and 0.1 <= x/M <= 10 the difference + between the approximation and the exact result is at most 22. For + L = 8 and 1.0 <= x/M <= 10.0 the difference is at most 15. In + both cases these are upper bounds on the error; it will usually be + much smaller.""" + + # The basic algorithm is the following: let log1p be the function + # log1p(x) = log(1+x). Then log(x/M) = log1p((x-M)/M). We use + # the reduction + # + # log1p(y) = 2*log1p(y/(1+sqrt(1+y))) + # + # repeatedly until the argument to log1p is small (< 2**-L in + # absolute value). For small y we can use the Taylor series + # expansion + # + # log1p(y) ~ y - y**2/2 + y**3/3 - ... - (-y)**T/T + # + # truncating at T such that y**T is small enough. The whole + # computation is carried out in a form of fixed-point arithmetic, + # with a real number z being represented by an integer + # approximation to z*M. To avoid loss of precision, the y below + # is actually an integer approximation to 2**R*y*M, where R is the + # number of reductions performed so far. + + y = x-M + # argument reduction; R = number of reductions performed + R = 0 + while (R <= L and abs(y) << L-R >= M or + R > L and abs(y) >> R-L >= M): + y = _div_nearest((M*y) << 1, + M + _sqrt_nearest(M*(M+_rshift_nearest(y, R)), M)) + R += 1 + + # Taylor series with T terms + T = -int(-10*len(str(M))//(3*L)) + yshift = _rshift_nearest(y, R) + w = _div_nearest(M, T) + for k in range(T-1, 0, -1): + w = _div_nearest(M, k) - _div_nearest(yshift*w, M) + + return _div_nearest(w*y, M) + +def _dlog10(c, e, p): + """Given integers c, e and p with c > 0, p >= 0, compute an integer + approximation to 10**p * log10(c*10**e), with an absolute error of + at most 1. Assumes that c*10**e is not exactly 1.""" + + # increase precision by 2; compensate for this by dividing + # final result by 100 + p += 2 + + # write c*10**e as d*10**f with either: + # f >= 0 and 1 <= d <= 10, or + # f <= 0 and 0.1 <= d <= 1. + # Thus for c*10**e close to 1, f = 0 + l = len(str(c)) + f = e+l - (e+l >= 1) + + if p > 0: + M = 10**p + k = e+p-f + if k >= 0: + c *= 10**k + else: + c = _div_nearest(c, 10**-k) + + log_d = _ilog(c, M) # error < 5 + 22 = 27 + log_10 = _log10_digits(p) # error < 1 + log_d = _div_nearest(log_d*M, log_10) + log_tenpower = f*M # exact + else: + log_d = 0 # error < 2.31 + log_tenpower = _div_nearest(f, 10**-p) # error < 0.5 + + return _div_nearest(log_tenpower+log_d, 100) + +def _dlog(c, e, p): + """Given integers c, e and p with c > 0, compute an integer + approximation to 10**p * log(c*10**e), with an absolute error of + at most 1. Assumes that c*10**e is not exactly 1.""" + + # Increase precision by 2. The precision increase is compensated + # for at the end with a division by 100. + p += 2 + + # rewrite c*10**e as d*10**f with either f >= 0 and 1 <= d <= 10, + # or f <= 0 and 0.1 <= d <= 1. Then we can compute 10**p * log(c*10**e) + # as 10**p * log(d) + 10**p*f * log(10). + l = len(str(c)) + f = e+l - (e+l >= 1) + + # compute approximation to 10**p*log(d), with error < 27 + if p > 0: + k = e+p-f + if k >= 0: + c *= 10**k + else: + c = _div_nearest(c, 10**-k) # error of <= 0.5 in c + + # _ilog magnifies existing error in c by a factor of at most 10 + log_d = _ilog(c, 10**p) # error < 5 + 22 = 27 + else: + # p <= 0: just approximate the whole thing by 0; error < 2.31 + log_d = 0 + + # compute approximation to f*10**p*log(10), with error < 11. + if f: + extra = len(str(abs(f)))-1 + if p + extra >= 0: + # error in f * _log10_digits(p+extra) < |f| * 1 = |f| + # after division, error < |f|/10**extra + 0.5 < 10 + 0.5 < 11 + f_log_ten = _div_nearest(f*_log10_digits(p+extra), 10**extra) + else: + f_log_ten = 0 + else: + f_log_ten = 0 + + # error in sum < 11+27 = 38; error after division < 0.38 + 0.5 < 1 + return _div_nearest(f_log_ten + log_d, 100) + +class _Log10Memoize(object): + """Class to compute, store, and allow retrieval of, digits of the + constant log(10) = 2.302585.... This constant is needed by + Decimal.ln, Decimal.log10, Decimal.exp and Decimal.__pow__.""" + def __init__(self): + self.digits = "23025850929940456840179914546843642076011014886" + + def getdigits(self, p): + """Given an integer p >= 0, return floor(10**p)*log(10). + + For example, self.getdigits(3) returns 2302. + """ + # digits are stored as a string, for quick conversion to + # integer in the case that we've already computed enough + # digits; the stored digits should always be correct + # (truncated, not rounded to nearest). + if p < 0: + raise ValueError("p should be nonnegative") + + if p >= len(self.digits): + # compute p+3, p+6, p+9, ... digits; continue until at + # least one of the extra digits is nonzero + extra = 3 + while True: + # compute p+extra digits, correct to within 1ulp + M = 10**(p+extra+2) + digits = str(_div_nearest(_ilog(10*M, M), 100)) + if digits[-extra:] != '0'*extra: + break + extra += 3 + # keep all reliable digits so far; remove trailing zeros + # and next nonzero digit + self.digits = digits.rstrip('0')[:-1] + return int(self.digits[:p+1]) + +_log10_digits = _Log10Memoize().getdigits + +def _iexp(x, M, L=8): + """Given integers x and M, M > 0, such that x/M is small in absolute + value, compute an integer approximation to M*exp(x/M). For 0 <= + x/M <= 2.4, the absolute error in the result is bounded by 60 (and + is usually much smaller).""" + + # Algorithm: to compute exp(z) for a real number z, first divide z + # by a suitable power R of 2 so that |z/2**R| < 2**-L. Then + # compute expm1(z/2**R) = exp(z/2**R) - 1 using the usual Taylor + # series + # + # expm1(x) = x + x**2/2! + x**3/3! + ... + # + # Now use the identity + # + # expm1(2x) = expm1(x)*(expm1(x)+2) + # + # R times to compute the sequence expm1(z/2**R), + # expm1(z/2**(R-1)), ... , exp(z/2), exp(z). + + # Find R such that x/2**R/M <= 2**-L + R = _nbits((x< M + T = -int(-10*len(str(M))//(3*L)) + y = _div_nearest(x, T) + Mshift = M<= 0: + cshift = c*10**shift + else: + cshift = c//10**-shift + quot, rem = divmod(cshift, _log10_digits(q)) + + # reduce remainder back to original precision + rem = _div_nearest(rem, 10**extra) + + # error in result of _iexp < 120; error after division < 0.62 + return _div_nearest(_iexp(rem, 10**p), 1000), quot - p + 3 + +def _dpower(xc, xe, yc, ye, p): + """Given integers xc, xe, yc and ye representing Decimals x = xc*10**xe and + y = yc*10**ye, compute x**y. Returns a pair of integers (c, e) such that: + + 10**(p-1) <= c <= 10**p, and + (c-1)*10**e < x**y < (c+1)*10**e + + in other words, c*10**e is an approximation to x**y with p digits + of precision, and with an error in c of at most 1. (This is + almost, but not quite, the same as the error being < 1ulp: when c + == 10**(p-1) we can only guarantee error < 10ulp.) + + We assume that: x is positive and not equal to 1, and y is nonzero. + """ + + # Find b such that 10**(b-1) <= |y| <= 10**b + b = len(str(abs(yc))) + ye + + # log(x) = lxc*10**(-p-b-1), to p+b+1 places after the decimal point + lxc = _dlog(xc, xe, p+b+1) + + # compute product y*log(x) = yc*lxc*10**(-p-b-1+ye) = pc*10**(-p-1) + shift = ye-b + if shift >= 0: + pc = lxc*yc*10**shift + else: + pc = _div_nearest(lxc*yc, 10**-shift) + + if pc == 0: + # we prefer a result that isn't exactly 1; this makes it + # easier to compute a correctly rounded result in __pow__ + if ((len(str(xc)) + xe >= 1) == (yc > 0)): # if x**y > 1: + coeff, exp = 10**(p-1)+1, 1-p + else: + coeff, exp = 10**p-1, -p + else: + coeff, exp = _dexp(pc, -(p+1), p+1) + coeff = _div_nearest(coeff, 10) + exp += 1 + + return coeff, exp + +def _log10_lb(c, correction = { + '1': 100, '2': 70, '3': 53, '4': 40, '5': 31, + '6': 23, '7': 16, '8': 10, '9': 5}): + """Compute a lower bound for 100*log10(c) for a positive integer c.""" + if c <= 0: + raise ValueError("The argument to _log10_lb should be nonnegative.") + str_c = str(c) + return 100*len(str_c) - correction[str_c[0]] + +##### Helper Functions #################################################### + +def _convert_other(other, raiseit=False, allow_float=False): + """Convert other to Decimal. + + Verifies that it's ok to use in an implicit construction. + If allow_float is true, allow conversion from float; this + is used in the comparison methods (__eq__ and friends). + + """ + if isinstance(other, Decimal): + return other + if isinstance(other, int): + return Decimal(other) + if allow_float and isinstance(other, float): + return Decimal.from_float(other) + + if raiseit: + raise TypeError("Unable to convert %s to Decimal" % other) + return NotImplemented + +def _convert_for_comparison(self, other, equality_op=False): + """Given a Decimal instance self and a Python object other, return + a pair (s, o) of Decimal instances such that "s op o" is + equivalent to "self op other" for any of the 6 comparison + operators "op". + + """ + if isinstance(other, Decimal): + return self, other + + # Comparison with a Rational instance (also includes integers): + # self op n/d <=> self*d op n (for n and d integers, d positive). + # A NaN or infinity can be left unchanged without affecting the + # comparison result. + if isinstance(other, _numbers.Rational): + if not self._is_special: + self = _dec_from_triple(self._sign, + str(int(self._int) * other.denominator), + self._exp) + return self, Decimal(other.numerator) + + # Comparisons with float and complex types. == and != comparisons + # with complex numbers should succeed, returning either True or False + # as appropriate. Other comparisons return NotImplemented. + if equality_op and isinstance(other, _numbers.Complex) and other.imag == 0: + other = other.real + if isinstance(other, float): + context = getcontext() + if equality_op: + context.flags[FloatOperation] = 1 + else: + context._raise_error(FloatOperation, + "strict semantics for mixing floats and Decimals are enabled") + return self, Decimal.from_float(other) + return NotImplemented, NotImplemented + + +##### Setup Specific Contexts ############################################ + +# The default context prototype used by Context() +# Is mutable, so that new contexts can have different default values + +DefaultContext = Context( + prec=28, rounding=ROUND_HALF_EVEN, + traps=[DivisionByZero, Overflow, InvalidOperation], + flags=[], + Emax=999999, + Emin=-999999, + capitals=1, + clamp=0 +) + +# Pre-made alternate contexts offered by the specification +# Don't change these; the user should be able to select these +# contexts and be able to reproduce results from other implementations +# of the spec. + +BasicContext = Context( + prec=9, rounding=ROUND_HALF_UP, + traps=[DivisionByZero, Overflow, InvalidOperation, Clamped, Underflow], + flags=[], +) + +ExtendedContext = Context( + prec=9, rounding=ROUND_HALF_EVEN, + traps=[], + flags=[], +) + + +##### crud for parsing strings ############################################# +# +# Regular expression used for parsing numeric strings. Additional +# comments: +# +# 1. Uncomment the two '\s*' lines to allow leading and/or trailing +# whitespace. But note that the specification disallows whitespace in +# a numeric string. +# +# 2. For finite numbers (not infinities and NaNs) the body of the +# number between the optional sign and the optional exponent must have +# at least one decimal digit, possibly after the decimal point. The +# lookahead expression '(?=\d|\.\d)' checks this. + +import re +_parser = re.compile(r""" # A numeric string consists of: +# \s* + (?P[-+])? # an optional sign, followed by either... + ( + (?=\d|\.\d) # ...a number (with at least one digit) + (?P\d*) # having a (possibly empty) integer part + (\.(?P\d*))? # followed by an optional fractional part + (E(?P[-+]?\d+))? # followed by an optional exponent, or... + | + Inf(inity)? # ...an infinity, or... + | + (?Ps)? # ...an (optionally signaling) + NaN # NaN + (?P\d*) # with (possibly empty) diagnostic info. + ) +# \s* + \Z +""", re.VERBOSE | re.IGNORECASE).match + +_all_zeros = re.compile('0*$').match +_exact_half = re.compile('50*$').match + +##### PEP3101 support functions ############################################## +# The functions in this section have little to do with the Decimal +# class, and could potentially be reused or adapted for other pure +# Python numeric classes that want to implement __format__ +# +# A format specifier for Decimal looks like: +# +# [[fill]align][sign][#][0][minimumwidth][,][.precision][type] + +_parse_format_specifier_regex = re.compile(r"""\A +(?: + (?P.)? + (?P[<>=^]) +)? +(?P[-+ ])? +(?P\#)? +(?P0)? +(?P(?!0)\d+)? +(?P,)? +(?:\.(?P0|(?!0)\d+))? +(?P[eEfFgGn%])? +\Z +""", re.VERBOSE|re.DOTALL) + +del re + +# The locale module is only needed for the 'n' format specifier. The +# rest of the PEP 3101 code functions quite happily without it, so we +# don't care too much if locale isn't present. +try: + import locale as _locale +except ImportError: + pass + +def _parse_format_specifier(format_spec, _localeconv=None): + """Parse and validate a format specifier. + + Turns a standard numeric format specifier into a dict, with the + following entries: + + fill: fill character to pad field to minimum width + align: alignment type, either '<', '>', '=' or '^' + sign: either '+', '-' or ' ' + minimumwidth: nonnegative integer giving minimum width + zeropad: boolean, indicating whether to pad with zeros + thousands_sep: string to use as thousands separator, or '' + grouping: grouping for thousands separators, in format + used by localeconv + decimal_point: string to use for decimal point + precision: nonnegative integer giving precision, or None + type: one of the characters 'eEfFgG%', or None + + """ + m = _parse_format_specifier_regex.match(format_spec) + if m is None: + raise ValueError("Invalid format specifier: " + format_spec) + + # get the dictionary + format_dict = m.groupdict() + + # zeropad; defaults for fill and alignment. If zero padding + # is requested, the fill and align fields should be absent. + fill = format_dict['fill'] + align = format_dict['align'] + format_dict['zeropad'] = (format_dict['zeropad'] is not None) + if format_dict['zeropad']: + if fill is not None: + raise ValueError("Fill character conflicts with '0'" + " in format specifier: " + format_spec) + if align is not None: + raise ValueError("Alignment conflicts with '0' in " + "format specifier: " + format_spec) + format_dict['fill'] = fill or ' ' + # PEP 3101 originally specified that the default alignment should + # be left; it was later agreed that right-aligned makes more sense + # for numeric types. See http://bugs.python.org/issue6857. + format_dict['align'] = align or '>' + + # default sign handling: '-' for negative, '' for positive + if format_dict['sign'] is None: + format_dict['sign'] = '-' + + # minimumwidth defaults to 0; precision remains None if not given + format_dict['minimumwidth'] = int(format_dict['minimumwidth'] or '0') + if format_dict['precision'] is not None: + format_dict['precision'] = int(format_dict['precision']) + + # if format type is 'g' or 'G' then a precision of 0 makes little + # sense; convert it to 1. Same if format type is unspecified. + if format_dict['precision'] == 0: + if format_dict['type'] is None or format_dict['type'] in 'gGn': + format_dict['precision'] = 1 + + # determine thousands separator, grouping, and decimal separator, and + # add appropriate entries to format_dict + if format_dict['type'] == 'n': + # apart from separators, 'n' behaves just like 'g' + format_dict['type'] = 'g' + if _localeconv is None: + _localeconv = _locale.localeconv() + if format_dict['thousands_sep'] is not None: + raise ValueError("Explicit thousands separator conflicts with " + "'n' type in format specifier: " + format_spec) + format_dict['thousands_sep'] = _localeconv['thousands_sep'] + format_dict['grouping'] = _localeconv['grouping'] + format_dict['decimal_point'] = _localeconv['decimal_point'] + else: + if format_dict['thousands_sep'] is None: + format_dict['thousands_sep'] = '' + format_dict['grouping'] = [3, 0] + format_dict['decimal_point'] = '.' + + return format_dict + +def _format_align(sign, body, spec): + """Given an unpadded, non-aligned numeric string 'body' and sign + string 'sign', add padding and alignment conforming to the given + format specifier dictionary 'spec' (as produced by + parse_format_specifier). + + """ + # how much extra space do we have to play with? + minimumwidth = spec['minimumwidth'] + fill = spec['fill'] + padding = fill*(minimumwidth - len(sign) - len(body)) + + align = spec['align'] + if align == '<': + result = sign + body + padding + elif align == '>': + result = padding + sign + body + elif align == '=': + result = sign + padding + body + elif align == '^': + half = len(padding)//2 + result = padding[:half] + sign + body + padding[half:] + else: + raise ValueError('Unrecognised alignment field') + + return result + +def _group_lengths(grouping): + """Convert a localeconv-style grouping into a (possibly infinite) + iterable of integers representing group lengths. + + """ + # The result from localeconv()['grouping'], and the input to this + # function, should be a list of integers in one of the + # following three forms: + # + # (1) an empty list, or + # (2) nonempty list of positive integers + [0] + # (3) list of positive integers + [locale.CHAR_MAX], or + + from itertools import chain, repeat + if not grouping: + return [] + elif grouping[-1] == 0 and len(grouping) >= 2: + return chain(grouping[:-1], repeat(grouping[-2])) + elif grouping[-1] == _locale.CHAR_MAX: + return grouping[:-1] + else: + raise ValueError('unrecognised format for grouping') + +def _insert_thousands_sep(digits, spec, min_width=1): + """Insert thousands separators into a digit string. + + spec is a dictionary whose keys should include 'thousands_sep' and + 'grouping'; typically it's the result of parsing the format + specifier using _parse_format_specifier. + + The min_width keyword argument gives the minimum length of the + result, which will be padded on the left with zeros if necessary. + + If necessary, the zero padding adds an extra '0' on the left to + avoid a leading thousands separator. For example, inserting + commas every three digits in '123456', with min_width=8, gives + '0,123,456', even though that has length 9. + + """ + + sep = spec['thousands_sep'] + grouping = spec['grouping'] + + groups = [] + for l in _group_lengths(grouping): + if l <= 0: + raise ValueError("group length should be positive") + # max(..., 1) forces at least 1 digit to the left of a separator + l = min(max(len(digits), min_width, 1), l) + groups.append('0'*(l - len(digits)) + digits[-l:]) + digits = digits[:-l] + min_width -= l + if not digits and min_width <= 0: + break + min_width -= len(sep) + else: + l = max(len(digits), min_width, 1) + groups.append('0'*(l - len(digits)) + digits[-l:]) + return sep.join(reversed(groups)) + +def _format_sign(is_negative, spec): + """Determine sign character.""" + + if is_negative: + return '-' + elif spec['sign'] in ' +': + return spec['sign'] + else: + return '' + +def _format_number(is_negative, intpart, fracpart, exp, spec): + """Format a number, given the following data: + + is_negative: true if the number is negative, else false + intpart: string of digits that must appear before the decimal point + fracpart: string of digits that must come after the point + exp: exponent, as an integer + spec: dictionary resulting from parsing the format specifier + + This function uses the information in spec to: + insert separators (decimal separator and thousands separators) + format the sign + format the exponent + add trailing '%' for the '%' type + zero-pad if necessary + fill and align if necessary + """ + + sign = _format_sign(is_negative, spec) + + if fracpart or spec['alt']: + fracpart = spec['decimal_point'] + fracpart + + if exp != 0 or spec['type'] in 'eE': + echar = {'E': 'E', 'e': 'e', 'G': 'E', 'g': 'e'}[spec['type']] + fracpart += "{0}{1:+}".format(echar, exp) + if spec['type'] == '%': + fracpart += '%' + + if spec['zeropad']: + min_width = spec['minimumwidth'] - len(fracpart) - len(sign) + else: + min_width = 0 + intpart = _insert_thousands_sep(intpart, spec, min_width) + + return _format_align(sign, intpart+fracpart, spec) + + +##### Useful Constants (internal use only) ################################ + +# Reusable defaults +_Infinity = Decimal('Inf') +_NegativeInfinity = Decimal('-Inf') +_NaN = Decimal('NaN') +_Zero = Decimal(0) +_One = Decimal(1) +_NegativeOne = Decimal(-1) + +# _SignedInfinity[sign] is infinity w/ that sign +_SignedInfinity = (_Infinity, _NegativeInfinity) + +# Constants related to the hash implementation; hash(x) is based +# on the reduction of x modulo _PyHASH_MODULUS +_PyHASH_MODULUS = sys.hash_info.modulus +# hash values to use for positive and negative infinities, and nans +_PyHASH_INF = sys.hash_info.inf +_PyHASH_NAN = sys.hash_info.nan + +# _PyHASH_10INV is the inverse of 10 modulo the prime _PyHASH_MODULUS +_PyHASH_10INV = pow(10, _PyHASH_MODULUS - 2, _PyHASH_MODULUS) +del sys diff --git a/shared/package-files/8b/8b90a1c3f99957578f8c2af8bc0faa53712eb21108af45f6802dce7e258183cb/blob b/shared/package-files/8b/8b90a1c3f99957578f8c2af8bc0faa53712eb21108af45f6802dce7e258183cb/blob new file mode 100644 index 0000000000000000000000000000000000000000..49f6e343732150432d551249cce9028e7e784969 Binary files /dev/null and b/shared/package-files/8b/8b90a1c3f99957578f8c2af8bc0faa53712eb21108af45f6802dce7e258183cb/blob differ diff --git a/shared/package-files/8b/8b90c2623ff71532b5856bc5736ec3488566bdcf66628a9bad443b4783bc23ed/blob b/shared/package-files/8b/8b90c2623ff71532b5856bc5736ec3488566bdcf66628a9bad443b4783bc23ed/blob new file mode 100644 index 0000000000000000000000000000000000000000..34d39d62c389addb0c1b96e9857a7d04a05390af --- /dev/null +++ b/shared/package-files/8b/8b90c2623ff71532b5856bc5736ec3488566bdcf66628a9bad443b4783bc23ed/blob @@ -0,0 +1,113 @@ +/*quarterly, seasonally adjusted, U.S. +fixed investment (y1), change in business inventories (y2), +1947Q1-1972Q4; +source: U.S. Department of Commerce, Bureau of Economic Analysis, +The National Income and Product Accounts of the United States, 1929-1974 +*/ + +<1947 Q1> + y1 y2 + 69.6 0.1 + 67.6 -0.9 + 69.5 -2.9 + 74.7 2.7 + 77.1 4.1 + 77.4 5.6 + 76.6 6.9 + 76.1 5.3 + 71.8 -0.3 + 68.9 -7.1 + 68.5 -2.5 + 70.6 -7.7 + 75.4 4.4 + 82.3 7.7 + 88.2 8.0 + 86.9 22.1 + 83.4 13.4 + 80.3 19.9 + 79.4 14.6 + 78.6 7.0 + 79.3 7.3 + 80.3 -2.7 + 75.3 5.4 + 80.6 7.2 + 83.9 3.9 + 84.2 5.1 + 84.4 1.9 + 83.8 -5.0 + 82.8 -3.4 + 84.1 -4.1 + 87.0 -2.7 + 88.5 1.5 + 92.1 5.9 + 96.1 8.0 + 98.3 7.8 + 98.8 9.2 + 96.6 7.5 + 97.4 5.5 + 97.6 4.9 + 96.6 5.4 + 96.2 2.5 + 95.3 2.9 + 96.4 3.7 + 94.9 -3.0 + 90.0 -6.8 + 87.2 -6.2 + 88.0 0.3 + 93.0 5.3 + 98.3 5.0 +101.6 13.0 +102.6 -0.4 +101.4 8.2 +104.9 13.5 +101.8 4.9 + 98.8 3.0 + 98.6 -3.9 + 97.7 -3.8 + 99.2 1.9 +101.3 6.6 +104.6 6.7 +106.1 10.6 +109.9 9.2 +111.1 8.0 +110.1 4.7 +110.7 7.6 +116.0 7.0 +118.5 9.3 +122.0 7.1 +124.0 6.1 +124.0 8.0 +124.9 7.3 +126.4 7.9 +133.4 13.4 +137.9 10.6 +140.1 12.4 +143.8 8.8 +147.5 13.5 +146.2 17.8 +145.0 15.1 +139.7 20.5 +136.4 14.6 +139.6 7.5 +141.1 12.2 +145.5 13.8 +148.9 6.3 +148.9 11.8 +150.7 9.2 +155.0 7.6 +159.1 9.8 +158.4 12.2 +158.1 13.4 +154.3 6.8 +151.8 2.9 +150.0 4.8 +150.4 6.3 +149.5 3.3 +154.3 7.9 +158.4 10.0 +162.1 5.0 +166.0 3.7 +174.3 4.8 +176.1 10.1 +178.2 12.1 +186.7 10.8 diff --git a/shared/package-files/8b/8b9121a163d486ee2495add7bb866f5c5d6554b3e223a660e21ea8f236a26a6a/blob b/shared/package-files/8b/8b9121a163d486ee2495add7bb866f5c5d6554b3e223a660e21ea8f236a26a6a/blob new file mode 100644 index 0000000000000000000000000000000000000000..bfa16aabf423d478af4ea2ab1910e454f5966028 --- /dev/null +++ b/shared/package-files/8b/8b9121a163d486ee2495add7bb866f5c5d6554b3e223a660e21ea8f236a26a6a/blob @@ -0,0 +1,6 @@ +from numpy.core import _dtype_ctypes + +_globals = globals() + +for item in _dtype_ctypes.__dir__(): + _globals[item] = getattr(_dtype_ctypes, item) diff --git a/shared/package-files/8b/8b9262fc79b943a382d24a96dab8a16757398cd7d7ea33766fd7e5ed6382a645/blob b/shared/package-files/8b/8b9262fc79b943a382d24a96dab8a16757398cd7d7ea33766fd7e5ed6382a645/blob new file mode 100644 index 0000000000000000000000000000000000000000..4180378fc0610a4a22f07895daa7345d06ef25f2 --- /dev/null +++ b/shared/package-files/8b/8b9262fc79b943a382d24a96dab8a16757398cd7d7ea33766fd7e5ed6382a645/blob @@ -0,0 +1,547 @@ +[build-system] +build-backend = 'mesonpy' +requires = [ + 'meson-python', + "cython>=3.0.13,<4", # Sync with CYTHON_MIN_VER in setup + "numpy>=2.0.0,<3", + "scipy>=1.13,<2", + "setuptools_scm>=9.2.0,<10", +] + +[project] +classifiers = [ + "Development Status :: 4 - Beta", + "Environment :: Console", + "Programming Language :: Cython", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", + "Operating System :: OS Independent", + "Intended Audience :: End Users/Desktop", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "Natural Language :: English", + "Topic :: Office/Business :: Financial", + "Topic :: Scientific/Engineering", +] +description = "Statistical computations and models for Python" +dynamic = ["version"] +keywords = [ + "statistics", + "econometrics", + "time series", + "regression", + "anova", + "statistical tests", + "linear models", + "generalized linear models", + "discrete choice models", + "robust linear models", + "mixed linear models", + "time series analysis", + "state space models", + "vector autoregression", + "volatility modeling", + "unit root tests", + "cointegration tests", + "nonparametric methods", + "multivariate statistics" +] +license = "BSD-3-Clause" +license-files = [ "LICENSE.txt" ] +maintainers = [ + { name = "statsmodels Developers", email = "pystatsmodels@googlegroups.com" }, +] +name = "statsmodels" +dependencies = [ + "numpy>=1.23.5,<3", # released March 2022 + "scipy>=1.8,!=1.9.2", # released Feb 2022, 1.9.2 blocked due to bug + "pandas>=1.4,!=2.1.0", # released Feb 2022, 2.1.0 blocked due to bug + "patsy>=0.5.6", # released December 2023 + "packaging>=21.3", # released Nov 2021 + "formulaic>=1.1.0", # released December 2024 +] +readme = "README.md" +requires-python = ">=3.10" + +[project.optional-dependencies] +test = [ + # test + "pytest>=8.4.1,<10", + "pytest-randomly", + "pytest-run-parallel", + "pytest-xdist", + "pytest-cov", + "coverage[toml]", + "meson", + 'ninja; sys_platform != "emscripten"', +] +doc = [ + # Requirements for doc build, in addition to requirements.txt + "sphinx", + # Avoid warnings which break Sphinx by pinning pandas + "pandas>=2.2.2,!=2.1.0", + "pydata-sphinx-theme", + "jupyter", + "notebook", + "nbsphinx", + "nbconvert", + "pyyaml", + "pandas-datareader", + "simplegeneric", + "seaborn", + "numpydoc", + # "theano-pymc; os_name != 'nt'", # disabled because it is not compatible with move recent Python version + "pymc; os_name != 'nt'", + "arviz; os_name != 'nt'", + "jinja2", + "pickleshare", + "pytest>=8.4.1,<10", +] +dev = [ + # build + "cython>=3.0.10,<4", + "setuptools_scm~=9.0", + "meson-python>=0.18.0", + "meson", + "ninja", + # run + "matplotlib>=3", + "colorama", + "joblib", + # Remove due to failures on azure CI + # cvxopt; os_name != "win32" + # Pin on Win32 + "pywinpty; os_name == 'nt'", + # code quality + "ruff", +] +polars = [ + # Polars support + "polars>=1.0.0", +] + +[project.urls] +homepage = "https://www.statsmodels.org" +documentation = "https://www.statsmodels.org/stable/index.html" +repository = "https://github.com/statsmodels/statsmodels" +changelog = "https://bashtage.github.io/arch/changes.html" +bugtracker = "https://github.com/statsmodels/statsmodels/issues" + +[tool.meson-python.args] +setup = ['--vsenv'] + +[tool.ruff] +line-length = 88 +fix = false +# Matches requires-python (>=3.10) and [tool.black]'s target-version below; +# also affects which pyupgrade (UP) rules apply. +target-version = "py310" +# archive/ is explicitly documented (archive/README) as deprecated, no +# longer maintained legacy code; excluded so it's not linted as if it +# were part of the active codebase. +extend-exclude = ["archive", "examples/python"] + +[tool.ruff.lint] +typing-modules = ["statsmodels.compat.python", "compat.python"] + +select = [ + # pyflakes + "F", + # pycodestyle + "E", "W", + # flake8-2020 + "YTT", + # flake8-bugbear + "B", + # flake8-quotes + "Q", + # flake8-debugger + "T10", + # flake8-gettext + "INT", + # pylint + "PL", + # flake8-pytest-style + "PT", + # misc lints + "PIE", + # flake8-pyi + "PYI", + # tidy imports + "TID", + # implicit string concatenation + "ISC", + # flake8-type-checking + "TC", + # comprehensions + "C4", + # pygrep-hooks + "PGH", + # Ruff-specific rules + "RUF", + # Perflint + "PERF", + # flynt + "FLY", + # flake8-future-annotations + "FA", + # flake8-import-conventions + "ICN", + # flake8-slots + "SLOT", + # flake8-raise + "RSE", + # flake8-async + "ASYNC", + # flake8-executable + "EXE", + # flake8-logging-format + "G", + # flake8-logging + "LOG", + # refurb + "FURB", + # flake8-bandit + "S", + # isort + "I", + # pyupgrade + "UP", + # flake8-builtins + "A", + # flake8-airflow (this project has no Airflow usage, so this and the + # frameworks below are all currently clean; enabled for completeness) + "AIR", + # flake8-commas: only COM812/COM819 fail (already in the never-enable + # list above), COM818 is otherwise clean + "COM", + # flake8-django (no Django usage, currently clean) + "DJ", + # flake8-datetimez + "DTZ", + # flake8-fastapi (no FastAPI usage, currently clean) + "FAST", + # pandas-vet + "PD", + # flake8-use-pathlib: os.path is used throughout, but fewer than half + # the rule's codes actually fire here, so the category is selected with + # the failing codes carved out below (see ignore list) rather than + # listing the larger set of passing codes individually. A full + # os.path -> pathlib migration is a bigger undertaking than a config + # change. + "PTH", + # pep8-naming: most of "N" fails on scientific/statistical variable + # naming conventions (e.g. X, Y, N806 alone is 2000+ hits), so only the + # individually-clean rules are selected rather than the whole category. + "N804", "N811", "N813", "N817", + # flake8-simplify: most of "SIM" still has real hits, so only the + # individually-clean rules are selected rather than the whole category. + "SIM107", "SIM109", "SIM110", "SIM112", "SIM116", "SIM202", "SIM208", + "SIM220", "SIM221", "SIM222", "SIM223", "SIM911", + # tryceratops: TRY003 alone (no vanilla-args messages in raise) is + # 1300+ hits, so only the individually-clean rules are selected rather + # than the whole category. + "TRY400", "TRY401", + # flake8-use-pathlib + "PTH", + "RET501", + "RET502", + "RET507", + "RET508", +] + +# pydocstyle ("D") is intentionally NOT selected above. This project already +# validates numpydoc docstring structure via tools/validate_docstrings.py +# (built on the numpydoc package), which understands our numpydoc convention +# far more precisely than ruff's generic pydocstyle checks do - even with +# `[tool.ruff.lint.pydocstyle] convention = "numpy"` set, D still produces +# tens of thousands of violations and disagrees with the dedicated +# validator in places. Do not add "D" to select. + +ignore = [ + # Required to allow black formatting + # Whitespace before ':' (Needed for black) + "E203", + # Line too long (### > 88 characters) (Needed for black) + "E501", + # Multiple statements on one line (colon) (E701) + "E701", + + ### Never enable: these conflict with the black/ruff-format formatter. + # ISC001 is deliberately NOT in this list: we keep ISC001 enabled and fix + # the occasional real violation by hand instead of suppressing it. + "COM812", # missing-trailing-comma: black decides this, not us + "COM819", # prohibited-trailing-comma: black decides this, not us + "Q003", # avoidable-escaped-quote + + ### Intentionally disabled + # Counts below are from `ruff check . --select ALL` + # (this project's conformance scope), refreshed 2026-07-27. + # Too many arguments to function call + "PLR0913", # 645 + # Too many returns + "PLR0911", # 12 + # Too many branches + "PLR0912", # 221 + # Too many statements + "PLR0915", # 221 + # Too many positional arguments + "PLR0917", # 617 + # Redefined loop name + "PLW2901", # 49 + # incorrect-dict-iterator, flags valid Series.items usage + "PERF102", # 5 + # try-except-in-loop, becomes useless in Python 3.11 + "PERF203", # 9 + # pytest-parametrize-names-wrong-type + "PT006", # 36 + # pytest-raises-with-multiple-statements + "PT012", # 15 + # Docstring contains ambiguous letter + "RUF002", # Do Not Enable + # Magic number + "PLR2004", # 717 + # import-outside-toplevel + "PLC0415", # 853 + + ### TODO: Enable gradually + # Too many uses of dict(...) instread of {...} + # Would be good enforce eventually + "C408", # 324 + # Too many places without match= in pytest raises + # Would be goot enforce eventually + "PT011", # 519 + "RUF059", # 342 + "PERF401", # 30 + # collection-literal-concatenation + "RUF005", # 138 + # mutable-class-default + "RUF012", # 177 + # repeated-equality-comparison + "PLR1714", # 27 + + ### New 2026-07-27 when enabling ASYNC, EXE, G, LOG, FURB, S, full ICN + # assert is used pervasively outside of tests in this codebase (input + # validation, invariant checks); not adopting flake8-bandit's blanket + # objection to it + "S101", # 1669 + # try/except/pass: mostly deliberate best-effort cleanup code + "S110", # 5 + # pickle usage: needed for model persistence, a documented feature + "S301", # 11 + # eval usage in a handful of expression-parsing helpers + "S307", # 3 + # urlopen used only for fetching public, hardcoded dataset URLs + "S310", # 2 + # subprocess with shell=True / without shell=True check: used in a couple + # of dev/CI helper scripts, not on untrusted input + "S602", # 2 + "S603", # 2 + + ### New 2026-07-27 when enabling A, DTZ, PD, PTH + # Variable/argument/import shadows a builtin name (e.g. `id`, `type`, + # `filter`); common in this codebase's mathematical notation and not + # worth a rename sweep + "A001", # 35 + "A002", # 10 + "A004", # 2 + # datetime()/datetime.now() without tzinfo; this project works with + # naive timestamps throughout (dataset dates, log timestamps) and does + # not need timezone-aware datetimes + "DTZ001", # 82 + "DTZ005", # 4 + # inplace=True is used deliberately in a number of places + "PD002", # 10 + # .pivot()/.unstack() and .stack() instead of .pivot_table()/.melt(): + # deliberate choices, not accidental API misuse + "PD010", # 25 + "PD013", # 13 + # .values instead of .to_numpy(): a pervasive, long-standing idiom + # throughout this codebase, not worth a mechanical rewrite + "PD011", # 308 + # os.path used throughout instead of pathlib; a real modernization + # opportunity, but a bigger migration than a quick fix + "PTH100", # 107 + "PTH103", # 4 + "PTH106", # 1 + "PTH107", # 6 + "PTH110", # 13 + "PTH111", # 1 + "PTH112", # 1 + "PTH113", # 1 + "PTH118", # 237 + "PTH119", # 1 + "PTH120", # 123 + "PTH122", # 4 + "PTH123", # 89 + "PTH204", # 4 + "PTH207", # 6 + "PTH208", # 3 +] + +[tool.ruff.lint.flake8-quotes] +multiline-quotes = "double" + +[tool.ruff.lint.isort] +# Mirrors original [tool.isort] below as closely as ruff's isort implementation +# allows. +combine-as-imports = true +force-sort-within-sections = true +known-first-party = ["statsmodels"] +known-third-party = ["Cython", "cython", "numpy", "matplotlib", "pandas", "patsy", "pytest", "cvxopt"] +section-order = ["future", "compat", "standard-library", "third-party", "first-party", "local-folder"] +split-on-trailing-comma = true + +[tool.ruff.lint.isort.sections] +# Mirrors known_compat from [tool.isort]. +compat = ["statsmodels.compat", "setuptools"] + +[tool.ruff.lint.per-file-ignores] +# relative imports allowed for asv_bench +"statsmodels/__init__.py" = ["PT028"] +"statsmodels/tsa/vector_ar/var_model.py" = ["PT028"] +"statsmodels/stats/rates.py" = ["PT028"] +"statsmodels/stats/proportion.py" = ["PT028"] +"statsmodels/stats/oneway.py" = ["PT028"] +"statsmodels/stats/multivariate.py" = ["PT028"] +"statsmodels/stats/diagnostic_gen.py" = ["PT028"] +"statsmodels/discrete/_diagnostics_count.py" = ["PT028"] +"statsmodels/examples/tests/test_notebooks.py" = ["F401"] +# Example scripts/notebooks are left as-is (not converted to pathlib). +"**/examples/**" = ["PTH"] +[tool.pytest.ini_options] +minversion = "8.4.1" +addopts = "--strict --durations=30 --junitxml=test-data.xml --randomly-dont-reset-seed" +testpaths = "statsmodels" +xfail_strict = true +empty_parameter_set_mark = "fail_at_collect" +filterwarnings = [ + "ignore:`formatargspec`:DeprecationWarning:statsmodels", + "ignore:Using or importing:DeprecationWarning", + "ignore:Session._key_changed is deprecated:DeprecationWarning", + "error:Support for multi-dimensional:DeprecationWarning", + "error:Mutating unit root tests is deprecated:FutureWarning", + "error:Lag selection has changed:DeprecationWarning", + "error:More than 20 figures have been opened:RuntimeWarning:", + "error:Inequality constraints incompatible::", + "error:Parameters are not consistent with a::", + "ignore:The --strict option is deprecated:pytest.PytestDeprecationWarning", + "error:The default for reindex:FutureWarning:", + "error:Values in x were outside bounds:RuntimeWarning:", + "error:The random_state property is deprecated:FutureWarning", + "error:random_state is deprecated:FutureWarning", + "error:seed is deprecated:FutureWarning", + "error:get_state is deprecated:FutureWarning", + "error:Conversion of an array with ndim:DeprecationWarning:", + "error:the matrix subclass is not the recommended::", + "error:The Dataframe Interchange Protocol::", + "error:Setting the shape:DeprecationWarning:", + "error:After 0.17 or January 2028:FutureWarning:", + "error:A future version of pandas:", + "error:The inplace keyword in::", + "error:Keyword arguments have been passed:FutureWarning:", + "error:After statsmodels 0.15 is released:FutureWarning:", + "error:Passing `None` as the seed currently:FutureWarning:", + "error:the 'random_state' keyword is deprecated:FutureWarning:", + "error:the 'seed' keyword is deprecated:FutureWarning:", + "error:the 'center' keyword is deprecated:FutureWarning:", + "error:Unpacking:FutureWarning:", + "error:The test statistic is outside:statsmodels.tools.sm_exceptions.InterpolationWarning:", + "error:The design matrix is:statsmodels.tools.sm_exceptions.SingularMatrixWarning:", + "error:Fast medcouple algorithm:UserWarning:", + "error:medcouple is undefined:UserWarning:", + "error:Provided `endog` series:UserWarning:", + # Strict checks disabled + # "error:overflow encountered in square:RuntimeWarning", + # "error:invalid value encountered in cos:RuntimeWarning", + # "error:divide by zero encountered in double_scalars:RuntimeWarning", + # "error:invalid value encountered in log:RuntimeWarning", + # "error:invalid value encountered in sqrt:RuntimeWarning:", + # "error:overflow encountered in double_scalars:RuntimeWarning:", + # "error:overflow encountered in multiply:RuntimeWarning:", +] +junit_family = "xunit2" +markers = [ + "example: mark a test that runs example code", + "matplotlib: mark a test that requires matplotlib", + "slow: mark a test as slow", + "smoke: mark a test as a smoke test", + "high_memory: mark a test as a high memory utilization test", + "low_precision: mark a test as low precision", + "todo: mark a test as incomplete", + "polars: mark a test that requires polars", + "singleton_randomstate: mark a test as changing the singleton RandomState", + "joblib: mark a test as using joblib for parallel computation", +] + +[tool.ruff.format] +quote-style = "double" + +[tool.coverage.run] +source = ["statsmodels"] +branch = true +plugins = ["Cython.Coverage"] +omit = [ + # print_version is untestable + "*/print_version.py", + # skip compatibility code + "*/compat/*", + # Unused file + "*/results/gee_generate_tests.py", + # Results for tests + "*/tests/results/*", + # Simulation checks + "*/tests/*_simulation_check.py", + # Misc non-test files in test directories + "*/tests/_*", + "*/tests/e*", + "*/tests/c*", + "*/tests/d*", + "*/tests/g*", + "*/tests/m*", + "*/tests/p*", + "*/tests/r*", + "*/tests/s*", + "*/_version.py", + "*/_pss_critical_values/*", +] + +[tool.coverage.report] +show_missing = true +ignore_errors = false +# Regexes for lines to exclude from consideration +exclude_also = [ + # Have to re-enable the standard pragma + "pragma: no cover", + + # Do not complain about missing debug-only code: + "def __repr__", + "if self\\.debug", + "if pdf_output:", + + # Do not complain if tests do not hit defensive assertion code: + "raise AssertionError", + "raise NotImplementedError", + "except NotImplementedError", + "except ImportError", + "except (HTTPError, URLError, SSLError, timeout)", + "except IGNORED_EXCEPTIONS", + + # Ignore pass + "pass", + + # Do not complain if non-runnable code is not run: + "if 0:", + "if __name__ == .__main__.:", +] + +[tool.coverage.html] +directory = "coverage_html_report" + + +[tool.black] +line-length = 88 +target-version = ["py310"] diff --git a/shared/package-files/8b/8b98ae07154d4192178886dad7c2939d83412ccd802457b11bfa7dba4356364e/blob b/shared/package-files/8b/8b98ae07154d4192178886dad7c2939d83412ccd802457b11bfa7dba4356364e/blob new file mode 100644 index 0000000000000000000000000000000000000000..2cd164693e57d39e70f6c85ae68b0843684b12ec --- /dev/null +++ b/shared/package-files/8b/8b98ae07154d4192178886dad7c2939d83412ccd802457b11bfa7dba4356364e/blob @@ -0,0 +1,461 @@ +#pragma once + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace c10 { + +/** + * Scalar represents a 0-dimensional tensor which contains a single element. + * Unlike a tensor, numeric literals (in C++) are implicitly convertible to + * Scalar (which is why, for example, we provide both add(Tensor) and + * add(Scalar) overloads for many operations). It may also be used in + * circumstances where you statically know a tensor is 0-dim and single size, + * but don't know its type. + */ +class C10_API Scalar { + public: + Scalar() : Scalar(int64_t(0)) {} + + void destroy() { + if (Tag::HAS_si == tag || Tag::HAS_sd == tag || Tag::HAS_sb == tag) { + raw::intrusive_ptr::decref(v.p); + v.p = nullptr; + } + } + + ~Scalar() { + destroy(); + } + +#define DEFINE_IMPLICIT_CTOR(type, name) \ + Scalar(type vv) : Scalar(vv, true) {} + + AT_FORALL_SCALAR_TYPES_AND7( + Half, + BFloat16, + Float8_e5m2, + Float8_e4m3fn, + Float8_e5m2fnuz, + Float8_e4m3fnuz, + ComplexHalf, + DEFINE_IMPLICIT_CTOR) + AT_FORALL_COMPLEX_TYPES(DEFINE_IMPLICIT_CTOR) + + // Helper constructors to allow Scalar creation from long and long long types + // As std::is_same_v is false(except Android), one needs to + // provide a constructor from either long or long long in addition to one from + // int64_t +#if defined(__APPLE__) || defined(__MACOSX) + static_assert( + std::is_same_v, + "int64_t is the same as long long on MacOS"); + Scalar(long vv) : Scalar(vv, true) {} +#endif +#if defined(__linux__) && !defined(__ANDROID__) + static_assert( + std::is_same_v, + "int64_t is the same as long on Linux"); + Scalar(long long vv) : Scalar(vv, true) {} +#endif + + Scalar(uint16_t vv) : Scalar(vv, true) {} + Scalar(uint32_t vv) : Scalar(vv, true) {} + Scalar(uint64_t vv) { + if (vv > static_cast(INT64_MAX)) { + tag = Tag::HAS_u; + v.u = vv; + } else { + tag = Tag::HAS_i; + // NB: no need to use convert, we've already tested convertibility + v.i = static_cast(vv); + } + } + +#undef DEFINE_IMPLICIT_CTOR + + // Value* is both implicitly convertible to SymbolicVariable and bool which + // causes ambiguity error. Specialized constructor for bool resolves this + // problem. + template < + typename T, + typename std::enable_if_t, bool>* = nullptr> + Scalar(T vv) : tag(Tag::HAS_b) { + v.i = convert(vv); + } + + template < + typename T, + typename std::enable_if_t, bool>* = + nullptr> + Scalar(T vv) : tag(Tag::HAS_sb) { + v.i = convert(vv); + } + +#define DEFINE_ACCESSOR(type, name) \ + type to##name() const { \ + if (Tag::HAS_d == tag) { \ + return checked_convert(v.d, #type); \ + } else if (Tag::HAS_z == tag) { \ + return checked_convert>(v.z, #type); \ + } \ + if (Tag::HAS_b == tag) { \ + return checked_convert(v.i, #type); \ + } else if (Tag::HAS_i == tag) { \ + return checked_convert(v.i, #type); \ + } else if (Tag::HAS_u == tag) { \ + return checked_convert(v.u, #type); \ + } else if (Tag::HAS_si == tag) { \ + return checked_convert( \ + toSymInt().guard_int(__FILE__, __LINE__), #type); \ + } else if (Tag::HAS_sd == tag) { \ + return checked_convert( \ + toSymFloat().guard_float(__FILE__, __LINE__), #type); \ + } else if (Tag::HAS_sb == tag) { \ + return checked_convert( \ + toSymBool().guard_bool(__FILE__, __LINE__), #type); \ + } \ + TORCH_CHECK(false) \ + } + + // TODO: Support ComplexHalf accessor + AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(DEFINE_ACCESSOR) + DEFINE_ACCESSOR(uint16_t, UInt16) + DEFINE_ACCESSOR(uint32_t, UInt32) + DEFINE_ACCESSOR(uint64_t, UInt64) + +#undef DEFINE_ACCESSOR + + SymInt toSymInt() const { + if (Tag::HAS_si == tag) { + return c10::SymInt(intrusive_ptr::reclaim_copy( + static_cast(v.p))); + } else { + return toLong(); + } + } + + SymFloat toSymFloat() const { + if (Tag::HAS_sd == tag) { + return c10::SymFloat(intrusive_ptr::reclaim_copy( + static_cast(v.p))); + } else { + return toDouble(); + } + } + + SymBool toSymBool() const { + if (Tag::HAS_sb == tag) { + return c10::SymBool(intrusive_ptr::reclaim_copy( + static_cast(v.p))); + } else { + return toBool(); + } + } + + // also support scalar.to(); + // Deleted for unsupported types, but specialized below for supported types + template + T to() const = delete; + + // audit uses of data_ptr + const void* data_ptr() const { + TORCH_INTERNAL_ASSERT(!isSymbolic()); + return static_cast(&v); + } + + bool isFloatingPoint() const { + return Tag::HAS_d == tag || Tag::HAS_sd == tag; + } + + C10_DEPRECATED_MESSAGE( + "isIntegral is deprecated. Please use the overload with 'includeBool' parameter instead.") + bool isIntegral() const { + return Tag::HAS_i == tag || Tag::HAS_si == tag || Tag::HAS_u == tag; + } + bool isIntegral(bool includeBool) const { + return Tag::HAS_i == tag || Tag::HAS_si == tag || Tag::HAS_u == tag || + (includeBool && isBoolean()); + } + + bool isComplex() const { + return Tag::HAS_z == tag; + } + bool isBoolean() const { + return Tag::HAS_b == tag || Tag::HAS_sb == tag; + } + + // you probably don't actually want these; they're mostly for testing + bool isSymInt() const { + return Tag::HAS_si == tag; + } + bool isSymFloat() const { + return Tag::HAS_sd == tag; + } + bool isSymBool() const { + return Tag::HAS_sb == tag; + } + + bool isSymbolic() const { + return Tag::HAS_si == tag || Tag::HAS_sd == tag || Tag::HAS_sb == tag; + } + + C10_ALWAYS_INLINE Scalar& operator=(Scalar&& other) noexcept { + if (&other == this) { + return *this; + } + + destroy(); + moveFrom(std::move(other)); + return *this; + } + + C10_ALWAYS_INLINE Scalar& operator=(const Scalar& other) { + if (&other == this) { + return *this; + } + + *this = Scalar(other); + return *this; + } + + Scalar operator-() const; + Scalar conj() const; + Scalar log() const; + + template < + typename T, + typename std::enable_if_t::value, int> = 0> + bool equal(T num) const { + if (isComplex()) { + TORCH_INTERNAL_ASSERT(!isSymbolic()); + auto val = v.z; + return (val.real() == num) && (val.imag() == T()); + } else if (isFloatingPoint()) { + TORCH_CHECK(!isSymbolic(), "NYI SymFloat equality"); + return v.d == num; + } else if (tag == Tag::HAS_i) { + if (overflows(v.i, /* strict_unsigned */ true)) { + return false; + } else { + return static_cast(v.i) == num; + } + } else if (tag == Tag::HAS_u) { + if (overflows(v.u, /* strict_unsigned */ true)) { + return false; + } else { + return static_cast(v.u) == num; + } + } else if (tag == Tag::HAS_si) { + TORCH_INTERNAL_ASSERT(false, "NYI SymInt equality"); + } else if (isBoolean()) { + // boolean scalar does not equal to a non boolean value + TORCH_INTERNAL_ASSERT(!isSymbolic()); + return false; + } else { + TORCH_INTERNAL_ASSERT(false); + } + } + + template < + typename T, + typename std::enable_if_t::value, int> = 0> + bool equal(T num) const { + if (isComplex()) { + TORCH_INTERNAL_ASSERT(!isSymbolic()); + return v.z == num; + } else if (isFloatingPoint()) { + TORCH_CHECK(!isSymbolic(), "NYI SymFloat equality"); + return (v.d == num.real()) && (num.imag() == T()); + } else if (tag == Tag::HAS_i) { + if (overflows(v.i, /* strict_unsigned */ true)) { + return false; + } else { + return static_cast(v.i) == num.real() && num.imag() == T(); + } + } else if (tag == Tag::HAS_u) { + if (overflows(v.u, /* strict_unsigned */ true)) { + return false; + } else { + return static_cast(v.u) == num.real() && num.imag() == T(); + } + } else if (tag == Tag::HAS_si) { + TORCH_INTERNAL_ASSERT(false, "NYI SymInt equality"); + } else if (isBoolean()) { + // boolean scalar does not equal to a non boolean value + TORCH_INTERNAL_ASSERT(!isSymbolic()); + return false; + } else { + TORCH_INTERNAL_ASSERT(false); + } + } + + bool equal(bool num) const { + if (isBoolean()) { + TORCH_INTERNAL_ASSERT(!isSymbolic()); + return static_cast(v.i) == num; + } else { + return false; + } + } + + ScalarType type() const { + if (isComplex()) { + return ScalarType::ComplexDouble; + } else if (isFloatingPoint()) { + return ScalarType::Double; + } else if (isIntegral(/*includeBool=*/false)) { + // Represent all integers as long, UNLESS it is unsigned and therefore + // unrepresentable as long + if (Tag::HAS_u == tag) { + return ScalarType::UInt64; + } + return ScalarType::Long; + } else if (isBoolean()) { + return ScalarType::Bool; + } else { + throw std::runtime_error("Unknown scalar type."); + } + } + + Scalar(Scalar&& rhs) noexcept : tag(rhs.tag) { + moveFrom(std::move(rhs)); + } + + Scalar(const Scalar& rhs) : tag(rhs.tag), v(rhs.v) { + if (isSymbolic()) { + c10::raw::intrusive_ptr::incref(v.p); + } + } + + Scalar(c10::SymInt si) { + if (auto m = si.maybe_as_int()) { + tag = Tag::HAS_i; + v.i = *m; + } else { + tag = Tag::HAS_si; + v.p = std::move(si).release(); + } + } + + Scalar(c10::SymFloat sd) { + if (sd.is_symbolic()) { + tag = Tag::HAS_sd; + v.p = std::move(sd).release(); + } else { + tag = Tag::HAS_d; + v.d = sd.as_float_unchecked(); + } + } + + Scalar(c10::SymBool sb) { + if (auto m = sb.maybe_as_bool()) { + tag = Tag::HAS_b; + v.i = *m; + } else { + tag = Tag::HAS_sb; + v.p = std::move(sb).release(); + } + } + + // We can't set v in the initializer list using the + // syntax v{ .member = ... } because it doesn't work on MSVC + private: + enum class Tag { HAS_d, HAS_i, HAS_u, HAS_z, HAS_b, HAS_sd, HAS_si, HAS_sb }; + + // Note [Meaning of HAS_u] + // ~~~~~~~~~~~~~~~~~~~~~~~ + // HAS_u is a bit special. On its face, it just means that we + // are holding an unsigned integer. However, we generally don't + // distinguish between different bit sizes in Scalar (e.g., we represent + // float as double), instead, it represents a mathematical notion + // of some quantity (integral versus floating point). So actually, + // HAS_u is used solely to represent unsigned integers that could + // not be represented as a signed integer. That means only uint64_t + // potentially can get this tag; smaller types like uint8_t fits into a + // regular int and so for BC reasons we keep as an int. + + // NB: assumes that self has already been cleared + // NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved) + C10_ALWAYS_INLINE void moveFrom(Scalar&& rhs) noexcept { + v = rhs.v; + tag = rhs.tag; + if (rhs.tag == Tag::HAS_si || rhs.tag == Tag::HAS_sd || + rhs.tag == Tag::HAS_sb) { + // Move out of scalar + rhs.tag = Tag::HAS_i; + rhs.v.i = 0; + } + } + + Tag tag; + + union v_t { + double d{}; + int64_t i; + // See Note [Meaning of HAS_u] + uint64_t u; + c10::complex z; + c10::intrusive_ptr_target* p; + // NOLINTNEXTLINE(modernize-use-equals-default) + v_t() {} // default constructor + } v; + + template < + typename T, + typename std::enable_if_t< + std::is_integral_v && !std::is_same_v, + bool>* = nullptr> + Scalar(T vv, bool) : tag(Tag::HAS_i) { + v.i = convert(vv); + } + + template < + typename T, + typename std::enable_if_t< + !std::is_integral_v && !c10::is_complex::value, + bool>* = nullptr> + Scalar(T vv, bool) : tag(Tag::HAS_d) { + v.d = convert(vv); + } + + template < + typename T, + typename std::enable_if_t::value, bool>* = nullptr> + Scalar(T vv, bool) : tag(Tag::HAS_z) { + v.z = convert(vv); + } +}; + +using OptionalScalarRef = c10::OptionalRef; + +// define the scalar.to() specializations +#define DEFINE_TO(T, name) \ + template <> \ + inline T Scalar::to() const { \ + return to##name(); \ + } +AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(DEFINE_TO) +DEFINE_TO(uint16_t, UInt16) +DEFINE_TO(uint32_t, UInt32) +DEFINE_TO(uint64_t, UInt64) +#undef DEFINE_TO + +} // namespace c10 diff --git a/shared/package-files/8b/8b99e880d840da023013d691988e76f16c3dc20737eb2fb046659b390e819fc0/blob b/shared/package-files/8b/8b99e880d840da023013d691988e76f16c3dc20737eb2fb046659b390e819fc0/blob new file mode 100644 index 0000000000000000000000000000000000000000..f152e0bce15c8896f06a24870ab711f5849a4ddc --- /dev/null +++ b/shared/package-files/8b/8b99e880d840da023013d691988e76f16c3dc20737eb2fb046659b390e819fc0/blob @@ -0,0 +1,32 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor random(const at::Tensor & self, int64_t from, c10::optional to, c10::optional generator=c10::nullopt); +TORCH_API at::Tensor & random_from_out(const at::Tensor & self, int64_t from, c10::optional to, c10::optional generator, at::Tensor & out); +TORCH_API at::Tensor & random_(at::Tensor & self, int64_t from, c10::optional to, c10::optional generator=c10::nullopt); +TORCH_API at::Tensor & random_meta_(at::Tensor & self, int64_t from, c10::optional to, c10::optional generator=c10::nullopt); +TORCH_API at::Tensor random(const at::Tensor & self, int64_t to, c10::optional generator=c10::nullopt); +TORCH_API at::Tensor & random_to_out(const at::Tensor & self, int64_t to, c10::optional generator, at::Tensor & out); +TORCH_API at::Tensor & random_(at::Tensor & self, int64_t to, c10::optional generator=c10::nullopt); +TORCH_API at::Tensor & random_meta_(at::Tensor & self, int64_t to, c10::optional generator=c10::nullopt); +TORCH_API at::Tensor random(const at::Tensor & self, c10::optional generator=c10::nullopt); +TORCH_API at::Tensor & random_out(const at::Tensor & self, c10::optional generator, at::Tensor & out); +TORCH_API at::Tensor & random_(at::Tensor & self, c10::optional generator=c10::nullopt); +TORCH_API at::Tensor & random_meta_(at::Tensor & self, c10::optional generator=c10::nullopt); +} // namespace native +} // namespace at diff --git a/shared/package-files/8b/8b9ede33ab32ae2505bc06eb5402e7ce20b0fc8e2510dcb305c25d39a1fbd725/blob b/shared/package-files/8b/8b9ede33ab32ae2505bc06eb5402e7ce20b0fc8e2510dcb305c25d39a1fbd725/blob new file mode 100644 index 0000000000000000000000000000000000000000..bf9a2d955fc23bb6c2043472e8292d4adc20d4ed Binary files /dev/null and b/shared/package-files/8b/8b9ede33ab32ae2505bc06eb5402e7ce20b0fc8e2510dcb305c25d39a1fbd725/blob differ diff --git a/shared/package-files/8b/8b9f5dff2e2f70ac88457066a9b8e3d18518df408da36edd42f904642f50d944/blob b/shared/package-files/8b/8b9f5dff2e2f70ac88457066a9b8e3d18518df408da36edd42f904642f50d944/blob new file mode 100644 index 0000000000000000000000000000000000000000..510c4ff254eb561fd93da5ca3bda66c2dc92770f --- /dev/null +++ b/shared/package-files/8b/8b9f5dff2e2f70ac88457066a9b8e3d18518df408da36edd42f904642f50d944/blob @@ -0,0 +1,921 @@ +"""Abstract linear algebra library. + +This module defines a class hierarchy that implements a kind of "lazy" +matrix representation, called the ``LinearOperator``. It can be used to do +linear algebra with extremely large sparse or structured matrices, without +representing those explicitly in memory. Such matrices can be added, +multiplied, transposed, etc. + +As a motivating example, suppose you want have a matrix where almost all of +the elements have the value one. The standard sparse matrix representation +skips the storage of zeros, but not ones. By contrast, a LinearOperator is +able to represent such matrices efficiently. First, we need a compact way to +represent an all-ones matrix:: + + >>> import numpy as np + >>> from scipy.sparse.linalg._interface import LinearOperator + >>> class Ones(LinearOperator): + ... def __init__(self, shape): + ... super().__init__(dtype=None, shape=shape) + ... def _matvec(self, x): + ... return np.repeat(x.sum(), self.shape[0]) + +Instances of this class emulate ``np.ones(shape)``, but using a constant +amount of storage, independent of ``shape``. The ``_matvec`` method specifies +how this linear operator multiplies with (operates on) a vector. We can now +add this operator to a sparse matrix that stores only offsets from one:: + + >>> from scipy.sparse.linalg._interface import aslinearoperator + >>> from scipy.sparse import csr_array + >>> offsets = csr_array([[1, 0, 2], [0, -1, 0], [0, 0, 3]]) + >>> A = aslinearoperator(offsets) + Ones(offsets.shape) + >>> A.dot([1, 2, 3]) + array([13, 4, 15]) + +The result is the same as that given by its dense, explicitly-stored +counterpart:: + + >>> (np.ones(A.shape, A.dtype) + offsets.toarray()).dot([1, 2, 3]) + array([13, 4, 15]) + +Several algorithms in the ``scipy.sparse`` library are able to operate on +``LinearOperator`` instances. +""" + +import warnings + +import numpy as np + +from scipy.sparse import issparse +from scipy.sparse._sputils import isshape, isintlike, asmatrix, is_pydata_spmatrix + +__all__ = ['LinearOperator', 'aslinearoperator'] + + +class LinearOperator: + """Common interface for performing matrix vector products + + Many iterative methods (e.g. cg, gmres) do not need to know the + individual entries of a matrix to solve a linear system A@x=b. + Such solvers only require the computation of matrix vector + products, A@v where v is a dense vector. This class serves as + an abstract interface between iterative solvers and matrix-like + objects. + + To construct a concrete LinearOperator, either pass appropriate + callables to the constructor of this class, or subclass it. + + A subclass must implement either one of the methods ``_matvec`` + and ``_matmat``, and the attributes/properties ``shape`` (pair of + integers) and ``dtype`` (may be None). It may call the ``__init__`` + on this class to have these attributes validated. Implementing + ``_matvec`` automatically implements ``_matmat`` (using a naive + algorithm) and vice-versa. + + Optionally, a subclass may implement ``_rmatvec`` or ``_adjoint`` + to implement the Hermitian adjoint (conjugate transpose). As with + ``_matvec`` and ``_matmat``, implementing either ``_rmatvec`` or + ``_adjoint`` implements the other automatically. Implementing + ``_adjoint`` is preferable; ``_rmatvec`` is mostly there for + backwards compatibility. + + Parameters + ---------- + shape : tuple + Matrix dimensions (M, N). + matvec : callable f(v) + Returns returns A @ v. + rmatvec : callable f(v) + Returns A^H @ v, where A^H is the conjugate transpose of A. + matmat : callable f(V) + Returns A @ V, where V is a dense matrix with dimensions (N, K). + dtype : dtype + Data type of the matrix. + rmatmat : callable f(V) + Returns A^H @ V, where V is a dense matrix with dimensions (M, K). + + Attributes + ---------- + args : tuple + For linear operators describing products etc. of other linear + operators, the operands of the binary operation. + ndim : int + Number of dimensions (this is always 2) + + See Also + -------- + aslinearoperator : Construct LinearOperators + + Notes + ----- + The user-defined matvec() function must properly handle the case + where v has shape (N,) as well as the (N,1) case. The shape of + the return type is handled internally by LinearOperator. + + It is highly recommended to explicitly specify the `dtype`, otherwise + it is determined automatically at the cost of a single matvec application + on `int8` zero vector using the promoted `dtype` of the output. + Python `int` could be difficult to automatically cast to numpy integers + in the definition of the `matvec` so the determination may be inaccurate. + It is assumed that `matmat`, `rmatvec`, and `rmatmat` would result in + the same dtype of the output given an `int8` input as `matvec`. + + LinearOperator instances can also be multiplied, added with each + other and exponentiated, all lazily: the result of these operations + is always a new, composite LinearOperator, that defers linear + operations to the original operators and combines the results. + + More details regarding how to subclass a LinearOperator and several + examples of concrete LinearOperator instances can be found in the + external project `PyLops `_. + + + Examples + -------- + >>> import numpy as np + >>> from scipy.sparse.linalg import LinearOperator + >>> def mv(v): + ... return np.array([2*v[0], 3*v[1]]) + ... + >>> A = LinearOperator((2,2), matvec=mv) + >>> A + <2x2 _CustomLinearOperator with dtype=int8> + >>> A.matvec(np.ones(2)) + array([ 2., 3.]) + >>> A @ np.ones(2) + array([ 2., 3.]) + + """ + + ndim = 2 + # Necessary for right matmul with numpy arrays. + __array_ufunc__ = None + + def __new__(cls, *args, **kwargs): + if cls is LinearOperator: + # Operate as _CustomLinearOperator factory. + return super().__new__(_CustomLinearOperator) + else: + obj = super().__new__(cls) + + if (type(obj)._matvec == LinearOperator._matvec + and type(obj)._matmat == LinearOperator._matmat): + warnings.warn("LinearOperator subclass should implement" + " at least one of _matvec and _matmat.", + category=RuntimeWarning, stacklevel=2) + + return obj + + def __init__(self, dtype, shape): + """Initialize this LinearOperator. + + To be called by subclasses. ``dtype`` may be None; ``shape`` should + be convertible to a length-2 tuple. + """ + if dtype is not None: + dtype = np.dtype(dtype) + + shape = tuple(shape) + if not isshape(shape): + raise ValueError(f"invalid shape {shape!r} (must be 2-d)") + + self.dtype = dtype + self.shape = shape + + def _init_dtype(self): + """Determine the dtype by executing `matvec` on an `int8` test vector. + + In `np.promote_types` hierarchy, the type `int8` is the smallest, + so we call `matvec` on `int8` and use the promoted dtype of the output + to set the default `dtype` of the `LinearOperator`. + We assume that `matmat`, `rmatvec`, and `rmatmat` would result in + the same dtype of the output given an `int8` input as `matvec`. + + Called from subclasses at the end of the __init__ routine. + """ + if self.dtype is None: + v = np.zeros(self.shape[-1], dtype=np.int8) + try: + matvec_v = np.asarray(self.matvec(v)) + except OverflowError: + # Python large `int` promoted to `np.int64`or `np.int32` + self.dtype = np.dtype(int) + else: + self.dtype = matvec_v.dtype + + def _matmat(self, X): + """Default matrix-matrix multiplication handler. + + Falls back on the user-defined _matvec method, so defining that will + define matrix multiplication (though in a very suboptimal way). + """ + + return np.hstack([self.matvec(col.reshape(-1,1)) for col in X.T]) + + def _matvec(self, x): + """Default matrix-vector multiplication handler. + + If self is a linear operator of shape (M, N), then this method will + be called on a shape (N,) or (N, 1) ndarray, and should return a + shape (M,) or (M, 1) ndarray. + + This default implementation falls back on _matmat, so defining that + will define matrix-vector multiplication as well. + """ + return self.matmat(x.reshape(-1, 1)) + + def matvec(self, x): + """Matrix-vector multiplication. + + Performs the operation y=A@x where A is an MxN linear + operator and x is a column vector or 1-d array. + + Parameters + ---------- + x : {matrix, ndarray} + An array with shape (N,) or (N,1). + + Returns + ------- + y : {matrix, ndarray} + A matrix or ndarray with shape (M,) or (M,1) depending + on the type and shape of the x argument. + + Notes + ----- + This matvec wraps the user-specified matvec routine or overridden + _matvec method to ensure that y has the correct shape and type. + + """ + + x = np.asanyarray(x) + + M,N = self.shape + + if x.shape != (N,) and x.shape != (N,1): + raise ValueError('dimension mismatch') + + y = self._matvec(x) + + if isinstance(x, np.matrix): + y = asmatrix(y) + else: + y = np.asarray(y) + + if x.ndim == 1: + y = y.reshape(M) + elif x.ndim == 2: + y = y.reshape(M,1) + else: + raise ValueError('invalid shape returned by user-defined matvec()') + + return y + + def rmatvec(self, x): + """Adjoint matrix-vector multiplication. + + Performs the operation y = A^H @ x where A is an MxN linear + operator and x is a column vector or 1-d array. + + Parameters + ---------- + x : {matrix, ndarray} + An array with shape (M,) or (M,1). + + Returns + ------- + y : {matrix, ndarray} + A matrix or ndarray with shape (N,) or (N,1) depending + on the type and shape of the x argument. + + Notes + ----- + This rmatvec wraps the user-specified rmatvec routine or overridden + _rmatvec method to ensure that y has the correct shape and type. + + """ + + x = np.asanyarray(x) + + M,N = self.shape + + if x.shape != (M,) and x.shape != (M,1): + raise ValueError('dimension mismatch') + + y = self._rmatvec(x) + + if isinstance(x, np.matrix): + y = asmatrix(y) + else: + y = np.asarray(y) + + if x.ndim == 1: + y = y.reshape(N) + elif x.ndim == 2: + y = y.reshape(N,1) + else: + raise ValueError('invalid shape returned by user-defined rmatvec()') + + return y + + def _rmatvec(self, x): + """Default implementation of _rmatvec; defers to adjoint.""" + if type(self)._adjoint == LinearOperator._adjoint: + # _adjoint not overridden, prevent infinite recursion + if (hasattr(self, "_rmatmat") + and type(self)._rmatmat != LinearOperator._rmatmat): + # Try to use _rmatmat as a fallback + return self._rmatmat(x.reshape(-1, 1)).reshape(-1) + raise NotImplementedError + else: + return self.H.matvec(x) + + def matmat(self, X): + """Matrix-matrix multiplication. + + Performs the operation y=A@X where A is an MxN linear + operator and X dense N*K matrix or ndarray. + + Parameters + ---------- + X : {matrix, ndarray} + An array with shape (N,K). + + Returns + ------- + Y : {matrix, ndarray} + A matrix or ndarray with shape (M,K) depending on + the type of the X argument. + + Notes + ----- + This matmat wraps any user-specified matmat routine or overridden + _matmat method to ensure that y has the correct type. + + """ + if not (issparse(X) or is_pydata_spmatrix(X)): + X = np.asanyarray(X) + + if X.ndim != 2: + raise ValueError(f'expected 2-d ndarray or matrix, not {X.ndim}-d') + + if X.shape[0] != self.shape[1]: + raise ValueError(f'dimension mismatch: {self.shape}, {X.shape}') + + try: + Y = self._matmat(X) + except Exception as e: + if issparse(X) or is_pydata_spmatrix(X): + raise TypeError( + "Unable to multiply a LinearOperator with a sparse matrix." + " Wrap the matrix in aslinearoperator first." + ) from e + raise + + if isinstance(Y, np.matrix): + Y = asmatrix(Y) + + return Y + + def rmatmat(self, X): + """Adjoint matrix-matrix multiplication. + + Performs the operation y = A^H @ x where A is an MxN linear + operator and x is a column vector or 1-d array, or 2-d array. + The default implementation defers to the adjoint. + + Parameters + ---------- + X : {matrix, ndarray} + A matrix or 2D array. + + Returns + ------- + Y : {matrix, ndarray} + A matrix or 2D array depending on the type of the input. + + Notes + ----- + This rmatmat wraps the user-specified rmatmat routine. + + """ + if not (issparse(X) or is_pydata_spmatrix(X)): + X = np.asanyarray(X) + + if X.ndim != 2: + raise ValueError('expected 2-d ndarray or matrix, not %d-d' + % X.ndim) + + if X.shape[0] != self.shape[0]: + raise ValueError(f'dimension mismatch: {self.shape}, {X.shape}') + + try: + Y = self._rmatmat(X) + except Exception as e: + if issparse(X) or is_pydata_spmatrix(X): + raise TypeError( + "Unable to multiply a LinearOperator with a sparse matrix." + " Wrap the matrix in aslinearoperator() first." + ) from e + raise + + if isinstance(Y, np.matrix): + Y = asmatrix(Y) + return Y + + def _rmatmat(self, X): + """Default implementation of _rmatmat defers to rmatvec or adjoint.""" + if type(self)._adjoint == LinearOperator._adjoint: + return np.hstack([self.rmatvec(col.reshape(-1, 1)) for col in X.T]) + else: + return self.H.matmat(X) + + def __call__(self, x): + return self@x + + def __mul__(self, x): + return self.dot(x) + + def __truediv__(self, other): + if not np.isscalar(other): + raise ValueError("Can only divide a linear operator by a scalar.") + + return _ScaledLinearOperator(self, 1.0/other) + + def dot(self, x): + """Matrix-matrix or matrix-vector multiplication. + + Parameters + ---------- + x : array_like + 1-d or 2-d array, representing a vector or matrix. + + Returns + ------- + Ax : array + 1-d or 2-d array (depending on the shape of x) that represents + the result of applying this linear operator on x. + + """ + if isinstance(x, LinearOperator): + return _ProductLinearOperator(self, x) + elif np.isscalar(x): + return _ScaledLinearOperator(self, x) + else: + if not issparse(x) and not is_pydata_spmatrix(x): + # Sparse matrices shouldn't be converted to numpy arrays. + x = np.asarray(x) + + if x.ndim == 1 or x.ndim == 2 and x.shape[1] == 1: + return self.matvec(x) + elif x.ndim == 2: + return self.matmat(x) + else: + raise ValueError(f'expected 1-d or 2-d array or matrix, got {x!r}') + + def __matmul__(self, other): + if np.isscalar(other): + raise ValueError("Scalar operands are not allowed, " + "use '*' instead") + return self.__mul__(other) + + def __rmatmul__(self, other): + if np.isscalar(other): + raise ValueError("Scalar operands are not allowed, " + "use '*' instead") + return self.__rmul__(other) + + def __rmul__(self, x): + if np.isscalar(x): + return _ScaledLinearOperator(self, x) + else: + return self._rdot(x) + + def _rdot(self, x): + """Matrix-matrix or matrix-vector multiplication from the right. + + Parameters + ---------- + x : array_like + 1-d or 2-d array, representing a vector or matrix. + + Returns + ------- + xA : array + 1-d or 2-d array (depending on the shape of x) that represents + the result of applying this linear operator on x from the right. + + Notes + ----- + This is copied from dot to implement right multiplication. + """ + if isinstance(x, LinearOperator): + return _ProductLinearOperator(x, self) + elif np.isscalar(x): + return _ScaledLinearOperator(self, x) + else: + if not issparse(x) and not is_pydata_spmatrix(x): + # Sparse matrices shouldn't be converted to numpy arrays. + x = np.asarray(x) + + # We use transpose instead of rmatvec/rmatmat to avoid + # unnecessary complex conjugation if possible. + if x.ndim == 1 or x.ndim == 2 and x.shape[0] == 1: + return self.T.matvec(x.T).T + elif x.ndim == 2: + return self.T.matmat(x.T).T + else: + raise ValueError(f'expected 1-d or 2-d array or matrix, got {x!r}') + + def __pow__(self, p): + if np.isscalar(p): + return _PowerLinearOperator(self, p) + else: + return NotImplemented + + def __add__(self, x): + if isinstance(x, LinearOperator): + return _SumLinearOperator(self, x) + else: + return NotImplemented + + def __neg__(self): + return _ScaledLinearOperator(self, -1) + + def __sub__(self, x): + return self.__add__(-x) + + def __repr__(self): + M,N = self.shape + if self.dtype is None: + dt = 'unspecified dtype' + else: + dt = 'dtype=' + str(self.dtype) + + return '<%dx%d %s with %s>' % (M, N, self.__class__.__name__, dt) + + def adjoint(self): + """Hermitian adjoint. + + Returns the Hermitian adjoint of self, aka the Hermitian + conjugate or Hermitian transpose. For a complex matrix, the + Hermitian adjoint is equal to the conjugate transpose. + + Can be abbreviated self.H instead of self.adjoint(). + + Returns + ------- + A_H : LinearOperator + Hermitian adjoint of self. + """ + return self._adjoint() + + H = property(adjoint) + + def transpose(self): + """Transpose this linear operator. + + Returns a LinearOperator that represents the transpose of this one. + Can be abbreviated self.T instead of self.transpose(). + """ + return self._transpose() + + T = property(transpose) + + def _adjoint(self): + """Default implementation of _adjoint; defers to rmatvec.""" + return _AdjointLinearOperator(self) + + def _transpose(self): + """ Default implementation of _transpose; defers to rmatvec + conj""" + return _TransposedLinearOperator(self) + + +class _CustomLinearOperator(LinearOperator): + """Linear operator defined in terms of user-specified operations.""" + + def __init__(self, shape, matvec, rmatvec=None, matmat=None, + dtype=None, rmatmat=None): + super().__init__(dtype, shape) + + self.args = () + + self.__matvec_impl = matvec + self.__rmatvec_impl = rmatvec + self.__rmatmat_impl = rmatmat + self.__matmat_impl = matmat + + self._init_dtype() + + def _matmat(self, X): + if self.__matmat_impl is not None: + return self.__matmat_impl(X) + else: + return super()._matmat(X) + + def _matvec(self, x): + return self.__matvec_impl(x) + + def _rmatvec(self, x): + func = self.__rmatvec_impl + if func is None: + raise NotImplementedError("rmatvec is not defined") + return self.__rmatvec_impl(x) + + def _rmatmat(self, X): + if self.__rmatmat_impl is not None: + return self.__rmatmat_impl(X) + else: + return super()._rmatmat(X) + + def _adjoint(self): + return _CustomLinearOperator(shape=(self.shape[1], self.shape[0]), + matvec=self.__rmatvec_impl, + rmatvec=self.__matvec_impl, + matmat=self.__rmatmat_impl, + rmatmat=self.__matmat_impl, + dtype=self.dtype) + + +class _AdjointLinearOperator(LinearOperator): + """Adjoint of arbitrary Linear Operator""" + + def __init__(self, A): + shape = (A.shape[1], A.shape[0]) + super().__init__(dtype=A.dtype, shape=shape) + self.A = A + self.args = (A,) + + def _matvec(self, x): + return self.A._rmatvec(x) + + def _rmatvec(self, x): + return self.A._matvec(x) + + def _matmat(self, x): + return self.A._rmatmat(x) + + def _rmatmat(self, x): + return self.A._matmat(x) + +class _TransposedLinearOperator(LinearOperator): + """Transposition of arbitrary Linear Operator""" + + def __init__(self, A): + shape = (A.shape[1], A.shape[0]) + super().__init__(dtype=A.dtype, shape=shape) + self.A = A + self.args = (A,) + + def _matvec(self, x): + # NB. np.conj works also on sparse matrices + return np.conj(self.A._rmatvec(np.conj(x))) + + def _rmatvec(self, x): + return np.conj(self.A._matvec(np.conj(x))) + + def _matmat(self, x): + # NB. np.conj works also on sparse matrices + return np.conj(self.A._rmatmat(np.conj(x))) + + def _rmatmat(self, x): + return np.conj(self.A._matmat(np.conj(x))) + +def _get_dtype(operators, dtypes=None): + if dtypes is None: + dtypes = [] + for obj in operators: + if obj is not None and hasattr(obj, 'dtype'): + dtypes.append(obj.dtype) + return np.result_type(*dtypes) + + +class _SumLinearOperator(LinearOperator): + def __init__(self, A, B): + if not isinstance(A, LinearOperator) or \ + not isinstance(B, LinearOperator): + raise ValueError('both operands have to be a LinearOperator') + if A.shape != B.shape: + raise ValueError(f'cannot add {A} and {B}: shape mismatch') + self.args = (A, B) + super().__init__(_get_dtype([A, B]), A.shape) + + def _matvec(self, x): + return self.args[0].matvec(x) + self.args[1].matvec(x) + + def _rmatvec(self, x): + return self.args[0].rmatvec(x) + self.args[1].rmatvec(x) + + def _rmatmat(self, x): + return self.args[0].rmatmat(x) + self.args[1].rmatmat(x) + + def _matmat(self, x): + return self.args[0].matmat(x) + self.args[1].matmat(x) + + def _adjoint(self): + A, B = self.args + return A.H + B.H + + +class _ProductLinearOperator(LinearOperator): + def __init__(self, A, B): + if not isinstance(A, LinearOperator) or \ + not isinstance(B, LinearOperator): + raise ValueError('both operands have to be a LinearOperator') + if A.shape[1] != B.shape[0]: + raise ValueError(f'cannot multiply {A} and {B}: shape mismatch') + super().