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mode 100644 index 0000000000000000000000000000000000000000..958eee50c9a6a630844801fe190562b3f8d3477c Binary files /dev/null and b/shared/package-files/56/561d36b5dc82ca96bc35be10cdd5619c30225c43b6590adcc1cbce7430c5179c/blob differ diff --git a/shared/package-files/56/561ec7f24a961f85fc6b7557d976765737bdb2bc096af90d1c0671749533fdb9/blob b/shared/package-files/56/561ec7f24a961f85fc6b7557d976765737bdb2bc096af90d1c0671749533fdb9/blob new file mode 100644 index 0000000000000000000000000000000000000000..8a92fd5135d5a3f4b05b3a71d1089e15d031fd59 --- /dev/null +++ b/shared/package-files/56/561ec7f24a961f85fc6b7557d976765737bdb2bc096af90d1c0671749533fdb9/blob @@ -0,0 +1,14 @@ +""" +Render to qt from agg +""" +from .. import backends + +backends._QT_FORCE_QT5_BINDING = True +from .backend_qtagg import ( # noqa: F401, E402 # pylint: disable=W0611 + _BackendQTAgg, FigureCanvasQTAgg, FigureManagerQT, NavigationToolbar2QT, + FigureCanvasAgg, FigureCanvasQT) + + +@_BackendQTAgg.export +class _BackendQT5Agg(_BackendQTAgg): + pass diff --git a/shared/package-files/56/56200dff83bb581b078ee68656999e720a8916f51ea86e52ea7123753ac149fd/blob b/shared/package-files/56/56200dff83bb581b078ee68656999e720a8916f51ea86e52ea7123753ac149fd/blob new file mode 100644 index 0000000000000000000000000000000000000000..e266fd2b7bcab18476d959b70fdcfb8c82b70675 --- /dev/null +++ b/shared/package-files/56/56200dff83bb581b078ee68656999e720a8916f51ea86e52ea7123753ac149fd/blob @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:56200dff83bb581b078ee68656999e720a8916f51ea86e52ea7123753ac149fd +size 342392 diff --git a/shared/package-files/56/562173cf3e5a16d3dc69ef89fd17b99d9aefbd1789fc63443d84f01b855ef8cc/blob b/shared/package-files/56/562173cf3e5a16d3dc69ef89fd17b99d9aefbd1789fc63443d84f01b855ef8cc/blob new file mode 100644 index 0000000000000000000000000000000000000000..58df5f06892d8e4e784d4d59a197dda98b40c1dc --- /dev/null +++ b/shared/package-files/56/562173cf3e5a16d3dc69ef89fd17b99d9aefbd1789fc63443d84f01b855ef8cc/blob @@ -0,0 +1,47 @@ +#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::_reshape_copy(Tensor self, SymInt[] size) -> Tensor +inline at::Tensor _reshape_copy(const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::_reshape_copy::call(self, c10::fromIntArrayRefSlow(size)); +} +namespace symint { + template ::value>> + at::Tensor _reshape_copy(const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::_reshape_copy::call(self, c10::fromIntArrayRefSlow(size)); + } +} + +// aten::_reshape_copy(Tensor self, SymInt[] size) -> Tensor +inline at::Tensor _reshape_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::_reshape_copy::call(self, size); +} +namespace symint { + template ::value>> + at::Tensor _reshape_copy(const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::_reshape_copy::call(self, size); + } +} + +} diff --git a/shared/package-files/56/5621b7f0f809b08853df1680aee82fc344cf3fa556a0fd95263ec51085b2a648/blob b/shared/package-files/56/5621b7f0f809b08853df1680aee82fc344cf3fa556a0fd95263ec51085b2a648/blob new file mode 100644 index 0000000000000000000000000000000000000000..93ce3be58fbea2a736072cddb0dc5d6454395cc2 --- /dev/null +++ b/shared/package-files/56/5621b7f0f809b08853df1680aee82fc344cf3fa556a0fd95263ec51085b2a648/blob @@ -0,0 +1,65 @@ +from __future__ import annotations + +import os +import sys +from contextlib import suppress +from errno import ENOSYS +from typing import cast + +from ._api import BaseFileLock +from ._util import ensure_directory_exists + +#: a flag to indicate if the fcntl API is available +has_fcntl = False +if sys.platform == "win32": # pragma: win32 cover + + class UnixFileLock(BaseFileLock): + """Uses the :func:`fcntl.flock` to hard lock the lock file on unix systems.""" + + def _acquire(self) -> None: + raise NotImplementedError + + def _release(self) -> None: + raise NotImplementedError + +else: # pragma: win32 no cover + try: + import fcntl + except ImportError: + pass + else: + has_fcntl = True + + class UnixFileLock(BaseFileLock): + """Uses the :func:`fcntl.flock` to hard lock the lock file on unix systems.""" + + def _acquire(self) -> None: + ensure_directory_exists(self.lock_file) + open_flags = os.O_RDWR | os.O_CREAT | os.O_TRUNC + fd = os.open(self.lock_file, open_flags, self._context.mode) + with suppress(PermissionError): # This locked is not owned by this UID + os.fchmod(fd, self._context.mode) + try: + fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) + except OSError as exception: + os.close(fd) + if exception.errno == ENOSYS: # NotImplemented error + msg = "FileSystem does not appear to support flock; user SoftFileLock instead" + raise NotImplementedError(msg) from exception + else: + self._context.lock_file_fd = fd + + def _release(self) -> None: + # Do not remove the lockfile: + # https://github.com/tox-dev/py-filelock/issues/31 + # https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition + fd = cast(int, self._context.lock_file_fd) + self._context.lock_file_fd = None + fcntl.flock(fd, fcntl.LOCK_UN) + os.close(fd) + + +__all__ = [ + "has_fcntl", + "UnixFileLock", +] diff --git a/shared/package-files/56/56243a12c4fe44781e453d01d2388a4dfbcfe0107b49a7010b64656cd569a966/blob b/shared/package-files/56/56243a12c4fe44781e453d01d2388a4dfbcfe0107b49a7010b64656cd569a966/blob new file mode 100644 index 0000000000000000000000000000000000000000..091f323d49c2d394fd29a23cb71e1040a0bda182 --- /dev/null +++ b/shared/package-files/56/56243a12c4fe44781e453d01d2388a4dfbcfe0107b49a7010b64656cd569a966/blob @@ -0,0 +1,1205 @@ +""" +Multivariate Conditional and Unconditional Kernel Regression +with Mixed Data Types + +References +---------- +[1] Racine, J., Li, Q. Nonparametric econometrics: theory and practice. + Princeton University Press. (2007) +[2] Racine, Jeff. "Nonparametric Econometrics: A Primer," Foundation + and Trends in Econometrics: Vol 3: No 1, pp1-88. (2008) + http://dx.doi.org/10.1561/0800000009 +[3] Racine, J., Li, Q. "Nonparametric Estimation of Distributions + with Categorical and Continuous Data." Working Paper. (2000) +[4] Racine, J. Li, Q. "Kernel Estimation of Multivariate Conditional + Distributions Annals of Economics and Finance 5, 211-235 (2004) +[5] Liu, R., Yang, L. "Kernel estimation of multivariate + cumulative distribution function." + Journal of Nonparametric Statistics (2008) +[6] Li, R., Ju, G. "Nonparametric Estimation of Multivariate CDF + with Categorical and Continuous Data." Working Paper +[7] Li, Q., Racine, J. "Cross-validated local linear nonparametric + regression" Statistica Sinica 14(2004), pp. 485-512 +[8] Racine, J.: "Consistent Significance Testing for Nonparametric + Regression" Journal of Business & Economics Statistics +[9] Racine, J., Hart, J., Li, Q., "Testing the Significance of + Categorical Predictor Variables in Nonparametric Regression + Models", 2006, Econometric Reviews 25, 523-544 + +""" + +from statsmodels.compat.pandas import deprecate_kwarg + +# TODO: make default behavior efficient=True above a certain n_obs +import copy +import warnings + +import numpy as np +from scipy import optimize +from scipy.stats.mstats import mquantiles + +from ._kernel_base import ( + EstimatorSettings, + GenericKDE, + LeaveOneOut, + _adjust_shape, + _compute_min_std_IQR, + _get_type_pos, + gpke, + initialize_generator, + kernel_func, +) + +__all__ = ["KernelCensoredReg", "KernelReg"] + + +class KernelReg(GenericKDE): + """ + Nonparametric kernel regression class + + Calculates the conditional mean ``E[y|X]`` where ``y = g(X) + e``. + Note that the "local constant" type of regression provided here is also + known as Nadaraya-Watson kernel regression; "local linear" is an extension + of that which suffers less from bias issues at the edge of the support. Note + that specifying a custom kernel works only with "local linear" kernel + regression. For example, a custom ``tricube`` kernel yields LOESS regression. + + Parameters + ---------- + endog : array_like + This is the dependent variable. + exog : array_like + The training data for the independent variable(s) + Each element in the list is a separate variable + var_type : str + The type of the variables, one character per variable: + + - c: continuous + - u: unordered (discrete) + - o: ordered (discrete) + + reg_type : {'lc', 'll'}, optional + Type of regression estimator. 'lc' means local constant and + 'll' local Linear estimator. Default is 'll' + bw : str or array_like, optional + Either a user-specified bandwidth or the method for bandwidth + selection. If a string, valid values are 'cv_ls' (least-squares + cross-validation) and 'aic' (AIC Hurvich bandwidth estimation). + Default is 'cv_ls'. User specified bandwidth must have as many + entries as the number of variables. + ckertype : str, optional + The kernel used for the continuous variables. + okertype : str, optional + The kernel used for the ordered discrete variables. + ukertype : str, optional + The kernel used for the unordered discrete variables. + defaults : EstimatorSettings instance, optional + The default values for the efficient bandwidth estimation. + rng : int, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional + If `rng` is None, the legacy global (singleton) ``RandomState`` + provided by ``numpy.random`` is used; this behavior is + deprecated and will change to creating a new ``Generator`` + using fresh entropy from the operating system in a future + release. 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. + + .. deprecated:: 0.15.0 + + In release 0.17.0 or after January 2028, whichever comes sooner, + using None will initialize a new numpy.random.default_rng using + system entropy. + + Attributes + ---------- + bw : ndarray + The bandwidth parameters. + """ + + @deprecate_kwarg("seed", "rng") + def __init__( + self, + endog, + exog, + var_type, + reg_type="ll", + bw="cv_ls", + ckertype="gaussian", + okertype="wangryzin", + ukertype="aitchisonaitken", + defaults=None, + *, + rng=None, + ): + self.var_type = var_type + self.data_type = var_type + self.reg_type = reg_type + self.ckertype = ckertype + self.okertype = okertype + self.ukertype = ukertype + if not ( + self.ckertype in kernel_func + and self.ukertype in kernel_func + and self.okertype in kernel_func + ): + raise ValueError( + "user specified kernel must be a supported " + "kernel from statsmodels.nonparametric.kernels." + ) + + self.k_vars = len(self.var_type) + self.endog = _adjust_shape(endog, 1) + self.exog = _adjust_shape(exog, self.k_vars) + self.data = np.column_stack((self.endog, self.exog)) + self.nobs = np.shape(self.exog)[0] + self.est = dict(lc=self._est_loc_constant, ll=self._est_loc_linear) + defaults = EstimatorSettings() if defaults is None else defaults + self._set_defaults(defaults) + self.seed = rng + self.rng = rng + self._generator = initialize_generator(rng) + if not isinstance(bw, str): + bw = np.asarray(bw) + if len(bw) != self.k_vars: + raise ValueError( + "bw must have the same dimension as the number of variables." + ) + if not self.efficient: + self.bw = self._compute_reg_bw(bw) + else: + self.bw = self._compute_efficient(bw) + + def _compute_reg_bw(self, bw): + if not isinstance(bw, str): + self._bw_method = "user-specified" + return np.asarray(bw) + else: + # The user specified a bandwidth selection method e.g., 'cv_ls' + self._bw_method = bw + # Workaround to avoid instance methods in __dict__ + if bw == "cv_ls": + res = self.cv_loo + else: # bw == 'aic' + res = self.aic_hurvich + X = np.std(self.exog, axis=0) + h0 = 1.06 * X * self.nobs ** (-1.0 / (4 + np.size(self.exog, axis=1))) + + func = self.est[self.reg_type] + bw_estimated = optimize.fmin( + res, x0=h0, args=(func,), maxiter=1e3, maxfun=1e3, disp=0 + ) + return bw_estimated + + def _est_loc_linear(self, bw, endog, exog, data_predict): + """ + Local linear estimator of g(x) in the regression ``y = g(x) + e`` + + Parameters + ---------- + bw : array_like + Vector of bandwidth value(s). + endog : 1D array_like + The dependent variable. + exog : 1D or 2D array_like + The independent variable(s). + data_predict : 1D array_like of length K, where K is the number of variables. + The point at which the conditional mean is estimated. + + Returns + ------- + mean : ndarray + The value of the conditional mean at `data_predict`. + mfx : ndarray + The marginal effects. + + Notes + ----- + See p. 81 in [1] and p.38 in [2] for the formulas. + Unlike other methods, this one requires that `data_predict` be 1D. + """ + nobs, k_vars = exog.shape + ker = gpke( + bw, + data=exog, + data_predict=data_predict, + var_type=self.var_type, + ckertype=self.ckertype, + ukertype=self.ukertype, + okertype=self.okertype, + tosum=False, + ) / float(nobs) + # Create the matrix on p.492 in [7], after the multiplication w/ K_h,ij + # See also p. 38 in [2] + # ix_cont = np.arange(self.k_vars) # Use all vars instead of continuous only + # Note: because ix_cont was defined here such that it selected all + # columns, I removed the indexing with it from exog/data_predict. + + # Convert ker to a 2-D array to make matrix operations below work + ker = ker[:, np.newaxis] + + M12 = exog - data_predict + M22 = np.dot(M12.T, M12 * ker) + M12 = (M12 * ker).sum(axis=0) + M = np.empty((k_vars + 1, k_vars + 1)) + M[0, 0] = ker.sum() + M[0, 1:] = M12 + M[1:, 0] = M12 + M[1:, 1:] = M22 + + ker_endog = ker * endog + V = np.empty((k_vars + 1, 1)) + V[0, 0] = ker_endog.sum() + V[1:, 0] = ((exog - data_predict) * ker_endog).sum(axis=0) + + mean_mfx = np.dot(np.linalg.pinv(M), V) + mean = mean_mfx[0] + mfx = mean_mfx[1:, :] + return mean, mfx + + def _est_loc_constant(self, bw, endog, exog, data_predict): + """ + Local constant estimator of g(x) in the regression + y = g(x) + e + + Parameters + ---------- + bw : array_like + Array of bandwidth value(s). + endog : 1D array_like + The dependent variable. + exog : 1D or 2D array_like + The independent variable(s). + data_predict : 1D or 2D array_like + The point(s) at which the conditional mean is estimated. + + Returns + ------- + G : ndarray + The value of the conditional mean at `data_predict`. + B_x : ndarray + The marginal effects. + """ + ker_x = gpke( + bw, + data=exog, + data_predict=data_predict, + var_type=self.var_type, + ckertype=self.ckertype, + ukertype=self.ukertype, + okertype=self.okertype, + tosum=False, + ) + ker_x = np.reshape(ker_x, np.shape(endog)) + G_numer = (ker_x * endog).sum(axis=0) + G_denom = ker_x.sum(axis=0) + G = G_numer / G_denom + nobs = exog.shape[0] + f_x = G_denom / float(nobs) + ker_xc = gpke( + bw, + data=exog, + data_predict=data_predict, + var_type=self.var_type, + ckertype="d_gaussian", + # okertype='wangryzin_reg', + tosum=False, + ) + + ker_xc = ker_xc[:, np.newaxis] + d_mx = -(endog * ker_xc).sum(axis=0) / float(nobs) # * np.prod(bw[:, ix_cont])) + d_fx = -ker_xc.sum(axis=0) / float(nobs) # * np.prod(bw[:, ix_cont])) + B_x = d_mx / f_x - G * d_fx / f_x + B_x = (G_numer * d_fx - G_denom * d_mx) / (G_denom**2) + # B_x = (f_x * d_mx - m_x * d_fx) / (f_x ** 2) + return G, B_x + + def aic_hurvich(self, bw, func=None): + """ + Computes the AIC Hurvich criteria for the estimation of the bandwidth + + Parameters + ---------- + bw : array_like + Vector of bandwidth value(s) at which to evaluate the criterion. + func : callable, optional + Unused here, needed in signature because it's used in `cv_loo`. + + Returns + ------- + aic : ndarray + The value of the AIC Hurvich criterion. + + References + ---------- + See ch.2 in [1] and p.35 in [2]. + """ + H = np.empty((self.nobs, self.nobs)) + for j in range(self.nobs): + H[:, j] = gpke( + bw, + data=self.exog, + data_predict=self.exog[j, :], + ckertype=self.ckertype, + ukertype=self.ukertype, + okertype=self.okertype, + var_type=self.var_type, + tosum=False, + ) + + denom = H.sum(axis=1) + H = H / denom + gx = KernelReg( + endog=self.endog, + exog=self.exog, + var_type=self.var_type, + reg_type=self.reg_type, + bw=bw, + defaults=EstimatorSettings(efficient=False), + ).fit()[0] + gx = np.reshape(gx, (self.nobs, 1)) + sigma = ((self.endog - gx) ** 2).sum(axis=0) / float(self.nobs) + + frac = (1 + np.trace(H) / float(self.nobs)) / ( + 1 - (np.trace(H) + 2) / float(self.nobs) + ) + # siga = np.dot(self.endog.T, (I - H).T) + # sigb = np.dot((I - H), self.endog) + # sigma = np.dot(siga, sigb) / float(self.nobs) + aic = np.log(sigma) + frac + return aic + + def cv_loo(self, bw, func): + r""" + The cross-validation function with leave-one-out estimator + + Parameters + ---------- + bw : array_like + Vector of bandwidth values. + func : callable + Returns the estimator of g(x). Can be either ``_est_loc_constant`` + (local constant) or ``_est_loc_linear`` (local_linear). + + Returns + ------- + L : float + The value of the CV function. + + Notes + ----- + Calculates the cross-validation least-squares function. This function + is minimized by compute_bw to calculate the optimal value of `bw`. + + For details see p.35 in [2] + + .. math:: CV(h)=n^{-1}\sum_{i=1}^{n}(Y_{i}-g_{-i}(X_{i}))^{2} + + where :math:`g_{-i}(X_{i})` is the leave-one-out estimator of g(X) + and :math:`h` is the vector of bandwidths + """ + LOO_X = LeaveOneOut(self.exog) + LOO_Y = LeaveOneOut(self.endog).__iter__() + L = 0 + for ii, X_not_i in enumerate(LOO_X): + Y = next(LOO_Y) + G = func(bw, endog=Y, exog=-X_not_i, data_predict=-self.exog[ii, :])[0] + L += (self.endog[ii] - G) ** 2 + + # Note: There might be a way to vectorize this. See p.72 in [1] + return L / self.nobs + + def r_squared(self): + r""" + Returns the R-Squared for the nonparametric regression + + Returns + ------- + r2 : float + The R-Squared statistic computed from the fitted mean. + + Notes + ----- + For more details see p.45 in [2] + The R-Squared is calculated by: + + .. math:: R^{2}=\frac{\left[\sum_{i=1}^{n} + (Y_{i}-\bar{y})(\hat{Y_{i}}-\bar{y}\right]^{2}}{\sum_{i=1}^{n} + (Y_{i}-\bar{y})^{2}\sum_{i=1}^{n}(\hat{Y_{i}}-\bar{y})^{2}}, + + where :math:`\hat{Y_{i}}` is the mean calculated in `fit` at the exog + points. + """ + Y = np.squeeze(self.endog) + Yhat = self.fit()[0] + Y_bar = np.mean(Yhat) + R2_numer = (((Y - Y_bar) * (Yhat - Y_bar)).sum()) ** 2 + R2_denom = ((Y - Y_bar) ** 2).sum(axis=0) * ((Yhat - Y_bar) ** 2).sum(axis=0) + return R2_numer / R2_denom + + def fit(self, data_predict=None): + """ + Returns the mean and marginal effects at the `data_predict` points + + Parameters + ---------- + data_predict : array_like, optional + Points at which to return the mean and marginal effects. If not + given, ``data_predict == exog``. + + Returns + ------- + mean : ndarray + The regression result for the mean (i.e., the actual curve). + mfx : ndarray + The marginal effects, i.e., the partial derivatives of the mean. + """ + func = self.est[self.reg_type] + if data_predict is None: + data_predict = self.exog + else: + data_predict = _adjust_shape(data_predict, self.k_vars) + + N_data_predict = np.shape(data_predict)[0] + mean = np.empty((N_data_predict,)) + mfx = np.empty((N_data_predict, self.k_vars)) + for i in range(N_data_predict): + mean_mfx = func( + self.bw, self.endog, self.exog, data_predict=data_predict[i, :] + ) + mean[i] = np.squeeze(mean_mfx[0]) + mfx_c = np.squeeze(mean_mfx[1]) + mfx[i, :] = mfx_c + + return mean, mfx + + def sig_test(self, var_pos, nboot=50, nested_res=25, pivot=False): + """ + Significance test for the variables in the regression + + Parameters + ---------- + var_pos : array_like of int + The position of the variable in exog to be tested. + nboot : int, optional + Number of bootstrap samples used to determine the distribution + of the test statistic in a finite sample. Default is 50. + nested_res : int, optional + Number of nested resamples used to calculate lambda when + `pivot` is True. Default is 25. + pivot : bool, optional + Pivot the test statistic by dividing by its standard error. + Significantly increases computational time, but pivot + statistics have more desirable properties. Default is False. + + Returns + ------- + sig : str + The level of significance: + + - `*` : at 90% confidence level + - `**` : at 95% confidence level + - `***` : at 99% confidence level + - "Not Significant" : if not significant + """ + var_pos = np.asarray(var_pos) + ix_cont, ix_ord, ix_unord = _get_type_pos(self.var_type) + if np.any(ix_cont[var_pos]): + if np.any(ix_ord[var_pos]) or np.any(ix_unord[var_pos]): + raise ValueError("Discrete variable in hypothesis. Must be continuous") + + Sig = TestRegCoefC(self, var_pos, nboot, nested_res, pivot, rng=self.rng) + else: + Sig = TestRegCoefD(self, var_pos, nboot, rng=self.rng) + + return Sig.sig + + def __repr__(self): + """Provide something sane to print""" + rpr = "KernelReg instance\n" + rpr += "Number of variables: k_vars = " + str(self.k_vars) + "\n" + rpr += "Number of samples: N = " + str(self.nobs) + "\n" + rpr += "Variable types: " + self.var_type + "\n" + rpr += "BW selection method: " + self._bw_method + "\n" + rpr += "Estimator type: " + self.reg_type + "\n" + return rpr + + def _get_class_vars_type(self): + """Helper method to be able to pass needed vars to _compute_subset""" + class_type = "KernelReg" + class_vars = (self.var_type, self.k_vars, self.reg_type) + return class_type, class_vars + + def _compute_dispersion(self, data): + """ + Computes the measure of dispersion + + The minimum of the standard deviation and interquartile range / 1.349 + + Parameters + ---------- + data : ndarray + The data array, with the dependent variable in the first column + and the independent variables in the remaining columns. + + Returns + ------- + ndarray + The minimum of the standard deviation and IQR / 1.349 for each + independent variable. + + References + ---------- + See the user guide for the np package in R. + In the notes on bwscaling option in npreg, npudens, npcdens there is + a discussion on the measure of dispersion + """ + data = data[:, 1:] + return _compute_min_std_IQR(data) + + +class KernelCensoredReg(KernelReg): + """ + Nonparametric censored regression + + Calculates the conditional mean ``E[y|X]`` where ``y = g(X) + e``, + where y is left-censored. Left censored variable Y is defined as + ``Y = min {Y', L}`` where ``L`` is the value at which ``Y`` is censored + and ``Y'`` is the true value of the variable. + + Parameters + ---------- + endog : array_like + This is the dependent variable. + exog : array_like + The training data for the independent variable(s) + Each element in the list is a separate variable + var_type : str + The type of the variables, one character per variable: + + - c: Continuous + - u: Unordered (Discrete) + - o: Ordered (Discrete) + + reg_type : str + Type of regression estimator + + - lc: Local Constant Estimator + - ll: Local Linear Estimator + + bw : str or array_like, optional + Either a user-specified bandwidth or + the method for bandwidth selection. + + - cv_ls: cross-validation least squares + - aic: AIC Hurvich Estimator + + ckertype : str, optional + The kernel used for the continuous variables. + okertype : str, optional + The kernel used for the ordered discrete variables. + ukertype : str, optional + The kernel used for the unordered discrete variables. + censor_val : float, optional + Value at which the dependent variable is censored. Default is 0. + defaults : EstimatorSettings instance, optional + The default values for the efficient bandwidth estimation + rng : int, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional + If `rng` is None, the legacy global (singleton) ``RandomState`` + provided by ``numpy.random`` is used; this behavior is + deprecated and will change to creating a new ``Generator`` + using fresh entropy from the operating system in a future + release. 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. + + .. deprecated:: 0.15.0 + + In release 0.17.0 or after January 2028, whichever comes sooner, + using None will initialize a new numpy.random.default_rng using + system entropy. + + Attributes + ---------- + bw : ndarray + The bandwidth parameters + """ + + @deprecate_kwarg("seed", "rng") + def __init__( + self, + endog, + exog, + var_type, + reg_type, + bw="cv_ls", + ckertype="gaussian", + ukertype="aitchison_aitken_reg", + okertype="wangryzin_reg", + censor_val=0, + defaults=None, + *, + rng=None, + ): + self.var_type = var_type + self.data_type = var_type + self.reg_type = reg_type + self.ckertype = ckertype + self.okertype = okertype + self.ukertype = ukertype + if not ( + self.ckertype in kernel_func + and self.ukertype in kernel_func + and self.okertype in kernel_func + ): + raise ValueError( + "user specified kernel must be a supported " + "kernel from statsmodels.nonparametric.kernels." + ) + + self.k_vars = len(self.var_type) + self.endog = _adjust_shape(endog, 1) + self.exog = _adjust_shape(exog, self.k_vars) + self.data = np.column_stack((self.endog, self.exog)) + self.nobs = np.shape(self.exog)[0] + self.est = dict(lc=self._est_loc_constant, ll=self._est_loc_linear) + self._generator = initialize_generator(rng) + defaults = EstimatorSettings() if defaults is None else defaults + self._set_defaults(defaults) + self.censor_val = censor_val + if self.censor_val is not None: + self.censored(censor_val) + else: + self.W_in = np.ones((self.nobs, 1)) + + if not self.efficient: + self.bw = self._compute_reg_bw(bw) + else: + self.bw = self._compute_efficient(bw) + + def censored(self, censor_val): + # see pp. 341-344 in [1] + self.d = (self.endog != censor_val) * 1.0 + ix = np.argsort(np.squeeze(self.endog)) + self.sortix = ix + self.sortix_rev = np.zeros(ix.shape, int) + self.sortix_rev[ix] = np.arange(len(ix)) + self.endog = np.squeeze(self.endog[ix]) + self.endog = _adjust_shape(self.endog, 1) + self.exog = np.squeeze(self.exog[ix]) + self.d = np.squeeze(self.d[ix]) + self.W_in = np.empty((self.nobs, 1)) + for i in range(1, self.nobs + 1): + P = 1 + for j in range(1, i): + P *= ((self.nobs - j) / (float(self.nobs) - j + 1)) ** self.d[j - 1] + self.W_in[i - 1, 0] = P * self.d[i - 1] / (float(self.nobs) - i + 1) + + def __repr__(self): + """Provide something sane to print""" + rpr = "KernelCensoredReg instance\n" + rpr += "Number of variables: k_vars = " + str(self.k_vars) + "\n" + rpr += "Number of samples: nobs = " + str(self.nobs) + "\n" + rpr += "Variable types: " + self.var_type + "\n" + rpr += "BW selection method: " + self._bw_method + "\n" + rpr += "Estimator type: " + self.reg_type + "\n" + return rpr + + def _est_loc_linear(self, bw, endog, exog, data_predict, W): + """ + Local linear estimator of g(x) in the regression ``y = g(x) + e`` + + Parameters + ---------- + bw : array_like + Vector of bandwidth value(s) + endog : 1D array_like + The dependent variable + exog : 1D or 2D array_like + The independent variable(s) + data_predict : 1D array_like of length K, where K is + the number of variables. The point at which + the conditional mean is estimated + W : array_like + The weights used to account for the censoring, as computed by + `censored`. + + Returns + ------- + mean : ndarray + The value of the conditional mean at data_predict + mfx : ndarray + The marginal effects. + + Notes + ----- + See p. 81 in [1] and p.38 in [2] for the formulas + Unlike other methods, this one requires that data_predict be 1D + """ + nobs, k_vars = exog.shape + ker = gpke( + bw, + data=exog, + data_predict=data_predict, + var_type=self.var_type, + ckertype=self.ckertype, + ukertype=self.ukertype, + okertype=self.okertype, + tosum=False, + ) + # Create the matrix on p.492 in [7], after the multiplication w/ K_h,ij + # See also p. 38 in [2] + + # Convert ker to a 2-D array to make matrix operations below work + ker = W * ker[:, np.newaxis] + + M12 = exog - data_predict + M22 = np.dot(M12.T, M12 * ker) + M12 = (M12 * ker).sum(axis=0) + M = np.empty((k_vars + 1, k_vars + 1)) + M[0, 0] = ker.sum() + M[0, 1:] = M12 + M[1:, 0] = M12 + M[1:, 1:] = M22 + + ker_endog = ker * endog + V = np.empty((k_vars + 1, 1)) + V[0, 0] = ker_endog.sum() + V[1:, 0] = ((exog - data_predict) * ker_endog).sum(axis=0) + + mean_mfx = np.dot(np.linalg.pinv(M), V) + mean = mean_mfx[0] + mfx = mean_mfx[1:, :] + return mean, mfx + + def cv_loo(self, bw, func): + r""" + The cross-validation function with leave-one-out + estimator + + Parameters + ---------- + bw : array_like + Vector of bandwidth values + func : callable + Returns the estimator of g(x). + Can be either ``_est_loc_constant`` (local constant) or + ``_est_loc_linear`` (local_linear). + + Returns + ------- + L : float + The value of the CV function + + Notes + ----- + Calculates the cross-validation least-squares + function. This function is minimized by compute_bw + to calculate the optimal value of bw + + For details see p.35 in [2] + + .. math:: CV(h)=n^{-1}\sum_{i=1}^{n}(Y_{i}-g_{-i}(X_{i}))^{2} + + where :math:`g_{-i}(X_{i})` is the leave-one-out estimator of g(X) + and :math:`h` is the vector of bandwidths + """ + LOO_X = LeaveOneOut(self.exog) + LOO_Y = LeaveOneOut(self.endog).__iter__() + LOO_W = LeaveOneOut(self.W_in).__iter__() + L = 0 + for ii, X_not_i in enumerate(LOO_X): + Y = next(LOO_Y) + w = next(LOO_W) + G = func(bw, endog=Y, exog=-X_not_i, data_predict=-self.exog[ii, :], W=w)[0] + L += (self.endog[ii] - G) ** 2 + + # Note: There might be a way to vectorize this. See p.72 in [1] + return L / self.nobs + + def fit(self, data_predict=None): + """ + Returns the mean and marginal effects at the `data_predict` points + + Parameters + ---------- + data_predict : array_like, optional + Points at which to return the mean and marginal effects. If not + given, ``data_predict == exog``. + + Returns + ------- + mean : ndarray + The regression result for the mean (i.e., the actual curve). + mfx : ndarray + The marginal effects, i.e., the partial derivatives of the mean. + """ + func = self.est[self.reg_type] + if data_predict is None: + data_predict = self.exog + else: + data_predict = _adjust_shape(data_predict, self.k_vars) + + N_data_predict = np.shape(data_predict)[0] + mean = np.empty((N_data_predict,)) + mfx = np.empty((N_data_predict, self.k_vars)) + for i in range(N_data_predict): + mean_mfx = func( + self.bw, + self.endog, + self.exog, + data_predict=data_predict[i, :], + W=self.W_in, + ) + mean[i] = np.squeeze(mean_mfx[0]) + mfx_c = np.squeeze(mean_mfx[1]) + mfx[i, :] = mfx_c + + return mean, mfx + + +class TestRegCoefC: + """ + Significance test for continuous variables in a nonparametric regression + + The null hypothesis is ``dE(Y|X)/dX_not_i = 0``, the alternative hypothesis + is ``dE(Y|X)/dX_not_i != 0``. + + Parameters + ---------- + model : KernelReg instance + This is the nonparametric regression model whose elements + are tested for significance. + test_vars : sequence of int + index of position of the continuous variables to be tested + for significance. e.g., (1,3,5) jointly tests variables at + position 1,3 and 5 for significance. + nboot : int, optional + Number of bootstrap samples used to determine the distribution + of the test statistic in a finite sample. Default is 400 + nested_res : int, optional + Number of nested resamples used to calculate lambda. Default is 400. + Must enable the pivot option + pivot : bool, optional + Pivot the test statistic by dividing by its standard error + Significantly increases computational time. But pivot statistics + have more desirable properties + (See references). Default is False. + rng : int, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional + If `rng` is None, the legacy global (singleton) ``RandomState`` + provided by ``numpy.random`` is used; this behavior is + deprecated and will change to creating a new ``Generator`` + using fresh entropy from the operating system in a future + release. 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. + + .. deprecated:: 0.15.0 + + In release 0.17.0 or after January 2028, whichever comes sooner, + using None will initialize a new numpy.random.default_rng using + system entropy. + + Attributes + ---------- + sig : str + The significance level of the variable(s) tested + "Not Significant": Not significant at the 90% confidence level + Fails to reject the null + "*": Significant at the 90% confidence level + "**": Significant at the 95% confidence level + "***": Significant at the 99% confidence level + + Notes + ----- + This class allows testing of joint hypothesis as long as all variables + are continuous. + + References + ---------- + Racine, J.: "Consistent Significance Testing for Nonparametric Regression" + Journal of Business & Economics Statistics. + + Chapter 12 in [1]. + """ + + # Significance of continuous vars in nonparametric regression + # Racine: Consistent Significance Testing for Nonparametric Regression + # Journal of Business & Economics Statistics + @deprecate_kwarg("seed", "rng") + def __init__( + self, model, test_vars, nboot=400, nested_res=400, pivot=False, rng=None + ): + self.nboot = nboot + self.nres = nested_res + self.test_vars = test_vars + self.model = model + self.bw = model.bw + self.var_type = model.var_type + self.k_vars = len(self.var_type) + self.endog = model.endog + self.exog = model.exog + self.gx = model.est[model.reg_type] + self.test_vars = test_vars + self.pivot = pivot + self._generator = initialize_generator(rng) + self.run() + + def run(self): + self.test_stat = self._compute_test_stat(self.endog, self.exog) + self.sig = self._compute_sig() + + def _compute_test_stat(self, Y, X): + """ + Computes the test statistic + + See p.371 in [8]. + """ + lam = self._compute_lambda(Y, X) + t = lam + if self.pivot: + se_lam = self._compute_se_lambda(Y, X) + t = lam / float(se_lam) + + return t + + def _compute_lambda(self, Y, X): + """Computes only lambda -- the main part of the test statistic""" + n = np.shape(X)[0] + Y = _adjust_shape(Y, 1) + X = _adjust_shape(X, self.k_vars) + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + category=FutureWarning, + ) + b = KernelReg( + Y, + X, + self.var_type, + self.model.reg_type, + self.bw, + defaults=EstimatorSettings(efficient=False), + ).fit()[1] + + b = b[:, self.test_vars] + b = np.reshape(b, (n, len(self.test_vars))) + # fct = np.std(b) # Pivot the statistic by dividing by SE + fct = 1.0 # Do not Pivot -- Bootstrapping works better if Pivot + lam = ((b / fct) ** 2).sum() / float(n) + return lam + + def _compute_se_lambda(self, Y, X): + """ + Calculates the SE of lambda by nested resampling + Used to pivot the statistic. + Bootstrapping works better with estimating pivotal statistics + but slows down computation significantly. + """ + n = np.shape(Y)[0] + lam = np.empty(shape=(self.nres,)) + for i in range(self.nres): + ind = self._integers(0, n, size=(n, 1)) + Y1 = Y[ind, 0] + X1 = X[ind, :] + lam[i] = self._compute_lambda(Y1, X1) + + se_lambda = np.std(lam) + return se_lambda + + def _compute_sig(self): + """ + Computes the significance value for the variable(s) tested + + The empirical distribution of the test statistic is obtained through + bootstrapping the sample. The null hypothesis is rejected if the test + statistic is larger than the 90, 95, 99 percentiles. + """ + t_dist = np.empty(shape=(self.nboot,)) + Y = self.endog + X = copy.deepcopy(self.exog) + n = np.shape(Y)[0] + + X[:, self.test_vars] = np.mean(X[:, self.test_vars], axis=0) + # Calculate the restricted mean. See p. 372 in [8] + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + category=FutureWarning, + ) + M = KernelReg( + Y, + X, + self.var_type, + self.model.reg_type, + self.bw, + defaults=EstimatorSettings(efficient=False), + ).fit()[0] + M = np.reshape(M, (n, 1)) + e = Y - M + e = e - np.mean(e) # recenter residuals + for i in range(self.nboot): + ind = self._integers(0, n, size=(n, 1)) + e_boot = e[ind, 0] + Y_boot = M + e_boot + t_dist[i] = self._compute_test_stat(Y_boot, self.exog) + + self.t_dist = t_dist + sig = "Not Significant" + if self.test_stat > mquantiles(t_dist, 0.9): + sig = "*" + if self.test_stat > mquantiles(t_dist, 0.95): + sig = "**" + if self.test_stat > mquantiles(t_dist, 0.99): + sig = "***" + + return sig + + def _integers(self, low: int, upper: int, size: tuple[int, int]): + if self._generator is None: + return np.random.randint(low, upper, size=size) + elif isinstance(self._generator, np.random.RandomState): + return self._generator.randint(low, upper, size=size) + else: + return self._generator.integers(low, upper, size=size) + + def _uniform(self, low: float, upper: float, size: tuple[int, int]): + if self._generator is None: + return np.random.uniform(low, upper, size=size) + return self._generator.uniform(low, upper, size=size) + + +class TestRegCoefD(TestRegCoefC): + """ + Significance test for the categorical variables in a nonparametric + regression + + Parameters + ---------- + model : Instance of KernelReg class + This is the nonparametric regression model whose elements + are tested for significance. + test_vars : sequence of int + index of position of the discrete variable to be tested + for significance. e.g., (3) tests variable at + position 3 for significance. + nboot : int, optional + Number of bootstrap samples used to determine the distribution + of the test statistic in a finite sample. Default is 400 + rng : int, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional + If `rng` is None, the legacy global (singleton) ``RandomState`` + provided by ``numpy.random`` is used; this behavior is + deprecated and will change to creating a new ``Generator`` + using fresh entropy from the operating system in a future + release. 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. + + .. deprecated:: 0.15.0 + + In release 0.17.0 or after January 2028, whichever comes sooner, + using None will initialize a new numpy.random.default_rng using + system entropy. + + Attributes + ---------- + sig : str + The significance level of the variable(s) tested + "Not Significant": Not significant at the 90% confidence level + Fails to reject the null + "*": Significant at the 90% confidence level + "**": Significant at the 95% confidence level + "***": Significant at the 99% confidence level + + Notes + ----- + This class currently does not allow joint hypothesis. + Only one variable can be tested at a time + + References + ---------- + See [9] and chapter 12 in [1]. + """ + + def _compute_test_stat(self, Y, X): + """Computes the test statistic""" + + dom_x = np.sort(np.unique(self.exog[:, self.test_vars])) + + n = np.shape(X)[0] + model = KernelReg( + Y, + X, + self.var_type, + self.model.reg_type, + self.bw, + defaults=EstimatorSettings(efficient=False), + rng=self._generator, + ) + X1 = copy.deepcopy(X) + X1[:, self.test_vars] = 0 + + m0 = model.fit(data_predict=X1)[0] + m0 = np.reshape(m0, (n, 1)) + zvec = np.zeros((n, 1)) + for i in dom_x[1:]: + X1[:, self.test_vars] = i + m1 = model.fit(data_predict=X1)[0] + m1 = np.reshape(m1, (n, 1)) + zvec += (m1 - m0) ** 2 + + avg = zvec.sum(axis=0) / float(n) + return avg + + def _compute_sig(self): + """Calculates the significance level of the variable tested""" + + m = self._est_cond_mean() + Y = self.endog + X = self.exog + n = np.shape(X)[0] + u = Y - m + u = u - np.mean(u) # center + fct1 = (1 - 5**0.5) / 2.0 + fct2 = (1 + 5**0.5) / 2.0 + u1 = fct1 * u + u2 = fct2 * u + r = fct2 / (5**0.5) + I_dist = np.empty((self.nboot, 1)) + for j in range(self.nboot): + u_boot = copy.deepcopy(u2) + + prob = self._uniform(0, 1, size=(n, 1)) + ind = prob < r + u_boot[ind] = u1[ind] + Y_boot = m + u_boot + I_dist[j] = self._compute_test_stat(Y_boot, X) + + sig = "Not Significant" + if self.test_stat > mquantiles(I_dist, 0.9): + sig = "*" + if self.test_stat > mquantiles(I_dist, 0.95): + sig = "**" + if self.test_stat > mquantiles(I_dist, 0.99): + sig = "***" + + return sig + + def _est_cond_mean(self): + """ + Calculates the expected conditional mean + m(X, Z=l) for all possible l + """ + self.dom_x = np.sort(np.unique(self.exog[:, self.test_vars])) + X = copy.deepcopy(self.exog) + m = 0 + for i in self.dom_x: + X[:, self.test_vars] = i + m += self.model.fit(data_predict=X)[0] + + m = m / float(len(self.dom_x)) + m = np.reshape(m, (np.shape(self.exog)[0], 1)) + return m diff --git a/shared/package-files/56/5625ccbe57902084adaae1cdd350a94d43d8c6ede3802e7b19a08e92e5d1b285/blob b/shared/package-files/56/5625ccbe57902084adaae1cdd350a94d43d8c6ede3802e7b19a08e92e5d1b285/blob new file mode 100644 index 0000000000000000000000000000000000000000..e140396d8cd73bdcc769dc79366ad70c3e302715 --- /dev/null +++ b/shared/package-files/56/5625ccbe57902084adaae1cdd350a94d43d8c6ede3802e7b19a08e92e5d1b285/blob @@ -0,0 +1,841 @@ +from .accumulationbounds import AccumBounds, AccumulationBounds # noqa: F401 +from .singularities import singularities +from sympy.core import Pow, S +from sympy.core.function import diff, expand_mul +from sympy.core.kind import NumberKind +from sympy.core.mod import Mod +from sympy.core.numbers import equal_valued +from sympy.core.relational import Relational +from sympy.core.symbol import Symbol, Dummy +from sympy.core.sympify import _sympify +from sympy.functions.elementary.complexes import Abs, im, re +from sympy.functions.elementary.exponential import exp, log +from sympy.functions.elementary.piecewise import Piecewise +from sympy.functions.elementary.trigonometric import ( + TrigonometricFunction, sin, cos, csc, sec) +from sympy.polys.polytools import degree, lcm_list +from sympy.sets.sets import (Interval, Intersection, FiniteSet, Union, + Complement) +from sympy.sets.fancysets import ImageSet +from sympy.utilities import filldedent +from sympy.utilities.iterables import iterable + + +def continuous_domain(f, symbol, domain): + """ + Returns the intervals in the given domain for which the function + is continuous. + This method is limited by the ability to determine the various + singularities and discontinuities of the given function. + + Parameters + ========== + + f : :py:class:`~.Expr` + The concerned function. + symbol : :py:class:`~.Symbol` + The variable for which the intervals are to be determined. + domain : :py:class:`~.Interval` + The domain over which the continuity of the symbol has to be checked. + + Examples + ======== + + >>> from sympy import Interval, Symbol, S, tan, log, pi, sqrt + >>> from sympy.calculus.util import continuous_domain + >>> x = Symbol('x') + >>> continuous_domain(1/x, x, S.Reals) + Union(Interval.open(-oo, 0), Interval.open(0, oo)) + >>> continuous_domain(tan(x), x, Interval(0, pi)) + Union(Interval.Ropen(0, pi/2), Interval.Lopen(pi/2, pi)) + >>> continuous_domain(sqrt(x - 2), x, Interval(-5, 5)) + Interval(2, 5) + >>> continuous_domain(log(2*x - 1), x, S.Reals) + Interval.open(1/2, oo) + + Returns + ======= + + :py:class:`~.Interval` + Union of all intervals where the function is continuous. + + Raises + ====== + + NotImplementedError + If the method to determine continuity of such a function + has not yet been developed. + + """ + from sympy.solvers.inequalities import solve_univariate_inequality + + if domain.is_subset(S.Reals): + constrained_interval = domain + for atom in f.atoms(Pow): + den = atom.exp.as_numer_denom()[1] + if den.is_even and den.is_nonzero: + constraint = solve_univariate_inequality(atom.base >= 0, + symbol).as_set() + constrained_interval = Intersection(constraint, + constrained_interval) + + for atom in f.atoms(log): + constraint = solve_univariate_inequality(atom.args[0] > 0, + symbol).as_set() + constrained_interval = Intersection(constraint, + constrained_interval) + + + return constrained_interval - singularities(f, symbol, domain) + + +def function_range(f, symbol, domain): + """ + Finds the range of a function in a given domain. + This method is limited by the ability to determine the singularities and + determine limits. + + Parameters + ========== + + f : :py:class:`~.Expr` + The concerned function. + symbol : :py:class:`~.Symbol` + The variable for which the range of function is to be determined. + domain : :py:class:`~.Interval` + The domain under which the range of the function has to be found. + + Examples + ======== + + >>> from sympy import Interval, Symbol, S, exp, log, pi, sqrt, sin, tan + >>> from sympy.calculus.util import function_range + >>> x = Symbol('x') + >>> function_range(sin(x), x, Interval(0, 2*pi)) + Interval(-1, 1) + >>> function_range(tan(x), x, Interval(-pi/2, pi/2)) + Interval(-oo, oo) + >>> function_range(1/x, x, S.Reals) + Union(Interval.open(-oo, 0), Interval.open(0, oo)) + >>> function_range(exp(x), x, S.Reals) + Interval.open(0, oo) + >>> function_range(log(x), x, S.Reals) + Interval(-oo, oo) + >>> function_range(sqrt(x), x, Interval(-5, 9)) + Interval(0, 3) + + Returns + ======= + + :py:class:`~.Interval` + Union of all ranges for all intervals under domain where function is + continuous. + + Raises + ====== + + NotImplementedError + If any of the intervals, in the given domain, for which function + is continuous are not finite or real, + OR if the critical points of the function on the domain cannot be found. + """ + + if domain is S.EmptySet: + return S.EmptySet + + period = periodicity(f, symbol) + if period == S.Zero: + # the expression is constant wrt symbol + return FiniteSet(f.expand()) + + from sympy.series.limits import limit + from sympy.solvers.solveset import solveset + + if period is not None: + if isinstance(domain, Interval): + if (domain.inf - domain.sup).is_infinite: + domain = Interval(0, period) + elif isinstance(domain, Union): + for sub_dom in domain.args: + if isinstance(sub_dom, Interval) and \ + ((sub_dom.inf - sub_dom.sup).is_infinite): + domain = Interval(0, period) + + intervals = continuous_domain(f, symbol, domain) + range_int = S.EmptySet + if isinstance(intervals,(Interval, FiniteSet)): + interval_iter = (intervals,) + + elif isinstance(intervals, Union): + interval_iter = intervals.args + + else: + raise NotImplementedError(filldedent(''' + Unable to find range for the given domain. + ''')) + + for interval in interval_iter: + if isinstance(interval, FiniteSet): + for singleton in interval: + if singleton in domain: + range_int += FiniteSet(f.subs(symbol, singleton)) + elif isinstance(interval, Interval): + vals = S.EmptySet + critical_points = S.EmptySet + critical_values = S.EmptySet + bounds = ((interval.left_open, interval.inf, '+'), + (interval.right_open, interval.sup, '-')) + + for is_open, limit_point, direction in bounds: + if is_open: + critical_values += FiniteSet(limit(f, symbol, limit_point, direction)) + vals += critical_values + + else: + vals += FiniteSet(f.subs(symbol, limit_point)) + + solution = solveset(f.diff(symbol), symbol, interval) + + if not iterable(solution): + raise NotImplementedError( + 'Unable to find critical points for {}'.format(f)) + if isinstance(solution, ImageSet): + raise NotImplementedError( + 'Infinite number of critical points for {}'.format(f)) + + critical_points += solution + + for critical_point in critical_points: + vals += FiniteSet(f.subs(symbol, critical_point)) + + left_open, right_open = False, False + + if critical_values is not S.EmptySet: + if critical_values.inf == vals.inf: + left_open = True + + if critical_values.sup == vals.sup: + right_open = True + + range_int += Interval(vals.inf, vals.sup, left_open, right_open) + else: + raise NotImplementedError(filldedent(''' + Unable to find range for the given domain. + ''')) + + return range_int + + +def not_empty_in(finset_intersection, *syms): + """ + Finds the domain of the functions in ``finset_intersection`` in which the + ``finite_set`` is not-empty. + + Parameters + ========== + + finset_intersection : Intersection of FiniteSet + The unevaluated intersection of FiniteSet containing + real-valued functions with Union of Sets + syms : Tuple of symbols + Symbol for which domain is to be found + + Raises + ====== + + NotImplementedError + The algorithms to find the non-emptiness of the given FiniteSet are + not yet implemented. + ValueError + The input is not valid. + RuntimeError + It is a bug, please report it to the github issue tracker + (https://github.com/sympy/sympy/issues). + + Examples + ======== + + >>> from sympy import FiniteSet, Interval, not_empty_in, oo + >>> from sympy.abc import x + >>> not_empty_in(FiniteSet(x/2).intersect(Interval(0, 1)), x) + Interval(0, 2) + >>> not_empty_in(FiniteSet(x, x**2).intersect(Interval(1, 2)), x) + Union(Interval(1, 2), Interval(-sqrt(2), -1)) + >>> not_empty_in(FiniteSet(x**2/(x + 2)).intersect(Interval(1, oo)), x) + Union(Interval.Lopen(-2, -1), Interval(2, oo)) + """ + + # TODO: handle piecewise defined functions + # TODO: handle transcendental functions + # TODO: handle multivariate functions + if len(syms) == 0: + raise ValueError("One or more symbols must be given in syms.") + + if finset_intersection is S.EmptySet: + return S.EmptySet + + if isinstance(finset_intersection, Union): + elm_in_sets = finset_intersection.args[0] + return Union(not_empty_in(finset_intersection.args[1], *syms), + elm_in_sets) + + if isinstance(finset_intersection, FiniteSet): + finite_set = finset_intersection + _sets = S.Reals + else: + finite_set = finset_intersection.args[1] + _sets = finset_intersection.args[0] + + if not isinstance(finite_set, FiniteSet): + raise ValueError('A FiniteSet must be given, not %s: %s' % + (type(finite_set), finite_set)) + + if len(syms) == 1: + symb = syms[0] + else: + raise NotImplementedError('more than one variables %s not handled' % + (syms,)) + + def elm_domain(expr, intrvl): + """ Finds the domain of an expression in any given interval """ + from sympy.solvers.solveset import solveset + + _start = intrvl.start + _end = intrvl.end + _singularities = solveset(expr.as_numer_denom()[1], symb, + domain=S.Reals) + + if intrvl.right_open: + if _end is S.Infinity: + _domain1 = S.Reals + else: + _domain1 = solveset(expr < _end, symb, domain=S.Reals) + else: + _domain1 = solveset(expr <= _end, symb, domain=S.Reals) + + if intrvl.left_open: + if _start is S.NegativeInfinity: + _domain2 = S.Reals + else: + _domain2 = solveset(expr > _start, symb, domain=S.Reals) + else: + _domain2 = solveset(expr >= _start, symb, domain=S.Reals) + + # domain in the interval + expr_with_sing = Intersection(_domain1, _domain2) + expr_domain = Complement(expr_with_sing, _singularities) + return expr_domain + + if isinstance(_sets, Interval): + return Union(*[elm_domain(element, _sets) for element in finite_set]) + + if isinstance(_sets, Union): + _domain = S.EmptySet + for intrvl in _sets.args: + _domain_element = Union(*[elm_domain(element, intrvl) + for element in finite_set]) + _domain = Union(_domain, _domain_element) + return _domain + + +def periodicity(f, symbol, check=False): + """ + Tests the given function for periodicity in the given symbol. + + Parameters + ========== + + f : :py:class:`~.Expr` + The concerned function. + symbol : :py:class:`~.Symbol` + The variable for which the period is to be determined. + check : bool, optional + The flag to verify whether the value being returned is a period or not. + + Returns + ======= + + period + The period of the function is returned. + ``None`` is returned when the function is aperiodic or has a complex period. + The value of $0$ is returned as the period of a constant function. + + Raises + ====== + + NotImplementedError + The value of the period computed cannot be verified. + + + Notes + ===== + + Currently, we do not support functions with a complex period. + The period of functions having complex periodic values such + as ``exp``, ``sinh`` is evaluated to ``None``. + + The value returned might not be the "fundamental" period of the given + function i.e. it may not be the smallest periodic value of the function. + + The verification of the period through the ``check`` flag is not reliable + due to internal simplification of the given expression. Hence, it is set + to ``False`` by default. + + Examples + ======== + >>> from sympy import periodicity, Symbol, sin, cos, tan, exp + >>> x = Symbol('x') + >>> f = sin(x) + sin(2*x) + sin(3*x) + >>> periodicity(f, x) + 2*pi + >>> periodicity(sin(x)*cos(x), x) + pi + >>> periodicity(exp(tan(2*x) - 1), x) + pi/2 + >>> periodicity(sin(4*x)**cos(2*x), x) + pi + >>> periodicity(exp(x), x) + """ + if symbol.kind is not NumberKind: + raise NotImplementedError("Cannot use symbol of kind %s" % symbol.kind) + temp = Dummy('x', real=True) + f = f.subs(symbol, temp) + symbol = temp + + def _check(orig_f, period): + '''Return the checked period or raise an error.''' + new_f = orig_f.subs(symbol, symbol + period) + if new_f.equals(orig_f): + return period + else: + raise NotImplementedError(filldedent(''' + The period of the given function cannot be verified. + When `%s` was replaced with `%s + %s` in `%s`, the result + was `%s` which was not recognized as being the same as + the original function. + So either the period was wrong or the two forms were + not recognized as being equal. + Set check=False to obtain the value.''' % + (symbol, symbol, period, orig_f, new_f))) + + orig_f = f + period = None + + if isinstance(f, Relational): + f = f.lhs - f.rhs + + f = f.simplify() + + if symbol not in f.free_symbols: + return S.Zero + + if isinstance(f, TrigonometricFunction): + try: + period = f.period(symbol) + except NotImplementedError: + pass + + if isinstance(f, Abs): + arg = f.args[0] + if isinstance(arg, (sec, csc, cos)): + # all but tan and cot might have a + # a period that is half as large + # so recast as sin + arg = sin(arg.args[0]) + period = periodicity(arg, symbol) + if period is not None and isinstance(arg, sin): + # the argument of Abs was a trigonometric other than + # cot or tan; test to see if the half-period + # is valid. Abs(arg) has behaviour equivalent to + # orig_f, so use that for test: + orig_f = Abs(arg) + try: + return _check(orig_f, period/2) + except NotImplementedError as err: + if check: + raise NotImplementedError(err) + # else let new orig_f and period be + # checked below + + if isinstance(f, exp) or (f.is_Pow and f.base == S.Exp1): + f = Pow(S.Exp1, expand_mul(f.exp)) + if im(f) != 0: + period_real = periodicity(re(f), symbol) + period_imag = periodicity(im(f), symbol) + if period_real is not None and period_imag is not None: + period = lcim([period_real, period_imag]) + + if f.is_Pow and f.base != S.Exp1: + base, expo = f.args + base_has_sym = base.has(symbol) + expo_has_sym = expo.has(symbol) + + if base_has_sym and not expo_has_sym: + period = periodicity(base, symbol) + + elif expo_has_sym and not base_has_sym: + period = periodicity(expo, symbol) + + else: + period = _periodicity(f.args, symbol) + + elif f.is_Mul: + coeff, g = f.as_independent(symbol, as_Add=False) + if isinstance(g, TrigonometricFunction) or not equal_valued(coeff, 1): + period = periodicity(g, symbol) + else: + period = _periodicity(g.args, symbol) + + elif f.is_Add: + k, g = f.as_independent(symbol) + if k is not S.Zero: + return periodicity(g, symbol) + + period = _periodicity(g.args, symbol) + + elif isinstance(f, Mod): + a, n = f.args + + if a == symbol: + period = n + elif isinstance(a, TrigonometricFunction): + period = periodicity(a, symbol) + #check if 'f' is linear in 'symbol' + elif (a.is_polynomial(symbol) and degree(a, symbol) == 1 and + symbol not in n.free_symbols): + period = Abs(n / a.diff(symbol)) + + elif isinstance(f, Piecewise): + pass # not handling Piecewise yet as the return type is not favorable + + elif period is None: + from sympy.solvers.decompogen import compogen, decompogen + g_s = decompogen(f, symbol) + num_of_gs = len(g_s) + if num_of_gs > 1: + for index, g in enumerate(reversed(g_s)): + start_index = num_of_gs - 1 - index + g = compogen(g_s[start_index:], symbol) + if g not in (orig_f, f): # Fix for issue 12620 + period = periodicity(g, symbol) + if period is not None: + break + + if period is not None: + if check: + return _check(orig_f, period) + return period + + return None + + +def _periodicity(args, symbol): + """ + Helper for `periodicity` to find the period of a list of simpler + functions. + It uses the `lcim` method to find the least common period of + all the functions. + + Parameters + ========== + + args : Tuple of :py:class:`~.Symbol` + All the symbols present in a function. + + symbol : :py:class:`~.Symbol` + The symbol over which the function is to be evaluated. + + Returns + ======= + + period + The least common period of the function for all the symbols + of the function. + ``None`` if for at least one of the symbols the function is aperiodic. + + """ + periods = [] + for f in args: + period = periodicity(f, symbol) + if period is None: + return None + + if period is not S.Zero: + periods.append(period) + + if len(periods) > 1: + return lcim(periods) + + if periods: + return periods[0] + + +def lcim(numbers): + """Returns the least common integral multiple of a list of numbers. + + The numbers can be rational or irrational or a mixture of both. + `None` is returned for incommensurable numbers. + + Parameters + ========== + + numbers : list + Numbers (rational and/or irrational) for which lcim is to be found. + + Returns + ======= + + number + lcim if it exists, otherwise ``None`` for incommensurable numbers. + + Examples + ======== + + >>> from sympy.calculus.util import lcim + >>> from sympy import S, pi + >>> lcim([S(1)/2, S(3)/4, S(5)/6]) + 15/2 + >>> lcim([2*pi, 3*pi, pi, pi/2]) + 6*pi + >>> lcim([S(1), 2*pi]) + """ + result = None + if all(num.is_irrational for num in numbers): + factorized_nums = [num.factor() for num in numbers] + factors_num = [num.as_coeff_Mul() for num in factorized_nums] + term = factors_num[0][1] + if all(factor == term for coeff, factor in factors_num): + common_term = term + coeffs = [coeff for coeff, factor in factors_num] + result = lcm_list(coeffs) * common_term + + elif all(num.is_rational for num in numbers): + result = lcm_list(numbers) + + else: + pass + + return result + +def is_convex(f, *syms, domain=S.Reals): + r"""Determines the convexity of the function passed in the argument. + + Parameters + ========== + + f : :py:class:`~.Expr` + The concerned function. + syms : Tuple of :py:class:`~.Symbol` + The variables with respect to which the convexity is to be determined. + domain : :py:class:`~.Interval`, optional + The domain over which the convexity of the function has to be checked. + If unspecified, S.Reals will be the default domain. + + Returns + ======= + + bool + The method returns ``True`` if the function is convex otherwise it + returns ``False``. + + Raises + ====== + + NotImplementedError + The check for the convexity of multivariate functions is not implemented yet. + + Notes + ===== + + To determine concavity of a function pass `-f` as the concerned function. + To determine logarithmic convexity of a function pass `\log(f)` as + concerned function. + To determine logarithmic concavity of a function pass `-\log(f)` as + concerned function. + + Currently, convexity check of multivariate functions is not handled. + + Examples + ======== + + >>> from sympy import is_convex, symbols, exp, oo, Interval + >>> x = symbols('x') + >>> is_convex(exp(x), x) + True + >>> is_convex(x**3, x, domain = Interval(-1, oo)) + False + >>> is_convex(1/x**2, x, domain=Interval.open(0, oo)) + True + + References + ========== + + .. [1] https://en.wikipedia.org/wiki/Convex_function + .. [2] http://www.ifp.illinois.edu/~angelia/L3_convfunc.pdf + .. [3] https://en.wikipedia.org/wiki/Logarithmically_convex_function + .. [4] https://en.wikipedia.org/wiki/Logarithmically_concave_function + .. [5] https://en.wikipedia.org/wiki/Concave_function + + """ + + if len(syms) > 1: + raise NotImplementedError( + "The check for the convexity of multivariate functions is not implemented yet.") + + from sympy.solvers.inequalities import solve_univariate_inequality + + f = _sympify(f) + var = syms[0] + if any(s in domain for s in singularities(f, var)): + return False + + condition = f.diff(var, 2) < 0 + if solve_univariate_inequality(condition, var, False, domain): + return False + return True + + +def stationary_points(f, symbol, domain=S.Reals): + """ + Returns the stationary points of a function (where derivative of the + function is 0) in the given domain. + + Parameters + ========== + + f : :py:class:`~.Expr` + The concerned function. + symbol : :py:class:`~.Symbol` + The variable for which the stationary points are to be determined. + domain : :py:class:`~.Interval` + The domain over which the stationary points have to be checked. + If unspecified, ``S.Reals`` will be the default domain. + + Returns + ======= + + Set + A set of stationary points for the function. If there are no + stationary point, an :py:class:`~.EmptySet` is returned. + + Examples + ======== + + >>> from sympy import Interval, Symbol, S, sin, pi, pprint, stationary_points + >>> x = Symbol('x') + + >>> stationary_points(1/x, x, S.Reals) + EmptySet + + >>> pprint(stationary_points(sin(x), x), use_unicode=False) + pi 3*pi + {2*n*pi + -- | n in Integers} U {2*n*pi + ---- | n in Integers} + 2 2 + + >>> stationary_points(sin(x),x, Interval(0, 4*pi)) + {pi/2, 3*pi/2, 5*pi/2, 7*pi/2} + + """ + from sympy.solvers.solveset import solveset + + if domain is S.EmptySet: + return S.EmptySet + + domain = continuous_domain(f, symbol, domain) + set = solveset(diff(f, symbol), symbol, domain) + + return set + + +def maximum(f, symbol, domain=S.Reals): + """ + Returns the maximum value of a function in the given domain. + + Parameters + ========== + + f : :py:class:`~.Expr` + The concerned function. + symbol : :py:class:`~.Symbol` + The variable for maximum value needs to be determined. + domain : :py:class:`~.Interval` + The domain over which the maximum have to be checked. + If unspecified, then the global maximum is returned. + + Returns + ======= + + number + Maximum value of the function in given domain. + + Examples + ======== + + >>> from sympy import Interval, Symbol, S, sin, cos, pi, maximum + >>> x = Symbol('x') + + >>> f = -x**2 + 2*x + 5 + >>> maximum(f, x, S.Reals) + 6 + + >>> maximum(sin(x), x, Interval(-pi, pi/4)) + sqrt(2)/2 + + >>> maximum(sin(x)*cos(x), x) + 1/2 + + """ + if isinstance(symbol, Symbol): + if domain is S.EmptySet: + raise ValueError("Maximum value not defined for empty domain.") + + return function_range(f, symbol, domain).sup + else: + raise ValueError("%s is not a valid symbol." % symbol) + + +def minimum(f, symbol, domain=S.Reals): + """ + Returns the minimum value of a function in the given domain. + + Parameters + ========== + + f : :py:class:`~.Expr` + The concerned function. + symbol : :py:class:`~.Symbol` + The variable for minimum value needs to be determined. + domain : :py:class:`~.Interval` + The domain over which the minimum have to be checked. + If unspecified, then the global minimum is returned. + + Returns + ======= + + number + Minimum value of the function in the given domain. + + Examples + ======== + + >>> from sympy import Interval, Symbol, S, sin, cos, minimum + >>> x = Symbol('x') + + >>> f = x**2 + 2*x + 5 + >>> minimum(f, x, S.Reals) + 4 + + >>> minimum(sin(x), x, Interval(2, 3)) + sin(3) + + >>> minimum(sin(x)*cos(x), x) + -1/2 + + """ + if isinstance(symbol, Symbol): + if domain is S.EmptySet: + raise ValueError("Minimum value not defined for empty domain.") + + return function_range(f, symbol, domain).inf + else: + raise ValueError("%s is not a valid symbol." % symbol) diff --git a/shared/package-files/56/5625dde86e5d3428f96dd588a61155f28936505d3c537a4a448d967e716af150/blob b/shared/package-files/56/5625dde86e5d3428f96dd588a61155f28936505d3c537a4a448d967e716af150/blob new file mode 100644 index 0000000000000000000000000000000000000000..f3dee77c683c26632c5191b287236e843cab3986 --- /dev/null +++ b/shared/package-files/56/5625dde86e5d3428f96dd588a61155f28936505d3c537a4a448d967e716af150/blob @@ -0,0 +1,212 @@ +#pragma once + +#include +#include +#include +#include + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace c10::cuda::impl { + +struct CUDAGuardImpl final : public c10::impl::DeviceGuardImplInterface { + static constexpr DeviceType static_type = DeviceType::CUDA; + + CUDAGuardImpl() = default; + explicit CUDAGuardImpl(DeviceType t) { + TORCH_INTERNAL_ASSERT(t == DeviceType::CUDA); + } + DeviceType type() const override { + return DeviceType::CUDA; + } + Device exchangeDevice(Device d) const override { + TORCH_INTERNAL_ASSERT(d.is_cuda()); + auto old_device_index = c10::cuda::ExchangeDevice(d.index()); + return Device(DeviceType::CUDA, old_device_index); + } + Device getDevice() const override { + DeviceIndex device = 0; + C10_CUDA_CHECK(c10::cuda::GetDevice(&device)); + return Device(DeviceType::CUDA, device); + } + c10::optional uncheckedGetDevice() const noexcept { + DeviceIndex device{-1}; + const auto err = C10_CUDA_ERROR_HANDLED(c10::cuda::GetDevice(&device)); + C10_CUDA_CHECK_WARN(err); + if (err != cudaSuccess) { + return c10::nullopt; + } + return Device(DeviceType::CUDA, device); + } + void setDevice(Device d) const override { + TORCH_INTERNAL_ASSERT(d.is_cuda()); + C10_CUDA_CHECK(c10::cuda::SetDevice(d.index())); + } + void uncheckedSetDevice(Device d) const noexcept override { + C10_CUDA_CHECK_WARN(c10::cuda::MaybeSetDevice(d.index())); + } + Stream getStream(Device d) const noexcept override { + return getCurrentCUDAStream(d.index()).unwrap(); + } + Stream getDefaultStream(Device d) const override { + return getDefaultCUDAStream(d.index()); + } + Stream getStreamFromGlobalPool(Device d, bool isHighPriority = false) + const override { + return getStreamFromPool(isHighPriority, d.index()); + } + // NB: These do NOT set the current device + Stream exchangeStream(Stream s) const noexcept override { + CUDAStream cs(s); + auto old_stream = getCurrentCUDAStream(s.device().index()); + setCurrentCUDAStream(cs); + return old_stream.unwrap(); + } + DeviceIndex deviceCount() const noexcept override { + return device_count(); + } + + // Event-related functions + void createEvent(cudaEvent_t* cuda_event, const EventFlag flag) const { + // Maps PyTorch's Event::Flag to CUDA flag + auto cuda_flag = cudaEventDefault; + switch (flag) { + case EventFlag::PYTORCH_DEFAULT: + case EventFlag::CUDA_EVENT_DISABLE_TIMING: + cuda_flag = cudaEventDisableTiming; + break; + case EventFlag::BACKEND_DEFAULT: + case EventFlag::CUDA_EVENT_DEFAULT: + cuda_flag = cudaEventDefault; + break; + default: + TORCH_CHECK(false, "CUDA event received unknown flag"); + } + + C10_CUDA_CHECK(cudaEventCreateWithFlags(cuda_event, cuda_flag)); + const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace(); + if (C10_UNLIKELY(interp)) { + (*interp)->trace_gpu_event_creation( + reinterpret_cast(cuda_event)); + } + } + + void destroyEvent(void* event, const DeviceIndex device_index) + const noexcept override { + if (!event) + return; + auto cuda_event = static_cast(event); + DeviceIndex orig_device{-1}; + C10_CUDA_CHECK_WARN(c10::cuda::GetDevice(&orig_device)); + C10_CUDA_CHECK_WARN(c10::cuda::SetDevice(device_index)); + const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace(); + if (C10_UNLIKELY(interp)) { + (*interp)->trace_gpu_event_deletion( + reinterpret_cast(cuda_event)); + } + C10_CUDA_CHECK_WARN(cudaEventDestroy(cuda_event)); + C10_CUDA_CHECK_WARN(c10::cuda::SetDevice(orig_device)); + } + + void record( + void** event, + const Stream& stream, + const DeviceIndex device_index, + const EventFlag flag) const override { + TORCH_CHECK( + device_index == -1 || device_index == stream.device_index(), + "Event device index ", + device_index, + " does not match recording stream's device index ", + stream.device_index(), + "."); + + cudaEvent_t cuda_event = static_cast(*event); + CUDAStream cuda_stream{stream}; + + // Moves to stream's device to record + const auto orig_device = getDevice(); + setDevice(stream.device()); + + // Creates the event (lazily) + if (!cuda_event) + createEvent(&cuda_event, flag); + C10_CUDA_CHECK(cudaEventRecord(cuda_event, cuda_stream)); + // Makes the void* point to the (possibly just allocated) CUDA event + *event = cuda_event; + const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace(); + if (C10_UNLIKELY(interp)) { + (*interp)->trace_gpu_event_record( + reinterpret_cast(cuda_event), + reinterpret_cast(cuda_stream.stream())); + } + + // Resets device + setDevice(orig_device); + } + + void block(void* event, const Stream& stream) const override { + if (!event) + return; + cudaEvent_t cuda_event = static_cast(event); + CUDAStream cuda_stream{stream}; + const auto orig_device = getDevice(); + setDevice(stream.device()); + C10_CUDA_CHECK(cudaStreamWaitEvent( + cuda_stream, + cuda_event, + /*flags (must be zero)=*/0)); + const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace(); + if (C10_UNLIKELY(interp)) { + (*interp)->trace_gpu_event_wait( + reinterpret_cast(cuda_event), + reinterpret_cast(cuda_stream.stream())); + } + setDevice(orig_device); + } + + // May be called from any device + bool queryEvent(void* event) const override { + if (!event) + return true; + cudaEvent_t cuda_event = static_cast(event); + const cudaError_t err = C10_CUDA_ERROR_HANDLED(cudaEventQuery(cuda_event)); + if (err != cudaErrorNotReady) { + C10_CUDA_CHECK(err); + } else { + // ignore and clear the error if not ready + (void)cudaGetLastError(); + } + return (err == cudaSuccess); + } + + // Stream-related functions + bool queryStream(const Stream& stream) const override { + CUDAStream cuda_stream{stream}; + return cuda_stream.query(); + } + + void synchronizeStream(const Stream& stream) const override { + CUDAStream cuda_stream{stream}; + cuda_stream.synchronize(); + } + + void recordDataPtrOnStream(const c10::DataPtr& data_ptr, const Stream& stream) + const override { + CUDAStream cuda_stream{stream}; + CUDACachingAllocator::recordStream(data_ptr, cuda_stream); + } +}; + +} // namespace c10::cuda::impl diff --git a/shared/package-files/56/5627831bc850c2f3bb9ecaafe77e4a9afaa5c219ac19f0acdebde802a9078900/blob b/shared/package-files/56/5627831bc850c2f3bb9ecaafe77e4a9afaa5c219ac19f0acdebde802a9078900/blob new file mode 100644 index 0000000000000000000000000000000000000000..6d03401eea9cc72a757bbbdc8a6ac0bda97f4556 --- /dev/null +++ b/shared/package-files/56/5627831bc850c2f3bb9ecaafe77e4a9afaa5c219ac19f0acdebde802a9078900/blob @@ -0,0 +1,39 @@ +#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 special_modified_bessel_k0 { + using schema = at::Tensor (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::special_modified_bessel_k0") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_modified_bessel_k0(Tensor self) -> Tensor") + static at::Tensor call(const at::Tensor & self); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self); +}; + +struct TORCH_API special_modified_bessel_k0_out { + using schema = at::Tensor & (const at::Tensor &, at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_modified_bessel_k0") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") + static at::Tensor & call(const at::Tensor & self, at::Tensor & out); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out); +}; + +}} // namespace at::_ops diff --git a/shared/package-files/56/562825f936033f38d1b61561663a9b6f3352aeed36351d6259f65f9a1ed621f2/blob b/shared/package-files/56/562825f936033f38d1b61561663a9b6f3352aeed36351d6259f65f9a1ed621f2/blob new file mode 100644 index 0000000000000000000000000000000000000000..fc5fdfa8ff84b659ac6c895274c7439a0e2da020 --- /dev/null +++ b/shared/package-files/56/562825f936033f38d1b61561663a9b6f3352aeed36351d6259f65f9a1ed621f2/blob @@ -0,0 +1,39 @@ +#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::softplus_backward.grad_input(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!) +inline at::Tensor & softplus_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { + return at::_ops::softplus_backward_grad_input::call(grad_output, self, beta, threshold, grad_input); +} +// aten::softplus_backward.grad_input(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!) +inline at::Tensor & softplus_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & grad_input) { + return at::_ops::softplus_backward_grad_input::call(grad_output, self, beta, threshold, grad_input); +} + +// aten::softplus_backward(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold) -> Tensor +inline at::Tensor softplus_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { + return at::_ops::softplus_backward::call(grad_output, self, beta, threshold); +} + +} diff --git a/shared/package-files/56/562d4b893ac95f9058d000cbc68264c02ec7dbb1f7d2d4cf33cf71674e00e465/blob b/shared/package-files/56/562d4b893ac95f9058d000cbc68264c02ec7dbb1f7d2d4cf33cf71674e00e465/blob new file mode 100644 index 0000000000000000000000000000000000000000..ac728968750297227c1be4aa3e444557c1899b03 --- /dev/null +++ b/shared/package-files/56/562d4b893ac95f9058d000cbc68264c02ec7dbb1f7d2d4cf33cf71674e00e465/blob @@ -0,0 +1,2 @@ +#include +#include diff --git a/shared/package-files/56/562d4d8bd25e6725fb9145fb60482f6a3d52ea4cba472532f2a2ff08e375520c/blob b/shared/package-files/56/562d4d8bd25e6725fb9145fb60482f6a3d52ea4cba472532f2a2ff08e375520c/blob new file mode 100644 index 0000000000000000000000000000000000000000..e2eeddc88f079f90cdc1df9ac8e6ba3b62578639 --- /dev/null +++ b/shared/package-files/56/562d4d8bd25e6725fb9145fb60482f6a3d52ea4cba472532f2a2ff08e375520c/blob @@ -0,0 +1,20 @@ +# coding: utf-8 +from __future__ import absolute_import +from six.moves import cStringIO +from .unparser import Unparser +from .printer import Printer + + +__version__ = '1.6.3' + + +def unparse(tree): + v = cStringIO() + Unparser(tree, file=v) + return v.getvalue() + + +def dump(tree): + v = cStringIO() + Printer(file=v).visit(tree) + return v.getvalue() diff --git a/shared/package-files/56/562d986ed52133d57160f6373c1bf17eed31658afa7c1cac9798103c9128f3c9/blob b/shared/package-files/56/562d986ed52133d57160f6373c1bf17eed31658afa7c1cac9798103c9128f3c9/blob new file mode 100644 index 0000000000000000000000000000000000000000..33c1f014b867cc069d532839b405c3f7696c5a22 --- /dev/null +++ b/shared/package-files/56/562d986ed52133d57160f6373c1bf17eed31658afa7c1cac9798103c9128f3c9/blob @@ -0,0 +1,285 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +from contourpy._contourpy import ( + ContourGenerator, + FillType, + LineType, + Mpl2005ContourGenerator, + Mpl2014ContourGenerator, + SerialContourGenerator, + ThreadedContourGenerator, + ZInterp, + max_threads, +) +from contourpy._version import __version__ +from contourpy.chunk import calc_chunk_sizes +from contourpy.convert import ( + convert_filled, + convert_lines, + convert_multi_filled, + convert_multi_lines, +) +from contourpy.dechunk import ( + dechunk_filled, + dechunk_lines, + dechunk_multi_filled, + dechunk_multi_lines, +) +from contourpy.enum_util import as_fill_type, as_line_type, as_z_interp + +if TYPE_CHECKING: + from typing import Any + + from numpy.typing import ArrayLike + + from ._contourpy import CoordinateArray, MaskArray + +__all__ = [ + "__version__", + "contour_generator", + "convert_filled", + "convert_lines", + "convert_multi_filled", + "convert_multi_lines", + "dechunk_filled", + "dechunk_lines", + "dechunk_multi_filled", + "dechunk_multi_lines", + "max_threads", + "FillType", + "LineType", + "ContourGenerator", + "Mpl2005ContourGenerator", + "Mpl2014ContourGenerator", + "SerialContourGenerator", + "ThreadedContourGenerator", + "ZInterp", +] + + +# Simple mapping of algorithm name to class name. +_class_lookup: dict[str, type[ContourGenerator]] = { + "mpl2005": Mpl2005ContourGenerator, + "mpl2014": Mpl2014ContourGenerator, + "serial": SerialContourGenerator, + "threaded": ThreadedContourGenerator, +} + + +def _remove_z_mask( + z: ArrayLike | np.ma.MaskedArray[Any, Any] | None, +) -> tuple[CoordinateArray, MaskArray | None]: + # Preserve mask if present. + z_array = np.ma.asarray(z, dtype=np.float64) # type: ignore[no-untyped-call] + z_masked = np.ma.masked_invalid(z_array, copy=False) # type: ignore[no-untyped-call] + + if np.ma.is_masked(z_masked): # type: ignore[no-untyped-call] + mask = np.ma.getmask(z_masked) # type: ignore[no-untyped-call] + else: + mask = None + + return np.ma.getdata(z_masked), mask # type: ignore[no-untyped-call] + + +def contour_generator( + x: ArrayLike | None = None, + y: ArrayLike | None = None, + z: ArrayLike | np.ma.MaskedArray[Any, Any] | None = None, + *, + name: str = "serial", + corner_mask: bool | None = None, + line_type: LineType | str | None = None, + fill_type: FillType | str | None = None, + chunk_size: int | tuple[int, int] | None = None, + chunk_count: int | tuple[int, int] | None = None, + total_chunk_count: int | None = None, + quad_as_tri: bool = False, + z_interp: ZInterp | str | None = ZInterp.Linear, + thread_count: int = 0, +) -> ContourGenerator: + """Create and return a :class:`~.ContourGenerator` object. + + The class and properties of the returned :class:`~.ContourGenerator` are determined by the + function arguments, with sensible defaults. + + Args: + x (array-like of shape (ny, nx) or (nx,), optional): The x-coordinates of the ``z`` values. + May be 2D with the same shape as ``z.shape``, or 1D with length ``nx = z.shape[1]``. + If not specified are assumed to be ``np.arange(nx)``. Must be ordered monotonically. + y (array-like of shape (ny, nx) or (ny,), optional): The y-coordinates of the ``z`` values. + May be 2D with the same shape as ``z.shape``, or 1D with length ``ny = z.shape[0]``. + If not specified are assumed to be ``np.arange(ny)``. Must be ordered monotonically. + z (array-like of shape (ny, nx), may be a masked array): The 2D gridded values to calculate + the contours of. May be a masked array, and any invalid values (``np.inf`` or + ``np.nan``) will also be masked out. + name (str): Algorithm name, one of ``"serial"``, ``"threaded"``, ``"mpl2005"`` or + ``"mpl2014"``, default ``"serial"``. + corner_mask (bool, optional): Enable/disable corner masking, which only has an effect if + ``z`` is a masked array. If ``False``, any quad touching a masked point is masked out. + If ``True``, only the triangular corners of quads nearest these points are always masked + out, other triangular corners comprising three unmasked points are contoured as usual. + If not specified, uses the default provided by the algorithm ``name``. + line_type (LineType or str, optional): The format of contour line data returned from calls + to :meth:`~.ContourGenerator.lines`, specified either as a :class:`~.LineType` or its + string equivalent such as ``"SeparateCode"``. + If not specified, uses the default provided by the algorithm ``name``. + The relationship between the :class:`~.LineType` enum and the data format returned from + :meth:`~.ContourGenerator.lines` is explained at :ref:`line_type`. + fill_type (FillType or str, optional): The format of filled contour data returned from calls + to :meth:`~.ContourGenerator.filled`, specified either as a :class:`~.FillType` or its + string equivalent such as ``"OuterOffset"``. + If not specified, uses the default provided by the algorithm ``name``. + The relationship between the :class:`~.FillType` enum and the data format returned from + :meth:`~.ContourGenerator.filled` is explained at :ref:`fill_type`. + chunk_size (int or tuple(int, int), optional): Chunk size in (y, x) directions, or the same + size in both directions if only one value is specified. + chunk_count (int or tuple(int, int), optional): Chunk count in (y, x) directions, or the + same count in both directions if only one value is specified. + total_chunk_count (int, optional): Total number of chunks. + quad_as_tri (bool): Enable/disable treating quads as 4 triangles, default ``False``. + If ``False``, a contour line within a quad is a straight line between points on two of + its edges. If ``True``, each full quad is divided into 4 triangles using a virtual point + at the centre (mean x, y of the corner points) and a contour line is piecewise linear + within those triangles. Corner-masked triangles are not affected by this setting, only + full unmasked quads. + z_interp (ZInterp or str, optional): How to interpolate ``z`` values when determining where + contour lines intersect the edges of quads and the ``z`` values of the central points of + quads, specified either as a :class:`~contourpy.ZInterp` or its string equivalent such + as ``"Log"``. Default is ``ZInterp.Linear``. + thread_count (int): Number of threads to use for contour calculation, default 0. Threads can + only be used with an algorithm ``name`` that supports threads (currently only + ``name="threaded"``) and there must be at least the same number of chunks as threads. + If ``thread_count=0`` and ``name="threaded"`` then it uses the maximum number of threads + as determined by the C++11 call ``std::thread::hardware_concurrency()``. If ``name`` is + something other than ``"threaded"`` then the ``thread_count`` will be set to ``1``. + + Return: + :class:`~.ContourGenerator`. + + Note: + A maximum of one of ``chunk_size``, ``chunk_count`` and ``total_chunk_count`` may be + specified. + + Warning: + The ``name="mpl2005"`` algorithm does not implement chunking for contour lines. + """ + x = np.asarray(x, dtype=np.float64) + y = np.asarray(y, dtype=np.float64) + z, mask = _remove_z_mask(z) + + # Check arguments: z. + if z.ndim != 2: + raise TypeError(f"Input z must be 2D, not {z.ndim}D") + + if z.shape[0] < 2 or z.shape[1] < 2: + raise TypeError(f"Input z must be at least a (2, 2) shaped array, but has shape {z.shape}") + + ny, nx = z.shape + + # Check arguments: x and y. + if x.ndim != y.ndim: + raise TypeError(f"Number of dimensions of x ({x.ndim}) and y ({y.ndim}) do not match") + + if x.ndim == 0: + x = np.arange(nx, dtype=np.float64) + y = np.arange(ny, dtype=np.float64) + x, y = np.meshgrid(x, y) + elif x.ndim == 1: + if len(x) != nx: + raise TypeError(f"Length of x ({len(x)}) must match number of columns in z ({nx})") + if len(y) != ny: + raise TypeError(f"Length of y ({len(y)}) must match number of rows in z ({ny})") + x, y = np.meshgrid(x, y) + elif x.ndim == 2: + if x.shape != z.shape: + raise TypeError(f"Shapes of x {x.shape} and z {z.shape} do not match") + if y.shape != z.shape: + raise TypeError(f"Shapes of y {y.shape} and z {z.shape} do not match") + else: + raise TypeError(f"Inputs x and y must be None, 1D or 2D, not {x.ndim}D") + + # Check mask shape just in case. + if mask is not None and mask.shape != z.shape: + raise ValueError("If mask is set it must be a 2D array with the same shape as z") + + # Check arguments: name. + if name not in _class_lookup: + raise ValueError(f"Unrecognised contour generator name: {name}") + + # Check arguments: chunk_size, chunk_count and total_chunk_count. + y_chunk_size, x_chunk_size = calc_chunk_sizes( + chunk_size, chunk_count, total_chunk_count, ny, nx) + + cls = _class_lookup[name] + + # Check arguments: corner_mask. + if corner_mask is None: + # Set it to default, which is True if the algorithm supports it. + corner_mask = cls.supports_corner_mask() + elif corner_mask and not cls.supports_corner_mask(): + raise ValueError(f"{name} contour generator does not support corner_mask=True") + + # Check arguments: line_type. + if line_type is None: + line_type = cls.default_line_type + else: + line_type = as_line_type(line_type) + + if not cls.supports_line_type(line_type): + raise ValueError(f"{name} contour generator does not support line_type {line_type}") + + # Check arguments: fill_type. + if fill_type is None: + fill_type = cls.default_fill_type + else: + fill_type = as_fill_type(fill_type) + + if not cls.supports_fill_type(fill_type): + raise ValueError(f"{name} contour generator does not support fill_type {fill_type}") + + # Check arguments: quad_as_tri. + if quad_as_tri and not cls.supports_quad_as_tri(): + raise ValueError(f"{name} contour generator does not support quad_as_tri=True") + + # Check arguments: z_interp. + if z_interp is None: + z_interp = ZInterp.Linear + else: + z_interp = as_z_interp(z_interp) + + if z_interp != ZInterp.Linear and not cls.supports_z_interp(): + raise ValueError(f"{name} contour generator does not support z_interp {z_interp}") + + # Check arguments: thread_count. + if thread_count not in (0, 1) and not cls.supports_threads(): + raise ValueError(f"{name} contour generator does not support thread_count {thread_count}") + + # Prepare args and kwargs for contour generator constructor. + args = [x, y, z, mask] + kwargs: dict[str, int | bool | LineType | FillType | ZInterp] = { + "x_chunk_size": x_chunk_size, + "y_chunk_size": y_chunk_size, + } + + if name not in ("mpl2005", "mpl2014"): + kwargs["line_type"] = line_type + kwargs["fill_type"] = fill_type + + if cls.supports_corner_mask(): + kwargs["corner_mask"] = corner_mask + + if cls.supports_quad_as_tri(): + kwargs["quad_as_tri"] = quad_as_tri + + if cls.supports_z_interp(): + kwargs["z_interp"] = z_interp + + if cls.supports_threads(): + kwargs["thread_count"] = thread_count + + # Create contour generator. + return cls(*args, **kwargs) diff --git a/shared/package-files/56/56325687bbec94c348a1074f14d525019234131b8b155e8476bcb061a223a905/blob b/shared/package-files/56/56325687bbec94c348a1074f14d525019234131b8b155e8476bcb061a223a905/blob new file mode 100644 index 0000000000000000000000000000000000000000..5c488dd9743d9676c0079196c3af85ecac832739 --- /dev/null +++ b/shared/package-files/56/56325687bbec94c348a1074f14d525019234131b8b155e8476bcb061a223a905/blob @@ -0,0 +1,841 @@ +""" + pygments.lexers.dotnet + ~~~~~~~~~~~~~~~~~~~~~~ + + Lexers for .net languages. + + :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" +import re + +from pygments.lexer import RegexLexer, DelegatingLexer, bygroups, include, \ + using, this, default, words +from pygments.token import Punctuation, Text, Comment, Operator, Keyword, \ + Name, String, Number, Literal, Other, Whitespace +from pygments.util import get_choice_opt +from pygments import unistring as uni + +from pygments.lexers.html import XmlLexer + +__all__ = ['CSharpLexer', 'NemerleLexer', 'BooLexer', 'VbNetLexer', + 'CSharpAspxLexer', 'VbNetAspxLexer', 'FSharpLexer', 'XppLexer'] + + +class CSharpLexer(RegexLexer): + """ + For C# source code. + + Additional options accepted: + + `unicodelevel` + Determines which Unicode characters this lexer allows for identifiers. + The possible values are: + + * ``none`` -- only the ASCII letters and numbers are allowed. This + is the fastest selection. + * ``basic`` -- all Unicode characters from the specification except + category ``Lo`` are allowed. + * ``full`` -- all Unicode characters as specified in the C# specs + are allowed. Note that this means a considerable slowdown since the + ``Lo`` category has more than 40,000 characters in it! + + The default value is ``basic``. + + .. versionadded:: 0.8 + """ + + name = 'C#' + url = 'https://docs.microsoft.com/en-us/dotnet/csharp/' + aliases = ['csharp', 'c#', 'cs'] + filenames = ['*.cs'] + mimetypes = ['text/x-csharp'] # inferred + + flags = re.MULTILINE | re.DOTALL + + # for the range of allowed unicode characters in identifiers, see + # http://www.ecma-international.org/publications/files/ECMA-ST/Ecma-334.pdf + + levels = { + 'none': r'@?[_a-zA-Z]\w*', + 'basic': ('@?[_' + uni.combine('Lu', 'Ll', 'Lt', 'Lm', 'Nl') + ']' + + '[' + uni.combine('Lu', 'Ll', 'Lt', 'Lm', 'Nl', 'Nd', 'Pc', + 'Cf', 'Mn', 'Mc') + ']*'), + 'full': ('@?(?:_|[^' + + uni.allexcept('Lu', 'Ll', 'Lt', 'Lm', 'Lo', 'Nl') + '])' + + '[^' + uni.allexcept('Lu', 'Ll', 'Lt', 'Lm', 'Lo', 'Nl', + 'Nd', 'Pc', 'Cf', 'Mn', 'Mc') + ']*'), + } + + tokens = {} + token_variants = True + + for levelname, cs_ident in levels.items(): + tokens[levelname] = { + 'root': [ + # method names + (r'^([ \t]*)((?:' + cs_ident + r'(?:\[\])?\s+)+?)' # return type + r'(' + cs_ident + ')' # method name + r'(\s*)(\()', # signature start + bygroups(Whitespace, using(this), Name.Function, Whitespace, + Punctuation)), + (r'^(\s*)(\[.*?\])', bygroups(Whitespace, Name.Attribute)), + (r'[^\S\n]+', Whitespace), + (r'(\\)(\n)', bygroups(Text, Whitespace)), # line continuation + (r'//.*?\n', Comment.Single), + (r'/[*].*?[*]/', Comment.Multiline), + (r'\n', Whitespace), + (words(( + '>>>=', '>>=', '<<=', '<=', '>=', '+=', '-=', '*=', '/=', + '%=', '&=', '|=', '^=', '??=', '=>', '??', '?.', '!=', '==', + '&&', '||', '>>>', '>>', '<<', '++', '--', '+', '-', '*', + '/', '%', '&', '|', '^', '<', '>', '?', '!', '~', '=', + )), Operator), + (r'=~|!=|==|<<|>>|[-+/*%=<>&^|]', Operator), + (r'[()\[\];:,.]', Punctuation), + (r'[{}]', Punctuation), + (r'@"(""|[^"])*"', String), + (r'\$?"(\\\\|\\[^\\]|[^"\\\n])*["\n]', String), + (r"'\\.'|'[^\\]'", String.Char), + (r"[0-9]+(\.[0-9]*)?([eE][+-][0-9]+)?" + r"[flFLdD]?|0[xX][0-9a-fA-F]+[Ll]?", Number), + (r'(#)([ \t]*)(if|endif|else|elif|define|undef|' + r'line|error|warning|region|endregion|pragma)\b(.*?)(\n)', + bygroups(Comment.Preproc, Whitespace, Comment.Preproc, + Comment.Preproc, Whitespace)), + (r'\b(extern)(\s+)(alias)\b', bygroups(Keyword, Whitespace, + Keyword)), + (r'(abstract|as|async|await|base|break|by|case|catch|' + r'checked|const|continue|default|delegate|' + r'do|else|enum|event|explicit|extern|false|finally|' + r'fixed|for|foreach|goto|if|implicit|in|interface|' + r'internal|is|let|lock|new|null|on|operator|' + r'out|override|params|private|protected|public|readonly|' + r'ref|return|sealed|sizeof|stackalloc|static|' + r'switch|this|throw|true|try|typeof|' + r'unchecked|unsafe|virtual|void|while|' + r'get|set|new|partial|yield|add|remove|value|alias|ascending|' + r'descending|from|group|into|orderby|select|thenby|where|' + r'join|equals)\b', Keyword), + (r'(global)(::)', bygroups(Keyword, Punctuation)), + (r'(bool|byte|char|decimal|double|dynamic|float|int|long|object|' + r'sbyte|short|string|uint|ulong|ushort|var)\b\??', Keyword.Type), + (r'(class|struct)(\s+)', bygroups(Keyword, Whitespace), 'class'), + (r'(namespace|using)(\s+)', bygroups(Keyword, Whitespace), 'namespace'), + (cs_ident, Name), + ], + 'class': [ + (cs_ident, Name.Class, '#pop'), + default('#pop'), + ], + 'namespace': [ + (r'(?=\()', Text, '#pop'), # using (resource) + ('(' + cs_ident + r'|\.)+', Name.Namespace, '#pop'), + ] + } + + def __init__(self, **options): + level = get_choice_opt(options, 'unicodelevel', list(self.tokens), 'basic') + if level not in self._all_tokens: + # compile the regexes now + self._tokens = self.__class__.process_tokendef(level) + else: + self._tokens = self._all_tokens[level] + + RegexLexer.__init__(self, **options) + + +class NemerleLexer(RegexLexer): + """ + For Nemerle source code. + + Additional options accepted: + + `unicodelevel` + Determines which Unicode characters this lexer allows for identifiers. + The possible values are: + + * ``none`` -- only the ASCII letters and numbers are allowed. This + is the fastest selection. + * ``basic`` -- all Unicode characters from the specification except + category ``Lo`` are allowed. + * ``full`` -- all Unicode characters as specified in the C# specs + are allowed. Note that this means a considerable slowdown since the + ``Lo`` category has more than 40,000 characters in it! + + The default value is ``basic``. + + .. versionadded:: 1.5 + """ + + name = 'Nemerle' + url = 'http://nemerle.org' + aliases = ['nemerle'] + filenames = ['*.n'] + mimetypes = ['text/x-nemerle'] # inferred + + flags = re.MULTILINE | re.DOTALL + + # for the range of allowed unicode characters in identifiers, see + # http://www.ecma-international.org/publications/files/ECMA-ST/Ecma-334.pdf + + levels = { + 'none': r'@?[_a-zA-Z]\w*', + 'basic': ('@?[_' + uni.combine('Lu', 'Ll', 'Lt', 'Lm', 'Nl') + ']' + + '[' + uni.combine('Lu', 'Ll', 'Lt', 'Lm', 'Nl', 'Nd', 'Pc', + 'Cf', 'Mn', 'Mc') + ']*'), + 'full': ('@?(?:_|[^' + + uni.allexcept('Lu', 'Ll', 'Lt', 'Lm', 'Lo', 'Nl') + '])' + + '[^' + uni.allexcept('Lu', 'Ll', 'Lt', 'Lm', 'Lo', 'Nl', + 'Nd', 'Pc', 'Cf', 'Mn', 'Mc') + ']*'), + } + + tokens = {} + token_variants = True + + for levelname, cs_ident in levels.items(): + tokens[levelname] = { + 'root': [ + # method names + (r'^([ \t]*)((?:' + cs_ident + r'(?:\[\])?\s+)+?)' # return type + r'(' + cs_ident + ')' # method name + r'(\s*)(\()', # signature start + bygroups(Whitespace, using(this), Name.Function, Whitespace, \ + Punctuation)), + (r'^(\s*)(\[.*?\])', bygroups(Whitespace, Name.Attribute)), + (r'[^\S\n]+', Whitespace), + (r'(\\)(\n)', bygroups(Text, Whitespace)), # line continuation + (r'//.*?\n', Comment.Single), + (r'/[*].*?[*]/', Comment.Multiline), + (r'\n', Whitespace), + (r'(\$)(\s*)(")', bygroups(String, Whitespace, String), + 'splice-string'), + (r'(\$)(\s*)(<#)', bygroups(String, Whitespace, String), + 'splice-string2'), + (r'<#', String, 'recursive-string'), + + (r'(<\[)(\s*)(' + cs_ident + ':)?', + bygroups(Keyword, Whitespace, Keyword)), + (r'\]\>', Keyword), + + # quasiquotation only + (r'\$' + cs_ident, Name), + (r'(\$)(\()', bygroups(Name, Punctuation), + 'splice-string-content'), + + (r'[~!%^&*()+=|\[\]:;,.<>/?-]', Punctuation), + (r'[{}]', Punctuation), + (r'@"(""|[^"])*"', String), + (r'"(\\\\|\\[^\\]|[^"\\\n])*["\n]', String), + (r"'\\.'|'[^\\]'", String.Char), + (r"0[xX][0-9a-fA-F]+[Ll]?", Number), + (r"[0-9](\.[0-9]*)?([eE][+-][0-9]+)?[flFLdD]?", Number), + (r'(#)([ \t]*)(if|endif|else|elif|define|undef|' + r'line|error|warning|region|endregion|pragma)\b', + bygroups(Comment.Preproc, Whitespace, Comment.Preproc), 'preproc'), + (r'\b(extern)(\s+)(alias)\b', bygroups(Keyword, Whitespace, Keyword)), + (r'(abstract|and|as|base|catch|def|delegate|' + r'enum|event|extern|false|finally|' + r'fun|implements|interface|internal|' + r'is|macro|match|matches|module|mutable|new|' + r'null|out|override|params|partial|private|' + r'protected|public|ref|sealed|static|' + r'syntax|this|throw|true|try|type|typeof|' + r'virtual|volatile|when|where|with|' + r'assert|assert2|async|break|checked|continue|do|else|' + r'ensures|for|foreach|if|late|lock|new|nolate|' + r'otherwise|regexp|repeat|requires|return|surroundwith|' + r'unchecked|unless|using|while|yield)\b', Keyword), + (r'(global)(::)', bygroups(Keyword, Punctuation)), + (r'(bool|byte|char|decimal|double|float|int|long|object|sbyte|' + r'short|string|uint|ulong|ushort|void|array|list)\b\??', + Keyword.Type), + (r'(:>?)(\s*)(' + cs_ident + r'\??)', + bygroups(Punctuation, Whitespace, Keyword.Type)), + (r'(class|struct|variant|module)(\s+)', + bygroups(Keyword, Whitespace), 'class'), + (r'(namespace|using)(\s+)', bygroups(Keyword, Whitespace), + 'namespace'), + (cs_ident, Name), + ], + 'class': [ + (cs_ident, Name.Class, '#pop') + ], + 'preproc': [ + (r'\w+', Comment.Preproc), + (r'[ \t]+', Whitespace), + (r'\n', Whitespace, '#pop') + ], + 'namespace': [ + (r'(?