__init__(_get_dtype([A, B]), + (A.shape[0], B.shape[1])) + self.args = (A, B) + + def _matvec(self, x): + return self.args[0].matvec(self.args[1].matvec(x)) + + def _rmatvec(self, x): + return self.args[1].rmatvec(self.args[0].rmatvec(x)) + + def _rmatmat(self, x): + return self.args[1].rmatmat(self.args[0].rmatmat(x)) + + def _matmat(self, x): + return self.args[0].matmat(self.args[1].matmat(x)) + + def _adjoint(self): + A, B = self.args + return B.H @ A.H + + +class _ScaledLinearOperator(LinearOperator): + def __init__(self, A, alpha): + if not isinstance(A, LinearOperator): + raise ValueError('LinearOperator expected as A') + if not np.isscalar(alpha): + raise ValueError('scalar expected as alpha') + if isinstance(A, _ScaledLinearOperator): + A, alpha_original = A.args + # Avoid in-place multiplication so that we don't accidentally mutate + # the original prefactor. + alpha = alpha * alpha_original + + dtype = _get_dtype([A], [type(alpha)]) + super().__init__(dtype, A.shape) + self.args = (A, alpha) + # Note: args[1] is alpha (a scalar), so use `*` below, not `@` + + def _matvec(self, x): + return self.args[1] * self.args[0].matvec(x) + + def _rmatvec(self, x): + return np.conj(self.args[1]) * self.args[0].rmatvec(x) + + def _rmatmat(self, x): + return np.conj(self.args[1]) * self.args[0].rmatmat(x) + + def _matmat(self, x): + return self.args[1] * self.args[0].matmat(x) + + def _adjoint(self): + A, alpha = self.args + return A.H * np.conj(alpha) + + +class _PowerLinearOperator(LinearOperator): + def __init__(self, A, p): + if not isinstance(A, LinearOperator): + raise ValueError('LinearOperator expected as A') + if A.shape[0] != A.shape[1]: + raise ValueError(f'square LinearOperator expected, got {A!r}') + if not isintlike(p) or p < 0: + raise ValueError('non-negative integer expected as p') + + super().__init__(_get_dtype([A]), A.shape) + self.args = (A, p) + + def _power(self, fun, x): + res = np.array(x, copy=True) + for i in range(self.args[1]): + res = fun(res) + return res + + def _matvec(self, x): + return self._power(self.args[0].matvec, x) + + def _rmatvec(self, x): + return self._power(self.args[0].rmatvec, x) + + def _rmatmat(self, x): + return self._power(self.args[0].rmatmat, x) + + def _matmat(self, x): + return self._power(self.args[0].matmat, x) + + def _adjoint(self): + A, p = self.args + return A.H ** p + + +class MatrixLinearOperator(LinearOperator): + def __init__(self, A): + super().__init__(A.dtype, A.shape) + self.A = A + self.__adj = None + self.args = (A,) + + def _matmat(self, X): + return self.A.dot(X) + + def _adjoint(self): + if self.__adj is None: + self.__adj = _AdjointMatrixOperator(self.A) + return self.__adj + + +class _AdjointMatrixOperator(MatrixLinearOperator): + def __init__(self, adjoint_array): + self.A = adjoint_array.T.conj() + self.args = (adjoint_array,) + self.shape = adjoint_array.shape[1], adjoint_array.shape[0] + + @property + def dtype(self): + return self.args[0].dtype + + def _adjoint(self): + return MatrixLinearOperator(self.args[0]) + + +class IdentityOperator(LinearOperator): + def __init__(self, shape, dtype=None): + super().__init__(dtype, shape) + + def _matvec(self, x): + return x + + def _rmatvec(self, x): + return x + + def _rmatmat(self, x): + return x + + def _matmat(self, x): + return x + + def _adjoint(self): + return self + + +def aslinearoperator(A): + """Return A as a LinearOperator. + + 'A' may be any of the following types: + - ndarray + - matrix + - sparse array (e.g. csr_array, lil_array, etc.) + - LinearOperator + - An object with .shape and .matvec attributes + + See the LinearOperator documentation for additional information. + + Notes + ----- + If 'A' has no .dtype attribute, the data type is determined by calling + :func:`LinearOperator.matvec()` - set the .dtype attribute to prevent this + call upon the linear operator creation. + + Examples + -------- + >>> import numpy as np + >>> from scipy.sparse.linalg import aslinearoperator + >>> M = np.array([[1,2,3],[4,5,6]], dtype=np.int32) + >>> aslinearoperator(M) + <2x3 MatrixLinearOperator with dtype=int32> + """ + if isinstance(A, LinearOperator): + return A + + elif isinstance(A, np.ndarray) or isinstance(A, np.matrix): + if A.ndim > 2: + raise ValueError('array must have ndim <= 2') + A = np.atleast_2d(np.asarray(A)) + return MatrixLinearOperator(A) + + elif issparse(A) or is_pydata_spmatrix(A): + return MatrixLinearOperator(A) + + else: + if hasattr(A, 'shape') and hasattr(A, 'matvec'): + rmatvec = None + rmatmat = None + dtype = None + + if hasattr(A, 'rmatvec'): + rmatvec = A.rmatvec + if hasattr(A, 'rmatmat'): + rmatmat = A.rmatmat + if hasattr(A, 'dtype'): + dtype = A.dtype + return LinearOperator(A.shape, A.matvec, rmatvec=rmatvec, + rmatmat=rmatmat, dtype=dtype) + + else: + raise TypeError('type not understood') diff --git a/shared/package-files/8b/8b9f6fe4750edf6e8b848fb49cfa169d2cafe88b2e0ddff1469e0d744bbc1946/blob b/shared/package-files/8b/8b9f6fe4750edf6e8b848fb49cfa169d2cafe88b2e0ddff1469e0d744bbc1946/blob new file mode 100644 index 0000000000000000000000000000000000000000..e8e58312e14e5cec8ef5ecd1af85c62c7aa016d8 --- /dev/null +++ b/shared/package-files/8b/8b9f6fe4750edf6e8b848fb49cfa169d2cafe88b2e0ddff1469e0d744bbc1946/blob @@ -0,0 +1,341 @@ +import itertools +import math +import sys + +from numba import jit, types +from numba.tests.support import TestCase, skip_unless_py314_or_later +from .complex_usecases import * +import unittest + +enable_pyobj_flags = {'forceobj': True} +no_pyobj_flags = {'nopython': True} + + +class BaseComplexTest(object): + + def basic_values(self): + reals = [-0.0, +0.0, 1, -1, +1.5, -3.5, + float('-inf'), float('+inf')] + if sys.platform != 'win32': + reals += [float('nan')] + return [complex(x, y) for x, y in itertools.product(reals, reals)] + + def more_values(self): + reals = [-0.0, +0.0, 1, -1, -math.pi, +math.pi, + float('-inf'), float('+inf')] + if sys.platform != 'win32': + reals += [float('nan')] + return [complex(x, y) for x, y in itertools.product(reals, reals)] + + def non_nan_values(self): + reals = [-0.0, +0.0, 1, -1, -math.pi, +math.pi, + float('inf'), float('-inf')] + return [complex(x, y) for x, y in itertools.product(reals, reals)] + + def run_unary(self, pyfunc, x_types, x_values, ulps=1, abs_tol=None, + flags=enable_pyobj_flags): + for tx in x_types: + cfunc = jit((tx,), **flags)(pyfunc) + prec = 'single' if tx in (types.float32, types.complex64) else 'double' + for vx in x_values: + try: + expected = pyfunc(vx) + except ValueError as e: + self.assertIn("math domain error", str(e)) + continue + got = cfunc(vx) + msg = 'for input %r with prec %r' % (vx, prec) + self.assertPreciseEqual(got, expected, prec=prec, + ulps=ulps, abs_tol=abs_tol, msg=msg) + + def run_binary(self, pyfunc, value_types, values, ulps=1, + flags=enable_pyobj_flags): + for tx, ty in value_types: + cfunc = jit((tx, ty), **flags)(pyfunc) + prec = ('single' + if set([tx, ty]) & set([types.float32, types.complex64]) + else 'double') + for vx, vy in values: + try: + expected = pyfunc(vx, vy) + except ValueError as e: + self.assertIn("math domain error", str(e)) + continue + except ZeroDivisionError: + continue + got = cfunc(vx, vy) + msg = 'for input %r with prec %r' % ((vx, vy), prec) + self.assertPreciseEqual(got, expected, prec=prec, + ulps=ulps, msg=msg) + + +class TestComplex(BaseComplexTest, TestCase): + + def test_real(self, flags=enable_pyobj_flags): + self.run_unary(real_usecase, [types.complex64, types.complex128], + self.basic_values(), flags=flags) + self.run_unary(real_usecase, [types.int8, types.int64], + [1, 0, -3], flags=flags) + self.run_unary(real_usecase, [types.float32, types.float64], + [1.5, -0.5], flags=flags) + + def test_real_npm(self): + self.test_real(flags=no_pyobj_flags) + + def test_imag(self, flags=enable_pyobj_flags): + self.run_unary(imag_usecase, [types.complex64, types.complex128], + self.basic_values(), flags=flags) + self.run_unary(imag_usecase, [types.int8, types.int64], + [1, 0, -3], flags=flags) + self.run_unary(imag_usecase, [types.float32, types.float64], + [1.5, -0.5], flags=flags) + + def test_imag_npm(self): + self.test_imag(flags=no_pyobj_flags) + + def test_conjugate(self, flags=enable_pyobj_flags): + self.run_unary(conjugate_usecase, [types.complex64, types.complex128], + self.basic_values(), flags=flags) + self.run_unary(conjugate_usecase, [types.int8, types.int64], + [1, 0, -3], flags=flags) + self.run_unary(conjugate_usecase, [types.float32, types.float64], + [1.5, -0.5], flags=flags) + + def test_conjugate_npm(self): + self.test_conjugate(flags=no_pyobj_flags) + + def test_div(self, flags=enable_pyobj_flags): + """ + Test complex.__div__ implementation with non-trivial values. + """ + # XXX Fold into test_operator? + values = list(itertools.product(self.more_values(), self.more_values())) + value_types = [(types.complex128, types.complex128), + (types.complex64, types.complex64)] + self.run_binary(div_usecase, value_types, values, flags=flags) + + def test_div_npm(self): + self.test_div(flags=no_pyobj_flags) + + +@skip_unless_py314_or_later +class TestComplexNaNRecovery(BaseComplexTest, TestCase): + """ + Verify CPython NaN-recovery for complex mul / div. + """ + + def special_values(self): + # 1e200 (large finite) is here so mul can overflow before recovering + reals = [-0.0, +0.0, 1, -1, 1e200, + float('-inf'), float('+inf'), float('nan')] + return [complex(x, y) for x, y in itertools.product(reals, reals)] + + def test_mul(self, flags=no_pyobj_flags): + values = list(itertools.product(self.special_values(), + self.special_values())) + value_types = [(types.complex128, types.complex128)] + self.run_binary(mul_usecase, value_types, values, flags=flags) + + def test_div(self, flags=no_pyobj_flags): + values = list(itertools.product(self.special_values(), + self.special_values())) + value_types = [(types.complex128, types.complex128)] + self.run_binary(div_usecase, value_types, values, flags=flags) + + def test_div_zero(self, flags=no_pyobj_flags): + cfunc = jit((types.complex128, types.complex128), **flags)(div_usecase) + for denom in [0j, complex(0.0, 0.0), complex(-0.0, -0.0), + complex(0.0, -0.0), complex(-0.0, 0.0)]: + with self.assertRaises(ZeroDivisionError): + cfunc(1+1j, denom) + + +class TestCMath(BaseComplexTest, TestCase): + """ + Tests for cmath module support. + """ + + def check_predicate_func(self, pyfunc, flags): + self.run_unary(pyfunc, [types.complex128, types.complex64], + self.basic_values(), flags=flags) + + def check_unary_func(self, pyfunc, flags, ulps=1, abs_tol=None, + values=None): + self.run_unary(pyfunc, [types.complex128], + values or self.more_values(), flags=flags, ulps=ulps, + abs_tol=abs_tol) + # Avoid discontinuities around pi when in single precision. + self.run_unary(pyfunc, [types.complex64], + values or self.basic_values(), flags=flags, ulps=ulps, + abs_tol=abs_tol) + + # Conversions + + def test_phase(self): + self.check_unary_func(phase_usecase, enable_pyobj_flags) + + def test_phase_npm(self): + self.check_unary_func(phase_usecase, no_pyobj_flags) + + def test_polar(self): + self.check_unary_func(polar_usecase, enable_pyobj_flags) + + def test_polar_npm(self): + self.check_unary_func(polar_usecase, no_pyobj_flags) + + def test_rect(self, flags=enable_pyobj_flags): + def do_test(tp, seed_values): + values = [(z.real, z.imag) for z in seed_values + if not math.isinf(z.imag) or z.real == 0] + self.run_binary(rect_usecase, [(tp, tp)], values, flags=flags) + do_test(types.float64, self.more_values()) + # Avoid discontinuities around pi when in single precision. + do_test(types.float32, self.basic_values()) + + def test_rect_npm(self): + self.test_rect(flags=no_pyobj_flags) + + # Classification + + def test_isnan(self, flags=enable_pyobj_flags): + self.check_predicate_func(isnan_usecase, enable_pyobj_flags) + + def test_isnan_npm(self): + self.check_predicate_func(isnan_usecase, no_pyobj_flags) + + def test_isinf(self, flags=enable_pyobj_flags): + self.check_predicate_func(isinf_usecase, enable_pyobj_flags) + + def test_isinf_npm(self): + self.check_predicate_func(isinf_usecase, no_pyobj_flags) + + def test_isfinite(self, flags=enable_pyobj_flags): + self.check_predicate_func(isfinite_usecase, enable_pyobj_flags) + + def test_isfinite_npm(self): + self.check_predicate_func(isfinite_usecase, no_pyobj_flags) + + # Power and logarithms + + def test_exp(self): + self.check_unary_func(exp_usecase, enable_pyobj_flags, ulps=2) + + def test_exp_npm(self): + # Aggressive optimization fixes the following subnormal float problem. + ## The two tests are failing due to subnormal float problems. + ## We are seeing (6.9532198665326e-310+2.1221202807e-314j) != 0j + self.check_unary_func(exp_usecase, no_pyobj_flags, ulps=2) + + def test_log(self): + self.check_unary_func(log_usecase, enable_pyobj_flags) + + def test_log_npm(self): + self.check_unary_func(log_usecase, no_pyobj_flags) + + def test_log_base(self, flags=enable_pyobj_flags): + values = list(itertools.product(self.more_values(), self.more_values())) + value_types = [(types.complex128, types.complex128), + (types.complex64, types.complex64)] + self.run_binary(log_base_usecase, value_types, values, flags=flags, + ulps=3) + + def test_log_base_npm(self): + self.test_log_base(flags=no_pyobj_flags) + + def test_log10(self): + self.check_unary_func(log10_usecase, enable_pyobj_flags) + + def test_log10_npm(self): + self.check_unary_func(log10_usecase, no_pyobj_flags) + + def test_sqrt(self): + self.check_unary_func(sqrt_usecase, enable_pyobj_flags) + + def test_sqrt_npm(self): + self.check_unary_func(sqrt_usecase, no_pyobj_flags) + # issue #3499, check large complex128 values, values cross the float32 + # representation limit threshold + values = [-10 ** i for i in range(36, 41)] + self.run_unary(sqrt_usecase, [types.complex128], + values, flags=no_pyobj_flags) + + # Trigonometric functions + + def test_acos(self): + self.check_unary_func(acos_usecase, enable_pyobj_flags, ulps=2) + + def test_acos_npm(self): + self.check_unary_func(acos_usecase, no_pyobj_flags, ulps=2) + + def test_asin(self): + self.check_unary_func(asin_usecase, enable_pyobj_flags, ulps=2) + + def test_asin_npm(self): + self.check_unary_func(asin_usecase, no_pyobj_flags, ulps=2) + + def test_atan(self): + self.check_unary_func(atan_usecase, enable_pyobj_flags, ulps=2,) + + def test_atan_npm(self): + self.check_unary_func(atan_usecase, no_pyobj_flags, ulps=2,) + + def test_cos(self): + self.check_unary_func(cos_usecase, enable_pyobj_flags, ulps=2) + + def test_cos_npm(self): + self.check_unary_func(cos_usecase, no_pyobj_flags, ulps=2) + + def test_sin(self): + # See test_sinh. + self.check_unary_func(sin_usecase, enable_pyobj_flags, abs_tol='eps') + + def test_sin_npm(self): + self.check_unary_func(sin_usecase, no_pyobj_flags, abs_tol='eps') + + def test_tan(self): + self.check_unary_func(tan_usecase, enable_pyobj_flags, ulps=2) + + def test_tan_npm(self): + self.check_unary_func(tan_usecase, enable_pyobj_flags, ulps=2) + + # Hyperbolic functions + + def test_acosh(self): + self.check_unary_func(acosh_usecase, enable_pyobj_flags) + + def test_acosh_npm(self): + self.check_unary_func(acosh_usecase, no_pyobj_flags) + + def test_asinh(self): + self.check_unary_func(asinh_usecase, enable_pyobj_flags, ulps=2) + + def test_asinh_npm(self): + self.check_unary_func(asinh_usecase, no_pyobj_flags, ulps=2) + + def test_atanh(self): + self.check_unary_func(atanh_usecase, enable_pyobj_flags, ulps=2) + + def test_atanh_npm(self): + self.check_unary_func(atanh_usecase, no_pyobj_flags, ulps=2) + + def test_cosh(self): + self.check_unary_func(cosh_usecase, enable_pyobj_flags, ulps=2) + + def test_cosh_npm(self): + self.check_unary_func(cosh_usecase, no_pyobj_flags, ulps=2) + + def test_sinh(self): + self.check_unary_func(sinh_usecase, enable_pyobj_flags, abs_tol='eps') + + def test_sinh_npm(self): + self.check_unary_func(sinh_usecase, no_pyobj_flags, abs_tol='eps') + + def test_tanh(self): + self.check_unary_func(tanh_usecase, enable_pyobj_flags, ulps=2) + + def test_tanh_npm(self): + self.check_unary_func(tanh_usecase, enable_pyobj_flags, ulps=2) + + +if __name__ == '__main__': + unittest.main() diff --git a/shared/package-files/8b/8b9fc65039f9df190eb3bfc0d68cdc0eed6070fa2b21afe4c48288f1988068d0/blob b/shared/package-files/8b/8b9fc65039f9df190eb3bfc0d68cdc0eed6070fa2b21afe4c48288f1988068d0/blob new file mode 100644 index 0000000000000000000000000000000000000000..c2cbae63f33651a0f44e287cb0fa6d5d4a25bc62 --- /dev/null +++ b/shared/package-files/8b/8b9fc65039f9df190eb3bfc0d68cdc0eed6070fa2b21afe4c48288f1988068d0/blob @@ -0,0 +1,44 @@ +import torch +import torch.nn.functional as F +from .expanded_weights_impl import implements_per_sample_grads +from .expanded_weights_utils import \ + forward_helper, set_grad_sample_if_exists, unpack_expanded_weight_or_tensor, is_batch_first +from typing import List, Optional + +@implements_per_sample_grads(F.linear) +class LinearPerSampleGrad(torch.autograd.Function): + @staticmethod + def forward(ctx, _, __, *expanded_args_and_kwargs): + if len(expanded_args_and_kwargs[0].shape) <= 1: + raise RuntimeError("Input does not have a batch dimension. Expanded Weights expected input " + f"of at least rank 2, got of rank {len(expanded_args_and_kwargs[0].shape)}") + expanded_kwargs = {'bias': expanded_args_and_kwargs[2] if len(expanded_args_and_kwargs) == 3 else None} + expanded_args = expanded_args_and_kwargs[:2] + ctx.batch_first = is_batch_first(expanded_args_and_kwargs) + output = forward_helper(F.linear, expanded_args, expanded_kwargs) + ctx.args = expanded_args + ctx.kwargs = expanded_kwargs + return output + + @staticmethod + def backward(ctx, grad_output): + input, weight = ctx.args + bias = ctx.kwargs['bias'] + results: List[Optional[torch.Tensor]] = [] + results.append(None) # for kwarg_names + results.append(None) # for op reference + + if input.requires_grad: + results.append(grad_output.matmul(unpack_expanded_weight_or_tensor(weight))) + else: + results.append(None) + results.extend([None] * 2) # weight and bias don't compute batched gradients + + if not ctx.batch_first: + grad_output = grad_output.transpose(0, 1) + input = input.transpose(0, 1) + + # weight and bias get their grad_sample fields set directly if they exist + set_grad_sample_if_exists(weight, lambda _: torch.einsum("n...i,n...j->nij", grad_output, input)) + set_grad_sample_if_exists(bias, lambda _: torch.einsum("n...k->nk", grad_output)) + return tuple(results) diff --git a/shared/package-files/8b/8b9fd73515667141ecfc6067f2b27f4a4afb3340139b737521f811d33c8a523b/blob b/shared/package-files/8b/8b9fd73515667141ecfc6067f2b27f4a4afb3340139b737521f811d33c8a523b/blob new file mode 100644 index 0000000000000000000000000000000000000000..22775645e3be15138c6fe92c670fdf268cc2e330 --- /dev/null +++ b/shared/package-files/8b/8b9fd73515667141ecfc6067f2b27f4a4afb3340139b737521f811d33c8a523b/blob @@ -0,0 +1,56 @@ +from __future__ import annotations + +import functools +from typing import Any + +import narwhals.stable.v1 as narwhals +import numpy +import pandas + + +@functools.singledispatch +def lag(data: Any, k: int = 1) -> Any: + """ + Shift the data indices forward or backward by the specified number of + intervals. + + Args: + data: The array-like dataset to be lagged along its first axis. + k: The number of intervals to shift back the observations by. For "lead" + shifts, use negative integers. + + Notes: + * For this transform to make sense, `data` must represent a time-series + collected at regularly spaced intervals. + * `k` is chosen as the offset parameter for compatibility with R. + """ + raise NotImplementedError( + f"No implementation of `lag` for data of type {type(data)}." + ) + + +@lag.register +def _(data: pandas.Series, offset: int = 1) -> pandas.Series: + return data.shift(offset) + + +@lag.register +def _(data: numpy.ndarray, offset: int = 1) -> numpy.ndarray: + if data.shape == () or offset == 0: + return data + + data = numpy.copy(data).astype(float) + + if offset > 0: + data[offset:] = data[:-offset] + data[:offset] = numpy.nan + else: + data[:offset] = data[-offset:] + data[offset:] = numpy.nan + + return data + + +@lag.register +def _(data: narwhals.Series, offset: int = 1) -> narwhals.Series: + return data.shift(offset) diff --git a/shared/package-files/8b/8ba054d3b7ea9a8fd954531d3abe5cbaf18bfa0e635d39c7082e31ab2a021ba0/blob b/shared/package-files/8b/8ba054d3b7ea9a8fd954531d3abe5cbaf18bfa0e635d39c7082e31ab2a021ba0/blob new file mode 100644 index 0000000000000000000000000000000000000000..866f32f7a9365bdfbfb816d3fdbaa92f7390254c --- /dev/null +++ b/shared/package-files/8b/8ba054d3b7ea9a8fd954531d3abe5cbaf18bfa0e635d39c7082e31ab2a021ba0/blob @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeimplicitautograd { + +TORCH_API bool _use_cudnn_rnn_flatten_weight(); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/shared/package-files/8b/8ba3d5ba862412d7d509da7ad8828cfba503ea44e5e04c88b39e86fa3ddd455e/blob b/shared/package-files/8b/8ba3d5ba862412d7d509da7ad8828cfba503ea44e5e04c88b39e86fa3ddd455e/blob new file mode 100644 index 0000000000000000000000000000000000000000..2028a9a4c12eabbb84e20ff1a4c2527e98dad883 --- /dev/null +++ b/shared/package-files/8b/8ba3d5ba862412d7d509da7ad8828cfba503ea44e5e04c88b39e86fa3ddd455e/blob @@ -0,0 +1,566 @@ +import argparse +import copy +import functools +import logging +import os +import shutil +import sys +import textwrap +from importlib import import_module +from typing import Union + +import torch +import torch.fx as fx + +from torch._dynamo.debug_utils import ( + AccuracyError, + backend_accuracy_fails, + BUCK_CMD_PREFIX, + BuckTargetWriter, + extra_imports, + generate_config_string, + helper_for_dump_minify, + InputReader, + InputWriter, + minifier_dir, + NNModuleToString, + NopInputReader, + run_fwd_maybe_bwd, + same_two_models, +) +from torch.fx.experimental.symbolic_shapes import fx_placeholder_targets +from torch.hub import tqdm + +from .. import config +from ..backends.registry import lookup_backend, register_debug_backend +from ..debug_utils import clone_inputs_retaining_gradness + +log = logging.getLogger(__name__) + + +inductor_config = import_module("torch._inductor.config") +use_buck = inductor_config.is_fbcode() + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# MAIN ENTRY POINT +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # + + +def wrap_backend_debug(unconfigured_compiler_fn, compiler_name: str): + """ + A minifier decorator that wraps the TorchDynamo produced Fx graph modules. + As opposed to wrap_compiler_debug, this wrapper intercepts at the + TorchDynamo produced Fx Graph Module. This makes it backend-agnostic to some + level, e.g., it is useful for minifying issues related to Aot Autograd + tracing. If an error is found, we minify and save the minified repro in + repro.tar.gz. + """ + + @functools.wraps(unconfigured_compiler_fn) + def debug_wrapper(gm, example_inputs, **kwargs): + compiler_fn = functools.partial(unconfigured_compiler_fn, **kwargs) + assert config.repro_after in ("dynamo", "aot", None) + + if config.repro_after == "dynamo": + + def add_paths(exc): + exc.minifier_path = os.path.join(minifier_dir(), "minifier_launcher.py") + if use_buck: + exc.buck_command = " ".join( + BUCK_CMD_PREFIX + + [BuckTargetWriter(exc.minifier_path).cmd_line_path] + ) + + if config.repro_level == 3: + dump_to_minify_after_dynamo(gm, example_inputs, compiler_name) + + # Check for either accuracy (level 4) or other type of failures. + if config.repro_level == 4: + # Check Accuracy + compiled_gm = compiler_fn(copy.deepcopy(gm), example_inputs) + if backend_accuracy_fails(gm, example_inputs, compiler_fn): + log.warning( + "Accuracy failed for the TorchDynamo produced graph. Creating script to minify the error." + ) + dump_to_minify_after_dynamo( + fx.GraphModule(gm, copy.deepcopy(gm.graph)), + example_inputs, + compiler_name, + ) + exc = AccuracyError("Bad accuracy detected.") + add_paths(exc) + raise exc + else: + try: + compiled_gm = compiler_fn(copy.deepcopy(gm), example_inputs) + run_fwd_maybe_bwd(compiled_gm, example_inputs) + except Exception as exc: + log.warning( + "Compiled Fx GraphModule failed. Creating script to minify the error." + ) + if config.repro_level == 1: + dump_state_fn = functools.partial( + dump_backend_state, compiler_name=compiler_name + ) + dump_state_fn( + fx.GraphModule(gm, copy.deepcopy(gm.graph)), example_inputs + ) + elif config.repro_level == 2: + dump_to_minify_after_dynamo( + fx.GraphModule(gm, copy.deepcopy(gm.graph)), + example_inputs, + compiler_name, + ) + add_paths(exc) + raise + else: + compiled_gm = compiler_fn(gm, example_inputs) + + return compiled_gm + + debug_wrapper._torchdynamo_orig_callable = unconfigured_compiler_fn # type: ignore[attr-defined] + if hasattr(unconfigured_compiler_fn, "compiler_name"): + debug_wrapper.__name__ = unconfigured_compiler_fn.compiler_name + if hasattr(unconfigured_compiler_fn, "get_compiler_config"): + debug_wrapper.get_compiler_config = unconfigured_compiler_fn.get_compiler_config # type: ignore[attr-defined] + return debug_wrapper + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# REPRO DUMPERS +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # + + +def generate_dynamo_fx_repro_string( + gm, + args, + compiler_name, + check_accuracy=False, + *, + stable_output=False, + save_dir=None, + command="run", +): + """ + Generate a repro string for backend-agnostic minified version. + """ + + model_str = NNModuleToString.convert(gm) + + # TODO: Figure out why torch.compile'd hash isn't work on this codepath + writer = InputWriter(save_dir, stable_hash=True) + for placeholder, arg in zip(fx_placeholder_targets(gm), args): + if isinstance(arg, (int, torch.SymInt)): + writer.symint(placeholder, arg) + elif isinstance(arg, torch.Tensor): + # TODO: improve these names with FQN + writer.tensor(placeholder, arg) + else: + raise TypeError(f"arg is neither SymInt/int nor torch.Tensor, {arg}") + load_args = "\n".join(writer.lines()) + + return textwrap.dedent( + f""" +from math import inf +import torch +from torch import tensor, device +import torch.fx as fx +import torch._dynamo +from torch._dynamo.testing import rand_strided +from torch._dynamo.debug_utils import run_fwd_maybe_bwd + +{generate_config_string(stable_output=stable_output)} + +{extra_imports} + +{model_str} +mod = Repro() + +{load_args} + +if __name__ == '__main__': + from torch._dynamo.repro.after_dynamo import run_repro + run_repro(mod, load_args, accuracy={check_accuracy!r}, command={command!r}, + save_dir={save_dir!r}, autocast={torch.is_autocast_enabled()!r}, backend={compiler_name!r}) +""" + ) + + +def dump_backend_repro_as_file(gm, args, compiler_name, check_accuracy=False): + """ + Saves the repro to a repro.py file + """ + curdir = os.getcwd() + subdir = os.path.join(os.getcwd(), "checkpoints") + if not os.path.exists(subdir): + os.makedirs(subdir, exist_ok=True) + file_name = os.path.join(subdir, f"minified_{len(gm.graph.nodes)}_nodes.py") + log.warning( + "Writing checkpoint with %s nodes to %s", len(gm.graph.nodes), file_name + ) + + with open(file_name, "w") as fd: + fd.write( + generate_dynamo_fx_repro_string( + gm, args, compiler_name, check_accuracy, save_dir=subdir + ) + ) + latest_repro = os.path.join(curdir, "repro.py") + log.warning("Copying %s to %s for convenience", file_name, latest_repro) + + if use_buck: + BuckTargetWriter(latest_repro).write() + + shutil.copyfile(file_name, latest_repro) + + +def dump_backend_state(gm, args, compiler_name, check_accuracy=False): + """ + Dumps the dynamo graph to repro the issue. + 1) It tries to convert Fx GraphModule to a string. If we can, it writes to a + repro.py file. + 2) If we can't convert Fx GraphModule to a string, we use to_folder to save + the module and save a tar file. + """ + assert NNModuleToString.can_convert_to_string(gm) + return dump_backend_repro_as_file(gm, args, compiler_name, check_accuracy) + # return dump_backend_repro_as_tarfile(gm, args, compiler_name) + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# MINIFIER DUMPER +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # + + +def dump_to_minify_after_dynamo(gm, args, compiler_name): + # TODO: factor this out + subdir = os.path.join(minifier_dir(), "checkpoints") + if not os.path.exists(subdir): + os.makedirs(subdir, exist_ok=True) + helper_for_dump_minify( + generate_dynamo_fx_repro_string( + gm, + args, + compiler_name, + check_accuracy=config.repro_level == 4, + save_dir=subdir, + command="minify", + ) + ) + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# MINIFIER BACKENDS +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # + + +@register_debug_backend +def dynamo_minifier_backend(gm, example_inputs, compiler_name): + from functorch.compile import minifier + + compiler_fn = lookup_backend(compiler_name) + + # TODO: It's inconsistent to pass SymInt inputs but REAL tensors. + # We should pass ints and look at the GraphModule placeholders + # to resolve them to SymInt (if necessary) + example_inputs = [ + i.node.hint if isinstance(i, torch.SymInt) else i for i in example_inputs + ] + + try: + compiled_gm = compiler_fn(gm, example_inputs) + run_fwd_maybe_bwd(compiled_gm, example_inputs) + raise ValueError("No issue was detected") + except Exception as exc: + orig_failure = str(exc) + log.warning( + "Compiled Fx GraphModule failed. Creating script to minify the error." + ) + dump_state_fn = functools.partial( + dump_backend_state, compiler_name=compiler_name + ) + dump_state_fn(fx.GraphModule(gm, copy.deepcopy(gm.graph)), example_inputs) + fails_fn = functools.partial( + backend_fails, + compiler_fn=compiler_fn, + orig_failure=orig_failure, + ) + minifier( + gm, + example_inputs, + module_fails=fails_fn, + dump_state=dump_state_fn, + ) + return gm + + +@register_debug_backend +def dynamo_accuracy_minifier_backend(gm, example_inputs, compiler_name): + from functorch.compile import minifier + + compiler_fn = lookup_backend(compiler_name) + + # Set the eval mode to remove randomness. + gm.eval() + + # Check Accuracy + if backend_accuracy_fails( + gm, example_inputs, compiler_fn, only_fwd=config.repro_forward_only + ): + log.warning("Accuracy failed for the TorchDynamo produced graph") + dump_state_fn = functools.partial( + dump_backend_state, compiler_name=compiler_name, check_accuracy=True + ) + fails_fn = functools.partial( + backend_accuracy_fails, + compiler_fn=compiler_fn, + only_fwd=config.repro_forward_only, + ) + dump_state_fn(fx.GraphModule(gm, copy.deepcopy(gm.graph)), example_inputs) + minifier( + gm, + example_inputs, + module_fails=fails_fn, + dump_state=dump_state_fn, + ) + else: + log.error("Input graph does not fail accuracy testing") + return gm + + +def backend_fails(gm, example_inputs, compiler_fn, orig_failure): + """ + Minifier uses this function to identify if the minified graph module fails + with the same error. + + One caveat is that minifier can potentially go into a wrong direction when + the resulting graph module fails for a different reason. To avoid this, we + save the string for the original exception and check similarity between new + and old exception. They can be somewhat different in some cases, when the + exception string depends on the failing node information. So, we have a + loose similarity metric to guide the minifier path. + """ + from difflib import SequenceMatcher + + try: + # Run the original gm to check eager validity + run_fwd_maybe_bwd(gm, clone_inputs_retaining_gradness(example_inputs)) + compiled_gm = compiler_fn(gm, example_inputs) + run_fwd_maybe_bwd(compiled_gm, clone_inputs_retaining_gradness(example_inputs)) + return False + except Exception as e: + new_failure = str(e) + if SequenceMatcher(None, orig_failure, new_failure).ratio() > 0.5: + return True + return False + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # +# REPRO MAIN +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # + + +def run_load_args(options, mod, load_args): + if not hasattr(load_args, "_version"): + log.warning( + "load_args does not have a _version attribute, please file a bug to PyTorch " + "and describe how you generate this repro script" + ) + else: + if load_args._version > 0: + log.warning( + "load_args is version %s, but this version of PyTorch only supports " + "version 0. We will try to run it anyway but there may be an incompatibility; " + "if so, try upgrading your version of PyTorch.", + load_args._version, + ) + + nop_reader = NopInputReader() + load_args(nop_reader) + + with tqdm(desc="Loading inputs", total=nop_reader.total) as pbar: + input_reader = InputReader(save_dir=options.save_dir, pbar=pbar) + load_args(input_reader) + args = input_reader.args + + return args + + +def repro_minify(options, mod, load_args): + args = run_load_args(options, mod, load_args) + + # Setup debug minifier compiler + if not options.accuracy: + compiler_fn = lookup_backend("dynamo_minifier_backend") + else: + compiler_fn = lookup_backend("dynamo_accuracy_minifier_backend") + + if options.backend is None: + raise RuntimeError( + "Compiler name is None - this likely means that a custom compiler " + "was called by torchdynamo. Please remove this error, import your " + "custom compiler function, and replace the backend=None " + "line in run_repro to backend=" + ) + + dynamo_minifier_backend = functools.partial( + compiler_fn, + compiler_name=options.backend, + ) + opt_mod = torch._dynamo.optimize(dynamo_minifier_backend)(mod) + + with torch.cuda.amp.autocast(enabled=options.autocast): + opt_mod(*args) + + +def repro_run(options, mod, load_args): + opt_mod = torch._dynamo.optimize(options.backend)(mod) + + if options.accuracy != "": + mod.eval() + opt_mod.eval() + + with torch.cuda.amp.autocast(enabled=options.autocast): + # TODO: disable clone + args = run_load_args(options, mod, load_args) + assert same_two_models(mod, mod, args), "Eager itself failed" + if not same_two_models(mod, opt_mod, args): + raise AccuracyError("Dynamo failed") + else: + with torch.cuda.amp.autocast(enabled=options.autocast): + args = run_load_args(options, mod, load_args) + ref = run_fwd_maybe_bwd( + mod, args, only_fwd=options.only_fwd, disable_clone=True + ) + del args + + args = run_load_args(options, mod, load_args) + res = run_fwd_maybe_bwd( + opt_mod, args, only_fwd=options.only_fwd, disable_clone=True + ) + + +def run_repro( + mod, + load_args, + *, + command="run", + accuracy: Union[bool, str] = "", + save_dir=None, + autocast=False, + backend="inductor", + **kwargs, +): + for k in kwargs: + log.warning( + "Unrecognized kwarg %s; perhaps this repro was made on a newer version of PyTorch", + k, + ) + + if accuracy is True: + accuracy = "accuracy" + elif accuracy is False: + accuracy = "" + + parser = argparse.ArgumentParser( + description=f"""\ +An after_dynamo repro script, typically triggering a bug in Dynamo or +AOTAutograd. When run with no arguments, this script defaults to running +'{command}'. Extra flags may be available; to find out more, try '{command} +--help'. There are also alternate subcommands available, see below. + +default settings on this script: + {accuracy=} + {save_dir=} +""", + formatter_class=argparse.RawTextHelpFormatter, + ) + + def common_flags(parser): + accuracy_group = parser.add_mutually_exclusive_group() + accuracy_group.add_argument( + "--no-accuracy", + dest="accuracy", + action="store_const", + const="", + default=accuracy, + help="do not test accuracy, just run the module and see if it errors", + ) + accuracy_group.add_argument( + "--accuracy", + action="store_const", + const="accuracy", + default=accuracy, + help="test accuracy", + ) + parser.add_argument( + "--save-dir", + type=str, + default=save_dir, + metavar="DIR", + help="directory where saved inputs live", + ) + parser.add_argument( + "--no-save-dir", + dest="save_dir", + action="store_const", + const=None, + help="don't use any directory for saved inputs", + ) + parser.add_argument( + "--no-isolate", + dest="isolate", + action="store_false", + default=False, + help="no isolate (doesn't do anything for after_dynamo)", + ) + parser.add_argument( + "--autocast", + default=autocast, + action="store_true", + help="use torch.cuda.amp.autocast", + ) + parser.add_argument( + "--no-autocast", + dest="autocast", + action="store_false", + help="don't use torch.cuda.amp.autocast", + ) + parser.add_argument( + "--backend", + type=str, + default=backend, + metavar="BACKEND", + help="torch.compile backend to use", + ) + + subparsers = parser.add_subparsers( + dest="command", metavar="{run,minify}", required=True + ) + + parser_run = subparsers.add_parser( + "run", + help="just run the repro", + ) + common_flags(parser_run) + parser_run.add_argument( + "--only-fwd", + action="store_true", + help="don't run backwards compilation for testing", + ) + + parser_minify = subparsers.add_parser( + "minify", help="run the minifier on the repro" + ) + common_flags(parser_minify) + + args = None + if len(sys.argv) <= 1: + args = [command, *sys.argv[1:]] + + options = parser.parse_args(args) + COMMAND_FNS = { + "minify": repro_minify, + "run": repro_run, + } + COMMAND_FNS[options.command](options, mod, load_args) diff --git a/shared/package-files/8b/8ba7b82882992c27f751fa5bdb557635ea45f1848558817b2c047bdbef7b46fd/blob b/shared/package-files/8b/8ba7b82882992c27f751fa5bdb557635ea45f1848558817b2c047bdbef7b46fd/blob new file mode 100644 index 0000000000000000000000000000000000000000..9ae57b4dc856f60189318e260b267a40970ce2e9 Binary files /dev/null and b/shared/package-files/8b/8ba7b82882992c27f751fa5bdb557635ea45f1848558817b2c047bdbef7b46fd/blob differ diff --git a/shared/package-files/8b/8babb286a74d5fb93bad98906f3c4b70386c0e3dfb4f31edf184a012887bec5c/blob b/shared/package-files/8b/8babb286a74d5fb93bad98906f3c4b70386c0e3dfb4f31edf184a012887bec5c/blob new file mode 100644 index 0000000000000000000000000000000000000000..9ea5ff406d799ffa1ed68158d09639d089957949 --- /dev/null +++ b/shared/package-files/8b/8babb286a74d5fb93bad98906f3c4b70386c0e3dfb4f31edf184a012887bec5c/blob @@ -0,0 +1,606 @@ +from typing import Any, Dict, Set, Tuple, Callable +from collections import OrderedDict +import torch +from torch.ao.quantization.fx._model_report.detector import ( + DetectorBase, + DETECTOR_OBS_ARGS_KEY, + DETECTOR_OBS_TO_INSERT_KEY, + DETECTOR_IS_POST_OBS_KEY, + DETECTOR_TARGET_NODE_KEY, + DetectorQConfigInfo +) +from torch.ao.quantization.fx._model_report.model_report_visualizer import ModelReportVisualizer +from torch.ao.quantization.fx.graph_module import GraphModule +from torch.ao.quantization.observer import ObserverBase +from torch.ao.quantization.qconfig_mapping import QConfigMapping, QConfig +from torch.ao.quantization.fx._equalize import EqualizationQConfig + +class ModelReport: + r""" + The ModelReport class aims to provide users an easy way to diagnose issues that they run into + with their models. The class works with all traceable GraphModules to help diagnose issues, + though the requirements on the type of model more-so depends on the specific report the user + is trying to generate. With respect to the reports, the ModelReport class is initialized with + a set of Detector classes, each of which generate reports on quantization configuration + issues a use might have. + + Currently supports generating reports on: + - Suggestions for per-channel vs. per-tensor quantization (nn.Module) + - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) + - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) + - Suggestions for outlier detection for all layers (Graph Modules) + + The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) + where needed for each detector to gather the information it needs, and then after callibration, the ModelReport + class compiles the report generated by each Detector class into a single report to return to the user. It also + has the capability to remove all the observers it inserted as well. + + * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule + + * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class + Make sure that these are all unique types of detectors [do not have more than 1 of the same class] + + * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. + This set is generated by calling the get_detector_name() of each detector + + * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest + The purpose of this is to keep track of what observers were inserted for each detector, so that they + can be removed at the end if desired + + * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not + This is to ensure we only insert observers once with the ModelReport instance + + * :attr:`_removed_observers` A boolean to track if we have removed observers already + The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport + instance. This also allows the functionality where we can generate the report multiple times + as long as we haven't removed the observers yet. + + Note: + This class was initially designed to work with the Fx Graph Mode workflow in mind. However, + full functionality is available as long as there is a traceable GraphModule that is being used. + One method to get a traceable GraphModule without going through the Fx workflow is to use + the QuantizationTracer class. + + General Flow for Fx workflow: + 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model + 2.) Prepare your model with prepare_fx + 3.) Call model_report.prepare_detailed_calibration to add relevant observers + 4.) Callibrate your model with data + 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers + Optional + 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance + 7.) To help in parsing report information and debugging, view report info as a: + - Table + - Histogram + - Line plot + 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions + + Example (with QuantizationTracer): + >>> # xdoctest: +SKIP + >>> # get the necessary qconfig + >>> config = PrepareCustomConfig() + >>> skipped_module_names, skipped_module_classes = get_skipped_module_name_and_classes(config, False) + + >>> # initialize our model and get GraphModule + >>> model = SomeModel() + >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) + >>> graph_module = GraphModule(model, tracer.trace(model)) + + >>> # get our set of detectors and ModelReport instance + >>> detector_set = set([DynamicStaticDetector(tolerance=0.5), InputWeightEqualizationDetector(ratio_threshold=0.7)]) + >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) + + >>> # now we insert the observers and callibrate the model + >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() + >>> for i in range(num_callibration_batches): + >>> example_input = get_callibration_input() + >>> tracer_model_with_observers(example_input) + + >>> # finally we generate the reports and optionally remove the observers we inserted + >>> reports = tracer_reporter.generate_model_report(remove_inserted_observers=True) + + >>> # Optional: we can generate the qconfig mapping based on the suggestions + >>> qconfigs = model_report.generate_qconfig_mapping() + + >>> # Optional: we can generate the equalization mapping based on the suggestions + >>> qconfigs = model_report.generate_equalization_mapping() + + >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired + >>> model_report_visualizer = tracer_reporter.generate_visualizer() + + """ + + def __init__(self, model: GraphModule, desired_report_detectors: Set[DetectorBase]): + + if len(desired_report_detectors) == 0: + raise ValueError("Should include at least 1 desired report") + + # keep track of the model we wish to generate report for + self._model: GraphModule = model + + # keep the reports private so they can't be modified + self._desired_report_detectors = desired_report_detectors + self._desired_detector_names = {detector.get_detector_name() for detector in desired_report_detectors} + + # keep a mapping of desired reports to observers of interest + # this is to get the readings, and to remove them, can create a large set + # this set can then be used to traverse the graph and remove added observers + self._detector_name_to_observer_fqns: Dict[str, Set[str]] = {} + + # initialize each report to have empty set of observers of interest + for desired_report in self._desired_detector_names: + self._detector_name_to_observer_fqns[desired_report] = set() + + # flags to ensure that we can only prepare and remove observers once + self._prepared_flag = False + self._removed_observers = False + + # store the reports that we generated for visualization purposes + # initially empty since no reports generated + self._generated_reports: Dict[str, Dict] = {} + + def get_desired_reports_names(self) -> Set[str]: + """ Returns a copy of the desired reports for viewing """ + return self._desired_detector_names.copy() + + def get_observers_of_interest(self) -> Dict[str, Set[str]]: + """ Returns a copy of the observers of interest for viewing """ + return self._detector_name_to_observer_fqns.copy() + + def prepare_detailed_calibration(self) -> GraphModule: + r""" + Takes in a graph model and inserts the following observers: + - ModelReportObserver + + Each observer is inserted based on the desired_reports into the relevant locations + + Right now, each report in self._desired_detector_names has independent insertions + However, if a module already has a Observer of the same type, the insertion will not occur + This is because all of the same type of Observer collect same information, so redundant + + Returns the same GraphModule with the observers inserted + """ + + # if already prepared once, cannot prepare again + if self._prepared_flag: + raise ValueError("Already ran preparing detailed callibration. Run the report generation next after callibration.") + + # loop through each detector, find where placements should be, and keep track + insert_observers_fqns: Dict[str, Any] = {} + + for detector in self._desired_report_detectors: + # determine observer points for each detector + obs_fqn_to_info = detector.determine_observer_insert_points(self._model) + # map each insert point to the observer to use + insert_observers_fqns.update(obs_fqn_to_info) + # update the set of observers this report cares about + self._detector_name_to_observer_fqns[detector.get_detector_name()] = set(obs_fqn_to_info.keys()) + + # now insert all the observers at their desired locations + for observer_fqn in insert_observers_fqns: + target_node = insert_observers_fqns[observer_fqn][DETECTOR_TARGET_NODE_KEY] + insert_obs = insert_observers_fqns[observer_fqn][DETECTOR_OBS_TO_INSERT_KEY] + insert_post = insert_observers_fqns[observer_fqn][DETECTOR_IS_POST_OBS_KEY] + observer_args = insert_observers_fqns[observer_fqn][DETECTOR_OBS_ARGS_KEY] + self._insert_observer_around_module( + observer_fqn, target_node, insert_obs, observer_args, insert_post + ) + + self._prepared_flag = True + + return self._model + + def _insert_observer_around_module( + self, + obs_fqn: str, + target_node: torch.fx.node.Node, + obs_to_insert: ObserverBase, + observer_args: Tuple, + insert_post: bool + ): + r""" + Helper function that inserts the observer into both the graph structure and the module of the model + + Args + node_fqn (str): The fully qualified name of the observer we want to insert + target_node (torch.fx.node.Node): The node in model we are inserting observers around + obs_to_insert (ObserverBase): The observer we are inserting around target_node + observer_args (Tuple): The arguments we want to pass into the observer + insert_post (bool): whether this is meant to be a post observer for this node + """ + # if we are inserting post, then our target node is the next node + if insert_post: + target_node = target_node.next + + with self._model.graph.inserting_before(target_node): + self._model.add_submodule(obs_fqn, obs_to_insert) + self._model.graph.create_node(op="call_module", target=obs_fqn, args=observer_args) + + # recompile model after inserts are made + self._model.recompile() + + def _get_node_from_fqn(self, node_fqn: str) -> torch.fx.node.Node: + r""" + Takes in a node fqn and returns the node based on the fqn + + Args + node_fqn (str): The fully qualified name of the node we want to find in model + + Returns the Node object of the given node_fqn otherwise returns None + """ + node_to_return = None + for node in self._model.graph.nodes: + # if the target matches the fqn, it's the node we are looking for + if node.target == node_fqn: + node_to_return = node + break + + if node_to_return is None: + raise ValueError("The node_fqn is was not found within the module.") + + # assert for MyPy + assert isinstance(node_to_return, torch.fx.node.Node) + + return node_to_return + + def generate_model_report( + self, remove_inserted_observers: bool + ) -> Dict[str, Tuple[str, Dict]]: + r""" + Generates all the requested reports. + + Note: + You should have callibrated the model with relevant data before calling this + + The reports generated are specified by the desired_reports specified in desired_reports + + Can optionally remove all the observers inserted by the ModelReport instance + + Args: + remove_inserted_observers (bool): True to remove the observers inserted by this ModelReport instance + + Returns a mapping of each desired report name to a tuple with: + The textual summary of that report information + A dictionary containing relevant statistics or information for that report + + Note: + Throws exception if we try to generate report on model we already removed observers from + Throws exception if we try to generate report without preparing for callibration + """ + # if we haven't prepped model for callibration, then we shouldn't generate report yet + if not self._prepared_flag: + raise Exception("Cannot generate report without preparing model for callibration") + + # if we already removed the observers, we cannot generate report + if self._removed_observers: + raise Exception("Cannot generate report on model you already removed observers from") + + # keep track of all the reports of interest and their outputs + reports_of_interest = {} + + for detector in self._desired_report_detectors: + # generate the individual report for the detector + report_output = detector.generate_detector_report(self._model) + reports_of_interest[detector.get_detector_name()] = report_output + + # if user wishes to remove inserted observers, go ahead and remove + if remove_inserted_observers: + self._removed_observers = True + # get the set of all Observers inserted by this instance of ModelReport + all_observers_of_interest: Set[str] = set() + for desired_report in self._detector_name_to_observer_fqns: + observers_of_interest = self._detector_name_to_observer_fqns[desired_report] + all_observers_of_interest.update(observers_of_interest) + + # go through all_observers_of_interest and remove them from the graph and model + for observer_fqn in all_observers_of_interest: + # remove the observer from the model + self._model.delete_submodule(observer_fqn) + + # remove the observer from the graph structure + node_obj = self._get_node_from_fqn(observer_fqn) + + if node_obj: + self._model.graph.erase_node(node_obj) + else: + raise ValueError("Node no longer exists in GraphModule structure") + + # remember to recompile the model + self._model.recompile() + + # save the generated reports for visualization purposes + saved_reports: Dict[str, Dict] = { + report_name : report_tuple[1] for report_name, report_tuple in reports_of_interest.items() + } + + self._generated_reports = saved_reports + + # return the reports of interest + return reports_of_interest + + def _is_same_info_for_same_key(self, info_dict_a: Dict, info_dict_b: Dict) -> bool: + r""" + Takes in two dictionaries and ensures that any common keys between the two have the same + values. + + Args: + info_dict_a (Dict): First dictionary we wish to compare + info_dict_b (Dict): Second dictionary we wish to compare + + Returns True if all shared keys have same values, false otherwise + """ + # get the set of keys for both + dict_a_keys: Set = set(info_dict_a.keys()) + dict_b_keys: Set = set(info_dict_b.keys()) + + # get the insersection keys and check if same value for both dicts + intersecting_keys: Set = dict_a_keys.intersection(dict_b_keys) + + for key in intersecting_keys: + dict_a_val = info_dict_a[key] + dict_b_val = info_dict_b[key] + + # if it's a tensor we have to handle separately + if type(dict_a_val) == torch.Tensor: + # if dict_b_val not tensor, automatically false + if type(dict_b_val) != torch.Tensor or sum(dict_a_val != dict_b_val) != 0: + return False + else: + # for non-tensor vals + if dict_a_val != dict_b_val: + return False + + # if no non matching shared keys found, return true + return True + + def _reformat_reports_for_visualizer(self) -> OrderedDict: + r""" + Takes the generated reports and reformats them into the format that is desired by the + ModelReportVisualizer + + Returns an OrderedDict mapping module_fqns to their features + """ + # we want to reorder and reformat the information so it is ordered in terms of order + # found in the model + + # first create new dict with all modules as keys and features under respective module + module_fqns_to_features: Dict[str, Dict] = {} + + for report_name in self._generated_reports: + # get mod -> feature dict and go through + module_info = self._generated_reports[report_name] + + for module_fqn in module_info: + # check if already in our accumulation dict + if module_fqn in module_fqns_to_features: + # we merge all the features together + new_info: Dict = module_info[module_fqn] + present_info: Dict = module_fqns_to_features[module_fqn] + + # merge them together into the new unioned dict + # same features keys -> same info, so okay if override + + # do safety check to make sure shared keys have same info + if self._is_same_info_for_same_key(new_info, present_info): + module_fqns_to_features[module_fqn] = {**new_info, **present_info} + else: + error_str = "You have the same key with different values across detectors. " + error_str += "Someone incorrectly implemented a detector with conflicting keys to existing detectors." + raise ValueError(error_str) + else: + # we just set it + module_fqns_to_features[module_fqn] = module_info[module_fqn] + + # our ordered dict so that modules can be ordered in order of how they appear in model + features_by_module: OrderedDict[str, Dict] = OrderedDict() + + # we loop through modules in graph in order + for fqn, module in self._model.named_modules(): + # find that fqn in fqns_to_features + if fqn in module_fqns_to_features: + # add it to our ordered dict + features_by_module[fqn] = module_fqns_to_features[fqn] + + # return the ordered dict of info we created + return features_by_module + + def generate_visualizer(self) -> ModelReportVisualizer: + r""" + Generates a ModelReportVisualizer instance using the reports generated + by the generate_model_report() method. + + Returns the generated ModelReportVisualizer instance initialized + + Note: + Throws exception if attempt to get visualizers without generating report + """ + # check if user has generated reports at least once + if len(self._generated_reports) == 0: + raise Exception("Unable to generate visualizers without first generating reports") + + # get the ordered dict mapping modules to their full set of collected features / stats + module_fqns_to_features: OrderedDict = self._reformat_reports_for_visualizer() + + # create and return ModelReportVisualizer instance + visualizer: ModelReportVisualizer = ModelReportVisualizer(module_fqns_to_features) + + return visualizer + + def _generate_qconfig_mapping_helper( + self, + detector_qconfig_info_combined: Dict[str, DetectorQConfigInfo], + generation_function: Callable + ) -> QConfigMapping: + r""" + This helper takes in the compiled detector qconfig info that + has been compiled together and merges it into a QConfigMapping + """ + # keep track of the qconfigmapping + qconfig_mapping = QConfigMapping() + + # loop through each module / fqn and attempt to create QConfigMapping + for fqn, module in self._model.named_modules(): + # if we have a qconfig info for this module + if fqn in detector_qconfig_info_combined: + qconfig_info_compiled = detector_qconfig_info_combined[fqn] + + # now generate the qconfig and add it to the mapping + generated_qconfig = generation_function(qconfig_info_compiled, module) + + # add to our config + qconfig_mapping.set_module_name(fqn, generated_qconfig) + + # return compiled mapping + return qconfig_mapping + + def _update_detector_quantizaiton_qconfig_info(self, combined_info: DetectorQConfigInfo, new_info: DetectorQConfigInfo): + r""" + Takes in the old and new information and updates the combined information. + + Args: + combined_info (DetectorQConfigInfo): The DetectorQConfigInfo we are compiling all of the information in + new_info (DetectorQConfigInfo): The DetectorQConfigInfo with the information we are trying to merge the new info + into it + """ + combined_info.is_activation_dynamic = combined_info.is_activation_dynamic or new_info.is_activation_dynamic + combined_info.is_weight_per_channel = combined_info.is_weight_per_channel or new_info.is_weight_per_channel + + def _update_detector_equalization_qconfig_info(self, combined_info: DetectorQConfigInfo, new_info: DetectorQConfigInfo): + r""" + Takes in the old and new information and updates the combined information. + + Args: + combined_info (DetectorQConfigInfo): The DetectorQConfigInfo we are compiling all of the information in + new_info (DetectorQConfigInfo): The DetectorQConfigInfo with the information we are trying to merge the new info + into it + """ + is_equalization_recommended = combined_info.is_equalization_recommended or new_info.is_equalization_recommended + combined_info.is_equalization_recommended = is_equalization_recommended + + def _generate_module_fqn_to_detector_info_mapping( + self, + update_qconfig_info_function: Callable + ) -> Dict[str, DetectorQConfigInfo]: + r""" + Generates a QConfigMapping based on the suggestions of the + ModelReport API. The generated mapping encompasses all the + different types of feedback from the different detectors + all into one place. + + These configs are based on the suggestions provided by the ModelReport API + and can only be generated once the reports have been generated. + + Args: + update_qconfig_info_function (Callable) takes in a function that takes in two DetectorQConfigInfo + and updates the one that is being compiled + + Returns a Dict mapping module_fqns to DetectorQConfigInfo objects + + Note: + Throws exception if we try to generate mapping on model we already removed observers from + Throws exception if we try to generate mapping without preparing for callibration + """ + # if we haven't prepped model for callibration, then we shouldn't generate mapping yet + if not self._prepared_flag: + raise Exception("Cannot generate report without preparing model for callibration") + + # if we already removed the observers, we cannot mapping + if self._removed_observers: + raise Exception("Cannot generate report on model you already removed observers from") + + # keep track of qconfig info for each module across detectors + detector_qconfig_info_combined: Dict[str, DetectorQConfigInfo] = {} + + for detector in self._desired_report_detectors: + # get the info from the detector + detector_info: Dict[str, DetectorQConfigInfo] = detector.get_qconfig_info(self._model) + + # we go through the modules + for module_fqn in detector_info: + # see if we already have info on it + if module_fqn in detector_qconfig_info_combined: + # we combine the current options with what is there + current_options = detector_qconfig_info_combined[module_fqn] + detector_options = detector_info[module_fqn] + + update_qconfig_info_function(current_options, detector_options) + else: + # we just use this for now + detector_qconfig_info_combined[module_fqn] = detector_info[module_fqn] + + return detector_qconfig_info_combined + + def generate_qconfig_mapping(self) -> QConfigMapping: + r""" + Generates a QConfigMapping based on the suggestions of the + ModelReport API. The generated mapping encompasses all the + different types of feedback from the different detectors + all into one place. + + These configs are based on the suggestions provided by the ModelReport API + and can only be generated once the reports have been generated. + + Returns a QConfigMapping for the quantization configuration + + Note: + Throws exception if we try to generate mapping on model we already removed observers from + Throws exception if we try to generate mapping without preparing for callibration + """ + # get the mapping info + detector_qconfig_info_combined = self._generate_module_fqn_to_detector_info_mapping( + self._update_detector_quantizaiton_qconfig_info + ) + + # we will do a bit of processing and remove fqns that don't have input weight recommended + + # now we generate the QConfig for each of the options + mapping: QConfigMapping = self._generate_qconfig_mapping_helper( + detector_qconfig_info_combined, + self._quantization_config_generator + ) + + # return the generated mapping + return mapping + + def _quantization_config_generator(self, detector_qconfig_info: DetectorQConfigInfo, module: torch.nn.Module) -> QConfig: + r""" + Returns the quantization configuration generated by the DetectorQConfigInfo object + """ + return detector_qconfig_info.generate_quantization_qconfig(module) + + def _equalization_config_generator( + self, + detector_qconfig_info: DetectorQConfigInfo, + module: torch.nn.Module + ) -> EqualizationQConfig: + r""" + We ignore the module argument here, and only focus on thedetector_qconfig_info + + Returns the equalization configuration generated by the DetectorQConfigInfo object + """ + return detector_qconfig_info.generate_equalization_qconfig() + + def generate_equalization_mapping(self) -> QConfigMapping: + r""" + Generates a QConfigMapping based on the suggestions of the + ModelReport API for equalization. The generated mapping encompasses all the + different types of feedback from the input-weight equalization detector. + + These configs are based on the suggestions provided by the ModelReport API + and can only be generated once the reports have been generated. + + Returns a QConfigMapping for the equalization configuration + """ + # get the mapping info + detector_qconfig_info_combined = self._generate_module_fqn_to_detector_info_mapping( + self._update_detector_equalization_qconfig_info + ) + + # now we generate the QConfig for each of the options + mapping: QConfigMapping = self._generate_qconfig_mapping_helper( + detector_qconfig_info_combined, + self._equalization_config_generator + ) + + # return the generated mapping + return mapping diff --git a/shared/package-files/8b/8bac1070af4cf22f337ff5aaa8184c8f638229a9af3fb4fff630b919418fd47e/blob b/shared/package-files/8b/8bac1070af4cf22f337ff5aaa8184c8f638229a9af3fb4fff630b919418fd47e/blob new file mode 100644 index 0000000000000000000000000000000000000000..f19de724fbd333e584bc7bfae55d0da98ad8703c --- /dev/null +++ b/shared/package-files/8b/8bac1070af4cf22f337ff5aaa8184c8f638229a9af3fb4fff630b919418fd47e/blob @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8bac1070af4cf22f337ff5aaa8184c8f638229a9af3fb4fff630b919418fd47e +size 208346 diff --git a/shared/package-files/8b/8badfd129686a5d20216b1aba16450f6f471722c8b6ddfc61278dd572283abc0/blob b/shared/package-files/8b/8badfd129686a5d20216b1aba16450f6f471722c8b6ddfc61278dd572283abc0/blob new file mode 100644 index 0000000000000000000000000000000000000000..dcf5da1e43adb591767e56102c48526c9e8e7710 --- /dev/null +++ b/shared/package-files/8b/8badfd129686a5d20216b1aba16450f6f471722c8b6ddfc61278dd572283abc0/blob @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8badfd129686a5d20216b1aba16450f6f471722c8b6ddfc61278dd572283abc0 +size 555268959 diff --git a/shared/package-files/8b/8bb075675e65561a64af676d362b7a55867696d0f0f5a15973451b8c155294c6/blob b/shared/package-files/8b/8bb075675e65561a64af676d362b7a55867696d0f0f5a15973451b8c155294c6/blob new file mode 100644 index 0000000000000000000000000000000000000000..772b04f3a7c758c27a441be322388b6a633ab93f --- /dev/null +++ b/shared/package-files/8b/8bb075675e65561a64af676d362b7a55867696d0f0f5a15973451b8c155294c6/blob @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cuda { + +TORCH_API ::std::vector _foreach_sign(at::TensorList self); +TORCH_API void _foreach_sign_(at::TensorList self); + +} // namespace cuda +} // namespace at diff --git a/shared/package-files/8b/8bb0c4a825ec10fb775f144093ab733132671db740246a713f00825d3b889e34/blob b/shared/package-files/8b/8bb0c4a825ec10fb775f144093ab733132671db740246a713f00825d3b889e34/blob new file mode 100644 index 0000000000000000000000000000000000000000..1761ba08a47ff67b7285b081659e0e474e81b0a2 --- /dev/null +++ b/shared/package-files/8b/8bb0c4a825ec10fb775f144093ab733132671db740246a713f00825d3b889e34/blob @@ -0,0 +1,31 @@ +""" +Test that all docstrings are the same: + +>>> len({f.__doc__ for f in (a, b, c, d)}) +1 +""" +from numba import guvectorize, int64, njit, vectorize + + +def a(): + """>>> x = 1""" + return 1 + + +@njit +def b(): + """>>> x = 1""" + return 1 + + +@guvectorize([(int64[:], int64, int64[:])], "(n),()->(n)") +def c(x, y, res): + """>>> x = 1""" + for i in range(x.shape[0]): + res[i] = x[i] + y + + +@vectorize([int64(int64, int64)]) +def d(x, y): + """>>> x = 1""" + return x + y diff --git a/shared/package-files/8b/8bb7d57be88bc8bb50c427c06b82317711feb9f70798c93b85b0861b94f1018e/blob b/shared/package-files/8b/8bb7d57be88bc8bb50c427c06b82317711feb9f70798c93b85b0861b94f1018e/blob new file mode 100644 index 0000000000000000000000000000000000000000..b3dd45b553bb72a47ff31ebdc766ef0bfc5a4370 --- /dev/null +++ b/shared/package-files/8b/8bb7d57be88bc8bb50c427c06b82317711feb9f70798c93b85b0861b94f1018e/blob @@ -0,0 +1,1008 @@ +StartFontMetrics 2.0 +Comment Copyright (c) 1989, 1991 Adobe Systems Incorporated. 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195 ; WX 400 ; N circumflex ; B 187 546 484 720 ; +C 196 ; WX 400 ; N tilde ; B 137 563 492 682 ; +C 197 ; WX 400 ; N macron ; B 193 597 489 656 ; +C 198 ; WX 400 ; N breve ; B 227 568 501 698 ; +C 199 ; WX 402 ; N dotaccent ; B 252 570 359 680 ; +C 200 ; WX 400 ; N dieresis ; B 172 572 487 682 ; +C 202 ; WX 400 ; N ring ; B 186 550 402 752 ; +C 203 ; WX 400 ; N cedilla ; B 62 -230 241 0 ; +C 205 ; WX 400 ; N hungarumlaut ; B 176 546 455 750 ; +C 206 ; WX 350 ; N ogonek ; B 68 -219 248 0 ; +C 207 ; WX 400 ; N caron ; B 213 557 510 731 ; +C 208 ; WX 1000 ; N emdash ; B 12 221 1024 279 ; +C 225 ; WX 880 ; N AE ; B -88 0 941 692 ; +C 227 ; WX 425 ; N ordfeminine ; B 77 265 460 590 ; +C 232 ; WX 571 ; N Lslash ; B 11 0 574 692 ; +C 233 ; WX 753 ; N Oslash ; B 79 -45 754 736 ; +C 234 ; WX 1020 ; N OE ; B 79 0 1081 692 ; +C 235 ; WX 389 ; N ordmasculine ; B 86 265 420 590 ; +C 241 ; WX 779 ; N ae ; B 34 -12 797 514 ; +C 245 ; WX 317 ; N dotlessi ; B 79 -12 299 502 ; +C 248 ; WX 318 ; N lslash ; B 45 -12 376 742 ; +C 249 ; WX 537 ; N oslash ; B 49 -39 522 529 ; +C 250 ; WX 806 ; N oe ; B 49 -12 824 502 ; +C 251 ; WX 577 ; N germandbls ; B -107 -242 630 742 ; +C -1 ; WX 370 ; N onesuperior ; B 90 272 326 680 ; +C -1 ; WX 570 ; N minus ; B 58 221 542 279 ; +C -1 ; WX 400 ; N degree ; B 152 404 428 680 ; +C -1 ; WX 537 ; N oacute ; B 49 -12 530 723 ; +C -1 ; WX 753 ; N Odieresis ; B 79 -15 754 848 ; +C -1 ; WX 537 ; N odieresis ; B 49 -12 532 682 ; +C -1 ; WX 596 ; N Eacute ; B 3 0 657 890 ; +C -1 ; WX 618 ; N ucircumflex ; B 89 -12 609 720 ; +C -1 ; WX 890 ; N onequarter ; B 97 -24 805 698 ; +C -1 ; WX 570 ; N logicalnot ; B 58 102 542 389 ; +C -1 ; WX 596 ; N Ecircumflex ; B 3 0 657 876 ; +C -1 ; WX 890 ; N onehalf ; B 71 -24 812 698 ; +C -1 ; WX 753 ; N Otilde ; B 79 -15 754 842 ; +C -1 ; WX 618 ; N uacute ; B 89 -12 609 723 ; +C -1 ; WX 453 ; N eacute ; B 45 -12 508 723 ; +C -1 ; WX 317 ; N iacute ; B 79 -12 398 723 ; +C -1 ; WX 596 ; N Egrave ; B 3 0 657 890 ; +C -1 ; WX 317 ; N icircumflex ; B 79 -12 383 720 ; +C -1 ; WX 618 ; N mu ; B 11 -232 609 502 ; +C -1 ; WX 270 ; N brokenbar ; B 130 -175 198 675 ; +C -1 ; WX 584 ; N thorn ; B 16 -242 580 700 ; +C -1 ; WX 624 ; N Aring ; B -58 0 623 861 ; +C -1 ; WX 468 ; N yacute ; B -40 -242 505 723 ; +C -1 ; WX 591 ; N Ydieresis ; B 96 0 744 848 ; +C -1 ; WX 1100 ; N trademark ; B 91 277 1094 692 ; +C -1 ; WX 836 ; N registered ; B 91 -15 819 707 ; +C -1 ; WX 537 ; N ocircumflex ; B 49 -12 522 720 ; +C -1 ; WX 624 ; N Agrave ; B -58 0 623 890 ; +C -1 ; WX 533 ; N Scaron ; B 34 -15 561 888 ; +C -1 ; WX 794 ; N Ugrave ; B 131 -15 880 890 ; +C -1 ; WX 596 ; N Edieresis ; B 3 0 657 848 ; +C -1 ; WX 794 ; N Uacute ; B 131 -15 880 890 ; +C -1 ; WX 537 ; N otilde ; B 49 -12 525 682 ; +C -1 ; WX 618 ; N ntilde ; B 63 -12 600 682 ; +C -1 ; WX 468 ; N ydieresis ; B -40 -242 513 682 ; +C -1 ; WX 624 ; N Aacute ; B -58 0 642 890 ; +C -1 ; WX 537 ; N eth ; B 47 -12 521 742 ; +C -1 ; WX 561 ; N acircumflex ; B 31 -12 563 720 ; +C -1 ; WX 561 ; N aring ; B 31 -12 563 752 ; +C -1 ; WX 753 ; N Ograve ; B 79 -15 754 890 ; +C -1 ; WX 441 ; N ccedilla ; B 46 -230 465 502 ; +C -1 ; WX 570 ; N multiply ; B 88 22 532 478 ; +C -1 ; WX 570 ; N divide ; B 58 25 542 475 ; +C -1 ; WX 370 ; N twosuperior ; B 35 272 399 680 ; +C -1 ; WX 763 ; N Ntilde ; B -4 0 855 842 ; +C -1 ; WX 618 ; N ugrave ; B 89 -12 609 723 ; +C -1 ; WX 794 ; N Ucircumflex ; B 131 -15 880 876 ; +C -1 ; WX 624 ; N Atilde ; B -58 0 623 842 ; +C -1 ; WX 468 ; N zcaron ; B 4 -12 484 731 ; +C -1 ; WX 317 ; N idieresis ; B 79 -12 398 682 ; +C -1 ; WX 624 ; N Acircumflex ; B -58 0 623 876 ; +C -1 ; WX 345 ; N Icircumflex ; B 5 0 453 876 ; +C -1 ; WX 591 ; N Yacute ; B 96 0 744 890 ; +C -1 ; WX 753 ; N Oacute ; B 79 -15 754 890 ; +C -1 ; WX 624 ; N Adieresis ; B -58 0 623 848 ; +C -1 ; WX 622 ; N Zcaron ; B -20 0 703 888 ; +C -1 ; WX 561 ; N agrave ; B 31 -12 563 723 ; +C -1 ; WX 370 ; N threesuperior ; B 59 265 389 680 ; +C -1 ; WX 537 ; N ograve ; B 49 -12 522 723 ; +C -1 ; WX 890 ; N threequarters ; B 105 -24 816 698 ; +C -1 ; WX 770 ; N Eth ; B 12 0 774 692 ; +C -1 ; WX 570 ; N plusminus ; B 58 0 542 556 ; +C -1 ; WX 618 ; N udieresis ; B 89 -12 609 682 ; +C -1 ; WX 453 ; N edieresis ; B 45 -12 490 682 ; +C -1 ; WX 561 ; N aacute ; B 31 -12 571 723 ; +C -1 ; WX 317 ; N igrave ; B 55 -12 299 723 ; +C -1 ; WX 345 ; N Idieresis ; B 5 0 461 848 ; +C -1 ; WX 561 ; N adieresis ; B 31 -12 563 682 ; +C -1 ; WX 345 ; N Iacute ; B 5 0 506 890 ; +C -1 ; WX 836 ; N copyright ; B 91 -15 819 707 ; +C -1 ; WX 345 ; N Igrave ; B 5 0 428 890 ; +C -1 ; WX 661 ; N Ccedilla ; B 79 -230 723 707 ; +C -1 ; WX 389 ; N scaron ; B 19 -12 457 731 ; +C -1 ; WX 453 ; N egrave ; B 45 -12 471 723 ; +C -1 ; WX 753 ; N Ocircumflex ; B 79 -15 754 876 ; +C -1 ; WX 604 ; N Thorn ; B 5 0 616 692 ; +C -1 ; WX 561 ; N atilde ; B 31 -12 563 682 ; +C -1 ; WX 794 ; N Udieresis ; B 131 -15 880 848 ; +C -1 ; WX 453 ; N ecircumflex ; B 45 -12 475 720 ; +EndCharMetrics 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quoteright -20 +KPX w quotedblright -30 +KPX w period -62 +KPX w comma -62 + +KPX x y 12 +KPX x w -6 +KPX x quoteright -40 +KPX x quotedblright -50 +KPX x q -6 +KPX x o -6 +KPX x e -6 +KPX x d -6 +KPX x c -6 + +KPX y quoteright -10 +KPX y quotedblright -20 +KPX y period -70 +KPX y emdash 40 +KPX y comma -60 + +KPX z quoteright -40 +KPX z quotedblright -50 +KPX z o -6 +KPX z e -6 +KPX z d -6 +KPX z c -6 +EndKernPairs +EndKernData +EndFontMetrics diff --git a/shared/package-files/8b/8bb850c565aa389fdc16f3a46965ad23d82adff60f2393fc2762b63185e8e6c9/blob b/shared/package-files/8b/8bb850c565aa389fdc16f3a46965ad23d82adff60f2393fc2762b63185e8e6c9/blob new file mode 100644 index 0000000000000000000000000000000000000000..de6633112c1f9951fd688e1fb43457a1ec11d6d8 --- /dev/null +++ b/shared/package-files/8b/8bb850c565aa389fdc16f3a46965ad23d82adff60f2393fc2762b63185e8e6c9/blob @@ -0,0 +1,18 @@ +Copyright (c) 2010-2020 Benjamin Peterson + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of +the Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/shared/package-files/8b/8bba1a38cadd44be6132d04665742d0768d1fe020372a96d73424c64f7931cc6/blob b/shared/package-files/8b/8bba1a38cadd44be6132d04665742d0768d1fe020372a96d73424c64f7931cc6/blob new file mode 100644 index 0000000000000000000000000000000000000000..be9a8c660781b2ff2c2485fdc5e0e9846b5b9e6d --- /dev/null +++ b/shared/package-files/8b/8bba1a38cadd44be6132d04665742d0768d1fe020372a96d73424c64f7931cc6/blob @@ -0,0 +1,27 @@ +import sys +from typing import Optional, Union, overload +from typing_extensions import Literal + +if sys.platform == "win32": + SND_FILENAME: int + SND_ALIAS: int + SND_LOOP: int + SND_MEMORY: int + SND_PURGE: int + SND_ASYNC: int + SND_NODEFAULT: int + SND_NOSTOP: int + SND_NOWAIT: int + + MB_ICONASTERISK: int + MB_ICONEXCLAMATION: int + MB_ICONHAND: int + MB_ICONQUESTION: int + MB_OK: int + def Beep(frequency: int, duration: int) -> None: ... + # Can actually accept anything ORed with 4, and if not it's definitely str, but that's inexpressible + @overload + def PlaySound(sound: Optional[bytes], flags: Literal[4]) -> None: ... + @overload + def PlaySound(sound: Optional[Union[str, bytes]], flags: int) -> None: ... + def MessageBeep(type: int = ...) -> None: ... diff --git a/shared/package-files/8b/8bbbffb486494564490858800c2c1538ea7fb7d568fda669fdc10bd2570e049f/blob b/shared/package-files/8b/8bbbffb486494564490858800c2c1538ea7fb7d568fda669fdc10bd2570e049f/blob new file mode 100644 index 0000000000000000000000000000000000000000..c6a9eaa95bc35cd80d7caa003737a760513e8201 --- /dev/null +++ b/shared/package-files/8b/8bbbffb486494564490858800c2c1538ea7fb7d568fda669fdc10bd2570e049f/blob @@ -0,0 +1,6 @@ +from .cache import Cache as Cache +from .decorators import cached as cached, cachedmethod as cachedmethod +from .lfu import LFUCache as LFUCache +from .lru import LRUCache as LRUCache +from .rr import RRCache as RRCache +from .ttl import TTLCache as TTLCache diff --git a/shared/package-files/8b/8bbc55b991109d8aee328e317ef188d22ce8cc8d6a26f70f3bc0ebeca454d158/blob b/shared/package-files/8b/8bbc55b991109d8aee328e317ef188d22ce8cc8d6a26f70f3bc0ebeca454d158/blob new file mode 100644 index 0000000000000000000000000000000000000000..d191099c30fe9f96c8087b8de64369a5d9b06897 --- /dev/null +++ b/shared/package-files/8b/8bbc55b991109d8aee328e317ef188d22ce8cc8d6a26f70f3bc0ebeca454d158/blob @@ -0,0 +1,213 @@ +""" + pygments.lexers.fortran + ~~~~~~~~~~~~~~~~~~~~~~~ + + Lexers for Fortran languages. + + :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" + +import re + +from pygments.lexer import RegexLexer, bygroups, include, words, using, default +from pygments.token import Text, Comment, Operator, Keyword, Name, String, \ + Number, Punctuation, Generic + +__all__ = ['FortranLexer', 'FortranFixedLexer'] + + +class FortranLexer(RegexLexer): + """ + Lexer for FORTRAN 90 code. + + .. versionadded:: 0.10 + """ + name = 'Fortran' + url = 'https://fortran-lang.org/' + aliases = ['fortran', 'f90'] + filenames = ['*.f03', '*.f90', '*.F03', '*.F90'] + mimetypes = ['text/x-fortran'] + flags = re.IGNORECASE | re.MULTILINE + + # Data Types: INTEGER, REAL, COMPLEX, LOGICAL, CHARACTER and DOUBLE PRECISION + # Operators: **, *, +, -, /, <, >, <=, >=, ==, /= + # Logical (?): NOT, AND, OR, EQV, NEQV + + # Builtins: + # http://gcc.gnu.org/onlinedocs/gcc-3.4.6/g77/Table-of-Intrinsic-Functions.html + + tokens = { + 'root': [ + (r'^#.*\n', Comment.Preproc), + (r'!.