=\()', Text, '#pop'), # using (resource) + ('(' + cs_ident + r'|\.)+', Name.Namespace, '#pop') + ], + 'splice-string': [ + (r'[^"$]', String), + (r'\$' + cs_ident, Name), + (r'(\$)(\()', bygroups(Name, Punctuation), + 'splice-string-content'), + (r'\\"', String), + (r'"', String, '#pop') + ], + 'splice-string2': [ + (r'[^#<>$]', String), + (r'\$' + cs_ident, Name), + (r'(\$)(\()', bygroups(Name, Punctuation), + 'splice-string-content'), + (r'<#', String, '#push'), + (r'#>', String, '#pop') + ], + 'recursive-string': [ + (r'[^#<>]', String), + (r'<#', String, '#push'), + (r'#>', String, '#pop') + ], + 'splice-string-content': [ + (r'if|match', Keyword), + (r'[~!%^&*+=|\[\]:;,.<>/?-\\"$ ]', Punctuation), + (cs_ident, Name), + (r'\d+', Number), + (r'\(', Punctuation, '#push'), + (r'\)', Punctuation, '#pop') + ] + } + + def __init__(self, **options): + level = get_choice_opt(options, 'unicodelevel', list(self.tokens), + 'basic') + if level not in self._all_tokens: + # compile the regexes now + self._tokens = self.__class__.process_tokendef(level) + else: + self._tokens = self._all_tokens[level] + + RegexLexer.__init__(self, **options) + + def analyse_text(text): + """Nemerle is quite similar to Python, but @if is relatively uncommon + elsewhere.""" + result = 0 + + if '@if' in text: + result += 0.1 + + return result + + +class BooLexer(RegexLexer): + """ + For Boo source code. + """ + + name = 'Boo' + url = 'https://github.com/boo-lang/boo' + aliases = ['boo'] + filenames = ['*.boo'] + mimetypes = ['text/x-boo'] + + tokens = { + 'root': [ + (r'\s+', Whitespace), + (r'(#|//).*$', Comment.Single), + (r'/[*]', Comment.Multiline, 'comment'), + (r'[]{}:(),.;[]', Punctuation), + (r'(\\)(\n)', bygroups(Text, Whitespace)), + (r'\\', Text), + (r'(in|is|and|or|not)\b', Operator.Word), + (r'/(\\\\|\\[^\\]|[^/\\\s])/', String.Regex), + (r'@/(\\\\|\\[^\\]|[^/\\])*/', String.Regex), + (r'=~|!=|==|<<|>>|[-+/*%=<>&^|]', Operator), + (r'(as|abstract|callable|constructor|destructor|do|import|' + r'enum|event|final|get|interface|internal|of|override|' + r'partial|private|protected|public|return|set|static|' + r'struct|transient|virtual|yield|super|and|break|cast|' + r'continue|elif|else|ensure|except|for|given|goto|if|in|' + r'is|isa|not|or|otherwise|pass|raise|ref|try|unless|when|' + r'while|from|as)\b', Keyword), + (r'def(?=\s+\(.*?\))', Keyword), + (r'(def)(\s+)', bygroups(Keyword, Whitespace), 'funcname'), + (r'(class)(\s+)', bygroups(Keyword, Whitespace), 'classname'), + (r'(namespace)(\s+)', bygroups(Keyword, Whitespace), 'namespace'), + (r'(?', Name.Attribute), + (r'\s+', Whitespace), + (r'\n', Whitespace), + (r'(rem\b.*?)(\n)', bygroups(Comment, Whitespace)), + (r"('.*?)(\n)", bygroups(Comment, Whitespace)), + (r'#If\s.*?\sThen|#ElseIf\s.*?\sThen|#Else|#End\s+If|#Const|' + r'#ExternalSource.*?\n|#End\s+ExternalSource|' + r'#Region.*?\n|#End\s+Region|#ExternalChecksum', + Comment.Preproc), + (r'[(){}!#,.:]', Punctuation), + (r'(Option)(\s+)(Strict|Explicit|Compare)(\s+)' + r'(On|Off|Binary|Text)', + bygroups(Keyword.Declaration, Whitespace, Keyword.Declaration, + Whitespace, Keyword.Declaration)), + (words(( + 'AddHandler', 'Alias', 'ByRef', 'ByVal', 'Call', 'Case', + 'Catch', 'CBool', 'CByte', 'CChar', 'CDate', 'CDec', 'CDbl', + 'CInt', 'CLng', 'CObj', 'Continue', 'CSByte', 'CShort', 'CSng', + 'CStr', 'CType', 'CUInt', 'CULng', 'CUShort', 'Declare', + 'Default', 'Delegate', 'DirectCast', 'Do', 'Each', 'Else', + 'ElseIf', 'EndIf', 'Erase', 'Error', 'Event', 'Exit', 'False', + 'Finally', 'For', 'Friend', 'Get', 'Global', 'GoSub', 'GoTo', + 'Handles', 'If', 'Implements', 'Inherits', 'Interface', 'Let', + 'Lib', 'Loop', 'Me', 'MustInherit', 'MustOverride', 'MyBase', + 'MyClass', 'Narrowing', 'New', 'Next', 'Not', 'Nothing', + 'NotInheritable', 'NotOverridable', 'Of', 'On', 'Operator', + 'Option', 'Optional', 'Overloads', 'Overridable', 'Overrides', + 'ParamArray', 'Partial', 'Private', 'Protected', 'Public', + 'RaiseEvent', 'ReadOnly', 'ReDim', 'RemoveHandler', 'Resume', + 'Return', 'Select', 'Set', 'Shadows', 'Shared', 'Single', + 'Static', 'Step', 'Stop', 'SyncLock', 'Then', 'Throw', 'To', + 'True', 'Try', 'TryCast', 'Wend', 'Using', 'When', 'While', + 'Widening', 'With', 'WithEvents', 'WriteOnly'), + prefix=r'(?>=|<<|>>|:=|' + r'<=|>=|<>|[-&*/\\^+=<>\[\]]', + Operator), + ('"', String, 'string'), + (r'(_)(\n)', bygroups(Text, Whitespace)), # Line continuation (must be before Name) + (uni_name + '[%&@!#$]?', Name), + ('#.*?#', Literal.Date), + (r'(\d+\.\d*|\d*\.\d+)(F[+-]?[0-9]+)?', Number.Float), + (r'\d+([SILDFR]|US|UI|UL)?', Number.Integer), + (r'&H[0-9a-f]+([SILDFR]|US|UI|UL)?', Number.Integer), + (r'&O[0-7]+([SILDFR]|US|UI|UL)?', Number.Integer), + ], + 'string': [ + (r'""', String), + (r'"C?', String, '#pop'), + (r'[^"]+', String), + ], + 'dim': [ + (uni_name, Name.Variable, '#pop'), + default('#pop'), # any other syntax + ], + 'funcname': [ + (uni_name, Name.Function, '#pop'), + ], + 'classname': [ + (uni_name, Name.Class, '#pop'), + ], + 'namespace': [ + (uni_name, Name.Namespace), + (r'\.', Name.Namespace), + default('#pop'), + ], + 'end': [ + (r'\s+', Whitespace), + (r'(Function|Sub|Property|Class|Structure|Enum|Module|Namespace)\b', + Keyword, '#pop'), + default('#pop'), + ] + } + + def analyse_text(text): + if re.search(r'^\s*(#If|Module|Namespace)', text, re.MULTILINE): + return 0.5 + + +class GenericAspxLexer(RegexLexer): + """ + Lexer for ASP.NET pages. + """ + + name = 'aspx-gen' + filenames = [] + mimetypes = [] + + flags = re.DOTALL + + tokens = { + 'root': [ + (r'(<%[@=#]?)(.*?)(%>)', bygroups(Name.Tag, Other, Name.Tag)), + (r'()(.*?)()', bygroups(using(XmlLexer), + Other, + using(XmlLexer))), + (r'(.+?)(?=<)', using(XmlLexer)), + (r'.+', using(XmlLexer)), + ], + } + + +# TODO support multiple languages within the same source file +class CSharpAspxLexer(DelegatingLexer): + """ + Lexer for highlighting C# within ASP.NET pages. + """ + + name = 'aspx-cs' + aliases = ['aspx-cs'] + filenames = ['*.aspx', '*.asax', '*.ascx', '*.ashx', '*.asmx', '*.axd'] + mimetypes = [] + + def __init__(self, **options): + super().__init__(CSharpLexer, GenericAspxLexer, **options) + + def analyse_text(text): + if re.search(r'Page\s*Language="C#"', text, re.I) is not None: + return 0.2 + elif re.search(r'script[^>]+language=["\']C#', text, re.I) is not None: + return 0.15 + + +class VbNetAspxLexer(DelegatingLexer): + """ + Lexer for highlighting Visual Basic.net within ASP.NET pages. + """ + + name = 'aspx-vb' + aliases = ['aspx-vb'] + filenames = ['*.aspx', '*.asax', '*.ascx', '*.ashx', '*.asmx', '*.axd'] + mimetypes = [] + + def __init__(self, **options): + super().__init__(VbNetLexer, GenericAspxLexer, **options) + + def analyse_text(text): + if re.search(r'Page\s*Language="Vb"', text, re.I) is not None: + return 0.2 + elif re.search(r'script[^>]+language=["\']vb', text, re.I) is not None: + return 0.15 + + +# Very close to functional.OcamlLexer +class FSharpLexer(RegexLexer): + """ + For the F# language (version 3.0). + + .. versionadded:: 1.5 + """ + + name = 'F#' + url = 'https://fsharp.org/' + aliases = ['fsharp', 'f#'] + filenames = ['*.fs', '*.fsi', '*.fsx'] + mimetypes = ['text/x-fsharp'] + + keywords = [ + 'abstract', 'as', 'assert', 'base', 'begin', 'class', 'default', + 'delegate', 'do!', 'do', 'done', 'downcast', 'downto', 'elif', 'else', + 'end', 'exception', 'extern', 'false', 'finally', 'for', 'function', + 'fun', 'global', 'if', 'inherit', 'inline', 'interface', 'internal', + 'in', 'lazy', 'let!', 'let', 'match', 'member', 'module', 'mutable', + 'namespace', 'new', 'null', 'of', 'open', 'override', 'private', 'public', + 'rec', 'return!', 'return', 'select', 'static', 'struct', 'then', 'to', + 'true', 'try', 'type', 'upcast', 'use!', 'use', 'val', 'void', 'when', + 'while', 'with', 'yield!', 'yield', + ] + # Reserved words; cannot hurt to color them as keywords too. + keywords += [ + 'atomic', 'break', 'checked', 'component', 'const', 'constraint', + 'constructor', 'continue', 'eager', 'event', 'external', 'fixed', + 'functor', 'include', 'method', 'mixin', 'object', 'parallel', + 'process', 'protected', 'pure', 'sealed', 'tailcall', 'trait', + 'virtual', 'volatile', + ] + keyopts = [ + '!=', '#', '&&', '&', r'\(', r'\)', r'\*', r'\+', ',', r'-\.', + '->', '-', r'\.\.', r'\.', '::', ':=', ':>', ':', ';;', ';', '<-', + r'<\]', '<', r'>\]', '>', r'\?\?', r'\?', r'\[<', r'\[\|', r'\[', r'\]', + '_', '`', r'\{', r'\|\]', r'\|', r'\}', '~', '<@@', '<@', '=', '@>', '@@>', + ] + + operators = r'[!$%&*+\./:<=>?@^|~-]' + word_operators = ['and', 'or', 'not'] + prefix_syms = r'[!?~]' + infix_syms = r'[=<>@^|&+\*/$%-]' + primitives = [ + 'sbyte', 'byte', 'char', 'nativeint', 'unativeint', 'float32', 'single', + 'float', 'double', 'int8', 'uint8', 'int16', 'uint16', 'int32', + 'uint32', 'int64', 'uint64', 'decimal', 'unit', 'bool', 'string', + 'list', 'exn', 'obj', 'enum', + ] + + # See http://msdn.microsoft.com/en-us/library/dd233181.aspx and/or + # http://fsharp.org/about/files/spec.pdf for reference. Good luck. + + tokens = { + 'escape-sequence': [ + (r'\\[\\"\'ntbrafv]', String.Escape), + (r'\\[0-9]{3}', String.Escape), + (r'\\u[0-9a-fA-F]{4}', String.Escape), + (r'\\U[0-9a-fA-F]{8}', String.Escape), + ], + 'root': [ + (r'\s+', Whitespace), + (r'\(\)|\[\]', Name.Builtin.Pseudo), + (r'\b(? and <| are weak + indicators.""" + result = 0 + if '|>' in text: + result += 0.05 + if '<|' in text: + result += 0.05 + + return result + + +class XppLexer(RegexLexer): + + """ + For X++ source code. This is based loosely on the CSharpLexer + + .. versionadded:: 2.15 + """ + + name = 'X++' + url = 'https://learn.microsoft.com/en-us/dynamics365/fin-ops-core/dev-itpro/dev-ref/xpp-language-reference' + aliases = ['xpp', 'x++'] + filenames = ['*.xpp'] + + flags = re.MULTILINE + + XPP_CHARS = ('@?(?:_|[^' + + uni.allexcept('Lu', 'Ll', 'Lt', 'Lm', 'Lo', 'Nl') + '])' + + '[^' + uni.allexcept('Lu', 'Ll', 'Lt', 'Lm', 'Lo', 'Nl', + 'Nd', 'Pc', 'Cf', 'Mn', 'Mc') + ']*'); + # Temporary, see + # https://github.com/thatch/regexlint/pull/49 + XPP_CHARS = XPP_CHARS.replace('\x00', '\x01') + + OPERATORS = ( + '<=', '>=', '+=', '-=', '*=', '/=', '!=', '==', + '&&', '||', '>>', '<<', '++', '--', '+', '-', '*', + '/', '%', '&', '|', '^', '<', '>', '?', '!', '~', '=', + ) + KEYWORDS = ('abstract','anytype','as','async','asc','at','avg','break','breakpoint','by','byref','case','catch', + 'changecompany','client','container','continue','count','crosscompany','default','delegate', + 'delete_from','desc','display','div','do','edit','else','element','eventhandler','exists','false','final', + 'firstfast','firstonly','firstonly10','firstonly100','firstonly1000','flush','for','forceliterals', + 'forcenestedloop','forceplaceholders','forceselectorder','forupdate','from','group','if','insert_recordset', + 'interface','is','join','like','maxof','minof','mod','new','next','nofetch','notexists','null','optimisticlock','order', + 'outer','pause','pessimisticlock','print','private','protected','public','repeatableread','retry','return', + 'reverse','select','server','setting','static','sum','super','switch','tablelock','this','throw','true','try','ttsabort','ttsbegin', + 'ttscommit','update_recordset','validtimestate','void','where','while','window') + RUNTIME_FUNCTIONS = ('_duration','abs','acos','any2Date','any2Enum','any2Guid','any2Int','any2Int64','any2Real','any2Str','anytodate', + 'anytoenum','anytoguid','anytoint','anytoint64','anytoreal','anytostr','asin','atan','beep','cTerm','char2Num','classIdGet', + 'corrFlagGet','corrFlagSet','cos','cosh','curExt','curUserId','date2Num','date2Str','datetime2Str','dayName','dayOfMth', + 'dayOfWk','dayOfYr','ddb','decRound','dg','dimOf','endMth','enum2str','exp','exp10','fV','fieldId2Name','fieldId2PName', + 'fieldName2Id','frac','funcName','getCurrentPartition','getCurrentPartitionRecId','getPrefix','guid2Str','idg','indexId2Name', + 'indexName2Id','int2Str','int642Str','intvMax','intvName','intvNo','intvNorm','log10','logN','match','max','min','mkDate','mthName', + 'mthOfYr','newGuid','nextMth','nextQtr','nextYr','num2Char','num2Date','num2Str','pmt','power','prevMth','prevQtr','prevYr', + 'prmIsDefault','pt','pv','rate','refPrintAll','round','runAs','sessionId','setPrefix','sin','sinh','sleep','sln','str2Date', + 'str2Datetime','str2Enum','str2Guid','str2Int','str2Int64','str2Num','str2Time','strAlpha','strCmp','strColSeq','strDel', + 'strFind','strFmt','strIns','strKeep','strLTrim','strLen','strLine','strLwr','strNFind','strPoke','strPrompt','strRTrim', + 'strRem','strRep','strScan','strUpr','subStr','syd','systemDateGet','systemDateSet','tableId2Name', + 'tableId2PName','tableName2Id','tan','tanh','term','time2Str','timeNow','today','trunc','typeOf','uint2Str','wkOfYr','year') + COMPILE_FUNCTIONS = ('attributeStr','classNum','classStr','configurationKeyNum','configurationKeyStr','dataEntityDataSourceStr','delegateStr', + 'dimensionHierarchyLevelStr','dimensionHierarchyStr','dimensionReferenceStr','dutyStr','enumCnt','enumLiteralStr','enumNum','enumStr', + 'extendedTypeNum','extendedTypeStr','fieldNum','fieldPName','fieldStr','formControlStr','formDataFieldStr','formDataSourceStr', + 'formMethodStr','formStr','identifierStr','indexNum','indexStr','licenseCodeNum','licenseCodeStr','literalStr','maxDate','maxInt', + 'measureStr','measurementStr','menuItemActionStr','menuItemDisplayStr','menuItemOutputStr','menuStr','methodStr','minInt','privilegeStr', + 'queryDatasourceStr','queryMethodStr','queryStr','reportStr','resourceStr','roleStr','ssrsReportStr','staticDelegateStr','staticMethodStr', + 'tableCollectionStr','tableFieldGroupStr','tableMethodStr','tableNum','tablePName','tableStaticMethodStr','tableStr','tileStr','varStr', + 'webActionItemStr','webDisplayContentItemStr','webFormStr','webMenuStr','webOutputContentItemStr','webReportStr','webSiteTempStr', + 'webStaticFileStr','webUrlItemStr','webWebPartStr','webletItemStr','webpageDefStr','websiteDefStr','workflowApprovalStr', + 'workflowCategoryStr','workflowTaskStr','workflowTypeStr') + + tokens = {} + + tokens = { + 'root': [ + # method names + (r'(\s*)\b(else|if)\b([^\n])', bygroups(Whitespace, Keyword, using(this))), # ensure that if is not treated like a function + (r'^([ \t]*)((?:' + XPP_CHARS + r'(?:\[\])?\s+)+?)' # return type + r'(' + XPP_CHARS + ')' # method name + r'(\s*)(\()', # signature start + bygroups(Whitespace, using(this), Name.Function, Whitespace, + Punctuation)), + (r'^(\s*)(\[)([^\n]*?)(\])', bygroups(Whitespace, Name.Attribute, Name.Variable.Class, Name.Attribute)), + (r'[^\S\n]+', Whitespace), + (r'(\\)(\n)', bygroups(Text, Whitespace)), # line continuation + (r'//[^\n]*?\n', Comment.Single), + (r'/[*][^\n]*?[*]/', Comment.Multiline), + (r'\n', Whitespace), + (words(OPERATORS), Operator), + (r'=~|!=|==|<<|>>|[-+/*%=<>&^|]', Operator), + (r'[()\[\];:,.#@]', Punctuation), + (r'[{}]', Punctuation), + (r'@"(""|[^"])*"', String), + (r'\$?"(\\\\|\\[^\\]|[^"\\\n])*["\n]', String), + (r"'\\.'|'[^\\]'", String.Char), + (r"[0-9]+(\.[0-9]*)?([eE][+-][0-9]+)?" + r"[flFLdD]?|0[xX][0-9a-fA-F]+[Ll]?", Number), + (words(KEYWORDS, suffix=r'\b'), Keyword), + (r'(boolean|int|int64|str|real|guid|date)\b\??', Keyword.Type), + (r'(class|struct|extends|implements)(\s+)', bygroups(Keyword, Whitespace), 'class'), + (r'('+XPP_CHARS+')(::)', bygroups(Name.Variable.Class, Punctuation)), + (r'(\s*)(\w+)(\s+\w+(,|=)?[^\n]*;)', bygroups(Whitespace, Name.Variable.Class, using(this))), # declaration + # x++ specific function to get field should highlight the classname + (r'(fieldNum\()('+XPP_CHARS+r')(\s*,\s*)('+XPP_CHARS+r')(\s*\))', + bygroups(using(this), Name.Variable.Class, using(this), Name.Property, using(this))), + # x++ specific function to get table should highlight the classname + (r'(tableNum\()('+XPP_CHARS+r')(\s*\))', + bygroups(using(this), Name.Variable.Class, using(this))), + (words(RUNTIME_FUNCTIONS, suffix=r'(?=\()'), Name.Function.Magic), + (words(COMPILE_FUNCTIONS, suffix=r'(?=\()'), Name.Function.Magic), + (XPP_CHARS, Name), + ], + 'class': [ + (XPP_CHARS, Name.Class, '#pop'), + default('#pop'), + ], + 'namespace': [ + (r'(?=\()', Text, '#pop'), # using (resource) + ('(' + XPP_CHARS + r'|\.)+', Name.Namespace, '#pop'), + ] + } diff --git a/shared/package-files/56/5637254ab13a7f25d7584d074a6c0fa33cde89d8b7a9f397d94291ce47025fd0/blob b/shared/package-files/56/5637254ab13a7f25d7584d074a6c0fa33cde89d8b7a9f397d94291ce47025fd0/blob new file mode 100644 index 0000000000000000000000000000000000000000..08d54433f81df2aef0b82ffb1db85ce1de61489b --- /dev/null +++ b/shared/package-files/56/5637254ab13a7f25d7584d074a6c0fa33cde89d8b7a9f397d94291ce47025fd0/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::_cast_Half(Tensor self, bool non_blocking=False) -> Tensor +inline at::Tensor _cast_Half(const at::Tensor & self, bool non_blocking=false) { + return at::_ops::_cast_Half::call(self, non_blocking); +} + +} diff --git a/shared/package-files/56/563906875503fa2d734c3035757a35b9e712b3a3f11dd190e6f94293f982098b/blob b/shared/package-files/56/563906875503fa2d734c3035757a35b9e712b3a3f11dd190e6f94293f982098b/blob new file mode 100644 index 0000000000000000000000000000000000000000..7c74ad3c86e54cb7e9939ed2bf96aa59cc6dcd06 --- /dev/null +++ b/shared/package-files/56/563906875503fa2d734c3035757a35b9e712b3a3f11dd190e6f94293f982098b/blob @@ -0,0 +1,35 @@ +import sys + + +def main(args=None): + if args is None: + args = sys.argv[1:] + + # TODO Handle library-wide options. Eg.: + # --unicodedata + # --verbose / other logging stuff + + # TODO Allow a way to run arbitrary modules? Useful for setting + # library-wide options and calling another library. Eg.: + # + # $ fonttools --unicodedata=... fontmake ... + # + # This allows for a git-like command where thirdparty commands + # can be added. Should we just try importing the fonttools + # module first and try without if it fails? + + if len(sys.argv) < 2: + sys.argv.append("help") + if sys.argv[1] == "-h" or sys.argv[1] == "--help": + sys.argv[1] = "help" + mod = "fontTools." + sys.argv[1] + sys.argv[1] = sys.argv[0] + " " + sys.argv[1] + del sys.argv[0] + + import runpy + + runpy.run_module(mod, run_name="__main__") + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/shared/package-files/56/56396fd018eb070fa3025504ec0605f95dfe1aa31824ef3068d3a92c144ab373/blob b/shared/package-files/56/56396fd018eb070fa3025504ec0605f95dfe1aa31824ef3068d3a92c144ab373/blob new file mode 100644 index 0000000000000000000000000000000000000000..36aafedd7aa36fc7f88928f9021e92dcc9fe95eb --- /dev/null +++ b/shared/package-files/56/56396fd018eb070fa3025504ec0605f95dfe1aa31824ef3068d3a92c144ab373/blob @@ -0,0 +1,104 @@ +import sys +from typing import Any, AsyncIterator, Awaitable, Callable, Iterable, Optional, Tuple, Union + +from . import events, protocols, transports + +_ClientConnectedCallback = Callable[[StreamReader, StreamWriter], Optional[Awaitable[None]]] + +if sys.version_info < (3, 8): + class IncompleteReadError(EOFError): + expected: Optional[int] + partial: bytes + def __init__(self, partial: bytes, expected: Optional[int]) -> None: ... + class LimitOverrunError(Exception): + consumed: int + def __init__(self, message: str, consumed: int) -> None: ... + +async def open_connection( + host: Optional[str] = ..., + port: Optional[Union[int, str]] = ..., + *, + loop: Optional[events.AbstractEventLoop] = ..., + limit: int = ..., + ssl_handshake_timeout: Optional[float] = ..., + **kwds: Any, +) -> Tuple[StreamReader, StreamWriter]: ... +async def start_server( + client_connected_cb: _ClientConnectedCallback, + host: Optional[str] = ..., + port: Optional[Union[int, str]] = ..., + *, + loop: Optional[events.AbstractEventLoop] = ..., + limit: int = ..., + ssl_handshake_timeout: Optional[float] = ..., + **kwds: Any, +) -> events.AbstractServer: ... + +if sys.platform != "win32": + if sys.version_info >= (3, 7): + from os import PathLike + + _PathType = Union[str, PathLike[str]] + else: + _PathType = str + async def open_unix_connection( + path: Optional[_PathType] = ..., *, loop: Optional[events.AbstractEventLoop] = ..., limit: int = ..., **kwds: Any + ) -> Tuple[StreamReader, StreamWriter]: ... + async def start_unix_server( + client_connected_cb: _ClientConnectedCallback, + path: Optional[_PathType] = ..., + *, + loop: Optional[events.AbstractEventLoop] = ..., + limit: int = ..., + **kwds: Any, + ) -> events.AbstractServer: ... + +class FlowControlMixin(protocols.Protocol): ... + +class StreamReaderProtocol(FlowControlMixin, protocols.Protocol): + def __init__( + self, + stream_reader: StreamReader, + client_connected_cb: Optional[_ClientConnectedCallback] = ..., + loop: Optional[events.AbstractEventLoop] = ..., + ) -> None: ... + def connection_made(self, transport: transports.BaseTransport) -> None: ... + def connection_lost(self, exc: Optional[Exception]) -> None: ... + def data_received(self, data: bytes) -> None: ... + def eof_received(self) -> bool: ... + +class StreamWriter: + def __init__( + self, + transport: transports.BaseTransport, + protocol: protocols.BaseProtocol, + reader: Optional[StreamReader], + loop: events.AbstractEventLoop, + ) -> None: ... + @property + def transport(self) -> transports.BaseTransport: ... + def write(self, data: bytes) -> None: ... + def writelines(self, data: Iterable[bytes]) -> None: ... + def write_eof(self) -> None: ... + def can_write_eof(self) -> bool: ... + def close(self) -> None: ... + if sys.version_info >= (3, 7): + def is_closing(self) -> bool: ... + async def wait_closed(self) -> None: ... + def get_extra_info(self, name: str, default: Any = ...) -> Any: ... + async def drain(self) -> None: ... + +class StreamReader: + def __init__(self, limit: int = ..., loop: Optional[events.AbstractEventLoop] = ...) -> None: ... + def exception(self) -> Exception: ... + def set_exception(self, exc: Exception) -> None: ... + def set_transport(self, transport: transports.BaseTransport) -> None: ... + def feed_eof(self) -> None: ... + def at_eof(self) -> bool: ... + def feed_data(self, data: bytes) -> None: ... + async def readline(self) -> bytes: ... + async def readuntil(self, separator: bytes = ...) -> bytes: ... + async def read(self, n: int = ...) -> bytes: ... + async def readexactly(self, n: int) -> bytes: ... + def __aiter__(self) -> AsyncIterator[bytes]: ... + async def __anext__(self) -> bytes: ... diff --git a/shared/package-files/56/5639df194837f65be5083c4ea0649e626f142f5db0642e724399fcf96f76783d/blob b/shared/package-files/56/5639df194837f65be5083c4ea0649e626f142f5db0642e724399fcf96f76783d/blob new file mode 100644 index 0000000000000000000000000000000000000000..8a36e0948332e324a41487efc757bc1b0acc50c6 --- /dev/null +++ b/shared/package-files/56/5639df194837f65be5083c4ea0649e626f142f5db0642e724399fcf96f76783d/blob @@ -0,0 +1,301 @@ +from __future__ import annotations + +import importlib +import types +import warnings + +__all__ = ["registry", "get_filesystem_class", "default"] + +# internal, mutable +_registry: dict[str, type] = {} + +# external, immutable +registry = types.MappingProxyType(_registry) +default = "file" + + +def register_implementation(name, cls, clobber=False, errtxt=None): + """Add implementation class to the registry + + Parameters + ---------- + name: str + Protocol name to associate with the class + cls: class or str + if a class: fsspec-compliant implementation class (normally inherits from + ``fsspec.AbstractFileSystem``, gets added straight to the registry. If a + str, the full path to an implementation class like package.module.class, + which gets added to known_implementations, + so the import is deferred until the filesystem is actually used. + clobber: bool (optional) + Whether to overwrite a protocol with the same name; if False, will raise + instead. + errtxt: str (optional) + If given, then a failure to import the given class will result in this + text being given. + """ + if isinstance(cls, str): + if name in known_implementations and clobber is False: + if cls != known_implementations[name]["class"]: + raise ValueError( + f"Name ({name}) already in the known_implementations and clobber " + f"is False" + ) + else: + known_implementations[name] = { + "class": cls, + "err": errtxt or f"{cls} import failed for protocol {name}", + } + + else: + if name in registry and clobber is False: + if _registry[name] is not cls: + raise ValueError( + f"Name ({name}) already in the registry and clobber is False" + ) + else: + _registry[name] = cls + + +# protocols mapped to the class which implements them. This dict can be +# updated with register_implementation +known_implementations = { + "abfs": { + "class": "adlfs.AzureBlobFileSystem", + "err": "Install adlfs to access Azure Datalake Gen2 and Azure Blob Storage", + }, + "adl": { + "class": "adlfs.AzureDatalakeFileSystem", + "err": "Install adlfs to access Azure Datalake Gen1", + }, + "arrow_hdfs": { + "class": "fsspec.implementations.arrow.HadoopFileSystem", + "err": "pyarrow and local java libraries required for HDFS", + }, + "asynclocal": { + "class": "morefs.asyn_local.AsyncLocalFileSystem", + "err": "Install 'morefs[asynclocalfs]' to use AsyncLocalFileSystem", + }, + "az": { + "class": "adlfs.AzureBlobFileSystem", + "err": "Install adlfs to access Azure Datalake Gen2 and Azure Blob Storage", + }, + "blockcache": {"class": "fsspec.implementations.cached.CachingFileSystem"}, + "box": { + "class": "boxfs.BoxFileSystem", + "err": "Please install boxfs to access BoxFileSystem", + }, + "cached": {"class": "fsspec.implementations.cached.CachingFileSystem"}, + "dask": { + "class": "fsspec.implementations.dask.DaskWorkerFileSystem", + "err": "Install dask distributed to access worker file system", + }, + "data": {"class": "fsspec.implementations.data.DataFileSystem"}, + "dbfs": { + "class": "fsspec.implementations.dbfs.DatabricksFileSystem", + "err": "Install the requests package to use the DatabricksFileSystem", + }, + "dir": {"class": "fsspec.implementations.dirfs.DirFileSystem"}, + "dropbox": { + "class": "dropboxdrivefs.DropboxDriveFileSystem", + "err": ( + 'DropboxFileSystem requires "dropboxdrivefs","requests" and "' + '"dropbox" to be installed' + ), + }, + "dvc": { + "class": "dvc.api.DVCFileSystem", + "err": "Install dvc to access DVCFileSystem", + }, + "file": {"class": "fsspec.implementations.local.LocalFileSystem"}, + "filecache": {"class": "fsspec.implementations.cached.WholeFileCacheFileSystem"}, + "ftp": {"class": "fsspec.implementations.ftp.FTPFileSystem"}, + "gcs": { + "class": "gcsfs.GCSFileSystem", + "err": "Please install gcsfs to access Google Storage", + }, + "gdrive": { + "class": "gdrivefs.GoogleDriveFileSystem", + "err": "Please install gdrivefs for access to Google Drive", + }, + "generic": {"class": "fsspec.generic.GenericFileSystem"}, + "git": { + "class": "fsspec.implementations.git.GitFileSystem", + "err": "Install pygit2 to browse local git repos", + }, + "github": { + "class": "fsspec.implementations.github.GithubFileSystem", + "err": "Install the requests package to use the github FS", + }, + "gs": { + "class": "gcsfs.GCSFileSystem", + "err": "Please install gcsfs to access Google Storage", + }, + "hdfs": { + "class": "fsspec.implementations.arrow.HadoopFileSystem", + "err": "pyarrow and local java libraries required for HDFS", + }, + "hf": { + "class": "huggingface_hub.HfFileSystem", + "err": "Install huggingface_hub to access HfFileSystem", + }, + "http": { + "class": "fsspec.implementations.http.HTTPFileSystem", + "err": 'HTTPFileSystem requires "requests" and "aiohttp" to be installed', + }, + "https": { + "class": "fsspec.implementations.http.HTTPFileSystem", + "err": 'HTTPFileSystem requires "requests" and "aiohttp" to be installed', + }, + "jlab": { + "class": "fsspec.implementations.jupyter.JupyterFileSystem", + "err": "Jupyter FS requires requests to be installed", + }, + "jupyter": { + "class": "fsspec.implementations.jupyter.JupyterFileSystem", + "err": "Jupyter FS requires requests to be installed", + }, + "lakefs": { + "class": "lakefs_spec.LakeFSFileSystem", + "err": "Please install lakefs-spec to access LakeFSFileSystem", + }, + "libarchive": { + "class": "fsspec.implementations.libarchive.LibArchiveFileSystem", + "err": "LibArchive requires to be installed", + }, + "local": {"class": "fsspec.implementations.local.LocalFileSystem"}, + "memory": {"class": "fsspec.implementations.memory.MemoryFileSystem"}, + "oci": { + "class": "ocifs.OCIFileSystem", + "err": "Install ocifs to access OCI Object Storage", + }, + "ocilake": { + "class": "ocifs.OCIFileSystem", + "err": "Install ocifs to access OCI Data Lake", + }, + "oss": { + "class": "ossfs.OSSFileSystem", + "err": "Install ossfs to access Alibaba Object Storage System", + }, + "reference": {"class": "fsspec.implementations.reference.ReferenceFileSystem"}, + "root": { + "class": "fsspec_xrootd.XRootDFileSystem", + "err": ( + "Install fsspec-xrootd to access xrootd storage system. " + "Note: 'root' is the protocol name for xrootd storage systems, " + "not referring to root directories" + ), + }, + "s3": {"class": "s3fs.S3FileSystem", "err": "Install s3fs to access S3"}, + "s3a": {"class": "s3fs.S3FileSystem", "err": "Install s3fs to access S3"}, + "sftp": { + "class": "fsspec.implementations.sftp.SFTPFileSystem", + "err": 'SFTPFileSystem requires "paramiko" to be installed', + }, + "simplecache": {"class": "fsspec.implementations.cached.SimpleCacheFileSystem"}, + "smb": { + "class": "fsspec.implementations.smb.SMBFileSystem", + "err": 'SMB requires "smbprotocol" or "smbprotocol[kerberos]" installed', + }, + "ssh": { + "class": "fsspec.implementations.sftp.SFTPFileSystem", + "err": 'SFTPFileSystem requires "paramiko" to be installed', + }, + "tar": {"class": "fsspec.implementations.tar.TarFileSystem"}, + "wandb": {"class": "wandbfs.WandbFS", "err": "Install wandbfs to access wandb"}, + "webdav": { + "class": "webdav4.fsspec.WebdavFileSystem", + "err": "Install webdav4 to access WebDAV", + }, + "webhdfs": { + "class": "fsspec.implementations.webhdfs.WebHDFS", + "err": 'webHDFS access requires "requests" to be installed', + }, + "zip": {"class": "fsspec.implementations.zip.ZipFileSystem"}, +} + + +def get_filesystem_class(protocol): + """Fetch named protocol implementation from the registry + + The dict ``known_implementations`` maps protocol names to the locations + of classes implementing the corresponding file-system. When used for the + first time, appropriate imports will happen and the class will be placed in + the registry. All subsequent calls will fetch directly from the registry. + + Some protocol implementations require additional dependencies, and so the + import may fail. In this case, the string in the "err" field of the + ``known_implementations`` will be given as the error message. + """ + if not protocol: + protocol = default + + if protocol not in registry: + if protocol not in known_implementations: + raise ValueError(f"Protocol not known: {protocol}") + bit = known_implementations[protocol] + try: + register_implementation(protocol, _import_class(bit["class"])) + except ImportError as e: + raise ImportError(bit["err"]) from e + cls = registry[protocol] + if getattr(cls, "protocol", None) in ("abstract", None): + cls.protocol = protocol + + return cls + + +s3_msg = """Your installed version of s3fs is very old and known to cause +severe performance issues, see also https://github.com/dask/dask/issues/10276 + +To fix, you should specify a lower version bound on s3fs, or +update the current installation. +""" + + +def _import_class(cls, minv=None): + """Take a string FQP and return the imported class or identifier + + cls is of the form "package.module.klass" or "package.module:subobject.klass" + """ + if ":" in cls: + mod, name = cls.rsplit(":", 1) + s3 = mod == "s3fs" + mod = importlib.import_module(mod) + if s3 and mod.__version__.split(".") < ["0", "5"]: + warnings.warn(s3_msg) + for part in name.split("."): + mod = getattr(mod, part) + return mod + else: + mod, name = cls.rsplit(".", 1) + s3 = mod == "s3fs" + mod = importlib.import_module(mod) + if s3 and mod.__version__.split(".") < ["0", "5"]: + warnings.warn(s3_msg) + return getattr(mod, name) + + +def filesystem(protocol, **storage_options): + """Instantiate filesystems for given protocol and arguments + + ``storage_options`` are specific to the protocol being chosen, and are + passed directly to the class. + """ + if protocol == "arrow_hdfs": + warnings.warn( + "The 'arrow_hdfs' protocol has been deprecated and will be " + "removed in the future. Specify it as 'hdfs'.", + DeprecationWarning, + ) + + cls = get_filesystem_class(protocol) + return cls(**storage_options) + + +def available_protocols(): + """Return a list of the implemented protocols. + + Note that any given protocol may require extra packages to be importable. + """ + return list(known_implementations) diff --git a/shared/package-files/56/563a1e1c187330875efa2ccd66fae72633c03e07e5497df26d155b2eb6f2a38b/blob b/shared/package-files/56/563a1e1c187330875efa2ccd66fae72633c03e07e5497df26d155b2eb6f2a38b/blob new file mode 100644 index 0000000000000000000000000000000000000000..583ae4923ceccbde2d5df17767e2903806abd9f4 --- /dev/null +++ b/shared/package-files/56/563a1e1c187330875efa2ccd66fae72633c03e07e5497df26d155b2eb6f2a38b/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 nonzero_numpy { + using schema = ::std::vector (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::nonzero_numpy") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "nonzero_numpy(Tensor self) -> Tensor[]") + static ::std::vector call(const at::Tensor & self); + static ::std::vector redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self); +}; + +}} // namespace at::_ops diff --git a/shared/package-files/56/563b9052bebaf2986ae5b707e34afde013e7641287cc97ff31005f33a0dbf7a5/blob b/shared/package-files/56/563b9052bebaf2986ae5b707e34afde013e7641287cc97ff31005f33a0dbf7a5/blob new file mode 100644 index 0000000000000000000000000000000000000000..fb5185ca60283bd56f795e9a956274c0b6e63325 Binary files /dev/null and b/shared/package-files/56/563b9052bebaf2986ae5b707e34afde013e7641287cc97ff31005f33a0dbf7a5/blob differ diff --git a/shared/package-files/56/563b9821194c63e2bbf616ab92d863cdc8052fbae59242cea00c0d7c4088959c/blob b/shared/package-files/56/563b9821194c63e2bbf616ab92d863cdc8052fbae59242cea00c0d7c4088959c/blob new file mode 100644 index 0000000000000000000000000000000000000000..1b0d21d5d38be9288a7e3c3405bd4ca449cfd808 --- /dev/null +++ b/shared/package-files/56/563b9821194c63e2bbf616ab92d863cdc8052fbae59242cea00c0d7c4088959c/blob @@ -0,0 +1,857 @@ +"""Principal Component Analysis.""" + +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause + +from math import lgamma, log, sqrt +from numbers import Integral, Real + +import numpy as np +from scipy import linalg +from scipy.sparse import issparse +from scipy.sparse.linalg import svds + +from ..base import _fit_context +from ..utils import check_random_state +from ..utils._arpack import _init_arpack_v0 +from ..utils._array_api import _convert_to_numpy, get_namespace +from ..utils._param_validation import Interval, RealNotInt, StrOptions +from ..utils.extmath import _randomized_svd, fast_logdet, stable_cumsum, svd_flip +from ..utils.sparsefuncs import _implicit_column_offset, mean_variance_axis +from ..utils.validation import check_is_fitted, validate_data +from ._base import _BasePCA + + +def _assess_dimension(spectrum, rank, n_samples): + """Compute the log-likelihood of a rank ``rank`` dataset. + + The dataset is assumed to be embedded in gaussian noise of shape(n, + dimf) having spectrum ``spectrum``. This implements the method of + T. P. Minka. + + Parameters + ---------- + spectrum : ndarray of shape (n_features,) + Data spectrum. + rank : int + Tested rank value. It should be strictly lower than n_features, + otherwise the method isn't specified (division by zero in equation + (31) from the paper). + n_samples : int + Number of samples. + + Returns + ------- + ll : float + The log-likelihood. + + References + ---------- + This implements the method of `Thomas P. Minka: + Automatic Choice of Dimensionality for PCA. NIPS 2000: 598-604 + `_ + """ + xp, _ = get_namespace(spectrum) + + n_features = spectrum.shape[0] + if not 1 <= rank < n_features: + raise ValueError("the tested rank should be in [1, n_features - 1]") + + eps = 1e-15 + + if spectrum[rank - 1] < eps: + # When the tested rank is associated with a small eigenvalue, there's + # no point in computing the log-likelihood: it's going to be very + # small and won't be the max anyway. Also, it can lead to numerical + # issues below when computing pa, in particular in log((spectrum[i] - + # spectrum[j]) because this will take the log of something very small. + return -xp.inf + + pu = -rank * log(2.0) + for i in range(1, rank + 1): + pu += ( + lgamma((n_features - i + 1) / 2.0) - log(xp.pi) * (n_features - i + 1) / 2.0 + ) + + pl = xp.sum(xp.log(spectrum[:rank])) + pl = -pl * n_samples / 2.0 + + v = max(eps, xp.sum(spectrum[rank:]) / (n_features - rank)) + pv = -log(v) * n_samples * (n_features - rank) / 2.0 + + m = n_features * rank - rank * (rank + 1.0) / 2.0 + pp = log(2.0 * xp.pi) * (m + rank) / 2.0 + + pa = 0.0 + spectrum_ = xp.asarray(spectrum, copy=True) + spectrum_[rank:n_features] = v + for i in range(rank): + for j in range(i + 1, spectrum.shape[0]): + pa += log( + (spectrum[i] - spectrum[j]) * (1.0 / spectrum_[j] - 1.0 / spectrum_[i]) + ) + log(n_samples) + + ll = pu + pl + pv + pp - pa / 2.0 - rank * log(n_samples) / 2.0 + + return ll + + +def _infer_dimension(spectrum, n_samples): + """Infers the dimension of a dataset with a given spectrum. + + The returned value will be in [1, n_features - 1]. + """ + xp, _ = get_namespace(spectrum) + + ll = xp.empty_like(spectrum) + ll[0] = -xp.inf # we don't want to return n_components = 0 + for rank in range(1, spectrum.shape[0]): + ll[rank] = _assess_dimension(spectrum, rank, n_samples) + return xp.argmax(ll) + + +class PCA(_BasePCA): + """Principal component analysis (PCA). + + Linear dimensionality reduction using Singular Value Decomposition of the + data to project it to a lower dimensional space. The input data is centered + but not scaled for each feature before applying the SVD. + + It uses the LAPACK implementation of the full SVD or a randomized truncated + SVD by the method of Halko et al. 2009, depending on the shape of the input + data and the number of components to extract. + + With sparse inputs, the ARPACK implementation of the truncated SVD can be + used (i.e. through :func:`scipy.sparse.linalg.svds`). Alternatively, one + may consider :class:`TruncatedSVD` where the data are not centered. + + Notice that this class only supports sparse inputs for some solvers such as + "arpack" and "covariance_eigh". See :class:`TruncatedSVD` for an + alternative with sparse data. + + For a usage example, see + :ref:`sphx_glr_auto_examples_decomposition_plot_pca_iris.py` + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + n_components : int, float or 'mle', default=None + Number of components to keep. + if n_components is not set all components are kept:: + + n_components == min(n_samples, n_features) + + If ``n_components == 'mle'`` and ``svd_solver == 'full'``, Minka's + MLE is used to guess the dimension. Use of ``n_components == 'mle'`` + will interpret ``svd_solver == 'auto'`` as ``svd_solver == 'full'``. + + If ``0 < n_components < 1`` and ``svd_solver == 'full'``, select the + number of components such that the amount of variance that needs to be + explained is greater than the percentage specified by n_components. + + If ``svd_solver == 'arpack'``, the number of components must be + strictly less than the minimum of n_features and n_samples. + + Hence, the None case results in:: + + n_components == min(n_samples, n_features) - 1 + + copy : bool, default=True + If False, data passed to fit are overwritten and running + fit(X).transform(X) will not yield the expected results, + use fit_transform(X) instead. + + whiten : bool, default=False + When True (False by default) the `components_` vectors are multiplied + by the square root of n_samples and then divided by the singular values + to ensure uncorrelated outputs with unit component-wise variances. + + Whitening will remove some information from the transformed signal + (the relative variance scales of the components) but can sometime + improve the predictive accuracy of the downstream estimators by + making their data respect some hard-wired assumptions. + + svd_solver : {'auto', 'full', 'covariance_eigh', 'arpack', 'randomized'},\ + default='auto' + "auto" : + The solver is selected by a default 'auto' policy is based on `X.shape` and + `n_components`: if the input data has fewer than 1000 features and + more than 10 times as many samples, then the "covariance_eigh" + solver is used. Otherwise, if the input data is larger than 500x500 + and the number of components to extract is lower than 80% of the + smallest dimension of the data, then the more efficient + "randomized" method is selected. Otherwise the exact "full" SVD is + computed and optionally truncated afterwards. + "full" : + Run exact full SVD calling the standard LAPACK solver via + `scipy.linalg.svd` and select the components by postprocessing + "covariance_eigh" : + Precompute the covariance matrix (on centered data), run a + classical eigenvalue decomposition on the covariance matrix + typically using LAPACK and select the components by postprocessing. + This solver is very efficient for n_samples >> n_features and small + n_features. It is, however, not tractable otherwise for large + n_features (large memory footprint required to materialize the + covariance matrix). Also note that compared to the "full" solver, + this solver effectively doubles the condition number and is + therefore less numerical stable (e.g. on input data with a large + range of singular values). + "arpack" : + Run SVD truncated to `n_components` calling ARPACK solver via + `scipy.sparse.linalg.svds`. It requires strictly + `0 < n_components < min(X.shape)` + "randomized" : + Run randomized SVD by the method of Halko et al. + + .. versionadded:: 0.18.0 + + .. versionchanged:: 1.5 + Added the 'covariance_eigh' solver. + + tol : float, default=0.0 + Tolerance for singular values computed by svd_solver == 'arpack'. + Must be of range [0.0, infinity). + + .. versionadded:: 0.18.0 + + iterated_power : int or 'auto', default='auto' + Number of iterations for the power method computed by + svd_solver == 'randomized'. + Must be of range [0, infinity). + + .. versionadded:: 0.18.0 + + n_oversamples : int, default=10 + This parameter is only relevant when `svd_solver="randomized"`. + It corresponds to the additional number of random vectors to sample the + range of `X` so as to ensure proper conditioning. See + :func:`~sklearn.utils.extmath.randomized_svd` for more details. + + .. versionadded:: 1.1 + + power_iteration_normalizer : {'auto', 'QR', 'LU', 'none'}, default='auto' + Power iteration normalizer for randomized SVD solver. + Not used by ARPACK. See :func:`~sklearn.utils.extmath.randomized_svd` + for more details. + + .. versionadded:: 1.1 + + random_state : int, RandomState instance or None, default=None + Used when the 'arpack' or 'randomized' solvers are used. Pass an int + for reproducible results across multiple function calls. + See :term:`Glossary `. + + .. versionadded:: 0.18.0 + + Attributes + ---------- + components_ : ndarray of shape (n_components, n_features) + Principal axes in feature space, representing the directions of + maximum variance in the data. Equivalently, the right singular + vectors of the centered input data, parallel to its eigenvectors. + The components are sorted by decreasing ``explained_variance_``. + + explained_variance_ : ndarray of shape (n_components,) + The amount of variance explained by each of the selected components. + The variance estimation uses `n_samples - 1` degrees of freedom. + + Equal to n_components largest eigenvalues + of the covariance matrix of X. + + .. versionadded:: 0.18 + + explained_variance_ratio_ : ndarray of shape (n_components,) + Percentage of variance explained by each of the selected components. + + If ``n_components`` is not set then all components are stored and the + sum of the ratios is equal to 1.0. + + singular_values_ : ndarray of shape (n_components,) + The singular values corresponding to each of the selected components. + The singular values are equal to the 2-norms of the ``n_components`` + variables in the lower-dimensional space. + + .. versionadded:: 0.19 + + mean_ : ndarray of shape (n_features,) + Per-feature empirical mean, estimated from the training set. + + Equal to `X.mean(axis=0)`. + + n_components_ : int + The estimated number of components. When n_components is set + to 'mle' or a number between 0 and 1 (with svd_solver == 'full') this + number is estimated from input data. Otherwise it equals the parameter + n_components, or the lesser value of n_features and n_samples + if n_components is None. + + n_samples_ : int + Number of samples in the training data. + + noise_variance_ : float + The estimated noise covariance following the Probabilistic PCA model + from Tipping and Bishop 1999. See "Pattern Recognition and + Machine Learning" by C. Bishop, 12.2.1 p. 574 or + http://www.miketipping.com/papers/met-mppca.pdf. It is required to + compute the estimated data covariance and score samples. + + Equal to the average of (min(n_features, n_samples) - n_components) + smallest eigenvalues of the covariance matrix of X. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + KernelPCA : Kernel Principal Component Analysis. + SparsePCA : Sparse Principal Component Analysis. + TruncatedSVD : Dimensionality reduction using truncated SVD. + IncrementalPCA : Incremental Principal Component Analysis. + + References + ---------- + For n_components == 'mle', this class uses the method from: + `Minka, T. P.. "Automatic choice of dimensionality for PCA". + In NIPS, pp. 598-604 `_ + + Implements the probabilistic PCA model from: + `Tipping, M. E., and Bishop, C. M. (1999). "Probabilistic principal + component analysis". Journal of the Royal Statistical Society: + Series B (Statistical Methodology), 61(3), 611-622. + `_ + via the score and score_samples methods. + + For svd_solver == 'arpack', refer to `scipy.sparse.linalg.svds`. + + For svd_solver == 'randomized', see: + :doi:`Halko, N., Martinsson, P. G., and Tropp, J. A. (2011). + "Finding structure with randomness: Probabilistic algorithms for + constructing approximate matrix decompositions". + SIAM review, 53(2), 217-288. + <10.1137/090771806>` + and also + :doi:`Martinsson, P. G., Rokhlin, V., and Tygert, M. (2011). + "A randomized algorithm for the decomposition of matrices". + Applied and Computational Harmonic Analysis, 30(1), 47-68. + <10.1016/j.acha.2010.02.003>` + + Examples + -------- + >>> import numpy as np + >>> from sklearn.decomposition import PCA + >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) + >>> pca = PCA(n_components=2) + >>> pca.fit(X) + PCA(n_components=2) + >>> print(pca.explained_variance_ratio_) + [0.9924 0.0075] + >>> print(pca.singular_values_) + [6.30061 0.54980] + + >>> pca = PCA(n_components=2, svd_solver='full') + >>> pca.fit(X) + PCA(n_components=2, svd_solver='full') + >>> print(pca.explained_variance_ratio_) + [0.9924 0.00755] + >>> print(pca.singular_values_) + [6.30061 0.54980] + + >>> pca = PCA(n_components=1, svd_solver='arpack') + >>> pca.fit(X) + PCA(n_components=1, svd_solver='arpack') + >>> print(pca.explained_variance_ratio_) + [0.99244] + >>> print(pca.singular_values_) + [6.30061] + """ + + _parameter_constraints: dict = { + "n_components": [ + Interval(Integral, 0, None, closed="left"), + Interval(RealNotInt, 0, 1, closed="neither"), + StrOptions({"mle"}), + None, + ], + "copy": ["boolean"], + "whiten": ["boolean"], + "svd_solver": [ + StrOptions({"auto", "full", "covariance_eigh", "arpack", "randomized"}) + ], + "tol": [Interval(Real, 0, None, closed="left")], + "iterated_power": [ + StrOptions({"auto"}), + Interval(Integral, 0, None, closed="left"), + ], + "n_oversamples": [Interval(Integral, 1, None, closed="left")], + "power_iteration_normalizer": [StrOptions({"auto", "QR", "LU", "none"})], + "random_state": ["random_state"], + } + + def __init__( + self, + n_components=None, + *, + copy=True, + whiten=False, + svd_solver="auto", + tol=0.0, + iterated_power="auto", + n_oversamples=10, + power_iteration_normalizer="auto", + random_state=None, + ): + self.n_components = n_components + self.copy = copy + self.whiten = whiten + self.svd_solver = svd_solver + self.tol = tol + self.iterated_power = iterated_power + self.n_oversamples = n_oversamples + self.power_iteration_normalizer = power_iteration_normalizer + self.random_state = random_state + + @_fit_context(prefer_skip_nested_validation=True) + def fit(self, X, y=None): + """Fit the model with X. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + Training data, where `n_samples` is the number of samples + and `n_features` is the number of features. + + y : Ignored + Ignored. + + Returns + ------- + self : object + Returns the instance itself. + """ + self._fit(X) + return self + + @_fit_context(prefer_skip_nested_validation=True) + def fit_transform(self, X, y=None): + """Fit the model with X and apply the dimensionality reduction on X. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + Training data, where `n_samples` is the number of samples + and `n_features` is the number of features. + + y : Ignored + Ignored. + + Returns + ------- + X_new : ndarray of shape (n_samples, n_components) + Transformed values. + + Notes + ----- + This method returns a Fortran-ordered array. To convert it to a + C-ordered array, use 'np.ascontiguousarray'. + """ + U, S, _, X, x_is_centered, xp = self._fit(X) + if U is not None: + U = U[:, : self.n_components_] + + if self.whiten: + # X_new = X * V / S * sqrt(n_samples) = U * sqrt(n_samples) + U *= sqrt(X.shape[0] - 1) + else: + # X_new = X * V = U * S * Vt * V = U * S + U *= S[: self.n_components_] + + return U + else: # solver="covariance_eigh" does not compute U at fit time. + return self._transform(X, xp, x_is_centered=x_is_centered) + + def _fit(self, X): + """Dispatch to the right submethod depending on the chosen solver.""" + xp, is_array_api_compliant = get_namespace(X) + + # Raise an error for sparse input and unsupported svd_solver + if issparse(X) and self.svd_solver not in ["auto", "arpack", "covariance_eigh"]: + raise TypeError( + 'PCA only support sparse inputs with the "arpack" and' + f' "covariance_eigh" solvers, while "{self.svd_solver}" was passed. See' + " TruncatedSVD for a possible alternative." + ) + if self.svd_solver == "arpack" and is_array_api_compliant: + raise ValueError( + "PCA with svd_solver='arpack' is not supported for Array API inputs." + ) + + # Validate the data, without ever forcing a copy as any solver that + # supports sparse input data and the `covariance_eigh` solver are + # written in a way to avoid the need for any inplace modification of + # the input data contrary to the other solvers. + # The copy will happen + # later, only if needed, once the solver negotiation below is done. + X = validate_data( + self, + X, + dtype=[xp.float64, xp.float32], + force_writeable=True, + accept_sparse=("csr", "csc"), + ensure_2d=True, + copy=False, + ) + self._fit_svd_solver = self.svd_solver + if self._fit_svd_solver == "auto" and issparse(X): + self._fit_svd_solver = "arpack" + + if self.n_components is None: + if self._fit_svd_solver != "arpack": + n_components = min(X.shape) + else: + n_components = min(X.shape) - 1 + else: + n_components = self.n_components + + if self._fit_svd_solver == "auto": + # Tall and skinny problems are best handled by precomputing the + # covariance matrix. + if X.shape[1] <= 1_000 and X.shape[0] >= 10 * X.shape[1]: + self._fit_svd_solver = "covariance_eigh" + # Small problem or n_components == 'mle', just call full PCA + elif max(X.shape) <= 500 or n_components == "mle": + self._fit_svd_solver = "full" + elif 1 <= n_components < 0.8 * min(X.shape): + self._fit_svd_solver = "randomized" + # This is also the case of n_components in (0, 1) + else: + self._fit_svd_solver = "full" + + # Call different fits for either full or truncated SVD + if self._fit_svd_solver in ("full", "covariance_eigh"): + return self._fit_full(X, n_components, xp, is_array_api_compliant) + elif self._fit_svd_solver in ["arpack", "randomized"]: + return self._fit_truncated(X, n_components, xp) + + def _fit_full(self, X, n_components, xp, is_array_api_compliant): + """Fit the model by computing full SVD on X.""" + n_samples, n_features = X.shape + + if n_components == "mle": + if n_samples < n_features: + raise ValueError( + "n_components='mle' is only supported if n_samples >= n_features" + ) + elif not 0 <= n_components <= min(n_samples, n_features): + raise ValueError( + f"n_components={n_components} must be between 0 and " + f"min(n_samples, n_features)={min(n_samples, n_features)} with " + f"svd_solver={self._fit_svd_solver!r}" + ) + + self.mean_ = xp.mean(X, axis=0) + # When X is a scipy sparse matrix, self.mean_ is a numpy matrix, so we need + # to transform it to a 1D array. Note that this is not the case when X + # is a scipy sparse array. + # TODO: remove the following two lines when scikit-learn only depends + # on scipy versions that no longer support scipy.sparse matrices. + self.mean_ = xp.reshape(xp.asarray(self.mean_), (-1,)) + + if self._fit_svd_solver == "full": + X_centered = xp.asarray(X, copy=True) if self.copy else X + X_centered -= self.mean_ + x_is_centered = not self.copy + + if not is_array_api_compliant: + # Use scipy.linalg with NumPy/SciPy inputs for the sake of not + # introducing unanticipated behavior changes. In the long run we + # could instead decide to always use xp.linalg.svd for all inputs, + # but that would make this code rely on numpy's SVD instead of + # scipy's. It's not 100% clear whether they use the same LAPACK + # solver by default though (assuming both are built against the + # same BLAS). + U, S, Vt = linalg.svd(X_centered, full_matrices=False) + else: + U, S, Vt = xp.linalg.svd(X_centered, full_matrices=False) + explained_variance_ = (S**2) / (n_samples - 1) + + else: + assert self._fit_svd_solver == "covariance_eigh" + # In the following, we center the covariance matrix C afterwards + # (without centering the data X first) to avoid an unnecessary copy + # of X. Note that the mean_ attribute is still needed to center + # test data in the transform method. + # + # Note: at the time of writing, `xp.cov` does not exist in the + # Array API standard: + # https://github.com/data-apis/array-api/issues/43 + # + # Besides, using `numpy.cov`, as of numpy 1.26.0, would not be + # memory efficient for our use case when `n_samples >> n_features`: + # `numpy.cov` centers a copy of the data before computing the + # matrix product instead of subtracting a small `(n_features, + # n_features)` square matrix from the gram matrix X.T @ X, as we do + # below. + x_is_centered = False + C = X.T @ X + C -= ( + n_samples + * xp.reshape(self.mean_, (-1, 1)) + * xp.reshape(self.mean_, (1, -1)) + ) + C /= n_samples - 1 + eigenvals, eigenvecs = xp.linalg.eigh(C) + + # When X is a scipy sparse matrix, the following two datastructures + # are returned as instances of the soft-deprecated numpy.matrix + # class. Note that this problem does not occur when X is a scipy + # sparse array (or another other kind of supported array). + # TODO: remove the following two lines when scikit-learn only + # depends on scipy versions that no longer support scipy.sparse + # matrices. + eigenvals = xp.reshape(xp.asarray(eigenvals), (-1,)) + eigenvecs = xp.asarray(eigenvecs) + + eigenvals = xp.flip(eigenvals, axis=0) + eigenvecs = xp.flip(eigenvecs, axis=1) + + # The covariance matrix C is positive semi-definite by + # construction. However, the eigenvalues returned by xp.linalg.eigh + # can be slightly negative due to numerical errors. This would be + # an issue for the subsequent sqrt, hence the manual clipping. + eigenvals[eigenvals < 0.0] = 0.0 + explained_variance_ = eigenvals + + # Re-construct SVD of centered X indirectly and make it consistent + # with the other solvers. + S = xp.sqrt(eigenvals * (n_samples - 1)) + Vt = eigenvecs.T + U = None + + # flip eigenvectors' sign to enforce deterministic output + U, Vt = svd_flip(U, Vt, u_based_decision=False) + + components_ = Vt + + # Get variance explained by singular values + total_var = xp.sum(explained_variance_) + explained_variance_ratio_ = explained_variance_ / total_var + singular_values_ = xp.asarray(S, copy=True) # Store the singular values. + + # Postprocess the number of components required + if n_components == "mle": + n_components = _infer_dimension(explained_variance_, n_samples) + elif 0 < n_components < 1.0: + # number of components for which the cumulated explained + # variance percentage is superior to the desired threshold + # side='right' ensures that number of features selected + # their variance is always greater than n_components float + # passed. More discussion in issue: #15669 + if is_array_api_compliant: + # Convert to numpy as xp.cumsum and xp.searchsorted are not + # part of the Array API standard yet: + # + # https://github.com/data-apis/array-api/issues/597 + # https://github.com/data-apis/array-api/issues/688 + # + # Furthermore, it's not always safe to call them for namespaces + # that already implement them: for instance as + # cupy.searchsorted does not accept a float as second argument. + explained_variance_ratio_np = _convert_to_numpy( + explained_variance_ratio_, xp=xp + ) + else: + explained_variance_ratio_np = explained_variance_ratio_ + ratio_cumsum = stable_cumsum(explained_variance_ratio_np) + n_components = np.searchsorted(ratio_cumsum, n_components, side="right") + 1 + + # Compute noise covariance using Probabilistic PCA model + # The sigma2 maximum likelihood (cf. eq. 12.46) + if n_components < min(n_features, n_samples): + self.noise_variance_ = xp.mean(explained_variance_[n_components:]) + else: + self.noise_variance_ = 0.0 + + self.n_samples_ = n_samples + self.n_components_ = n_components + # Assign a copy of the result of the truncation of the components in + # order to: + # - release the memory used by the discarded components, + # - ensure that the kept components are allocated contiguously in + # memory to make the transform method faster by leveraging cache + # locality. + self.components_ = xp.asarray(components_[:n_components, :], copy=True) + + # We do the same for the other arrays for the sake of consistency. + self.explained_variance_ = xp.asarray( + explained_variance_[:n_components], copy=True + ) + self.explained_variance_ratio_ = xp.asarray( + explained_variance_ratio_[:n_components], copy=True + ) + self.singular_values_ = xp.asarray(singular_values_[:n_components], copy=True) + + return U, S, Vt, X, x_is_centered, xp + + def _fit_truncated(self, X, n_components, xp): + """Fit the model by computing truncated SVD (by ARPACK or randomized) + on X. + """ + n_samples, n_features = X.shape + + svd_solver = self._fit_svd_solver + if isinstance(n_components, str): + raise ValueError( + "n_components=%r cannot be a string with svd_solver='%s'" + % (n_components, svd_solver) + ) + elif not 1 <= n_components <= min(n_samples, n_features): + raise ValueError( + "n_components=%r must be between 1 and " + "min(n_samples, n_features)=%r with " + "svd_solver='%s'" + % (n_components, min(n_samples, n_features), svd_solver) + ) + elif svd_solver == "arpack" and n_components == min(n_samples, n_features): + raise ValueError( + "n_components=%r must be strictly less than " + "min(n_samples, n_features)=%r with " + "svd_solver='%s'" + % (n_components, min(n_samples, n_features), svd_solver) + ) + + random_state = check_random_state(self.random_state) + + # Center data + total_var = None + if issparse(X): + self.mean_, var = mean_variance_axis(X, axis=0) + total_var = var.sum() * n_samples / (n_samples - 1) # ddof=1 + X_centered = _implicit_column_offset(X, self.mean_) + x_is_centered = False + else: + self.mean_ = xp.mean(X, axis=0) + X_centered = xp.asarray(X, copy=True) if self.copy else X + X_centered -= self.mean_ + x_is_centered = not self.copy + + if svd_solver == "arpack": + v0 = _init_arpack_v0(min(X.shape), random_state) + U, S, Vt = svds(X_centered, k=n_components, tol=self.tol, v0=v0) + # svds doesn't abide by scipy.linalg.svd/randomized_svd + # conventions, so reverse its outputs. + S = S[::-1] + # flip eigenvectors' sign to enforce deterministic output + U, Vt = svd_flip(U[:, ::-1], Vt[::-1], u_based_decision=False) + + elif svd_solver == "randomized": + # sign flipping is done inside + U, S, Vt = _randomized_svd( + X_centered, + n_components=n_components, + n_oversamples=self.n_oversamples, + n_iter=self.iterated_power, + power_iteration_normalizer=self.power_iteration_normalizer, + flip_sign=False, + random_state=random_state, + ) + U, Vt = svd_flip(U, Vt, u_based_decision=False) + + self.n_samples_ = n_samples + self.components_ = Vt + self.n_components_ = n_components + + # Get variance explained by singular values + self.explained_variance_ = (S**2) / (n_samples - 1) + + # Workaround in-place variance calculation since at the time numpy + # did not have a way to calculate variance in-place. + # + # TODO: update this code to either: + # * Use the array-api variance calculation, unless memory usage suffers + # * Update sklearn.utils.extmath._incremental_mean_and_var to support array-api + # See: https://github.com/scikit-learn/scikit-learn/pull/18689#discussion_r1335540991 + if total_var is None: + N = X.shape[0] - 1 + X_centered **= 2 + total_var = xp.sum(X_centered) / N + + self.explained_variance_ratio_ = self.explained_variance_ / total_var + self.singular_values_ = xp.asarray(S, copy=True) # Store the singular values. + + if self.n_components_ < min(n_features, n_samples): + self.noise_variance_ = total_var - xp.sum(self.explained_variance_) + self.noise_variance_ /= min(n_features, n_samples) - n_components + else: + self.noise_variance_ = 0.0 + + return U, S, Vt, X, x_is_centered, xp + + def score_samples(self, X): + """Return the log-likelihood of each sample. + + See. "Pattern Recognition and Machine Learning" + by C. Bishop, 12.2.1 p. 574 + or http://www.miketipping.com/papers/met-mppca.pdf + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data. + + Returns + ------- + ll : ndarray of shape (n_samples,) + Log-likelihood of each sample under the current model. + """ + check_is_fitted(self) + xp, _ = get_namespace(X) + X = validate_data(self, X, dtype=[xp.float64, xp.float32], reset=False) + Xr = X - self.mean_ + n_features = X.shape[1] + precision = self.get_precision() + log_like = -0.5 * xp.sum(Xr * (Xr @ precision), axis=1) + log_like -= 0.5 * (n_features * log(2.0 * np.pi) - fast_logdet(precision)) + return log_like + + def score(self, X, y=None): + """Return the average log-likelihood of all samples. + + See. "Pattern Recognition and Machine Learning" + by C. Bishop, 12.2.1 p. 574 + or http://www.miketipping.com/papers/met-mppca.pdf + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data. + + y : Ignored + Ignored. + + Returns + ------- + ll : float + Average log-likelihood of the samples under the current model. + """ + xp, _ = get_namespace(X) + return float(xp.mean(self.score_samples(X))) + + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.transformer_tags.preserves_dtype = ["float64", "float32"] + tags.array_api_support = True + tags.input_tags.sparse = self.svd_solver in ( + "auto", + "arpack", + "covariance_eigh", + ) + return tags diff --git a/shared/package-files/56/563bae0d769e16d7de0c6d77333e35c68f398272b7223c3890bbdfadbc2d0d76/blob b/shared/package-files/56/563bae0d769e16d7de0c6d77333e35c68f398272b7223c3890bbdfadbc2d0d76/blob new file mode 100644 index 0000000000000000000000000000000000000000..ffb6bd6faefcc095f20b54d2f860f41498e4da3e --- /dev/null +++ b/shared/package-files/56/563bae0d769e16d7de0c6d77333e35c68f398272b7223c3890bbdfadbc2d0d76/blob @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:563bae0d769e16d7de0c6d77333e35c68f398272b7223c3890bbdfadbc2d0d76 +size 912912 diff --git a/shared/package-files/56/563cf6e58e87fe2874884136006d246b13bd53c3f07174aba1fcea9cde170702/blob b/shared/package-files/56/563cf6e58e87fe2874884136006d246b13bd53c3f07174aba1fcea9cde170702/blob new file mode 100644 index 0000000000000000000000000000000000000000..b599cb345b8af2b98c972bd0f43d6eb014fa77fb --- /dev/null +++ b/shared/package-files/56/563cf6e58e87fe2874884136006d246b13bd53c3f07174aba1fcea9cde170702/blob @@ -0,0 +1,551 @@ +import pytest + +import numpy as np +from numpy.testing import ( + assert_, assert_equal, assert_array_equal, assert_almost_equal, + assert_array_almost_equal, assert_raises, assert_raises_regex, + ) +from numpy.lib.index_tricks import ( + mgrid, ogrid, ndenumerate, fill_diagonal, diag_indices, diag_indices_from, + index_exp, ndindex, r_, s_, ix_ + ) + + +class TestRavelUnravelIndex: + def test_basic(self): + assert_equal(np.unravel_index(2, (2, 2)), (1, 0)) + + # test that new shape argument works properly + assert_equal(np.unravel_index(indices=2, + shape=(2, 2)), + (1, 0)) + + # test that an invalid second keyword argument + # is properly handled, including the old name `dims`. + with assert_raises(TypeError): + np.unravel_index(indices=2, hape=(2, 2)) + + with assert_raises(TypeError): + np.unravel_index(2, hape=(2, 2)) + + with assert_raises(TypeError): + np.unravel_index(254, ims=(17, 94)) + + with assert_raises(TypeError): + np.unravel_index(254, dims=(17, 94)) + + assert_equal(np.ravel_multi_index((1, 0), (2, 2)), 2) + assert_equal(np.unravel_index(254, (17, 94)), (2, 66)) + assert_equal(np.ravel_multi_index((2, 66), (17, 94)), 254) + assert_raises(ValueError, np.unravel_index, -1, (2, 2)) + assert_raises(TypeError, np.unravel_index, 0.5, (2, 2)) + assert_raises(ValueError, np.unravel_index, 4, (2, 2)) + assert_raises(ValueError, np.ravel_multi_index, (-3, 1), (2, 2)) + assert_raises(ValueError, np.ravel_multi_index, (2, 1), (2, 2)) + assert_raises(ValueError, np.ravel_multi_index, (0, -3), (2, 2)) + assert_raises(ValueError, np.ravel_multi_index, (0, 2), (2, 2)) + assert_raises(TypeError, np.ravel_multi_index, (0.1, 0.), (2, 2)) + + assert_equal(np.unravel_index((2*3 + 1)*6 + 4, (4, 3, 6)), [2, 1, 4]) + assert_equal( + np.ravel_multi_index([2, 1, 4], (4, 3, 6)), (2*3 + 1)*6 + 4) + + arr = np.array([[3, 6, 6], [4, 5, 1]]) + assert_equal(np.ravel_multi_index(arr, (7, 6)), [22, 41, 37]) + assert_equal( + np.ravel_multi_index(arr, (7, 6), order='F'), [31, 41, 13]) + assert_equal( + np.ravel_multi_index(arr, (4, 6), mode='clip'), [22, 23, 19]) + assert_equal(np.ravel_multi_index(arr, (4, 4), mode=('clip', 'wrap')), + [12, 13, 13]) + assert_equal(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9)), 1621) + + assert_equal(np.unravel_index(np.array([22, 41, 37]), (7, 6)), + [[3, 6, 6], [4, 5, 1]]) + assert_equal( + np.unravel_index(np.array([31, 41, 13]), (7, 6), order='F'), + [[3, 6, 6], [4, 5, 1]]) + assert_equal(np.unravel_index(1621, (6, 7, 8, 9)), [3, 1, 4, 1]) + + def test_empty_indices(self): + msg1 = 'indices must be integral: the provided empty sequence was' + msg2 = 'only int indices permitted' + assert_raises_regex(TypeError, msg1, np.unravel_index, [], (10, 3, 5)) + assert_raises_regex(TypeError, msg1, np.unravel_index, (), (10, 3, 5)) + assert_raises_regex(TypeError, msg2, np.unravel_index, np.array([]), + (10, 3, 5)) + assert_equal(np.unravel_index(np.array([],dtype=int), (10, 3, 5)), + [[], [], []]) + assert_raises_regex(TypeError, msg1, np.ravel_multi_index, ([], []), + (10, 3)) + assert_raises_regex(TypeError, msg1, np.ravel_multi_index, ([], ['abc']), + (10, 3)) + assert_raises_regex(TypeError, msg2, np.ravel_multi_index, + (np.array([]), np.array([])), (5, 3)) + assert_equal(np.ravel_multi_index( + (np.array([], dtype=int), np.array([], dtype=int)), (5, 3)), []) + assert_equal(np.ravel_multi_index(np.array([[], []], dtype=int), + (5, 3)), []) + + def test_big_indices(self): + # ravel_multi_index for big indices (issue #7546) + if np.intp == np.int64: + arr = ([1, 29], [3, 5], [3, 117], [19, 2], + [2379, 1284], [2, 2], [0, 1]) + assert_equal( + np.ravel_multi_index(arr, (41, 7, 120, 36, 2706, 8, 6)), + [5627771580, 117259570957]) + + # test unravel_index for big indices (issue #9538) + assert_raises(ValueError, np.unravel_index, 1, (2**32-1, 2**31+1)) + + # test overflow checking for too big array (issue #7546) + dummy_arr = ([0],[0]) + half_max = np.iinfo(np.intp).max // 2 + assert_equal( + np.ravel_multi_index(dummy_arr, (half_max, 2)), [0]) + assert_raises(ValueError, + np.ravel_multi_index, dummy_arr, (half_max+1, 2)) + assert_equal( + np.ravel_multi_index(dummy_arr, (half_max, 2), order='F'), [0]) + assert_raises(ValueError, + np.ravel_multi_index, dummy_arr, (half_max+1, 2), order='F') + + def test_dtypes(self): + # Test with different data types + for dtype in [np.int16, np.uint16, np.int32, + np.uint32, np.int64, np.uint64]: + coords = np.array( + [[1, 0, 1, 2, 3, 4], [1, 6, 1, 3, 2, 0]], dtype=dtype) + shape = (5, 8) + uncoords = 8*coords[0]+coords[1] + assert_equal(np.ravel_multi_index(coords, shape), uncoords) + assert_equal(coords, np.unravel_index(uncoords, shape)) + uncoords = coords[0]+5*coords[1] + assert_equal( + np.ravel_multi_index(coords, shape, order='F'), uncoords) + assert_equal(coords, np.unravel_index(uncoords, shape, order='F')) + + coords = np.array( + [[1, 0, 1, 2, 3, 4], [1, 6, 1, 3, 2, 0], [1, 3, 1, 0, 9, 5]], + dtype=dtype) + shape = (5, 8, 10) + uncoords = 10*(8*coords[0]+coords[1])+coords[2] + assert_equal(np.ravel_multi_index(coords, shape), uncoords) + assert_equal(coords, np.unravel_index(uncoords, shape)) + uncoords = coords[0]+5*(coords[1]+8*coords[2]) + assert_equal( + np.ravel_multi_index(coords, shape, order='F'), uncoords) + assert_equal(coords, np.unravel_index(uncoords, shape, order='F')) + + def test_clipmodes(self): + # Test clipmodes + assert_equal( + np.ravel_multi_index([5, 1, -1, 2], (4, 3, 7, 12), mode='wrap'), + np.ravel_multi_index([1, 1, 6, 2], (4, 3, 7, 12))) + assert_equal(np.ravel_multi_index([5, 1, -1, 2], (4, 3, 7, 12), + mode=( + 'wrap', 'raise', 'clip', 'raise')), + np.ravel_multi_index([1, 1, 0, 2], (4, 3, 7, 12))) + assert_raises( + ValueError, np.ravel_multi_index, [5, 1, -1, 2], (4, 3, 7, 12)) + + def test_writeability(self): + # See gh-7269 + x, y = np.unravel_index([1, 2, 3], (4, 5)) + assert_(x.flags.writeable) + assert_(y.flags.writeable) + + def test_0d(self): + # gh-580 + x = np.unravel_index(0, ()) + assert_equal(x, ()) + + assert_raises_regex(ValueError, "0d array", np.unravel_index, [0], ()) + assert_raises_regex( + ValueError, "out of bounds", np.unravel_index, [1], ()) + + @pytest.mark.parametrize("mode", ["clip", "wrap", "raise"]) + def test_empty_array_ravel(self, mode): + res = np.ravel_multi_index( + np.zeros((3, 0), dtype=np.intp), (2, 1, 0), mode=mode) + assert(res.shape == (0,)) + + with assert_raises(ValueError): + np.ravel_multi_index( + np.zeros((3, 1), dtype=np.intp), (2, 1, 0), mode=mode) + + def test_empty_array_unravel(self): + res = np.unravel_index(np.zeros(0, dtype=np.intp), (2, 1, 0)) + # res is a tuple of three empty arrays + assert(len(res) == 3) + assert(all(a.shape == (0,) for a in res)) + + with assert_raises(ValueError): + np.unravel_index([1], (2, 1, 0)) + +class TestGrid: + def test_basic(self): + a = mgrid[-1:1:10j] + b = mgrid[-1:1:0.1] + assert_(a.shape == (10,)) + assert_(b.shape == (20,)) + assert_(a[0] == -1) + assert_almost_equal(a[-1], 1) + assert_(b[0] == -1) + assert_almost_equal(b[1]-b[0], 0.1, 11) + assert_almost_equal(b[-1], b[0]+19*0.1, 11) + assert_almost_equal(a[1]-a[0], 2.0/9.0, 11) + + def test_linspace_equivalence(self): + y, st = np.linspace(2, 10, retstep=True) + assert_almost_equal(st, 8/49.0) + assert_array_almost_equal(y, mgrid[2:10:50j], 13) + + def test_nd(self): + c = mgrid[-1:1:10j, -2:2:10j] + d = mgrid[-1:1:0.1, -2:2:0.2] + assert_(c.shape == (2, 10, 10)) + assert_(d.shape == (2, 20, 20)) + assert_array_equal(c[0][0, :], -np.ones(10, 'd')) + assert_array_equal(c[1][:, 0], -2*np.ones(10, 'd')) + assert_array_almost_equal(c[0][-1, :], np.ones(10, 'd'), 11) + assert_array_almost_equal(c[1][:, -1], 2*np.ones(10, 'd'), 11) + assert_array_almost_equal(d[0, 1, :] - d[0, 0, :], + 0.1*np.ones(20, 'd'), 11) + assert_array_almost_equal(d[1, :, 1] - d[1, :, 0], + 0.2*np.ones(20, 'd'), 11) + + def test_sparse(self): + grid_full = mgrid[-1:1:10j, -2:2:10j] + grid_sparse = ogrid[-1:1:10j, -2:2:10j] + + # sparse grids can be made dense by broadcasting + grid_broadcast = np.broadcast_arrays(*grid_sparse) + for f, b in zip(grid_full, grid_broadcast): + assert_equal(f, b) + + @pytest.mark.parametrize("start, stop, step, expected", [ + (None, 10, 10j, (200, 10)), + (-10, 20, None, (1800, 30)), + ]) + def test_mgrid_size_none_handling(self, start, stop, step, expected): + # regression test None value handling for + # start and step values used by mgrid; + # internally, this aims to cover previously + # unexplored code paths in nd_grid() + grid = mgrid[start:stop:step, start:stop:step] + # need a smaller grid to explore one of the + # untested code paths + grid_small = mgrid[start:stop:step] + assert_equal(grid.size, expected[0]) + assert_equal(grid_small.size, expected[1]) + + def test_accepts_npfloating(self): + # regression test for #16466 + grid64 = mgrid[0.1:0.33:0.1, ] + grid32 = mgrid[np.float32(0.1):np.float32(0.33):np.float32(0.1), ] + assert_(grid32.dtype == np.float64) + assert_array_almost_equal(grid64, grid32) + + # different code path for single slice + grid64 = mgrid[0.1:0.33:0.1] + grid32 = mgrid[np.float32(0.1):np.float32(0.33):np.float32(0.1)] + assert_(grid32.dtype == np.float64) + assert_array_almost_equal(grid64, grid32) + + def test_accepts_longdouble(self): + # regression tests for #16945 + grid64 = mgrid[0.1:0.33:0.1, ] + grid128 = mgrid[ + np.longdouble(0.1):np.longdouble(0.33):np.longdouble(0.1), + ] + assert_(grid128.dtype == np.longdouble) + assert_array_almost_equal(grid64, grid128) + + grid128c_a = mgrid[0:np.longdouble(1):3.4j] + grid128c_b = mgrid[0:np.longdouble(1):3.4j, ] + assert_(grid128c_a.dtype == grid128c_b.dtype == np.longdouble) + assert_array_equal(grid128c_a, grid128c_b[0]) + + # different code path for single slice + grid64 = mgrid[0.1:0.33:0.1] + grid128 = mgrid[ + np.longdouble(0.1):np.longdouble(0.33):np.longdouble(0.1) + ] + assert_(grid128.dtype == np.longdouble) + assert_array_almost_equal(grid64, grid128) + + def test_accepts_npcomplexfloating(self): + # Related to #16466 + assert_array_almost_equal( + mgrid[0.1:0.3:3j, ], mgrid[0.1:0.3:np.complex64(3j), ] + ) + + # different code path for single slice + assert_array_almost_equal( + mgrid[0.1:0.3:3j], mgrid[0.1:0.3:np.complex64(3j)] + ) + + # Related to #16945 + grid64_a = mgrid[0.1:0.3:3.3j] + grid64_b = mgrid[0.1:0.3:3.3j, ][0] + assert_(grid64_a.dtype == grid64_b.dtype == np.float64) + assert_array_equal(grid64_a, grid64_b) + + grid128_a = mgrid[0.1:0.3:np.clongdouble(3.3j)] + grid128_b = mgrid[0.1:0.3:np.clongdouble(3.3j), ][0] + assert_(grid128_a.dtype == grid128_b.dtype == np.longdouble) + assert_array_equal(grid64_a, grid64_b) + + +class TestConcatenator: + def test_1d(self): + assert_array_equal(r_[1, 2, 3, 4, 5, 6], np.array([1, 2, 3, 4, 5, 6])) + b = np.ones(5) + c = r_[b, 0, 0, b] + assert_array_equal(c, [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1]) + + def test_mixed_type(self): + g = r_[10.1, 1:10] + assert_(g.dtype == 'f8') + + def test_more_mixed_type(self): + g = r_[-10.1, np.array([1]), np.array([2, 3, 4]), 10.0] + assert_(g.dtype == 'f8') + + def test_complex_step(self): + # Regression test for #12262 + g = r_[0:36:100j] + assert_(g.shape == (100,)) + + # Related to #16466 + g = r_[0:36:np.complex64(100j)] + assert_(g.shape == (100,)) + + def test_2d(self): + b = np.random.rand(5, 5) + c = np.random.rand(5, 5) + d = r_['1', b, c] # append columns + assert_(d.shape == (5, 10)) + assert_array_equal(d[:, :5], b) + assert_array_equal(d[:, 5:], c) + d = r_[b, c] + assert_(d.shape == (10, 5)) + assert_array_equal(d[:5, :], b) + assert_array_equal(d[5:, :], c) + + def test_0d(self): + assert_equal(r_[0, np.array(1), 2], [0, 1, 2]) + assert_equal(r_[[0, 1, 2], np.array(3)], [0, 1, 2, 3]) + assert_equal(r_[np.array(0), [1, 2, 3]], [0, 1, 2, 3]) + + +class TestNdenumerate: + def test_basic(self): + a = np.array([[1, 2], [3, 4]]) + assert_equal(list(ndenumerate(a)), + [((0, 0), 1), ((0, 1), 2), ((1, 0), 3), ((1, 1), 4)]) + + +class TestIndexExpression: + def test_regression_1(self): + # ticket #1196 + a = np.arange(2) + assert_equal(a[:-1], a[s_[:-1]]) + assert_equal(a[:-1], a[index_exp[:-1]]) + + def test_simple_1(self): + a = np.random.rand(4, 5, 6) + + assert_equal(a[:, :3, [1, 2]], a[index_exp[:, :3, [1, 2]]]) + assert_equal(a[:, :3, [1, 2]], a[s_[:, :3, [1, 2]]]) + + +class TestIx_: + def test_regression_1(self): + # Test empty untyped inputs create outputs of indexing type, gh-5804 + a, = np.ix_(range(0)) + assert_equal(a.dtype, np.intp) + + a, = np.ix_([]) + assert_equal(a.dtype, np.intp) + + # but if the type is specified, don't change it + a, = np.ix_(np.array([], dtype=np.float32)) + assert_equal(a.dtype, np.float32) + + def test_shape_and_dtype(self): + sizes = (4, 5, 3, 2) + # Test both lists and arrays + for func in (range, np.arange): + arrays = np.ix_(*[func(sz) for sz in sizes]) + for k, (a, sz) in enumerate(zip(arrays, sizes)): + assert_equal(a.shape[k], sz) + assert_(all(sh == 1 for j, sh in enumerate(a.shape) if j != k)) + assert_(np.issubdtype(a.dtype, np.integer)) + + def test_bool(self): + bool_a = [True, False, True, True] + int_a, = np.nonzero(bool_a) + assert_equal(np.ix_(bool_a)[0], int_a) + + def test_1d_only(self): + idx2d = [[1, 2, 3], [4, 5, 6]] + assert_raises(ValueError, np.ix_, idx2d) + + def test_repeated_input(self): + length_of_vector = 5 + x = np.arange(length_of_vector) + out = ix_(x, x) + assert_equal(out[0].shape, (length_of_vector, 1)) + assert_equal(out[1].shape, (1, length_of_vector)) + # check that input shape is not modified + assert_equal(x.shape, (length_of_vector,)) + + +def test_c_(): + a = np.c_[np.array([[1, 2, 3]]), 0, 0, np.array([[4, 5, 6]])] + assert_equal(a, [[1, 2, 3, 0, 0, 4, 5, 6]]) + + +class TestFillDiagonal: + def test_basic(self): + a = np.zeros((3, 3), int) + fill_diagonal(a, 5) + assert_array_equal( + a, np.array([[5, 0, 0], + [0, 5, 0], + [0, 0, 5]]) + ) + + def test_tall_matrix(self): + a = np.zeros((10, 3), int) + fill_diagonal(a, 5) + assert_array_equal( + a, np.array([[5, 0, 0], + [0, 5, 0], + [0, 0, 5], + [0, 0, 0], + [0, 0, 0], + [0, 0, 0], + [0, 0, 0], + [0, 0, 0], + [0, 0, 0], + [0, 0, 0]]) + ) + + def test_tall_matrix_wrap(self): + a = np.zeros((10, 3), int) + fill_diagonal(a, 5, True) + assert_array_equal( + a, np.array([[5, 0, 0], + [0, 5, 0], + [0, 0, 5], + [0, 0, 0], + [5, 0, 0], + [0, 5, 0], + [0, 0, 5], + [0, 0, 0], + [5, 0, 0], + [0, 5, 0]]) + ) + + def test_wide_matrix(self): + a = np.zeros((3, 10), int) + fill_diagonal(a, 5) + assert_array_equal( + a, np.array([[5, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 5, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 5, 0, 0, 0, 0, 0, 0, 0]]) + ) + + def test_operate_4d_array(self): + a = np.zeros((3, 3, 3, 3), int) + fill_diagonal(a, 4) + i = np.array([0, 1, 2]) + assert_equal(np.where(a != 0), (i, i, i, i)) + + def test_low_dim_handling(self): + # raise error with low dimensionality + a = np.zeros(3, int) + with assert_raises_regex(ValueError, "at least 2-d"): + fill_diagonal(a, 5) + + def test_hetero_shape_handling(self): + # raise error with high dimensionality and + # shape mismatch + a = np.zeros((3,3,7,3), int) + with assert_raises_regex(ValueError, "equal length"): + fill_diagonal(a, 2) + + +def test_diag_indices(): + di = diag_indices(4) + a = np.array([[1, 2, 3, 4], + [5, 6, 7, 8], + [9, 10, 11, 12], + [13, 14, 15, 16]]) + a[di] = 100 + assert_array_equal( + a, np.array([[100, 2, 3, 4], + [5, 100, 7, 8], + [9, 10, 100, 12], + [13, 14, 15, 100]]) + ) + + # Now, we create indices to manipulate a 3-d array: + d3 = diag_indices(2, 3) + + # And use it to set the diagonal of a zeros array to 1: + a = np.zeros((2, 2, 2), int) + a[d3] = 1 + assert_array_equal( + a, np.array([[[1, 0], + [0, 0]], + [[0, 0], + [0, 1]]]) + ) + + +class TestDiagIndicesFrom: + + def test_diag_indices_from(self): + x = np.random.random((4, 4)) + r, c = diag_indices_from(x) + assert_array_equal(r, np.arange(4)) + assert_array_equal(c, np.arange(4)) + + def test_error_small_input(self): + x = np.ones(7) + with assert_raises_regex(ValueError, "at least 2-d"): + diag_indices_from(x) + + def test_error_shape_mismatch(self): + x = np.zeros((3, 3, 2, 3), int) + with assert_raises_regex(ValueError, "equal length"): + diag_indices_from(x) + + +def test_ndindex(): + x = list(ndindex(1, 2, 3)) + expected = [ix for ix, e in ndenumerate(np.zeros((1, 2, 3)))] + assert_array_equal(x, expected) + + x = list(ndindex((1, 2, 3))) + assert_array_equal(x, expected) + + # Test use of scalars and tuples + x = list(ndindex((3,))) + assert_array_equal(x, list(ndindex(3))) + + # Make sure size argument is optional + x = list(ndindex()) + assert_equal(x, [()]) + + x = list(ndindex(())) + assert_equal(x, [()]) + + # Make sure 0-sized ndindex works correctly + x = list(ndindex(*[0])) + assert_equal(x, []) diff --git a/shared/package-files/56/563dcf55580ef635d85c2c376da6f3269d00624d3d914e0cde871258b0929f8b/blob b/shared/package-files/56/563dcf55580ef635d85c2c376da6f3269d00624d3d914e0cde871258b0929f8b/blob new file mode 100644 index 0000000000000000000000000000000000000000..664d00b340054e2d944b0713a9ae40acf54cefd2 --- /dev/null +++ b/shared/package-files/56/563dcf55580ef635d85c2c376da6f3269d00624d3d914e0cde871258b0929f8b/blob @@ -0,0 +1,1297 @@ +import numpy as np + +from statsmodels.tools.tools import Bunch + +llf = np.array([-242.89663276735]) + +nobs = np.array([202]) + +k = np.array([3]) + +k_exog = np.array([1]) + +sigma = np.array([.8053519404535]) + +chi2 = np.array([15723.381396967]) + +df_model = np.array([2]) + +k_ar = np.array([1]) + +k_ma = np.array([1]) + +params = np.array([ + .99479180506163, + -.84461527652809, + .64859174799221]) + +cov_params = np.array([ + .00008904968254, + -.00023560410507, + .00012795903324, + -.00023560410507, + .00131628534915, + -.00022462340695, + .00012795903324, + -.00022462340695, + .0005651128627]).reshape(3, 3) + +xb = np.array([ + 0, + 0, + .02869686298072, + .05651443824172, + .0503994859755, + .06887971609831, + .05940540507436, + .08067328482866, + .08167565613985, + .06429278105497, + .07087650150061, + .06886467337608, + .06716959923506, + .08230647444725, + .07099691033363, + .08401278406382, + .07996553182602, + .07354256510735, + .09366323798895, + .08811800926924, + .10296355187893, + .08846370875835, + .0852297320962, + .08700425922871, + .09751411527395, + .09737934917212, + .11228405684233, + .1053489819169, + .12352022528648, + .16439816355705, + .1643835157156, + .19891132414341, + .17551273107529, + .17827558517456, + .19562774896622, + .21028305590153, + .23767858743668, + .24580039083958, + .28269505500793, + .29883882403374, + .31247469782829, + .35402658581734, + .37410452961922, + .39106267690659, + .42040377855301, + .44518512487411, + .43608102202415, + .44340893626213, + .44959822297096, + .40977239608765, + .42118826508522, + .40079545974731, + .38357082009315, + .36902260780334, + .35673499107361, + .36137464642525, + .38031083345413, + .47139286994934, + .47323387861252, + .60994738340378, + .69538277387619, + .7825602889061, + .84117436408997, + .9657689332962, + 1.0109325647354, + .95897275209427, + .96013957262039, + .9461076259613, + .9342554807663, + .83413934707642, + .83968591690063, + .84437066316605, + .83330947160721, + .8990553021431, + .87949693202972, + .86297762393951, + .89407861232758, + .93536442518234, + 1.0303052663803, + 1.1104937791824, + 1.1481873989105, + 1.2851470708847, + 1.4458787441254, + 1.5515991449356, + 1.7309991121292, + 1.8975404500961, + 1.8579913377762, + 1.8846583366394, + 1.9672524929047, + 1.9469071626663, + 2.0048115253448, + 1.9786299467087, + 1.8213576078415, + 1.6284521818161, + 1.7508568763733, + 1.5689061880112, + 1.2950873374939, + 1.2290096282959, + 1.1882168054581, + 1.1537625789642, + 1.1697143316269, + 1.1681711673737, + 1.106795668602, + 1.0849931240082, + 1.006507396698, + 1.0453414916992, + .98803448677063, + .95465070009232, + 1.0165599584579, + .67838954925537, + .69311982393265, + .69054269790649, + .76345545053482, + .84005492925644, + .87471830844879, + .91901183128357, + .92638796567917, + .96265280246735, + 1.0083012580872, + 1.0618740320206, + 1.0921038389206, + 1.2077431678772, + 1.2303256988525, + 1.174311041832, + 1.3072115182877, + 1.314337015152, + 1.3503924608231, + 1.5760731697083, + 1.5264053344727, + 1.34929728508, + 1.304829955101, + 1.2522557973862, + 1.222869515419, + 1.198047041893, + 1.1770839691162, + 1.1743944883347, + 1.1571066379547, + 1.1274864673615, + 1.0574153661728, + 1.058304309845, + .99898308515549, + .9789143204689, + 1.0070173740387, + 1.000718832016, + 1.0104174613953, + 1.0486439466476, + 1.0058424472809, + .98470783233643, + 1.0119106769562, + 1.0649236440659, + 1.0346088409424, + 1.0540577173233, + 1.0704846382141, + .97923594713211, + .90216588973999, + .9271782040596, + .85819715261459, + .75488126277924, + .78776079416275, + .77047789096832, + .77089905738831, + .8313245177269, + .82229107618332, + .90476810932159, + .94439232349396, + 1.0379292964935, + 1.1469690799713, + 1.1489590406418, + 1.2257302999496, + 1.1554099321365, + 1.1260533332825, + .9811190366745, + .8436843752861, + .95287209749222, + .90993344783783, + .94875508546829, + 1.0115815401077, + .94450175762177, + .87282890081406, + .91741597652435, + .98511207103729, + .9972335100174, + 1.0975805521011, + 1.1823329925537, + 1.1487929821014, + 1.270641207695, + 1.2083609104156, + 1.696394443512, + 1.4628355503082, + 1.4307631254196, + 1.5087975263596, + 1.1542117595673, + 1.2262620925903, + 1.3880327939987, + 1.3853038549423, + 1.4396153688431, + 1.7208145856857, + 1.678991317749, + 2.110867023468, + 1.524417757988, + .57946246862411, + .56406193971634, + .74643105268478]) + +y = np.array([ + np.nan, + 28.979999542236, + 29.178695678711, + 29.40651512146, + 29.420400619507, + 29.608880996704, + 29.609405517578, + 29.830673217773, + 29.921676635742, + 29.874292373657, + 29.990877151489, + 30.048864364624, + 30.10717010498, + 30.292304992676, + 30.290996551514, + 30.464012145996, + 30.519966125488, + 30.553541183472, + 30.783664703369, + 30.838117599487, + 31.042964935303, + 31.038463592529, + 31.105230331421, + 31.207004547119, + 31.377513885498, + 31.477378845215, + 31.692283630371, + 31.755348205566, + 32.003520965576, + 32.444396972656, + 32.61438369751, + 33.048908233643, + 33.07551574707, + 33.278274536133, + 33.595630645752, + 33.91028213501, + 34.337677001953, + 34.645801544189, + 35.182697296143, + 35.598838806152, + 36.012474060059, + 36.654026031494, + 37.174102783203, + 37.691062927246, + 38.320404052734, + 38.94518661499, + 39.336082458496, + 39.843410491943, + 40.349597930908, + 40.509769439697, + 41.021186828613, + 41.300796508789, + 41.583572387695, + 41.869022369385, + 42.156734466553, + 42.561374664307, + 43.080310821533, + 44.171394348145, + 44.673233032227, + 46.209945678711, + 47.495380401611, + 48.882556915283, + 50.141174316406, + 51.965770721436, + 53.310932159424, + 53.958972930908, + 54.960140228271, + 55.84610748291, + 56.734252929688, + 56.934139251709, + 57.839687347412, + 58.744373321533, + 59.533309936523, + 60.899055480957, + 61.679496765137, + 62.46297454834, + 63.594078063965, + 64.83536529541, + 66.530303955078, + 68.210494995117, + 69.64818572998, + 71.885147094727, + 74.445877075195, + 76.751594543457, + 79.731002807617, + 82.797538757324, + 84.457992553711, + 86.584655761719, + 89.167251586914, + 91.046905517578, + 93.504814147949, + 95.378631591797, + 96.22135925293, + 96.628448486328, + 99.250854492188, + 99.668907165527, + 99.195091247559, + 100.0290145874, + 100.98822021484, + 101.95376586914, + 103.26971435547, + 104.46817779541, + 105.20679473877, + 106.1849899292, + 106.70650482178, + 108.0453414917, + 108.68803405762, + 109.45465087891, + 110.91656494141, + 109.37838745117, + 110.19312286377, + 110.89054107666, + 112.16345977783, + 113.54005432129, + 114.67472076416, + 115.91901397705, + 116.92639160156, + 118.16265106201, + 119.50830078125, + 120.96187591553, + 122.29209899902, + 124.30773925781, + 125.7303237915, + 126.57431030273, + 128.8072052002, + 130.21432495117, + 131.85038757324, + 134.97607421875, + 136.22640991211, + 136.44931030273, + 137.50482177734, + 138.45225524902, + 139.5228729248, + 140.59803771973, + 141.67707824707, + 142.87438964844, + 143.95710754395, + 144.92749023438, + 145.55741882324, + 146.65830993652, + 147.29898071289, + 148.17890930176, + 149.40701293945, + 150.40071105957, + 151.51042175293, + 152.84864807129, + 153.60585021973, + 154.48471069336, + 155.7119140625, + 157.16493225098, + 158.03460693359, + 159.25405883789, + 160.47047424316, + 160.87922668457, + 161.30215454102, + 162.42718505859, + 162.85820007324, + 162.95487976074, + 163.98776245117, + 164.67047119141, + 165.47090148926, + 166.73132324219, + 167.52229309082, + 169.00477600098, + 170.24440002441, + 171.93792724609, + 173.84696960449, + 175.04895019531, + 176.82572937012, + 177.55540466309, + 178.52604675293, + 178.58113098145, + 178.54368591309, + 180.25286865234, + 180.90992736816, + 182.14875793457, + 183.61158752441, + 184.14450073242, + 184.5728302002, + 185.81741333008, + 187.28511047363, + 188.39723205566, + 190.19758605957, + 191.98233032227, + 192.94879150391, + 195.07064819336, + 195.90835571289, + 200.89639282227, + 200.86282348633, + 202.13075256348, + 204.20880126953, + 203.05419921875, + 204.80026245117, + 207.3080291748, + 208.72329711914, + 210.57261657715, + 214.21580505371, + 215.67597961426, + 220.72087097168, + 218.41342163086, + 212.75346374512, + 213.23506164551, + 215.21542358398]) + +resid = np.array([ + np.nan, + .17000007629395, + .17130389809608, + -.03651398047805, + .11960058659315, + -.05888139456511, + .14059536159039, + .00932686589658, + -.11167634278536, + .04570783302188, + -.0108770346269, + -.00886330008507, + .10282856971025, + -.07230624556541, + .08900293707848, + -.0240114107728, + -.03996651992202, + .13645842671394, + -.03366377204657, + .10188252478838, + -.09296332299709, + -.01846401393414, + .01477065030485, + .0729955881834, + .00248436117545, + .10262141376734, + -.04228436201811, + 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.28950771689415, + .95181107521057, + 1.1148544549942, + .75411820411682, + 1.2484039068222, + 1.1690024137497, + -.1975435167551, + .24200716614723, + .6153416633606, + -.06725100427866, + .45309436321259, + -.10480991750956, + -.97863000631332, + -1.2213591337204, + .8715478181839, + -1.1508584022522, + -1.7689031362534, + -.39508575201035, + -.22900961339474, + -.18821682035923, + .14623281359673, + .03029025532305, + -.36817568540573, + -.10679569840431, + -.48499462008476, + .29349562525749, + -.34534454345703, + -.18803144991398, + .44535079598427, + -2.2165644168854, + .12161350995302, + .00687709869817, + .50946187973022, + .53653997182846, + .25995117425919, + .32527860999107, + .08098815381527, + .27360898256302, + .33735024929047, + .39170032739639, + .23812144994736, + .80789774656296, + .19225835800171, + -.33032417297363, + .92568749189377, + .09278241544962, + .28566908836365, + 1.5496014356613, + -.27607008814812, + -1.1263961791992, + -.24930645525455, + -.30482992529869, + -.15224970877171, + -.12287864089012, + -.09804095327854, + .02291300706565, + -.07438835501671, + -.15710659325123, + -.42748948931694, + .04259072244167, + -.35830733180046, + -.09898918122053, + .22108262777328, + -.00701736938208, + .0992873236537, + .28958559036255, + -.24864092469215, + -.10584850609303, + .21528913080692, + .38809850811958, + -.16492980718613, + .16538816690445, + .1459391862154, + -.57048463821411, + -.47923597693443, + .19784018397331, + -.4271782040596, + -.65820020437241, + .24511873722076, + -.0877638310194, + .02952514961362, + .42909786105156, + -.03132146969438, + .57771807909012, + .29522883892059, + .6555985212326, + .76207375526428, + .05302781611681, + .55105316638947, + -.42574247717857, + -.15540990233421, + -.92604118585587, + -.88112819194794, + .75632172822952, + -.25287514925003, + .29006350040436, + .45125409960747, + -.41159069538116, + -.44450175762177, + .32716807723045, + .48259317874908, + .11487878113985, + .70277869701385, + .60241633653641, + -.18233296275139, + .85120695829391, + -.37064728140831, + 3.2916390895844, + -1.4963974952698, + -.16283248364925, + .56923681497574, + -2.3088004589081, + .51979947090149, + 1.1197309494019, + .02996650896966, + .40969428420067, + 1.9223841428757, + -.21881568431854, + 2.9340152740479, + -3.8318600654602, + -6.239429473877, + -.08245316892862, + 1.2339268922806, + 1.1695692539215]) + +yr = np.array([ + np.nan, + .17000007629395, + .17130389809608, + -.03651398047805, + .11960058659315, + -.05888139456511, + .14059536159039, + .00932686589658, + -.11167634278536, + .04570783302188, + -.0108770346269, + -.00886330008507, + .10282856971025, + -.07230624556541, + .08900293707848, + -.0240114107728, + -.03996651992202, + .13645842671394, + -.03366377204657, + .10188252478838, + -.09296332299709, + -.01846401393414, + .01477065030485, + .0729955881834, + .00248436117545, + .10262141376734, + -.04228436201811, + .12465056031942, + .27647939324379, + .00560382334515, + .23561419546604, + -.1489082723856, + .02448422275484, + .12172746658325, + .10437148809433, + .18971465528011, + .06232447177172, + .25419962406158, + .11730266362429, + .10116269439459, + .2875237762928, + .14597341418266, + .12589547038078, + .20893961191177, + .17959471046925, + -.04518361017108, + .06391899287701, + .05659105628729, + -.24960128962994, + .09022761881351, + -.12118522822857, + -.10079623758793, + -.08357158303261, + -.06902338564396, + .04326653853059, + .13862533867359, + .61968916654587, + .02860714122653, + .92676383256912, + .59005337953568, + .60461646318436, + .41744044423103, + .85882639884949, + .33423033356667, + -.31093180179596, + .04102724045515, + -.06013804674149, + -.04610994458199, + -.63425624370575, + .06586220860481, + .06031560897827, + -.04437142238021, + .46668976545334, + -.09905604273081, + -.07949769496918, + .23702463507652, + .30592212080956, + .66463404893875, + .56969320774078, + .28950771689415, + .95181107521057, + 1.1148544549942, + .75411820411682, + 1.2484039068222, + 1.1690024137497, + -.1975435167551, + .24200716614723, + .6153416633606, + -.06725100427866, + .45309436321259, + -.10480991750956, + -.97863000631332, + -1.2213591337204, + .8715478181839, + -1.1508584022522, + -1.7689031362534, + -.39508575201035, + -.22900961339474, + -.18821682035923, + .14623281359673, + .03029025532305, + -.36817568540573, + -.10679569840431, + -.48499462008476, + .29349562525749, + -.34534454345703, + -.18803144991398, + .44535079598427, + -2.2165644168854, + .12161350995302, + .00687709869817, + .50946187973022, + .53653997182846, + .25995117425919, + .32527860999107, + .08098815381527, + .27360898256302, + .33735024929047, + .39170032739639, + .23812144994736, + .80789774656296, + .19225835800171, + -.33032417297363, + .92568749189377, + .09278241544962, + .28566908836365, + 1.5496014356613, + -.27607008814812, + -1.1263961791992, + -.24930645525455, + -.30482992529869, + -.15224970877171, + -.12287864089012, + -.09804095327854, + .02291300706565, + -.07438835501671, + -.15710659325123, + -.42748948931694, + .04259072244167, + -.35830733180046, + -.09898918122053, + .22108262777328, + -.00701736938208, + .0992873236537, + .28958559036255, + -.24864092469215, + -.10584850609303, + .21528913080692, + .38809850811958, + -.16492980718613, + .16538816690445, + .1459391862154, + -.57048463821411, + -.47923597693443, + .19784018397331, + -.4271782040596, + -.65820020437241, + .24511873722076, + -.0877638310194, + .02952514961362, + .42909786105156, + -.03132146969438, + .57771807909012, + .29522883892059, + .6555985212326, + .76207375526428, + .05302781611681, + .55105316638947, + -.42574247717857, + -.15540990233421, + -.92604118585587, + -.88112819194794, + .75632172822952, + -.25287514925003, + .29006350040436, + .45125409960747, + -.41159069538116, + -.44450175762177, + .32716807723045, + .48259317874908, + .11487878113985, + .70277869701385, + .60241633653641, + -.18233296275139, + .85120695829391, + -.37064728140831, + 3.2916390895844, + -1.4963974952698, + -.16283248364925, + .56923681497574, + -2.3088004589081, + .51979947090149, + 1.1197309494019, + .02996650896966, + .40969428420067, + 1.9223841428757, + -.21881568431854, + 2.9340152740479, + -3.8318600654602, + -6.239429473877, + -.08245316892862, + 1.2339268922806, + 1.1695692539215]) + +mse = np.array([ + 1.1112809181213, + .6632194519043, + .65879660844803, + .65575885772705, + .65364873409271, + .65217137336731, + .65113133192062, + .6503963470459, + .64987552165985, + .64950579404831, + .64924287796021, + .64905577898026, + .64892256259918, + .64882761240005, + .64875996112823, + .64871168136597, + .64867728948593, + .64865279197693, + .64863526821136, + .64862281084061, + .64861387014389, + .64860755205154, + .64860302209854, + .64859980344772, + .64859747886658, + .64859586954117, + .64859467744827, + .64859384298325, + .6485932469368, + .64859282970428, + .64859253168106, + 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.64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068, + .64859175682068]) + +stdp = np.array([ + 0, + 0, + .02869686298072, + .05651443824172, + .0503994859755, + .06887971609831, + .05940540507436, + .08067328482866, + .08167565613985, + .06429278105497, + .07087650150061, + .06886467337608, + .06716959923506, + .08230647444725, + .07099691033363, + .08401278406382, + .07996553182602, + .07354256510735, + .09366323798895, + .08811800926924, + .10296355187893, + .08846370875835, + .0852297320962, + .08700425922871, + .09751411527395, + .09737934917212, + .11228405684233, + .1053489819169, + .12352022528648, + .16439816355705, + .1643835157156, + .19891132414341, + .17551273107529, + .17827558517456, + .19562774896622, + .21028305590153, + .23767858743668, + .24580039083958, + .28269505500793, + .29883882403374, + .31247469782829, + .35402658581734, + .37410452961922, + .39106267690659, + .42040377855301, + .44518512487411, + .43608102202415, + .44340893626213, + .44959822297096, + .40977239608765, + .42118826508522, + .40079545974731, + .38357082009315, + .36902260780334, + .35673499107361, + .36137464642525, + .38031083345413, + .47139286994934, + .47323387861252, + .60994738340378, + .69538277387619, + .7825602889061, + .84117436408997, + .9657689332962, + 1.0109325647354, + .95897275209427, + .96013957262039, + .9461076259613, + .9342554807663, + .83413934707642, + .83968591690063, + .84437066316605, + .83330947160721, + .8990553021431, + .87949693202972, + .86297762393951, + .89407861232758, + .93536442518234, + 1.0303052663803, + 1.1104937791824, + 1.1481873989105, + 1.2851470708847, + 1.4458787441254, + 1.5515991449356, + 1.7309991121292, + 1.8975404500961, + 1.8579913377762, + 1.8846583366394, + 1.9672524929047, + 1.9469071626663, + 2.0048115253448, + 1.9786299467087, + 1.8213576078415, + 1.6284521818161, + 1.7508568763733, + 1.5689061880112, + 1.2950873374939, + 1.2290096282959, + 1.1882168054581, + 1.1537625789642, + 1.1697143316269, + 1.1681711673737, + 1.106795668602, + 1.0849931240082, + 1.006507396698, + 1.0453414916992, + .98803448677063, + .95465070009232, + 1.0165599584579, + .67838954925537, + .69311982393265, + .69054269790649, + .76345545053482, + .84005492925644, + .87471830844879, + .91901183128357, + .92638796567917, + .96265280246735, + 1.0083012580872, + 1.0618740320206, + 1.0921038389206, + 1.2077431678772, + 1.2303256988525, + 1.174311041832, + 1.3072115182877, + 1.314337015152, + 1.3503924608231, + 1.5760731697083, + 1.5264053344727, + 1.34929728508, + 1.304829955101, + 1.2522557973862, + 1.222869515419, + 1.198047041893, + 1.1770839691162, + 1.1743944883347, + 1.1571066379547, + 1.1274864673615, + 1.0574153661728, + 1.058304309845, + .99898308515549, + .9789143204689, + 1.0070173740387, + 1.000718832016, + 1.0104174613953, + 1.0486439466476, + 1.0058424472809, + .98470783233643, + 1.0119106769562, + 1.0649236440659, + 1.0346088409424, + 1.0540577173233, + 1.0704846382141, + .97923594713211, + .90216588973999, + .9271782040596, + .85819715261459, + .75488126277924, + .78776079416275, + .77047789096832, + .77089905738831, + .8313245177269, + .82229107618332, + .90476810932159, + .94439232349396, + 1.0379292964935, + 1.1469690799713, + 1.1489590406418, + 1.2257302999496, + 1.1554099321365, + 1.1260533332825, + .9811190366745, + .8436843752861, + .95287209749222, + .90993344783783, + .94875508546829, + 1.0115815401077, + .94450175762177, + .87282890081406, + .91741597652435, + .98511207103729, + .9972335100174, + 1.0975805521011, + 1.1823329925537, + 1.1487929821014, + 1.270641207695, + 1.2083609104156, + 1.696394443512, + 1.4628355503082, + 1.4307631254196, + 1.5087975263596, + 1.1542117595673, + 1.2262620925903, + 1.3880327939987, + 1.3853038549423, + 1.4396153688431, + 1.7208145856857, + 1.678991317749, + 2.110867023468, + 1.524417757988, + .57946246862411, + .56406193971634, + .74643105268478]) + +icstats = np.array([ + 202, + np.nan, + -242.89663276735, + 3, + 491.79326553469, + 501.7180686269]) + + +results = Bunch( + llf=llf, + nobs=nobs, + k=k, + k_exog=k_exog, + sigma=sigma, + chi2=chi2, + df_model=df_model, + k_ar=k_ar, + k_ma=k_ma, + params=params, + cov_params=cov_params, + xb=xb, + y=y, + resid=resid, + yr=yr, + mse=mse, + stdp=stdp, + icstats=icstats +) diff --git a/shared/package-files/56/563e1558fcad6e16795bbefe59ab34fffabc06bf514d4d6f467045ac7f0cdf3c/blob b/shared/package-files/56/563e1558fcad6e16795bbefe59ab34fffabc06bf514d4d6f467045ac7f0cdf3c/blob new file mode 100644 index 0000000000000000000000000000000000000000..d960b287e20fbc363dbe53bc7676bd6bf28c6759 --- /dev/null +++ b/shared/package-files/56/563e1558fcad6e16795bbefe59ab34fffabc06bf514d4d6f467045ac7f0cdf3c/blob @@ -0,0 +1,227 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef __FreeBSD__ +#include +#include +#define thp_bswap16(x) bswap16(x) +#define thp_bswap32(x) bswap32(x) +#define thp_bswap64(x) bswap64(x) +#elif defined(__APPLE__) +#include +#define thp_bswap16(x) OSSwapInt16(x) +#define thp_bswap32(x) OSSwapInt32(x) +#define thp_bswap64(x) OSSwapInt64(x) +#elif defined(__GNUC__) && !defined(__MINGW32__) +#include +#define thp_bswap16(x) bswap_16(x) +#define thp_bswap32(x) bswap_32(x) +#define thp_bswap64(x) bswap_64(x) +#elif defined _WIN32 || defined _WIN64 +#define thp_bswap16(x) _byteswap_ushort(x) +#define thp_bswap32(x) _byteswap_ulong(x) +#define thp_bswap64(x) _byteswap_uint64(x) +#endif + +#if __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__ +#define to_be16(x) thp_bswap16(x) +#define from_be16(x) thp_bswap16(x) +#define to_be32(x) thp_bswap32(x) +#define from_be32(x) thp_bswap32(x) +#define to_be64(x) thp_bswap64(x) +#define from_be64(x) thp_bswap64(x) +#define to_le16(x) (x) +#define from_le16(x) (x) +#define to_le32(x) (x) +#define from_le32(x) (x) +#define to_le64(x) (x) +#define from_le64(x) (x) +#elif __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ +#define to_be16(x) (x) +#define from_be16(x) (x) +#define to_be32(x) (x) +#define from_be32(x) (x) +#define to_be64(x) (x) +#define from_be64(x) (x) +#define to_le16(x) thp_bswap16(x) +#define from_le16(x) thp_bswap16(x) +#define to_le32(x) thp_bswap32(x) +#define from_le32(x) thp_bswap32(x) +#define to_le64(x) thp_bswap64(x) +#define from_le64(x) thp_bswap64(x) +#else +#error Unexpected or undefined __BYTE_ORDER__ +#endif + +namespace torch { +namespace utils { + +enum THPByteOrder { THP_LITTLE_ENDIAN = 0, THP_BIG_ENDIAN = 1 }; + +TORCH_API THPByteOrder THP_nativeByteOrder(); + +TORCH_API void THP_decodeInt16Buffer( + int16_t* dst, + const uint8_t* src, + bool do_byte_swap, + size_t len); +TORCH_API void THP_decodeInt32Buffer( + int32_t* dst, + const uint8_t* src, + bool do_byte_swap, + size_t len); +TORCH_API void THP_decodeInt64Buffer( + int64_t* dst, + const uint8_t* src, + bool do_byte_swap, + size_t len); +TORCH_API void THP_decodeHalfBuffer( + c10::Half* dst, + const uint8_t* src, + bool do_byte_swap, + size_t len); +TORCH_API void THP_decodeFloatBuffer( + float* dst, + const uint8_t* src, + bool do_byte_swap, + size_t len); +TORCH_API void THP_decodeDoubleBuffer( + double* dst, + const uint8_t* src, + bool do_byte_swap, + size_t len); +TORCH_API void THP_decodeBoolBuffer( + bool* dst, + const uint8_t* src, + bool do_byte_swap, + size_t len); +TORCH_API void THP_decodeBFloat16Buffer( + at::BFloat16* dst, + const uint8_t* src, + bool do_byte_swap, + size_t len); +TORCH_API void THP_decodeComplexFloatBuffer( + c10::complex* dst, + const uint8_t* src, + bool do_byte_swap, + size_t len); +TORCH_API void THP_decodeComplexDoubleBuffer( + c10::complex* dst, + const uint8_t* src, + bool do_byte_swap, + size_t len); + +TORCH_API void THP_decodeInt16Buffer( + int16_t* dst, + const uint8_t* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_decodeInt32Buffer( + int32_t* dst, + const uint8_t* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_decodeInt64Buffer( + int64_t* dst, + const uint8_t* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_decodeHalfBuffer( + c10::Half* dst, + const uint8_t* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_decodeFloatBuffer( + float* dst, + const uint8_t* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_decodeDoubleBuffer( + double* dst, + const uint8_t* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_decodeBoolBuffer( + bool* dst, + const uint8_t* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_decodeBFloat16Buffer( + at::BFloat16* dst, + const uint8_t* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_decodeFloat8_e5m2Buffer( + at::Float8_e5m2* dst, + const uint8_t* src, + size_t len); +TORCH_API void THP_decodeFloat8_e4m3fnBuffer( + at::Float8_e4m3fn* dst, + const uint8_t* src, + size_t len); +TORCH_API void THP_decodeFloat8_e5m2fnuzBuffer( + at::Float8_e5m2fnuz* dst, + const uint8_t* src, + size_t len); +TORCH_API void THP_decodeFloat8_e4m3fnuzBuffer( + at::Float8_e4m3fnuz* dst, + const uint8_t* src, + size_t len); +TORCH_API void THP_decodeComplexFloatBuffer( + c10::complex* dst, + const uint8_t* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_decodeComplexDoubleBuffer( + c10::complex* dst, + const uint8_t* src, + THPByteOrder order, + size_t len); + +TORCH_API void THP_encodeInt16Buffer( + uint8_t* dst, + const int16_t* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_encodeInt32Buffer( + uint8_t* dst, + const int32_t* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_encodeInt64Buffer( + uint8_t* dst, + const int64_t* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_encodeFloatBuffer( + uint8_t* dst, + const float* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_encodeDoubleBuffer( + uint8_t* dst, + const double* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_encodeComplexFloatBuffer( + uint8_t* dst, + const c10::complex* src, + THPByteOrder order, + size_t len); +TORCH_API void THP_encodeComplexDoubleBuffer( + uint8_t* dst, + const c10::complex* src, + THPByteOrder order, + size_t len); + +} // namespace utils +} // namespace torch diff --git a/shared/package-files/56/563e435ddf0f296b765370c217a43555179ae3317e4104ce4494b5b4c3b59e4b/blob b/shared/package-files/56/563e435ddf0f296b765370c217a43555179ae3317e4104ce4494b5b4c3b59e4b/blob new file mode 100644 index 0000000000000000000000000000000000000000..77ceba91198bb20f151d7cf0ccb52a84794b5de8 --- /dev/null +++ b/shared/package-files/56/563e435ddf0f296b765370c217a43555179ae3317e4104ce4494b5b4c3b59e4b/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 det { + using schema = at::Tensor (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::det") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "det(Tensor self) -> Tensor") + static at::Tensor call(const at::Tensor & self); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self); +}; + +}} // namespace at::_ops diff --git a/shared/package-files/56/563fa8988b3ade8702d35f49f4e1940ba66e3adae9fe83cd64743e2e400353a9/blob b/shared/package-files/56/563fa8988b3ade8702d35f49f4e1940ba66e3adae9fe83cd64743e2e400353a9/blob new file mode 100644 index 0000000000000000000000000000000000000000..83a4be1f8fc5d1856a36836ed0e48e20d2fab4e2 --- /dev/null +++ b/shared/package-files/56/563fa8988b3ade8702d35f49f4e1940ba66e3adae9fe83cd64743e2e400353a9/blob @@ -0,0 +1,189 @@ +""" +Key bindings, for scrolling up and down through pages. + +This are separate bindings, because GNU readline doesn't have them, but +they are very useful for navigating through long multiline buffers, like in +Vi, Emacs, etc... +""" +from __future__ import annotations + +from prompt_toolkit.key_binding.key_processor import KeyPressEvent + +__all__ = [ + "scroll_forward", + "scroll_backward", + "scroll_half_page_up", + "scroll_half_page_down", + "scroll_one_line_up", + "scroll_one_line_down", +] + +E = KeyPressEvent + + +def scroll_forward(event: E, half: bool = False) -> None: + """ + Scroll window down. + """ + w = event.app.layout.current_window + b = event.app.current_buffer + + if w and w.render_info: + info = w.render_info + ui_content = info.ui_content + + # Height to scroll. + scroll_height = info.window_height + if half: + scroll_height //= 2 + + # Calculate how many lines is equivalent to that vertical space. + y = b.document.cursor_position_row + 1 + height = 0 + while y < ui_content.line_count: + line_height = info.get_height_for_line(y) + + if height + line_height < scroll_height: + height += line_height + y += 1 + else: + break + + b.cursor_position = b.document.translate_row_col_to_index(y, 0) + + +def scroll_backward(event: E, half: bool = False) -> None: + """ + Scroll window up. + """ + w = event.app.layout.current_window + b = event.app.current_buffer + + if w and w.render_info: + info = w.render_info + + # Height to scroll. + scroll_height = info.window_height + if half: + scroll_height //= 2 + + # Calculate how many lines is equivalent to that vertical space. + y = max(0, b.document.cursor_position_row - 1) + height = 0 + while y > 0: + line_height = info.get_height_for_line(y) + + if height + line_height < scroll_height: + height += line_height + y -= 1 + else: + break + + b.cursor_position = b.document.translate_row_col_to_index(y, 0) + + +def scroll_half_page_down(event: E) -> None: + """ + Same as ControlF, but only scroll half a page. + """ + scroll_forward(event, half=True) + + +def scroll_half_page_up(event: E) -> None: + """ + Same as ControlB, but only scroll half a page. + """ + scroll_backward(event, half=True) + + +def scroll_one_line_down(event: E) -> None: + """ + scroll_offset += 1 + """ + w = event.app.layout.current_window + b = event.app.current_buffer + + if w: + # When the cursor is at the top, move to the next line. (Otherwise, only scroll.) + if w.render_info: + info = w.render_info + + if w.vertical_scroll < info.content_height - info.window_height: + if info.cursor_position.y <= info.configured_scroll_offsets.top: + b.cursor_position += b.document.get_cursor_down_position() + + w.vertical_scroll += 1 + + +def scroll_one_line_up(event: E) -> None: + """ + scroll_offset -= 1 + """ + w = event.app.layout.current_window + b = event.app.current_buffer + + if w: + # When the cursor is at the bottom, move to the previous line. (Otherwise, only scroll.) + if w.render_info: + info = w.render_info + + if w.vertical_scroll > 0: + first_line_height = info.get_height_for_line(info.first_visible_line()) + + cursor_up = info.cursor_position.y - ( + info.window_height + - 1 + - first_line_height + - info.configured_scroll_offsets.bottom + ) + + # Move cursor up, as many steps as the height of the first line. + # TODO: not entirely correct yet, in case of line wrapping and many long lines. + for _ in range(max(0, cursor_up)): + b.cursor_position += b.document.get_cursor_up_position() + + # Scroll window + w.vertical_scroll -= 1 + + +def scroll_page_down(event: E) -> None: + """ + Scroll page down. (Prefer the cursor at the top of the page, after scrolling.) + """ + w = event.app.layout.current_window + b = event.app.current_buffer + + if w and w.render_info: + # Scroll down one page. + line_index = max(w.render_info.last_visible_line(), w.vertical_scroll + 1) + w.vertical_scroll = line_index + + b.cursor_position = b.document.translate_row_col_to_index(line_index, 0) + b.cursor_position += b.document.get_start_of_line_position( + after_whitespace=True + ) + + +def scroll_page_up(event: E) -> None: + """ + Scroll page up. (Prefer the cursor at the bottom of the page, after scrolling.) + """ + w = event.app.layout.current_window + b = event.app.current_buffer + + if w and w.render_info: + # Put cursor at the first visible line. (But make sure that the cursor + # moves at least one line up.) + line_index = max( + 0, + min(w.render_info.first_visible_line(), b.document.cursor_position_row - 1), + ) + + b.cursor_position = b.document.translate_row_col_to_index(line_index, 0) + b.cursor_position += b.document.get_start_of_line_position( + after_whitespace=True + ) + + # Set the scroll offset. We can safely set it to zero; the Window will + # make sure that it scrolls at least until the cursor becomes visible. + w.vertical_scroll = 0 diff --git a/shared/package-files/56/5641a9e909f66d0b990a6d4fb2093bc4490352081f4974b9844e06dbfdfb5822/blob b/shared/package-files/56/5641a9e909f66d0b990a6d4fb2093bc4490352081f4974b9844e06dbfdfb5822/blob new file mode 100644 index 0000000000000000000000000000000000000000..68b26ed1381133db9de0ba7cb2187578fb7d680d --- /dev/null +++ b/shared/package-files/56/5641a9e909f66d0b990a6d4fb2093bc4490352081f4974b9844e06dbfdfb5822/blob @@ -0,0 +1,20 @@ +#pragma once + +#include +#include + +namespace at { +struct TensorIterator; + +namespace native { + +using _compute_linear_combination_fn = void(*)( + TensorIterator& iter, + int64_t in_stride, + int64_t coeff_stride, + int64_t num_summations +); + +DECLARE_DISPATCH(_compute_linear_combination_fn, _compute_linear_combination_stub); + +}} // namespace at::native diff --git a/shared/package-files/56/5642d1b0a514557a37ceb8405e7f6233ea4ac926c62157f35a8a290e199c78c0/blob b/shared/package-files/56/5642d1b0a514557a37ceb8405e7f6233ea4ac926c62157f35a8a290e199c78c0/blob new file mode 100644 index 0000000000000000000000000000000000000000..bc8eb7a55b8a20a8c800507b620d0afef1d477a4 Binary files /dev/null and b/shared/package-files/56/5642d1b0a514557a37ceb8405e7f6233ea4ac926c62157f35a8a290e199c78c0/blob differ diff --git a/shared/package-files/56/5643ad9376af2ed3a5d23bdae0172e6ec19fbb325d68d62661e90050f4cf118f/blob b/shared/package-files/56/5643ad9376af2ed3a5d23bdae0172e6ec19fbb325d68d62661e90050f4cf118f/blob new file mode 100644 index 0000000000000000000000000000000000000000..24f873e339c639ea39f9091d4431e8871ec1ca47 --- /dev/null +++ b/shared/package-files/56/5643ad9376af2ed3a5d23bdae0172e6ec19fbb325d68d62661e90050f4cf118f/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 cuda { + +TORCH_API at::Tensor bmm(const at::Tensor & self, const at::Tensor & mat2); +TORCH_API at::Tensor & bmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat2); +TORCH_API at::Tensor & bmm_outf(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out); + +} // namespace cuda +} // namespace at diff --git a/shared/package-files/56/5645a590a79bb3c1ff657da38e6fb44a23430625c5374a0fb6699fdde3e116b5/blob b/shared/package-files/56/5645a590a79bb3c1ff657da38e6fb44a23430625c5374a0fb6699fdde3e116b5/blob new file mode 100644 index 0000000000000000000000000000000000000000..775ba0954feda1dab5d4e3dd8232e135db3d08d6 Binary files /dev/null and b/shared/package-files/56/5645a590a79bb3c1ff657da38e6fb44a23430625c5374a0fb6699fdde3e116b5/blob differ diff --git a/shared/package-files/56/5646119d7e7043b4628400fa918aa2748fe16fd36028948013419a62e94da476/blob b/shared/package-files/56/5646119d7e7043b4628400fa918aa2748fe16fd36028948013419a62e94da476/blob new file mode 100644 index 0000000000000000000000000000000000000000..5dbb4e8baf00a4086f54ee6b796de6a89e462fb2 --- /dev/null +++ b/shared/package-files/56/5646119d7e7043b4628400fa918aa2748fe16fd36028948013419a62e94da476/blob @@ -0,0 +1,359 @@ +from typing import Optional, Union +from urllib.parse import urlparse + +import dns.asyncbackend +import dns.asyncquery +import dns.inet +import dns.message +import dns.query + + +class Nameserver: + def __init__(self): + pass + + def __str__(self): + raise NotImplementedError + + def kind(self) -> str: + raise NotImplementedError + + def is_always_max_size(self) -> bool: + raise NotImplementedError + + def answer_nameserver(self) -> str: + raise NotImplementedError + + def answer_port(self) -> int: + raise NotImplementedError + + def query( + self, + request: dns.message.QueryMessage, + timeout: float, + source: Optional[str], + source_port: int, + max_size: bool, + one_rr_per_rrset: bool = False, + ignore_trailing: bool = False, + ) -> dns.message.Message: + raise NotImplementedError + + async def async_query( + self, + request: dns.message.QueryMessage, + timeout: float, + source: Optional[str], + source_port: int, + max_size: bool, + backend: dns.asyncbackend.Backend, + one_rr_per_rrset: bool = False, + ignore_trailing: bool = False, + ) -> dns.message.Message: + raise NotImplementedError + + +class AddressAndPortNameserver(Nameserver): + def __init__(self, address: str, port: int): + super().__init__() + self.address = address + self.port = port + + def kind(self) -> str: + raise NotImplementedError + + def is_always_max_size(self) -> bool: + return False + + def __str__(self): + ns_kind = self.kind() + return f"{ns_kind}:{self.address}@{self.port}" + + def answer_nameserver(self) -> str: + return self.address + + def answer_port(self) -> int: + return self.port + + +class Do53Nameserver(AddressAndPortNameserver): + def __init__(self, address: str, port: int = 53): + super().__init__(address, port) + + def kind(self): + return "Do53" + + def query( + self, + request: dns.message.QueryMessage, + timeout: float, + source: Optional[str], + source_port: int, + max_size: bool, + one_rr_per_rrset: bool = False, + ignore_trailing: bool = False, + ) -> dns.message.Message: + if max_size: + response = dns.query.tcp( + request, + self.address, + timeout=timeout, + port=self.port, + source=source, + source_port=source_port, + one_rr_per_rrset=one_rr_per_rrset, + ignore_trailing=ignore_trailing, + ) + else: + response = dns.query.udp( + request, + self.address, + timeout=timeout, + port=self.port, + source=source, + source_port=source_port, + raise_on_truncation=True, + one_rr_per_rrset=one_rr_per_rrset, + ignore_trailing=ignore_trailing, + ignore_errors=True, + ignore_unexpected=True, + ) + return response + + async def async_query( + self, + request: dns.message.QueryMessage, + timeout: float, + source: Optional[str], + source_port: int, + max_size: bool, + backend: dns.asyncbackend.Backend, + one_rr_per_rrset: bool = False, + ignore_trailing: bool = False, + ) -> dns.message.Message: + if max_size: + response = await dns.asyncquery.tcp( + request, + self.address, + timeout=timeout, + port=self.port, + source=source, + source_port=source_port, + backend=backend, + one_rr_per_rrset=one_rr_per_rrset, + ignore_trailing=ignore_trailing, + ) + else: + response = await dns.asyncquery.udp( + request, + self.address, + timeout=timeout, + port=self.port, + source=source, + source_port=source_port, + raise_on_truncation=True, + backend=backend, + one_rr_per_rrset=one_rr_per_rrset, + ignore_trailing=ignore_trailing, + ignore_errors=True, + ignore_unexpected=True, + ) + return response + + +class DoHNameserver(Nameserver): + def __init__( + self, + url: str, + bootstrap_address: Optional[str] = None, + verify: Union[bool, str] = True, + want_get: bool = False, + ): + super().__init__() + self.url = url + self.bootstrap_address = bootstrap_address + self.verify = verify + self.want_get = want_get + + def kind(self): + return "DoH" + + def is_always_max_size(self) -> bool: + return True + + def __str__(self): + return self.url + + def answer_nameserver(self) -> str: + return self.url + + def answer_port(self) -> int: + port = urlparse(self.url).port + if port is None: + port = 443 + return port + + def query( + self, + request: dns.message.QueryMessage, + timeout: float, + source: Optional[str], + source_port: int, + max_size: bool = False, + one_rr_per_rrset: bool = False, + ignore_trailing: bool = False, + ) -> dns.message.Message: + return dns.query.https( + request, + self.url, + timeout=timeout, + source=source, + source_port=source_port, + bootstrap_address=self.bootstrap_address, + one_rr_per_rrset=one_rr_per_rrset, + ignore_trailing=ignore_trailing, + verify=self.verify, + post=(not self.want_get), + ) + + async def async_query( + self, + request: dns.message.QueryMessage, + timeout: float, + source: Optional[str], + source_port: int, + max_size: bool, + backend: dns.asyncbackend.Backend, + one_rr_per_rrset: bool = False, + ignore_trailing: bool = False, + ) -> dns.message.Message: + return await dns.asyncquery.https( + request, + self.url, + timeout=timeout, + source=source, + source_port=source_port, + bootstrap_address=self.bootstrap_address, + one_rr_per_rrset=one_rr_per_rrset, + ignore_trailing=ignore_trailing, + verify=self.verify, + post=(not self.want_get), + ) + + +class DoTNameserver(AddressAndPortNameserver): + def __init__( + self, + address: str, + port: int = 853, + hostname: Optional[str] = None, + verify: Union[bool, str] = True, + ): + super().__init__(address, port) + self.hostname = hostname + self.verify = verify + + def kind(self): + return "DoT" + + def query( + self, + request: dns.message.QueryMessage, + timeout: float, + source: Optional[str], + source_port: int, + max_size: bool = False, + one_rr_per_rrset: bool = False, + ignore_trailing: bool = False, + ) -> dns.message.Message: + return dns.query.tls( + request, + self.address, + port=self.port, + timeout=timeout, + one_rr_per_rrset=one_rr_per_rrset, + ignore_trailing=ignore_trailing, + server_hostname=self.hostname, + verify=self.verify, + ) + + async def async_query( + self, + request: dns.message.QueryMessage, + timeout: float, + source: Optional[str], + source_port: int, + max_size: bool, + backend: dns.asyncbackend.Backend, + one_rr_per_rrset: bool = False, + ignore_trailing: bool = False, + ) -> dns.message.Message: + return await dns.asyncquery.tls( + request, + self.address, + port=self.port, + timeout=timeout, + one_rr_per_rrset=one_rr_per_rrset, + ignore_trailing=ignore_trailing, + server_hostname=self.hostname, + verify=self.verify, + ) + + +class DoQNameserver(AddressAndPortNameserver): + def __init__( + self, + address: str, + port: int = 853, + verify: Union[bool, str] = True, + server_hostname: Optional[str] = None, + ): + super().__init__(address, port) + self.verify = verify + self.server_hostname = server_hostname + + def kind(self): + return "DoQ" + + def query( + self, + request: dns.message.QueryMessage, + timeout: float, + source: Optional[str], + source_port: int, + max_size: bool = False, + one_rr_per_rrset: bool = False, + ignore_trailing: bool = False, + ) -> dns.message.Message: + return dns.query.quic( + request, + self.address, + port=self.port, + timeout=timeout, + one_rr_per_rrset=one_rr_per_rrset, + ignore_trailing=ignore_trailing, + verify=self.verify, + server_hostname=self.server_hostname, + ) + + async def async_query( + self, + request: dns.message.QueryMessage, + timeout: float, + source: Optional[str], + source_port: int, + max_size: bool, + backend: dns.asyncbackend.Backend, + one_rr_per_rrset: bool = False, + ignore_trailing: bool = False, + ) -> dns.message.Message: + return await dns.asyncquery.quic( + request, + self.address, + port=self.port, + timeout=timeout, + one_rr_per_rrset=one_rr_per_rrset, + ignore_trailing=ignore_trailing, + verify=self.verify, + server_hostname=self.server_hostname, + ) diff --git a/shared/package-files/56/56496724da7238138ed04b02aa5dfd17e03f3830063abcc620bc2f92b5436833/blob b/shared/package-files/56/56496724da7238138ed04b02aa5dfd17e03f3830063abcc620bc2f92b5436833/blob new file mode 100644 index 0000000000000000000000000000000000000000..92c2d64d67c4726b0d33b3f55c1c9716dc60198b --- /dev/null +++ b/shared/package-files/56/56496724da7238138ed04b02aa5dfd17e03f3830063abcc620bc2f92b5436833/blob @@ -0,0 +1,161 @@ +"""Machinery for documenting traitlets config options with Sphinx. + +This includes: + +- A Sphinx extension defining directives and roles for config options. +- A function to generate an rst file given an Application instance. + +To make this documentation, first set this module as an extension in Sphinx's +conf.py:: + + extensions = [ + # ... + 'traitlets.config.sphinxdoc', + ] + +Autogenerate the config documentation by running code like this before +Sphinx builds:: + + from traitlets.config.sphinxdoc import write_doc + from myapp import MyApplication + + writedoc('config/options.rst', # File to write + 'MyApp config options', # Title + MyApplication() + ) + +The generated rST syntax looks like this:: + + .. configtrait:: Application.log_datefmt + + Description goes here. + + Cross reference like this: :configtrait:`Application.log_datefmt`. +""" +from collections import defaultdict +from textwrap import dedent + +from traitlets import Undefined +from traitlets.utils.text import indent + + +def setup(app): + """Registers the Sphinx extension. + + You shouldn't need to call this directly; configure Sphinx to use this + module instead. + """ + app.add_object_type("configtrait", "configtrait", objname="Config option") + metadata = {"parallel_read_safe": True, "parallel_write_safe": True} + return metadata + + +def interesting_default_value(dv): + if (dv is None) or (dv is Undefined): + return False + if isinstance(dv, (str, list, tuple, dict, set)): + return bool(dv) + return True + + +def format_aliases(aliases): + fmted = [] + for a in aliases: + dashes = "-" if len(a) == 1 else "--" + fmted.append(f"``{dashes}{a}``") + return ", ".join(fmted) + + +def class_config_rst_doc(cls, trait_aliases): + """Generate rST documentation for this class' config options. + + Excludes traits defined on parent classes. + """ + lines = [] + classname = cls.__name__ + for _, trait in sorted(cls.class_traits(config=True).items()): + ttype = trait.__class__.__name__ + + fullname = classname + "." + trait.name + lines += [".. configtrait:: " + fullname, ""] + + help = trait.help.rstrip() or "No description" + lines.append(indent(dedent(help)) + "\n") + + # Choices or type + if "Enum" in ttype: + # include Enum choices + lines.append(indent(":options: " + ", ".join("``%r``" % x for x in trait.values))) + else: + lines.append(indent(":trait type: " + ttype)) + + # Default value + # Ignore boring default values like None, [] or '' + if interesting_default_value(trait.default_value): + try: + dvr = trait.default_value_repr() + except Exception: + dvr = None # ignore defaults we can't construct + if dvr is not None: + if len(dvr) > 64: + dvr = dvr[:61] + "..." + # Double up backslashes, so they get to the rendered docs + dvr = dvr.replace("\\n", "\\\\n") + lines.append(indent(":default: ``%s``" % dvr)) + + # Command line aliases + if trait_aliases[fullname]: + fmt_aliases = format_aliases(trait_aliases[fullname]) + lines.append(indent(":CLI option: " + fmt_aliases)) + + # Blank line + lines.append("") + + return "\n".join(lines) + + +def reverse_aliases(app): + """Produce a mapping of trait names to lists of command line aliases.""" + res = defaultdict(list) + for alias, trait in app.aliases.items(): + res[trait].append(alias) + + # Flags also often act as aliases for a boolean trait. + # Treat flags which set one trait to True as aliases. + for flag, (cfg, _) in app.flags.items(): + if len(cfg) == 1: + classname = list(cfg)[0] + cls_cfg = cfg[classname] + if len(cls_cfg) == 1: + traitname = list(cls_cfg)[0] + if cls_cfg[traitname] is True: + res[classname + "." + traitname].append(flag) + + return res + + +def write_doc(path, title, app, preamble=None): + """Write a rst file documenting config options for a traitlets application. + + Parameters + ---------- + path : str + The file to be written + title : str + The human-readable title of the document + app : traitlets.config.Application + An instance of the application class to be documented + preamble : str + Extra text to add just after the title (optional) + """ + trait_aliases = reverse_aliases(app) + with open(path, "w") as f: + f.write(title + "\n") + f.write(("=" * len(title)) + "\n") + f.write("\n") + if preamble is not None: + f.write(preamble + "\n\n") + + for c in app._classes_inc_parents(): + f.write(class_config_rst_doc(c, trait_aliases)) + f.write("\n") diff --git a/shared/package-files/56/564c14cab191be17a8429c304e53fe65b464a83d6175f5d304e3232bab785e97/blob b/shared/package-files/56/564c14cab191be17a8429c304e53fe65b464a83d6175f5d304e3232bab785e97/blob new file mode 100644 index 0000000000000000000000000000000000000000..682610aeb908a47d756da38a18543493e753b970 --- /dev/null +++ b/shared/package-files/56/564c14cab191be17a8429c304e53fe65b464a83d6175f5d304e3232bab785e97/blob @@ -0,0 +1,1505 @@ +from __future__ import annotations + +import ast +import builtins +import collections +import dataclasses +import enum +import functools +import importlib +import inspect +import itertools +import logging +import math +import os +import re +import sys +import textwrap +import types +import weakref +from inspect import currentframe, getframeinfo +from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union +from weakref import ReferenceType + + +try: + import numpy as np +except ModuleNotFoundError: + np = None # type: ignore[assignment] + +import torch +import torch.utils._device +from torch._dynamo.source import ( + is_from_local_source, + TensorProperty, + TensorPropertySource, +) + +from torch._guards import ( + DuplicateInputs, + Guard, + GuardBuilderBase, + GuardEnvExpr, + GuardSource, + Source, +) + +from torch._logging import structured +from torch.fx.experimental.symbolic_shapes import ( + EqualityConstraint, + is_symbolic, + SYMPY_INTERP, +) +from torch.utils._traceback import format_frame, report_compile_source_on_error +from torch.utils.weak import TensorWeakRef + +from . import config, convert_frame, exc, mutation_guard +from .eval_frame import set_guard_error_hook +from .source import AttrSource, DefaultsSource, LocalSource, TypeSource +from .types import CacheEntry, ExtraState, GuardedCode, GuardFail, GuardFn # noqa: F401 +from .utils import ( + common_constant_types, + dict_keys_repr, + guard_failures, + istype, + key_is_id, + key_to_id, + orig_code_map, + tensor_always_has_static_shape, + tuple_iterator_getitem, + tuple_iterator_len, +) + +log = logging.getLogger(__name__) +guards_log = torch._logging.getArtifactLogger(__name__, "guards") +recompiles_log = torch._logging.getArtifactLogger(__name__, "recompiles") +recompiles_verbose_log = torch._logging.getArtifactLogger( + __name__, "recompiles_verbose" +) +verbose_guards_log = torch._logging.getArtifactLogger(__name__, "verbose_guards") + +TensorGuards = torch._C._dynamo.guards.TensorGuards +check_obj_id = torch._C._dynamo.guards.check_obj_id +check_type_id = torch._C._dynamo.guards.check_type_id +dict_version = torch._C._dynamo.guards.dict_version + + +# For user stack printing +@functools.lru_cache(None) +def uninteresting_files(): + import torch._dynamo.external_utils + + mods = [ + torch._dynamo.external_utils, + ] + return {inspect.getfile(m) for m in mods} + + +CLOSURE_VARS = { + "___check_type_id": check_type_id, + "___check_obj_id": check_obj_id, + "___odict_getitem": collections.OrderedDict.__getitem__, + "___key_to_id": key_to_id, + "___dict_version": dict_version, + "___dict_contains": lambda a, b: a in b, + "___tuple_iterator_len": tuple_iterator_len, + "___tuple_iterator_getitem": tuple_iterator_getitem, + "__math_isnan": math.isnan, + "__numpy_isnan": None if np is None else np.isnan, + "inf": float("inf"), + "__load_module": importlib.import_module, + "utils_device": torch.utils._device, + "device": torch.device, + "___from_numpy": + # If not numpy array, piggy back on e.g. tensor guards to check type + (lambda a: torch.as_tensor(a) if isinstance(a, (np.generic, np.ndarray)) else a), + "torch": torch, + "inspect": inspect, +} + +if sys.version_info[:2] <= (3, 8): + # [Note: Python Version <= 3.8] + # This branch should be dropped when we drop support for Python 3.8. + # Reason: 'ast.unparse' function was introduced in Python 3.9. + + try: + import astunparse # type: ignore[import] + + def _ast_unparse(node: ast.AST) -> str: + return astunparse.unparse(node).replace("\n", "") + + HAS_UNPARSE_FUNCTIONS = True + except ImportError: + HAS_UNPARSE_FUNCTIONS = False + pass +else: + HAS_UNPARSE_FUNCTIONS = True + + def _ast_unparse(node: ast.AST) -> str: + return ast.unparse(node).replace("\n", "") + + +def strip_function_call(name): + """ + "___odict_getitem(a, 1)" => "a" + "a.layers[slice(2)][0]._xyz" ==> "a" + "getattr(a.layers[slice(2)][0]._abc, '0')" ==> "a" + "getattr(getattr(a.x[3], '0'), '3')" ==> "a" + "a.layers[slice(None, -1, None)][0]._xyz" ==> "a" + """ + # recursively find valid object name in function + valid_name = re.compile("[A-Za-z_].*") + curr = "" + for char in name: + if char in " (": + curr = "" + elif char in "),[]": + if curr and curr != "None" and valid_name.match(curr): + return strip_function_call(curr) + else: + curr += char + + return strip_getattr_getitem(name) + + +def strip_getattr_getitem(name): + """ + "a[1]" => "a" + "a.foo" => "a" + """ + return re.split(r"[.\[]", name)[0] + + +def get_verbose_code_part(code_part, guard): + extra = "" + if guard.user_stack: + for fs in reversed(guard.user_stack): + if fs.filename not in uninteresting_files(): + extra = f" # {format_frame(fs, line=True)}" + break + elif guard.stack: + extra = f" # {format_frame(guard.stack.summary()[-1])}" + + return f"{code_part:<60}{extra}" + + +def convert_to_concrete_values(size_or_stride): + converted: List[Optional[int]] = [] + for dim in size_or_stride: + if not is_symbolic(dim): + converted.append(dim) + else: + assert isinstance(dim, torch.SymInt) + converted.append(dim.node.maybe_as_int()) + return converted + + +def get_tensor_guard_code_part(value, name, sizes, strides): + pytype = type(value) + dispatch_key = ( + torch._C._dispatch_keys(value) | torch._C._dispatch_tls_local_include_set() + ) - torch._C._dispatch_tls_local_exclude_set() + dtype = value.dtype + device_index = value.device.index + requires_grad = value.requires_grad + guard_str = ( + f"check_tensor({name}, {pytype.__qualname__}, {dispatch_key}, {dtype}, " + f"device={device_index}, requires_grad={requires_grad}, size={sizes}, stride={strides})" + ) + return guard_str + + +# The ready to eval generated code (possibly multiple parts) for a guard, plus +# the original guard object that created it for provenance +@dataclasses.dataclass +class GuardCodeList: + code_list: List[str] + guard: Guard + + +class GuardBuilder(GuardBuilderBase): + def __init__( + self, + id_ref: Callable[[Any], str], + source_ref: Callable[[Source], str], + lookup_weakrefs: Callable[[object], ReferenceType[object]], + local_scope: Dict[str, object], + global_scope: Dict[str, object], + check_fn_manager: CheckFunctionManager, + ): + self.id_ref = id_ref + self.source_ref = source_ref + self.lookup_weakrefs = lookup_weakrefs + self.scope: Dict[str, Dict[str, object]] = {"L": local_scope, "G": global_scope} + self.scope["__builtins__"] = builtins.__dict__.copy() + for ( + name, + package_module, + ) in torch.package.package_importer._package_imported_modules.items(): + name = name.replace(">", "_").replace("<", "_").replace(".", "_dot_") + # Write the package module into the scope so that we can import it + self.scope["__builtins__"][name] = package_module + # Write the demangled name to the scope so that we can use it + self.scope[name] = package_module + + self.argnames: List[str] = [] + # Code is python expression strings generated for each guard + self.code: List[GuardCodeList] = [] + # shape_env_code is only used by builder and is used for + # shape env code. This exists only because we need to make sure + # shape env guards get run after tensor match guards (since the + # tensor match guards make sure we actually have tensors) + self.shape_env_code: List[GuardCodeList] = [] + + # [Note - On Eager Tensor Guards] + # Most of the time, we generate Python code in a guard to directly + # check various properties. However, tensors are a bit special; + # it is too slow to check their properties one-by-one in Python. + # Instead, there is a C++ function TensorGuards.check which takes + # all of the tensor arguments and checks them all against compile-time + # examples entirely in C++. Thus, every time we process a + # TENSOR_MATCH guard, we just add another entry to + # tensor_check_names/tensor_check_examples, saying "for this local, + # check it against this example", and it all ends up getting + # swept up into a single call to ___check_tensors. Invariant: + # len(tensor_check_names) == len(tensor_check_examples). + # TODO: something here + self.tensor_check_names: List[str] = [] + self.tensor_check_examples: List[torch.Tensor] = [] + self.tensor_check_guards: List[Guard] = [] + + self.check_fn_manager: CheckFunctionManager = check_fn_manager + # Keep track of weak references of objects with ID_MATCH guard. This + # info is stored alongside optimized_code and check_fn and is used to + # limit the number of cache entries with same ID_MATCH'd object. + self.id_matched_objs: Dict[str, ReferenceType[object]] = {} + + # Warning: use this with care! This lets you access what the current + # value of the value you are guarding on is. You probably don't want + # to actually durably save this value though (because it's specific + # to this frame!) Instead, you should be reading out some property + # (like its type) which is what you permanently install into the + # guard code. + def get(self, name: str) -> Any: + return eval(name, self.scope, CLOSURE_VARS) + + # Registers the usage of the source name referenced by the + # string (or stored in the Guard) as being guarded upon. It's important + # to call this before generating some code that makes use of 'guard', + # because without this call, we won't actually bind the variable + # you reference in the actual guard closure (oops!) + def arg_ref(self, guard: Union[str, Guard]) -> str: + name: str + if isinstance(guard, str): + name = guard + else: + name = guard.name + base = strip_getattr_getitem(strip_function_call(name)) + if base not in self.argnames: + if re.match(r"[a-zA-Z0-9_]+", base): + if re.match(r"^\d+$", base): + log.warning("invalid var name: %s", guard) + self.argnames.append(base) + + return name + + def _guard_on_attribute(self, guard: Guard, attr_name: str, guard_fn): + attr_source = AttrSource(guard.originating_source, attr_name) + # Copy the stack info + new_guard = Guard( + attr_source, guard_fn, stack=guard.stack, user_stack=guard.user_stack + ) + new_guard.create(self) + + def TYPE_MATCH(self, guard: Guard) -> None: + # ___check_type_id is same as `id(type(x)) == y` + t = type(self.get(guard.name)) + obj_id = self.id_ref(t) + code = f"___check_type_id({self.arg_ref(guard)}, {obj_id})" + self._produce_guard_code(guard, [code]) + + def DICT_VERSION(self, guard: Guard): + # ___check_dict_version is same as `dict_version(x) == y` + ref = self.arg_ref(guard) + version = dict_version(self.get(guard.name)) + code = f"___dict_version({ref}) == {version}" + self._produce_guard_code(guard, [code]) + + def DICT_CONTAINS(self, guard: Guard, key: str, invert: bool): + dict_ref = self.arg_ref(guard) + + maybe_not = "not " if invert else "" + code = f"{maybe_not}___dict_contains({key!r}, {dict_ref})" + return self._produce_guard_code(guard, [code]) + + def BOOL_FALSE(self, guard: Guard): + # Guard on the runtime value being 'False', + # can be faster than seemingly equivalent checks like DICT_KEYS for empty dict + # + # WARNING: this guard is not safe to use generally. It only works if the runtime + # value is of a type that supports bool(), and some types e.g. Tensor do not. + # Only use this guard in cases you can guarantee the runtime type will be friendly. + # (e.g. Specialized NNModule with mutation protection via setattr) + # + # Why not simply check the runtime type inside this guard? It's slow enough to defeat + # the purpose of using this guard, which itself is supposed to be a faster alternative + # to DICT_KEYS. + ref = self.arg_ref(guard) + code = f"not {ref}" + self._produce_guard_code(guard, [code]) + + def ID_MATCH(self, guard: Guard): + # ___check_obj_id is same as `id(x) == y` + if isinstance(guard.originating_source, TypeSource): + # optional optimization to produce cleaner/faster guard code + return self.TYPE_MATCH( + Guard(guard.originating_source.base, GuardBuilder.TYPE_MATCH) # type: ignore[arg-type] + ) + + ref = self.arg_ref(guard) + val = self.get(guard.name) + code = f"___check_obj_id({ref}, {self.id_ref(val)})" + self._produce_guard_code(guard, [code]) + + # Keep track of ID_MATCH'd objects. This will be used to modify the + # cache size logic + if isinstance(guard.originating_source, LocalSource): + # TODO(janimesh) - This is currently restricted to nn.Module objects + # because many other ID_MATCH'd objects fail - like DeviceMesh. + # Increase the scope of ID_MATCH'd objects. + if isinstance(val, torch.nn.Module): + local_name = guard.originating_source.local_name + weak_id = self.lookup_weakrefs(val) + if weak_id is not None: + self.id_matched_objs[local_name] = weak_id + + def NAME_MATCH(self, guard: Guard): + obj = self.get(guard.name) + self._guard_on_attribute(guard, "__name__", GuardBuilder.EQUALS_MATCH) + + def DATA_PTR_MATCH(self, guard: Guard): + obj = self.get(guard.name) + code = f"{self.arg_ref(guard)}.data_ptr() == {obj.data_ptr()}" + self._produce_guard_code(guard, [code]) + + def HASATTR(self, guard: Guard): + assert isinstance( + guard.originating_source, AttrSource + ), f"invalid source {guard.name}" + base_source = guard.originating_source.base + base = base_source.name() + attr = guard.originating_source.member + + ref = self.arg_ref(base) + val = hasattr(self.get(base), attr) + code = None + if val: + code = f"hasattr({ref}, {attr!r})" + else: + code = f"not hasattr({ref}, {attr!r})" + + self._produce_guard_code(guard, [code], provided_guarded_object=self.get(base)) + + def FUNCTORCH_STACK_MATCH(self, guard: Guard): + # Invalidate functorch code if current level is different than + # the one when FX graph was generated + # if torch._C._functorch.peek_interpreter_stack() is not None: + cis = torch._functorch.pyfunctorch.retrieve_all_functorch_interpreters() + states = [ci.get_state() for ci in cis] + code = [f"torch._functorch.pyfunctorch.compare_functorch_state({states})"] + self._produce_guard_code(guard, code) + + def EQUALS_MATCH(self, guard: Guard): + ref = self.arg_ref(guard) + val = self.get(guard.name) + t = type(val) + if np: + np_types: Tuple[Type[Any], ...] = ( + np.int8, + np.int16, + np.int32, + np.int64, + np.uint8, + np.uint16, + np.uint32, + np.uint64, + np.float16, + np.float32, + np.float64, + ) + else: + np_types = () + ok_types = tuple( + common_constant_types + | { + type, + list, + tuple, + set, + frozenset, + slice, + range, + torch.Size, + *np_types, + } + ) + if istype(val, dict): + assert all( + istype(x, ok_types) for x in itertools.chain(val.keys(), val.values()) + ) + else: + assert istype( + val, + ok_types, + ), f"Unexpected type {type(val)}, not in {ok_types}" + + # Special case for nan because float("nan") == float("nan") evaluates to False + if istype(val, float) and math.isnan(val): + self.TYPE_MATCH(guard) + code = list() + code.append(f"__math_isnan({ref})") + self._produce_guard_code(guard, code) + return + # Python math library doesn't support complex nan, so we need to use numpy + elif istype(val, complex) and np.isnan(val): + self.TYPE_MATCH(guard) + code = list() + code.append(f"__numpy_isnan({ref})") + self._produce_guard_code(guard, code) + return + + code = list() + + # If matching equality against list/tuple, we must also check that + # the internal types match. (TODO: what about nested lists?) + if istype(val, (list, tuple)): + # NB: SEQUENCE_LENGTH takes care of the outer __check_type_id test + self.SEQUENCE_LENGTH(guard) + + for idx, elem in enumerate(val): + code.append( + f"___check_type_id({ref}[{idx}], {self.id_ref(type(elem))})" + ) + else: + # Add type check to prevent equality check between tensor and non-tensor. + self.TYPE_MATCH(guard) + + if istype(val, torch.Size): + val = tuple(val) + + # Code object can not be compared against their string representation + # I.e `eval(f"{compile('2+2','','exec')!r}")` raises SyntaxError + assert not istype(val, types.CodeType) + + # TODO: It feels like it would be better to just implement our own + # equality test in C that handles all of the necessary type checking + # and NaN tests + code.append(f"{ref} == {val!r}") + self._produce_guard_code(guard, code) + + def CONSTANT_MATCH(self, guard: Guard): + val = self.get(guard.name) + if istype(val, (bool, type(None), types.CodeType)): + self.ID_MATCH(guard) + else: + self.EQUALS_MATCH(guard) + + def NN_MODULE(self, guard: Guard): + self.ID_MATCH(guard) + ref = self.arg_ref(guard) + val = self.get(guard.name) + + def setup_guard(): + assert istype(val.training, bool) + self._guard_on_attribute(guard, "training", GuardBuilder.CONSTANT_MATCH) + + if hasattr(val, "training"): + # There are cases where a monkeypatched object has a guard made between __new__ and __init__ + setup_guard() + else: + exc.unimplemented(f"Guard setup for uninitialized class {type(val)}") + + def FUNCTION_MATCH(self, guard: Guard): + """things like torch.add and user defined functions""" + if guard.is_local(): + return self.ID_MATCH(guard) + + def CLOSURE_MATCH(self, guard: Guard): + """matches a closure by __code__ id.""" + if guard.is_local(): + val = self.get(guard.name) + # Strictly only want user-defined functions + if type(val) == types.FunctionType and hasattr(val, "__code__"): + self._guard_on_attribute(guard, "__code__", GuardBuilder.HASATTR) + self._guard_on_attribute(guard, "__code__", GuardBuilder.FUNCTION_MATCH) + else: + self.FUNCTION_MATCH(guard) + + def BUILTIN_MATCH(self, guard: Guard): + return self.FUNCTION_MATCH(guard) + + def PYMODULE_MATCH(self, guard: Guard): + return self.FUNCTION_MATCH(guard) + + def SEQUENCE_LENGTH(self, guard): + # This guard is used to check lenght of PySequence objects like list, + # tuple, collections.deque etc + ref = self.arg_ref(guard) + value = self.get(guard.name) + t = type(value) + + self.TYPE_MATCH(guard) + code = list() + if len(value) == 0: + code.append(f"not {ref}") + else: + code.append(f"len({ref}) == {len(value)}") + + self._produce_guard_code(guard, code) + + def DICT_LENGTH(self, guard): + self.SEQUENCE_LENGTH(guard) + + def TUPLE_ITERATOR_LEN(self, guard): + ref = self.arg_ref(guard) + value = self.get(guard.name) + t = type(value) + + self.TYPE_MATCH(guard) + code = list() + code.append(f"___tuple_iterator_len({ref}) == {tuple_iterator_len(value)}") + + self._produce_guard_code(guard, code) + + # TODO(voz): Deduplicate w/ AOTAutograd dupe input guards + def DUPLICATE_INPUT(self, guard, source_b): + ref_a = self.arg_ref(guard) + ref_b = self.arg_ref(source_b.name()) + + code = [f"{ref_b} is {ref_a}"] + self._produce_guard_code(guard, code) + + def DICT_KEYS(self, guard): + # Guard on the keys and their order + ref = self.arg_ref(guard) + value = self.get(guard.name) + t = type(value) + + self.TYPE_MATCH(guard) + code = list() + any_key_is_id = any(key_is_id(k) for k in value.keys()) + const_keys_repr = dict_keys_repr( + key_to_id(value), + local=is_from_local_source(guard.originating_source), + ) + if any_key_is_id: + code.append(f"___key_to_id({ref}) == {const_keys_repr}") + else: + code.append(f"list({ref}.keys()) == {const_keys_repr}") + + self._produce_guard_code(guard, code) + + def WEAKREF_ALIVE(self, guard): + self._produce_guard_code(guard, [f"{self.arg_ref(guard)} is not None"]) + + def NN_MODULE_PARAM_NAMES(self, guard): + ref = self.arg_ref(guard) + value = self.get(guard.name) + t = type(value) + keys = {k for k, v in value.named_parameters()} + + self.TYPE_MATCH(guard) + code = list() + code.append(f"{{k for k, v in {ref}.named_parameters()}} == {keys!r}") + + self._produce_guard_code(guard, code) + + def DICT_CONST_KEYS(self, guard): + """Constant keys match""" + ref = self.arg_ref(guard) + value = self.get(guard.name) + t = type(value) + + self.TYPE_MATCH(guard) + code = list() + code.append(f"list({ref}.keys()) == {list(value.keys())!r}") + + self._produce_guard_code(guard, code) + + def OBJECT_MUTATION(self, guard: Guard): + mutation_guard.watch(self.get(guard.name), self.check_fn_manager) + + def GRAD_MODE(self, guard: Guard): + pass # we always guard on this via GlobalStateGuard() + + def DETERMINISTIC_ALGORITHMS(self, guard: Guard): + pass # we always guard on this via GlobalStateGuard() + + def TORCH_FUNCTION_STATE(self, guard: Guard): + pass # we always guard on this via GlobalStateGuard() + + def DEFAULT_DEVICE(self, guard: Guard): + """Guard on CURRENT_DEVICE per torch.utils._device""" + assert guard.source is GuardSource.GLOBAL + import torch.utils._device as m + + self._produce_guard_code( + guard, [f"utils_device.CURRENT_DEVICE == {m.CURRENT_DEVICE!r}"] + ) + + def BACKEND_MATCH(self, guard: Guard): + """Guard on backend matching based on id of current_backend""" + assert guard.source is GuardSource.GLOBAL + backend_id = ( + f"{id(torch._dynamo.eval_frame.guarded_backend_cache.current_backend)}" + ) + code = [f"___check_current_backend({backend_id})"] + self._produce_guard_code(guard, code) + + def SHAPE_ENV(self, guard: Guard): + # Let's handle ShapeEnv guards. To do this, we will resolve + # shape variables to sources from tracked_fakes. This must happen after + # tensor checks. + assert guard.name == "" + output_graph = self.check_fn_manager.output_graph + # NB: self.output_graph can be None in the debug_nops tests + fs = output_graph.tracked_fakes + input_contexts = [a.symbolic_context for a in fs] + + def get_sources(t_id, dim): + # Looks up base sources mapped to a tensor id and uses them to create + # sources for the corresponding tensor dimension. + return [ + TensorPropertySource(source, TensorProperty.SIZE, dim) + for source in output_graph.tracked_fakes_id_to_source[t_id] + ] + + if output_graph.export_constraints: + from sympy import Symbol + + source_pairs: List[Tuple[Source, Source]] = [] + derived_equalities: List[ # type: ignore[type-arg] + Tuple[Source, Union[Source, Symbol], Callable] + ] = [] + phantom_symbols: Dict[str, Symbol] = {} + for constraint in output_graph.export_constraints: + if constraint.t_id in output_graph.tracked_fakes_id_to_source: + torch.export.dynamic_shapes._process_equalities( + constraint, + get_sources, + output_graph.shape_env, + source_pairs, + derived_equalities, + phantom_symbols, + ) + else: + log.warning("Untracked tensor used in export constraints") + equalities_inputs = EqualityConstraint( + source_pairs=source_pairs, + derived_equalities=derived_equalities, + phantom_symbols=list(phantom_symbols.values()), + warn_only=False, + ) + else: + equalities_inputs = None + guards = output_graph.shape_env.produce_guards( + [a.fake for a in fs], + [a.source for a in fs], + input_contexts=input_contexts, + equalities_inputs=equalities_inputs, + source_ref=self.source_ref, + # Export keeps static. + ignore_static=(not self.check_fn_manager.output_graph.export), + ) + # When exporting, we may work with the shape constraints some more in + # postprocessing, so don't freeze yet + if not self.check_fn_manager.output_graph.export: + output_graph.shape_env.freeze() + for shape_guard in guards: + self._produce_guard_code(guard, [shape_guard], shape_env=True) + + def TENSOR_MATCH(self, guard: Guard, value=None): + if guard.is_nn_module() or guard.originating_source.is_dict_key(): + self.ID_MATCH(guard) + else: + if isinstance(value, TensorWeakRef): + value = value() + + value = value if value is not None else self.get(guard.name) + assert isinstance(value, torch.Tensor) + + tensor_name = self.arg_ref(guard) + # [Note - On Export Tensor Guards] + # + # In eager mode, tensor guards are evaluated through C++, in guards.cpp + # see [Note - On Eager Tensor Guards] for more info. + # + # In export mode, we instead maintain parallel logic between C++ and python + # here, with an exception of checking the dispatch key - with the idea that a dispatch key + # is an entirely runtime notion that would make no sense to keep in an exported graph. + # + # Now, this idea is okay, but to paraphrase @ezyang, this mental model is sufficient for now, although + # not entirely true. + # For example, suppose one of the input tensors had the negative dispatch key. + # You should end up with a graph that is specialized for tensors that have a negative dispatch key. + # If you allow a Tensor that does NOT have this bit set, you will accidentally run it "as if" it were negated. + # Now, negative key only shows up for complex numbers, and most likely, the exported to target doesn't + # support this feature at all, but the point stands that :some: tensor state only shows up on dispatch key. + # TODO(voz): Either populate a dispatch_key check into the guards, or error on users passing in an unsupported + # subset of keys during export. + # + # The list of tensor fields and calls we care about can be found in `terms` below. + # TODO(voz): We are missing storage offset in all our tensor guards? + code: List[str] = list() + if self.check_fn_manager.output_graph.export: + self.TYPE_MATCH(guard) + terms = [ + "dtype", + "device", + "requires_grad", + "ndimension()", + ] + + for term in terms: + real_value = self.get(tensor_name + "." + term) + if istype(real_value, (torch.device, torch.dtype)): + # copy pasted from EQUALS_MATCH + code.append(f"str({tensor_name}.{term}) == {str(real_value)!r}") + else: + code.append(f"{tensor_name}.{term} == {real_value}") + else: + self.tensor_check_names.append(tensor_name) + self.tensor_check_examples.append(value) + self.tensor_check_guards.append(guard) + + # A frame is valid for reuse with dynamic dimensions if the new + # (user-requested) dynamic dimensions are a subset of the old + # (already compiled) dynamic dimensions. + # + # It's a little non-obvious why you'd want this: in particular, + # if an already compiled frame matches all of the guards, why + # not just use it, why force a recompile? + # + # We force it for two reasons: + # + # - The user *required* us to compile with a new dynamic dimension, + # we should not ignore that and serve up the old, specialized + # frame. Listen to the user! + # + # - In fact, we are obligated to *raise an error* if we fail to + # make the requested dimension dynamic. If we don't + # recompile, we can't tell if that dimension can actually be + # made dynamic. + # + # If the new dynamic dims are a subset of the old, we already know + # we can make them dynamic (since we made them dynamic in old). + # This is slightly unsound, because maybe your input size is + # [s0, s0, s1] and so you can do it dynamic if you say dynamic + # dims {0, 1, 2} but you can't if you only do {0, 2} (because now + # the second s0 is specialized). But we're not entirely sure if + # this is a good idea anyway lol... (if you want to try removing + # this logic, be my guest! -- ezyang 2024) + # + assert guard.source is not None + static, reason = tensor_always_has_static_shape( + value, is_tensor=True, guard_source=guard.source + ) + if not static: + if hasattr(value, "_dynamo_dynamic_indices"): + code.append( + f"(({tensor_name}._dynamo_dynamic_indices.issubset({value._dynamo_dynamic_indices})) if hasattr({tensor_name}, '_dynamo_dynamic_indices') else True)" # noqa: B950 + ) + # In the case of us not having any dynamic dimension indices, we compiled the frame with no chance of + # raising for this specific tensor - and any inputs with more dynamic user directives specified must be recompiled. + else: + code.append( + f"hasattr({tensor_name}, '_dynamo_dynamic_indices') == False" + ) + if len(code) > 0: + self._produce_guard_code(guard, code) + + # A util that appends guarded code, or, in the case of export, adds data onto guards + def _produce_guard_code( + self, guard, code_list, provided_guarded_object=None, shape_env=False + ): + # WARNING: It is important that cur_frame/caller do NOT stay in + # the current frame, because they will keep things live longer + # than they should. See TestMisc.test_release_module_memory + cur_frame = currentframe() + assert cur_frame is not None + caller = cur_frame.f_back + del cur_frame + assert caller is not None + func_name = getframeinfo(caller)[2] + del caller + # We use func_name for export, so might as well get a nice defensive check out of it + assert func_name in dir( + self.__class__ + ), f"_produce_guard_code must be called from inside GuardedCode. Called from {func_name}" + + if shape_env: + self.shape_env_code.append(GuardCodeList(code_list, guard)) + else: + self.code.append(GuardCodeList(code_list, guard)) + + # Not all guards have names, some can be installed globally (see asserts on HAS_GRAD) + if provided_guarded_object is None: + name_valid = guard.name is not None and guard.name != "" + + guarded_object = self.get(guard.name) if name_valid else None + else: + guarded_object = provided_guarded_object + + guarded_object_type = ( + weakref.ref(type(guarded_object)) if guarded_object is not None else None + ) + obj_ref = None + # Not necessary to have weakref for Enum type, but there is a bug that + # makes hasattr(guarded_object.__class__, "__weakref__") return True. + if hasattr(guarded_object.__class__, "__weakref__") and not isinstance( + guarded_object, enum.Enum + ): + obj_ref = weakref.ref(guarded_object) + + guard.set_export_info( + func_name, + guarded_object_type, + code_list, + obj_ref, + ) + + +# Common Sub-Expression Elimination for Python expressions. +# +# There are 2 steps to this pass: +# 1. Count the frequency of each sub-expression (i.e. inner +# node in the AST tree) +# +# 2. Replace those that occur more than once by a fresh variable 'v'. +# 'v' will be defined in the 'preface' list (output argument to +# 'NodeTransformer') +# +# NB: the use of 'ast.unparse' while visiting the nodes makes this pass +# quadratic on the depth of the tree. +# +# NB: this pass creates a new variable for each AST node that is repeated +# more than 'USE_THRESHOLD'. e.g. if 'a.b.c.d' is used 10 times, 'a.b.c' +# and 'a.b' are also used 10 times. So, there will be a new variable for +# each of them. +class PyExprCSEPass: + # Maximum number of times a given expression can be used without being + # replaced by a fresh variable. + USE_THRESHOLD = 1 + + # Ad-Hoc: AST nodes this pass focuses on. + ALLOWED_NODE_TYPES = (ast.Attribute, ast.Call, ast.Subscript) + + @dataclasses.dataclass + class Config: + expr_count: Dict[str, int] + expr_to_name: Dict[str, str] + + class ExprCounter(ast.NodeVisitor): + def __init__(self, config: PyExprCSEPass.Config) -> None: + self._config = config + + def visit(self, node: ast.AST) -> Any: + if isinstance(node, PyExprCSEPass.ALLOWED_NODE_TYPES): + self._config.expr_count[_ast_unparse(node)] += 1 + super().visit(node) + + class Replacer(ast.NodeTransformer): + def __init__( + self, + config: PyExprCSEPass.Config, + gen_name: Callable[[], str], + ) -> None: + super().__init__() + self._config = config + self._gen_name = gen_name + self.preface: List[str] = [] + + def visit(self, node: ast.AST) -> Any: + if isinstance(node, PyExprCSEPass.ALLOWED_NODE_TYPES): + expr = _ast_unparse(node) + + # Replacement only occurs if a given expression is used more + # than once. + if self._config.expr_count[expr] > PyExprCSEPass.USE_THRESHOLD: + if expr not in self._config.expr_to_name: + # Parent 'visit' is called so that we CSE the inner expressions first. + # + # The resulting expression is used as right-hand-side of the variable + # assignment. i.e. we are CSE-ing the children before the parents. + # + # Indexing still uses the old 'node', since that's what was counted + # by the 'NodeVisitor'. + node_ = super().visit(node) + expr_ = _ast_unparse(node_) + var_name = self._gen_name() + self.preface.append(f"{var_name} = {expr_}") + self._config.expr_to_name[expr] = var_name + else: + var_name = self._config.expr_to_name[expr] + return ast.Name(var_name, ast.Load()) + + return super().visit(node) + + def __init__(self) -> None: + self._counter = 0 + self._config = self.Config( + expr_count=collections.defaultdict(lambda: 0), expr_to_name={} + ) + + def _new_var(self, prefix: str = "_var") -> str: + name = f"{prefix}{self._counter}" + self._counter += 1 + return name + + def count(self, exprs: List[str]) -> None: + counter = self.ExprCounter(self._config) + for e in exprs: + try: + counter.visit(ast.parse(e)) + except SyntaxError as ex: + log.exception("Failed to visit expr at line %s.\n%s", ex.lineno, e) + raise + + def replace(self, expr: str) -> Tuple[List[str], str]: + replacer = self.Replacer(self._config, self._new_var) + new_node = replacer.visit(ast.parse(expr)) + return replacer.preface, _ast_unparse(new_node) + + +def must_add_nn_module_guards(guard): + # For config.guard_nn_modules=False, we can skip all the guards that + # originate from inside of nn module except for a few categories. + return ( + # Guard for defaults + isinstance(guard.originating_source, DefaultsSource) + # Guard using dict tags if the config flag is set + or ( + config.guard_nn_modules_using_dict_tags + and guard.create_fn is GuardBuilder.NN_MODULE + ) + ) + + +class DeletedGuardFn: + pass + + +# NB: Naively, you'd expect this to only be a function that produces +# the callable that constitutes the guard. However, there is some +# delicate handling for invalidating this check function when the +# locals/globals get invalidated, so there's some extra state +# we have to hold in this manager class. +class CheckFunctionManager: + def __init__( + self, + output_graph=None, + guard_fail_fn: Optional[Callable[[GuardFail], None]] = None, + ): + guards = output_graph.guards if output_graph else None + self._weakrefs: Dict[int, ReferenceType[object]] = {} + self.output_graph = output_graph + w_builder = None + + def source_ref(source): + guard_source = source.guard_source() + if guard_source is GuardSource.CONSTANT: + # No need to track constants + return source.name() + assert w_builder + r_builder = w_builder() + assert r_builder is not None + return r_builder.arg_ref(source.name()) + + builder = GuardBuilder( + self.id_ref, + source_ref, + self.lookup_weakrefs, + output_graph.local_scope, + output_graph.global_scope, + self, + ) + + # Break retain cycle. See test_release_scope_memory + def cleanup_builder(weak_b): + b = weak_b() + if b: + b.scope = None + + # Break retain cycle. See test_release_input_memory + w_builder = weakref.ref(builder, cleanup_builder) + + for guard in sorted(guards or [], key=Guard.sort_key): + if ( + not config.guard_nn_modules + and guard.is_nn_module() + # Default func args must be guarded on. + # TODO: we could make use of 'DefaultsSource' and offer a .guard.is_defaults() API + and "__defaults__" not in guard.name + and "__kwdefaults__" not in guard.name + and (config.skip_nnmodule_hook_guards or "hooks" not in guard.name) + ): + continue + + guard.create(builder) + self.check_fn = self.compile_check_fn(builder, guards, guard_fail_fn) + # Keep track of weak references of objects with ID_MATCH guard. This + # info is stored alongside optimized_code and check_fn and is used to + # limit the number of cache entries with same ID_MATCH'd object. + # TODO(janimesh) - Currently this information is stored as an attr on + # the check_fn itself to avoid changing CacehEntry datastructure in + # eval_frame.c. In future, we should probably replace check_fn with a + # queryable data structure such that this information is already present + # in some form. + self.check_fn.id_matched_objs = builder.id_matched_objs + + # NB - We have to very careful of cleaning up here. Because of the + # invalidate function, we can create a weakref finalizer that keeps + # `self` alive for very long. Sometimes by mistake, we can run + # invalidate for a type/object (check id_ref method) that Python can + # leak by design, preventing us from calling the finalizer. In that + # case, the `self` will be alive even though the cache entry will be + # deleted (check invalidate method), which can cause a memory leak, + # e.g., not setting output_graph = None can keep hold of nn_modules. + self._weakrefs.clear() + self.output_graph = None + + def compile_check_fn(self, builder, guards_out, guard_fail_fn): + # see parallel handling of ".0" / "___implicit0" in _eval_frame.c + largs = builder.argnames + largs += ["**___kwargs_ignored"] + + guards_log.debug("GUARDS:") + + # Don't report this guard, it's always the same, useless! + code_parts = ["___check_global_state()"] + verbose_code_parts = code_parts[:] + structured_guard_fns = [] + + def add_code_part(code_part, guard, log_only=False): + verbose_code_part = get_verbose_code_part(code_part, guard) + guards_log.debug("%s", verbose_code_part) + + structured_guard_fns.append( + lambda: { + "code": code_part, + "stack": structured.from_traceback(guard.stack.summary()) + if guard.stack + else None, + "user_stack": structured.from_traceback(guard.user_stack) + if guard.user_stack + else None, + } + ) + + if verbose_guards_log.isEnabledFor(logging.DEBUG): + maybe_stack = "" + maybe_user_stack = "" + if guard is not None: + if guard.stack: + maybe_stack = f"\nStack:\n{''.join(guard.stack.format())}" + if guard.user_stack: + maybe_user_stack = ( + f"\nUser stack:\n{''.join(guard.user_stack.format())}" + ) + verbose_guards_log.debug( + "Guard: %s%s%s", + code_part, + maybe_stack, + maybe_user_stack, + ) + + if not log_only: + code_parts.append(code_part) + verbose_code_parts.append(verbose_code_part) + + seen = set() + for gcl in builder.code: + for code in gcl.code_list: + if code not in seen: + add_code_part(code, gcl.guard) + seen.add(code) + + tensor_check_names = builder.tensor_check_names + check_tensors_fn = None + check_tensors_verbose_fn = None + if tensor_check_names: + assert ( + not self.output_graph.export + ), "Illegal to set tensor_check_names in export." + tensor_check_examples = builder.tensor_check_examples + + dynamic_dims_sizes = [ + convert_to_concrete_values( + self.output_graph.tensor_weakref_to_sizes_strides[t]["size"] + ) + for t in tensor_check_examples + ] + + dynamic_dims_strides = [ + convert_to_concrete_values( + self.output_graph.tensor_weakref_to_sizes_strides[t]["stride"] + ) + for t in tensor_check_examples + ] + + tensor_guards = TensorGuards( + *tensor_check_examples, + dynamic_dims_sizes=dynamic_dims_sizes, + dynamic_dims_strides=dynamic_dims_strides, + ) + check_tensors_fn = tensor_guards.check + check_tensors_verbose_fn = tensor_guards.check_verbose + tensor_check_args = ", ".join( + tensor_check_names + ["tensor_check_names=tensor_check_names"] + ) + # Do this manually, to un-stagger the guards in log message + code_parts.append(f"___check_tensors({tensor_check_args})") + verbose_code_parts.append(f"___check_tensors({tensor_check_args})") + tensor_check_guards = builder.tensor_check_guards + + for i, name in enumerate(tensor_check_names): + # This is a copy of what guards.cpp checks against + # Keep this in sync with TensorCheck constructor + t = tensor_check_examples[i] + sizes = dynamic_dims_sizes[i] + strides = dynamic_dims_strides[i] + code_part = get_tensor_guard_code_part(t, name, sizes, strides) + add_code_part(code_part, tensor_check_guards[i], log_only=True) + + aotautograd_guards: List[GuardEnvExpr] = ( + self.output_graph.tracing_context.guards_context.aotautograd_guards + if self.output_graph + else [] + ) + for guard in aotautograd_guards: + if isinstance(guard, DuplicateInputs): + source_a = guard.input_source_a + source_b = guard.input_source_b + add_code_part(f"{source_a.name()} is {source_b.name()}", None) + else: + raise RuntimeError(f"Unknown GuardEnvExpr: {guard}") + + # TODO: the "guard" here is actually just the top level SHAPE_ENV + # which is useless. Get ShapeEnv to pass in more provenance. + for gcl in builder.shape_env_code: + for code in gcl.code_list: + add_code_part(code, gcl.guard) + + # OK, all done generating guards + torch._logging.trace_structured( + "dynamo_guards", payload_fn=lambda: [f() for f in structured_guard_fns] + ) + + global_state = convert_frame.initial_global_state + if global_state is None: + # we should only hit this case in NopTests() + global_state = convert_frame.GlobalStateGuard() + closure_vars = { + "___check_tensors": check_tensors_fn, + "___check_tensors_verbose": check_tensors_verbose_fn, + "___check_global_state": global_state.check, + "___check_current_backend": torch._dynamo.eval_frame.check_current_backend, + "tensor_check_names": tensor_check_names, + **SYMPY_INTERP, + **CLOSURE_VARS, + } + + unique_code_parts = list(unique(code_parts)) + make_guard_fn_args = ", ".join(closure_vars.keys()) + guard_body, pycode = build_guard_function(unique_code_parts, make_guard_fn_args) + + if os.environ.get("TORCHDYNAMO_PRINT_GUARDS", None) == "1": + print("GUARDS\n", guard_body) + + out: Dict[str, Any] = dict() + + # We don't put builder.scope as the globals in exec call because + # guard_fn.__globals__ becomes equal to builder.scope. This causes + # guard_fn to hold a referece to f_locals sitting in builder.scope["L"] + globals_for_guard_fn = {"G": builder.scope["G"]} + try: + exec(pycode, globals_for_guard_fn, out) + except SyntaxError as ex: + log.exception("Failed to exec guard at line %s.\n%s", ex.lineno, pycode) + raise + guard_fn = out["___make_guard_fn"](*closure_vars.values()) + guard_fn.closure_vars = closure_vars + # TODO(whc) maybe '.code_parts' was only kept around for the guard callback? so we don't need both + guard_fn.args = largs + guard_fn.code_parts = code_parts + guard_fn.verbose_code_parts = verbose_code_parts + # Grab only G, but preserve "G" because guards access it as "G" + guard_fn.global_scope = globals_for_guard_fn + guard_fn.guard_fail_fn = guard_fail_fn + # will be populated by a non-owning reference to CacheEntry/ExtraState + # when the CacheEntry is constructed + guard_fn.cache_entry = None + guard_fn.extra_state = None + return guard_fn + + def invalidate(self): + # Some tests reveal that CheckFunctionManager has no attribute + # check_fn, but this case should not be of any concern. + # This case doesn't seem easy to repro. + if ( + hasattr(self, "check_fn") + and self.check_fn is not DeletedGuardFn + and (cache_entry := self.check_fn.cache_entry) is not None + and (extra_state := self.check_fn.extra_state) is not None + ): + assert isinstance(cache_entry, CacheEntry) + assert isinstance(extra_state, ExtraState) + extra_state.invalidate(cache_entry) + self.check_fn.cache_entry = None + self.check_fn.extra_state = None + self.check_fn = DeletedGuardFn + + def id_ref(self, obj): + """add a weakref, return the id""" + try: + if id(obj) not in self._weakrefs: + # We will clear the _weakrefs dict at the end of __init__ + # function, which will delete the callbacks as well. Therefore, + # we are using a finalizer which is kept alive. + self._weakrefs[id(obj)] = weakref.ref(obj) + weakref.finalize(obj, self.invalidate) + except TypeError: + pass # cannot weakref bool object + return id(obj) + + def lookup_weakrefs(self, obj): + """Lookup the _weakrefs created in id_ref function for ID_MATCH'd objects""" + if id(obj) in self._weakrefs: + return self._weakrefs[id(obj)] + return None + + +def build_guard_function(code_parts, closure_args) -> Tuple[str, str]: + from torch._inductor.utils import IndentedBuffer + + if HAS_UNPARSE_FUNCTIONS: + csepass = PyExprCSEPass() + csepass.count(code_parts) + + def replace(expr: str) -> Tuple[List[str], str]: + return csepass.replace(expr) + + else: + + def replace(expr: str) -> Tuple[List[str], str]: + return [], expr + + # Generate the inner body of the guard function. + # i.e. if-chain of the guard expressions. + guard_body = IndentedBuffer() + for expr in code_parts: + preface, expr = replace(expr) + guard_body.writelines(preface) + guard_body.writeline(f"if not ({expr}):") + with guard_body.indent(): + guard_body.writeline("return False") + + # Wrap the inner body into the actual guard function. + guard = IndentedBuffer() + guard.writeline("def guard(L):") + with guard.indent(): + guard.splice(guard_body) + guard.writeline("return True") + + # Wrap the whole guard function into another function + # with the closure variables. + make_guard_fn = IndentedBuffer() + make_guard_fn.writeline(f"def ___make_guard_fn({closure_args}):") + with make_guard_fn.indent(): + make_guard_fn.splice(guard) + make_guard_fn.writeline("return guard") + + return guard_body.getvalue(), make_guard_fn.getvalue() + + +def is_recompiles_enabled(): + return torch._logging._internal.log_state.is_artifact_enabled("recompiles") + + +def is_recompiles_verbose_enabled(): + return torch._logging._internal.log_state.is_artifact_enabled("recompiles_verbose") + + +def get_guard_fail_reason( + guard_fn: GuardFn, + code: types.CodeType, + f_locals: Dict[str, object], +) -> str: + """ + Return the reason why `guard_fn` failed. + Updates `guard_failures` with the generated reason. + Only the first failed check of guard_fn is reported. + """ + scope = {"L": f_locals, "G": guard_fn.global_scope["G"]} + scope.update(guard_fn.closure_vars) + scope["___check_tensors"] = scope["___check_tensors_verbose"] + reasons: List[str] = [] + for part in guard_fn.verbose_code_parts: + global_scope = dict(guard_fn.global_scope) + global_scope["__compile_source__"] = part + with report_compile_source_on_error(): + try: + fail_reason = eval(part, global_scope, scope) + except Exception as e: + if is_recompiles_verbose_enabled(): + continue + else: + raise + # Only ___check_tensors knows how to return a fancy fail reason; + # for everything else we just report the code that failed + + if isinstance(fail_reason, bool) and not fail_reason: + fail_reason = part + if isinstance(fail_reason, str): + reasons.append(fail_reason) + if not is_recompiles_verbose_enabled(): + break + + reason_str = "\n".join(reasons) + guard_failures[orig_code_map[code]].append(reason_str) + + try: + if guard_fn.guard_fail_fn is not None: + guard_fn.guard_fail_fn( + GuardFail(reason_str or "unknown reason", orig_code_map[code]) + ) + except Exception as e: + log.exception( + "Failure in guard_fail_fn callback - raising here will cause a NULL Error on guard eval", + ) + + return reason_str + + +def get_and_maybe_log_recompilation_reason( + cache_entry, frame: types.FrameType +) -> List[str]: + """ + Return the list of guard failure reasons using cache_entry. + Logs the recompilation reason if `recompiles` logging is enabled. + Raises a RecompileError if `config.error_on_recompile` is enabled. + """ + reasons = [] + while cache_entry is not None: + reason = get_guard_fail_reason( + cache_entry.check_fn, cache_entry.code, frame.f_locals + ) + if reason: + reasons.append(reason) + cache_entry = cache_entry.next + + code = frame.f_code + + # at least one of "recompiles" or "recompiles_verbose" is enabled + do_recompiles_log = is_recompiles_enabled() or is_recompiles_verbose_enabled() + + if do_recompiles_log or config.error_on_recompile: + if is_recompiles_verbose_enabled(): + failures = "\n\n".join( + f"guard {i} failures:\n" + textwrap.indent(reason, "- ") + for i, reason in enumerate(reasons) + ) + else: + failures = textwrap.indent("\n".join(reasons), "- ") + guard_failure_details = ( + f"triggered by the following guard failure(s):\n{failures}" + ) + message = ( + f"Recompiling function {code.co_name} in {code.co_filename}:{code.co_firstlineno}\n" + f"{textwrap.indent(guard_failure_details, ' ')}" + ) + if do_recompiles_log: + if is_recompiles_verbose_enabled(): + recompiles_verbose_log.debug(message) + else: + recompiles_log.debug(message) + if config.error_on_recompile: + raise exc.RecompileError(message) + + return reasons + + +def guard_error_hook( + guard_fn: GuardFn, + code: types.CodeType, + f_locals: Dict[str, object], + index: int, + last: bool, +): + print( + f"ERROR RUNNING GUARDS {code.co_name} {code.co_filename}:{code.co_firstlineno}" + ) + print("lambda " + ", ".join(guard_fn.args) + ":") + print(" ", " and\n ".join(guard_fn.code_parts)) + local_scope = {"L": f_locals, **guard_fn.closure_vars} + for guard in guard_fn.code_parts: + try: + eval(guard, guard_fn.global_scope, local_scope) + except: # noqa: B001,E722 + print(f"Malformed guard:\n{guard}") + + +set_guard_error_hook(guard_error_hook) + + +def unique(seq): + seen = set() + for x in seq: + if x not in seen: + yield x + seen.add(x) + + +def make_dupe_guard(obj_source, dupe_source): + # Note - we may end up in a situation where we invoke something like + # def fn(x, y) + # with fn(x, x) + # Prior to the addition of tracking to all relevant objects, we would handle this just fine by + # eagerly re-entering VB and rewrapping inputs, correctly creating graphargs and placeholders. However, + # with tracking on inputs, duplicate inputs or aliased relationships may end up getting erased here - + # In the fn(x, x) example call above look like a graph with a single input. + # In order to ensure that we do not reuse fn(x, x) for fn(x, y), we create a duplicate input guard. + + # Note - we may not have a source, that is fine, it just means we had an object that is safe to have + # leave unsourced - like a local list created and discharged entirely within a local scope. + if dupe_source and dupe_source != obj_source: + ser_source_is_local = is_from_local_source(dupe_source) + source_is_local = is_from_local_source(obj_source) + # Note - both must be local, or global, or we will run afoul of a lack of merging in how we currently + # reconcile guards builder scopes in compile_check_fn. This technically means we miss a guard here, + # so maybe we should do this refactor before we land this... + # TODO(voz): Combine local and global guard builders. + if ser_source_is_local == source_is_local: + # Note - this is a little aggressive - these being duplicate input does not always matter. + # However, this should always be a sound guard to add here. + return functools.partial(GuardBuilder.DUPLICATE_INPUT, source_b=dupe_source) + return None + + +def install_guard(*guards, skip=0): + """ + Add dynamo guards to the current tracing context. + + Args: + guards: guard(s) to add + skip: number of stack frames to ignore for debug stack trace + """ + from torch._guards import TracingContext + + collect_debug_stack = guards_log.isEnabledFor( + logging.DEBUG + ) or verbose_guards_log.isEnabledFor(logging.DEBUG) + add = TracingContext.get().guards_context.dynamo_guards.add + for guard in guards: + assert isinstance(guard, Guard) + add(guard, collect_debug_stack=collect_debug_stack, skip=skip + 1) diff --git a/shared/package-files/56/564d6cca251b3adad0ba10b91ca657e113d82ad7406f2a53b17f9f668c7c1ad7/blob b/shared/package-files/56/564d6cca251b3adad0ba10b91ca657e113d82ad7406f2a53b17f9f668c7c1ad7/blob new file mode 100644 index 0000000000000000000000000000000000000000..5bc4de797a4407701148a316c5c054b5b666428c --- /dev/null +++ b/shared/package-files/56/564d6cca251b3adad0ba10b91ca657e113d82ad7406f2a53b17f9f668c7c1ad7/blob @@ -0,0 +1,114 @@ +""" +@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.containers import ( + RepeatedCompositeFieldContainer as google___protobuf___internal___containers___RepeatedCompositeFieldContainer, +) + +from google.protobuf.message import ( + Message as google___protobuf___message___Message, +) + +from google.protobuf.source_context_pb2 import ( + SourceContext as google___protobuf___source_context_pb2___SourceContext, +) + +from google.protobuf.type_pb2 import ( + Option as google___protobuf___type_pb2___Option, + SyntaxValue as google___protobuf___type_pb2___SyntaxValue, +) + +from typing import ( + Iterable as typing___Iterable, + Optional as typing___Optional, + Text as typing___Text, +) + +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 Api(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + name: typing___Text = ... + version: typing___Text = ... + syntax: google___protobuf___type_pb2___SyntaxValue = ... + + @property + def methods(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___Method]: ... + + @property + def options(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[google___protobuf___type_pb2___Option]: ... + + @property + def source_context(self) -> google___protobuf___source_context_pb2___SourceContext: ... + + @property + def mixins(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___Mixin]: ... + + def __init__(self, + *, + name : typing___Optional[typing___Text] = None, + methods : typing___Optional[typing___Iterable[type___Method]] = None, + options : typing___Optional[typing___Iterable[google___protobuf___type_pb2___Option]] = None, + version : typing___Optional[typing___Text] = None, + source_context : typing___Optional[google___protobuf___source_context_pb2___SourceContext] = None, + mixins : typing___Optional[typing___Iterable[type___Mixin]] = None, + syntax : typing___Optional[google___protobuf___type_pb2___SyntaxValue] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"source_context",b"source_context"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"methods",b"methods",u"mixins",b"mixins",u"name",b"name",u"options",b"options",u"source_context",b"source_context",u"syntax",b"syntax",u"version",b"version"]) -> None: ... +type___Api = Api + +class Method(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + name: typing___Text = ... + request_type_url: typing___Text = ... + request_streaming: builtin___bool = ... + response_type_url: typing___Text = ... + response_streaming: builtin___bool = ... + syntax: google___protobuf___type_pb2___SyntaxValue = ... + + @property + def options(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[google___protobuf___type_pb2___Option]: ... + + def __init__(self, + *, + name : typing___Optional[typing___Text] = None, + request_type_url : typing___Optional[typing___Text] = None, + request_streaming : typing___Optional[builtin___bool] = None, + response_type_url : typing___Optional[typing___Text] = None, + response_streaming : typing___Optional[builtin___bool] = None, + options : typing___Optional[typing___Iterable[google___protobuf___type_pb2___Option]] = None, + syntax : typing___Optional[google___protobuf___type_pb2___SyntaxValue] = None, + ) -> None: ... + def ClearField(self, field_name: typing_extensions___Literal[u"name",b"name",u"options",b"options",u"request_streaming",b"request_streaming",u"request_type_url",b"request_type_url",u"response_streaming",b"response_streaming",u"response_type_url",b"response_type_url",u"syntax",b"syntax"]) -> None: ... +type___Method = Method + +class Mixin(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + name: typing___Text = ... + root: typing___Text = ... + + def __init__(self, + *, + name : typing___Optional[typing___Text] = None, + root : typing___Optional[typing___Text] = None, + ) -> None: ... + def ClearField(self, field_name: typing_extensions___Literal[u"name",b"name",u"root",b"root"]) -> None: ... +type___Mixin = Mixin diff --git a/shared/package-files/56/564f153bbe095146991ef7a3be45890519e693ece9987c2392c3026680435837/blob b/shared/package-files/56/564f153bbe095146991ef7a3be45890519e693ece9987c2392c3026680435837/blob new file mode 100644 index 0000000000000000000000000000000000000000..5d20d7db7e3ccc1d77c5949830cf7b610d88355f --- /dev/null +++ b/shared/package-files/56/564f153bbe095146991ef7a3be45890519e693ece9987c2392c3026680435837/blob @@ -0,0 +1,130 @@ +import os.path +from pathlib import Path +from typing import Any, Callable, Optional, Tuple, Union + +import numpy as np +from PIL import Image + +from .utils import check_integrity, download_url, verify_str_arg +from .vision import VisionDataset + + +class SVHN(VisionDataset): + """`SVHN `_ Dataset. + Note: The SVHN dataset assigns the label `10` to the digit `0`. However, in this Dataset, + we assign the label `0` to the digit `0` to be compatible with PyTorch loss functions which + expect the class labels to be in the range `[0, C-1]` + + .. warning:: + + This class needs `scipy `_ to load data from `.mat` format. + + Args: + root (str or ``pathlib.Path``): Root directory of the dataset where the data is stored. + split (string): One of {'train', 'test', 'extra'}. + Accordingly dataset is selected. 'extra' is Extra training set. + transform (callable, optional): A function/transform that takes in a PIL image + and returns a transformed version. E.g, ``transforms.RandomCrop`` + target_transform (callable, optional): A function/transform that takes in the + target and transforms it. + download (bool, optional): If true, downloads the dataset from the internet and + puts it in root directory. If dataset is already downloaded, it is not + downloaded again. + + """ + + split_list = { + "train": [ + "http://ufldl.stanford.edu/housenumbers/train_32x32.mat", + "train_32x32.mat", + "e26dedcc434d2e4c54c9b2d4a06d8373", + ], + "test": [ + "http://ufldl.stanford.edu/housenumbers/test_32x32.mat", + "test_32x32.mat", + "eb5a983be6a315427106f1b164d9cef3", + ], + "extra": [ + "http://ufldl.stanford.edu/housenumbers/extra_32x32.mat", + "extra_32x32.mat", + "a93ce644f1a588dc4d68dda5feec44a7", + ], + } + + def __init__( + self, + root: Union[str, Path], + split: str = "train", + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ) -> None: + super().__init__(root, transform=transform, target_transform=target_transform) + self.split = verify_str_arg(split, "split", tuple(self.split_list.keys())) + self.url = self.split_list[split][0] + self.filename = self.split_list[split][1] + self.file_md5 = self.split_list[split][2] + + if download: + self.download() + + if not self._check_integrity(): + raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") + + # import here rather than at top of file because this is + # an optional dependency for torchvision + import scipy.io as sio + + # reading(loading) mat file as array + loaded_mat = sio.loadmat(os.path.join(self.root, self.filename)) + + self.data = loaded_mat["X"] + # loading from the .mat file gives an np.ndarray of type np.uint8 + # converting to np.int64, so that we have a LongTensor after + # the conversion from the numpy array + # the squeeze is needed to obtain a 1D tensor + self.labels = loaded_mat["y"].astype(np.int64).squeeze() + + # the svhn dataset assigns the class label "10" to the digit 0 + # this makes it inconsistent with several loss functions + # which expect the class labels to be in the range [0, C-1] + np.place(self.labels, self.labels == 10, 0) + self.data = np.transpose(self.data, (3, 2, 0, 1)) + + def __getitem__(self, index: int) -> Tuple[Any, Any]: + """ + Args: + index (int): Index + + Returns: + tuple: (image, target) where target is index of the target class. + """ + img, target = self.data[index], int(self.labels[index]) + + # doing this so that it is consistent with all other datasets + # to return a PIL Image + img = Image.fromarray(np.transpose(img, (1, 2, 0))) + + if self.transform is not None: + img = self.transform(img) + + if self.target_transform is not None: + target = self.target_transform(target) + + return img, target + + def __len__(self) -> int: + return len(self.data) + + def _check_integrity(self) -> bool: + root = self.root + md5 = self.split_list[self.split][2] + fpath = os.path.join(root, self.filename) + return check_integrity(fpath, md5) + + def download(self) -> None: + md5 = self.split_list[self.split][2] + download_url(self.url, self.root, self.filename, md5) + + def extra_repr(self) -> str: + return "Split: {split}".format(**self.