*\n', Comment), + include('strings'), + include('core'), + (r'[a-z][\w$]*', Name), + include('nums'), + (r'[\s]+', Text.Whitespace), + ], + 'core': [ + # Statements + + (r'\b(DO)(\s+)(CONCURRENT)\b', bygroups(Keyword, Text.Whitespace, Keyword)), + (r'\b(GO)(\s*)(TO)\b', bygroups(Keyword, Text.Whitespace, Keyword)), + + (words(( + 'ABSTRACT', 'ACCEPT', 'ALL', 'ALLSTOP', 'ALLOCATABLE', 'ALLOCATE', + 'ARRAY', 'ASSIGN', 'ASSOCIATE', 'ASYNCHRONOUS', 'BACKSPACE', 'BIND', + 'BLOCK', 'BLOCKDATA', 'BYTE', 'CALL', 'CASE', 'CLASS', 'CLOSE', + 'CODIMENSION', 'COMMON', 'CONTIGUOUS', 'CONTAINS', + 'CONTINUE', 'CRITICAL', 'CYCLE', 'DATA', 'DEALLOCATE', 'DECODE', + 'DEFERRED', 'DIMENSION', 'DO', 'ELEMENTAL', 'ELSE', 'ENCODE', 'END', + 'ENDASSOCIATE', 'ENDBLOCK', 'ENDDO', 'ENDENUM', 'ENDFORALL', + 'ENDFUNCTION', 'ENDIF', 'ENDINTERFACE', 'ENDMODULE', 'ENDPROGRAM', + 'ENDSELECT', 'ENDSUBMODULE', 'ENDSUBROUTINE', 'ENDTYPE', 'ENDWHERE', + 'ENTRY', 'ENUM', 'ENUMERATOR', 'EQUIVALENCE', 'ERROR STOP', 'EXIT', + 'EXTENDS', 'EXTERNAL', 'EXTRINSIC', 'FILE', 'FINAL', 'FORALL', 'FORMAT', + 'FUNCTION', 'GENERIC', 'IF', 'IMAGES', 'IMPLICIT', + 'IMPORT', 'IMPURE', 'INCLUDE', 'INQUIRE', 'INTENT', 'INTERFACE', + 'INTRINSIC', 'IS', 'LOCK', 'MEMORY', 'MODULE', 'NAMELIST', 'NULLIFY', + 'NONE', 'NON_INTRINSIC', 'NON_OVERRIDABLE', 'NOPASS', 'ONLY', 'OPEN', + 'OPTIONAL', 'OPTIONS', 'PARAMETER', 'PASS', 'PAUSE', 'POINTER', 'PRINT', + 'PRIVATE', 'PROGRAM', 'PROCEDURE', 'PROTECTED', 'PUBLIC', 'PURE', 'READ', + 'RECURSIVE', 'RESULT', 'RETURN', 'REWIND', 'SAVE', 'SELECT', 'SEQUENCE', + 'STOP', 'SUBMODULE', 'SUBROUTINE', 'SYNC', 'SYNCALL', 'SYNCIMAGES', + 'SYNCMEMORY', 'TARGET', 'THEN', 'TYPE', 'UNLOCK', 'USE', 'VALUE', + 'VOLATILE', 'WHERE', 'WRITE', 'WHILE'), prefix=r'\b', suffix=r'\s*\b'), + Keyword), + + # Data Types + (words(( + 'CHARACTER', 'COMPLEX', 'DOUBLE PRECISION', 'DOUBLE COMPLEX', 'INTEGER', + 'LOGICAL', 'REAL', 'C_INT', 'C_SHORT', 'C_LONG', 'C_LONG_LONG', + 'C_SIGNED_CHAR', 'C_SIZE_T', 'C_INT8_T', 'C_INT16_T', 'C_INT32_T', + 'C_INT64_T', 'C_INT_LEAST8_T', 'C_INT_LEAST16_T', 'C_INT_LEAST32_T', + 'C_INT_LEAST64_T', 'C_INT_FAST8_T', 'C_INT_FAST16_T', 'C_INT_FAST32_T', + 'C_INT_FAST64_T', 'C_INTMAX_T', 'C_INTPTR_T', 'C_FLOAT', 'C_DOUBLE', + 'C_LONG_DOUBLE', 'C_FLOAT_COMPLEX', 'C_DOUBLE_COMPLEX', + 'C_LONG_DOUBLE_COMPLEX', 'C_BOOL', 'C_CHAR', 'C_PTR', 'C_FUNPTR'), + prefix=r'\b', suffix=r'\s*\b'), + Keyword.Type), + + # Operators + (r'(\*\*|\*|\+|-|\/|<|>|<=|>=|==|\/=|=)', Operator), + + (r'(::)', Keyword.Declaration), + + (r'[()\[\],:&%;.]', Punctuation), + # Intrinsics + (words(( + 'Abort', 'Abs', 'Access', 'AChar', 'ACos', 'ACosH', 'AdjustL', + 'AdjustR', 'AImag', 'AInt', 'Alarm', 'All', 'Allocated', 'ALog', + 'AMax', 'AMin', 'AMod', 'And', 'ANInt', 'Any', 'ASin', 'ASinH', + 'Associated', 'ATan', 'ATanH', 'Atomic_Define', 'Atomic_Ref', + 'BesJ', 'BesJN', 'Bessel_J0', 'Bessel_J1', 'Bessel_JN', 'Bessel_Y0', + 'Bessel_Y1', 'Bessel_YN', 'BesY', 'BesYN', 'BGE', 'BGT', 'BLE', + 'BLT', 'Bit_Size', 'BTest', 'CAbs', 'CCos', 'Ceiling', 'CExp', + 'Char', 'ChDir', 'ChMod', 'CLog', 'Cmplx', 'Command_Argument_Count', + 'Complex', 'Conjg', 'Cos', 'CosH', 'Count', 'CPU_Time', 'CShift', + 'CSin', 'CSqRt', 'CTime', 'C_Loc', 'C_Associated', + 'C_Null_Ptr', 'C_Null_Funptr', 'C_F_Pointer', 'C_F_ProcPointer', + 'C_Null_Char', 'C_Alert', 'C_Backspace', 'C_Form_Feed', 'C_FunLoc', + 'C_Sizeof', 'C_New_Line', 'C_Carriage_Return', + 'C_Horizontal_Tab', 'C_Vertical_Tab', 'DAbs', 'DACos', 'DASin', + 'DATan', 'Date_and_Time', 'DbesJ', 'DbesJN', 'DbesY', + 'DbesYN', 'Dble', 'DCos', 'DCosH', 'DDiM', 'DErF', + 'DErFC', 'DExp', 'Digits', 'DiM', 'DInt', 'DLog', 'DMax', + 'DMin', 'DMod', 'DNInt', 'Dot_Product', 'DProd', 'DSign', 'DSinH', + 'DShiftL', 'DShiftR', 'DSin', 'DSqRt', 'DTanH', 'DTan', 'DTime', + 'EOShift', 'Epsilon', 'ErF', 'ErFC', 'ErFC_Scaled', 'ETime', + 'Execute_Command_Line', 'Exit', 'Exp', 'Exponent', 'Extends_Type_Of', + 'FDate', 'FGet', 'FGetC', 'FindLoc', 'Float', 'Floor', 'Flush', + 'FNum', 'FPutC', 'FPut', 'Fraction', 'FSeek', 'FStat', 'FTell', + 'Gamma', 'GError', 'GetArg', 'Get_Command', 'Get_Command_Argument', + 'Get_Environment_Variable', 'GetCWD', 'GetEnv', 'GetGId', 'GetLog', + 'GetPId', 'GetUId', 'GMTime', 'HostNm', 'Huge', 'Hypot', 'IAbs', + 'IAChar', 'IAll', 'IAnd', 'IAny', 'IArgC', 'IBClr', 'IBits', + 'IBSet', 'IChar', 'IDate', 'IDiM', 'IDInt', 'IDNInt', 'IEOr', + 'IErrNo', 'IFix', 'Imag', 'ImagPart', 'Image_Index', 'Index', + 'Int', 'IOr', 'IParity', 'IRand', 'IsaTty', 'IShft', 'IShftC', + 'ISign', 'Iso_C_Binding', 'Is_Contiguous', 'Is_Iostat_End', + 'Is_Iostat_Eor', 'ITime', 'Kill', 'Kind', 'LBound', 'LCoBound', + 'Len', 'Len_Trim', 'LGe', 'LGt', 'Link', 'LLe', 'LLt', 'LnBlnk', + 'Loc', 'Log', 'Log_Gamma', 'Logical', 'Long', 'LShift', 'LStat', + 'LTime', 'MaskL', 'MaskR', 'MatMul', 'Max', 'MaxExponent', + 'MaxLoc', 'MaxVal', 'MClock', 'Merge', 'Merge_Bits', 'Move_Alloc', + 'Min', 'MinExponent', 'MinLoc', 'MinVal', 'Mod', 'Modulo', 'MvBits', + 'Nearest', 'New_Line', 'NInt', 'Norm2', 'Not', 'Null', 'Num_Images', + 'Or', 'Pack', 'Parity', 'PError', 'Precision', 'Present', 'Product', + 'Radix', 'Rand', 'Random_Number', 'Random_Seed', 'Range', 'Real', + 'RealPart', 'Rename', 'Repeat', 'Reshape', 'RRSpacing', 'RShift', + 'Same_Type_As', 'Scale', 'Scan', 'Second', 'Selected_Char_Kind', + 'Selected_Int_Kind', 'Selected_Real_Kind', 'Set_Exponent', 'Shape', + 'ShiftA', 'ShiftL', 'ShiftR', 'Short', 'Sign', 'Signal', 'SinH', + 'Sin', 'Sleep', 'Sngl', 'Spacing', 'Spread', 'SqRt', 'SRand', + 'Stat', 'Storage_Size', 'Sum', 'SymLnk', 'System', 'System_Clock', + 'Tan', 'TanH', 'Time', 'This_Image', 'Tiny', 'TrailZ', 'Transfer', + 'Transpose', 'Trim', 'TtyNam', 'UBound', 'UCoBound', 'UMask', + 'Unlink', 'Unpack', 'Verify', 'XOr', 'ZAbs', 'ZCos', 'ZExp', + 'ZLog', 'ZSin', 'ZSqRt'), prefix=r'\b', suffix=r'\s*\b'), + Name.Builtin), + + # Booleans + (r'\.(true|false)\.', Name.Builtin), + # Comparing Operators + (r'\.(eq|ne|lt|le|gt|ge|not|and|or|eqv|neqv)\.', Operator.Word), + ], + + 'strings': [ + (r'"(\\[0-7]+|\\[^0-7]|[^"\\])*"', String.Double), + (r"'(\\[0-7]+|\\[^0-7]|[^'\\])*'", String.Single), + ], + + 'nums': [ + (r'\d+(?![.e])(_([1-9]|[a-z]\w*))?', Number.Integer), + (r'[+-]?\d*\.\d+([ed][-+]?\d+)?(_([1-9]|[a-z]\w*))?', Number.Float), + (r'[+-]?\d+\.\d*([ed][-+]?\d+)?(_([1-9]|[a-z]\w*))?', Number.Float), + (r'[+-]?\d+(\.\d*)?[ed][-+]?\d+(_([1-9]|[a-z]\w*))?', Number.Float), + ], + } + + +class FortranFixedLexer(RegexLexer): + """ + Lexer for fixed format Fortran. + + .. versionadded:: 2.1 + """ + name = 'FortranFixed' + aliases = ['fortranfixed'] + filenames = ['*.f', '*.F'] + + flags = re.IGNORECASE + + def _lex_fortran(self, match, ctx=None): + """Lex a line just as free form fortran without line break.""" + lexer = FortranLexer() + text = match.group(0) + "\n" + for index, token, value in lexer.get_tokens_unprocessed(text): + value = value.replace('\n', '') + if value != '': + yield index, token, value + + tokens = { + 'root': [ + (r'[C*].*\n', Comment), + (r'#.*\n', Comment.Preproc), + (r' {0,4}!.*\n', Comment), + (r'(.{5})', Name.Label, 'cont-char'), + (r'.*\n', using(FortranLexer)), + ], + 'cont-char': [ + (' ', Text, 'code'), + ('0', Comment, 'code'), + ('.', Generic.Strong, 'code'), + ], + 'code': [ + (r'(.{66})(.*)(\n)', + bygroups(_lex_fortran, Comment, Text.Whitespace), 'root'), + (r'(.*)(\n)', bygroups(_lex_fortran, Text.Whitespace), 'root'), + default('root'), + ] + } diff --git a/shared/package-files/8b/8bc0648758f4ee980fea922fcc5c45219eee0254757d03826c81e1eefd38c70f/blob b/shared/package-files/8b/8bc0648758f4ee980fea922fcc5c45219eee0254757d03826c81e1eefd38c70f/blob new file mode 100644 index 0000000000000000000000000000000000000000..51f21afd4d3e401bfe4021ff308cd2d5c36e5c4b --- /dev/null +++ b/shared/package-files/8b/8bc0648758f4ee980fea922fcc5c45219eee0254757d03826c81e1eefd38c70f/blob @@ -0,0 +1,9 @@ +from ._typedefs cimport intp_t + +from cython cimport floating + +cdef int simultaneous_sort( + floating *dist, + intp_t *idx, + intp_t size, +) noexcept nogil diff --git a/shared/package-files/8b/8bc0e900307c66122e70a73c96df497d86d000d46f47aeeee7da107f9c229ef8/blob b/shared/package-files/8b/8bc0e900307c66122e70a73c96df497d86d000d46f47aeeee7da107f9c229ef8/blob new file mode 100644 index 0000000000000000000000000000000000000000..9710d0b2fe2f2ac1fc9e19c1c9b4688807efd6d7 --- /dev/null +++ b/shared/package-files/8b/8bc0e900307c66122e70a73c96df497d86d000d46f47aeeee7da107f9c229ef8/blob @@ -0,0 +1,26 @@ +#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER) + /* + * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics, + * whether or not the build options for those features are specified. + * Therefore, we must test #definitions of CPU features when option native/host + * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise + * the test will be broken and leads to enable all possible features. + */ + #if !defined(__AVX512FP16__) + #error "HOST/ARCH doesn't support Sapphire Rapids AVX512FP16 features" + #endif +#endif + +#include + +int main(int argc, char **argv) +{ +/* clang has a bug regarding our spr coode, see gh-23730. */ +#if __clang__ +#error +#endif + __m512h a = _mm512_loadu_ph((void*)argv[argc-1]); + __m512h temp = _mm512_fmadd_ph(a, a, a); + _mm512_storeu_ph((void*)(argv[argc-1]), temp); + return 0; +} diff --git a/shared/package-files/8b/8bc152afd712148ae1d7a3bc87b7289bc2cab5df67b0b63cc221214a9935fda6/blob b/shared/package-files/8b/8bc152afd712148ae1d7a3bc87b7289bc2cab5df67b0b63cc221214a9935fda6/blob new file mode 100644 index 0000000000000000000000000000000000000000..f14a90b1d88a1bbf424a9b48772eaf3e5b4c2978 --- /dev/null +++ b/shared/package-files/8b/8bc152afd712148ae1d7a3bc87b7289bc2cab5df67b0b63cc221214a9935fda6/blob @@ -0,0 +1,31 @@ +""" +Charset-Normalizer +~~~~~~~~~~~~~~ +The Real First Universal Charset Detector. +A library that helps you read text from an unknown charset encoding. +Motivated by chardet, This package is trying to resolve the issue by taking a new approach. +All IANA character set names for which the Python core library provides codecs are supported. + +Basic usage: + >>> from charset_normalizer import from_bytes + >>> results = from_bytes('Bсеки човек има право на образование. Oбразованието трябва да бъде безплатно, поне що се отнася до началното и основното образование.'.encode('utf_8')) + >>> "utf_8" in results + True + >>> best_result = results.best() + >>> str(best_result) + 'Bсеки човек има право на образование. Oбразованието трябва да бъде безплатно, поне що се отнася до началното и основното образование.' + +Others methods and usages are available - see the full documentation +at . +:copyright: (c) 2021 by Ahmed TAHRI +:license: MIT, see LICENSE for more details. +""" +from charset_normalizer.api import from_fp, from_path, from_bytes, normalize +from charset_normalizer.legacy import detect +from charset_normalizer.version import __version__, VERSION +from charset_normalizer.models import CharsetMatch, CharsetMatches + +# Backward-compatible v1 imports +from charset_normalizer.models import CharsetNormalizerMatch +import charset_normalizer.api as CharsetDetector +CharsetNormalizerMatches = CharsetDetector diff --git a/shared/package-files/8b/8bc19c6d27178483db3047bbbae3205ea87f714da6666d9fea1b29220996e6e3/blob b/shared/package-files/8b/8bc19c6d27178483db3047bbbae3205ea87f714da6666d9fea1b29220996e6e3/blob new file mode 100644 index 0000000000000000000000000000000000000000..2b27b4b036e5f2ed93a1ea88cd7d7144eb5615d4 --- /dev/null +++ b/shared/package-files/8b/8bc19c6d27178483db3047bbbae3205ea87f714da6666d9fea1b29220996e6e3/blob @@ -0,0 +1,35 @@ +from sympy.crypto.crypto import (cycle_list, + encipher_shift, encipher_affine, encipher_substitution, + check_and_join, encipher_vigenere, decipher_vigenere, bifid5_square, + bifid6_square, encipher_hill, decipher_hill, + encipher_bifid5, encipher_bifid6, decipher_bifid5, + decipher_bifid6, encipher_kid_rsa, decipher_kid_rsa, + kid_rsa_private_key, kid_rsa_public_key, decipher_rsa, rsa_private_key, + rsa_public_key, encipher_rsa, lfsr_connection_polynomial, + lfsr_autocorrelation, lfsr_sequence, encode_morse, decode_morse, + elgamal_private_key, elgamal_public_key, decipher_elgamal, + encipher_elgamal, dh_private_key, dh_public_key, dh_shared_key, + padded_key, encipher_bifid, decipher_bifid, bifid_square, bifid5, + bifid6, bifid10, decipher_gm, encipher_gm, gm_public_key, + gm_private_key, bg_private_key, bg_public_key, encipher_bg, decipher_bg, + encipher_rot13, decipher_rot13, encipher_atbash, decipher_atbash, + encipher_railfence, decipher_railfence) + +__all__ = [ + 'cycle_list', 'encipher_shift', 'encipher_affine', + 'encipher_substitution', 'check_and_join', 'encipher_vigenere', + 'decipher_vigenere', 'bifid5_square', 'bifid6_square', 'encipher_hill', + 'decipher_hill', 'encipher_bifid5', 'encipher_bifid6', 'decipher_bifid5', + 'decipher_bifid6', 'encipher_kid_rsa', 'decipher_kid_rsa', + 'kid_rsa_private_key', 'kid_rsa_public_key', 'decipher_rsa', + 'rsa_private_key', 'rsa_public_key', 'encipher_rsa', + 'lfsr_connection_polynomial', 'lfsr_autocorrelation', 'lfsr_sequence', + 'encode_morse', 'decode_morse', 'elgamal_private_key', + 'elgamal_public_key', 'decipher_elgamal', 'encipher_elgamal', + 'dh_private_key', 'dh_public_key', 'dh_shared_key', 'padded_key', + 'encipher_bifid', 'decipher_bifid', 'bifid_square', 'bifid5', 'bifid6', + 'bifid10', 'decipher_gm', 'encipher_gm', 'gm_public_key', + 'gm_private_key', 'bg_private_key', 'bg_public_key', 'encipher_bg', + 'decipher_bg', 'encipher_rot13', 'decipher_rot13', 'encipher_atbash', + 'decipher_atbash', 'encipher_railfence', 'decipher_railfence', +] diff --git a/shared/package-files/8b/8bc4ba21bd4e4237082c0fa5e2093392d3197b5f1369e50d238f4f2d1a7a3815/blob b/shared/package-files/8b/8bc4ba21bd4e4237082c0fa5e2093392d3197b5f1369e50d238f4f2d1a7a3815/blob new file mode 100644 index 0000000000000000000000000000000000000000..e33cba5e44d4f7c62a479db7ea215f7a06ad6efa --- /dev/null +++ b/shared/package-files/8b/8bc4ba21bd4e4237082c0fa5e2093392d3197b5f1369e50d238f4f2d1a7a3815/blob @@ -0,0 +1,90 @@ +import functools +import warnings +import re +import textwrap +import email.message + +from ._text import FoldedCase +from ._compat import pypy_partial + + +# Do not remove prior to 2024-01-01 or Python 3.14 +_warn = functools.partial( + warnings.warn, + "Implicit None on return values is deprecated and will raise KeyErrors.", + DeprecationWarning, + stacklevel=pypy_partial(2), +) + + +class Message(email.message.Message): + multiple_use_keys = set( + map( + FoldedCase, + [ + 'Classifier', + 'Obsoletes-Dist', + 'Platform', + 'Project-URL', + 'Provides-Dist', + 'Provides-Extra', + 'Requires-Dist', + 'Requires-External', + 'Supported-Platform', + 'Dynamic', + ], + ) + ) + """ + Keys that may be indicated multiple times per PEP 566. + """ + + def __new__(cls, orig: email.message.Message): + res = super().__new__(cls) + vars(res).update(vars(orig)) + return res + + def __init__(self, *args, **kwargs): + self._headers = self._repair_headers() + + # suppress spurious error from mypy + def __iter__(self): + return super().__iter__() + + def __getitem__(self, item): + """ + Warn users that a ``KeyError`` can be expected when a + mising key is supplied. Ref python/importlib_metadata#371. + """ + res = super().__getitem__(item) + if res is None: + _warn() + return res + + def _repair_headers(self): + def redent(value): + "Correct for RFC822 indentation" + if not value or '\n' not in value: + return value + return textwrap.dedent(' ' * 8 + value) + + headers = [(key, redent(value)) for key, value in vars(self)['_headers']] + if self._payload: + headers.append(('Description', self.get_payload())) + return headers + + @property + def json(self): + """ + Convert PackageMetadata to a JSON-compatible format + per PEP 0566. + """ + + def transform(key): + value = self.get_all(key) if key in self.multiple_use_keys else self[key] + if key == 'Keywords': + value = re.split(r'\s+', value) + tk = key.lower().replace('-', '_') + return tk, value + + return dict(map(transform, map(FoldedCase, self))) diff --git a/shared/package-files/8b/8bc5c04347850a8836e85c3dc95d186f5ca002a298075c3d0b3f67d1f8fc8195/blob b/shared/package-files/8b/8bc5c04347850a8836e85c3dc95d186f5ca002a298075c3d0b3f67d1f8fc8195/blob new file mode 100644 index 0000000000000000000000000000000000000000..5948570178f3e6e79d1ff574241d09d4d8ed78de --- /dev/null +++ b/shared/package-files/8b/8bc5c04347850a8836e85c3dc95d186f5ca002a298075c3d0b3f67d1f8fc8195/blob @@ -0,0 +1,27 @@ +"""Filetype information. +""" + +from typing import Tuple + +from pip._internal.utils.misc import splitext + +WHEEL_EXTENSION = ".whl" +BZ2_EXTENSIONS: Tuple[str, ...] = (".tar.bz2", ".tbz") +XZ_EXTENSIONS: Tuple[str, ...] = ( + ".tar.xz", + ".txz", + ".tlz", + ".tar.lz", + ".tar.lzma", +) +ZIP_EXTENSIONS: Tuple[str, ...] = (".zip", WHEEL_EXTENSION) +TAR_EXTENSIONS: Tuple[str, ...] = (".tar.gz", ".tgz", ".tar") +ARCHIVE_EXTENSIONS = ZIP_EXTENSIONS + BZ2_EXTENSIONS + TAR_EXTENSIONS + XZ_EXTENSIONS + + +def is_archive_file(name: str) -> bool: + """Return True if `name` is a considered as an archive file.""" + ext = splitext(name)[1].lower() + if ext in ARCHIVE_EXTENSIONS: + return True + return False diff --git a/shared/package-files/8b/8bc63bfa0e1f38f74bbeea6027c8bfcded69034846a5f0e00283c21af08f1712/blob b/shared/package-files/8b/8bc63bfa0e1f38f74bbeea6027c8bfcded69034846a5f0e00283c21af08f1712/blob new file mode 100644 index 0000000000000000000000000000000000000000..060c866213e8ebed53f9fc3653c99ab8641e28bd --- /dev/null +++ b/shared/package-files/8b/8bc63bfa0e1f38f74bbeea6027c8bfcded69034846a5f0e00283c21af08f1712/blob @@ -0,0 +1,44 @@ +.. _olivetti_faces_dataset: + +The Olivetti faces dataset +-------------------------- + +`This dataset contains a set of face images`_ taken between April 1992 and +April 1994 at AT&T Laboratories Cambridge. The +:func:`sklearn.datasets.fetch_olivetti_faces` function is the data +fetching / caching function that downloads the data +archive from AT&T. + +.. _This dataset contains a set of face images: https://cam-orl.co.uk/facedatabase.html + +As described on the original website: + + There are ten different images of each of 40 distinct subjects. For some + subjects, the images were taken at different times, varying the lighting, + facial expressions (open / closed eyes, smiling / not smiling) and facial + details (glasses / no glasses). All the images were taken against a dark + homogeneous background with the subjects in an upright, frontal position + (with tolerance for some side movement). + +**Data Set Characteristics:** + +================= ===================== +Classes 40 +Samples total 400 +Dimensionality 4096 +Features real, between 0 and 1 +================= ===================== + +The image is quantized to 256 grey levels and stored as unsigned 8-bit +integers; the loader will convert these to floating point values on the +interval [0, 1], which are easier to work with for many algorithms. + +The "target" for this database is an integer from 0 to 39 indicating the +identity of the person pictured; however, with only 10 examples per class, this +relatively small dataset is more interesting from an unsupervised or +semi-supervised perspective. + +The original dataset consisted of 92 x 112, while the version available here +consists of 64x64 images. + +When using these images, please give credit to AT&T Laboratories Cambridge. diff --git a/shared/package-files/8b/8bcfcf75c446f4ec48946aef9d10228ff485895d9bfbd5c10a3d064179a0b915/blob b/shared/package-files/8b/8bcfcf75c446f4ec48946aef9d10228ff485895d9bfbd5c10a3d064179a0b915/blob new file mode 100644 index 0000000000000000000000000000000000000000..444a0e0858a8436c875d1da34dcc2139104a22a6 Binary files /dev/null and b/shared/package-files/8b/8bcfcf75c446f4ec48946aef9d10228ff485895d9bfbd5c10a3d064179a0b915/blob differ diff --git a/shared/package-files/8b/8bd06325add2a90be7a8329564557715bb6a80a6600fb773a86b5df7d7ed4ed9/blob b/shared/package-files/8b/8bd06325add2a90be7a8329564557715bb6a80a6600fb773a86b5df7d7ed4ed9/blob new file mode 100644 index 0000000000000000000000000000000000000000..5100a9527dd43f1d7d9728118fb035b37efe28a2 --- /dev/null +++ b/shared/package-files/8b/8bd06325add2a90be7a8329564557715bb6a80a6600fb773a86b5df7d7ed4ed9/blob @@ -0,0 +1,26 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + + +} diff --git a/shared/package-files/8b/8bd0b0a40ea8b37d6a2012602ed2934613477d1fc4ad0fbd656d6ab91755f137/blob b/shared/package-files/8b/8bd0b0a40ea8b37d6a2012602ed2934613477d1fc4ad0fbd656d6ab91755f137/blob new file mode 100644 index 0000000000000000000000000000000000000000..f675519ee18294bca908f29ccd553f05f4cd0971 --- /dev/null +++ b/shared/package-files/8b/8bd0b0a40ea8b37d6a2012602ed2934613477d1fc4ad0fbd656d6ab91755f137/blob @@ -0,0 +1,170 @@ + +import torch +import torch.utils._pytree as pytree +from torch.fx.experimental.proxy_tensor import ( + disable_proxy_modes_tracing, + ProxyTorchDispatchMode, + track_tensor_tree, + maybe_handle_decomp, +) +from torch._C import DispatchKey +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_tensor import FakeTensorMode +from torch._prims_common import elementwise_dtypes, ELEMENTWISE_TYPE_PROMOTION_KIND +from torch._higher_order_ops.utils import autograd_not_implemented + +# TODO to figure out a more generic approach +ALLOWABLE_OPS = [ + torch.ops.aten.linear.default, + torch.ops.aten.mm.default, + torch.ops.aten.conv2d.default, + torch.ops.aten.convolution.default, + torch.ops.aten.mul.Tensor, + torch.ops.aten.mul.Scalar, + torch.ops.aten.div.Tensor, + torch.ops.aten.div.Scalar, +] + + +class OutDtypeOperator(HigherOrderOperator): + """ + The out_dtype operator takes an existing ATen functional operator, an + `out_dtype` argument, and arguments to the original operator, and executes + the original operator and returns a Tensor with the `out_dtype` precision. + This operator does not mandate a compute precision so it allows the + representation to not be opinionated about the exact implementation. + + The general implementation for all operators will be the following: + 1. Promote inputs dtypes based on default PyTorch dtype promotion rules, + using the dtypes of all input Tensors/Scalars and the `out_dtype` + arugument. + 2. Execute the operator + 3. Cast the output to `out_dtype` + """ + + + def __init__(self): + super().__init__("out_dtype") + # TODO(ydwu4): Subclassing HigherOrderOperator causes __module__ to + # become different (torch._higher_order_ops.out_dtype) which will result + # in torch.fx to record the op incorrectly in the graph. + self.__module__ = "torch.ops.higher_order" + + def __call__(self, op, output_dtype, *args): + if not isinstance(op, torch._ops.OpOverload): + raise ValueError("out_dtype's first argument must be an OpOverload") + if op._schema.is_mutable: + raise ValueError("out_dtype's first argument needs to be a functional operator") + if not ( + len(op._schema.returns) == 1 and + isinstance(op._schema.returns[0].type, torch.TensorType) + ): + raise ValueError( + "out_dtype's can only apply to ops that return a single tensor" + f"Instead got {[r.type for r in op._schema.returns]}" + ) + + if op not in ALLOWABLE_OPS: + raise ValueError( + f"out_dtype only allows the following operators: {ALLOWABLE_OPS}." + ) + + res = super().__call__(op, output_dtype, *args) + + return res + + +out_dtype = OutDtypeOperator() + +def trace_out_dtype(proxy_mode, func_overload, op, output_dtype, *args): + # NB: Long-term we should put the decomposition logic into + # ProxyTorchDispatchMode so that people do not need to call maybe_handle_decomp + # in all HigherOrderOp proxy implementations. + r = maybe_handle_decomp(proxy_mode, func_overload, (op, output_dtype, *args), {}) + if r is not NotImplemented: + return r + + with disable_proxy_modes_tracing(): + # This is a simplified implementation of this operator just for tracing. + # Actual implementation may also first promote the arguments + out = op(*args).to(dtype=output_dtype) + + node_args = (op, output_dtype, *args) + proxy_args = pytree.tree_map(proxy_mode.tracer.unwrap_proxy, node_args) + out_proxy = proxy_mode.tracer.create_proxy( + "call_function", func_overload, proxy_args, {}, name="out_dtype" + ) + return track_tensor_tree(out, out_proxy, constant=None, tracer=proxy_mode.tracer) + + +@out_dtype.py_impl(DispatchKey.CompositeExplicitAutograd) +def out_dtype_dense( + op: torch._ops.OpOverload, + output_dtype: torch.dtype, + *args +): + if is_int_mm(op, output_dtype, args): + return torch._int_mm(*args) + return out_dtype_fallback(op, output_dtype, *args) + + +def is_int_mm(op, output_dtype, args): + return ( + op == torch.ops.aten.mm.default and + output_dtype == torch.int32 and + len(args) == 2 and + args[0].dtype == torch.int8 and + args[1].dtype == torch.int8 and + args[0].is_cuda and + args[1].is_cuda + ) + + +def out_dtype_fallback(op, output_dtype, *args): + flat_inputs = pytree.arg_tree_leaves(*args) + [torch.ones(1, dtype=output_dtype)] + promote_dtype: torch.dtype = elementwise_dtypes( + *flat_inputs, + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, + )[0] + + casted_args = pytree.tree_map_only( + torch.Tensor, lambda arg: arg.to(dtype=promote_dtype), args + ) + res = op(*casted_args).to(dtype=output_dtype) + return res + + +out_dtype.py_impl(DispatchKey.Autograd)(autograd_not_implemented(out_dtype, deferred_error=True)) + + +@out_dtype.py_impl(ProxyTorchDispatchMode) +def out_dtype_proxy( + mode: ProxyTorchDispatchMode, + op: torch._ops.OpOverload, + output_dtype: torch.dtype, + *args +): + if mode.enable_tracing: + return trace_out_dtype(mode, out_dtype, op, output_dtype, *args) + else: + return out_dtype(op, output_dtype, *args) + + +@out_dtype.py_impl(FakeTensorMode) +def out_dtype_fake_tensor_mode( + mode: FakeTensorMode, + op: torch._ops.OpOverload, + output_dtype: torch.dtype, + *args +): + with mode: + return out_dtype_dense(op, output_dtype, *args) + + +@out_dtype.py_functionalize_impl +def out_dtype_func(ctx, op, output_dtype, *args): + unwrapped_args = tuple(ctx.unwrap_tensors(arg) for arg in args) + + with ctx.redispatch_to_next(): + res = out_dtype(op, output_dtype, *unwrapped_args) + return ctx.wrap_tensors(res) diff --git a/shared/package-files/8b/8bd71b4b5c6aa3167696724e605b42b40fcdbcb4c18e04487cad25365f12db0f/blob b/shared/package-files/8b/8bd71b4b5c6aa3167696724e605b42b40fcdbcb4c18e04487cad25365f12db0f/blob new file mode 100644 index 0000000000000000000000000000000000000000..d5543f9c6fd05675e5c904f4c99faad9f8880154 --- /dev/null +++ b/shared/package-files/8b/8bd71b4b5c6aa3167696724e605b42b40fcdbcb4c18e04487cad25365f12db0f/blob @@ -0,0 +1,26 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + + +} diff --git a/shared/package-files/8b/8bd789832cf147fbc01f710ffa206f5f58f88260868492be9f5031eeb5002942/blob b/shared/package-files/8b/8bd789832cf147fbc01f710ffa206f5f58f88260868492be9f5031eeb5002942/blob new file mode 100644 index 0000000000000000000000000000000000000000..3eb4b0f86cef4b693e7921662172e3dde314a55d --- /dev/null +++ b/shared/package-files/8b/8bd789832cf147fbc01f710ffa206f5f58f88260868492be9f5031eeb5002942/blob @@ -0,0 +1,30 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::nested_to_padded_tensor(Tensor self, float padding, int[]? output_size=None) -> Tensor +inline at::Tensor nested_to_padded_tensor(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=c10::nullopt) { + return at::_ops::nested_to_padded_tensor::call(self, padding, output_size); +} + +} diff --git a/shared/package-files/8b/8bd7a4e43e958028f30bf7e3648d81ecc3e437966ed0e290fcfa578d571a0fd2/blob b/shared/package-files/8b/8bd7a4e43e958028f30bf7e3648d81ecc3e437966ed0e290fcfa578d571a0fd2/blob new file mode 100644 index 0000000000000000000000000000000000000000..30be9e118c7b3743ceea014271325ca7ceb83743 --- /dev/null +++ b/shared/package-files/8b/8bd7a4e43e958028f30bf7e3648d81ecc3e437966ed0e290fcfa578d571a0fd2/blob @@ -0,0 +1,143711 @@ +{ + "initialization": { + "distribution": "Normal(0,0.01)", + "seed": 20261021, + "pretrained_weights": false + }, + "training_data": "all 19880 supplied training shots per condition; all 130 conditions", + "train_manifest_sha256": "ce46c21e3595cfc5885bbe2b6481105317d2e6267498ec4572601469825914b8", + "validation_manifest_sha256": "482bb7268c6c827ebbf1f756ee6994750acfcd011adab08eaa2ada703e5faea1", + "calibration_exception": "Supplied even-fitted DEMs include validation syndromes but no logical labels or test data.", + "optimizer": "L-BFGS-B binary cross entropy plus 1e-5 L2, maximum 300 iterations", + "selection": "First trained candidate; validation reported without parameter fitting or hyperparameter selection", + "test_data_used": false, + "released_per_shot_predictions_used": false, + "history": [ + { + "group": "d3_bX_center_3_5", + "train_loss": 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All13 durations perbasis are included. Transductive condition-mean gates are excluded. This is not an independent confirmation dataset." + }, + "confidence_joint1": { + "declared_plan": { + "hypothesis": "Adding fully differentiated expert confidence may let the jointly trained gate use complementary expert certainty beyond syndrome density and history alone", + "parents": [ + "fresh2_full/weights-1536.npz", + "reserved1_full/weights-1536.npz" + ], + "initialization": "Same session-trained parents and exact Adam states as joint_mixture1; same gate seed20261310 preserves its41input hidden weights; append4zero input rows; zero output gives same initial equal mixture", + "features": "Existing41syndrome/basis/duration features plus clipped logits/8 for each expert and sqrt(z^2+1e-4) for each;clip probabilities to[1e-6,1-1e-6] and logit to[-8,8];45inputs,32SiLU hidden;shot-local,label-free,ID-free,fixedscales;full derivative through predictions", + "loss": "Stable mixture BCE; total multiplier on expert BCE gradient is responsibility+(1-2*y)*exp(-expert_BCE)*(gate-only derivative dL/dq); all prediction-to-gate paths differentiated; labels used only in loss", + "training": "Matched joint_mixture1:2048updates B1024;seed20261311 yields same uniform draws over516880rows/26conditions;expertLR.0001,gateLR.001;separate restored expert Adam clocks,fresh gate Adam;globalnorm1,decay1e-4", + "selection_steps": [ + 0, + 512, + 1024, + 2048 + ], + "validation": "Full133120 supplied validation shots per listed checkpoint; perbasis choose incumbent or rawjoint/equalphysical,all13durations; tiesretainincumbent. Step0 is fixedmean of trainedparents.1536has screening only and is not a full-selection checkpoint.", + "audit_scope": "All training labels are eligible; no independent reserved-training-label audit claim. Parent initialization provenance retained.", + "controls": "CPU scalar finite differences/exact autodiff with nonzero confidence couplings passed;two1update CPU restoration/sampling audit passed;four-case portable CPU runtime passed (order/subsets/delegation exact,independent mixture max5.96e-8,CPU priors vs GPUcache max3.58e-7);actual GPUcontrol still required before training; composed joint->robust->confidence CPU runtime passed after using distinct confidence cache attributes; actual joint/robust sources unchanged; constructed joint->logicalresidual->confidence CPU runtime also passed retained paths/order/distinct caches", + "runtime": "Two exact neural passes share one physical-prior computation; preserve all retained incumbent dispatches;fullreplay/scoring/readonlyrelocation/projection before any publication", + "interpretation": "Matched recipe extension; no physical-regime identification or independent reserved-label audit claim", + "gpu": "One H100; serialized after completed branch verification", + "test_data_used": false, + "parameter_counts": { + "expert0": 29505, + "expert1": 29505, + "gate": 1505, + "total": 60515 + }, + "physical_blend_clarification_before_training": "Equal physical blends use the existing calibrated/ensemble physical pass3 decoder output from turbo1/predictions, checked against runtime config and DEM/plans/weights. Raw feedback5_cache pass3 is only the neural conditioning prior/diagnostic. Corrected before any GPU training or selection in this study.", + "status": "Launched after fixed branch extraction verification", + "queued_utc": "2026-10-21T16:04:11.785757+00:00", + "launch_rationale": "Fixed branch diagnostics show modest, basis-dependent gate gains and a stronger individual Zexpert. The previously prepared matched confidence-input extension tests whether the gate can better use expert certainty. Recipe and selection checkpoints remain unchanged.", + "launch_utc": "2026-10-21T16:11:35.399436+00:00" + }, + "physical_blend_reference": { + "package": "/run-state/workspace/research/context_upgrades/turbo1/package", + "predictions": "/run-state/workspace/research/context_upgrades/turbo1/predictions", + "component": "Existing physical pass3 decoder output, including its training-fitted calibration/ensemble; exactly the hybrid3.py blend_turbo path", + "verified_config": "turbo settings equal; physical DEM/plans and calibration/ensemble weights have identical SHA256", + "verified_files": [ + "dems/surface_code_bX_d5_r01_center_5_5.dem", + "dems/surface_code_bX_d5_r03_center_5_5.dem", + "dems/surface_code_bX_d5_r05_center_5_5.dem", + "dems/surface_code_bX_d5_r07_center_5_5.dem", + "dems/surface_code_bX_d5_r09_center_5_5.dem", + "dems/surface_code_bX_d5_r11_center_5_5.dem", + "dems/surface_code_bX_d5_r13_center_5_5.dem", + "dems/surface_code_bX_d5_r15_center_5_5.dem", + "dems/surface_code_bX_d5_r17_center_5_5.dem", + "dems/surface_code_bX_d5_r19_center_5_5.dem", + "dems/surface_code_bX_d5_r21_center_5_5.dem", + "dems/surface_code_bX_d5_r23_center_5_5.dem", + "dems/surface_code_bX_d5_r25_center_5_5.dem", + "dems/surface_code_bZ_d5_r01_center_5_5.dem", + "dems/surface_code_bZ_d5_r03_center_5_5.dem", + "dems/surface_code_bZ_d5_r05_center_5_5.dem", + "dems/surface_code_bZ_d5_r07_center_5_5.dem", + "dems/surface_code_bZ_d5_r09_center_5_5.dem", + "dems/surface_code_bZ_d5_r11_center_5_5.dem", + "dems/surface_code_bZ_d5_r13_center_5_5.dem", + "dems/surface_code_bZ_d5_r15_center_5_5.dem", + "dems/surface_code_bZ_d5_r17_center_5_5.dem", + "dems/surface_code_bZ_d5_r19_center_5_5.dem", + "dems/surface_code_bZ_d5_r21_center_5_5.dem", + "dems/surface_code_bZ_d5_r23_center_5_5.dem", + "dems/surface_code_bZ_d5_r25_center_5_5.dem", + "plans/surface_code_bX_d5_r01_center_5_5.npz", + "plans/surface_code_bZ_d5_r01_center_5_5.npz", + "turbo_plans/surface_code_bX_d5_r03_center_5_5.npz", + "turbo_plans/surface_code_bX_d5_r05_center_5_5.npz", + "turbo_plans/surface_code_bX_d5_r07_center_5_5.npz", + "turbo_plans/surface_code_bX_d5_r09_center_5_5.npz", + "turbo_plans/surface_code_bX_d5_r11_center_5_5.npz", + "turbo_plans/surface_code_bX_d5_r13_center_5_5.npz", + "turbo_plans/surface_code_bX_d5_r15_center_5_5.npz", + "turbo_plans/surface_code_bX_d5_r17_center_5_5.npz", + "turbo_plans/surface_code_bX_d5_r19_center_5_5.npz", + "turbo_plans/surface_code_bX_d5_r21_center_5_5.npz", + "turbo_plans/surface_code_bX_d5_r23_center_5_5.npz", + "turbo_plans/surface_code_bX_d5_r25_center_5_5.npz", + "turbo_plans/surface_code_bZ_d5_r03_center_5_5.npz", + "turbo_plans/surface_code_bZ_d5_r05_center_5_5.npz", + "turbo_plans/surface_code_bZ_d5_r07_center_5_5.npz", + "turbo_plans/surface_code_bZ_d5_r09_center_5_5.npz", + "turbo_plans/surface_code_bZ_d5_r11_center_5_5.npz", + "turbo_plans/surface_code_bZ_d5_r13_center_5_5.npz", + "turbo_plans/surface_code_bZ_d5_r15_center_5_5.npz", + "turbo_plans/surface_code_bZ_d5_r17_center_5_5.npz", + "turbo_plans/surface_code_bZ_d5_r19_center_5_5.npz", + "turbo_plans/surface_code_bZ_d5_r21_center_5_5.npz", + "turbo_plans/surface_code_bZ_d5_r23_center_5_5.npz", + "turbo_plans/surface_code_bZ_d5_r25_center_5_5.npz", + "weights/turbo1-d5_bX_center_5_5.npz", + "weights/turbo1-d5_bZ_center_5_5.npz" + ], + "clarification": "Use these ID-aligned probabilities, not the raw pass3 column in feedback5_cache. 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Use this complete basis route, retaining the original strongest decoder for every other condition.", + "selection_history": { + "adaptive": true, + "inspected_completed_validation_summaries": { + "d5_latent_readout": { + "source_sha256": "46170313ee66cb86bcdce4c5ae2c7cdad520b48154ebab900d938045ffd852ef", + "choices": [ + { + "step": 1024, + "net_corrected_by_group": { + "d5_bX_center_5_5": -22, + "d5_bZ_center_5_5": 13 + } + }, + { + "step": 2048, + "net_corrected_by_group": { + "d5_bX_center_5_5": -28, + "d5_bZ_center_5_5": -7 + } + }, + { + "step": 4096, + "net_corrected_by_group": { + "d5_bX_center_5_5": -17, + "d5_bZ_center_5_5": 8 + } + } + ] + }, + "d5_conditional_quality": { + "source_sha256": "41f2c7036f9fa84dab5a0f3366f2e55a0652501227c4cd454685aceae1e75ac6", + "choices": [ + { + "step": 1024, + "net_corrected_by_group": { + "d5_bX_center_5_5": -19, + "d5_bZ_center_5_5": 16 + } + }, + { + "step": 2048, + "net_corrected_by_group": { + "d5_bX_center_5_5": -23, + "d5_bZ_center_5_5": -9 + } + }, + { + "step": 4096, + "net_corrected_by_group": { + "d5_bX_center_5_5": -18, + "d5_bZ_center_5_5": 2 + } + } + ] + }, + "d5_joint_conditional_quality": { + "source_sha256": "5812de90f9bade912440a39324fb0af3f6fa08d883efb7f8ea220df2aa0293e3", + "choices": [ + { + "step": 1024, + "net_corrected_by_group": { + "d5_bX_center_5_5": -30, + "d5_bZ_center_5_5": -64 + } + }, + { + "step": 2048, + "net_corrected_by_group": { + "d5_bX_center_5_5": -89, + "d5_bZ_center_5_5": -57 + } + }, + { + "step": 4096, + "net_corrected_by_group": { + "d5_bX_center_5_5": -62, + "d5_bZ_center_5_5": -59 + } + } + ] + } + }, + "choice": "Conditional-quality1024 for all d5 Z rounds above1; original incumbent everywhere else, including both d5 r1 conditions.", + "new_candidates": 1, + "comparison": "One fixed composition against the strongest then-current full130-condition incumbent. 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New dispatch is absence/presence of the existing per-condition extension. Execute actual infer.sh on all26d5, score all130; compare with cached own-model outputs. Any improvement requires second all26 replay, read-only relocation, arbitrary IDs/order/subsets, and runtime projection below3600seconds before atomic qecbench.package publication.", + "resources": { + "GPU": 1, + "predecessor": "d5_allsite_likelihood_prior_lift terminal resource state", + "additional_optimizer_updates": 0 + }, + "test_data_used": false, + "dependencies": { + "/run-state/workspace/research/operations/prepare_z_quality_composition.py": "2adc39b2349cb552ca187ffd9f68a4114ef38e7296816b18c027b940b63d4fcb", + "/workspace/research/d5_conditional_quality/export.py": "b64f53e77393f8526f5681ea33f91bd48948b1daa2d5f7e52e6378b167a5bae1", + "/workspace/research/d5_conditional_quality/runtime.py": "e10a663879b2c21e0464554532709d5853c330966a635bb0049e00a4afd80d8e", + "/workspace/research/d5_conditional_quality/PROPOSAL.json": "0aa6abf80cce73727e2a6099c76277075f1cdf493455fddf2def54ae21cd25bc", + "/workspace/research/d5_conditional_quality/prepared_controls.json": "ffd2358dcb2f38008d1068c04288d6e91c68f8b079619aed2baa9822113ecfb3", + "/workspace/research/d5_conditional_quality/fit1/provenance.json": "6968788df0715c80f976c5aa3ba6108a82f2ff777ad24aecbf594f962785dd3d", + "/workspace/research/d5_conditional_quality/fit1/sampling_audit.json": "815e2f018ac8f808358ec6867e81cb46f852f0d8ad1f9917fea537ae0d96dba9", + "/workspace/research/d5_conditional_quality/paired_sampling_audit.json": "49d59302ee9ca48f58d96ccd0317e2aa21db28537d4f19ae29aad65575658fa5", + "/workspace/research/d5_conditional_quality/fit1/weights-0.npz": "80f988ddc72891435733d9711eae08cd9ce5ce1cd9973c9436c093adc5cb7232", + "/workspace/research/d5_conditional_quality/fit1/weights-1024.npz": "f7c28b7b5e8f77a52958b0cb798b940550490d17a0b914622d8248f8482d20ab", + "/run-state/workspace/research/context_upgrades/d3_latent_readout1/package/model_config.json": "4be528609a15d46592885fe48a89de82d20cde4cf6c4915effb0bcc27bb6d508", + "/run-state/workspace/research/context_upgrades/d3_latent_readout1/package/model_manifest.json": "89813c76eff14a4d0e209c2cfc51cb594c1f7958a15e03d7747fcc21a8a57dae", + "/run-state/workspace/research/context_upgrades/d3_latent_readout1/package/training_provenance.json": "e3af855461bae99251f8fc37cee5bc1667d77a773e2c3da29e39b4316b794d2f" + } + }, + "training": { + "status": "complete", + "run_config": { + "steps": 4096, + "batch": 256, + "control": false, + "seed_offset": 0, + "settings": { + "steps": 4096, + "batch": 256, + "sampling_seed": 20268620, + "learning_rate": 0.02, + "checkpoints": [ + 1024, + 2048, + 4096 + ], + "stream": "Absolute step s uses NumPydefault_rng(SeedSequence([seed,s])); uniform one of sorted26conditions then256independent uniform rowdraws. 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Let input complementary-check observations determine the local latent innovation probability while preserving the conditional mean physical error rate. This tests time-varying correlation strength around the same frozen physical parent.", + "reference": { + "package": "/run-state/workspace/research/context_upgrades/d3_latent_readout1/package", + "pins": { + "model_config.json": "4be528609a15d46592885fe48a89de82d20cde4cf6c4915effb0bcc27bb6d508", + "model_manifest.json": "89813c76eff14a4d0e209c2cfc51cb594c1f7958a15e03d7747fcc21a8a57dae", + "exact.py": "869dfacb295c419c156d1070fc2afdd6e20d02ac1840e45e72346b2b289f4825" + } + }, + "geometry": "Fixed central data family12 coordinate(5,5), same target-CSS interior readout signatures as completed constant-quality study. Exactly2 adjacent complementary checks supply features; boundary channels unchanged.", + "model": { + "trainable_scalars": 10, + "groups": [ + "d5_bX_center_5_5", + "d5_bZ_center_5_5" + ], + "parameters_per_group": [ + "base occupancy logit", + "persistence logit", + "amplitude logit", + "current complementary feature coefficient", + "window complementary feature coefficient" + ], + "features": "Current two-complementary-check detection mean; mean over those checks at available interior detector times max(1,t-2)..min(R-1,t+2). Fixed input-only linear pooling; padded slots zero. No logical labels, target-plane bits, population means, batch statistics or IDs.", + "innovation_pi_t": ".001+.499*sigmoid(theta0+theta3*current_t+theta4*window_t)", + "rho": ".999*sigmoid(theta1)", + "alpha": ".99*sigmoid(theta2)", + "transition": "P(Q_t=1|Q_previous)=rho*Q_previous+(1-rho)*pi_t; initialquality prior pi_first.", + "conditional_prior_mean": "m_first=pi_first; m_t=rho*m_previous+(1-rho)*pi_t, calculated before target-plane evidence.", + "rates": "pQ=p+alpha*min(p/m_t,(1-p)/(1-m_t))*(Q-m_t), retaining conditional prior averagep.", + "interpretation": "Effective conditional discriminative correlation decoder, not an identified microscopic mechanism.", + "frozen_X": "Published own-session two physical MLP experts, confidence-dependent gate and selected pretrained axial residual; derivatives include all confidence/axial input paths.", + "frozen_Z": "Published own-session masked encoder, selected endpoint projection and physical MLP.", + "precision": "HIGHEST float32 neural equations; validated fulljointwidth15forward and segmentedwidth14directional derivative engines; new engine can differ numerically from original stationaryimplementation. Neutralindependentreference andnativeincumbent differences separately recorded." + }, + "initialization": { + "seed": 20268610, + "distribution": "Regenerate original constant-quality study first3 logits pergroup using same NumPyseed and3-draw grouporder, normal(.0,.01) jitter around pi.1/rho.8/alpha.25. Append two exactzero conditional coefficients without extra RNG draws. No trainedlatentparameters inherited; ten freshparameters.", + "nonzero_amplitude": "Mean-preserving stationarycorrelation haszero amplitude derivative atzero; originalnonzeroamplitude retained.", + "inherited_weights": "Frozenphysical/encoder/gate/axialweights are own-session checkpoints, all ancestorsrandominitialization inthissession; exact originalconstant-study parent retained evenif then-currentd5hasadditionalrefinements." + }, + "implementation": { + "batch": 256, + "optimizer": "Independent activebasis Adam,gradientnormclip1,no weightdecay,dropout,EMA orcalibration.", + "learning_rate": 0.02, + "training_priors": "Use own-session saved fulltraining priors, opaqueID equalityalignment only; record actual source hashes. Validation priors from previously verified own packagedruntime cache; verify actual assembledreplay beforepromotion.", + "inference_priors": "Recompute from supplied detections with existing TurboDecoder pass3X/pass5Z and original prior-generation batch512. No precomputed per-shot predictions or training-ID mapping in inference." + }, + "training": { + "steps": 4096, + "batch": 256, + "sampling_seed": 20268620, + "learning_rate": 0.02, + "checkpoints": [ + 1024, + 2048, + 4096 + ], + "stream": "Absolute step s uses NumPydefault_rng(SeedSequence([seed,s])); uniform one of sorted26conditions then256independent uniform rowdraws. All516880d5trainingrows eligible. Recordcounts,seen,descriptorSHA,groupAdamclocks andbypasses.", + "r1": "Bothr1conditions sampled/logged but skipoptimizer because no interiorqualitychannels. Retain exactincumbentr1runtime/probabilities in every fullcandidate evaluation.", + "selection": "Exactly one GLOBAL d5 choice among1024/2048/4096 andthen-currentincumbent, equalX/Z and13durations. Retainbothr1andstrongestd3. No extra checkpoints,basis/durationchoices,blends,EMAorcalibration. 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full selected subset cases", + "scope": "Local contract check; not a Docker/network/mount-isolation test" + } + } +} diff --git a/shared/package-files/8b/8bd94705246c0e4be83426c5f5bee66740b67c70a3294e42282137d1cf82c4c7/blob b/shared/package-files/8b/8bd94705246c0e4be83426c5f5bee66740b67c70a3294e42282137d1cf82c4c7/blob new file mode 100644 index 0000000000000000000000000000000000000000..c3d34d2fd38887877980727bceaaa215129bf283 --- /dev/null +++ b/shared/package-files/8b/8bd94705246c0e4be83426c5f5bee66740b67c70a3294e42282137d1cf82c4c7/blob @@ -0,0 +1,44 @@ +# This file is not meant for public use and will be removed in SciPy v2.0.0. +# Use the `scipy.sparse` namespace for importing the functions +# included below. + +from scipy._lib.deprecation import _sub_module_deprecation + + +__all__ = [ # noqa: F822 + 'block_diag', + 'bmat', + 'bsr_matrix', + 'check_random_state', + 'coo_matrix', + 'csc_matrix', + 'csr_hstack', + 'csr_matrix', + 'dia_matrix', + 'diags', + 'eye', + 'get_index_dtype', + 'hstack', + 'identity', + 'isscalarlike', + 'issparse', + 'kron', + 'kronsum', + 'numbers', + 'rand', + 'random', + 'rng_integers', + 'spdiags', + 'upcast', + 'vstack', +] + + +def __dir__(): + return __all__ + + +def __getattr__(name): + return _sub_module_deprecation(sub_package="sparse", module="construct", + private_modules=["_construct"], all=__all__, + attribute=name) diff --git a/shared/package-files/8b/8bdd5d35070e8a340795eebaf3b3b7c491d6015cb94aafe4fa4a3bf67a25391a/blob b/shared/package-files/8b/8bdd5d35070e8a340795eebaf3b3b7c491d6015cb94aafe4fa4a3bf67a25391a/blob new file mode 100644 index 0000000000000000000000000000000000000000..18c3c7a9ab5b7cf9cc3434781de8d1ae0bbfbe26 --- /dev/null +++ b/shared/package-files/8b/8bdd5d35070e8a340795eebaf3b3b7c491d6015cb94aafe4fa4a3bf67a25391a/blob @@ -0,0 +1,35 @@ +from typing import Any + +from yaml.error import YAMLError + +class ReaderError(YAMLError): + name: Any + character: Any + position: Any + encoding: Any + reason: Any + def __init__(self, name, position, character, encoding, reason) -> None: ... + +class Reader: + name: Any + stream: Any + stream_pointer: Any + eof: Any + buffer: Any + pointer: Any + raw_buffer: Any + raw_decode: Any + encoding: Any + index: Any + line: Any + column: Any + def __init__(self, stream) -> None: ... + def peek(self, index=...): ... + def prefix(self, length=...): ... + def forward(self, length=...): ... + def get_mark(self): ... + def determine_encoding(self): ... + NON_PRINTABLE: Any + def check_printable(self, data): ... + def update(self, length): ... + def update_raw(self, size=...): ... diff --git a/shared/package-files/8b/8bdf35419504042bdfd38db6940b63678b87c057267b1b92b9b3c03117ca177a/blob b/shared/package-files/8b/8bdf35419504042bdfd38db6940b63678b87c057267b1b92b9b3c03117ca177a/blob new file mode 100644 index 0000000000000000000000000000000000000000..aff962e441c52e61545a5eb7a349a01e7e1852c1 --- /dev/null +++ b/shared/package-files/8b/8bdf35419504042bdfd38db6940b63678b87c057267b1b92b9b3c03117ca177a/blob @@ -0,0 +1,22 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor permute(const at::Tensor & self, at::IntArrayRef dims); +TORCH_API at::Tensor permute_sparse_coo(const at::Tensor & self, at::IntArrayRef dims); +} // namespace native +} // namespace at diff --git a/shared/package-files/8b/8be3a4918b600cf7fe226010c2b6e6fed73e7f182e5876f9924ecb218a0f16b0/blob b/shared/package-files/8b/8be3a4918b600cf7fe226010c2b6e6fed73e7f182e5876f9924ecb218a0f16b0/blob new file mode 100644 index 0000000000000000000000000000000000000000..8c1875691fbc710e4e7aafed20cb137afe680e5c --- /dev/null +++ b/shared/package-files/8b/8be3a4918b600cf7fe226010c2b6e6fed73e7f182e5876f9924ecb218a0f16b0/blob @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API void _foreach_asin_out(at::TensorList out, at::TensorList self); +TORCH_API void _foreach_asin_outf(at::TensorList self, at::TensorList out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/shared/package-files/8b/8be4ceed5adf99b3f6d46e479b60cb9b5d2bdcac860d4783a067e3b0e1d4ce2d/blob b/shared/package-files/8b/8be4ceed5adf99b3f6d46e479b60cb9b5d2bdcac860d4783a067e3b0e1d4ce2d/blob new file mode 100644 index 0000000000000000000000000000000000000000..666922adc709ea04f02aad43b80cefd380deef29 --- /dev/null +++ b/shared/package-files/8b/8be4ceed5adf99b3f6d46e479b60cb9b5d2bdcac860d4783a067e3b0e1d4ce2d/blob @@ -0,0 +1,75 @@ +# mypy: ignore-errors + +import inspect +import itertools + +from . import _funcs_impl, _reductions_impl +from ._normalizations import normalizer + +# _funcs_impl.py contains functions which mimic NumPy's eponymous equivalents, +# and consume/return PyTorch tensors/dtypes. +# They are also type annotated. +# Pull these functions from _funcs_impl and decorate them with @normalizer, which +# - Converts any input `np.ndarray`, `torch._numpy.ndarray`, list of lists, Python scalars, etc into a `torch.Tensor`. +# - Maps NumPy dtypes to PyTorch dtypes +# - If the input to the `axis` kwarg is an ndarray, it maps it into a tuple +# - Implements the semantics for the `out=` arg +# - Wraps back the outputs into `torch._numpy.ndarrays` + + +def _public_functions(mod): + def is_public_function(f): + return inspect.isfunction(f) and not f.__name__.startswith("_") + + return inspect.getmembers(mod, is_public_function) + + +# We fill in __all__ in the loop below +__all__ = [] + +# decorate implementer functions with argument normalizers and export to the top namespace +for name, func in itertools.chain( + _public_functions(_funcs_impl), _public_functions(_reductions_impl) +): + if name in ["percentile", "quantile", "median"]: + decorated = normalizer(func, promote_scalar_result=True) + elif name == "einsum": + # normalized manually + decorated = func + else: + decorated = normalizer(func) + + decorated.__qualname__ = name + decorated.__name__ = name + vars()[name] = decorated + __all__.append(name) + + +""" +Vendored objects from numpy.lib.index_tricks +""" + + +class IndexExpression: + """ + Written by Konrad Hinsen + last revision: 1999-7-23 + + Cosmetic changes by T. Oliphant 2001 + """ + + def __init__(self, maketuple): + self.maketuple = maketuple + + def __getitem__(self, item): + if self.maketuple and not isinstance(item, tuple): + return (item,) + else: + return item + + +index_exp = IndexExpression(maketuple=True) +s_ = IndexExpression(maketuple=False) + + +__all__ += ["index_exp", "s_"] diff --git a/shared/package-files/8b/8be562c08bb9c2bb5c7e5eb932e766ae2f58611919798b941223f8e0f1641ada/blob b/shared/package-files/8b/8be562c08bb9c2bb5c7e5eb932e766ae2f58611919798b941223f8e0f1641ada/blob new file mode 100644 index 0000000000000000000000000000000000000000..ab4978759b02d05a269e8c1feeba11aef9438e51 --- /dev/null +++ b/shared/package-files/8b/8be562c08bb9c2bb5c7e5eb932e766ae2f58611919798b941223f8e0f1641ada/blob @@ -0,0 +1,2473 @@ +error(0.006014839548147809384) D0 +error(0.009959960935856326802) D0 D1 +error(0.01264271510186945786) D0 D4 +error(0.002468534479109814848) D0 D4 ^ D5 +error(0.01130314504322646289) D0 D7 +error(0.002739388483720264084) D0 D7 ^ D5 +error(0.001305310087185718778) D0 D8 +error(0.00183853519115541081) D0 D8 ^ D5 +error(0.007666164796921177103) D1 D2 +error(0.004846254036976339885) D1 D2 ^ D10 +error(0.0300534662004016441) D1 D7 +error(0.001383850345310868495) D1 D7 ^ D9 +error(0.003467794794583309911) D1 D7 ^ D9 D10 +error(0.0001143727689921466753) D1 D7 ^ D10 +error(0.002318898184400041036) D1 D8 +error(0.0009020826978319663797) D1 D8 ^ D9 +error(0.0001375227917901545758) D1 D8 ^ D10 +error(0.0001826446343252678575) D1 D8 ^ D10 ^ D9 +error(0.001370975498864088248) D1 D11 +error(0.0005071586951654475164) D1 D11 ^ D10 +error(0.01215808074440048389) D1 L0 +error(0.005214060029468524371) D1 L0 ^ D9 +error(0.01166383778173952016) D2 +error(0.005000000000000000104) D2 D3 +error(0.01734289301939373126) D2 D8 +error(0.003118819916410177015) D2 D8 ^ D10 +error(0.004379037648481394138) D2 D11 +error(0.004058094302248410265) D2 D11 ^ D10 +error(0.01899895244295335406) D3 D11 +error(0.007969546877955773281) D3 L0 +error(0.009957291433651803761) D4 +error(0.01632932662208725141) D4 D7 +error(0.02048841538829885775) D4 D7 ^ D5 +error(0.01432068897443614271) D4 D12 +error(0.003724222745789971568) D4 D12 ^ D13 +error(0.008010401095168906069) D4 D15 +error(0.007940226548819344018) D4 D15 ^ D5 +error(0.003047688540304718287) D4 D15 ^ D13 +error(0.001113551153159442111) D4 D15 ^ D13 ^ D5 +error(0.0012261922121419445) D4 D16 +error(0.001293311027307580789) D4 D16 ^ D5 D13 +error(0.0002766506363411279324) D4 D16 ^ D13 +error(0.01220941266226805225) D4 ^ D5 +error(0.001340836656243296664) D4 ^ D13 +error(0.002787819927005772032) D5 D13 ^ D4 +error(0.0357382989899303824) D5 +error(0.007869341179138700584) D5 D6 +error(0.0004992884905115992956) D5 D6 ^ D4 +error(0.0002964391581778778721) D5 D6 ^ D7 D8 +error(0.0001957883314196005203) D5 D6 ^ D7 D16 +error(0.0127468593986014532) D5 D6 ^ D8 +error(0.002027113369458129113) D5 D6 ^ D8 D16 +error(0.001680777882927654652) D5 D6 ^ D16 +error(0.000510964933352025261) D5 D6 ^ D16 ^ D4 +error(0.01605069201953910479) D5 D10 +error(0.009985933114777419128) D5 D10 ^ D7 D8 +error(0.02565843058292602286) D5 D13 +error(0.001864362289368129985) D5 D13 ^ D15 D16 +error(0.0009030108125920386763) D5 D13 ^ D16 +error(0.002168943494147704959) D5 D17 +error(0.001623587268677913588) D5 D17 ^ D7 D15 +error(0.0002161019940614549329) D5 D17 ^ D7 D16 +error(0.000388632672172653233) D5 D17 ^ D15 D16 +error(0.001498498304452828417) D5 D18 +error(0.001856993221836747681) D5 D18 ^ D7 D8 +error(0.0009804196430601077195) D5 D18 ^ D7 D15 +error(0.001569512860033841139) D5 D18 ^ D7 D16 +error(0.0004097649005875169032) D5 D18 ^ D8 D16 +error(0.001862686936830267124) D5 D18 ^ D15 D16 +error(0.003326821728693895794) D5 ^ D0 +error(0.002976987691969855063) D5 ^ D8 +error(0.004219216288335304009) D7 D15 ^ D5 +error(0.04521988472312658824) D6 +error(0.003596024184132560076) D6 D13 +error(0.000353809852654799644) D6 D13 ^ D4 +error(0.003114589733093941094) D6 D13 ^ D16 +error(0.0003537892199881977127) D6 D13 ^ D16 ^ D4 +error(0.0268149874189349581) D6 D14 +error(0.002693575443238237482) D6 D14 ^ D16 +error(0.0009947049708068399828) D6 D18 +error(0.0004489571233011255941) D6 D18 ^ D7 D8 +error(0.0001023647480353978632) D6 D18 ^ D7 D16 +error(0.001017738624743863216) D6 D18 ^ D8 D16 +error(0.01063065913824745752) D6 ^ D8 +error(0.001485361101119797361) D6 ^ D16 ^ D8 +error(0.006921833905805777239) D7 D8 +error(0.001270997322123811838) D7 D8 ^ D5 +error(0.0001580133974676078783) D7 D8 ^ D9 +error(0.000568343241847281801) D7 D8 ^ D10 +error(0.002537074408032832188) D7 D8 ^ D10 D18 +error(0.0009005770998163306097) D7 D8 ^ D10 ^ D9 +error(0.0003403736328788634535) D7 D8 ^ D18 +error(0.03158800500128303157) D7 D15 +error(0.004053201805270884896) D7 D15 ^ D17 D18 +error(0.003287314687101230461) D7 D16 +error(0.000362912345862538815) D7 D16 ^ D17 D18 +error(0.001818820937996893771) D7 D19 +error(0.0001853416030457494853) D7 D19 ^ D10 +error(0.001711555130697444208) D7 D19 ^ D10 D18 +error(0.000272762609427849021) D7 D19 ^ D18 +error(0.01000000000000000021) D7 L0 +error(0.0145008332864979498) D7 L0 ^ D9 +error(0.004207393336629383467) D9 D17 ^ D7 L0 +error(0.01000000000000000021) D8 +error(0.01606367370460309169) D8 D11 +error(0.01490581269190633697) D8 D11 ^ D10 +error(0.02339721966187317467) D8 D16 +error(0.004593006776778153256) D8 D16 ^ D18 +error(0.004633022736341869084) D8 D19 +error(0.00323703015503416314) D8 D19 ^ D10 +error(0.004090640469596825389) D8 D19 ^ D18 +error(0.002101065562810008717) D8 D19 ^ D18 ^ D10 +error(0.03403075680915557816) D9 +error(0.01711079948107072785) D9 D10 +error(0.0008296038195846473741) D9 D10 ^ D1 L0 +error(0.00992947227087518855) D9 D10 ^ D7 L0 +error(0.0290017331886132379) D9 D17 +error(0.02760105671953077058) D10 +error(0.002819593978225994144) D10 D17 +error(0.003137767389894948866) D10 D17 ^ D7 L0 +error(0.0006968140446665368809) D10 D17 ^ D19 L0 +error(0.000505172529929820641) D10 D17 ^ D19 L0 ^ D7 L0 +error(0.02081610292411238683) D10 D18 +error(0.001206124809337631677) D10 D18 ^ D7 L0 +error(0.002463255996633777374) D10 D18 ^ D19 L0 +error(0.0009926967000886714396) D10 ^ D1 L0 +error(0.0151721271562197408) D10 ^ D11 L0 +error(0.0004219488338511960513) D10 ^ D11 L0 ^ D1 L0 ^ D9 +error(0.0004047947112935211403) D10 ^ D11 L0 ^ D9 +error(0.001663150487453687832) D10 ^ D19 L0 +error(0.00455585972925812762) D11 D19 ^ D10 +error(0.01883905370747077437) D11 D19 +error(0.007658596738439791239) D11 L0 +error(4.761136287833082036e-05) D11 L0 ^ D1 L0 ^ D9 +error(0.001906628054554462348) D11 L0 ^ D9 +error(0.01751672136136445471) D12 +error(0.01785065649831395901) D12 D15 +error(0.02043771586639645671) D12 D15 ^ D13 +error(0.01432068897443614271) D12 D20 +error(0.003724222745789971568) D12 D20 ^ D21 +error(0.008010401095168906069) D12 D23 +error(0.007940226548819344018) D12 D23 ^ D13 +error(0.003047688540304718287) D12 D23 ^ D21 +error(0.001113551153159442111) D12 D23 ^ D21 ^ D13 +error(0.0012261922121419445) D12 D24 +error(0.001293311027307580789) D12 D24 ^ D13 D21 +error(0.0002766506363411279324) D12 D24 ^ D21 +error(0.0127754705552621823) D12 ^ D13 +error(0.001340836656243296664) D12 ^ D21 +error(0.002787819927005772032) D13 D21 ^ D12 +error(0.01563106638893721131) D13 +error(0.01056384291876393856) D13 D14 +error(0.0002904294784450725487) D13 D14 ^ D12 +error(0.0002058388941412996195) D13 D14 ^ D15 D16 +error(0.0001957883314196005203) D13 D14 ^ D15 D24 +error(0.01111726604515600632) D13 D14 ^ D16 +error(0.002027113369458129113) D13 D14 ^ D16 D24 +error(0.001680777882927654652) D13 D14 ^ D24 +error(0.000510964933352025261) D13 D14 ^ D24 ^ D12 +error(0.01295778001257064625) D13 D18 +error(0.01037953181937149687) D13 D18 ^ D15 D16 +error(0.02565843058292602286) D13 D21 +error(0.001864362289368129985) D13 D21 ^ D23 D24 +error(0.0009030108125920386763) D13 D21 ^ D24 +error(0.002168943494147704959) D13 D25 +error(0.001623587268677913588) D13 D25 ^ D15 D23 +error(0.0002161019940614549329) D13 D25 ^ D15 D24 +error(0.000388632672172653233) D13 D25 ^ D23 D24 +error(0.001498498304452828417) D13 D26 +error(0.001856993221836747681) D13 D26 ^ D15 D16 +error(0.0009804196430601077195) D13 D26 ^ D15 D23 +error(0.001569512860033841139) D13 D26 ^ D15 D24 +error(0.0004097649005875169032) D13 D26 ^ D16 D24 +error(0.001862686936830267124) D13 D26 ^ D23 D24 +error(0.004219216288335304009) D15 D23 ^ D13 +error(0.01703946026454849702) D14 +error(0.003596024184132560076) D14 D21 +error(0.000353809852654799644) D14 D21 ^ D12 +error(0.003114589733093941094) D14 D21 ^ D24 +error(0.0003537892199881977127) D14 D21 ^ D24 ^ D12 +error(0.0268149874189349581) D14 D22 +error(0.002693575443238237482) D14 D22 ^ D24 +error(0.0009947049708068399828) D14 D26 +error(0.0004489571233011255941) D14 D26 ^ D15 D16 +error(0.0001023647480353978632) D14 D26 ^ D15 D24 +error(0.001017738624743863216) D14 D26 ^ D16 D24 +error(0.01180107044904601943) D14 ^ D16 +error(0.001485361101119797361) D14 ^ D24 ^ D16 +error(0.008909961818705179268) D15 D16 +error(0.000172764079590881711) D15 D16 ^ D5 +error(0.0001034000690297430145) D15 D16 ^ D13 +error(0.0004460207984751748565) D15 D16 ^ D17 D18 +error(0.002537074408032832188) D15 D16 ^ D18 D26 +error(0.0003403736328788634535) D15 D16 ^ D26 +error(0.03158800500128303157) D15 D23 +error(0.004053201805270884896) D15 D23 ^ D25 D26 +error(0.003287314687101230461) D15 D24 +error(0.000362912345862538815) D15 D24 ^ D25 D26 +error(0.001818820937996893771) D15 D27 +error(0.0001853416030457494853) D15 D27 ^ D18 +error(0.001711555130697444208) D15 D27 ^ D18 D26 +error(0.000272762609427849021) D15 D27 ^ D26 +error(0.01000000000000000021) D15 L0 +error(0.01235521784681438417) D15 L0 ^ D17 +error(0.004207393336629383467) D17 D25 ^ D15 L0 +error(0.01000000000000000021) D16 +error(0.01785301659321773815) D16 D19 +error(0.01637401583216853418) D16 D19 ^ D18 +error(0.02339721966187317467) D16 D24 +error(0.004593006776778153256) D16 D24 ^ D26 +error(0.004633022736341869084) D16 D27 +error(0.00323703015503416314) D16 D27 ^ D18 +error(0.004090640469596825389) D16 D27 ^ D26 +error(0.002101065562810008717) D16 D27 ^ D26 ^ D18 +error(0.007349448775310450134) D17 +error(0.009492030802785636837) D17 D18 +error(0.001887289238295643528) D17 D18 ^ D7 L0 +error(0.01247787028000646274) D17 D18 ^ D15 L0 +error(0.0007927547980242701528) D17 D18 ^ D19 L0 +error(0.0004780626237276926682) D17 D18 ^ D19 L0 ^ D7 L0 +error(0.0290017331886132379) D17 D25 +error(0.0002014583196037038619) D17 ^ D5 ^ D7 L0 +error(0.0004166580577437174488) D17 ^ D5 ^ D15 L0 +error(0.001290031617734563882) D17 ^ D15 L0 ^ D7 L0 +error(0.0107231597008022872) D18 +error(0.002819593978225994144) D18 D25 +error(0.003137767389894948866) D18 D25 ^ D15 L0 +error(0.0006968140446665368809) D18 D25 ^ D27 L0 +error(0.000505172529929820641) D18 D25 ^ D27 L0 ^ D15 L0 +error(0.02081610292411238683) D18 D26 +error(0.001206124809337631677) D18 D26 ^ D15 L0 +error(0.002463255996633777374) D18 D26 ^ D27 L0 +error(0.0002751468606974653234) D18 ^ D6 ^ D8 +error(0.0004594173010041198999) D18 ^ D6 ^ D16 +error(0.009220825108482943316) D18 ^ D19 L0 +error(0.001663150487453687832) D18 ^ D27 L0 +error(0.00455585972925812762) D19 D27 ^ D18 +error(0.01883905370747077437) D19 D27 +error(0.005000000000000000104) D19 L0 +error(0.01751672136136445471) D20 +error(0.01785065649831395901) D20 D23 +error(0.02043771586639645671) D20 D23 ^ D21 +error(0.01432068897443614271) D20 D28 +error(0.003724222745789971568) D20 D28 ^ D29 +error(0.008010401095168906069) D20 D31 +error(0.007940226548819344018) D20 D31 ^ D21 +error(0.003047688540304718287) D20 D31 ^ D29 +error(0.001113551153159442111) D20 D31 ^ D29 ^ D21 +error(0.0012261922121419445) D20 D32 +error(0.001293311027307580789) D20 D32 ^ D21 D29 +error(0.0002766506363411279324) D20 D32 ^ D29 +error(0.0127754705552621823) D20 ^ D21 +error(0.001340836656243296664) D20 ^ D29 +error(0.002787819927005772032) D21 D29 ^ D20 +error(0.01563106638893721131) D21 +error(0.01056384291876393856) D21 D22 +error(0.0002904294784450725487) D21 D22 ^ D20 +error(0.0002058388941412996195) D21 D22 ^ D23 D24 +error(0.0001957883314196005203) D21 D22 ^ D23 D32 +error(0.01111726604515600632) D21 D22 ^ D24 +error(0.002027113369458129113) D21 D22 ^ D24 D32 +error(0.001680777882927654652) D21 D22 ^ D32 +error(0.000510964933352025261) D21 D22 ^ D32 ^ D20 +error(0.01295778001257064625) D21 D26 +error(0.01037953181937149687) D21 D26 ^ D23 D24 +error(0.02565843058292602286) D21 D29 +error(0.001864362289368129985) D21 D29 ^ D31 D32 +error(0.0009030108125920386763) D21 D29 ^ D32 +error(0.002168943494147704959) D21 D33 +error(0.001623587268677913588) D21 D33 ^ D23 D31 +error(0.0002161019940614549329) D21 D33 ^ D23 D32 +error(0.000388632672172653233) D21 D33 ^ D31 D32 +error(0.001498498304452828417) D21 D34 +error(0.001856993221836747681) D21 D34 ^ D23 D24 +error(0.0009804196430601077195) D21 D34 ^ D23 D31 +error(0.001569512860033841139) D21 D34 ^ D23 D32 +error(0.0004097649005875169032) D21 D34 ^ D24 D32 +error(0.001862686936830267124) D21 D34 ^ D31 D32 +error(0.004219216288335304009) D23 D31 ^ D21 +error(0.01703946026454849702) D22 +error(0.003596024184132560076) D22 D29 +error(0.000353809852654799644) D22 D29 ^ D20 +error(0.003114589733093941094) D22 D29 ^ D32 +error(0.0003537892199881977127) D22 D29 ^ D32 ^ D20 +error(0.0268149874189349581) D22 D30 +error(0.002693575443238237482) D22 D30 ^ D32 +error(0.0009947049708068399828) D22 D34 +error(0.0004489571233011255941) D22 D34 ^ D23 D24 +error(0.0001023647480353978632) D22 D34 ^ D23 D32 +error(0.001017738624743863216) D22 D34 ^ D24 D32 +error(0.01180107044904601943) D22 ^ D24 +error(0.001485361101119797361) D22 ^ D32 ^ D24 +error(0.008909961818705179268) D23 D24 +error(0.000172764079590881711) D23 D24 ^ D13 +error(0.0001034000690297430145) D23 D24 ^ D21 +error(0.0004460207984751748565) D23 D24 ^ D25 D26 +error(0.002537074408032832188) D23 D24 ^ D26 D34 +error(0.0003403736328788634535) D23 D24 ^ D34 +error(0.03158800500128303157) D23 D31 +error(0.004053201805270884896) D23 D31 ^ D33 D34 +error(0.003287314687101230461) D23 D32 +error(0.000362912345862538815) D23 D32 ^ D33 D34 +error(0.001818820937996893771) D23 D35 +error(0.0001853416030457494853) D23 D35 ^ D26 +error(0.001711555130697444208) D23 D35 ^ D26 D34 +error(0.000272762609427849021) D23 D35 ^ D34 +error(0.01000000000000000021) D23 L0 +error(0.01235521784681438417) D23 L0 ^ D25 +error(0.004207393336629383467) D25 D33 ^ D23 L0 +error(0.01000000000000000021) D24 +error(0.01785301659321773815) D24 D27 +error(0.01637401583216853418) D24 D27 ^ D26 +error(0.02339721966187317467) D24 D32 +error(0.004593006776778153256) D24 D32 ^ D34 +error(0.004633022736341869084) D24 D35 +error(0.00323703015503416314) D24 D35 ^ D26 +error(0.004090640469596825389) D24 D35 ^ D34 +error(0.002101065562810008717) D24 D35 ^ D34 ^ D26 +error(0.007349448775310450134) D25 +error(0.009492030802785636837) D25 D26 +error(0.001887289238295643528) D25 D26 ^ D15 L0 +error(0.01247787028000646274) D25 D26 ^ D23 L0 +error(0.0007927547980242701528) D25 D26 ^ D27 L0 +error(0.0004780626237276926682) D25 D26 ^ D27 L0 ^ D15 L0 +error(0.0290017331886132379) D25 D33 +error(0.0002014583196037038619) D25 ^ D13 ^ D15 L0 +error(0.0004166580577437174488) D25 ^ D13 ^ D23 L0 +error(0.001290031617734563882) D25 ^ D23 L0 ^ D15 L0 +error(0.0107231597008022872) D26 +error(0.002819593978225994144) D26 D33 +error(0.003137767389894948866) D26 D33 ^ D23 L0 +error(0.0006968140446665368809) D26 D33 ^ D35 L0 +error(0.000505172529929820641) D26 D33 ^ D35 L0 ^ D23 L0 +error(0.02081610292411238683) D26 D34 +error(0.001206124809337631677) D26 D34 ^ D23 L0 +error(0.002463255996633777374) D26 D34 ^ D35 L0 +error(0.0002751468606974653234) D26 ^ D14 ^ D16 +error(0.0004594173010041198999) D26 ^ D14 ^ D24 +error(0.009220825108482943316) D26 ^ D27 L0 +error(0.001663150487453687832) D26 ^ D35 L0 +error(0.00455585972925812762) D27 D35 ^ D26 +error(0.01883905370747077437) D27 D35 +error(0.005000000000000000104) D27 L0 +error(0.01751672136136445471) D28 +error(0.01785065649831395901) D28 D31 +error(0.02043771586639645671) D28 D31 ^ D29 +error(0.01432068897443614271) D28 D36 +error(0.003724222745789971568) D28 D36 ^ D37 +error(0.008010401095168906069) D28 D39 +error(0.007940226548819344018) D28 D39 ^ D29 +error(0.003047688540304718287) D28 D39 ^ D37 +error(0.001113551153159442111) D28 D39 ^ D37 ^ D29 +error(0.0012261922121419445) D28 D40 +error(0.001293311027307580789) D28 D40 ^ D29 D37 +error(0.0002766506363411279324) D28 D40 ^ D37 +error(0.0127754705552621823) D28 ^ D29 +error(0.001340836656243296664) D28 ^ D37 +error(0.002787819927005772032) D29 D37 ^ D28 +error(0.01563106638893721131) D29 +error(0.01056384291876393856) D29 D30 +error(0.0002904294784450725487) D29 D30 ^ D28 +error(0.0002058388941412996195) D29 D30 ^ D31 D32 +error(0.0001957883314196005203) D29 D30 ^ D31 D40 +error(0.01111726604515600632) D29 D30 ^ D32 +error(0.002027113369458129113) D29 D30 ^ D32 D40 +error(0.001680777882927654652) D29 D30 ^ D40 +error(0.000510964933352025261) D29 D30 ^ D40 ^ D28 +error(0.01295778001257064625) D29 D34 +error(0.01037953181937149687) D29 D34 ^ D31 D32 +error(0.02565843058292602286) D29 D37 +error(0.001864362289368129985) D29 D37 ^ D39 D40 +error(0.0009030108125920386763) D29 D37 ^ D40 +error(0.002168943494147704959) D29 D41 +error(0.001623587268677913588) D29 D41 ^ D31 D39 +error(0.0002161019940614549329) D29 D41 ^ D31 D40 +error(0.000388632672172653233) D29 D41 ^ D39 D40 +error(0.001498498304452828417) D29 D42 +error(0.001856993221836747681) D29 D42 ^ D31 D32 +error(0.0009804196430601077195) D29 D42 ^ D31 D39 +error(0.001569512860033841139) D29 D42 ^ D31 D40 +error(0.0004097649005875169032) D29 D42 ^ D32 D40 +error(0.001862686936830267124) D29 D42 ^ D39 D40 +error(0.004219216288335304009) D31 D39 ^ D29 +error(0.01703946026454849702) D30 +error(0.003596024184132560076) D30 D37 +error(0.000353809852654799644) D30 D37 ^ D28 +error(0.003114589733093941094) D30 D37 ^ D40 +error(0.0003537892199881977127) D30 D37 ^ D40 ^ D28 +error(0.0268149874189349581) D30 D38 +error(0.002693575443238237482) D30 D38 ^ D40 +error(0.0009947049708068399828) D30 D42 +error(0.0004489571233011255941) D30 D42 ^ D31 D32 +error(0.0001023647480353978632) D30 D42 ^ D31 D40 +error(0.001017738624743863216) D30 D42 ^ D32 D40 +error(0.01180107044904601943) D30 ^ D32 +error(0.001485361101119797361) D30 ^ D40 ^ D32 +error(0.008909961818705179268) D31 D32 +error(0.000172764079590881711) D31 D32 ^ D21 +error(0.0001034000690297430145) D31 D32 ^ D29 +error(0.0004460207984751748565) D31 D32 ^ D33 D34 +error(0.002537074408032832188) D31 D32 ^ D34 D42 +error(0.0003403736328788634535) D31 D32 ^ D42 +error(0.03158800500128303157) D31 D39 +error(0.004053201805270884896) D31 D39 ^ D41 D42 +error(0.003287314687101230461) D31 D40 +error(0.000362912345862538815) D31 D40 ^ D41 D42 +error(0.001818820937996893771) D31 D43 +error(0.0001853416030457494853) D31 D43 ^ D34 +error(0.001711555130697444208) D31 D43 ^ D34 D42 +error(0.000272762609427849021) D31 D43 ^ D42 +error(0.01000000000000000021) D31 L0 +error(0.01235521784681438417) D31 L0 ^ D33 +error(0.004207393336629383467) D33 D41 ^ D31 L0 +error(0.01000000000000000021) D32 +error(0.01785301659321773815) D32 D35 +error(0.01637401583216853418) D32 D35 ^ D34 +error(0.02339721966187317467) D32 D40 +error(0.004593006776778153256) D32 D40 ^ D42 +error(0.004633022736341869084) D32 D43 +error(0.00323703015503416314) D32 D43 ^ D34 +error(0.004090640469596825389) D32 D43 ^ D42 +error(0.002101065562810008717) D32 D43 ^ D42 ^ D34 +error(0.007349448775310450134) D33 +error(0.009492030802785636837) D33 D34 +error(0.001887289238295643528) D33 D34 ^ D23 L0 +error(0.01247787028000646274) D33 D34 ^ D31 L0 +error(0.0007927547980242701528) D33 D34 ^ D35 L0 +error(0.0004780626237276926682) D33 D34 ^ D35 L0 ^ D23 L0 +error(0.0290017331886132379) D33 D41 +error(0.0002014583196037038619) D33 ^ D21 ^ D23 L0 +error(0.0004166580577437174488) D33 ^ D21 ^ D31 L0 +error(0.001290031617734563882) D33 ^ D31 L0 ^ D23 L0 +error(0.0107231597008022872) D34 +error(0.002819593978225994144) D34 D41 +error(0.003137767389894948866) D34 D41 ^ D31 L0 +error(0.0006968140446665368809) D34 D41 ^ D43 L0 +error(0.000505172529929820641) D34 D41 ^ D43 L0 ^ D31 L0 +error(0.02081610292411238683) D34 D42 +error(0.001206124809337631677) D34 D42 ^ D31 L0 +error(0.002463255996633777374) D34 D42 ^ D43 L0 +error(0.0002751468606974653234) D34 ^ D22 ^ D24 +error(0.0004594173010041198999) D34 ^ D22 ^ D32 +error(0.009220825108482943316) D34 ^ D35 L0 +error(0.001663150487453687832) D34 ^ D43 L0 +error(0.00455585972925812762) D35 D43 ^ D34 +error(0.01883905370747077437) D35 D43 +error(0.005000000000000000104) D35 L0 +error(0.01751672136136445471) D36 +error(0.01785065649831395901) D36 D39 +error(0.02043771586639645671) D36 D39 ^ D37 +error(0.01432068897443614271) D36 D44 +error(0.003724222745789971568) D36 D44 ^ D45 +error(0.008010401095168906069) D36 D47 +error(0.007940226548819344018) D36 D47 ^ D37 +error(0.003047688540304718287) D36 D47 ^ D45 +error(0.001113551153159442111) D36 D47 ^ D45 ^ D37 +error(0.0012261922121419445) D36 D48 +error(0.001293311027307580789) D36 D48 ^ D37 D45 +error(0.0002766506363411279324) D36 D48 ^ D45 +error(0.0127754705552621823) D36 ^ D37 +error(0.001340836656243296664) D36 ^ D45 +error(0.002787819927005772032) D37 D45 ^ D36 +error(0.01563106638893721131) D37 +error(0.01056384291876393856) D37 D38 +error(0.0002904294784450725487) D37 D38 ^ D36 +error(0.0002058388941412996195) D37 D38 ^ D39 D40 +error(0.0001957883314196005203) D37 D38 ^ D39 D48 +error(0.01111726604515600632) D37 D38 ^ D40 +error(0.002027113369458129113) D37 D38 ^ D40 D48 +error(0.001680777882927654652) D37 D38 ^ D48 +error(0.000510964933352025261) D37 D38 ^ D48 ^ D36 +error(0.01295778001257064625) D37 D42 +error(0.01037953181937149687) D37 D42 ^ D39 D40 +error(0.02565843058292602286) D37 D45 +error(0.001864362289368129985) D37 D45 ^ D47 D48 +error(0.0009030108125920386763) D37 D45 ^ D48 +error(0.002168943494147704959) D37 D49 +error(0.001623587268677913588) D37 D49 ^ D39 D47 +error(0.0002161019940614549329) D37 D49 ^ D39 D48 +error(0.000388632672172653233) D37 D49 ^ D47 D48 +error(0.001498498304452828417) D37 D50 +error(0.001856993221836747681) D37 D50 ^ D39 D40 +error(0.0009804196430601077195) D37 D50 ^ D39 D47 +error(0.001569512860033841139) D37 D50 ^ D39 D48 +error(0.0004097649005875169032) D37 D50 ^ D40 D48 +error(0.001862686936830267124) D37 D50 ^ D47 D48 +error(0.004219216288335304009) D39 D47 ^ D37 +error(0.01703946026454849702) D38 +error(0.003596024184132560076) D38 D45 +error(0.000353809852654799644) D38 D45 ^ D36 +error(0.003114589733093941094) D38 D45 ^ D48 +error(0.0003537892199881977127) D38 D45 ^ D48 ^ D36 +error(0.0268149874189349581) D38 D46 +error(0.002693575443238237482) D38 D46 ^ D48 +error(0.0009947049708068399828) D38 D50 +error(0.0004489571233011255941) D38 D50 ^ D39 D40 +error(0.0001023647480353978632) D38 D50 ^ D39 D48 +error(0.001017738624743863216) D38 D50 ^ D40 D48 +error(0.01180107044904601943) D38 ^ D40 +error(0.001485361101119797361) D38 ^ D48 ^ D40 +error(0.008909961818705179268) D39 D40 +error(0.000172764079590881711) D39 D40 ^ D29 +error(0.0001034000690297430145) D39 D40 ^ D37 +error(0.0004460207984751748565) D39 D40 ^ D41 D42 +error(0.002537074408032832188) D39 D40 ^ D42 D50 +error(0.0003403736328788634535) D39 D40 ^ D50 +error(0.03158800500128303157) D39 D47 +error(0.004053201805270884896) D39 D47 ^ D49 D50 +error(0.003287314687101230461) D39 D48 +error(0.000362912345862538815) D39 D48 ^ D49 D50 +error(0.001818820937996893771) D39 D51 +error(0.0001853416030457494853) D39 D51 ^ D42 +error(0.001711555130697444208) D39 D51 ^ D42 D50 +error(0.000272762609427849021) D39 D51 ^ D50 +error(0.01000000000000000021) D39 L0 +error(0.01235521784681438417) D39 L0 ^ D41 +error(0.004207393336629383467) D41 D49 ^ D39 L0 +error(0.01000000000000000021) D40 +error(0.01785301659321773815) D40 D43 +error(0.01637401583216853418) D40 D43 ^ D42 +error(0.02339721966187317467) D40 D48 +error(0.004593006776778153256) D40 D48 ^ D50 +error(0.004633022736341869084) D40 D51 +error(0.00323703015503416314) D40 D51 ^ D42 +error(0.004090640469596825389) D40 D51 ^ D50 +error(0.002101065562810008717) D40 D51 ^ D50 ^ D42 +error(0.007349448775310450134) D41 +error(0.009492030802785636837) D41 D42 +error(0.001887289238295643528) D41 D42 ^ D31 L0 +error(0.01247787028000646274) D41 D42 ^ D39 L0 +error(0.0007927547980242701528) D41 D42 ^ D43 L0 +error(0.0004780626237276926682) D41 D42 ^ D43 L0 ^ D31 L0 +error(0.0290017331886132379) D41 D49 +error(0.0002014583196037038619) D41 ^ D29 ^ D31 L0 +error(0.0004166580577437174488) D41 ^ D29 ^ D39 L0 +error(0.001290031617734563882) D41 ^ D39 L0 ^ D31 L0 +error(0.0107231597008022872) D42 +error(0.002819593978225994144) D42 D49 +error(0.003137767389894948866) D42 D49 ^ D39 L0 +error(0.0006968140446665368809) D42 D49 ^ D51 L0 +error(0.000505172529929820641) D42 D49 ^ D51 L0 ^ D39 L0 +error(0.02081610292411238683) D42 D50 +error(0.001206124809337631677) D42 D50 ^ D39 L0 +error(0.002463255996633777374) D42 D50 ^ D51 L0 +error(0.0002751468606974653234) D42 ^ D30 ^ D32 +error(0.0004594173010041198999) D42 ^ D30 ^ D40 +error(0.009220825108482943316) D42 ^ D43 L0 +error(0.001663150487453687832) D42 ^ D51 L0 +error(0.00455585972925812762) D43 D51 ^ D42 +error(0.01883905370747077437) D43 D51 +error(0.005000000000000000104) D43 L0 +error(0.01751672136136445471) D44 +error(0.01785065649831395901) D44 D47 +error(0.02043771586639645671) D44 D47 ^ D45 +error(0.01432068897443614271) D44 D52 +error(0.003724222745789971568) D44 D52 ^ D53 +error(0.008010401095168906069) D44 D55 +error(0.007940226548819344018) D44 D55 ^ D45 +error(0.003047688540304718287) D44 D55 ^ D53 +error(0.001113551153159442111) D44 D55 ^ D53 ^ D45 +error(0.0012261922121419445) D44 D56 +error(0.001293311027307580789) D44 D56 ^ D45 D53 +error(0.0002766506363411279324) D44 D56 ^ D53 +error(0.0127754705552621823) D44 ^ D45 +error(0.001340836656243296664) D44 ^ D53 +error(0.002787819927005772032) D45 D53 ^ D44 +error(0.01563106638893721131) D45 +error(0.01056384291876393856) D45 D46 +error(0.0002904294784450725487) D45 D46 ^ D44 +error(0.0002058388941412996195) D45 D46 ^ D47 D48 +error(0.0001957883314196005203) D45 D46 ^ D47 D56 +error(0.01111726604515600632) D45 D46 ^ D48 +error(0.002027113369458129113) D45 D46 ^ D48 D56 +error(0.001680777882927654652) D45 D46 ^ D56 +error(0.000510964933352025261) D45 D46 ^ D56 ^ D44 +error(0.01295778001257064625) D45 D50 +error(0.01037953181937149687) D45 D50 ^ D47 D48 +error(0.02565843058292602286) D45 D53 +error(0.001864362289368129985) D45 D53 ^ D55 D56 +error(0.0009030108125920386763) D45 D53 ^ D56 +error(0.002168943494147704959) D45 D57 +error(0.001623587268677913588) D45 D57 ^ D47 D55 +error(0.0002161019940614549329) D45 D57 ^ D47 D56 +error(0.000388632672172653233) D45 D57 ^ D55 D56 +error(0.001498498304452828417) D45 D58 +error(0.001856993221836747681) D45 D58 ^ D47 D48 +error(0.0009804196430601077195) D45 D58 ^ D47 D55 +error(0.001569512860033841139) D45 D58 ^ D47 D56 +error(0.0004097649005875169032) D45 D58 ^ D48 D56 +error(0.001862686936830267124) D45 D58 ^ D55 D56 +error(0.004219216288335304009) D47 D55 ^ D45 +error(0.01703946026454849702) D46 +error(0.003596024184132560076) D46 D53 +error(0.000353809852654799644) D46 D53 ^ D44 +error(0.003114589733093941094) D46 D53 ^ D56 +error(0.0003537892199881977127) D46 D53 ^ D56 ^ D44 +error(0.0268149874189349581) D46 D54 +error(0.002693575443238237482) D46 D54 ^ D56 +error(0.0009947049708068399828) D46 D58 +error(0.0004489571233011255941) D46 D58 ^ D47 D48 +error(0.0001023647480353978632) D46 D58 ^ D47 D56 +error(0.001017738624743863216) D46 D58 ^ D48 D56 +error(0.01180107044904601943) D46 ^ D48 +error(0.001485361101119797361) D46 ^ D56 ^ D48 +error(0.008909961818705179268) D47 D48 +error(0.000172764079590881711) D47 D48 ^ D37 +error(0.0001034000690297430145) D47 D48 ^ D45 +error(0.0004460207984751748565) D47 D48 ^ D49 D50 +error(0.002537074408032832188) D47 D48 ^ D50 D58 +error(0.0003403736328788634535) D47 D48 ^ D58 +error(0.03158800500128303157) D47 D55 +error(0.004053201805270884896) D47 D55 ^ D57 D58 +error(0.003287314687101230461) D47 D56 +error(0.000362912345862538815) D47 D56 ^ D57 D58 +error(0.001818820937996893771) D47 D59 +error(0.0001853416030457494853) D47 D59 ^ D50 +error(0.001711555130697444208) D47 D59 ^ D50 D58 +error(0.000272762609427849021) D47 D59 ^ D58 +error(0.01000000000000000021) D47 L0 +error(0.01235521784681438417) D47 L0 ^ D49 +error(0.004207393336629383467) D49 D57 ^ D47 L0 +error(0.01000000000000000021) D48 +error(0.01785301659321773815) D48 D51 +error(0.01637401583216853418) D48 D51 ^ D50 +error(0.02339721966187317467) D48 D56 +error(0.004593006776778153256) D48 D56 ^ D58 +error(0.004633022736341869084) D48 D59 +error(0.00323703015503416314) D48 D59 ^ D50 +error(0.004090640469596825389) D48 D59 ^ D58 +error(0.002101065562810008717) D48 D59 ^ D58 ^ D50 +error(0.007349448775310450134) D49 +error(0.009492030802785636837) D49 D50 +error(0.001887289238295643528) D49 D50 ^ D39 L0 +error(0.01247787028000646274) D49 D50 ^ D47 L0 +error(0.0007927547980242701528) D49 D50 ^ D51 L0 +error(0.0004780626237276926682) D49 D50 ^ D51 L0 ^ D39 L0 +error(0.0290017331886132379) D49 D57 +error(0.0002014583196037038619) D49 ^ D37 ^ D39 L0 +error(0.0004166580577437174488) D49 ^ D37 ^ D47 L0 +error(0.001290031617734563882) D49 ^ D47 L0 ^ D39 L0 +error(0.0107231597008022872) D50 +error(0.002819593978225994144) D50 D57 +error(0.003137767389894948866) D50 D57 ^ D47 L0 +error(0.0006968140446665368809) D50 D57 ^ D59 L0 +error(0.000505172529929820641) D50 D57 ^ D59 L0 ^ D47 L0 +error(0.02081610292411238683) D50 D58 +error(0.001206124809337631677) D50 D58 ^ D47 L0 +error(0.002463255996633777374) D50 D58 ^ D59 L0 +error(0.0002751468606974653234) D50 ^ D38 ^ D40 +error(0.0004594173010041198999) D50 ^ D38 ^ D48 +error(0.009220825108482943316) D50 ^ D51 L0 +error(0.001663150487453687832) D50 ^ D59 L0 +error(0.00455585972925812762) D51 D59 ^ D50 +error(0.01883905370747077437) D51 D59 +error(0.005000000000000000104) D51 L0 +error(0.01751672136136445471) D52 +error(0.01785065649831395901) D52 D55 +error(0.02043771586639645671) D52 D55 ^ D53 +error(0.01432068897443614271) D52 D60 +error(0.003724222745789971568) D52 D60 ^ D61 +error(0.008010401095168906069) D52 D63 +error(0.007940226548819344018) D52 D63 ^ D53 +error(0.003047688540304718287) D52 D63 ^ D61 +error(0.001113551153159442111) D52 D63 ^ D61 ^ D53 +error(0.0012261922121419445) D52 D64 +error(0.001293311027307580789) D52 D64 ^ D53 D61 +error(0.0002766506363411279324) D52 D64 ^ D61 +error(0.0127754705552621823) D52 ^ D53 +error(0.001340836656243296664) D52 ^ D61 +error(0.002787819927005772032) D53 D61 ^ D52 +error(0.01563106638893721131) D53 +error(0.01056384291876393856) D53 D54 +error(0.0002904294784450725487) D53 D54 ^ D52 +error(0.0002058388941412996195) D53 D54 ^ D55 D56 +error(0.0001957883314196005203) D53 D54 ^ D55 D64 +error(0.01111726604515600632) D53 D54 ^ D56 +error(0.002027113369458129113) D53 D54 ^ D56 D64 +error(0.001680777882927654652) D53 D54 ^ D64 +error(0.000510964933352025261) D53 D54 ^ D64 ^ D52 +error(0.01295778001257064625) D53 D58 +error(0.01037953181937149687) D53 D58 ^ D55 D56 +error(0.02565843058292602286) D53 D61 +error(0.001864362289368129985) D53 D61 ^ D63 D64 +error(0.0009030108125920386763) D53 D61 ^ D64 +error(0.002168943494147704959) D53 D65 +error(0.001623587268677913588) D53 D65 ^ D55 D63 +error(0.0002161019940614549329) D53 D65 ^ D55 D64 +error(0.000388632672172653233) D53 D65 ^ D63 D64 +error(0.001498498304452828417) D53 D66 +error(0.001856993221836747681) D53 D66 ^ D55 D56 +error(0.0009804196430601077195) D53 D66 ^ D55 D63 +error(0.001569512860033841139) D53 D66 ^ D55 D64 +error(0.0004097649005875169032) D53 D66 ^ D56 D64 +error(0.001862686936830267124) D53 D66 ^ D63 D64 +error(0.004219216288335304009) D55 D63 ^ D53 +error(0.01703946026454849702) D54 +error(0.003596024184132560076) D54 D61 +error(0.000353809852654799644) D54 D61 ^ D52 +error(0.003114589733093941094) D54 D61 ^ D64 +error(0.0003537892199881977127) D54 D61 ^ D64 ^ D52 +error(0.0268149874189349581) D54 D62 +error(0.002693575443238237482) D54 D62 ^ D64 +error(0.0009947049708068399828) D54 D66 +error(0.0004489571233011255941) D54 D66 ^ D55 D56 +error(0.0001023647480353978632) D54 D66 ^ D55 D64 +error(0.001017738624743863216) D54 D66 ^ D56 D64 +error(0.01180107044904601943) D54 ^ D56 +error(0.001485361101119797361) D54 ^ D64 ^ D56 +error(0.008909961818705179268) D55 D56 +error(0.000172764079590881711) D55 D56 ^ D45 +error(0.0001034000690297430145) D55 D56 ^ D53 +error(0.0004460207984751748565) D55 D56 ^ D57 D58 +error(0.002537074408032832188) D55 D56 ^ D58 D66 +error(0.0003403736328788634535) D55 D56 ^ D66 +error(0.03158800500128303157) D55 D63 +error(0.004053201805270884896) D55 D63 ^ D65 D66 +error(0.003287314687101230461) D55 D64 +error(0.000362912345862538815) D55 D64 ^ D65 D66 +error(0.001818820937996893771) D55 D67 +error(0.0001853416030457494853) D55 D67 ^ D58 +error(0.001711555130697444208) D55 D67 ^ D58 D66 +error(0.000272762609427849021) D55 D67 ^ D66 +error(0.01000000000000000021) D55 L0 +error(0.01235521784681438417) D55 L0 ^ D57 +error(0.004207393336629383467) D57 D65 ^ D55 L0 +error(0.01000000000000000021) D56 +error(0.01785301659321773815) D56 D59 +error(0.01637401583216853418) D56 D59 ^ D58 +error(0.02339721966187317467) D56 D64 +error(0.004593006776778153256) D56 D64 ^ D66 +error(0.004633022736341869084) D56 D67 +error(0.00323703015503416314) D56 D67 ^ D58 +error(0.004090640469596825389) D56 D67 ^ D66 +error(0.002101065562810008717) D56 D67 ^ D66 ^ D58 +error(0.007349448775310450134) D57 +error(0.009492030802785636837) D57 D58 +error(0.001887289238295643528) D57 D58 ^ D47 L0 +error(0.01247787028000646274) D57 D58 ^ D55 L0 +error(0.0007927547980242701528) D57 D58 ^ D59 L0 +error(0.0004780626237276926682) D57 D58 ^ D59 L0 ^ D47 L0 +error(0.0290017331886132379) D57 D65 +error(0.0002014583196037038619) D57 ^ D45 ^ D47 L0 +error(0.0004166580577437174488) D57 ^ D45 ^ D55 L0 +error(0.001290031617734563882) D57 ^ D55 L0 ^ D47 L0 +error(0.0107231597008022872) D58 +error(0.002819593978225994144) D58 D65 +error(0.003137767389894948866) D58 D65 ^ D55 L0 +error(0.0006968140446665368809) D58 D65 ^ D67 L0 +error(0.000505172529929820641) D58 D65 ^ D67 L0 ^ D55 L0 +error(0.02081610292411238683) D58 D66 +error(0.001206124809337631677) D58 D66 ^ D55 L0 +error(0.002463255996633777374) D58 D66 ^ D67 L0 +error(0.0002751468606974653234) D58 ^ D46 ^ D48 +error(0.0004594173010041198999) D58 ^ D46 ^ D56 +error(0.009220825108482943316) D58 ^ D59 L0 +error(0.001663150487453687832) D58 ^ D67 L0 +error(0.00455585972925812762) D59 D67 ^ D58 +error(0.01883905370747077437) D59 D67 +error(0.005000000000000000104) D59 L0 +error(0.01751672136136445471) D60 +error(0.01785065649831395901) D60 D63 +error(0.02043771586639645671) D60 D63 ^ D61 +error(0.01432068897443614271) D60 D68 +error(0.003724222745789971568) D60 D68 ^ D69 +error(0.008010401095168906069) D60 D71 +error(0.007940226548819344018) D60 D71 ^ D61 +error(0.003047688540304718287) D60 D71 ^ D69 +error(0.001113551153159442111) D60 D71 ^ D69 ^ D61 +error(0.0012261922121419445) D60 D72 +error(0.001293311027307580789) D60 D72 ^ D61 D69 +error(0.0002766506363411279324) D60 D72 ^ D69 +error(0.0127754705552621823) D60 ^ D61 +error(0.001340836656243296664) D60 ^ D69 +error(0.002787819927005772032) D61 D69 ^ D60 +error(0.01563106638893721131) D61 +error(0.01056384291876393856) D61 D62 +error(0.0002904294784450725487) D61 D62 ^ D60 +error(0.0002058388941412996195) D61 D62 ^ D63 D64 +error(0.0001957883314196005203) D61 D62 ^ D63 D72 +error(0.01111726604515600632) D61 D62 ^ D64 +error(0.002027113369458129113) D61 D62 ^ D64 D72 +error(0.001680777882927654652) D61 D62 ^ D72 +error(0.000510964933352025261) D61 D62 ^ D72 ^ D60 +error(0.01295778001257064625) D61 D66 +error(0.01037953181937149687) D61 D66 ^ D63 D64 +error(0.02565843058292602286) D61 D69 +error(0.001864362289368129985) D61 D69 ^ D71 D72 +error(0.0009030108125920386763) D61 D69 ^ D72 +error(0.002168943494147704959) D61 D73 +error(0.001623587268677913588) D61 D73 ^ D63 D71 +error(0.0002161019940614549329) D61 D73 ^ D63 D72 +error(0.000388632672172653233) D61 D73 ^ D71 D72 +error(0.001498498304452828417) D61 D74 +error(0.001856993221836747681) D61 D74 ^ D63 D64 +error(0.0009804196430601077195) D61 D74 ^ D63 D71 +error(0.001569512860033841139) D61 D74 ^ D63 D72 +error(0.0004097649005875169032) D61 D74 ^ D64 D72 +error(0.001862686936830267124) D61 D74 ^ D71 D72 +error(0.004219216288335304009) D63 D71 ^ D61 +error(0.01703946026454849702) D62 +error(0.003596024184132560076) D62 D69 +error(0.000353809852654799644) D62 D69 ^ D60 +error(0.003114589733093941094) D62 D69 ^ D72 +error(0.0003537892199881977127) D62 D69 ^ D72 ^ D60 +error(0.0268149874189349581) D62 D70 +error(0.002693575443238237482) D62 D70 ^ D72 +error(0.0009947049708068399828) D62 D74 +error(0.0004489571233011255941) D62 D74 ^ D63 D64 +error(0.0001023647480353978632) D62 D74 ^ D63 D72 +error(0.001017738624743863216) D62 D74 ^ D64 D72 +error(0.01180107044904601943) D62 ^ D64 +error(0.001485361101119797361) D62 ^ D72 ^ D64 +error(0.008909961818705179268) D63 D64 +error(0.000172764079590881711) D63 D64 ^ D53 +error(0.0001034000690297430145) D63 D64 ^ D61 +error(0.0004460207984751748565) D63 D64 ^ D65 D66 +error(0.002537074408032832188) D63 D64 ^ D66 D74 +error(0.0003403736328788634535) D63 D64 ^ D74 +error(0.03158800500128303157) D63 D71 +error(0.004053201805270884896) D63 D71 ^ D73 D74 +error(0.003287314687101230461) D63 D72 +error(0.000362912345862538815) D63 D72 ^ D73 D74 +error(0.001818820937996893771) D63 D75 +error(0.0001853416030457494853) D63 D75 ^ D66 +error(0.001711555130697444208) D63 D75 ^ D66 D74 +error(0.000272762609427849021) D63 D75 ^ D74 +error(0.01000000000000000021) D63 L0 +error(0.01235521784681438417) D63 L0 ^ D65 +error(0.004207393336629383467) D65 D73 ^ D63 L0 +error(0.01000000000000000021) D64 +error(0.01785301659321773815) D64 D67 +error(0.01637401583216853418) D64 D67 ^ D66 +error(0.02339721966187317467) D64 D72 +error(0.004593006776778153256) D64 D72 ^ D74 +error(0.004633022736341869084) D64 D75 +error(0.00323703015503416314) D64 D75 ^ D66 +error(0.004090640469596825389) D64 D75 ^ D74 +error(0.002101065562810008717) D64 D75 ^ D74 ^ D66 +error(0.007349448775310450134) D65 +error(0.009492030802785636837) D65 D66 +error(0.001887289238295643528) D65 D66 ^ D55 L0 +error(0.01247787028000646274) D65 D66 ^ D63 L0 +error(0.0007927547980242701528) D65 D66 ^ D67 L0 +error(0.0004780626237276926682) D65 D66 ^ D67 L0 ^ D55 L0 +error(0.0290017331886132379) D65 D73 +error(0.0002014583196037038619) D65 ^ D53 ^ D55 L0 +error(0.0004166580577437174488) D65 ^ D53 ^ D63 L0 +error(0.001290031617734563882) D65 ^ D63 L0 ^ D55 L0 +error(0.0107231597008022872) D66 +error(0.002819593978225994144) D66 D73 +error(0.003137767389894948866) D66 D73 ^ D63 L0 +error(0.0006968140446665368809) D66 D73 ^ D75 L0 +error(0.000505172529929820641) D66 D73 ^ D75 L0 ^ D63 L0 +error(0.02081610292411238683) D66 D74 +error(0.001206124809337631677) D66 D74 ^ D63 L0 +error(0.002463255996633777374) D66 D74 ^ D75 L0 +error(0.0002751468606974653234) D66 ^ D54 ^ D56 +error(0.0004594173010041198999) D66 ^ D54 ^ D64 +error(0.009220825108482943316) D66 ^ D67 L0 +error(0.001663150487453687832) D66 ^ D75 L0 +error(0.00455585972925812762) D67 D75 ^ D66 +error(0.01883905370747077437) D67 D75 +error(0.005000000000000000104) D67 L0 +error(0.01751672136136445471) D68 +error(0.01785065649831395901) D68 D71 +error(0.02043771586639645671) D68 D71 ^ D69 +error(0.01432068897443614271) D68 D76 +error(0.003724222745789971568) D68 D76 ^ D77 +error(0.008010401095168906069) D68 D79 +error(0.007940226548819344018) D68 D79 ^ D69 +error(0.003047688540304718287) D68 D79 ^ D77 +error(0.001113551153159442111) D68 D79 ^ D77 ^ D69 +error(0.0012261922121419445) D68 D80 +error(0.001293311027307580789) D68 D80 ^ D69 D77 +error(0.0002766506363411279324) D68 D80 ^ D77 +error(0.0127754705552621823) D68 ^ D69 +error(0.001340836656243296664) D68 ^ D77 +error(0.002787819927005772032) D69 D77 ^ D68 +error(0.01563106638893721131) D69 +error(0.01056384291876393856) D69 D70 +error(0.0002904294784450725487) D69 D70 ^ D68 +error(0.0002058388941412996195) D69 D70 ^ D71 D72 +error(0.0001957883314196005203) D69 D70 ^ D71 D80 +error(0.01111726604515600632) D69 D70 ^ D72 +error(0.002027113369458129113) D69 D70 ^ D72 D80 +error(0.001680777882927654652) D69 D70 ^ D80 +error(0.000510964933352025261) D69 D70 ^ D80 ^ D68 +error(0.01295778001257064625) D69 D74 +error(0.01037953181937149687) D69 D74 ^ D71 D72 +error(0.02565843058292602286) D69 D77 +error(0.001864362289368129985) D69 D77 ^ D79 D80 +error(0.0009030108125920386763) D69 D77 ^ D80 +error(0.002168943494147704959) D69 D81 +error(0.001623587268677913588) D69 D81 ^ D71 D79 +error(0.0002161019940614549329) D69 D81 ^ D71 D80 +error(0.000388632672172653233) D69 D81 ^ D79 D80 +error(0.001498498304452828417) D69 D82 +error(0.001856993221836747681) D69 D82 ^ D71 D72 +error(0.0009804196430601077195) D69 D82 ^ D71 D79 +error(0.001569512860033841139) D69 D82 ^ D71 D80 +error(0.0004097649005875169032) D69 D82 ^ D72 D80 +error(0.001862686936830267124) D69 D82 ^ D79 D80 +error(0.004219216288335304009) D71 D79 ^ D69 +error(0.01703946026454849702) D70 +error(0.003596024184132560076) D70 D77 +error(0.000353809852654799644) D70 D77 ^ D68 +error(0.003114589733093941094) D70 D77 ^ D80 +error(0.0003537892199881977127) D70 D77 ^ D80 ^ D68 +error(0.0268149874189349581) D70 D78 +error(0.002693575443238237482) D70 D78 ^ D80 +error(0.0009947049708068399828) D70 D82 +error(0.0004489571233011255941) D70 D82 ^ D71 D72 +error(0.0001023647480353978632) D70 D82 ^ D71 D80 +error(0.001017738624743863216) D70 D82 ^ D72 D80 +error(0.01180107044904601943) D70 ^ D72 +error(0.001485361101119797361) D70 ^ D80 ^ D72 +error(0.008909961818705179268) D71 D72 +error(0.000172764079590881711) D71 D72 ^ D61 +error(0.0001034000690297430145) D71 D72 ^ D69 +error(0.0004460207984751748565) D71 D72 ^ D73 D74 +error(0.002537074408032832188) D71 D72 ^ D74 D82 +error(0.0003403736328788634535) D71 D72 ^ D82 +error(0.03158800500128303157) D71 D79 +error(0.004053201805270884896) D71 D79 ^ D81 D82 +error(0.003287314687101230461) D71 D80 +error(0.000362912345862538815) D71 D80 ^ D81 D82 +error(0.001818820937996893771) D71 D83 +error(0.0001853416030457494853) D71 D83 ^ D74 +error(0.001711555130697444208) D71 D83 ^ D74 D82 +error(0.000272762609427849021) D71 D83 ^ D82 +error(0.01000000000000000021) D71 L0 +error(0.01235521784681438417) D71 L0 ^ D73 +error(0.004207393336629383467) D73 D81 ^ D71 L0 +error(0.01000000000000000021) D72 +error(0.01785301659321773815) D72 D75 +error(0.01637401583216853418) D72 D75 ^ D74 +error(0.02339721966187317467) D72 D80 +error(0.004593006776778153256) D72 D80 ^ D82 +error(0.004633022736341869084) D72 D83 +error(0.00323703015503416314) D72 D83 ^ D74 +error(0.004090640469596825389) D72 D83 ^ D82 +error(0.002101065562810008717) D72 D83 ^ D82 ^ D74 +error(0.007349448775310450134) D73 +error(0.009492030802785636837) D73 D74 +error(0.001887289238295643528) D73 D74 ^ D63 L0 +error(0.01247787028000646274) D73 D74 ^ D71 L0 +error(0.0007927547980242701528) D73 D74 ^ D75 L0 +error(0.0004780626237276926682) D73 D74 ^ D75 L0 ^ D63 L0 +error(0.0290017331886132379) D73 D81 +error(0.0002014583196037038619) D73 ^ D61 ^ D63 L0 +error(0.0004166580577437174488) D73 ^ D61 ^ D71 L0 +error(0.001290031617734563882) D73 ^ D71 L0 ^ D63 L0 +error(0.0107231597008022872) D74 +error(0.002819593978225994144) D74 D81 +error(0.003137767389894948866) D74 D81 ^ D71 L0 +error(0.0006968140446665368809) D74 D81 ^ D83 L0 +error(0.000505172529929820641) D74 D81 ^ D83 L0 ^ D71 L0 +error(0.02081610292411238683) D74 D82 +error(0.001206124809337631677) D74 D82 ^ D71 L0 +error(0.002463255996633777374) D74 D82 ^ D83 L0 +error(0.0002751468606974653234) D74 ^ D62 ^ D64 +error(0.0004594173010041198999) D74 ^ D62 ^ D72 +error(0.009220825108482943316) D74 ^ D75 L0 +error(0.001663150487453687832) D74 ^ D83 L0 +error(0.00455585972925812762) D75 D83 ^ D74 +error(0.01883905370747077437) D75 D83 +error(0.005000000000000000104) D75 L0 +error(0.01751672136136445471) D76 +error(0.01785065649831395901) D76 D79 +error(0.02043771586639645671) D76 D79 ^ D77 +error(0.01432068897443614271) D76 D84 +error(0.003724222745789971568) D76 D84 ^ D85 +error(0.008010401095168906069) D76 D87 +error(0.007940226548819344018) D76 D87 ^ D77 +error(0.003047688540304718287) D76 D87 ^ D85 +error(0.001113551153159442111) D76 D87 ^ D85 ^ D77 +error(0.0012261922121419445) D76 D88 +error(0.001293311027307580789) D76 D88 ^ D77 D85 +error(0.0002766506363411279324) D76 D88 ^ D85 +error(0.0127754705552621823) D76 ^ D77 +error(0.001340836656243296664) D76 ^ D85 +error(0.002787819927005772032) D77 D85 ^ D76 +error(0.01563106638893721131) D77 +error(0.01056384291876393856) D77 D78 +error(0.0002904294784450725487) D77 D78 ^ D76 +error(0.0002058388941412996195) D77 D78 ^ D79 D80 +error(0.0001957883314196005203) D77 D78 ^ D79 D88 +error(0.01111726604515600632) D77 D78 ^ D80 +error(0.002027113369458129113) D77 D78 ^ D80 D88 +error(0.001680777882927654652) D77 D78 ^ D88 +error(0.000510964933352025261) D77 D78 ^ D88 ^ D76 +error(0.01295778001257064625) D77 D82 +error(0.01037953181937149687) D77 D82 ^ D79 D80 +error(0.02565843058292602286) D77 D85 +error(0.001864362289368129985) D77 D85 ^ D87 D88 +error(0.0009030108125920386763) D77 D85 ^ D88 +error(0.002168943494147704959) D77 D89 +error(0.001623587268677913588) D77 D89 ^ D79 D87 +error(0.0002161019940614549329) D77 D89 ^ D79 D88 +error(0.000388632672172653233) D77 D89 ^ D87 D88 +error(0.001498498304452828417) D77 D90 +error(0.001856993221836747681) D77 D90 ^ D79 D80 +error(0.0009804196430601077195) D77 D90 ^ D79 D87 +error(0.001569512860033841139) D77 D90 ^ D79 D88 +error(0.0004097649005875169032) D77 D90 ^ D80 D88 +error(0.001862686936830267124) D77 D90 ^ D87 D88 +error(0.004219216288335304009) D79 D87 ^ D77 +error(0.01703946026454849702) D78 +error(0.003596024184132560076) D78 D85 +error(0.000353809852654799644) D78 D85 ^ D76 +error(0.003114589733093941094) D78 D85 ^ D88 +error(0.0003537892199881977127) D78 D85 ^ D88 ^ D76 +error(0.0268149874189349581) D78 D86 +error(0.002693575443238237482) D78 D86 ^ D88 +error(0.0009947049708068399828) D78 D90 +error(0.0004489571233011255941) D78 D90 ^ D79 D80 +error(0.0001023647480353978632) D78 D90 ^ D79 D88 +error(0.001017738624743863216) D78 D90 ^ D80 D88 +error(0.01180107044904601943) D78 ^ D80 +error(0.001485361101119797361) D78 ^ D88 ^ D80 +error(0.008909961818705179268) D79 D80 +error(0.000172764079590881711) D79 D80 ^ D69 +error(0.0001034000690297430145) D79 D80 ^ D77 +error(0.0004460207984751748565) D79 D80 ^ D81 D82 +error(0.002537074408032832188) D79 D80 ^ D82 D90 +error(0.0003403736328788634535) D79 D80 ^ D90 +error(0.03158800500128303157) D79 D87 +error(0.004053201805270884896) D79 D87 ^ D89 D90 +error(0.003287314687101230461) D79 D88 +error(0.000362912345862538815) D79 D88 ^ D89 D90 +error(0.001818820937996893771) D79 D91 +error(0.0001853416030457494853) D79 D91 ^ D82 +error(0.001711555130697444208) D79 D91 ^ D82 D90 +error(0.000272762609427849021) D79 D91 ^ D90 +error(0.01000000000000000021) D79 L0 +error(0.01235521784681438417) D79 L0 ^ D81 +error(0.004207393336629383467) D81 D89 ^ D79 L0 +error(0.01000000000000000021) D80 +error(0.01785301659321773815) D80 D83 +error(0.01637401583216853418) D80 D83 ^ D82 +error(0.02339721966187317467) D80 D88 +error(0.004593006776778153256) D80 D88 ^ D90 +error(0.004633022736341869084) D80 D91 +error(0.00323703015503416314) D80 D91 ^ D82 +error(0.004090640469596825389) D80 D91 ^ D90 +error(0.002101065562810008717) D80 D91 ^ D90 ^ D82 +error(0.007349448775310450134) D81 +error(0.009492030802785636837) D81 D82 +error(0.001887289238295643528) D81 D82 ^ D71 L0 +error(0.01247787028000646274) D81 D82 ^ D79 L0 +error(0.0007927547980242701528) D81 D82 ^ D83 L0 +error(0.0004780626237276926682) D81 D82 ^ D83 L0 ^ D71 L0 +error(0.0290017331886132379) D81 D89 +error(0.0002014583196037038619) D81 ^ D69 ^ D71 L0 +error(0.0004166580577437174488) D81 ^ D69 ^ D79 L0 +error(0.001290031617734563882) D81 ^ D79 L0 ^ D71 L0 +error(0.0107231597008022872) D82 +error(0.002819593978225994144) D82 D89 +error(0.003137767389894948866) D82 D89 ^ D79 L0 +error(0.0006968140446665368809) D82 D89 ^ D91 L0 +error(0.000505172529929820641) D82 D89 ^ D91 L0 ^ D79 L0 +error(0.02081610292411238683) D82 D90 +error(0.001206124809337631677) D82 D90 ^ D79 L0 +error(0.002463255996633777374) D82 D90 ^ D91 L0 +error(0.0002751468606974653234) D82 ^ D70 ^ D72 +error(0.0004594173010041198999) D82 ^ D70 ^ D80 +error(0.009220825108482943316) D82 ^ D83 L0 +error(0.001663150487453687832) D82 ^ D91 L0 +error(0.00455585972925812762) D83 D91 ^ D82 +error(0.01883905370747077437) D83 D91 +error(0.005000000000000000104) D83 L0 +error(0.01751672136136445471) D84 +error(0.01785065649831395901) D84 D87 +error(0.02043771586639645671) D84 D87 ^ D85 +error(0.01432068897443614271) D84 D92 +error(0.003724222745789971568) D84 D92 ^ D93 +error(0.008010401095168906069) D84 D95 +error(0.007940226548819344018) D84 D95 ^ D85 +error(0.003047688540304718287) D84 D95 ^ D93 +error(0.001113551153159442111) D84 D95 ^ D93 ^ D85 +error(0.0012261922121419445) D84 D96 +error(0.001293311027307580789) D84 D96 ^ D85 D93 +error(0.0002766506363411279324) D84 D96 ^ D93 +error(0.0127754705552621823) D84 ^ D85 +error(0.001340836656243296664) D84 ^ D93 +error(0.002787819927005772032) D85 D93 ^ D84 +error(0.01563106638893721131) D85 +error(0.01056384291876393856) D85 D86 +error(0.0002904294784450725487) D85 D86 ^ D84 +error(0.0002058388941412996195) D85 D86 ^ D87 D88 +error(0.0001957883314196005203) D85 D86 ^ D87 D96 +error(0.01111726604515600632) D85 D86 ^ D88 +error(0.002027113369458129113) D85 D86 ^ D88 D96 +error(0.001680777882927654652) D85 D86 ^ D96 +error(0.000510964933352025261) D85 D86 ^ D96 ^ D84 +error(0.01295778001257064625) D85 D90 +error(0.01037953181937149687) D85 D90 ^ D87 D88 +error(0.02565843058292602286) D85 D93 +error(0.001864362289368129985) D85 D93 ^ D95 D96 +error(0.0009030108125920386763) D85 D93 ^ D96 +error(0.002168943494147704959) D85 D97 +error(0.001623587268677913588) D85 D97 ^ D87 D95 +error(0.0002161019940614549329) D85 D97 ^ D87 D96 +error(0.000388632672172653233) D85 D97 ^ D95 D96 +error(0.001498498304452828417) D85 D98 +error(0.001856993221836747681) D85 D98 ^ D87 D88 +error(0.0009804196430601077195) D85 D98 ^ D87 D95 +error(0.001569512860033841139) D85 D98 ^ D87 D96 +error(0.0004097649005875169032) D85 D98 ^ D88 D96 +error(0.001862686936830267124) D85 D98 ^ D95 D96 +error(0.004219216288335304009) D87 D95 ^ D85 +error(0.01703946026454849702) D86 +error(0.003596024184132560076) D86 D93 +error(0.000353809852654799644) D86 D93 ^ D84 +error(0.003114589733093941094) D86 D93 ^ D96 +error(0.0003537892199881977127) D86 D93 ^ D96 ^ D84 +error(0.0268149874189349581) D86 D94 +error(0.002693575443238237482) D86 D94 ^ D96 +error(0.0009947049708068399828) D86 D98 +error(0.0004489571233011255941) D86 D98 ^ D87 D88 +error(0.0001023647480353978632) D86 D98 ^ D87 D96 +error(0.001017738624743863216) D86 D98 ^ D88 D96 +error(0.01180107044904601943) D86 ^ D88 +error(0.001485361101119797361) D86 ^ D96 ^ D88 +error(0.008909961818705179268) D87 D88 +error(0.000172764079590881711) D87 D88 ^ D77 +error(0.0001034000690297430145) D87 D88 ^ D85 +error(0.0004460207984751748565) D87 D88 ^ D89 D90 +error(0.002537074408032832188) D87 D88 ^ D90 D98 +error(0.0003403736328788634535) D87 D88 ^ D98 +error(0.03158800500128303157) D87 D95 +error(0.004053201805270884896) D87 D95 ^ D97 D98 +error(0.003287314687101230461) D87 D96 +error(0.000362912345862538815) D87 D96 ^ D97 D98 +error(0.001818820937996893771) D87 D99 +error(0.0001853416030457494853) D87 D99 ^ D90 +error(0.001711555130697444208) D87 D99 ^ D90 D98 +error(0.000272762609427849021) D87 D99 ^ D98 +error(0.01000000000000000021) D87 L0 +error(0.01235521784681438417) D87 L0 ^ D89 +error(0.004207393336629383467) D89 D97 ^ D87 L0 +error(0.01000000000000000021) D88 +error(0.01785301659321773815) D88 D91 +error(0.01637401583216853418) D88 D91 ^ D90 +error(0.02339721966187317467) D88 D96 +error(0.004593006776778153256) D88 D96 ^ D98 +error(0.004633022736341869084) D88 D99 +error(0.00323703015503416314) D88 D99 ^ D90 +error(0.004090640469596825389) D88 D99 ^ D98 +error(0.002101065562810008717) D88 D99 ^ D98 ^ D90 +error(0.007349448775310450134) D89 +error(0.009492030802785636837) D89 D90 +error(0.001887289238295643528) D89 D90 ^ D79 L0 +error(0.01247787028000646274) D89 D90 ^ D87 L0 +error(0.0007927547980242701528) D89 D90 ^ D91 L0 +error(0.0004780626237276926682) D89 D90 ^ D91 L0 ^ D79 L0 +error(0.0290017331886132379) D89 D97 +error(0.0002014583196037038619) D89 ^ D77 ^ D79 L0 +error(0.0004166580577437174488) D89 ^ D77 ^ D87 L0 +error(0.001290031617734563882) D89 ^ D87 L0 ^ D79 L0 +error(0.0107231597008022872) D90 +error(0.002819593978225994144) D90 D97 +error(0.003137767389894948866) D90 D97 ^ D87 L0 +error(0.0006968140446665368809) D90 D97 ^ D99 L0 +error(0.000505172529929820641) D90 D97 ^ D99 L0 ^ D87 L0 +error(0.02081610292411238683) D90 D98 +error(0.001206124809337631677) D90 D98 ^ D87 L0 +error(0.002463255996633777374) D90 D98 ^ D99 L0 +error(0.0002751468606974653234) D90 ^ D78 ^ D80 +error(0.0004594173010041198999) D90 ^ D78 ^ D88 +error(0.009220825108482943316) D90 ^ D91 L0 +error(0.001663150487453687832) D90 ^ D99 L0 +error(0.00455585972925812762) D91 D99 ^ D90 +error(0.01883905370747077437) D91 D99 +error(0.005000000000000000104) D91 L0 +error(0.01751672136136445471) D92 +error(0.01785065649831395901) D92 D95 +error(0.02043771586639645671) D92 D95 ^ D93 +error(0.01432068897443614271) D92 D100 +error(0.003724222745789971568) D92 D100 ^ D101 +error(0.008010401095168906069) D92 D103 +error(0.007940226548819344018) D92 D103 ^ D93 +error(0.003047688540304718287) D92 D103 ^ D101 +error(0.001113551153159442111) D92 D103 ^ D101 ^ D93 +error(0.0012261922121419445) D92 D104 +error(0.001293311027307580789) D92 D104 ^ D93 D101 +error(0.0002766506363411279324) D92 D104 ^ D101 +error(0.0127754705552621823) D92 ^ D93 +error(0.001340836656243296664) D92 ^ D101 +error(0.002787819927005772032) D93 D101 ^ D92 +error(0.01563106638893721131) D93 +error(0.01056384291876393856) D93 D94 +error(0.0002904294784450725487) D93 D94 ^ D92 +error(0.0002058388941412996195) D93 D94 ^ D95 D96 +error(0.0001957883314196005203) D93 D94 ^ D95 D104 +error(0.01111726604515600632) D93 D94 ^ D96 +error(0.002027113369458129113) D93 D94 ^ D96 D104 +error(0.001680777882927654652) D93 D94 ^ D104 +error(0.000510964933352025261) D93 D94 ^ D104 ^ D92 +error(0.01295778001257064625) D93 D98 +error(0.01037953181937149687) D93 D98 ^ D95 D96 +error(0.02565843058292602286) D93 D101 +error(0.001864362289368129985) D93 D101 ^ D103 D104 +error(0.0009030108125920386763) D93 D101 ^ D104 +error(0.002168943494147704959) D93 D105 +error(0.001623587268677913588) D93 D105 ^ D95 D103 +error(0.0002161019940614549329) D93 D105 ^ D95 D104 +error(0.000388632672172653233) D93 D105 ^ D103 D104 +error(0.001498498304452828417) D93 D106 +error(0.001856993221836747681) D93 D106 ^ D95 D96 +error(0.0009804196430601077195) D93 D106 ^ D95 D103 +error(0.001569512860033841139) D93 D106 ^ D95 D104 +error(0.0004097649005875169032) D93 D106 ^ D96 D104 +error(0.001862686936830267124) D93 D106 ^ D103 D104 +error(0.004219216288335304009) D95 D103 ^ D93 +error(0.01703946026454849702) D94 +error(0.003596024184132560076) D94 D101 +error(0.000353809852654799644) D94 D101 ^ D92 +error(0.003114589733093941094) D94 D101 ^ D104 +error(0.0003537892199881977127) D94 D101 ^ D104 ^ D92 +error(0.0268149874189349581) D94 D102 +error(0.002693575443238237482) D94 D102 ^ D104 +error(0.0009947049708068399828) D94 D106 +error(0.0004489571233011255941) D94 D106 ^ D95 D96 +error(0.0001023647480353978632) D94 D106 ^ D95 D104 +error(0.001017738624743863216) D94 D106 ^ D96 D104 +error(0.01180107044904601943) D94 ^ D96 +error(0.001485361101119797361) D94 ^ D104 ^ D96 +error(0.008909961818705179268) D95 D96 +error(0.000172764079590881711) D95 D96 ^ D85 +error(0.0001034000690297430145) D95 D96 ^ D93 +error(0.0004460207984751748565) D95 D96 ^ D97 D98 +error(0.002537074408032832188) D95 D96 ^ D98 D106 +error(0.0003403736328788634535) D95 D96 ^ D106 +error(0.03158800500128303157) D95 D103 +error(0.004053201805270884896) D95 D103 ^ D105 D106 +error(0.003287314687101230461) D95 D104 +error(0.000362912345862538815) D95 D104 ^ D105 D106 +error(0.001818820937996893771) D95 D107 +error(0.0001853416030457494853) D95 D107 ^ D98 +error(0.001711555130697444208) D95 D107 ^ D98 D106 +error(0.000272762609427849021) D95 D107 ^ D106 +error(0.01000000000000000021) D95 L0 +error(0.01235521784681438417) D95 L0 ^ D97 +error(0.004207393336629383467) D97 D105 ^ D95 L0 +error(0.01000000000000000021) D96 +error(0.01785301659321773815) D96 D99 +error(0.01637401583216853418) D96 D99 ^ D98 +error(0.02339721966187317467) D96 D104 +error(0.004593006776778153256) D96 D104 ^ D106 +error(0.004633022736341869084) D96 D107 +error(0.00323703015503416314) D96 D107 ^ D98 +error(0.004090640469596825389) D96 D107 ^ D106 +error(0.002101065562810008717) D96 D107 ^ D106 ^ D98 +error(0.007349448775310450134) D97 +error(0.009492030802785636837) D97 D98 +error(0.001887289238295643528) D97 D98 ^ D87 L0 +error(0.01247787028000646274) D97 D98 ^ D95 L0 +error(0.0007927547980242701528) D97 D98 ^ D99 L0 +error(0.0004780626237276926682) D97 D98 ^ D99 L0 ^ D87 L0 +error(0.0290017331886132379) D97 D105 +error(0.0002014583196037038619) D97 ^ D85 ^ D87 L0 +error(0.0004166580577437174488) D97 ^ D85 ^ D95 L0 +error(0.001290031617734563882) D97 ^ D95 L0 ^ D87 L0 +error(0.0107231597008022872) D98 +error(0.002819593978225994144) D98 D105 +error(0.003137767389894948866) D98 D105 ^ D95 L0 +error(0.0006968140446665368809) D98 D105 ^ D107 L0 +error(0.000505172529929820641) D98 D105 ^ D107 L0 ^ D95 L0 +error(0.02081610292411238683) D98 D106 +error(0.001206124809337631677) D98 D106 ^ D95 L0 +error(0.002463255996633777374) D98 D106 ^ D107 L0 +error(0.0002751468606974653234) D98 ^ D86 ^ D88 +error(0.0004594173010041198999) D98 ^ D86 ^ D96 +error(0.009220825108482943316) D98 ^ D99 L0 +error(0.001663150487453687832) D98 ^ D107 L0 +error(0.00455585972925812762) D99 D107 ^ D98 +error(0.01883905370747077437) D99 D107 +error(0.005000000000000000104) D99 L0 +error(0.01751672136136445471) D100 +error(0.01785065649831395901) D100 D103 +error(0.02043771586639645671) D100 D103 ^ D101 +error(0.01432068897443614271) D100 D108 +error(0.003724222745789971568) D100 D108 ^ D109 +error(0.008010401095168906069) D100 D111 +error(0.007940226548819344018) D100 D111 ^ D101 +error(0.003047688540304718287) D100 D111 ^ D109 +error(0.001113551153159442111) D100 D111 ^ D109 ^ D101 +error(0.0012261922121419445) D100 D112 +error(0.001293311027307580789) D100 D112 ^ D101 D109 +error(0.0002766506363411279324) D100 D112 ^ D109 +error(0.0127754705552621823) D100 ^ D101 +error(0.001340836656243296664) D100 ^ D109 +error(0.002787819927005772032) D101 D109 ^ D100 +error(0.01563106638893721131) D101 +error(0.01056384291876393856) D101 D102 +error(0.0002904294784450725487) D101 D102 ^ D100 +error(0.0002058388941412996195) D101 D102 ^ D103 D104 +error(0.0001957883314196005203) D101 D102 ^ D103 D112 +error(0.01111726604515600632) D101 D102 ^ D104 +error(0.002027113369458129113) D101 D102 ^ D104 D112 +error(0.001680777882927654652) D101 D102 ^ D112 +error(0.000510964933352025261) D101 D102 ^ D112 ^ D100 +error(0.01295778001257064625) D101 D106 +error(0.01037953181937149687) D101 D106 ^ D103 D104 +error(0.02565843058292602286) D101 D109 +error(0.001864362289368129985) D101 D109 ^ D111 D112 +error(0.0009030108125920386763) D101 D109 ^ D112 +error(0.002168943494147704959) D101 D113 +error(0.001623587268677913588) D101 D113 ^ D103 D111 +error(0.0002161019940614549329) D101 D113 ^ D103 D112 +error(0.000388632672172653233) D101 D113 ^ D111 D112 +error(0.001498498304452828417) D101 D114 +error(0.001856993221836747681) D101 D114 ^ D103 D104 +error(0.0009804196430601077195) D101 D114 ^ D103 D111 +error(0.001569512860033841139) D101 D114 ^ D103 D112 +error(0.0004097649005875169032) D101 D114 ^ D104 D112 +error(0.001862686936830267124) D101 D114 ^ D111 D112 +error(0.004219216288335304009) D103 D111 ^ D101 +error(0.01703946026454849702) D102 +error(0.003596024184132560076) D102 D109 +error(0.000353809852654799644) D102 D109 ^ D100 +error(0.003114589733093941094) D102 D109 ^ D112 +error(0.0003537892199881977127) D102 D109 ^ D112 ^ D100 +error(0.0268149874189349581) D102 D110 +error(0.002693575443238237482) D102 D110 ^ D112 +error(0.0009947049708068399828) D102 D114 +error(0.0004489571233011255941) D102 D114 ^ D103 D104 +error(0.0001023647480353978632) D102 D114 ^ D103 D112 +error(0.001017738624743863216) D102 D114 ^ D104 D112 +error(0.01180107044904601943) D102 ^ D104 +error(0.001485361101119797361) D102 ^ D112 ^ D104 +error(0.008909961818705179268) D103 D104 +error(0.000172764079590881711) D103 D104 ^ D93 +error(0.0001034000690297430145) D103 D104 ^ D101 +error(0.0004460207984751748565) D103 D104 ^ D105 D106 +error(0.002537074408032832188) D103 D104 ^ D106 D114 +error(0.0003403736328788634535) D103 D104 ^ D114 +error(0.03158800500128303157) D103 D111 +error(0.004053201805270884896) D103 D111 ^ D113 D114 +error(0.003287314687101230461) D103 D112 +error(0.000362912345862538815) D103 D112 ^ D113 D114 +error(0.001818820937996893771) D103 D115 +error(0.0001853416030457494853) D103 D115 ^ D106 +error(0.001711555130697444208) D103 D115 ^ D106 D114 +error(0.000272762609427849021) D103 D115 ^ D114 +error(0.01000000000000000021) D103 L0 +error(0.01235521784681438417) D103 L0 ^ D105 +error(0.004207393336629383467) D105 D113 ^ D103 L0 +error(0.01000000000000000021) D104 +error(0.01785301659321773815) D104 D107 +error(0.01637401583216853418) D104 D107 ^ D106 +error(0.02339721966187317467) D104 D112 +error(0.004593006776778153256) D104 D112 ^ D114 +error(0.004633022736341869084) D104 D115 +error(0.00323703015503416314) D104 D115 ^ D106 +error(0.004090640469596825389) D104 D115 ^ D114 +error(0.002101065562810008717) D104 D115 ^ D114 ^ D106 +error(0.007349448775310450134) D105 +error(0.009492030802785636837) D105 D106 +error(0.001887289238295643528) D105 D106 ^ D95 L0 +error(0.01247787028000646274) D105 D106 ^ D103 L0 +error(0.0007927547980242701528) D105 D106 ^ D107 L0 +error(0.0004780626237276926682) D105 D106 ^ D107 L0 ^ D95 L0 +error(0.0290017331886132379) D105 D113 +error(0.0002014583196037038619) D105 ^ D93 ^ D95 L0 +error(0.0004166580577437174488) D105 ^ D93 ^ D103 L0 +error(0.001290031617734563882) D105 ^ D103 L0 ^ D95 L0 +error(0.0107231597008022872) D106 +error(0.002819593978225994144) D106 D113 +error(0.003137767389894948866) D106 D113 ^ D103 L0 +error(0.0006968140446665368809) D106 D113 ^ D115 L0 +error(0.000505172529929820641) D106 D113 ^ D115 L0 ^ D103 L0 +error(0.02081610292411238683) D106 D114 +error(0.001206124809337631677) D106 D114 ^ D103 L0 +error(0.002463255996633777374) D106 D114 ^ D115 L0 +error(0.0002751468606974653234) D106 ^ D94 ^ D96 +error(0.0004594173010041198999) D106 ^ D94 ^ D104 +error(0.009220825108482943316) D106 ^ D107 L0 +error(0.001663150487453687832) D106 ^ D115 L0 +error(0.00455585972925812762) D107 D115 ^ D106 +error(0.01883905370747077437) D107 D115 +error(0.005000000000000000104) D107 L0 +error(0.01751672136136445471) D108 +error(0.01785065649831395901) D108 D111 +error(0.02043771586639645671) D108 D111 ^ D109 +error(0.01432068897443614271) D108 D116 +error(0.003724222745789971568) D108 D116 ^ D117 +error(0.008010401095168906069) D108 D119 +error(0.007940226548819344018) D108 D119 ^ D109 +error(0.003047688540304718287) D108 D119 ^ D117 +error(0.001113551153159442111) D108 D119 ^ D117 ^ D109 +error(0.0012261922121419445) D108 D120 +error(0.001293311027307580789) D108 D120 ^ D109 D117 +error(0.0002766506363411279324) D108 D120 ^ D117 +error(0.0127754705552621823) D108 ^ D109 +error(0.001340836656243296664) D108 ^ D117 +error(0.002787819927005772032) D109 D117 ^ D108 +error(0.01563106638893721131) D109 +error(0.01056384291876393856) D109 D110 +error(0.0002904294784450725487) D109 D110 ^ D108 +error(0.0002058388941412996195) D109 D110 ^ D111 D112 +error(0.0001957883314196005203) D109 D110 ^ D111 D120 +error(0.01111726604515600632) D109 D110 ^ D112 +error(0.002027113369458129113) D109 D110 ^ D112 D120 +error(0.001680777882927654652) D109 D110 ^ D120 +error(0.000510964933352025261) D109 D110 ^ D120 ^ D108 +error(0.01295778001257064625) D109 D114 +error(0.01037953181937149687) D109 D114 ^ D111 D112 +error(0.02565843058292602286) D109 D117 +error(0.001864362289368129985) D109 D117 ^ D119 D120 +error(0.0009030108125920386763) D109 D117 ^ D120 +error(0.002168943494147704959) D109 D121 +error(0.001623587268677913588) D109 D121 ^ D111 D119 +error(0.0002161019940614549329) D109 D121 ^ D111 D120 +error(0.000388632672172653233) D109 D121 ^ D119 D120 +error(0.001498498304452828417) D109 D122 +error(0.001856993221836747681) D109 D122 ^ D111 D112 +error(0.0009804196430601077195) D109 D122 ^ D111 D119 +error(0.001569512860033841139) D109 D122 ^ D111 D120 +error(0.0004097649005875169032) D109 D122 ^ D112 D120 +error(0.001862686936830267124) D109 D122 ^ D119 D120 +error(0.004219216288335304009) D111 D119 ^ D109 +error(0.01703946026454849702) D110 +error(0.003596024184132560076) D110 D117 +error(0.000353809852654799644) D110 D117 ^ D108 +error(0.003114589733093941094) D110 D117 ^ D120 +error(0.0003537892199881977127) D110 D117 ^ D120 ^ D108 +error(0.0268149874189349581) D110 D118 +error(0.002693575443238237482) D110 D118 ^ D120 +error(0.0009947049708068399828) D110 D122 +error(0.0004489571233011255941) D110 D122 ^ D111 D112 +error(0.0001023647480353978632) D110 D122 ^ D111 D120 +error(0.001017738624743863216) D110 D122 ^ D112 D120 +error(0.01180107044904601943) D110 ^ D112 +error(0.001485361101119797361) D110 ^ D120 ^ D112 +error(0.008909961818705179268) D111 D112 +error(0.000172764079590881711) D111 D112 ^ D101 +error(0.0001034000690297430145) D111 D112 ^ D109 +error(0.0004460207984751748565) D111 D112 ^ D113 D114 +error(0.002537074408032832188) D111 D112 ^ D114 D122 +error(0.0003403736328788634535) D111 D112 ^ D122 +error(0.03158800500128303157) D111 D119 +error(0.004053201805270884896) D111 D119 ^ D121 D122 +error(0.003287314687101230461) D111 D120 +error(0.000362912345862538815) D111 D120 ^ D121 D122 +error(0.001818820937996893771) D111 D123 +error(0.0001853416030457494853) D111 D123 ^ D114 +error(0.001711555130697444208) D111 D123 ^ D114 D122 +error(0.000272762609427849021) D111 D123 ^ D122 +error(0.01000000000000000021) D111 L0 +error(0.01235521784681438417) D111 L0 ^ D113 +error(0.004207393336629383467) D113 D121 ^ D111 L0 +error(0.01000000000000000021) D112 +error(0.01785301659321773815) D112 D115 +error(0.01637401583216853418) D112 D115 ^ D114 +error(0.02339721966187317467) D112 D120 +error(0.004593006776778153256) D112 D120 ^ D122 +error(0.004633022736341869084) D112 D123 +error(0.00323703015503416314) D112 D123 ^ D114 +error(0.004090640469596825389) D112 D123 ^ D122 +error(0.002101065562810008717) D112 D123 ^ D122 ^ D114 +error(0.007349448775310450134) D113 +error(0.009492030802785636837) D113 D114 +error(0.001887289238295643528) D113 D114 ^ D103 L0 +error(0.01247787028000646274) D113 D114 ^ D111 L0 +error(0.0007927547980242701528) D113 D114 ^ D115 L0 +error(0.0004780626237276926682) D113 D114 ^ D115 L0 ^ D103 L0 +error(0.0290017331886132379) D113 D121 +error(0.0002014583196037038619) D113 ^ D101 ^ D103 L0 +error(0.0004166580577437174488) D113 ^ D101 ^ D111 L0 +error(0.001290031617734563882) D113 ^ D111 L0 ^ D103 L0 +error(0.0107231597008022872) D114 +error(0.002819593978225994144) D114 D121 +error(0.003137767389894948866) D114 D121 ^ D111 L0 +error(0.0006968140446665368809) D114 D121 ^ D123 L0 +error(0.000505172529929820641) D114 D121 ^ D123 L0 ^ D111 L0 +error(0.02081610292411238683) D114 D122 +error(0.001206124809337631677) D114 D122 ^ D111 L0 +error(0.002463255996633777374) D114 D122 ^ D123 L0 +error(0.0002751468606974653234) D114 ^ D102 ^ D104 +error(0.0004594173010041198999) D114 ^ D102 ^ D112 +error(0.009220825108482943316) D114 ^ D115 L0 +error(0.001663150487453687832) D114 ^ D123 L0 +error(0.00455585972925812762) D115 D123 ^ D114 +error(0.01883905370747077437) D115 D123 +error(0.005000000000000000104) D115 L0 +error(0.01751672136136445471) D116 +error(0.01785065649831395901) D116 D119 +error(0.02043771586639645671) D116 D119 ^ D117 +error(0.01432068897443614271) D116 D124 +error(0.003724222745789971568) D116 D124 ^ D125 +error(0.008010401095168906069) D116 D127 +error(0.007940226548819344018) D116 D127 ^ D117 +error(0.003047688540304718287) D116 D127 ^ D125 +error(0.001113551153159442111) D116 D127 ^ D125 ^ D117 +error(0.0012261922121419445) D116 D128 +error(0.001293311027307580789) D116 D128 ^ D117 D125 +error(0.0002766506363411279324) D116 D128 ^ D125 +error(0.0127754705552621823) D116 ^ D117 +error(0.001340836656243296664) D116 ^ D125 +error(0.002787819927005772032) D117 D125 ^ D116 +error(0.01563106638893721131) D117 +error(0.01056384291876393856) D117 D118 +error(0.0002904294784450725487) D117 D118 ^ D116 +error(0.0002058388941412996195) D117 D118 ^ D119 D120 +error(0.0001957883314196005203) D117 D118 ^ D119 D128 +error(0.01111726604515600632) D117 D118 ^ D120 +error(0.002027113369458129113) D117 D118 ^ D120 D128 +error(0.001680777882927654652) D117 D118 ^ D128 +error(0.000510964933352025261) D117 D118 ^ D128 ^ D116 +error(0.01295778001257064625) D117 D122 +error(0.01037953181937149687) D117 D122 ^ D119 D120 +error(0.02565843058292602286) D117 D125 +error(0.001864362289368129985) D117 D125 ^ D127 D128 +error(0.0009030108125920386763) D117 D125 ^ D128 +error(0.002168943494147704959) D117 D129 +error(0.001623587268677913588) D117 D129 ^ D119 D127 +error(0.0002161019940614549329) D117 D129 ^ D119 D128 +error(0.000388632672172653233) D117 D129 ^ D127 D128 +error(0.001498498304452828417) D117 D130 +error(0.001856993221836747681) D117 D130 ^ D119 D120 +error(0.0009804196430601077195) D117 D130 ^ D119 D127 +error(0.001569512860033841139) D117 D130 ^ D119 D128 +error(0.0004097649005875169032) D117 D130 ^ D120 D128 +error(0.001862686936830267124) D117 D130 ^ D127 D128 +error(0.004219216288335304009) D119 D127 ^ D117 +error(0.01703946026454849702) D118 +error(0.003596024184132560076) D118 D125 +error(0.000353809852654799644) D118 D125 ^ D116 +error(0.003114589733093941094) D118 D125 ^ D128 +error(0.0003537892199881977127) D118 D125 ^ D128 ^ D116 +error(0.0268149874189349581) D118 D126 +error(0.002693575443238237482) D118 D126 ^ D128 +error(0.0009947049708068399828) D118 D130 +error(0.0004489571233011255941) D118 D130 ^ D119 D120 +error(0.0001023647480353978632) D118 D130 ^ D119 D128 +error(0.001017738624743863216) D118 D130 ^ D120 D128 +error(0.01180107044904601943) D118 ^ D120 +error(0.001485361101119797361) D118 ^ D128 ^ D120 +error(0.008909961818705179268) D119 D120 +error(0.000172764079590881711) D119 D120 ^ D109 +error(0.0001034000690297430145) D119 D120 ^ D117 +error(0.0004460207984751748565) D119 D120 ^ D121 D122 +error(0.002537074408032832188) D119 D120 ^ D122 D130 +error(0.0003403736328788634535) D119 D120 ^ D130 +error(0.03158800500128303157) D119 D127 +error(0.004053201805270884896) D119 D127 ^ D129 D130 +error(0.003287314687101230461) D119 D128 +error(0.000362912345862538815) D119 D128 ^ D129 D130 +error(0.001818820937996893771) D119 D131 +error(0.0001853416030457494853) D119 D131 ^ D122 +error(0.001711555130697444208) D119 D131 ^ D122 D130 +error(0.000272762609427849021) D119 D131 ^ D130 +error(0.01000000000000000021) D119 L0 +error(0.01235521784681438417) D119 L0 ^ D121 +error(0.004207393336629383467) D121 D129 ^ D119 L0 +error(0.01000000000000000021) D120 +error(0.01785301659321773815) D120 D123 +error(0.01637401583216853418) D120 D123 ^ D122 +error(0.02339721966187317467) D120 D128 +error(0.004593006776778153256) D120 D128 ^ D130 +error(0.004633022736341869084) D120 D131 +error(0.00323703015503416314) D120 D131 ^ D122 +error(0.004090640469596825389) D120 D131 ^ D130 +error(0.002101065562810008717) D120 D131 ^ D130 ^ D122 +error(0.007349448775310450134) D121 +error(0.009492030802785636837) D121 D122 +error(0.001887289238295643528) D121 D122 ^ D111 L0 +error(0.01247787028000646274) D121 D122 ^ D119 L0 +error(0.0007927547980242701528) D121 D122 ^ D123 L0 +error(0.0004780626237276926682) D121 D122 ^ D123 L0 ^ D111 L0 +error(0.0290017331886132379) D121 D129 +error(0.0002014583196037038619) D121 ^ D109 ^ D111 L0 +error(0.0004166580577437174488) D121 ^ D109 ^ D119 L0 +error(0.001290031617734563882) D121 ^ D119 L0 ^ D111 L0 +error(0.0107231597008022872) D122 +error(0.002819593978225994144) D122 D129 +error(0.003137767389894948866) D122 D129 ^ D119 L0 +error(0.0006968140446665368809) D122 D129 ^ D131 L0 +error(0.000505172529929820641) D122 D129 ^ D131 L0 ^ D119 L0 +error(0.02081610292411238683) D122 D130 +error(0.001206124809337631677) D122 D130 ^ D119 L0 +error(0.002463255996633777374) D122 D130 ^ D131 L0 +error(0.0002751468606974653234) D122 ^ D110 ^ D112 +error(0.0004594173010041198999) D122 ^ D110 ^ D120 +error(0.009220825108482943316) D122 ^ D123 L0 +error(0.001663150487453687832) D122 ^ D131 L0 +error(0.00455585972925812762) D123 D131 ^ D122 +error(0.01883905370747077437) D123 D131 +error(0.005000000000000000104) D123 L0 +error(0.01751672136136445471) D124 +error(0.01785065649831395901) D124 D127 +error(0.02043771586639645671) D124 D127 ^ D125 +error(0.01432068897443614271) D124 D132 +error(0.003724222745789971568) D124 D132 ^ D133 +error(0.008010401095168906069) D124 D135 +error(0.007940226548819344018) D124 D135 ^ D125 +error(0.003047688540304718287) D124 D135 ^ D133 +error(0.001113551153159442111) D124 D135 ^ D133 ^ D125 +error(0.0012261922121419445) D124 D136 +error(0.001293311027307580789) D124 D136 ^ D125 D133 +error(0.0002766506363411279324) D124 D136 ^ D133 +error(0.0127754705552621823) D124 ^ D125 +error(0.001340836656243296664) D124 ^ D133 +error(0.002787819927005772032) D125 D133 ^ D124 +error(0.01563106638893721131) D125 +error(0.01056384291876393856) D125 D126 +error(0.0002904294784450725487) D125 D126 ^ D124 +error(0.0002058388941412996195) D125 D126 ^ D127 D128 +error(0.0001957883314196005203) D125 D126 ^ D127 D136 +error(0.01111726604515600632) D125 D126 ^ D128 +error(0.002027113369458129113) D125 D126 ^ D128 D136 +error(0.001680777882927654652) D125 D126 ^ D136 +error(0.000510964933352025261) D125 D126 ^ D136 ^ D124 +error(0.01295778001257064625) D125 D130 +error(0.01037953181937149687) D125 D130 ^ D127 D128 +error(0.02565843058292602286) D125 D133 +error(0.001864362289368129985) D125 D133 ^ D135 D136 +error(0.0009030108125920386763) D125 D133 ^ D136 +error(0.002168943494147704959) D125 D137 +error(0.001623587268677913588) D125 D137 ^ D127 D135 +error(0.0002161019940614549329) D125 D137 ^ D127 D136 +error(0.000388632672172653233) D125 D137 ^ D135 D136 +error(0.001498498304452828417) D125 D138 +error(0.001856993221836747681) D125 D138 ^ D127 D128 +error(0.0009804196430601077195) D125 D138 ^ D127 D135 +error(0.001569512860033841139) D125 D138 ^ D127 D136 +error(0.0004097649005875169032) D125 D138 ^ D128 D136 +error(0.001862686936830267124) D125 D138 ^ D135 D136 +error(0.004219216288335304009) D127 D135 ^ D125 +error(0.01703946026454849702) D126 +error(0.003596024184132560076) D126 D133 +error(0.000353809852654799644) D126 D133 ^ D124 +error(0.003114589733093941094) D126 D133 ^ D136 +error(0.0003537892199881977127) D126 D133 ^ D136 ^ D124 +error(0.0268149874189349581) D126 D134 +error(0.002693575443238237482) D126 D134 ^ D136 +error(0.0009947049708068399828) D126 D138 +error(0.0004489571233011255941) D126 D138 ^ D127 D128 +error(0.0001023647480353978632) D126 D138 ^ D127 D136 +error(0.001017738624743863216) D126 D138 ^ D128 D136 +error(0.01180107044904601943) D126 ^ D128 +error(0.001485361101119797361) D126 ^ D136 ^ D128 +error(0.008909961818705179268) D127 D128 +error(0.000172764079590881711) D127 D128 ^ D117 +error(0.0001034000690297430145) D127 D128 ^ D125 +error(0.0004460207984751748565) D127 D128 ^ D129 D130 +error(0.002537074408032832188) D127 D128 ^ D130 D138 +error(0.0003403736328788634535) D127 D128 ^ D138 +error(0.03158800500128303157) D127 D135 +error(0.004053201805270884896) D127 D135 ^ D137 D138 +error(0.003287314687101230461) D127 D136 +error(0.000362912345862538815) D127 D136 ^ D137 D138 +error(0.001818820937996893771) D127 D139 +error(0.0001853416030457494853) D127 D139 ^ D130 +error(0.001711555130697444208) D127 D139 ^ D130 D138 +error(0.000272762609427849021) D127 D139 ^ D138 +error(0.01000000000000000021) D127 L0 +error(0.01235521784681438417) D127 L0 ^ D129 +error(0.004207393336629383467) D129 D137 ^ D127 L0 +error(0.01000000000000000021) D128 +error(0.01785301659321773815) D128 D131 +error(0.01637401583216853418) D128 D131 ^ D130 +error(0.02339721966187317467) D128 D136 +error(0.004593006776778153256) D128 D136 ^ D138 +error(0.004633022736341869084) D128 D139 +error(0.00323703015503416314) D128 D139 ^ D130 +error(0.004090640469596825389) D128 D139 ^ D138 +error(0.002101065562810008717) D128 D139 ^ D138 ^ D130 +error(0.007349448775310450134) D129 +error(0.009492030802785636837) D129 D130 +error(0.001887289238295643528) D129 D130 ^ D119 L0 +error(0.01247787028000646274) D129 D130 ^ D127 L0 +error(0.0007927547980242701528) D129 D130 ^ D131 L0 +error(0.0004780626237276926682) D129 D130 ^ D131 L0 ^ D119 L0 +error(0.0290017331886132379) D129 D137 +error(0.0002014583196037038619) D129 ^ D117 ^ D119 L0 +error(0.0004166580577437174488) D129 ^ D117 ^ D127 L0 +error(0.001290031617734563882) D129 ^ D127 L0 ^ D119 L0 +error(0.0107231597008022872) D130 +error(0.002819593978225994144) D130 D137 +error(0.003137767389894948866) D130 D137 ^ D127 L0 +error(0.0006968140446665368809) D130 D137 ^ D139 L0 +error(0.000505172529929820641) D130 D137 ^ D139 L0 ^ D127 L0 +error(0.02081610292411238683) D130 D138 +error(0.001206124809337631677) D130 D138 ^ D127 L0 +error(0.002463255996633777374) D130 D138 ^ D139 L0 +error(0.0002751468606974653234) D130 ^ D118 ^ D120 +error(0.0004594173010041198999) D130 ^ D118 ^ D128 +error(0.009220825108482943316) D130 ^ D131 L0 +error(0.001663150487453687832) D130 ^ D139 L0 +error(0.00455585972925812762) D131 D139 ^ D130 +error(0.01883905370747077437) D131 D139 +error(0.005000000000000000104) D131 L0 +error(0.01751672136136445471) D132 +error(0.01785065649831395901) D132 D135 +error(0.02043771586639645671) D132 D135 ^ D133 +error(0.01432068897443614271) D132 D140 +error(0.003724222745789971568) D132 D140 ^ D141 +error(0.008010401095168906069) D132 D143 +error(0.007940226548819344018) D132 D143 ^ D133 +error(0.003047688540304718287) D132 D143 ^ D141 +error(0.001113551153159442111) D132 D143 ^ D141 ^ D133 +error(0.0012261922121419445) D132 D144 +error(0.001293311027307580789) D132 D144 ^ D133 D141 +error(0.0002766506363411279324) D132 D144 ^ D141 +error(0.0127754705552621823) D132 ^ D133 +error(0.001340836656243296664) D132 ^ D141 +error(0.002787819927005772032) D133 D141 ^ D132 +error(0.01563106638893721131) D133 +error(0.01056384291876393856) D133 D134 +error(0.0002904294784450725487) D133 D134 ^ D132 +error(0.0002058388941412996195) D133 D134 ^ D135 D136 +error(0.0001957883314196005203) D133 D134 ^ D135 D144 +error(0.01111726604515600632) D133 D134 ^ D136 +error(0.002027113369458129113) D133 D134 ^ D136 D144 +error(0.001680777882927654652) D133 D134 ^ D144 +error(0.000510964933352025261) D133 D134 ^ D144 ^ D132 +error(0.01295778001257064625) D133 D138 +error(0.01037953181937149687) D133 D138 ^ D135 D136 +error(0.02565843058292602286) D133 D141 +error(0.001864362289368129985) D133 D141 ^ D143 D144 +error(0.0009030108125920386763) D133 D141 ^ D144 +error(0.002168943494147704959) D133 D145 +error(0.001623587268677913588) D133 D145 ^ D135 D143 +error(0.0002161019940614549329) D133 D145 ^ D135 D144 +error(0.000388632672172653233) D133 D145 ^ D143 D144 +error(0.001498498304452828417) D133 D146 +error(0.001856993221836747681) D133 D146 ^ D135 D136 +error(0.0009804196430601077195) D133 D146 ^ D135 D143 +error(0.001569512860033841139) D133 D146 ^ D135 D144 +error(0.0004097649005875169032) D133 D146 ^ D136 D144 +error(0.001862686936830267124) D133 D146 ^ D143 D144 +error(0.004219216288335304009) D135 D143 ^ D133 +error(0.01703946026454849702) D134 +error(0.003596024184132560076) D134 D141 +error(0.000353809852654799644) D134 D141 ^ D132 +error(0.003114589733093941094) D134 D141 ^ D144 +error(0.0003537892199881977127) D134 D141 ^ D144 ^ D132 +error(0.0268149874189349581) D134 D142 +error(0.002693575443238237482) D134 D142 ^ D144 +error(0.0009947049708068399828) D134 D146 +error(0.0004489571233011255941) D134 D146 ^ D135 D136 +error(0.0001023647480353978632) D134 D146 ^ D135 D144 +error(0.001017738624743863216) D134 D146 ^ D136 D144 +error(0.01180107044904601943) D134 ^ D136 +error(0.001485361101119797361) D134 ^ D144 ^ D136 +error(0.008909961818705179268) D135 D136 +error(0.000172764079590881711) D135 D136 ^ D125 +error(0.0001034000690297430145) D135 D136 ^ D133 +error(0.0004460207984751748565) D135 D136 ^ D137 D138 +error(0.002537074408032832188) D135 D136 ^ D138 D146 +error(0.0003403736328788634535) D135 D136 ^ D146 +error(0.03158800500128303157) D135 D143 +error(0.004053201805270884896) D135 D143 ^ D145 D146 +error(0.003287314687101230461) D135 D144 +error(0.000362912345862538815) D135 D144 ^ D145 D146 +error(0.001818820937996893771) D135 D147 +error(0.0001853416030457494853) D135 D147 ^ D138 +error(0.001711555130697444208) D135 D147 ^ D138 D146 +error(0.000272762609427849021) D135 D147 ^ D146 +error(0.01000000000000000021) D135 L0 +error(0.01235521784681438417) D135 L0 ^ D137 +error(0.004207393336629383467) D137 D145 ^ D135 L0 +error(0.01000000000000000021) D136 +error(0.01785301659321773815) D136 D139 +error(0.01637401583216853418) D136 D139 ^ D138 +error(0.02339721966187317467) D136 D144 +error(0.004593006776778153256) D136 D144 ^ D146 +error(0.004633022736341869084) D136 D147 +error(0.00323703015503416314) D136 D147 ^ D138 +error(0.004090640469596825389) D136 D147 ^ D146 +error(0.002101065562810008717) D136 D147 ^ D146 ^ D138 +error(0.007349448775310450134) D137 +error(0.009492030802785636837) D137 D138 +error(0.001887289238295643528) D137 D138 ^ D127 L0 +error(0.01247787028000646274) D137 D138 ^ D135 L0 +error(0.0007927547980242701528) D137 D138 ^ D139 L0 +error(0.0004780626237276926682) D137 D138 ^ D139 L0 ^ D127 L0 +error(0.0290017331886132379) D137 D145 +error(0.0002014583196037038619) D137 ^ D125 ^ D127 L0 +error(0.0004166580577437174488) D137 ^ D125 ^ D135 L0 +error(0.001290031617734563882) D137 ^ D135 L0 ^ D127 L0 +error(0.0107231597008022872) D138 +error(0.002819593978225994144) D138 D145 +error(0.003137767389894948866) D138 D145 ^ D135 L0 +error(0.0006968140446665368809) D138 D145 ^ D147 L0 +error(0.000505172529929820641) D138 D145 ^ D147 L0 ^ D135 L0 +error(0.02081610292411238683) D138 D146 +error(0.001206124809337631677) D138 D146 ^ D135 L0 +error(0.002463255996633777374) D138 D146 ^ D147 L0 +error(0.0002751468606974653234) D138 ^ D126 ^ D128 +error(0.0004594173010041198999) D138 ^ D126 ^ D136 +error(0.009220825108482943316) D138 ^ D139 L0 +error(0.001663150487453687832) D138 ^ D147 L0 +error(0.00455585972925812762) D139 D147 ^ D138 +error(0.01883905370747077437) D139 D147 +error(0.005000000000000000104) D139 L0 +error(0.01751672136136445471) D140 +error(0.01785065649831395901) D140 D143 +error(0.02043771586639645671) D140 D143 ^ D141 +error(0.01432068897443614271) D140 D148 +error(0.003724222745789971568) D140 D148 ^ D149 +error(0.008010401095168906069) D140 D151 +error(0.007940226548819344018) D140 D151 ^ D141 +error(0.003047688540304718287) D140 D151 ^ D149 +error(0.001113551153159442111) D140 D151 ^ D149 ^ D141 +error(0.0012261922121419445) D140 D152 +error(0.001293311027307580789) D140 D152 ^ D141 D149 +error(0.0002766506363411279324) D140 D152 ^ D149 +error(0.0127754705552621823) D140 ^ D141 +error(0.001340836656243296664) D140 ^ D149 +error(0.002787819927005772032) D141 D149 ^ D140 +error(0.01563106638893721131) D141 +error(0.01056384291876393856) D141 D142 +error(0.0002904294784450725487) D141 D142 ^ D140 +error(0.0002058388941412996195) D141 D142 ^ D143 D144 +error(0.0001957883314196005203) D141 D142 ^ D143 D152 +error(0.01111726604515600632) D141 D142 ^ D144 +error(0.002027113369458129113) D141 D142 ^ D144 D152 +error(0.001680777882927654652) D141 D142 ^ D152 +error(0.000510964933352025261) D141 D142 ^ D152 ^ D140 +error(0.01295778001257064625) D141 D146 +error(0.01037953181937149687) D141 D146 ^ D143 D144 +error(0.02565843058292602286) D141 D149 +error(0.001864362289368129985) D141 D149 ^ D151 D152 +error(0.0009030108125920386763) D141 D149 ^ D152 +error(0.002168943494147704959) D141 D153 +error(0.001623587268677913588) D141 D153 ^ D143 D151 +error(0.0002161019940614549329) D141 D153 ^ D143 D152 +error(0.000388632672172653233) D141 D153 ^ D151 D152 +error(0.001498498304452828417) D141 D154 +error(0.001856993221836747681) D141 D154 ^ D143 D144 +error(0.0009804196430601077195) D141 D154 ^ D143 D151 +error(0.001569512860033841139) D141 D154 ^ D143 D152 +error(0.0004097649005875169032) D141 D154 ^ D144 D152 +error(0.001862686936830267124) D141 D154 ^ D151 D152 +error(0.004219216288335304009) D143 D151 ^ D141 +error(0.01703946026454849702) D142 +error(0.003596024184132560076) D142 D149 +error(0.000353809852654799644) D142 D149 ^ D140 +error(0.003114589733093941094) D142 D149 ^ D152 +error(0.0003537892199881977127) D142 D149 ^ D152 ^ D140 +error(0.0268149874189349581) D142 D150 +error(0.002693575443238237482) D142 D150 ^ D152 +error(0.0009947049708068399828) D142 D154 +error(0.0004489571233011255941) D142 D154 ^ D143 D144 +error(0.0001023647480353978632) D142 D154 ^ D143 D152 +error(0.001017738624743863216) D142 D154 ^ D144 D152 +error(0.01180107044904601943) D142 ^ D144 +error(0.001485361101119797361) D142 ^ D152 ^ D144 +error(0.008909961818705179268) D143 D144 +error(0.000172764079590881711) D143 D144 ^ D133 +error(0.0001034000690297430145) D143 D144 ^ D141 +error(0.0004460207984751748565) D143 D144 ^ D145 D146 +error(0.002537074408032832188) D143 D144 ^ D146 D154 +error(0.0003403736328788634535) D143 D144 ^ D154 +error(0.03158800500128303157) D143 D151 +error(0.004053201805270884896) D143 D151 ^ D153 D154 +error(0.003287314687101230461) D143 D152 +error(0.000362912345862538815) D143 D152 ^ D153 D154 +error(0.001818820937996893771) D143 D155 +error(0.0001853416030457494853) D143 D155 ^ D146 +error(0.001711555130697444208) D143 D155 ^ D146 D154 +error(0.000272762609427849021) D143 D155 ^ D154 +error(0.01000000000000000021) D143 L0 +error(0.01235521784681438417) D143 L0 ^ D145 +error(0.004207393336629383467) D145 D153 ^ D143 L0 +error(0.01000000000000000021) D144 +error(0.01785301659321773815) D144 D147 +error(0.01637401583216853418) D144 D147 ^ D146 +error(0.02339721966187317467) D144 D152 +error(0.004593006776778153256) D144 D152 ^ D154 +error(0.004633022736341869084) D144 D155 +error(0.00323703015503416314) D144 D155 ^ D146 +error(0.004090640469596825389) D144 D155 ^ D154 +error(0.002101065562810008717) D144 D155 ^ D154 ^ D146 +error(0.007349448775310450134) D145 +error(0.009492030802785636837) D145 D146 +error(0.001887289238295643528) D145 D146 ^ D135 L0 +error(0.01247787028000646274) D145 D146 ^ D143 L0 +error(0.0007927547980242701528) D145 D146 ^ D147 L0 +error(0.0004780626237276926682) D145 D146 ^ D147 L0 ^ D135 L0 +error(0.0290017331886132379) D145 D153 +error(0.0002014583196037038619) D145 ^ D133 ^ D135 L0 +error(0.0004166580577437174488) D145 ^ D133 ^ D143 L0 +error(0.001290031617734563882) D145 ^ D143 L0 ^ D135 L0 +error(0.0107231597008022872) D146 +error(0.002819593978225994144) D146 D153 +error(0.003137767389894948866) D146 D153 ^ D143 L0 +error(0.0006968140446665368809) D146 D153 ^ D155 L0 +error(0.000505172529929820641) D146 D153 ^ D155 L0 ^ D143 L0 +error(0.02081610292411238683) D146 D154 +error(0.001206124809337631677) D146 D154 ^ D143 L0 +error(0.002463255996633777374) D146 D154 ^ D155 L0 +error(0.0002751468606974653234) D146 ^ D134 ^ D136 +error(0.0004594173010041198999) D146 ^ D134 ^ D144 +error(0.009220825108482943316) D146 ^ D147 L0 +error(0.001663150487453687832) D146 ^ D155 L0 +error(0.00455585972925812762) D147 D155 ^ D146 +error(0.01883905370747077437) D147 D155 +error(0.005000000000000000104) D147 L0 +error(0.01751672136136445471) D148 +error(0.01785065649831395901) D148 D151 +error(0.02043771586639645671) D148 D151 ^ D149 +error(0.01432068897443614271) D148 D156 +error(0.003724222745789971568) D148 D156 ^ D157 +error(0.008010401095168906069) D148 D159 +error(0.007940226548819344018) D148 D159 ^ D149 +error(0.003047688540304718287) D148 D159 ^ D157 +error(0.001113551153159442111) D148 D159 ^ D157 ^ D149 +error(0.0012261922121419445) D148 D160 +error(0.001293311027307580789) D148 D160 ^ D149 D157 +error(0.0002766506363411279324) D148 D160 ^ D157 +error(0.0127754705552621823) D148 ^ D149 +error(0.001340836656243296664) D148 ^ D157 +error(0.002787819927005772032) D149 D157 ^ D148 +error(0.01563106638893721131) D149 +error(0.01056384291876393856) D149 D150 +error(0.0002904294784450725487) D149 D150 ^ D148 +error(0.0002058388941412996195) D149 D150 ^ D151 D152 +error(0.0001957883314196005203) D149 D150 ^ D151 D160 +error(0.01111726604515600632) D149 D150 ^ D152 +error(0.002027113369458129113) D149 D150 ^ D152 D160 +error(0.001680777882927654652) D149 D150 ^ D160 +error(0.000510964933352025261) D149 D150 ^ D160 ^ D148 +error(0.01295778001257064625) D149 D154 +error(0.01037953181937149687) D149 D154 ^ D151 D152 +error(0.02565843058292602286) D149 D157 +error(0.001864362289368129985) D149 D157 ^ D159 D160 +error(0.0009030108125920386763) D149 D157 ^ D160 +error(0.002168943494147704959) D149 D161 +error(0.001623587268677913588) D149 D161 ^ D151 D159 +error(0.0002161019940614549329) D149 D161 ^ D151 D160 +error(0.000388632672172653233) D149 D161 ^ D159 D160 +error(0.001498498304452828417) D149 D162 +error(0.001856993221836747681) D149 D162 ^ D151 D152 +error(0.0009804196430601077195) D149 D162 ^ D151 D159 +error(0.001569512860033841139) D149 D162 ^ D151 D160 +error(0.0004097649005875169032) D149 D162 ^ D152 D160 +error(0.001862686936830267124) D149 D162 ^ D159 D160 +error(0.004219216288335304009) D151 D159 ^ D149 +error(0.01703946026454849702) D150 +error(0.003596024184132560076) D150 D157 +error(0.000353809852654799644) D150 D157 ^ D148 +error(0.003114589733093941094) D150 D157 ^ D160 +error(0.0003537892199881977127) D150 D157 ^ D160 ^ D148 +error(0.0268149874189349581) D150 D158 +error(0.002693575443238237482) D150 D158 ^ D160 +error(0.0009947049708068399828) D150 D162 +error(0.0004489571233011255941) D150 D162 ^ D151 D152 +error(0.0001023647480353978632) D150 D162 ^ D151 D160 +error(0.001017738624743863216) D150 D162 ^ D152 D160 +error(0.01180107044904601943) D150 ^ D152 +error(0.001485361101119797361) D150 ^ D160 ^ D152 +error(0.008909961818705179268) D151 D152 +error(0.000172764079590881711) D151 D152 ^ D141 +error(0.0001034000690297430145) D151 D152 ^ D149 +error(0.0004460207984751748565) D151 D152 ^ D153 D154 +error(0.002537074408032832188) D151 D152 ^ D154 D162 +error(0.0003403736328788634535) D151 D152 ^ D162 +error(0.03158800500128303157) D151 D159 +error(0.004053201805270884896) D151 D159 ^ D161 D162 +error(0.003287314687101230461) D151 D160 +error(0.000362912345862538815) D151 D160 ^ D161 D162 +error(0.001818820937996893771) D151 D163 +error(0.0001853416030457494853) D151 D163 ^ D154 +error(0.001711555130697444208) D151 D163 ^ D154 D162 +error(0.000272762609427849021) D151 D163 ^ D162 +error(0.01000000000000000021) D151 L0 +error(0.01235521784681438417) D151 L0 ^ D153 +error(0.004207393336629383467) D153 D161 ^ D151 L0 +error(0.01000000000000000021) D152 +error(0.01785301659321773815) D152 D155 +error(0.01637401583216853418) D152 D155 ^ D154 +error(0.02339721966187317467) D152 D160 +error(0.004593006776778153256) D152 D160 ^ D162 +error(0.004633022736341869084) D152 D163 +error(0.00323703015503416314) D152 D163 ^ D154 +error(0.004090640469596825389) D152 D163 ^ D162 +error(0.002101065562810008717) D152 D163 ^ D162 ^ D154 +error(0.007349448775310450134) D153 +error(0.009492030802785636837) D153 D154 +error(0.001887289238295643528) D153 D154 ^ D143 L0 +error(0.01247787028000646274) D153 D154 ^ D151 L0 +error(0.0007927547980242701528) D153 D154 ^ D155 L0 +error(0.0004780626237276926682) D153 D154 ^ D155 L0 ^ D143 L0 +error(0.0290017331886132379) D153 D161 +error(0.0002014583196037038619) D153 ^ D141 ^ D143 L0 +error(0.0004166580577437174488) D153 ^ D141 ^ D151 L0 +error(0.001290031617734563882) D153 ^ D151 L0 ^ D143 L0 +error(0.0107231597008022872) D154 +error(0.002819593978225994144) D154 D161 +error(0.003137767389894948866) D154 D161 ^ D151 L0 +error(0.0006968140446665368809) D154 D161 ^ D163 L0 +error(0.000505172529929820641) D154 D161 ^ D163 L0 ^ D151 L0 +error(0.02081610292411238683) D154 D162 +error(0.001206124809337631677) D154 D162 ^ D151 L0 +error(0.002463255996633777374) D154 D162 ^ D163 L0 +error(0.0002751468606974653234) D154 ^ D142 ^ D144 +error(0.0004594173010041198999) D154 ^ D142 ^ D152 +error(0.009220825108482943316) D154 ^ D155 L0 +error(0.001663150487453687832) D154 ^ D163 L0 +error(0.00455585972925812762) D155 D163 ^ D154 +error(0.01883905370747077437) D155 D163 +error(0.005000000000000000104) D155 L0 +error(0.01751672136136445471) D156 +error(0.01785065649831395901) D156 D159 +error(0.02043771586639645671) D156 D159 ^ D157 +error(0.01432068897443614271) D156 D164 +error(0.003724222745789971568) D156 D164 ^ D165 +error(0.008010401095168906069) D156 D167 +error(0.007940226548819344018) D156 D167 ^ D157 +error(0.003047688540304718287) D156 D167 ^ D165 +error(0.001113551153159442111) D156 D167 ^ D165 ^ D157 +error(0.0012261922121419445) D156 D168 +error(0.001293311027307580789) D156 D168 ^ D157 D165 +error(0.0002766506363411279324) D156 D168 ^ D165 +error(0.0127754705552621823) D156 ^ D157 +error(0.001340836656243296664) D156 ^ D165 +error(0.002787819927005772032) D157 D165 ^ D156 +error(0.01563106638893721131) D157 +error(0.01056384291876393856) D157 D158 +error(0.0002904294784450725487) D157 D158 ^ D156 +error(0.0002058388941412996195) D157 D158 ^ D159 D160 +error(0.0001957883314196005203) D157 D158 ^ D159 D168 +error(0.01111726604515600632) D157 D158 ^ D160 +error(0.002027113369458129113) D157 D158 ^ D160 D168 +error(0.001680777882927654652) D157 D158 ^ D168 +error(0.000510964933352025261) D157 D158 ^ D168 ^ D156 +error(0.01295778001257064625) D157 D162 +error(0.01037953181937149687) D157 D162 ^ D159 D160 +error(0.02565843058292602286) D157 D165 +error(0.001864362289368129985) D157 D165 ^ D167 D168 +error(0.0009030108125920386763) D157 D165 ^ D168 +error(0.002168943494147704959) D157 D169 +error(0.001623587268677913588) D157 D169 ^ D159 D167 +error(0.0002161019940614549329) D157 D169 ^ D159 D168 +error(0.000388632672172653233) D157 D169 ^ D167 D168 +error(0.001498498304452828417) D157 D170 +error(0.001856993221836747681) D157 D170 ^ D159 D160 +error(0.0009804196430601077195) D157 D170 ^ D159 D167 +error(0.001569512860033841139) D157 D170 ^ D159 D168 +error(0.0004097649005875169032) D157 D170 ^ D160 D168 +error(0.001862686936830267124) D157 D170 ^ D167 D168 +error(0.004219216288335304009) D159 D167 ^ D157 +error(0.01703946026454849702) D158 +error(0.003596024184132560076) D158 D165 +error(0.000353809852654799644) D158 D165 ^ D156 +error(0.003114589733093941094) D158 D165 ^ D168 +error(0.0003537892199881977127) D158 D165 ^ D168 ^ D156 +error(0.0268149874189349581) D158 D166 +error(0.002693575443238237482) D158 D166 ^ D168 +error(0.0009947049708068399828) D158 D170 +error(0.0004489571233011255941) D158 D170 ^ D159 D160 +error(0.0001023647480353978632) D158 D170 ^ D159 D168 +error(0.001017738624743863216) D158 D170 ^ D160 D168 +error(0.01180107044904601943) D158 ^ D160 +error(0.001485361101119797361) D158 ^ D168 ^ D160 +error(0.008909961818705179268) D159 D160 +error(0.000172764079590881711) D159 D160 ^ D149 +error(0.0001034000690297430145) D159 D160 ^ D157 +error(0.0004460207984751748565) D159 D160 ^ D161 D162 +error(0.002537074408032832188) D159 D160 ^ D162 D170 +error(0.0003403736328788634535) D159 D160 ^ D170 +error(0.03158800500128303157) D159 D167 +error(0.004053201805270884896) D159 D167 ^ D169 D170 +error(0.003287314687101230461) D159 D168 +error(0.000362912345862538815) D159 D168 ^ D169 D170 +error(0.001818820937996893771) D159 D171 +error(0.0001853416030457494853) D159 D171 ^ D162 +error(0.001711555130697444208) D159 D171 ^ D162 D170 +error(0.000272762609427849021) D159 D171 ^ D170 +error(0.01000000000000000021) D159 L0 +error(0.01235521784681438417) D159 L0 ^ D161 +error(0.004207393336629383467) D161 D169 ^ D159 L0 +error(0.01000000000000000021) D160 +error(0.01785301659321773815) D160 D163 +error(0.01637401583216853418) D160 D163 ^ D162 +error(0.02339721966187317467) D160 D168 +error(0.004593006776778153256) D160 D168 ^ D170 +error(0.004633022736341869084) D160 D171 +error(0.00323703015503416314) D160 D171 ^ D162 +error(0.004090640469596825389) D160 D171 ^ D170 +error(0.002101065562810008717) D160 D171 ^ D170 ^ D162 +error(0.007349448775310450134) D161 +error(0.009492030802785636837) D161 D162 +error(0.001887289238295643528) D161 D162 ^ D151 L0 +error(0.01247787028000646274) D161 D162 ^ D159 L0 +error(0.0007927547980242701528) D161 D162 ^ D163 L0 +error(0.0004780626237276926682) D161 D162 ^ D163 L0 ^ D151 L0 +error(0.0290017331886132379) D161 D169 +error(0.0002014583196037038619) D161 ^ D149 ^ D151 L0 +error(0.0004166580577437174488) D161 ^ D149 ^ D159 L0 +error(0.001290031617734563882) D161 ^ D159 L0 ^ D151 L0 +error(0.0107231597008022872) D162 +error(0.002819593978225994144) D162 D169 +error(0.003137767389894948866) D162 D169 ^ D159 L0 +error(0.0006968140446665368809) D162 D169 ^ D171 L0 +error(0.000505172529929820641) D162 D169 ^ D171 L0 ^ D159 L0 +error(0.02081610292411238683) D162 D170 +error(0.001206124809337631677) D162 D170 ^ D159 L0 +error(0.002463255996633777374) D162 D170 ^ D171 L0 +error(0.0002751468606974653234) D162 ^ D150 ^ D152 +error(0.0004594173010041198999) D162 ^ D150 ^ D160 +error(0.009220825108482943316) D162 ^ D163 L0 +error(0.001663150487453687832) D162 ^ D171 L0 +error(0.00455585972925812762) D163 D171 ^ D162 +error(0.01883905370747077437) D163 D171 +error(0.005000000000000000104) D163 L0 +error(0.01751672136136445471) D164 +error(0.01785065649831395901) D164 D167 +error(0.02043771586639645671) D164 D167 ^ D165 +error(0.01432068897443614271) D164 D172 +error(0.003724222745789971568) D164 D172 ^ D173 +error(0.008010401095168906069) D164 D175 +error(0.007940226548819344018) D164 D175 ^ D165 +error(0.003047688540304718287) D164 D175 ^ D173 +error(0.001113551153159442111) D164 D175 ^ D173 ^ D165 +error(0.0012261922121419445) D164 D176 +error(0.001293311027307580789) D164 D176 ^ D165 D173 +error(0.0002766506363411279324) D164 D176 ^ D173 +error(0.0127754705552621823) D164 ^ D165 +error(0.001340836656243296664) D164 ^ D173 +error(0.002787819927005772032) D165 D173 ^ D164 +error(0.01563106638893721131) D165 +error(0.01056384291876393856) D165 D166 +error(0.0002904294784450725487) D165 D166 ^ D164 +error(0.0002058388941412996195) D165 D166 ^ D167 D168 +error(0.0001957883314196005203) D165 D166 ^ D167 D176 +error(0.01111726604515600632) D165 D166 ^ D168 +error(0.002027113369458129113) D165 D166 ^ D168 D176 +error(0.001680777882927654652) D165 D166 ^ D176 +error(0.000510964933352025261) D165 D166 ^ D176 ^ D164 +error(0.01295778001257064625) D165 D170 +error(0.01037953181937149687) D165 D170 ^ D167 D168 +error(0.02565843058292602286) D165 D173 +error(0.001864362289368129985) D165 D173 ^ D175 D176 +error(0.0009030108125920386763) D165 D173 ^ D176 +error(0.002168943494147704959) D165 D177 +error(0.001623587268677913588) D165 D177 ^ D167 D175 +error(0.0002161019940614549329) D165 D177 ^ D167 D176 +error(0.000388632672172653233) D165 D177 ^ D175 D176 +error(0.001498498304452828417) D165 D178 +error(0.001856993221836747681) D165 D178 ^ D167 D168 +error(0.0009804196430601077195) D165 D178 ^ D167 D175 +error(0.001569512860033841139) D165 D178 ^ D167 D176 +error(0.0004097649005875169032) D165 D178 ^ D168 D176 +error(0.001862686936830267124) D165 D178 ^ D175 D176 +error(0.004219216288335304009) D167 D175 ^ D165 +error(0.01703946026454849702) D166 +error(0.003596024184132560076) D166 D173 +error(0.000353809852654799644) D166 D173 ^ D164 +error(0.003114589733093941094) D166 D173 ^ D176 +error(0.0003537892199881977127) D166 D173 ^ D176 ^ D164 +error(0.0268149874189349581) D166 D174 +error(0.002693575443238237482) D166 D174 ^ D176 +error(0.0009947049708068399828) D166 D178 +error(0.0004489571233011255941) D166 D178 ^ D167 D168 +error(0.0001023647480353978632) D166 D178 ^ D167 D176 +error(0.001017738624743863216) D166 D178 ^ D168 D176 +error(0.01180107044904601943) D166 ^ D168 +error(0.001485361101119797361) D166 ^ D176 ^ D168 +error(0.008909961818705179268) D167 D168 +error(0.000172764079590881711) D167 D168 ^ D157 +error(0.0001034000690297430145) D167 D168 ^ D165 +error(0.0004460207984751748565) D167 D168 ^ D169 D170 +error(0.002537074408032832188) D167 D168 ^ D170 D178 +error(0.0003403736328788634535) D167 D168 ^ D178 +error(0.03158800500128303157) D167 D175 +error(0.004053201805270884896) D167 D175 ^ D177 D178 +error(0.003287314687101230461) D167 D176 +error(0.000362912345862538815) D167 D176 ^ D177 D178 +error(0.001818820937996893771) D167 D179 +error(0.0001853416030457494853) D167 D179 ^ D170 +error(0.001711555130697444208) D167 D179 ^ D170 D178 +error(0.000272762609427849021) D167 D179 ^ D178 +error(0.01000000000000000021) D167 L0 +error(0.01235521784681438417) D167 L0 ^ D169 +error(0.004207393336629383467) D169 D177 ^ D167 L0 +error(0.01000000000000000021) D168 +error(0.01785301659321773815) D168 D171 +error(0.01637401583216853418) D168 D171 ^ D170 +error(0.02339721966187317467) D168 D176 +error(0.004593006776778153256) D168 D176 ^ D178 +error(0.004633022736341869084) D168 D179 +error(0.00323703015503416314) D168 D179 ^ D170 +error(0.004090640469596825389) D168 D179 ^ D178 +error(0.002101065562810008717) D168 D179 ^ D178 ^ D170 +error(0.007349448775310450134) D169 +error(0.009492030802785636837) D169 D170 +error(0.001887289238295643528) D169 D170 ^ D159 L0 +error(0.01247787028000646274) D169 D170 ^ D167 L0 +error(0.0007927547980242701528) D169 D170 ^ D171 L0 +error(0.0004780626237276926682) D169 D170 ^ D171 L0 ^ D159 L0 +error(0.0290017331886132379) D169 D177 +error(0.0002014583196037038619) D169 ^ D157 ^ D159 L0 +error(0.0004166580577437174488) D169 ^ D157 ^ D167 L0 +error(0.001290031617734563882) D169 ^ D167 L0 ^ D159 L0 +error(0.0107231597008022872) D170 +error(0.002819593978225994144) D170 D177 +error(0.003137767389894948866) D170 D177 ^ D167 L0 +error(0.0006968140446665368809) D170 D177 ^ D179 L0 +error(0.000505172529929820641) D170 D177 ^ D179 L0 ^ D167 L0 +error(0.02081610292411238683) D170 D178 +error(0.001206124809337631677) D170 D178 ^ D167 L0 +error(0.002463255996633777374) D170 D178 ^ D179 L0 +error(0.0002751468606974653234) D170 ^ D158 ^ D160 +error(0.0004594173010041198999) D170 ^ D158 ^ D168 +error(0.009220825108482943316) D170 ^ D171 L0 +error(0.001663150487453687832) D170 ^ D179 L0 +error(0.00455585972925812762) D171 D179 ^ D170 +error(0.01883905370747077437) D171 D179 +error(0.005000000000000000104) D171 L0 +error(0.01925075188032962861) D172 +error(0.01985347796842761373) D172 D175 +error(0.01709160401663562326) D172 D175 ^ D173 +error(0.0169945436967199881) D172 D180 +error(0.01113017870071546407) D172 D181 +error(0.008925184322359086542) D172 D181 ^ D173 +error(0.001087708414143065058) D172 D182 +error(0.0009183448662986461826) D172 D182 ^ D173 +error(0.01762060416364190016) D172 ^ D173 +error(0.03808906947941175652) D173 +error(0.01078991202197674033) D173 D174 +error(0.0134728288846554644) D173 D174 ^ D176 +error(0.00269945891117282042) D173 D174 ^ D176 D182 +error(0.002678840172641844871) D173 D174 ^ D182 +error(0.01021974258515253101) D173 D178 +error(0.01043506392589704616) D173 D178 ^ D175 D176 +error(0.000829870635800795931) D173 ^ D172 ^ D174 +error(0.00165634512383596084) D173 ^ D175 L0 +error(0.0008567884406648901404) D173 ^ D181 L0 +error(0.005096936525580127858) D175 D181 ^ D173 +error(0.0007348722800744330675) D173 ^ D182 +error(0.0003710750036516944597) D173 ^ D182 ^ D172 ^ D174 +error(0.05337299308844348222) D174 +error(0.008854686414539502184) D174 ^ D176 +error(0.006011818068283811267) D174 ^ D182 +error(0.002998385934093448103) D176 D182 ^ D174 +error(0.008905413293389344981) D175 D176 +error(0.000172764079590881711) D175 D176 ^ D165 +error(0.002395876762930221511) D175 D176 ^ D173 +error(0.001625455595571512288) D175 D176 ^ D174 +error(0.0005954826252501619092) D175 D176 ^ D177 D178 +error(0.001084317711497137906) D175 D176 ^ D178 +error(0.0395360335294304352) D175 D181 +error(0.004981222011927470723) D175 D182 +error(0.0009857363768163715859) D175 D182 ^ D173 +error(0.0004799213067820802233) D175 D182 ^ D174 +error(0.003543939151561256891) D175 D183 +error(0.003192190352879872156) D175 D183 ^ D178 +error(0.01000000000000000021) D175 L0 +error(0.01950746316044099302) D175 L0 ^ D177 +error(0.002158868531299172611) D175 L0 ^ D178 +error(0.01000000000000000021) D176 +error(0.01973378848257803866) D176 D179 +error(0.01701773718866270504) D176 D179 ^ D178 +error(0.02081916671545180203) D176 D182 +error(0.00107030681462752064) D176 D182 ^ D173 +error(0.00858962945672469419) D176 D183 +error(0.002610400073093903026) D176 D183 ^ D178 +error(0.03828760669849980841) D177 +error(0.005791662857336990836) D177 D178 +error(0.001887289238295643528) D177 D178 ^ D167 L0 +error(0.01109398466133069798) D177 D178 ^ D175 L0 +error(0.0009053038090202449907) D177 D178 ^ D179 L0 +error(0.0004780626237276926682) D177 D178 ^ D179 L0 ^ D167 L0 +error(0.0002014583196037038619) D177 ^ D165 ^ D167 L0 +error(0.0004166580577437174488) D177 ^ D165 ^ D175 L0 +error(0.001290031617734563882) D177 ^ D175 L0 ^ D167 L0 +error(0.03898654309300965404) D178 +error(0.0002751468606974653234) D178 ^ D166 ^ D168 +error(0.0004594173010041198999) D178 ^ D166 ^ D176 +error(0.01165549636150209865) D178 ^ D179 L0 +error(0.006511349223561001785) D178 ^ D183 L0 +error(0.005581258370559874851) D179 D183 ^ D178 +error(0.02021069326842731689) D179 D183 +error(0.005000000000000000104) D179 L0 +error(0.0171379207776263115) D180 +error(0.01536074247083696331) D180 D181 +error(0.01373879340783211098) D181 D182 +error(0.004241828644153815796) D181 D182 ^ D173 +error(0.02895233922051143605) D181 L0 +error(0.03334172239242454844) D182 +error(0.02091798772417075014) D182 D183 +error(0.0007091171173097788553) D182 ^ D172 ^ D174 +error(0.01387079969824839103) D183 L0 +detector(5, 4, 0) D0 +detector(7, 4, 0) D1 +detector(7, 6, 0) D2 +detector(9, 6, 0) D3 +detector(5, 4, 1) D4 +detector(6, 5, 1) D5 +detector(6, 7, 1) D6 +detector(7, 4, 1) D7 +detector(7, 6, 1) D8 +detector(8, 3, 1) D9 +detector(8, 5, 1) D10 +detector(9, 6, 1) D11 +detector(5, 4, 2) D12 +detector(6, 5, 2) D13 +detector(6, 7, 2) D14 +detector(7, 4, 2) D15 +detector(7, 6, 2) D16 +detector(8, 3, 2) D17 +detector(8, 5, 2) D18 +detector(9, 6, 2) D19 +detector(5, 4, 3) D20 +detector(6, 5, 3) D21 +detector(6, 7, 3) D22 +detector(7, 4, 3) D23 +detector(7, 6, 3) D24 +detector(8, 3, 3) D25 +detector(8, 5, 3) D26 +detector(9, 6, 3) D27 +detector(5, 4, 4) D28 +detector(6, 5, 4) D29 +detector(6, 7, 4) D30 +detector(7, 4, 4) D31 +detector(7, 6, 4) D32 +detector(8, 3, 4) D33 +detector(8, 5, 4) D34 +detector(9, 6, 4) D35 +detector(5, 4, 5) D36 +detector(6, 5, 5) D37 +detector(6, 7, 5) D38 +detector(7, 4, 5) D39 +detector(7, 6, 5) D40 +detector(8, 3, 5) D41 +detector(8, 5, 5) D42 +detector(9, 6, 5) D43 +detector(5, 4, 6) D44 +detector(6, 5, 6) D45 +detector(6, 7, 6) D46 +detector(7, 4, 6) D47 +detector(7, 6, 6) D48 +detector(8, 3, 6) D49 +detector(8, 5, 6) D50 +detector(9, 6, 6) D51 +detector(5, 4, 7) D52 +detector(6, 5, 7) D53 +detector(6, 7, 7) D54 +detector(7, 4, 7) D55 +detector(7, 6, 7) D56 +detector(8, 3, 7) D57 +detector(8, 5, 7) D58 +detector(9, 6, 7) D59 +detector(5, 4, 8) D60 +detector(6, 5, 8) D61 +detector(6, 7, 8) D62 +detector(7, 4, 8) D63 +detector(7, 6, 8) D64 +detector(8, 3, 8) D65 +detector(8, 5, 8) D66 +detector(9, 6, 8) D67 +detector(5, 4, 9) D68 +detector(6, 5, 9) D69 +detector(6, 7, 9) D70 +detector(7, 4, 9) D71 +detector(7, 6, 9) D72 +detector(8, 3, 9) D73 +detector(8, 5, 9) D74 +detector(9, 6, 9) D75 +detector(5, 4, 10) D76 +detector(6, 5, 10) D77 +detector(6, 7, 10) D78 +detector(7, 4, 10) D79 +detector(7, 6, 10) D80 +detector(8, 3, 10) D81 +detector(8, 5, 10) D82 +detector(9, 6, 10) D83 +detector(5, 4, 11) D84 +detector(6, 5, 11) D85 +detector(6, 7, 11) D86 +detector(7, 4, 11) D87 +detector(7, 6, 11) D88 +detector(8, 3, 11) D89 +detector(8, 5, 11) D90 +detector(9, 6, 11) D91 +detector(5, 4, 12) D92 +detector(6, 5, 12) D93 +detector(6, 7, 12) D94 +detector(7, 4, 12) D95 +detector(7, 6, 12) D96 +detector(8, 3, 12) D97 +detector(8, 5, 12) D98 +detector(9, 6, 12) D99 +detector(5, 4, 13) D100 +detector(6, 5, 13) D101 +detector(6, 7, 13) D102 +detector(7, 4, 13) D103 +detector(7, 6, 13) D104 +detector(8, 3, 13) D105 +detector(8, 5, 13) D106 +detector(9, 6, 13) D107 +detector(5, 4, 14) D108 +detector(6, 5, 14) D109 +detector(6, 7, 14) D110 +detector(7, 4, 14) D111 +detector(7, 6, 14) D112 +detector(8, 3, 14) D113 +detector(8, 5, 14) D114 +detector(9, 6, 14) D115 +detector(5, 4, 15) D116 +detector(6, 5, 15) D117 +detector(6, 7, 15) D118 +detector(7, 4, 15) D119 +detector(7, 6, 15) D120 +detector(8, 3, 15) D121 +detector(8, 5, 15) D122 +detector(9, 6, 15) D123 +detector(5, 4, 16) D124 +detector(6, 5, 16) D125 +detector(6, 7, 16) D126 +detector(7, 4, 16) D127 +detector(7, 6, 16) D128 +detector(8, 3, 16) D129 +detector(8, 5, 16) D130 +detector(9, 6, 16) D131 +detector(5, 4, 17) D132 +detector(6, 5, 17) D133 +detector(6, 7, 17) D134 +detector(7, 4, 17) D135 +detector(7, 6, 17) D136 +detector(8, 3, 17) D137 +detector(8, 5, 17) D138 +detector(9, 6, 17) D139 +detector(5, 4, 18) D140 +detector(6, 5, 18) D141 +detector(6, 7, 18) D142 +detector(7, 4, 18) D143 +detector(7, 6, 18) D144 +detector(8, 3, 18) D145 +detector(8, 5, 18) D146 +detector(9, 6, 18) D147 +detector(5, 4, 19) D148 +detector(6, 5, 19) D149 +detector(6, 7, 19) D150 +detector(7, 4, 19) D151 +detector(7, 6, 19) D152 +detector(8, 3, 19) D153 +detector(8, 5, 19) D154 +detector(9, 6, 19) D155 +detector(5, 4, 20) D156 +detector(6, 5, 20) D157 +detector(6, 7, 20) D158 +detector(7, 4, 20) D159 +detector(7, 6, 20) D160 +detector(8, 3, 20) D161 +detector(8, 5, 20) D162 +detector(9, 6, 20) D163 +detector(5, 4, 21) D164 +detector(6, 5, 21) D165 +detector(6, 7, 21) D166 +detector(7, 4, 21) D167 +detector(7, 6, 21) D168 +detector(8, 3, 21) D169 +detector(8, 5, 21) D170 +detector(9, 6, 21) D171 +detector(5, 4, 22) D172 +detector(6, 5, 22) D173 +detector(6, 7, 22) D174 +detector(7, 4, 22) D175 +detector(7, 6, 22) D176 +detector(8, 3, 22) D177 +detector(8, 5, 22) D178 +detector(9, 6, 22) D179 +detector(5, 4, 23) D180 +detector(7, 4, 23) D181 +detector(7, 6, 23) D182 +detector(9, 6, 23) D183 diff --git a/shared/package-files/8b/8be664e2c7ed2b970c4154368ea4b14cca5523e818c97375ebea794ef9a35117/blob b/shared/package-files/8b/8be664e2c7ed2b970c4154368ea4b14cca5523e818c97375ebea794ef9a35117/blob new file mode 100644 index 0000000000000000000000000000000000000000..14c62b4d1b905ec71d8ab97dfdb3f18638d707f1 --- /dev/null +++ b/shared/package-files/8b/8be664e2c7ed2b970c4154368ea4b14cca5523e818c97375ebea794ef9a35117/blob @@ -0,0 +1,25 @@ +import sys + +from setuptools.command.egg_info import egg_info as _egg_info + +class egg_info(_egg_info): + def run(self): + if 'sdist' in sys.argv: + import warnings + import textwrap + msg = textwrap.dedent(""" + `build_src` is being run, this may lead to missing + files in your sdist! You want to use distutils.sdist + instead of the setuptools version: + + from distutils.command.sdist import sdist + cmdclass={'sdist': sdist}" + + See numpy's setup.py or gh-7131 for details.""") + warnings.warn(msg, UserWarning, stacklevel=2) + + # We need to ensure that build_src has been executed in order to give + # setuptools' egg_info command real filenames instead of functions which + # generate files. + self.run_command("build_src") + _egg_info.run(self) diff --git a/shared/package-files/8b/8be6fd310330aee857a05fb6259f13dc1a000f2800378975f76cde7445015b1b/blob b/shared/package-files/8b/8be6fd310330aee857a05fb6259f13dc1a000f2800378975f76cde7445015b1b/blob new file mode 100644 index 0000000000000000000000000000000000000000..b3a4b85f05e8eb782b40c0c28f4e52cdc43c0e7d --- /dev/null +++ b/shared/package-files/8b/8be6fd310330aee857a05fb6259f13dc1a000f2800378975f76cde7445015b1b/blob @@ -0,0 +1,190 @@ +#pragma once + +#include +#include +#include +#include + +#include + +namespace c10::impl { + +struct C10_API PyObjectSlot { + public: + PyObjectSlot(); + + ~PyObjectSlot(); + + void maybe_destroy_pyobj(); + + // Associate the TensorImpl with the specified PyObject, and, if necessary, + // also tag the interpreter. + // + // NB: This lives in a header so that we can inline away the switch on status + // + // NB: THIS FUNCTION CAN RAISE AN EXCEPTION. Make sure to clean up after + // PyObject if necessary! + void init_pyobj( + PyInterpreter* self_interpreter, + PyObject* pyobj, + PyInterpreterStatus status) { + impl::PyInterpreter* expected = nullptr; + switch (status) { + case impl::PyInterpreterStatus::DEFINITELY_UNINITIALIZED: + // caller guarantees there is no multithreaded access; if there is + // no data race OK to do a relaxed store + pyobj_interpreter_.store(self_interpreter, std::memory_order_relaxed); + break; + case impl::PyInterpreterStatus::TAGGED_BY_US: + // no tagging is necessary, the tag is already correct + break; + case impl::PyInterpreterStatus::MAYBE_UNINITIALIZED: + // attempt to claim this TensorImpl with the specified interpreter + // tag + if (pyobj_interpreter_.compare_exchange_strong( + expected, self_interpreter, std::memory_order_acq_rel)) { + break; + } + // test if, actually, it was already tagged by us! this situation can't + // be caused by a race, but it could be caused by a situation + // where someone conservatively tagged the tensor as MAYBE_UNINITIALIZED + // (because they didn't pre-check the tag) when actually it was + // owned by the interpreter + if (expected == self_interpreter) { + break; + } + // fallthrough, we lost the race. We are guaranteed not to lose the + // race with ourself, as calls to init_pyobj with the same interpreter + // ID must be sequentialized by the GIL + [[fallthrough]]; + case impl::PyInterpreterStatus::TAGGED_BY_OTHER: + TORCH_CHECK( + false, + "cannot allocate PyObject for Tensor on interpreter ", + self_interpreter, + " that has already been used by another torch deploy interpreter ", + pyobj_interpreter_.load()); + } + + // we are the ONLY thread that can have gotten to this point. It is not + // possible to conflict with another zero interpreter as access is protected + // by GIL + // NB: owns_pyobj tag is initially false + pyobj_ = pyobj; + } + + // Query the PyObject interpreter. This may return null if there is no + // interpreter. This is racy! + PyInterpreter* pyobj_interpreter(); + + PyObject* _unchecked_untagged_pyobj() const; + + // Test the interpreter tag. If tagged for the current interpreter, return + // a non-nullopt (but possibly null) PyObject. If (possibly) untagged, + // returns a nullopt. If it is definitely invalid, raises an error. + // + // If `ignore_hermetic_tls` is false and this function is called from a + // hermetic context (ie, `HermeticPyObjectTLS::get_state()` is true), then + // nullopt is returned. If `ignore_hermetic_tls` is true, then the hermetic + // context is ignored, allowing you to check the interpreter tag of a + // nonhermetic PyObject from within a hermetic context. This is necessary + // because there are some cases where the deallocator function of a + // nonhermetic PyObject is called from within a hermetic context, so it must + // be properly treated as a nonhermetic PyObject. + // + // NB: this lives in header so that we can avoid actually creating the + // c10::optional + c10::optional check_pyobj( + PyInterpreter* self_interpreter, + bool ignore_hermetic_tls = false) const { + // Note [Memory ordering on Python interpreter tag] + impl::PyInterpreter* interpreter = + pyobj_interpreter_.load(std::memory_order_acquire); + if (interpreter == nullptr) { + // NB: This never returns DEFINITELY_UNINITIALIZED because there is + // always the possibility that another thread races to initialize + // after we query here. The only time when we can conclude a tensor + // is definitely uninitialized is when we have just allocated it and + // it cannot have escaped to other threads yet + return c10::nullopt; + } else if (interpreter == self_interpreter) { + // NB: pyobj_ could still be null! + if (!ignore_hermetic_tls && c10::impl::HermeticPyObjectTLS::get_state()) { + return c10::nullopt; + } else { + return c10::make_optional(_unchecked_untagged_pyobj()); + } + } else { + TORCH_CHECK( + false, + "cannot access PyObject for Tensor on interpreter ", + (*self_interpreter)->name(), + " that has already been used by another torch deploy interpreter ", + (*pyobj_interpreter_.load())->name()); + } + } + + // Clear the PyObject field for an interpreter, in situations where we + // statically know the tensor is tagged with our interpreter. + void unchecked_clear_pyobj(PyInterpreter* interpreter); + + PyInterpreter& load_pyobj_interpreter() const; + + // Check if the PyObjectSlot's interpreter is the same as the specified + // interpreter + bool check_interpreter(PyInterpreter* interpreter); + + // Check if the PyObjectSlot is holding a PyObject, owned or non-owned + bool has_pyobj_nonhermetic(); + + bool owns_pyobj(); + + void set_owns_pyobj(bool b); + + private: + // This field contains the interpreter tag for this object. See + // Note [Python interpreter tag] for general context + // + // Note [Memory ordering on Python interpreter tag] + // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + // What memory_order do we need when accessing this atomic? We don't + // need a single total modification order (as provided by + // memory_order_seq_cst) as pyobj_interpreter_ is monotonic: it can only + // transition from -1 to some positive integer and never changes afterwards. + // Because there is only one modification, it trivially already has a total + // modification order (e.g., we don't need fences or locked instructions on + // x86) + // + // In fact, one could make a reasonable argument that relaxed reads are OK, + // due to the presence of external locking (GIL) to ensure that interactions + // with other data structures are still correctly synchronized, so that + // we fall in the "Single-Location Data Structures" case as described in + // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p2055r0.pdf + // However, on x86, it doesn't matter if I use acquire or relaxed on the load + // as I get the same assembly in both cases. So I just use the more + // conservative acquire (which will impede compiler optimizations but I don't + // care) + std::atomic pyobj_interpreter_; + + // This field contains a reference to a PyObject representing this Tensor. + // If pyobj is nullptr, when we transfer Tensor to Python, we allocate a new + // PyObject for it and set this field. This field does not have to be + // protected by an atomic as it is only allowed to be accessed when you hold + // the GIL, or during destruction of the tensor. + // + // When a PyObject dies, you are obligated to clear this field + // (otherwise, you will try to use-after-free the pyobj); this currently + // occurs in THPVariable_clear in torch/csrc/autograd/python_variable.cpp + // + // NB: Ordinarily, this should not be a strong reference, as if the + // PyObject owns the Tensor, this would create a reference cycle. + // However, sometimes this ownership flips. To track who owns + // who, this has a single pointer tag indicating whether or not the + // C++ object owns the PyObject (the common case, zero, means PyObject + // owns the C++ object); see _unchecked_untagged_pyobj for raw access + // or check_pyobj for checked access. See references to PyObject + // resurrection in torch/csrc/autograd/python_variable.cpp + PyObject* pyobj_; +}; + +} // namespace c10::impl diff --git a/shared/package-files/8b/8becf99c2ba72e1eb017e6ecd3faef34ebd2df8a6924c4fe64da237b54a9eae0/blob b/shared/package-files/8b/8becf99c2ba72e1eb017e6ecd3faef34ebd2df8a6924c4fe64da237b54a9eae0/blob new file mode 100644 index 0000000000000000000000000000000000000000..0fbad8ca3d2fe79e987130288adf1a5877348d9e Binary files /dev/null and b/shared/package-files/8b/8becf99c2ba72e1eb017e6ecd3faef34ebd2df8a6924c4fe64da237b54a9eae0/blob differ diff --git a/shared/package-files/8b/8bf401ff49268abc529561cc0f3154fcb4588c4c4a358a0a288acfcba9fc5a13/blob b/shared/package-files/8b/8bf401ff49268abc529561cc0f3154fcb4588c4c4a358a0a288acfcba9fc5a13/blob new file mode 100644 index 0000000000000000000000000000000000000000..1e1afe4470e56bdfcfeb0f5cfa0614767cbd387d --- /dev/null +++ b/shared/package-files/8b/8bf401ff49268abc529561cc0f3154fcb4588c4c4a358a0a288acfcba9fc5a13/blob @@ -0,0 +1,21 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API ::std::vector nonzero_numpy(const at::Tensor & self); +} // namespace native +} // namespace at diff --git a/shared/package-files/8b/8bf5037194555f42b1cd25c71559913ee195c224a6e9d927e44a7eb0c23952e4/blob b/shared/package-files/8b/8bf5037194555f42b1cd25c71559913ee195c224a6e9d927e44a7eb0c23952e4/blob new file mode 100644 index 0000000000000000000000000000000000000000..b14e910be0b8abbdfaa838849a28e381ed34fab3 --- /dev/null +++ b/shared/package-files/8b/8bf5037194555f42b1cd25c71559913ee195c224a6e9d927e44a7eb0c23952e4/blob @@ -0,0 +1,464 @@ +from typing import Any, Dict, List, Optional, Tuple, Union + +from torchgen.api.types import ( + BaseCppType, + BaseCType, + boolT, + CType, + deviceT, + doubleT, + generatorT, + layoutT, + ListCType, + longT, + memoryFormatT, + NamedCType, + OptionalCType, + scalarT, + scalarTypeT, + stringT, + SymIntT, + VectorCType, +) + +from torchgen.model import ( + Argument, + BaseTy, + BaseType, + FunctionSchema, + ListType, + OperatorName, + OptionalType, + Return, + TensorOptionsArguments, + Type, +) + + +_valueT: Optional[BaseCppType] = None + + +# A ValueT is an IR type which represents the computation of a Tensor. In other +# words, a PyTorch user will do operations on lazy tensors, and each output lazy +# tensor internally tracks a ValueT representing the IR node that would have +# actually produced the value of this tensor for real. +# +# This is configurable because different lazy tensor backends (LTC vs XLA) will +# have different IR representations. (Though, arguably, after unification they +# shouldn't!) +def getValueT() -> BaseCppType: + global _valueT + if not _valueT: + raise NotImplementedError( + "The value type needs to be set with setValueT() in run_gen_lazy_tensor()" + ) + + return _valueT + + +def setValueT(val: BaseCppType) -> None: + global _valueT + _valueT = val + + +# this is a bad hack. I need to refactor the data model to represent each arg in the schema as an object, +# making it easier to represent special properties of an arg. +tensorListValueT = BaseCppType("torch::lazy", "Value") + + +def process_ir_type( + typ: Type, properties: "LazyIrProperties", *, symint: bool +) -> Union[BaseCType, VectorCType, OptionalCType, ListCType]: + """ + This function takes a type from NativeFunctions and converts it for use with + lazy tensor codegen. + + Type conversion for lazy currently consists of + (1) changing at::Tensors into lazy::Values + (2) wrapping everything in a BaseCType + (3) making cpp-reference types into cpp-value types (e.g. vector instead of IntArrayRef) + + (1) converts at::Tensors to lazy::Values (which wrap lazy::Nodes, with which Lazy IR represents tensors.) + There is special handling for Optional[Tensor] or List[Tensor], etc- hence 'tensor-like' + + This is incomplete- there are assertions in places that it's expected to need to add + more types as the codegen is used with more operators. + """ + if isinstance(typ, BaseType): + if typ.name == BaseTy.Tensor: + return BaseCType(getValueT()) + elif typ.name == BaseTy.Scalar: + if properties.TreatScalarsAsConstants: + return BaseCType(scalarT) + # at::scalar has special handling, + # and is wrapped in an lazy::Value just like at::tensor + return BaseCType(getValueT()) + elif typ.name == BaseTy.ScalarType: + return BaseCType(scalarTypeT) + elif typ.name == BaseTy.int: + return BaseCType(longT) + elif typ.name == BaseTy.SymInt: + if symint: + return BaseCType(getValueT()) + else: + return BaseCType(longT) + elif typ.name == BaseTy.bool: + return BaseCType(boolT) + elif typ.name == BaseTy.float: + return BaseCType(doubleT) + elif typ.name == BaseTy.str: + return BaseCType(stringT) + elif typ.name == BaseTy.Device: + return BaseCType(deviceT) + elif typ.name == BaseTy.Generator: + return BaseCType(generatorT) + elif typ.name == BaseTy.Layout: + return BaseCType(layoutT) + elif typ.name == BaseTy.MemoryFormat: + return BaseCType(memoryFormatT) + else: + raise AssertionError(f"TODO add support for type {repr(typ)}") + elif isinstance(typ, OptionalType): + return OptionalCType(process_ir_type(typ.elem, properties, symint=symint)) + elif isinstance(typ, ListType): + if str(typ.elem) == "Tensor?": + # TODO(whc) is this actually correct? or should it use a Vector like above + return ListCType(OptionalCType(BaseCType(getValueT()))) + elif str(typ.elem) == "Tensor": + # this is a TensorList which comes in from GetTensorList as a Value + return BaseCType(tensorListValueT) + elif typ.elem == BaseType(BaseTy.SymInt): + # TODO: return a value type. The problem here is analogous to + # the problem with tensorListValueT: if you have SymInt[] you + # cannot conveniently save the list of Value directly, as nodes + # expect to save values as a vector for ALL arguments. So you + # need a separate IR node that represents all of the size nodes + # assembled into a list. I'm not an LTC dev so I don't want to + # figure it out right now. Y'all figure it out... + return VectorCType(BaseCType(longT)) + + else: + return VectorCType(process_ir_type(typ.elem, properties, symint=symint)) + else: + raise AssertionError(f"unrecognized type {repr(typ)}") + + +# TODO: Determining this based off of CType is bad; this should be computed +# from Type directly; then the same logic as process_ir_type can be used +# +# Invariant: passed typ should be an *owning* CType (e.g., we will report +# that ArrayRef is NOT a value type) +def isValueType(typ: CType, properties: "Optional[LazyIrProperties]" = None) -> bool: + """ + Given a type, determine if it is a Value-like type. This is equivalent to + being Tensor-like, but assumes the type has already been transformed. + """ + if isinstance(typ, BaseCType): + # I am regretting my naming conventions, but now we are wrapping at::scalar in + # lazy value, while preserving other 'scalar' types as scalars in the IR + treat_scalars_as_constants = properties and properties.TreatScalarsAsConstants + return ( + typ.type == getValueT() + or (typ.type == scalarT and not treat_scalars_as_constants) + or typ.type == SymIntT + ) + elif typ == VectorCType(BaseCType(SymIntT)): + # TODO: report True for this + return False + elif isinstance(typ, (OptionalCType, ListCType, VectorCType)): + return isValueType(typ.elem, properties) + return False + + +def isSymIntType(typ: Type) -> bool: + return isinstance(typ, BaseType) and typ.name == BaseTy.SymInt + + +def isWrappedScalarType(typ: Type) -> bool: + """ + Given a type, determine if it is a c10::scalar which we will wrap in a lazy Value. + Since we literally change the type from scalarT to valueT, information is lost. + This function helps build a list of wrapped scalars to save that information + """ + if isinstance(typ, BaseType): + # I am regretting my naming conventions, but now we are wrapping at::scalar in + # lazy value, while preserving other 'scalar' types as scalars in the IR + return typ.name == BaseTy.Scalar + elif isinstance(typ, (OptionalType, ListType)): + return isWrappedScalarType(typ.elem) + return False + + +# TODO: dedupe with Type.is_generator_like +def isGeneratorType(typ: Type) -> bool: + if isinstance(typ, BaseType): + return typ.name == BaseTy.Generator + elif isinstance(typ, (OptionalType)): + return isGeneratorType(typ.elem) + return False + + +# This class caches a few derived properties computed from an Argument +# and LazyIrProperties +class LazyArgument: + name: str + orig_type: Type + lazy_type_: Optional[CType] + is_wrapped_scalar: bool + is_generator: bool + # TODO: this is lies, it is false for symint list + is_symint_or_list: bool + + # Whether or not we are treating this as symint or not + symint: bool + + # true if this argument is or contains a lazy IR value + is_lazy_value: bool + + def __init__(self, arg: Argument, properties: "LazyIrProperties", *, symint: bool): + self.name = arg.name + self.orig_type = arg.type + self.symint = symint + self.is_optional = isinstance(arg.type, OptionalType) + self.is_generator = isGeneratorType(arg.type) + self.lazy_type_ = process_ir_type(arg.type, properties, symint=symint) + self.is_wrapped_scalar = isWrappedScalarType(arg.type) + self.is_symint_or_list = symint and ( + isSymIntType(arg.type) + or (isinstance(arg.type, OptionalType) and isSymIntType(arg.type.elem)) + # TODO: lists of symints are not currently treated as value types + # or (isinstance(arg.type, ListType) and isSymIntType(arg.type.elem)) + ) + + self.is_lazy_value = isValueType(self.lazy_type, properties) + + @property + def lazy_type(self) -> CType: + assert ( + self.lazy_type_ is not None + ), f"Attempted to access lazy_type for invalid argument {self.name}" + return self.lazy_type_ + + +class LazyIrProperties: + """Collection of properties for an IR node + + The property groups are listed below. Each group is mutually + exclusive, meaning that only one property from each group can be True + at any one time. The properties can be accessed as if they were normal + attributes. The mutual exclusivity is automatically handled. + """ + + Properties: Tuple[Tuple[str, ...], ...] = ( + ( + "ShapePrecompute", # Assume shape has been precomputed + "ShapeCompute", # Need to compute the shape on construction + "ShapeCache", # Utilize the shape cache to defer computation + ), + ( + "Lower", # Codegen full lower function + "LowerDeclOnly", # Codegen only lower function declaration + ), + ( + "CanBeReused", # Codegen full reuse function + "CanBeReusedDeclOnly", # Codegen only reuse function declaration + ), + ( + "CreateFn", # Codegen full create function + "CreateFnDeclOnly", # Codegen only create function declaration + ), + ( + "TreatScalarsAsConstants", # Treat Scalars as constants instead of handling like values + ), + ) + + def __init__(self, *default_properties: str): + properties: Dict[Tuple[str, ...], Optional[str]] = dict.fromkeys( + LazyIrProperties.Properties + ) + self.__dict__["properties"] = properties + for p in default_properties: + setattr(self, p, True) + + def __getattr__(self, key: str) -> Any: + properties = self.__dict__["properties"] + for values in LazyIrProperties.Properties: + if key in values: + return properties[values] == key + + return self.__getattribute__(key) + + def __setattr__(self, key: str, value: Any) -> Any: + properties = self.__dict__["properties"] + for values in LazyIrProperties.Properties: + if key in values: + properties[values] = key if value else None + return value + + raise KeyError(f"Invalid property: {key}") + + +# Inspired by a FunctionSchema object, a LazyIrSchema holds the schema of a Lazy IR node. +# Unlike a FunctionSchema, it has no round-trippable string form (relating to the YAML), +# but carries type information from a native FunctionSchema modified for use with IR nodes, +# and preserving original argument names. +# +# TODO: This is not idiomatic with how other torchgen APIs transform on schema. +class LazyIrSchema: + # The name of the operator this function schema describes. + name: "OperatorName" + + positional_args: Tuple[LazyArgument, ...] + keyword_args: Tuple[LazyArgument, ...] + + # TODO: Need to handle collisions with argument names at some point + returns: Tuple["Return", ...] + + # if this schema has a Generator arg, list its orig ctype/name but don't + # build a LazyArgument since lazy IR doesn't support it + generator_arg: Optional[NamedCType] = None + + # original function schema + func: FunctionSchema + + # Whether or not we are code-genning for SymInt or not + symint: bool + + properties: LazyIrProperties = LazyIrProperties( + # default properties + "ShapePrecompute", + "Lower", + "CanBeReused", + ) + opkind: Optional[str] = None + + def __init__( + self, + func: FunctionSchema, + properties: Optional[LazyIrProperties] = None, + *, + symint: bool, + ): + if properties: + self.properties = properties + + self.func = func + self.symint = symint + positional_args: List[LazyArgument] = [] + for arg_field in ["pre_self_positional", "self_arg", "post_self_positional"]: + if arg_field == "self_arg" and func.arguments.self_arg is not None: + arg = func.arguments.self_arg.argument + positional_args.append( + LazyArgument(arg, self.properties, symint=symint) + ) + elif getattr(func.arguments, arg_field) is not None: + positional_args.extend( + LazyArgument(arg, self.properties, symint=symint) + for arg in getattr(func.arguments, arg_field) + ) + self.positional_args = tuple(positional_args) + + keyword_args: List[LazyArgument] = [] + for arg_field in [ + "pre_tensor_options_kwarg_only", + "tensor_options", + "post_tensor_options_kwarg_only", + "out", + ]: + curr_args = getattr(func.arguments, arg_field) + if curr_args is not None: + if isinstance(curr_args, TensorOptionsArguments): + curr_args = curr_args.all() + for arg in curr_args: + if isGeneratorType(arg.type): + assert ( + self.generator_arg is None + ), "We expect there is only one generator arg" + self.generator_arg = NamedCType( + arg.name, arg.type # type:ignore[arg-type] + ) + keyword_args.extend( + LazyArgument(arg, self.properties, symint=symint) + for arg in curr_args + ) + self.keyword_args = tuple(keyword_args) + self.name = func.name + self.returns = func.returns + + @property + def node_name(self) -> str: + """ + Return camel-case version of op in node. + + Note: This function also appends any `overload_name` in the operation. + For example, if the op is `bitwise_and.Tensor`, the returned name + will be `BitwiseAndTensor`. + """ + op_name = f"{self.name.name}_{self.name.overload_name}".lower() + return "".join(word.capitalize() or "" for word in op_name.split("_")) + + @property + def aten_name(self) -> str: + return str(self.name.name) + + @property + def base_name(self) -> str: + return f"{self.name.name.base}" + + def filtered_args( + self, + positional: bool = True, + keyword: bool = True, + values: bool = True, + scalars: bool = True, + generator: bool = True, + ) -> List[LazyArgument]: + # This function maintains the sorted order of arguments but provides different filtered views. + # Some parts of the code care about kwargs vs args (TS lowerings), + # other parts care about whether they need to wrap the arg in a lazy value or leave it alone. + # Generators are special cased, as they are needed for fallback/shape-inference but not supported + # in TS lowerings and therefore also omitted from lazy IR. + args: List[LazyArgument] = [] + if positional: + args.extend(self.positional_args) + if keyword: + args.extend(self.keyword_args) + + if values and scalars and generator: + return args + elif values and scalars: + return [a for a in args if not a.is_generator] + elif values: + return [a for a in args if a.is_lazy_value] + elif scalars: + return [ + a + for a in args + if not a.is_lazy_value and (generator or not a.is_generator) + ] + + return [] + + @property + def positional_values(self) -> List[LazyArgument]: + return self.filtered_args( + positional=True, keyword=False, values=True, scalars=False + ) + + @property + def positional_scalars(self) -> List[LazyArgument]: + return self.filtered_args( + positional=True, keyword=False, values=False, scalars=True + ) + + @property + def keyword_values(self) -> List[LazyArgument]: + return self.filtered_args( + positional=False, keyword=True, values=True, scalars=False + ) + + @property + def keyword_scalars(self) -> List[LazyArgument]: + return self.filtered_args( + positional=False, keyword=True, values=False, scalars=True + ) diff --git a/shared/package-files/8b/8bf72570c57d3d10c9c243ad14f3201dd6eea8ecd5db5942b49c399be11f07d4/blob b/shared/package-files/8b/8bf72570c57d3d10c9c243ad14f3201dd6eea8ecd5db5942b49c399be11f07d4/blob new file mode 100644 index 0000000000000000000000000000000000000000..2643c297a11c7c19013e7cf61eef0cbbd02a89c0 --- /dev/null +++ b/shared/package-files/8b/8bf72570c57d3d10c9c243ad14f3201dd6eea8ecd5db5942b49c399be11f07d4/blob @@ -0,0 +1,25 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor range(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step=1, c10::optional dtype={}, c10::optional layout={}, c10::optional device={}, c10::optional pin_memory={}); +TORCH_API at::Tensor & range_out(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out); +TORCH_API at::Tensor & range_cuda_out(const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out); +TORCH_API at::Tensor range(const at::Scalar & start, const at::Scalar & end, c10::optional dtype={}, c10::optional layout={}, c10::optional device={}, c10::optional pin_memory={}); +TORCH_API at::Tensor & range_out_no_step(const at::Scalar & start, const at::Scalar & end, at::Tensor & out); +} // namespace native +} // namespace at diff --git a/shared/package-files/8b/8bf75e3c92a774f032ee17f8f1297a66e3f2977b453c213aef3d1235ab70bb01/blob b/shared/package-files/8b/8bf75e3c92a774f032ee17f8f1297a66e3f2977b453c213aef3d1235ab70bb01/blob new file mode 100644 index 0000000000000000000000000000000000000000..96f824955bf098d86e54cd8bce3bf0015f976ec2 --- /dev/null +++ b/shared/package-files/8b/8bf75e3c92a774f032ee17f8f1297a66e3f2977b453c213aef3d1235ab70bb01/blob @@ -0,0 +1,381 @@ +"""Configuration management setup + +Some terminology: +- name + As written in config files. +- value + Value associated with a name +- key + Name combined with it's section (section.name) +- variant + A single word describing where the configuration key-value pair came from +""" + +import configparser +import locale +import os +import sys +from typing import Any, Dict, Iterable, List, NewType, Optional, Tuple + +from pip._internal.exceptions import ( + ConfigurationError, + ConfigurationFileCouldNotBeLoaded, +) +from pip._internal.utils import appdirs +from pip._internal.utils.compat import WINDOWS +from pip._internal.utils.logging import getLogger +from pip._internal.utils.misc import ensure_dir, enum + +RawConfigParser = configparser.RawConfigParser # Shorthand +Kind = NewType("Kind", str) + +CONFIG_BASENAME = "pip.ini" if WINDOWS else "pip.conf" +ENV_NAMES_IGNORED = "version", "help" + +# The kinds of configurations there are. +kinds = enum( + USER="user", # User Specific + GLOBAL="global", # System Wide + SITE="site", # [Virtual] Environment Specific + ENV="env", # from PIP_CONFIG_FILE + ENV_VAR="env-var", # from Environment Variables +) +OVERRIDE_ORDER = kinds.GLOBAL, kinds.USER, kinds.SITE, kinds.ENV, kinds.ENV_VAR +VALID_LOAD_ONLY = kinds.USER, kinds.GLOBAL, kinds.SITE + +logger = getLogger(__name__) + + +# NOTE: Maybe use the optionx attribute to normalize keynames. +def _normalize_name(name: str) -> str: + """Make a name consistent regardless of source (environment or file)""" + name = name.lower().replace("_", "-") + if name.startswith("--"): + name = name[2:] # only prefer long opts + return name + + +def _disassemble_key(name: str) -> List[str]: + if "." not in name: + error_message = ( + "Key does not contain dot separated section and key. " + "Perhaps you wanted to use 'global.{}' instead?" + ).format(name) + raise ConfigurationError(error_message) + return name.split(".", 1) + + +def get_configuration_files() -> Dict[Kind, List[str]]: + global_config_files = [ + os.path.join(path, CONFIG_BASENAME) for path in appdirs.site_config_dirs("pip") + ] + + site_config_file = os.path.join(sys.prefix, CONFIG_BASENAME) + legacy_config_file = os.path.join( + os.path.expanduser("~"), + "pip" if WINDOWS else ".pip", + CONFIG_BASENAME, + ) + new_config_file = os.path.join(appdirs.user_config_dir("pip"), CONFIG_BASENAME) + return { + kinds.GLOBAL: global_config_files, + kinds.SITE: [site_config_file], + kinds.USER: [legacy_config_file, new_config_file], + } + + +class Configuration: + """Handles management of configuration. + + Provides an interface to accessing and managing configuration files. + + This class converts provides an API that takes "section.key-name" style + keys and stores the value associated with it as "key-name" under the + section "section". + + This allows for a clean interface wherein the both the section and the + key-name are preserved in an easy to manage form in the configuration files + and the data stored is also nice. + """ + + def __init__(self, isolated: bool, load_only: Optional[Kind] = None) -> None: + super().__init__() + + if load_only is not None and load_only not in VALID_LOAD_ONLY: + raise ConfigurationError( + "Got invalid value for load_only - should be one of {}".format( + ", ".join(map(repr, VALID_LOAD_ONLY)) + ) + ) + self.isolated = isolated + self.load_only = load_only + + # Because we keep track of where we got the data from + self._parsers: Dict[Kind, List[Tuple[str, RawConfigParser]]] = { + variant: [] for variant in OVERRIDE_ORDER + } + self._config: Dict[Kind, Dict[str, Any]] = { + variant: {} for variant in OVERRIDE_ORDER + } + self._modified_parsers: List[Tuple[str, RawConfigParser]] = [] + + def load(self) -> None: + """Loads configuration from configuration files and environment""" + self._load_config_files() + if not self.isolated: + self._load_environment_vars() + + def get_file_to_edit(self) -> Optional[str]: + """Returns the file with highest priority in configuration""" + assert self.load_only is not None, "Need to be specified a file to be editing" + + try: + return self._get_parser_to_modify()[0] + except IndexError: + return None + + def items(self) -> Iterable[Tuple[str, Any]]: + """Returns key-value pairs like dict.items() representing the loaded + configuration + """ + return self._dictionary.items() + + def get_value(self, key: str) -> Any: + """Get a value from the configuration.""" + orig_key = key + key = _normalize_name(key) + try: + return self._dictionary[key] + except KeyError: + # disassembling triggers a more useful error message than simply + # "No such key" in the case that the key isn't in the form command.option + _disassemble_key(key) + raise ConfigurationError(f"No such key - {orig_key}") + + def set_value(self, key: str, value: Any) -> None: + """Modify a value in the configuration.""" + key = _normalize_name(key) + self._ensure_have_load_only() + + assert self.load_only + fname, parser = self._get_parser_to_modify() + + if parser is not None: + section, name = _disassemble_key(key) + + # Modify the parser and the configuration + if not parser.has_section(section): + parser.add_section(section) + parser.set(section, name, value) + + self._config[self.load_only][key] = value + self._mark_as_modified(fname, parser) + + def unset_value(self, key: str) -> None: + """Unset a value in the configuration.""" + orig_key = key + key = _normalize_name(key) + self._ensure_have_load_only() + + assert self.load_only + if key not in self._config[self.load_only]: + raise ConfigurationError(f"No such key - {orig_key}") + + fname, parser = self._get_parser_to_modify() + + if parser is not None: + section, name = _disassemble_key(key) + if not ( + parser.has_section(section) and parser.remove_option(section, name) + ): + # The option was not removed. + raise ConfigurationError( + "Fatal Internal error [id=1]. Please report as a bug." + ) + + # The section may be empty after the option was removed. + if not parser.items(section): + parser.remove_section(section) + self._mark_as_modified(fname, parser) + + del self._config[self.load_only][key] + + def save(self) -> None: + """Save the current in-memory state.""" + self._ensure_have_load_only() + + for fname, parser in self._modified_parsers: + logger.info("Writing to %s", fname) + + # Ensure directory exists. + ensure_dir(os.path.dirname(fname)) + + # Ensure directory's permission(need to be writeable) + try: + with open(fname, "w") as f: + parser.write(f) + except OSError as error: + raise ConfigurationError( + f"An error occurred while writing to the configuration file " + f"{fname}: {error}" + ) + + # + # Private routines + # + + def _ensure_have_load_only(self) -> None: + if self.load_only is None: + raise ConfigurationError("Needed a specific file to be modifying.") + logger.debug("Will be working with %s variant only", self.load_only) + + @property + def _dictionary(self) -> Dict[str, Any]: + """A dictionary representing the loaded configuration.""" + # NOTE: Dictionaries are not populated if not loaded. So, conditionals + # are not needed here. + retval = {} + + for variant in OVERRIDE_ORDER: + retval.update(self._config[variant]) + + return retval + + def _load_config_files(self) -> None: + """Loads configuration from configuration files""" + config_files = dict(self.iter_config_files()) + if config_files[kinds.ENV][0:1] == [os.devnull]: + logger.debug( + "Skipping loading configuration files due to " + "environment's PIP_CONFIG_FILE being os.devnull" + ) + return + + for variant, files in config_files.items(): + for fname in files: + # If there's specific variant set in `load_only`, load only + # that variant, not the others. + if self.load_only is not None and variant != self.load_only: + logger.debug("Skipping file '%s' (variant: %s)", fname, variant) + continue + + parser = self._load_file(variant, fname) + + # Keeping track of the parsers used + self._parsers[variant].append((fname, parser)) + + def _load_file(self, variant: Kind, fname: str) -> RawConfigParser: + logger.verbose("For variant '%s', will try loading '%s'", variant, fname) + parser = self._construct_parser(fname) + + for section in parser.sections(): + items = parser.items(section) + self._config[variant].update(self._normalized_keys(section, items)) + + return parser + + def _construct_parser(self, fname: str) -> RawConfigParser: + parser = configparser.RawConfigParser() + # If there is no such file, don't bother reading it but create the + # parser anyway, to hold the data. + # Doing this is useful when modifying and saving files, where we don't + # need to construct a parser. + if os.path.exists(fname): + locale_encoding = locale.getpreferredencoding(False) + try: + parser.read(fname, encoding=locale_encoding) + except UnicodeDecodeError: + # See https://github.com/pypa/pip/issues/4963 + raise ConfigurationFileCouldNotBeLoaded( + reason=f"contains invalid {locale_encoding} characters", + fname=fname, + ) + except configparser.Error as error: + # See https://github.com/pypa/pip/issues/4893 + raise ConfigurationFileCouldNotBeLoaded(error=error) + return parser + + def _load_environment_vars(self) -> None: + """Loads configuration from environment variables""" + self._config[kinds.ENV_VAR].update( + self._normalized_keys(":env:", self.get_environ_vars()) + ) + + def _normalized_keys( + self, section: str, items: Iterable[Tuple[str, Any]] + ) -> Dict[str, Any]: + """Normalizes items to construct a dictionary with normalized keys. + + This routine is where the names become keys and are made the same + regardless of source - configuration files or environment. + """ + normalized = {} + for name, val in items: + key = section + "." + _normalize_name(name) + normalized[key] = val + return normalized + + def get_environ_vars(self) -> Iterable[Tuple[str, str]]: + """Returns a generator with all environmental vars with prefix PIP_""" + for key, val in os.environ.items(): + if key.startswith("PIP_"): + name = key[4:].lower() + if name not in ENV_NAMES_IGNORED: + yield name, val + + # XXX: This is patched in the tests. + def iter_config_files(self) -> Iterable[Tuple[Kind, List[str]]]: + """Yields variant and configuration files associated with it. + + This should be treated like items of a dictionary. + """ + # SMELL: Move the conditions out of this function + + # environment variables have the lowest priority + config_file = os.environ.get("PIP_CONFIG_FILE", None) + if config_file is not None: + yield kinds.ENV, [config_file] + else: + yield kinds.ENV, [] + + config_files = get_configuration_files() + + # at the base we have any global configuration + yield kinds.GLOBAL, config_files[kinds.GLOBAL] + + # per-user configuration next + should_load_user_config = not self.isolated and not ( + config_file and os.path.exists(config_file) + ) + if should_load_user_config: + # The legacy config file is overridden by the new config file + yield kinds.USER, config_files[kinds.USER] + + # finally virtualenv configuration first trumping others + yield kinds.SITE, config_files[kinds.SITE] + + def get_values_in_config(self, variant: Kind) -> Dict[str, Any]: + """Get values present in a config file""" + return self._config[variant] + + def _get_parser_to_modify(self) -> Tuple[str, RawConfigParser]: + # Determine which parser to modify + assert self.load_only + parsers = self._parsers[self.load_only] + if not parsers: + # This should not happen if everything works correctly. + raise ConfigurationError( + "Fatal Internal error [id=2]. Please report as a bug." + ) + + # Use the highest priority parser. + return parsers[-1] + + # XXX: This is patched in the tests. + def _mark_as_modified(self, fname: str, parser: RawConfigParser) -> None: + file_parser_tuple = (fname, parser) + if file_parser_tuple not in self._modified_parsers: + self._modified_parsers.append(file_parser_tuple) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({self._dictionary!r})" diff --git a/shared/package-files/8b/8bf9697a7ce2aeade01e9fd3cc82655625d123530fcbb19652a8dc2fd816f528/blob b/shared/package-files/8b/8bf9697a7ce2aeade01e9fd3cc82655625d123530fcbb19652a8dc2fd816f528/blob new file mode 100644 index 0000000000000000000000000000000000000000..9c8aeadc45ae291f363bb4850b30bab4fb14214d --- /dev/null +++ b/shared/package-files/8b/8bf9697a7ce2aeade01e9fd3cc82655625d123530fcbb19652a8dc2fd816f528/blob @@ -0,0 +1,26 @@ +import torch + +from torch._export.db.case import export_case + + +class MyAutogradFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, x): + return x.clone() + + @staticmethod + def backward(ctx, grad_output): + return grad_output + 1 + + +@export_case( + example_inputs=(torch.randn(3, 2),), +) +class AutogradFunction(torch.nn.Module): + """ + TorchDynamo does not keep track of backward() on autograd functions. We recommend to + use `allow_in_graph` to mitigate this problem. + """ + + def forward(self, x): + return MyAutogradFunction.apply(x) diff --git a/shared/package-files/8b/8bfac4c0356c58b4922bb3354a10abde367bf55f4cc10638a27d4217c3c143be/blob b/shared/package-files/8b/8bfac4c0356c58b4922bb3354a10abde367bf55f4cc10638a27d4217c3c143be/blob new file mode 100644 index 0000000000000000000000000000000000000000..2316cdbbc75611b73d7c768c5679d6c74ffb5679 --- /dev/null +++ b/shared/package-files/8b/8bfac4c0356c58b4922bb3354a10abde367bf55f4cc10638a27d4217c3c143be/blob @@ -0,0 +1,82 @@ +import codecs +import sys +from typing import Any, Callable, Dict, Optional, Text, Tuple, Union + +# For convenience: +_Handler = Callable[[Exception], Tuple[Text, int]] +_String = Union[bytes, str] +_Errors = Union[str, Text, None] +if sys.version_info < (3, 0): + _Decodable = Union[bytes, Text] + _Encodable = Union[bytes, Text] +else: + _Decodable = bytes + _Encodable = str + +# This type is not exposed; it is defined in unicodeobject.c +class _EncodingMap(object): + def size(self) -> int: ... + +_MapT = Union[Dict[int, int], _EncodingMap] + +def register(__search_function: Callable[[str], Any]) -> None: ... +def register_error(__errors: Union[str, Text], __handler: _Handler) -> None: ... +def lookup(__encoding: Union[str, Text]) -> codecs.CodecInfo: ... +def lookup_error(__name: Union[str, Text]) -> _Handler: ... +def decode(obj: Any, encoding: Union[str, Text] = ..., errors: _Errors = ...) -> Any: ... +def encode(obj: Any, encoding: Union[str, Text] = ..., errors: _Errors = ...) -> Any: ... +def charmap_build(__map: Text) -> _MapT: ... +def ascii_decode(__data: _Decodable, __errors: _Errors = ...) -> Tuple[Text, int]: ... +def ascii_encode(__str: _Encodable, __errors: _Errors = ...) -> Tuple[bytes, int]: ... + +if sys.version_info < (3, 2): + def charbuffer_encode(__data: _Encodable, __errors: _Errors = ...) -> Tuple[bytes, int]: ... + +def charmap_decode(__data: _Decodable, __errors: _Errors = ..., __mapping: Optional[_MapT] = ...) -> Tuple[Text, int]: ... +def charmap_encode(__str: _Encodable, __errors: _Errors = ..., __mapping: Optional[_MapT] = ...) -> Tuple[bytes, int]: ... +def escape_decode(__data: _String, __errors: _Errors = ...) -> Tuple[str, int]: ... +def escape_encode(__data: bytes, __errors: _Errors = ...) -> Tuple[bytes, int]: ... +def latin_1_decode(__data: _Decodable, __errors: _Errors = ...) -> Tuple[Text, int]: ... +def latin_1_encode(__str: _Encodable, __errors: _Errors = ...) -> Tuple[bytes, int]: ... +def raw_unicode_escape_decode(__data: _String, __errors: _Errors = ...) -> Tuple[Text, int]: ... +def raw_unicode_escape_encode(__str: _Encodable, __errors: _Errors = ...) -> Tuple[bytes, int]: ... +def readbuffer_encode(__data: _String, __errors: _Errors = ...) -> Tuple[bytes, int]: ... +def unicode_escape_decode(__data: _String, __errors: _Errors = ...) -> Tuple[Text, int]: ... +def unicode_escape_encode(__str: _Encodable, __errors: _Errors = ...) -> Tuple[bytes, int]: ... + +if sys.version_info < (3, 8): + def unicode_internal_decode(__obj: _String, __errors: _Errors = ...) -> Tuple[Text, int]: ... + def unicode_internal_encode(__obj: _String, __errors: _Errors = ...) -> Tuple[bytes, int]: ... + +def utf_16_be_decode(__data: _Decodable, __errors: _Errors = ..., __final: int = ...) -> Tuple[Text, int]: ... +def utf_16_be_encode(__str: _Encodable, __errors: _Errors = ...) -> Tuple[bytes, int]: ... +def utf_16_decode(__data: _Decodable, __errors: _Errors = ..., __final: int = ...) -> Tuple[Text, int]: ... +def utf_16_encode(__str: _Encodable, __errors: _Errors = ..., __byteorder: int = ...) -> Tuple[bytes, int]: ... +def utf_16_ex_decode( + __data: _Decodable, __errors: _Errors = ..., __byteorder: int = ..., __final: int = ... +) -> Tuple[Text, int, int]: ... +def utf_16_le_decode(__data: _Decodable, __errors: _Errors = ..., __final: int = ...) -> Tuple[Text, int]: ... +def utf_16_le_encode(__str: _Encodable, __errors: _Errors = ...) -> Tuple[bytes, int]: ... +def utf_32_be_decode(__data: _Decodable, __errors: _Errors = ..., __final: int = ...) -> Tuple[Text, int]: ... +def utf_32_be_encode(__str: _Encodable, __errors: _Errors = ...) -> Tuple[bytes, int]: ... +def utf_32_decode(__data: _Decodable, __errors: _Errors = ..., __final: int = ...) -> Tuple[Text, int]: ... +def utf_32_encode(__str: _Encodable, __errors: _Errors = ..., __byteorder: int = ...) -> Tuple[bytes, int]: ... +def utf_32_ex_decode( + __data: _Decodable, __errors: _Errors = ..., __byteorder: int = ..., __final: int = ... +) -> Tuple[Text, int, int]: ... +def utf_32_le_decode(__data: _Decodable, __errors: _Errors = ..., __final: int = ...) -> Tuple[Text, int]: ... +def utf_32_le_encode(__str: _Encodable, __errors: _Errors = ...) -> Tuple[bytes, int]: ... +def utf_7_decode(__data: _Decodable, __errors: _Errors = ..., __final: int = ...) -> Tuple[Text, int]: ... +def utf_7_encode(__str: _Encodable, __errors: _Errors = ...) -> Tuple[bytes, int]: ... +def utf_8_decode(__data: _Decodable, __errors: _Errors = ..., __final: int = ...) -> Tuple[Text, int]: ... +def utf_8_encode(__str: _Encodable, __errors: _Errors = ...) -> Tuple[bytes, int]: ... + +if sys.platform == "win32": + def mbcs_decode(__data: _Decodable, __errors: _Errors = ..., __final: int = ...) -> Tuple[Text, int]: ... + def mbcs_encode(__str: _Encodable, __errors: _Errors = ...) -> Tuple[bytes, int]: ... + if sys.version_info >= (3, 0): + def code_page_decode(__codepage: int, __data: bytes, __errors: _Errors = ..., __final: int = ...) -> Tuple[Text, int]: ... + def code_page_encode(__code_page: int, __str: Text, __errors: _Errors = ...) -> Tuple[bytes, int]: ... + if sys.version_info >= (3, 6): + def oem_decode(__data: bytes, __errors: _Errors = ..., __final: int = ...) -> Tuple[Text, int]: ... + def oem_encode(__str: Text, __errors: _Errors = ...) -> Tuple[bytes, int]: ... diff --git a/shared/package-files/8b/8bfc8eb445d7ae4d7ee07e5d82f37189d933560c00516f6a5a8c45281b02602c/blob b/shared/package-files/8b/8bfc8eb445d7ae4d7ee07e5d82f37189d933560c00516f6a5a8c45281b02602c/blob new file mode 100644 index 0000000000000000000000000000000000000000..e47030a4a605f74b7f5305c90ad3b64d80ff0d95 --- /dev/null +++ b/shared/package-files/8b/8bfc8eb445d7ae4d7ee07e5d82f37189d933560c00516f6a5a8c45281b02602c/blob @@ -0,0 +1,34 @@ +#!/usr/bin/env python +# Copyright (c) 2017-2019, Intel Corporation +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# * Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# * Redistributions in binary form must reproduce the above copyright +# notice, this list of conditions and the following disclaimer in the +# documentation and/or other materials provided with the distribution. +# * Neither the name of Intel Corporation nor the names of its contributors +# may be used to endorse or promote products derived from this software +# without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +from ._pydfti import (fft, ifft, fft2, ifft2, fftn, ifftn, rfft, irfft, + rfft_numpy, irfft_numpy, rfftn_numpy, irfftn_numpy) + +from ._version import __version__ +import mkl_fft.interfaces + +__all__ = ['fft', 'ifft', 'fft2', 'ifft2', 'fftn', 'ifftn', 'rfft', 'irfft', + 'rfft_numpy', 'irfft_numpy', 'rfftn_numpy', 'irfftn_numpy', 'interfaces'] diff --git a/shared/package-files/8b/8bfc9c25474ac923bde4bf89030bfce928ead3b465d2b38561a9f7949c72b610/blob b/shared/package-files/8b/8bfc9c25474ac923bde4bf89030bfce928ead3b465d2b38561a9f7949c72b610/blob new file mode 100644 index 0000000000000000000000000000000000000000..d1392f5407c0c7f24843631436b17281a15a9c55 --- /dev/null +++ b/shared/package-files/8b/8bfc9c25474ac923bde4bf89030bfce928ead3b465d2b38561a9f7949c72b610/blob @@ -0,0 +1,675 @@ +# Generates ADInplaceOrViewType.h/cpp +# +# NOTE: If any changes are being made to the ADInplaceOrView codegen please also check +# if updates are needed in torch/csrc/autograd/autograd_not_implemented_fallback.cpp +# The fallback is expected to mimick this codegen, so we should keep the two in sync. + +from typing import Dict, List, Optional, Tuple + +from torchgen.api import cpp +from torchgen.api.autograd import ( + dispatch_strategy, + gen_differentiable_outputs, + NativeFunctionWithDifferentiabilityInfo, +) +from torchgen.api.types import ( + BaseCType, + Binding, + boolT, + ConstRefCType, + CType, + DispatcherSignature, + intArrayRefT, + longT, + OptionalCType, + symIntArrayRefT, + SymIntT, + # See Note [Nested Arg Types] + tensorT, +) +from torchgen.code_template import CodeTemplate +from torchgen.context import with_native_function +from torchgen.model import ( + NativeFunction, + SchemaKind, + SelfArgument, + TensorOptionsArguments, + Type, +) +from torchgen.utils import FileManager + +from .context import with_native_function_with_differentiability_info +from .gen_trace_type import ( + get_return_value, + MANUAL_AUTOGRAD, + tie_return_values, + type_wrapper_name, +) + +# See NOTE [ Autograd View Variables ] in variable.h for details. +# If you update list VIEW_FUNCTIONS or RETURNS_VIEWS_OF_INPUT, +# you **MUST** also update the public list of view ops accordingly in +# docs/source/tensor_view.rst. Note not all ATen functions are exposed to public, +# e.g alias & sparse_coo_tensor_with_dims_and_tensors. +# +# A map: function name => name of the argument that all outputs are view of + +VIEW_FUNCTIONS_WITH_METADATA_CHANGE = [ + "view_as_complex", + "view_as_real", + "_conj", + "_neg_view", + "_nested_get_values", + "_nested_view_from_buffer", + "_nested_view_from_jagged", +] + +VIEW_FUNCTIONS = { + "numpy_T": "self", + "alias": "self", + "as_strided": "self", + "diagonal": "self", + "expand": "self", + "permute": "self", + "select": "self", + "slice": "self", + "slice_inverse": "self", + "split": "self", + "split_with_sizes": "self", + "squeeze": "self", + "t": "self", + "transpose": "self", + "unfold": "self", + "unsqueeze": "self", + "flatten": "self", + "view": "self", + "unbind": "self", + "_indices": "self", + "_values": "self", + "indices": "self", + "values": "self", + "crow_indices": "self", + "col_indices": "self", + "ccol_indices": "self", + "row_indices": "self", + # sparse_coo ctor output should really be views of both indices and values, + # but we only supports making as view of a single variable, and indices is + # discrete anyways. + # FIXME: clone indices on construction. + "sparse_coo_tensor_with_dims_and_tensors": "values", + "_reshape_alias": "self", + "_test_autograd_multiple_dispatch_view": "self", +} + +for key in VIEW_FUNCTIONS_WITH_METADATA_CHANGE: + VIEW_FUNCTIONS[key] = "self" + +# note: some VIEW_FUNCTIONS are just compositions of the view functions above +# this list contains both the root view functions and any that are purely composed +# of viewing functions, and is used by the JIT to determine when an operator +# may return a view of its inputs; however they may sometimes return a copy. +# (e.g. `contiguous`) +RETURNS_VIEWS_OF_INPUT = set(VIEW_FUNCTIONS.keys()).union( + { + "chunk", + "detach", + "contiguous", + "reshape", + "reshape_as", + "expand_as", + "view_as", + "real", + "imag", + "narrow", + "movedim", + "tensor_split", + "swapdims", + "swapaxes", + "mT", + "mH", + "adjoint", + "matrix_H", + } +) + +# These are the functions we consider views for the purposes of validating +# StorageImpl and TensorImpl in gen_variable_type. +# `_unsafe_view` is not included in VIEW_FUNCTIONS above because it is not a +# view for the purposes of ADInplaceOrView kernel, we do not want to call as_view +# See NOTE [Unsafe View] for more info. +ALL_VIEW_FUNCTIONS = { + **VIEW_FUNCTIONS, + "_unsafe_view": "self", +} + +ARRAYREF_TO_VEC = CodeTemplate( + """\ +auto ${vec} = ${arg}.vec(); +""" +) + +OPTIONAL_TO_VAL = CodeTemplate( + """\ +auto ${val} = ${arg}.value_or(${default}); +""" +) + +CALL_DISPATCH = CodeTemplate( + """\ +at::_ops::${unambiguous_name}::call(${unpacked_args})""" +) + +REVERSE_VIEW_DISPATCH = CodeTemplate( + """\ +${reverse_name}(${unpacked_args})""" +) + +MULTI_OUTPUT_VIEW_ITERATION = CodeTemplate( + """\ +for (auto ${view_idx} : c10::irange(${var}.size())) { + ${body} +} +""" +) + +SETUP_REPLAY_VIEW_IF_NOT_SUPPORT_AS_STRIDED_OR_VIEW_WITH_METADATA_CHANGE = CodeTemplate( + """\ +std::unique_ptr func(nullptr); +std::function rev_func=nullptr; +if (${is_view_with_metadata_change} || + !self.unsafeGetTensorImpl()->support_as_strided() || + self.unsafeGetTensorImpl()->is_python_dispatch() || + c10::AutogradState::get_tls_state().get_view_replay_enabled()) { + ${replay_view_func} + ${reverse_replay_view_func} +} +""" +) + +REPLAY_VIEW_FUNC = CodeTemplate( + """\ +func = std::make_unique<${view_func_name}>(${view_func_args}); +""" +) + +REVERSE_REPLAY_VIEW_LAMBDA_FUNC = CodeTemplate( + """\ +rev_func = [=](const at::Tensor& ${input_view}) { + return ${reverse_replay_view_call}; +}; +""" +) + +METHOD_DEFINITION = CodeTemplate( + """\ +${return_type} ${type_wrapper_name}(${formals}) { + ${type_definition_body} +} +""" +) + +WRAPPER_REGISTRATION = CodeTemplate( + """\ +m.impl("${unqual_operator_name_with_overload}", + TORCH_FN(${class_type}::${type_wrapper_name}) +); +""" +) + +AUTOGRAD_NOT_IMPLEMENTED_REGISTRATION = CodeTemplate( + """\ +m.impl("${unqual_operator_name_with_overload}", torch::autograd::autogradNotImplementedFallback()); +""" +) + +INPLACE_REDISPATCH = CodeTemplate( + """\ +{ + at::AutoDispatchBelowADInplaceOrView guard; + at::_ops::${unambiguous_name}::redispatch(${unpacked_args}); +} +""" +) + +ASSIGN_RETURN_VALUE = CodeTemplate( + """\ +${return_values} = ${rhs_value}; +""" +) + +VIEW_REDISPATCH = CodeTemplate( + """\ +${assign_return_values} ([&]() { + at::AutoDispatchBelowADInplaceOrView guard; + return at::_ops::${unambiguous_name}::redispatch(${unpacked_args}); +})(); +""" +) + +TMP_VAR = "_tmp" + + +# FIXME: Ideally these functions should be methods on Type class, but we have a +# comment in codegen/model.py there saying these concepts are not well defined. +# Thus we put a version that commonly used by autograd codegen here. +def is_tensor_type(t: Type) -> bool: + # TODO: Should handle optional here? + return t.is_tensor_like() and t.is_list_like() is None + + +def is_tensor_list_type(t: Type) -> bool: + # TODO: Should handle optional here? + return t.is_tensor_like() and t.is_list_like() is not None + + +UNPACK_TENSOR = CodeTemplate( + """\ +auto${ref} ${arg_name}_ = unpack${suffix}(${arg_name}, "${arg_name}", ${arg_pos});""" +) + + +def unpacked_name(arg_name: str) -> str: + return arg_name + "_" + + +# e.g. select.int -> select_copy_int_inverse() +def inverse_view_name(f: NativeFunction) -> str: + copy_variant = f"{f.root_name}_copy" + overload = f"{f.func.name.overload_name}" + if overload != "": + overload = "_" + overload + return f"{copy_variant}{overload}_inverse" + + +def extract_bindings(f: NativeFunction) -> List[Binding]: + return [ + r + for a in f.func.schema_order_arguments() + for r in cpp.argument( + a, + method=False, + symint=True, + cpp_no_default_args=set(), + faithful=False, + has_tensor_options=False, + ) + ] + + +@with_native_function +def unpack_args(f: NativeFunction) -> Tuple[List[str], List[Binding]]: + body: List[str] = [] + unpacked_bindings: List[Binding] = [] + + for i, binding in enumerate(extract_bindings(f)): + assert not isinstance(binding.argument, SelfArgument) + if isinstance(binding.argument, TensorOptionsArguments): + raise RuntimeError("VariableKernel shouldn't take TensorOptions") + + is_nullable = binding.argument.type.is_nullable() + if not binding.argument.type.is_tensor_like() or is_nullable: + unpacked_bindings.append(binding) + continue + + is_tensor_list = is_tensor_list_type(binding.argument.type) + ref = (not is_nullable) and not is_tensor_list + suffix = "_opt" if is_nullable and not is_tensor_list else "" + body.append( + UNPACK_TENSOR.substitute( + arg_name=binding.name, + arg_pos=i, + suffix=suffix, + ref="&" if ref else "", + ) + ) + unpacked_bindings.append( + Binding( + name=unpacked_name(binding.name), + nctype=binding.nctype, + argument=binding.argument, + default=binding.default, + ) + ) + + return body, unpacked_bindings + + +def get_base_name(f: NativeFunction) -> str: + return f.func.name.name.base # TODO: should be str(f.func.name.name)? + + +def get_view_info(f: NativeFunction) -> Optional[str]: + base_name = get_base_name(f) + view_info = VIEW_FUNCTIONS.get(base_name, None) + if view_info is None and base_name in RETURNS_VIEWS_OF_INPUT: + view_info = "self" + return view_info + + +def emit_view_func( + f: NativeFunction, bindings: List[Binding], view_idx: Optional[str] = None +) -> str: + """Generate an additional lambda function to recover views in backward when as_strided is not supported. + See Note [View + Inplace update for base tensor] and [View + Inplace update for view tensor] for more details. + """ + # TODO: Clean this logic up if we get rid of reverse view funcs or reify them. + input_base = "input_base" + replay_view_func = "" + updated_args: List[str] = [] + known_view_arg_simple_types: List[CType] = [ + BaseCType(longT), + OptionalCType(BaseCType(longT)), + BaseCType(SymIntT), + OptionalCType(BaseCType(SymIntT)), + BaseCType(boolT), + BaseCType(intArrayRefT), + BaseCType(symIntArrayRefT), + ConstRefCType(BaseCType(tensorT)), + ConstRefCType(OptionalCType(BaseCType(tensorT))), + ] + for binding in bindings: + arg, arg_type = binding.name, binding.nctype.type + if arg == "self": + updated_args.append(input_base) + continue + if arg_type not in known_view_arg_simple_types: + known_types_str = ", ".join([str(t) for t in known_view_arg_simple_types]) + raise TypeError( + f"You are adding an {arg_type} {arg} argument to op {cpp.name(f.func)} in addition to known types: " + f"{known_types_str}. Please update the list or materialize it so that it can be closed " + "over by value, also add a test in pytorch/xla/test/test_operations.py where this code " + "is exercised." + ) + if arg_type == BaseCType(intArrayRefT) or arg_type == BaseCType( + symIntArrayRefT + ): + # It's not safe to close over IntArrayRef by value, since this is a + # reference type, so materialize a vector to close over by value + arg_vec = arg + "_vec" + replay_view_func += ARRAYREF_TO_VEC.substitute(arg=arg, vec=arg_vec) + updated_args.append(arg_vec) + elif arg_type == OptionalCType(BaseCType(longT)): + # Materialize int64_t? to int64_t + arg_value = arg + "_val" + replay_view_func += OPTIONAL_TO_VAL.substitute( + arg=arg, val=arg_value, default="0" + ) + updated_args.append(arg_value) + elif arg_type == ConstRefCType(BaseCType(tensorT)) or arg_type == ConstRefCType( + OptionalCType(BaseCType(tensorT)) + ): + # NB: Closing over a tensor. If a user modifies this tensor, this will be silently + # incorrect. The proper thing to do is to store the version counter and copy on write. + updated_args.append(arg) + else: + updated_args.append(arg) + + from .gen_view_funcs import view_func_name + + view_func_args = [b.name for b in bindings if b.name != "self"] + if view_idx is not None: + view_func_args.append(f"{view_idx}") + replay_view_func += REPLAY_VIEW_FUNC.substitute( + view_func_name=view_func_name(f, include_namespace=True), + view_func_args=view_func_args, + ) + + input_view = "input_view" + reverse_unpacked_args = [ + "self", + f"{input_view}", + # inverse_return_mode= + "at::functionalization::InverseReturnMode::AlwaysView", + *(() if view_idx is None else (f"{view_idx}",)), + # skip input_base arg + *updated_args[1:], + ] + + from torchgen.api.functionalization import reverse_name + + reverse_replay_view_call = REVERSE_VIEW_DISPATCH.substitute( + reverse_name=reverse_name(f, include_namespace=True), + unpacked_args=reverse_unpacked_args, + ) + reverse_replay_view_func = REVERSE_REPLAY_VIEW_LAMBDA_FUNC.substitute( + input_view=input_view, reverse_replay_view_call=reverse_replay_view_call + ) + + is_view_with_metadata_change = ( + "true" if cpp.name(f.func) in VIEW_FUNCTIONS_WITH_METADATA_CHANGE else "false" + ) + + return SETUP_REPLAY_VIEW_IF_NOT_SUPPORT_AS_STRIDED_OR_VIEW_WITH_METADATA_CHANGE.substitute( + is_view_with_metadata_change=is_view_with_metadata_change, + replay_view_func=replay_view_func, + reverse_replay_view_func=reverse_replay_view_func, + ) + + +def emit_view_body( + fn: NativeFunctionWithDifferentiabilityInfo, var: str +) -> Tuple[str, str]: + # See NOTE [ Autograd View Variables ] in variable.h for details. + f = fn.func + base_name = get_base_name(f) + view_info = get_view_info(f) + call = "" + differentiable_outputs = gen_differentiable_outputs(fn) + differentiable_output_vars = {r.name for r in differentiable_outputs} + if not isinstance(view_info, str): + raise TypeError( + f"The view info should be a string for {base_name}, but it is: {view_info}" + ) + if len(differentiable_output_vars) == 0: + # no output is differentiable (.indices() for SparseTensors for example) + rhs_value = ( + f"as_view({view_info}, {var}, " + f"/* is_bw_differentiable */ false, /* is_fw_differentiable */ false)" + ) + elif len(differentiable_output_vars) == 1: + # Single differentiable output (Tensor or Tensor[]) + return_info = differentiable_outputs[0] + # We only support simple Tensor or a TensorList for functions that return views + if not is_tensor_type(return_info.type) and not is_tensor_list_type( + return_info.type + ): + raise RuntimeError( + f"{base_name} that return differentiable views can only return Tensor or Tensor[]" + ) + + # See Note [ View + Inplace detection] + def get_creation_meta_in_mode(original: str) -> str: + creation_meta_with_grad_mode = f"(at::GradMode::is_enabled() ? {original} : CreationMeta::NO_GRAD_MODE)" + return f"InferenceMode::is_enabled() ? CreationMeta::INFERENCE_MODE : {creation_meta_with_grad_mode}" + + # Only allow rebasing of the history if we return a single Tensor + # If we are in a no grad block, raise a warning + # See NOTE [ View + Inplace detection ] for more details about this logic + if is_tensor_list_type(return_info.type): + creation_meta = get_creation_meta_in_mode("CreationMeta::MULTI_OUTPUT_NODE") + view_idx = "view_idx" + view_func = emit_view_func( + f, extract_bindings(f), view_idx=view_idx + ).strip() + as_view_call = ( + f"as_view(/* base */ {view_info}, /* output */ {var}[{view_idx}], " + "/* is_bw_differentiable */ true, /* is_fw_differentiable */ true, " + "/* view_func */ std::move(func), /* rev_view_func */ rev_func, " + f"/* creation_meta */ {creation_meta});" + ) + call += MULTI_OUTPUT_VIEW_ITERATION.substitute( + var=var, view_idx=view_idx, body=f"{view_func}\n{as_view_call}" + ) + rhs_value = f"std::move({var})" + else: + call += emit_view_func(f, extract_bindings(f), view_idx=None) + creation_meta = get_creation_meta_in_mode("CreationMeta::DEFAULT") + rhs_value = ( + f"as_view(/* base */ {view_info}, /* output */ {var}, /* is_bw_differentiable */ true, " + "/* is_fw_differentiable */ true, " + f"/* view_func */ std::move(func), /* rev_view_func */ rev_func, /* creation_meta */ {creation_meta})" + ) + else: + # This could be supported but we don't need it at the moment, so keeping things simple. + raise RuntimeError( + "Function that return multiple differentiable output " + "when at least one of them is view is not supported." + ) + return call, rhs_value + + +def modifies_arguments(f: NativeFunction) -> bool: + return f.func.kind() in [SchemaKind.inplace, SchemaKind.out] + + +@with_native_function_with_differentiability_info +def emit_inplace_or_view_body(fn: NativeFunctionWithDifferentiabilityInfo) -> List[str]: + f = fn.func + inplace_view_body: List[str] = [] + + dispatcher_sig = DispatcherSignature.from_schema(f.func) + dispatcher_exprs = dispatcher_sig.exprs() + + # code-generated ADInplaceOrView kernels plumb and recompute dispatch keys directly through the kernel for performance. + # See Note [Plumbing Keys Through The Dispatcher] for details. + dispatch_key_set = "ks & c10::after_ADInplaceOrView_keyset" + redispatch_args = ", ".join([dispatch_key_set] + [a.expr for a in dispatcher_exprs]) + + # Note that this calls the slow, dispatching variants of manual_cpp_binding ops. + # We could probably work harder to ensure that the fast variants are called instead, but the perf benefit would be minimal. + if modifies_arguments(f): # inplace op + inplace_view_body.append( + INPLACE_REDISPATCH.substitute( + unambiguous_name=f.func.name.unambiguous_name(), + unpacked_args=redispatch_args, + ) + ) + for r in cpp.return_names(f): + inplace_view_body.append(f"increment_version({r});") + else: + assert get_view_info(f) is not None + inplace_view_body.append( + VIEW_REDISPATCH.substitute( + assign_return_values="auto " + TMP_VAR + " = ", + unambiguous_name=f.func.name.unambiguous_name(), + unpacked_args=redispatch_args, + ) + ) + call, rhs_value = emit_view_body(fn, TMP_VAR) + inplace_view_body.append(call) + assert rhs_value is not None + inplace_view_body.append( + ASSIGN_RETURN_VALUE.substitute( + return_values=tie_return_values(f), rhs_value=rhs_value + ) + ) + if f.func.returns: + inplace_view_body.append(f"return {get_return_value(f)};") + return inplace_view_body + + +@with_native_function +def gen_formals(f: NativeFunction) -> str: + return ", ".join( + # code-generated autograd kernels plumb and recompute dispatch keys directly through the kernel for performance. + # See Note [Plumbing Keys Through The Dispatcher] for details. + ["c10::DispatchKeySet ks"] + + [ + f'{cpp.argument_type(a, binds="__placeholder__", symint=True).cpp_type()} {a.name}' + for a in f.func.schema_order_arguments() + ] + ) + + +@with_native_function_with_differentiability_info +def inplace_or_view_method_definition( + fn: NativeFunctionWithDifferentiabilityInfo, +) -> Optional[str]: + f = fn.func + if get_view_info(f) is None and ( + # For functions that modify their inputs but don't return them, + # we can't give them autograd support. + # See https://github.com/pytorch/pytorch/issues/53796 + not modifies_arguments(f) + or len(f.func.returns) == 0 + ): + return None + return METHOD_DEFINITION.substitute( + return_type=cpp.returns_type(f.func.returns, symint=True).cpp_type(), + type_wrapper_name=type_wrapper_name(f), + formals=gen_formals(f), + type_definition_body=emit_inplace_or_view_body(fn), + ) + + +@with_native_function_with_differentiability_info +def inplace_or_view_method_registration( + fn: NativeFunctionWithDifferentiabilityInfo, +) -> Optional[str]: + f = fn.func + if get_view_info(f) is None and ( + not modifies_arguments(f) or len(f.func.returns) == 0 + ): + return None + return WRAPPER_REGISTRATION.substitute( + unqual_operator_name_with_overload=f.func.name, + type_wrapper_name=type_wrapper_name(f), + class_type="ADInplaceOrView", + ) + + +def use_derived(fn: NativeFunctionWithDifferentiabilityInfo) -> bool: + f = fn.func + name = cpp.name(f.func) + return name not in MANUAL_AUTOGRAD and dispatch_strategy(fn) == "use_derived" + + +def gen_inplace_or_view_type_env( + fn: NativeFunctionWithDifferentiabilityInfo, +) -> Dict[str, List[str]]: + definition = inplace_or_view_method_definition(fn) + registration = inplace_or_view_method_registration(fn) + + return { + "ops_headers": ( + [f"#include "] + if definition is not None + else [] + ), + "inplace_or_view_method_definitions": [definition] + if definition is not None + else [], + "inplace_or_view_wrapper_registrations": [registration] + if registration is not None + else [], + } + + +def gen_inplace_or_view_type( + out: str, + native_yaml_path: str, + tags_yaml_path: str, + fns_with_infos: List[NativeFunctionWithDifferentiabilityInfo], + template_path: str, +) -> None: + # NOTE: see Note [Sharded File] at the top of the VariableType.cpp + # template regarding sharding of the generated files. + num_shards = 2 + + fm = FileManager(install_dir=out, template_dir=template_path, dry_run=False) + fm.write_sharded( + "ADInplaceOrViewType.cpp", + [fn for fn in fns_with_infos if use_derived(fn)], + key_fn=lambda fn: fn.func.root_name, + base_env={ + "generated_comment": "@" + + f"generated from {fm.template_dir_for_comments()}/ADInplaceOrViewType.cpp", + }, + env_callable=gen_inplace_or_view_type_env, + num_shards=2, + sharded_keys={ + "ops_headers", + "inplace_or_view_method_definitions", + "inplace_or_view_wrapper_registrations", + }, + ) diff --git a/shared/package-files/8b/8bfca34869b3f9a3b2fc71b02cbac41512af6d1f8ab17d2564e65320f88ede10/blob b/shared/package-files/8b/8bfca34869b3f9a3b2fc71b02cbac41512af6d1f8ab17d2564e65320f88ede10/blob new file mode 100644 index 0000000000000000000000000000000000000000..5b59661ea42d673be0c0ed4b8288406dcdd45826 --- /dev/null +++ b/shared/package-files/8b/8bfca34869b3f9a3b2fc71b02cbac41512af6d1f8ab17d2564e65320f88ede10/blob @@ -0,0 +1,20 @@ +# Encoding file: cp737, single-byte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diff --git a/shared/package-files/8c/8c017ef1938a38138f03199253de2b2fd5870fedaf53d763980ff31d9686b3bf/blob b/shared/package-files/8c/8c017ef1938a38138f03199253de2b2fd5870fedaf53d763980ff31d9686b3bf/blob new file mode 100644 index 0000000000000000000000000000000000000000..d5d69b9d648b3280c96becb53e8d6af90c9a57e2 --- /dev/null +++ b/shared/package-files/8c/8c017ef1938a38138f03199253de2b2fd5870fedaf53d763980ff31d9686b3bf/blob @@ -0,0 +1,198 @@ +from typing import List, Optional + +import torch +from torch.backends._nnapi.serializer import _NnapiSerializer + +ANEURALNETWORKS_PREFER_LOW_POWER = 0 +ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1 +ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2 + + +class NnapiModule(torch.nn.Module): + """Torch Module that wraps an NNAPI Compilation. + + This module handles preparing the weights, initializing the + NNAPI TorchBind object, and adjusting the memory formats + of all inputs and outputs. + """ + + # _nnapi.Compilation is defined + comp: Optional[torch.classes._nnapi.Compilation] # type: ignore[name-defined] + weights: List[torch.Tensor] + out_templates: List[torch.Tensor] + + def __init__( + self, + shape_compute_module: torch.nn.Module, + ser_model: torch.Tensor, + weights: List[torch.Tensor], + inp_mem_fmts: List[int], + out_mem_fmts: List[int], + compilation_preference: int, + relax_f32_to_f16: bool, + ): + super().