__dict__) diff --git a/shared/package-files/56/5652bcc2d84bb33915968e96a2b1f17c53beb242a11ed79abd839a80779d0821/blob b/shared/package-files/56/5652bcc2d84bb33915968e96a2b1f17c53beb242a11ed79abd839a80779d0821/blob new file mode 100644 index 0000000000000000000000000000000000000000..cb48dae2c536a7b1e2568244bf2058ce934238f0 --- /dev/null +++ b/shared/package-files/56/5652bcc2d84bb33915968e96a2b1f17c53beb242a11ed79abd839a80779d0821/blob @@ -0,0 +1,51 @@ +# NOTE: This test is sensitive to line numbers as it checks breakpoints +from numba import njit, types +import numpy as np +from numba.tests.gdb_support import GdbMIDriver +from numba.tests.support import TestCase, needs_subprocess +import unittest + + +@needs_subprocess +class Test(TestCase): + + def test(self): + @njit(debug=True) + def foo(x): + z = np.ones_like(x) # break here + return x, z + + tmp = np.ones(5) + foo(tmp) + + driver = GdbMIDriver(__file__) + driver.set_breakpoint(line=15) + driver.run() + driver.check_hit_breakpoint(1) + driver.stack_list_arguments(2) + llvm_intp = f"i{types.intp.bitwidth}" + expect = ( + '[frame={level="0",args=[{name="x",type="array(float64, 1d, C) ' + f'({{i8*, i8*, {llvm_intp}, {llvm_intp}, double*, ' + f'[1 x {llvm_intp}], [1 x {llvm_intp}]}})"}}]}}]' + ) + driver.assert_output(expect) + driver.stack_list_variables(1) + # 'z' should be zero-init + expect = ('{name="z",value="{meminfo = 0x0, parent = 0x0, nitems = 0, ' + 'itemsize = 0, data = 0x0, shape = {0}, strides = {0}}"}') + driver.assert_output(expect) + driver.set_breakpoint(line=16) + driver.cont() + driver.check_hit_breakpoint(2) + driver.stack_list_variables(1) + # 'z' should be populated + expect = (r'^.*\{name="z",value="\{meminfo = 0x[0-9a-f]+ .*, ' + r'parent = 0x0, nitems = 5, itemsize = 8, ' + r'data = 0x[0-9a-f]+, shape = \{5\}, strides = \{8\}\}.*$') + driver.assert_regex_output(expect) + driver.quit() + + +if __name__ == '__main__': + unittest.main() diff --git a/shared/package-files/56/565454d155b79ca728b997f7ca4b7c23e8ad2bd520ed89561831c99d8b95b438/blob b/shared/package-files/56/565454d155b79ca728b997f7ca4b7c23e8ad2bd520ed89561831c99d8b95b438/blob new file mode 100644 index 0000000000000000000000000000000000000000..1176edc2208a7ef30b2265f30f8e60a194fdc232 --- /dev/null +++ b/shared/package-files/56/565454d155b79ca728b997f7ca4b7c23e8ad2bd520ed89561831c99d8b95b438/blob @@ -0,0 +1,27 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeMetaFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace meta { + +struct TORCH_API structured_scatter_reduce_two : public at::impl::MetaBase { + + + void meta(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self); +}; + +} // namespace native +} // namespace at diff --git a/shared/package-files/56/56549f6a15910c6ee83b2922aefd8b06bbe24d0c739afb966802d06cf10023b5/blob b/shared/package-files/56/56549f6a15910c6ee83b2922aefd8b06bbe24d0c739afb966802d06cf10023b5/blob new file mode 100644 index 0000000000000000000000000000000000000000..633429959ef4371a2ff58d482b244e9c4f832e9f Binary files /dev/null and b/shared/package-files/56/56549f6a15910c6ee83b2922aefd8b06bbe24d0c739afb966802d06cf10023b5/blob differ diff --git a/shared/package-files/56/5659af72d45a42d8ee62f42ea1edaddae7e26fbe13b9affbaac895aecde3fa9d/blob b/shared/package-files/56/5659af72d45a42d8ee62f42ea1edaddae7e26fbe13b9affbaac895aecde3fa9d/blob new file mode 100644 index 0000000000000000000000000000000000000000..bb62e2223d515288bd5aa6c5a70028279c4f6d30 --- /dev/null +++ b/shared/package-files/56/5659af72d45a42d8ee62f42ea1edaddae7e26fbe13b9affbaac895aecde3fa9d/blob @@ -0,0 +1,9 @@ +import numpy as np +from pytest import raises as assert_raises + +import scipy.sparse.linalg._isolve.utils as utils + + +def test_make_system_bad_shape(): + assert_raises(ValueError, + utils.make_system, np.zeros((5,3)), None, np.zeros(4), np.zeros(4)) diff --git a/shared/package-files/56/565b18e09c1951c905e6ec43ad596b2510559ef4e6bae6a3774877ec13246cea/blob b/shared/package-files/56/565b18e09c1951c905e6ec43ad596b2510559ef4e6bae6a3774877ec13246cea/blob new file mode 100644 index 0000000000000000000000000000000000000000..a62b5c3298c9ceb95e9fd62a462828f8a7155843 --- /dev/null +++ b/shared/package-files/56/565b18e09c1951c905e6ec43ad596b2510559ef4e6bae6a3774877ec13246cea/blob @@ -0,0 +1,447 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +import io +import os +import re +import sys +import types + +import pytest +from unittest import mock + +import pyarrow as pa + +try: + import pyarrow.parquet as pq + from pyarrow.tests.parquet.common import _write_table +except ImportError: + pq = None + +try: + import pandas as pd + import pandas.testing as tm + + from pyarrow.tests.parquet.common import alltypes_sample +except ImportError: + pd = tm = None + + +# Marks all of the tests in this module +# Ignore these with pytest ... -m 'not parquet' +pytestmark = pytest.mark.parquet + + +@pytest.mark.pandas +def test_pass_separate_metadata(): + # ARROW-471 + df = alltypes_sample(size=10000) + + a_table = pa.Table.from_pandas(df) + + buf = io.BytesIO() + _write_table(a_table, buf, compression='snappy', version='2.6') + + buf.seek(0) + metadata = pq.read_metadata(buf) + + buf.seek(0) + + fileh = pq.ParquetFile(buf, metadata=metadata) + + tm.assert_frame_equal(df, fileh.read().to_pandas()) + + +@pytest.mark.pandas +def test_read_single_row_group(): + # ARROW-471 + N, K = 10000, 4 + df = alltypes_sample(size=N) + + a_table = pa.Table.from_pandas(df) + + buf = io.BytesIO() + _write_table(a_table, buf, row_group_size=N / K, + compression='snappy', version='2.6') + + buf.seek(0) + + pf = pq.ParquetFile(buf) + + assert pf.num_row_groups == K + + row_groups = [pf.read_row_group(i) for i in range(K)] + result = pa.concat_tables(row_groups) + tm.assert_frame_equal(df, result.to_pandas()) + + +@pytest.mark.pandas +def test_read_single_row_group_with_column_subset(): + N, K = 10000, 4 + df = alltypes_sample(size=N) + a_table = pa.Table.from_pandas(df) + + buf = io.BytesIO() + _write_table(a_table, buf, row_group_size=N / K, + compression='snappy', version='2.6') + + buf.seek(0) + pf = pq.ParquetFile(buf) + + cols = list(df.columns[:2]) + row_groups = [pf.read_row_group(i, columns=cols) for i in range(K)] + result = pa.concat_tables(row_groups) + tm.assert_frame_equal(df[cols], result.to_pandas()) + + # ARROW-4267: Selection of duplicate columns still leads to these columns + # being read uniquely. + row_groups = [pf.read_row_group(i, columns=cols + cols) for i in range(K)] + result = pa.concat_tables(row_groups) + tm.assert_frame_equal(df[cols], result.to_pandas()) + + +@pytest.mark.pandas +def test_read_multiple_row_groups(): + N, K = 10000, 4 + df = alltypes_sample(size=N) + + a_table = pa.Table.from_pandas(df) + + buf = io.BytesIO() + _write_table(a_table, buf, row_group_size=N / K, + compression='snappy', version='2.6') + + buf.seek(0) + + pf = pq.ParquetFile(buf) + + assert pf.num_row_groups == K + + result = pf.read_row_groups(range(K)) + tm.assert_frame_equal(df, result.to_pandas()) + + +@pytest.mark.pandas +def test_read_multiple_row_groups_with_column_subset(): + N, K = 10000, 4 + df = alltypes_sample(size=N) + a_table = pa.Table.from_pandas(df) + + buf = io.BytesIO() + _write_table(a_table, buf, row_group_size=N / K, + compression='snappy', version='2.6') + + buf.seek(0) + pf = pq.ParquetFile(buf) + + cols = list(df.columns[:2]) + result = pf.read_row_groups(range(K), columns=cols) + tm.assert_frame_equal(df[cols], result.to_pandas()) + + # ARROW-4267: Selection of duplicate columns still leads to these columns + # being read uniquely. + result = pf.read_row_groups(range(K), columns=cols + cols) + tm.assert_frame_equal(df[cols], result.to_pandas()) + + +@pytest.mark.pandas +def test_scan_contents(): + N, K = 10000, 4 + df = alltypes_sample(size=N) + a_table = pa.Table.from_pandas(df) + + buf = io.BytesIO() + _write_table(a_table, buf, row_group_size=N / K, + compression='snappy', version='2.6') + + buf.seek(0) + pf = pq.ParquetFile(buf) + + assert pf.scan_contents() == 10000 + assert pf.scan_contents(df.columns[:4]) == 10000 + + +def test_parquet_file_pass_directory_instead_of_file(tempdir): + # ARROW-7208 + path = tempdir / 'directory' + os.mkdir(str(path)) + + msg = f"Cannot open for reading: path '{str(path)}' is a directory" + with pytest.raises(IOError) as exc: + pq.ParquetFile(path) + if exc.errisinstance(PermissionError) and sys.platform == 'win32': + return # Windows CI can get a PermissionError here. + exc.match(msg) + + +def test_read_column_invalid_index(): + table = pa.table([pa.array([4, 5]), pa.array(["foo", "bar"])], + names=['ints', 'strs']) + bio = pa.BufferOutputStream() + pq.write_table(table, bio) + f = pq.ParquetFile(bio.getvalue()) + assert f.reader.read_column(0).to_pylist() == [4, 5] + assert f.reader.read_column(1).to_pylist() == ["foo", "bar"] + for index in (-1, 2): + with pytest.raises((ValueError, IndexError)): + f.reader.read_column(index) + + +@pytest.mark.pandas +@pytest.mark.parametrize('batch_size', [300, 1000, 1300]) +def test_iter_batches_columns_reader(tempdir, batch_size): + total_size = 3000 + chunk_size = 1000 + # TODO: Add categorical support + df = alltypes_sample(size=total_size) + + filename = tempdir / 'pandas_roundtrip.parquet' + arrow_table = pa.Table.from_pandas(df) + _write_table(arrow_table, filename, version='2.6', + chunk_size=chunk_size) + + file_ = pq.ParquetFile(filename) + for columns in [df.columns[:10], df.columns[10:]]: + batches = file_.iter_batches(batch_size=batch_size, columns=columns) + batch_starts = range(0, total_size+batch_size, batch_size) + for batch, start in zip(batches, batch_starts): + end = min(total_size, start + batch_size) + tm.assert_frame_equal( + batch.to_pandas(), + df.iloc[start:end, :].loc[:, columns].reset_index(drop=True) + ) + + +@pytest.mark.pandas +@pytest.mark.parametrize('chunk_size', [1000]) +def test_iter_batches_reader(tempdir, chunk_size): + df = alltypes_sample(size=10000, categorical=True) + + filename = tempdir / 'pandas_roundtrip.parquet' + arrow_table = pa.Table.from_pandas(df) + assert arrow_table.schema.pandas_metadata is not None + + _write_table(arrow_table, filename, version='2.6', + chunk_size=chunk_size) + + file_ = pq.ParquetFile(filename) + + def get_all_batches(f): + for row_group in range(f.num_row_groups): + batches = f.iter_batches( + batch_size=900, + row_groups=[row_group], + ) + + for batch in batches: + yield batch + + batches = list(get_all_batches(file_)) + batch_no = 0 + + for i in range(file_.num_row_groups): + tm.assert_frame_equal( + batches[batch_no].to_pandas(), + file_.read_row_groups([i]).to_pandas().head(900) + ) + + batch_no += 1 + + tm.assert_frame_equal( + batches[batch_no].to_pandas().reset_index(drop=True), + file_.read_row_groups([i]).to_pandas().iloc[900:].reset_index( + drop=True + ) + ) + + batch_no += 1 + + +@pytest.mark.pandas +@pytest.mark.parametrize('pre_buffer', [False, True]) +def test_pre_buffer(pre_buffer): + N, K = 10000, 4 + df = alltypes_sample(size=N) + a_table = pa.Table.from_pandas(df) + + buf = io.BytesIO() + _write_table(a_table, buf, row_group_size=N / K, + compression='snappy', version='2.6') + + buf.seek(0) + pf = pq.ParquetFile(buf, pre_buffer=pre_buffer) + assert pf.read().num_rows == N + + +def test_parquet_file_explicitly_closed(tempdir): + """ + Unopened files should be closed explicitly after use, + and previously opened files should be left open. + Applies to read_table, ParquetDataset, and ParquetFile + """ + # create test parquet file + fn = tempdir.joinpath('file.parquet') + table = pa.table({'col1': [0, 1], 'col2': [0, 1]}) + pq.write_table(table, fn) + + # ParquetFile with opened file (will leave open) + with open(fn, 'rb') as f: + with pq.ParquetFile(f) as p: + p.read() + assert not f.closed + assert not p.closed + assert not f.closed # opened input file was not closed + assert not p.closed # parquet file obj reports as not closed + assert f.closed + assert p.closed # parquet file being closed reflects underlying file + + # ParquetFile with unopened file (will close) + with pq.ParquetFile(fn) as p: + p.read() + assert not p.closed + assert p.closed # parquet file obj reports as closed + + +@pytest.mark.s3 +@pytest.mark.parametrize("use_uri", (True, False)) +def test_parquet_file_with_filesystem(s3_example_fs, use_uri): + s3_fs, s3_uri, s3_path = s3_example_fs + + args = (s3_uri if use_uri else s3_path,) + kwargs = {} if use_uri else dict(filesystem=s3_fs) + + table = pa.table({"a": range(10)}) + pq.write_table(table, s3_path, filesystem=s3_fs) + + parquet_file = pq.ParquetFile(*args, **kwargs) + assert parquet_file.read() == table + assert not parquet_file.closed + parquet_file.close() + assert parquet_file.closed + + with pq.ParquetFile(*args, **kwargs) as f: + assert f.read() == table + assert not f.closed + assert f.closed + + +def test_read_statistics(): + table = pa.table({"value": pa.array([-1, None, 3])}) + buf = io.BytesIO() + _write_table(table, buf) + buf.seek(0) + + statistics = pq.ParquetFile(buf).read().columns[0].chunks[0].statistics + assert statistics.is_null_count_exact is True + assert statistics.null_count == 1 + assert statistics.distinct_count is None + # TODO: add tests for is_distinct_count_exact == None and True + # once Python API allows + assert statistics.is_distinct_count_exact is False + assert statistics.min == -1 + assert statistics.is_min_exact + assert statistics.max == 3 + assert statistics.is_max_exact + assert repr(statistics) == ("arrow.ArrayStatistics<" + "null_count=1, distinct_count=None, " + "min=-1, is_min_exact=True, " + "max=3, is_max_exact=True>") + + +def test_read_undefined_logical_type(parquet_test_datadir): + test_file = f"{parquet_test_datadir}/unknown-logical-type.parquet" + + table = pq.ParquetFile(test_file).read() + assert table.column_names == ["column with known type", "column with unknown type"] + assert table["column with unknown type"].to_pylist() == [ + b"unknown string 1", + b"unknown string 2", + b"unknown string 3" + ] + + +def test_parquet_file_fsspec_support(): + pytest.importorskip("fsspec") + + table = pa.table({"a": range(10)}) + pq.write_table(table, "fsspec+memory://example.parquet") + table2 = pq.read_table("fsspec+memory://example.parquet") + assert table.equals(table2) + + msg = "Unrecognized filesystem type in URI" + with pytest.raises(pa.ArrowInvalid, match=msg): + pq.read_table("non-existing://example.parquet") + + +def test_parquet_file_fsspec_support_through_filesystem_argument(): + try: + from fsspec.implementations.memory import MemoryFileSystem + except ImportError: + pytest.skip("fsspec is not installed, skipping test") + + table = pa.table({"b": range(10)}) + + fs = MemoryFileSystem() + fs.mkdir("/path/to/prefix", create_parents=True) + assert fs.exists("/path/to/prefix") + + fs_str = "fsspec+memory://path/to/prefix" + pq.write_table(table, "b.parquet", filesystem=fs_str) + table2 = pq.read_table("fsspec+memory://path/to/prefix/b.parquet") + assert table.equals(table2) + + +def test_parquet_file_hugginface_support(): + try: + from fsspec.implementations.memory import MemoryFileSystem + except ImportError: + pytest.skip("fsspec is not installed, skipping Hugging Face test") + + fake_hf_module = types.ModuleType("huggingface_hub") + fake_hf_module.HfFileSystem = MemoryFileSystem + with mock.patch.dict("sys.modules", {"huggingface_hub": fake_hf_module}): + uri = "hf://datasets/apache/arrow/test.parquet" + table = pa.table({"a": range(10)}) + pq.write_table(table, uri) + table2 = pq.read_table(uri) + assert table.equals(table2) + + +def test_fsspec_uri_raises_if_fsspec_is_not_available(): + # sadly cannot patch sys.modules because cython will still be able to import fsspec + try: + import fsspec # noqa: F401 + except ImportError: + pass + else: + pytest.skip("fsspec is available, skipping test") + + msg = re.escape( + "`fsspec` is required to handle `fsspec+://` and `hf://` URIs.") + with pytest.raises(ImportError, match=msg): + pq.read_table("fsspec+memory://example.parquet") + + +def test_iter_batches_raises_batch_size_zero(tempdir): + # See https://github.com/apache/arrow/issues/46811 + schema = pa.schema([]) + empty_table = pa.Table.from_batches([], schema=schema) + parquet_file_path = tempdir / "empty_file.parquet" + pq.write_table(empty_table, parquet_file_path) + parquet_file = pq.ParquetFile(parquet_file_path) + with pytest.raises(ValueError): + parquet_file.iter_batches(batch_size=0) diff --git a/shared/package-files/56/565ef8fb81829dc98a4be79bd3ac584d2e4a66d9a63a5ff80e55dc78583f95b6/blob b/shared/package-files/56/565ef8fb81829dc98a4be79bd3ac584d2e4a66d9a63a5ff80e55dc78583f95b6/blob new file mode 100644 index 0000000000000000000000000000000000000000..c367f50b59e639a50bc03f3b45b6b81bce71ab53 --- /dev/null +++ b/shared/package-files/56/565ef8fb81829dc98a4be79bd3ac584d2e4a66d9a63a5ff80e55dc78583f95b6/blob @@ -0,0 +1,17 @@ +from typing import Any + +from yaml.error import MarkedYAMLError + +class ComposerError(MarkedYAMLError): ... + +class Composer: + anchors: Any + def __init__(self) -> None: ... + def check_node(self): ... + def get_node(self): ... + def get_single_node(self): ... + def compose_document(self): ... + def compose_node(self, parent, index): ... + def compose_scalar_node(self, anchor): ... + def compose_sequence_node(self, anchor): ... + def compose_mapping_node(self, anchor): ... diff --git a/shared/package-files/56/566141a62fbfb4d6bccb3366bbf7ee7dec263d39c253ac9c34219c377e1405c7/blob b/shared/package-files/56/566141a62fbfb4d6bccb3366bbf7ee7dec263d39c253ac9c34219c377e1405c7/blob new file mode 100644 index 0000000000000000000000000000000000000000..01a1ad3c6f4cb5c0c9b99d1ce35fea92e7618ff5 --- /dev/null +++ b/shared/package-files/56/566141a62fbfb4d6bccb3366bbf7ee7dec263d39c253ac9c34219c377e1405c7/blob @@ -0,0 +1,14 @@ +from .geometry import cauchy_geometry, spider_geometry +from .optim import ( + tangential_byrd_omojokun, + constrained_tangential_byrd_omojokun, + normal_byrd_omojokun, +) + +__all__ = [ + "cauchy_geometry", + "spider_geometry", + "tangential_byrd_omojokun", + "constrained_tangential_byrd_omojokun", + "normal_byrd_omojokun", +] diff --git a/shared/package-files/56/566520ef07b56189e8e87e331e08c2a0f63ba533b7cf314792714c0bca18c047/blob b/shared/package-files/56/566520ef07b56189e8e87e331e08c2a0f63ba533b7cf314792714c0bca18c047/blob new file mode 100644 index 0000000000000000000000000000000000000000..3d5aa4bc61b19036f868c36bf3fe189c1a9608fb --- /dev/null +++ b/shared/package-files/56/566520ef07b56189e8e87e331e08c2a0f63ba533b7cf314792714c0bca18c047/blob @@ -0,0 +1,67 @@ +from socket import _RetAddress, socket +from threading import Thread +from typing import Dict, Protocol, Tuple + +from paramiko.channel import Channel +from paramiko.message import Message +from paramiko.pkey import PKey +from paramiko.transport import Transport + +class _AgentProxy(Protocol): + def connect(self) -> None: ... + def close(self) -> None: ... + +cSSH2_AGENTC_REQUEST_IDENTITIES: bytes +SSH2_AGENT_IDENTITIES_ANSWER: int +cSSH2_AGENTC_SIGN_REQUEST: bytes +SSH2_AGENT_SIGN_RESPONSE: int + +class AgentSSH: + def __init__(self) -> None: ... + def get_keys(self) -> Tuple[AgentKey, ...]: ... + +class AgentProxyThread(Thread): + def __init__(self, agent: _AgentProxy) -> None: ... + def run(self) -> None: ... + +class AgentLocalProxy(AgentProxyThread): + def __init__(self, agent: AgentServerProxy) -> None: ... + def get_connection(self) -> Tuple[socket, _RetAddress]: ... + +class AgentRemoteProxy(AgentProxyThread): + def __init__(self, agent: AgentClientProxy, chan: Channel) -> None: ... + def get_connection(self) -> Tuple[socket, _RetAddress]: ... + +class AgentClientProxy: + thread: Thread + def __init__(self, chanRemote: Channel) -> None: ... + def __del__(self) -> None: ... + def connect(self) -> None: ... + def close(self) -> None: ... + +class AgentServerProxy(AgentSSH): + thread: Thread + def __init__(self, t: Transport) -> None: ... + def __del__(self) -> None: ... + def connect(self) -> None: ... + def close(self) -> None: ... + def get_env(self) -> Dict[str, str]: ... + +class AgentRequestHandler: + def __init__(self, chanClient: Channel) -> None: ... + def __del__(self) -> None: ... + def close(self) -> None: ... + +class Agent(AgentSSH): + def __init__(self) -> None: ... + def close(self) -> None: ... + +class AgentKey(PKey): + agent: AgentSSH + blob: bytes + public_blob: None + name: str + def __init__(self, agent: AgentSSH, blob: bytes) -> None: ... + def asbytes(self) -> bytes: ... + def get_name(self) -> str: ... + def sign_ssh_data(self, data: bytes) -> Message: ... diff --git a/shared/package-files/56/56664ff52e693ee705c72a80395f74f049965d066f8028162e6949253525d0b1/blob b/shared/package-files/56/56664ff52e693ee705c72a80395f74f049965d066f8028162e6949253525d0b1/blob new file mode 100644 index 0000000000000000000000000000000000000000..001638c2f2a964eb2cc75660f66a1bcc447d7a57 Binary files /dev/null and b/shared/package-files/56/56664ff52e693ee705c72a80395f74f049965d066f8028162e6949253525d0b1/blob differ diff --git a/shared/package-files/56/5666818020fce78b306322e8b8330725fe0b2fa8943606a310e41fd295231e38/blob b/shared/package-files/56/5666818020fce78b306322e8b8330725fe0b2fa8943606a310e41fd295231e38/blob new file mode 100644 index 0000000000000000000000000000000000000000..2854dbc8b1e53f298ac3b135eac1f06e73940152 Binary files /dev/null and b/shared/package-files/56/5666818020fce78b306322e8b8330725fe0b2fa8943606a310e41fd295231e38/blob differ diff --git a/shared/package-files/56/5666bb7eda54f19534e85cecf9cca49fce014790c8cd7bb2eaf3b1646851eba8/blob b/shared/package-files/56/5666bb7eda54f19534e85cecf9cca49fce014790c8cd7bb2eaf3b1646851eba8/blob new file mode 100644 index 0000000000000000000000000000000000000000..ec70beeaf4891efb41581a92b643f2cb358bafde --- /dev/null +++ b/shared/package-files/56/5666bb7eda54f19534e85cecf9cca49fce014790c8cd7bb2eaf3b1646851eba8/blob @@ -0,0 +1,661 @@ +from abc import ABC, abstractmethod +import getpass +import sys, os, pickle +import tempfile +import types +import re +from typing import ( + TypeVar, Type, List, Dict, Iterator, Callable, Union, Optional, Sequence, + Tuple, Iterable, IO, Any, TYPE_CHECKING, Collection +) +if TYPE_CHECKING: + from .parsers.lalr_interactive_parser import InteractiveParser + from .tree import ParseTree + from .visitors import Transformer + if sys.version_info >= (3, 8): + from typing import Literal + else: + from typing_extensions import Literal + from .parser_frontends import ParsingFrontend + +from .exceptions import ConfigurationError, assert_config, UnexpectedInput +from .utils import Serialize, SerializeMemoizer, FS, isascii, logger +from .load_grammar import load_grammar, FromPackageLoader, Grammar, verify_used_files, PackageResource, sha256_digest +from .tree import Tree +from .common import LexerConf, ParserConf, _ParserArgType, _LexerArgType + +from .lexer import Lexer, BasicLexer, TerminalDef, LexerThread, Token +from .parse_tree_builder import ParseTreeBuilder +from .parser_frontends import _validate_frontend_args, _get_lexer_callbacks, _deserialize_parsing_frontend, _construct_parsing_frontend +from .grammar import Rule + + +try: + import regex + _has_regex = True +except ImportError: + _has_regex = False + + +###{standalone + + +class PostLex(ABC): + @abstractmethod + def process(self, stream: Iterator[Token]) -> Iterator[Token]: + return stream + + always_accept: Iterable[str] = () + +class LarkOptions(Serialize): + """Specifies the options for Lark + + """ + + start: List[str] + debug: bool + strict: bool + transformer: 'Optional[Transformer]' + propagate_positions: Union[bool, str] + maybe_placeholders: bool + cache: Union[bool, str] + regex: bool + g_regex_flags: int + keep_all_tokens: bool + tree_class: Optional[Callable[[str, List], Any]] + parser: _ParserArgType + lexer: _LexerArgType + ambiguity: 'Literal["auto", "resolve", "explicit", "forest"]' + postlex: Optional[PostLex] + priority: 'Optional[Literal["auto", "normal", "invert"]]' + lexer_callbacks: Dict[str, Callable[[Token], Token]] + use_bytes: bool + ordered_sets: bool + edit_terminals: Optional[Callable[[TerminalDef], TerminalDef]] + import_paths: 'List[Union[str, Callable[[Union[None, str, PackageResource], str], Tuple[str, str]]]]' + source_path: Optional[str] + + OPTIONS_DOC = r""" + **=== General Options ===** + + start + The start symbol. Either a string, or a list of strings for multiple possible starts (Default: "start") + debug + Display debug information and extra warnings. Use only when debugging (Default: ``False``) + When used with Earley, it generates a forest graph as "sppf.png", if 'dot' is installed. + strict + Throw an exception on any potential ambiguity, including shift/reduce conflicts, and regex collisions. + transformer + Applies the transformer to every parse tree (equivalent to applying it after the parse, but faster) + propagate_positions + Propagates positional attributes into the 'meta' attribute of all tree branches. + Sets attributes: (line, column, end_line, end_column, start_pos, end_pos, + container_line, container_column, container_end_line, container_end_column) + Accepts ``False``, ``True``, or a callable, which will filter which nodes to ignore when propagating. + maybe_placeholders + When ``True``, the ``[]`` operator returns ``None`` when not matched. + When ``False``, ``[]`` behaves like the ``?`` operator, and returns no value at all. + (default= ``True``) + cache + Cache the results of the Lark grammar analysis, for x2 to x3 faster loading. LALR only for now. + + - When ``False``, does nothing (default) + - When ``True``, caches to a temporary file in the local directory + - When given a string, caches to the path pointed by the string + regex + When True, uses the ``regex`` module instead of the stdlib ``re``. + g_regex_flags + Flags that are applied to all terminals (both regex and strings) + keep_all_tokens + Prevent the tree builder from automagically removing "punctuation" tokens (Default: ``False``) + tree_class + Lark will produce trees comprised of instances of this class instead of the default ``lark.Tree``. + + **=== Algorithm Options ===** + + parser + Decides which parser engine to use. Accepts "earley" or "lalr". (Default: "earley"). + (there is also a "cyk" option for legacy) + lexer + Decides whether or not to use a lexer stage + + - "auto" (default): Choose for me based on the parser + - "basic": Use a basic lexer + - "contextual": Stronger lexer (only works with parser="lalr") + - "dynamic": Flexible and powerful (only with parser="earley") + - "dynamic_complete": Same as dynamic, but tries *every* variation of tokenizing possible. + ambiguity + Decides how to handle ambiguity in the parse. Only relevant if parser="earley" + + - "resolve": The parser will automatically choose the simplest derivation + (it chooses consistently: greedy for tokens, non-greedy for rules) + - "explicit": The parser will return all derivations wrapped in "_ambig" tree nodes (i.e. a forest). + - "forest": The parser will return the root of the shared packed parse forest. + + **=== Misc. / Domain Specific Options ===** + + postlex + Lexer post-processing (Default: ``None``) Only works with the basic and contextual lexers. + priority + How priorities should be evaluated - "auto", ``None``, "normal", "invert" (Default: "auto") + lexer_callbacks + Dictionary of callbacks for the lexer. May alter tokens during lexing. Use with caution. + use_bytes + Accept an input of type ``bytes`` instead of ``str``. + ordered_sets + Should Earley use ordered-sets to achieve stable output (~10% slower than regular sets. Default: True) + edit_terminals + A callback for editing the terminals before parse. + import_paths + A List of either paths or loader functions to specify from where grammars are imported + source_path + Override the source of from where the grammar was loaded. Useful for relative imports and unconventional grammar loading + **=== End of Options ===** + """ + if __doc__: + __doc__ += OPTIONS_DOC + + + # Adding a new option needs to be done in multiple places: + # - In the dictionary below. This is the primary truth of which options `Lark.__init__` accepts + # - In the docstring above. It is used both for the docstring of `LarkOptions` and `Lark`, and in readthedocs + # - As an attribute of `LarkOptions` above + # - Potentially in `_LOAD_ALLOWED_OPTIONS` below this class, when the option doesn't change how the grammar is loaded + # - Potentially in `lark.tools.__init__`, if it makes sense, and it can easily be passed as a cmd argument + _defaults: Dict[str, Any] = { + 'debug': False, + 'strict': False, + 'keep_all_tokens': False, + 'tree_class': None, + 'cache': False, + 'postlex': None, + 'parser': 'earley', + 'lexer': 'auto', + 'transformer': None, + 'start': 'start', + 'priority': 'auto', + 'ambiguity': 'auto', + 'regex': False, + 'propagate_positions': False, + 'lexer_callbacks': {}, + 'maybe_placeholders': True, + 'edit_terminals': None, + 'g_regex_flags': 0, + 'use_bytes': False, + 'ordered_sets': True, + 'import_paths': [], + 'source_path': None, + '_plugins': {}, + } + + def __init__(self, options_dict: Dict[str, Any]) -> None: + o = dict(options_dict) + + options = {} + for name, default in self._defaults.items(): + if name in o: + value = o.pop(name) + if isinstance(default, bool) and name not in ('cache', 'use_bytes', 'propagate_positions'): + value = bool(value) + else: + value = default + + options[name] = value + + if isinstance(options['start'], str): + options['start'] = [options['start']] + + self.__dict__['options'] = options + + + assert_config(self.parser, ('earley', 'lalr', 'cyk', None)) + + if self.parser == 'earley' and self.transformer: + raise ConfigurationError('Cannot specify an embedded transformer when using the Earley algorithm. ' + 'Please use your transformer on the resulting parse tree, or use a different algorithm (i.e. LALR)') + + if o: + raise ConfigurationError("Unknown options: %s" % o.keys()) + + def __getattr__(self, name: str) -> Any: + try: + return self.__dict__['options'][name] + except KeyError as e: + raise AttributeError(e) + + def __setattr__(self, name: str, value: str) -> None: + assert_config(name, self.options.keys(), "%r isn't a valid option. Expected one of: %s") + self.options[name] = value + + def serialize(self, memo = None) -> Dict[str, Any]: + return self.options + + @classmethod + def deserialize(cls, data: Dict[str, Any], memo: Dict[int, Union[TerminalDef, Rule]]) -> "LarkOptions": + return cls(data) + + +# Options that can be passed to the Lark parser, even when it was loaded from cache/standalone. +# These options are only used outside of `load_grammar`. +_LOAD_ALLOWED_OPTIONS = {'postlex', 'transformer', 'lexer_callbacks', 'use_bytes', 'debug', 'g_regex_flags', 'regex', 'propagate_positions', 'tree_class', '_plugins'} + +_VALID_PRIORITY_OPTIONS = ('auto', 'normal', 'invert', None) +_VALID_AMBIGUITY_OPTIONS = ('auto', 'resolve', 'explicit', 'forest') + + +_T = TypeVar('_T', bound="Lark") + +class Lark(Serialize): + """Main interface for the library. + + It's mostly a thin wrapper for the many different parsers, and for the tree constructor. + + Parameters: + grammar: a string or file-object containing the grammar spec (using Lark's ebnf syntax) + options: a dictionary controlling various aspects of Lark. + + Example: + >>> Lark(r'''start: "foo" ''') + Lark(...) + """ + + source_path: str + source_grammar: str + grammar: 'Grammar' + options: LarkOptions + lexer: Lexer + parser: 'ParsingFrontend' + terminals: Collection[TerminalDef] + + def __init__(self, grammar: 'Union[Grammar, str, IO[str]]', **options) -> None: + self.options = LarkOptions(options) + re_module: types.ModuleType + + # Set regex or re module + use_regex = self.options.regex + if use_regex: + if _has_regex: + re_module = regex + else: + raise ImportError('`regex` module must be installed if calling `Lark(regex=True)`.') + else: + re_module = re + + # Some, but not all file-like objects have a 'name' attribute + if self.options.source_path is None: + try: + self.source_path = grammar.name # type: ignore[union-attr] + except AttributeError: + self.source_path = '' + else: + self.source_path = self.options.source_path + + # Drain file-like objects to get their contents + try: + read = grammar.read # type: ignore[union-attr] + except AttributeError: + pass + else: + grammar = read() + + cache_fn = None + cache_sha256 = None + if isinstance(grammar, str): + self.source_grammar = grammar + if self.options.use_bytes: + if not isascii(grammar): + raise ConfigurationError("Grammar must be ascii only, when use_bytes=True") + + if self.options.cache: + if self.options.parser != 'lalr': + raise ConfigurationError("cache only works with parser='lalr' for now") + + unhashable = ('transformer', 'postlex', 'lexer_callbacks', 'edit_terminals', '_plugins') + options_str = ''.join(k+str(v) for k, v in options.items() if k not in unhashable) + from . import __version__ + s = grammar + options_str + __version__ + str(sys.version_info[:2]) + cache_sha256 = sha256_digest(s) + + if isinstance(self.options.cache, str): + cache_fn = self.options.cache + else: + if self.options.cache is not True: + raise ConfigurationError("cache argument must be bool or str") + + try: + username = getpass.getuser() + except Exception: + # The exception raised may be ImportError or OSError in + # the future. For the cache, we don't care about the + # specific reason - we just want a username. + username = "unknown" + + cache_fn = tempfile.gettempdir() + "/.lark_cache_%s_%s_%s_%s.tmp" % (username, cache_sha256, *sys.version_info[:2]) + + old_options = self.options + try: + with FS.open(cache_fn, 'rb') as f: + logger.debug('Loading grammar from cache: %s', cache_fn) + # Remove options that aren't relevant for loading from cache + for name in (set(options) - _LOAD_ALLOWED_OPTIONS): + del options[name] + file_sha256 = f.readline().rstrip(b'\n') + cached_used_files = pickle.load(f) + if file_sha256 == cache_sha256.encode('utf8') and verify_used_files(cached_used_files): + cached_parser_data = pickle.load(f) + self._load(cached_parser_data, **options) + return + except FileNotFoundError: + # The cache file doesn't exist; parse and compose the grammar as normal + pass + except Exception: # We should probably narrow done which errors we catch here. + logger.exception("Failed to load Lark from cache: %r. We will try to carry on.", cache_fn) + + # In theory, the Lark instance might have been messed up by the call to `_load`. + # In practice the only relevant thing that might have been overwritten should be `options` + self.options = old_options + + + # Parse the grammar file and compose the grammars + self.grammar, used_files = load_grammar(grammar, self.source_path, self.options.import_paths, self.options.keep_all_tokens) + else: + assert isinstance(grammar, Grammar) + self.grammar = grammar + + + if self.options.lexer == 'auto': + if self.options.parser == 'lalr': + self.options.lexer = 'contextual' + elif self.options.parser == 'earley': + if self.options.postlex is not None: + logger.info("postlex can't be used with the dynamic lexer, so we use 'basic' instead. " + "Consider using lalr with contextual instead of earley") + self.options.lexer = 'basic' + else: + self.options.lexer = 'dynamic' + elif self.options.parser == 'cyk': + self.options.lexer = 'basic' + else: + assert False, self.options.parser + lexer = self.options.lexer + if isinstance(lexer, type): + assert issubclass(lexer, Lexer) # XXX Is this really important? Maybe just ensure interface compliance + else: + assert_config(lexer, ('basic', 'contextual', 'dynamic', 'dynamic_complete')) + if self.options.postlex is not None and 'dynamic' in lexer: + raise ConfigurationError("Can't use postlex with a dynamic lexer. Use basic or contextual instead") + + if self.options.ambiguity == 'auto': + if self.options.parser == 'earley': + self.options.ambiguity = 'resolve' + else: + assert_config(self.options.parser, ('earley', 'cyk'), "%r doesn't support disambiguation. Use one of these parsers instead: %s") + + if self.options.priority == 'auto': + self.options.priority = 'normal' + + if self.options.priority not in _VALID_PRIORITY_OPTIONS: + raise ConfigurationError("invalid priority option: %r. Must be one of %r" % (self.options.priority, _VALID_PRIORITY_OPTIONS)) + if self.options.ambiguity not in _VALID_AMBIGUITY_OPTIONS: + raise ConfigurationError("invalid ambiguity option: %r. Must be one of %r" % (self.options.ambiguity, _VALID_AMBIGUITY_OPTIONS)) + + if self.options.parser is None: + terminals_to_keep = '*' + elif self.options.postlex is not None: + terminals_to_keep = set(self.options.postlex.always_accept) + else: + terminals_to_keep = set() + + # Compile the EBNF grammar into BNF + self.terminals, self.rules, self.ignore_tokens = self.grammar.compile(self.options.start, terminals_to_keep) + + if self.options.edit_terminals: + for t in self.terminals: + self.options.edit_terminals(t) + + self._terminals_dict = {t.name: t for t in self.terminals} + + # If the user asked to invert the priorities, negate them all here. + if self.options.priority == 'invert': + for rule in self.rules: + if rule.options.priority is not None: + rule.options.priority = -rule.options.priority + for term in self.terminals: + term.priority = -term.priority + # Else, if the user asked to disable priorities, strip them from the + # rules and terminals. This allows the Earley parsers to skip an extra forest walk + # for improved performance, if you don't need them (or didn't specify any). + elif self.options.priority is None: + for rule in self.rules: + if rule.options.priority is not None: + rule.options.priority = None + for term in self.terminals: + term.priority = 0 + + # TODO Deprecate lexer_callbacks? + self.lexer_conf = LexerConf( + self.terminals, re_module, self.ignore_tokens, self.options.postlex, + self.options.lexer_callbacks, self.options.g_regex_flags, use_bytes=self.options.use_bytes, strict=self.options.strict + ) + + if self.options.parser: + self.parser = self._build_parser() + elif lexer: + self.lexer = self._build_lexer() + + if cache_fn: + logger.debug('Saving grammar to cache: %s', cache_fn) + try: + with FS.open(cache_fn, 'wb') as f: + assert cache_sha256 is not None + f.write(cache_sha256.encode('utf8') + b'\n') + pickle.dump(used_files, f) + self.save(f, _LOAD_ALLOWED_OPTIONS) + except IOError as e: + logger.exception("Failed to save Lark to cache: %r.", cache_fn, e) + + if __doc__: + __doc__ += "\n\n" + LarkOptions.OPTIONS_DOC + + __serialize_fields__ = 'parser', 'rules', 'options' + + def _build_lexer(self, dont_ignore: bool=False) -> BasicLexer: + lexer_conf = self.lexer_conf + if dont_ignore: + from copy import copy + lexer_conf = copy(lexer_conf) + lexer_conf.ignore = () + return BasicLexer(lexer_conf) + + def _prepare_callbacks(self) -> None: + self._callbacks = {} + # we don't need these callbacks if we aren't building a tree + if self.options.ambiguity != 'forest': + self._parse_tree_builder = ParseTreeBuilder( + self.rules, + self.options.tree_class or Tree, + self.options.propagate_positions, + self.options.parser != 'lalr' and self.options.ambiguity == 'explicit', + self.options.maybe_placeholders + ) + self._callbacks = self._parse_tree_builder.create_callback(self.options.transformer) + self._callbacks.update(_get_lexer_callbacks(self.options.transformer, self.terminals)) + + def _build_parser(self) -> "ParsingFrontend": + self._prepare_callbacks() + _validate_frontend_args(self.options.parser, self.options.lexer) + parser_conf = ParserConf(self.rules, self._callbacks, self.options.start) + return _construct_parsing_frontend( + self.options.parser, + self.options.lexer, + self.lexer_conf, + parser_conf, + options=self.options + ) + + def save(self, f, exclude_options: Collection[str] = ()) -> None: + """Saves the instance into the given file object + + Useful for caching and multiprocessing. + """ + if self.options.parser != 'lalr': + raise NotImplementedError("Lark.save() is only implemented for the LALR(1) parser.") + data, m = self.memo_serialize([TerminalDef, Rule]) + if exclude_options: + data["options"] = {n: v for n, v in data["options"].items() if n not in exclude_options} + pickle.dump({'data': data, 'memo': m}, f, protocol=pickle.HIGHEST_PROTOCOL) + + @classmethod + def load(cls: Type[_T], f) -> _T: + """Loads an instance from the given file object + + Useful for caching and multiprocessing. + """ + inst = cls.__new__(cls) + return inst._load(f) + + def _deserialize_lexer_conf(self, data: Dict[str, Any], memo: Dict[int, Union[TerminalDef, Rule]], options: LarkOptions) -> LexerConf: + lexer_conf = LexerConf.deserialize(data['lexer_conf'], memo) + lexer_conf.callbacks = options.lexer_callbacks or {} + lexer_conf.re_module = regex if options.regex else re + lexer_conf.use_bytes = options.use_bytes + lexer_conf.g_regex_flags = options.g_regex_flags + lexer_conf.skip_validation = True + lexer_conf.postlex = options.postlex + return lexer_conf + + def _load(self: _T, f: Any, **kwargs) -> _T: + if isinstance(f, dict): + d = f + else: + d = pickle.load(f) + memo_json = d['memo'] + data = d['data'] + + assert memo_json + memo = SerializeMemoizer.deserialize(memo_json, {'Rule': Rule, 'TerminalDef': TerminalDef}, {}) + options = dict(data['options']) + if (set(kwargs) - _LOAD_ALLOWED_OPTIONS) & set(LarkOptions._defaults): + raise ConfigurationError("Some options are not allowed when loading a Parser: {}" + .format(set(kwargs) - _LOAD_ALLOWED_OPTIONS)) + options.update(kwargs) + self.options = LarkOptions.deserialize(options, memo) + self.rules = [Rule.deserialize(r, memo) for r in data['rules']] + self.source_path = '' + _validate_frontend_args(self.options.parser, self.options.lexer) + self.lexer_conf = self._deserialize_lexer_conf(data['parser'], memo, self.options) + self.terminals = self.lexer_conf.terminals + self._prepare_callbacks() + self._terminals_dict = {t.name: t for t in self.terminals} + self.parser = _deserialize_parsing_frontend( + data['parser'], + memo, + self.lexer_conf, + self._callbacks, + self.options, # Not all, but multiple attributes are used + ) + return self + + @classmethod + def _load_from_dict(cls, data, memo, **kwargs): + inst = cls.__new__(cls) + return inst._load({'data': data, 'memo': memo}, **kwargs) + + @classmethod + def open(cls: Type[_T], grammar_filename: str, rel_to: Optional[str]=None, **options) -> _T: + """Create an instance of Lark with the grammar given by its filename + + If ``rel_to`` is provided, the function will find the grammar filename in relation to it. + + Example: + + >>> Lark.open("grammar_file.lark", rel_to=__file__, parser="lalr") + Lark(...) + + """ + if rel_to: + basepath = os.path.dirname(rel_to) + grammar_filename = os.path.join(basepath, grammar_filename) + with open(grammar_filename, encoding='utf8') as f: + return cls(f, **options) + + @classmethod + def open_from_package(cls: Type[_T], package: str, grammar_path: str, search_paths: 'Sequence[str]'=[""], **options) -> _T: + """Create an instance of Lark with the grammar loaded from within the package `package`. + This allows grammar loading from zipapps. + + Imports in the grammar will use the `package` and `search_paths` provided, through `FromPackageLoader` + + Example: + + Lark.open_from_package(__name__, "example.lark", ("grammars",), parser=...) + """ + package_loader = FromPackageLoader(package, search_paths) + full_path, text = package_loader(None, grammar_path) + options.setdefault('source_path', full_path) + options.setdefault('import_paths', []) + options['import_paths'].append(package_loader) + return cls(text, **options) + + def __repr__(self): + return 'Lark(open(%r), parser=%r, lexer=%r, ...)' % (self.source_path, self.options.parser, self.options.lexer) + + + def lex(self, text: str, dont_ignore: bool=False) -> Iterator[Token]: + """Only lex (and postlex) the text, without parsing it. Only relevant when lexer='basic' + + When dont_ignore=True, the lexer will return all tokens, even those marked for %ignore. + + :raises UnexpectedCharacters: In case the lexer cannot find a suitable match. + """ + lexer: Lexer + if not hasattr(self, 'lexer') or dont_ignore: + lexer = self._build_lexer(dont_ignore) + else: + lexer = self.lexer + lexer_thread = LexerThread.from_text(lexer, text) + stream = lexer_thread.lex(None) + if self.options.postlex: + return self.options.postlex.process(stream) + return stream + + def get_terminal(self, name: str) -> TerminalDef: + """Get information about a terminal""" + return self._terminals_dict[name] + + def parse_interactive(self, text: Optional[str]=None, start: Optional[str]=None) -> 'InteractiveParser': + """Start an interactive parsing session. + + Parameters: + text (str, optional): Text to be parsed. Required for ``resume_parse()``. + start (str, optional): Start symbol + + Returns: + A new InteractiveParser instance. + + See Also: ``Lark.parse()`` + """ + return self.parser.parse_interactive(text, start=start) + + def parse(self, text: str, start: Optional[str]=None, on_error: 'Optional[Callable[[UnexpectedInput], bool]]'=None) -> 'ParseTree': + """Parse the given text, according to the options provided. + + Parameters: + text (str): Text to be parsed. + start (str, optional): Required if Lark was given multiple possible start symbols (using the start option). + on_error (function, optional): if provided, will be called on UnexpectedToken error. Return true to resume parsing. + LALR only. See examples/advanced/error_handling.py for an example of how to use on_error. + + Returns: + If a transformer is supplied to ``__init__``, returns whatever is the + result of the transformation. Otherwise, returns a Tree instance. + + :raises UnexpectedInput: On a parse error, one of these sub-exceptions will rise: + ``UnexpectedCharacters``, ``UnexpectedToken``, or ``UnexpectedEOF``. + For convenience, these sub-exceptions also inherit from ``ParserError`` and ``LexerError``. + + """ + return self.parser.parse(text, start=start, on_error=on_error) + + +###} diff --git a/shared/package-files/56/56678668d6a1892cc1253e114d7301f010ea40bbce65c8d67bfc518a620f3f62/blob b/shared/package-files/56/56678668d6a1892cc1253e114d7301f010ea40bbce65c8d67bfc518a620f3f62/blob new file mode 100644 index 0000000000000000000000000000000000000000..617bc805032a1dc9e83f784aaf0cc7117b967bd7 --- /dev/null +++ b/shared/package-files/56/56678668d6a1892cc1253e114d7301f010ea40bbce65c8d67bfc518a620f3f62/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 at::Tensor conj_physical(const at::Tensor & self); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/shared/package-files/56/5667c31dcece0b6d20cad709faa0d16a2a57ee42563742cfa16ea8c0cca4dc48/blob b/shared/package-files/56/5667c31dcece0b6d20cad709faa0d16a2a57ee42563742cfa16ea8c0cca4dc48/blob new file mode 100644 index 0000000000000000000000000000000000000000..2947ba7b8c72ac09497f796aaaec8edfb133a948 --- /dev/null +++ b/shared/package-files/56/5667c31dcece0b6d20cad709faa0d16a2a57ee42563742cfa16ea8c0cca4dc48/blob @@ -0,0 +1,1331 @@ +from __future__ import annotations + +from datetime import timedelta +import operator +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Literal, + TypeVar, + cast, + overload, +) +import warnings + +import numpy as np + +from pandas._libs import ( + algos as libalgos, + lib, +) +from pandas._libs.arrays import NDArrayBacked +from pandas._libs.tslibs import ( + BaseOffset, + NaT, + NaTType, + Timedelta, + add_overflowsafe, + astype_overflowsafe, + dt64arr_to_periodarr as c_dt64arr_to_periodarr, + get_unit_from_dtype, + iNaT, + parsing, + period as libperiod, + to_offset, +) +from pandas._libs.tslibs.dtypes import ( + FreqGroup, + PeriodDtypeBase, + freq_to_period_freqstr, +) +from pandas._libs.tslibs.fields import isleapyear_arr +from pandas._libs.tslibs.offsets import ( + Tick, + delta_to_tick, +) +from pandas._libs.tslibs.period import ( + DIFFERENT_FREQ, + IncompatibleFrequency, + Period, + get_period_field_arr, + period_asfreq_arr, +) +from pandas.util._decorators import ( + cache_readonly, + doc, +) +from pandas.util._exceptions import find_stack_level + +from pandas.core.dtypes.common import ( + ensure_object, + pandas_dtype, +) +from pandas.core.dtypes.dtypes import ( + DatetimeTZDtype, + PeriodDtype, +) +from pandas.core.dtypes.generic import ( + ABCIndex, + ABCPeriodIndex, + ABCSeries, + ABCTimedeltaArray, +) +from pandas.core.dtypes.missing import isna + +from pandas.core.arrays import datetimelike as dtl +import pandas.core.common as com + +if TYPE_CHECKING: + from collections.abc import Sequence + + from pandas._typing import ( + AnyArrayLike, + Dtype, + FillnaOptions, + NpDtype, + NumpySorter, + NumpyValueArrayLike, + Self, + npt, + ) + + from pandas.core.arrays import ( + DatetimeArray, + TimedeltaArray, + ) + from pandas.core.arrays.base import ExtensionArray + + +BaseOffsetT = TypeVar("BaseOffsetT", bound=BaseOffset) + + +_shared_doc_kwargs = { + "klass": "PeriodArray", +} + + +def _field_accessor(name: str, docstring: str | None = None): + def f(self): + base = self.dtype._dtype_code + result = get_period_field_arr(name, self.asi8, base) + return result + + f.__name__ = name + f.__doc__ = docstring + return property(f) + + +# error: Definition of "_concat_same_type" in base class "NDArrayBacked" is +# incompatible with definition in base class "ExtensionArray" +class PeriodArray(dtl.DatelikeOps, libperiod.PeriodMixin): # type: ignore[misc] + """ + Pandas ExtensionArray for storing Period data. + + Users should use :func:`~pandas.array` to create new instances. + + Parameters + ---------- + values : Union[PeriodArray, Series[period], ndarray[int], PeriodIndex] + The data to store. These should be arrays that can be directly + converted to ordinals without inference or copy (PeriodArray, + ndarray[int64]), or a box around such an array (Series[period], + PeriodIndex). + dtype : PeriodDtype, optional + A PeriodDtype instance from which to extract a `freq`. If both + `freq` and `dtype` are specified, then the frequencies must match. + freq : str or DateOffset + The `freq` to use for the array. Mostly applicable when `values` + is an ndarray of integers, when `freq` is required. When `values` + is a PeriodArray (or box around), it's checked that ``values.freq`` + matches `freq`. + copy : bool, default False + Whether to copy the ordinals before storing. + + Attributes + ---------- + None + + Methods + ------- + None + + See Also + -------- + Period: Represents a period of time. + PeriodIndex : Immutable Index for period data. + period_range: Create a fixed-frequency PeriodArray. + array: Construct a pandas array. + + Notes + ----- + There are two components to a PeriodArray + + - ordinals : integer ndarray + - freq : pd.tseries.offsets.Offset + + The values are physically stored as a 1-D ndarray of integers. These are + called "ordinals" and represent some kind of offset from a base. + + The `freq` indicates the span covered by each element of the array. + All elements in the PeriodArray have the same `freq`. + + Examples + -------- + >>> pd.arrays.PeriodArray(pd.PeriodIndex(['2023-01-01', + ... '2023-01-02'], freq='D')) + + ['2023-01-01', '2023-01-02'] + Length: 2, dtype: period[D] + """ + + # array priority higher than numpy scalars + __array_priority__ = 1000 + _typ = "periodarray" # ABCPeriodArray + _internal_fill_value = np.int64(iNaT) + _recognized_scalars = (Period,) + _is_recognized_dtype = lambda x: isinstance( + x, PeriodDtype + ) # check_compatible_with checks freq match + _infer_matches = ("period",) + + @property + def _scalar_type(self) -> type[Period]: + return Period + + # Names others delegate to us + _other_ops: list[str] = [] + _bool_ops: list[str] = ["is_leap_year"] + _object_ops: list[str] = ["start_time", "end_time", "freq"] + _field_ops: list[str] = [ + "year", + "month", + "day", + "hour", + "minute", + "second", + "weekofyear", + "weekday", + "week", + "dayofweek", + "day_of_week", + "dayofyear", + "day_of_year", + "quarter", + "qyear", + "days_in_month", + "daysinmonth", + ] + _datetimelike_ops: list[str] = _field_ops + _object_ops + _bool_ops + _datetimelike_methods: list[str] = ["strftime", "to_timestamp", "asfreq"] + + _dtype: PeriodDtype + + # -------------------------------------------------------------------- + # Constructors + + def __init__( + self, values, dtype: Dtype | None = None, freq=None, copy: bool = False + ) -> None: + if freq is not None: + # GH#52462 + warnings.warn( + "The 'freq' keyword in the PeriodArray constructor is deprecated " + "and will be removed in a future version. Pass 'dtype' instead", + FutureWarning, + stacklevel=find_stack_level(), + ) + freq = validate_dtype_freq(dtype, freq) + dtype = PeriodDtype(freq) + + if dtype is not None: + dtype = pandas_dtype(dtype) + if not isinstance(dtype, PeriodDtype): + raise ValueError(f"Invalid dtype {dtype} for PeriodArray") + + if isinstance(values, ABCSeries): + values = values._values + if not isinstance(values, type(self)): + raise TypeError("Incorrect dtype") + + elif isinstance(values, ABCPeriodIndex): + values = values._values + + if isinstance(values, type(self)): + if dtype is not None and dtype != values.dtype: + raise raise_on_incompatible(values, dtype.freq) + values, dtype = values._ndarray, values.dtype + + if not copy: + values = np.asarray(values, dtype="int64") + else: + values = np.array(values, dtype="int64", copy=copy) + if dtype is None: + raise ValueError("dtype is not specified and cannot be inferred") + dtype = cast(PeriodDtype, dtype) + NDArrayBacked.__init__(self, values, dtype) + + # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked" + @classmethod + def _simple_new( # type: ignore[override] + cls, + values: npt.NDArray[np.int64], + dtype: PeriodDtype, + ) -> Self: + # alias for PeriodArray.__init__ + assertion_msg = "Should be numpy array of type i8" + assert isinstance(values, np.ndarray) and values.dtype == "i8", assertion_msg + return cls(values, dtype=dtype) + + @classmethod + def _from_sequence( + cls, + scalars, + *, + dtype: Dtype | None = None, + copy: bool = False, + ) -> Self: + if dtype is not None: + dtype = pandas_dtype(dtype) + if dtype and isinstance(dtype, PeriodDtype): + freq = dtype.freq + else: + freq = None + + if isinstance(scalars, cls): + validate_dtype_freq(scalars.dtype, freq) + if copy: + scalars = scalars.copy() + return scalars + + periods = np.asarray(scalars, dtype=object) + + freq = freq or libperiod.extract_freq(periods) + ordinals = libperiod.extract_ordinals(periods, freq) + dtype = PeriodDtype(freq) + return cls(ordinals, dtype=dtype) + + @classmethod + def _from_sequence_of_strings( + cls, strings, *, dtype: Dtype | None = None, copy: bool = False + ) -> Self: + return cls._from_sequence(strings, dtype=dtype, copy=copy) + + @classmethod + def _from_datetime64(cls, data, freq, tz=None) -> Self: + """ + Construct a PeriodArray from a datetime64 array + + Parameters + ---------- + data : ndarray[datetime64[ns], datetime64[ns, tz]] + freq : str or Tick + tz : tzinfo, optional + + Returns + ------- + PeriodArray[freq] + """ + if isinstance(freq, BaseOffset): + freq = freq_to_period_freqstr(freq.n, freq.name) + data, freq = dt64arr_to_periodarr(data, freq, tz) + dtype = PeriodDtype(freq) + return cls(data, dtype=dtype) + + @classmethod + def _generate_range(cls, start, end, periods, freq): + periods = dtl.validate_periods(periods) + + if freq is not None: + freq = Period._maybe_convert_freq(freq) + + if start is not None or end is not None: + subarr, freq = _get_ordinal_range(start, end, periods, freq) + else: + raise ValueError("Not enough parameters to construct Period range") + + return subarr, freq + + @classmethod + def _from_fields(cls, *, fields: dict, freq) -> Self: + subarr, freq = _range_from_fields(freq=freq, **fields) + dtype = PeriodDtype(freq) + return cls._simple_new(subarr, dtype=dtype) + + # ----------------------------------------------------------------- + # DatetimeLike Interface + + # error: Argument 1 of "_unbox_scalar" is incompatible with supertype + # "DatetimeLikeArrayMixin"; supertype defines the argument type as + # "Union[Union[Period, Any, Timedelta], NaTType]" + def _unbox_scalar( # type: ignore[override] + self, + value: Period | NaTType, + ) -> np.int64: + if value is NaT: + # error: Item "Period" of "Union[Period, NaTType]" has no attribute "value" + return np.int64(value._value) # type: ignore[union-attr] + elif isinstance(value, self._scalar_type): + self._check_compatible_with(value) + return np.int64(value.ordinal) + else: + raise ValueError(f"'value' should be a Period. Got '{value}' instead.") + + def _scalar_from_string(self, value: str) -> Period: + return Period(value, freq=self.freq) + + # error: Argument 1 of "_check_compatible_with" is incompatible with + # supertype "DatetimeLikeArrayMixin"; supertype defines the argument type + # as "Period | Timestamp | Timedelta | NaTType" + def _check_compatible_with(self, other: Period | NaTType | PeriodArray) -> None: # type: ignore[override] + if other is NaT: + return + # error: Item "NaTType" of "Period | NaTType | PeriodArray" has no + # attribute "freq" + self._require_matching_freq(other.freq) # type: ignore[union-attr] + + # -------------------------------------------------------------------- + # Data / Attributes + + @cache_readonly + def dtype(self) -> PeriodDtype: + return self._dtype + + # error: Cannot override writeable attribute with read-only property + @property # type: ignore[override] + def freq(self) -> BaseOffset: + """ + Return the frequency object for this PeriodArray. + """ + return self.dtype.freq + + @property + def freqstr(self) -> str: + return freq_to_period_freqstr(self.freq.n, self.freq.name) + + def __array__( + self, dtype: NpDtype | None = None, copy: bool | None = None + ) -> np.ndarray: + if dtype == "i8": + # For NumPy 1.x compatibility we cannot use copy=None. And + # `copy=False` has the meaning of `copy=None` here: + if not copy: + return np.asarray(self.asi8, dtype=dtype) + else: + return np.array(self.asi8, dtype=dtype) + + if copy is False: + warnings.warn( + "Starting with NumPy 2.0, the behavior of the 'copy' keyword has " + "changed and passing 'copy=False' raises an error when returning " + "a zero-copy NumPy array is not possible. pandas will follow " + "this behavior starting with pandas 3.0.\nThis conversion to " + "NumPy requires a copy, but 'copy=False' was passed. Consider " + "using 'np.asarray(..)' instead.", + FutureWarning, + stacklevel=find_stack_level(), + ) + + if dtype == bool: + return ~self._isnan + + # This will raise TypeError for non-object dtypes + return np.array(list(self), dtype=object) + + def __arrow_array__(self, type=None): + """ + Convert myself into a pyarrow Array. + """ + import pyarrow + + from pandas.core.arrays.arrow.extension_types import ArrowPeriodType + + if type is not None: + if pyarrow.types.is_integer(type): + return pyarrow.array(self._ndarray, mask=self.isna(), type=type) + elif isinstance(type, ArrowPeriodType): + # ensure we have the same freq + if self.freqstr != type.freq: + raise TypeError( + "Not supported to convert PeriodArray to array with different " + f"'freq' ({self.freqstr} vs {type.freq})" + ) + else: + raise TypeError( + f"Not supported to convert PeriodArray to '{type}' type" + ) + + period_type = ArrowPeriodType(self.freqstr) + storage_array = pyarrow.array(self._ndarray, mask=self.isna(), type="int64") + return pyarrow.ExtensionArray.from_storage(period_type, storage_array) + + # -------------------------------------------------------------------- + # Vectorized analogues of Period properties + + year = _field_accessor( + "year", + """ + The year of the period. + + Examples + -------- + >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y") + >>> idx.year + Index([2023, 2024, 2025], dtype='int64') + """, + ) + month = _field_accessor( + "month", + """ + The month as January=1, December=12. + + Examples + -------- + >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M") + >>> idx.month + Index([1, 2, 3], dtype='int64') + """, + ) + day = _field_accessor( + "day", + """ + The days of the period. + + Examples + -------- + >>> idx = pd.PeriodIndex(['2020-01-31', '2020-02-28'], freq='D') + >>> idx.day + Index([31, 28], dtype='int64') + """, + ) + hour = _field_accessor( + "hour", + """ + The hour of the period. + + Examples + -------- + >>> idx = pd.PeriodIndex(["2023-01-01 10:00", "2023-01-01 11:00"], freq='h') + >>> idx.hour + Index([10, 11], dtype='int64') + """, + ) + minute = _field_accessor( + "minute", + """ + The minute of the period. + + Examples + -------- + >>> idx = pd.PeriodIndex(["2023-01-01 10:30:00", + ... "2023-01-01 11:50:00"], freq='min') + >>> idx.minute + Index([30, 50], dtype='int64') + """, + ) + second = _field_accessor( + "second", + """ + The second of the period. + + Examples + -------- + >>> idx = pd.PeriodIndex(["2023-01-01 10:00:30", + ... "2023-01-01 10:00:31"], freq='s') + >>> idx.second + Index([30, 31], dtype='int64') + """, + ) + weekofyear = _field_accessor( + "week", + """ + The week ordinal of the year. + + Examples + -------- + >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M") + >>> idx.week # It can be written `weekofyear` + Index([5, 9, 13], dtype='int64') + """, + ) + week = weekofyear + day_of_week = _field_accessor( + "day_of_week", + """ + The day of the week with Monday=0, Sunday=6. + + Examples + -------- + >>> idx = pd.PeriodIndex(["2023-01-01", "2023-01-02", "2023-01-03"], freq="D") + >>> idx.weekday + Index([6, 0, 1], dtype='int64') + """, + ) + dayofweek = day_of_week + weekday = dayofweek + dayofyear = day_of_year = _field_accessor( + "day_of_year", + """ + The ordinal day of the year. + + Examples + -------- + >>> idx = pd.PeriodIndex(["2023-01-10", "2023-02-01", "2023-03-01"], freq="D") + >>> idx.dayofyear + Index([10, 32, 60], dtype='int64') + + >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y") + >>> idx + PeriodIndex(['2023', '2024', '2025'], dtype='period[Y-DEC]') + >>> idx.dayofyear + Index([365, 366, 365], dtype='int64') + """, + ) + quarter = _field_accessor( + "quarter", + """ + The quarter of the date. + + Examples + -------- + >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M") + >>> idx.quarter + Index([1, 1, 1], dtype='int64') + """, + ) + qyear = _field_accessor("qyear") + days_in_month = _field_accessor( + "days_in_month", + """ + The number of days in the month. + + Examples + -------- + For Series: + + >>> period = pd.period_range('2020-1-1 00:00', '2020-3-1 00:00', freq='M') + >>> s = pd.Series(period) + >>> s + 0 2020-01 + 1 2020-02 + 2 2020-03 + dtype: period[M] + >>> s.dt.days_in_month + 0 31 + 1 29 + 2 31 + dtype: int64 + + For PeriodIndex: + + >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M") + >>> idx.days_in_month # It can be also entered as `daysinmonth` + Index([31, 28, 31], dtype='int64') + """, + ) + daysinmonth = days_in_month + + @property + def is_leap_year(self) -> npt.NDArray[np.bool_]: + """ + Logical indicating if the date belongs to a leap year. + + Examples + -------- + >>> idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="Y") + >>> idx.is_leap_year + array([False, True, False]) + """ + return isleapyear_arr(np.asarray(self.year)) + + def to_timestamp(self, freq=None, how: str = "start") -> DatetimeArray: + """ + Cast to DatetimeArray/Index. + + Parameters + ---------- + freq : str or DateOffset, optional + Target frequency. The default is 'D' for week or longer, + 's' otherwise. + how : {'s', 'e', 'start', 'end'} + Whether to use the start or end of the time period being converted. + + Returns + ------- + DatetimeArray/Index + + Examples + -------- + >>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M") + >>> idx.to_timestamp() + DatetimeIndex(['2023-01-01', '2023-02-01', '2023-03-01'], + dtype='datetime64[ns]', freq='MS') + """ + from pandas.core.arrays import DatetimeArray + + how = libperiod.validate_end_alias(how) + + end = how == "E" + if end: + if freq == "B" or self.freq == "B": + # roll forward to ensure we land on B date + adjust = Timedelta(1, "D") - Timedelta(1, "ns") + return self.to_timestamp(how="start") + adjust + else: + adjust = Timedelta(1, "ns") + return (self + self.freq).to_timestamp(how="start") - adjust + + if freq is None: + freq_code = self._dtype._get_to_timestamp_base() + dtype = PeriodDtypeBase(freq_code, 1) + freq = dtype._freqstr + base = freq_code + else: + freq = Period._maybe_convert_freq(freq) + base = freq._period_dtype_code + + new_parr = self.asfreq(freq, how=how) + + new_data = libperiod.periodarr_to_dt64arr(new_parr.asi8, base) + dta = DatetimeArray._from_sequence(new_data) + + if self.freq.name == "B": + # See if we can retain BDay instead of Day in cases where + # len(self) is too small for infer_freq to distinguish between them + diffs = libalgos.unique_deltas(self.asi8) + if len(diffs) == 1: + diff = diffs[0] + if diff == self.dtype._n: + dta._freq = self.freq + elif diff == 1: + dta._freq = self.freq.base + # TODO: other cases? + return dta + else: + return dta._with_freq("infer") + + # -------------------------------------------------------------------- + + def _box_func(self, x) -> Period | NaTType: + return Period._from_ordinal(ordinal=x, freq=self.freq) + + @doc(**_shared_doc_kwargs, other="PeriodIndex", other_name="PeriodIndex") + def asfreq(self, freq=None, how: str = "E") -> Self: + """ + Convert the {klass} to the specified frequency `freq`. + + Equivalent to applying :meth:`pandas.Period.asfreq` with the given arguments + to each :class:`~pandas.Period` in this {klass}. + + Parameters + ---------- + freq : str + A frequency. + how : str {{'E', 'S'}}, default 'E' + Whether the elements should be aligned to the end + or start within pa period. + + * 'E', 'END', or 'FINISH' for end, + * 'S', 'START', or 'BEGIN' for start. + + January 31st ('END') vs. January 1st ('START') for example. + + Returns + ------- + {klass} + The transformed {klass} with the new frequency. + + See Also + -------- + {other}.asfreq: Convert each Period in a {other_name} to the given frequency. + Period.asfreq : Convert a :class:`~pandas.Period` object to the given frequency. + + Examples + -------- + >>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='Y') + >>> pidx + PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'], + dtype='period[Y-DEC]') + + >>> pidx.asfreq('M') + PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12', + '2015-12'], dtype='period[M]') + + >>> pidx.asfreq('M', how='S') + PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01', + '2015-01'], dtype='period[M]') + """ + how = libperiod.validate_end_alias(how) + if isinstance(freq, BaseOffset) and hasattr(freq, "_period_dtype_code"): + freq = PeriodDtype(freq)._freqstr + freq = Period._maybe_convert_freq(freq) + + base1 = self._dtype._dtype_code + base2 = freq._period_dtype_code + + asi8 = self.asi8 + # self.freq.n can't be negative or 0 + end = how == "E" + if end: + ordinal = asi8 + self.dtype._n - 1 + else: + ordinal = asi8 + + new_data = period_asfreq_arr(ordinal, base1, base2, end) + + if self._hasna: + new_data[self._isnan] = iNaT + + dtype = PeriodDtype(freq) + return type(self)(new_data, dtype=dtype) + + # ------------------------------------------------------------------ + # Rendering Methods + + def _formatter(self, boxed: bool = False): + if boxed: + return str + return "'{}'".format + + def _format_native_types( + self, *, na_rep: str | float = "NaT", date_format=None, **kwargs + ) -> npt.NDArray[np.object_]: + """ + actually format my specific types + """ + return libperiod.period_array_strftime( + self.asi8, self.dtype._dtype_code, na_rep, date_format + ) + + # ------------------------------------------------------------------ + + def astype(self, dtype, copy: bool = True): + # We handle Period[T] -> Period[U] + # Our parent handles everything else. + dtype = pandas_dtype(dtype) + if dtype == self._dtype: + if not copy: + return self + else: + return self.copy() + if isinstance(dtype, PeriodDtype): + return self.asfreq(dtype.freq) + + if lib.is_np_dtype(dtype, "M") or isinstance(dtype, DatetimeTZDtype): + # GH#45038 match PeriodIndex behavior. + tz = getattr(dtype, "tz", None) + unit = dtl.dtype_to_unit(dtype) + return self.to_timestamp().tz_localize(tz).as_unit(unit) + + return super().astype(dtype, copy=copy) + + def searchsorted( + self, + value: NumpyValueArrayLike | ExtensionArray, + side: Literal["left", "right"] = "left", + sorter: NumpySorter | None = None, + ) -> npt.NDArray[np.intp] | np.intp: + npvalue = self._validate_setitem_value(value).view("M8[ns]") + + # Cast to M8 to get datetime-like NaT placement, + # similar to dtl._period_dispatch + m8arr = self._ndarray.view("M8[ns]") + return m8arr.searchsorted(npvalue, side=side, sorter=sorter) + + def _pad_or_backfill( + self, + *, + method: FillnaOptions, + limit: int | None = None, + limit_area: Literal["inside", "outside"] | None = None, + copy: bool = True, + ) -> Self: + # view as dt64 so we get treated as timelike in core.missing, + # similar to dtl._period_dispatch + dta = self.view("M8[ns]") + result = dta._pad_or_backfill( + method=method, limit=limit, limit_area=limit_area, copy=copy + ) + if copy: + return cast("Self", result.view(self.dtype)) + else: + return self + + def fillna( + self, value=None, method=None, limit: int | None = None, copy: bool = True + ) -> Self: + if method is not None: + # view as dt64 so we get treated as timelike in core.missing, + # similar to dtl._period_dispatch + dta = self.view("M8[ns]") + result = dta.fillna(value=value, method=method, limit=limit, copy=copy) + # error: Incompatible return value type (got "Union[ExtensionArray, + # ndarray[Any, Any]]", expected "PeriodArray") + return result.view(self.dtype) # type: ignore[return-value] + return super().fillna(value=value, method=method, limit=limit, copy=copy) + + # ------------------------------------------------------------------ + # Arithmetic Methods + + def _addsub_int_array_or_scalar( + self, other: np.ndarray | int, op: Callable[[Any, Any], Any] + ) -> Self: + """ + Add or subtract array of integers. + + Parameters + ---------- + other : np.ndarray[int64] or int + op : {operator.add, operator.sub} + + Returns + ------- + result : PeriodArray + """ + assert op in [operator.add, operator.sub] + if op is operator.sub: + other = -other + res_values = add_overflowsafe(self.asi8, np.asarray(other, dtype="i8")) + return type(self)(res_values, dtype=self.dtype) + + def _add_offset(self, other: BaseOffset): + assert not isinstance(other, Tick) + + self._require_matching_freq(other, base=True) + return self._addsub_int_array_or_scalar(other.n, operator.add) + + # TODO: can we de-duplicate with Period._add_timedeltalike_scalar? + def _add_timedeltalike_scalar(self, other): + """ + Parameters + ---------- + other : timedelta, Tick, np.timedelta64 + + Returns + ------- + PeriodArray + """ + if not isinstance(self.freq, Tick): + # We cannot add timedelta-like to non-tick PeriodArray + raise raise_on_incompatible(self, other) + + if isna(other): + # i.e. np.timedelta64("NaT") + return super()._add_timedeltalike_scalar(other) + + td = np.asarray(Timedelta(other).asm8) + return self._add_timedelta_arraylike(td) + + def _add_timedelta_arraylike( + self, other: TimedeltaArray | npt.NDArray[np.timedelta64] + ) -> Self: + """ + Parameters + ---------- + other : TimedeltaArray or ndarray[timedelta64] + + Returns + ------- + PeriodArray + """ + if not self.dtype._is_tick_like(): + # We cannot add timedelta-like to non-tick PeriodArray + raise TypeError( + f"Cannot add or subtract timedelta64[ns] dtype from {self.dtype}" + ) + + dtype = np.dtype(f"m8[{self.dtype._td64_unit}]") + + # Similar to _check_timedeltalike_freq_compat, but we raise with a + # more specific exception message if necessary. + try: + delta = astype_overflowsafe( + np.asarray(other), dtype=dtype, copy=False, round_ok=False + ) + except ValueError as err: + # e.g. if we have minutes freq and try to add 30s + # "Cannot losslessly convert units" + raise IncompatibleFrequency( + "Cannot add/subtract timedelta-like from PeriodArray that is " + "not an integer multiple of the PeriodArray's freq." + ) from err + + res_values = add_overflowsafe(self.asi8, np.asarray(delta.view("i8"))) + return type(self)(res_values, dtype=self.dtype) + + def _check_timedeltalike_freq_compat(self, other): + """ + Arithmetic operations with timedelta-like scalars or array `other` + are only valid if `other` is an integer multiple of `self.freq`. + If the operation is valid, find that integer multiple. Otherwise, + raise because the operation is invalid. + + Parameters + ---------- + other : timedelta, np.timedelta64, Tick, + ndarray[timedelta64], TimedeltaArray, TimedeltaIndex + + Returns + ------- + multiple : int or ndarray[int64] + + Raises + ------ + IncompatibleFrequency + """ + assert self.dtype._is_tick_like() # checked by calling function + + dtype = np.dtype(f"m8[{self.dtype._td64_unit}]") + + if isinstance(other, (timedelta, np.timedelta64, Tick)): + td = np.asarray(Timedelta(other).asm8) + else: + td = np.asarray(other) + + try: + delta = astype_overflowsafe(td, dtype=dtype, copy=False, round_ok=False) + except ValueError as err: + raise raise_on_incompatible(self, other) from err + + delta = delta.view("i8") + return lib.item_from_zerodim(delta) + + +def raise_on_incompatible(left, right) -> IncompatibleFrequency: + """ + Helper function to render a consistent error message when raising + IncompatibleFrequency. + + Parameters + ---------- + left : PeriodArray + right : None, DateOffset, Period, ndarray, or timedelta-like + + Returns + ------- + IncompatibleFrequency + Exception to be raised by the caller. + """ + # GH#24283 error message format depends on whether right is scalar + if isinstance(right, (np.ndarray, ABCTimedeltaArray)) or right is None: + other_freq = None + elif isinstance(right, BaseOffset): + other_freq = freq_to_period_freqstr(right.n, right.name) + elif isinstance(right, (ABCPeriodIndex, PeriodArray, Period)): + other_freq = right.freqstr + else: + other_freq = delta_to_tick(Timedelta(right)).freqstr + + own_freq = freq_to_period_freqstr(left.freq.n, left.freq.name) + msg = DIFFERENT_FREQ.format( + cls=type(left).__name__, own_freq=own_freq, other_freq=other_freq + ) + return IncompatibleFrequency(msg) + + +# ------------------------------------------------------------------- +# Constructor Helpers + + +def period_array( + data: Sequence[Period | str | None] | AnyArrayLike, + freq: str | Tick | BaseOffset | None = None, + copy: bool = False, +) -> PeriodArray: + """ + Construct a new PeriodArray from a sequence of Period scalars. + + Parameters + ---------- + data : Sequence of Period objects + A sequence of Period objects. These are required to all have + the same ``freq.`` Missing values can be indicated by ``None`` + or ``pandas.NaT``. + freq : str, Tick, or Offset + The frequency of every element of the array. This can be specified + to avoid inferring the `freq` from `data`. + copy : bool, default False + Whether to ensure a copy of the data is made. + + Returns + ------- + PeriodArray + + See Also + -------- + PeriodArray + pandas.PeriodIndex + + Examples + -------- + >>> period_array([pd.Period('2017', freq='Y'), + ... pd.Period('2018', freq='Y')]) + + ['2017', '2018'] + Length: 2, dtype: period[Y-DEC] + + >>> period_array([pd.Period('2017', freq='Y'), + ... pd.Period('2018', freq='Y'), + ... pd.NaT]) + + ['2017', '2018', 'NaT'] + Length: 3, dtype: period[Y-DEC] + + Integers that look like years are handled + + >>> period_array([2000, 2001, 2002], freq='D') + + ['2000-01-01', '2001-01-01', '2002-01-01'] + Length: 3, dtype: period[D] + + Datetime-like strings may also be passed + + >>> period_array(['2000-Q1', '2000-Q2', '2000-Q3', '2000-Q4'], freq='Q') + + ['2000Q1', '2000Q2', '2000Q3', '2000Q4'] + Length: 4, dtype: period[Q-DEC] + """ + data_dtype = getattr(data, "dtype", None) + + if lib.is_np_dtype(data_dtype, "M"): + return PeriodArray._from_datetime64(data, freq) + if isinstance(data_dtype, PeriodDtype): + out = PeriodArray(data) + if freq is not None: + if freq == data_dtype.freq: + return out + return out.asfreq(freq) + return out + + # other iterable of some kind + if not isinstance(data, (np.ndarray, list, tuple, ABCSeries)): + data = list(data) + + arrdata = np.asarray(data) + + dtype: PeriodDtype | None + if freq: + dtype = PeriodDtype(freq) + else: + dtype = None + + if arrdata.dtype.kind == "f" and len(arrdata) > 0: + raise TypeError("PeriodIndex does not allow floating point in construction") + + if arrdata.dtype.kind in "iu": + arr = arrdata.astype(np.int64, copy=False) + # error: Argument 2 to "from_ordinals" has incompatible type "Union[str, + # Tick, None]"; expected "Union[timedelta, BaseOffset, str]" + ordinals = libperiod.from_ordinals(arr, freq) # type: ignore[arg-type] + return PeriodArray(ordinals, dtype=dtype) + + data = ensure_object(arrdata) + if freq is None: + freq = libperiod.extract_freq(data) + dtype = PeriodDtype(freq) + return PeriodArray._from_sequence(data, dtype=dtype) + + +@overload +def validate_dtype_freq(dtype, freq: BaseOffsetT) -> BaseOffsetT: + ... + + +@overload +def validate_dtype_freq(dtype, freq: timedelta | str | None) -> BaseOffset: + ... + + +def validate_dtype_freq( + dtype, freq: BaseOffsetT | BaseOffset | timedelta | str | None +) -> BaseOffsetT: + """ + If both a dtype and a freq are available, ensure they match. If only + dtype is available, extract the implied freq. + + Parameters + ---------- + dtype : dtype + freq : DateOffset or None + + Returns + ------- + freq : DateOffset + + Raises + ------ + ValueError : non-period dtype + IncompatibleFrequency : mismatch between dtype and freq + """ + if freq is not None: + freq = to_offset(freq, is_period=True) + + if dtype is not None: + dtype = pandas_dtype(dtype) + if not isinstance(dtype, PeriodDtype): + raise ValueError("dtype must be PeriodDtype") + if freq is None: + freq = dtype.freq + elif freq != dtype.freq: + raise IncompatibleFrequency("specified freq and dtype are different") + # error: Incompatible return value type (got "Union[BaseOffset, Any, None]", + # expected "BaseOffset") + return freq # type: ignore[return-value] + + +def dt64arr_to_periodarr( + data, freq, tz=None +) -> tuple[npt.NDArray[np.int64], BaseOffset]: + """ + Convert an datetime-like array to values Period ordinals. + + Parameters + ---------- + data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]] + freq : Optional[Union[str, Tick]] + Must match the `freq` on the `data` if `data` is a DatetimeIndex + or Series. + tz : Optional[tzinfo] + + Returns + ------- + ordinals : ndarray[int64] + freq : Tick + The frequency extracted from the Series or DatetimeIndex if that's + used. + + """ + if not isinstance(data.dtype, np.dtype) or data.dtype.kind != "M": + raise ValueError(f"Wrong dtype: {data.dtype}") + + if freq is None: + if isinstance(data, ABCIndex): + data, freq = data._values, data.freq + elif isinstance(data, ABCSeries): + data, freq = data._values, data.dt.freq + + elif isinstance(data, (ABCIndex, ABCSeries)): + data = data._values + + reso = get_unit_from_dtype(data.dtype) + freq = Period._maybe_convert_freq(freq) + base = freq._period_dtype_code + return c_dt64arr_to_periodarr(data.view("i8"), base, tz, reso=reso), freq + + +def _get_ordinal_range(start, end, periods, freq, mult: int = 1): + if com.count_not_none(start, end, periods) != 2: + raise ValueError( + "Of the three parameters: start, end, and periods, " + "exactly two must be specified" + ) + + if freq is not None: + freq = to_offset(freq, is_period=True) + mult = freq.n + + if start is not None: + start = Period(start, freq) + if end is not None: + end = Period(end, freq) + + is_start_per = isinstance(start, Period) + is_end_per = isinstance(end, Period) + + if is_start_per and is_end_per and start.freq != end.freq: + raise ValueError("start and end must have same freq") + if start is NaT or end is NaT: + raise ValueError("start and end must not be NaT") + + if freq is None: + if is_start_per: + freq = start.freq + elif is_end_per: + freq = end.freq + else: # pragma: no cover + raise ValueError("Could not infer freq from start/end") + mult = freq.n + + if periods is not None: + periods = periods * mult + if start is None: + data = np.arange( + end.ordinal - periods + mult, end.ordinal + 1, mult, dtype=np.int64 + ) + else: + data = np.arange( + start.ordinal, start.ordinal + periods, mult, dtype=np.int64 + ) + else: + data = np.arange(start.ordinal, end.ordinal + 1, mult, dtype=np.int64) + + return data, freq + + +def _range_from_fields( + year=None, + month=None, + quarter=None, + day=None, + hour=None, + minute=None, + second=None, + freq=None, +) -> tuple[np.ndarray, BaseOffset]: + if hour is None: + hour = 0 + if minute is None: + minute = 0 + if second is None: + second = 0 + if day is None: + day = 1 + + ordinals = [] + + if quarter is not None: + if freq is None: + freq = to_offset("Q", is_period=True) + base = FreqGroup.FR_QTR.value + else: + freq = to_offset(freq, is_period=True) + base = libperiod.freq_to_dtype_code(freq) + if base != FreqGroup.FR_QTR.value: + raise AssertionError("base must equal FR_QTR") + + freqstr = freq.freqstr + year, quarter = _make_field_arrays(year, quarter) + for y, q in zip(year, quarter): + calendar_year, calendar_month = parsing.quarter_to_myear(y, q, freqstr) + val = libperiod.period_ordinal( + calendar_year, calendar_month, 1, 1, 1, 1, 0, 0, base + ) + ordinals.append(val) + else: + freq = to_offset(freq, is_period=True) + base = libperiod.freq_to_dtype_code(freq) + arrays = _make_field_arrays(year, month, day, hour, minute, second) + for y, mth, d, h, mn, s in zip(*arrays): + ordinals.append(libperiod.period_ordinal(y, mth, d, h, mn, s, 0, 0, base)) + + return np.array(ordinals, dtype=np.int64), freq + + +def _make_field_arrays(*fields) -> list[np.ndarray]: + length = None + for x in fields: + if isinstance(x, (list, np.ndarray, ABCSeries)): + if length is not None and len(x) != length: + raise ValueError("Mismatched Period array lengths") + if length is None: + length = len(x) + + # error: Argument 2 to "repeat" has incompatible type "Optional[int]"; expected + # "Union[Union[int, integer[Any]], Union[bool, bool_], ndarray, Sequence[Union[int, + # integer[Any]]], Sequence[Union[bool, bool_]], Sequence[Sequence[Any]]]" + return [ + np.asarray(x) + if isinstance(x, (np.ndarray, list, ABCSeries)) + else np.repeat(x, length) # type: ignore[arg-type] + for x in fields + ] diff --git a/shared/package-files/56/5668580ce550f79698302dbd6a39bd6bef6e852c6e0b0d60666fb6d70b533c39/blob b/shared/package-files/56/5668580ce550f79698302dbd6a39bd6bef6e852c6e0b0d60666fb6d70b533c39/blob new file mode 100644 index 0000000000000000000000000000000000000000..a86cfee57b39a8f330e2ad0effbdc36e4cad57bf --- /dev/null +++ b/shared/package-files/56/5668580ce550f79698302dbd6a39bd6bef6e852c6e0b0d60666fb6d70b533c39/blob @@ -0,0 +1,28 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace native { +TORCH_API at::Tensor remainder(const at::Tensor & self, const at::Scalar & other); +TORCH_API at::Tensor & remainder_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out); +TORCH_API at::Tensor & remainder_(at::Tensor & self, const at::Scalar & other); +struct TORCH_API structured_remainder_out : public at::meta::structured_remainder_Tensor { +void impl(const at::Tensor & self, const at::Tensor & other, const at::Tensor & out); +}; +TORCH_API at::Tensor & remainder_Scalar_Tensor_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out); +TORCH_API at::Tensor remainder(const at::Scalar & self, const at::Tensor & other); +} // namespace native +} // namespace at diff --git a/shared/package-files/56/566b5dbaa8c09b9da0833a776ff9f8a23e6c1920c787c9a45b1e94e42667c9a1/blob b/shared/package-files/56/566b5dbaa8c09b9da0833a776ff9f8a23e6c1920c787c9a45b1e94e42667c9a1/blob new file mode 100644 index 0000000000000000000000000000000000000000..d094ba5bd51cba847a648ed86d1d329f4abbb1dd Binary files /dev/null and b/shared/package-files/56/566b5dbaa8c09b9da0833a776ff9f8a23e6c1920c787c9a45b1e94e42667c9a1/blob differ diff --git a/shared/package-files/56/566ba90b67b3511de9e6d819c74aea5c41c0b73b229015b6620688c6e1b03b90/blob b/shared/package-files/56/566ba90b67b3511de9e6d819c74aea5c41c0b73b229015b6620688c6e1b03b90/blob new file mode 100644 index 0000000000000000000000000000000000000000..122e6009bd71e77ae39f55da5cf056500ff526a9 --- /dev/null +++ b/shared/package-files/56/566ba90b67b3511de9e6d819c74aea5c41c0b73b229015b6620688c6e1b03b90/blob @@ -0,0 +1,48 @@ +import numpy as np +import scipy.special as sc +from numpy.testing import assert_almost_equal, assert_array_equal + + +class TestPdtr: + def test(self): + val = sc.pdtr(0, 1) + assert_almost_equal(val, np.exp(-1)) + + def test_m_zero(self): + val = sc.pdtr([0, 1, 2], 0) + assert_array_equal(val, [1, 1, 1]) + + def test_rounding(self): + double_val = sc.pdtr([0.1, 1.1, 2.1], 1.0) + int_val = sc.pdtr([0, 1, 2], 1.0) + assert_array_equal(double_val, int_val) + + def test_inf(self): + val = sc.pdtr(np.inf, 1.0) + assert_almost_equal(val, 1.0) + + def test_domain(self): + val = sc.pdtr(-1.1, 1.0) + assert np.isnan(val) + +class TestPdtrc: + def test_value(self): + val = sc.pdtrc(0, 1) + assert_almost_equal(val, 1 - np.exp(-1)) + + def test_m_zero(self): + val = sc.pdtrc([0, 1, 2], 0.0) + assert_array_equal(val, [0, 0, 0]) + + def test_rounding(self): + double_val = sc.pdtrc([0.1, 1.1, 2.1], 1.0) + int_val = sc.pdtrc([0, 1, 2], 1.0) + assert_array_equal(double_val, int_val) + + def test_inf(self): + val = sc.pdtrc(np.inf, 1.0) + assert_almost_equal(val, 0.0) + + def test_domain(self): + val = sc.pdtrc(-1.1, 1.0) + assert np.isnan(val) diff --git a/shared/package-files/56/5671ef63dc847a761de43990d44f0adf2a1c63bb645ca2ccc91572047585290f/blob b/shared/package-files/56/5671ef63dc847a761de43990d44f0adf2a1c63bb645ca2ccc91572047585290f/blob new file mode 100644 index 0000000000000000000000000000000000000000..4ca467b9ad64eddbc8f625230c5562da65a151d0 --- /dev/null +++ b/shared/package-files/56/5671ef63dc847a761de43990d44f0adf2a1c63bb645ca2ccc91572047585290f/blob @@ -0,0 +1,39 @@ +#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::hardsigmoid_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) +inline at::Tensor & hardsigmoid_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::hardsigmoid_backward_grad_input::call(grad_output, self, grad_input); +} +// aten::hardsigmoid_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) +inline at::Tensor & hardsigmoid_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { + return at::_ops::hardsigmoid_backward_grad_input::call(grad_output, self, grad_input); +} + +// aten::hardsigmoid_backward(Tensor grad_output, Tensor self) -> Tensor +inline at::Tensor hardsigmoid_backward(const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::hardsigmoid_backward::call(grad_output, self); +} + +} diff --git a/shared/package-files/56/56776b6c7cc73dd2e4e9440d69b498eb8cfc1ea9060f46d201317014d5e1578f/blob b/shared/package-files/56/56776b6c7cc73dd2e4e9440d69b498eb8cfc1ea9060f46d201317014d5e1578f/blob new file mode 100644 index 0000000000000000000000000000000000000000..c8ac982cf1df2b85cb0fd201078a7211e58aeeb0 --- /dev/null +++ b/shared/package-files/56/56776b6c7cc73dd2e4e9440d69b498eb8cfc1ea9060f46d201317014d5e1578f/blob @@ -0,0 +1,37 @@ +""" +Utilities for converting data types into structured JSON for dumping. +""" + +import traceback +from typing import Dict, Sequence + +import torch._logging._internal + + +INTERN_TABLE: Dict[str, int] = {} + + +def intern_string(s: str) -> int: + r = INTERN_TABLE.get(s, None) + if r is None: + r = len(INTERN_TABLE) + INTERN_TABLE[s] = r + torch._logging._internal.trace_structured( + "str", lambda: (s, r), suppress_context=True + ) + return r + + +def from_traceback(tb: Sequence[traceback.FrameSummary]) -> object: + r = [] + for frame in tb: + # dict naming convention here coincides with + # python/combined_traceback.cpp + r.append( + { + "line": frame.lineno, + "name": frame.name, + "filename": intern_string(frame.filename), + } + ) + return r diff --git a/shared/package-files/56/567860b355d9addfdc00ab699b9aaecd043bd9596368d37ab70148b6b21a73c7/blob b/shared/package-files/56/567860b355d9addfdc00ab699b9aaecd043bd9596368d37ab70148b6b21a73c7/blob new file mode 100644 index 0000000000000000000000000000000000000000..3b35319b6b3a9eed962f1b5adc3e1a6845a18f17 --- /dev/null +++ b/shared/package-files/56/567860b355d9addfdc00ab699b9aaecd043bd9596368d37ab70148b6b21a73c7/blob @@ -0,0 +1,28 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace native { +TORCH_API at::Tensor upsample_nearest2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, c10::optional> scale_factors); +struct TORCH_API structured_upsample_nearest2d_out_cpu : public at::meta::structured_upsample_nearest2d { +void impl(const at::Tensor & self, at::ArrayRef output_size, c10::optional scales_h, c10::optional scales_w, const at::Tensor & out); +}; +struct TORCH_API structured_upsample_nearest2d_out_cuda : public at::meta::structured_upsample_nearest2d { +void impl(const at::Tensor & self, at::ArrayRef output_size, c10::optional scales_h, c10::optional scales_w, const at::Tensor & out); +}; +TORCH_API at::Tensor upsample_nearest2d_quantized_cpu(const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt); +} // namespace native +} // namespace at diff --git a/shared/package-files/56/5678e80e6cb4a4a428b9b5d740943f3258e80d88b515c2dd35907628dfd3408b/blob b/shared/package-files/56/5678e80e6cb4a4a428b9b5d740943f3258e80d88b515c2dd35907628dfd3408b/blob new file mode 100644 index 0000000000000000000000000000000000000000..4b24ad960e4f2359e0363a216ad91a00d2910d24 --- /dev/null +++ b/shared/package-files/56/5678e80e6cb4a4a428b9b5d740943f3258e80d88b515c2dd35907628dfd3408b/blob @@ -0,0 +1,143 @@ +from __future__ import annotations + +from typing import TextIO + +from prompt_toolkit.cursor_shapes import CursorShape +from prompt_toolkit.data_structures import Size +from prompt_toolkit.styles import Attrs + +from .base import Output +from .color_depth import ColorDepth +from .flush_stdout import flush_stdout + +__all__ = ["PlainTextOutput"] + + +class PlainTextOutput(Output): + """ + Output that won't include any ANSI escape sequences. + + Useful when stdout is not a terminal. Maybe stdout is redirected to a file. + In this case, if `print_formatted_text` is used, for instance, we don't + want to include formatting. + + (The code is mostly identical to `Vt100_Output`, but without the + formatting.) + """ + + def __init__(self, stdout: TextIO) -> None: + assert all(hasattr(stdout, a) for a in ("write", "flush")) + + self.stdout: TextIO = stdout + self._buffer: list[str] = [] + + def fileno(self) -> int: + "There is no sensible default for fileno()." + return self.stdout.fileno() + + def encoding(self) -> str: + return "utf-8" + + def write(self, data: str) -> None: + self._buffer.append(data) + + def write_raw(self, data: str) -> None: + self._buffer.append(data) + + def set_title(self, title: str) -> None: + pass + + def clear_title(self) -> None: + pass + + def flush(self) -> None: + if not self._buffer: + return + + data = "".join(self._buffer) + self._buffer = [] + flush_stdout(self.stdout, data) + + def erase_screen(self) -> None: + pass + + def enter_alternate_screen(self) -> None: + pass + + def quit_alternate_screen(self) -> None: + pass + + def enable_mouse_support(self) -> None: + pass + + def disable_mouse_support(self) -> None: + pass + + def erase_end_of_line(self) -> None: + pass + + def erase_down(self) -> None: + pass + + def reset_attributes(self) -> None: + pass + + def set_attributes(self, attrs: Attrs, color_depth: ColorDepth) -> None: + pass + + def disable_autowrap(self) -> None: + pass + + def enable_autowrap(self) -> None: + pass + + def cursor_goto(self, row: int = 0, column: int = 0) -> None: + pass + + def cursor_up(self, amount: int) -> None: + pass + + def cursor_down(self, amount: int) -> None: + self._buffer.append("\n") + + def cursor_forward(self, amount: int) -> None: + self._buffer.append(" " * amount) + + def cursor_backward(self, amount: int) -> None: + pass + + def hide_cursor(self) -> None: + pass + + def show_cursor(self) -> None: + pass + + def set_cursor_shape(self, cursor_shape: CursorShape) -> None: + pass + + def reset_cursor_shape(self) -> None: + pass + + def ask_for_cpr(self) -> None: + pass + + def bell(self) -> None: + pass + + def enable_bracketed_paste(self) -> None: + pass + + def disable_bracketed_paste(self) -> None: + pass + + def scroll_buffer_to_prompt(self) -> None: + pass + + def get_size(self) -> Size: + return Size(rows=40, columns=80) + + def get_rows_below_cursor_position(self) -> int: + return 8 + + def get_default_color_depth(self) -> ColorDepth: + return ColorDepth.DEPTH_1_BIT diff --git a/shared/package-files/56/567c18af9293fc52ca7dcd3057b76d373edb36d682f55210b7ea33e7a1dbe441/blob b/shared/package-files/56/567c18af9293fc52ca7dcd3057b76d373edb36d682f55210b7ea33e7a1dbe441/blob new file mode 100644 index 0000000000000000000000000000000000000000..5f7d99efd12d3052e5442238bf826d13b5acf6b3 --- /dev/null +++ b/shared/package-files/56/567c18af9293fc52ca7dcd3057b76d373edb36d682f55210b7ea33e7a1dbe441/blob @@ -0,0 +1,76 @@ +import sys +from typing import Literal, Any + +import numpy as np +import numpy.typing as npt + +if sys.version_info >= (3, 11): + from typing import assert_type +else: + from typing_extensions import assert_type + +f8: np.float64 +AR_f8: npt.NDArray[np.float64] +AR_i8: npt.NDArray[np.int64] + +assert_type(np.absolute.__doc__, str) +assert_type(np.absolute.types, list[str]) + +assert_type(np.absolute.__name__, Literal["absolute"]) +assert_type(np.absolute.ntypes, Literal[20]) +assert_type(np.absolute.identity, None) +assert_type(np.absolute.nin, Literal[1]) +assert_type(np.absolute.nin, Literal[1]) +assert_type(np.absolute.nout, Literal[1]) +assert_type(np.absolute.nargs, Literal[2]) +assert_type(np.absolute.signature, None) +assert_type(np.absolute(f8), Any) +assert_type(np.absolute(AR_f8), npt.NDArray[Any]) +assert_type(np.absolute.at(AR_f8, AR_i8), None) + +assert_type(np.add.__name__, Literal["add"]) +assert_type(np.add.ntypes, Literal[22]) +assert_type(np.add.identity, Literal[0]) +assert_type(np.add.nin, Literal[2]) +assert_type(np.add.nout, Literal[1]) +assert_type(np.add.nargs, Literal[3]) +assert_type(np.add.signature, None) +assert_type(np.add(f8, f8), Any) +assert_type(np.add(AR_f8, f8), npt.NDArray[Any]) +assert_type(np.add.at(AR_f8, AR_i8, f8), None) +assert_type(np.add.reduce(AR_f8, axis=0), Any) +assert_type(np.add.accumulate(AR_f8), npt.NDArray[Any]) +assert_type(np.add.reduceat(AR_f8, AR_i8), npt.NDArray[Any]) +assert_type(np.add.outer(f8, f8), Any) +assert_type(np.add.outer(AR_f8, f8), npt.NDArray[Any]) + +assert_type(np.frexp.__name__, Literal["frexp"]) +assert_type(np.frexp.ntypes, Literal[4]) +assert_type(np.frexp.identity, None) +assert_type(np.frexp.nin, Literal[1]) +assert_type(np.frexp.nout, Literal[2]) +assert_type(np.frexp.nargs, Literal[3]) +assert_type(np.frexp.signature, None) +assert_type(np.frexp(f8), tuple[Any, Any]) +assert_type(np.frexp(AR_f8), tuple[npt.NDArray[Any], npt.NDArray[Any]]) + +assert_type(np.divmod.__name__, Literal["divmod"]) +assert_type(np.divmod.ntypes, Literal[15]) +assert_type(np.divmod.identity, None) +assert_type(np.divmod.nin, Literal[2]) +assert_type(np.divmod.nout, Literal[2]) +assert_type(np.divmod.nargs, Literal[4]) +assert_type(np.divmod.signature, None) +assert_type(np.divmod(f8, f8), tuple[Any, Any]) +assert_type(np.divmod(AR_f8, f8), tuple[npt.NDArray[Any], npt.NDArray[Any]]) + +assert_type(np.matmul.__name__, Literal["matmul"]) +assert_type(np.matmul.ntypes, Literal[19]) +assert_type(np.matmul.identity, None) +assert_type(np.matmul.nin, Literal[2]) +assert_type(np.matmul.nout, Literal[1]) +assert_type(np.matmul.nargs, Literal[3]) +assert_type(np.matmul.signature, Literal["(n?,k),(k,m?)->(n?,m?)"]) +assert_type(np.matmul.identity, None) +assert_type(np.matmul(AR_f8, AR_f8), Any) +assert_type(np.matmul(AR_f8, AR_f8, axes=[(0, 1), (0, 1), (0, 1)]), Any) diff --git a/shared/package-files/56/567fc4fab7b1b7393e5c62e307301256d390d2032be9c259138681d2f7ae7461/blob b/shared/package-files/56/567fc4fab7b1b7393e5c62e307301256d390d2032be9c259138681d2f7ae7461/blob new file mode 100644 index 0000000000000000000000000000000000000000..d2b9723e36fb0fcc3592dacf19ad10481b79a3d2 Binary files /dev/null and b/shared/package-files/56/567fc4fab7b1b7393e5c62e307301256d390d2032be9c259138681d2f7ae7461/blob differ diff --git a/shared/package-files/56/5684b25c17c6d8db80a8777f58045eed41f57aaabd8da3b9be28d130d9370054/blob b/shared/package-files/56/5684b25c17c6d8db80a8777f58045eed41f57aaabd8da3b9be28d130d9370054/blob new file mode 100644 index 0000000000000000000000000000000000000000..cfdd031fa27b5299780f7227b5d4b27f0deacc35 --- /dev/null +++ b/shared/package-files/56/5684b25c17c6d8db80a8777f58045eed41f57aaabd8da3b9be28d130d9370054/blob @@ -0,0 +1,196 @@ +from abc import ABCMeta, abstractmethod +from typing import ClassVar, Optional, Union + +from cryptography.hazmat.backends.interfaces import EllipticCurveBackend +from cryptography.hazmat.primitives.asymmetric import AsymmetricVerificationContext +from cryptography.hazmat.primitives.asymmetric.utils import Prehashed +from cryptography.hazmat.primitives.hashes import HashAlgorithm +from cryptography.hazmat.primitives.serialization import Encoding, KeySerializationEncryption, PrivateFormat, PublicFormat +from cryptography.x509 import ObjectIdentifier + +class EllipticCurve(metaclass=ABCMeta): + @property + @abstractmethod + def key_size(self) -> int: ... + @property + @abstractmethod + def name(self) -> str: ... + +class BrainpoolP256R1(EllipticCurve): + key_size: int = ... + name: str = ... + +class BrainpoolP384R1(EllipticCurve): + key_size: int = ... + name: str = ... + +class BrainpoolP512R1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECP192R1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECP224R1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECP256K1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECP256R1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECP384R1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECP521R1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECT163K1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECT163R2(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECT233K1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECT233R1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECT283K1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECT283R1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECT409K1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECT409R1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECT571K1(EllipticCurve): + key_size: int = ... + name: str = ... + +class SECT571R1(EllipticCurve): + key_size: int = ... + name: str = ... + +class EllipticCurveOID(object): + SECP192R1: ClassVar[ObjectIdentifier] + SECP224R1: ClassVar[ObjectIdentifier] + SECP256K1: ClassVar[ObjectIdentifier] + SECP256R1: ClassVar[ObjectIdentifier] + SECP384R1: ClassVar[ObjectIdentifier] + SECP521R1: ClassVar[ObjectIdentifier] + BRAINPOOLP256R1: ClassVar[ObjectIdentifier] + BRAINPOOLP384R1: ClassVar[ObjectIdentifier] + BRAINPOOLP512R1: ClassVar[ObjectIdentifier] + SECT163K1: ClassVar[ObjectIdentifier] + SECT163R2: ClassVar[ObjectIdentifier] + SECT233K1: ClassVar[ObjectIdentifier] + SECT233R1: ClassVar[ObjectIdentifier] + SECT283K1: ClassVar[ObjectIdentifier] + SECT283R1: ClassVar[ObjectIdentifier] + SECT409K1: ClassVar[ObjectIdentifier] + SECT409R1: ClassVar[ObjectIdentifier] + SECT571K1: ClassVar[ObjectIdentifier] + SECT571R1: ClassVar[ObjectIdentifier] + +class EllipticCurvePrivateKey(metaclass=ABCMeta): + @property + @abstractmethod + def curve(self) -> EllipticCurve: ... + @property + @abstractmethod + def key_size(self) -> int: ... + @abstractmethod + def exchange(self, algorithm: ECDH, peer_public_key: EllipticCurvePublicKey) -> bytes: ... + @abstractmethod + def public_key(self) -> EllipticCurvePublicKey: ... + @abstractmethod + def sign(self, data: bytes, signature_algorithm: EllipticCurveSignatureAlgorithm) -> bytes: ... + +class EllipticCurvePrivateKeyWithSerialization(EllipticCurvePrivateKey): + @abstractmethod + def private_bytes( + self, encoding: Encoding, format: PrivateFormat, encryption_algorithm: KeySerializationEncryption + ) -> bytes: ... + @abstractmethod + def private_numbers(self) -> EllipticCurvePrivateNumbers: ... + +class EllipticCurvePrivateNumbers(object): + @property + def private_value(self) -> int: ... + @property + def public_numbers(self) -> EllipticCurvePublicNumbers: ... + def __init__(self, private_value: int, public_numbers: EllipticCurvePublicNumbers) -> None: ... + def private_key(self, backend: Optional[EllipticCurveBackend] = ...) -> EllipticCurvePrivateKey: ... + +class EllipticCurvePublicKey(metaclass=ABCMeta): + @property + @abstractmethod + def curve(self) -> EllipticCurve: ... + @property + @abstractmethod + def key_size(self) -> int: ... + @classmethod + def from_encoded_point(cls, curve: EllipticCurve, data: bytes) -> EllipticCurvePublicKey: ... + @abstractmethod + def public_bytes(self, encoding: Encoding, format: PublicFormat) -> bytes: ... + @abstractmethod + def public_numbers(self) -> EllipticCurvePublicNumbers: ... + @abstractmethod + def verifier( + self, signature: bytes, signature_algorithm: EllipticCurveSignatureAlgorithm + ) -> AsymmetricVerificationContext: ... + @abstractmethod + def verify(self, signature: bytes, data: bytes, signature_algorithm: EllipticCurveSignatureAlgorithm) -> None: ... + +EllipticCurvePublicKeyWithSerialization = EllipticCurvePublicKey + +class EllipticCurvePublicNumbers(object): + @property + def curve(self) -> EllipticCurve: ... + @property + def x(self) -> int: ... + @property + def y(self) -> int: ... + def __init__(self, x: int, y: int, curve: EllipticCurve) -> None: ... + @classmethod + def from_encoded_point(cls, curve: EllipticCurve, data: bytes) -> EllipticCurvePublicNumbers: ... + def public_key(self, backend: Optional[EllipticCurveBackend] = ...) -> EllipticCurvePublicKey: ... + +class EllipticCurveSignatureAlgorithm(metaclass=ABCMeta): + @property + @abstractmethod + def algorithm(self) -> Union[HashAlgorithm, Prehashed]: ... + +class ECDH(object): ... + +class ECDSA(EllipticCurveSignatureAlgorithm): + def __init__(self, algorithm: Union[HashAlgorithm, Prehashed]): ... + @property + def algorithm(self) -> Union[HashAlgorithm, Prehashed]: ... + +def derive_private_key( + private_value: int, curve: EllipticCurve, backend: Optional[EllipticCurveBackend] = ... +) -> EllipticCurvePrivateKey: ... +def generate_private_key(curve: EllipticCurve, backend: Optional[EllipticCurveBackend] = ...) -> EllipticCurvePrivateKey: ... +def get_curve_for_oid(oid: ObjectIdentifier) -> EllipticCurve: ... diff --git a/shared/package-files/56/5684c82cd0eb516bd9c742caf7ff7b9412d014ab521a60ede102ce1fccd8be5a/blob b/shared/package-files/56/5684c82cd0eb516bd9c742caf7ff7b9412d014ab521a60ede102ce1fccd8be5a/blob new file mode 100644 index 0000000000000000000000000000000000000000..5cf02fe868b04d3fd1ff145e57332475d7466b57 --- /dev/null +++ b/shared/package-files/56/5684c82cd0eb516bd9c742caf7ff7b9412d014ab521a60ede102ce1fccd8be5a/blob @@ -0,0 +1,175 @@ +""" +============================ +Typing (:mod:`numpy.typing`) +============================ + +.. versionadded:: 1.20 + +Large parts of the NumPy API have :pep:`484`-style type annotations. In +addition a number of type aliases are available to users, most prominently +the two below: + +- `ArrayLike`: objects that can be converted to arrays +- `DTypeLike`: objects that can be converted to dtypes + +.. _typing-extensions: https://pypi.org/project/typing-extensions/ + +Mypy plugin +----------- + +.. versionadded:: 1.21 + +.. automodule:: numpy.typing.mypy_plugin + +.. currentmodule:: numpy.typing + +Differences from the runtime NumPy API +-------------------------------------- + +NumPy is very flexible. Trying to describe the full range of +possibilities statically would result in types that are not very +helpful. For that reason, the typed NumPy API is often stricter than +the runtime NumPy API. This section describes some notable +differences. + +ArrayLike +~~~~~~~~~ + +The `ArrayLike` type tries to avoid creating object arrays. For +example, + +.. code-block:: python + + >>> np.array(x**2 for x in range(10)) + array( at ...>, dtype=object) + +is valid NumPy code which will create a 0-dimensional object +array. Type checkers will complain about the above example when using +the NumPy types however. If you really intended to do the above, then +you can either use a ``# type: ignore`` comment: + +.. code-block:: python + + >>> np.array(x**2 for x in range(10)) # type: ignore + +or explicitly type the array like object as `~typing.Any`: + +.. code-block:: python + + >>> from typing import Any + >>> array_like: Any = (x**2 for x in range(10)) + >>> np.array(array_like) + array( at ...>, dtype=object) + +ndarray +~~~~~~~ + +It's possible to mutate the dtype of an array at runtime. For example, +the following code is valid: + +.. code-block:: python + + >>> x = np.array([1, 2]) + >>> x.dtype = np.bool_ + +This sort of mutation is not allowed by the types. Users who want to +write statically typed code should instead use the `numpy.ndarray.view` +method to create a view of the array with a different dtype. + +DTypeLike +~~~~~~~~~ + +The `DTypeLike` type tries to avoid creation of dtype objects using +dictionary of fields like below: + +.. code-block:: python + + >>> x = np.dtype({"field1": (float, 1), "field2": (int, 3)}) + +Although this is valid NumPy code, the type checker will complain about it, +since its usage is discouraged. +Please see : :ref:`Data type objects ` + +Number precision +~~~~~~~~~~~~~~~~ + +The precision of `numpy.number` subclasses is treated as a covariant generic +parameter (see :class:`~NBitBase`), simplifying the annotating of processes +involving precision-based casting. + +.. code-block:: python + + >>> from typing import TypeVar + >>> import numpy as np + >>> import numpy.typing as npt + + >>> T = TypeVar("T", bound=npt.NBitBase) + >>> def func(a: "np.floating[T]", b: "np.floating[T]") -> "np.floating[T]": + ... ... + +Consequently, the likes of `~numpy.float16`, `~numpy.float32` and +`~numpy.float64` are still sub-types of `~numpy.floating`, but, contrary to +runtime, they're not necessarily considered as sub-classes. + +Timedelta64 +~~~~~~~~~~~ + +The `~numpy.timedelta64` class is not considered a subclass of +`~numpy.signedinteger`, the former only inheriting from `~numpy.generic` +while static type checking. + +0D arrays +~~~~~~~~~ + +During runtime numpy aggressively casts any passed 0D arrays into their +corresponding `~numpy.generic` instance. Until the introduction of shape +typing (see :pep:`646`) it is unfortunately not possible to make the +necessary distinction between 0D and >0D arrays. While thus not strictly +correct, all operations are that can potentially perform a 0D-array -> scalar +cast are currently annotated as exclusively returning an `ndarray`. + +If it is known in advance that an operation _will_ perform a +0D-array -> scalar cast, then one can consider manually remedying the +situation with either `typing.cast` or a ``# type: ignore`` comment. + +Record array dtypes +~~~~~~~~~~~~~~~~~~~ + +The dtype of `numpy.recarray`, and the `numpy.rec` functions in general, +can be specified in one of two ways: + +* Directly via the ``dtype`` argument. +* With up to five helper arguments that operate via `numpy.format_parser`: + ``formats``, ``names``, ``titles``, ``aligned`` and ``byteorder``. + +These two approaches are currently typed as being mutually exclusive, +*i.e.* if ``dtype`` is specified than one may not specify ``formats``. +While this mutual exclusivity is not (strictly) enforced during runtime, +combining both dtype specifiers can lead to unexpected or even downright +buggy behavior. + +API +--- + +""" +# NOTE: The API section will be appended with additional entries +# further down in this file + +from numpy._typing import ( + ArrayLike, + DTypeLike, + NBitBase, + NDArray, +) + +__all__ = ["ArrayLike", "DTypeLike", "NBitBase", "NDArray"] + +if __doc__ is not None: + from numpy._typing._add_docstring import _docstrings + __doc__ += _docstrings + __doc__ += '\n.. autoclass:: numpy.typing.NBitBase\n' + del _docstrings + +from numpy._pytesttester import PytestTester +test = PytestTester(__name__) +del PytestTester diff --git a/shared/package-files/56/56851e6586b568ee6b53af5d0e9bf839be45398cabde79be789de6d24bad8b45/blob b/shared/package-files/56/56851e6586b568ee6b53af5d0e9bf839be45398cabde79be789de6d24bad8b45/blob new file mode 100644 index 0000000000000000000000000000000000000000..2e4332adfa216bb42f3726d1c6be0a238844b5ae --- /dev/null +++ b/shared/package-files/56/56851e6586b568ee6b53af5d0e9bf839be45398cabde79be789de6d24bad8b45/blob @@ -0,0 +1,505 @@ +""" + pygments.lexers.objective + ~~~~~~~~~~~~~~~~~~~~~~~~~ + + Lexers for Objective-C family languages. + + :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" + +import re + +from pygments.lexer import RegexLexer, include, bygroups, using, this, words, \ + inherit, default +from pygments.token import Text, Keyword, Name, String, Operator, \ + Number, Punctuation, Literal, Comment + +from pygments.lexers.c_cpp import CLexer, CppLexer + +__all__ = ['ObjectiveCLexer', 'ObjectiveCppLexer', 'LogosLexer', 'SwiftLexer'] + + +def objective(baselexer): + """ + Generate a subclass of baselexer that accepts the Objective-C syntax + extensions. + """ + + # Have to be careful not to accidentally match JavaDoc/Doxygen syntax here, + # since that's quite common in ordinary C/C++ files. It's OK to match + # JavaDoc/Doxygen keywords that only apply to Objective-C, mind. + # + # The upshot of this is that we CANNOT match @class or @interface + _oc_keywords = re.compile(r'@(?:end|implementation|protocol)') + + # Matches [ ? identifier ( identifier ? ] | identifier? : ) + # (note the identifier is *optional* when there is a ':'!) + _oc_message = re.compile(r'\[\s*[a-zA-Z_]\w*\s+' + r'(?:[a-zA-Z_]\w*\s*\]|' + r'(?:[a-zA-Z_]\w*)?:)') + + class GeneratedObjectiveCVariant(baselexer): + """ + Implements Objective-C syntax on top of an existing C family lexer. + """ + + tokens = { + 'statements': [ + (r'@"', String, 'string'), + (r'@(YES|NO)', Number), + (r"@'(\\.|\\[0-7]{1,3}|\\x[a-fA-F0-9]{1,2}|[^\\\'\n])'", String.Char), + (r'@(\d+\.\d*|\.\d+|\d+)[eE][+-]?\d+[lL]?', Number.Float), + (r'@(\d+\.\d*|\.\d+|\d+[fF])[fF]?', Number.Float), + (r'@0x[0-9a-fA-F]+[Ll]?', Number.Hex), + (r'@0[0-7]+[Ll]?', Number.Oct), + (r'@\d+[Ll]?', Number.Integer), + (r'@\(', Literal, 'literal_number'), + (r'@\[', Literal, 'literal_array'), + (r'@\{', Literal, 'literal_dictionary'), + (words(( + '@selector', '@private', '@protected', '@public', '@encode', + '@synchronized', '@try', '@throw', '@catch', '@finally', + '@end', '@property', '@synthesize', '__bridge', '__bridge_transfer', + '__autoreleasing', '__block', '__weak', '__strong', 'weak', 'strong', + 'copy', 'retain', 'assign', 'unsafe_unretained', 'atomic', 'nonatomic', + 'readonly', 'readwrite', 'setter', 'getter', 'typeof', 'in', + 'out', 'inout', 'release', 'class', '@dynamic', '@optional', + '@required', '@autoreleasepool', '@import'), suffix=r'\b'), + Keyword), + (words(('id', 'instancetype', 'Class', 'IMP', 'SEL', 'BOOL', + 'IBOutlet', 'IBAction', 'unichar'), suffix=r'\b'), + Keyword.Type), + (r'@(true|false|YES|NO)\n', Name.Builtin), + (r'(YES|NO|nil|self|super)\b', Name.Builtin), + # Carbon types + (r'(Boolean|UInt8|SInt8|UInt16|SInt16|UInt32|SInt32)\b', Keyword.Type), + # Carbon built-ins + (r'(TRUE|FALSE)\b', Name.Builtin), + (r'(@interface|@implementation)(\s+)', bygroups(Keyword, Text), + ('#pop', 'oc_classname')), + (r'(@class|@protocol)(\s+)', bygroups(Keyword, Text), + ('#pop', 'oc_forward_classname')), + # @ can also prefix other expressions like @{...} or @(...) + (r'@', Punctuation), + inherit, + ], + 'oc_classname': [ + # interface definition that inherits + (r'([a-zA-Z$_][\w$]*)(\s*:\s*)([a-zA-Z$_][\w$]*)?(\s*)(\{)', + bygroups(Name.Class, Text, Name.Class, Text, Punctuation), + ('#pop', 'oc_ivars')), + (r'([a-zA-Z$_][\w$]*)(\s*:\s*)([a-zA-Z$_][\w$]*)?', + bygroups(Name.Class, Text, Name.Class), '#pop'), + # interface definition for a category + (r'([a-zA-Z$_][\w$]*)(\s*)(\([a-zA-Z$_][\w$]*\))(\s*)(\{)', + bygroups(Name.Class, Text, Name.Label, Text, Punctuation), + ('#pop', 'oc_ivars')), + (r'([a-zA-Z$_][\w$]*)(\s*)(\([a-zA-Z$_][\w$]*\))', + bygroups(Name.Class, Text, Name.Label), '#pop'), + # simple interface / implementation + (r'([a-zA-Z$_][\w$]*)(\s*)(\{)', + bygroups(Name.Class, Text, Punctuation), ('#pop', 'oc_ivars')), + (r'([a-zA-Z$_][\w$]*)', Name.Class, '#pop') + ], + 'oc_forward_classname': [ + (r'([a-zA-Z$_][\w$]*)(\s*,\s*)', + bygroups(Name.Class, Text), 'oc_forward_classname'), + (r'([a-zA-Z$_][\w$]*)(\s*;?)', + bygroups(Name.Class, Text), '#pop') + ], + 'oc_ivars': [ + include('whitespace'), + include('statements'), + (';', Punctuation), + (r'\{', Punctuation, '#push'), + (r'\}', Punctuation, '#pop'), + ], + 'root': [ + # methods + (r'^([-+])(\s*)' # method marker + r'(\(.*?\))?(\s*)' # return type + r'([a-zA-Z$_][\w$]*:?)', # begin of method name + bygroups(Punctuation, Text, using(this), + Text, Name.Function), + 'method'), + inherit, + ], + 'method': [ + include('whitespace'), + # TODO unsure if ellipses are allowed elsewhere, see + # discussion in Issue 789 + (r',', Punctuation), + (r'\.\.\.', Punctuation), + (r'(\(.*?\))(\s*)([a-zA-Z$_][\w$]*)', + bygroups(using(this), Text, Name.Variable)), + (r'[a-zA-Z$_][\w$]*:', Name.Function), + (';', Punctuation, '#pop'), + (r'\{', Punctuation, 'function'), + default('#pop'), + ], + 'literal_number': [ + (r'\(', Punctuation, 'literal_number_inner'), + (r'\)', Literal, '#pop'), + include('statement'), + ], + 'literal_number_inner': [ + (r'\(', Punctuation, '#push'), + (r'\)', Punctuation, '#pop'), + include('statement'), + ], + 'literal_array': [ + (r'\[', Punctuation, 'literal_array_inner'), + (r'\]', Literal, '#pop'), + include('statement'), + ], + 'literal_array_inner': [ + (r'\[', Punctuation, '#push'), + (r'\]', Punctuation, '#pop'), + include('statement'), + ], + 'literal_dictionary': [ + (r'\}', Literal, '#pop'), + include('statement'), + ], + } + + def analyse_text(text): + if _oc_keywords.search(text): + return 1.0 + elif '@"' in text: # strings + return 0.8 + elif re.search('@[0-9]+', text): + return 0.7 + elif _oc_message.search(text): + return 0.8 + return 0 + + def get_tokens_unprocessed(self, text, stack=('root',)): + from pygments.lexers._cocoa_builtins import COCOA_INTERFACES, \ + COCOA_PROTOCOLS, COCOA_PRIMITIVES + + for index, token, value in \ + baselexer.get_tokens_unprocessed(self, text, stack): + if token is Name or token is Name.Class: + if value in COCOA_INTERFACES or value in COCOA_PROTOCOLS \ + or value in COCOA_PRIMITIVES: + token = Name.Builtin.Pseudo + + yield index, token, value + + return GeneratedObjectiveCVariant + + +class ObjectiveCLexer(objective(CLexer)): + """ + For Objective-C source code with preprocessor directives. + """ + + name = 'Objective-C' + url = 'https://developer.apple.com/library/archive/documentation/Cocoa/Conceptual/ProgrammingWithObjectiveC/Introduction/Introduction.html' + aliases = ['objective-c', 'objectivec', 'obj-c', 'objc'] + filenames = ['*.m', '*.h'] + mimetypes = ['text/x-objective-c'] + priority = 0.05 # Lower than C + + +class ObjectiveCppLexer(objective(CppLexer)): + """ + For Objective-C++ source code with preprocessor directives. + """ + + name = 'Objective-C++' + aliases = ['objective-c++', 'objectivec++', 'obj-c++', 'objc++'] + filenames = ['*.mm', '*.hh'] + mimetypes = ['text/x-objective-c++'] + priority = 0.05 # Lower than C++ + + +class LogosLexer(ObjectiveCppLexer): + """ + For Logos + Objective-C source code with preprocessor directives. + + .. versionadded:: 1.6 + """ + + name = 'Logos' + aliases = ['logos'] + filenames = ['*.x', '*.xi', '*.xm', '*.xmi'] + mimetypes = ['text/x-logos'] + priority = 0.25 + + tokens = { + 'statements': [ + (r'(%orig|%log)\b', Keyword), + (r'(%c)\b(\()(\s*)([a-zA-Z$_][\w$]*)(\s*)(\))', + bygroups(Keyword, Punctuation, Text, Name.Class, Text, Punctuation)), + (r'(%init)\b(\()', + bygroups(Keyword, Punctuation), 'logos_init_directive'), + (r'(%init)(?=\s*;)', bygroups(Keyword)), + (r'(%hook|%group)(\s+)([a-zA-Z$_][\w$]+)', + bygroups(Keyword, Text, Name.Class), '#pop'), + (r'(%subclass)(\s+)', bygroups(Keyword, Text), + ('#pop', 'logos_classname')), + inherit, + ], + 'logos_init_directive': [ + (r'\s+', Text), + (',', Punctuation, ('logos_init_directive', '#pop')), + (r'([a-zA-Z$_][\w$]*)(\s*)(=)(\s*)([^);]*)', + bygroups(Name.Class, Text, Punctuation, Text, Text)), + (r'([a-zA-Z$_][\w$]*)', Name.Class), + (r'\)', Punctuation, '#pop'), + ], + 'logos_classname': [ + (r'([a-zA-Z$_][\w$]*)(\s*:\s*)([a-zA-Z$_][\w$]*)?', + bygroups(Name.Class, Text, Name.Class), '#pop'), + (r'([a-zA-Z$_][\w$]*)', Name.Class, '#pop') + ], + 'root': [ + (r'(%subclass)(\s+)', bygroups(Keyword, Text), + 'logos_classname'), + (r'(%hook|%group)(\s+)([a-zA-Z$_][\w$]+)', + bygroups(Keyword, Text, Name.Class)), + (r'(%config)(\s*\(\s*)(\w+)(\s*=)(.*?)(\)\s*)', + bygroups(Keyword, Text, Name.Variable, Text, String, Text)), + (r'(%ctor)(\s*)(\{)', bygroups(Keyword, Text, Punctuation), + 'function'), + (r'(%new)(\s*)(\()(.*?)(\))', + bygroups(Keyword, Text, Keyword, String, Keyword)), + (r'(\s*)(%end)(\s*)', bygroups(Text, Keyword, Text)), + inherit, + ], + } + + _logos_keywords = re.compile(r'%(?:hook|ctor|init|c\()') + + def analyse_text(text): + if LogosLexer._logos_keywords.search(text): + return 1.0 + return 0 + + +class SwiftLexer(RegexLexer): + """ + For Swift source. + + .. versionadded:: 2.0 + """ + name = 'Swift' + url = 'https://www.swift.org/' + filenames = ['*.swift'] + aliases = ['swift'] + mimetypes = ['text/x-swift'] + + tokens = { + 'root': [ + # Whitespace and Comments + (r'\n', Text), + (r'\s+', Text), + (r'//', Comment.Single, 'comment-single'), + (r'/\*', Comment.Multiline, 'comment-multi'), + (r'#(if|elseif|else|endif|available)\b', Comment.Preproc, 'preproc'), + + # Keywords + include('keywords'), + + # Global Types + (words(( + 'Array', 'AutoreleasingUnsafeMutablePointer', 'BidirectionalReverseView', + 'Bit', 'Bool', 'CFunctionPointer', 'COpaquePointer', 'CVaListPointer', + 'Character', 'ClosedInterval', 'CollectionOfOne', 'ContiguousArray', + 'Dictionary', 'DictionaryGenerator', 'DictionaryIndex', 'Double', + 'EmptyCollection', 'EmptyGenerator', 'EnumerateGenerator', + 'EnumerateSequence', 'FilterCollectionView', + 'FilterCollectionViewIndex', 'FilterGenerator', 'FilterSequenceView', + 'Float', 'Float80', 'FloatingPointClassification', 'GeneratorOf', + 'GeneratorOfOne', 'GeneratorSequence', 'HalfOpenInterval', 'HeapBuffer', + 'HeapBufferStorage', 'ImplicitlyUnwrappedOptional', 'IndexingGenerator', + 'Int', 'Int16', 'Int32', 'Int64', 'Int8', 'LazyBidirectionalCollection', + 'LazyForwardCollection', 'LazyRandomAccessCollection', + 'LazySequence', 'MapCollectionView', 'MapSequenceGenerator', + 'MapSequenceView', 'MirrorDisposition', 'ObjectIdentifier', 'OnHeap', + 'Optional', 'PermutationGenerator', 'QuickLookObject', + 'RandomAccessReverseView', 'Range', 'RangeGenerator', 'RawByte', 'Repeat', + 'ReverseBidirectionalIndex', 'ReverseRandomAccessIndex', 'SequenceOf', + 'SinkOf', 'Slice', 'StaticString', 'StrideThrough', 'StrideThroughGenerator', + 'StrideTo', 'StrideToGenerator', 'String', 'UInt', 'UInt16', 'UInt32', + 'UInt64', 'UInt8', 'UTF16', 'UTF32', 'UTF8', 'UnicodeDecodingResult', + 'UnicodeScalar', 'Unmanaged', 'UnsafeBufferPointer', + 'UnsafeBufferPointerGenerator', 'UnsafeMutableBufferPointer', + 'UnsafeMutablePointer', 'UnsafePointer', 'Zip2', 'ZipGenerator2', + # Protocols + 'AbsoluteValuable', 'AnyObject', 'ArrayLiteralConvertible', + 'BidirectionalIndexType', 'BitwiseOperationsType', + 'BooleanLiteralConvertible', 'BooleanType', 'CVarArgType', + 'CollectionType', 'Comparable', 'DebugPrintable', + 'DictionaryLiteralConvertible', 'Equatable', + 'ExtendedGraphemeClusterLiteralConvertible', + 'ExtensibleCollectionType', 'FloatLiteralConvertible', + 'FloatingPointType', 'ForwardIndexType', 'GeneratorType', 'Hashable', + 'IntegerArithmeticType', 'IntegerLiteralConvertible', 'IntegerType', + 'IntervalType', 'MirrorType', 'MutableCollectionType', 'MutableSliceable', + 'NilLiteralConvertible', 'OutputStreamType', 'Printable', + 'RandomAccessIndexType', 'RangeReplaceableCollectionType', + 'RawOptionSetType', 'RawRepresentable', 'Reflectable', 'SequenceType', + 'SignedIntegerType', 'SignedNumberType', 'SinkType', 'Sliceable', + 'Streamable', 'Strideable', 'StringInterpolationConvertible', + 'StringLiteralConvertible', 'UnicodeCodecType', + 'UnicodeScalarLiteralConvertible', 'UnsignedIntegerType', + '_ArrayBufferType', '_BidirectionalIndexType', '_CocoaStringType', + '_CollectionType', '_Comparable', '_ExtensibleCollectionType', + '_ForwardIndexType', '_Incrementable', '_IntegerArithmeticType', + '_IntegerType', '_ObjectiveCBridgeable', '_RandomAccessIndexType', + '_RawOptionSetType', '_SequenceType', '_Sequence_Type', + '_SignedIntegerType', '_SignedNumberType', '_Sliceable', '_Strideable', + '_SwiftNSArrayRequiredOverridesType', '_SwiftNSArrayType', + '_SwiftNSCopyingType', '_SwiftNSDictionaryRequiredOverridesType', + '_SwiftNSDictionaryType', '_SwiftNSEnumeratorType', + '_SwiftNSFastEnumerationType', '_SwiftNSStringRequiredOverridesType', + '_SwiftNSStringType', '_UnsignedIntegerType', + # Variables + 'C_ARGC', 'C_ARGV', 'Process', + # Typealiases + 'Any', 'AnyClass', 'BooleanLiteralType', 'CBool', 'CChar', 'CChar16', + 'CChar32', 'CDouble', 'CFloat', 'CInt', 'CLong', 'CLongLong', 'CShort', + 'CSignedChar', 'CUnsignedInt', 'CUnsignedLong', 'CUnsignedShort', + 'CWideChar', 'ExtendedGraphemeClusterType', 'Float32', 'Float64', + 'FloatLiteralType', 'IntMax', 'IntegerLiteralType', 'StringLiteralType', + 'UIntMax', 'UWord', 'UnicodeScalarType', 'Void', 'Word', + # Foundation/Cocoa + 'NSErrorPointer', 'NSObjectProtocol', 'Selector'), suffix=r'\b'), + Name.Builtin), + # Functions + (words(( + 'abs', 'advance', 'alignof', 'alignofValue', 'assert', 'assertionFailure', + 'contains', 'count', 'countElements', 'debugPrint', 'debugPrintln', + 'distance', 'dropFirst', 'dropLast', 'dump', 'enumerate', 'equal', + 'extend', 'fatalError', 'filter', 'find', 'first', 'getVaList', 'indices', + 'insert', 'isEmpty', 'join', 'last', 'lazy', 'lexicographicalCompare', + 'map', 'max', 'maxElement', 'min', 'minElement', 'numericCast', 'overlaps', + 'partition', 'precondition', 'preconditionFailure', 'prefix', 'print', + 'println', 'reduce', 'reflect', 'removeAll', 'removeAtIndex', 'removeLast', + 'removeRange', 'reverse', 'sizeof', 'sizeofValue', 'sort', 'sorted', + 'splice', 'split', 'startsWith', 'stride', 'strideof', 'strideofValue', + 'suffix', 'swap', 'toDebugString', 'toString', 'transcode', + 'underestimateCount', 'unsafeAddressOf', 'unsafeBitCast', 'unsafeDowncast', + 'withExtendedLifetime', 'withUnsafeMutablePointer', + 'withUnsafeMutablePointers', 'withUnsafePointer', 'withUnsafePointers', + 'withVaList'), suffix=r'\b'), + Name.Builtin.Pseudo), + + # Implicit Block Variables + (r'\$\d+', Name.Variable), + + # Binary Literal + (r'0b[01_]+', Number.Bin), + # Octal Literal + (r'0o[0-7_]+', Number.Oct), + # Hexadecimal Literal + (r'0x[0-9a-fA-F_]+', Number.Hex), + # Decimal Literal + (r'[0-9][0-9_]*(\.[0-9_]+[eE][+\-]?[0-9_]+|' + r'\.[0-9_]*|[eE][+\-]?[0-9_]+)', Number.Float), + (r'[0-9][0-9_]*', Number.Integer), + # String Literal + (r'"', String, 'string'), + + # Operators and Punctuation + (r'[(){}\[\].,:;=@#`?]|->|[<&?](?=\w)|(?<=\w)[>!?]', Punctuation), + (r'[/=\-+!*%<>&|^?~]+', Operator), + + # Identifier + (r'[a-zA-Z_]\w*', Name) + ], + 'keywords': [ + (words(( + 'as', 'async', 'await', 'break', 'case', 'catch', 'continue', 'default', 'defer', + 'do', 'else', 'fallthrough', 'for', 'guard', 'if', 'in', 'is', + 'repeat', 'return', '#selector', 'switch', 'throw', 'try', + 'where', 'while'), suffix=r'\b'), + Keyword), + (r'@availability\([^)]+\)', Keyword.Reserved), + (words(( + 'associativity', 'convenience', 'dynamic', 'didSet', 'final', + 'get', 'indirect', 'infix', 'inout', 'lazy', 'left', 'mutating', + 'none', 'nonmutating', 'optional', 'override', 'postfix', + 'precedence', 'prefix', 'Protocol', 'required', 'rethrows', + 'right', 'set', 'throws', 'Type', 'unowned', 'weak', 'willSet', + '@availability', '@autoclosure', '@noreturn', + '@NSApplicationMain', '@NSCopying', '@NSManaged', '@objc', + '@UIApplicationMain', '@IBAction', '@IBDesignable', + '@IBInspectable', '@IBOutlet'), suffix=r'\b'), + Keyword.Reserved), + (r'(as|dynamicType|false|is|nil|self|Self|super|true|__COLUMN__' + r'|__FILE__|__FUNCTION__|__LINE__|_' + r'|#(?:file|line|column|function))\b', Keyword.Constant), + (r'import\b', Keyword.Declaration, 'module'), + (r'(class|enum|extension|struct|protocol)(\s+)([a-zA-Z_]\w*)', + bygroups(Keyword.Declaration, Text, Name.Class)), + (r'(func)(\s+)([a-zA-Z_]\w*)', + bygroups(Keyword.Declaration, Text, Name.Function)), + (r'(var|let)(\s+)([a-zA-Z_]\w*)', bygroups(Keyword.Declaration, + Text, Name.Variable)), + (words(( + 'actor', 'associatedtype', 'class', 'deinit', 'enum', 'extension', 'func', 'import', + 'init', 'internal', 'let', 'operator', 'private', 'protocol', 'public', + 'static', 'struct', 'subscript', 'typealias', 'var'), suffix=r'\b'), + Keyword.Declaration) + ], + 'comment': [ + (r':param: [a-zA-Z_]\w*|:returns?:|(FIXME|MARK|TODO):', + Comment.Special) + ], + + # Nested + 'comment-single': [ + (r'\n', Text, '#pop'), + include('comment'), + (r'[^\n]', Comment.Single) + ], + 'comment-multi': [ + include('comment'), + (r'[^*/]', Comment.Multiline), + (r'/\*', Comment.Multiline, '#push'), + (r'\*/', Comment.Multiline, '#pop'), + (r'[*/]', Comment.Multiline) + ], + 'module': [ + (r'\n', Text, '#pop'), + (r'[a-zA-Z_]\w*', Name.Class), + include('root') + ], + 'preproc': [ + (r'\n', Text, '#pop'), + include('keywords'), + (r'[A-Za-z]\w*', Comment.Preproc), + include('root') + ], + 'string': [ + (r'\\\(', String.Interpol, 'string-intp'), + (r'"', String, '#pop'), + (r"""\\['"\\nrt]|\\x[0-9a-fA-F]{2}|\\[0-7]{1,3}""" + r"""|\\u[0-9a-fA-F]{4}|\\U[0-9a-fA-F]{8}""", String.Escape), + (r'[^\\"]+', String), + (r'\\', String) + ], + 'string-intp': [ + (r'\(', String.Interpol, '#push'), + (r'\)', String.Interpol, '#pop'), + include('root') + ] + } + + def get_tokens_unprocessed(self, text): + from pygments.lexers._cocoa_builtins import COCOA_INTERFACES, \ + COCOA_PROTOCOLS, COCOA_PRIMITIVES + + for index, token, value in \ + RegexLexer.get_tokens_unprocessed(self, text): + if token is Name or token is Name.Class: + if value in COCOA_INTERFACES or value in COCOA_PROTOCOLS \ + or value in COCOA_PRIMITIVES: + token = Name.Builtin.Pseudo + + yield index, token, value diff --git a/shared/package-files/56/5687b07e11ecf0c194a47ffec082d05889f5fc1baa4c5cdc7bf75e67a329ebfe/blob b/shared/package-files/56/5687b07e11ecf0c194a47ffec082d05889f5fc1baa4c5cdc7bf75e67a329ebfe/blob new file mode 100644 index 0000000000000000000000000000000000000000..bd74aa318198233e38437970067b24eaeafa2b34 --- /dev/null +++ b/shared/package-files/56/5687b07e11ecf0c194a47ffec082d05889f5fc1baa4c5cdc7bf75e67a329ebfe/blob @@ -0,0 +1,44 @@ +#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::exp2(Tensor self) -> Tensor +inline at::Tensor exp2(const at::Tensor & self) { + return at::_ops::exp2::call(self); +} + +// aten::exp2_(Tensor(a!) self) -> Tensor(a!) +inline at::Tensor & exp2_(at::Tensor & self) { + return at::_ops::exp2_::call(self); +} + +// aten::exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & exp2_out(at::Tensor & out, const at::Tensor & self) { + return at::_ops::exp2_out::call(self, out); +} +// aten::exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & exp2_outf(const at::Tensor & self, at::Tensor & out) { + return at::_ops::exp2_out::call(self, out); +} + +} diff --git a/shared/package-files/56/568c40fb25e8c3f0918a4275b659e2d4fb0000e4f50f65fc3ed4d9c893864175/blob b/shared/package-files/56/568c40fb25e8c3f0918a4275b659e2d4fb0000e4f50f65fc3ed4d9c893864175/blob new file mode 100644 index 0000000000000000000000000000000000000000..69f394afb65191fe4cc52519fbc52959d2e1dd76 --- /dev/null +++ b/shared/package-files/56/568c40fb25e8c3f0918a4275b659e2d4fb0000e4f50f65fc3ed4d9c893864175/blob @@ -0,0 +1,50 @@ +import pytest + +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +class TestDataFrameSetItem: + def test_isetitem_ea_df(self): + # GH#49922 + df = DataFrame([[1, 2, 3], [4, 5, 6]]) + rhs = DataFrame([[11, 12], [13, 14]], dtype="Int64") + + df.isetitem([0, 1], rhs) + expected = DataFrame( + { + 0: Series([11, 13], dtype="Int64"), + 1: Series([12, 14], dtype="Int64"), + 2: [3, 6], + } + ) + tm.assert_frame_equal(df, expected) + + def test_isetitem_ea_df_scalar_indexer(self): + # GH#49922 + df = DataFrame([[1, 2, 3], [4, 5, 6]]) + rhs = DataFrame([[11], [13]], dtype="Int64") + + df.isetitem(2, rhs) + expected = DataFrame( + { + 0: [1, 4], + 1: [2, 5], + 2: Series([11, 13], dtype="Int64"), + } + ) + tm.assert_frame_equal(df, expected) + + def test_isetitem_dimension_mismatch(self): + # GH#51701 + df = DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]}) + value = df.copy() + with pytest.raises(ValueError, match="Got 2 positions but value has 3 columns"): + df.isetitem([1, 2], value) + + value = df.copy() + with pytest.raises(ValueError, match="Got 2 positions but value has 1 columns"): + df.isetitem([1, 2], value[["a"]]) diff --git a/shared/package-files/56/568ea0601a40247033b65abc9b7807fbd1aa29493148e1e0d9f93a700a478051/blob b/shared/package-files/56/568ea0601a40247033b65abc9b7807fbd1aa29493148e1e0d9f93a700a478051/blob new file mode 100644 index 0000000000000000000000000000000000000000..2789d51ec2ce3096c64b41af90b5a416ceef9f5b --- /dev/null +++ b/shared/package-files/56/568ea0601a40247033b65abc9b7807fbd1aa29493148e1e0d9f93a700a478051/blob @@ -0,0 +1,89 @@ +""" +Test extension array that has custom attribute information (not stored on the dtype). + +""" +from __future__ import annotations + +import numbers +from typing import TYPE_CHECKING + +import numpy as np + +from pandas.core.dtypes.base import ExtensionDtype + +import pandas as pd +from pandas.core.arrays import ExtensionArray + +if TYPE_CHECKING: + from pandas._typing import type_t + + +class FloatAttrDtype(ExtensionDtype): + type = float + name = "float_attr" + na_value = np.nan + + @classmethod + def construct_array_type(cls) -> type_t[FloatAttrArray]: + """ + Return the array type associated with this dtype. + + Returns + ------- + type + """ + return FloatAttrArray + + +class FloatAttrArray(ExtensionArray): + dtype = FloatAttrDtype() + __array_priority__ = 1000 + + def __init__(self, values, attr=None) -> None: + if not isinstance(values, np.ndarray): + raise TypeError("Need to pass a numpy array of float64 dtype as values") + if not values.dtype == "float64": + raise TypeError("Need to pass a numpy array of float64 dtype as values") + self.data = values + self.attr = attr + + @classmethod + def _from_sequence(cls, scalars, *, dtype=None, copy=False): + if not copy: + data = np.asarray(scalars, dtype="float64") + else: + data = np.array(scalars, dtype="float64", copy=copy) + return cls(data) + + def __getitem__(self, item): + if isinstance(item, numbers.Integral): + return self.data[item] + else: + # slice, list-like, mask + item = pd.api.indexers.check_array_indexer(self, item) + return type(self)(self.data[item], self.attr) + + def __len__(self) -> int: + return len(self.data) + + def isna(self): + return np.isnan(self.data) + + def take(self, indexer, allow_fill=False, fill_value=None): + from pandas.api.extensions import take + + data = self.data + if allow_fill and fill_value is None: + fill_value = self.dtype.na_value + + result = take(data, indexer, fill_value=fill_value, allow_fill=allow_fill) + return type(self)(result, self.attr) + + def copy(self): + return type(self)(self.data.copy(), self.attr) + + @classmethod + def _concat_same_type(cls, to_concat): + data = np.concatenate([x.data for x in to_concat]) + attr = to_concat[0].attr if len(to_concat) else None + return cls(data, attr) diff --git a/shared/package-files/56/568eb9c241a993e5474e047261d55be46eb53026d79850cdb6fd97059d5fb090/blob b/shared/package-files/56/568eb9c241a993e5474e047261d55be46eb53026d79850cdb6fd97059d5fb090/blob new file mode 100644 index 0000000000000000000000000000000000000000..2dae661c9692f12b1cc4759e7dd9b1c681ff763b --- /dev/null +++ b/shared/package-files/56/568eb9c241a993e5474e047261d55be46eb53026d79850cdb6fd97059d5fb090/blob @@ -0,0 +1,73 @@ +"""El Nino dataset, 1950 - 2010""" +from statsmodels.datasets import utils as du + +__docformat__ = "restructuredtext" + +COPYRIGHT = """This data is in the public domain.""" + +TITLE = """El Nino - Sea Surface Temperatures""" + +SOURCE = """ +National Oceanic and Atmospheric Administration's National Weather Service + +ERSST.V3B dataset, Nino 1+2 +http://www.cpc.ncep.noaa.gov/data/indices/ +""" + +DESCRSHORT = """Averaged monthly sea surface temperature - Pacific Ocean.""" + +DESCRLONG = """This data contains the averaged monthly sea surface +temperature in degrees Celsius of the Pacific Ocean, between 0-10 degrees South +and 90-80 degrees West, from 1950 to 2010. This dataset was obtained from +NOAA. +""" + +NOTE = """:: + + Number of Observations - 61 x 12 + + Number of Variables - 1 + + Variable name definitions:: + + TEMPERATURE - average sea surface temperature in degrees Celsius + (12 columns, one per month). +""" + + +def load_pandas(): + """ + Load the El Nino data and return a Dataset class. + + Returns + ------- + Dataset + A `Dataset` instance with ``data`` and ``names`` attributes. + + Notes + ----- + The elnino Dataset instance does not contain endog and exog attributes. + """ + data = _get_data() + dataset = du.Dataset(data=data, names=list(data.columns)) + return dataset + + +def load(): + """ + Load the El Nino data and return a Dataset class. + + Returns + ------- + Dataset + A `Dataset` instance with ``data`` and ``names`` attributes. + + Notes + ----- + The elnino Dataset instance does not contain endog and exog attributes. + """ + return load_pandas() + + +def _get_data(): + return du.load_csv(__file__, "elnino.csv", convert_float=True) diff --git a/shared/package-files/56/568f09a6d16bf8a8367f6704a0383c2ed10b4a354dc9c71d258ef858ffe4aad7/blob b/shared/package-files/56/568f09a6d16bf8a8367f6704a0383c2ed10b4a354dc9c71d258ef858ffe4aad7/blob new file mode 100644 index 0000000000000000000000000000000000000000..ccfdaa8adb7879ba852eab9ce55b602e11dad06d Binary files /dev/null and b/shared/package-files/56/568f09a6d16bf8a8367f6704a0383c2ed10b4a354dc9c71d258ef858ffe4aad7/blob differ diff --git a/shared/package-files/56/568f46e6065a1934d0c253616c8f19240eed5dee8949878d0a42938221bc73f9/blob b/shared/package-files/56/568f46e6065a1934d0c253616c8f19240eed5dee8949878d0a42938221bc73f9/blob new file mode 100644 index 0000000000000000000000000000000000000000..28d713fbb4464f75f4bf98f23d581f373069a70b Binary files /dev/null and b/shared/package-files/56/568f46e6065a1934d0c253616c8f19240eed5dee8949878d0a42938221bc73f9/blob differ diff --git a/shared/package-files/56/5690ec210c05373f68d3870fb53676a4fa7b2db4c881b43fae6c60572e0a41d3/blob b/shared/package-files/56/5690ec210c05373f68d3870fb53676a4fa7b2db4c881b43fae6c60572e0a41d3/blob new file mode 100644 index 0000000000000000000000000000000000000000..446a4de530b1c65fabcc5f03c88dcbbbfbcfb387 --- /dev/null +++ b/shared/package-files/56/5690ec210c05373f68d3870fb53676a4fa7b2db4c881b43fae6c60572e0a41d3/blob @@ -0,0 +1,28 @@ +#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 at::Tensor special_chebyshev_polynomial_t(const at::Scalar & x, const at::Tensor & n); +TORCH_API at::Tensor & special_chebyshev_polynomial_t_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n); +TORCH_API at::Tensor & special_chebyshev_polynomial_t_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); +TORCH_API at::Tensor special_chebyshev_polynomial_t(const at::Tensor & x, const at::Scalar & n); +TORCH_API at::Tensor & special_chebyshev_polynomial_t_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n); +TORCH_API at::Tensor & special_chebyshev_polynomial_t_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/shared/package-files/56/56914a1d79fcbab672f1fd6194f663dc4bde2a5b0a9ea51b070a09d16dc09122/blob b/shared/package-files/56/56914a1d79fcbab672f1fd6194f663dc4bde2a5b0a9ea51b070a09d16dc09122/blob new file mode 100644 index 0000000000000000000000000000000000000000..795a1faf2c36dac0d952e4e58d24e91f32fee6b0 Binary files /dev/null and b/shared/package-files/56/56914a1d79fcbab672f1fd6194f663dc4bde2a5b0a9ea51b070a09d16dc09122/blob differ diff --git a/shared/package-files/56/5691b2820b0c6ea77608bd934525f5228ce9d46e153083cd4bb14c304fa13c0c/blob b/shared/package-files/56/5691b2820b0c6ea77608bd934525f5228ce9d46e153083cd4bb14c304fa13c0c/blob new file mode 100644 index 0000000000000000000000000000000000000000..e84a694df754f33419212ad23c94aaf84bfc1e5a --- /dev/null +++ b/shared/package-files/56/5691b2820b0c6ea77608bd934525f5228ce9d46e153083cd4bb14c304fa13c0c/blob @@ -0,0 +1,3172 @@ +from collections import Counter, defaultdict, OrderedDict +from itertools import ( + chain, combinations, combinations_with_replacement, cycle, islice, + permutations, product, groupby +) +# For backwards compatibility +from itertools import product as cartes # noqa: F401 +from operator import gt + + + +# this is the logical location of these functions +from sympy.utilities.enumerative import ( + multiset_partitions_taocp, list_visitor, MultisetPartitionTraverser) + +from sympy.utilities.misc import as_int +from sympy.utilities.decorator import deprecated + + +def is_palindromic(s, i=0, j=None): + """ + Return True if the sequence is the same from left to right as it + is from right to left in the whole sequence (default) or in the + Python slice ``s[i: j]``; else False. + + Examples + ======== + + >>> from sympy.utilities.iterables import is_palindromic + >>> is_palindromic([1, 0, 1]) + True + >>> is_palindromic('abcbb') + False + >>> is_palindromic('abcbb', 1) + False + + Normal Python slicing is performed in place so there is no need to + create a slice of the sequence for testing: + + >>> is_palindromic('abcbb', 1, -1) + True + >>> is_palindromic('abcbb', -4, -1) + True + + See Also + ======== + + sympy.ntheory.digits.is_palindromic: tests integers + + """ + i, j, _ = slice(i, j).indices(len(s)) + m = (j - i)//2 + # if length is odd, middle element will be ignored + return all(s[i + k] == s[j - 1 - k] for k in range(m)) + + +def flatten(iterable, levels=None, cls=None): # noqa: F811 + """ + Recursively denest iterable containers. + + >>> from sympy import flatten + + >>> flatten([1, 2, 3]) + [1, 2, 3] + >>> flatten([1, 2, [3]]) + [1, 2, 3] + >>> flatten([1, [2, 3], [4, 5]]) + [1, 2, 3, 4, 5] + >>> flatten([1.0, 2, (1, None)]) + [1.0, 2, 1, None] + + If you want to denest only a specified number of levels of + nested containers, then set ``levels`` flag to the desired + number of levels:: + + >>> ls = [[(-2, -1), (1, 2)], [(0, 0)]] + + >>> flatten(ls, levels=1) + [(-2, -1), (1, 2), (0, 0)] + + If cls argument is specified, it will only flatten instances of that + class, for example: + + >>> from sympy import Basic, S + >>> class MyOp(Basic): + ... pass + ... + >>> flatten([MyOp(S(1), MyOp(S(2), S(3)))], cls=MyOp) + [1, 2, 3] + + adapted from https://kogs-www.informatik.uni-hamburg.de/~meine/python_tricks + """ + from sympy.tensor.array import NDimArray + if levels is not None: + if not levels: + return iterable + elif levels > 0: + levels -= 1 + else: + raise ValueError( + "expected non-negative number of levels, got %s" % levels) + + if cls is None: + def reducible(x): + return is_sequence(x, set) + else: + def reducible(x): + return isinstance(x, cls) + + result = [] + + for el in iterable: + if reducible(el): + if hasattr(el, 'args') and not isinstance(el, NDimArray): + el = el.args + result.extend(flatten(el, levels=levels, cls=cls)) + else: + result.append(el) + + return result + + +def unflatten(iter, n=2): + """Group ``iter`` into tuples of length ``n``. Raise an error if + the length of ``iter`` is not a multiple of ``n``. + """ + if n < 1 or len(iter) % n: + raise ValueError('iter length is not a multiple of %i' % n) + return list(zip(*(iter[i::n] for i in range(n)))) + + +def reshape(seq, how): + """Reshape the sequence according to the template in ``how``. + + Examples + ======== + + >>> from sympy.utilities import reshape + >>> seq = list(range(1, 9)) + + >>> reshape(seq, [4]) # lists of 4 + [[1, 2, 3, 4], [5, 6, 7, 8]] + + >>> reshape(seq, (4,)) # tuples of 4 + [(1, 2, 3, 4), (5, 6, 7, 8)] + + >>> reshape(seq, (2, 2)) # tuples of 4 + [(1, 2, 3, 4), (5, 6, 7, 8)] + + >>> reshape(seq, (2, [2])) # (i, i, [i, i]) + [(1, 2, [3, 4]), (5, 6, [7, 8])] + + >>> reshape(seq, ((2,), [2])) # etc.... + [((1, 2), [3, 4]), ((5, 6), [7, 8])] + + >>> reshape(seq, (1, [2], 1)) + [(1, [2, 3], 4), (5, [6, 7], 8)] + + >>> reshape(tuple(seq), ([[1], 1, (2,)],)) + (([[1], 2, (3, 4)],), ([[5], 6, (7, 8)],)) + + >>> reshape(tuple(seq), ([1], 1, (2,))) + (([1], 2, (3, 4)), ([5], 6, (7, 8))) + + >>> reshape(list(range(12)), [2, [3], {2}, (1, (3,), 1)]) + [[0, 1, [2, 3, 4], {5, 6}, (7, (8, 9, 10), 11)]] + + """ + m = sum(flatten(how)) + n, rem = divmod(len(seq), m) + if m < 0 or rem: + raise ValueError('template must sum to positive number ' + 'that divides the length of the sequence') + i = 0 + container = type(how) + rv = [None]*n + for k in range(len(rv)): + _rv = [] + for hi in how: + if isinstance(hi, int): + _rv.extend(seq[i: i + hi]) + i += hi + else: + n = sum(flatten(hi)) + hi_type = type(hi) + _rv.append(hi_type(reshape(seq[i: i + n], hi)[0])) + i += n + rv[k] = container(_rv) + return type(seq)(rv) + + +def group(seq, multiple=True): + """ + Splits a sequence into a list of lists of equal, adjacent elements. + + Examples + ======== + + >>> from sympy import group + + >>> group([1, 1, 1, 2, 2, 3]) + [[1, 1, 1], [2, 2], [3]] + >>> group([1, 1, 1, 2, 2, 3], multiple=False) + [(1, 3), (2, 2), (3, 1)] + >>> group([1, 1, 3, 2, 2, 1], multiple=False) + [(1, 2), (3, 1), (2, 2), (1, 1)] + + See Also + ======== + + multiset + + """ + if multiple: + return [(list(g)) for _, g in groupby(seq)] + return [(k, len(list(g))) for k, g in groupby(seq)] + + +def _iproduct2(iterable1, iterable2): + '''Cartesian product of two possibly infinite iterables''' + + it1 = iter(iterable1) + it2 = iter(iterable2) + + elems1 = [] + elems2 = [] + + sentinel = object() + def append(it, elems): + e = next(it, sentinel) + if e is not sentinel: + elems.append(e) + + n = 0 + append(it1, elems1) + append(it2, elems2) + + while n <= len(elems1) + len(elems2): + for m in range(n-len(elems1)+1, len(elems2)): + yield (elems1[n-m], elems2[m]) + n += 1 + append(it1, elems1) + append(it2, elems2) + + +def iproduct(*iterables): + ''' + Cartesian product of iterables. + + Generator of the Cartesian product of iterables. This is analogous to + itertools.product except that it works with infinite iterables and will + yield any item from the infinite product eventually. + + Examples + ======== + + >>> from sympy.utilities.iterables import iproduct + >>> sorted(iproduct([1,2], [3,4])) + [(1, 3), (1, 4), (2, 3), (2, 4)] + + With an infinite iterator: + + >>> from sympy import S + >>> (3,) in iproduct(S.Integers) + True + >>> (3, 4) in iproduct(S.Integers, S.Integers) + True + + .. seealso:: + + `itertools.product + `_ + ''' + if len(iterables) == 0: + yield () + return + elif len(iterables) == 1: + for e in iterables[0]: + yield (e,) + elif len(iterables) == 2: + yield from _iproduct2(*iterables) + else: + first, others = iterables[0], iterables[1:] + for ef, eo in _iproduct2(first, iproduct(*others)): + yield (ef,) + eo + + +def multiset(seq): + """Return the hashable sequence in multiset form with values being the + multiplicity of the item in the sequence. + + Examples + ======== + + >>> from sympy.utilities.iterables import multiset + >>> multiset('mississippi') + {'i': 4, 'm': 1, 'p': 2, 's': 4} + + See Also + ======== + + group + + """ + return dict(Counter(seq).items()) + + + + +def ibin(n, bits=None, str=False): + """Return a list of length ``bits`` corresponding to the binary value + of ``n`` with small bits to the right (last). If bits is omitted, the + length will be the number required to represent ``n``. If the bits are + desired in reversed order, use the ``[::-1]`` slice of the returned list. + + If a sequence of all bits-length lists starting from ``[0, 0,..., 0]`` + through ``[1, 1, ..., 1]`` are desired, pass a non-integer for bits, e.g. + ``'all'``. + + If the bit *string* is desired pass ``str=True``. + + Examples + ======== + + >>> from sympy.utilities.iterables import ibin + >>> ibin(2) + [1, 0] + >>> ibin(2, 4) + [0, 0, 1, 0] + + If all lists corresponding to 0 to 2**n - 1, pass a non-integer + for bits: + + >>> bits = 2 + >>> for i in ibin(2, 'all'): + ... print(i) + (0, 0) + (0, 1) + (1, 0) + (1, 1) + + If a bit string is desired of a given length, use str=True: + + >>> n = 123 + >>> bits = 10 + >>> ibin(n, bits, str=True) + '0001111011' + >>> ibin(n, bits, str=True)[::-1] # small bits left + '1101111000' + >>> list(ibin(3, 'all', str=True)) + ['000', '001', '010', '011', '100', '101', '110', '111'] + + """ + if n < 0: + raise ValueError("negative numbers are not allowed") + n = as_int(n) + + if bits is None: + bits = 0 + else: + try: + bits = as_int(bits) + except ValueError: + bits = -1 + else: + if n.bit_length() > bits: + raise ValueError( + "`bits` must be >= {}".format(n.bit_length())) + + if not str: + if bits >= 0: + return [1 if i == "1" else 0 for i in bin(n)[2:].rjust(bits, "0")] + else: + return variations(range(2), n, repetition=True) + else: + if bits >= 0: + return bin(n)[2:].rjust(bits, "0") + else: + return (bin(i)[2:].rjust(n, "0") for i in range(2**n)) + + +def variations(seq, n, repetition=False): + r"""Returns an iterator over the n-sized variations of ``seq`` (size N). + ``repetition`` controls whether items in ``seq`` can appear more than once; + + Examples + ======== + + ``variations(seq, n)`` will return `\frac{N!}{(N - n)!}` permutations without + repetition of ``seq``'s elements: + + >>> from sympy import variations + >>> list(variations([1, 2], 2)) + [(1, 2), (2, 1)] + + ``variations(seq, n, True)`` will return the `N^n` permutations obtained + by allowing repetition of elements: + + >>> list(variations([1, 2], 2, repetition=True)) + [(1, 1), (1, 2), (2, 1), (2, 2)] + + If you ask for more items than are in the set you get the empty set unless + you allow repetitions: + + >>> list(variations([0, 1], 3, repetition=False)) + [] + >>> list(variations([0, 1], 3, repetition=True))[:4] + [(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1)] + + .. seealso:: + + `itertools.permutations + `_, + `itertools.product + `_ + """ + if not repetition: + seq = tuple(seq) + if len(seq) < n: + return iter(()) # 0 length iterator + return permutations(seq, n) + else: + if n == 0: + return iter(((),)) # yields 1 empty tuple + else: + return product(seq, repeat=n) + + +def subsets(seq, k=None, repetition=False): + r"""Generates all `k`-subsets (combinations) from an `n`-element set, ``seq``. + + A `k`-subset of an `n`-element set is any subset of length exactly `k`. The + number of `k`-subsets of an `n`-element set is given by ``binomial(n, k)``, + whereas there are `2^n` subsets all together. If `k` is ``None`` then all + `2^n` subsets will be returned from shortest to longest. + + Examples + ======== + + >>> from sympy import subsets + + ``subsets(seq, k)`` will return the + `\frac{n!}{k!(n - k)!}` `k`-subsets (combinations) + without repetition, i.e. once an item has been removed, it can no + longer be "taken": + + >>> list(subsets([1, 2], 2)) + [(1, 2)] + >>> list(subsets([1, 2])) + [(), (1,), (2,), (1, 2)] + >>> list(subsets([1, 2, 3], 2)) + [(1, 2), (1, 3), (2, 3)] + + + ``subsets(seq, k, repetition=True)`` will return the + `\frac{(n - 1 + k)!}{k!(n - 1)!}` + combinations *with* repetition: + + >>> list(subsets([1, 2], 2, repetition=True)) + [(1, 1), (1, 2), (2, 2)] + + If you ask for more items than are in the set you get the empty set unless + you allow repetitions: + + >>> list(subsets([0, 1], 3, repetition=False)) + [] + >>> list(subsets([0, 1], 3, repetition=True)) + [(0, 0, 0), (0, 0, 1), (0, 1, 1), (1, 1, 1)] + + """ + if k is None: + if not repetition: + return chain.from_iterable((combinations(seq, k) + for k in range(len(seq) + 1))) + else: + return chain.from_iterable((combinations_with_replacement(seq, k) + for k in range(len(seq) + 1))) + else: + if not repetition: + return combinations(seq, k) + else: + return combinations_with_replacement(seq, k) + + +def filter_symbols(iterator, exclude): + """ + Only yield elements from `iterator` that do not occur in `exclude`. + + Parameters + ========== + + iterator : iterable + iterator to take elements from + + exclude : iterable + elements to exclude + + Returns + ======= + + iterator : iterator + filtered iterator + """ + exclude = set(exclude) + for s in iterator: + if s not in exclude: + yield s + +def numbered_symbols(prefix='x', cls=None, start=0, exclude=(), *args, **assumptions): + """ + Generate an infinite stream of Symbols consisting of a prefix and + increasing subscripts provided that they do not occur in ``exclude``. + + Parameters + ========== + + prefix : str, optional + The prefix to use. By default, this function will generate symbols of + the form "x0", "x1", etc. + + cls : class, optional + The class to use. By default, it uses ``Symbol``, but you can also use ``Wild`` + or ``Dummy``. + + start : int, optional + The start number. By default, it is 0. + + Returns + ======= + + sym : Symbol + The subscripted symbols. + """ + exclude = set(exclude or []) + if cls is None: + # We can't just make the default cls=Symbol because it isn't + # imported yet. + from sympy.core import Symbol + cls = Symbol + + while True: + name = '%s%s' % (prefix, start) + s = cls(name, *args, **assumptions) + if s not in exclude: + yield s + start += 1 + + +def capture(func): + """Return the printed output of func(). + + ``func`` should be a function without arguments that produces output with + print statements. + + >>> from sympy.utilities.iterables import capture + >>> from sympy import pprint + >>> from sympy.abc import x + >>> def foo(): + ... print('hello world!') + ... + >>> 'hello' in capture(foo) # foo, not foo() + True + >>> capture(lambda: pprint(2/x)) + '2\\n-\\nx\\n' + + """ + from io import StringIO + import sys + + stdout = sys.stdout + sys.stdout = file = StringIO() + try: + func() + finally: + sys.stdout = stdout + return file.getvalue() + + +def sift(seq, keyfunc, binary=False): + """ + Sift the sequence, ``seq`` according to ``keyfunc``. + + Returns + ======= + + When ``binary`` is ``False`` (default), the output is a dictionary + where elements of ``seq`` are stored in a list keyed to the value + of keyfunc for that element. If ``binary`` is True then a tuple + with lists ``T`` and ``F`` are returned where ``T`` is a list + containing elements of seq for which ``keyfunc`` was ``True`` and + ``F`` containing those elements for which ``keyfunc`` was ``False``; + a ValueError is raised if the ``keyfunc`` is not binary. + + Examples + ======== + + >>> from sympy.utilities import sift + >>> from sympy.abc import x, y + >>> from sympy import sqrt, exp, pi, Tuple + + >>> sift(range(5), lambda x: x % 2) + {0: [0, 2, 4], 1: [1, 3]} + + sift() returns a defaultdict() object, so any key that has no matches will + give []. + + >>> sift([x], lambda x: x.is_commutative) + {True: [x]} + >>> _[False] + [] + + Sometimes you will not know how many keys you will get: + + >>> sift([sqrt(x), exp(x), (y**x)**2], + ... lambda x: x.as_base_exp()[0]) + {E: [exp(x)], x: [sqrt(x)], y: [y**(2*x)]} + + Sometimes you expect the results to be binary; the + results can be unpacked by setting ``binary`` to True: + + >>> sift(range(4), lambda x: x % 2, binary=True) + ([1, 3], [0, 2]) + >>> sift(Tuple(1, pi), lambda x: x.is_rational, binary=True) + ([1], [pi]) + + A ValueError is raised if the predicate was not actually binary + (which is a good test for the logic where sifting is used and + binary results were expected): + + >>> unknown = exp(1) - pi # the rationality of this is unknown + >>> args = Tuple(1, pi, unknown) + >>> sift(args, lambda x: x.is_rational, binary=True) + Traceback (most recent call last): + ... + ValueError: keyfunc gave non-binary output + + The non-binary sifting shows that there were 3 keys generated: + + >>> set(sift(args, lambda x: x.is_rational).keys()) + {None, False, True} + + If you need to sort the sifted items it might be better to use + ``ordered`` which can economically apply multiple sort keys + to a sequence while sorting. + + See Also + ======== + + ordered + + """ + if not binary: + m = defaultdict(list) + for i in seq: + m[keyfunc(i)].append(i) + return m + sift = F, T = [], [] + for i in seq: + try: + sift[keyfunc(i)].append(i) + except (IndexError, TypeError): + raise ValueError('keyfunc gave non-binary output') + return T, F + + +def take(iter, n): + """Return ``n`` items from ``iter`` iterator. """ + return [ value for _, value in zip(range(n), iter) ] + + +def dict_merge(*dicts): + """Merge dictionaries into a single dictionary. """ + merged = {} + + for dict in dicts: + merged.update(dict) + + return merged + + +def common_prefix(*seqs): + """Return the subsequence that is a common start of sequences in ``seqs``. + + >>> from sympy.utilities.iterables import common_prefix + >>> common_prefix(list(range(3))) + [0, 1, 2] + >>> common_prefix(list(range(3)), list(range(4))) + [0, 1, 2] + >>> common_prefix([1, 2, 3], [1, 2, 5]) + [1, 2] + >>> common_prefix([1, 2, 3], [1, 3, 5]) + [1] + """ + if not all(seqs): + return [] + elif len(seqs) == 1: + return seqs[0] + i = 0 + for i in range(min(len(s) for s in seqs)): + if not all(seqs[j][i] == seqs[0][i] for j in range(len(seqs))): + break + else: + i += 1 + return seqs[0][:i] + + +def common_suffix(*seqs): + """Return the subsequence that is a common ending of sequences in ``seqs``. + + >>> from sympy.utilities.iterables import common_suffix + >>> common_suffix(list(range(3))) + [0, 1, 2] + >>> common_suffix(list(range(3)), list(range(4))) + [] + >>> common_suffix([1, 2, 3], [9, 2, 3]) + [2, 3] + >>> common_suffix([1, 2, 3], [9, 7, 3]) + [3] + """ + + if not all(seqs): + return [] + elif len(seqs) == 1: + return seqs[0] + i = 0 + for i in range(-1, -min(len(s) for s in seqs) - 1, -1): + if not all(seqs[j][i] == seqs[0][i] for j in range(len(seqs))): + break + else: + i -= 1 + if i == -1: + return [] + else: + return seqs[0][i + 1:] + + +def prefixes(seq): + """ + Generate all prefixes of a sequence. + + Examples + ======== + + >>> from sympy.utilities.iterables import prefixes + + >>> list(prefixes([1,2,3,4])) + [[1], [1, 2], [1, 2, 3], [1, 2, 3, 4]] + + """ + n = len(seq) + + for i in range(n): + yield seq[:i + 1] + + +def postfixes(seq): + """ + Generate all postfixes of a sequence. + + Examples + ======== + + >>> from sympy.utilities.iterables import postfixes + + >>> list(postfixes([1,2,3,4])) + [[4], [3, 4], [2, 3, 4], [1, 2, 3, 4]] + + """ + n = len(seq) + + for i in range(n): + yield seq[n - i - 1:] + + +def topological_sort(graph, key=None): + r""" + Topological sort of graph's vertices. + + Parameters + ========== + + graph : tuple[list, list[tuple[T, T]] + A tuple consisting of a list of vertices and a list of edges of + a graph to be sorted topologically. + + key : callable[T] (optional) + Ordering key for vertices on the same level. By default the natural + (e.g. lexicographic) ordering is used (in this case the base type + must implement ordering relations). + + Examples + ======== + + Consider a graph:: + + +---+ +---+ +---+ + | 7 |\ | 5 | | 3 | + +---+ \ +---+ +---+ + | _\___/ ____ _/ | + | / \___/ \ / | + V V V V | + +----+ +---+ | + | 11 | | 8 | | + +----+ +---+ | + | | \____ ___/ _ | + | \ \ / / \ | + V \ V V / V V + +---+ \ +---+ | +----+ + | 2 | | | 9 | | | 10 | + +---+ | +---+ | +----+ + \________/ + + where vertices are integers. This graph can be encoded using + elementary Python's data structures as follows:: + + >>> V = [2, 3, 5, 7, 8, 9, 10, 11] + >>> E = [(7, 11), (7, 8), (5, 11), (3, 8), (3, 10), + ... (11, 2), (11, 9), (11, 10), (8, 9)] + + To compute a topological sort for graph ``(V, E)`` issue:: + + >>> from sympy.utilities.iterables import topological_sort + + >>> topological_sort((V, E)) + [3, 5, 7, 8, 11, 2, 9, 10] + + If specific tie breaking approach is needed, use ``key`` parameter:: + + >>> topological_sort((V, E), key=lambda v: -v) + [7, 5, 11, 3, 10, 8, 9, 2] + + Only acyclic graphs can be sorted. If the input graph has a cycle, + then ``ValueError`` will be raised:: + + >>> topological_sort((V, E + [(10, 7)])) + Traceback (most recent call last): + ... + ValueError: cycle detected + + References + ========== + + .. [1] https://en.wikipedia.org/wiki/Topological_sorting + + """ + V, E = graph + + L = [] + S = set(V) + E = list(E) + + for v, u in E: + S.discard(u) + + if key is None: + def key(value): + return value + + S = sorted(S, key=key, reverse=True) + + while S: + node = S.pop() + L.append(node) + + for u, v in list(E): + if u == node: + E.remove((u, v)) + + for _u, _v in E: + if v == _v: + break + else: + kv = key(v) + + for i, s in enumerate(S): + ks = key(s) + + if kv > ks: + S.insert(i, v) + break + else: + S.append(v) + + if E: + raise ValueError("cycle detected") + else: + return L + + +def strongly_connected_components(G): + r""" + Strongly connected components of a directed graph in reverse topological + order. + + + Parameters + ========== + + graph : tuple[list, list[tuple[T, T]] + A tuple consisting of a list of vertices and a list of edges of + a graph whose strongly connected components are to be found. + + + Examples + ======== + + Consider a directed graph (in dot notation):: + + digraph { + A -> B + A -> C + B -> C + C -> B + B -> D + } + + .. graphviz:: + + digraph { + A -> B + A -> C + B -> C + C -> B + B -> D + } + + where vertices are the letters A, B, C and D. This graph can be encoded + using Python's elementary data structures as follows:: + + >>> V = ['A', 'B', 'C', 'D'] + >>> E = [('A', 'B'), ('A', 'C'), ('B', 'C'), ('C', 'B'), ('B', 'D')] + + The strongly connected components of this graph can be computed as + + >>> from sympy.utilities.iterables import strongly_connected_components + + >>> strongly_connected_components((V, E)) + [['D'], ['B', 'C'], ['A']] + + This also gives the components in reverse topological order. + + Since the subgraph containing B and C has a cycle they must be together in + a strongly connected component. A and D are connected to the rest of the + graph but not in a cyclic manner so they appear as their own strongly + connected components. + + + Notes + ===== + + The vertices of the graph must be hashable for the data structures used. + If the vertices are unhashable replace them with integer indices. + + This function uses Tarjan's algorithm to compute the strongly connected + components in `O(|V|+|E|)` (linear) time. + + + References + ========== + + .. [1] https://en.wikipedia.org/wiki/Strongly_connected_component + .. [2] https://en.wikipedia.org/wiki/Tarjan%27s_strongly_connected_components_algorithm + + + See Also + ======== + + sympy.utilities.iterables.connected_components + + """ + # Map from a vertex to its neighbours + V, E = G + Gmap = {vi: [] for vi in V} + for v1, v2 in E: + Gmap[v1].append(v2) + return _strongly_connected_components(V, Gmap) + + +def _strongly_connected_components(V, Gmap): + """More efficient internal routine for strongly_connected_components""" + # + # Here V is an iterable of vertices and Gmap is a dict mapping each vertex + # to a list of neighbours e.g.: + # + # V = [0, 1, 2, 3] + # Gmap = {0: [2, 3], 1: [0]} + # + # For a large graph these data structures can often be created more + # efficiently then those expected by strongly_connected_components() which + # in this case would be + # + # V = [0, 1, 2, 3] + # Gmap = [(0, 2), (0, 3), (1, 0)] + # + # XXX: Maybe this should be the recommended function to use instead... + # + + # Non-recursive Tarjan's algorithm: + lowlink = {} + indices = {} + stack = OrderedDict() + callstack = [] + components = [] + nomore = object() + + def start(v): + index = len(stack) + indices[v] = lowlink[v] = index + stack[v] = None + callstack.append((v, iter(Gmap[v]))) + + def finish(v1): + # Finished a component? + if lowlink[v1] == indices[v1]: + component = [stack.popitem()[0]] + while component[-1] is not v1: + component.append(stack.popitem()[0]) + components.append(component[::-1]) + v2, _ = callstack.pop() + if callstack: + v1, _ = callstack[-1] + lowlink[v1] = min(lowlink[v1], lowlink[v2]) + + for v in V: + if v in indices: + continue + start(v) + while callstack: + v1, it1 = callstack[-1] + v2 = next(it1, nomore) + # Finished children of v1? + if v2 is nomore: + finish(v1) + # Recurse on v2 + elif v2 not in indices: + start(v2) + elif v2 in stack: + lowlink[v1] = min(lowlink[v1], indices[v2]) + + # Reverse topological sort order: + return components + + +def connected_components(G): + r""" + Connected components of an undirected graph or weakly connected components + of a directed graph. + + + Parameters + ========== + + graph : tuple[list, list[tuple[T, T]] + A tuple consisting of a list of vertices and a list of edges of + a graph whose connected components are to be found. + + + Examples + ======== + + + Given an undirected graph:: + + graph { + A -- B + C -- D + } + + .. graphviz:: + + graph { + A -- B + C -- D + } + + We can find the connected components using this function if we include + each edge in both directions:: + + >>> from sympy.utilities.iterables import connected_components + + >>> V = ['A', 'B', 'C', 'D'] + >>> E = [('A', 'B'), ('B', 'A'), ('C', 'D'), ('D', 'C')] + >>> connected_components((V, E)) + [['A', 'B'], ['C', 'D']] + + The weakly connected components of a directed graph can found the same + way. + + + Notes + ===== + + The vertices of the graph must be hashable for the data structures used. + If the vertices are unhashable replace them with integer indices. + + This function uses Tarjan's algorithm to compute the connected components + in `O(|V|+|E|)` (linear) time. + + + References + ========== + + .. [1] https://en.wikipedia.org/wiki/Component_%28graph_theory%29 + .. [2] https://en.wikipedia.org/wiki/Tarjan%27s_strongly_connected_components_algorithm + + + See Also + ======== + + sympy.utilities.iterables.strongly_connected_components + + """ + # Duplicate edges both ways so that the graph is effectively undirected + # and return the strongly connected components: + V, E = G + E_undirected = [] + for v1, v2 in E: + E_undirected.extend([(v1, v2), (v2, v1)]) + return strongly_connected_components((V, E_undirected)) + + +def rotate_left(x, y): + """ + Left rotates a list x by the number of steps specified + in y. + + Examples + ======== + + >>> from sympy.utilities.iterables import rotate_left + >>> a = [0, 1, 2] + >>> rotate_left(a, 1) + [1, 2, 0] + """ + if len(x) == 0: + return [] + y = y % len(x) + return x[y:] + x[:y] + + +def rotate_right(x, y): + """ + Right rotates a list x by the number of steps specified + in y. + + Examples + ======== + + >>> from sympy.utilities.iterables import rotate_right + >>> a = [0, 1, 2] + >>> rotate_right(a, 1) + [2, 0, 1] + """ + if len(x) == 0: + return [] + y = len(x) - y % len(x) + return x[y:] + x[:y] + + +def least_rotation(x, key=None): + ''' + Returns the number of steps of left rotation required to + obtain lexicographically minimal string/list/tuple, etc. + + Examples + ======== + + >>> from sympy.utilities.iterables import least_rotation, rotate_left + >>> a = [3, 1, 5, 1, 2] + >>> least_rotation(a) + 3 + >>> rotate_left(a, _) + [1, 2, 3, 1, 5] + + References + ========== + + .. [1] https://en.wikipedia.org/wiki/Lexicographically_minimal_string_rotation + + ''' + from sympy.functions.elementary.miscellaneous import Id + if key is None: key = Id + S = x + x # Concatenate string to it self to avoid modular arithmetic + f = [-1] * len(S) # Failure function + k = 0 # Least rotation of string found so far + for j in range(1,len(S)): + sj = S[j] + i = f[j-k-1] + while i != -1 and sj != S[k+i+1]: + if key(sj) < key(S[k+i+1]): + k = j-i-1 + i = f[i] + if sj != S[k+i+1]: + if key(sj) < key(S[k]): + k = j + f[j-k] = -1 + else: + f[j-k] = i+1 + return k + + +def multiset_combinations(m, n, g=None): + """ + Return the unique combinations of size ``n`` from multiset ``m``. + + Examples + ======== + + >>> from sympy.utilities.iterables import multiset_combinations + >>> from itertools import combinations + >>> [''.join(i) for i in multiset_combinations('baby', 3)] + ['abb', 'aby', 'bby'] + + >>> def count(f, s): return len(list(f(s, 3))) + + The number of combinations depends on the number of letters; the + number of unique combinations depends on how the letters are + repeated. + + >>> s1 = 'abracadabra' + >>> s2 = 'banana tree' + >>> count(combinations, s1), count(multiset_combinations, s1) + (165, 23) + >>> count(combinations, s2), count(multiset_combinations, s2) + (165, 54) + + """ + from sympy.core.sorting import ordered + if g is None: + if isinstance(m, dict): + if any(as_int(v) < 0 for v in m.values()): + raise ValueError('counts cannot be negative') + N = sum(m.values()) + if n > N: + return + g = [[k, m[k]] for k in ordered(m)] + else: + m = list(m) + N = len(m) + if n > N: + return + try: + m = multiset(m) + g = [(k, m[k]) for k in ordered(m)] + except TypeError: + m = list(ordered(m)) + g = [list(i) for i in group(m, multiple=False)] + del m + else: + # not checking counts since g is intended for internal use + N = sum(v for k, v in g) + if n > N or not n: + yield [] + else: + for i, (k, v) in enumerate(g): + if v >= n: + yield [k]*n + v = n - 1 + for v in range(min(n, v), 0, -1): + for j in multiset_combinations(None, n - v, g[i + 1:]): + rv = [k]*v + j + if len(rv) == n: + yield rv + +def multiset_permutations(m, size=None, g=None): + """ + Return the unique permutations of multiset ``m``. + + Examples + ======== + + >>> from sympy.utilities.iterables import multiset_permutations + >>> from sympy import factorial + >>> [''.join(i) for i in multiset_permutations('aab')] + ['aab', 'aba', 'baa'] + >>> factorial(len('banana')) + 720 + >>> len(list(multiset_permutations('banana'))) + 60 + """ + from sympy.core.sorting import ordered + if g is None: + if isinstance(m, dict): + if any(as_int(v) < 0 for v in m.values()): + raise ValueError('counts cannot be negative') + g = [[k, m[k]] for k in ordered(m)] + else: + m = list(ordered(m)) + g = [list(i) for i in group(m, multiple=False)] + del m + do = [gi for gi in g if gi[1] > 0] + SUM = sum([gi[1] for gi in do]) + if not do or size is not None and (size > SUM or size < 1): + if not do and size is None or size == 0: + yield [] + return + elif size == 1: + for k, v in do: + yield [k] + elif len(do) == 1: + k, v = do[0] + v = v if size is None else (size if size <= v else 0) + yield [k for i in range(v)] + elif all(v == 1 for k, v in do): + for p in permutations([k for k, v in do], size): + yield list(p) + else: + size = size if size is not None else SUM + for i, (k, v) in enumerate(do): + do[i][1] -= 1 + for j in multiset_permutations(None, size - 1, do): + if j: + yield [k] + j + do[i][1] += 1 + + +def _partition(seq, vector, m=None): + """ + Return the partition of seq as specified by the partition vector. + + Examples + ======== + + >>> from sympy.utilities.iterables import _partition + >>> _partition('abcde', [1, 0, 1, 2, 0]) + [['b', 'e'], ['a', 'c'], ['d']] + + Specifying the number of bins in the partition is optional: + + >>> _partition('abcde', [1, 0, 1, 2, 0], 3) + [['b', 'e'], ['a', 'c'], ['d']] + + The output of _set_partitions can be passed as follows: + + >>> output = (3, [1, 0, 1, 2, 0]) + >>> _partition('abcde', *output) + [['b', 'e'], ['a', 'c'], ['d']] + + See Also + ======== + + combinatorics.partitions.Partition.from_rgs + + """ + if m is None: + m = max(vector) + 1 + elif isinstance(vector, int): # entered as m, vector + vector, m = m, vector + p = [[] for i in range(m)] + for i, v in enumerate(vector): + p[v].append(seq[i]) + return p + + +def _set_partitions(n): + """Cycle through all partitions of n elements, yielding the + current number of partitions, ``m``, and a mutable list, ``q`` + such that ``element[i]`` is in part ``q[i]`` of the partition. + + NOTE: ``q`` is modified in place and generally should not be changed + between function calls. + + Examples + ======== + + >>> from sympy.utilities.iterables import _set_partitions, _partition + >>> for m, q in _set_partitions(3): + ... print('%s %s %s' % (m, q, _partition('abc', q, m))) + 1 [0, 0, 0] [['a', 'b', 'c']] + 2 [0, 0, 1] [['a', 'b'], ['c']] + 2 [0, 1, 0] [['a', 'c'], ['b']] + 2 [0, 1, 1] [['a'], ['b', 'c']] + 3 [0, 1, 2] [['a'], ['b'], ['c']] + + Notes + ===== + + This algorithm is similar to, and solves the same problem as, + Algorithm 7.2.1.5H, from volume 4A of Knuth's The Art of Computer + Programming. Knuth uses the term "restricted growth string" where + this code refers to a "partition vector". In each case, the meaning is + the same: the value in the ith element of the vector specifies to + which part the ith set element is to be assigned. + + At the lowest level, this code implements an n-digit big-endian + counter (stored in the array q) which is incremented (with carries) to + get the next partition in the sequence. A special twist is that a + digit is constrained to be at most one greater than the maximum of all + the digits to the left of it. The array p maintains this maximum, so + that the code can efficiently decide when a digit can be incremented + in place or whether it needs to be reset to 0 and trigger a carry to + the next digit. The enumeration starts with all the digits 0 (which + corresponds to all the set elements being assigned to the same 0th + part), and ends with 0123...n, which corresponds to each set element + being assigned to a different, singleton, part. + + This routine was rewritten to use 0-based lists while trying to + preserve the beauty and efficiency of the original algorithm. + + References + ========== + + .. [1] Nijenhuis, Albert and Wilf, Herbert. (1978) Combinatorial Algorithms, + 2nd Ed, p 91, algorithm "nexequ". Available online from + https://www.math.upenn.edu/~wilf/website/CombAlgDownld.html (viewed + November 17, 2012). + + """ + p = [0]*n + q = [0]*n + nc = 1 + yield nc, q + while nc != n: + m = n + while 1: + m -= 1 + i = q[m] + if p[i] != 1: + break + q[m] = 0 + i += 1 + q[m] = i + m += 1 + nc += m - n + p[0] += n - m + if i == nc: + p[nc] = 0 + nc += 1 + p[i - 1] -= 1 + p[i] += 1 + yield nc, q + + +def multiset_partitions(multiset, m=None): + """ + Return unique partitions of the given multiset (in list form). + If ``m`` is None, all multisets will be returned, otherwise only + partitions with ``m`` parts will be returned. + + If ``multiset`` is an integer, a range [0, 1, ..., multiset - 1] + will be supplied. + + Examples + ======== + + >>> from sympy.utilities.iterables import multiset_partitions + >>> list(multiset_partitions([1, 2, 3, 4], 2)) + [[[1, 2, 3], [4]], [[1, 2, 4], [3]], [[1, 2], [3, 4]], + [[1, 3, 4], [2]], [[1, 3], [2, 4]], [[1, 4], [2, 3]], + [[1], [2, 3, 4]]] + >>> list(multiset_partitions([1, 2, 3, 4], 1)) + [[[1, 2, 3, 4]]] + + Only unique partitions are returned and these will be returned in a + canonical order regardless of the order of the input: + + >>> a = [1, 2, 2, 1] + >>> ans = list(multiset_partitions(a, 2)) + >>> a.sort() + >>> list(multiset_partitions(a, 2)) == ans + True + >>> a = range(3, 1, -1) + >>> (list(multiset_partitions(a)) == + ... list(multiset_partitions(sorted(a)))) + True + + If m is omitted then all partitions will be returned: + + >>> list(multiset_partitions([1, 1, 2])) + [[[1, 1, 2]], [[1, 1], [2]], [[1, 2], [1]], [[1], [1], [2]]] + >>> list(multiset_partitions([1]*3)) + [[[1, 1, 1]], [[1], [1, 1]], [[1], [1], [1]]] + + Counting + ======== + + The number of partitions of a set is given by the bell number: + + >>> from sympy import bell + >>> len(list(multiset_partitions(5))) == bell(5) == 52 + True + + The number of partitions of length k from a set of size n is given by the + Stirling Number of the 2nd kind: + + >>> from sympy.functions.combinatorial.numbers import stirling + >>> stirling(5, 2) == len(list(multiset_partitions(5, 2))) == 15 + True + + These comments on counting apply to *sets*, not multisets. + + Notes + ===== + + When all the elements are the same in the multiset, the order + of the returned partitions is determined by the ``partitions`` + routine. If one is counting partitions then it is better to use + the ``nT`` function. + + See Also + ======== + + partitions + sympy.combinatorics.partitions.Partition + sympy.combinatorics.partitions.IntegerPartition + sympy.functions.combinatorial.numbers.nT + + """ + # This function looks at the supplied input and dispatches to + # several special-case routines as they apply. + if isinstance(multiset, int): + n = multiset + if m and m > n: + return + multiset = list(range(n)) + if m == 1: + yield [multiset[:]] + return + + # If m is not None, it can sometimes be faster to use + # MultisetPartitionTraverser.enum_range() even for inputs + # which are sets. Since the _set_partitions code is quite + # fast, this is only advantageous when the overall set + # partitions outnumber those with the desired number of parts + # by a large factor. (At least 60.) Such a switch is not + # currently implemented. + for nc, q in _set_partitions(n): + if m is None or nc == m: + rv = [[] for i in range(nc)] + for i in range(n): + rv[q[i]].append(multiset[i]) + yield rv + return + + if len(multiset) == 1 and isinstance(multiset, str): + multiset = [multiset] + + if not has_variety(multiset): + # Only one component, repeated n times. The resulting + # partitions correspond to partitions of integer n. + n = len(multiset) + if m and m > n: + return + if m == 1: + yield [multiset[:]] + return + x = multiset[:1] + for size, p in partitions(n, m, size=True): + if m is None or size == m: + rv = [] + for k in sorted(p): + rv.extend([x*k]*p[k]) + yield rv + else: + from sympy.core.sorting import ordered + multiset = list(ordered(multiset)) + n = len(multiset) + if m and m > n: + return + if m == 1: + yield [multiset[:]] + return + + # Split the information of the multiset into two lists - + # one of the elements themselves, and one (of the same length) + # giving the number of repeats for the corresponding element. + elements, multiplicities = zip(*group(multiset, False)) + + if len(elements) < len(multiset): + # General case - multiset with more than one distinct element + # and at least one element repeated more than once. + if m: + mpt = MultisetPartitionTraverser() + for state in mpt.enum_range(multiplicities, m-1, m): + yield list_visitor(state, elements) + else: + for state in multiset_partitions_taocp(multiplicities): + yield list_visitor(state, elements) + else: + # Set partitions case - no repeated elements. Pretty much + # same as int argument case above, with same possible, but + # currently unimplemented optimization for some cases when + # m is not None + for nc, q in _set_partitions(n): + if m is None or nc == m: + rv = [[] for i in range(nc)] + for i in range(n): + rv[q[i]].append(i) + yield [[multiset[j] for j in i] for i in rv] + + +def partitions(n, m=None, k=None, size=False): + """Generate all partitions of positive integer, n. + + Parameters + ========== + + m : integer (default gives partitions of all sizes) + limits number of parts in partition (mnemonic: m, maximum parts) + k : integer (default gives partitions number from 1 through n) + limits the numbers that are kept in the partition (mnemonic: k, keys) + size : bool (default False, only partition is returned) + when ``True`` then (M, P) is returned where M is the sum of the + multiplicities and P is the generated partition. + + Each partition is represented as a dictionary, mapping an integer + to the number of copies of that integer in the partition. For example, + the first partition of 4 returned is {4: 1}, "4: one of them". + + Examples + ======== + + >>> from sympy.utilities.iterables import partitions + + The numbers appearing in the partition (the key of the returned dict) + are limited with k: + + >>> for p in partitions(6, k=2): # doctest: +SKIP + ... print(p) + {2: 3} + {1: 2, 2: 2} + {1: 4, 2: 1} + {1: 6} + + The maximum number of parts in the partition (the sum of the values in + the returned dict) are limited with m (default value, None, gives + partitions from 1 through n): + + >>> for p in partitions(6, m=2): # doctest: +SKIP + ... print(p) + ... + {6: 1} + {1: 1, 5: 1} + {2: 1, 4: 1} + {3: 2} + + References + ========== + + .. [1] modified from Tim Peter's version to allow for k and m values: + https://code.activestate.com/recipes/218332-generator-for-integer-partitions/ + + See Also + ======== + + sympy.combinatorics.partitions.Partition + sympy.combinatorics.partitions.IntegerPartition + + """ + if (n <= 0 or + m is not None and m < 1 or + k is not None and k < 1 or + m and k and m*k < n): + # the empty set is the only way to handle these inputs + # and returning {} to represent it is consistent with + # the counting convention, e.g. nT(0) == 1. + if size: + yield 0, {} + else: + yield {} + return + + if m is None: + m = n + else: + m = min(m, n) + k = min(k or n, n) + + n, m, k = as_int(n), as_int(m), as_int(k) + q, r = divmod(n, k) + ms = {k: q} + keys = [k] # ms.keys(), from largest to smallest + if r: + ms[r] = 1 + keys.append(r) + room = m - q - bool(r) + if size: + yield sum(ms.values()), ms.copy() + else: + yield ms.copy() + + while keys != [1]: + # Reuse any 1's. + if keys[-1] == 1: + del keys[-1] + reuse = ms.pop(1) + room += reuse + else: + reuse = 0 + + while 1: + # Let i be the smallest key larger than 1. Reuse one + # instance of i. + i = keys[-1] + newcount = ms[i] = ms[i] - 1 + reuse += i + if newcount == 0: + del keys[-1], ms[i] + room += 1 + + # Break the remainder into pieces of size i-1. + i -= 1 + q, r = divmod(reuse, i) + need = q + bool(r) + if need > room: + if not keys: + return + continue + + ms[i] = q + keys.append(i) + if r: + ms[r] = 1 + keys.append(r) + break + room -= need + if size: + yield sum(ms.values()), ms.copy() + else: + yield ms.copy() + + +def ordered_partitions(n, m=None, sort=True): + """Generates ordered partitions of integer ``n``. + + Parameters + ========== + + m : integer (default None) + The default value gives partitions of all sizes else only + those with size m. In addition, if ``m`` is not None then + partitions are generated *in place* (see examples). + sort : bool (default True) + Controls whether partitions are + returned in sorted order when ``m`` is not None; when False, + the partitions are returned as fast as possible with elements + sorted, but when m|n the partitions will not be in + ascending lexicographical order. + + Examples + ======== + + >>> from sympy.utilities.iterables import ordered_partitions + + All partitions of 5 in ascending lexicographical: + + >>> for p in ordered_partitions(5): + ... print(p) + [1, 1, 1, 1, 1] + [1, 1, 1, 2] + [1, 1, 3] + [1, 2, 2] + [1, 4] + [2, 3] + [5] + + Only partitions of 5 with two parts: + + >>> for p in ordered_partitions(5, 2): + ... print(p) + [1, 4] + [2, 3] + + When ``m`` is given, a given list objects will be used more than + once for speed reasons so you will not see the correct partitions + unless you make a copy of each as it is generated: + + >>> [p for p in ordered_partitions(7, 3)] + [[1, 1, 1], [1, 1, 1], [1, 1, 1], [2, 2, 2]] + >>> [list(p) for p in ordered_partitions(7, 3)] + [[1, 1, 5], [1, 2, 4], [1, 3, 3], [2, 2, 3]] + + When ``n`` is a multiple of ``m``, the elements are still sorted + but the partitions themselves will be *unordered* if sort is False; + the default is to return them in ascending lexicographical order. + + >>> for p in ordered_partitions(6, 2): + ... print(p) + [1, 5] + [2, 4] + [3, 3] + + But if speed is more important than ordering, sort can be set to + False: + + >>> for p in ordered_partitions(6, 2, sort=False): + ... print(p) + [1, 5] + [3, 3] + [2, 4] + + References + ========== + + .. [1] Generating Integer Partitions, [online], + Available: https://jeromekelleher.net/generating-integer-partitions.html + .. [2] Jerome Kelleher and Barry O'Sullivan, "Generating All + Partitions: A Comparison Of Two Encodings", [online], + Available: https://arxiv.org/pdf/0909.2331v2.pdf + """ + if n < 1 or m is not None and m < 1: + # the empty set is the only way to handle these inputs + # and returning {} to represent it is consistent with + # the counting convention, e.g. nT(0) == 1. + yield [] + return + + if m is None: + # The list `a`'s leading elements contain the partition in which + # y is the biggest element and x is either the same as y or the + # 2nd largest element; v and w are adjacent element indices + # to which x and y are being assigned, respectively. + a = [1]*n + y = -1 + v = n + while v > 0: + v -= 1 + x = a[v] + 1 + while y >= 2 * x: + a[v] = x + y -= x + v += 1 + w = v + 1 + while x <= y: + a[v] = x + a[w] = y + yield a[:w + 1] + x += 1 + y -= 1 + a[v] = x + y + y = a[v] - 1 + yield a[:w] + elif m == 1: + yield [n] + elif n == m: + yield [1]*n + else: + # recursively generate partitions of size m + for b in range(1, n//m + 1): + a = [b]*m + x = n - b*m + if not x: + if sort: + yield a + elif not sort and x <= m: + for ax in ordered_partitions(x, sort=False): + mi = len(ax) + a[-mi:] = [i + b for i in ax] + yield a + a[-mi:] = [b]*mi + else: + for mi in range(1, m): + for ax in ordered_partitions(x, mi, sort=True): + a[-mi:] = [i + b for i in ax] + yield a + a[-mi:] = [b]*mi + + +def binary_partitions(n): + """ + Generates the binary partition of n. + + A binary partition consists only of numbers that are + powers of two. Each step reduces a `2^{k+1}` to `2^k` and + `2^k`. Thus 16 is converted to 8 and 8. + + Examples + ======== + + >>> from sympy.utilities.iterables import binary_partitions + >>> for i in binary_partitions(5): + ... print(i) + ... + [4, 1] + [2, 2, 1] + [2, 1, 1, 1] + [1, 1, 1, 1, 1] + + References + ========== + + .. [1] TAOCP 4, section 7.2.1.5, problem 64 + + """ + from math import ceil, log + power = int(2**(ceil(log(n, 2)))) + acc = 0 + partition = [] + while power: + if acc + power <= n: + partition.append(power) + acc += power + power >>= 1 + + last_num = len(partition) - 1 - (n & 1) + while last_num >= 0: + yield partition + if partition[last_num] == 2: + partition[last_num] = 1 + partition.append(1) + last_num -= 1 + continue + partition.append(1) + partition[last_num] >>= 1 + x = partition[last_num + 1] = partition[last_num] + last_num += 1 + while x > 1: + if x <= len(partition) - last_num - 1: + del partition[-x + 1:] + last_num += 1 + partition[last_num] = x + else: + x >>= 1 + yield [1]*n + + +def has_dups(seq): + """Return True if there are any duplicate elements in ``seq``. + + Examples + ======== + + >>> from sympy import has_dups, Dict, Set + >>> has_dups((1, 2, 1)) + True + >>> has_dups(range(3)) + False + >>> all(has_dups(c) is False for c in (set(), Set(), dict(), Dict())) + True + """ + from sympy.core.containers import Dict + from sympy.sets.sets import Set + if isinstance(seq, (dict, set, Dict, Set)): + return False + unique = set() + try: + return any(True for s in seq if s in unique or unique.add(s)) + except TypeError: + return len(seq) != len(list(uniq(seq))) + + +def has_variety(seq): + """Return True if there are any different elements in ``seq``. + + Examples + ======== + + >>> from sympy import has_variety + + >>> has_variety((1, 2, 1)) + True + >>> has_variety((1, 1, 1)) + False + """ + for i, s in enumerate(seq): + if i == 0: + sentinel = s + else: + if s != sentinel: + return True + return False + + +def uniq(seq, result=None): + """ + Yield unique elements from ``seq`` as an iterator. The second + parameter ``result`` is used internally; it is not necessary + to pass anything for this. + + Note: changing the sequence during iteration will raise a + RuntimeError if the size of the sequence is known; if you pass + an iterator and advance the iterator you will change the + output of this routine but there will be no warning. + + Examples + ======== + + >>> from sympy.utilities.iterables import uniq + >>> dat = [1, 4, 1, 5, 4, 2, 1, 2] + >>> type(uniq(dat)) in (list, tuple) + False + + >>> list(uniq(dat)) + [1, 4, 5, 2] + >>> list(uniq(x for x in dat)) + [1, 4, 5, 2] + >>> list(uniq([[1], [2, 1], [1]])) + [[1], [2, 1]] + """ + try: + n = len(seq) + except TypeError: + n = None + def check(): + # check that size of seq did not change during iteration; + # if n == None the object won't support size changing, e.g. + # an iterator can't be changed + if n is not None and len(seq) != n: + raise RuntimeError('sequence changed size during iteration') + try: + seen = set() + result = result or [] + for i, s in enumerate(seq): + if not (s in seen or seen.add(s)): + yield s + check() + except TypeError: + if s not in result: + yield s + check() + result.append(s) + if hasattr(seq, '__getitem__'): + yield from uniq(seq[i + 1:], result) + else: + yield from uniq(seq, result) + + +def generate_bell(n): + """Return permutations of [0, 1, ..., n - 1] such that each permutation + differs from the last by the exchange of a single pair of neighbors. + The ``n!