__init__() + self.shape_compute_module = shape_compute_module + self.ser_model = ser_model + self.weights = weights + self.inp_mem_fmts = inp_mem_fmts + self.out_mem_fmts = out_mem_fmts + self.out_templates = [] + self.comp = None + self.compilation_preference = compilation_preference + self.relax_f32_to_f16 = relax_f32_to_f16 + + @torch.jit.export + def init(self, args: List[torch.Tensor]): + assert self.comp is None + self.out_templates = self.shape_compute_module.prepare(self.ser_model, args) # type: ignore[operator] + self.weights = [w.contiguous() for w in self.weights] + comp = torch.classes._nnapi.Compilation() + comp.init2( + self.ser_model, + self.weights, + self.compilation_preference, + self.relax_f32_to_f16, + ) + + self.comp = comp + + def forward(self, args: List[torch.Tensor]) -> List[torch.Tensor]: + if self.comp is None: + self.init(args) + comp = self.comp + assert comp is not None + outs = [torch.empty_like(out) for out in self.out_templates] + + assert len(args) == len(self.inp_mem_fmts) + fixed_args = [] + for idx in range(len(args)): + fmt = self.inp_mem_fmts[idx] + # These constants match the values in DimOrder in serializer.py + # TODO: See if it's possible to use those directly. + if fmt == 0: + fixed_args.append(args[idx].contiguous()) + elif fmt == 1: + fixed_args.append(args[idx].permute(0, 2, 3, 1).contiguous()) + else: + raise Exception("Invalid mem_fmt") + comp.run(fixed_args, outs) + assert len(outs) == len(self.out_mem_fmts) + for idx in range(len(self.out_templates)): + fmt = self.out_mem_fmts[idx] + # These constants match the values in DimOrder in serializer.py + # TODO: See if it's possible to use those directly. + if fmt in (0, 2): + pass + elif fmt == 1: + outs[idx] = outs[idx].permute(0, 3, 1, 2) + else: + raise Exception("Invalid mem_fmt") + return outs + + +def convert_model_to_nnapi( + model, + inputs, + serializer=None, + return_shapes=None, + use_int16_for_qint16=False, + compilation_preference=ANEURALNETWORKS_PREFER_SUSTAINED_SPEED, + relax_f32_to_f16=False, +): + ( + shape_compute_module, + ser_model_tensor, + used_weights, + inp_mem_fmts, + out_mem_fmts, + retval_count, + ) = process_for_nnapi( + model, inputs, serializer, return_shapes, use_int16_for_qint16 + ) + + nnapi_model = NnapiModule( + shape_compute_module, + ser_model_tensor, + used_weights, + inp_mem_fmts, + out_mem_fmts, + compilation_preference, + relax_f32_to_f16, + ) + + class NnapiInterfaceWrapper(torch.nn.Module): + """NNAPI list-ifying and de-list-ifying wrapper. + + NNAPI always expects a list of inputs and provides a list of outputs. + This module allows us to accept inputs as separate arguments. + It returns results as either a single tensor or tuple, + matching the original module. + """ + + def __init__(self, mod): + super().__init__() + self.mod = mod + + wrapper_model_py = NnapiInterfaceWrapper(nnapi_model) + wrapper_model = torch.jit.script(wrapper_model_py) + # TODO: Maybe make these names match the original. + arg_list = ", ".join(f"arg_{idx}" for idx in range(len(inputs))) + if retval_count < 0: + ret_expr = "retvals[0]" + else: + ret_expr = "".join(f"retvals[{idx}], " for idx in range(retval_count)) + wrapper_model.define( + f"def forward(self, {arg_list}):\n" + f" retvals = self.mod([{arg_list}])\n" + f" return {ret_expr}\n" + ) + return wrapper_model + + +def process_for_nnapi( + model, inputs, serializer=None, return_shapes=None, use_int16_for_qint16=False +): + model = torch.jit.freeze(model) + + if isinstance(inputs, torch.Tensor): + inputs = [inputs] + + serializer = serializer or _NnapiSerializer( + config=None, use_int16_for_qint16=use_int16_for_qint16 + ) + ( + ser_model, + used_weights, + inp_mem_fmts, + out_mem_fmts, + shape_compute_lines, + retval_count, + ) = serializer.serialize_model(model, inputs, return_shapes) + ser_model_tensor = torch.tensor(ser_model, dtype=torch.int32) + + # We have to create a new class here every time this function is called + # because module.define adds a method to the *class*, not the instance. + class ShapeComputeModule(torch.nn.Module): + """Code-gen-ed module for tensor shape computation. + + module.prepare will mutate ser_model according to the computed operand + shapes, based on the shapes of args. Returns a list of output templates. + """ + + pass + + shape_compute_module = torch.jit.script(ShapeComputeModule()) + real_shape_compute_lines = [ + "def prepare(self, ser_model: torch.Tensor, args: List[torch.Tensor]) -> List[torch.Tensor]:\n", + ] + [f" {line}\n" for line in shape_compute_lines] + shape_compute_module.define("".join(real_shape_compute_lines)) + + return ( + shape_compute_module, + ser_model_tensor, + used_weights, + inp_mem_fmts, + out_mem_fmts, + retval_count, + ) diff --git a/shared/package-files/8c/8c0440db777a3d550f3cecc262d9f15ef7f403a744d62bc1e8bf17d9b97ebcd2/blob b/shared/package-files/8c/8c0440db777a3d550f3cecc262d9f15ef7f403a744d62bc1e8bf17d9b97ebcd2/blob new file mode 100644 index 0000000000000000000000000000000000000000..bdf19e8d7362272c50d36ff36fcc7c5918a98afb --- /dev/null +++ b/shared/package-files/8c/8c0440db777a3d550f3cecc262d9f15ef7f403a744d62bc1e8bf17d9b97ebcd2/blob @@ -0,0 +1,401 @@ +// Copyright © 2022 Apple Inc. + +#pragma once + +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +// this implementation is based on CUDACachingAllocator. +// It utilizes Metal Heaps to improve the performance with buffer allocation. +// Do not include this header. Use MPSAllocatorInterface.h instead. +// TODO: Unify the logic with CUDACachingAllocator and remove redundant code. +namespace at::mps::HeapAllocator { + +static const size_t kMaxSmallAlloc = MB(1); // largest "small" allocation is 1 MiB +static const size_t kMinLargeAlloc = MB(10); // allocations between 1 and 10 MiB may use kLargeHeap +static const size_t kRoundLarge = MB(2); // round up large allocations to 2 MiB +static const size_t kSmallHeap = MB(8); // "small" allocations are packed in 8 MiB heaps +static const size_t kLargeHeap = MB(32); // "large" allocations may be packed in 32 MiB heaps +static const size_t kXLargeHeapD = MB(128); // "extra large" allocations on Discrete devices may be packed in 128 MiB heaps +static const size_t kXLargeHeapU = MB(1024); // "extra large" allocations on Unified devices may be packed in 1 GiB heaps +static const size_t kMaxScalarAlloc = (sizeof(int64_t)); // largest "scalar" allocation + +// buffer pools could be customized with a combination of usage flags +enum UsageFlags : uint32_t { + PRIVATE = 0, + SMALL = (1 << 0), // small heaps have sizes of kSmallHeap, and large ones kLargeHeap + SHARED = (1 << 1), // shared pools allocated on devices with unified memory; otherwise, private between host/device + MANAGED = (1 << 2), // managed storage mode + HAZARD = (1 << 3), // enables Automatic Hazard Tracking for the resources allocated on the pool + SCALAR = (1 << 4), // used to import CPU scalar values to GPU and use them in MPS Stream +}; +// debug verbosity flags +enum DebugVerbosity : uint32_t { + SILENT = 0, + PROFILING = (1 << 0), // print generic profiling data for total system memory usage + ALLOCATIONS = (1 << 1), // print buffer allocations + RECYCLES = (1 << 2), // print buffer recycling + RELEASES = (1 << 3), // print buffer releases + LARGE_ONLY = (1 << 4), // only log large buffer pool transactions +}; + +struct HeapBlock; + +struct BufferBlock { + id buffer; + void* cpu_ptr = nullptr; // stores the pointer to CPU mapping of a Shared MTLBuffer + size_t size; // size after alignment + size_t requested_size; // requested size (before alignment) + // buffer shape is used for retrieving base of views in cached graphs + std::vector shape; + bool in_use = false; + HeapBlock* heap; + id_t buf_id; + // counter to candidate least recently used buffers for garbage collection + uint32_t gc_count = 0; + uint32_t use_count = 0; + // counter to assign unique ids to buffer blocks + static uint64_t buffer_counter; + // Metal events used to sync GPU/CPU operations on the shared-storage buffers + MPSEventPtr event; + + BufferBlock(size_t Size, size_t RequestedSize = 0, const id Buffer = nullptr, + HeapBlock* Heap = nullptr) : + buffer(Buffer), size(Size), requested_size(RequestedSize), + heap(Heap), buf_id(Buffer ? ++buffer_counter : 0) { } + + static bool Comparator(const BufferBlock* a, const BufferBlock* b) { + return (a->size != b->size) ? a->size < b->size : (uintptr_t)a->buffer < (uintptr_t)b->buffer; + } + static size_t alignUp(size_t Size, size_t Alignment) { + assert(((Alignment - 1) & Alignment) == 0); + return ((Size + Alignment - 1) & ~(Alignment - 1)); + } + uint32_t retainCount() const { return [buffer retainCount]; } +}; +typedef bool (*BufferComparison)(const BufferBlock*, const BufferBlock*); + +struct BufferPool; +struct AllocParams { + AllocParams(size_t Alloc_Size, size_t Requested_Size, BufferPool* Pool) : + search_key(Alloc_Size), pool(Pool), requested_size(Requested_Size) { } + size_t size() const { return search_key.size; } + + BufferBlock search_key; + BufferPool* pool; + BufferBlock* buffer_block = nullptr; + size_t requested_size; + // true if we exceed the low watermark limit. In this case + // we apply strategies to relieve the pressure before allocation. + bool has_memory_pressure = false; + // true if we're allocating on a unified memory device + bool has_unified_memory = true; +}; + +struct HeapBlock { + id heap; + struct { size_t total, available; } size; + BufferPool* pool; + unsigned int n_buffers = 0; + id_t heap_id; + // indicates if we split this heap to sub-allocate 'several' buffers (otherwise single buffer) + bool is_split; + // counter to assign unique ids to heap blocks + static uint64_t heap_counter; + + HeapBlock(size_t Size, const id Heap = nullptr, BufferPool *Pool = nullptr) : + heap(Heap), size({.total = Size, .available = Size}), pool(Pool), + heap_id(Heap ? ++heap_counter : 0), is_split(true) { } + + static MTLResourceOptions getOptions(uint32_t usage) { + // TODO: check the caching performance of write-combined mode + MTLResourceOptions options = MTLResourceCPUCacheModeDefaultCache; + + if (usage & UsageFlags::MANAGED) + options |= MTLResourceStorageModeManaged; + else if (usage & UsageFlags::SHARED) + options |= MTLResourceStorageModeShared; + else + options |= MTLResourceStorageModePrivate; + + options |= (usage & UsageFlags::HAZARD) ? MTLResourceHazardTrackingModeTracked : MTLResourceHazardTrackingModeUntracked; + + return options; + } + + static HeapBlock* createHeapBlock(AllocParams& params, id device, uint32_t usage) { + HeapBlock *heapBlock = nullptr; + bool is_split = true; + const size_t size = params.size(); + MTLHeapDescriptor *d = [MTLHeapDescriptor new]; + if (d) { + const size_t kXLargeHeap = params.has_unified_memory ? kXLargeHeapU : kXLargeHeapD; + if (size <= kMaxSmallAlloc) { + d.size = kSmallHeap; + } else if (size < kMinLargeAlloc) { + d.size = kLargeHeap; + } else if (size < kXLargeHeap / 2 && !params.has_memory_pressure) { + d.size = kXLargeHeap; + } else { + d.size = kRoundLarge * ((size + kRoundLarge - 1) / kRoundLarge); + is_split = false; + } + d.storageMode = (usage & UsageFlags::SHARED) ? MTLStorageModeShared : MTLStorageModePrivate; + d.cpuCacheMode = MTLCPUCacheModeDefaultCache; + // this automatically handles Metal buffer access synchronizations at the + // cost of slightly lower performance. + d.hazardTrackingMode = (usage & UsageFlags::HAZARD) ? MTLHazardTrackingModeTracked : MTLHazardTrackingModeUntracked; + d.resourceOptions = getOptions(usage); + d.type = MTLHeapTypeAutomatic; + id heap = [device newHeapWithDescriptor: d]; + if (heap) { + [heap setPurgeableState:MTLPurgeableStateNonVolatile]; + const size_t heap_size = heapAvailableSize(heap); + heapBlock = new HeapBlock(heap_size, heap, params.pool); + if (heapBlock) { + heapBlock->is_split = is_split; + } + } + [d release]; + } + return heapBlock; + } + static bool Comparator(const HeapBlock* a, const HeapBlock* b) { + return (a->size.available != b->size.available) ? a->size.available < b->size.available : + (uintptr_t)a->heap < (uintptr_t)b->heap; + } + static NSUInteger heapAvailableSize(id heap, size_t Alignment = vm_page_size) { + return [heap maxAvailableSizeWithAlignment:Alignment]; + } + NSUInteger Size() { + return [heap size]; + } + id newMTLBuffer(size_t length, uint32_t usage) { + id buf = [heap newBufferWithLength:length options:getOptions(usage)]; + if (buf) { + updateAvailableSize(); + n_buffers++; + } + return buf; + } + // returns the retainCount before releasing the buffer + uint32_t releaseMTLBuffer(id& buffer) { + const uint32_t retainCount = [buffer retainCount]; + [buffer release]; + buffer = nil; + updateAvailableSize(); + n_buffers--; + return retainCount; + } + // returns the retainCount before releasing the heap + uint32_t releaseMTLHeap() { + const uint32_t retainCount = [heap retainCount]; + TORCH_INTERNAL_ASSERT(!n_buffers); // assert if heap isn't empty + [heap setPurgeableState:MTLPurgeableStateEmpty]; + [heap release]; + heap = nil; + size.available = 0; + return retainCount; + } + uint32_t retainCount() const { return [heap retainCount]; } + void updateAvailableSize() { size.available = heapAvailableSize(heap); } +}; +typedef bool (*HeapComparison)(const HeapBlock*, const HeapBlock*); + +struct BufferPool { + enum class Kind { + PRIVATE_SMALL, + PRIVATE_LARGE, + SHARED_SMALL, + SHARED_LARGE, + SCALAR, + }; + + BufferPool(const id Device, uint32_t Usage) : + device(Device), usage(Usage), + heaps(HeapBlock::Comparator), available_buffers(BufferBlock::Comparator) { } + + const id device; + // usage flags to customize the pool for various purposes (see UsageFlags enum) + const uint32_t usage; + // total number of buffers in the pool + uint32_t n_buffers = 0; + // total allocations size on this pool + size_t allocated_size = 0; + // total memory available in the pool + size_t available_size = 0; + // list of heaps ordered by their "available" (not total) memory size + std::set heaps; + // list of only "available" buffers in the pool (i.e., buffers not in-use) + std::set available_buffers; + // list of buffers that are in a state of "limbo" where they've already been freed + // from PyTorch-side, but were not returned to pool due to still being + // in-use by command buffers with retainCount > 1. In this state, the buffer is + // neither ready to be recycled, nor could be returned to pool as available. + // These buffers will be returned to pool once the command buffer's + // completionHandler callbacks are called. + std::unordered_set buffers_pending_free; + // list of heaps pending size update + std::unordered_set heaps_pending_update; +}; + +class MPSHeapAllocatorImpl { +public: + explicit MPSHeapAllocatorImpl() : + m_device(at::mps::MPSDevice::getInstance()->device()), + m_max_buffer_size([m_device maxBufferLength]), + m_stream(getDefaultMPSStream()), + m_event_pool(getMPSEventPool()) { + init_allocator(); + } + ~MPSHeapAllocatorImpl() { + emptyCache(); + } + // interface exposed to at::Allocator + id malloc(size_t size, uint32_t usage); + // frees a buffer and returns it into buffer pool + void free(void* ptr); + // releases all the cached buffers and their associated heaps + void emptyCache(); + // free inactive buffers that are pending to be freed + void freeInactiveBuffers(); + // returns true if buffer was allocated from the shared pool + bool isSharedBuffer(const void* ptr); + // get the requested unaligned size of an MTLBuffer + ssize_t getUnalignedBufferSize(const void* ptr); + // set the shape of a base tensor from a view tensor + void setBufferShape(const void* ptr, const IntArrayRef& shape); + // retrieve the shape of a base tensor from a view tensor + IntArrayRef getBufferShape(const void* ptr); + // get the unique ID of the buffer + id_t getBufferId(const void* ptr); + // allocate a buffer from a specialized pool to import CPU scalars into GPU + id allocScalarBufferWithValue(void* value, size_t size); + // returns a CPU-mapping of the input buffer and its retainCount, + // if only it has Shared storage-mode and allocated on MPSAllocator + std::pair getSharedBufferPtr(const void* buffer); + // records events for a list of MTLBuffers (list is used to lock the mutex once) + // returns true if records any event (given if passed buffers exist and are shared-storage) + bool recordEvents(c10::ArrayRef buffers); + // waits for the event to signal the completion of GPU execution + // on the passed shared buffers (list is used to lock the mutex once) + // returns true if actually waited on any event + bool waitForEvents(c10::ArrayRef buffers); + // this indicates how far (in Megabytes) the current total allocations are from the + // low watermark limit which is used to detect if we're under memory pressure + // This returns zero if we've reached the low watermark limit + ssize_t getLowWatermarkValue(); + // (see m_low_watermark_ratio for description) + void setLowWatermarkRatio(double ratio); + // (see m_high_watermark_ratio for description) + void setHighWatermarkRatio(double ratio); + // (see m_low_watermark_limit for description) + size_t getLowWatermarkLimit() const { return m_low_watermark_limit; } + // (see m_max_total_allowed_size for description) + size_t getHighWatermarkLimit() const { return m_max_total_allowed_size; } + // (see m_total_allocated_memory for description) + size_t getTotalAllocatedMemory() const { return m_total_allocated_memory; } + // (see m_current_allocated_memory for description) + size_t getCurrentAllocatedMemory() const { return m_current_allocated_memory; } + // total GPU memory allocated in the process by Metal driver; including + // implicit allocations from MPS/MPSGraph frameworks and MPSHeapAllocatorImpl. + size_t getDriverAllocatedMemory() const { return current_allocated_size(); } + // (see enum DebugVerbosity for description) + uint32_t getDebugVerbosity() const { return m_debug_verbosity; } + // returns the device that we allocate from + inline id Device() const { return m_device; } + + // TODO: make a common function to do size unit conversions in PyTorch. + inline std::string format_size(uint64_t size) const; + +private: + // (see m_high_watermark_ratio for description) + constexpr static double default_high_watermark_ratio = 1.7; + // we set the allowed upper bound to twice the size of recommendedMaxWorkingSetSize. + constexpr static double default_high_watermark_upper_bound = 2.0; + // (see m_low_watermark_ratio for description) + // on unified memory, we could allocate beyond the recommendedMaxWorkingSetSize + constexpr static double default_low_watermark_ratio_unified = 1.4; + constexpr static double default_low_watermark_ratio_discrete = 1.0; + + const id m_device; + std::recursive_mutex m_mutex; + // allocated buffers by device pointer + ska::flat_hash_map m_allocated_buffers; + // using a container for pools to simplify iterating them + ska::flat_hash_map> m_pools; + // total memory allocated by HeapAllocator (including blocks in pools) + size_t m_total_allocated_memory = 0; + // currently active memory allocations in use (i.e., blocks not in pools) + size_t m_current_allocated_memory = 0; + // max buffer size allowed by Metal + size_t m_max_buffer_size = 0; + // maximum total size allowed to be allocated + size_t m_max_total_allowed_size = 0; + // high watermark ratio is a hard limit for the total allowed allocations + // 0. : disables high watermark limit (may cause system failure if system-wide OOM occurs) + // 1. : recommended maximum allocation size (i.e., device.recommendedMaxWorkingSetSize) + // >1.: allows limits beyond the device.recommendedMaxWorkingSetSize + // e.g., value 0.95 means we allocate up to 95% of recommended maximum + // allocation size; beyond that, the allocations would fail with OOM error. + double m_high_watermark_ratio; + // low watermark ratio is a soft limit to attempt limiting memory allocations up to the lower watermark + // level by garbage collection or committing command buffers more frequently (a.k.a, adaptive commit). + // Value between 0 to m_high_watermark_ratio (setting 0.0 disables adaptive commit and garbage collection) + // e.g., value 0.9 means we 'attempt' to limit allocations up to 90% of recommended maximum + // allocation size. + double m_low_watermark_ratio; + // low watermark size limit (in Bytes) at the time we initialize the allocator + size_t m_low_watermark_limit; + // use "PYTORCH_DEBUG_MPS_ALLOCATOR" env-var to set debug verbosity + uint32_t m_debug_verbosity; + // default MPS stream + MPSStream* m_stream; + // we hold a reference to MPSEventPool so it could get destroyed after MPSAllocator + std::shared_ptr m_event_pool; + + void init_allocator(); + void init_buffer_pools(); + HeapBlock* get_free_heap(AllocParams& params); + bool get_free_buffer(AllocParams& params); + BufferBlock* get_allocated_buffer_block(const void* ptr); + BufferBlock* alloc_buffer_block(size_t size, uint32_t usage); + bool alloc_buffer(AllocParams& params); + void free_buffer(BufferBlock* buffer_block); + // returns true if the container heap is also released + bool release_buffer(BufferBlock* buffer_block, bool remove_empty_heap = true); + void release_buffers(BufferPool& pool); + bool release_available_cached_buffers(AllocParams& params); + bool release_cached_buffers(); + // free unused cached blocks to reclaim GPU memory if memory pressure is high + void garbage_collect_cached_buffers(AllocParams& params); + // returns the suitable buffer pool type for the usage or + // requested/allocated sizes + BufferPool& get_pool(size_t requested_size, size_t aligned_size, uint32_t usage); + // returns the aligned allocation size that is optimized + // for the buffers to get reused frequently + size_t get_allocation_size(size_t size, uint32_t usage) const; + // maximum size of device memory available for allocation in current process + // Note: the recommendedMaxWorkingSetSize is typically 75% of the total system memory. + size_t max_device_size() const { return [m_device recommendedMaxWorkingSetSize]; } + // there are implicit allocations from MPS backend, so we need to query the 'device' for + // total allocated size instead of manually tracking in MPSAllocator + size_t current_allocated_size() const { return [m_device currentAllocatedSize]; } + + bool trigger_memory_callbacks(BufferBlock* buffer_block, IMpsAllocatorCallback::EventType event) const { + for (const auto& name : MPSAllocatorCallbacksRegistry()->Keys()) { + MPSAllocatorCallbacksRegistry()->Create(name)->executeMPSAllocatorCallback(buffer_block ? buffer_block->buffer : nullptr, event); + } + return true; + } +}; + +} // namespace at::mps::HeapAllocator diff --git a/shared/package-files/8c/8c062769618cb94686f7ddf2c4bc2bd5e4fa275bade3709c05a29db3809edfed/blob b/shared/package-files/8c/8c062769618cb94686f7ddf2c4bc2bd5e4fa275bade3709c05a29db3809edfed/blob new file mode 100644 index 0000000000000000000000000000000000000000..ed6b4686a06defdc3eac4e1f6427fb0569c2d48d --- /dev/null +++ b/shared/package-files/8c/8c062769618cb94686f7ddf2c4bc2bd5e4fa275bade3709c05a29db3809edfed/blob @@ -0,0 +1,41 @@ +from pandas import ( + Index, + NaT, + Period, + PeriodIndex, +) +import pandas._testing as tm + + +class TestFillNA: + def test_fillna_period(self): + # GH#11343 + idx = PeriodIndex(["2011-01-01 09:00", NaT, "2011-01-01 11:00"], freq="h") + + exp = PeriodIndex( + ["2011-01-01 09:00", "2011-01-01 10:00", "2011-01-01 11:00"], freq="h" + ) + result = idx.fillna(Period("2011-01-01 10:00", freq="h")) + tm.assert_index_equal(result, exp) + + exp = Index( + [ + Period("2011-01-01 09:00", freq="h"), + "x", + Period("2011-01-01 11:00", freq="h"), + ], + dtype=object, + ) + result = idx.fillna("x") + tm.assert_index_equal(result, exp) + + exp = Index( + [ + Period("2011-01-01 09:00", freq="h"), + Period("2011-01-01", freq="D"), + Period("2011-01-01 11:00", freq="h"), + ], + dtype=object, + ) + result = idx.fillna(Period("2011-01-01", freq="D")) + tm.assert_index_equal(result, exp) diff --git a/shared/package-files/8c/8c074b30cbddc5db3364221d546c51670ee2e7068806a6f846ac247ad26a32be/blob b/shared/package-files/8c/8c074b30cbddc5db3364221d546c51670ee2e7068806a6f846ac247ad26a32be/blob new file mode 100644 index 0000000000000000000000000000000000000000..c21998013cf2e8fce4278a9f531b12f154b1e7f9 --- /dev/null +++ b/shared/package-files/8c/8c074b30cbddc5db3364221d546c51670ee2e7068806a6f846ac247ad26a32be/blob @@ -0,0 +1,3 @@ +class CertificateError(ValueError): ... + +def match_hostname(cert, hostname): ... diff --git a/shared/package-files/8c/8c0935c2e7613e22c1861b72753351959e37f7ad4ba85bb7b98b7a71503f8d5d/blob b/shared/package-files/8c/8c0935c2e7613e22c1861b72753351959e37f7ad4ba85bb7b98b7a71503f8d5d/blob new file mode 100644 index 0000000000000000000000000000000000000000..dd7b57df9baed18b172dc8398a61a49e9435f82a --- /dev/null +++ b/shared/package-files/8c/8c0935c2e7613e22c1861b72753351959e37f7ad4ba85bb7b98b7a71503f8d5d/blob @@ -0,0 +1,72 @@ +import locale + +import pytest + +from pandas._config import detect_console_encoding + + +class MockEncoding: + """ + Used to add a side effect when accessing the 'encoding' property. If the + side effect is a str in nature, the value will be returned. Otherwise, the + side effect should be an exception that will be raised. + """ + + def __init__(self, encoding) -> None: + super().__init__() + self.val = encoding + + @property + def encoding(self): + return self.raise_or_return(self.val) + + @staticmethod + def raise_or_return(val): + if isinstance(val, str): + return val + else: + raise val + + +@pytest.mark.parametrize("empty,filled", [["stdin", "stdout"], ["stdout", "stdin"]]) +def test_detect_console_encoding_from_stdout_stdin(monkeypatch, empty, filled): + # Ensures that when sys.stdout.encoding or sys.stdin.encoding is used when + # they have values filled. + # GH 21552 + with monkeypatch.context() as context: + context.setattr(f"sys.{empty}", MockEncoding("")) + context.setattr(f"sys.{filled}", MockEncoding(filled)) + assert detect_console_encoding() == filled + + +@pytest.mark.parametrize("encoding", [AttributeError, OSError, "ascii"]) +def test_detect_console_encoding_fallback_to_locale(monkeypatch, encoding): + # GH 21552 + with monkeypatch.context() as context: + context.setattr("locale.getpreferredencoding", lambda: "foo") + context.setattr("sys.stdout", MockEncoding(encoding)) + assert detect_console_encoding() == "foo" + + +@pytest.mark.parametrize( + "std,locale", + [ + ["ascii", "ascii"], + ["ascii", locale.Error], + [AttributeError, "ascii"], + [AttributeError, locale.Error], + [OSError, "ascii"], + [OSError, locale.Error], + ], +) +def test_detect_console_encoding_fallback_to_default(monkeypatch, std, locale): + # When both the stdout/stdin encoding and locale preferred encoding checks + # fail (or return 'ascii', we should default to the sys default encoding. + # GH 21552 + with monkeypatch.context() as context: + context.setattr( + "locale.getpreferredencoding", lambda: MockEncoding.raise_or_return(locale) + ) + context.setattr("sys.stdout", MockEncoding(std)) + context.setattr("sys.getdefaultencoding", lambda: "sysDefaultEncoding") + assert detect_console_encoding() == "sysDefaultEncoding" diff --git a/shared/package-files/8c/8c0b31952c785db21c12ee89a6919f196f37c6988d2a6e67dbc5f4a76b374c80/blob b/shared/package-files/8c/8c0b31952c785db21c12ee89a6919f196f37c6988d2a6e67dbc5f4a76b374c80/blob new file mode 100644 index 0000000000000000000000000000000000000000..08a84b188a8f27ae695f85cd30bcb2c8da627a41 --- /dev/null +++ b/shared/package-files/8c/8c0b31952c785db21c12ee89a6919f196f37c6988d2a6e67dbc5f4a76b374c80/blob @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8c0b31952c785db21c12ee89a6919f196f37c6988d2a6e67dbc5f4a76b374c80 +size 112528 diff --git a/shared/package-files/8c/8c0f0a6d4813e3846e46484aa0783fa2b03637603be3107bb8ab5506f9b96143/blob b/shared/package-files/8c/8c0f0a6d4813e3846e46484aa0783fa2b03637603be3107bb8ab5506f9b96143/blob new file mode 100644 index 0000000000000000000000000000000000000000..3591a49f685b15e65cb000928bff700eef846ca3 --- /dev/null +++ b/shared/package-files/8c/8c0f0a6d4813e3846e46484aa0783fa2b03637603be3107bb8ab5506f9b96143/blob @@ -0,0 +1,338 @@ +##===----------------------------------------------------------------------===## +# +# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +# See https://llvm.org/LICENSE.txt for license information. +# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +# +##===----------------------------------------------------------------------===## +# +# Find OpenMP Target offloading Support for various compilers. +# +##===----------------------------------------------------------------------===## + +#[========================================================================[.rst: +FindOpenMPTarget +---------------- + +Finds OpenMP Target Offloading Support. + +This module can be used to detect OpenMP target offloading support in a +compiler. If the compiler support OpenMP Offloading to a specified target, the +flags required to compile offloading code to that target are output for each +target. + +This module will automatically include OpenMP support if it was not loaded +already. It does not need to be included separately to get full OpenMP support. + +Variables +^^^^^^^^^ + +The module exposes the components ``NVPTX`` and ``AMDGPU``. Each of these +controls the various offloading targets to search OpenMP target offloasing +support for. + +Depending on the enabled components the following variables will be set: + +``OpenMPTarget_FOUND`` + Variable indicating that OpenMP target offloading flags for all requested + targets have been found. + +This module will set the following variables per language in your +project, where ```` is one of NVPTX or AMDGPU + +``OpenMPTarget__FOUND`` + Variable indicating if OpenMP support for the ```` was detected. +``OpenMPTarget__FLAGS`` + OpenMP compiler flags for offloading to ````, separated by spaces. + +For linking with OpenMP code written in ````, the following +variables are provided: + +``OpenMPTarget__LIBRARIES`` + A list of libraries needed to link with OpenMP code written in ````. + +Additionally, the module provides :prop_tgt:`IMPORTED` targets: + +``OpenMPTarget::OpenMPTarget_`` + Target for using OpenMP offloading to ````. + +If the specific architecture of the target is needed, it can be manually +specified by setting a variable to the desired architecture. Variables can also +be used to override the standard flag searching for a given compiler. + +``OpenMPTarget__ARCH`` + Sets the architecture of ```` to compile for. Such as `sm_70` for NVPTX + or `gfx908` for AMDGPU. + +``OpenMPTarget__DEVICE`` + Sets the name of the device to offload to. + +``OpenMPTarget__FLAGS`` + Sets the compiler flags for offloading to ````. + +#]========================================================================] + +# TODO: Support Fortran +# TODO: Support multiple offloading targets by setting the "OpenMPTarget" target +# to include flags for all components loaded +# TODO: Configure target architecture without a variable (component NVPTX_SM_70) +# TODO: Test more compilers + +cmake_policy(PUSH) +cmake_policy(VERSION 3.13.4) + +find_package(OpenMP ${OpenMPTarget_FIND_VERSION} REQUIRED) + +# Find the offloading flags for each compiler. +function(_OPENMP_TARGET_DEVICE_FLAG_CANDIDATES LANG DEVICE) + if(NOT OpenMPTarget_${LANG}_FLAGS) + unset(OpenMPTarget_FLAG_CANDIDATES) + + set(OMPTarget_FLAGS_Clang "-fopenmp-targets=${DEVICE}") + set(OMPTarget_FLAGS_GNU "-foffload=${DEVICE}=\"-lm -latomic\"") + set(OMPTarget_FLAGS_XL "-qoffload") + set(OMPTarget_FLAGS_PGI "-mp=${DEVICE}") + set(OMPTarget_FLAGS_NVHPC "-mp=${DEVICE}") + + if(DEFINED OMPTarget_FLAGS_${CMAKE_${LANG}_COMPILER_ID}) + set(OpenMPTarget_FLAG_CANDIDATES "${OMPTarget_FLAGS_${CMAKE_${LANG}_COMPILER_ID}}") + endif() + + set(OpenMPTarget_${LANG}_FLAG_CANDIDATES "${OpenMPTarget_FLAG_CANDIDATES}" PARENT_SCOPE) + else() + set(OpenMPTarget_${LANG}_FLAG_CANDIDATES "${OpenMPTarget_${LANG}_FLAGS}" PARENT_SCOPE) + endif() +endfunction() + +# Get the coded name of the device for each compiler. +function(_OPENMP_TARGET_DEVICE_CANDIDATES LANG DEVICE) + if (NOT OpenMPTarget_${DEVICE}_DEVICE) + unset(OpenMPTarget_DEVICE_CANDIDATES) + + # Check each supported device. + if("${DEVICE}" STREQUAL "NVPTX") + if ("${CMAKE_SIZEOF_VOID_P}" STREQUAL "4") + set(OMPTarget_DEVICE_Clang "nvptx32-nvidia-cuda") + else() + set(OMPTarget_DEVICE_Clang "nvptx64-nvidia-cuda") + endif() + set(OMPTarget_DEVICE_GNU "nvptx-none") + set(OMPTarget_DEVICE_XL "") + set(OMPTarget_DEVICE_PGI "gpu") + set(OMPTarget_DEVICE_NVHPC "gpu") + + if(DEFINED OMPTarget_DEVICE_${CMAKE_${LANG}_COMPILER_ID}) + set(OpenMPTarget_DEVICE_CANDIDATES "${OMPTarget_DEVICE_${CMAKE_${LANG}_COMPILER_ID}}") + endif() + elseif("${DEVICE}" STREQUAL "AMDGPU") + set(OMPTarget_DEVICE_Clang "amdgcn-amd-amdhsa") + set(OMPTarget_DEVICE_GNU "hsa") + + if(DEFINED OMPTarget_DEVICE_${CMAKE_${LANG}_COMPILER_ID}) + set(OpenMPTarget_DEVICE_CANDIDATES "${OMPTarget_DEVICE_${CMAKE_${LANG}_COMPILER_ID}}") + endif() + endif() + set(OpenMPTarget_${LANG}_DEVICE_CANDIDATES "${OpenMPTarget_DEVICE_CANDIDATES}" PARENT_SCOPE) + else() + set(OpenMPTarget_${LANG}_DEVICE_CANDIDATES "${OpenMPTarget_${LANG}_DEVICE}" PARENT_SCOPE) + endif() +endfunction() + +# Get flags for setting the device's architecture for each compiler. +function(_OPENMP_TARGET_DEVICE_ARCH_CANDIDATES LANG DEVICE DEVICE_FLAG) + if(OpenMPTarget_${DEVICE}_ARCH) + # Only Clang supports selecting the architecture for now. + set(OMPTarget_ARCH_Clang "-Xopenmp-target=${DEVICE_FLAG} -march=${OpenMPTarget_${DEVICE}_ARCH}") + + if(DEFINED OMPTarget_ARCH_${CMAKE_${LANG}_COMPILER_ID}) + set(OpenMPTarget_DEVICE_ARCH_CANDIDATES "${OMPTarget_ARCH_${CMAKE_${LANG}_COMPILER_ID}}") + endif() + set(OpenMPTarget_${LANG}_DEVICE_ARCH_CANDIDATES "${OpenMPTarget_DEVICE_ARCH_CANDIDATES}" PARENT_SCOPE) + else() + set(OpenMPTarget_${LANG}_DEVICE_ARCH_CANDIDATES "" PARENT_SCOPE) + endif() +endfunction() + +set(OpenMPTarget_C_CXX_TEST_SOURCE +"#include +int main(void) { + int isHost; +#pragma omp target map(from: isHost) + { isHost = omp_is_initial_device(); } + return isHost; +}") + +function(_OPENMP_TARGET_WRITE_SOURCE_FILE LANG SRC_FILE_CONTENT_VAR SRC_FILE_NAME SRC_FILE_FULLPATH) + set(WORK_DIR ${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/FindOpenMPTarget) + if("${LANG}" STREQUAL "C") + set(SRC_FILE "${WORK_DIR}/${SRC_FILE_NAME}.c") + file(WRITE "${SRC_FILE}" "${OpenMPTarget_C_CXX_${SRC_FILE_CONTENT_VAR}}") + elseif("${LANG}" STREQUAL "CXX") + set(SRC_FILE "${WORK_DIR}/${SRC_FILE_NAME}.cpp") + file(WRITE "${SRC_FILE}" "${OpenMPTarget_C_CXX_${SRC_FILE_CONTENT_VAR}}") + endif() + set(${SRC_FILE_FULLPATH} "${SRC_FILE}" PARENT_SCOPE) +endfunction() + +# Get the candidate flags and try to compile a test application. If it compiles +# and all the flags are found, we assume the compiler supports offloading. +function(_OPENMP_TARGET_DEVICE_GET_FLAGS LANG DEVICE OPENMP_FLAG_VAR OPENMP_LIB_VAR OPENMP_DEVICE_VAR OPENMP_ARCH_VAR) + _OPENMP_TARGET_DEVICE_CANDIDATES(${LANG} ${DEVICE}) + _OPENMP_TARGET_DEVICE_FLAG_CANDIDATES(${LANG} "${OpenMPTarget_${LANG}_DEVICE_CANDIDATES}") + _OPENMP_TARGET_DEVICE_ARCH_CANDIDATES(${LANG} ${DEVICE} "${OpenMPTarget_${LANG}_DEVICE_CANDIDATES}") + _OPENMP_TARGET_WRITE_SOURCE_FILE("${LANG}" "TEST_SOURCE" OpenMPTargetTryFlag _OPENMP_TEST_SRC) + + # Try to compile a test application with the found flags. + try_compile(OpenMPTarget_COMPILE_RESULT ${CMAKE_BINARY_DIR} ${_OPENMP_TEST_SRC} + CMAKE_FLAGS "-DCOMPILE_DEFINITIONS:STRING=${OpenMP_${LANG}_FLAGS} ${OpenMPTarget_${LANG}_FLAG_CANDIDATES} ${OpenMPTarget_${LANG}_DEVICE_ARCH_CANDIDATES}" + "-DINCLUDE_DIRECTORIES:STRING=${OpenMP_${LANG}_INCLUDE_DIR}" + LINK_LIBRARIES ${CMAKE_${LANG}_VERBOSE_FLAG} + OUTPUT_VARIABLE OpenMP_TRY_COMPILE_OUTPUT + ) + + file(APPEND ${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/CMakeOutput.log + "Detecting OpenMP ${CMAKE_${LANG}_COMPILER_ID} ${DEVICE} target support with the following Flags: + ${OpenMP_${LANG}_FLAGS} ${OpenMPTarget_${LANG}_FLAG_CANDIDATES} ${OpenMPTarget_${LANG}_DEVICE_ARCH_CANDIDATES} + With the following output:\n ${OpenMP_TRY_COMPILE_OUTPUT}\n") + + # If compilation was successful and the device was found set the return variables. + if (OpenMPTarget_COMPILE_RESULT AND DEFINED OpenMPTarget_${LANG}_DEVICE_CANDIDATES) + file(APPEND ${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/CMakeOutput.log + "Compilation successful, adding flags for ${DEVICE}.\n\n") + + # Clang has a seperate library for target offloading. + if(CMAKE_${LANG}_COMPILER_ID STREQUAL "Clang") + find_library(OpenMPTarget_libomptarget_LIBRARY + NAMES omptarget + HINTS ${CMAKE_${LANG}_IMPLICIT_LINK_DIRECTORIES} + ) + mark_as_advanced(OpenMPTarget_libomptarget_LIBRARY) + set("${OPENMP_LIB_VAR}" "${OpenMPTarget_libomptarget_LIBRARY}" PARENT_SCOPE) + else() + unset("${OPENMP_LIB_VAR}" PARENT_SCOPE) + endif() + set("${OPENMP_DEVICE_VAR}" "${OpenMPTarget_${LANG}_DEVICE_CANDIDATES}" PARENT_SCOPE) + set("${OPENMP_FLAG_VAR}" "${OpenMPTarget_${LANG}_FLAG_CANDIDATES}" PARENT_SCOPE) + set("${OPENMP_ARCH_VAR}" "${OpenMPTarget_${LANG}_DEVICE_ARCH_CANDIDATES}" PARENT_SCOPE) + else() + unset("${OPENMP_DEVICE_VAR}" PARENT_SCOPE) + unset("${OPENMP_FLAG_VAR}" PARENT_SCOPE) + unset("${OPENMP_ARCH_VAR}" PARENT_SCOPE) + endif() +endfunction() + +# Load the compiler support for each device. +foreach(LANG IN ITEMS C CXX) + # Cache the version in case CMake doesn't load the OpenMP package this time + set(OpenMP_${LANG}_VERSION ${OpenMP_${LANG}_VERSION} + CACHE STRING "OpenMP Version" FORCE) + mark_as_advanced(OpenMP_${LANG}_VERSION) + foreach(DEVICE IN ITEMS NVPTX AMDGPU) + if(CMAKE_${LANG}_COMPILER_LOADED) + if(NOT DEFINED OpenMPTarget_${LANG}_FLAGS OR NOT DEFINED OpenMPTarget_${LANG}_DEVICE) + _OPENMP_TARGET_DEVICE_GET_FLAGS(${LANG} ${DEVICE} + OpenMPTarget_${DEVICE}_FLAGS_WORK + OpenMPTarget_${DEVICE}_LIBS_WORK + OpenMPTarget_${DEVICE}_DEVICE_WORK + OpenMPTarget_${DEVICE}_ARCHS_WORK) + + separate_arguments(_OpenMPTarget_${DEVICE}_FLAGS NATIVE_COMMAND "${OpenMPTarget_${DEVICE}_FLAGS_WORK}") + separate_arguments(_OpenMPTarget_${DEVICE}_ARCHS NATIVE_COMMAND "${OpenMPTarget_${DEVICE}_ARCHS_WORK}") + set(OpenMPTarget_${DEVICE}_FLAGS ${_OpenMPTarget_${DEVICE}_FLAGS} + CACHE STRING "${DEVICE} target compile flags for OpenMP target offloading" FORCE) + set(OpenMPTarget_${DEVICE}_ARCH ${_OpenMPTarget_${DEVICE}_ARCHS} + CACHE STRING "${DEVICE} target architecture flags for OpenMP target offloading" FORCE) + set(OpenMPTarget_${DEVICE}_LIBRARIES ${OpenMPTarget_${DEVICE}_LIBS_WORK} + CACHE STRING "${DEVICE} target libraries for OpenMP target offloading" FORCE) + mark_as_advanced(OpenMPTarget_${DEVICE}_FLAGS OpenMPTarget_${DEVICE}_ARCH OpenMPTarget_${DEVICE}_LIBRARIES) + endif() + endif() + endforeach() +endforeach() + +if(OpenMPTarget_FIND_COMPONENTS) + set(OpenMPTarget_FINDLIST ${OpenMPTarget_FIND_COMPONENTS}) +else() + set(OpenMPTarget_FINDLIST NVPTX) +endif() + +unset(_OpenMPTarget_MIN_VERSION) + +# Attempt to find each requested device. +foreach(LANG IN ITEMS C CXX) + foreach(DEVICE IN LISTS OpenMPTarget_FINDLIST) + if(CMAKE_${LANG}_COMPILER_LOADED) + set(OpenMPTarget_${DEVICE}_VERSION "${OpenMP_${LANG}_VERSION}") + set(OpenMPTarget_${DEVICE}_VERSION_MAJOR "${OpenMP_${LANG}_VERSION}_MAJOR") + set(OpenMPTarget_${DEVICE}_VERSION_MINOR "${OpenMP_${LANG}_VERSION}_MINOR") + set(OpenMPTarget_${DEVICE}_FIND_QUIETLY ${OpenMPTarget_FIND_QUIETLY}) + set(OpenMPTarget_${DEVICE}_FIND_REQUIRED ${OpenMPTarget_FIND_REQUIRED}) + set(OpenMPTarget_${DEVICE}_FIND_VERSION ${OpenMPTarget_FIND_VERSION}) + set(OpenMPTarget_${DEVICE}_FIND_VERSION_EXACT ${OpenMPTarget_FIND_VERSION_EXACT}) + + # OpenMP target offloading is only supported in OpenMP 4.0 an newer. + if(OpenMPTarget_${DEVICE}_VERSION AND ("${OpenMPTarget_${DEVICE}_VERSION}" VERSION_LESS "4.0")) + message(SEND_ERROR "FindOpenMPTarget requires at least OpenMP 4.0") + endif() + + set(FPHSA_NAME_MISMATCHED TRUE) + find_package_handle_standard_args(OpenMPTarget_${DEVICE} + REQUIRED_VARS OpenMPTarget_${DEVICE}_FLAGS + VERSION_VAR OpenMPTarget_${DEVICE}_VERSION) + + if(OpenMPTarget_${DEVICE}_FOUND) + if(DEFINED OpenMPTarget_${DEVICE}_VERSION) + if(NOT _OpenMPTarget_MIN_VERSION OR _OpenMPTarget_MIN_VERSION VERSION_GREATER OpenMPTarget_${LANG}_VERSION) + set(_OpenMPTarget_MIN_VERSION OpenMPTarget_${DEVICE}_VERSION) + endif() + endif() + # Create a new target. + if(NOT TARGET OpenMPTarget::OpenMPTarget_${DEVICE}) + add_library(OpenMPTarget::OpenMPTarget_${DEVICE} INTERFACE IMPORTED) + endif() + # Get compiler flags for offloading to the device and architecture and + # set the target features. Include the normal OpenMP flags as well. + set_property(TARGET OpenMPTarget::OpenMPTarget_${DEVICE} PROPERTY + INTERFACE_COMPILE_OPTIONS + "$<$:${OpenMPTarget_${DEVICE}_FLAGS}>" + "$<$:${OpenMPTarget_${DEVICE}_ARCH}>" + "$<$:${OpenMP_${LANG}_FLAGS}>") + set_property(TARGET OpenMPTarget::OpenMPTarget_${DEVICE} PROPERTY + INTERFACE_INCLUDE_DIRECTORIES "$") + set_property(TARGET OpenMPTarget::OpenMPTarget_${DEVICE} PROPERTY + INTERFACE_LINK_LIBRARIES + "${OpenMPTarget_${DEVICE}_LIBRARIES}" + "${OpenMP_${LANG}_LIBRARIES}") + # The offloading flags must also be passed during the linking phase so + # the compiler can pass the binary to the correct toolchain. + set_property(TARGET OpenMPTarget::OpenMPTarget_${DEVICE} PROPERTY + INTERFACE_LINK_OPTIONS + "$<$:${OpenMPTarget_${DEVICE}_FLAGS}>" + "$<$:${OpenMPTarget_${DEVICE}_ARCH}>" + "$<$:${OpenMP_${LANG}_FLAGS}>") + # Combine all the flags if not using the target for convenience. + set(OpenMPTarget_${DEVICE}_FLAGS ${OpenMP_${LANG}_FLAGS} + ${OpenMPTarget_${DEVICE}_FLAGS} + ${OpenMPTarget_${DEVICE}_ARCH} + CACHE STRING "${DEVICE} target compile flags for OpenMP target offloading" FORCE) + endif() + endif() + endforeach() +endforeach() + +unset(_OpenMPTarget_REQ_VARS) +foreach(DEVICE IN LISTS OpenMPTarget_FINDLIST) + list(APPEND _OpenMPTarget_REQ_VARS "OpenMPTarget_${DEVICE}_FOUND") +endforeach() + +find_package_handle_standard_args(OpenMPTarget + REQUIRED_VARS ${_OpenMPTarget_REQ_VARS} + VERSION_VAR ${_OpenMPTarget_MIN_VERSION} + HANDLE_COMPONENTS) + +if(NOT (CMAKE_C_COMPILER_LOADED OR CMAKE_CXX_COMPILER_LOADED) OR CMAKE_Fortran_COMPILER_LOADED) + message(SEND_ERROR "FindOpenMPTarget requires the C or CXX languages to be enabled") +endif() + +unset(OpenMPTarget_C_CXX_TEST_SOURCE) +cmake_policy(POP) diff --git a/shared/package-files/8c/8c0f1d9d12dd9f8682e1e9d879bd5888e5ee402ce042d40715f1de805b265ad3/blob b/shared/package-files/8c/8c0f1d9d12dd9f8682e1e9d879bd5888e5ee402ce042d40715f1de805b265ad3/blob new file mode 100644 index 0000000000000000000000000000000000000000..8f23051dc682395ba92b2fcc4043162abaa8ec47 --- /dev/null +++ b/shared/package-files/8c/8c0f1d9d12dd9f8682e1e9d879bd5888e5ee402ce042d40715f1de805b265ad3/blob @@ -0,0 +1,15 @@ +#pragma once + +#include +#include + +namespace caffe2 { +class TypeMeta; +} // namespace caffe2 + +namespace c10 { +C10_API void set_default_dtype(caffe2::TypeMeta dtype); +C10_API const caffe2::TypeMeta get_default_dtype(); +C10_API ScalarType get_default_dtype_as_scalartype(); +C10_API const caffe2::TypeMeta get_default_complex_dtype(); +} // namespace c10 diff --git a/shared/package-files/8c/8c0f5eff425ee511da1c447068609841b84f92b0ddefc7b637872b8c857470a9/blob b/shared/package-files/8c/8c0f5eff425ee511da1c447068609841b84f92b0ddefc7b637872b8c857470a9/blob new file mode 100644 index 0000000000000000000000000000000000000000..f9c9c5b6aa1966bd64e16303593de041ff5eca75 --- /dev/null +++ b/shared/package-files/8c/8c0f5eff425ee511da1c447068609841b84f92b0ddefc7b637872b8c857470a9/blob @@ -0,0 +1,28 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API _dimV { + using schema = int64_t (const at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_dimV") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_dimV(Tensor self) -> int") + static int64_t call(const at::Tensor & self); + static int64_t redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self); +}; + +}} // namespace at::_ops diff --git a/shared/package-files/8c/8c0f6abda67468e026e81c4d3ba6c3a87248950657ffe95f8ad23285aabc666a/blob b/shared/package-files/8c/8c0f6abda67468e026e81c4d3ba6c3a87248950657ffe95f8ad23285aabc666a/blob new file mode 100644 index 0000000000000000000000000000000000000000..306af9b2a536d9d610f3616dda3a96911b3a0eb3 --- /dev/null +++ b/shared/package-files/8c/8c0f6abda67468e026e81c4d3ba6c3a87248950657ffe95f8ad23285aabc666a/blob @@ -0,0 +1,1751 @@ +error(0.006018222588128467089) D0 +error(0.005112438030459798277) D0 D1 +error(0.0227277272686524888) D0 D4 +error(0.0002020984342416418057) D0 D4 ^ D5 +error(0.005596392374062400359) D0 D7 +error(0.001337333394197824134) D0 D7 ^ D5 +error(0.001528716445309385996) D0 D8 +error(0.002049448405587175111) D0 D8 ^ D5 +error(0.009430800333370582475) D1 D2 +error(0.007042369394341871369) D1 D2 ^ D10 +error(0.02760981603310543389) D1 D7 +error(0.001929110929971969814) D1 D7 ^ D9 +error(0.002389061210813362476) D1 D7 ^ D9 D10 +error(0.002417269828584028314) D1 D7 ^ D10 +error(0.001840250317789958283) D1 D8 +error(4.623697452329296378e-05) D1 D8 ^ D9 +error(0.001245897594560458959) D1 D8 ^ D10 +error(0.001170938681571742027) D1 D8 ^ D10 ^ D9 +error(0.002166732967798507339) D1 D11 +error(0.002559044635051800545) D1 D11 ^ D10 +error(0.01141911478070931074) D1 L0 +error(0.003853711992303913633) D1 L0 ^ D9 +error(0.01365285075072551757) D2 +error(0.01255406270177994443) D2 D3 +error(0.01483407689955998886) D2 D8 +error(0.004027872808748624543) D2 D8 ^ D10 +error(0.01126712506800254393) D2 D11 +error(0.008322526150587277252) D2 D11 ^ D10 +error(0.02572479702819235881) D3 D11 +error(0.009134562953210235287) D3 L0 +error(0.005000000000000000104) D4 +error(0.01574703576908046201) D4 D7 +error(0.01172638436789839173) D4 D7 ^ D5 +error(0.02120182655647323902) D4 D12 +error(0.004005051107944217724) D4 D12 ^ D13 +error(0.003257070752525762988) D4 D15 +error(0.004022286150863349508) D4 D15 ^ D5 +error(0.002543293195019577166) D4 D15 ^ D13 +error(0.001504391012008346896) D4 D15 ^ D13 ^ D5 +error(0.002614549345611351036) D4 D16 +error(0.0003652254927584783358) D4 D16 ^ D5 +error(0.001411437394529351875) D4 D16 ^ D5 D13 +error(0.0003879027308221025152) D4 D16 ^ D13 +error(0.006042713315135925072) D4 ^ D5 +error(0.002811504541598291648) D4 ^ D13 +error(0.003679817133241939341) D5 D13 ^ D4 +error(0.02247253570971357445) D5 +error(0.007617352240241407474) D5 D6 +error(0.000887162096888913709) D5 D6 ^ D4 +error(0.0007251573378451043597) D5 D6 ^ D7 D8 +error(0.0001957930849328015286) D5 D6 ^ D7 D16 +error(0.01324645101727105201) D5 D6 ^ D8 +error(0.002085122492311126778) D5 D6 ^ D8 D16 +error(0.001977684631864129981) D5 D6 ^ D16 +error(3.270351685940596236e-06) D5 D6 ^ D16 ^ D4 +error(0.0157304085309798343) D5 D10 +error(0.01151786723308933363) D5 D10 ^ D7 D8 +error(0.02919543128697425077) D5 D13 +error(0.002628432669318331218) D5 D13 ^ D15 D16 +error(0.001244045587690730979) D5 D13 ^ D16 +error(0.001774401289933531713) D5 D17 +error(0.00215930058179150848) D5 D17 ^ D7 D15 +error(0.0003512834228198727328) D5 D17 ^ D7 D16 +error(0.0002152771204412623973) D5 D17 ^ D15 D16 +error(0.003671177367358601732) D5 D18 +error(0.002426203061533186139) D5 D18 ^ D7 D8 +error(0.001594528899234590224) D5 D18 ^ D7 D15 +error(0.001908863009923503868) D5 D18 ^ D7 D16 +error(0.0006777420681469889779) D5 D18 ^ D8 D16 +error(0.002089629819013069569) D5 D18 ^ D15 D16 +error(0.005709173266486284107) D5 ^ D0 +error(0.00210270216426732123) D5 ^ D8 +error(0.004017245732850435735) D7 D15 ^ D5 +error(0.04682746423002089736) D6 +error(0.002369887455031276322) D6 D13 +error(0.0003318766923448692526) D6 D13 ^ D4 +error(0.002074857726409925964) D6 D13 ^ D16 +error(0.03289266697592603539) D6 D14 +error(0.003227722995460973777) D6 D14 ^ D16 +error(0.001321883351880175864) D6 D18 +error(0.0001061570292203804219) D6 D18 ^ D7 D8 +error(0.001480679150158425344) D6 D18 ^ D8 D16 +error(0.01777135568000335827) D6 ^ D8 +error(0.00136231486461977757) D6 ^ D16 ^ D8 +error(0.0173756234208292254) D7 D8 +error(7.739806790802663432e-05) D7 D8 ^ D5 +error(0.0009210559731429083206) D7 D8 ^ D9 +error(0.00400912219928547392) D7 D8 ^ D10 +error(0.00213473588335926813) D7 D8 ^ D10 D18 +error(0.0004131054937049005119) D7 D8 ^ D10 ^ D9 +error(0.001545759997001574463) D7 D8 ^ D18 +error(0.04539877534963716876) D7 D15 +error(0.003308806725389073101) D7 D15 ^ D17 D18 +error(0.002580122891698501727) D7 D16 +error(0.0004324137196982047134) D7 D16 ^ D17 D18 +error(0.00147646061331719214) D7 D19 +error(0.001161790667520634906) D7 D19 ^ D10 +error(0.001294277265455211342) D7 D19 ^ D10 D18 +error(0.0004861687020259467451) D7 D19 ^ D18 +error(0.01000000000000000021) D7 L0 +error(0.01052566078570786601) D7 L0 ^ D9 +error(0.003705356755689873452) D9 D17 ^ D7 L0 +error(0.02163975455408657239) D8 +error(0.01945930274580606745) D8 D11 +error(0.02111139900238914735) D8 D11 ^ D10 +error(0.01586367019697373579) D8 D16 +error(0.003329144510032204431) D8 D16 ^ D18 +error(0.007704981069138553892) D8 D19 +error(0.006337428984486745583) D8 D19 ^ D10 +error(0.008606358000911078221) D8 D19 ^ D18 +error(0.001500625098945613427) D8 D19 ^ D18 ^ D10 +error(0.03233948577033358973) D9 +error(0.01485896342174500805) D9 D10 +error(0.003243488560349256245) D9 D10 ^ D1 L0 +error(0.0178691478671848536) D9 D10 ^ D7 L0 +error(0.01996525495835424144) D9 D17 +error(0.03862988876395519189) D10 +error(0.004920888807233343536) D10 D17 +error(0.003458651288054707777) D10 D17 ^ D7 L0 +error(0.02134289816389397729) D10 D18 +error(0.0008976811780191280843) D10 D18 ^ D7 L0 +error(0.003921709342996214549) D10 D18 ^ D19 L0 +error(0.001271008367170607407) D10 ^ D2 +error(0.0007852141360654214157) D10 ^ D8 +error(0.01693253259474453154) D10 ^ D11 L0 +error(0.0004558428257494107744) D10 ^ D11 L0 ^ D1 L0 ^ D9 +error(0.00204782118831032655) D10 ^ D19 L0 +error(0.005331285191324799597) D11 D19 ^ D10 +error(0.02414180985073517693) D11 D19 +error(0.005343406900730185947) D11 L0 +error(0.0008819589258063926639) D11 L0 ^ D1 L0 ^ D9 +error(0.005000000000000000104) D12 +error(0.01467144407408177594) D12 D15 +error(0.01487322813926130263) D12 D15 ^ D13 +error(0.02120182655647323902) D12 D20 +error(0.004005051107944217724) D12 D20 ^ D21 +error(0.003257070752525762988) D12 D23 +error(0.004022286150863349508) D12 D23 ^ D13 +error(0.002543293195019577166) D12 D23 ^ D21 +error(0.001504391012008346896) D12 D23 ^ D21 ^ D13 +error(0.002614549345611351036) D12 D24 +error(0.0003652254927584783358) D12 D24 ^ D13 +error(0.001411437394529351875) D12 D24 ^ D13 D21 +error(0.0003879027308221025152) D12 D24 ^ D21 +error(0.005141525429393459246) D12 ^ D13 +error(0.002811504541598291648) D12 ^ D21 +error(0.003679817133241939341) D13 D21 ^ D12 +error(0.01000000000000000021) D13 +error(0.008917134013459478495) D13 D14 +error(0.0002294357728164152742) D13 D14 ^ D12 +error(0.0004097416596508378925) D13 D14 ^ D15 D16 +error(0.0001957930849328015286) D13 D14 ^ D15 D24 +error(0.01166828223472706448) D13 D14 ^ D16 +error(0.002085122492311126778) D13 D14 ^ D16 D24 +error(0.001977684631864129981) D13 D14 ^ D24 +error(3.270351685940596236e-06) D13 D14 ^ D24 ^ D12 +error(0.01465787481814465748) D13 D18 +error(0.01137750691802791314) D13 D18 ^ D15 D16 +error(0.02919543128697425077) D13 D21 +error(0.002628432669318331218) D13 D21 ^ D23 D24 +error(0.001244045587690730979) D13 D21 ^ D24 +error(0.001774401289933531713) D13 D25 +error(0.00215930058179150848) D13 D25 ^ D15 D23 +error(0.0003512834228198727328) D13 D25 ^ D15 D24 +error(0.0002152771204412623973) D13 D25 ^ D23 D24 +error(0.003671177367358601732) D13 D26 +error(0.002426203061533186139) D13 D26 ^ D15 D16 +error(0.001594528899234590224) D13 D26 ^ D15 D23 +error(0.001908863009923503868) D13 D26 ^ D15 D24 +error(0.0006777420681469889779) D13 D26 ^ D16 D24 +error(0.002089629819013069569) D13 D26 ^ D23 D24 +error(0.004017245732850435735) D15 D23 ^ D13 +error(0.0149590643735124252) D14 +error(0.002369887455031276322) D14 D21 +error(0.0003318766923448692526) D14 D21 ^ D12 +error(0.002074857726409925964) D14 D21 ^ D24 +error(0.03289266697592603539) D14 D22 +error(0.003227722995460973777) D14 D22 ^ D24 +error(0.001321883351880175864) D14 D26 +error(0.0001061570292203804219) D14 D26 ^ D15 D16 +error(0.001480679150158425344) D14 D26 ^ D16 D24 +error(0.01455314546477868079) D14 ^ D16 +error(0.00136231486461977757) D14 ^ D24 ^ D16 +error(0.01288970954529138936) D15 D16 +error(0.0006777685174510479724) D15 D16 ^ D17 D18 +error(0.001866442831601738267) D15 D16 ^ D18 +error(0.00213473588335926813) D15 D16 ^ D18 D26 +error(0.001545759997001574463) D15 D16 ^ D26 +error(0.04539877534963716876) D15 D23 +error(0.003308806725389073101) D15 D23 ^ D25 D26 +error(0.002580122891698501727) D15 D24 +error(0.0004324137196982047134) D15 D24 ^ D25 D26 +error(0.00147646061331719214) D15 D27 +error(0.001161790667520634906) D15 D27 ^ D18 +error(0.001294277265455211342) D15 D27 ^ D18 D26 +error(0.0004861687020259467451) D15 D27 ^ D26 +error(0.01000000000000000021) D15 L0 +error(0.01100459450826842857) D15 L0 ^ D17 +error(0.003705356755689873452) D17 D25 ^ D15 L0 +error(0.01833470896650363224) D16 +error(0.0189340821389175698) D16 D19 +error(0.02135328730744307305) D16 D19 ^ D18 +error(0.01586367019697373579) D16 D24 +error(0.003329144510032204431) D16 D24 ^ D26 +error(0.007704981069138553892) D16 D27 +error(0.006337428984486745583) D16 D27 ^ D18 +error(0.008606358000911078221) D16 D27 ^ D26 +error(0.001500625098945613427) D16 D27 ^ D26 ^ D18 +error(0.007129452538042223286) D17 +error(0.01393549343122455104) D17 D18 +error(0.004097831678638559107) D17 D18 ^ D7 L0 +error(0.01848965772592321699) D17 D18 ^ D15 L0 +error(0.0003632959610633380398) D17 D18 ^ D19 L0 +error(0.0001629166182058437587) D17 D18 ^ D19 L0 ^ D7 L0 +error(0.01996525495835424144) D17 D25 +error(0.0007253352123544244299) D17 ^ D5 ^ D7 L0 +error(0.000434599230980333524) D17 ^ D5 ^ D15 L0 +error(0.001220824660818315994) D17 ^ D15 L0 ^ D7 L0 +error(0.02095150869270260424) D18 +error(0.004920888807233343536) D18 D25 +error(0.003458651288054707777) D18 D25 ^ D15 L0 +error(0.02134289816389397729) D18 D26 +error(0.0008976811780191280843) D18 D26 ^ D15 L0 +error(0.003921709342996214549) D18 D26 ^ D27 L0 +error(0.0002055227307604762482) D18 ^ D6 ^ D8 +error(0.0005592180442147452529) D18 ^ D6 ^ D16 +error(0.0007083109441602856255) D18 ^ D8 +error(0.01120617835675017868) D18 ^ D19 L0 +error(0.00204782118831032655) D18 ^ D27 L0 +error(0.005331285191324799597) D19 D27 ^ D18 +error(0.02414180985073517693) D19 D27 +error(0.005000000000000000104) D19 L0 +error(0.005000000000000000104) D20 +error(0.01467144407408177594) D20 D23 +error(0.01487322813926130263) D20 D23 ^ D21 +error(0.02120182655647323902) D20 D28 +error(0.004005051107944217724) D20 D28 ^ D29 +error(0.003257070752525762988) D20 D31 +error(0.004022286150863349508) D20 D31 ^ D21 +error(0.002543293195019577166) D20 D31 ^ D29 +error(0.001504391012008346896) D20 D31 ^ D29 ^ D21 +error(0.002614549345611351036) D20 D32 +error(0.0003652254927584783358) D20 D32 ^ D21 +error(0.001411437394529351875) D20 D32 ^ D21 D29 +error(0.0003879027308221025152) D20 D32 ^ D29 +error(0.005141525429393459246) D20 ^ D21 +error(0.002811504541598291648) D20 ^ D29 +error(0.003679817133241939341) D21 D29 ^ D20 +error(0.01000000000000000021) D21 +error(0.008917134013459478495) D21 D22 +error(0.0002294357728164152742) D21 D22 ^ D20 +error(0.0004097416596508378925) D21 D22 ^ D23 D24 +error(0.0001957930849328015286) D21 D22 ^ D23 D32 +error(0.01166828223472706448) D21 D22 ^ D24 +error(0.002085122492311126778) D21 D22 ^ D24 D32 +error(0.001977684631864129981) D21 D22 ^ D32 +error(3.270351685940596236e-06) D21 D22 ^ D32 ^ D20 +error(0.01465787481814465748) D21 D26 +error(0.01137750691802791314) D21 D26 ^ D23 D24 +error(0.02919543128697425077) D21 D29 +error(0.002628432669318331218) D21 D29 ^ D31 D32 +error(0.001244045587690730979) D21 D29 ^ D32 +error(0.001774401289933531713) D21 D33 +error(0.00215930058179150848) D21 D33 ^ D23 D31 +error(0.0003512834228198727328) D21 D33 ^ D23 D32 +error(0.0002152771204412623973) D21 D33 ^ D31 D32 +error(0.003671177367358601732) D21 D34 +error(0.002426203061533186139) D21 D34 ^ D23 D24 +error(0.001594528899234590224) D21 D34 ^ D23 D31 +error(0.001908863009923503868) D21 D34 ^ D23 D32 +error(0.0006777420681469889779) D21 D34 ^ D24 D32 +error(0.002089629819013069569) D21 D34 ^ D31 D32 +error(0.004017245732850435735) D23 D31 ^ D21 +error(0.0149590643735124252) D22 +error(0.002369887455031276322) D22 D29 +error(0.0003318766923448692526) D22 D29 ^ D20 +error(0.002074857726409925964) D22 D29 ^ D32 +error(0.03289266697592603539) D22 D30 +error(0.003227722995460973777) D22 D30 ^ D32 +error(0.001321883351880175864) D22 D34 +error(0.0001061570292203804219) D22 D34 ^ D23 D24 +error(0.001480679150158425344) D22 D34 ^ D24 D32 +error(0.01455314546477868079) D22 ^ D24 +error(0.00136231486461977757) D22 ^ D32 ^ D24 +error(0.01288970954529138936) D23 D24 +error(0.0006777685174510479724) D23 D24 ^ D25 D26 +error(0.001866442831601738267) D23 D24 ^ D26 +error(0.00213473588335926813) D23 D24 ^ D26 D34 +error(0.001545759997001574463) D23 D24 ^ D34 +error(0.04539877534963716876) D23 D31 +error(0.003308806725389073101) D23 D31 ^ D33 D34 +error(0.002580122891698501727) D23 D32 +error(0.0004324137196982047134) D23 D32 ^ D33 D34 +error(0.00147646061331719214) D23 D35 +error(0.001161790667520634906) D23 D35 ^ D26 +error(0.001294277265455211342) D23 D35 ^ D26 D34 +error(0.0004861687020259467451) D23 D35 ^ D34 +error(0.01000000000000000021) D23 L0 +error(0.01100459450826842857) D23 L0 ^ D25 +error(0.003705356755689873452) D25 D33 ^ D23 L0 +error(0.01833470896650363224) D24 +error(0.0189340821389175698) D24 D27 +error(0.02135328730744307305) D24 D27 ^ D26 +error(0.01586367019697373579) D24 D32 +error(0.003329144510032204431) D24 D32 ^ D34 +error(0.007704981069138553892) D24 D35 +error(0.006337428984486745583) D24 D35 ^ D26 +error(0.008606358000911078221) D24 D35 ^ D34 +error(0.001500625098945613427) D24 D35 ^ D34 ^ D26 +error(0.007129452538042223286) D25 +error(0.01393549343122455104) D25 D26 +error(0.004097831678638559107) D25 D26 ^ D15 L0 +error(0.01848965772592321699) D25 D26 ^ D23 L0 +error(0.0003632959610633380398) D25 D26 ^ D27 L0 +error(0.0001629166182058437587) D25 D26 ^ D27 L0 ^ D15 L0 +error(0.01996525495835424144) D25 D33 +error(0.0007253352123544244299) D25 ^ D13 ^ D15 L0 +error(0.000434599230980333524) D25 ^ D13 ^ D23 L0 +error(0.001220824660818315994) D25 ^ D23 L0 ^ D15 L0 +error(0.02095150869270260424) D26 +error(0.004920888807233343536) D26 D33 +error(0.003458651288054707777) D26 D33 ^ D23 L0 +error(0.02134289816389397729) D26 D34 +error(0.0008976811780191280843) D26 D34 ^ D23 L0 +error(0.003921709342996214549) D26 D34 ^ D35 L0 +error(0.0002055227307604762482) D26 ^ D14 ^ D16 +error(0.0005592180442147452529) D26 ^ D14 ^ D24 +error(0.0007083109441602856255) D26 ^ D16 +error(0.01120617835675017868) D26 ^ D27 L0 +error(0.00204782118831032655) D26 ^ D35 L0 +error(0.005331285191324799597) D27 D35 ^ D26 +error(0.02414180985073517693) D27 D35 +error(0.005000000000000000104) D27 L0 +error(0.005000000000000000104) D28 +error(0.01467144407408177594) D28 D31 +error(0.01487322813926130263) D28 D31 ^ D29 +error(0.02120182655647323902) D28 D36 +error(0.004005051107944217724) D28 D36 ^ D37 +error(0.003257070752525762988) D28 D39 +error(0.004022286150863349508) D28 D39 ^ D29 +error(0.002543293195019577166) D28 D39 ^ D37 +error(0.001504391012008346896) D28 D39 ^ D37 ^ D29 +error(0.002614549345611351036) D28 D40 +error(0.0003652254927584783358) D28 D40 ^ D29 +error(0.001411437394529351875) D28 D40 ^ D29 D37 +error(0.0003879027308221025152) D28 D40 ^ D37 +error(0.005141525429393459246) D28 ^ D29 +error(0.002811504541598291648) D28 ^ D37 +error(0.003679817133241939341) D29 D37 ^ D28 +error(0.01000000000000000021) D29 +error(0.008917134013459478495) D29 D30 +error(0.0002294357728164152742) D29 D30 ^ D28 +error(0.0004097416596508378925) D29 D30 ^ D31 D32 +error(0.0001957930849328015286) D29 D30 ^ D31 D40 +error(0.01166828223472706448) D29 D30 ^ D32 +error(0.002085122492311126778) D29 D30 ^ D32 D40 +error(0.001977684631864129981) D29 D30 ^ D40 +error(3.270351685940596236e-06) D29 D30 ^ D40 ^ D28 +error(0.01465787481814465748) D29 D34 +error(0.01137750691802791314) D29 D34 ^ D31 D32 +error(0.02919543128697425077) D29 D37 +error(0.002628432669318331218) D29 D37 ^ D39 D40 +error(0.001244045587690730979) D29 D37 ^ D40 +error(0.001774401289933531713) D29 D41 +error(0.00215930058179150848) D29 D41 ^ D31 D39 +error(0.0003512834228198727328) D29 D41 ^ D31 D40 +error(0.0002152771204412623973) D29 D41 ^ D39 D40 +error(0.003671177367358601732) D29 D42 +error(0.002426203061533186139) D29 D42 ^ D31 D32 +error(0.001594528899234590224) D29 D42 ^ D31 D39 +error(0.001908863009923503868) D29 D42 ^ D31 D40 +error(0.0006777420681469889779) D29 D42 ^ D32 D40 +error(0.002089629819013069569) D29 D42 ^ D39 D40 +error(0.004017245732850435735) D31 D39 ^ D29 +error(0.0149590643735124252) D30 +error(0.002369887455031276322) D30 D37 +error(0.0003318766923448692526) D30 D37 ^ D28 +error(0.002074857726409925964) D30 D37 ^ D40 +error(0.03289266697592603539) D30 D38 +error(0.003227722995460973777) D30 D38 ^ D40 +error(0.001321883351880175864) D30 D42 +error(0.0001061570292203804219) D30 D42 ^ D31 D32 +error(0.001480679150158425344) D30 D42 ^ D32 D40 +error(0.01455314546477868079) D30 ^ D32 +error(0.00136231486461977757) D30 ^ D40 ^ D32 +error(0.01288970954529138936) D31 D32 +error(0.0006777685174510479724) D31 D32 ^ D33 D34 +error(0.001866442831601738267) D31 D32 ^ D34 +error(0.00213473588335926813) D31 D32 ^ D34 D42 +error(0.001545759997001574463) D31 D32 ^ D42 +error(0.04539877534963716876) D31 D39 +error(0.003308806725389073101) D31 D39 ^ D41 D42 +error(0.002580122891698501727) D31 D40 +error(0.0004324137196982047134) D31 D40 ^ D41 D42 +error(0.00147646061331719214) D31 D43 +error(0.001161790667520634906) D31 D43 ^ D34 +error(0.001294277265455211342) D31 D43 ^ D34 D42 +error(0.0004861687020259467451) D31 D43 ^ D42 +error(0.01000000000000000021) D31 L0 +error(0.01100459450826842857) D31 L0 ^ D33 +error(0.003705356755689873452) D33 D41 ^ D31 L0 +error(0.01833470896650363224) D32 +error(0.0189340821389175698) D32 D35 +error(0.02135328730744307305) D32 D35 ^ D34 +error(0.01586367019697373579) D32 D40 +error(0.003329144510032204431) D32 D40 ^ D42 +error(0.007704981069138553892) D32 D43 +error(0.006337428984486745583) D32 D43 ^ D34 +error(0.008606358000911078221) D32 D43 ^ D42 +error(0.001500625098945613427) D32 D43 ^ D42 ^ D34 +error(0.007129452538042223286) D33 +error(0.01393549343122455104) D33 D34 +error(0.004097831678638559107) D33 D34 ^ D23 L0 +error(0.01848965772592321699) D33 D34 ^ D31 L0 +error(0.0003632959610633380398) D33 D34 ^ D35 L0 +error(0.0001629166182058437587) D33 D34 ^ D35 L0 ^ D23 L0 +error(0.01996525495835424144) D33 D41 +error(0.0007253352123544244299) D33 ^ D21 ^ D23 L0 +error(0.000434599230980333524) D33 ^ D21 ^ D31 L0 +error(0.001220824660818315994) D33 ^ D31 L0 ^ D23 L0 +error(0.02095150869270260424) D34 +error(0.004920888807233343536) D34 D41 +error(0.003458651288054707777) D34 D41 ^ D31 L0 +error(0.02134289816389397729) D34 D42 +error(0.0008976811780191280843) D34 D42 ^ D31 L0 +error(0.003921709342996214549) D34 D42 ^ D43 L0 +error(0.0002055227307604762482) D34 ^ D22 ^ D24 +error(0.0005592180442147452529) D34 ^ D22 ^ D32 +error(0.0007083109441602856255) D34 ^ D24 +error(0.01120617835675017868) D34 ^ D35 L0 +error(0.00204782118831032655) D34 ^ D43 L0 +error(0.005331285191324799597) D35 D43 ^ D34 +error(0.02414180985073517693) D35 D43 +error(0.005000000000000000104) D35 L0 +error(0.005000000000000000104) D36 +error(0.01467144407408177594) D36 D39 +error(0.01487322813926130263) D36 D39 ^ D37 +error(0.02120182655647323902) D36 D44 +error(0.004005051107944217724) D36 D44 ^ D45 +error(0.003257070752525762988) D36 D47 +error(0.004022286150863349508) D36 D47 ^ D37 +error(0.002543293195019577166) D36 D47 ^ D45 +error(0.001504391012008346896) D36 D47 ^ D45 ^ D37 +error(0.002614549345611351036) D36 D48 +error(0.0003652254927584783358) D36 D48 ^ D37 +error(0.001411437394529351875) D36 D48 ^ D37 D45 +error(0.0003879027308221025152) D36 D48 ^ D45 +error(0.005141525429393459246) D36 ^ D37 +error(0.002811504541598291648) D36 ^ D45 +error(0.003679817133241939341) D37 D45 ^ D36 +error(0.01000000000000000021) D37 +error(0.008917134013459478495) D37 D38 +error(0.0002294357728164152742) D37 D38 ^ D36 +error(0.0004097416596508378925) D37 D38 ^ D39 D40 +error(0.0001957930849328015286) D37 D38 ^ D39 D48 +error(0.01166828223472706448) D37 D38 ^ D40 +error(0.002085122492311126778) D37 D38 ^ D40 D48 +error(0.001977684631864129981) D37 D38 ^ D48 +error(3.270351685940596236e-06) D37 D38 ^ D48 ^ D36 +error(0.01465787481814465748) D37 D42 +error(0.01137750691802791314) D37 D42 ^ D39 D40 +error(0.02919543128697425077) D37 D45 +error(0.002628432669318331218) D37 D45 ^ D47 D48 +error(0.001244045587690730979) D37 D45 ^ D48 +error(0.001774401289933531713) D37 D49 +error(0.00215930058179150848) D37 D49 ^ D39 D47 +error(0.0003512834228198727328) D37 D49 ^ D39 D48 +error(0.0002152771204412623973) D37 D49 ^ D47 D48 +error(0.003671177367358601732) D37 D50 +error(0.002426203061533186139) D37 D50 ^ D39 D40 +error(0.001594528899234590224) D37 D50 ^ D39 D47 +error(0.001908863009923503868) D37 D50 ^ D39 D48 +error(0.0006777420681469889779) D37 D50 ^ D40 D48 +error(0.002089629819013069569) D37 D50 ^ D47 D48 +error(0.004017245732850435735) D39 D47 ^ D37 +error(0.0149590643735124252) D38 +error(0.002369887455031276322) D38 D45 +error(0.0003318766923448692526) D38 D45 ^ D36 +error(0.002074857726409925964) D38 D45 ^ D48 +error(0.03289266697592603539) D38 D46 +error(0.003227722995460973777) D38 D46 ^ D48 +error(0.001321883351880175864) D38 D50 +error(0.0001061570292203804219) D38 D50 ^ D39 D40 +error(0.001480679150158425344) D38 D50 ^ D40 D48 +error(0.01455314546477868079) D38 ^ D40 +error(0.00136231486461977757) D38 ^ D48 ^ D40 +error(0.01288970954529138936) D39 D40 +error(0.0006777685174510479724) D39 D40 ^ D41 D42 +error(0.001866442831601738267) D39 D40 ^ D42 +error(0.00213473588335926813) D39 D40 ^ D42 D50 +error(0.001545759997001574463) D39 D40 ^ D50 +error(0.04539877534963716876) D39 D47 +error(0.003308806725389073101) D39 D47 ^ D49 D50 +error(0.002580122891698501727) D39 D48 +error(0.0004324137196982047134) D39 D48 ^ D49 D50 +error(0.00147646061331719214) D39 D51 +error(0.001161790667520634906) D39 D51 ^ D42 +error(0.001294277265455211342) D39 D51 ^ D42 D50 +error(0.0004861687020259467451) D39 D51 ^ D50 +error(0.01000000000000000021) D39 L0 +error(0.01100459450826842857) D39 L0 ^ D41 +error(0.003705356755689873452) D41 D49 ^ D39 L0 +error(0.01833470896650363224) D40 +error(0.0189340821389175698) D40 D43 +error(0.02135328730744307305) D40 D43 ^ D42 +error(0.01586367019697373579) D40 D48 +error(0.003329144510032204431) D40 D48 ^ D50 +error(0.007704981069138553892) D40 D51 +error(0.006337428984486745583) D40 D51 ^ D42 +error(0.008606358000911078221) D40 D51 ^ D50 +error(0.001500625098945613427) D40 D51 ^ D50 ^ D42 +error(0.007129452538042223286) D41 +error(0.01393549343122455104) D41 D42 +error(0.004097831678638559107) D41 D42 ^ D31 L0 +error(0.01848965772592321699) D41 D42 ^ D39 L0 +error(0.0003632959610633380398) D41 D42 ^ D43 L0 +error(0.0001629166182058437587) D41 D42 ^ D43 L0 ^ D31 L0 +error(0.01996525495835424144) D41 D49 +error(0.0007253352123544244299) D41 ^ D29 ^ D31 L0 +error(0.000434599230980333524) D41 ^ D29 ^ D39 L0 +error(0.001220824660818315994) D41 ^ D39 L0 ^ D31 L0 +error(0.02095150869270260424) D42 +error(0.004920888807233343536) D42 D49 +error(0.003458651288054707777) D42 D49 ^ D39 L0 +error(0.02134289816389397729) D42 D50 +error(0.0008976811780191280843) D42 D50 ^ D39 L0 +error(0.003921709342996214549) D42 D50 ^ D51 L0 +error(0.0002055227307604762482) D42 ^ D30 ^ D32 +error(0.0005592180442147452529) D42 ^ D30 ^ D40 +error(0.0007083109441602856255) D42 ^ D32 +error(0.01120617835675017868) D42 ^ D43 L0 +error(0.00204782118831032655) D42 ^ D51 L0 +error(0.005331285191324799597) D43 D51 ^ D42 +error(0.02414180985073517693) D43 D51 +error(0.005000000000000000104) D43 L0 +error(0.005000000000000000104) D44 +error(0.01467144407408177594) D44 D47 +error(0.01487322813926130263) D44 D47 ^ D45 +error(0.02120182655647323902) D44 D52 +error(0.004005051107944217724) D44 D52 ^ D53 +error(0.003257070752525762988) D44 D55 +error(0.004022286150863349508) D44 D55 ^ D45 +error(0.002543293195019577166) D44 D55 ^ D53 +error(0.001504391012008346896) D44 D55 ^ D53 ^ D45 +error(0.002614549345611351036) D44 D56 +error(0.0003652254927584783358) D44 D56 ^ D45 +error(0.001411437394529351875) D44 D56 ^ D45 D53 +error(0.0003879027308221025152) D44 D56 ^ D53 +error(0.005141525429393459246) D44 ^ D45 +error(0.002811504541598291648) D44 ^ D53 +error(0.003679817133241939341) D45 D53 ^ D44 +error(0.01000000000000000021) D45 +error(0.008917134013459478495) D45 D46 +error(0.0002294357728164152742) D45 D46 ^ D44 +error(0.0004097416596508378925) D45 D46 ^ D47 D48 +error(0.0001957930849328015286) D45 D46 ^ D47 D56 +error(0.01166828223472706448) D45 D46 ^ D48 +error(0.002085122492311126778) D45 D46 ^ D48 D56 +error(0.001977684631864129981) D45 D46 ^ D56 +error(3.270351685940596236e-06) D45 D46 ^ D56 ^ D44 +error(0.01465787481814465748) D45 D50 +error(0.01137750691802791314) D45 D50 ^ D47 D48 +error(0.02919543128697425077) D45 D53 +error(0.002628432669318331218) D45 D53 ^ D55 D56 +error(0.001244045587690730979) D45 D53 ^ D56 +error(0.001774401289933531713) D45 D57 +error(0.00215930058179150848) D45 D57 ^ D47 D55 +error(0.0003512834228198727328) D45 D57 ^ D47 D56 +error(0.0002152771204412623973) D45 D57 ^ D55 D56 +error(0.003671177367358601732) D45 D58 +error(0.002426203061533186139) D45 D58 ^ D47 D48 +error(0.001594528899234590224) D45 D58 ^ D47 D55 +error(0.001908863009923503868) D45 D58 ^ D47 D56 +error(0.0006777420681469889779) D45 D58 ^ D48 D56 +error(0.002089629819013069569) D45 D58 ^ D55 D56 +error(0.004017245732850435735) D47 D55 ^ D45 +error(0.0149590643735124252) D46 +error(0.002369887455031276322) D46 D53 +error(0.0003318766923448692526) D46 D53 ^ D44 +error(0.002074857726409925964) D46 D53 ^ D56 +error(0.03289266697592603539) D46 D54 +error(0.003227722995460973777) D46 D54 ^ D56 +error(0.001321883351880175864) D46 D58 +error(0.0001061570292203804219) D46 D58 ^ D47 D48 +error(0.001480679150158425344) D46 D58 ^ D48 D56 +error(0.01455314546477868079) D46 ^ D48 +error(0.00136231486461977757) D46 ^ D56 ^ D48 +error(0.01288970954529138936) D47 D48 +error(0.0006777685174510479724) D47 D48 ^ D49 D50 +error(0.001866442831601738267) D47 D48 ^ D50 +error(0.00213473588335926813) D47 D48 ^ D50 D58 +error(0.001545759997001574463) D47 D48 ^ D58 +error(0.04539877534963716876) D47 D55 +error(0.003308806725389073101) D47 D55 ^ D57 D58 +error(0.002580122891698501727) D47 D56 +error(0.0004324137196982047134) D47 D56 ^ D57 D58 +error(0.00147646061331719214) D47 D59 +error(0.001161790667520634906) D47 D59 ^ D50 +error(0.001294277265455211342) D47 D59 ^ D50 D58 +error(0.0004861687020259467451) D47 D59 ^ D58 +error(0.01000000000000000021) D47 L0 +error(0.01100459450826842857) D47 L0 ^ D49 +error(0.003705356755689873452) D49 D57 ^ D47 L0 +error(0.01833470896650363224) D48 +error(0.0189340821389175698) D48 D51 +error(0.02135328730744307305) D48 D51 ^ D50 +error(0.01586367019697373579) D48 D56 +error(0.003329144510032204431) D48 D56 ^ D58 +error(0.007704981069138553892) D48 D59 +error(0.006337428984486745583) D48 D59 ^ D50 +error(0.008606358000911078221) D48 D59 ^ D58 +error(0.001500625098945613427) D48 D59 ^ D58 ^ D50 +error(0.007129452538042223286) D49 +error(0.01393549343122455104) D49 D50 +error(0.004097831678638559107) D49 D50 ^ D39 L0 +error(0.01848965772592321699) D49 D50 ^ D47 L0 +error(0.0003632959610633380398) D49 D50 ^ D51 L0 +error(0.0001629166182058437587) D49 D50 ^ D51 L0 ^ D39 L0 +error(0.01996525495835424144) D49 D57 +error(0.0007253352123544244299) D49 ^ D37 ^ D39 L0 +error(0.000434599230980333524) D49 ^ D37 ^ D47 L0 +error(0.001220824660818315994) D49 ^ D47 L0 ^ D39 L0 +error(0.02095150869270260424) D50 +error(0.004920888807233343536) D50 D57 +error(0.003458651288054707777) D50 D57 ^ D47 L0 +error(0.02134289816389397729) D50 D58 +error(0.0008976811780191280843) D50 D58 ^ D47 L0 +error(0.003921709342996214549) D50 D58 ^ D59 L0 +error(0.0002055227307604762482) D50 ^ D38 ^ D40 +error(0.0005592180442147452529) D50 ^ D38 ^ D48 +error(0.0007083109441602856255) D50 ^ D40 +error(0.01120617835675017868) D50 ^ D51 L0 +error(0.00204782118831032655) D50 ^ D59 L0 +error(0.005331285191324799597) D51 D59 ^ D50 +error(0.02414180985073517693) D51 D59 +error(0.005000000000000000104) D51 L0 +error(0.005000000000000000104) D52 +error(0.01467144407408177594) D52 D55 +error(0.01487322813926130263) D52 D55 ^ D53 +error(0.02120182655647323902) D52 D60 +error(0.004005051107944217724) D52 D60 ^ D61 +error(0.003257070752525762988) D52 D63 +error(0.004022286150863349508) D52 D63 ^ D53 +error(0.002543293195019577166) D52 D63 ^ D61 +error(0.001504391012008346896) D52 D63 ^ D61 ^ D53 +error(0.002614549345611351036) D52 D64 +error(0.0003652254927584783358) D52 D64 ^ D53 +error(0.001411437394529351875) D52 D64 ^ D53 D61 +error(0.0003879027308221025152) D52 D64 ^ D61 +error(0.005141525429393459246) D52 ^ D53 +error(0.002811504541598291648) D52 ^ D61 +error(0.003679817133241939341) D53 D61 ^ D52 +error(0.01000000000000000021) D53 +error(0.008917134013459478495) D53 D54 +error(0.0002294357728164152742) D53 D54 ^ D52 +error(0.0004097416596508378925) D53 D54 ^ D55 D56 +error(0.0001957930849328015286) D53 D54 ^ D55 D64 +error(0.01166828223472706448) D53 D54 ^ D56 +error(0.002085122492311126778) D53 D54 ^ D56 D64 +error(0.001977684631864129981) D53 D54 ^ D64 +error(3.270351685940596236e-06) D53 D54 ^ D64 ^ D52 +error(0.01465787481814465748) D53 D58 +error(0.01137750691802791314) D53 D58 ^ D55 D56 +error(0.02919543128697425077) D53 D61 +error(0.002628432669318331218) D53 D61 ^ D63 D64 +error(0.001244045587690730979) D53 D61 ^ D64 +error(0.001774401289933531713) D53 D65 +error(0.00215930058179150848) D53 D65 ^ D55 D63 +error(0.0003512834228198727328) D53 D65 ^ D55 D64 +error(0.0002152771204412623973) D53 D65 ^ D63 D64 +error(0.003671177367358601732) D53 D66 +error(0.002426203061533186139) D53 D66 ^ D55 D56 +error(0.001594528899234590224) D53 D66 ^ D55 D63 +error(0.001908863009923503868) D53 D66 ^ D55 D64 +error(0.0006777420681469889779) D53 D66 ^ D56 D64 +error(0.002089629819013069569) D53 D66 ^ D63 D64 +error(0.004017245732850435735) D55 D63 ^ D53 +error(0.0149590643735124252) D54 +error(0.002369887455031276322) D54 D61 +error(0.0003318766923448692526) D54 D61 ^ D52 +error(0.002074857726409925964) D54 D61 ^ D64 +error(0.03289266697592603539) D54 D62 +error(0.003227722995460973777) D54 D62 ^ D64 +error(0.001321883351880175864) D54 D66 +error(0.0001061570292203804219) D54 D66 ^ D55 D56 +error(0.001480679150158425344) D54 D66 ^ D56 D64 +error(0.01455314546477868079) D54 ^ D56 +error(0.00136231486461977757) D54 ^ D64 ^ D56 +error(0.01288970954529138936) D55 D56 +error(0.0006777685174510479724) D55 D56 ^ D57 D58 +error(0.001866442831601738267) D55 D56 ^ D58 +error(0.00213473588335926813) D55 D56 ^ D58 D66 +error(0.001545759997001574463) D55 D56 ^ D66 +error(0.04539877534963716876) D55 D63 +error(0.003308806725389073101) D55 D63 ^ D65 D66 +error(0.002580122891698501727) D55 D64 +error(0.0004324137196982047134) D55 D64 ^ D65 D66 +error(0.00147646061331719214) D55 D67 +error(0.001161790667520634906) D55 D67 ^ D58 +error(0.001294277265455211342) D55 D67 ^ D58 D66 +error(0.0004861687020259467451) D55 D67 ^ D66 +error(0.01000000000000000021) D55 L0 +error(0.01100459450826842857) D55 L0 ^ D57 +error(0.003705356755689873452) D57 D65 ^ D55 L0 +error(0.01833470896650363224) D56 +error(0.0189340821389175698) D56 D59 +error(0.02135328730744307305) D56 D59 ^ D58 +error(0.01586367019697373579) D56 D64 +error(0.003329144510032204431) D56 D64 ^ D66 +error(0.007704981069138553892) D56 D67 +error(0.006337428984486745583) D56 D67 ^ D58 +error(0.008606358000911078221) D56 D67 ^ D66 +error(0.001500625098945613427) D56 D67 ^ D66 ^ D58 +error(0.007129452538042223286) D57 +error(0.01393549343122455104) D57 D58 +error(0.004097831678638559107) D57 D58 ^ D47 L0 +error(0.01848965772592321699) D57 D58 ^ D55 L0 +error(0.0003632959610633380398) D57 D58 ^ D59 L0 +error(0.0001629166182058437587) D57 D58 ^ D59 L0 ^ D47 L0 +error(0.01996525495835424144) D57 D65 +error(0.0007253352123544244299) D57 ^ D45 ^ D47 L0 +error(0.000434599230980333524) D57 ^ D45 ^ D55 L0 +error(0.001220824660818315994) D57 ^ D55 L0 ^ D47 L0 +error(0.02095150869270260424) D58 +error(0.004920888807233343536) D58 D65 +error(0.003458651288054707777) D58 D65 ^ D55 L0 +error(0.02134289816389397729) D58 D66 +error(0.0008976811780191280843) D58 D66 ^ D55 L0 +error(0.003921709342996214549) D58 D66 ^ D67 L0 +error(0.0002055227307604762482) D58 ^ D46 ^ D48 +error(0.0005592180442147452529) D58 ^ D46 ^ D56 +error(0.0007083109441602856255) D58 ^ D48 +error(0.01120617835675017868) D58 ^ D59 L0 +error(0.00204782118831032655) D58 ^ D67 L0 +error(0.005331285191324799597) D59 D67 ^ D58 +error(0.02414180985073517693) D59 D67 +error(0.005000000000000000104) D59 L0 +error(0.005000000000000000104) D60 +error(0.01467144407408177594) D60 D63 +error(0.01487322813926130263) D60 D63 ^ D61 +error(0.02120182655647323902) D60 D68 +error(0.004005051107944217724) D60 D68 ^ D69 +error(0.003257070752525762988) D60 D71 +error(0.004022286150863349508) D60 D71 ^ D61 +error(0.002543293195019577166) D60 D71 ^ D69 +error(0.001504391012008346896) D60 D71 ^ D69 ^ D61 +error(0.002614549345611351036) D60 D72 +error(0.0003652254927584783358) D60 D72 ^ D61 +error(0.001411437394529351875) D60 D72 ^ D61 D69 +error(0.0003879027308221025152) D60 D72 ^ D69 +error(0.005141525429393459246) D60 ^ D61 +error(0.002811504541598291648) D60 ^ D69 +error(0.003679817133241939341) D61 D69 ^ D60 +error(0.01000000000000000021) D61 +error(0.008917134013459478495) D61 D62 +error(0.0002294357728164152742) D61 D62 ^ D60 +error(0.0004097416596508378925) D61 D62 ^ D63 D64 +error(0.0001957930849328015286) D61 D62 ^ D63 D72 +error(0.01166828223472706448) D61 D62 ^ D64 +error(0.002085122492311126778) D61 D62 ^ D64 D72 +error(0.001977684631864129981) D61 D62 ^ D72 +error(3.270351685940596236e-06) D61 D62 ^ D72 ^ D60 +error(0.01465787481814465748) D61 D66 +error(0.01137750691802791314) D61 D66 ^ D63 D64 +error(0.02919543128697425077) D61 D69 +error(0.002628432669318331218) D61 D69 ^ D71 D72 +error(0.001244045587690730979) D61 D69 ^ D72 +error(0.001774401289933531713) D61 D73 +error(0.00215930058179150848) D61 D73 ^ D63 D71 +error(0.0003512834228198727328) D61 D73 ^ D63 D72 +error(0.0002152771204412623973) D61 D73 ^ D71 D72 +error(0.003671177367358601732) D61 D74 +error(0.002426203061533186139) D61 D74 ^ D63 D64 +error(0.001594528899234590224) D61 D74 ^ D63 D71 +error(0.001908863009923503868) D61 D74 ^ D63 D72 +error(0.0006777420681469889779) D61 D74 ^ D64 D72 +error(0.002089629819013069569) D61 D74 ^ D71 D72 +error(0.004017245732850435735) D63 D71 ^ D61 +error(0.0149590643735124252) D62 +error(0.002369887455031276322) D62 D69 +error(0.0003318766923448692526) D62 D69 ^ D60 +error(0.002074857726409925964) D62 D69 ^ D72 +error(0.03289266697592603539) D62 D70 +error(0.003227722995460973777) D62 D70 ^ D72 +error(0.001321883351880175864) D62 D74 +error(0.0001061570292203804219) D62 D74 ^ D63 D64 +error(0.001480679150158425344) D62 D74 ^ D64 D72 +error(0.01455314546477868079) D62 ^ D64 +error(0.00136231486461977757) D62 ^ D72 ^ D64 +error(0.01288970954529138936) D63 D64 +error(0.0006777685174510479724) D63 D64 ^ D65 D66 +error(0.001866442831601738267) D63 D64 ^ D66 +error(0.00213473588335926813) D63 D64 ^ D66 D74 +error(0.001545759997001574463) D63 D64 ^ D74 +error(0.04539877534963716876) D63 D71 +error(0.003308806725389073101) D63 D71 ^ D73 D74 +error(0.002580122891698501727) D63 D72 +error(0.0004324137196982047134) D63 D72 ^ D73 D74 +error(0.00147646061331719214) D63 D75 +error(0.001161790667520634906) D63 D75 ^ D66 +error(0.001294277265455211342) D63 D75 ^ D66 D74 +error(0.0004861687020259467451) D63 D75 ^ D74 +error(0.01000000000000000021) D63 L0 +error(0.01100459450826842857) D63 L0 ^ D65 +error(0.003705356755689873452) D65 D73 ^ D63 L0 +error(0.01833470896650363224) D64 +error(0.0189340821389175698) D64 D67 +error(0.02135328730744307305) D64 D67 ^ D66 +error(0.01586367019697373579) D64 D72 +error(0.003329144510032204431) D64 D72 ^ D74 +error(0.007704981069138553892) D64 D75 +error(0.006337428984486745583) D64 D75 ^ D66 +error(0.008606358000911078221) D64 D75 ^ D74 +error(0.001500625098945613427) D64 D75 ^ D74 ^ D66 +error(0.007129452538042223286) D65 +error(0.01393549343122455104) D65 D66 +error(0.004097831678638559107) D65 D66 ^ D55 L0 +error(0.01848965772592321699) D65 D66 ^ D63 L0 +error(0.0003632959610633380398) D65 D66 ^ D67 L0 +error(0.0001629166182058437587) D65 D66 ^ D67 L0 ^ D55 L0 +error(0.01996525495835424144) D65 D73 +error(0.0007253352123544244299) D65 ^ D53 ^ D55 L0 +error(0.000434599230980333524) D65 ^ D53 ^ D63 L0 +error(0.001220824660818315994) D65 ^ D63 L0 ^ D55 L0 +error(0.02095150869270260424) D66 +error(0.004920888807233343536) D66 D73 +error(0.003458651288054707777) D66 D73 ^ D63 L0 +error(0.02134289816389397729) D66 D74 +error(0.0008976811780191280843) D66 D74 ^ D63 L0 +error(0.003921709342996214549) D66 D74 ^ D75 L0 +error(0.0002055227307604762482) D66 ^ D54 ^ D56 +error(0.0005592180442147452529) D66 ^ D54 ^ D64 +error(0.0007083109441602856255) D66 ^ D56 +error(0.01120617835675017868) D66 ^ D67 L0 +error(0.00204782118831032655) D66 ^ D75 L0 +error(0.005331285191324799597) D67 D75 ^ D66 +error(0.02414180985073517693) D67 D75 +error(0.005000000000000000104) D67 L0 +error(0.005000000000000000104) D68 +error(0.01467144407408177594) D68 D71 +error(0.01487322813926130263) D68 D71 ^ D69 +error(0.02120182655647323902) D68 D76 +error(0.004005051107944217724) D68 D76 ^ D77 +error(0.003257070752525762988) D68 D79 +error(0.004022286150863349508) D68 D79 ^ D69 +error(0.002543293195019577166) D68 D79 ^ D77 +error(0.001504391012008346896) D68 D79 ^ D77 ^ D69 +error(0.002614549345611351036) D68 D80 +error(0.0003652254927584783358) D68 D80 ^ D69 +error(0.001411437394529351875) D68 D80 ^ D69 D77 +error(0.0003879027308221025152) D68 D80 ^ D77 +error(0.005141525429393459246) D68 ^ D69 +error(0.002811504541598291648) D68 ^ D77 +error(0.003679817133241939341) D69 D77 ^ D68 +error(0.01000000000000000021) D69 +error(0.008917134013459478495) D69 D70 +error(0.0002294357728164152742) D69 D70 ^ D68 +error(0.0004097416596508378925) D69 D70 ^ D71 D72 +error(0.0001957930849328015286) D69 D70 ^ D71 D80 +error(0.01166828223472706448) D69 D70 ^ D72 +error(0.002085122492311126778) D69 D70 ^ D72 D80 +error(0.001977684631864129981) D69 D70 ^ D80 +error(3.270351685940596236e-06) D69 D70 ^ D80 ^ D68 +error(0.01465787481814465748) D69 D74 +error(0.01137750691802791314) D69 D74 ^ D71 D72 +error(0.02919543128697425077) D69 D77 +error(0.002628432669318331218) D69 D77 ^ D79 D80 +error(0.001244045587690730979) D69 D77 ^ D80 +error(0.001774401289933531713) D69 D81 +error(0.00215930058179150848) D69 D81 ^ D71 D79 +error(0.0003512834228198727328) D69 D81 ^ D71 D80 +error(0.0002152771204412623973) D69 D81 ^ D79 D80 +error(0.003671177367358601732) D69 D82 +error(0.002426203061533186139) D69 D82 ^ D71 D72 +error(0.001594528899234590224) D69 D82 ^ D71 D79 +error(0.001908863009923503868) D69 D82 ^ D71 D80 +error(0.0006777420681469889779) D69 D82 ^ D72 D80 +error(0.002089629819013069569) D69 D82 ^ D79 D80 +error(0.004017245732850435735) D71 D79 ^ D69 +error(0.0149590643735124252) D70 +error(0.002369887455031276322) D70 D77 +error(0.0003318766923448692526) D70 D77 ^ D68 +error(0.002074857726409925964) D70 D77 ^ D80 +error(0.03289266697592603539) D70 D78 +error(0.003227722995460973777) D70 D78 ^ D80 +error(0.001321883351880175864) D70 D82 +error(0.0001061570292203804219) D70 D82 ^ D71 D72 +error(0.001480679150158425344) D70 D82 ^ D72 D80 +error(0.01455314546477868079) D70 ^ D72 +error(0.00136231486461977757) D70 ^ D80 ^ D72 +error(0.01288970954529138936) D71 D72 +error(0.0006777685174510479724) D71 D72 ^ D73 D74 +error(0.001866442831601738267) D71 D72 ^ D74 +error(0.00213473588335926813) D71 D72 ^ D74 D82 +error(0.001545759997001574463) D71 D72 ^ D82 +error(0.04539877534963716876) D71 D79 +error(0.003308806725389073101) D71 D79 ^ D81 D82 +error(0.002580122891698501727) D71 D80 +error(0.0004324137196982047134) D71 D80 ^ D81 D82 +error(0.00147646061331719214) D71 D83 +error(0.001161790667520634906) D71 D83 ^ D74 +error(0.001294277265455211342) D71 D83 ^ D74 D82 +error(0.0004861687020259467451) D71 D83 ^ D82 +error(0.01000000000000000021) D71 L0 +error(0.01100459450826842857) D71 L0 ^ D73 +error(0.003705356755689873452) D73 D81 ^ D71 L0 +error(0.01833470896650363224) D72 +error(0.0189340821389175698) D72 D75 +error(0.02135328730744307305) D72 D75 ^ D74 +error(0.01586367019697373579) D72 D80 +error(0.003329144510032204431) D72 D80 ^ D82 +error(0.007704981069138553892) D72 D83 +error(0.006337428984486745583) D72 D83 ^ D74 +error(0.008606358000911078221) D72 D83 ^ D82 +error(0.001500625098945613427) D72 D83 ^ D82 ^ D74 +error(0.007129452538042223286) D73 +error(0.01393549343122455104) D73 D74 +error(0.004097831678638559107) D73 D74 ^ D63 L0 +error(0.01848965772592321699) D73 D74 ^ D71 L0 +error(0.0003632959610633380398) D73 D74 ^ D75 L0 +error(0.0001629166182058437587) D73 D74 ^ D75 L0 ^ D63 L0 +error(0.01996525495835424144) D73 D81 +error(0.0007253352123544244299) D73 ^ D61 ^ D63 L0 +error(0.000434599230980333524) D73 ^ D61 ^ D71 L0 +error(0.001220824660818315994) D73 ^ D71 L0 ^ D63 L0 +error(0.02095150869270260424) D74 +error(0.004920888807233343536) D74 D81 +error(0.003458651288054707777) D74 D81 ^ D71 L0 +error(0.02134289816389397729) D74 D82 +error(0.0008976811780191280843) D74 D82 ^ D71 L0 +error(0.003921709342996214549) D74 D82 ^ D83 L0 +error(0.0002055227307604762482) D74 ^ D62 ^ D64 +error(0.0005592180442147452529) D74 ^ D62 ^ D72 +error(0.0007083109441602856255) D74 ^ D64 +error(0.01120617835675017868) D74 ^ D75 L0 +error(0.00204782118831032655) D74 ^ D83 L0 +error(0.005331285191324799597) D75 D83 ^ D74 +error(0.02414180985073517693) D75 D83 +error(0.005000000000000000104) D75 L0 +error(0.005000000000000000104) D76 +error(0.01467144407408177594) D76 D79 +error(0.01487322813926130263) D76 D79 ^ D77 +error(0.02120182655647323902) D76 D84 +error(0.004005051107944217724) D76 D84 ^ D85 +error(0.003257070752525762988) D76 D87 +error(0.004022286150863349508) D76 D87 ^ D77 +error(0.002543293195019577166) D76 D87 ^ D85 +error(0.001504391012008346896) D76 D87 ^ D85 ^ D77 +error(0.002614549345611351036) D76 D88 +error(0.0003652254927584783358) D76 D88 ^ D77 +error(0.001411437394529351875) D76 D88 ^ D77 D85 +error(0.0003879027308221025152) D76 D88 ^ D85 +error(0.005141525429393459246) D76 ^ D77 +error(0.002811504541598291648) D76 ^ D85 +error(0.003679817133241939341) D77 D85 ^ D76 +error(0.01000000000000000021) D77 +error(0.008917134013459478495) D77 D78 +error(0.0002294357728164152742) D77 D78 ^ D76 +error(0.0004097416596508378925) D77 D78 ^ D79 D80 +error(0.0001957930849328015286) D77 D78 ^ D79 D88 +error(0.01166828223472706448) D77 D78 ^ D80 +error(0.002085122492311126778) D77 D78 ^ D80 D88 +error(0.001977684631864129981) D77 D78 ^ D88 +error(3.270351685940596236e-06) D77 D78 ^ D88 ^ D76 +error(0.01465787481814465748) D77 D82 +error(0.01137750691802791314) D77 D82 ^ D79 D80 +error(0.02919543128697425077) D77 D85 +error(0.002628432669318331218) D77 D85 ^ D87 D88 +error(0.001244045587690730979) D77 D85 ^ D88 +error(0.001774401289933531713) D77 D89 +error(0.00215930058179150848) D77 D89 ^ D79 D87 +error(0.0003512834228198727328) D77 D89 ^ D79 D88 +error(0.0002152771204412623973) D77 D89 ^ D87 D88 +error(0.003671177367358601732) D77 D90 +error(0.002426203061533186139) D77 D90 ^ D79 D80 +error(0.001594528899234590224) D77 D90 ^ D79 D87 +error(0.001908863009923503868) D77 D90 ^ D79 D88 +error(0.0006777420681469889779) D77 D90 ^ D80 D88 +error(0.002089629819013069569) D77 D90 ^ D87 D88 +error(0.004017245732850435735) D79 D87 ^ D77 +error(0.0149590643735124252) D78 +error(0.002369887455031276322) D78 D85 +error(0.0003318766923448692526) D78 D85 ^ D76 +error(0.002074857726409925964) D78 D85 ^ D88 +error(0.03289266697592603539) D78 D86 +error(0.003227722995460973777) D78 D86 ^ D88 +error(0.001321883351880175864) D78 D90 +error(0.0001061570292203804219) D78 D90 ^ D79 D80 +error(0.001480679150158425344) D78 D90 ^ D80 D88 +error(0.01455314546477868079) D78 ^ D80 +error(0.00136231486461977757) D78 ^ D88 ^ D80 +error(0.01288970954529138936) D79 D80 +error(0.0006777685174510479724) D79 D80 ^ D81 D82 +error(0.001866442831601738267) D79 D80 ^ D82 +error(0.00213473588335926813) D79 D80 ^ D82 D90 +error(0.001545759997001574463) D79 D80 ^ D90 +error(0.04539877534963716876) D79 D87 +error(0.003308806725389073101) D79 D87 ^ D89 D90 +error(0.002580122891698501727) D79 D88 +error(0.0004324137196982047134) D79 D88 ^ D89 D90 +error(0.00147646061331719214) D79 D91 +error(0.001161790667520634906) D79 D91 ^ D82 +error(0.001294277265455211342) D79 D91 ^ D82 D90 +error(0.0004861687020259467451) D79 D91 ^ D90 +error(0.01000000000000000021) D79 L0 +error(0.01100459450826842857) D79 L0 ^ D81 +error(0.003705356755689873452) D81 D89 ^ D79 L0 +error(0.01833470896650363224) D80 +error(0.0189340821389175698) D80 D83 +error(0.02135328730744307305) D80 D83 ^ D82 +error(0.01586367019697373579) D80 D88 +error(0.003329144510032204431) D80 D88 ^ D90 +error(0.007704981069138553892) D80 D91 +error(0.006337428984486745583) D80 D91 ^ D82 +error(0.008606358000911078221) D80 D91 ^ D90 +error(0.001500625098945613427) D80 D91 ^ D90 ^ D82 +error(0.007129452538042223286) D81 +error(0.01393549343122455104) D81 D82 +error(0.004097831678638559107) D81 D82 ^ D71 L0 +error(0.01848965772592321699) D81 D82 ^ D79 L0 +error(0.0003632959610633380398) D81 D82 ^ D83 L0 +error(0.0001629166182058437587) D81 D82 ^ D83 L0 ^ D71 L0 +error(0.01996525495835424144) D81 D89 +error(0.0007253352123544244299) D81 ^ D69 ^ D71 L0 +error(0.000434599230980333524) D81 ^ D69 ^ D79 L0 +error(0.001220824660818315994) D81 ^ D79 L0 ^ D71 L0 +error(0.02095150869270260424) D82 +error(0.004920888807233343536) D82 D89 +error(0.003458651288054707777) D82 D89 ^ D79 L0 +error(0.02134289816389397729) D82 D90 +error(0.0008976811780191280843) D82 D90 ^ D79 L0 +error(0.003921709342996214549) D82 D90 ^ D91 L0 +error(0.0002055227307604762482) D82 ^ D70 ^ D72 +error(0.0005592180442147452529) D82 ^ D70 ^ D80 +error(0.0007083109441602856255) D82 ^ D72 +error(0.01120617835675017868) D82 ^ D83 L0 +error(0.00204782118831032655) D82 ^ D91 L0 +error(0.005331285191324799597) D83 D91 ^ D82 +error(0.02414180985073517693) D83 D91 +error(0.005000000000000000104) D83 L0 +error(0.005000000000000000104) D84 +error(0.01467144407408177594) D84 D87 +error(0.01487322813926130263) D84 D87 ^ D85 +error(0.02120182655647323902) D84 D92 +error(0.004005051107944217724) D84 D92 ^ D93 +error(0.003257070752525762988) D84 D95 +error(0.004022286150863349508) D84 D95 ^ D85 +error(0.002543293195019577166) D84 D95 ^ D93 +error(0.001504391012008346896) D84 D95 ^ D93 ^ D85 +error(0.002614549345611351036) D84 D96 +error(0.0003652254927584783358) D84 D96 ^ D85 +error(0.001411437394529351875) D84 D96 ^ D85 D93 +error(0.0003879027308221025152) D84 D96 ^ D93 +error(0.005141525429393459246) D84 ^ D85 +error(0.002811504541598291648) D84 ^ D93 +error(0.003679817133241939341) D85 D93 ^ D84 +error(0.01000000000000000021) D85 +error(0.008917134013459478495) D85 D86 +error(0.0002294357728164152742) D85 D86 ^ D84 +error(0.0004097416596508378925) D85 D86 ^ D87 D88 +error(0.0001957930849328015286) D85 D86 ^ D87 D96 +error(0.01166828223472706448) D85 D86 ^ D88 +error(0.002085122492311126778) D85 D86 ^ D88 D96 +error(0.001977684631864129981) D85 D86 ^ D96 +error(3.270351685940596236e-06) D85 D86 ^ D96 ^ D84 +error(0.01465787481814465748) D85 D90 +error(0.01137750691802791314) D85 D90 ^ D87 D88 +error(0.02919543128697425077) D85 D93 +error(0.002628432669318331218) D85 D93 ^ D95 D96 +error(0.001244045587690730979) D85 D93 ^ D96 +error(0.001774401289933531713) D85 D97 +error(0.00215930058179150848) D85 D97 ^ D87 D95 +error(0.0003512834228198727328) D85 D97 ^ D87 D96 +error(0.0002152771204412623973) D85 D97 ^ D95 D96 +error(0.003671177367358601732) D85 D98 +error(0.002426203061533186139) D85 D98 ^ D87 D88 +error(0.001594528899234590224) D85 D98 ^ D87 D95 +error(0.001908863009923503868) D85 D98 ^ D87 D96 +error(0.0006777420681469889779) D85 D98 ^ D88 D96 +error(0.002089629819013069569) D85 D98 ^ D95 D96 +error(0.004017245732850435735) D87 D95 ^ D85 +error(0.0149590643735124252) D86 +error(0.002369887455031276322) D86 D93 +error(0.0003318766923448692526) D86 D93 ^ D84 +error(0.002074857726409925964) D86 D93 ^ D96 +error(0.03289266697592603539) D86 D94 +error(0.003227722995460973777) D86 D94 ^ D96 +error(0.001321883351880175864) D86 D98 +error(0.0001061570292203804219) D86 D98 ^ D87 D88 +error(0.001480679150158425344) D86 D98 ^ D88 D96 +error(0.01455314546477868079) D86 ^ D88 +error(0.00136231486461977757) D86 ^ D96 ^ D88 +error(0.01288970954529138936) D87 D88 +error(0.0006777685174510479724) D87 D88 ^ D89 D90 +error(0.001866442831601738267) D87 D88 ^ D90 +error(0.00213473588335926813) D87 D88 ^ D90 D98 +error(0.001545759997001574463) D87 D88 ^ D98 +error(0.04539877534963716876) D87 D95 +error(0.003308806725389073101) D87 D95 ^ D97 D98 +error(0.002580122891698501727) D87 D96 +error(0.0004324137196982047134) D87 D96 ^ D97 D98 +error(0.00147646061331719214) D87 D99 +error(0.001161790667520634906) D87 D99 ^ D90 +error(0.001294277265455211342) D87 D99 ^ D90 D98 +error(0.0004861687020259467451) D87 D99 ^ D98 +error(0.01000000000000000021) D87 L0 +error(0.01100459450826842857) D87 L0 ^ D89 +error(0.003705356755689873452) D89 D97 ^ D87 L0 +error(0.01833470896650363224) D88 +error(0.0189340821389175698) D88 D91 +error(0.02135328730744307305) D88 D91 ^ D90 +error(0.01586367019697373579) D88 D96 +error(0.003329144510032204431) D88 D96 ^ D98 +error(0.007704981069138553892) D88 D99 +error(0.006337428984486745583) D88 D99 ^ D90 +error(0.008606358000911078221) D88 D99 ^ D98 +error(0.001500625098945613427) D88 D99 ^ D98 ^ D90 +error(0.007129452538042223286) D89 +error(0.01393549343122455104) D89 D90 +error(0.004097831678638559107) D89 D90 ^ D79 L0 +error(0.01848965772592321699) D89 D90 ^ D87 L0 +error(0.0003632959610633380398) D89 D90 ^ D91 L0 +error(0.0001629166182058437587) D89 D90 ^ D91 L0 ^ D79 L0 +error(0.01996525495835424144) D89 D97 +error(0.0007253352123544244299) D89 ^ D77 ^ D79 L0 +error(0.000434599230980333524) D89 ^ D77 ^ D87 L0 +error(0.001220824660818315994) D89 ^ D87 L0 ^ D79 L0 +error(0.02095150869270260424) D90 +error(0.004920888807233343536) D90 D97 +error(0.003458651288054707777) D90 D97 ^ D87 L0 +error(0.02134289816389397729) D90 D98 +error(0.0008976811780191280843) D90 D98 ^ D87 L0 +error(0.003921709342996214549) D90 D98 ^ D99 L0 +error(0.0002055227307604762482) D90 ^ D78 ^ D80 +error(0.0005592180442147452529) D90 ^ D78 ^ D88 +error(0.0007083109441602856255) D90 ^ D80 +error(0.01120617835675017868) D90 ^ D91 L0 +error(0.00204782118831032655) D90 ^ D99 L0 +error(0.005331285191324799597) D91 D99 ^ D90 +error(0.02414180985073517693) D91 D99 +error(0.005000000000000000104) D91 L0 +error(0.005000000000000000104) D92 +error(0.01467144407408177594) D92 D95 +error(0.01487322813926130263) D92 D95 ^ D93 +error(0.02120182655647323902) D92 D100 +error(0.004005051107944217724) D92 D100 ^ D101 +error(0.003257070752525762988) D92 D103 +error(0.004022286150863349508) D92 D103 ^ D93 +error(0.002543293195019577166) D92 D103 ^ D101 +error(0.001504391012008346896) D92 D103 ^ D101 ^ D93 +error(0.002614549345611351036) D92 D104 +error(0.0003652254927584783358) D92 D104 ^ D93 +error(0.001411437394529351875) D92 D104 ^ D93 D101 +error(0.0003879027308221025152) D92 D104 ^ D101 +error(0.005141525429393459246) D92 ^ D93 +error(0.002811504541598291648) D92 ^ D101 +error(0.003679817133241939341) D93 D101 ^ D92 +error(0.01000000000000000021) D93 +error(0.008917134013459478495) D93 D94 +error(0.0002294357728164152742) D93 D94 ^ D92 +error(0.0004097416596508378925) D93 D94 ^ D95 D96 +error(0.0001957930849328015286) D93 D94 ^ D95 D104 +error(0.01166828223472706448) D93 D94 ^ D96 +error(0.002085122492311126778) D93 D94 ^ D96 D104 +error(0.001977684631864129981) D93 D94 ^ D104 +error(3.270351685940596236e-06) D93 D94 ^ D104 ^ D92 +error(0.01465787481814465748) D93 D98 +error(0.01137750691802791314) D93 D98 ^ D95 D96 +error(0.02919543128697425077) D93 D101 +error(0.002628432669318331218) D93 D101 ^ D103 D104 +error(0.001244045587690730979) D93 D101 ^ D104 +error(0.001774401289933531713) D93 D105 +error(0.00215930058179150848) D93 D105 ^ D95 D103 +error(0.0003512834228198727328) D93 D105 ^ D95 D104 +error(0.0002152771204412623973) D93 D105 ^ D103 D104 +error(0.003671177367358601732) D93 D106 +error(0.002426203061533186139) D93 D106 ^ D95 D96 +error(0.001594528899234590224) D93 D106 ^ D95 D103 +error(0.001908863009923503868) D93 D106 ^ D95 D104 +error(0.0006777420681469889779) D93 D106 ^ D96 D104 +error(0.002089629819013069569) D93 D106 ^ D103 D104 +error(0.004017245732850435735) D95 D103 ^ D93 +error(0.0149590643735124252) D94 +error(0.002369887455031276322) D94 D101 +error(0.0003318766923448692526) D94 D101 ^ D92 +error(0.002074857726409925964) D94 D101 ^ D104 +error(0.03289266697592603539) D94 D102 +error(0.003227722995460973777) D94 D102 ^ D104 +error(0.001321883351880175864) D94 D106 +error(0.0001061570292203804219) D94 D106 ^ D95 D96 +error(0.001480679150158425344) D94 D106 ^ D96 D104 +error(0.01455314546477868079) D94 ^ D96 +error(0.00136231486461977757) D94 ^ D104 ^ D96 +error(0.01288970954529138936) D95 D96 +error(0.0006777685174510479724) D95 D96 ^ D97 D98 +error(0.001866442831601738267) D95 D96 ^ D98 +error(0.00213473588335926813) D95 D96 ^ D98 D106 +error(0.001545759997001574463) D95 D96 ^ D106 +error(0.04539877534963716876) D95 D103 +error(0.003308806725389073101) D95 D103 ^ D105 D106 +error(0.002580122891698501727) D95 D104 +error(0.0004324137196982047134) D95 D104 ^ D105 D106 +error(0.00147646061331719214) D95 D107 +error(0.001161790667520634906) D95 D107 ^ D98 +error(0.001294277265455211342) D95 D107 ^ D98 D106 +error(0.0004861687020259467451) D95 D107 ^ D106 +error(0.01000000000000000021) D95 L0 +error(0.01100459450826842857) D95 L0 ^ D97 +error(0.003705356755689873452) D97 D105 ^ D95 L0 +error(0.01833470896650363224) D96 +error(0.0189340821389175698) D96 D99 +error(0.02135328730744307305) D96 D99 ^ D98 +error(0.01586367019697373579) D96 D104 +error(0.003329144510032204431) D96 D104 ^ D106 +error(0.007704981069138553892) D96 D107 +error(0.006337428984486745583) D96 D107 ^ D98 +error(0.008606358000911078221) D96 D107 ^ D106 +error(0.001500625098945613427) D96 D107 ^ D106 ^ D98 +error(0.007129452538042223286) D97 +error(0.01393549343122455104) D97 D98 +error(0.004097831678638559107) D97 D98 ^ D87 L0 +error(0.01848965772592321699) D97 D98 ^ D95 L0 +error(0.0003632959610633380398) D97 D98 ^ D99 L0 +error(0.0001629166182058437587) D97 D98 ^ D99 L0 ^ D87 L0 +error(0.01996525495835424144) D97 D105 +error(0.0007253352123544244299) D97 ^ D85 ^ D87 L0 +error(0.000434599230980333524) D97 ^ D85 ^ D95 L0 +error(0.001220824660818315994) D97 ^ D95 L0 ^ D87 L0 +error(0.02095150869270260424) D98 +error(0.004920888807233343536) D98 D105 +error(0.003458651288054707777) D98 D105 ^ D95 L0 +error(0.02134289816389397729) D98 D106 +error(0.0008976811780191280843) D98 D106 ^ D95 L0 +error(0.003921709342996214549) D98 D106 ^ D107 L0 +error(0.0002055227307604762482) D98 ^ D86 ^ D88 +error(0.0005592180442147452529) D98 ^ D86 ^ D96 +error(0.0007083109441602856255) D98 ^ D88 +error(0.01120617835675017868) D98 ^ D99 L0 +error(0.00204782118831032655) D98 ^ D107 L0 +error(0.005331285191324799597) D99 D107 ^ D98 +error(0.02414180985073517693) D99 D107 +error(0.005000000000000000104) D99 L0 +error(0.005000000000000000104) D100 +error(0.01467144407408177594) D100 D103 +error(0.01487322813926130263) D100 D103 ^ D101 +error(0.02120182655647323902) D100 D108 +error(0.004005051107944217724) D100 D108 ^ D109 +error(0.003257070752525762988) D100 D111 +error(0.004022286150863349508) D100 D111 ^ D101 +error(0.002543293195019577166) D100 D111 ^ D109 +error(0.001504391012008346896) D100 D111 ^ D109 ^ D101 +error(0.002614549345611351036) D100 D112 +error(0.0003652254927584783358) D100 D112 ^ D101 +error(0.001411437394529351875) D100 D112 ^ D101 D109 +error(0.0003879027308221025152) D100 D112 ^ D109 +error(0.005141525429393459246) D100 ^ D101 +error(0.002811504541598291648) D100 ^ D109 +error(0.003679817133241939341) D101 D109 ^ D100 +error(0.01000000000000000021) D101 +error(0.008917134013459478495) D101 D102 +error(0.0002294357728164152742) D101 D102 ^ D100 +error(0.0004097416596508378925) D101 D102 ^ D103 D104 +error(0.0001957930849328015286) D101 D102 ^ D103 D112 +error(0.01166828223472706448) D101 D102 ^ D104 +error(0.002085122492311126778) D101 D102 ^ D104 D112 +error(0.001977684631864129981) D101 D102 ^ D112 +error(3.270351685940596236e-06) D101 D102 ^ D112 ^ D100 +error(0.01465787481814465748) D101 D106 +error(0.01137750691802791314) D101 D106 ^ D103 D104 +error(0.02919543128697425077) D101 D109 +error(0.002628432669318331218) D101 D109 ^ D111 D112 +error(0.001244045587690730979) D101 D109 ^ D112 +error(0.001774401289933531713) D101 D113 +error(0.00215930058179150848) D101 D113 ^ D103 D111 +error(0.0003512834228198727328) D101 D113 ^ D103 D112 +error(0.0002152771204412623973) D101 D113 ^ D111 D112 +error(0.003671177367358601732) D101 D114 +error(0.002426203061533186139) D101 D114 ^ D103 D104 +error(0.001594528899234590224) D101 D114 ^ D103 D111 +error(0.001908863009923503868) D101 D114 ^ D103 D112 +error(0.0006777420681469889779) D101 D114 ^ D104 D112 +error(0.002089629819013069569) D101 D114 ^ D111 D112 +error(0.004017245732850435735) D103 D111 ^ D101 +error(0.0149590643735124252) D102 +error(0.002369887455031276322) D102 D109 +error(0.0003318766923448692526) D102 D109 ^ D100 +error(0.002074857726409925964) D102 D109 ^ D112 +error(0.03289266697592603539) D102 D110 +error(0.003227722995460973777) D102 D110 ^ D112 +error(0.001321883351880175864) D102 D114 +error(0.0001061570292203804219) D102 D114 ^ D103 D104 +error(0.001480679150158425344) D102 D114 ^ D104 D112 +error(0.01455314546477868079) D102 ^ D104 +error(0.00136231486461977757) D102 ^ D112 ^ D104 +error(0.01288970954529138936) D103 D104 +error(0.0006777685174510479724) D103 D104 ^ D105 D106 +error(0.001866442831601738267) D103 D104 ^ D106 +error(0.00213473588335926813) D103 D104 ^ D106 D114 +error(0.001545759997001574463) D103 D104 ^ D114 +error(0.04539877534963716876) D103 D111 +error(0.003308806725389073101) D103 D111 ^ D113 D114 +error(0.002580122891698501727) D103 D112 +error(0.0004324137196982047134) D103 D112 ^ D113 D114 +error(0.00147646061331719214) D103 D115 +error(0.001161790667520634906) D103 D115 ^ D106 +error(0.001294277265455211342) D103 D115 ^ D106 D114 +error(0.0004861687020259467451) D103 D115 ^ D114 +error(0.01000000000000000021) D103 L0 +error(0.01100459450826842857) D103 L0 ^ D105 +error(0.003705356755689873452) D105 D113 ^ D103 L0 +error(0.01833470896650363224) D104 +error(0.0189340821389175698) D104 D107 +error(0.02135328730744307305) D104 D107 ^ D106 +error(0.01586367019697373579) D104 D112 +error(0.003329144510032204431) D104 D112 ^ D114 +error(0.007704981069138553892) D104 D115 +error(0.006337428984486745583) D104 D115 ^ D106 +error(0.008606358000911078221) D104 D115 ^ D114 +error(0.001500625098945613427) D104 D115 ^ D114 ^ D106 +error(0.007129452538042223286) D105 +error(0.01393549343122455104) D105 D106 +error(0.004097831678638559107) D105 D106 ^ D95 L0 +error(0.01848965772592321699) D105 D106 ^ D103 L0 +error(0.0003632959610633380398) D105 D106 ^ D107 L0 +error(0.0001629166182058437587) D105 D106 ^ D107 L0 ^ D95 L0 +error(0.01996525495835424144) D105 D113 +error(0.0007253352123544244299) D105 ^ D93 ^ D95 L0 +error(0.000434599230980333524) D105 ^ D93 ^ D103 L0 +error(0.001220824660818315994) D105 ^ D103 L0 ^ D95 L0 +error(0.02095150869270260424) D106 +error(0.004920888807233343536) D106 D113 +error(0.003458651288054707777) D106 D113 ^ D103 L0 +error(0.02134289816389397729) D106 D114 +error(0.0008976811780191280843) D106 D114 ^ D103 L0 +error(0.003921709342996214549) D106 D114 ^ D115 L0 +error(0.0002055227307604762482) D106 ^ D94 ^ D96 +error(0.0005592180442147452529) D106 ^ D94 ^ D104 +error(0.0007083109441602856255) D106 ^ D96 +error(0.01120617835675017868) D106 ^ D107 L0 +error(0.00204782118831032655) D106 ^ D115 L0 +error(0.005331285191324799597) D107 D115 ^ D106 +error(0.02414180985073517693) D107 D115 +error(0.005000000000000000104) D107 L0 +error(0.005000000000000000104) D108 +error(0.01467144407408177594) D108 D111 +error(0.01487322813926130263) D108 D111 ^ D109 +error(0.02120182655647323902) D108 D116 +error(0.004005051107944217724) D108 D116 ^ D117 +error(0.003257070752525762988) D108 D119 +error(0.004022286150863349508) D108 D119 ^ D109 +error(0.002543293195019577166) D108 D119 ^ D117 +error(0.001504391012008346896) D108 D119 ^ D117 ^ D109 +error(0.002614549345611351036) D108 D120 +error(0.0003652254927584783358) D108 D120 ^ D109 +error(0.001411437394529351875) D108 D120 ^ D109 D117 +error(0.0003879027308221025152) D108 D120 ^ D117 +error(0.005141525429393459246) D108 ^ D109 +error(0.002811504541598291648) D108 ^ D117 +error(0.003679817133241939341) D109 D117 ^ D108 +error(0.01000000000000000021) D109 +error(0.008917134013459478495) D109 D110 +error(0.0002294357728164152742) D109 D110 ^ D108 +error(0.0004097416596508378925) D109 D110 ^ D111 D112 +error(0.0001957930849328015286) D109 D110 ^ D111 D120 +error(0.01166828223472706448) D109 D110 ^ D112 +error(0.002085122492311126778) D109 D110 ^ D112 D120 +error(0.001977684631864129981) D109 D110 ^ D120 +error(3.270351685940596236e-06) D109 D110 ^ D120 ^ D108 +error(0.01465787481814465748) D109 D114 +error(0.01137750691802791314) D109 D114 ^ D111 D112 +error(0.02919543128697425077) D109 D117 +error(0.002628432669318331218) D109 D117 ^ D119 D120 +error(0.001244045587690730979) D109 D117 ^ D120 +error(0.001774401289933531713) D109 D121 +error(0.00215930058179150848) D109 D121 ^ D111 D119 +error(0.0003512834228198727328) D109 D121 ^ D111 D120 +error(0.0002152771204412623973) D109 D121 ^ D119 D120 +error(0.003671177367358601732) D109 D122 +error(0.002426203061533186139) D109 D122 ^ D111 D112 +error(0.001594528899234590224) D109 D122 ^ D111 D119 +error(0.001908863009923503868) D109 D122 ^ D111 D120 +error(0.0006777420681469889779) D109 D122 ^ D112 D120 +error(0.002089629819013069569) D109 D122 ^ D119 D120 +error(0.004017245732850435735) D111 D119 ^ D109 +error(0.0149590643735124252) D110 +error(0.002369887455031276322) D110 D117 +error(0.0003318766923448692526) D110 D117 ^ D108 +error(0.002074857726409925964) D110 D117 ^ D120 +error(0.03289266697592603539) D110 D118 +error(0.003227722995460973777) D110 D118 ^ D120 +error(0.001321883351880175864) D110 D122 +error(0.0001061570292203804219) D110 D122 ^ D111 D112 +error(0.001480679150158425344) D110 D122 ^ D112 D120 +error(0.01455314546477868079) D110 ^ D112 +error(0.00136231486461977757) D110 ^ D120 ^ D112 +error(0.01288970954529138936) D111 D112 +error(0.0006777685174510479724) D111 D112 ^ D113 D114 +error(0.001866442831601738267) D111 D112 ^ D114 +error(0.00213473588335926813) D111 D112 ^ D114 D122 +error(0.001545759997001574463) D111 D112 ^ D122 +error(0.04539877534963716876) D111 D119 +error(0.003308806725389073101) D111 D119 ^ D121 D122 +error(0.002580122891698501727) D111 D120 +error(0.0004324137196982047134) D111 D120 ^ D121 D122 +error(0.00147646061331719214) D111 D123 +error(0.001161790667520634906) D111 D123 ^ D114 +error(0.001294277265455211342) D111 D123 ^ D114 D122 +error(0.0004861687020259467451) D111 D123 ^ D122 +error(0.01000000000000000021) D111 L0 +error(0.01100459450826842857) D111 L0 ^ D113 +error(0.003705356755689873452) D113 D121 ^ D111 L0 +error(0.01833470896650363224) D112 +error(0.0189340821389175698) D112 D115 +error(0.02135328730744307305) D112 D115 ^ D114 +error(0.01586367019697373579) D112 D120 +error(0.003329144510032204431) D112 D120 ^ D122 +error(0.007704981069138553892) D112 D123 +error(0.006337428984486745583) D112 D123 ^ D114 +error(0.008606358000911078221) D112 D123 ^ D122 +error(0.001500625098945613427) D112 D123 ^ D122 ^ D114 +error(0.007129452538042223286) D113 +error(0.01393549343122455104) D113 D114 +error(0.004097831678638559107) D113 D114 ^ D103 L0 +error(0.01848965772592321699) D113 D114 ^ D111 L0 +error(0.0003632959610633380398) D113 D114 ^ D115 L0 +error(0.0001629166182058437587) D113 D114 ^ D115 L0 ^ D103 L0 +error(0.01996525495835424144) D113 D121 +error(0.0007253352123544244299) D113 ^ D101 ^ D103 L0 +error(0.000434599230980333524) D113 ^ D101 ^ D111 L0 +error(0.001220824660818315994) D113 ^ D111 L0 ^ D103 L0 +error(0.02095150869270260424) D114 +error(0.004920888807233343536) D114 D121 +error(0.003458651288054707777) D114 D121 ^ D111 L0 +error(0.02134289816389397729) D114 D122 +error(0.0008976811780191280843) D114 D122 ^ D111 L0 +error(0.003921709342996214549) D114 D122 ^ D123 L0 +error(0.0002055227307604762482) D114 ^ D102 ^ D104 +error(0.0005592180442147452529) D114 ^ D102 ^ D112 +error(0.0007083109441602856255) D114 ^ D104 +error(0.01120617835675017868) D114 ^ D115 L0 +error(0.00204782118831032655) D114 ^ D123 L0 +error(0.005331285191324799597) D115 D123 ^ D114 +error(0.02414180985073517693) D115 D123 +error(0.005000000000000000104) D115 L0 +error(0.005000000000000000104) D116 +error(0.01467144407408177594) D116 D119 +error(0.01487322813926130263) D116 D119 ^ D117 +error(0.02120182655647323902) D116 D124 +error(0.004005051107944217724) D116 D124 ^ D125 +error(0.003257070752525762988) D116 D127 +error(0.004022286150863349508) D116 D127 ^ D117 +error(0.002543293195019577166) D116 D127 ^ D125 +error(0.001504391012008346896) D116 D127 ^ D125 ^ D117 +error(0.002614549345611351036) D116 D128 +error(0.0003652254927584783358) D116 D128 ^ D117 +error(0.001411437394529351875) D116 D128 ^ D117 D125 +error(0.0003879027308221025152) D116 D128 ^ D125 +error(0.005141525429393459246) D116 ^ D117 +error(0.002811504541598291648) D116 ^ D125 +error(0.003679817133241939341) D117 D125 ^ D116 +error(0.01000000000000000021) D117 +error(0.008917134013459478495) D117 D118 +error(0.0002294357728164152742) D117 D118 ^ D116 +error(0.0004097416596508378925) D117 D118 ^ D119 D120 +error(0.0001957930849328015286) D117 D118 ^ D119 D128 +error(0.01166828223472706448) D117 D118 ^ D120 +error(0.002085122492311126778) D117 D118 ^ D120 D128 +error(0.001977684631864129981) D117 D118 ^ D128 +error(3.270351685940596236e-06) D117 D118 ^ D128 ^ D116 +error(0.01465787481814465748) D117 D122 +error(0.01137750691802791314) D117 D122 ^ D119 D120 +error(0.02919543128697425077) D117 D125 +error(0.002628432669318331218) D117 D125 ^ D127 D128 +error(0.001244045587690730979) D117 D125 ^ D128 +error(0.001774401289933531713) D117 D129 +error(0.00215930058179150848) D117 D129 ^ D119 D127 +error(0.0003512834228198727328) D117 D129 ^ D119 D128 +error(0.0002152771204412623973) D117 D129 ^ D127 D128 +error(0.003671177367358601732) D117 D130 +error(0.002426203061533186139) D117 D130 ^ D119 D120 +error(0.001594528899234590224) D117 D130 ^ D119 D127 +error(0.001908863009923503868) D117 D130 ^ D119 D128 +error(0.0006777420681469889779) D117 D130 ^ D120 D128 +error(0.002089629819013069569) D117 D130 ^ D127 D128 +error(0.004017245732850435735) D119 D127 ^ D117 +error(0.0149590643735124252) D118 +error(0.002369887455031276322) D118 D125 +error(0.0003318766923448692526) D118 D125 ^ D116 +error(0.002074857726409925964) D118 D125 ^ D128 +error(0.03289266697592603539) D118 D126 +error(0.003227722995460973777) D118 D126 ^ D128 +error(0.001321883351880175864) D118 D130 +error(0.0001061570292203804219) D118 D130 ^ D119 D120 +error(0.001480679150158425344) D118 D130 ^ D120 D128 +error(0.01455314546477868079) D118 ^ D120 +error(0.00136231486461977757) D118 ^ D128 ^ D120 +error(0.01288970954529138936) D119 D120 +error(0.0006777685174510479724) D119 D120 ^ D121 D122 +error(0.001866442831601738267) D119 D120 ^ D122 +error(0.00213473588335926813) D119 D120 ^ D122 D130 +error(0.001545759997001574463) D119 D120 ^ D130 +error(0.04539877534963716876) D119 D127 +error(0.003308806725389073101) D119 D127 ^ D129 D130 +error(0.002580122891698501727) D119 D128 +error(0.0004324137196982047134) D119 D128 ^ D129 D130 +error(0.00147646061331719214) D119 D131 +error(0.001161790667520634906) D119 D131 ^ D122 +error(0.001294277265455211342) D119 D131 ^ D122 D130 +error(0.0004861687020259467451) D119 D131 ^ D130 +error(0.01000000000000000021) D119 L0 +error(0.01100459450826842857) D119 L0 ^ D121 +error(0.003705356755689873452) D121 D129 ^ D119 L0 +error(0.01833470896650363224) D120 +error(0.0189340821389175698) D120 D123 +error(0.02135328730744307305) D120 D123 ^ D122 +error(0.01586367019697373579) D120 D128 +error(0.003329144510032204431) D120 D128 ^ D130 +error(0.007704981069138553892) D120 D131 +error(0.006337428984486745583) D120 D131 ^ D122 +error(0.008606358000911078221) D120 D131 ^ D130 +error(0.001500625098945613427) D120 D131 ^ D130 ^ D122 +error(0.007129452538042223286) D121 +error(0.01393549343122455104) D121 D122 +error(0.004097831678638559107) D121 D122 ^ D111 L0 +error(0.01848965772592321699) D121 D122 ^ D119 L0 +error(0.0003632959610633380398) D121 D122 ^ D123 L0 +error(0.0001629166182058437587) D121 D122 ^ D123 L0 ^ D111 L0 +error(0.01996525495835424144) D121 D129 +error(0.0007253352123544244299) D121 ^ D109 ^ D111 L0 +error(0.000434599230980333524) D121 ^ D109 ^ D119 L0 +error(0.001220824660818315994) D121 ^ D119 L0 ^ D111 L0 +error(0.02095150869270260424) D122 +error(0.004920888807233343536) D122 D129 +error(0.003458651288054707777) D122 D129 ^ D119 L0 +error(0.02134289816389397729) D122 D130 +error(0.0008976811780191280843) D122 D130 ^ D119 L0 +error(0.003921709342996214549) D122 D130 ^ D131 L0 +error(0.0002055227307604762482) D122 ^ D110 ^ D112 +error(0.0005592180442147452529) D122 ^ D110 ^ D120 +error(0.0007083109441602856255) D122 ^ D112 +error(0.01120617835675017868) D122 ^ D123 L0 +error(0.00204782118831032655) D122 ^ D131 L0 +error(0.005331285191324799597) D123 D131 ^ D122 +error(0.02414180985073517693) D123 D131 +error(0.005000000000000000104) D123 L0 +error(0.005000000000000000104) D124 +error(0.0162496209508275341) D124 D127 +error(0.01578489085899240432) D124 D127 ^ D125 +error(0.02254504150509828442) D124 D132 +error(0.007263368292792921933) D124 D133 +error(0.00642951229822774032) D124 D133 ^ D125 +error(0.003813464625347072652) D124 D134 +error(0.001965784939689887469) D124 D134 ^ D125 +error(0.009355126031258776884) D124 ^ D125 +error(0.0006286445156290599917) D124 ^ D126 +error(0.03425199487456091069) D125 +error(0.01080646029812032741) D125 D126 +error(0.01153399319543317939) D125 D126 ^ D128 +error(0.002695177414852898985) D125 D126 ^ D128 D134 +error(0.002299858746869936667) D125 D126 ^ D134 +error(0.01508754361751855899) D125 D130 +error(0.01308731235472786018) D125 D130 ^ D127 D128 +error(0.001308316607519280882) D125 ^ D124 ^ D126 +error(0.001651945229625426679) D125 ^ D133 L0 +error(0.009269490341631363844) D127 D133 ^ D125 +error(0.0454664505418074516) D126 +error(0.0163580251700988881) D126 ^ D128 +error(0.007078470778404606298) D126 ^ D134 +error(0.003895837629788516561) D128 D134 ^ D126 +error(0.01360033884913293065) D127 D128 +error(0.001858241452114893109) D127 D128 ^ D125 +error(0.001365551191615431859) D127 D128 ^ D129 D130 +error(0.003501292128335610834) D127 D128 ^ D130 +error(0.0500048327697436637) D127 D133 +error(0.0007245087947987270596) D127 D134 +error(0.004954133413815862974) D127 D134 ^ D125 +error(0.0001184701880579148715) D127 D134 ^ D126 +error(0.002665722838255353295) D127 D135 +error(0.00295778262971048032) D127 D135 ^ D130 +error(0.01183584473603477467) D127 L0 +error(0.01536325763628302796) D127 L0 ^ D129 +error(0.004715989541254326373) D127 L0 ^ D130 +error(0.02382610527904693506) D128 +error(0.01758719486904114851) D128 D131 +error(0.02380608245728029967) D128 D131 ^ D130 +error(0.01513885445498827816) D128 D134 +error(0.002819136972445986013) D128 D134 ^ D125 +error(0.02040408476177009434) D128 D135 +error(0.006899479038359508307) D128 D135 ^ D130 +error(0.02334252237453055684) D129 +error(0.01509028238227375952) D129 D130 +error(0.004097831678638559107) D129 D130 ^ D119 L0 +error(0.0189063109504975374) D129 D130 ^ D127 L0 +error(0.0002776126152802611455) D129 D130 ^ D131 L0 +error(0.0001629166182058437587) D129 D130 ^ D131 L0 ^ D119 L0 +error(0.0007253352123544244299) D129 ^ D117 ^ D119 L0 +error(0.000434599230980333524) D129 ^ D117 ^ D127 L0 +error(0.001220824660818315994) D129 ^ D127 L0 ^ D119 L0 +error(0.05253321423927768269) D130 +error(0.0002055227307604762482) D130 ^ D118 ^ D120 +error(0.0005592180442147452529) D130 ^ D118 ^ D128 +error(0.0007083109441602856255) D130 ^ D120 +error(0.007517307650297300366) D130 ^ D131 L0 +error(0.004589551379556004555) D130 ^ D135 L0 +error(0.006696541374361833499) D131 D135 ^ D130 +error(0.02388514687046786045) D131 D135 +error(0.005000000000000000104) D131 L0 +error(0.0169341676751426605) D132 +error(0.01426923950073588632) D132 D133 +error(0.01451720809790842635) D133 D134 +error(0.004451974030540664651) D133 D134 ^ D125 +error(0.0324800399540740789) D133 L0 +error(0.02763732586505380368) D134 +error(0.01638124641799343362) D134 D135 +error(0.009680506330525230629) D135 L0 +detector(3, 2, 0) D0 +detector(5, 2, 0) D1 +detector(5, 4, 0) D2 +detector(7, 4, 0) D3 +detector(3, 2, 1) D4 +detector(4, 3, 1) D5 +detector(4, 5, 1) D6 +detector(5, 2, 1) D7 +detector(5, 4, 1) D8 +detector(6, 1, 1) D9 +detector(6, 3, 1) D10 +detector(7, 4, 1) D11 +detector(3, 2, 2) D12 +detector(4, 3, 2) D13 +detector(4, 5, 2) D14 +detector(5, 2, 2) D15 +detector(5, 4, 2) D16 +detector(6, 1, 2) D17 +detector(6, 3, 2) D18 +detector(7, 4, 2) D19 +detector(3, 2, 3) D20 +detector(4, 3, 3) D21 +detector(4, 5, 3) D22 +detector(5, 2, 3) D23 +detector(5, 4, 3) D24 +detector(6, 1, 3) D25 +detector(6, 3, 3) D26 +detector(7, 4, 3) D27 +detector(3, 2, 4) D28 +detector(4, 3, 4) D29 +detector(4, 5, 4) D30 +detector(5, 2, 4) D31 +detector(5, 4, 4) D32 +detector(6, 1, 4) D33 +detector(6, 3, 4) D34 +detector(7, 4, 4) D35 +detector(3, 2, 5) D36 +detector(4, 3, 5) D37 +detector(4, 5, 5) D38 +detector(5, 2, 5) D39 +detector(5, 4, 5) D40 +detector(6, 1, 5) D41 +detector(6, 3, 5) D42 +detector(7, 4, 5) D43 +detector(3, 2, 6) D44 +detector(4, 3, 6) D45 +detector(4, 5, 6) D46 +detector(5, 2, 6) D47 +detector(5, 4, 6) D48 +detector(6, 1, 6) D49 +detector(6, 3, 6) D50 +detector(7, 4, 6) D51 +detector(3, 2, 7) D52 +detector(4, 3, 7) D53 +detector(4, 5, 7) D54 +detector(5, 2, 7) D55 +detector(5, 4, 7) D56 +detector(6, 1, 7) D57 +detector(6, 3, 7) D58 +detector(7, 4, 7) D59 +detector(3, 2, 8) D60 +detector(4, 3, 8) D61 +detector(4, 5, 8) D62 +detector(5, 2, 8) D63 +detector(5, 4, 8) D64 +detector(6, 1, 8) D65 +detector(6, 3, 8) D66 +detector(7, 4, 8) D67 +detector(3, 2, 9) D68 +detector(4, 3, 9) D69 +detector(4, 5, 9) D70 +detector(5, 2, 9) D71 +detector(5, 4, 9) D72 +detector(6, 1, 9) D73 +detector(6, 3, 9) D74 +detector(7, 4, 9) D75 +detector(3, 2, 10) D76 +detector(4, 3, 10) D77 +detector(4, 5, 10) D78 +detector(5, 2, 10) D79 +detector(5, 4, 10) D80 +detector(6, 1, 10) D81 +detector(6, 3, 10) D82 +detector(7, 4, 10) D83 +detector(3, 2, 11) D84 +detector(4, 3, 11) D85 +detector(4, 5, 11) D86 +detector(5, 2, 11) D87 +detector(5, 4, 11) D88 +detector(6, 1, 11) D89 +detector(6, 3, 11) D90 +detector(7, 4, 11) D91 +detector(3, 2, 12) D92 +detector(4, 3, 12) D93 +detector(4, 5, 12) D94 +detector(5, 2, 12) D95 +detector(5, 4, 12) D96 +detector(6, 1, 12) D97 +detector(6, 3, 12) D98 +detector(7, 4, 12) D99 +detector(3, 2, 13) D100 +detector(4, 3, 13) D101 +detector(4, 5, 13) D102 +detector(5, 2, 13) D103 +detector(5, 4, 13) D104 +detector(6, 1, 13) D105 +detector(6, 3, 13) D106 +detector(7, 4, 13) D107 +detector(3, 2, 14) D108 +detector(4, 3, 14) D109 +detector(4, 5, 14) D110 +detector(5, 2, 14) D111 +detector(5, 4, 14) D112 +detector(6, 1, 14) D113 +detector(6, 3, 14) D114 +detector(7, 4, 14) D115 +detector(3, 2, 15) D116 +detector(4, 3, 15) D117 +detector(4, 5, 15) D118 +detector(5, 2, 15) D119 +detector(5, 4, 15) D120 +detector(6, 1, 15) D121 +detector(6, 3, 15) D122 +detector(7, 4, 15) D123 +detector(3, 2, 16) D124 +detector(4, 3, 16) D125 +detector(4, 5, 16) D126 +detector(5, 2, 16) D127 +detector(5, 4, 16) D128 +detector(6, 1, 16) D129 +detector(6, 3, 16) D130 +detector(7, 4, 16) D131 +detector(3, 2, 17) D132 +detector(5, 2, 17) D133 +detector(5, 4, 17) D134 +detector(7, 4, 17) D135 diff --git a/shared/package-files/8c/8c111a7aa72a0a8a713824968db84ff338d72dff95902045499799d46f08d545/blob b/shared/package-files/8c/8c111a7aa72a0a8a713824968db84ff338d72dff95902045499799d46f08d545/blob new file mode 100644 index 0000000000000000000000000000000000000000..973efdd76a8ab16cd118f17f720796bba1cf618f --- /dev/null +++ b/shared/package-files/8c/8c111a7aa72a0a8a713824968db84ff338d72dff95902045499799d46f08d545/blob @@ -0,0 +1,46 @@ +""" +@generated by mypy-protobuf. Do not edit manually! +isort:skip_file +""" +from google.protobuf.descriptor import ( + Descriptor as google___protobuf___descriptor___Descriptor, + FileDescriptor as google___protobuf___descriptor___FileDescriptor, +) + +from google.protobuf.internal.well_known_types import ( + Duration as google___protobuf___internal___well_known_types___Duration, +) + +from google.protobuf.message import ( + Message as google___protobuf___message___Message, +) + +from typing import ( + Optional as typing___Optional, +) + +from typing_extensions import ( + Literal as typing_extensions___Literal, +) + + +builtin___bool = bool +builtin___bytes = bytes +builtin___float = float +builtin___int = int + + +DESCRIPTOR: google___protobuf___descriptor___FileDescriptor = ... + +class Duration(google___protobuf___message___Message, google___protobuf___internal___well_known_types___Duration): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + seconds: builtin___int = ... + nanos: builtin___int = ... + + def __init__(self, + *, + seconds : typing___Optional[builtin___int] = None, + nanos : typing___Optional[builtin___int] = None, + ) -> None: ... + def ClearField(self, field_name: typing_extensions___Literal[u"nanos",b"nanos",u"seconds",b"seconds"]) -> None: ... +type___Duration = Duration diff --git a/shared/package-files/8c/8c120f4aab1fe939c11b2c1112b2d9ad0be6a83ab6e2f5c6317559afffb908d2/blob b/shared/package-files/8c/8c120f4aab1fe939c11b2c1112b2d9ad0be6a83ab6e2f5c6317559afffb908d2/blob new file mode 100644 index 0000000000000000000000000000000000000000..45eb8dc6b0b68fd074278175e5ab0dbab4db0914 --- /dev/null +++ b/shared/package-files/8c/8c120f4aab1fe939c11b2c1112b2d9ad0be6a83ab6e2f5c6317559afffb908d2/blob @@ -0,0 +1,254 @@ +import numpy as np + +from .. import links +from ..models import Model +from ..utils import MaskedModel, partition_tree_shuffle +from ._explainer import Explainer + + +class PermutationExplainer(Explainer): + """This method approximates the Shapley values by iterating through permutations of the inputs. + + This is a model agnostic explainer that guarantees local accuracy (additivity) by iterating completely + through an entire permutation of the features in both forward and reverse directions (antithetic sampling). + If we do this once, then we get the exact SHAP values for models with up to second order interaction effects. + We can iterate this many times over many random permutations to get better SHAP value estimates for models + with higher order interactions. This sequential ordering formulation also allows for easy reuse of + model evaluations and the ability to efficiently avoid evaluating the model when the background values + for a feature are the same as the current input value. We can also account for hierarchical data + structures with partition trees, something not currently implemented for KernalExplainer or SamplingExplainer. + """ + + def __init__( + self, model, masker, link=links.identity, feature_names=None, linearize_link=True, seed=None, **call_args + ): + """Build an explainers.Permutation object for the given model using the given masker object. + + Parameters + ---------- + model : function + A callable python object that executes the model given a set of input data samples. + + masker : function or numpy.array or pandas.DataFrame + A callable python object used to "mask" out hidden features of the form ``masker(binary_mask, x)``. + It takes a single input sample and a binary mask and returns a matrix of masked samples. These + masked samples are evaluated using the model function and the outputs are then averaged. + As a shortcut for the standard masking using by SHAP you can pass a background data matrix + instead of a function and that matrix will be used for masking. To use a clustering + game structure you can pass a ``shap.maskers.Tabular(data, clustering="correlation")`` object. + + seed: None or int + Seed for reproducibility + + **call_args : valid argument to the __call__ method + These arguments are saved and passed to the __call__ method as the new default values for these arguments. + + """ + # setting seed for random generation: if seed is not None, then shap values computation should be reproducible + np.random.seed(seed) + + if masker is None: + raise ValueError("masker cannot be None.") + + super().__init__(model, masker, link=link, linearize_link=linearize_link, feature_names=feature_names) + + if not isinstance(self.model, Model): + self.model = Model(self.model) + + # if we have gotten default arguments for the call function we need to wrap ourselves in a new class that + # has a call function with those new default arguments + if len(call_args) > 0: + # this signature should match the __call__ signature of the class defined below + class PermutationExplainer(self.__class__): + def __call__( + self, + *args, + max_evals=500, + main_effects=False, + error_bounds=False, + batch_size="auto", + outputs=None, + silent=False, + ): + return super().__call__( + *args, + max_evals=max_evals, + main_effects=main_effects, + error_bounds=error_bounds, + batch_size=batch_size, + outputs=outputs, + silent=silent, + ) + + PermutationExplainer.__call__.__doc__ = self.__class__.__call__.__doc__ + self.__class__ = PermutationExplainer + for k, v in call_args.items(): + self.__call__.__kwdefaults__[k] = v + + # note that changes to this function signature should be copied to the default call argument wrapper above + def __call__( + self, + *args, + max_evals=500, + main_effects=False, + error_bounds=False, + batch_size="auto", + outputs=None, + silent=False, + ): + """Explain the output of the model on the given arguments.""" + return super().__call__( + *args, + max_evals=max_evals, + main_effects=main_effects, + error_bounds=error_bounds, + batch_size=batch_size, + outputs=outputs, + silent=silent, + ) + + def explain_row(self, *row_args, max_evals, main_effects, error_bounds, batch_size, outputs, silent): + """Explains a single row and returns the tuple (row_values, row_expected_values, row_mask_shapes).""" + # build a masked version of the model for the current input sample + fm = MaskedModel(self.model, self.masker, self.link, self.linearize_link, *row_args) + + # by default we run 10 permutations forward and backward + if max_evals == "auto": + max_evals = 10 * 2 * len(fm) + + # compute any custom clustering for this row + row_clustering = None + if getattr(self.masker, "clustering", None) is not None: + if isinstance(self.masker.clustering, np.ndarray): + row_clustering = self.masker.clustering + elif callable(self.masker.clustering): + row_clustering = self.masker.clustering(*row_args) + else: + raise NotImplementedError( + "The masker passed has a .clustering attribute that is not yet supported by the Permutation explainer!" + ) + + # loop over many permutations + inds = fm.varying_inputs() + inds_mask = np.zeros(len(fm), dtype=bool) + inds_mask[inds] = True + masks = np.zeros(2 * len(inds) + 1, dtype=int) + masks[0] = MaskedModel.delta_mask_noop_value + npermutations = max_evals // (2 * len(inds) + 1) + row_values = None + row_values_history = None + history_pos = 0 + main_effect_values = None + if len(inds) > 0: + for _ in range(npermutations): + # shuffle the indexes so we get a random permutation ordering + if row_clustering is not None: + # [TODO] This is shuffle does not work when inds is not a complete set of integers from 0 to M TODO: still true? + # assert len(inds) == len(fm), "Need to support partition shuffle when not all the inds vary!!" + partition_tree_shuffle(inds, inds_mask, row_clustering) + else: + np.random.shuffle(inds) + + # create a large batch of masks to evaluate + i = 1 + for ind in inds: + masks[i] = ind + i += 1 + for ind in inds: + masks[i] = ind + i += 1 + + # evaluate the masked model + outputs = fm(masks, zero_index=0, batch_size=batch_size) + + if row_values is None: + row_values = np.zeros((len(fm),) + outputs.shape[1:]) + + if error_bounds: + row_values_history = np.zeros( + ( + 2 * npermutations, + len(fm), + ) + + outputs.shape[1:] + ) + + # update our SHAP value estimates + i = 0 + for ind in inds: # forward + row_values[ind] += outputs[i + 1] - outputs[i] + if error_bounds: + row_values_history[history_pos][ind] = outputs[i + 1] - outputs[i] + i += 1 + history_pos += 1 + for ind in inds: # backward + row_values[ind] += outputs[i] - outputs[i + 1] + if error_bounds: + row_values_history[history_pos][ind] = outputs[i] - outputs[i + 1] + i += 1 + history_pos += 1 + + if npermutations == 0: + raise ValueError( + f"max_evals={max_evals} is too low for the Permutation explainer, it must be at least 2 * num_features + 1 = {2 * len(inds) + 1}!" + ) + + expected_value = outputs[0] + + # compute the main effects if we need to + if main_effects: + main_effect_values = fm.main_effects(inds, batch_size=batch_size) + else: + masks = np.zeros(1, dtype=int) + outputs = fm(masks, zero_index=0, batch_size=1) + expected_value = outputs[0] + row_values = np.zeros((len(fm),) + outputs.shape[1:]) + if error_bounds: + row_values_history = np.zeros( + ( + 2 * npermutations, + len(fm), + ) + + outputs.shape[1:] + ) + + return { + "values": row_values / (2 * npermutations), + "expected_values": expected_value, + "mask_shapes": fm.mask_shapes, + "main_effects": main_effect_values, + "clustering": row_clustering, + "error_std": None if row_values_history is None else row_values_history.std(0), + "output_names": self.model.output_names if hasattr(self.model, "output_names") else None, + } + + def shap_values(self, X, npermutations=10, main_effects=False, error_bounds=False, batch_evals=True, silent=False): + """Legacy interface to estimate the SHAP values for a set of samples. + + Parameters + ---------- + X : numpy.array or pandas.DataFrame or any scipy.sparse matrix + A matrix of samples (# samples x # features) on which to explain the model's output. + + npermutations : int + Number of times to cycle through all the features, re-evaluating the model at each step. + Each cycle evaluates the model function 2 * (# features + 1) times on a data matrix of + (# background data samples) rows. An exception to this is when PermutationExplainer can + avoid evaluating the model because a feature's value is the same in X and the background + dataset (which is common for example with sparse features). + + Returns + ------- + array or list + For models with a single output this returns a matrix of SHAP values + (# samples x # features). Each row sums to the difference between the model output for that + sample and the expected value of the model output (which is stored as expected_value + attribute of the explainer). For models with vector outputs this returns a list + of such matrices, one for each output. + + """ + explanation = self(X, max_evals=npermutations * X.shape[1], main_effects=main_effects) + return explanation.values + + def __str__(self): + return "shap.explainers.PermutationExplainer()" diff --git a/shared/package-files/8c/8c127f751e6ad5a65e9a659d07d19778e2599ee16129f2b59e7d5ba552992106/blob b/shared/package-files/8c/8c127f751e6ad5a65e9a659d07d19778e2599ee16129f2b59e7d5ba552992106/blob new file mode 100644 index 0000000000000000000000000000000000000000..8153497a1bd4bb8e7ab8d5dea39f443199ef567e Binary files /dev/null and b/shared/package-files/8c/8c127f751e6ad5a65e9a659d07d19778e2599ee16129f2b59e7d5ba552992106/blob differ diff --git a/shared/package-files/8c/8c132d96bd25db413934aa58a2cce77ca7527be30f7a8c93b546a5f6157bcec7/blob b/shared/package-files/8c/8c132d96bd25db413934aa58a2cce77ca7527be30f7a8c93b546a5f6157bcec7/blob new file mode 100644 index 0000000000000000000000000000000000000000..c5683dfe3dcc4a3b5a729aa5ff53bda38aabbf84 --- /dev/null +++ b/shared/package-files/8c/8c132d96bd25db413934aa58a2cce77ca7527be30f7a8c93b546a5f6157bcec7/blob @@ -0,0 +1,22 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API ::std::tuple linalg_ldl_factor(const at::Tensor & self, bool hermitian=false); +TORCH_API ::std::tuple linalg_ldl_factor_out(const at::Tensor & self, bool hermitian, at::Tensor & LD, at::Tensor & pivots); +} // namespace native +} // namespace at diff --git a/shared/package-files/8c/8c1552cafa1dc2320ec4ab84b206042c797e2422cb34f2d30350eac6ad91dd48/blob b/shared/package-files/8c/8c1552cafa1dc2320ec4ab84b206042c797e2422cb34f2d30350eac6ad91dd48/blob new file mode 100644 index 0000000000000000000000000000000000000000..2d24d382431c80369bc02dd0f07b979b54acde93 --- /dev/null +++ b/shared/package-files/8c/8c1552cafa1dc2320ec4ab84b206042c797e2422cb34f2d30350eac6ad91dd48/blob @@ -0,0 +1,84 @@ +from pathlib import Path + +import numpy as np +import pandas as pd + +from statsmodels.tools.testing import Holder + +cur_dir = Path(__file__).resolve().parent + +results_meth = Holder() +results_meth.type = "ML" +results_meth.method = "BFGS" +results_meth.scoring = 3 +results_meth.start = np.array([ + 1.44771372395646, 0.0615237727637243, 0.604926837329731, 0.98389051740736, + 6.25859738441389, 0 + ]) +results_meth.n = 36 +results_meth.nobs = 36 +results_meth.df_null = 34 +results_meth.df_residual = 30 +results_meth.loglik = 104.148028405343 +results_meth.vcov = np.array([ + 0.00115682165449043, -0.000665413980696048, -0.000924081767589657, + -0.000924126199147583, 0.000941505276523348, -1.44829373972985e-05, + -0.000665413980696048, 0.00190019966824938, 4.45163588328844e-06, + 6.23668249663711e-06, -0.00216418558500309, 4.18754929463506e-05, + -0.000924081767589657, 4.45163588328844e-06, 0.0023369966334575, + 0.000924223263225116, 0.000168988804218447, 1.14762434349836e-07, + -0.000924126199147583, 6.23668249663711e-06, 0.000924223263225116, + 0.00282071714820361, 0.000331499252772628, 1.93773358431975e-07, + 0.000941505276523348, -0.00216418558500309, 0.000168988804218447, + 0.000331499252772628, 3.20761137509433, -0.0581708456538647, + -1.44829373972985e-05, 4.18754929463506e-05, 1.14762434349836e-07, + 1.93773358431975e-07, -0.0581708456538647, 0.00107353277853341 + ]).reshape(6, 6, order="F") +results_meth.pseudo_r_squared = 0.905194911478503 +results_meth.y = np.array([ + 0.815, 0.803, 0.803, 0.808, 0.855, 0.813, 0.816, 0.827, 0.829, 0.776, + 0.786, 0.822, 0.891, 0.894, 0.894, 0.869, 0.914, 0.889, 0.885, 0.898, + 0.896, 0.86, 0.887, 0.88, 0.936, 0.913, 0.9, 0.912, 0.935, 0.928, 0.915, + 0.916, 0.929, 0.92, 0.916, 0.926 + ]) + +# > cat_items(summ_meth, prefix="results_meth.") +# duplicate deleted + +results_meth.residuals_type = "sweighted2" +results_meth.iterations = np.array([ + 12, 3 + ]) + +results_meth.table_mean = np.array([ + 1.44224319715775, 0.0698572427112336, 0.607345321898288, 0.973547608125426, + 0.0340120810079364, 0.0435912797271355, 0.0483424930413969, + 0.0531104241011462, 42.4038504677562, 1.60255085761448, 12.5633843785881, + 18.3306314080896, 0, 0.109033850726723, 3.35661710796797e-36, + 4.71401008973566e-75 + ]).reshape(4, 4, order="F") + +results_meth.table_precision = np.array([ + 8.22828526376512, -0.0347054296138766, 1.79098056245575, + 0.0327648100640521, 4.59429065633335, -1.05922877459173, + 4.34223794561173e-06, 0.289495603466561 + ]).reshape(2, 4, order="F") + +results_meth.aic = -196.296056810686 +results_meth.bic = -186.79494317995 + +results_meth.table_mean_oim = np.array([ + 1.44224320770907, 0.069857238768632, 0.607345313356895, 0.973547591731571, + 0.0340453325782864, 0.0435867955242771, 0.0490089283252544, + 0.053386889034385, 42.362435567127, 1.60271563734762, 12.3925442590004, + 18.2357056075048, 0, 0.108997449531221, 2.86797597854623e-35, + 2.68762966306205e-74 + ]).reshape(4, 4, order="F") + +results_meth.table_precision_oim = np.array([ + 8.22828540005571, -0.0347054322904486, 1.83887205150239, + 0.0336205378385678, 4.4746372611042, -1.0322688012039, + 7.65411434417314e-06, 0.301946212204644 + ]).reshape(2, 4, order="F") + +results_meth.resid = pd.read_csv(Path(cur_dir).joinpath("resid_methylation.csv")) diff --git a/shared/package-files/8c/8c1693bd82afa9dd9dd79900ad2aae5ed3ffbef43031dab0816d3cdf1bb2bb21/blob b/shared/package-files/8c/8c1693bd82afa9dd9dd79900ad2aae5ed3ffbef43031dab0816d3cdf1bb2bb21/blob new file mode 100644 index 0000000000000000000000000000000000000000..2504ecda3d0743690112ba62f15c20e07d8c6c07 Binary files /dev/null and b/shared/package-files/8c/8c1693bd82afa9dd9dd79900ad2aae5ed3ffbef43031dab0816d3cdf1bb2bb21/blob differ diff --git a/shared/package-files/8c/8c1bfc42e4228be8a45a494fefafc33c261f9acf3c7e98faa4f39a08e2bab343/blob b/shared/package-files/8c/8c1bfc42e4228be8a45a494fefafc33c261f9acf3c7e98faa4f39a08e2bab343/blob new file mode 100644 index 0000000000000000000000000000000000000000..555ec73acd9b2e9ccbb5ea145db77862e2c54dbf --- /dev/null +++ b/shared/package-files/8c/8c1bfc42e4228be8a45a494fefafc33c261f9acf3c7e98faa4f39a08e2bab343/blob @@ -0,0 +1,252 @@ +from typing import ( + Any, + Hashable, + Literal, +) + +import numpy as np + +from pandas._typing import npt + +def unique_label_indices( + labels: np.ndarray, # const int64_t[:] +) -> np.ndarray: ... + +class Factorizer: + count: int + uniques: Any + def __init__(self, size_hint: int) -> None: ... + def get_count(self) -> int: ... + def factorize( + self, + values: np.ndarray, + na_sentinel=..., + na_value=..., + mask=..., + ) -> npt.NDArray[np.intp]: ... + +class ObjectFactorizer(Factorizer): + table: PyObjectHashTable + uniques: ObjectVector + +class Int64Factorizer(Factorizer): + table: Int64HashTable + uniques: Int64Vector + +class UInt64Factorizer(Factorizer): + table: UInt64HashTable + uniques: UInt64Vector + +class Int32Factorizer(Factorizer): + table: Int32HashTable + uniques: Int32Vector + +class UInt32Factorizer(Factorizer): + table: UInt32HashTable + uniques: UInt32Vector + +class Int16Factorizer(Factorizer): + table: Int16HashTable + uniques: Int16Vector + +class UInt16Factorizer(Factorizer): + table: UInt16HashTable + uniques: UInt16Vector + +class Int8Factorizer(Factorizer): + table: Int8HashTable + uniques: Int8Vector + +class UInt8Factorizer(Factorizer): + table: UInt8HashTable + uniques: UInt8Vector + +class Float64Factorizer(Factorizer): + table: Float64HashTable + uniques: Float64Vector + +class Float32Factorizer(Factorizer): + table: Float32HashTable + uniques: Float32Vector + +class Complex64Factorizer(Factorizer): + table: Complex64HashTable + uniques: Complex64Vector + +class Complex128Factorizer(Factorizer): + table: Complex128HashTable + uniques: Complex128Vector + +class Int64Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.int64]: ... + +class Int32Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.int32]: ... + +class Int16Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.int16]: ... + +class Int8Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.int8]: ... + +class UInt64Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.uint64]: ... + +class UInt32Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.uint32]: ... + +class UInt16Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.uint16]: ... + +class UInt8Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.uint8]: ... + +class Float64Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.float64]: ... + +class Float32Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.float32]: ... + +class Complex128Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.complex128]: ... + +class Complex64Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.complex64]: ... + +class StringVector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.object_]: ... + +class ObjectVector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.object_]: ... + +class HashTable: + # NB: The base HashTable class does _not_ actually have these methods; + # we are putting them here for the sake of mypy to avoid + # reproducing them in each subclass below. + def __init__(self, size_hint: int = ..., uses_mask: bool = ...) -> None: ... + def __len__(self) -> int: ... + def __contains__(self, key: Hashable) -> bool: ... + def sizeof(self, deep: bool = ...) -> int: ... + def get_state(self) -> dict[str, int]: ... + # TODO: `val/key` type is subclass-specific + def get_item(self, val): ... # TODO: return type? + def set_item(self, key, val) -> None: ... + def get_na(self): ... # TODO: return type? + def set_na(self, val) -> None: ... + def map_locations( + self, + values: np.ndarray, # np.ndarray[subclass-specific] + mask: npt.NDArray[np.bool_] | None = ..., + ) -> None: ... + def lookup( + self, + values: np.ndarray, # np.ndarray[subclass-specific] + mask: npt.NDArray[np.bool_] | None = ..., + ) -> npt.NDArray[np.intp]: ... + def get_labels( + self, + values: np.ndarray, # np.ndarray[subclass-specific] + uniques, # SubclassTypeVector + count_prior: int = ..., + na_sentinel: int = ..., + na_value: object = ..., + mask=..., + ) -> npt.NDArray[np.intp]: ... + def unique( + self, + values: np.ndarray, # np.ndarray[subclass-specific] + return_inverse: bool = ..., + mask=..., + ) -> ( + tuple[ + np.ndarray, # np.ndarray[subclass-specific] + npt.NDArray[np.intp], + ] + | np.ndarray + ): ... # np.ndarray[subclass-specific] + def factorize( + self, + values: np.ndarray, # np.ndarray[subclass-specific] + na_sentinel: int = ..., + na_value: object = ..., + mask=..., + ignore_na: bool = True, + ) -> tuple[np.ndarray, npt.NDArray[np.intp]]: ... # np.ndarray[subclass-specific] + +class Complex128HashTable(HashTable): ... +class Complex64HashTable(HashTable): ... +class Float64HashTable(HashTable): ... +class Float32HashTable(HashTable): ... + +class Int64HashTable(HashTable): + # Only Int64HashTable has get_labels_groupby, map_keys_to_values + def get_labels_groupby( + self, + values: npt.NDArray[np.int64], # const int64_t[:] + ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.int64]]: ... + def map_keys_to_values( + self, + keys: npt.NDArray[np.int64], + values: npt.NDArray[np.int64], # const int64_t[:] + ) -> None: ... + +class Int32HashTable(HashTable): ... +class Int16HashTable(HashTable): ... +class Int8HashTable(HashTable): ... +class UInt64HashTable(HashTable): ... +class UInt32HashTable(HashTable): ... +class UInt16HashTable(HashTable): ... +class UInt8HashTable(HashTable): ... +class StringHashTable(HashTable): ... +class PyObjectHashTable(HashTable): ... +class IntpHashTable(HashTable): ... + +def duplicated( + values: np.ndarray, + keep: Literal["last", "first", False] = ..., + mask: npt.NDArray[np.bool_] | None = ..., +) -> npt.NDArray[np.bool_]: ... +def mode( + values: np.ndarray, dropna: bool, mask: npt.NDArray[np.bool_] | None = ... +) -> np.ndarray: ... +def value_count( + values: np.ndarray, + dropna: bool, + mask: npt.NDArray[np.bool_] | None = ..., +) -> tuple[np.ndarray, npt.NDArray[np.int64], int]: ... # np.ndarray[same-as-values] + +# arr and values should have same dtype +def ismember( + arr: np.ndarray, + values: np.ndarray, +) -> npt.NDArray[np.bool_]: ... +def object_hash(obj) -> int: ... +def objects_are_equal(a, b) -> bool: ... diff --git a/shared/package-files/8c/8c1dd7081980b5ca9ff966fd3f0056eae23290fa4c3ed86e5cb2748224cacd91/blob b/shared/package-files/8c/8c1dd7081980b5ca9ff966fd3f0056eae23290fa4c3ed86e5cb2748224cacd91/blob new file mode 100644 index 0000000000000000000000000000000000000000..638d0d1534deba060140ffda3b61950a0b4f815d --- /dev/null +++ b/shared/package-files/8c/8c1dd7081980b5ca9ff966fd3f0056eae23290fa4c3ed86e5cb2748224cacd91/blob @@ -0,0 +1,16 @@ +import numpy as np +from numpy.testing import assert_, assert_equal, assert_array_equal + +class TestView: + def test_type(self): + x = np.array([1, 2, 3]) + assert_(isinstance(x.view(np.matrix), np.matrix)) + + def test_keywords(self): + x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)]) + # We must be specific about the endianness here: + y = x.view(dtype=' Tuple[str, Callable]: + if gui in registered: + return gui, registered[gui] + + if gui not in backends: + raise UnknownBackend(gui) + + if gui in aliases: + return get_inputhook_name_and_func(aliases[gui]) + + gui_mod = gui + if gui.startswith("qt"): + gui = set_qt_api(gui) + gui_mod = "qt" + + mod = importlib.import_module("IPython.terminal.pt_inputhooks." + gui_mod) + return gui, mod.inputhook diff --git a/shared/package-files/8c/8c1ef367737d84833d69d5bbaa54c0cb270b616f67a5a58a2d1cdaa1570b29c3/blob b/shared/package-files/8c/8c1ef367737d84833d69d5bbaa54c0cb270b616f67a5a58a2d1cdaa1570b29c3/blob new file mode 100644 index 0000000000000000000000000000000000000000..ec2f44ecd4dd7988bf337cd4fd9d4c64d40c2aa2 --- /dev/null +++ b/shared/package-files/8c/8c1ef367737d84833d69d5bbaa54c0cb270b616f67a5a58a2d1cdaa1570b29c3/blob @@ -0,0 +1,5 @@ +Wheel-Version: 1.0 +Generator: setuptools (75.8.2) +Root-Is-Purelib: true +Tag: py3-none-any + diff --git a/shared/package-files/8c/8c27202167a6de5c8f6b902384093d4ba7d6234ee1935ae0d2ff1ca03edb12d2/blob b/shared/package-files/8c/8c27202167a6de5c8f6b902384093d4ba7d6234ee1935ae0d2ff1ca03edb12d2/blob new file mode 100644 index 0000000000000000000000000000000000000000..359dbcd246168d3e81968341cf3412077ccbd3b7 --- /dev/null +++ b/shared/package-files/8c/8c27202167a6de5c8f6b902384093d4ba7d6234ee1935ae0d2ff1ca03edb12d2/blob @@ -0,0 +1,487 @@ +import pytest +import types +from unittest.mock import MagicMock + +from bs4 import ( + CSS, + BeautifulSoup, + ResultSet, +) + +from . import ( + SoupTest, + SOUP_SIEVE_PRESENT, +) + +if SOUP_SIEVE_PRESENT: + from soupsieve import SelectorSyntaxError + + +@pytest.mark.skipif(not SOUP_SIEVE_PRESENT, reason="Soup Sieve not installed") +class TestCSSSelectors(SoupTest): + """Test basic CSS selector functionality. + + This functionality is implemented in soupsieve, which has a much + more comprehensive test suite, so this is basically an extra check + that soupsieve works as expected. + """ + + HTML = """ + + + +The title + + + +Hello there. +
+
+