`` permutations are returned as an iterator. In order to obtain + the next permutation from a random starting permutation, use the + ``next_trotterjohnson`` method of the Permutation class (which generates + the same sequence in a different manner). + + Examples + ======== + + >>> from itertools import permutations + >>> from sympy.utilities.iterables import generate_bell + >>> from sympy import zeros, Matrix + + This is the sort of permutation used in the ringing of physical bells, + and does not produce permutations in lexicographical order. Rather, the + permutations differ from each other by exactly one inversion, and the + position at which the swapping occurs varies periodically in a simple + fashion. Consider the first few permutations of 4 elements generated + by ``permutations`` and ``generate_bell``: + + >>> list(permutations(range(4)))[:5] + [(0, 1, 2, 3), (0, 1, 3, 2), (0, 2, 1, 3), (0, 2, 3, 1), (0, 3, 1, 2)] + >>> list(generate_bell(4))[:5] + [(0, 1, 2, 3), (0, 1, 3, 2), (0, 3, 1, 2), (3, 0, 1, 2), (3, 0, 2, 1)] + + Notice how the 2nd and 3rd lexicographical permutations have 3 elements + out of place whereas each "bell" permutation always has only two + elements out of place relative to the previous permutation (and so the + signature (+/-1) of a permutation is opposite of the signature of the + previous permutation). + + How the position of inversion varies across the elements can be seen + by tracing out where the largest number appears in the permutations: + + >>> m = zeros(4, 24) + >>> for i, p in enumerate(generate_bell(4)): + ... m[:, i] = Matrix([j - 3 for j in list(p)]) # make largest zero + >>> m.print_nonzero('X') + [XXX XXXXXX XXXXXX XXX] + [XX XX XXXX XX XXXX XX XX] + [X XXXX XX XXXX XX XXXX X] + [ XXXXXX XXXXXX XXXXXX ] + + See Also + ======== + + sympy.combinatorics.permutations.Permutation.next_trotterjohnson + + References + ========== + + .. [1] https://en.wikipedia.org/wiki/Method_ringing + + .. [2] https://stackoverflow.com/questions/4856615/recursive-permutation/4857018 + + .. [3] https://web.archive.org/web/20160313023044/http://programminggeeks.com/bell-algorithm-for-permutation/ + + .. [4] https://en.wikipedia.org/wiki/Steinhaus%E2%80%93Johnson%E2%80%93Trotter_algorithm + + .. [5] Generating involutions, derangements, and relatives by ECO + Vincent Vajnovszki, DMTCS vol 1 issue 12, 2010 + + """ + n = as_int(n) + if n < 1: + raise ValueError('n must be a positive integer') + if n == 1: + yield (0,) + elif n == 2: + yield (0, 1) + yield (1, 0) + elif n == 3: + yield from [(0, 1, 2), (0, 2, 1), (2, 0, 1), (2, 1, 0), (1, 2, 0), (1, 0, 2)] + else: + m = n - 1 + op = [0] + [-1]*m + l = list(range(n)) + while True: + yield tuple(l) + # find biggest element with op + big = None, -1 # idx, value + for i in range(n): + if op[i] and l[i] > big[1]: + big = i, l[i] + i, _ = big + if i is None: + break # there are no ops left + # swap it with neighbor in the indicated direction + j = i + op[i] + l[i], l[j] = l[j], l[i] + op[i], op[j] = op[j], op[i] + # if it landed at the end or if the neighbor in the same + # direction is bigger then turn off op + if j == 0 or j == m or l[j + op[j]] > l[j]: + op[j] = 0 + # any element bigger to the left gets +1 op + for i in range(j): + if l[i] > l[j]: + op[i] = 1 + # any element bigger to the right gets -1 op + for i in range(j + 1, n): + if l[i] > l[j]: + op[i] = -1 + + +def generate_involutions(n): + """ + Generates involutions. + + An involution is a permutation that when multiplied + by itself equals the identity permutation. In this + implementation the involutions are generated using + Fixed Points. + + Alternatively, an involution can be considered as + a permutation that does not contain any cycles with + a length that is greater than two. + + Examples + ======== + + >>> from sympy.utilities.iterables import generate_involutions + >>> list(generate_involutions(3)) + [(0, 1, 2), (0, 2, 1), (1, 0, 2), (2, 1, 0)] + >>> len(list(generate_involutions(4))) + 10 + + References + ========== + + .. [1] https://mathworld.wolfram.com/PermutationInvolution.html + + """ + idx = list(range(n)) + for p in permutations(idx): + for i in idx: + if p[p[i]] != i: + break + else: + yield p + + +def multiset_derangements(s): + """Generate derangements of the elements of s *in place*. + + Examples + ======== + + >>> from sympy.utilities.iterables import multiset_derangements, uniq + + Because the derangements of multisets (not sets) are generated + in place, copies of the return value must be made if a collection + of derangements is desired or else all values will be the same: + + >>> list(uniq([i for i in multiset_derangements('1233')])) + [[None, None, None, None]] + >>> [i.copy() for i in multiset_derangements('1233')] + [['3', '3', '1', '2'], ['3', '3', '2', '1']] + >>> [''.join(i) for i in multiset_derangements('1233')] + ['3312', '3321'] + """ + from sympy.core.sorting import ordered + # create multiset dictionary of hashable elements or else + # remap elements to integers + try: + ms = multiset(s) + except TypeError: + # give each element a canonical integer value + key = dict(enumerate(ordered(uniq(s)))) + h = [] + for si in s: + for k in key: + if key[k] == si: + h.append(k) + break + for i in multiset_derangements(h): + yield [key[j] for j in i] + return + + mx = max(ms.values()) # max repetition of any element + n = len(s) # the number of elements + + ## special cases + + # 1) one element has more than half the total cardinality of s: no + # derangements are possible. + if mx*2 > n: + return + + # 2) all elements appear once: singletons + if len(ms) == n: + yield from _set_derangements(s) + return + + # find the first element that is repeated the most to place + # in the following two special cases where the selection + # is unambiguous: either there are two elements with multiplicity + # of mx or else there is only one with multiplicity mx + for M in ms: + if ms[M] == mx: + break + + inonM = [i for i in range(n) if s[i] != M] # location of non-M + iM = [i for i in range(n) if s[i] == M] # locations of M + rv = [None]*n + + # 3) half are the same + if 2*mx == n: + # M goes into non-M locations + for i in inonM: + rv[i] = M + # permutations of non-M go to M locations + for p in multiset_permutations([s[i] for i in inonM]): + for i, pi in zip(iM, p): + rv[i] = pi + yield rv + # clean-up (and encourages proper use of routine) + rv[:] = [None]*n + return + + # 4) single repeat covers all but 1 of the non-repeats: + # if there is one repeat then the multiset of the values + # of ms would be {mx: 1, 1: n - mx}, i.e. there would + # be n - mx + 1 values with the condition that n - 2*mx = 1 + if n - 2*mx == 1 and len(ms.values()) == n - mx + 1: + for i, i1 in enumerate(inonM): + ifill = inonM[:i] + inonM[i+1:] + for j in ifill: + rv[j] = M + for p in permutations([s[j] for j in ifill]): + rv[i1] = s[i1] + for j, pi in zip(iM, p): + rv[j] = pi + k = i1 + for j in iM: + rv[j], rv[k] = rv[k], rv[j] + yield rv + k = j + # clean-up (and encourages proper use of routine) + rv[:] = [None]*n + return + + ## general case is handled with 3 helpers: + # 1) `finish_derangements` will place the last two elements + # which have arbitrary multiplicities, e.g. for multiset + # {c: 3, a: 2, b: 2}, the last two elements are a and b + # 2) `iopen` will tell where a given element can be placed + # 3) `do` will recursively place elements into subsets of + # valid locations + + def finish_derangements(): + """Place the last two elements into the partially completed + derangement, and yield the results. + """ + + a = take[1][0] # penultimate element + a_ct = take[1][1] + b = take[0][0] # last element to be placed + b_ct = take[0][1] + + # split the indexes of the not-already-assigned elements of rv into + # three categories + forced_a = [] # positions which must have an a + forced_b = [] # positions which must have a b + open_free = [] # positions which could take either + for i in range(len(s)): + if rv[i] is None: + if s[i] == a: + forced_b.append(i) + elif s[i] == b: + forced_a.append(i) + else: + open_free.append(i) + + if len(forced_a) > a_ct or len(forced_b) > b_ct: + # No derangement possible + return + + for i in forced_a: + rv[i] = a + for i in forced_b: + rv[i] = b + for a_place in combinations(open_free, a_ct - len(forced_a)): + for a_pos in a_place: + rv[a_pos] = a + for i in open_free: + if rv[i] is None: # anything not in the subset is set to b + rv[i] = b + yield rv + # Clean up/undo the final placements + for i in open_free: + rv[i] = None + + # additional cleanup - clear forced_a, forced_b + for i in forced_a: + rv[i] = None + for i in forced_b: + rv[i] = None + + def iopen(v): + # return indices at which element v can be placed in rv: + # locations which are not already occupied if that location + # does not already contain v in the same location of s + return [i for i in range(n) if rv[i] is None and s[i] != v] + + def do(j): + if j == 1: + # handle the last two elements (regardless of multiplicity) + # with a special method + yield from finish_derangements() + else: + # place the mx elements of M into a subset of places + # into which it can be replaced + M, mx = take[j] + for i in combinations(iopen(M), mx): + # place M + for ii in i: + rv[ii] = M + # recursively place the next element + yield from do(j - 1) + # mark positions where M was placed as once again + # open for placement of other elements + for ii in i: + rv[ii] = None + + # process elements in order of canonically decreasing multiplicity + take = sorted(ms.items(), key=lambda x:(x[1], x[0])) + yield from do(len(take) - 1) + rv[:] = [None]*n + + +def random_derangement(t, choice=None, strict=True): + """Return a list of elements in which none are in the same positions + as they were originally. If an element fills more than half of the positions + then an error will be raised since no derangement is possible. To obtain + a derangement of as many items as possible--with some of the most numerous + remaining in their original positions--pass `strict=False`. To produce a + pseudorandom derangment, pass a pseudorandom selector like `choice` (see + below). + + Examples + ======== + + >>> from sympy.utilities.iterables import random_derangement + >>> t = 'SymPy: a CAS in pure Python' + >>> d = random_derangement(t) + >>> all(i != j for i, j in zip(d, t)) + True + + A predictable result can be obtained by using a pseudorandom + generator for the choice: + + >>> from sympy.core.random import seed, choice as c + >>> seed(1) + >>> d = [''.join(random_derangement(t, c)) for i in range(5)] + >>> assert len(set(d)) != 1 # we got different values + + By reseeding, the same sequence can be obtained: + + >>> seed(1) + >>> d2 = [''.join(random_derangement(t, c)) for i in range(5)] + >>> assert d == d2 + """ + if choice is None: + import secrets + choice = secrets.choice + def shuffle(rv): + '''Knuth shuffle''' + for i in range(len(rv) - 1, 0, -1): + x = choice(rv[:i + 1]) + j = rv.index(x) + rv[i], rv[j] = rv[j], rv[i] + def pick(rv, n): + '''shuffle rv and return the first n values + ''' + shuffle(rv) + return rv[:n] + ms = multiset(t) + tot = len(t) + ms = sorted(ms.items(), key=lambda x: x[1]) + # if there are not enough spaces for the most + # plentiful element to move to then some of them + # will have to stay in place + M, mx = ms[-1] + n = len(t) + xs = 2*mx - tot + if xs > 0: + if strict: + raise ValueError('no derangement possible') + opts = [i for (i, c) in enumerate(t) if c == ms[-1][0]] + pick(opts, xs) + stay = sorted(opts[:xs]) + rv = list(t) + for i in reversed(stay): + rv.pop(i) + rv = random_derangement(rv, choice) + for i in stay: + rv.insert(i, ms[-1][0]) + return ''.join(rv) if type(t) is str else rv + # the normal derangement calculated from here + if n == len(ms): + # approx 1/3 will succeed + rv = list(t) + while True: + shuffle(rv) + if all(i != j for i,j in zip(rv, t)): + break + else: + # general case + rv = [None]*n + while True: + j = 0 + while j > -len(ms): # do most numerous first + j -= 1 + e, c = ms[j] + opts = [i for i in range(n) if rv[i] is None and t[i] != e] + if len(opts) < c: + for i in range(n): + rv[i] = None + break # try again + pick(opts, c) + for i in range(c): + rv[opts[i]] = e + else: + return rv + return rv + + +def _set_derangements(s): + """ + yield derangements of items in ``s`` which are assumed to contain + no repeated elements + """ + if len(s) < 2: + return + if len(s) == 2: + yield [s[1], s[0]] + return + if len(s) == 3: + yield [s[1], s[2], s[0]] + yield [s[2], s[0], s[1]] + return + for p in permutations(s): + if not any(i == j for i, j in zip(p, s)): + yield list(p) + + +def generate_derangements(s): + """ + Return unique derangements of the elements of iterable ``s``. + + Examples + ======== + + >>> from sympy.utilities.iterables import generate_derangements + >>> list(generate_derangements([0, 1, 2])) + [[1, 2, 0], [2, 0, 1]] + >>> list(generate_derangements([0, 1, 2, 2])) + [[2, 2, 0, 1], [2, 2, 1, 0]] + >>> list(generate_derangements([0, 1, 1])) + [] + + See Also + ======== + + sympy.functions.combinatorial.factorials.subfactorial + + """ + if not has_dups(s): + yield from _set_derangements(s) + else: + for p in multiset_derangements(s): + yield list(p) + + +def necklaces(n, k, free=False): + """ + A routine to generate necklaces that may (free=True) or may not + (free=False) be turned over to be viewed. The "necklaces" returned + are comprised of ``n`` integers (beads) with ``k`` different + values (colors). Only unique necklaces are returned. + + Examples + ======== + + >>> from sympy.utilities.iterables import necklaces, bracelets + >>> def show(s, i): + ... return ''.join(s[j] for j in i) + + The "unrestricted necklace" is sometimes also referred to as a + "bracelet" (an object that can be turned over, a sequence that can + be reversed) and the term "necklace" is used to imply a sequence + that cannot be reversed. So ACB == ABC for a bracelet (rotate and + reverse) while the two are different for a necklace since rotation + alone cannot make the two sequences the same. + + (mnemonic: Bracelets can be viewed Backwards, but Not Necklaces.) + + >>> B = [show('ABC', i) for i in bracelets(3, 3)] + >>> N = [show('ABC', i) for i in necklaces(3, 3)] + >>> set(N) - set(B) + {'ACB'} + + >>> list(necklaces(4, 2)) + [(0, 0, 0, 0), (0, 0, 0, 1), (0, 0, 1, 1), + (0, 1, 0, 1), (0, 1, 1, 1), (1, 1, 1, 1)] + + >>> [show('.o', i) for i in bracelets(4, 2)] + ['....', '...o', '..oo', '.o.o', '.ooo', 'oooo'] + + References + ========== + + .. [1] https://mathworld.wolfram.com/Necklace.html + + .. [2] Frank Ruskey, Carla Savage, and Terry Min Yih Wang, + Generating necklaces, Journal of Algorithms 13 (1992), 414-430; + https://doi.org/10.1016/0196-6774(92)90047-G + + """ + # The FKM algorithm + if k == 0 and n > 0: + return + a = [0]*n + yield tuple(a) + if n == 0: + return + while True: + i = n - 1 + while a[i] == k - 1: + i -= 1 + if i == -1: + return + a[i] += 1 + for j in range(n - i - 1): + a[j + i + 1] = a[j] + if n % (i + 1) == 0 and (not free or all(a <= a[j::-1] + a[-1:j:-1] for j in range(n - 1))): + # No need to test j = n - 1. + yield tuple(a) + + +def bracelets(n, k): + """Wrapper to necklaces to return a free (unrestricted) necklace.""" + return necklaces(n, k, free=True) + + +def generate_oriented_forest(n): + """ + This algorithm generates oriented forests. + + An oriented graph is a directed graph having no symmetric pair of directed + edges. A forest is an acyclic graph, i.e., it has no cycles. A forest can + also be described as a disjoint union of trees, which are graphs in which + any two vertices are connected by exactly one simple path. + + Examples + ======== + + >>> from sympy.utilities.iterables import generate_oriented_forest + >>> list(generate_oriented_forest(4)) + [[0, 1, 2, 3], [0, 1, 2, 2], [0, 1, 2, 1], [0, 1, 2, 0], \ + [0, 1, 1, 1], [0, 1, 1, 0], [0, 1, 0, 1], [0, 1, 0, 0], [0, 0, 0, 0]] + + References + ========== + + .. [1] T. Beyer and S.M. Hedetniemi: constant time generation of + rooted trees, SIAM J. Computing Vol. 9, No. 4, November 1980 + + .. [2] https://stackoverflow.com/questions/1633833/oriented-forest-taocp-algorithm-in-python + + """ + P = list(range(-1, n)) + while True: + yield P[1:] + if P[n] > 0: + P[n] = P[P[n]] + else: + for p in range(n - 1, 0, -1): + if P[p] != 0: + target = P[p] - 1 + for q in range(p - 1, 0, -1): + if P[q] == target: + break + offset = p - q + for i in range(p, n + 1): + P[i] = P[i - offset] + break + else: + break + + +def minlex(seq, directed=True, key=None): + r""" + Return the rotation of the sequence in which the lexically smallest + elements appear first, e.g. `cba \rightarrow acb`. + + The sequence returned is a tuple, unless the input sequence is a string + in which case a string is returned. + + If ``directed`` is False then the smaller of the sequence and the + reversed sequence is returned, e.g. `cba \rightarrow abc`. + + If ``key`` is not None then it is used to extract a comparison key from each element in iterable. + + Examples + ======== + + >>> from sympy.combinatorics.polyhedron import minlex + >>> minlex((1, 2, 0)) + (0, 1, 2) + >>> minlex((1, 0, 2)) + (0, 2, 1) + >>> minlex((1, 0, 2), directed=False) + (0, 1, 2) + + >>> minlex('11010011000', directed=True) + '00011010011' + >>> minlex('11010011000', directed=False) + '00011001011' + + >>> minlex(('bb', 'aaa', 'c', 'a')) + ('a', 'bb', 'aaa', 'c') + >>> minlex(('bb', 'aaa', 'c', 'a'), key=len) + ('c', 'a', 'bb', 'aaa') + + """ + from sympy.functions.elementary.miscellaneous import Id + if key is None: key = Id + best = rotate_left(seq, least_rotation(seq, key=key)) + if not directed: + rseq = seq[::-1] + rbest = rotate_left(rseq, least_rotation(rseq, key=key)) + best = min(best, rbest, key=key) + + # Convert to tuple, unless we started with a string. + return tuple(best) if not isinstance(seq, str) else best + + +def runs(seq, op=gt): + """Group the sequence into lists in which successive elements + all compare the same with the comparison operator, ``op``: + op(seq[i + 1], seq[i]) is True from all elements in a run. + + Examples + ======== + + >>> from sympy.utilities.iterables import runs + >>> from operator import ge + >>> runs([0, 1, 2, 2, 1, 4, 3, 2, 2]) + [[0, 1, 2], [2], [1, 4], [3], [2], [2]] + >>> runs([0, 1, 2, 2, 1, 4, 3, 2, 2], op=ge) + [[0, 1, 2, 2], [1, 4], [3], [2, 2]] + """ + cycles = [] + seq = iter(seq) + try: + run = [next(seq)] + except StopIteration: + return [] + while True: + try: + ei = next(seq) + except StopIteration: + break + if op(ei, run[-1]): + run.append(ei) + continue + else: + cycles.append(run) + run = [ei] + if run: + cycles.append(run) + return cycles + + +def sequence_partitions(l, n, /): + r"""Returns the partition of sequence $l$ into $n$ bins + + Explanation + =========== + + Given the sequence $l_1 \cdots l_m \in V^+$ where + $V^+$ is the Kleene plus of $V$ + + The set of $n$ partitions of $l$ is defined as: + + .. math:: + \{(s_1, \cdots, s_n) | s_1 \in V^+, \cdots, s_n \in V^+, + s_1 \cdots s_n = l_1 \cdots l_m\} + + Parameters + ========== + + l : Sequence[T] + A nonempty sequence of any Python objects + + n : int + A positive integer + + Yields + ====== + + out : list[Sequence[T]] + A list of sequences with concatenation equals $l$. + This should conform with the type of $l$. + + Examples + ======== + + >>> from sympy.utilities.iterables import sequence_partitions + >>> for out in sequence_partitions([1, 2, 3, 4], 2): + ... print(out) + [[1], [2, 3, 4]] + [[1, 2], [3, 4]] + [[1, 2, 3], [4]] + + Notes + ===== + + This is modified version of EnricoGiampieri's partition generator + from https://stackoverflow.com/questions/13131491/partition-n-items-into-k-bins-in-python-lazily + + See Also + ======== + + sequence_partitions_empty + """ + # Asserting l is nonempty is done only for sanity check + if n == 1 and l: + yield [l] + return + for i in range(1, len(l)): + for part in sequence_partitions(l[i:], n - 1): + yield [l[:i]] + part + + +def sequence_partitions_empty(l, n, /): + r"""Returns the partition of sequence $l$ into $n$ bins with + empty sequence + + Explanation + =========== + + Given the sequence $l_1 \cdots l_m \in V^*$ where + $V^*$ is the Kleene star of $V$ + + The set of $n$ partitions of $l$ is defined as: + + .. math:: + \{(s_1, \cdots, s_n) | s_1 \in V^*, \cdots, s_n \in V^*, + s_1 \cdots s_n = l_1 \cdots l_m\} + + There are more combinations than :func:`sequence_partitions` because + empty sequence can fill everywhere, so we try to provide different + utility for this. + + Parameters + ========== + + l : Sequence[T] + A sequence of any Python objects (can be possibly empty) + + n : int + A positive integer + + Yields + ====== + + out : list[Sequence[T]] + A list of sequences with concatenation equals $l$. + This should conform with the type of $l$. + + Examples + ======== + + >>> from sympy.utilities.iterables import sequence_partitions_empty + >>> for out in sequence_partitions_empty([1, 2, 3, 4], 2): + ... print(out) + [[], [1, 2, 3, 4]] + [[1], [2, 3, 4]] + [[1, 2], [3, 4]] + [[1, 2, 3], [4]] + [[1, 2, 3, 4], []] + + See Also + ======== + + sequence_partitions + """ + if n < 1: + return + if n == 1: + yield [l] + return + for i in range(0, len(l) + 1): + for part in sequence_partitions_empty(l[i:], n - 1): + yield [l[:i]] + part + + +def kbins(l, k, ordered=None): + """ + Return sequence ``l`` partitioned into ``k`` bins. + + Examples + ======== + + The default is to give the items in the same order, but grouped + into k partitions without any reordering: + + >>> from sympy.utilities.iterables import kbins + >>> for p in kbins(list(range(5)), 2): + ... print(p) + ... + [[0], [1, 2, 3, 4]] + [[0, 1], [2, 3, 4]] + [[0, 1, 2], [3, 4]] + [[0, 1, 2, 3], [4]] + + The ``ordered`` flag is either None (to give the simple partition + of the elements) or is a 2 digit integer indicating whether the order of + the bins and the order of the items in the bins matters. Given:: + + A = [[0], [1, 2]] + B = [[1, 2], [0]] + C = [[2, 1], [0]] + D = [[0], [2, 1]] + + the following values for ``ordered`` have the shown meanings:: + + 00 means A == B == C == D + 01 means A == B + 10 means A == D + 11 means A == A + + >>> for ordered_flag in [None, 0, 1, 10, 11]: + ... print('ordered = %s' % ordered_flag) + ... for p in kbins(list(range(3)), 2, ordered=ordered_flag): + ... print(' %s' % p) + ... + ordered = None + [[0], [1, 2]] + [[0, 1], [2]] + ordered = 0 + [[0, 1], [2]] + [[0, 2], [1]] + [[0], [1, 2]] + ordered = 1 + [[0], [1, 2]] + [[0], [2, 1]] + [[1], [0, 2]] + [[1], [2, 0]] + [[2], [0, 1]] + [[2], [1, 0]] + ordered = 10 + [[0, 1], [2]] + [[2], [0, 1]] + [[0, 2], [1]] + [[1], [0, 2]] + [[0], [1, 2]] + [[1, 2], [0]] + ordered = 11 + [[0], [1, 2]] + [[0, 1], [2]] + [[0], [2, 1]] + [[0, 2], [1]] + [[1], [0, 2]] + [[1, 0], [2]] + [[1], [2, 0]] + [[1, 2], [0]] + [[2], [0, 1]] + [[2, 0], [1]] + [[2], [1, 0]] + [[2, 1], [0]] + + See Also + ======== + + partitions, multiset_partitions + + """ + if ordered is None: + yield from sequence_partitions(l, k) + elif ordered == 11: + for pl in multiset_permutations(l): + pl = list(pl) + yield from sequence_partitions(pl, k) + elif ordered == 00: + yield from multiset_partitions(l, k) + elif ordered == 10: + for p in multiset_partitions(l, k): + for perm in permutations(p): + yield list(perm) + elif ordered == 1: + for kgot, p in partitions(len(l), k, size=True): + if kgot != k: + continue + for li in multiset_permutations(l): + rv = [] + i = j = 0 + li = list(li) + for size, multiplicity in sorted(p.items()): + for m in range(multiplicity): + j = i + size + rv.append(li[i: j]) + i = j + yield rv + else: + raise ValueError( + 'ordered must be one of 00, 01, 10 or 11, not %s' % ordered) + + +def permute_signs(t): + """Return iterator in which the signs of non-zero elements + of t are permuted. + + Examples + ======== + + >>> from sympy.utilities.iterables import permute_signs + >>> list(permute_signs((0, 1, 2))) + [(0, 1, 2), (0, -1, 2), (0, 1, -2), (0, -1, -2)] + """ + for signs in product(*[(1, -1)]*(len(t) - t.count(0))): + signs = list(signs) + yield type(t)([i*signs.pop() if i else i for i in t]) + + +def signed_permutations(t): + """Return iterator in which the signs of non-zero elements + of t and the order of the elements are permuted. + + Examples + ======== + + >>> from sympy.utilities.iterables import signed_permutations + >>> list(signed_permutations((0, 1, 2))) + [(0, 1, 2), (0, -1, 2), (0, 1, -2), (0, -1, -2), (0, 2, 1), + (0, -2, 1), (0, 2, -1), (0, -2, -1), (1, 0, 2), (-1, 0, 2), + (1, 0, -2), (-1, 0, -2), (1, 2, 0), (-1, 2, 0), (1, -2, 0), + (-1, -2, 0), (2, 0, 1), (-2, 0, 1), (2, 0, -1), (-2, 0, -1), + (2, 1, 0), (-2, 1, 0), (2, -1, 0), (-2, -1, 0)] + """ + return (type(t)(i) for j in permutations(t) + for i in permute_signs(j)) + + +def rotations(s, dir=1): + """Return a generator giving the items in s as list where + each subsequent list has the items rotated to the left (default) + or right (``dir=-1``) relative to the previous list. + + Examples + ======== + + >>> from sympy import rotations + >>> list(rotations([1,2,3])) + [[1, 2, 3], [2, 3, 1], [3, 1, 2]] + >>> list(rotations([1,2,3], -1)) + [[1, 2, 3], [3, 1, 2], [2, 3, 1]] + """ + seq = list(s) + for i in range(len(seq)): + yield seq + seq = rotate_left(seq, dir) + + +def roundrobin(*iterables): + """roundrobin recipe taken from itertools documentation: + https://docs.python.org/3/library/itertools.html#itertools-recipes + + roundrobin('ABC', 'D', 'EF') --> A D E B F C + + Recipe credited to George Sakkis + """ + nexts = cycle(iter(it).__next__ for it in iterables) + + pending = len(iterables) + while pending: + try: + for nxt in nexts: + yield nxt() + except StopIteration: + pending -= 1 + nexts = cycle(islice(nexts, pending)) + + + +class NotIterable: + """ + Use this as mixin when creating a class which is not supposed to + return true when iterable() is called on its instances because + calling list() on the instance, for example, would result in + an infinite loop. + """ + pass + + +def iterable(i, exclude=(str, dict, NotIterable)): + """ + Return a boolean indicating whether ``i`` is SymPy iterable. + True also indicates that the iterator is finite, e.g. you can + call list(...) on the instance. + + When SymPy is working with iterables, it is almost always assuming + that the iterable is not a string or a mapping, so those are excluded + by default. If you want a pure Python definition, make exclude=None. To + exclude multiple items, pass them as a tuple. + + You can also set the _iterable attribute to True or False on your class, + which will override the checks here, including the exclude test. + + As a rule of thumb, some SymPy functions use this to check if they should + recursively map over an object. If an object is technically iterable in + the Python sense but does not desire this behavior (e.g., because its + iteration is not finite, or because iteration might induce an unwanted + computation), it should disable it by setting the _iterable attribute to False. + + See also: is_sequence + + Examples + ======== + + >>> from sympy.utilities.iterables import iterable + >>> from sympy import Tuple + >>> things = [[1], (1,), set([1]), Tuple(1), (j for j in [1, 2]), {1:2}, '1', 1] + >>> for i in things: + ... print('%s %s' % (iterable(i), type(i))) + True <... 'list'> + True <... 'tuple'> + True <... 'set'> + True + True <... 'generator'> + False <... 'dict'> + False <... 'str'> + False <... 'int'> + + >>> iterable({}, exclude=None) + True + >>> iterable({}, exclude=str) + True + >>> iterable("no", exclude=str) + False + + """ + if hasattr(i, '_iterable'): + return i._iterable + try: + iter(i) + except TypeError: + return False + if exclude: + return not isinstance(i, exclude) + return True + + +def is_sequence(i, include=None): + """ + Return a boolean indicating whether ``i`` is a sequence in the SymPy + sense. If anything that fails the test below should be included as + being a sequence for your application, set 'include' to that object's + type; multiple types should be passed as a tuple of types. + + Note: although generators can generate a sequence, they often need special + handling to make sure their elements are captured before the generator is + exhausted, so these are not included by default in the definition of a + sequence. + + See also: iterable + + Examples + ======== + + >>> from sympy.utilities.iterables import is_sequence + >>> from types import GeneratorType + >>> is_sequence([]) + True + >>> is_sequence(set()) + False + >>> is_sequence('abc') + False + >>> is_sequence('abc', include=str) + True + >>> generator = (c for c in 'abc') + >>> is_sequence(generator) + False + >>> is_sequence(generator, include=(str, GeneratorType)) + True + + """ + return (hasattr(i, '__getitem__') and + iterable(i) or + bool(include) and + isinstance(i, include)) + + +@deprecated( + """ + Using postorder_traversal from the sympy.utilities.iterables submodule is + deprecated. + + Instead, use postorder_traversal from the top-level sympy namespace, like + + sympy.postorder_traversal + """, + deprecated_since_version="1.10", + active_deprecations_target="deprecated-traversal-functions-moved") +def postorder_traversal(node, keys=None): + from sympy.core.traversal import postorder_traversal as _postorder_traversal + return _postorder_traversal(node, keys=keys) + + +@deprecated( + """ + Using interactive_traversal from the sympy.utilities.iterables submodule + is deprecated. + + Instead, use interactive_traversal from the top-level sympy namespace, + like + + sympy.interactive_traversal + """, + deprecated_since_version="1.10", + active_deprecations_target="deprecated-traversal-functions-moved") +def interactive_traversal(expr): + from sympy.interactive.traversal import interactive_traversal as _interactive_traversal + return _interactive_traversal(expr) + + +@deprecated( + """ + Importing default_sort_key from sympy.utilities.iterables is deprecated. + Use from sympy import default_sort_key instead. + """, + deprecated_since_version="1.10", +active_deprecations_target="deprecated-sympy-core-compatibility", +) +def default_sort_key(*args, **kwargs): + from sympy import default_sort_key as _default_sort_key + return _default_sort_key(*args, **kwargs) + + +@deprecated( + """ + Importing default_sort_key from sympy.utilities.iterables is deprecated. + Use from sympy import default_sort_key instead. + """, + deprecated_since_version="1.10", +active_deprecations_target="deprecated-sympy-core-compatibility", +) +def ordered(*args, **kwargs): + from sympy import ordered as _ordered + return _ordered(*args, **kwargs) diff --git a/shared/package-files/56/569318a3168345477b972293d086b9318dbcdb6c79a4df1dbd1f7c57ed7a296e/blob b/shared/package-files/56/569318a3168345477b972293d086b9318dbcdb6c79a4df1dbd1f7c57ed7a296e/blob new file mode 100644 index 0000000000000000000000000000000000000000..5b1ac77954b86137899996a2e8406c89ed082213 --- /dev/null +++ b/shared/package-files/56/569318a3168345477b972293d086b9318dbcdb6c79a4df1dbd1f7c57ed7a296e/blob @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:569318a3168345477b972293d086b9318dbcdb6c79a4df1dbd1f7c57ed7a296e +size 2733821792 diff --git a/shared/package-files/56/56944549c98227170ca64fe6a5c15391e4ea6df01206e1ed7d7b75aee14ced6c/blob b/shared/package-files/56/56944549c98227170ca64fe6a5c15391e4ea6df01206e1ed7d7b75aee14ced6c/blob new file mode 100644 index 0000000000000000000000000000000000000000..86bb9af4c160ff9a9355f2c0982e531d1548928b --- /dev/null +++ b/shared/package-files/56/56944549c98227170ca64fe6a5c15391e4ea6df01206e1ed7d7b75aee14ced6c/blob @@ -0,0 +1,331 @@ +# -*- coding: utf-8 -*- +""" +Class and program to colorize python source code for ANSI terminals. + +Based on an HTML code highlighter by Jurgen Hermann found at: +http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/52298 + +Modifications by Fernando Perez (fperez@colorado.edu). + +Information on the original HTML highlighter follows: + +MoinMoin - Python Source Parser + +Title: Colorize Python source using the built-in tokenizer + +Submitter: Jurgen Hermann +Last Updated:2001/04/06 + +Version no:1.2 + +Description: + +This code is part of MoinMoin (http://moin.sourceforge.net/) and converts +Python source code to HTML markup, rendering comments, keywords, +operators, numeric and string literals in different colors. + +It shows how to use the built-in keyword, token and tokenize modules to +scan Python source code and re-emit it with no changes to its original +formatting (which is the hard part). +""" + +__all__ = ['ANSICodeColors', 'Parser'] + +_scheme_default = 'Linux' + + +# Imports +import keyword +import os +import sys +import token +import tokenize + +generate_tokens = tokenize.generate_tokens + +from IPython.utils.coloransi import TermColors, InputTermColors,ColorScheme, ColorSchemeTable +from .colorable import Colorable +from io import StringIO + +############################################################################# +### Python Source Parser (does Highlighting) +############################################################################# + +_KEYWORD = token.NT_OFFSET + 1 +_TEXT = token.NT_OFFSET + 2 + +#**************************************************************************** +# Builtin color schemes + +Colors = TermColors # just a shorthand + +# Build a few color schemes +NoColor = ColorScheme( + 'NoColor',{ + 'header' : Colors.NoColor, + token.NUMBER : Colors.NoColor, + token.OP : Colors.NoColor, + token.STRING : Colors.NoColor, + tokenize.COMMENT : Colors.NoColor, + token.NAME : Colors.NoColor, + token.ERRORTOKEN : Colors.NoColor, + + _KEYWORD : Colors.NoColor, + _TEXT : Colors.NoColor, + + 'in_prompt' : InputTermColors.NoColor, # Input prompt + 'in_number' : InputTermColors.NoColor, # Input prompt number + 'in_prompt2' : InputTermColors.NoColor, # Continuation prompt + 'in_normal' : InputTermColors.NoColor, # color off (usu. Colors.Normal) + + 'out_prompt' : Colors.NoColor, # Output prompt + 'out_number' : Colors.NoColor, # Output prompt number + + 'normal' : Colors.NoColor # color off (usu. Colors.Normal) + } ) + +LinuxColors = ColorScheme( + 'Linux',{ + 'header' : Colors.LightRed, + token.NUMBER : Colors.LightCyan, + token.OP : Colors.Yellow, + token.STRING : Colors.LightBlue, + tokenize.COMMENT : Colors.LightRed, + token.NAME : Colors.Normal, + token.ERRORTOKEN : Colors.Red, + + _KEYWORD : Colors.LightGreen, + _TEXT : Colors.Yellow, + + 'in_prompt' : InputTermColors.Green, + 'in_number' : InputTermColors.LightGreen, + 'in_prompt2' : InputTermColors.Green, + 'in_normal' : InputTermColors.Normal, # color off (usu. Colors.Normal) + + 'out_prompt' : Colors.Red, + 'out_number' : Colors.LightRed, + + 'normal' : Colors.Normal # color off (usu. Colors.Normal) + } ) + +NeutralColors = ColorScheme( + 'Neutral',{ + 'header' : Colors.Red, + token.NUMBER : Colors.Cyan, + token.OP : Colors.Blue, + token.STRING : Colors.Blue, + tokenize.COMMENT : Colors.Red, + token.NAME : Colors.Normal, + token.ERRORTOKEN : Colors.Red, + + _KEYWORD : Colors.Green, + _TEXT : Colors.Blue, + + 'in_prompt' : InputTermColors.Blue, + 'in_number' : InputTermColors.LightBlue, + 'in_prompt2' : InputTermColors.Blue, + 'in_normal' : InputTermColors.Normal, # color off (usu. Colors.Normal) + + 'out_prompt' : Colors.Red, + 'out_number' : Colors.LightRed, + + 'normal' : Colors.Normal # color off (usu. Colors.Normal) + } ) + +# Hack: the 'neutral' colours are not very visible on a dark background on +# Windows. Since Windows command prompts have a dark background by default, and +# relatively few users are likely to alter that, we will use the 'Linux' colours, +# designed for a dark background, as the default on Windows. Changing it here +# avoids affecting the prompt colours rendered by prompt_toolkit, where the +# neutral defaults do work OK. + +if os.name == 'nt': + NeutralColors = LinuxColors.copy(name='Neutral') + +LightBGColors = ColorScheme( + 'LightBG',{ + 'header' : Colors.Red, + token.NUMBER : Colors.Cyan, + token.OP : Colors.Blue, + token.STRING : Colors.Blue, + tokenize.COMMENT : Colors.Red, + token.NAME : Colors.Normal, + token.ERRORTOKEN : Colors.Red, + + + _KEYWORD : Colors.Green, + _TEXT : Colors.Blue, + + 'in_prompt' : InputTermColors.Blue, + 'in_number' : InputTermColors.LightBlue, + 'in_prompt2' : InputTermColors.Blue, + 'in_normal' : InputTermColors.Normal, # color off (usu. Colors.Normal) + + 'out_prompt' : Colors.Red, + 'out_number' : Colors.LightRed, + + 'normal' : Colors.Normal # color off (usu. Colors.Normal) + } ) + +# Build table of color schemes (needed by the parser) +ANSICodeColors = ColorSchemeTable([NoColor,LinuxColors,LightBGColors, NeutralColors], + _scheme_default) + +Undefined = object() + +class Parser(Colorable): + """ Format colored Python source. + """ + + def __init__(self, color_table=None, out = sys.stdout, parent=None, style=None): + """ Create a parser with a specified color table and output channel. + + Call format() to process code. + """ + + super(Parser, self).__init__(parent=parent) + + self.color_table = color_table if color_table else ANSICodeColors + self.out = out + self.pos = None + self.lines = None + self.raw = None + if not style: + self.style = self.default_style + else: + self.style = style + + + def format(self, raw, out=None, scheme=Undefined): + import warnings + if scheme is not Undefined: + warnings.warn('The `scheme` argument of IPython.utils.PyColorize:Parser.format is deprecated since IPython 6.0.' + 'It will have no effect. Set the parser `style` directly.', + stacklevel=2) + return self.format2(raw, out)[0] + + def format2(self, raw, out = None): + """ Parse and send the colored source. + + If out and scheme are not specified, the defaults (given to + constructor) are used. + + out should be a file-type object. Optionally, out can be given as the + string 'str' and the parser will automatically return the output in a + string.""" + + string_output = 0 + if out == 'str' or self.out == 'str' or \ + isinstance(self.out, StringIO): + # XXX - I don't really like this state handling logic, but at this + # point I don't want to make major changes, so adding the + # isinstance() check is the simplest I can do to ensure correct + # behavior. + out_old = self.out + self.out = StringIO() + string_output = 1 + elif out is not None: + self.out = out + else: + raise ValueError('`out` or `self.out` should be file-like or the value `"str"`') + + # Fast return of the unmodified input for NoColor scheme + if self.style == 'NoColor': + error = False + self.out.write(raw) + if string_output: + return raw, error + return None, error + + # local shorthands + colors = self.color_table[self.style].colors + self.colors = colors # put in object so __call__ sees it + + # Remove trailing whitespace and normalize tabs + self.raw = raw.expandtabs().rstrip() + + # store line offsets in self.lines + self.lines = [0, 0] + pos = 0 + raw_find = self.raw.find + lines_append = self.lines.append + while True: + pos = raw_find('\n', pos) + 1 + if not pos: + break + lines_append(pos) + lines_append(len(self.raw)) + + # parse the source and write it + self.pos = 0 + text = StringIO(self.raw) + + error = False + try: + for atoken in generate_tokens(text.readline): + self(*atoken) + except tokenize.TokenError as ex: + msg = ex.args[0] + line = ex.args[1][0] + self.out.write("%s\n\n*** ERROR: %s%s%s\n" % + (colors[token.ERRORTOKEN], + msg, self.raw[self.lines[line]:], + colors.normal) + ) + error = True + self.out.write(colors.normal+'\n') + if string_output: + output = self.out.getvalue() + self.out = out_old + return (output, error) + return (None, error) + + + def _inner_call_(self, toktype, toktext, start_pos): + """like call but write to a temporary buffer""" + buff = StringIO() + srow, scol = start_pos + colors = self.colors + owrite = buff.write + + # line separator, so this works across platforms + linesep = os.linesep + + # calculate new positions + oldpos = self.pos + newpos = self.lines[srow] + scol + self.pos = newpos + len(toktext) + + # send the original whitespace, if needed + if newpos > oldpos: + owrite(self.raw[oldpos:newpos]) + + # skip indenting tokens + if toktype in [token.INDENT, token.DEDENT]: + self.pos = newpos + buff.seek(0) + return buff.read() + + # map token type to a color group + if token.LPAR <= toktype <= token.OP: + toktype = token.OP + elif toktype == token.NAME and keyword.iskeyword(toktext): + toktype = _KEYWORD + color = colors.get(toktype, colors[_TEXT]) + + # Triple quoted strings must be handled carefully so that backtracking + # in pagers works correctly. We need color terminators on _each_ line. + if linesep in toktext: + toktext = toktext.replace(linesep, '%s%s%s' % + (colors.normal,linesep,color)) + + # send text + owrite('%s%s%s' % (color,toktext,colors.normal)) + buff.seek(0) + return buff.read() + + + def __call__(self, toktype, toktext, start_pos, end_pos, line): + """ Token handler, with syntax highlighting.""" + self.out.write( + self._inner_call_(toktype, toktext, start_pos)) diff --git a/shared/package-files/56/5695c819ce3892ec785fe6c12ca2c3b079296dcd3fd3b627a183e67e9a84e1be/blob b/shared/package-files/56/5695c819ce3892ec785fe6c12ca2c3b079296dcd3fd3b627a183e67e9a84e1be/blob new file mode 100644 index 0000000000000000000000000000000000000000..16957f3219887a1ff1efd63af3637313fa7f4fd2 --- /dev/null +++ b/shared/package-files/56/5695c819ce3892ec785fe6c12ca2c3b079296dcd3fd3b627a183e67e9a84e1be/blob @@ -0,0 +1,26 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace native { +struct TORCH_API structured_fractional_max_pool2d_out_cpu : public at::meta::structured_fractional_max_pool2d { +void impl(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, const at::Tensor & output, const at::Tensor & indices); +}; +struct TORCH_API structured_fractional_max_pool2d_out_cuda : public at::meta::structured_fractional_max_pool2d { +void impl(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, const at::Tensor & output, const at::Tensor & indices); +}; +} // namespace native +} // namespace at diff --git a/shared/package-files/56/56976c9e2a8a13deb90901908fad52e22d58f0aa245059b73970aea3b54bc7f0/blob b/shared/package-files/56/56976c9e2a8a13deb90901908fad52e22d58f0aa245059b73970aea3b54bc7f0/blob new file mode 100644 index 0000000000000000000000000000000000000000..946f1b44746743e286f5a8d06816572df52b8c47 --- /dev/null +++ b/shared/package-files/56/56976c9e2a8a13deb90901908fad52e22d58f0aa245059b73970aea3b54bc7f0/blob @@ -0,0 +1,59 @@ +#---------------------------------------------------------------- +# Generated CMake target import file for configuration "Release". +#---------------------------------------------------------------- + +# Commands may need to know the format version. +set(CMAKE_IMPORT_FILE_VERSION 1) + +# Import target "c10_cuda" for configuration "Release" +set_property(TARGET c10_cuda APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(c10_cuda PROPERTIES + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libc10_cuda.so" + IMPORTED_SONAME_RELEASE "libc10_cuda.so" + ) + +list(APPEND _cmake_import_check_targets c10_cuda ) +list(APPEND _cmake_import_check_files_for_c10_cuda "${_IMPORT_PREFIX}/lib/libc10_cuda.so" ) + +# Import target "c10" for configuration "Release" +set_property(TARGET c10 APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(c10 PROPERTIES + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libc10.so" + IMPORTED_SONAME_RELEASE "libc10.so" + ) + +list(APPEND _cmake_import_check_targets c10 ) +list(APPEND _cmake_import_check_files_for_c10 "${_IMPORT_PREFIX}/lib/libc10.so" ) + +# Import target "torch_cpu" for configuration "Release" +set_property(TARGET torch_cpu APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(torch_cpu PROPERTIES + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libtorch_cpu.so" + IMPORTED_SONAME_RELEASE "libtorch_cpu.so" + ) + +list(APPEND _cmake_import_check_targets torch_cpu ) +list(APPEND _cmake_import_check_files_for_torch_cpu "${_IMPORT_PREFIX}/lib/libtorch_cpu.so" ) + +# Import target "torch_cuda" for configuration "Release" +set_property(TARGET torch_cuda APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(torch_cuda PROPERTIES + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libtorch_cuda.so" + IMPORTED_SONAME_RELEASE "libtorch_cuda.so" + ) + +list(APPEND _cmake_import_check_targets torch_cuda ) +list(APPEND _cmake_import_check_files_for_torch_cuda "${_IMPORT_PREFIX}/lib/libtorch_cuda.so" ) + +# Import target "torch" for configuration "Release" +set_property(TARGET torch APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE) +set_target_properties(torch PROPERTIES + IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libtorch.so" + IMPORTED_SONAME_RELEASE "libtorch.so" + ) + +list(APPEND _cmake_import_check_targets torch ) +list(APPEND _cmake_import_check_files_for_torch "${_IMPORT_PREFIX}/lib/libtorch.so" ) + +# Commands beyond this point should not need to know the version. +set(CMAKE_IMPORT_FILE_VERSION) diff --git a/shared/package-files/56/5698c36139a83034d9718ec2b06ce29c187447f3880aec0eca45826e60914195/blob b/shared/package-files/56/5698c36139a83034d9718ec2b06ce29c187447f3880aec0eca45826e60914195/blob new file mode 100644 index 0000000000000000000000000000000000000000..4fd4d555845aa9dddb42d86c4f4968f86d30014d --- /dev/null +++ b/shared/package-files/56/5698c36139a83034d9718ec2b06ce29c187447f3880aec0eca45826e60914195/blob @@ -0,0 +1,85 @@ +""" +This file tests the generic aspects of ArrayMethod. At the time of writing +this is private API, but when added, public API may be added here. +""" + +from __future__ import annotations + +import sys +import types +from typing import Any + +import pytest + +import numpy as np +from numpy.core._multiarray_umath import _get_castingimpl as get_castingimpl + + +class TestResolveDescriptors: + # Test mainly error paths of the resolve_descriptors function, + # note that the `casting_unittests` tests exercise this non-error paths. + + # Casting implementations are the main/only current user: + method = get_castingimpl(type(np.dtype("d")), type(np.dtype("f"))) + + @pytest.mark.parametrize("args", [ + (True,), # Not a tuple. + ((None,)), # Too few elements + ((None, None, None),), # Too many + ((None, None),), # Input dtype is None, which is invalid. + ((np.dtype("d"), True),), # Output dtype is not a dtype + ((np.dtype("f"), None),), # Input dtype does not match method + ]) + def test_invalid_arguments(self, args): + with pytest.raises(TypeError): + self.method._resolve_descriptors(*args) + + +class TestSimpleStridedCall: + # Test mainly error paths of the resolve_descriptors function, + # note that the `casting_unittests` tests exercise this non-error paths. + + # Casting implementations are the main/only current user: + method = get_castingimpl(type(np.dtype("d")), type(np.dtype("f"))) + + @pytest.mark.parametrize(["args", "error"], [ + ((True,), TypeError), # Not a tuple + (((None,),), TypeError), # Too few elements + ((None, None), TypeError), # Inputs are not arrays. + (((None, None, None),), TypeError), # Too many + (((np.arange(3), np.arange(3)),), TypeError), # Incorrect dtypes + (((np.ones(3, dtype=">d"), np.ones(3, dtype=" None: + """Test `ndarray.__class_getitem__`.""" + alias = cls[Any, Any] + assert isinstance(alias, types.GenericAlias) + assert alias.__origin__ is cls + + @pytest.mark.parametrize("arg_len", range(4)) + def test_subscript_tup(self, cls: type[np.ndarray], arg_len: int) -> None: + arg_tup = (Any,) * arg_len + if arg_len in (1, 2): + assert cls[arg_tup] + else: + match = f"Too {'few' if arg_len == 0 else 'many'} arguments" + with pytest.raises(TypeError, match=match): + cls[arg_tup] diff --git a/shared/package-files/56/56995a7e4cbe4c6b501cdff0114bd22af926fa44e3388f2cec9d3b40b4076834/blob b/shared/package-files/56/56995a7e4cbe4c6b501cdff0114bd22af926fa44e3388f2cec9d3b40b4076834/blob new file mode 100644 index 0000000000000000000000000000000000000000..5ad00b4311a2fadc2c4f5fcfd5223ea427d41fa1 --- /dev/null +++ b/shared/package-files/56/56995a7e4cbe4c6b501cdff0114bd22af926fa44e3388f2cec9d3b40b4076834/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 compositeimplicitautograd { + +TORCH_API at::Tensor frobenius_norm(const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false); +TORCH_API at::Tensor & frobenius_norm_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false); +TORCH_API at::Tensor & frobenius_norm_outf(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/shared/package-files/56/569a886ba64263e6b7d788e429d6191f6d49aeb4b9f99dbf0f6d3faed6ffcdc6/blob b/shared/package-files/56/569a886ba64263e6b7d788e429d6191f6d49aeb4b9f99dbf0f6d3faed6ffcdc6/blob new file mode 100644 index 0000000000000000000000000000000000000000..edc4efd811868010515bc98366f777d52554cc3c --- /dev/null +++ b/shared/package-files/56/569a886ba64263e6b7d788e429d6191f6d49aeb4b9f99dbf0f6d3faed6ffcdc6/blob @@ -0,0 +1,39 @@ +#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 gru_input { + using schema = ::std::tuple (const at::Tensor &, const at::Tensor &, at::TensorList, bool, int64_t, double, bool, bool, bool); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::gru") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "input") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "gru.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor)") + static ::std::tuple call(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); + static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first); +}; + +struct TORCH_API gru_data { + using schema = ::std::tuple (const at::Tensor &, const at::Tensor &, const at::Tensor &, at::TensorList, bool, int64_t, double, bool, bool); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::gru") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "data") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor)") + static ::std::tuple call(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); + static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional); +}; + +}} // namespace at::_ops diff --git a/shared/package-files/56/569cbfc563e01cd749c3e2b534a6df3836f369c8123c4d2fcd02689aa24aeaa9/blob b/shared/package-files/56/569cbfc563e01cd749c3e2b534a6df3836f369c8123c4d2fcd02689aa24aeaa9/blob new file mode 100644 index 0000000000000000000000000000000000000000..fa03033a8f89191224d7ab36db142e6ed3716aa5 Binary files /dev/null and b/shared/package-files/56/569cbfc563e01cd749c3e2b534a6df3836f369c8123c4d2fcd02689aa24aeaa9/blob differ diff --git a/shared/package-files/56/569d4d57305f33c8f3d15246868a7750e9ca5dad6698948d237bb48afffe37bd/blob b/shared/package-files/56/569d4d57305f33c8f3d15246868a7750e9ca5dad6698948d237bb48afffe37bd/blob new file mode 100644 index 0000000000000000000000000000000000000000..fe83e56b6be6d8605c473d05cfabbd3468697a32 --- /dev/null +++ b/shared/package-files/56/569d4d57305f33c8f3d15246868a7750e9ca5dad6698948d237bb48afffe37bd/blob @@ -0,0 +1,101 @@ +import sys +from hashlib import _Hash +from typing import Any, ClassVar, Dict, Generic, Optional, Set, TypeVar, Union + +from cryptography.hazmat.primitives import hashes +from cryptography.hazmat.primitives.asymmetric.ec import ( + EllipticCurvePrivateKey, + EllipticCurvePrivateKeyWithSerialization, + EllipticCurvePrivateNumbers, + EllipticCurvePublicKey, + EllipticCurvePublicKeyWithSerialization, + EllipticCurvePublicNumbers, +) +from cryptography.hazmat.primitives.asymmetric.ed25519 import Ed25519PrivateKey, Ed25519PublicKey +from cryptography.hazmat.primitives.asymmetric.rsa import ( + RSAPrivateKey, + RSAPrivateKeyWithSerialization, + RSAPrivateNumbers, + RSAPublicKey, + RSAPublicKeyWithSerialization, + RSAPublicNumbers, +) +from cryptography.hazmat.primitives.asymmetric.utils import Prehashed +from cryptography.hazmat.primitives.hashes import HashAlgorithm + +requires_cryptography = Set[str] + +def get_default_algorithms() -> Dict[str, Algorithm[Any]]: ... + +_K = TypeVar("_K") + +class Algorithm(Generic[_K]): + def prepare_key(self, key: _K) -> _K: ... + def sign(self, msg: bytes, key: _K) -> bytes: ... + def verify(self, msg: bytes, key: _K, sig: bytes) -> bool: ... + @staticmethod + def to_jwk(key_obj: _K) -> str: ... + @staticmethod + def from_jwk(jwk: str) -> _K: ... + +class NoneAlgorithm(Algorithm[None]): + def prepare_key(self, key: Optional[str]) -> None: ... + +class _HashAlg: + def __call__(self, arg: Union[bytes, bytearray, memoryview] = ...) -> _Hash: ... + +class HMACAlgorithm(Algorithm[bytes]): + SHA256: ClassVar[_HashAlg] + SHA384: ClassVar[_HashAlg] + SHA512: ClassVar[_HashAlg] + hash_alg: _HashAlg + def __init__(self, hash_alg: _HashAlg) -> None: ... + def prepare_key(self, key: Union[str, bytes]) -> bytes: ... + @staticmethod + def to_jwk(key_obj: Union[str, bytes]) -> str: ... + @staticmethod + def from_jwk(jwk: Union[str, bytes]) -> bytes: ... + +# Only defined if cryptography is installed. +class RSAAlgorithm(Algorithm[Any]): + SHA256: ClassVar[hashes.SHA256] + SHA384: ClassVar[hashes.SHA384] + SHA512: ClassVar[hashes.SHA512] + hash_alg: Union[HashAlgorithm, Prehashed] + def __init__(self, hash_alg: Union[HashAlgorithm, Prehashed]) -> None: ... + def prepare_key(self, key: Union[bytes, str, RSAPrivateKey, RSAPublicKey]) -> Union[RSAPrivateKey, RSAPublicKey]: ... + @staticmethod + def from_jwk(jwk: Union[str, bytes, Dict[str, Any]]) -> Union[RSAPrivateKey, RSAPublicKey]: ... + def sign(self, msg: bytes, key: RSAPrivateKey) -> bytes: ... + def verify(self, msg: bytes, key: RSAPublicKey, sig: bytes) -> bool: ... + +# Only defined if cryptography is installed. +class ECAlgorithm(Algorithm[Any]): + SHA256: ClassVar[hashes.SHA256] + SHA384: ClassVar[hashes.SHA384] + SHA512: ClassVar[hashes.SHA512] + hash_alg: Union[HashAlgorithm, Prehashed] + def __init__(self, hash_alg: Union[HashAlgorithm, Prehashed]) -> None: ... + def prepare_key( + self, key: Union[bytes, str, EllipticCurvePrivateKey, EllipticCurvePublicKey] + ) -> Union[EllipticCurvePrivateKey, EllipticCurvePublicKey]: ... + @staticmethod + def to_jwk(key_obj: Union[EllipticCurvePrivateKeyWithSerialization, EllipticCurvePublicKeyWithSerialization]) -> str: ... + @staticmethod + def from_jwk(jwk: Union[str, bytes]) -> Union[EllipticCurvePrivateKey, EllipticCurvePublicKey]: ... + def sign(self, msg: bytes, key: EllipticCurvePrivateKey) -> bytes: ... + def verify(self, msg: bytes, key: EllipticCurvePublicKey, sig: bytes) -> bool: ... + +# Only defined if cryptography is installed. Types should be tightened when +# cryptography gets type hints. +# See https://github.com/python/typeshed/issues/2542 +class RSAPSSAlgorithm(RSAAlgorithm): + def sign(self, msg: bytes, key: Any) -> bytes: ... + def verify(self, msg: bytes, key: Any, sig: bytes) -> bool: ... + +# Only defined if cryptography is installed. +class Ed25519Algorithm(Algorithm[Any]): + def __init__(self, **kwargs: Any) -> None: ... + def prepare_key(self, key: Union[str, bytes, Ed25519PrivateKey, Ed25519PublicKey]) -> Any: ... + def sign(self, msg: Union[str, bytes], key: Ed25519PrivateKey) -> bytes: ... + def verify(self, msg: Union[str, bytes], key: Ed25519PublicKey, sig: Union[str, bytes]) -> bool: ... diff --git a/shared/package-files/56/569da10acd97d93411a337e50cdc0984d298279159a89619718e4e62f207853d/blob b/shared/package-files/56/569da10acd97d93411a337e50cdc0984d298279159a89619718e4e62f207853d/blob new file mode 100644 index 0000000000000000000000000000000000000000..82886d3c774b78de0526b2bce2bfc06db6f23a92 --- /dev/null +++ b/shared/package-files/56/569da10acd97d93411a337e50cdc0984d298279159a89619718e4e62f207853d/blob @@ -0,0 +1,34 @@ +""" +:mod:`torch.distributed.optim` exposes DistributedOptimizer, which takes a list +of remote parameters (:class:`~torch.distributed.rpc.RRef`) and runs the +optimizer locally on the workers where the parameters live. The distributed +optimizer can use any of the local optimizer :ref:`optimizer-algorithms` to +apply the gradients on each worker. +""" +import torch +from torch import optim + +from .apply_optimizer_in_backward import ( + _apply_optimizer_in_backward, + _get_in_backward_optimizers, +) +from .functional_adadelta import _FunctionalAdadelta + +from .functional_adagrad import _FunctionalAdagrad +from .functional_adam import _FunctionalAdam +from .functional_adamax import _FunctionalAdamax +from .functional_adamw import _FunctionalAdamW +from .functional_rmsprop import _FunctionalRMSprop +from .functional_rprop import _FunctionalRprop +from .functional_sgd import _FunctionalSGD +from .named_optimizer import _NamedOptimizer +from .utils import as_functional_optim + + +# DistributedOptimizer imports torch.distributed.rpc names, so gate availability +# based on RPC being available. +if hasattr(torch._C, "_rpc_init"): + from .optimizer import DistributedOptimizer + +from .post_localSGD_optimizer import PostLocalSGDOptimizer +from .zero_redundancy_optimizer import ZeroRedundancyOptimizer diff --git a/shared/package-files/56/56a0bc0ea9b79c604b309812e79ae5ecf3a879ad59ba0adcb74ec3f7aebbab98/blob b/shared/package-files/56/56a0bc0ea9b79c604b309812e79ae5ecf3a879ad59ba0adcb74ec3f7aebbab98/blob new file mode 100644 index 0000000000000000000000000000000000000000..f265d57b649dda335d6c4124d401d229154c8bc4 --- /dev/null +++ b/shared/package-files/56/56a0bc0ea9b79c604b309812e79ae5ecf3a879ad59ba0adcb74ec3f7aebbab98/blob @@ -0,0 +1,412 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include +#include +#include +#include +#include + +namespace torch::jit { +struct Node; +struct Value; +struct Graph; +struct Module; + +namespace tracer { + +using ::c10::ivalue::Shared; + +using ::c10::IValue; +using ::c10::ivalue::Future; + +using ::c10::ArrayRef; +using ::c10::TupleType; +using ::c10::TupleTypePtr; +using ::c10::ivalue::ConstantString; + +using torch::autograd::Variable; +using variable_list = std::vector; + +TORCH_API std::atomic& getTracerStateWarnMode(); + +struct TORCH_API TracingState + : public std::enable_shared_from_this { + TracingState(); + ~TracingState(); + + // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes) + std::shared_ptr graph; + // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes) + bool warn = getTracerStateWarnMode(); + // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes) + bool strict = true; + // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes) + bool force_outplace = false; + // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes) + std::function lookup_var_name_fn = + [](const Variable& var) { return ""; }; + + void enterFrame() { + env_stack.emplace_back(); + } + + void leaveFrame() { + env_stack.pop_back(); + } + + void setValue(const IValue& v, Value* value); + void delValue(const IValue& var); + Value* getValue(const IValue& var); + Value* getOutput(const IValue& var, size_t i); + bool hasValue(const IValue& var) const; + + Node* createNode(c10::Symbol op_name, size_t num_outputs); + void insertNode(Node* node); + + private: + using WeakIValue = at::WeakIValue; + + struct WeakIValueHasher { + size_t operator()(const WeakIValue& t) const { + return t.hash(); + } + }; + + struct WeakIValueEq { + bool operator()(const WeakIValue& t1, const WeakIValue& t2) const { + return t1.isSameIdentity(t2); + } + }; + + using Frame = + std::unordered_map; + std::vector env_stack; +}; + +// This is meant to be used as a thread local place, where we can store extra +// info that gets lost when we call into ATen from Python bindings. One example +// for when this happens is when we get an IntArrayRef argument with e.g. sizes +// for view. When tracing, those might be tensors, which let us encode extra +// data dependencies, but once they get to the ATen call where we actually have +// the tracing logic, they get converted into a raw IntArrayRef, and we loose +// all information. To prevent this, we temporarily stash it in here. +// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init) +struct ArgumentStash { + struct IntArrayRefTrace : std::vector { + IntArrayRefTrace(int size) : std::vector(size, nullptr) {} + }; + + static bool empty() { + return stash.intlists.empty(); + } + + TORCH_API static void stashIntArrayRefElem( + const std::string& arg_name, + size_t size, + size_t idx, + const Variable& var); + + static bool hasIntArrayRef(const std::string& arg_name) { + return stash.intlists.count(arg_name) > 0; + } + + static IntArrayRefTrace popIntArrayRef(const std::string& arg_name) { + auto info = std::move(stash.intlists.at(arg_name)); + stash.intlists.erase(arg_name); + return info; + } + + // Value stashing: Use these methods to stash arguments which correspond + // to regular Value*'s in the graph. i.e. they don't require special + // handling like in the case of IntArrayRefs + TORCH_API static void stashValue( + const std::string& arg_name, + size_t idx, + const Variable& var, + const c10::TypePtr& type = nullptr); + + static bool hasValue(const std::string& arg_name) { + return stash.values.count(arg_name) > 0; + } + + static Value* popValue(const std::string& arg_name) { + auto info = stash.values.at(arg_name); + stash.values.erase(arg_name); + return info; + } + + private: + static thread_local ArgumentStash stash; + std::unordered_map intlists; + std::unordered_map values; +}; + +// Retrieve or set the current tracing state. Returns a nullptr if tracing is +// disabled. +TORCH_API const std::shared_ptr& getTracingState(); +TORCH_API void setTracingState(std::shared_ptr state); + +inline bool isTracing() { + return static_cast(getTracingState()); +} + +using warn_fn_type = void (*)(const std::string& msg); +TORCH_API extern const char* WARN_PYTHON_DATAFLOW; +TORCH_API extern const char* WARN_CONSTRUCTOR; +TORCH_API extern const char* WARN_RESIZE; +TORCH_API extern const char* STRICT_TRACER_MSG; +TORCH_API void _do_warn(const char* _reason, const char* _kind); +inline void warn(const char* _reason, const char* _kind = nullptr) { + if (const auto& state = getTracingState()) { + if (!state->warn) + return; + _do_warn(_reason, _kind); + } +} +TORCH_API void setWarn(warn_fn_type fn); + +struct TORCH_API NoWarn { + NoWarn() : state(getTracingState()) { + if (state) { + prev = state->warn; + state->warn = false; + } + } + ~NoWarn() { + if (state) { + state->warn = prev; + } + } + std::shared_ptr state; + bool prev{false}; +}; + +struct WithNestedTracingFrame { + WithNestedTracingFrame() { + getTracingState()->enterFrame(); + } + + ~WithNestedTracingFrame() { + getTracingState()->leaveFrame(); + } +}; +TORCH_API void recordSourceLocation(Node* n); +TORCH_API void setRecordSourceLocation(void (*v)(Node*)); + +TORCH_API std::vector pythonCallstack(); +TORCH_API void setPythonCallstack(std::vector (*v)()); + +// Having finished adding a new 'node' to the graph IR 'setValueTrace' +// associates this node with an output variable, so that further operations +// involving this variable know which node in the IR to reference. +TORCH_API void setValueTrace(const IValue& v, Value* value); + +TORCH_API void delValueTrace(const IValue& var); + +TORCH_API std::function pauseTracing(); + +TORCH_API Value* getValueTrace(const IValue& var); + +TORCH_API std::pair, Stack> trace( + Stack inputs, + const std::function& traced_fn, + std::function var_name_lookup_fn, + bool strict = true, + bool force_outplace = false, + Module* self = nullptr, + const std::vector& argument_names = {}); + +TORCH_API void abandon(); + +// NB: those serve both as an intermediate steps in addInputs below, +// as well as the overloads that terminate template recursion +TORCH_API void addInputs(Node* n, const char* name, int64_t value); +TORCH_API void addInputs(Node* n, const char* name, c10::SymInt value); +TORCH_API void addInputs( + Node* n, + const char* name, + c10::optional value); +TORCH_API void addInputs(Node* n, const char* name, bool value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::optional& value); +TORCH_API void addInputs(Node* n, const char* name, double value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::optional& value); +TORCH_API void addInputs(Node* n, const char* name, const at::Scalar& value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::optional& value); +TORCH_API void addInputs(Node* n, const char* name, const at::Tensor& value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::optional& value); +TORCH_API void addInputs(Node* n, const char* name, ArrayRef value); +TORCH_API void addInputs(Node* n, const char* name, c10::SymIntArrayRef value); +TORCH_API void addInputs( + Node* n, + const char* name, + c10::optional value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::optional>& value); +TORCH_API void addInputs( + Node* n, + const char* name, + const at::OptionalIntArrayRef& opt_value); +TORCH_API void addInputs( + Node* n, + const char* name, + const at::OptionalSymIntArrayRef& opt_value); +TORCH_API void addInputs( + Node* n, + const char* name, + ArrayRef value, + bool allow_undefined = false); +TORCH_API void addInputs( + Node* n, + const char* name, + std::vector value, + bool allow_undefined = false); +TORCH_API void addInputs( + Node* n, + const char* name, + at::ITensorListRef value, + bool allow_undefined = false); +TORCH_API void addInputs( + Node* n, + const char* name, + const List>& value); +TORCH_API void addInputs( + Node* n, + const char* name, + ArrayRef> value, + const c10::ClassTypePtr& class_type); +TORCH_API void addInputs(Node* n, const char* name, ArrayRef value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::optional>& value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::string_view value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::optional& value); +TORCH_API void addInputs(Node* n, const char* name, at::Device value); +TORCH_API void addInputs(Node* n, const char* name, c10::Stream stream); +TORCH_API void addInputs(Node* n, const char* name, at::Layout value); +TORCH_API void addInputs(Node* n, const char* name, at::ScalarType value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::optional& value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::optional& value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::optional& value); +TORCH_API void addInputs(Node* n, const char* name, at::MemoryFormat value); +TORCH_API void addInputs( + Node* n, + const char* name, + c10::optional value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::optional& value); +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::optional& value); + +inline void addInputs( + Node* n, + const char* name, + const std::vector& value) { + AT_ERROR("Tracing a list of bool type is currently not supported!"); +} + +template +void addInputs(Node* n, const char* name, ArrayRef value) { + AT_ERROR("Tracing a list of arbitrary type is currently not supported!"); +} +template +void addInputs( + Node* n, + const char* name, + const std::unordered_map& value) { + AT_ERROR("Tracing a dict of arbitrary types is currently not supported!"); +} + +template +void addInputs(Node* n, const char* name, std::array value) { + throw std::runtime_error( + "Found an unsupported argument type in the JIT tracer. File a bug report."); +} + +TORCH_API void addInputs( + Node* n, + const char* name, + const c10::intrusive_ptr& obj); + +TORCH_API void ensureUniqueIfOutOfPlaced( + const char* name, + const at::Tensor& tensor); +TORCH_API void ensureUniqueIfOutOfPlaced( + const char* name, + const c10::optional& tensor); + +template < + typename T, + typename = torch::enable_if_t< + (!std::is_convertible_v, at::TensorList> && + !std::is_convertible_v, c10::List> && + !std::is_convertible_v, at::Tensor> && + !std::is_convertible_v< + torch::decay_t, + c10::intrusive_ptr>)>> +void addOutput(Node* node, T&&) { + AT_ERROR( + "Found an unsupported argument type ", + c10::demangle_type(), + " in the JIT tracer. File a bug report."); +} +TORCH_API void addOutput(Node* node, const at::Tensor& tensor); +TORCH_API void setOutput(Value* value, const at::Tensor& output); +TORCH_API void addOutput(Node* node, const std::vector& list); +TORCH_API void addOutput(Node* node, const c10::List& list); +TORCH_API void addOutput( + Node* node, + const c10::intrusive_ptr& output); + +TORCH_API autograd::Variable getSizeOf( + const autograd::Variable& var, + int64_t dim); + +TORCH_API autograd::Variable getNumelOf(const autograd::Variable& var); + +} // namespace tracer +} // namespace torch::jit diff --git a/shared/package-files/56/56a147374ff499d23e8374ad0b3d474af95dfdc3da65777f3bdc206add359074/blob b/shared/package-files/56/56a147374ff499d23e8374ad0b3d474af95dfdc3da65777f3bdc206add359074/blob new file mode 100644 index 0000000000000000000000000000000000000000..9fe78901626378f642429b2c781bf86322a45d3f --- /dev/null +++ b/shared/package-files/56/56a147374ff499d23e8374ad0b3d474af95dfdc3da65777f3bdc206add359074/blob @@ -0,0 +1,125 @@ +import os +import os.path +import pathlib +from typing import Any, Callable, Optional, Sequence, Tuple, Union + +from PIL import Image + +from .utils import download_and_extract_archive, verify_str_arg +from .vision import VisionDataset + + +class OxfordIIITPet(VisionDataset): + """`Oxford-IIIT Pet Dataset `_. + + Args: + root (str or ``pathlib.Path``): Root directory of the dataset. + split (string, optional): The dataset split, supports ``"trainval"`` (default) or ``"test"``. + target_types (string, sequence of strings, optional): Types of target to use. Can be ``category`` (default) or + ``segmentation``. Can also be a list to output a tuple with all specified target types. The types represent: + + - ``category`` (int): Label for one of the 37 pet categories. + - ``segmentation`` (PIL image): Segmentation trimap of the image. + + If empty, ``None`` will be returned as target. + + transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed + version. E.g, ``transforms.RandomCrop``. + target_transform (callable, optional): A function/transform that takes in the target and transforms it. + download (bool, optional): If True, downloads the dataset from the internet and puts it into + ``root/oxford-iiit-pet``. If dataset is already downloaded, it is not downloaded again. + """ + + _RESOURCES = ( + ("https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz", "5c4f3ee8e5d25df40f4fd59a7f44e54c"), + ("https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz", "95a8c909bbe2e81eed6a22bccdf3f68f"), + ) + _VALID_TARGET_TYPES = ("category", "segmentation") + + def __init__( + self, + root: Union[str, pathlib.Path], + split: str = "trainval", + target_types: Union[Sequence[str], str] = "category", + transforms: Optional[Callable] = None, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + download: bool = False, + ): + self._split = verify_str_arg(split, "split", ("trainval", "test")) + if isinstance(target_types, str): + target_types = [target_types] + self._target_types = [ + verify_str_arg(target_type, "target_types", self._VALID_TARGET_TYPES) for target_type in target_types + ] + + super().__init__(root, transforms=transforms, transform=transform, target_transform=target_transform) + self._base_folder = pathlib.Path(self.root) / "oxford-iiit-pet" + self._images_folder = self._base_folder / "images" + self._anns_folder = self._base_folder / "annotations" + self._segs_folder = self._anns_folder / "trimaps" + + if download: + self._download() + + if not self._check_exists(): + raise RuntimeError("Dataset not found. You can use download=True to download it") + + image_ids = [] + self._labels = [] + with open(self._anns_folder / f"{self._split}.txt") as file: + for line in file: + image_id, label, *_ = line.strip().split() + image_ids.append(image_id) + self._labels.append(int(label) - 1) + + self.classes = [ + " ".join(part.title() for part in raw_cls.split("_")) + for raw_cls, _ in sorted( + {(image_id.rsplit("_", 1)[0], label) for image_id, label in zip(image_ids, self._labels)}, + key=lambda image_id_and_label: image_id_and_label[1], + ) + ] + self.class_to_idx = dict(zip(self.classes, range(len(self.classes)))) + + self._images = [self._images_folder / f"{image_id}.jpg" for image_id in image_ids] + self._segs = [self._segs_folder / f"{image_id}.png" for image_id in image_ids] + + def __len__(self) -> int: + return len(self._images) + + def __getitem__(self, idx: int) -> Tuple[Any, Any]: + image = Image.open(self._images[idx]).convert("RGB") + + target: Any = [] + for target_type in self._target_types: + if target_type == "category": + target.append(self._labels[idx]) + else: # target_type == "segmentation" + target.append(Image.open(self._segs[idx])) + + if not target: + target = None + elif len(target) == 1: + target = target[0] + else: + target = tuple(target) + + if self.transforms: + image, target = self.transforms(image, target) + + return image, target + + def _check_exists(self) -> bool: + for folder in (self._images_folder, self._anns_folder): + if not (os.path.exists(folder) and os.path.isdir(folder)): + return False + else: + return True + + def _download(self) -> None: + if self._check_exists(): + return + + for url, md5 in self._RESOURCES: + download_and_extract_archive(url, download_root=str(self._base_folder), md5=md5) diff --git a/shared/package-files/56/56a1d075b9361f149caf456fec04975387816fdbed302f170596d39c8b7a5360/blob b/shared/package-files/56/56a1d075b9361f149caf456fec04975387816fdbed302f170596d39c8b7a5360/blob new file mode 100644 index 0000000000000000000000000000000000000000..05d4b0d5e867b57afe6b09d8508d208fa5c35f64 Binary files /dev/null and b/shared/package-files/56/56a1d075b9361f149caf456fec04975387816fdbed302f170596d39c8b7a5360/blob differ diff --git a/shared/package-files/56/56a3ddde4887c362cce726f4603b63fd1c7e91294e5289f65b7712bb5778f4a6/blob b/shared/package-files/56/56a3ddde4887c362cce726f4603b63fd1c7e91294e5289f65b7712bb5778f4a6/blob new file mode 100644 index 0000000000000000000000000000000000000000..593c4edaec65885cef39e3fed9af9069543dd556 --- /dev/null +++ b/shared/package-files/56/56a3ddde4887c362cce726f4603b63fd1c7e91294e5289f65b7712bb5778f4a6/blob @@ -0,0 +1,86 @@ +import sys +from typing import List, Optional, Sequence, Tuple, Union, overload + +from _tracemalloc import * + +def get_object_traceback(obj: object) -> Optional[Traceback]: ... +def take_snapshot() -> Snapshot: ... + +class DomainFilter: + inclusive: bool + domain: int + def __init__(self, inclusive: bool, domain: int) -> None: ... + +class Filter: + domain: Optional[int] + inclusive: bool + lineno: Optional[int] + filename_pattern: str + all_frames: bool + def __init__( + self, + inclusive: bool, + filename_pattern: str, + lineno: Optional[int] = ..., + all_frames: bool = ..., + domain: Optional[int] = ..., + ) -> None: ... + +class Statistic: + count: int + size: int + traceback: Traceback + def __init__(self, traceback: Traceback, size: int, count: int) -> None: ... + +class StatisticDiff: + count: int + count_diff: int + size: int + size_diff: int + traceback: Traceback + def __init__(self, traceback: Traceback, size: int, size_diff: int, count: int, count_diff: int) -> None: ... + +_FrameTupleT = Tuple[str, int] + +class Frame: + filename: str + lineno: int + def __init__(self, frame: _FrameTupleT) -> None: ... + +if sys.version_info >= (3, 9): + _TraceTupleT = Union[Tuple[int, int, Sequence[_FrameTupleT], Optional[int]], Tuple[int, int, Sequence[_FrameTupleT]]] +else: + _TraceTupleT = Tuple[int, int, Sequence[_FrameTupleT]] + +class Trace: + domain: int + size: int + traceback: Traceback + def __init__(self, trace: _TraceTupleT) -> None: ... + +class Traceback(Sequence[Frame]): + if sys.version_info >= (3, 9): + total_nframe: Optional[int] + def __init__(self, frames: Sequence[_FrameTupleT], total_nframe: Optional[int] = ...) -> None: ... + else: + def __init__(self, frames: Sequence[_FrameTupleT]) -> None: ... + if sys.version_info >= (3, 7): + def format(self, limit: Optional[int] = ..., most_recent_first: bool = ...) -> List[str]: ... + else: + def format(self, limit: Optional[int] = ...) -> List[str]: ... + @overload + def __getitem__(self, i: int) -> Frame: ... + @overload + def __getitem__(self, s: slice) -> Sequence[Frame]: ... + def __len__(self) -> int: ... + +class Snapshot: + def __init__(self, traces: Sequence[_TraceTupleT], traceback_limit: int) -> None: ... + def compare_to(self, old_snapshot: Snapshot, key_type: str, cumulative: bool = ...) -> List[StatisticDiff]: ... + def dump(self, filename: str) -> None: ... + def filter_traces(self, filters: Sequence[Union[DomainFilter, Filter]]) -> Snapshot: ... + @staticmethod + def load(filename: str) -> Snapshot: ... + def statistics(self, key_type: str, cumulative: bool = ...) -> List[Statistic]: ... + traceback_limit: int + traces: Sequence[Trace] diff --git a/shared/package-files/56/56a52a776276f9d2ad96d66db183fc78f2a85f58ec343a53caa95c9a97c2fa5a/blob b/shared/package-files/56/56a52a776276f9d2ad96d66db183fc78f2a85f58ec343a53caa95c9a97c2fa5a/blob new file mode 100644 index 0000000000000000000000000000000000000000..a50779cf6d264408f1f99a086a25cb5c9d525588 --- /dev/null +++ b/shared/package-files/56/56a52a776276f9d2ad96d66db183fc78f2a85f58ec343a53caa95c9a97c2fa5a/blob @@ -0,0 +1,67 @@ +""" +`pylab` is a historic interface and its use is strongly discouraged. The equivalent +replacement is `matplotlib.pyplot`. See :ref:`api_interfaces` for a full overview +of Matplotlib interfaces. + +`pylab` was designed to support a MATLAB-like way of working with all plotting related +functions directly available in the global namespace. This was achieved through a +wildcard import (``from pylab import *``). + +.. warning:: + The use of `pylab` is discouraged for the following reasons: + + ``from pylab import *`` imports all the functions from `matplotlib.pyplot`, `numpy`, + `numpy.fft`, `numpy.linalg`, and `numpy.random`, and some additional functions into + the global namespace. + + Such a pattern is considered bad practice in modern python, as it clutters the global + namespace. Even more severely, in the case of `pylab`, this will overwrite some + builtin functions (e.g. the builtin `sum` will be replaced by `numpy.sum`), which + can lead to unexpected behavior. + +""" + +from matplotlib.cbook import flatten, silent_list + +import matplotlib as mpl + +from matplotlib.dates import ( + date2num, num2date, datestr2num, drange, DateFormatter, DateLocator, + RRuleLocator, YearLocator, MonthLocator, WeekdayLocator, DayLocator, + HourLocator, MinuteLocator, SecondLocator, rrule, MO, TU, WE, TH, FR, + SA, SU, YEARLY, MONTHLY, WEEKLY, DAILY, HOURLY, MINUTELY, SECONDLY, + relativedelta) + +# bring all the symbols in so folks can import them from +# pylab in one fell swoop + +## We are still importing too many things from mlab; more cleanup is needed. + +from matplotlib.mlab import ( + detrend, detrend_linear, detrend_mean, detrend_none, window_hanning, + window_none) + +from matplotlib import cbook, mlab, pyplot as plt +from matplotlib.pyplot import * + +from numpy import * +from numpy.fft import * +from numpy.random import * +from numpy.linalg import * + +import numpy as np +import numpy.ma as ma + +# don't let numpy's datetime hide stdlib +import datetime + +# This is needed, or bytes will be numpy.random.bytes from +# "from numpy.random import *" above +bytes = __import__("builtins").bytes +# We also don't want the numpy version of these functions +abs = __import__("builtins").abs +bool = __import__("builtins").bool +max = __import__("builtins").max +min = __import__("builtins").min +pow = __import__("builtins").pow +round = __import__("builtins").round diff --git a/shared/package-files/56/56aac2a7abf671e0aa8572d582c9662988dc085efa5bbe09a76c74dfc31afaa8/blob b/shared/package-files/56/56aac2a7abf671e0aa8572d582c9662988dc085efa5bbe09a76c74dfc31afaa8/blob new file mode 100644 index 0000000000000000000000000000000000000000..75316d26bdacd8c7d882685f43056f616142d24c --- /dev/null +++ b/shared/package-files/56/56aac2a7abf671e0aa8572d582c9662988dc085efa5bbe09a76c74dfc31afaa8/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 meta { + +TORCH_API at::Tensor as_strided(const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional storage_offset=c10::nullopt); +TORCH_API at::Tensor as_strided_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional storage_offset=c10::nullopt); + +} // namespace meta +} // namespace at diff --git a/shared/package-files/56/56ae000d5391f3ba8171b2fd04c397ec339bb3a4c9406108893dcb8af1f89a48/blob b/shared/package-files/56/56ae000d5391f3ba8171b2fd04c397ec339bb3a4c9406108893dcb8af1f89a48/blob new file mode 100644 index 0000000000000000000000000000000000000000..da5120d6edf180f7fbbe88ac342b4d0e4b383e50 --- /dev/null +++ b/shared/package-files/56/56ae000d5391f3ba8171b2fd04c397ec339bb3a4c9406108893dcb8af1f89a48/blob @@ -0,0 +1,6 @@ +# Whether to disable showing progress on compilation passes +# Need to add a new config otherwise wil get a circular import if dynamo config is imported here +disable_progress = True + +# If True this also shows the node names in each pass, for small models this is great but larger models it's quite noisy +verbose_progress = False diff --git a/shared/package-files/56/56b1707aca5ac95c91c0c3d55b154ce30ec38d9f1750e811e5881e867abd93d3/blob b/shared/package-files/56/56b1707aca5ac95c91c0c3d55b154ce30ec38d9f1750e811e5881e867abd93d3/blob new file mode 100644 index 0000000000000000000000000000000000000000..4c87162bfcbad518cc9d1dc8404acc48316e5a37 --- /dev/null +++ b/shared/package-files/56/56b1707aca5ac95c91c0c3d55b154ce30ec38d9f1750e811e5881e867abd93d3/blob @@ -0,0 +1,14 @@ +# Copyright (C) 2019 Anaconda, Inc +# SPDX-License-Identifier: BSD-3-Clause +import conda.plugins + +from .cli import cli + + +@conda.plugins.hookimpl +def conda_subcommands(): + yield conda.plugins.CondaSubcommand( + name="content-trust", + summary="Signing and verification tools for Conda", + action=cli, + ) diff --git a/shared/package-files/56/56b1bb8e3492f99c71013243faf6c42449a257bf1aed38c4927b6e9a9e57408f/mol_graph.py b/shared/package-files/56/56b1bb8e3492f99c71013243faf6c42449a257bf1aed38c4927b6e9a9e57408f/mol_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..1bcee1eac98ed92a5cfb9eb556e4bdcafc349578 --- /dev/null +++ b/shared/package-files/56/56b1bb8e3492f99c71013243faf6c42449a257bf1aed38c4927b6e9a9e57408f/mol_graph.py @@ -0,0 +1,416 @@ +""" mol_graph.py. + +Classes to featurize molecules into a graph with onehot concat feats on atoms +and bonds. Inspired by the dgllife library. + +""" +from rdkit import Chem +import numpy as np +import torch +import dgl +import ms_pred.nn_utils.nn_utils as nn_utils + +atom_feat_registry = {} +bond_feat_registry = {} + + +def register_bond_feat(cls): + """register_bond_feat.""" + bond_feat_registry[cls.name] = {"fn": cls.featurize, "feat_size": cls.feat_size} + return cls + + +def register_atom_feat(cls): + """register_atom_feat.""" + atom_feat_registry[cls.name] = {"fn": cls.featurize, "feat_size": cls.feat_size} + return cls + + +class MolDGLGraph: + def __init__( + self, + atom_feats: list = [ + "a_onehot", + "a_degree", + "a_hybrid", + "a_formal", + "a_radical", + "a_ring", + "a_mass", + "a_chiral", + ], + bond_feats: list = ["b_degree"], + pe_embed_k: int = 0, + ): + """__init__ + + Args: + atom_feats (list) + bond_feats (list) + pe_embed_k (int) + + """ + self.pe_embed_k = pe_embed_k + self.atom_feats = atom_feats + self.bond_feats = bond_feats + self.a_featurizers = [] + self.b_featurizers = [] + + self.num_atom_feats = 0 + self.num_bond_feats = 0 + + for i in self.atom_feats: + if i not in atom_feat_registry: + raise ValueError(f"Feat {i} not recognized") + feat_obj = atom_feat_registry[i] + self.num_atom_feats += feat_obj["feat_size"] + self.a_featurizers.append(feat_obj["fn"]) + + for i in self.bond_feats: + if i not in bond_feat_registry: + raise ValueError(f"Feat {i} not recognized") + feat_obj = bond_feat_registry[i] + self.num_bond_feats += feat_obj["feat_size"] + self.b_featurizers.append(feat_obj["fn"]) + + self.num_atom_feats += self.pe_embed_k + + def get_mol_graph( + self, + mol: Chem.Mol, + bigraph: str = True, + ) -> dict: + """get_mol_graph. + + Args: + mol (Chem.Mol): + bigraph (bool): If true, double all edges. + + Return: + dict: + "atom_feats": np.ndarray (|N| x d_n) + "bond_feats": np.ndarray (|E| x d_e) + "bond_tuples": np.ndarray (|E| x 2) + + """ + all_atoms = mol.GetAtoms() + all_bonds = mol.GetBonds() + bond_feats = [] + bond_tuples = [] + atom_feats = [] + for bond in all_bonds: + strt = bond.GetBeginAtomIdx() + end = bond.GetEndAtomIdx() + bond_tuples.append((strt, end)) + bond_feat = [] + for fn in self.b_featurizers: + bond_feat.extend(fn(bond)) + bond_feats.append(bond_feat) + + for atom in all_atoms: + atom_feat = [] + for fn in self.a_featurizers: + atom_feat.extend(fn(atom)) + atom_feats.append(atom_feat) + + atom_feats = np.array(atom_feats) + bond_feats = np.array(bond_feats) + bond_tuples = np.array(bond_tuples) + + # Add doubles + if bigraph: + rev_bonds = np.vstack([bond_tuples[:, 1], bond_tuples[:, 0]]).transpose( + 1, 0 + ) + bond_tuples = np.vstack([bond_tuples, rev_bonds]) + bond_feats = np.vstack([bond_feats, bond_feats]) + return { + "atom_feats": atom_feats, + "bond_feats": bond_feats, + "bond_tuples": bond_tuples, + } + + def get_dgl_graph(self, mol: Chem.Mol, bigraph: str = True): + """get_dgl_graph. + + Args: + mol (Chem.Mol): + bigraph (bool): If true, double all edges. + + Return: + dgl graph object + """ + mol_graph = self.get_mol_graph(mol, bigraph=bigraph) + + bond_inds = torch.from_numpy(mol_graph["bond_tuples"]).long() + bond_feats = torch.from_numpy(mol_graph["bond_feats"]).float() + atom_feats = torch.from_numpy(mol_graph["atom_feats"]).float() + + g = dgl.graph( + data=(bond_inds[:, 0], bond_inds[:, 1]), num_nodes=atom_feats.shape[0] + ) + g.ndata["h"] = atom_feats + g.edata["e"] = bond_feats + + if self.pe_embed_k > 0: + pe_embeds = nn_utils.random_walk_pe( + g, + k=self.pe_embed_k, + ) + g.ndata["h"] = torch.cat((g.ndata["h"], pe_embeds), -1) + + return g + + +class FeatBase: + """FeatBase. + + Extend this class for atom and bond featurizers + + """ + + feat_size = 0 + name = "base" + + @classmethod + def featurize(cls, x) -> list: + raise NotImplementedError() + + +@register_atom_feat +class AtomOneHot(FeatBase): + name = "a_onehot" + allowable_set = [ + "C", + "N", + "O", + "S", + "F", + "Si", + "P", + "Cl", + "Br", + "Mg", + "Na", + "Ca", + "Fe", + "As", + "Al", + "I", + "B", + "V", + "K", + "Tl", + "Yb", + "Sb", + "Sn", + "Ag", + "Pd", + "Co", + "Se", + "Ti", + "Zn", + "H", + "Li", + "Ge", + "Cu", + "Au", + "Ni", + "Cd", + "In", + "Mn", + "Zr", + "Cr", + "Pt", + "Hg", + "Pb", + ] + feat_size = len(allowable_set) + 1 + + @classmethod + def featurize(cls, x) -> int: + return one_hot_encoding(x.GetSymbol(), cls.allowable_set, True) + + +@register_atom_feat +class AtomDegree(FeatBase): + name = "a_degree" + allowable_set = list(range(11)) + feat_size = len(allowable_set) + 1 + 2 + + @classmethod + def featurize(cls, x) -> int: + deg = [x.GetDegree(), x.GetTotalDegree()] + onehot = one_hot_encoding(deg, cls.allowable_set, True) + return deg + onehot + + +@register_atom_feat +class AtomHybrid(FeatBase): + + name = "a_hybrid" + allowable_set = [ + Chem.rdchem.HybridizationType.SP, + Chem.rdchem.HybridizationType.SP2, + Chem.rdchem.HybridizationType.SP3, + Chem.rdchem.HybridizationType.SP3D, + Chem.rdchem.HybridizationType.SP3D2, + ] + feat_size = len(allowable_set) + 1 + + @classmethod + def featurize(cls, x) -> int: + onehot = one_hot_encoding(x.GetHybridization(), cls.allowable_set, True) + return onehot + + +@register_atom_feat +class AtomFormal(FeatBase): + + name = "a_formal" + allowable_set = list(range(-2, 3)) + feat_size = len(allowable_set) + 1 + + @classmethod + def featurize(cls, x) -> int: + onehot = one_hot_encoding(x.GetFormalCharge(), cls.allowable_set, True) + return onehot + + +@register_atom_feat +class AtomRadical(FeatBase): + + name = "a_radical" + allowable_set = list(range(5)) + feat_size = len(allowable_set) + 1 + + @classmethod + def featurize(cls, x) -> int: + onehot = one_hot_encoding(x.GetNumRadicalElectrons(), cls.allowable_set, True) + return onehot + + +@register_atom_feat +class AtomRing(FeatBase): + + name = "a_ring" + allowable_set = [True, False] + feat_size = len(allowable_set) * 2 + + @classmethod + def featurize(cls, x) -> int: + onehot_ring = one_hot_encoding(x.IsInRing(), cls.allowable_set, False) + onehot_aromatic = one_hot_encoding(x.GetIsAromatic(), cls.allowable_set, False) + return onehot_ring + onehot_aromatic + + +@register_atom_feat +class AtomChiral(FeatBase): + + name = "a_chiral" + allowable_set = [ + Chem.rdchem.ChiralType.CHI_UNSPECIFIED, + Chem.rdchem.ChiralType.CHI_TETRAHEDRAL_CW, + Chem.rdchem.ChiralType.CHI_TETRAHEDRAL_CCW, + Chem.rdchem.ChiralType.CHI_OTHER, + ] + feat_size = len(allowable_set) + 1 + + @classmethod + def featurize(cls, x) -> int: + chiral_onehot = one_hot_encoding(x.GetChiralTag(), cls.allowable_set, True) + return chiral_onehot + + +@register_atom_feat +class AtomMass(FeatBase): + + name = "a_mass" + coef = 0.01 + feat_size = 1 + + @classmethod + def featurize(cls, x) -> int: + return [x.GetMass() * cls.coef] + + +@register_bond_feat +class BondDegree(FeatBase): + + name = "b_degree" + allowable_set = [ + Chem.rdchem.BondType.SINGLE, + Chem.rdchem.BondType.DOUBLE, + Chem.rdchem.BondType.TRIPLE, + Chem.rdchem.BondType.AROMATIC, + ] + feat_size = len(allowable_set) + 1 + + @classmethod + def featurize(cls, x) -> int: + return one_hot_encoding(x.GetBondType(), cls.allowable_set, True) + + +@register_bond_feat +class BondStereo(FeatBase): + + name = "b_stereo" + allowable_set = [ + Chem.rdchem.BondStereo.STEREONONE, + Chem.rdchem.BondStereo.STEREOANY, + Chem.rdchem.BondStereo.STEREOZ, + Chem.rdchem.BondStereo.STEREOE, + Chem.rdchem.BondStereo.STEREOCIS, + Chem.rdchem.BondStereo.STEREOTRANS, + ] + feat_size = len(allowable_set) + 1 + + @classmethod + def featurize(cls, x) -> int: + return one_hot_encoding(x.GetStereo(), cls.allowable_set, True) + + +@register_bond_feat +class BondConj(FeatBase): + + name = "b_ring" + feat_size = 2 + + @classmethod + def featurize(cls, x) -> int: + return one_hot_encoding(x.IsInRing(), [False, True], False) + + +@register_bond_feat +class BondConj(FeatBase): + + name = "b_conj" + feat_size = 2 + + @classmethod + def featurize(cls, x) -> int: + return one_hot_encoding(x.GetIsConjugated(), [False, True], False) + + +def one_hot_encoding(x, allowable_set, encode_unknown=False) -> list: + """One_hot encoding. + + Code taken from dgllife library + https://lifesci.dgl.ai/_modules/dgllife/utils/featurizers.html + + Args: + x: Val to encode + allowable_set: Options + encode_unknown: If true, encode unk + + Return: + list of bools + """ + + if encode_unknown and (allowable_set[-1] is not None): + allowable_set.append(None) + + if encode_unknown and (x not in allowable_set): + x = None + + return list(map(lambda s: int(x == s), allowable_set)) diff --git a/shared/package-files/56/56b32974d0816450421972b81d81b9e08b2a1b6f9e111d786b3da7dc28c45c02/blob b/shared/package-files/56/56b32974d0816450421972b81d81b9e08b2a1b6f9e111d786b3da7dc28c45c02/blob new file mode 100644 index 0000000000000000000000000000000000000000..fd8be03e251b18c255f5df3de1f6173c2a246e8e --- /dev/null +++ b/shared/package-files/56/56b32974d0816450421972b81d81b9e08b2a1b6f9e111d786b3da7dc28c45c02/blob @@ -0,0 +1,91 @@ +#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::fft_rfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor +inline at::Tensor fft_rfft2(const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft2::call(self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); +} +namespace symint { + template ::value>> + at::Tensor fft_rfft2(const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft2::call(self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } +} + +// aten::fft_rfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor +inline at::Tensor fft_rfft2_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft2::call(self, s, dim, norm); +} +namespace symint { + template ::value>> + at::Tensor fft_rfft2(const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft2::call(self, s, dim, norm); + } +} + +// aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & fft_rfft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft2_out::call(self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); +} +namespace symint { + template ::value>> + at::Tensor & fft_rfft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft2_out::call(self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } +} + +// aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & fft_rfft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_rfft2_out::call(self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); +} +namespace symint { + template ::value>> + at::Tensor & fft_rfft2_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_rfft2_out::call(self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } +} + +// aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & fft_rfft2_symint_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft2_out::call(self, s, dim, norm, out); +} +namespace symint { + template ::value>> + at::Tensor & fft_rfft2_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft2_out::call(self, s, dim, norm, out); + } +} + +// aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & fft_rfft2_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_rfft2_out::call(self, s, dim, norm, out); +} +namespace symint { + template ::value>> + at::Tensor & fft_rfft2_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_rfft2_out::call(self, s, dim, norm, out); + } +} + +} diff --git a/shared/package-files/56/56b3bb75cf45bdf58c01c1aee4015648403890c4409885d95020bd8bf064f024/blob b/shared/package-files/56/56b3bb75cf45bdf58c01c1aee4015648403890c4409885d95020bd8bf064f024/blob new file mode 100644 index 0000000000000000000000000000000000000000..16baec799a0056dcdf077dec9de5e55ee65c915b --- /dev/null +++ b/shared/package-files/56/56b3bb75cf45bdf58c01c1aee4015648403890c4409885d95020bd8bf064f024/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::resolve_conj(Tensor(a) self) -> Tensor(a) +inline at::Tensor resolve_conj(const at::Tensor & self) { + return at::_ops::resolve_conj::call(self); +} + +} diff --git a/shared/package-files/56/56b54bb7f01a0ecf31d750dd59916901e39678e52f48413929cc77b2d5014485/blob b/shared/package-files/56/56b54bb7f01a0ecf31d750dd59916901e39678e52f48413929cc77b2d5014485/blob new file mode 100644 index 0000000000000000000000000000000000000000..0842cf0c82b485b16717ac19c78b4d51098769eb --- /dev/null +++ b/shared/package-files/56/56b54bb7f01a0ecf31d750dd59916901e39678e52f48413929cc77b2d5014485/blob @@ -0,0 +1,1081 @@ +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause + +import pickle +import re +import warnings + +import numpy as np +import pytest +import scipy.sparse as sp +from numpy.testing import assert_allclose + +import sklearn +from sklearn import config_context, datasets +from sklearn.base import ( + BaseEstimator, + OutlierMixin, + TransformerMixin, + clone, + is_classifier, + is_clusterer, + is_outlier_detector, + is_regressor, +) +from sklearn.cluster import KMeans +from sklearn.decomposition import PCA +from sklearn.ensemble import IsolationForest +from sklearn.exceptions import InconsistentVersionWarning +from sklearn.metrics import get_scorer +from sklearn.model_selection import GridSearchCV, KFold +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import StandardScaler +from sklearn.svm import SVC, SVR +from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor +from sklearn.utils._mocking import MockDataFrame +from sklearn.utils._set_output import _get_output_config +from sklearn.utils._testing import ( + _convert_container, + assert_array_equal, +) +from sklearn.utils.validation import _check_n_features, validate_data + + +############################################################################# +# A few test classes +class MyEstimator(BaseEstimator): + def __init__(self, l1=0, empty=None): + self.l1 = l1 + self.empty = empty + + +class K(BaseEstimator): + def __init__(self, c=None, d=None): + self.c = c + self.d = d + + +class T(BaseEstimator): + def __init__(self, a=None, b=None): + self.a = a + self.b = b + + +class NaNTag(BaseEstimator): + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.input_tags.allow_nan = True + return tags + + +class NoNaNTag(BaseEstimator): + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.input_tags.allow_nan = False + return tags + + +class OverrideTag(NaNTag): + def __sklearn_tags__(self): + tags = super().__sklearn_tags__() + tags.input_tags.allow_nan = False + return tags + + +class DiamondOverwriteTag(NaNTag, NoNaNTag): + pass + + +class InheritDiamondOverwriteTag(DiamondOverwriteTag): + pass + + +class ModifyInitParams(BaseEstimator): + """Deprecated behavior. + Equal parameters but with a type cast. + Doesn't fulfill a is a + """ + + def __init__(self, a=np.array([0])): + self.a = a.copy() + + +class Buggy(BaseEstimator): + "A buggy estimator that does not set its parameters right." + + def __init__(self, a=None): + self.a = 1 + + +class NoEstimator: + def __init__(self): + pass + + def fit(self, X=None, y=None): + return self + + def predict(self, X=None): + return None + + +class VargEstimator(BaseEstimator): + """scikit-learn estimators shouldn't have vargs.""" + + def __init__(self, *vargs): + pass + + +############################################################################# +# The tests + + +def test_clone(): + # Tests that clone creates a correct deep copy. + # We create an estimator, make a copy of its original state + # (which, in this case, is the current state of the estimator), + # and check that the obtained copy is a correct deep copy. + + from sklearn.feature_selection import SelectFpr, f_classif + + selector = SelectFpr(f_classif, alpha=0.1) + new_selector = clone(selector) + assert selector is not new_selector + assert selector.get_params() == new_selector.get_params() + + selector = SelectFpr(f_classif, alpha=np.zeros((10, 2))) + new_selector = clone(selector) + assert selector is not new_selector + + +def test_clone_2(): + # Tests that clone doesn't copy everything. + # We first create an estimator, give it an own attribute, and + # make a copy of its original state. Then we check that the copy doesn't + # have the specific attribute we manually added to the initial estimator. + + from sklearn.feature_selection import SelectFpr, f_classif + + selector = SelectFpr(f_classif, alpha=0.1) + selector.own_attribute = "test" + new_selector = clone(selector) + assert not hasattr(new_selector, "own_attribute") + + +def test_clone_buggy(): + # Check that clone raises an error on buggy estimators. + buggy = Buggy() + buggy.a = 2 + with pytest.raises(RuntimeError): + clone(buggy) + + no_estimator = NoEstimator() + with pytest.raises(TypeError): + clone(no_estimator) + + varg_est = VargEstimator() + with pytest.raises(RuntimeError): + clone(varg_est) + + est = ModifyInitParams() + with pytest.raises(RuntimeError): + clone(est) + + +def test_clone_empty_array(): + # Regression test for cloning estimators with empty arrays + clf = MyEstimator(empty=np.array([])) + clf2 = clone(clf) + assert_array_equal(clf.empty, clf2.empty) + + clf = MyEstimator(empty=sp.csr_matrix(np.array([[0]]))) + clf2 = clone(clf) + assert_array_equal(clf.empty.data, clf2.empty.data) + + +def test_clone_nan(): + # Regression test for cloning estimators with default parameter as np.nan + clf = MyEstimator(empty=np.nan) + clf2 = clone(clf) + + assert clf.empty is clf2.empty + + +def test_clone_dict(): + # test that clone creates a clone of a dict + orig = {"a": MyEstimator()} + cloned = clone(orig) + assert orig["a"] is not cloned["a"] + + +def test_clone_sparse_matrices(): + sparse_matrix_classes = [ + cls + for name in dir(sp) + if name.endswith("_matrix") and type(cls := getattr(sp, name)) is type + ] + + for cls in sparse_matrix_classes: + sparse_matrix = cls(np.eye(5)) + clf = MyEstimator(empty=sparse_matrix) + clf_cloned = clone(clf) + assert clf.empty.__class__ is clf_cloned.empty.__class__ + assert_array_equal(clf.empty.toarray(), clf_cloned.empty.toarray()) + + +def test_clone_estimator_types(): + # Check that clone works for parameters that are types rather than + # instances + clf = MyEstimator(empty=MyEstimator) + clf2 = clone(clf) + + assert clf.empty is clf2.empty + + +def test_clone_class_rather_than_instance(): + # Check that clone raises expected error message when + # cloning class rather than instance + msg = "You should provide an instance of scikit-learn estimator" + with pytest.raises(TypeError, match=msg): + clone(MyEstimator) + + +def test_repr(): + # Smoke test the repr of the base estimator. + my_estimator = MyEstimator() + repr(my_estimator) + test = T(K(), K()) + assert repr(test) == "T(a=K(), b=K())" + + some_est = T(a=["long_params"] * 1000) + assert len(repr(some_est)) == 485 + + +def test_str(): + # Smoke test the str of the base estimator + my_estimator = MyEstimator() + str(my_estimator) + + +def test_get_params(): + test = T(K(), K) + + assert "a__d" in test.get_params(deep=True) + assert "a__d" not in test.get_params(deep=False) + + test.set_params(a__d=2) + assert test.a.d == 2 + + with pytest.raises(ValueError): + test.set_params(a__a=2) + + +# TODO(1.8): Remove this test when the deprecation is removed +def test_is_estimator_type_class(): + with pytest.warns(FutureWarning, match="passing a class to.*is deprecated"): + assert is_classifier(SVC) + + with pytest.warns(FutureWarning, match="passing a class to.*is deprecated"): + assert is_regressor(SVR) + + with pytest.warns(FutureWarning, match="passing a class to.*is deprecated"): + assert is_clusterer(KMeans) + + with pytest.warns(FutureWarning, match="passing a class to.*is deprecated"): + assert is_outlier_detector(IsolationForest) + + +@pytest.mark.parametrize( + "estimator, expected_result", + [ + (SVC(), True), + (GridSearchCV(SVC(), {"C": [0.1, 1]}), True), + (Pipeline([("svc", SVC())]), True), + (Pipeline([("svc_cv", GridSearchCV(SVC(), {"C": [0.1, 1]}))]), True), + (SVR(), False), + (GridSearchCV(SVR(), {"C": [0.1, 1]}), False), + (Pipeline([("svr", SVR())]), False), + (Pipeline([("svr_cv", GridSearchCV(SVR(), {"C": [0.1, 1]}))]), False), + ], +) +def test_is_classifier(estimator, expected_result): + assert is_classifier(estimator) == expected_result + + +@pytest.mark.parametrize( + "estimator, expected_result", + [ + (SVR(), True), + (GridSearchCV(SVR(), {"C": [0.1, 1]}), True), + (Pipeline([("svr", SVR())]), True), + (Pipeline([("svr_cv", GridSearchCV(SVR(), {"C": [0.1, 1]}))]), True), + (SVC(), False), + (GridSearchCV(SVC(), {"C": [0.1, 1]}), False), + (Pipeline([("svc", SVC())]), False), + (Pipeline([("svc_cv", GridSearchCV(SVC(), {"C": [0.1, 1]}))]), False), + ], +) +def test_is_regressor(estimator, expected_result): + assert is_regressor(estimator) == expected_result + + +@pytest.mark.parametrize( + "estimator, expected_result", + [ + (KMeans(), True), + (GridSearchCV(KMeans(), {"n_clusters": [3, 8]}), True), + (Pipeline([("km", KMeans())]), True), + (Pipeline([("km_cv", GridSearchCV(KMeans(), {"n_clusters": [3, 8]}))]), True), + (SVC(), False), + (GridSearchCV(SVC(), {"C": [0.1, 1]}), False), + (Pipeline([("svc", SVC())]), False), + (Pipeline([("svc_cv", GridSearchCV(SVC(), {"C": [0.1, 1]}))]), False), + ], +) +def test_is_clusterer(estimator, expected_result): + assert is_clusterer(estimator) == expected_result + + +def test_set_params(): + # test nested estimator parameter setting + clf = Pipeline([("svc", SVC())]) + + # non-existing parameter in svc + with pytest.raises(ValueError): + clf.set_params(svc__stupid_param=True) + + # non-existing parameter of pipeline + with pytest.raises(ValueError): + clf.set_params(svm__stupid_param=True) + + # we don't currently catch if the things in pipeline are estimators + # bad_pipeline = Pipeline([("bad", NoEstimator())]) + # with pytest.raises(AttributeError): + # bad_pipeline.set_params(bad__stupid_param=True) + + +def test_set_params_passes_all_parameters(): + # Make sure all parameters are passed together to set_params + # of nested estimator. Regression test for #9944 + + class TestDecisionTree(DecisionTreeClassifier): + def set_params(self, **kwargs): + super().set_params(**kwargs) + # expected_kwargs is in test scope + assert kwargs == expected_kwargs + return self + + expected_kwargs = {"max_depth": 5, "min_samples_leaf": 2} + for est in [ + Pipeline([("estimator", TestDecisionTree())]), + GridSearchCV(TestDecisionTree(), {}), + ]: + est.set_params(estimator__max_depth=5, estimator__min_samples_leaf=2) + + +def test_set_params_updates_valid_params(): + # Check that set_params tries to set SVC().C, not + # DecisionTreeClassifier().C + gscv = GridSearchCV(DecisionTreeClassifier(), {}) + gscv.set_params(estimator=SVC(), estimator__C=42.0) + assert gscv.estimator.C == 42.0 + + +@pytest.mark.parametrize( + "tree,dataset", + [ + ( + DecisionTreeClassifier(max_depth=2, random_state=0), + datasets.make_classification(random_state=0), + ), + ( + DecisionTreeRegressor(max_depth=2, random_state=0), + datasets.make_regression(random_state=0), + ), + ], +) +def test_score_sample_weight(tree, dataset): + rng = np.random.RandomState(0) + # check that the score with and without sample weights are different + X, y = dataset + + tree.fit(X, y) + # generate random sample weights + sample_weight = rng.randint(1, 10, size=len(y)) + score_unweighted = tree.score(X, y) + score_weighted = tree.score(X, y, sample_weight=sample_weight) + msg = "Unweighted and weighted scores are unexpectedly equal" + assert score_unweighted != score_weighted, msg + + +def test_clone_pandas_dataframe(): + class DummyEstimator(TransformerMixin, BaseEstimator): + """This is a dummy class for generating numerical features + + This feature extractor extracts numerical features from pandas data + frame. + + Parameters + ---------- + + df: pandas data frame + The pandas data frame parameter. + + Notes + ----- + """ + + def __init__(self, df=None, scalar_param=1): + self.df = df + self.scalar_param = scalar_param + + def fit(self, X, y=None): + pass + + def transform(self, X): + pass + + # build and clone estimator + d = np.arange(10) + df = MockDataFrame(d) + e = DummyEstimator(df, scalar_param=1) + cloned_e = clone(e) + + # the test + assert (e.df == cloned_e.df).values.all() + assert e.scalar_param == cloned_e.scalar_param + + +def test_clone_protocol(): + """Checks that clone works with `__sklearn_clone__` protocol.""" + + class FrozenEstimator(BaseEstimator): + def __init__(self, fitted_estimator): + self.fitted_estimator = fitted_estimator + + def __getattr__(self, name): + return getattr(self.fitted_estimator, name) + + def __sklearn_clone__(self): + return self + + def fit(self, *args, **kwargs): + return self + + def fit_transform(self, *args, **kwargs): + return self.fitted_estimator.transform(*args, **kwargs) + + X = np.array([[-1, -1], [-2, -1], [-3, -2]]) + pca = PCA().fit(X) + components = pca.components_ + + frozen_pca = FrozenEstimator(pca) + assert_allclose(frozen_pca.components_, components) + + # Calling PCA methods such as `get_feature_names_out` still works + assert_array_equal(frozen_pca.get_feature_names_out(), pca.get_feature_names_out()) + + # Fitting on a new data does not alter `components_` + X_new = np.asarray([[-1, 2], [3, 4], [1, 2]]) + frozen_pca.fit(X_new) + assert_allclose(frozen_pca.components_, components) + + # `fit_transform` does not alter state + frozen_pca.fit_transform(X_new) + assert_allclose(frozen_pca.components_, components) + + # Cloning estimator is a no-op + clone_frozen_pca = clone(frozen_pca) + assert clone_frozen_pca is frozen_pca + assert_allclose(clone_frozen_pca.components_, components) + + +def test_pickle_version_warning_is_not_raised_with_matching_version(): + iris = datasets.load_iris() + tree = DecisionTreeClassifier().fit(iris.data, iris.target) + tree_pickle = pickle.dumps(tree) + assert b"_sklearn_version" in tree_pickle + + with warnings.catch_warnings(): + warnings.simplefilter("error") + tree_restored = pickle.loads(tree_pickle) + + # test that we can predict with the restored decision tree classifier + score_of_original = tree.score(iris.data, iris.target) + score_of_restored = tree_restored.score(iris.data, iris.target) + assert score_of_original == score_of_restored + + +class TreeBadVersion(DecisionTreeClassifier): + def __getstate__(self): + return dict(self.__dict__.items(), _sklearn_version="something") + + +pickle_error_message = ( + "Trying to unpickle estimator {estimator} from " + "version {old_version} when using version " + "{current_version}. This might " + "lead to breaking code or invalid results. " + "Use at your own risk." +) + + +def test_pickle_version_warning_is_issued_upon_different_version(): + iris = datasets.load_iris() + tree = TreeBadVersion().fit(iris.data, iris.target) + tree_pickle_other = pickle.dumps(tree) + message = pickle_error_message.format( + estimator="TreeBadVersion", + old_version="something", + current_version=sklearn.__version__, + ) + with pytest.warns(UserWarning, match=message) as warning_record: + pickle.loads(tree_pickle_other) + + message = warning_record.list[0].message + assert isinstance(message, InconsistentVersionWarning) + assert message.estimator_name == "TreeBadVersion" + assert message.original_sklearn_version == "something" + assert message.current_sklearn_version == sklearn.__version__ + + +class TreeNoVersion(DecisionTreeClassifier): + def __getstate__(self): + return self.__dict__ + + +def test_pickle_version_warning_is_issued_when_no_version_info_in_pickle(): + iris = datasets.load_iris() + # TreeNoVersion has no getstate, like pre-0.18 + tree = TreeNoVersion().fit(iris.data, iris.target) + + tree_pickle_noversion = pickle.dumps(tree) + assert b"_sklearn_version" not in tree_pickle_noversion + message = pickle_error_message.format( + estimator="TreeNoVersion", + old_version="pre-0.18", + current_version=sklearn.__version__, + ) + # check we got the warning about using pre-0.18 pickle + with pytest.warns(UserWarning, match=message): + pickle.loads(tree_pickle_noversion) + + +def test_pickle_version_no_warning_is_issued_with_non_sklearn_estimator(): + iris = datasets.load_iris() + tree = TreeNoVersion().fit(iris.data, iris.target) + tree_pickle_noversion = pickle.dumps(tree) + try: + module_backup = TreeNoVersion.__module__ + TreeNoVersion.__module__ = "notsklearn" + + with warnings.catch_warnings(): + warnings.simplefilter("error") + + pickle.loads(tree_pickle_noversion) + finally: + TreeNoVersion.__module__ = module_backup + + +class DontPickleAttributeMixin: + def __getstate__(self): + data = self.__dict__.copy() + data["_attribute_not_pickled"] = None + return data + + def __setstate__(self, state): + state["_restored"] = True + self.__dict__.update(state) + + +class MultiInheritanceEstimator(DontPickleAttributeMixin, BaseEstimator): + def __init__(self, attribute_pickled=5): + self.attribute_pickled = attribute_pickled + self._attribute_not_pickled = None + + +def test_pickling_when_getstate_is_overwritten_by_mixin(): + estimator = MultiInheritanceEstimator() + estimator._attribute_not_pickled = "this attribute should not be pickled" + + serialized = pickle.dumps(estimator) + estimator_restored = pickle.loads(serialized) + assert estimator_restored.attribute_pickled == 5 + assert estimator_restored._attribute_not_pickled is None + assert estimator_restored._restored + + +def test_pickling_when_getstate_is_overwritten_by_mixin_outside_of_sklearn(): + try: + estimator = MultiInheritanceEstimator() + text = "this attribute should not be pickled" + estimator._attribute_not_pickled = text + old_mod = type(estimator).__module__ + type(estimator).__module__ = "notsklearn" + + serialized = estimator.__getstate__() + assert serialized == {"_attribute_not_pickled": None, "attribute_pickled": 5} + + serialized["attribute_pickled"] = 4 + estimator.__setstate__(serialized) + assert estimator.attribute_pickled == 4 + assert estimator._restored + finally: + type(estimator).__module__ = old_mod + + +class SingleInheritanceEstimator(BaseEstimator): + def __init__(self, attribute_pickled=5): + self.attribute_pickled = attribute_pickled + self._attribute_not_pickled = None + + def __getstate__(self): + state = super().__getstate__() + state["_attribute_not_pickled"] = None + return state + + +def test_pickling_works_when_getstate_is_overwritten_in_the_child_class(): + estimator = SingleInheritanceEstimator() + estimator._attribute_not_pickled = "this attribute should not be pickled" + + serialized = pickle.dumps(estimator) + estimator_restored = pickle.loads(serialized) + assert estimator_restored.attribute_pickled == 5 + assert estimator_restored._attribute_not_pickled is None + + +def test_tag_inheritance(): + # test that changing tags by inheritance is not allowed + + nan_tag_est = NaNTag() + no_nan_tag_est = NoNaNTag() + assert nan_tag_est.__sklearn_tags__().input_tags.allow_nan + assert not no_nan_tag_est.__sklearn_tags__().input_tags.allow_nan + + redefine_tags_est = OverrideTag() + assert not redefine_tags_est.__sklearn_tags__().input_tags.allow_nan + + diamond_tag_est = DiamondOverwriteTag() + assert diamond_tag_est.__sklearn_tags__().input_tags.allow_nan + + inherit_diamond_tag_est = InheritDiamondOverwriteTag() + assert inherit_diamond_tag_est.__sklearn_tags__().input_tags.allow_nan + + +def test_raises_on_get_params_non_attribute(): + class MyEstimator(BaseEstimator): + def __init__(self, param=5): + pass + + def fit(self, X, y=None): + return self + + est = MyEstimator() + msg = "'MyEstimator' object has no attribute 'param'" + + with pytest.raises(AttributeError, match=msg): + est.get_params() + + +def test_repr_mimebundle_(): + # Checks the display configuration flag controls the json output + tree = DecisionTreeClassifier() + output = tree._repr_mimebundle_() + assert "text/plain" in output + assert "text/html" in output + + with config_context(display="text"): + output = tree._repr_mimebundle_() + assert "text/plain" in output + assert "text/html" not in output + + +def test_repr_html_wraps(): + # Checks the display configuration flag controls the html output + tree = DecisionTreeClassifier() + + output = tree._repr_html_() + assert " + + + + + + + + + + diff --git a/shared/package-files/5d/5d5d2ba8dbf382b1157a25a2e821fe45e703370d1b08ee852df970c4b29f8b13/blob b/shared/package-files/5d/5d5d2ba8dbf382b1157a25a2e821fe45e703370d1b08ee852df970c4b29f8b13/blob new file mode 100644 index 0000000000000000000000000000000000000000..57a5999e055083a5aebe599ac25fa68736d3e767 --- /dev/null +++ b/shared/package-files/5d/5d5d2ba8dbf382b1157a25a2e821fe45e703370d1b08ee852df970c4b29f8b13/blob @@ -0,0 +1,39 @@ +#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 _sparse_sum_backward { + using schema = at::Tensor (const at::Tensor &, const at::Tensor &, at::IntArrayRef); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_sparse_sum_backward") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_sparse_sum_backward(Tensor grad, Tensor self, int[] dim) -> Tensor") + static at::Tensor call(const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim); +}; + +struct TORCH_API _sparse_sum_backward_out { + using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::IntArrayRef, at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_sparse_sum_backward") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_sparse_sum_backward.out(Tensor grad, Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)") + static at::Tensor & call(const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out); +}; + +}} // namespace at::_ops diff --git a/shared/package-files/5d/5d5de06a5042b1bb67679ecf26845b8bcd8c81762169a89f6c60ede87a302dd4/blob b/shared/package-files/5d/5d5de06a5042b1bb67679ecf26845b8bcd8c81762169a89f6c60ede87a302dd4/blob new file mode 100644 index 0000000000000000000000000000000000000000..bd527d3ecd965fb0962901601c88af4ca24d092c --- /dev/null +++ b/shared/package-files/5d/5d5de06a5042b1bb67679ecf26845b8bcd8c81762169a89f6c60ede87a302dd4/blob @@ -0,0 +1,40 @@ +# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29, +# with extension for dataclasses and type annotation. + +from __future__ import annotations + +import dataclasses +from typing import Optional + +from torch.onnx._internal.diagnostics.infra.sarif import ( + _address, + _artifact_location, + _property_bag, + _region, +) + + +@dataclasses.dataclass +class PhysicalLocation(object): + """A physical location relevant to a result. Specifies a reference to a programming artifact together with a range of bytes or characters within that artifact.""" + + address: Optional[_address.Address] = dataclasses.field( + default=None, metadata={"schema_property_name": "address"} + ) + artifact_location: Optional[ + _artifact_location.ArtifactLocation + ] = dataclasses.field( + default=None, metadata={"schema_property_name": "artifactLocation"} + ) + context_region: Optional[_region.Region] = dataclasses.field( + default=None, metadata={"schema_property_name": "contextRegion"} + ) + properties: Optional[_property_bag.PropertyBag] = dataclasses.field( + default=None, metadata={"schema_property_name": "properties"} + ) + region: Optional[_region.Region] = dataclasses.field( + default=None, metadata={"schema_property_name": "region"} + ) + + +# flake8: noqa diff --git a/shared/package-files/5d/5d61cffe9ccdda8e6b4242928276563bf54c7404933ce41aa77e5e6ef5a7457c/blob b/shared/package-files/5d/5d61cffe9ccdda8e6b4242928276563bf54c7404933ce41aa77e5e6ef5a7457c/blob new file mode 100644 index 0000000000000000000000000000000000000000..9a15673353d2d32c0cd9abdcf0eae33b2fa9f0ab --- /dev/null +++ b/shared/package-files/5d/5d61cffe9ccdda8e6b4242928276563bf54c7404933ce41aa77e5e6ef5a7457c/blob @@ -0,0 +1,76 @@ +{{py: + +""" +Dataset abstractions for sequential data access. + +Template file for easily generate fused types consistent code using Tempita +(https://github.com/cython/cython/blob/master/Cython/Tempita/_tempita.py). + +Generated file: _seq_dataset.pxd + +Each class is duplicated for all dtypes (float and double). The keywords +between double braces are substituted during the build. +""" + +# name_suffix, c_type +dtypes = [('64', 'float64_t'), + ('32', 'float32_t')] + +}} +"""Dataset abstractions for sequential data access.""" + +from ._typedefs cimport float32_t, float64_t, intp_t, uint32_t + +# SequentialDataset and its two concrete subclasses are (optionally randomized) +# iterators over the rows of a matrix X and corresponding target values y. + +{{for name_suffix, c_type in dtypes}} + +#------------------------------------------------------------------------------ + +cdef class SequentialDataset{{name_suffix}}: + cdef int current_index + cdef int[::1] index + cdef int *index_data_ptr + cdef Py_ssize_t n_samples + cdef uint32_t seed + + cdef void shuffle(self, uint32_t seed) noexcept nogil + cdef int _get_next_index(self) noexcept nogil + cdef int _get_random_index(self) noexcept nogil + + cdef void _sample(self, {{c_type}} **x_data_ptr, int **x_ind_ptr, + int *nnz, {{c_type}} *y, {{c_type}} *sample_weight, + int current_index) noexcept nogil + cdef void next(self, {{c_type}} **x_data_ptr, int **x_ind_ptr, + int *nnz, {{c_type}} *y, {{c_type}} *sample_weight) noexcept nogil + cdef int random(self, {{c_type}} **x_data_ptr, int **x_ind_ptr, + int *nnz, {{c_type}} *y, {{c_type}} *sample_weight) noexcept nogil + + +cdef class ArrayDataset{{name_suffix}}(SequentialDataset{{name_suffix}}): + cdef const {{c_type}}[:, ::1] X + cdef const {{c_type}}[::1] Y + cdef const {{c_type}}[::1] sample_weights + cdef Py_ssize_t n_features + cdef intp_t X_stride + cdef {{c_type}} *X_data_ptr + cdef {{c_type}} *Y_data_ptr + cdef const int[::1] feature_indices + cdef int *feature_indices_ptr + cdef {{c_type}} *sample_weight_data + + +cdef class CSRDataset{{name_suffix}}(SequentialDataset{{name_suffix}}): + cdef const {{c_type}}[::1] X_data + cdef const int[::1] X_indptr + cdef const int[::1] X_indices + cdef const {{c_type}}[::1] Y + cdef const {{c_type}}[::1] sample_weights + cdef {{c_type}} *X_data_ptr + cdef int *X_indptr_ptr + cdef int *X_indices_ptr + cdef {{c_type}} *Y_data_ptr + cdef {{c_type}} *sample_weight_data + +{{endfor}} diff --git a/shared/package-files/5d/5d64415878874f5953814e6adc14a7386fd8a33e14bde94ce088413e1f1a1b26/blob b/shared/package-files/5d/5d64415878874f5953814e6adc14a7386fd8a33e14bde94ce088413e1f1a1b26/blob new file mode 100644 index 0000000000000000000000000000000000000000..7c8a79c5493afa763ad1bc5499d5074892c4aafc --- /dev/null +++ b/shared/package-files/5d/5d64415878874f5953814e6adc14a7386fd8a33e14bde94ce088413e1f1a1b26/blob @@ -0,0 +1,1073 @@ + +# This module is back-ported from CMake 3.17 and above to work with CMake 3.10 + +# Distributed under the OSI-approved BSD 3-Clause License. See accompanying +# file Copyright.txt or https://cmake.org/licensing for details. + +#[=======================================================================[.rst: +FindCUDAToolkit +--------------- + +.. versionadded:: 3.17 + +This script locates the NVIDIA CUDA toolkit and the associated libraries, but +does not require the ``CUDA`` language be enabled for a given project. This +module does not search for the NVIDIA CUDA Samples. + +.. versionadded:: 3.19 + QNX support. + +Search Behavior +^^^^^^^^^^^^^^^ + +The CUDA Toolkit search behavior uses the following order: + +1. If the ``CUDA`` language has been enabled we will use the directory + containing the compiler as the first search location for ``nvcc``. + +2. If the ``CUDAToolkit_ROOT`` cmake configuration variable (e.g., + ``-DCUDAToolkit_ROOT=/some/path``) *or* environment variable is defined, it + will be searched. If both an environment variable **and** a + configuration variable are specified, the *configuration* variable takes + precedence. + + The directory specified here must be such that the executable ``nvcc`` or + the appropriate ``version.txt`` file can be found underneath the specified + directory. + +3. If the CUDA_PATH environment variable is defined, it will be searched + for ``nvcc``. + +4. The user's path is searched for ``nvcc`` using :command:`find_program`. If + this is found, no subsequent search attempts are performed. Users are + responsible for ensuring that the first ``nvcc`` to show up in the path is + the desired path in the event that multiple CUDA Toolkits are installed. + +5. On Unix systems, if the symbolic link ``/usr/local/cuda`` exists, this is + used. No subsequent search attempts are performed. No default symbolic link + location exists for the Windows platform. + +6. The platform specific default install locations are searched. If exactly one + candidate is found, this is used. The default CUDA Toolkit install locations + searched are: + + +-------------+-------------------------------------------------------------+ + | Platform | Search Pattern | + +=============+=============================================================+ + | macOS | ``/Developer/NVIDIA/CUDA-X.Y`` | + +-------------+-------------------------------------------------------------+ + | Other Unix | ``/usr/local/cuda-X.Y`` | + +-------------+-------------------------------------------------------------+ + | Windows | ``C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.Y`` | + +-------------+-------------------------------------------------------------+ + + Where ``X.Y`` would be a specific version of the CUDA Toolkit, such as + ``/usr/local/cuda-9.0`` or + ``C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0`` + + .. note:: + + When multiple CUDA Toolkits are installed in the default location of a + system(e.g., both ``/usr/local/cuda-9.0`` and ``/usr/local/cuda-10.0`` + exist but the ``/usr/local/cuda`` symbolic link does **not** exist), this + package is marked as **not** found. + + There are too many factors involved in making an automatic decision in + the presence of multiple CUDA Toolkits being installed. In this + situation, users are encouraged to either (1) set ``CUDAToolkit_ROOT`` or + (2) ensure that the correct ``nvcc`` executable shows up in ``$PATH`` for + :command:`find_program` to find. + +Arguments +^^^^^^^^^ + +``[]`` + The ``[]`` argument requests a version with which the package found + should be compatible. See :ref:`find_package version format ` + for more details. + +Options +^^^^^^^ + +``REQUIRED`` + If specified, configuration will error if a suitable CUDA Toolkit is not + found. + +``QUIET`` + If specified, the search for a suitable CUDA Toolkit will not produce any + messages. + +``EXACT`` + If specified, the CUDA Toolkit is considered found only if the exact + ``VERSION`` specified is recovered. + +Imported targets +^^^^^^^^^^^^^^^^ + +An :ref:`imported target ` named ``CUDA::toolkit`` is provided. + +This module defines :prop_tgt:`IMPORTED` targets for each +of the following libraries that are part of the CUDAToolkit: + +- :ref:`CUDA Runtime Library` +- :ref:`CUDA Driver Library` +- :ref:`cuBLAS` +- :ref:`cuFFT` +- :ref:`cuRAND` +- :ref:`cuSOLVER` +- :ref:`cuSPARSE` +- :ref:`cuPTI` +- :ref:`NPP` +- :ref:`nvBLAS` +- :ref:`nvGRAPH` +- :ref:`nvJPEG` +- :ref:`nvidia-ML` +- :ref:`nvRTC` +- :ref:`nvToolsExt` +- :ref:`OpenCL` +- :ref:`cuLIBOS` + +.. _`cuda_toolkit_rt_lib`: + +CUDA Runtime Library +"""""""""""""""""""" + +The CUDA Runtime library (cudart) are what most applications will typically +need to link against to make any calls such as `cudaMalloc`, and `cudaFree`. + +Targets Created: + +- ``CUDA::cudart`` +- ``CUDA::cudart_static`` + +.. _`cuda_toolkit_driver_lib`: + +CUDA Driver Library +"""""""""""""""""""" + +The CUDA Driver library (cuda) are used by applications that use calls +such as `cuMemAlloc`, and `cuMemFree`. + +Targets Created: + +- ``CUDA::cuda_driver`` + +.. _`cuda_toolkit_cuBLAS`: + +cuBLAS +"""""" + +The `cuBLAS `_ library. + +Targets Created: + +- ``CUDA::cublas`` +- ``CUDA::cublas_static`` +- ``CUDA::cublasLt`` starting in CUDA 10.1 +- ``CUDA::cublasLt_static`` starting in CUDA 10.1 + +.. _`cuda_toolkit_cuFFT`: + +cuFFT +""""" + +The `cuFFT `_ library. + +Targets Created: + +- ``CUDA::cufft`` +- ``CUDA::cufftw`` +- ``CUDA::cufft_static`` +- ``CUDA::cufft_static_nocallback`` starting in CUDA 9.2, requires CMake 3.23+ +- ``CUDA::cufftw_static`` + +cuRAND +"""""" + +The `cuRAND `_ library. + +Targets Created: + +- ``CUDA::curand`` +- ``CUDA::curand_static`` + +.. _`cuda_toolkit_cuSOLVER`: + +cuSOLVER +"""""""" + +The `cuSOLVER `_ library. + +Targets Created: + +- ``CUDA::cusolver`` +- ``CUDA::cusolver_static`` + +.. _`cuda_toolkit_cuSPARSE`: + +cuSPARSE +"""""""" + +The `cuSPARSE `_ library. + +Targets Created: + +- ``CUDA::cusparse`` +- ``CUDA::cusparse_static`` + +.. _`cuda_toolkit_cupti`: + +cupti +""""" + +The `NVIDIA CUDA Profiling Tools Interface `_. + +Targets Created: + +- ``CUDA::cupti`` +- ``CUDA::cupti_static`` + +.. _`cuda_toolkit_NPP`: + +NPP +""" + +The `NPP `_ libraries. + +Targets Created: + +- `nppc`: + + - ``CUDA::nppc`` + - ``CUDA::nppc_static`` + +- `nppial`: Arithmetic and logical operation functions in `nppi_arithmetic_and_logical_operations.h` + + - ``CUDA::nppial`` + - ``CUDA::nppial_static`` + +- `nppicc`: Color conversion and sampling functions in `nppi_color_conversion.h` + + - ``CUDA::nppicc`` + - ``CUDA::nppicc_static`` + +- `nppicom`: JPEG compression and decompression functions in `nppi_compression_functions.h` + Removed starting in CUDA 11.0, use :ref:`nvJPEG` instead. + + - ``CUDA::nppicom`` + - ``CUDA::nppicom_static`` + +- `nppidei`: Data exchange and initialization functions in `nppi_data_exchange_and_initialization.h` + + - ``CUDA::nppidei`` + - ``CUDA::nppidei_static`` + +- `nppif`: Filtering and computer vision functions in `nppi_filter_functions.h` + + - ``CUDA::nppif`` + - ``CUDA::nppif_static`` + +- `nppig`: Geometry transformation functions found in `nppi_geometry_transforms.h` + + - ``CUDA::nppig`` + - ``CUDA::nppig_static`` + +- `nppim`: Morphological operation functions found in `nppi_morphological_operations.h` + + - ``CUDA::nppim`` + - ``CUDA::nppim_static`` + +- `nppist`: Statistics and linear transform in `nppi_statistics_functions.h` and `nppi_linear_transforms.h` + + - ``CUDA::nppist`` + - ``CUDA::nppist_static`` + +- `nppisu`: Memory support functions in `nppi_support_functions.h` + + - ``CUDA::nppisu`` + - ``CUDA::nppisu_static`` + +- `nppitc`: Threshold and compare operation functions in `nppi_threshold_and_compare_operations.h` + + - ``CUDA::nppitc`` + - ``CUDA::nppitc_static`` + +- `npps`: + + - ``CUDA::npps`` + - ``CUDA::npps_static`` + +.. _`cuda_toolkit_nvBLAS`: + +nvBLAS +"""""" + +The `nvBLAS `_ libraries. +This is a shared library only. + +Targets Created: + +- ``CUDA::nvblas`` + +.. _`cuda_toolkit_nvGRAPH`: + +nvGRAPH +""""""" + +The `nvGRAPH `_ library. +Removed starting in CUDA 11.0 + +Targets Created: + +- ``CUDA::nvgraph`` +- ``CUDA::nvgraph_static`` + + +.. _`cuda_toolkit_nvJPEG`: + +nvJPEG +"""""" + +The `nvJPEG `_ library. +Introduced in CUDA 10. + +Targets Created: + +- ``CUDA::nvjpeg`` +- ``CUDA::nvjpeg_static`` + +.. _`cuda_toolkit_nvRTC`: + +nvRTC +""""" + +The `nvRTC `_ (Runtime Compilation) library. +This is a shared library only. + +Targets Created: + +- ``CUDA::nvrtc`` + +.. _`cuda_toolkit_nvml`: + +nvidia-ML +""""""""" + +The `NVIDIA Management Library `_. +This is a shared library only. + +Targets Created: + +- ``CUDA::nvml`` + +.. _`cuda_toolkit_nvToolsExt`: + +nvToolsExt +"""""""""" + +The `NVIDIA Tools Extension `_. +This is a shared library only. + +Targets Created: + +- ``CUDA::nvToolsExt`` + +.. _`cuda_toolkit_opencl`: + +OpenCL +"""""" + +The `NVIDIA OpenCL Library `_. +This is a shared library only. + +Targets Created: + +- ``CUDA::OpenCL`` + +.. _`cuda_toolkit_cuLIBOS`: + +cuLIBOS +""""""" + +The cuLIBOS library is a backend thread abstraction layer library which is +static only. The ``CUDA::cublas_static``, ``CUDA::cusparse_static``, +``CUDA::cufft_static``, ``CUDA::curand_static``, and (when implemented) NPP +libraries all automatically have this dependency linked. + +Target Created: + +- ``CUDA::culibos`` + +**Note**: direct usage of this target by consumers should not be necessary. + +.. _`cuda_toolkit_cuRAND`: + + + +Result variables +^^^^^^^^^^^^^^^^ + +``CUDAToolkit_FOUND`` + A boolean specifying whether or not the CUDA Toolkit was found. + +``CUDAToolkit_VERSION`` + The exact version of the CUDA Toolkit found (as reported by + ``nvcc --version`` or ``version.txt``). + +``CUDAToolkit_VERSION_MAJOR`` + The major version of the CUDA Toolkit. + +``CUDAToolkit_VERSION_MINOR`` + The minor version of the CUDA Toolkit. + +``CUDAToolkit_VERSION_PATCH`` + The patch version of the CUDA Toolkit. + +``CUDAToolkit_BIN_DIR`` + The path to the CUDA Toolkit library directory that contains the CUDA + executable ``nvcc``. + +``CUDAToolkit_INCLUDE_DIRS`` + The path to the CUDA Toolkit ``include`` folder containing the header files + required to compile a project linking against CUDA. + +``CUDAToolkit_LIBRARY_DIR`` + The path to the CUDA Toolkit library directory that contains the CUDA + Runtime library ``cudart``. + +``CUDAToolkit_LIBRARY_ROOT`` + .. versionadded:: 3.18 + + The path to the CUDA Toolkit directory containing the nvvm directory and + version.txt. + +``CUDAToolkit_TARGET_DIR`` + The path to the CUDA Toolkit directory including the target architecture + when cross-compiling. When not cross-compiling this will be equivalent to + the parent directory of ``CUDAToolkit_BIN_DIR``. + +``CUDAToolkit_NVCC_EXECUTABLE`` + The path to the NVIDIA CUDA compiler ``nvcc``. Note that this path may + **not** be the same as + :variable:`CMAKE_CUDA_COMPILER _COMPILER>`. ``nvcc`` must be + found to determine the CUDA Toolkit version as well as determining other + features of the Toolkit. This variable is set for the convenience of + modules that depend on this one. + + +#]=======================================================================] + +# NOTE: much of this was simply extracted from FindCUDA.cmake. + +# James Bigler, NVIDIA Corp (nvidia.com - jbigler) +# Abe Stephens, SCI Institute -- http://www.sci.utah.edu/~abe/FindCuda.html +# +# Copyright (c) 2008 - 2009 NVIDIA Corporation. All rights reserved. +# +# Copyright (c) 2007-2009 +# Scientific Computing and Imaging Institute, University of Utah +# +# This code is licensed under the MIT License. See the FindCUDA.cmake script +# for the text of the license. + +# The MIT License +# +# License for the specific language governing rights and limitations under +# 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. +# +############################################################################### + +# The toolkit is located during compiler detection for CUDA and stored in CMakeCUDACompiler.cmake as +# CMAKE_CUDA_COMPILER_TOOLKIT_ROOT and CMAKE_CUDA_COMPILER_LIBRARY_ROOT. +# We compute the rest based on those here to avoid re-searching and to avoid finding a possibly +# different installation. +if(CMAKE_CUDA_COMPILER_TOOLKIT_ROOT) + set(CUDAToolkit_ROOT_DIR "${CMAKE_CUDA_COMPILER_TOOLKIT_ROOT}") + set(CUDAToolkit_LIBRARY_ROOT "${CMAKE_CUDA_COMPILER_LIBRARY_ROOT}") + set(CUDAToolkit_VERSION "${CMAKE_CUDA_COMPILER_TOOLKIT_VERSION}") + + if(CUDAToolkit_VERSION MATCHES [=[([0-9]+)\.([0-9]+)\.([0-9]+)]=]) + set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}") + set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}") + set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}") + endif() +else() + function(_CUDAToolkit_find_root_dir ) + cmake_parse_arguments(arg "" "" "SEARCH_PATHS;FIND_FLAGS" ${ARGN}) + + if(NOT CUDAToolkit_BIN_DIR) + if(NOT CUDAToolkit_SENTINEL_FILE) + find_program(CUDAToolkit_NVCC_EXECUTABLE + NAMES nvcc nvcc.exe + PATHS ${arg_SEARCH_PATHS} + ${arg_FIND_FLAGS} + ) + endif() + + if(NOT CUDAToolkit_NVCC_EXECUTABLE) + find_file(CUDAToolkit_SENTINEL_FILE + NAMES version.txt + PATHS ${arg_SEARCH_PATHS} + NO_DEFAULT_PATH + ) + endif() + + if(EXISTS "${CUDAToolkit_NVCC_EXECUTABLE}") + # If NVCC exists then invoke it to find the toolkit location. + # This allows us to support wrapper scripts (e.g. ccache or colornvcc), CUDA Toolkit, + # NVIDIA HPC SDK, and distro's splayed layouts + execute_process(COMMAND ${CUDAToolkit_NVCC_EXECUTABLE} "-v" "__cmake_determine_cuda" + OUTPUT_VARIABLE _CUDA_NVCC_OUT ERROR_VARIABLE _CUDA_NVCC_OUT) + if(_CUDA_NVCC_OUT MATCHES "\\#\\$ TOP=([^\r\n]*)") + get_filename_component(CUDAToolkit_BIN_DIR "${CMAKE_MATCH_1}/bin" ABSOLUTE) + else() + get_filename_component(CUDAToolkit_BIN_DIR "${CUDAToolkit_NVCC_EXECUTABLE}" DIRECTORY) + endif() + unset(_CUDA_NVCC_OUT) + + mark_as_advanced(CUDAToolkit_BIN_DIR) + set(CUDAToolkit_BIN_DIR "${CUDAToolkit_BIN_DIR}" CACHE PATH "" FORCE) + endif() + + if(CUDAToolkit_SENTINEL_FILE) + get_filename_component(CUDAToolkit_BIN_DIR ${CUDAToolkit_SENTINEL_FILE} DIRECTORY ABSOLUTE) + set(CUDAToolkit_BIN_DIR "${CUDAToolkit_BIN_DIR}/bin") + + set(CUDAToolkit_BIN_DIR "${CUDAToolkit_BIN_DIR}" CACHE PATH "" FORCE) + mark_as_advanced(CUDAToolkit_BIN_DIR) + endif() + endif() + + if(CUDAToolkit_BIN_DIR) + get_filename_component(CUDAToolkit_ROOT_DIR ${CUDAToolkit_BIN_DIR} DIRECTORY ABSOLUTE) + set(CUDAToolkit_ROOT_DIR "${CUDAToolkit_ROOT_DIR}" PARENT_SCOPE) + endif() + + endfunction() + + # For NVCC we can easily deduce the SDK binary directory from the compiler path. + if(CMAKE_CUDA_COMPILER_LOADED AND NOT CUDAToolkit_BIN_DIR AND CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA") + get_filename_component(CUDAToolkit_BIN_DIR "${CMAKE_CUDA_COMPILER}" DIRECTORY) + set(CUDAToolkit_BIN_DIR "${CUDAToolkit_BIN_DIR}" CACHE PATH "") + # Try language provided path first. + _CUDAToolkit_find_root_dir(SEARCH_PATHS "${CUDAToolkit_BIN_DIR}" FIND_FLAGS NO_DEFAULT_PATH) + mark_as_advanced(CUDAToolkit_BIN_DIR) + endif() + + # Try user provided path + if(NOT CUDAToolkit_ROOT_DIR AND CUDAToolkit_ROOT) + _CUDAToolkit_find_root_dir(SEARCH_PATHS "${CUDAToolkit_ROOT}" FIND_FLAGS PATH_SUFFIXES bin NO_DEFAULT_PATH) + endif() + if(NOT CUDAToolkit_ROOT_DIR) + _CUDAToolkit_find_root_dir(FIND_FLAGS PATHS ENV CUDA_PATH PATH_SUFFIXES bin) + endif() + + # If the user specified CUDAToolkit_ROOT but the toolkit could not be found, this is an error. + if(NOT CUDAToolkit_ROOT_DIR AND (DEFINED CUDAToolkit_ROOT OR DEFINED ENV{CUDAToolkit_ROOT})) + # Declare error messages now, print later depending on find_package args. + set(fail_base "Could not find nvcc executable in path specified by") + set(cuda_root_fail "${fail_base} CUDAToolkit_ROOT=${CUDAToolkit_ROOT}") + set(env_cuda_root_fail "${fail_base} environment variable CUDAToolkit_ROOT=$ENV{CUDAToolkit_ROOT}") + + if(CUDAToolkit_FIND_REQUIRED) + if(DEFINED CUDAToolkit_ROOT) + message(FATAL_ERROR ${cuda_root_fail}) + elseif(DEFINED ENV{CUDAToolkit_ROOT}) + message(FATAL_ERROR ${env_cuda_root_fail}) + endif() + else() + if(NOT CUDAToolkit_FIND_QUIETLY) + if(DEFINED CUDAToolkit_ROOT) + message(STATUS ${cuda_root_fail}) + elseif(DEFINED ENV{CUDAToolkit_ROOT}) + message(STATUS ${env_cuda_root_fail}) + endif() + endif() + set(CUDAToolkit_FOUND FALSE) + unset(fail_base) + unset(cuda_root_fail) + unset(env_cuda_root_fail) + return() + endif() + endif() + + # CUDAToolkit_ROOT cmake / env variable not specified, try platform defaults. + # + # - Linux: /usr/local/cuda-X.Y + # - macOS: /Developer/NVIDIA/CUDA-X.Y + # - Windows: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.Y + # + # We will also search the default symlink location /usr/local/cuda first since + # if CUDAToolkit_ROOT is not specified, it is assumed that the symlinked + # directory is the desired location. + if(NOT CUDAToolkit_ROOT_DIR) + if(UNIX) + if(NOT APPLE) + set(platform_base "/usr/local/cuda-") + else() + set(platform_base "/Developer/NVIDIA/CUDA-") + endif() + else() + set(platform_base "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v") + endif() + + # Build out a descending list of possible cuda installations, e.g. + file(GLOB possible_paths "${platform_base}*") + # Iterate the glob results and create a descending list. + set(versions) + foreach(p ${possible_paths}) + # Extract version number from end of string + string(REGEX MATCH "[0-9][0-9]?\\.[0-9]$" p_version ${p}) + if(IS_DIRECTORY ${p} AND p_version) + list(APPEND versions ${p_version}) + endif() + endforeach() + + # Sort numerically in descending order, so we try the newest versions first. + if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.18) + list(SORT versions COMPARE NATURAL ORDER DESCENDING) + elseif(versions) + # Alphabetical sort here is not ideal but better than nothing + list(SORT versions) + list(REVERSE versions) + endif() + + # With a descending list of versions, populate possible paths to search. + set(search_paths) + foreach(v ${versions}) + list(APPEND search_paths "${platform_base}${v}") + endforeach() + + # Force the global default /usr/local/cuda to the front on Unix. + if(UNIX) + list(INSERT search_paths 0 "/usr/local/cuda") + endif() + + # Now search for the toolkit again using the platform default search paths. + _CUDAToolkit_find_root_dir(SEARCH_PATHS "${search_paths}" FIND_FLAGS PATH_SUFFIXES bin) + + # We are done with these variables now, cleanup for caller. + unset(platform_base) + unset(possible_paths) + unset(versions) + unset(search_paths) + + if(NOT CUDAToolkit_ROOT_DIR) + if(CUDAToolkit_FIND_REQUIRED) + message(FATAL_ERROR "Could not find nvcc, please set CUDAToolkit_ROOT.") + elseif(NOT CUDAToolkit_FIND_QUIETLY) + message(STATUS "Could not find nvcc, please set CUDAToolkit_ROOT.") + endif() + + set(CUDAToolkit_FOUND FALSE) + return() + endif() + endif() +endif() + +if(NOT CUDAToolkit_BIN_DIR) + set(CUDAToolkit_BIN_DIR "${CUDAToolkit_ROOT_DIR}/bin") +endif() + +if(NOT CUDAToolkit_NVCC_EXECUTABLE) + set(CUDAToolkit_NVCC_EXECUTABLE "${CUDAToolkit_BIN_DIR}/nvcc${CMAKE_EXECUTABLE_SUFFIX}") +endif() + +if(CMAKE_CUDA_COMPILER_TOOLKIT_VERSION) + set(CUDAToolkit_VERSION "${CMAKE_CUDA_COMPILER_TOOLKIT_VERSION}") +else() + function(_CUDAToolkit_find_version_file result_variable) + # We first check for a non-scattered installation to prefer it over a scattered installation. + if(CUDAToolkit_ROOT AND EXISTS "${CUDAToolkit_ROOT}/version.txt") + set(${result_variable} "${CUDAToolkit_ROOT}/version.txt" PARENT_SCOPE) + elseif(CUDAToolkit_ROOT_DIR AND EXISTS "${CUDAToolkit_ROOT_DIR}/version.txt") + set(${result_variable} "${CUDAToolkit_ROOT_DIR}/version.txt" PARENT_SCOPE) + elseif(CMAKE_SYSROOT_LINK AND EXISTS "${CMAKE_SYSROOT_LINK}/usr/lib/cuda/version.txt") + set(${result_variable} "${CMAKE_SYSROOT_LINK}/usr/lib/cuda/version.txt" PARENT_SCOPE) + elseif(EXISTS "${CMAKE_SYSROOT}/usr/lib/cuda/version.txt") + set(${result_variable} "${CMAKE_SYSROOT}/usr/lib/cuda/version.txt" PARENT_SCOPE) + endif() + endfunction() + + _CUDAToolkit_find_version_file( _CUDAToolkit_version_file ) + if(_CUDAToolkit_version_file) + # CUDAToolkit_LIBRARY_ROOT contains the device library and version file. + get_filename_component(CUDAToolkit_LIBRARY_ROOT "${_CUDAToolkit_version_file}" DIRECTORY ABSOLUTE) + endif() + unset(_CUDAToolkit_version_file) + + if(CUDAToolkit_NVCC_EXECUTABLE AND + CMAKE_CUDA_COMPILER_VERSION AND + CUDAToolkit_NVCC_EXECUTABLE STREQUAL CMAKE_CUDA_COMPILER) + # Need to set these based off the already computed CMAKE_CUDA_COMPILER_VERSION value + # This if statement will always match, but is used to provide variables for MATCH 1,2,3... + if(CMAKE_CUDA_COMPILER_VERSION MATCHES [=[([0-9]+)\.([0-9]+)\.([0-9]+)]=]) + set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}") + set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}") + set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}") + set(CUDAToolkit_VERSION "${CMAKE_CUDA_COMPILER_VERSION}") + endif() + elseif(CUDAToolkit_NVCC_EXECUTABLE) + # Compute the version by invoking nvcc + execute_process(COMMAND ${CUDAToolkit_NVCC_EXECUTABLE} "--version" OUTPUT_VARIABLE NVCC_OUT) + if(NVCC_OUT MATCHES [=[ V([0-9]+)\.([0-9]+)\.([0-9]+)]=]) + set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}") + set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}") + set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}") + set(CUDAToolkit_VERSION "${CMAKE_MATCH_1}.${CMAKE_MATCH_2}.${CMAKE_MATCH_3}") + endif() + unset(NVCC_OUT) + else() + _CUDAToolkit_find_version_file(version_file) + if(version_file) + file(READ "${version_file}" VERSION_INFO) + if(VERSION_INFO MATCHES [=[CUDA Version ([0-9]+)\.([0-9]+)\.([0-9]+)]=]) + set(CUDAToolkit_VERSION_MAJOR "${CMAKE_MATCH_1}") + set(CUDAToolkit_VERSION_MINOR "${CMAKE_MATCH_2}") + set(CUDAToolkit_VERSION_PATCH "${CMAKE_MATCH_3}") + set(CUDAToolkit_VERSION "${CMAKE_MATCH_1}.${CMAKE_MATCH_2}.${CMAKE_MATCH_3}") + endif() + endif() + endif() +endif() + +# Find target directory when crosscompiling. +if(CMAKE_CROSSCOMPILING) + if(CMAKE_SYSTEM_PROCESSOR STREQUAL "armv7-a") + # Support for NVPACK + set(CUDAToolkit_TARGET_NAME "armv7-linux-androideabi") + elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "arm") + set(CUDAToolkit_TARGET_NAME "armv7-linux-gnueabihf") + elseif(CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64") + if(ANDROID_ARCH_NAME STREQUAL "arm64") + set(CUDAToolkit_TARGET_NAME "aarch64-linux-androideabi") + elseif(CMAKE_SYSTEM_NAME STREQUAL "QNX") + set(CUDAToolkit_TARGET_NAME "aarch64-qnx") + else() + set(CUDAToolkit_TARGET_NAME "aarch64-linux") + endif(ANDROID_ARCH_NAME STREQUAL "arm64") + elseif(CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64") + set(CUDAToolkit_TARGET_NAME "x86_64-linux") + endif() + + if(EXISTS "${CUDAToolkit_ROOT_DIR}/targets/${CUDAToolkit_TARGET_NAME}") + set(CUDAToolkit_TARGET_DIR "${CUDAToolkit_ROOT_DIR}/targets/${CUDAToolkit_TARGET_NAME}") + # add known CUDA target root path to the set of directories we search for programs, libraries and headers + list(PREPEND CMAKE_FIND_ROOT_PATH "${CUDAToolkit_TARGET_DIR}") + + # Mark that we need to pop the root search path changes after we have + # found all cuda libraries so that searches for our cross-compilation + # libraries work when another cuda sdk is in CMAKE_PREFIX_PATH or + # PATh + set(_CUDAToolkit_Pop_ROOT_PATH True) + endif() +endif() + +# If not already set we can simply use the toolkit root or it's a scattered installation. +if(NOT CUDAToolkit_TARGET_DIR) + # Not cross compiling + set(CUDAToolkit_TARGET_DIR "${CUDAToolkit_ROOT_DIR}") + # Now that we have the real ROOT_DIR, find components inside it. + list(APPEND CMAKE_PREFIX_PATH ${CUDAToolkit_ROOT_DIR}) + + # Mark that we need to pop the prefix path changes after we have + # found the cudart library. + set(_CUDAToolkit_Pop_Prefix True) +endif() + +# CUDAToolkit_TARGET_DIR always points to the directory containing the include directory. +# On a scattered installation /usr, on a non-scattered something like /usr/local/cuda or /usr/local/cuda-10.2/targets/aarch64-linux. +if(EXISTS "${CUDAToolkit_TARGET_DIR}/include/cuda_runtime.h") + set(CUDAToolkit_INCLUDE_DIR "${CUDAToolkit_TARGET_DIR}/include") +elseif(NOT CUDAToolkit_FIND_QUIETLY) + message(STATUS "Unable to find cuda_runtime.h in \"${CUDAToolkit_TARGET_DIR}/include\" for CUDAToolkit_INCLUDE_DIR.") +endif() + +# The NVHPC layout moves math library headers and libraries to a sibling directory. +# Create a separate variable so this directory can be selectively added to math targets. +if(NOT EXISTS "${CUDAToolkit_INCLUDE_DIR}/cublas_v2.h") + set(CUDAToolkit_MATH_INCLUDE_DIR "${CUDAToolkit_TARGET_DIR}/../../math_libs/include") + get_filename_component(CUDAToolkit_MATH_INCLUDE_DIR "${CUDAToolkit_MATH_INCLUDE_DIR}" ABSOLUTE) + if(NOT EXISTS "${CUDAToolkit_MATH_INCLUDE_DIR}/cublas_v2.h") + if(NOT CUDAToolkit_FIND_QUIETLY) + message(STATUS "Unable to find cublas_v2.h in either \"${CUDAToolkit_INCLUDE_DIR}\" or \"${CUDAToolkit_MATH_INCLUDE_DIR}\"") + endif() + unset(CUDAToolkit_MATH_INCLUDE_DIR) + endif() +endif() + +# Find the CUDA Runtime Library libcudart +find_library(CUDA_CUDART + NAMES cudart + PATH_SUFFIXES lib64 lib/x64 +) +find_library(CUDA_CUDART + NAMES cudart + PATH_SUFFIXES lib64/stubs lib/x64/stubs +) + +if(NOT CUDA_CUDART AND NOT CUDAToolkit_FIND_QUIETLY) + message(STATUS "Unable to find cudart library.") +endif() + +if(_CUDAToolkit_Pop_Prefix) + list(REMOVE_AT CMAKE_PREFIX_PATH -1) + unset(_CUDAToolkit_Pop_Prefix) +endif() + +#----------------------------------------------------------------------------- +# Perform version comparison and validate all required variables are set. +include(FindPackageHandleStandardArgs) +find_package_handle_standard_args(CUDAToolkit + REQUIRED_VARS + CUDAToolkit_INCLUDE_DIR + CUDAToolkit_VERSION + CUDA_CUDART + CUDAToolkit_BIN_DIR + VERSION_VAR + CUDAToolkit_VERSION +) + +mark_as_advanced(CUDA_CUDART + CUDAToolkit_INCLUDE_DIR + CUDAToolkit_NVCC_EXECUTABLE + CUDAToolkit_SENTINEL_FILE + ) + +#----------------------------------------------------------------------------- +# Construct result variables +if(CUDAToolkit_FOUND) + set(CUDAToolkit_INCLUDE_DIRS ${CUDAToolkit_INCLUDE_DIR}) + get_filename_component(CUDAToolkit_LIBRARY_DIR ${CUDA_CUDART} DIRECTORY ABSOLUTE) +endif() + +#----------------------------------------------------------------------------- +# Construct import targets +if(CUDAToolkit_FOUND) + + function(_CUDAToolkit_find_and_add_import_lib lib_name) + cmake_parse_arguments(arg "" "" "ALT;DEPS;EXTRA_HINTS;EXTRA_PATH_SUFFIXES;EXTRA_INCLUDE_DIRS" ${ARGN}) + + set(search_names ${lib_name} ${arg_ALT}) + + find_library(CUDA_${lib_name}_LIBRARY + NAMES ${search_names} + HINTS ${CUDAToolkit_LIBRARY_DIR} + ENV CUDA_PATH + ${arg_EXTRA_HINTS} + PATH_SUFFIXES nvidia/current lib64 lib/x64 lib + ${arg_EXTRA_PATH_SUFFIXES} + ) + # Don't try any stub directories until we have exhausted all other + # search locations. + find_library(CUDA_${lib_name}_LIBRARY + NAMES ${search_names} + HINTS ${CUDAToolkit_LIBRARY_DIR} + ENV CUDA_PATH + ${arg_EXTRA_HINTS} + PATH_SUFFIXES lib64/stubs lib/x64/stubs lib/stubs stubs + # Support NVHPC splayed math library layout + ../../math_libs/${CUDAToolkit_VERSION_MAJOR}.${CUDAToolkit_VERSION_MINOR}/lib64 + ../../math_libs/lib64 + ) + + mark_as_advanced(CUDA_${lib_name}_LIBRARY) + + if(NOT TARGET CUDA::${lib_name} AND CUDA_${lib_name}_LIBRARY) + add_library(CUDA::${lib_name} UNKNOWN IMPORTED) + set_property(TARGET CUDA::${lib_name} APPEND PROPERTY + INTERFACE_INCLUDE_DIRECTORIES "${CUDAToolkit_INCLUDE_DIRS}") + set_property(TARGET CUDA::${lib_name} APPEND PROPERTY + INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${CUDAToolkit_INCLUDE_DIRS}") + if(DEFINED CUDAToolkit_MATH_INCLUDE_DIR) + string(FIND ${CUDA_${lib_name}_LIBRARY} "math_libs" math_libs) + if(NOT ${math_libs} EQUAL -1) + set_property(TARGET CUDA::${lib_name} APPEND PROPERTY + INTERFACE_INCLUDE_DIRECTORIES "${CUDAToolkit_MATH_INCLUDE_DIRS}") + set_property(TARGET CUDA::${lib_name} APPEND PROPERTY + INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${CUDAToolkit_MATH_INCLUDE_DIRS}") + endif() + endif() + set_property(TARGET CUDA::${lib_name} PROPERTY IMPORTED_LOCATION "${CUDA_${lib_name}_LIBRARY}") + foreach(dep ${arg_DEPS}) + if(TARGET CUDA::${dep}) + set_property(TARGET CUDA::${lib_name} APPEND PROPERTY + INTERFACE_LINK_LIBRARIES CUDA::${dep}) + endif() + endforeach() + if(arg_EXTRA_INCLUDE_DIRS) + set_property(TARGET CUDA::${lib_name} APPEND PROPERTY + INTERFACE_INCLUDE_DIRECTORIES "${arg_EXTRA_INCLUDE_DIRS}") + set_property(TARGET CUDA::${lib_name} APPEND PROPERTY + INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${arg_EXTRA_INCLUDE_DIRS}") + endif() + endif() + endfunction() + + if(NOT TARGET CUDA::toolkit) + add_library(CUDA::toolkit IMPORTED INTERFACE) + set_property(TARGET CUDA::toolkit APPEND PROPERTY + INTERFACE_INCLUDE_DIRECTORIES "${CUDAToolkit_INCLUDE_DIRS}") + set_property(TARGET CUDA::toolkit APPEND PROPERTY + INTERFACE_SYSTEM_INCLUDE_DIRECTORIES "${CUDAToolkit_INCLUDE_DIRS}") + endif() + + _CUDAToolkit_find_and_add_import_lib(cuda_driver ALT cuda) + + _CUDAToolkit_find_and_add_import_lib(cudart) + _CUDAToolkit_find_and_add_import_lib(cudart_static) + + # setup dependencies that are required for cudart_static when building + # on linux. These are generally only required when using the CUDA toolkit + # when CUDA language is disabled + if(NOT TARGET CUDA::cudart_static_deps + AND TARGET CUDA::cudart_static) + + add_library(CUDA::cudart_static_deps IMPORTED INTERFACE) + set_property(TARGET CUDA::cudart_static APPEND PROPERTY + INTERFACE_LINK_LIBRARIES CUDA::cudart_static_deps) + + if(UNIX AND (CMAKE_C_COMPILER OR CMAKE_CXX_COMPILER)) + find_package(Threads REQUIRED) + set_property(TARGET CUDA::cudart_static_deps APPEND PROPERTY + INTERFACE_LINK_LIBRARIES Threads::Threads ${CMAKE_DL_LIBS}) + endif() + + if(UNIX AND NOT APPLE AND NOT (CMAKE_SYSTEM_NAME STREQUAL "QNX")) + # On Linux, you must link against librt when using the static cuda runtime. + find_library(CUDAToolkit_rt_LIBRARY rt) + mark_as_advanced(CUDAToolkit_rt_LIBRARY) + if(NOT CUDAToolkit_rt_LIBRARY) + message(WARNING "Could not find librt library, needed by CUDA::cudart_static") + else() + set_property(TARGET CUDA::cudart_static_deps APPEND PROPERTY + INTERFACE_LINK_LIBRARIES ${CUDAToolkit_rt_LIBRARY}) + endif() + endif() + endif() + + _CUDAToolkit_find_and_add_import_lib(culibos) # it's a static library + foreach(cuda_lib cublasLt cufft curand cusparse nppc nvjpeg) + _CUDAToolkit_find_and_add_import_lib(${cuda_lib}) + _CUDAToolkit_find_and_add_import_lib(${cuda_lib}_static DEPS culibos) + endforeach() + + if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 11.0.0) + # cublas depends on cublasLt + # https://docs.nvidia.com/cuda/archive/11.0/cublas/index.html#static-library + _CUDAToolkit_find_and_add_import_lib(cublas DEPS cublasLt) + _CUDAToolkit_find_and_add_import_lib(cublas_static DEPS cublasLt_static) + else() + _CUDAToolkit_find_and_add_import_lib(cublas) + _CUDAToolkit_find_and_add_import_lib(cublas_static DEPS culibos) + endif() + + # cuFFTW depends on cuFFT + _CUDAToolkit_find_and_add_import_lib(cufftw DEPS cufft) + _CUDAToolkit_find_and_add_import_lib(cufftw_static DEPS cufft_static) + if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 9.2) + _CUDAToolkit_find_and_add_import_lib(cufft_static_nocallback DEPS culibos) + endif() + + # cuSOLVER depends on cuBLAS, and cuSPARSE + _CUDAToolkit_find_and_add_import_lib(cusolver DEPS cublas cusparse) + _CUDAToolkit_find_and_add_import_lib(cusolver_static DEPS cublas_static cusparse_static culibos) + + + if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL 10.1.2) + # cusolver depends on liblapack_static.a starting with CUDA 10.1 update 2, + # https://docs.nvidia.com/cuda/archive/11.5.0/cusolver/index.html#static-link-lapack + _CUDAToolkit_find_and_add_import_lib(cusolver_lapack_static ALT lapack_static) # implementation detail static lib + _CUDAToolkit_find_and_add_import_lib(cusolver_static DEPS cusolver_lapack_static) + endif() + + if(CUDAToolkit_VERSION VERSION_GREATER 11.2.1) + # cusolver depends on libcusolver_metis and cublasLt + # https://docs.nvidia.com/cuda/archive/11.2.2/cusolver/index.html#link-dependency + _CUDAToolkit_find_and_add_import_lib(cusolver DEPS cublasLt) + + _CUDAToolkit_find_and_add_import_lib(cusolver_metis_static ALT metis_static) # implementation detail static lib + _CUDAToolkit_find_and_add_import_lib(cusolver_static DEPS cusolver_metis_static cublasLt_static) + endif() + + # nvGRAPH depends on cuRAND, and cuSOLVER. + _CUDAToolkit_find_and_add_import_lib(nvgraph DEPS curand cusolver) + _CUDAToolkit_find_and_add_import_lib(nvgraph_static DEPS curand_static cusolver_static) + + # Process the majority of the NPP libraries. + foreach(cuda_lib nppial nppicc nppidei nppif nppig nppim nppist nppitc npps nppicom nppisu) + _CUDAToolkit_find_and_add_import_lib(${cuda_lib} DEPS nppc) + _CUDAToolkit_find_and_add_import_lib(${cuda_lib}_static DEPS nppc_static) + endforeach() + + find_path(CUDAToolkit_CUPTI_INCLUDE_DIR cupti.h PATHS + "${CUDAToolkit_ROOT_DIR}/extras/CUPTI/include" + "${CUDAToolkit_INCLUDE_DIR}/../extras/CUPTI/include" + "${CUDAToolkit_INCLUDE_DIR}" + NO_DEFAULT_PATH) + mark_as_advanced(CUDAToolkit_CUPTI_INCLUDE_DIR) + + if(CUDAToolkit_CUPTI_INCLUDE_DIR) + _CUDAToolkit_find_and_add_import_lib(cupti + EXTRA_PATH_SUFFIXES ../extras/CUPTI/lib64/ + ../extras/CUPTI/lib/ + EXTRA_INCLUDE_DIRS "${CUDAToolkit_CUPTI_INCLUDE_DIR}") + _CUDAToolkit_find_and_add_import_lib(cupti_static + EXTRA_PATH_SUFFIXES ../extras/CUPTI/lib64/ + ../extras/CUPTI/lib/ + EXTRA_INCLUDE_DIRS "${CUDAToolkit_CUPTI_INCLUDE_DIR}") + endif() + + _CUDAToolkit_find_and_add_import_lib(nvrtc DEPS cuda_driver) + + _CUDAToolkit_find_and_add_import_lib(nvml ALT nvidia-ml nvml) + + # nvtools can be installed outside the CUDA toolkit directory, + # so search the NVTOOLSEXT_PATH windows only environment variable + set(nvToolsExt_EXTRA_PATH) + if(WIN32) + set(nvToolsExt_EXTRA_PATH "C:\\Program Files\\NVIDIA Corporation\\NvToolsExt") + endif() + + find_path(CUDAToolkit_nvToolsExt_INCLUDE_DIR nvToolsExt.h + PATHS "${CUDAToolkit_INCLUDE_DIR}" + "${CUDAToolkit_ROOT_DIR}" + ENV NVTOOLSEXT_PATH + "${nvToolsExt_EXTRA_PATH}" + PATH_SUFFIXES include + NO_DEFAULT_PATH) + mark_as_advanced(CUDAToolkit_nvToolsExt_INCLUDE_DIR) + + if(CUDAToolkit_nvToolsExt_INCLUDE_DIR) + _CUDAToolkit_find_and_add_import_lib(nvToolsExt + ALT nvToolsExt64 nvToolsExt64_1 + EXTRA_HINTS ENV NVTOOLSEXT_PATH + "${nvToolsExt_EXTRA_PATH}" + EXTRA_INCLUDE_DIRS "${CUDAToolkit_nvToolsExt_INCLUDE_DIR}") + endif() + + _CUDAToolkit_find_and_add_import_lib(OpenCL) +endif() + +unset(CUDAToolkit_ROOT_DIR) + +if(_CUDAToolkit_Pop_ROOT_PATH) + list(REMOVE_AT CMAKE_FIND_ROOT_PATH 0) + unset(_CUDAToolkit_Pop_ROOT_PATH) +endif() diff --git a/shared/package-files/5d/5d65012555d608e4754866f748c0ac6c1b467e98674bba5e628eba37d0ea30d4/blob b/shared/package-files/5d/5d65012555d608e4754866f748c0ac6c1b467e98674bba5e628eba37d0ea30d4/blob new file mode 100644 index 0000000000000000000000000000000000000000..bd96863997d2b24756dbb309809e0d503347229a --- /dev/null +++ b/shared/package-files/5d/5d65012555d608e4754866f748c0ac6c1b467e98674bba5e628eba37d0ea30d4/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 compositeexplicitautogradnonfunctional { + +TORCH_API at::Tensor asinh(const at::Tensor & self); +TORCH_API at::Tensor & asinh_(at::Tensor & self); + +} // namespace compositeexplicitautogradnonfunctional +} // namespace at diff --git a/shared/package-files/5d/5d6662bf22e3c58945e3553892d526bd42c2d8f32fc4ab3653a0470d745955d1/blob b/shared/package-files/5d/5d6662bf22e3c58945e3553892d526bd42c2d8f32fc4ab3653a0470d745955d1/blob new file mode 100644 index 0000000000000000000000000000000000000000..d274206a7d6dffb6d9101378370210dd0cb8e01f --- /dev/null +++ b/shared/package-files/5d/5d6662bf22e3c58945e3553892d526bd42c2d8f32fc4ab3653a0470d745955d1/blob @@ -0,0 +1,306 @@ +""" +Tests for closeness centrality. +""" +import pytest + +import networkx as nx + + +class TestClosenessCentrality: + @classmethod + def setup_class(cls): + cls.K = nx.krackhardt_kite_graph() + cls.P3 = nx.path_graph(3) + cls.P4 = nx.path_graph(4) + cls.K5 = nx.complete_graph(5) + + cls.C4 = nx.cycle_graph(4) + cls.T = nx.balanced_tree(r=2, h=2) + cls.Gb = nx.Graph() + cls.Gb.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (2, 4), (4, 5), (3, 5)]) + + F = nx.florentine_families_graph() + cls.F = F + + cls.LM = nx.les_miserables_graph() + + # Create random undirected, unweighted graph for testing incremental version + cls.undirected_G = nx.fast_gnp_random_graph(n=100, p=0.6, seed=123) + cls.undirected_G_cc = nx.closeness_centrality(cls.undirected_G) + + def test_wf_improved(self): + G = nx.union(self.P4, nx.path_graph([4, 5, 6])) + c = nx.closeness_centrality(G) + cwf = nx.closeness_centrality(G, wf_improved=False) + res = {0: 0.25, 1: 0.375, 2: 0.375, 3: 0.25, 4: 0.222, 5: 0.333, 6: 0.222} + wf_res = {0: 0.5, 1: 0.75, 2: 0.75, 3: 0.5, 4: 0.667, 5: 1.0, 6: 0.667} + for n in G: + assert c[n] == pytest.approx(res[n], abs=1e-3) + assert cwf[n] == pytest.approx(wf_res[n], abs=1e-3) + + def test_digraph(self): + G = nx.path_graph(3, create_using=nx.DiGraph()) + c = nx.closeness_centrality(G) + cr = nx.closeness_centrality(G.reverse()) + d = {0: 0.0, 1: 0.500, 2: 0.667} + dr = {0: 0.667, 1: 0.500, 2: 0.0} + for n in sorted(self.P3): + assert c[n] == pytest.approx(d[n], abs=1e-3) + assert cr[n] == pytest.approx(dr[n], abs=1e-3) + + def test_k5_closeness(self): + c = nx.closeness_centrality(self.K5) + d = {0: 1.000, 1: 1.000, 2: 1.000, 3: 1.000, 4: 1.000} + for n in sorted(self.K5): + assert c[n] == pytest.approx(d[n], abs=1e-3) + + def test_p3_closeness(self): + c = nx.closeness_centrality(self.P3) + d = {0: 0.667, 1: 1.000, 2: 0.667} + for n in sorted(self.P3): + assert c[n] == pytest.approx(d[n], abs=1e-3) + + def test_krackhardt_closeness(self): + c = nx.closeness_centrality(self.K) + d = { + 0: 0.529, + 1: 0.529, + 2: 0.500, + 3: 0.600, + 4: 0.500, + 5: 0.643, + 6: 0.643, + 7: 0.600, + 8: 0.429, + 9: 0.310, + } + for n in sorted(self.K): + assert c[n] == pytest.approx(d[n], abs=1e-3) + + def test_florentine_families_closeness(self): + c = nx.closeness_centrality(self.F) + d = { + "Acciaiuoli": 0.368, + "Albizzi": 0.483, + "Barbadori": 0.4375, + "Bischeri": 0.400, + "Castellani": 0.389, + "Ginori": 0.333, + "Guadagni": 0.467, + "Lamberteschi": 0.326, + "Medici": 0.560, + "Pazzi": 0.286, + "Peruzzi": 0.368, + "Ridolfi": 0.500, + "Salviati": 0.389, + "Strozzi": 0.4375, + "Tornabuoni": 0.483, + } + for n in sorted(self.F): + assert c[n] == pytest.approx(d[n], abs=1e-3) + + def test_les_miserables_closeness(self): + c = nx.closeness_centrality(self.LM) + d = { + "Napoleon": 0.302, + "Myriel": 0.429, + "MlleBaptistine": 0.413, + "MmeMagloire": 0.413, + "CountessDeLo": 0.302, + "Geborand": 0.302, + "Champtercier": 0.302, + "Cravatte": 0.302, + "Count": 0.302, + "OldMan": 0.302, + "Valjean": 0.644, + "Labarre": 0.394, + "Marguerite": 0.413, + "MmeDeR": 0.394, + "Isabeau": 0.394, + "Gervais": 0.394, + "Listolier": 0.341, + "Tholomyes": 0.392, + "Fameuil": 0.341, + "Blacheville": 0.341, + "Favourite": 0.341, + "Dahlia": 0.341, + "Zephine": 0.341, + "Fantine": 0.461, + "MmeThenardier": 0.461, + "Thenardier": 0.517, + "Cosette": 0.478, + "Javert": 0.517, + "Fauchelevent": 0.402, + "Bamatabois": 0.427, + "Perpetue": 0.318, + "Simplice": 0.418, + "Scaufflaire": 0.394, + "Woman1": 0.396, + "Judge": 0.404, + "Champmathieu": 0.404, + "Brevet": 0.404, + "Chenildieu": 0.404, + "Cochepaille": 0.404, + "Pontmercy": 0.373, + "Boulatruelle": 0.342, + "Eponine": 0.396, + "Anzelma": 0.352, + "Woman2": 0.402, + "MotherInnocent": 0.398, + "Gribier": 0.288, + "MmeBurgon": 0.344, + "Jondrette": 0.257, + "Gavroche": 0.514, + "Gillenormand": 0.442, + "Magnon": 0.335, + "MlleGillenormand": 0.442, + "MmePontmercy": 0.315, + "MlleVaubois": 0.308, + "LtGillenormand": 0.365, + "Marius": 0.531, + "BaronessT": 0.352, + "Mabeuf": 0.396, + "Enjolras": 0.481, + "Combeferre": 0.392, + "Prouvaire": 0.357, + "Feuilly": 0.392, + "Courfeyrac": 0.400, + "Bahorel": 0.394, + "Bossuet": 0.475, + "Joly": 0.394, + "Grantaire": 0.358, + "MotherPlutarch": 0.285, + "Gueulemer": 0.463, + "Babet": 0.463, + "Claquesous": 0.452, + "Montparnasse": 0.458, + "Toussaint": 0.402, + "Child1": 0.342, + "Child2": 0.342, + "Brujon": 0.380, + "MmeHucheloup": 0.353, + } + for n in sorted(self.LM): + assert c[n] == pytest.approx(d[n], abs=1e-3) + + def test_weighted_closeness(self): + edges = [ + ("s", "u", 10), + ("s", "x", 5), + ("u", "v", 1), + ("u", "x", 2), + ("v", "y", 1), + ("x", "u", 3), + ("x", "v", 5), + ("x", "y", 2), + ("y", "s", 7), + ("y", "v", 6), + ] + XG = nx.Graph() + XG.add_weighted_edges_from(edges) + c = nx.closeness_centrality(XG, distance="weight") + d = {"y": 0.200, "x": 0.286, "s": 0.138, "u": 0.235, "v": 0.200} + for n in sorted(XG): + assert c[n] == pytest.approx(d[n], abs=1e-3) + + # + # Tests for incremental closeness centrality. + # + @staticmethod + def pick_add_edge(g): + u = nx.utils.arbitrary_element(g) + possible_nodes = set(g.nodes()) + neighbors = list(g.neighbors(u)) + [u] + possible_nodes.difference_update(neighbors) + v = nx.utils.arbitrary_element(possible_nodes) + return (u, v) + + @staticmethod + def pick_remove_edge(g): + u = nx.utils.arbitrary_element(g) + possible_nodes = list(g.neighbors(u)) + v = nx.utils.arbitrary_element(possible_nodes) + return (u, v) + + def test_directed_raises(self): + with pytest.raises(nx.NetworkXNotImplemented): + dir_G = nx.gn_graph(n=5) + prev_cc = None + edge = self.pick_add_edge(dir_G) + insert = True + nx.incremental_closeness_centrality(dir_G, edge, prev_cc, insert) + + def test_wrong_size_prev_cc_raises(self): + with pytest.raises(nx.NetworkXError): + G = self.undirected_G.copy() + edge = self.pick_add_edge(G) + insert = True + prev_cc = self.undirected_G_cc.copy() + prev_cc.pop(0) + nx.incremental_closeness_centrality(G, edge, prev_cc, insert) + + def test_wrong_nodes_prev_cc_raises(self): + with pytest.raises(nx.NetworkXError): + G = self.undirected_G.copy() + edge = self.pick_add_edge(G) + insert = True + prev_cc = self.undirected_G_cc.copy() + num_nodes = len(prev_cc) + prev_cc.pop(0) + prev_cc[num_nodes] = 0.5 + nx.incremental_closeness_centrality(G, edge, prev_cc, insert) + + def test_zero_centrality(self): + G = nx.path_graph(3) + prev_cc = nx.closeness_centrality(G) + edge = self.pick_remove_edge(G) + test_cc = nx.incremental_closeness_centrality(G, edge, prev_cc, insertion=False) + G.remove_edges_from([edge]) + real_cc = nx.closeness_centrality(G) + shared_items = set(test_cc.items()) & set(real_cc.items()) + assert len(shared_items) == len(real_cc) + assert 0 in test_cc.values() + + def test_incremental(self): + # Check that incremental and regular give same output + G = self.undirected_G.copy() + prev_cc = None + for i in range(5): + if i % 2 == 0: + # Remove an edge + insert = False + edge = self.pick_remove_edge(G) + else: + # Add an edge + insert = True + edge = self.pick_add_edge(G) + + # start = timeit.default_timer() + test_cc = nx.incremental_closeness_centrality(G, edge, prev_cc, insert) + # inc_elapsed = (timeit.default_timer() - start) + # print(f"incremental time: {inc_elapsed}") + + if insert: + G.add_edges_from([edge]) + else: + G.remove_edges_from([edge]) + + # start = timeit.default_timer() + real_cc = nx.closeness_centrality(G) + # reg_elapsed = (timeit.default_timer() - start) + # print(f"regular time: {reg_elapsed}") + # Example output: + # incremental time: 0.208 + # regular time: 0.276 + # incremental time: 0.00683 + # regular time: 0.260 + # incremental time: 0.0224 + # regular time: 0.278 + # incremental time: 0.00804 + # regular time: 0.208 + # incremental time: 0.00947 + # regular time: 0.188 + + assert set(test_cc.items()) == set(real_cc.items()) + + prev_cc = test_cc diff --git a/shared/package-files/5d/5d69709e14981813debb06cab6a76298f2bc1af59e2a6fdd60ee622a0ea24137/blob b/shared/package-files/5d/5d69709e14981813debb06cab6a76298f2bc1af59e2a6fdd60ee622a0ea24137/blob new file mode 100644 index 0000000000000000000000000000000000000000..3336692ddb41f4dbb6532842233f09e890c9b86f --- /dev/null +++ b/shared/package-files/5d/5d69709e14981813debb06cab6a76298f2bc1af59e2a6fdd60ee622a0ea24137/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_exp(at::TensorList self); +TORCH_API void _foreach_exp_(at::TensorList self); + +} // namespace cuda +} // namespace at diff --git a/shared/package-files/5d/5d6c30bc7da7200987f24e7d6918e72c15506d3c0abddfc5cddb1134048224cc/blob b/shared/package-files/5d/5d6c30bc7da7200987f24e7d6918e72c15506d3c0abddfc5cddb1134048224cc/blob new file mode 100644 index 0000000000000000000000000000000000000000..9b5e1bec4b81616c99a00021d91f78845e7cd283 --- /dev/null +++ b/shared/package-files/5d/5d6c30bc7da7200987f24e7d6918e72c15506d3c0abddfc5cddb1134048224cc/blob @@ -0,0 +1,12 @@ +from matplotlib.tri import Triangulation + +import numpy as np + +class TriAnalyzer: + def __init__(self, triangulation: Triangulation) -> None: ... + @property + def scale_factors(self) -> tuple[float, float]: ... + def circle_ratios(self, rescale: bool = ...) -> np.ndarray: ... + def get_flat_tri_mask( + self, min_circle_ratio: float = ..., rescale: bool = ... + ) -> np.ndarray: ... diff --git a/shared/package-files/5d/5d721ec0da7d4720faf50e7cad277d20a7d86842dd18345d69c6353cddbc20a0/blob b/shared/package-files/5d/5d721ec0da7d4720faf50e7cad277d20a7d86842dd18345d69c6353cddbc20a0/blob new file mode 100644 index 0000000000000000000000000000000000000000..46262d34d914238c1e4a707889ede292bda4708c Binary files /dev/null and b/shared/package-files/5d/5d721ec0da7d4720faf50e7cad277d20a7d86842dd18345d69c6353cddbc20a0/blob differ diff --git a/shared/package-files/5d/5d72bd8a5dc6d360c4172e3fd1bb2558153779606317225ebd03195648eb8c29/blob b/shared/package-files/5d/5d72bd8a5dc6d360c4172e3fd1bb2558153779606317225ebd03195648eb8c29/blob new file mode 100644 index 0000000000000000000000000000000000000000..e8720d4a84a5b6de0177bbf47b12d819e20a1638 --- /dev/null +++ b/shared/package-files/5d/5d72bd8a5dc6d360c4172e3fd1bb2558153779606317225ebd03195648eb8c29/blob @@ -0,0 +1,40 @@ +BSD 3-Clause License + +Copyright (c) 2012, Anaconda, Inc. +All rights reserved. + +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 the copyright holder 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 HOLDER 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. + +--- + +Conda incorporates the following libraries into its distributed code: + +* appdirs, licensed as MIT +* auxlib, licensed as ISC +* boltons, licensed as BSD-3-Clause +* py-cpuinfo, licensed as MIT +* distro, licensed as Apache-2.0 +* frozendict, license as LGPL-3.0 +* toolz, licensed as BSD-3-Clause +* tqdm, licensed as MIT diff --git a/shared/package-files/5d/5d7435894c68963bdc081e67bb26f91676fc593016a53192389896231f5369e5/blob b/shared/package-files/5d/5d7435894c68963bdc081e67bb26f91676fc593016a53192389896231f5369e5/blob new file mode 100644 index 0000000000000000000000000000000000000000..780e8b66f64a14eea6659add1d934151197e4f0c --- /dev/null +++ b/shared/package-files/5d/5d7435894c68963bdc081e67bb26f91676fc593016a53192389896231f5369e5/blob @@ -0,0 +1,23 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace native { +struct TORCH_API structured_logit_backward_out : public at::meta::structured_logit_backward { +void impl(const at::Tensor & grad_output, const at::Tensor & self, c10::optional eps, const at::Tensor & grad_input); +}; +} // namespace native +} // namespace at diff --git a/shared/package-files/5d/5d78fbdb38fedc01f68cf05469c555e864a963d6388a7ff7f20f1c49ff147d88/blob b/shared/package-files/5d/5d78fbdb38fedc01f68cf05469c555e864a963d6388a7ff7f20f1c49ff147d88/blob new file mode 100644 index 0000000000000000000000000000000000000000..1033d908eb22d38e58b46fd0e4a042dc31b3aca9 --- /dev/null +++ b/shared/package-files/5d/5d78fbdb38fedc01f68cf05469c555e864a963d6388a7ff7f20f1c49ff147d88/blob @@ -0,0 +1,143 @@ +from datetime import datetime + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, +) + +# The various methods we support +downsample_methods = [ + "min", + "max", + "first", + "last", + "sum", + "mean", + "sem", + "median", + "prod", + "var", + "std", + "ohlc", + "quantile", +] +upsample_methods = ["count", "size"] +series_methods = ["nunique"] +resample_methods = downsample_methods + upsample_methods + series_methods + + +@pytest.fixture(params=downsample_methods) +def downsample_method(request): + """Fixture for parametrization of Grouper downsample methods.""" + return request.param + + +@pytest.fixture(params=resample_methods) +def resample_method(request): + """Fixture for parametrization of Grouper resample methods.""" + return request.param + + +@pytest.fixture +def _index_start(): + """Fixture for parametrization of index, series and frame.""" + return datetime(2005, 1, 1) + + +@pytest.fixture +def _index_end(): + """Fixture for parametrization of index, series and frame.""" + return datetime(2005, 1, 10) + + +@pytest.fixture +def _index_freq(): + """Fixture for parametrization of index, series and frame.""" + return "D" + + +@pytest.fixture +def _index_name(): + """Fixture for parametrization of index, series and frame.""" + return None + + +@pytest.fixture +def index(_index_factory, _index_start, _index_end, _index_freq, _index_name): + """ + Fixture for parametrization of date_range, period_range and + timedelta_range indexes + """ + return _index_factory(_index_start, _index_end, freq=_index_freq, name=_index_name) + + +@pytest.fixture +def _static_values(index): + """ + Fixture for parametrization of values used in parametrization of + Series and DataFrames with date_range, period_range and + timedelta_range indexes + """ + return np.arange(len(index)) + + +@pytest.fixture +def _series_name(): + """ + Fixture for parametrization of Series name for Series used with + date_range, period_range and timedelta_range indexes + """ + return None + + +@pytest.fixture +def series(index, _series_name, _static_values): + """ + Fixture for parametrization of Series with date_range, period_range and + timedelta_range indexes + """ + return Series(_static_values, index=index, name=_series_name) + + +@pytest.fixture +def empty_series_dti(series): + """ + Fixture for parametrization of empty Series with date_range, + period_range and timedelta_range indexes + """ + return series[:0] + + +@pytest.fixture +def frame(index, _series_name, _static_values): + """ + Fixture for parametrization of DataFrame with date_range, period_range + and timedelta_range indexes + """ + # _series_name is intentionally unused + return DataFrame({"value": _static_values}, index=index) + + +@pytest.fixture +def empty_frame_dti(series): + """ + Fixture for parametrization of empty DataFrame with date_range, + period_range and timedelta_range indexes + """ + index = series.index[:0] + return DataFrame(index=index) + + +@pytest.fixture +def series_and_frame(frame_or_series, series, frame): + """ + Fixture for parametrization of Series and DataFrame with date_range, + period_range and timedelta_range indexes + """ + if frame_or_series == Series: + return series + if frame_or_series == DataFrame: + return frame diff --git a/shared/package-files/5d/5d7a11093a1f6510de786b0e9d67902ab33a57f637cd8f5e2603cf6c5c609a18/blob b/shared/package-files/5d/5d7a11093a1f6510de786b0e9d67902ab33a57f637cd8f5e2603cf6c5c609a18/blob new file mode 100644 index 0000000000000000000000000000000000000000..ce218265d4a824c717565e4390387c97c0f1519b --- /dev/null +++ b/shared/package-files/5d/5d7a11093a1f6510de786b0e9d67902ab33a57f637cd8f5e2603cf6c5c609a18/blob @@ -0,0 +1,1948 @@ +# Tix.py -- Tix widget wrappers. +# +# For Tix, see http://tix.sourceforge.net +# +# - Sudhir Shenoy (sshenoy@gol.com), Dec. 1995. +# based on an idea of Jean-Marc Lugrin (lugrin@ms.com) +# +# NOTE: In order to minimize changes to Tkinter.py, some of the code here +# (TixWidget.__init__) has been taken from Tkinter (Widget.__init__) +# and will break if there are major changes in Tkinter. +# +# The Tix widgets are represented by a class hierarchy in python with proper +# inheritance of base classes. +# +# As a result after creating a 'w = StdButtonBox', I can write +# w.ok['text'] = 'Who Cares' +# or w.ok['bg'] = w['bg'] +# or even w.ok.invoke() +# etc. +# +# Compare the demo tixwidgets.py to the original Tcl program and you will +# appreciate the advantages. +# +# NOTE: This module is deprecated since Python 3.6. + +import os +import warnings +import tkinter +from tkinter import * +from tkinter import _cnfmerge + +warnings.warn( + 'The Tix Tk extension is unmaintained, and the tkinter.tix wrapper module' + ' is deprecated in favor of tkinter.ttk', + DeprecationWarning, + stacklevel=2, + ) + +# Some more constants (for consistency with Tkinter) +WINDOW = 'window' +TEXT = 'text' +STATUS = 'status' +IMMEDIATE = 'immediate' +IMAGE = 'image' +IMAGETEXT = 'imagetext' +BALLOON = 'balloon' +AUTO = 'auto' +ACROSSTOP = 'acrosstop' + +# A few useful constants for the Grid widget +ASCII = 'ascii' +CELL = 'cell' +COLUMN = 'column' +DECREASING = 'decreasing' +INCREASING = 'increasing' +INTEGER = 'integer' +MAIN = 'main' +MAX = 'max' +REAL = 'real' +ROW = 'row' +S_REGION = 's-region' +X_REGION = 'x-region' +Y_REGION = 'y-region' + +# Some constants used by Tkinter dooneevent() +TCL_DONT_WAIT = 1 << 1 +TCL_WINDOW_EVENTS = 1 << 2 +TCL_FILE_EVENTS = 1 << 3 +TCL_TIMER_EVENTS = 1 << 4 +TCL_IDLE_EVENTS = 1 << 5 +TCL_ALL_EVENTS = 0 + +# BEWARE - this is implemented by copying some code from the Widget class +# in Tkinter (to override Widget initialization) and is therefore +# liable to break. + +# Could probably add this to Tkinter.Misc +class tixCommand: + """The tix commands provide access to miscellaneous elements + of Tix's internal state and the Tix application context. + Most of the information manipulated by these commands pertains + to the application as a whole, or to a screen or + display, rather than to a particular window. + + This is a mixin class, assumed to be mixed to Tkinter.Tk + that supports the self.tk.call method. + """ + + def tix_addbitmapdir(self, directory): + """Tix maintains a list of directories under which + the tix_getimage and tix_getbitmap commands will + search for image files. The standard bitmap directory + is $TIX_LIBRARY/bitmaps. The addbitmapdir command + adds directory into this list. By using this + command, the image files of an applications can + also be located using the tix_getimage or tix_getbitmap + command. + """ + return self.tk.call('tix', 'addbitmapdir', directory) + + def tix_cget(self, option): + """Returns the current value of the configuration + option given by option. Option may be any of the + options described in the CONFIGURATION OPTIONS section. + """ + return self.tk.call('tix', 'cget', option) + + def tix_configure(self, cnf=None, **kw): + """Query or modify the configuration options of the Tix application + context. If no option is specified, returns a dictionary all of the + available options. If option is specified with no value, then the + command returns a list describing the one named option (this list + will be identical to the corresponding sublist of the value + returned if no option is specified). If one or more option-value + pairs are specified, then the command modifies the given option(s) + to have the given value(s); in this case the command returns an + empty string. Option may be any of the configuration options. + """ + # Copied from Tkinter.py + if kw: + cnf = _cnfmerge((cnf, kw)) + elif cnf: + cnf = _cnfmerge(cnf) + if cnf is None: + return self._getconfigure('tix', 'configure') + if isinstance(cnf, str): + return self._getconfigure1('tix', 'configure', '-'+cnf) + return self.tk.call(('tix', 'configure') + self._options(cnf)) + + def tix_filedialog(self, dlgclass=None): + """Returns the file selection dialog that may be shared among + different calls from this application. This command will create a + file selection dialog widget when it is called the first time. This + dialog will be returned by all subsequent calls to tix_filedialog. + An optional dlgclass parameter can be passed to specified what type + of file selection dialog widget is desired. Possible options are + tix FileSelectDialog or tixExFileSelectDialog. + """ + if dlgclass is not None: + return self.tk.call('tix', 'filedialog', dlgclass) + else: + return self.tk.call('tix', 'filedialog') + + def tix_getbitmap(self, name): + """Locates a bitmap file of the name name.xpm or name in one of the + bitmap directories (see the tix_addbitmapdir command above). By + using tix_getbitmap, you can avoid hard coding the pathnames of the + bitmap files in your application. When successful, it returns the + complete pathname of the bitmap file, prefixed with the character + '@'. The returned value can be used to configure the -bitmap + option of the TK and Tix widgets. + """ + return self.tk.call('tix', 'getbitmap', name) + + def tix_getimage(self, name): + """Locates an image file of the name name.xpm, name.xbm or name.ppm + in one of the bitmap directories (see the addbitmapdir command + above). If more than one file with the same name (but different + extensions) exist, then the image type is chosen according to the + depth of the X display: xbm images are chosen on monochrome + displays and color images are chosen on color displays. By using + tix_ getimage, you can avoid hard coding the pathnames of the + image files in your application. When successful, this command + returns the name of the newly created image, which can be used to + configure the -image option of the Tk and Tix widgets. + """ + return self.tk.call('tix', 'getimage', name) + + def tix_option_get(self, name): + """Gets the options maintained by the Tix + scheme mechanism. Available options include: + + active_bg active_fg bg + bold_font dark1_bg dark1_fg + dark2_bg dark2_fg disabled_fg + fg fixed_font font + inactive_bg inactive_fg input1_bg + input2_bg italic_font light1_bg + light1_fg light2_bg light2_fg + menu_font output1_bg output2_bg + select_bg select_fg selector + """ + # could use self.tk.globalgetvar('tixOption', name) + return self.tk.call('tix', 'option', 'get', name) + + def tix_resetoptions(self, newScheme, newFontSet, newScmPrio=None): + """Resets the scheme and fontset of the Tix application to + newScheme and newFontSet, respectively. This affects only those + widgets created after this call. Therefore, it is best to call the + resetoptions command before the creation of any widgets in a Tix + application. + + The optional parameter newScmPrio can be given to reset the + priority level of the Tk options set by the Tix schemes. + + Because of the way Tk handles the X option database, after Tix has + been has imported and inited, it is not possible to reset the color + schemes and font sets using the tix config command. Instead, the + tix_resetoptions command must be used. + """ + if newScmPrio is not None: + return self.tk.call('tix', 'resetoptions', newScheme, newFontSet, newScmPrio) + else: + return self.tk.call('tix', 'resetoptions', newScheme, newFontSet) + +class Tk(tkinter.Tk, tixCommand): + """Toplevel widget of Tix which represents mostly the main window + of an application. It has an associated Tcl interpreter.""" + def __init__(self, screenName=None, baseName=None, className='Tix'): + tkinter.Tk.__init__(self, screenName, baseName, className) + tixlib = os.environ.get('TIX_LIBRARY') + self.tk.eval('global auto_path; lappend auto_path [file dir [info nameof]]') + if tixlib is not None: + self.tk.eval('global auto_path; lappend auto_path {%s}' % tixlib) + self.tk.eval('global tcl_pkgPath; lappend tcl_pkgPath {%s}' % tixlib) + # Load Tix - this should work dynamically or statically + # If it's static, tcl/tix8.1/pkgIndex.tcl should have + # 'load {} Tix' + # If it's dynamic under Unix, tcl/tix8.1/pkgIndex.tcl should have + # 'load libtix8.1.8.3.so Tix' + self.tk.eval('package require Tix') + + def destroy(self): + # For safety, remove the delete_window binding before destroy + self.protocol("WM_DELETE_WINDOW", "") + tkinter.Tk.destroy(self) + +# The Tix 'tixForm' geometry manager +class Form: + """The Tix Form geometry manager + + Widgets can be arranged by specifying attachments to other widgets. + See Tix documentation for complete details""" + + def config(self, cnf={}, **kw): + self.tk.call('tixForm', self._w, *self._options(cnf, kw)) + + form = config + + def __setitem__(self, key, value): + Form.form(self, {key: value}) + + def check(self): + return self.tk.call('tixForm', 'check', self._w) + + def forget(self): + self.tk.call('tixForm', 'forget', self._w) + + def grid(self, xsize=0, ysize=0): + if (not xsize) and (not ysize): + x = self.tk.call('tixForm', 'grid', self._w) + y = self.tk.splitlist(x) + z = () + for x in y: + z = z + (self.tk.getint(x),) + return z + return self.tk.call('tixForm', 'grid', self._w, xsize, ysize) + + def info(self, option=None): + if not option: + return self.tk.call('tixForm', 'info', self._w) + if option[0] != '-': + option = '-' + option + return self.tk.call('tixForm', 'info', self._w, option) + + def slaves(self): + return [self._nametowidget(x) for x in + self.tk.splitlist( + self.tk.call( + 'tixForm', 'slaves', self._w))] + + + +tkinter.Widget.__bases__ = tkinter.Widget.__bases__ + (Form,) + +class TixWidget(tkinter.Widget): + """A TixWidget class is used to package all (or most) Tix widgets. + + Widget initialization is extended in two ways: + 1) It is possible to give a list of options which must be part of + the creation command (so called Tix 'static' options). These cannot be + given as a 'config' command later. + 2) It is possible to give the name of an existing TK widget. These are + child widgets created automatically by a Tix mega-widget. The Tk call + to create these widgets is therefore bypassed in TixWidget.__init__ + + Both options are for use by subclasses only. + """ + def __init__ (self, master=None, widgetName=None, + static_options=None, cnf={}, kw={}): + # Merge keywords and dictionary arguments + if kw: + cnf = _cnfmerge((cnf, kw)) + else: + cnf = _cnfmerge(cnf) + + # Move static options into extra. static_options must be + # a list of keywords (or None). + extra=() + + # 'options' is always a static option + if static_options: + static_options.append('options') + else: + static_options = ['options'] + + for k,v in list(cnf.items()): + if k in static_options: + extra = extra + ('-' + k, v) + del cnf[k] + + self.widgetName = widgetName + self._setup(master, cnf) + + # If widgetName is None, this is a dummy creation call where the + # corresponding Tk widget has already been created by Tix + if widgetName: + self.tk.call(widgetName, self._w, *extra) + + # Non-static options - to be done via a 'config' command + if cnf: + Widget.config(self, cnf) + + # Dictionary to hold subwidget names for easier access. We can't + # use the children list because the public Tix names may not be the + # same as the pathname component + self.subwidget_list = {} + + # We set up an attribute access function so that it is possible to + # do w.ok['text'] = 'Hello' rather than w.subwidget('ok')['text'] = 'Hello' + # when w is a StdButtonBox. + # We can even do w.ok.invoke() because w.ok is subclassed from the + # Button class if you go through the proper constructors + def __getattr__(self, name): + if name in self.subwidget_list: + return self.subwidget_list[name] + raise AttributeError(name) + + def set_silent(self, value): + """Set a variable without calling its action routine""" + self.tk.call('tixSetSilent', self._w, value) + + def subwidget(self, name): + """Return the named subwidget (which must have been created by + the sub-class).""" + n = self._subwidget_name(name) + if not n: + raise TclError("Subwidget " + name + " not child of " + self._name) + # Remove header of name and leading dot + n = n[len(self._w)+1:] + return self._nametowidget(n) + + def subwidgets_all(self): + """Return all subwidgets.""" + names = self._subwidget_names() + if not names: + return [] + retlist = [] + for name in names: + name = name[len(self._w)+1:] + try: + retlist.append(self._nametowidget(name)) + except: + # some of the widgets are unknown e.g. border in LabelFrame + pass + return retlist + + def _subwidget_name(self,name): + """Get a subwidget name (returns a String, not a Widget !)""" + try: + return self.tk.call(self._w, 'subwidget', name) + except TclError: + return None + + def _subwidget_names(self): + """Return the name of all subwidgets.""" + try: + x = self.tk.call(self._w, 'subwidgets', '-all') + return self.tk.splitlist(x) + except TclError: + return None + + def config_all(self, option, value): + """Set configuration options for all subwidgets (and self).""" + if option == '': + return + elif not isinstance(option, str): + option = repr(option) + if not isinstance(value, str): + value = repr(value) + names = self._subwidget_names() + for name in names: + self.tk.call(name, 'configure', '-' + option, value) + # These are missing from Tkinter + def image_create(self, imgtype, cnf={}, master=None, **kw): + if master is None: + master = self + if kw and cnf: cnf = _cnfmerge((cnf, kw)) + elif kw: cnf = kw + options = () + for k, v in cnf.items(): + if callable(v): + v = self._register(v) + options = options + ('-'+k, v) + return master.tk.call(('image', 'create', imgtype,) + options) + def image_delete(self, imgname): + try: + self.tk.call('image', 'delete', imgname) + except TclError: + # May happen if the root was destroyed + pass + +# Subwidgets are child widgets created automatically by mega-widgets. +# In python, we have to create these subwidgets manually to mirror their +# existence in Tk/Tix. +class TixSubWidget(TixWidget): + """Subwidget class. + + This is used to mirror child widgets automatically created + by Tix/Tk as part of a mega-widget in Python (which is not informed + of this)""" + + def __init__(self, master, name, + destroy_physically=1, check_intermediate=1): + if check_intermediate: + path = master._subwidget_name(name) + try: + path = path[len(master._w)+1:] + plist = path.split('.') + except: + plist = [] + + if not check_intermediate: + # immediate descendant + TixWidget.__init__(self, master, None, None, {'name' : name}) + else: + # Ensure that the intermediate widgets exist + parent = master + for i in range(len(plist) - 1): + n = '.'.join(plist[:i+1]) + try: + w = master._nametowidget(n) + parent = w + except KeyError: + # Create the intermediate widget + parent = TixSubWidget(parent, plist[i], + destroy_physically=0, + check_intermediate=0) + # The Tk widget name is in plist, not in name + if plist: + name = plist[-1] + TixWidget.__init__(self, parent, None, None, {'name' : name}) + self.destroy_physically = destroy_physically + + def destroy(self): + # For some widgets e.g., a NoteBook, when we call destructors, + # we must be careful not to destroy the frame widget since this + # also destroys the parent NoteBook thus leading to an exception + # in Tkinter when it finally calls Tcl to destroy the NoteBook + for c in list(self.children.values()): c.destroy() + if self._name in self.master.children: + del self.master.children[self._name] + if self._name in self.master.subwidget_list: + del self.master.subwidget_list[self._name] + if self.destroy_physically: + # This is bypassed only for a few widgets + self.tk.call('destroy', self._w) + + +# Useful class to create a display style - later shared by many items. +# Contributed by Steffen Kremser +class DisplayStyle: + """DisplayStyle - handle configuration options shared by + (multiple) Display Items""" + + def __init__(self, itemtype, cnf={}, *, master=None, **kw): + if master is None: + if 'refwindow' in kw: + master = kw['refwindow'] + elif 'refwindow' in cnf: + master = cnf['refwindow'] + else: + master = tkinter._get_default_root('create display style') + self.tk = master.tk + self.stylename = self.tk.call('tixDisplayStyle', itemtype, + *self._options(cnf,kw) ) + + def __str__(self): + return self.stylename + + def _options(self, cnf, kw): + if kw and cnf: + cnf = _cnfmerge((cnf, kw)) + elif kw: + cnf = kw + opts = () + for k, v in cnf.items(): + opts = opts + ('-'+k, v) + return opts + + def delete(self): + self.tk.call(self.stylename, 'delete') + + def __setitem__(self,key,value): + self.tk.call(self.stylename, 'configure', '-%s'%key, value) + + def config(self, cnf={}, **kw): + return self._getconfigure( + self.stylename, 'configure', *self._options(cnf,kw)) + + def __getitem__(self,key): + return self.tk.call(self.stylename, 'cget', '-%s'%key) + + +###################################################### +### The Tix Widget classes - in alphabetical order ### +###################################################### + +class Balloon(TixWidget): + """Balloon help widget. + + Subwidget Class + --------- ----- + label Label + message Message""" + + # FIXME: It should inherit -superclass tixShell + def __init__(self, master=None, cnf={}, **kw): + # static seem to be -installcolormap -initwait -statusbar -cursor + static = ['options', 'installcolormap', 'initwait', 'statusbar', + 'cursor'] + TixWidget.