An H1

+

Some text

+

Some more text

+

An H2

+

Another

+Bob +

Another H2

+me + +span1a1 +span1a2 test + +span2a1 + + + +
+ +
+ + + + + + + + +

English

+

English UK

+

English US

+

French

+
+ + +""" + + def setup_method(self): + self.soup = BeautifulSoup(self.HTML, 'html.parser') + + def assert_selects(self, selector, expected_ids, **kwargs): + results = self.soup.select(selector, **kwargs) + assert isinstance(results, ResultSet) + el_ids = [el['id'] for el in results] + el_ids.sort() + expected_ids.sort() + assert expected_ids == el_ids, "Selector %s, expected [%s], got [%s]" % ( + selector, ', '.join(expected_ids), ', '.join(el_ids) + ) + + assertSelect = assert_selects + + def assert_select_multiple(self, *tests): + for selector, expected_ids in tests: + self.assert_selects(selector, expected_ids) + + def test_precompiled(self): + sel = self.soup.css.compile('div') + + els = self.soup.select(sel) + assert len(els) == 4 + for div in els: + assert div.name == 'div' + + el = self.soup.select_one(sel) + assert 'main' == el['id'] + + def test_one_tag_one(self): + els = self.soup.select('title') + assert len(els) == 1 + assert els[0].name == 'title' + assert els[0].contents == ['The title'] + + def test_one_tag_many(self): + els = self.soup.select('div') + assert len(els) == 4 + for div in els: + assert div.name == 'div' + + el = self.soup.select_one('div') + assert 'main' == el['id'] + + def test_select_one_returns_none_if_no_match(self): + match = self.soup.select_one('nonexistenttag') + assert None == match + + + def test_tag_in_tag_one(self): + els = self.soup.select('div div') + self.assert_selects('div div', ['inner', 'data1']) + + def test_tag_in_tag_many(self): + for selector in ('html div', 'html body div', 'body div'): + self.assert_selects(selector, ['data1', 'main', 'inner', 'footer']) + + + def test_limit(self): + self.assert_selects('html div', ['main'], limit=1) + self.assert_selects('html body div', ['inner', 'main'], limit=2) + self.assert_selects('body div', ['data1', 'main', 'inner', 'footer'], + limit=10) + + def test_tag_no_match(self): + assert len(self.soup.select('del')) == 0 + + def test_invalid_tag(self): + with pytest.raises(SelectorSyntaxError): + self.soup.select('tag%t') + + def test_select_dashed_tag_ids(self): + self.assert_selects('custom-dashed-tag', ['dash1', 'dash2']) + + def test_select_dashed_by_id(self): + dashed = self.soup.select('custom-dashed-tag[id=\"dash2\"]') + assert dashed[0].name == 'custom-dashed-tag' + assert dashed[0]['id'] == 'dash2' + + def test_dashed_tag_text(self): + assert self.soup.select('body > custom-dashed-tag')[0].text == 'Hello there.' + + def test_select_dashed_matches_find_all(self): + assert self.soup.select('custom-dashed-tag') == self.soup.find_all('custom-dashed-tag') + + def test_header_tags(self): + self.assert_select_multiple( + ('h1', ['header1']), + ('h2', ['header2', 'header3']), + ) + + def test_class_one(self): + for selector in ('.onep', 'p.onep', 'html p.onep'): + els = self.soup.select(selector) + assert len(els) == 1 + assert els[0].name == 'p' + assert els[0]['class'] == ['onep'] + + def test_class_mismatched_tag(self): + els = self.soup.select('div.onep') + assert len(els) == 0 + + def test_one_id(self): + for selector in ('div#inner', '#inner', 'div div#inner'): + self.assert_selects(selector, ['inner']) + + def test_bad_id(self): + els = self.soup.select('#doesnotexist') + assert len(els) == 0 + + def test_items_in_id(self): + els = self.soup.select('div#inner p') + assert len(els) == 3 + for el in els: + assert el.name == 'p' + assert els[1]['class'] == ['onep'] + assert not els[0].has_attr('class') + + def test_a_bunch_of_emptys(self): + for selector in ('div#main del', 'div#main div.oops', 'div div#main'): + assert len(self.soup.select(selector)) == 0 + + def test_multi_class_support(self): + for selector in ('.class1', 'p.class1', '.class2', 'p.class2', + '.class3', 'p.class3', 'html p.class2', 'div#inner .class2'): + self.assert_selects(selector, ['pmulti']) + + def test_multi_class_selection(self): + for selector in ('.class1.class3', '.class3.class2', + '.class1.class2.class3'): + self.assert_selects(selector, ['pmulti']) + + def test_child_selector(self): + self.assert_selects('.s1 > a', ['s1a1', 's1a2']) + self.assert_selects('.s1 > a span', ['s1a2s1']) + + def test_child_selector_id(self): + self.assert_selects('.s1 > a#s1a2 span', ['s1a2s1']) + + def test_attribute_equals(self): + self.assert_select_multiple( + ('p[class="onep"]', ['p1']), + ('p[id="p1"]', ['p1']), + ('[class="onep"]', ['p1']), + ('[id="p1"]', ['p1']), + ('link[rel="stylesheet"]', ['l1']), + ('link[type="text/css"]', ['l1']), + ('link[href="blah.css"]', ['l1']), + ('link[href="no-blah.css"]', []), + ('[rel="stylesheet"]', ['l1']), + ('[type="text/css"]', ['l1']), + ('[href="blah.css"]', ['l1']), + ('[href="no-blah.css"]', []), + ('p[href="no-blah.css"]', []), + ('[href="no-blah.css"]', []), + ) + + def test_attribute_tilde(self): + self.assert_select_multiple( + ('p[class~="class1"]', ['pmulti']), + ('p[class~="class2"]', ['pmulti']), + ('p[class~="class3"]', ['pmulti']), + ('[class~="class1"]', ['pmulti']), + ('[class~="class2"]', ['pmulti']), + ('[class~="class3"]', ['pmulti']), + ('a[rel~="friend"]', ['bob']), + ('a[rel~="met"]', ['bob']), + ('[rel~="friend"]', ['bob']), + ('[rel~="met"]', ['bob']), + ) + + def test_attribute_startswith(self): + self.assert_select_multiple( + ('[rel^="style"]', ['l1']), + ('link[rel^="style"]', ['l1']), + ('notlink[rel^="notstyle"]', []), + ('[rel^="notstyle"]', []), + ('link[rel^="notstyle"]', []), + ('link[href^="bla"]', ['l1']), + ('a[href^="http://"]', ['bob', 'me']), + ('[href^="http://"]', ['bob', 'me']), + ('[id^="p"]', ['pmulti', 'p1']), + ('[id^="m"]', ['me', 'main']), + ('div[id^="m"]', ['main']), + ('a[id^="m"]', ['me']), + ('div[data-tag^="dashed"]', ['data1']) + ) + + def test_attribute_endswith(self): + self.assert_select_multiple( + ('[href$=".css"]', ['l1']), + ('link[href$=".css"]', ['l1']), + ('link[id$="1"]', ['l1']), + ('[id$="1"]', ['data1', 'l1', 'p1', 'header1', 's1a1', 's2a1', 's1a2s1', 'dash1']), + ('div[id$="1"]', ['data1']), + ('[id$="noending"]', []), + ) + + def test_attribute_contains(self): + self.assert_select_multiple( + # From test_attribute_startswith + ('[rel*="style"]', ['l1']), + ('link[rel*="style"]', ['l1']), + ('notlink[rel*="notstyle"]', []), + ('[rel*="notstyle"]', []), + ('link[rel*="notstyle"]', []), + ('link[href*="bla"]', ['l1']), + ('[href*="http://"]', ['bob', 'me']), + ('[id*="p"]', ['pmulti', 'p1']), + ('div[id*="m"]', ['main']), + ('a[id*="m"]', ['me']), + # From test_attribute_endswith + ('[href*=".css"]', ['l1']), + ('link[href*=".css"]', ['l1']), + ('link[id*="1"]', ['l1']), + ('[id*="1"]', ['data1', 'l1', 'p1', 'header1', 's1a1', 's1a2', 's2a1', 's1a2s1', 'dash1']), + ('div[id*="1"]', ['data1']), + ('[id*="noending"]', []), + # New for this test + ('[href*="."]', ['bob', 'me', 'l1']), + ('a[href*="."]', ['bob', 'me']), + ('link[href*="."]', ['l1']), + ('div[id*="n"]', ['main', 'inner']), + ('div[id*="nn"]', ['inner']), + ('div[data-tag*="edval"]', ['data1']) + ) + + def test_attribute_exact_or_hypen(self): + self.assert_select_multiple( + ('p[lang|="en"]', ['lang-en', 'lang-en-gb', 'lang-en-us']), + ('[lang|="en"]', ['lang-en', 'lang-en-gb', 'lang-en-us']), + ('p[lang|="fr"]', ['lang-fr']), + ('p[lang|="gb"]', []), + ) + + def test_attribute_exists(self): + self.assert_select_multiple( + ('[rel]', ['l1', 'bob', 'me']), + ('link[rel]', ['l1']), + ('a[rel]', ['bob', 'me']), + ('[lang]', ['lang-en', 'lang-en-gb', 'lang-en-us', 'lang-fr']), + ('p[class]', ['p1', 'pmulti']), + ('[blah]', []), + ('p[blah]', []), + ('div[data-tag]', ['data1']) + ) + + def test_quoted_space_in_selector_name(self): + html = """
nope
+
yes
+ """ + soup = BeautifulSoup(html, 'html.parser') + [chosen] = soup.select('div[style="display: right"]') + assert "yes" == chosen.string + + def test_unsupported_pseudoclass(self): + with pytest.raises(NotImplementedError): + self.soup.select("a:no-such-pseudoclass") + + with pytest.raises(SelectorSyntaxError): + self.soup.select("a:nth-of-type(a)") + + def test_nth_of_type(self): + # Try to select first paragraph + els = self.soup.select('div#inner p:nth-of-type(1)') + assert len(els) == 1 + assert els[0].string == 'Some text' + + # Try to select third paragraph + els = self.soup.select('div#inner p:nth-of-type(3)') + assert len(els) == 1 + assert els[0].string == 'Another' + + # Try to select (non-existent!) fourth paragraph + els = self.soup.select('div#inner p:nth-of-type(4)') + assert len(els) == 0 + + # Zero will select no tags. + els = self.soup.select('div p:nth-of-type(0)') + assert len(els) == 0 + + def test_nth_of_type_direct_descendant(self): + els = self.soup.select('div#inner > p:nth-of-type(1)') + assert len(els) == 1 + assert els[0].string == 'Some text' + + def test_id_child_selector_nth_of_type(self): + self.assert_selects('#inner > p:nth-of-type(2)', ['p1']) + + def test_select_on_element(self): + # Other tests operate on the tree; this operates on an element + # within the tree. + inner = self.soup.find("div", id="main") + selected = inner.select("div") + # The
tag was selected. The