__init__(self, master, 'tixBalloon', static, cnf, kw) + self.subwidget_list['label'] = _dummyLabel(self, 'label', + destroy_physically=0) + self.subwidget_list['message'] = _dummyLabel(self, 'message', + destroy_physically=0) + + def bind_widget(self, widget, cnf={}, **kw): + """Bind balloon widget to another. + One balloon widget may be bound to several widgets at the same time""" + self.tk.call(self._w, 'bind', widget._w, *self._options(cnf, kw)) + + def unbind_widget(self, widget): + self.tk.call(self._w, 'unbind', widget._w) + +class ButtonBox(TixWidget): + """ButtonBox - A container for pushbuttons. + Subwidgets are the buttons added with the add method. + """ + def __init__(self, master=None, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixButtonBox', + ['orientation', 'options'], cnf, kw) + + def add(self, name, cnf={}, **kw): + """Add a button with given name to box.""" + + btn = self.tk.call(self._w, 'add', name, *self._options(cnf, kw)) + self.subwidget_list[name] = _dummyButton(self, name) + return btn + + def invoke(self, name): + if name in self.subwidget_list: + self.tk.call(self._w, 'invoke', name) + +class ComboBox(TixWidget): + """ComboBox - an Entry field with a dropdown menu. The user can select a + choice by either typing in the entry subwidget or selecting from the + listbox subwidget. + + Subwidget Class + --------- ----- + entry Entry + arrow Button + slistbox ScrolledListBox + tick Button + cross Button : present if created with the fancy option""" + + # FIXME: It should inherit -superclass tixLabelWidget + def __init__ (self, master=None, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixComboBox', + ['editable', 'dropdown', 'fancy', 'options'], + cnf, kw) + self.subwidget_list['label'] = _dummyLabel(self, 'label') + self.subwidget_list['entry'] = _dummyEntry(self, 'entry') + self.subwidget_list['arrow'] = _dummyButton(self, 'arrow') + self.subwidget_list['slistbox'] = _dummyScrolledListBox(self, + 'slistbox') + try: + self.subwidget_list['tick'] = _dummyButton(self, 'tick') + self.subwidget_list['cross'] = _dummyButton(self, 'cross') + except TypeError: + # unavailable when -fancy not specified + pass + + # align + + def add_history(self, str): + self.tk.call(self._w, 'addhistory', str) + + def append_history(self, str): + self.tk.call(self._w, 'appendhistory', str) + + def insert(self, index, str): + self.tk.call(self._w, 'insert', index, str) + + def pick(self, index): + self.tk.call(self._w, 'pick', index) + +class Control(TixWidget): + """Control - An entry field with value change arrows. The user can + adjust the value by pressing the two arrow buttons or by entering + the value directly into the entry. The new value will be checked + against the user-defined upper and lower limits. + + Subwidget Class + --------- ----- + incr Button + decr Button + entry Entry + label Label""" + + # FIXME: It should inherit -superclass tixLabelWidget + def __init__ (self, master=None, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixControl', ['options'], cnf, kw) + self.subwidget_list['incr'] = _dummyButton(self, 'incr') + self.subwidget_list['decr'] = _dummyButton(self, 'decr') + self.subwidget_list['label'] = _dummyLabel(self, 'label') + self.subwidget_list['entry'] = _dummyEntry(self, 'entry') + + def decrement(self): + self.tk.call(self._w, 'decr') + + def increment(self): + self.tk.call(self._w, 'incr') + + def invoke(self): + self.tk.call(self._w, 'invoke') + + def update(self): + self.tk.call(self._w, 'update') + +class DirList(TixWidget): + """DirList - displays a list view of a directory, its previous + directories and its sub-directories. The user can choose one of + the directories displayed in the list or change to another directory. + + Subwidget Class + --------- ----- + hlist HList + hsb Scrollbar + vsb Scrollbar""" + + # FIXME: It should inherit -superclass tixScrolledHList + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixDirList', ['options'], cnf, kw) + self.subwidget_list['hlist'] = _dummyHList(self, 'hlist') + self.subwidget_list['vsb'] = _dummyScrollbar(self, 'vsb') + self.subwidget_list['hsb'] = _dummyScrollbar(self, 'hsb') + + def chdir(self, dir): + self.tk.call(self._w, 'chdir', dir) + +class DirTree(TixWidget): + """DirTree - Directory Listing in a hierarchical view. + Displays a tree view of a directory, its previous directories and its + sub-directories. The user can choose one of the directories displayed + in the list or change to another directory. + + Subwidget Class + --------- ----- + hlist HList + hsb Scrollbar + vsb Scrollbar""" + + # FIXME: It should inherit -superclass tixScrolledHList + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixDirTree', ['options'], cnf, kw) + self.subwidget_list['hlist'] = _dummyHList(self, 'hlist') + self.subwidget_list['vsb'] = _dummyScrollbar(self, 'vsb') + self.subwidget_list['hsb'] = _dummyScrollbar(self, 'hsb') + + def chdir(self, dir): + self.tk.call(self._w, 'chdir', dir) + +class DirSelectBox(TixWidget): + """DirSelectBox - Motif style file select box. + It is generally used for + the user to choose a file. FileSelectBox stores the files mostly + recently selected into a ComboBox widget so that they can be quickly + selected again. + + Subwidget Class + --------- ----- + selection ComboBox + filter ComboBox + dirlist ScrolledListBox + filelist ScrolledListBox""" + + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixDirSelectBox', ['options'], cnf, kw) + self.subwidget_list['dirlist'] = _dummyDirList(self, 'dirlist') + self.subwidget_list['dircbx'] = _dummyFileComboBox(self, 'dircbx') + +class ExFileSelectBox(TixWidget): + """ExFileSelectBox - MS Windows style file select box. + It provides a convenient method for the user to select files. + + Subwidget Class + --------- ----- + cancel Button + ok Button + hidden Checkbutton + types ComboBox + dir ComboBox + file ComboBox + dirlist ScrolledListBox + filelist ScrolledListBox""" + + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixExFileSelectBox', ['options'], cnf, kw) + self.subwidget_list['cancel'] = _dummyButton(self, 'cancel') + self.subwidget_list['ok'] = _dummyButton(self, 'ok') + self.subwidget_list['hidden'] = _dummyCheckbutton(self, 'hidden') + self.subwidget_list['types'] = _dummyComboBox(self, 'types') + self.subwidget_list['dir'] = _dummyComboBox(self, 'dir') + self.subwidget_list['dirlist'] = _dummyDirList(self, 'dirlist') + self.subwidget_list['file'] = _dummyComboBox(self, 'file') + self.subwidget_list['filelist'] = _dummyScrolledListBox(self, 'filelist') + + def filter(self): + self.tk.call(self._w, 'filter') + + def invoke(self): + self.tk.call(self._w, 'invoke') + + +# Should inherit from a Dialog class +class DirSelectDialog(TixWidget): + """The DirSelectDialog widget presents the directories in the file + system in a dialog window. The user can use this dialog window to + navigate through the file system to select the desired directory. + + Subwidgets Class + ---------- ----- + dirbox DirSelectDialog""" + + # FIXME: It should inherit -superclass tixDialogShell + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixDirSelectDialog', + ['options'], cnf, kw) + self.subwidget_list['dirbox'] = _dummyDirSelectBox(self, 'dirbox') + # cancel and ok buttons are missing + + def popup(self): + self.tk.call(self._w, 'popup') + + def popdown(self): + self.tk.call(self._w, 'popdown') + + +# Should inherit from a Dialog class +class ExFileSelectDialog(TixWidget): + """ExFileSelectDialog - MS Windows style file select dialog. + It provides a convenient method for the user to select files. + + Subwidgets Class + ---------- ----- + fsbox ExFileSelectBox""" + + # FIXME: It should inherit -superclass tixDialogShell + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixExFileSelectDialog', + ['options'], cnf, kw) + self.subwidget_list['fsbox'] = _dummyExFileSelectBox(self, 'fsbox') + + def popup(self): + self.tk.call(self._w, 'popup') + + def popdown(self): + self.tk.call(self._w, 'popdown') + +class FileSelectBox(TixWidget): + """ExFileSelectBox - Motif style file select box. + It is generally used for + the user to choose a file. FileSelectBox stores the files mostly + recently selected into a ComboBox widget so that they can be quickly + selected again. + + Subwidget Class + --------- ----- + selection ComboBox + filter ComboBox + dirlist ScrolledListBox + filelist ScrolledListBox""" + + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixFileSelectBox', ['options'], cnf, kw) + self.subwidget_list['dirlist'] = _dummyScrolledListBox(self, 'dirlist') + self.subwidget_list['filelist'] = _dummyScrolledListBox(self, 'filelist') + self.subwidget_list['filter'] = _dummyComboBox(self, 'filter') + self.subwidget_list['selection'] = _dummyComboBox(self, 'selection') + + def apply_filter(self): # name of subwidget is same as command + self.tk.call(self._w, 'filter') + + def invoke(self): + self.tk.call(self._w, 'invoke') + +# Should inherit from a Dialog class +class FileSelectDialog(TixWidget): + """FileSelectDialog - Motif style file select dialog. + + Subwidgets Class + ---------- ----- + btns StdButtonBox + fsbox FileSelectBox""" + + # FIXME: It should inherit -superclass tixStdDialogShell + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixFileSelectDialog', + ['options'], cnf, kw) + self.subwidget_list['btns'] = _dummyStdButtonBox(self, 'btns') + self.subwidget_list['fsbox'] = _dummyFileSelectBox(self, 'fsbox') + + def popup(self): + self.tk.call(self._w, 'popup') + + def popdown(self): + self.tk.call(self._w, 'popdown') + +class FileEntry(TixWidget): + """FileEntry - Entry field with button that invokes a FileSelectDialog. + The user can type in the filename manually. Alternatively, the user can + press the button widget that sits next to the entry, which will bring + up a file selection dialog. + + Subwidgets Class + ---------- ----- + button Button + entry Entry""" + + # FIXME: It should inherit -superclass tixLabelWidget + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixFileEntry', + ['dialogtype', 'options'], cnf, kw) + self.subwidget_list['button'] = _dummyButton(self, 'button') + self.subwidget_list['entry'] = _dummyEntry(self, 'entry') + + def invoke(self): + self.tk.call(self._w, 'invoke') + + def file_dialog(self): + # FIXME: return python object + pass + +class HList(TixWidget, XView, YView): + """HList - Hierarchy display widget can be used to display any data + that have a hierarchical structure, for example, file system directory + trees. The list entries are indented and connected by branch lines + according to their places in the hierarchy. + + Subwidgets - None""" + + def __init__ (self,master=None,cnf={}, **kw): + TixWidget.__init__(self, master, 'tixHList', + ['columns', 'options'], cnf, kw) + + def add(self, entry, cnf={}, **kw): + return self.tk.call(self._w, 'add', entry, *self._options(cnf, kw)) + + def add_child(self, parent=None, cnf={}, **kw): + if parent is None: + parent = '' + return self.tk.call( + self._w, 'addchild', parent, *self._options(cnf, kw)) + + def anchor_set(self, entry): + self.tk.call(self._w, 'anchor', 'set', entry) + + def anchor_clear(self): + self.tk.call(self._w, 'anchor', 'clear') + + def column_width(self, col=0, width=None, chars=None): + if not chars: + return self.tk.call(self._w, 'column', 'width', col, width) + else: + return self.tk.call(self._w, 'column', 'width', col, + '-char', chars) + + def delete_all(self): + self.tk.call(self._w, 'delete', 'all') + + def delete_entry(self, entry): + self.tk.call(self._w, 'delete', 'entry', entry) + + def delete_offsprings(self, entry): + self.tk.call(self._w, 'delete', 'offsprings', entry) + + def delete_siblings(self, entry): + self.tk.call(self._w, 'delete', 'siblings', entry) + + def dragsite_set(self, index): + self.tk.call(self._w, 'dragsite', 'set', index) + + def dragsite_clear(self): + self.tk.call(self._w, 'dragsite', 'clear') + + def dropsite_set(self, index): + self.tk.call(self._w, 'dropsite', 'set', index) + + def dropsite_clear(self): + self.tk.call(self._w, 'dropsite', 'clear') + + def header_create(self, col, cnf={}, **kw): + self.tk.call(self._w, 'header', 'create', col, *self._options(cnf, kw)) + + def header_configure(self, col, cnf={}, **kw): + if cnf is None: + return self._getconfigure(self._w, 'header', 'configure', col) + self.tk.call(self._w, 'header', 'configure', col, + *self._options(cnf, kw)) + + def header_cget(self, col, opt): + return self.tk.call(self._w, 'header', 'cget', col, opt) + + def header_exists(self, col): + # A workaround to Tix library bug (issue #25464). + # The documented command is "exists", but only erroneous "exist" is + # accepted. + return self.tk.getboolean(self.tk.call(self._w, 'header', 'exist', col)) + header_exist = header_exists + + def header_delete(self, col): + self.tk.call(self._w, 'header', 'delete', col) + + def header_size(self, col): + return self.tk.call(self._w, 'header', 'size', col) + + def hide_entry(self, entry): + self.tk.call(self._w, 'hide', 'entry', entry) + + def indicator_create(self, entry, cnf={}, **kw): + self.tk.call( + self._w, 'indicator', 'create', entry, *self._options(cnf, kw)) + + def indicator_configure(self, entry, cnf={}, **kw): + if cnf is None: + return self._getconfigure( + self._w, 'indicator', 'configure', entry) + self.tk.call( + self._w, 'indicator', 'configure', entry, *self._options(cnf, kw)) + + def indicator_cget(self, entry, opt): + return self.tk.call(self._w, 'indicator', 'cget', entry, opt) + + def indicator_exists(self, entry): + return self.tk.call (self._w, 'indicator', 'exists', entry) + + def indicator_delete(self, entry): + self.tk.call(self._w, 'indicator', 'delete', entry) + + def indicator_size(self, entry): + return self.tk.call(self._w, 'indicator', 'size', entry) + + def info_anchor(self): + return self.tk.call(self._w, 'info', 'anchor') + + def info_bbox(self, entry): + return self._getints( + self.tk.call(self._w, 'info', 'bbox', entry)) or None + + def info_children(self, entry=None): + c = self.tk.call(self._w, 'info', 'children', entry) + return self.tk.splitlist(c) + + def info_data(self, entry): + return self.tk.call(self._w, 'info', 'data', entry) + + def info_dragsite(self): + return self.tk.call(self._w, 'info', 'dragsite') + + def info_dropsite(self): + return self.tk.call(self._w, 'info', 'dropsite') + + def info_exists(self, entry): + return self.tk.call(self._w, 'info', 'exists', entry) + + def info_hidden(self, entry): + return self.tk.call(self._w, 'info', 'hidden', entry) + + def info_next(self, entry): + return self.tk.call(self._w, 'info', 'next', entry) + + def info_parent(self, entry): + return self.tk.call(self._w, 'info', 'parent', entry) + + def info_prev(self, entry): + return self.tk.call(self._w, 'info', 'prev', entry) + + def info_selection(self): + c = self.tk.call(self._w, 'info', 'selection') + return self.tk.splitlist(c) + + def item_cget(self, entry, col, opt): + return self.tk.call(self._w, 'item', 'cget', entry, col, opt) + + def item_configure(self, entry, col, cnf={}, **kw): + if cnf is None: + return self._getconfigure(self._w, 'item', 'configure', entry, col) + self.tk.call(self._w, 'item', 'configure', entry, col, + *self._options(cnf, kw)) + + def item_create(self, entry, col, cnf={}, **kw): + self.tk.call( + self._w, 'item', 'create', entry, col, *self._options(cnf, kw)) + + def item_exists(self, entry, col): + return self.tk.call(self._w, 'item', 'exists', entry, col) + + def item_delete(self, entry, col): + self.tk.call(self._w, 'item', 'delete', entry, col) + + def entrycget(self, entry, opt): + return self.tk.call(self._w, 'entrycget', entry, opt) + + def entryconfigure(self, entry, cnf={}, **kw): + if cnf is None: + return self._getconfigure(self._w, 'entryconfigure', entry) + self.tk.call(self._w, 'entryconfigure', entry, + *self._options(cnf, kw)) + + def nearest(self, y): + return self.tk.call(self._w, 'nearest', y) + + def see(self, entry): + self.tk.call(self._w, 'see', entry) + + def selection_clear(self, cnf={}, **kw): + self.tk.call(self._w, 'selection', 'clear', *self._options(cnf, kw)) + + def selection_includes(self, entry): + return self.tk.call(self._w, 'selection', 'includes', entry) + + def selection_set(self, first, last=None): + self.tk.call(self._w, 'selection', 'set', first, last) + + def show_entry(self, entry): + return self.tk.call(self._w, 'show', 'entry', entry) + +class InputOnly(TixWidget): + """InputOnly - Invisible widget. Unix only. + + Subwidgets - None""" + + def __init__ (self,master=None,cnf={}, **kw): + TixWidget.__init__(self, master, 'tixInputOnly', None, cnf, kw) + +class LabelEntry(TixWidget): + """LabelEntry - Entry field with label. Packages an entry widget + and a label into one mega widget. It can be used to simplify the creation + of ``entry-form'' type of interface. + + Subwidgets Class + ---------- ----- + label Label + entry Entry""" + + def __init__ (self,master=None,cnf={}, **kw): + TixWidget.__init__(self, master, 'tixLabelEntry', + ['labelside','options'], cnf, kw) + self.subwidget_list['label'] = _dummyLabel(self, 'label') + self.subwidget_list['entry'] = _dummyEntry(self, 'entry') + +class LabelFrame(TixWidget): + """LabelFrame - Labelled Frame container. Packages a frame widget + and a label into one mega widget. To create widgets inside a + LabelFrame widget, one creates the new widgets relative to the + frame subwidget and manage them inside the frame subwidget. + + Subwidgets Class + ---------- ----- + label Label + frame Frame""" + + def __init__ (self,master=None,cnf={}, **kw): + TixWidget.__init__(self, master, 'tixLabelFrame', + ['labelside','options'], cnf, kw) + self.subwidget_list['label'] = _dummyLabel(self, 'label') + self.subwidget_list['frame'] = _dummyFrame(self, 'frame') + + +class ListNoteBook(TixWidget): + """A ListNoteBook widget is very similar to the TixNoteBook widget: + it can be used to display many windows in a limited space using a + notebook metaphor. The notebook is divided into a stack of pages + (windows). At one time only one of these pages can be shown. + The user can navigate through these pages by + choosing the name of the desired page in the hlist subwidget.""" + + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixListNoteBook', ['options'], cnf, kw) + # Is this necessary? It's not an exposed subwidget in Tix. + self.subwidget_list['pane'] = _dummyPanedWindow(self, 'pane', + destroy_physically=0) + self.subwidget_list['hlist'] = _dummyHList(self, 'hlist') + self.subwidget_list['shlist'] = _dummyScrolledHList(self, 'shlist') + + def add(self, name, cnf={}, **kw): + self.tk.call(self._w, 'add', name, *self._options(cnf, kw)) + self.subwidget_list[name] = TixSubWidget(self, name) + return self.subwidget_list[name] + + def page(self, name): + return self.subwidget(name) + + def pages(self): + # Can't call subwidgets_all directly because we don't want .nbframe + names = self.tk.splitlist(self.tk.call(self._w, 'pages')) + ret = [] + for x in names: + ret.append(self.subwidget(x)) + return ret + + def raise_page(self, name): # raise is a python keyword + self.tk.call(self._w, 'raise', name) + +class Meter(TixWidget): + """The Meter widget can be used to show the progress of a background + job which may take a long time to execute. + """ + + def __init__(self, master=None, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixMeter', + ['options'], cnf, kw) + +class NoteBook(TixWidget): + """NoteBook - Multi-page container widget (tabbed notebook metaphor). + + Subwidgets Class + ---------- ----- + nbframe NoteBookFrame + page widgets added dynamically with the add method""" + + def __init__ (self,master=None,cnf={}, **kw): + TixWidget.__init__(self,master,'tixNoteBook', ['options'], cnf, kw) + self.subwidget_list['nbframe'] = TixSubWidget(self, 'nbframe', + destroy_physically=0) + + def add(self, name, cnf={}, **kw): + self.tk.call(self._w, 'add', name, *self._options(cnf, kw)) + self.subwidget_list[name] = TixSubWidget(self, name) + return self.subwidget_list[name] + + def delete(self, name): + self.tk.call(self._w, 'delete', name) + self.subwidget_list[name].destroy() + del self.subwidget_list[name] + + def page(self, name): + return self.subwidget(name) + + def pages(self): + # Can't call subwidgets_all directly because we don't want .nbframe + names = self.tk.splitlist(self.tk.call(self._w, 'pages')) + ret = [] + for x in names: + ret.append(self.subwidget(x)) + return ret + + def raise_page(self, name): # raise is a python keyword + self.tk.call(self._w, 'raise', name) + + def raised(self): + return self.tk.call(self._w, 'raised') + +class NoteBookFrame(TixWidget): + # FIXME: This is dangerous to expose to be called on its own. + pass + +class OptionMenu(TixWidget): + """OptionMenu - creates a menu button of options. + + Subwidget Class + --------- ----- + menubutton Menubutton + menu Menu""" + + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixOptionMenu', ['options'], cnf, kw) + self.subwidget_list['menubutton'] = _dummyMenubutton(self, 'menubutton') + self.subwidget_list['menu'] = _dummyMenu(self, 'menu') + + def add_command(self, name, cnf={}, **kw): + self.tk.call(self._w, 'add', 'command', name, *self._options(cnf, kw)) + + def add_separator(self, name, cnf={}, **kw): + self.tk.call(self._w, 'add', 'separator', name, *self._options(cnf, kw)) + + def delete(self, name): + self.tk.call(self._w, 'delete', name) + + def disable(self, name): + self.tk.call(self._w, 'disable', name) + + def enable(self, name): + self.tk.call(self._w, 'enable', name) + +class PanedWindow(TixWidget): + """PanedWindow - Multi-pane container widget + allows the user to interactively manipulate the sizes of several + panes. The panes can be arranged either vertically or horizontally.The + user changes the sizes of the panes by dragging the resize handle + between two panes. + + Subwidgets Class + ---------- ----- + g/p widgets added dynamically with the add method.""" + + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixPanedWindow', ['orientation', 'options'], cnf, kw) + + # add delete forget panecget paneconfigure panes setsize + def add(self, name, cnf={}, **kw): + self.tk.call(self._w, 'add', name, *self._options(cnf, kw)) + self.subwidget_list[name] = TixSubWidget(self, name, + check_intermediate=0) + return self.subwidget_list[name] + + def delete(self, name): + self.tk.call(self._w, 'delete', name) + self.subwidget_list[name].destroy() + del self.subwidget_list[name] + + def forget(self, name): + self.tk.call(self._w, 'forget', name) + + def panecget(self, entry, opt): + return self.tk.call(self._w, 'panecget', entry, opt) + + def paneconfigure(self, entry, cnf={}, **kw): + if cnf is None: + return self._getconfigure(self._w, 'paneconfigure', entry) + self.tk.call(self._w, 'paneconfigure', entry, *self._options(cnf, kw)) + + def panes(self): + names = self.tk.splitlist(self.tk.call(self._w, 'panes')) + return [self.subwidget(x) for x in names] + +class PopupMenu(TixWidget): + """PopupMenu widget can be used as a replacement of the tk_popup command. + The advantage of the Tix PopupMenu widget is it requires less application + code to manipulate. + + + Subwidgets Class + ---------- ----- + menubutton Menubutton + menu Menu""" + + # FIXME: It should inherit -superclass tixShell + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixPopupMenu', ['options'], cnf, kw) + self.subwidget_list['menubutton'] = _dummyMenubutton(self, 'menubutton') + self.subwidget_list['menu'] = _dummyMenu(self, 'menu') + + def bind_widget(self, widget): + self.tk.call(self._w, 'bind', widget._w) + + def unbind_widget(self, widget): + self.tk.call(self._w, 'unbind', widget._w) + + def post_widget(self, widget, x, y): + self.tk.call(self._w, 'post', widget._w, x, y) + +class ResizeHandle(TixWidget): + """Internal widget to draw resize handles on Scrolled widgets.""" + def __init__(self, master, cnf={}, **kw): + # There seems to be a Tix bug rejecting the configure method + # Let's try making the flags -static + flags = ['options', 'command', 'cursorfg', 'cursorbg', + 'handlesize', 'hintcolor', 'hintwidth', + 'x', 'y'] + # In fact, x y height width are configurable + TixWidget.__init__(self, master, 'tixResizeHandle', + flags, cnf, kw) + + def attach_widget(self, widget): + self.tk.call(self._w, 'attachwidget', widget._w) + + def detach_widget(self, widget): + self.tk.call(self._w, 'detachwidget', widget._w) + + def hide(self, widget): + self.tk.call(self._w, 'hide', widget._w) + + def show(self, widget): + self.tk.call(self._w, 'show', widget._w) + +class ScrolledHList(TixWidget): + """ScrolledHList - HList with automatic scrollbars.""" + + # FIXME: It should inherit -superclass tixScrolledWidget + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixScrolledHList', ['options'], + cnf, kw) + self.subwidget_list['hlist'] = _dummyHList(self, 'hlist') + self.subwidget_list['vsb'] = _dummyScrollbar(self, 'vsb') + self.subwidget_list['hsb'] = _dummyScrollbar(self, 'hsb') + +class ScrolledListBox(TixWidget): + """ScrolledListBox - Listbox with automatic scrollbars.""" + + # FIXME: It should inherit -superclass tixScrolledWidget + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixScrolledListBox', ['options'], cnf, kw) + self.subwidget_list['listbox'] = _dummyListbox(self, 'listbox') + self.subwidget_list['vsb'] = _dummyScrollbar(self, 'vsb') + self.subwidget_list['hsb'] = _dummyScrollbar(self, 'hsb') + +class ScrolledText(TixWidget): + """ScrolledText - Text with automatic scrollbars.""" + + # FIXME: It should inherit -superclass tixScrolledWidget + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixScrolledText', ['options'], cnf, kw) + self.subwidget_list['text'] = _dummyText(self, 'text') + self.subwidget_list['vsb'] = _dummyScrollbar(self, 'vsb') + self.subwidget_list['hsb'] = _dummyScrollbar(self, 'hsb') + +class ScrolledTList(TixWidget): + """ScrolledTList - TList with automatic scrollbars.""" + + # FIXME: It should inherit -superclass tixScrolledWidget + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixScrolledTList', ['options'], + cnf, kw) + self.subwidget_list['tlist'] = _dummyTList(self, 'tlist') + self.subwidget_list['vsb'] = _dummyScrollbar(self, 'vsb') + self.subwidget_list['hsb'] = _dummyScrollbar(self, 'hsb') + +class ScrolledWindow(TixWidget): + """ScrolledWindow - Window with automatic scrollbars.""" + + # FIXME: It should inherit -superclass tixScrolledWidget + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixScrolledWindow', ['options'], cnf, kw) + self.subwidget_list['window'] = _dummyFrame(self, 'window') + self.subwidget_list['vsb'] = _dummyScrollbar(self, 'vsb') + self.subwidget_list['hsb'] = _dummyScrollbar(self, 'hsb') + +class Select(TixWidget): + """Select - Container of button subwidgets. It can be used to provide + radio-box or check-box style of selection options for the user. + + Subwidgets are buttons added dynamically using the add method.""" + + # FIXME: It should inherit -superclass tixLabelWidget + def __init__(self, master, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixSelect', + ['allowzero', 'radio', 'orientation', 'labelside', + 'options'], + cnf, kw) + self.subwidget_list['label'] = _dummyLabel(self, 'label') + + def add(self, name, cnf={}, **kw): + self.tk.call(self._w, 'add', name, *self._options(cnf, kw)) + self.subwidget_list[name] = _dummyButton(self, name) + return self.subwidget_list[name] + + def invoke(self, name): + self.tk.call(self._w, 'invoke', name) + +class Shell(TixWidget): + """Toplevel window. + + Subwidgets - None""" + + def __init__ (self,master=None,cnf={}, **kw): + TixWidget.__init__(self, master, 'tixShell', ['options', 'title'], cnf, kw) + +class DialogShell(TixWidget): + """Toplevel window, with popup popdown and center methods. + It tells the window manager that it is a dialog window and should be + treated specially. The exact treatment depends on the treatment of + the window manager. + + Subwidgets - None""" + + # FIXME: It should inherit from Shell + def __init__ (self,master=None,cnf={}, **kw): + TixWidget.__init__(self, master, + 'tixDialogShell', + ['options', 'title', 'mapped', + 'minheight', 'minwidth', + 'parent', 'transient'], cnf, kw) + + def popdown(self): + self.tk.call(self._w, 'popdown') + + def popup(self): + self.tk.call(self._w, 'popup') + + def center(self): + self.tk.call(self._w, 'center') + +class StdButtonBox(TixWidget): + """StdButtonBox - Standard Button Box (OK, Apply, Cancel and Help) """ + + def __init__(self, master=None, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixStdButtonBox', + ['orientation', 'options'], cnf, kw) + self.subwidget_list['ok'] = _dummyButton(self, 'ok') + self.subwidget_list['apply'] = _dummyButton(self, 'apply') + self.subwidget_list['cancel'] = _dummyButton(self, 'cancel') + self.subwidget_list['help'] = _dummyButton(self, 'help') + + def invoke(self, name): + if name in self.subwidget_list: + self.tk.call(self._w, 'invoke', name) + +class TList(TixWidget, XView, YView): + """TList - Hierarchy display widget which can be + used to display data in a tabular format. The list entries of a TList + widget are similar to the entries in the Tk listbox widget. The main + differences are (1) the TList widget can display the list entries in a + two dimensional format and (2) you can use graphical images as well as + multiple colors and fonts for the list entries. + + Subwidgets - None""" + + def __init__ (self,master=None,cnf={}, **kw): + TixWidget.__init__(self, master, 'tixTList', ['options'], cnf, kw) + + def active_set(self, index): + self.tk.call(self._w, 'active', 'set', index) + + def active_clear(self): + self.tk.call(self._w, 'active', 'clear') + + def anchor_set(self, index): + self.tk.call(self._w, 'anchor', 'set', index) + + def anchor_clear(self): + self.tk.call(self._w, 'anchor', 'clear') + + def delete(self, from_, to=None): + self.tk.call(self._w, 'delete', from_, to) + + def dragsite_set(self, index): + self.tk.call(self._w, 'dragsite', 'set', index) + + def dragsite_clear(self): + self.tk.call(self._w, 'dragsite', 'clear') + + def dropsite_set(self, index): + self.tk.call(self._w, 'dropsite', 'set', index) + + def dropsite_clear(self): + self.tk.call(self._w, 'dropsite', 'clear') + + def insert(self, index, cnf={}, **kw): + self.tk.call(self._w, 'insert', index, *self._options(cnf, kw)) + + def info_active(self): + return self.tk.call(self._w, 'info', 'active') + + def info_anchor(self): + return self.tk.call(self._w, 'info', 'anchor') + + def info_down(self, index): + return self.tk.call(self._w, 'info', 'down', index) + + def info_left(self, index): + return self.tk.call(self._w, 'info', 'left', index) + + def info_right(self, index): + return self.tk.call(self._w, 'info', 'right', index) + + def info_selection(self): + c = self.tk.call(self._w, 'info', 'selection') + return self.tk.splitlist(c) + + def info_size(self): + return self.tk.call(self._w, 'info', 'size') + + def info_up(self, index): + return self.tk.call(self._w, 'info', 'up', index) + + def nearest(self, x, y): + return self.tk.call(self._w, 'nearest', x, y) + + def see(self, index): + self.tk.call(self._w, 'see', index) + + def selection_clear(self, cnf={}, **kw): + self.tk.call(self._w, 'selection', 'clear', *self._options(cnf, kw)) + + def selection_includes(self, index): + return self.tk.call(self._w, 'selection', 'includes', index) + + def selection_set(self, first, last=None): + self.tk.call(self._w, 'selection', 'set', first, last) + +class Tree(TixWidget): + """Tree - The tixTree widget can be used to display hierarchical + data in a tree form. The user can adjust + the view of the tree by opening or closing parts of the tree.""" + + # FIXME: It should inherit -superclass tixScrolledWidget + def __init__(self, master=None, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixTree', + ['options'], cnf, kw) + self.subwidget_list['hlist'] = _dummyHList(self, 'hlist') + self.subwidget_list['vsb'] = _dummyScrollbar(self, 'vsb') + self.subwidget_list['hsb'] = _dummyScrollbar(self, 'hsb') + + def autosetmode(self): + '''This command calls the setmode method for all the entries in this + Tree widget: if an entry has no child entries, its mode is set to + none. Otherwise, if the entry has any hidden child entries, its mode is + set to open; otherwise its mode is set to close.''' + self.tk.call(self._w, 'autosetmode') + + def close(self, entrypath): + '''Close the entry given by entryPath if its mode is close.''' + self.tk.call(self._w, 'close', entrypath) + + def getmode(self, entrypath): + '''Returns the current mode of the entry given by entryPath.''' + return self.tk.call(self._w, 'getmode', entrypath) + + def open(self, entrypath): + '''Open the entry given by entryPath if its mode is open.''' + self.tk.call(self._w, 'open', entrypath) + + def setmode(self, entrypath, mode='none'): + '''This command is used to indicate whether the entry given by + entryPath has children entries and whether the children are visible. mode + must be one of open, close or none. If mode is set to open, a (+) + indicator is drawn next the entry. If mode is set to close, a (-) + indicator is drawn next the entry. If mode is set to none, no + indicators will be drawn for this entry. The default mode is none. The + open mode indicates the entry has hidden children and this entry can be + opened by the user. The close mode indicates that all the children of the + entry are now visible and the entry can be closed by the user.''' + self.tk.call(self._w, 'setmode', entrypath, mode) + + +# Could try subclassing Tree for CheckList - would need another arg to init +class CheckList(TixWidget): + """The CheckList widget + displays a list of items to be selected by the user. CheckList acts + similarly to the Tk checkbutton or radiobutton widgets, except it is + capable of handling many more items than checkbuttons or radiobuttons. + """ + # FIXME: It should inherit -superclass tixTree + def __init__(self, master=None, cnf={}, **kw): + TixWidget.__init__(self, master, 'tixCheckList', + ['options', 'radio'], cnf, kw) + self.subwidget_list['hlist'] = _dummyHList(self, 'hlist') + self.subwidget_list['vsb'] = _dummyScrollbar(self, 'vsb') + self.subwidget_list['hsb'] = _dummyScrollbar(self, 'hsb') + + def autosetmode(self): + '''This command calls the setmode method for all the entries in this + Tree widget: if an entry has no child entries, its mode is set to + none. Otherwise, if the entry has any hidden child entries, its mode is + set to open; otherwise its mode is set to close.''' + self.tk.call(self._w, 'autosetmode') + + def close(self, entrypath): + '''Close the entry given by entryPath if its mode is close.''' + self.tk.call(self._w, 'close', entrypath) + + def getmode(self, entrypath): + '''Returns the current mode of the entry given by entryPath.''' + return self.tk.call(self._w, 'getmode', entrypath) + + def open(self, entrypath): + '''Open the entry given by entryPath if its mode is open.''' + self.tk.call(self._w, 'open', entrypath) + + def getselection(self, mode='on'): + '''Returns a list of items whose status matches status. If status is + not specified, the list of items in the "on" status will be returned. + Mode can be on, off, default''' + return self.tk.splitlist(self.tk.call(self._w, 'getselection', mode)) + + def getstatus(self, entrypath): + '''Returns the current status of entryPath.''' + return self.tk.call(self._w, 'getstatus', entrypath) + + def setstatus(self, entrypath, mode='on'): + '''Sets the status of entryPath to be status. A bitmap will be + displayed next to the entry its status is on, off or default.''' + self.tk.call(self._w, 'setstatus', entrypath, mode) + + +########################################################################### +### The subclassing below is used to instantiate the subwidgets in each ### +### mega widget. This allows us to access their methods directly. ### +########################################################################### + +class _dummyButton(Button, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyCheckbutton(Checkbutton, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyEntry(Entry, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyFrame(Frame, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyLabel(Label, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyListbox(Listbox, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyMenu(Menu, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyMenubutton(Menubutton, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyScrollbar(Scrollbar, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyText(Text, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyScrolledListBox(ScrolledListBox, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + self.subwidget_list['listbox'] = _dummyListbox(self, 'listbox') + self.subwidget_list['vsb'] = _dummyScrollbar(self, 'vsb') + self.subwidget_list['hsb'] = _dummyScrollbar(self, 'hsb') + +class _dummyHList(HList, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyScrolledHList(ScrolledHList, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + self.subwidget_list['hlist'] = _dummyHList(self, 'hlist') + self.subwidget_list['vsb'] = _dummyScrollbar(self, 'vsb') + self.subwidget_list['hsb'] = _dummyScrollbar(self, 'hsb') + +class _dummyTList(TList, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyComboBox(ComboBox, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, ['fancy',destroy_physically]) + self.subwidget_list['label'] = _dummyLabel(self, 'label') + self.subwidget_list['entry'] = _dummyEntry(self, 'entry') + self.subwidget_list['arrow'] = _dummyButton(self, 'arrow') + + self.subwidget_list['slistbox'] = _dummyScrolledListBox(self, + 'slistbox') + try: + self.subwidget_list['tick'] = _dummyButton(self, 'tick') + #cross Button : present if created with the fancy option + self.subwidget_list['cross'] = _dummyButton(self, 'cross') + except TypeError: + # unavailable when -fancy not specified + pass + +class _dummyDirList(DirList, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + self.subwidget_list['hlist'] = _dummyHList(self, 'hlist') + self.subwidget_list['vsb'] = _dummyScrollbar(self, 'vsb') + self.subwidget_list['hsb'] = _dummyScrollbar(self, 'hsb') + +class _dummyDirSelectBox(DirSelectBox, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + self.subwidget_list['dirlist'] = _dummyDirList(self, 'dirlist') + self.subwidget_list['dircbx'] = _dummyFileComboBox(self, 'dircbx') + +class _dummyExFileSelectBox(ExFileSelectBox, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + self.subwidget_list['cancel'] = _dummyButton(self, 'cancel') + self.subwidget_list['ok'] = _dummyButton(self, 'ok') + self.subwidget_list['hidden'] = _dummyCheckbutton(self, 'hidden') + self.subwidget_list['types'] = _dummyComboBox(self, 'types') + self.subwidget_list['dir'] = _dummyComboBox(self, 'dir') + self.subwidget_list['dirlist'] = _dummyScrolledListBox(self, 'dirlist') + self.subwidget_list['file'] = _dummyComboBox(self, 'file') + self.subwidget_list['filelist'] = _dummyScrolledListBox(self, 'filelist') + +class _dummyFileSelectBox(FileSelectBox, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + self.subwidget_list['dirlist'] = _dummyScrolledListBox(self, 'dirlist') + self.subwidget_list['filelist'] = _dummyScrolledListBox(self, 'filelist') + self.subwidget_list['filter'] = _dummyComboBox(self, 'filter') + self.subwidget_list['selection'] = _dummyComboBox(self, 'selection') + +class _dummyFileComboBox(ComboBox, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + self.subwidget_list['dircbx'] = _dummyComboBox(self, 'dircbx') + +class _dummyStdButtonBox(StdButtonBox, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + self.subwidget_list['ok'] = _dummyButton(self, 'ok') + self.subwidget_list['apply'] = _dummyButton(self, 'apply') + self.subwidget_list['cancel'] = _dummyButton(self, 'cancel') + self.subwidget_list['help'] = _dummyButton(self, 'help') + +class _dummyNoteBookFrame(NoteBookFrame, TixSubWidget): + def __init__(self, master, name, destroy_physically=0): + TixSubWidget.__init__(self, master, name, destroy_physically) + +class _dummyPanedWindow(PanedWindow, TixSubWidget): + def __init__(self, master, name, destroy_physically=1): + TixSubWidget.__init__(self, master, name, destroy_physically) + +######################## +### Utility Routines ### +######################## + +#mike Should tixDestroy be exposed as a wrapper? - but not for widgets. + +def OptionName(widget): + '''Returns the qualified path name for the widget. Normally used to set + default options for subwidgets. See tixwidgets.py''' + return widget.tk.call('tixOptionName', widget._w) + +# Called with a dictionary argument of the form +# {'*.c':'C source files', '*.txt':'Text Files', '*':'All files'} +# returns a string which can be used to configure the fsbox file types +# in an ExFileSelectBox. i.e., +# '{{*} {* - All files}} {{*.c} {*.c - C source files}} {{*.txt} {*.txt - Text Files}}' +def FileTypeList(dict): + s = '' + for type in dict.keys(): + s = s + '{{' + type + '} {' + type + ' - ' + dict[type] + '}} ' + return s + +# Still to be done: +# tixIconView +class CObjView(TixWidget): + """This file implements the Canvas Object View widget. This is a base + class of IconView. It implements automatic placement/adjustment of the + scrollbars according to the canvas objects inside the canvas subwidget. + The scrollbars are adjusted so that the canvas is just large enough + to see all the objects. + """ + # FIXME: It should inherit -superclass tixScrolledWidget + pass + + +class Grid(TixWidget, XView, YView): + '''The Tix Grid command creates a new window and makes it into a + tixGrid widget. Additional options, may be specified on the command + line or in the option database to configure aspects such as its cursor + and relief. + + A Grid widget displays its contents in a two dimensional grid of cells. + Each cell may contain one Tix display item, which may be in text, + graphics or other formats. See the DisplayStyle class for more information + about Tix display items. Individual cells, or groups of cells, can be + formatted with a wide range of attributes, such as its color, relief and + border. + + Subwidgets - None''' + # valid specific resources as of Tk 8.4 + # editdonecmd, editnotifycmd, floatingcols, floatingrows, formatcmd, + # highlightbackground, highlightcolor, leftmargin, itemtype, selectmode, + # selectunit, topmargin, + def __init__(self, master=None, cnf={}, **kw): + static= [] + self.cnf= cnf + TixWidget.__init__(self, master, 'tixGrid', static, cnf, kw) + + # valid options as of Tk 8.4 + # anchor, bdtype, cget, configure, delete, dragsite, dropsite, entrycget, + # edit, entryconfigure, format, geometryinfo, info, index, move, nearest, + # selection, set, size, unset, xview, yview + def anchor_clear(self): + """Removes the selection anchor.""" + self.tk.call(self, 'anchor', 'clear') + + def anchor_get(self): + "Get the (x,y) coordinate of the current anchor cell" + return self._getints(self.tk.call(self, 'anchor', 'get')) + + def anchor_set(self, x, y): + """Set the selection anchor to the cell at (x, y).""" + self.tk.call(self, 'anchor', 'set', x, y) + + def delete_row(self, from_, to=None): + """Delete rows between from_ and to inclusive. + If to is not provided, delete only row at from_""" + if to is None: + self.tk.call(self, 'delete', 'row', from_) + else: + self.tk.call(self, 'delete', 'row', from_, to) + + def delete_column(self, from_, to=None): + """Delete columns between from_ and to inclusive. + If to is not provided, delete only column at from_""" + if to is None: + self.tk.call(self, 'delete', 'column', from_) + else: + self.tk.call(self, 'delete', 'column', from_, to) + + def edit_apply(self): + """If any cell is being edited, de-highlight the cell and applies + the changes.""" + self.tk.call(self, 'edit', 'apply') + + def edit_set(self, x, y): + """Highlights the cell at (x, y) for editing, if the -editnotify + command returns True for this cell.""" + self.tk.call(self, 'edit', 'set', x, y) + + def entrycget(self, x, y, option): + "Get the option value for cell at (x,y)" + if option and option[0] != '-': + option = '-' + option + return self.tk.call(self, 'entrycget', x, y, option) + + def entryconfigure(self, x, y, cnf=None, **kw): + return self._configure(('entryconfigure', x, y), cnf, kw) + + # def format + # def index + + def info_exists(self, x, y): + "Return True if display item exists at (x,y)" + return self._getboolean(self.tk.call(self, 'info', 'exists', x, y)) + + def info_bbox(self, x, y): + # This seems to always return '', at least for 'text' displayitems + return self.tk.call(self, 'info', 'bbox', x, y) + + def move_column(self, from_, to, offset): + """Moves the range of columns from position FROM through TO by + the distance indicated by OFFSET. For example, move_column(2, 4, 1) + moves the columns 2,3,4 to columns 3,4,5.""" + self.tk.call(self, 'move', 'column', from_, to, offset) + + def move_row(self, from_, to, offset): + """Moves the range of rows from position FROM through TO by + the distance indicated by OFFSET. + For example, move_row(2, 4, 1) moves the rows 2,3,4 to rows 3,4,5.""" + self.tk.call(self, 'move', 'row', from_, to, offset) + + def nearest(self, x, y): + "Return coordinate of cell nearest pixel coordinate (x,y)" + return self._getints(self.tk.call(self, 'nearest', x, y)) + + # def selection adjust + # def selection clear + # def selection includes + # def selection set + # def selection toggle + + def set(self, x, y, itemtype=None, **kw): + args= self._options(self.cnf, kw) + if itemtype is not None: + args= ('-itemtype', itemtype) + args + self.tk.call(self, 'set', x, y, *args) + + def size_column(self, index, **kw): + """Queries or sets the size of the column given by + INDEX. INDEX may be any non-negative + integer that gives the position of a given column. + INDEX can also be the string "default"; in this case, this command + queries or sets the default size of all columns. + When no option-value pair is given, this command returns a tuple + containing the current size setting of the given column. When + option-value pairs are given, the corresponding options of the + size setting of the given column are changed. Options may be one + of the following: + pad0 pixels + Specifies the paddings to the left of a column. + pad1 pixels + Specifies the paddings to the right of a column. + size val + Specifies the width of a column. Val may be: + "auto" -- the width of the column is set to the + width of the widest cell in the column; + a valid Tk screen distance unit; + or a real number following by the word chars + (e.g. 3.4chars) that sets the width of the column to the + given number of characters.""" + return self.tk.splitlist(self.tk.call(self._w, 'size', 'column', index, + *self._options({}, kw))) + + def size_row(self, index, **kw): + """Queries or sets the size of the row given by + INDEX. INDEX may be any non-negative + integer that gives the position of a given row . + INDEX can also be the string "default"; in this case, this command + queries or sets the default size of all rows. + When no option-value pair is given, this command returns a list con- + taining the current size setting of the given row . When option-value + pairs are given, the corresponding options of the size setting of the + given row are changed. Options may be one of the following: + pad0 pixels + Specifies the paddings to the top of a row. + pad1 pixels + Specifies the paddings to the bottom of a row. + size val + Specifies the height of a row. Val may be: + "auto" -- the height of the row is set to the + height of the highest cell in the row; + a valid Tk screen distance unit; + or a real number following by the word chars + (e.g. 3.4chars) that sets the height of the row to the + given number of characters.""" + return self.tk.splitlist(self.tk.call( + self, 'size', 'row', index, *self._options({}, kw))) + + def unset(self, x, y): + """Clears the cell at (x, y) by removing its display item.""" + self.tk.call(self._w, 'unset', x, y) + + +class ScrolledGrid(Grid): + '''Scrolled Grid widgets''' + + # FIXME: It should inherit -superclass tixScrolledWidget + def __init__(self, master=None, cnf={}, **kw): + static= [] + self.cnf= cnf + TixWidget.__init__(self, master, 'tixScrolledGrid', static, cnf, kw) diff --git a/shared/package-files/5d/5d7b238a111ba401377e9c0e157730483fa2081ea5badca34b5fd12c67aa1d05/blob b/shared/package-files/5d/5d7b238a111ba401377e9c0e157730483fa2081ea5badca34b5fd12c67aa1d05/blob new file mode 100644 index 0000000000000000000000000000000000000000..e3ac872861c170fd9dd1521075555f0a205109b2 --- /dev/null +++ b/shared/package-files/5d/5d7b238a111ba401377e9c0e157730483fa2081ea5badca34b5fd12c67aa1d05/blob @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5d7b238a111ba401377e9c0e157730483fa2081ea5badca34b5fd12c67aa1d05 +size 415194 diff --git a/shared/package-files/5d/5d7b9d899fb1407a614cb2ade46b6d2400ba797634e9fed7d770fc4b7e372760/blob b/shared/package-files/5d/5d7b9d899fb1407a614cb2ade46b6d2400ba797634e9fed7d770fc4b7e372760/blob new file mode 100644 index 0000000000000000000000000000000000000000..818588f7403182b1bf747b3f0d3c9b79b14ef1b3 --- /dev/null +++ b/shared/package-files/5d/5d7b9d899fb1407a614cb2ade46b6d2400ba797634e9fed7d770fc4b7e372760/blob @@ -0,0 +1,1556 @@ +'''This module implements specialized container datatypes providing +alternatives to Python's general purpose built-in containers, dict, +list, set, and tuple. + +* namedtuple factory function for creating tuple subclasses with named fields +* deque list-like container with fast appends and pops on either end +* ChainMap dict-like class for creating a single view of multiple mappings +* Counter dict subclass for counting hashable objects +* OrderedDict dict subclass that remembers the order entries were added +* defaultdict dict subclass that calls a factory function to supply missing values +* UserDict wrapper around dictionary objects for easier dict subclassing +* UserList wrapper around list objects for easier list subclassing +* UserString wrapper around string objects for easier string subclassing + +''' + +__all__ = [ + 'ChainMap', + 'Counter', + 'OrderedDict', + 'UserDict', + 'UserList', + 'UserString', + 'defaultdict', + 'deque', + 'namedtuple', +] + +import _collections_abc +import sys as _sys + +from itertools import chain as _chain +from itertools import repeat as _repeat +from itertools import starmap as _starmap +from keyword import iskeyword as _iskeyword +from operator import eq as _eq +from operator import itemgetter as _itemgetter +from reprlib import recursive_repr as _recursive_repr +from _weakref import proxy as _proxy + +try: + from _collections import deque +except ImportError: + pass +else: + _collections_abc.MutableSequence.register(deque) + +try: + from _collections import defaultdict +except ImportError: + pass + + +################################################################################ +### OrderedDict +################################################################################ + +class _OrderedDictKeysView(_collections_abc.KeysView): + + def __reversed__(self): + yield from reversed(self._mapping) + +class _OrderedDictItemsView(_collections_abc.ItemsView): + + def __reversed__(self): + for key in reversed(self._mapping): + yield (key, self._mapping[key]) + +class _OrderedDictValuesView(_collections_abc.ValuesView): + + def __reversed__(self): + for key in reversed(self._mapping): + yield self._mapping[key] + +class _Link(object): + __slots__ = 'prev', 'next', 'key', '__weakref__' + +class OrderedDict(dict): + 'Dictionary that remembers insertion order' + # An inherited dict maps keys to values. + # The inherited dict provides __getitem__, __len__, __contains__, and get. + # The remaining methods are order-aware. + # Big-O running times for all methods are the same as regular dictionaries. + + # The internal self.__map dict maps keys to links in a doubly linked list. + # The circular doubly linked list starts and ends with a sentinel element. + # The sentinel element never gets deleted (this simplifies the algorithm). + # The sentinel is in self.__hardroot with a weakref proxy in self.__root. + # The prev links are weakref proxies (to prevent circular references). + # Individual links are kept alive by the hard reference in self.__map. + # Those hard references disappear when a key is deleted from an OrderedDict. + + def __init__(self, other=(), /, **kwds): + '''Initialize an ordered dictionary. The signature is the same as + regular dictionaries. Keyword argument order is preserved. + ''' + try: + self.__root + except AttributeError: + self.__hardroot = _Link() + self.__root = root = _proxy(self.__hardroot) + root.prev = root.next = root + self.__map = {} + self.__update(other, **kwds) + + def __setitem__(self, key, value, + dict_setitem=dict.__setitem__, proxy=_proxy, Link=_Link): + 'od.__setitem__(i, y) <==> od[i]=y' + # Setting a new item creates a new link at the end of the linked list, + # and the inherited dictionary is updated with the new key/value pair. + if key not in self: + self.__map[key] = link = Link() + root = self.__root + last = root.prev + link.prev, link.next, link.key = last, root, key + last.next = link + root.prev = proxy(link) + dict_setitem(self, key, value) + + def __delitem__(self, key, dict_delitem=dict.__delitem__): + 'od.__delitem__(y) <==> del od[y]' + # Deleting an existing item uses self.__map to find the link which gets + # removed by updating the links in the predecessor and successor nodes. + dict_delitem(self, key) + link = self.__map.pop(key) + link_prev = link.prev + link_next = link.next + link_prev.next = link_next + link_next.prev = link_prev + link.prev = None + link.next = None + + def __iter__(self): + 'od.__iter__() <==> iter(od)' + # Traverse the linked list in order. + root = self.__root + curr = root.next + while curr is not root: + yield curr.key + curr = curr.next + + def __reversed__(self): + 'od.__reversed__() <==> reversed(od)' + # Traverse the linked list in reverse order. + root = self.__root + curr = root.prev + while curr is not root: + yield curr.key + curr = curr.prev + + def clear(self): + 'od.clear() -> None. Remove all items from od.' + root = self.__root + root.prev = root.next = root + self.__map.clear() + dict.clear(self) + + def popitem(self, last=True): + '''Remove and return a (key, value) pair from the dictionary. + + Pairs are returned in LIFO order if last is true or FIFO order if false. + ''' + if not self: + raise KeyError('dictionary is empty') + root = self.__root + if last: + link = root.prev + link_prev = link.prev + link_prev.next = root + root.prev = link_prev + else: + link = root.next + link_next = link.next + root.next = link_next + link_next.prev = root + key = link.key + del self.__map[key] + value = dict.pop(self, key) + return key, value + + def move_to_end(self, key, last=True): + '''Move an existing element to the end (or beginning if last is false). + + Raise KeyError if the element does not exist. + ''' + link = self.__map[key] + link_prev = link.prev + link_next = link.next + soft_link = link_next.prev + link_prev.next = link_next + link_next.prev = link_prev + root = self.__root + if last: + last = root.prev + link.prev = last + link.next = root + root.prev = soft_link + last.next = link + else: + first = root.next + link.prev = root + link.next = first + first.prev = soft_link + root.next = link + + def __sizeof__(self): + sizeof = _sys.getsizeof + n = len(self) + 1 # number of links including root + size = sizeof(self.__dict__) # instance dictionary + size += sizeof(self.__map) * 2 # internal dict and inherited dict + size += sizeof(self.__hardroot) * n # link objects + size += sizeof(self.__root) * n # proxy objects + return size + + update = __update = _collections_abc.MutableMapping.update + + def keys(self): + "D.keys() -> a set-like object providing a view on D's keys" + return _OrderedDictKeysView(self) + + def items(self): + "D.items() -> a set-like object providing a view on D's items" + return _OrderedDictItemsView(self) + + def values(self): + "D.values() -> an object providing a view on D's values" + return _OrderedDictValuesView(self) + + __ne__ = _collections_abc.MutableMapping.__ne__ + + __marker = object() + + def pop(self, key, default=__marker): + '''od.pop(k[,d]) -> v, remove specified key and return the corresponding + value. If key is not found, d is returned if given, otherwise KeyError + is raised. + + ''' + if key in self: + result = self[key] + del self[key] + return result + if default is self.__marker: + raise KeyError(key) + return default + + def setdefault(self, key, default=None): + '''Insert key with a value of default if key is not in the dictionary. + + Return the value for key if key is in the dictionary, else default. + ''' + if key in self: + return self[key] + self[key] = default + return default + + @_recursive_repr() + def __repr__(self): + 'od.__repr__() <==> repr(od)' + if not self: + return '%s()' % (self.__class__.__name__,) + return '%s(%r)' % (self.__class__.__name__, list(self.items())) + + def __reduce__(self): + 'Return state information for pickling' + inst_dict = vars(self).copy() + for k in vars(OrderedDict()): + inst_dict.pop(k, None) + return self.__class__, (), inst_dict or None, None, iter(self.items()) + + def copy(self): + 'od.copy() -> a shallow copy of od' + return self.__class__(self) + + @classmethod + def fromkeys(cls, iterable, value=None): + '''Create a new ordered dictionary with keys from iterable and values set to value. + ''' + self = cls() + for key in iterable: + self[key] = value + return self + + def __eq__(self, other): + '''od.__eq__(y) <==> od==y. Comparison to another OD is order-sensitive + while comparison to a regular mapping is order-insensitive. + + ''' + if isinstance(other, OrderedDict): + return dict.__eq__(self, other) and all(map(_eq, self, other)) + return dict.__eq__(self, other) + + def __ior__(self, other): + self.update(other) + return self + + def __or__(self, other): + if not isinstance(other, dict): + return NotImplemented + new = self.__class__(self) + new.update(other) + return new + + def __ror__(self, other): + if not isinstance(other, dict): + return NotImplemented + new = self.__class__(other) + new.update(self) + return new + + +try: + from _collections import OrderedDict +except ImportError: + # Leave the pure Python version in place. + pass + + +################################################################################ +### namedtuple +################################################################################ + +try: + from _collections import _tuplegetter +except ImportError: + _tuplegetter = lambda index, doc: property(_itemgetter(index), doc=doc) + +def namedtuple(typename, field_names, *, rename=False, defaults=None, module=None): + """Returns a new subclass of tuple with named fields. + + >>> Point = namedtuple('Point', ['x', 'y']) + >>> Point.__doc__ # docstring for the new class + 'Point(x, y)' + >>> p = Point(11, y=22) # instantiate with positional args or keywords + >>> p[0] + p[1] # indexable like a plain tuple + 33 + >>> x, y = p # unpack like a regular tuple + >>> x, y + (11, 22) + >>> p.x + p.y # fields also accessible by name + 33 + >>> d = p._asdict() # convert to a dictionary + >>> d['x'] + 11 + >>> Point(**d) # convert from a dictionary + Point(x=11, y=22) + >>> p._replace(x=100) # _replace() is like str.replace() but targets named fields + Point(x=100, y=22) + + """ + + # Validate the field names. At the user's option, either generate an error + # message or automatically replace the field name with a valid name. + if isinstance(field_names, str): + field_names = field_names.replace(',', ' ').split() + field_names = list(map(str, field_names)) + typename = _sys.intern(str(typename)) + + if rename: + seen = set() + for index, name in enumerate(field_names): + if (not name.isidentifier() + or _iskeyword(name) + or name.startswith('_') + or name in seen): + field_names[index] = f'_{index}' + seen.add(name) + + for name in [typename] + field_names: + if type(name) is not str: + raise TypeError('Type names and field names must be strings') + if not name.isidentifier(): + raise ValueError('Type names and field names must be valid ' + f'identifiers: {name!r}') + if _iskeyword(name): + raise ValueError('Type names and field names cannot be a ' + f'keyword: {name!r}') + + seen = set() + for name in field_names: + if name.startswith('_') and not rename: + raise ValueError('Field names cannot start with an underscore: ' + f'{name!r}') + if name in seen: + raise ValueError(f'Encountered duplicate field name: {name!r}') + seen.add(name) + + field_defaults = {} + if defaults is not None: + defaults = tuple(defaults) + if len(defaults) > len(field_names): + raise TypeError('Got more default values than field names') + field_defaults = dict(reversed(list(zip(reversed(field_names), + reversed(defaults))))) + + # Variables used in the methods and docstrings + field_names = tuple(map(_sys.intern, field_names)) + num_fields = len(field_names) + arg_list = ', '.join(field_names) + if num_fields == 1: + arg_list += ',' + repr_fmt = '(' + ', '.join(f'{name}=%r' for name in field_names) + ')' + tuple_new = tuple.__new__ + _dict, _tuple, _len, _map, _zip = dict, tuple, len, map, zip + + # Create all the named tuple methods to be added to the class namespace + + namespace = { + '_tuple_new': tuple_new, + '__builtins__': {}, + '__name__': f'namedtuple_{typename}', + } + code = f'lambda _cls, {arg_list}: _tuple_new(_cls, ({arg_list}))' + __new__ = eval(code, namespace) + __new__.__name__ = '__new__' + __new__.__doc__ = f'Create new instance of {typename}({arg_list})' + if defaults is not None: + __new__.__defaults__ = defaults + + @classmethod + def _make(cls, iterable): + result = tuple_new(cls, iterable) + if _len(result) != num_fields: + raise TypeError(f'Expected {num_fields} arguments, got {len(result)}') + return result + + _make.__func__.__doc__ = (f'Make a new {typename} object from a sequence ' + 'or iterable') + + def _replace(self, /, **kwds): + result = self._make(_map(kwds.pop, field_names, self)) + if kwds: + raise ValueError(f'Got unexpected field names: {list(kwds)!r}') + return result + + _replace.__doc__ = (f'Return a new {typename} object replacing specified ' + 'fields with new values') + + def __repr__(self): + 'Return a nicely formatted representation string' + return self.__class__.__name__ + repr_fmt % self + + def _asdict(self): + 'Return a new dict which maps field names to their values.' + return _dict(_zip(self._fields, self)) + + def __getnewargs__(self): + 'Return self as a plain tuple. Used by copy and pickle.' + return _tuple(self) + + # Modify function metadata to help with introspection and debugging + for method in ( + __new__, + _make.__func__, + _replace, + __repr__, + _asdict, + __getnewargs__, + ): + method.__qualname__ = f'{typename}.{method.__name__}' + + # Build-up the class namespace dictionary + # and use type() to build the result class + class_namespace = { + '__doc__': f'{typename}({arg_list})', + '__slots__': (), + '_fields': field_names, + '_field_defaults': field_defaults, + '__new__': __new__, + '_make': _make, + '_replace': _replace, + '__repr__': __repr__, + '_asdict': _asdict, + '__getnewargs__': __getnewargs__, + '__match_args__': field_names, + } + for index, name in enumerate(field_names): + doc = _sys.intern(f'Alias for field number {index}') + class_namespace[name] = _tuplegetter(index, doc) + + result = type(typename, (tuple,), class_namespace) + + # For pickling to work, the __module__ variable needs to be set to the frame + # where the named tuple is created. Bypass this step in environments where + # sys._getframe is not defined (Jython for example) or sys._getframe is not + # defined for arguments greater than 0 (IronPython), or where the user has + # specified a particular module. + if module is None: + try: + module = _sys._getframe(1).f_globals.get('__name__', '__main__') + except (AttributeError, ValueError): + pass + if module is not None: + result.__module__ = module + + return result + + +######################################################################## +### Counter +######################################################################## + +def _count_elements(mapping, iterable): + 'Tally elements from the iterable.' + mapping_get = mapping.get + for elem in iterable: + mapping[elem] = mapping_get(elem, 0) + 1 + +try: # Load C helper function if available + from _collections import _count_elements +except ImportError: + pass + +class Counter(dict): + '''Dict subclass for counting hashable items. Sometimes called a bag + or multiset. Elements are stored as dictionary keys and their counts + are stored as dictionary values. + + >>> c = Counter('abcdeabcdabcaba') # count elements from a string + + >>> c.most_common(3) # three most common elements + [('a', 5), ('b', 4), ('c', 3)] + >>> sorted(c) # list all unique elements + ['a', 'b', 'c', 'd', 'e'] + >>> ''.join(sorted(c.elements())) # list elements with repetitions + 'aaaaabbbbcccdde' + >>> sum(c.values()) # total of all counts + 15 + + >>> c['a'] # count of letter 'a' + 5 + >>> for elem in 'shazam': # update counts from an iterable + ... c[elem] += 1 # by adding 1 to each element's count + >>> c['a'] # now there are seven 'a' + 7 + >>> del c['b'] # remove all 'b' + >>> c['b'] # now there are zero 'b' + 0 + + >>> d = Counter('simsalabim') # make another counter + >>> c.update(d) # add in the second counter + >>> c['a'] # now there are nine 'a' + 9 + + >>> c.clear() # empty the counter + >>> c + Counter() + + Note: If a count is set to zero or reduced to zero, it will remain + in the counter until the entry is deleted or the counter is cleared: + + >>> c = Counter('aaabbc') + >>> c['b'] -= 2 # reduce the count of 'b' by two + >>> c.most_common() # 'b' is still in, but its count is zero + [('a', 3), ('c', 1), ('b', 0)] + + ''' + # References: + # http://en.wikipedia.org/wiki/Multiset + # http://www.gnu.org/software/smalltalk/manual-base/html_node/Bag.html + # http://www.demo2s.com/Tutorial/Cpp/0380__set-multiset/Catalog0380__set-multiset.htm + # http://code.activestate.com/recipes/259174/ + # Knuth, TAOCP Vol. II section 4.6.3 + + def __init__(self, iterable=None, /, **kwds): + '''Create a new, empty Counter object. And if given, count elements + from an input iterable. Or, initialize the count from another mapping + of elements to their counts. + + >>> c = Counter() # a new, empty counter + >>> c = Counter('gallahad') # a new counter from an iterable + >>> c = Counter({'a': 4, 'b': 2}) # a new counter from a mapping + >>> c = Counter(a=4, b=2) # a new counter from keyword args + + ''' + super().__init__() + self.update(iterable, **kwds) + + def __missing__(self, key): + 'The count of elements not in the Counter is zero.' + # Needed so that self[missing_item] does not raise KeyError + return 0 + + def total(self): + 'Sum of the counts' + return sum(self.values()) + + def most_common(self, n=None): + '''List the n most common elements and their counts from the most + common to the least. If n is None, then list all element counts. + + >>> Counter('abracadabra').most_common(3) + [('a', 5), ('b', 2), ('r', 2)] + + ''' + # Emulate Bag.sortedByCount from Smalltalk + if n is None: + return sorted(self.items(), key=_itemgetter(1), reverse=True) + + # Lazy import to speedup Python startup time + import heapq + return heapq.nlargest(n, self.items(), key=_itemgetter(1)) + + def elements(self): + '''Iterator over elements repeating each as many times as its count. + + >>> c = Counter('ABCABC') + >>> sorted(c.elements()) + ['A', 'A', 'B', 'B', 'C', 'C'] + + # Knuth's example for prime factors of 1836: 2**2 * 3**3 * 17**1 + >>> prime_factors = Counter({2: 2, 3: 3, 17: 1}) + >>> product = 1 + >>> for factor in prime_factors.elements(): # loop over factors + ... product *= factor # and multiply them + >>> product + 1836 + + Note, if an element's count has been set to zero or is a negative + number, elements() will ignore it. + + ''' + # Emulate Bag.do from Smalltalk and Multiset.begin from C++. + return _chain.from_iterable(_starmap(_repeat, self.items())) + + # Override dict methods where necessary + + @classmethod + def fromkeys(cls, iterable, v=None): + # There is no equivalent method for counters because the semantics + # would be ambiguous in cases such as Counter.fromkeys('aaabbc', v=2). + # Initializing counters to zero values isn't necessary because zero + # is already the default value for counter lookups. Initializing + # to one is easily accomplished with Counter(set(iterable)). For + # more exotic cases, create a dictionary first using a dictionary + # comprehension or dict.fromkeys(). + raise NotImplementedError( + 'Counter.fromkeys() is undefined. Use Counter(iterable) instead.') + + def update(self, iterable=None, /, **kwds): + '''Like dict.update() but add counts instead of replacing them. + + Source can be an iterable, a dictionary, or another Counter instance. + + >>> c = Counter('which') + >>> c.update('witch') # add elements from another iterable + >>> d = Counter('watch') + >>> c.update(d) # add elements from another counter + >>> c['h'] # four 'h' in which, witch, and watch + 4 + + ''' + # The regular dict.update() operation makes no sense here because the + # replace behavior results in the some of original untouched counts + # being mixed-in with all of the other counts for a mismash that + # doesn't have a straight-forward interpretation in most counting + # contexts. Instead, we implement straight-addition. Both the inputs + # and outputs are allowed to contain zero and negative counts. + + if iterable is not None: + if isinstance(iterable, _collections_abc.Mapping): + if self: + self_get = self.get + for elem, count in iterable.items(): + self[elem] = count + self_get(elem, 0) + else: + # fast path when counter is empty + super().update(iterable) + else: + _count_elements(self, iterable) + if kwds: + self.update(kwds) + + def subtract(self, iterable=None, /, **kwds): + '''Like dict.update() but subtracts counts instead of replacing them. + Counts can be reduced below zero. Both the inputs and outputs are + allowed to contain zero and negative counts. + + Source can be an iterable, a dictionary, or another Counter instance. + + >>> c = Counter('which') + >>> c.subtract('witch') # subtract elements from another iterable + >>> c.subtract(Counter('watch')) # subtract elements from another counter + >>> c['h'] # 2 in which, minus 1 in witch, minus 1 in watch + 0 + >>> c['w'] # 1 in which, minus 1 in witch, minus 1 in watch + -1 + + ''' + if iterable is not None: + self_get = self.get + if isinstance(iterable, _collections_abc.Mapping): + for elem, count in iterable.items(): + self[elem] = self_get(elem, 0) - count + else: + for elem in iterable: + self[elem] = self_get(elem, 0) - 1 + if kwds: + self.subtract(kwds) + + def copy(self): + 'Return a shallow copy.' + return self.__class__(self) + + def __reduce__(self): + return self.__class__, (dict(self),) + + def __delitem__(self, elem): + 'Like dict.__delitem__() but does not raise KeyError for missing values.' + if elem in self: + super().__delitem__(elem) + + def __eq__(self, other): + 'True if all counts agree. Missing counts are treated as zero.' + if not isinstance(other, Counter): + return NotImplemented + return all(self[e] == other[e] for c in (self, other) for e in c) + + def __ne__(self, other): + 'True if any counts disagree. Missing counts are treated as zero.' + if not isinstance(other, Counter): + return NotImplemented + return not self == other + + def __le__(self, other): + 'True if all counts in self are a subset of those in other.' + if not isinstance(other, Counter): + return NotImplemented + return all(self[e] <= other[e] for c in (self, other) for e in c) + + def __lt__(self, other): + 'True if all counts in self are a proper subset of those in other.' + if not isinstance(other, Counter): + return NotImplemented + return self <= other and self != other + + def __ge__(self, other): + 'True if all counts in self are a superset of those in other.' + if not isinstance(other, Counter): + return NotImplemented + return all(self[e] >= other[e] for c in (self, other) for e in c) + + def __gt__(self, other): + 'True if all counts in self are a proper superset of those in other.' + if not isinstance(other, Counter): + return NotImplemented + return self >= other and self != other + + def __repr__(self): + if not self: + return f'{self.__class__.__name__}()' + try: + # dict() preserves the ordering returned by most_common() + d = dict(self.most_common()) + except TypeError: + # handle case where values are not orderable + d = dict(self) + return f'{self.__class__.__name__}({d!r})' + + # Multiset-style mathematical operations discussed in: + # Knuth TAOCP Volume II section 4.6.3 exercise 19 + # and at http://en.wikipedia.org/wiki/Multiset + # + # Outputs guaranteed to only include positive counts. + # + # To strip negative and zero counts, add-in an empty counter: + # c += Counter() + # + # Results are ordered according to when an element is first + # encountered in the left operand and then by the order + # encountered in the right operand. + # + # When the multiplicities are all zero or one, multiset operations + # are guaranteed to be equivalent to the corresponding operations + # for regular sets. + # Given counter multisets such as: + # cp = Counter(a=1, b=0, c=1) + # cq = Counter(c=1, d=0, e=1) + # The corresponding regular sets would be: + # sp = {'a', 'c'} + # sq = {'c', 'e'} + # All of the following relations would hold: + # set(cp + cq) == sp | sq + # set(cp - cq) == sp - sq + # set(cp | cq) == sp | sq + # set(cp & cq) == sp & sq + # (cp == cq) == (sp == sq) + # (cp != cq) == (sp != sq) + # (cp <= cq) == (sp <= sq) + # (cp < cq) == (sp < sq) + # (cp >= cq) == (sp >= sq) + # (cp > cq) == (sp > sq) + + def __add__(self, other): + '''Add counts from two counters. + + >>> Counter('abbb') + Counter('bcc') + Counter({'b': 4, 'c': 2, 'a': 1}) + + ''' + if not isinstance(other, Counter): + return NotImplemented + result = Counter() + for elem, count in self.items(): + newcount = count + other[elem] + if newcount > 0: + result[elem] = newcount + for elem, count in other.items(): + if elem not in self and count > 0: + result[elem] = count + return result + + def __sub__(self, other): + ''' Subtract count, but keep only results with positive counts. + + >>> Counter('abbbc') - Counter('bccd') + Counter({'b': 2, 'a': 1}) + + ''' + if not isinstance(other, Counter): + return NotImplemented + result = Counter() + for elem, count in self.items(): + newcount = count - other[elem] + if newcount > 0: + result[elem] = newcount + for elem, count in other.items(): + if elem not in self and count < 0: + result[elem] = 0 - count + return result + + def __or__(self, other): + '''Union is the maximum of value in either of the input counters. + + >>> Counter('abbb') | Counter('bcc') + Counter({'b': 3, 'c': 2, 'a': 1}) + + ''' + if not isinstance(other, Counter): + return NotImplemented + result = Counter() + for elem, count in self.items(): + other_count = other[elem] + newcount = other_count if count < other_count else count + if newcount > 0: + result[elem] = newcount + for elem, count in other.items(): + if elem not in self and count > 0: + result[elem] = count + return result + + def __and__(self, other): + ''' Intersection is the minimum of corresponding counts. + + >>> Counter('abbb') & Counter('bcc') + Counter({'b': 1}) + + ''' + if not isinstance(other, Counter): + return NotImplemented + result = Counter() + for elem, count in self.items(): + other_count = other[elem] + newcount = count if count < other_count else other_count + if newcount > 0: + result[elem] = newcount + return result + + def __pos__(self): + 'Adds an empty counter, effectively stripping negative and zero counts' + result = Counter() + for elem, count in self.items(): + if count > 0: + result[elem] = count + return result + + def __neg__(self): + '''Subtracts from an empty counter. Strips positive and zero counts, + and flips the sign on negative counts. + + ''' + result = Counter() + for elem, count in self.items(): + if count < 0: + result[elem] = 0 - count + return result + + def _keep_positive(self): + '''Internal method to strip elements with a negative or zero count''' + nonpositive = [elem for elem, count in self.items() if not count > 0] + for elem in nonpositive: + del self[elem] + return self + + def __iadd__(self, other): + '''Inplace add from another counter, keeping only positive counts. + + >>> c = Counter('abbb') + >>> c += Counter('bcc') + >>> c + Counter({'b': 4, 'c': 2, 'a': 1}) + + ''' + for elem, count in other.items(): + self[elem] += count + return self._keep_positive() + + def __isub__(self, other): + '''Inplace subtract counter, but keep only results with positive counts. + + >>> c = Counter('abbbc') + >>> c -= Counter('bccd') + >>> c + Counter({'b': 2, 'a': 1}) + + ''' + for elem, count in other.items(): + self[elem] -= count + return self._keep_positive() + + def __ior__(self, other): + '''Inplace union is the maximum of value from either counter. + + >>> c = Counter('abbb') + >>> c |= Counter('bcc') + >>> c + Counter({'b': 3, 'c': 2, 'a': 1}) + + ''' + for elem, other_count in other.items(): + count = self[elem] + if other_count > count: + self[elem] = other_count + return self._keep_positive() + + def __iand__(self, other): + '''Inplace intersection is the minimum of corresponding counts. + + >>> c = Counter('abbb') + >>> c &= Counter('bcc') + >>> c + Counter({'b': 1}) + + ''' + for elem, count in self.items(): + other_count = other[elem] + if other_count < count: + self[elem] = other_count + return self._keep_positive() + + +######################################################################## +### ChainMap +######################################################################## + +class ChainMap(_collections_abc.MutableMapping): + ''' A ChainMap groups multiple dicts (or other mappings) together + to create a single, updateable view. + + The underlying mappings are stored in a list. That list is public and can + be accessed or updated using the *maps* attribute. There is no other + state. + + Lookups search the underlying mappings successively until a key is found. + In contrast, writes, updates, and deletions only operate on the first + mapping. + + ''' + + def __init__(self, *maps): + '''Initialize a ChainMap by setting *maps* to the given mappings. + If no mappings are provided, a single empty dictionary is used. + + ''' + self.maps = list(maps) or [{}] # always at least one map + + def __missing__(self, key): + raise KeyError(key) + + def __getitem__(self, key): + for mapping in self.maps: + try: + return mapping[key] # can't use 'key in mapping' with defaultdict + except KeyError: + pass + return self.__missing__(key) # support subclasses that define __missing__ + + def get(self, key, default=None): + return self[key] if key in self else default + + def __len__(self): + return len(set().union(*self.maps)) # reuses stored hash values if possible + + def __iter__(self): + d = {} + for mapping in reversed(self.maps): + d.update(dict.fromkeys(mapping)) # reuses stored hash values if possible + return iter(d) + + def __contains__(self, key): + return any(key in m for m in self.maps) + + def __bool__(self): + return any(self.maps) + + @_recursive_repr() + def __repr__(self): + return f'{self.__class__.__name__}({", ".join(map(repr, self.maps))})' + + @classmethod + def fromkeys(cls, iterable, *args): + 'Create a ChainMap with a single dict created from the iterable.' + return cls(dict.fromkeys(iterable, *args)) + + def copy(self): + 'New ChainMap or subclass with a new copy of maps[0] and refs to maps[1:]' + return self.__class__(self.maps[0].copy(), *self.maps[1:]) + + __copy__ = copy + + def new_child(self, m=None, **kwargs): # like Django's Context.push() + '''New ChainMap with a new map followed by all previous maps. + If no map is provided, an empty dict is used. + Keyword arguments update the map or new empty dict. + ''' + if m is None: + m = kwargs + elif kwargs: + m.update(kwargs) + return self.__class__(m, *self.maps) + + @property + def parents(self): # like Django's Context.pop() + 'New ChainMap from maps[1:].' + return self.__class__(*self.maps[1:]) + + def __setitem__(self, key, value): + self.maps[0][key] = value + + def __delitem__(self, key): + try: + del self.maps[0][key] + except KeyError: + raise KeyError(f'Key not found in the first mapping: {key!r}') + + def popitem(self): + 'Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty.' + try: + return self.maps[0].popitem() + except KeyError: + raise KeyError('No keys found in the first mapping.') + + def pop(self, key, *args): + 'Remove *key* from maps[0] and return its value. Raise KeyError if *key* not in maps[0].' + try: + return self.maps[0].pop(key, *args) + except KeyError: + raise KeyError(f'Key not found in the first mapping: {key!r}') + + def clear(self): + 'Clear maps[0], leaving maps[1:] intact.' + self.maps[0].clear() + + def __ior__(self, other): + self.maps[0].update(other) + return self + + def __or__(self, other): + if not isinstance(other, _collections_abc.Mapping): + return NotImplemented + m = self.copy() + m.maps[0].update(other) + return m + + def __ror__(self, other): + if not isinstance(other, _collections_abc.Mapping): + return NotImplemented + m = dict(other) + for child in reversed(self.maps): + m.update(child) + return self.__class__(m) + + +################################################################################ +### UserDict +################################################################################ + +class UserDict(_collections_abc.MutableMapping): + + # Start by filling-out the abstract methods + def __init__(self, dict=None, /, **kwargs): + self.data = {} + if dict is not None: + self.update(dict) + if kwargs: + self.update(kwargs) + + def __len__(self): + return len(self.data) + + def __getitem__(self, key): + if key in self.data: + return self.data[key] + if hasattr(self.__class__, "__missing__"): + return self.__class__.__missing__(self, key) + raise KeyError(key) + + def __setitem__(self, key, item): + self.data[key] = item + + def __delitem__(self, key): + del self.data[key] + + def __iter__(self): + return iter(self.data) + + # Modify __contains__ to work correctly when __missing__ is present + def __contains__(self, key): + return key in self.data + + # Now, add the methods in dicts but not in MutableMapping + def __repr__(self): + return repr(self.data) + + def __or__(self, other): + if isinstance(other, UserDict): + return self.__class__(self.data | other.data) + if isinstance(other, dict): + return self.__class__(self.data | other) + return NotImplemented + + def __ror__(self, other): + if isinstance(other, UserDict): + return self.__class__(other.data | self.data) + if isinstance(other, dict): + return self.__class__(other | self.data) + return NotImplemented + + def __ior__(self, other): + if isinstance(other, UserDict): + self.data |= other.data + else: + self.data |= other + return self + + def __copy__(self): + inst = self.__class__.__new__(self.__class__) + inst.__dict__.update(self.__dict__) + # Create a copy and avoid triggering descriptors + inst.__dict__["data"] = self.__dict__["data"].copy() + return inst + + def copy(self): + if self.__class__ is UserDict: + return UserDict(self.data.copy()) + import copy + data = self.data + try: + self.data = {} + c = copy.copy(self) + finally: + self.data = data + c.update(self) + return c + + @classmethod + def fromkeys(cls, iterable, value=None): + d = cls() + for key in iterable: + d[key] = value + return d + + +################################################################################ +### UserList +################################################################################ + +class UserList(_collections_abc.MutableSequence): + """A more or less complete user-defined wrapper around list objects.""" + + def __init__(self, initlist=None): + self.data = [] + if initlist is not None: + # XXX should this accept an arbitrary sequence? + if type(initlist) == type(self.data): + self.data[:] = initlist + elif isinstance(initlist, UserList): + self.data[:] = initlist.data[:] + else: + self.data = list(initlist) + + def __repr__(self): + return repr(self.data) + + def __lt__(self, other): + return self.data < self.__cast(other) + + def __le__(self, other): + return self.data <= self.__cast(other) + + def __eq__(self, other): + return self.data == self.__cast(other) + + def __gt__(self, other): + return self.data > self.__cast(other) + + def __ge__(self, other): + return self.data >= self.__cast(other) + + def __cast(self, other): + return other.data if isinstance(other, UserList) else other + + def __contains__(self, item): + return item in self.data + + def __len__(self): + return len(self.data) + + def __getitem__(self, i): + if isinstance(i, slice): + return self.__class__(self.data[i]) + else: + return self.data[i] + + def __setitem__(self, i, item): + self.data[i] = item + + def __delitem__(self, i): + del self.data[i] + + def __add__(self, other): + if isinstance(other, UserList): + return self.__class__(self.data + other.data) + elif isinstance(other, type(self.data)): + return self.__class__(self.data + other) + return self.__class__(self.data + list(other)) + + def __radd__(self, other): + if isinstance(other, UserList): + return self.__class__(other.data + self.data) + elif isinstance(other, type(self.data)): + return self.__class__(other + self.data) + return self.__class__(list(other) + self.data) + + def __iadd__(self, other): + if isinstance(other, UserList): + self.data += other.data + elif isinstance(other, type(self.data)): + self.data += other + else: + self.data += list(other) + return self + + def __mul__(self, n): + return self.__class__(self.data * n) + + __rmul__ = __mul__ + + def __imul__(self, n): + self.data *= n + return self + + def __copy__(self): + inst = self.__class__.__new__(self.__class__) + inst.__dict__.update(self.__dict__) + # Create a copy and avoid triggering descriptors + inst.__dict__["data"] = self.__dict__["data"][:] + return inst + + def append(self, item): + self.data.append(item) + + def insert(self, i, item): + self.data.insert(i, item) + + def pop(self, i=-1): + return self.data.pop(i) + + def remove(self, item): + self.data.remove(item) + + def clear(self): + self.data.clear() + + def copy(self): + return self.__class__(self) + + def count(self, item): + return self.data.count(item) + + def index(self, item, *args): + return self.data.index(item, *args) + + def reverse(self): + self.data.reverse() + + def sort(self, /, *args, **kwds): + self.data.sort(*args, **kwds) + + def extend(self, other): + if isinstance(other, UserList): + self.data.extend(other.data) + else: + self.data.extend(other) + + +################################################################################ +### UserString +################################################################################ + +class UserString(_collections_abc.Sequence): + + def __init__(self, seq): + if isinstance(seq, str): + self.data = seq + elif isinstance(seq, UserString): + self.data = seq.data[:] + else: + self.data = str(seq) + + def __str__(self): + return str(self.data) + + def __repr__(self): + return repr(self.data) + + def __int__(self): + return int(self.data) + + def __float__(self): + return float(self.data) + + def __complex__(self): + return complex(self.data) + + def __hash__(self): + return hash(self.data) + + def __getnewargs__(self): + return (self.data[:],) + + def __eq__(self, string): + if isinstance(string, UserString): + return self.data == string.data + return self.data == string + + def __lt__(self, string): + if isinstance(string, UserString): + return self.data < string.data + return self.data < string + + def __le__(self, string): + if isinstance(string, UserString): + return self.data <= string.data + return self.data <= string + + def __gt__(self, string): + if isinstance(string, UserString): + return self.data > string.data + return self.data > string + + def __ge__(self, string): + if isinstance(string, UserString): + return self.data >= string.data + return self.data >= string + + def __contains__(self, char): + if isinstance(char, UserString): + char = char.data + return char in self.data + + def __len__(self): + return len(self.data) + + def __getitem__(self, index): + return self.__class__(self.data[index]) + + def __add__(self, other): + if isinstance(other, UserString): + return self.__class__(self.data + other.data) + elif isinstance(other, str): + return self.__class__(self.data + other) + return self.__class__(self.data + str(other)) + + def __radd__(self, other): + if isinstance(other, str): + return self.__class__(other + self.data) + return self.__class__(str(other) + self.data) + + def __mul__(self, n): + return self.__class__(self.data * n) + + __rmul__ = __mul__ + + def __mod__(self, args): + return self.__class__(self.data % args) + + def __rmod__(self, template): + return self.__class__(str(template) % self) + + # the following methods are defined in alphabetical order: + def capitalize(self): + return self.__class__(self.data.capitalize()) + + def casefold(self): + return self.__class__(self.data.casefold()) + + def center(self, width, *args): + return self.__class__(self.data.center(width, *args)) + + def count(self, sub, start=0, end=_sys.maxsize): + if isinstance(sub, UserString): + sub = sub.data + return self.data.count(sub, start, end) + + def removeprefix(self, prefix, /): + if isinstance(prefix, UserString): + prefix = prefix.data + return self.__class__(self.data.removeprefix(prefix)) + + def removesuffix(self, suffix, /): + if isinstance(suffix, UserString): + suffix = suffix.data + return self.__class__(self.data.removesuffix(suffix)) + + def encode(self, encoding='utf-8', errors='strict'): + encoding = 'utf-8' if encoding is None else encoding + errors = 'strict' if errors is None else errors + return self.data.encode(encoding, errors) + + def endswith(self, suffix, start=0, end=_sys.maxsize): + return self.data.endswith(suffix, start, end) + + def expandtabs(self, tabsize=8): + return self.__class__(self.data.expandtabs(tabsize)) + + def find(self, sub, start=0, end=_sys.maxsize): + if isinstance(sub, UserString): + sub = sub.data + return self.data.find(sub, start, end) + + def format(self, /, *args, **kwds): + return self.data.format(*args, **kwds) + + def format_map(self, mapping): + return self.data.format_map(mapping) + + def index(self, sub, start=0, end=_sys.maxsize): + return self.data.index(sub, start, end) + + def isalpha(self): + return self.data.isalpha() + + def isalnum(self): + return self.data.isalnum() + + def isascii(self): + return self.data.isascii() + + def isdecimal(self): + return self.data.isdecimal() + + def isdigit(self): + return self.data.isdigit() + + def isidentifier(self): + return self.data.isidentifier() + + def islower(self): + return self.data.islower() + + def isnumeric(self): + return self.data.isnumeric() + + def isprintable(self): + return self.data.isprintable() + + def isspace(self): + return self.data.isspace() + + def istitle(self): + return self.data.istitle() + + def isupper(self): + return self.data.isupper() + + def join(self, seq): + return self.data.join(seq) + + def ljust(self, width, *args): + return self.__class__(self.data.ljust(width, *args)) + + def lower(self): + return self.__class__(self.data.lower()) + + def lstrip(self, chars=None): + return self.__class__(self.data.lstrip(chars)) + + maketrans = str.maketrans + + def partition(self, sep): + return self.data.partition(sep) + + def replace(self, old, new, maxsplit=-1): + if isinstance(old, UserString): + old = old.data + if isinstance(new, UserString): + new = new.data + return self.__class__(self.data.replace(old, new, maxsplit)) + + def rfind(self, sub, start=0, end=_sys.maxsize): + if isinstance(sub, UserString): + sub = sub.data + return self.data.rfind(sub, start, end) + + def rindex(self, sub, start=0, end=_sys.maxsize): + return self.data.rindex(sub, start, end) + + def rjust(self, width, *args): + return self.__class__(self.data.rjust(width, *args)) + + def rpartition(self, sep): + return self.data.rpartition(sep) + + def rstrip(self, chars=None): + return self.__class__(self.data.rstrip(chars)) + + def split(self, sep=None, maxsplit=-1): + return self.data.split(sep, maxsplit) + + def rsplit(self, sep=None, maxsplit=-1): + return self.data.rsplit(sep, maxsplit) + + def splitlines(self, keepends=False): + return self.data.splitlines(keepends) + + def startswith(self, prefix, start=0, end=_sys.maxsize): + return self.data.startswith(prefix, start, end) + + def strip(self, chars=None): + return self.__class__(self.data.strip(chars)) + + def swapcase(self): + return self.__class__(self.data.swapcase()) + + def title(self): + return self.__class__(self.data.title()) + + def translate(self, *args): + return self.__class__(self.data.translate(*args)) + + def upper(self): + return self.__class__(self.data.upper()) + + def zfill(self, width): + return self.__class__(self.data.zfill(width)) diff --git a/shared/package-files/5d/5d7ff67a5a87ba4177b687840eea7040f94dffc504bf7fb218a951d00f769fbe/blob b/shared/package-files/5d/5d7ff67a5a87ba4177b687840eea7040f94dffc504bf7fb218a951d00f769fbe/blob new file mode 100644 index 0000000000000000000000000000000000000000..f5f23581bd9dc519a33753dbe171e00ee29c28d4 --- /dev/null +++ b/shared/package-files/5d/5d7ff67a5a87ba4177b687840eea7040f94dffc504bf7fb218a951d00f769fbe/blob @@ -0,0 +1,805 @@ +""" +Adjust subplot layouts so that there are no overlapping Axes or Axes +decorations. All Axes decorations are dealt with (labels, ticks, titles, +ticklabels) and some dependent artists are also dealt with (colorbar, +suptitle). + +Layout is done via `~matplotlib.gridspec`, with one constraint per gridspec, +so it is possible to have overlapping Axes if the gridspecs overlap (i.e. +using `~matplotlib.gridspec.GridSpecFromSubplotSpec`). Axes placed using +``figure.subplots()`` or ``figure.add_subplots()`` will participate in the +layout. Axes manually placed via ``figure.add_axes()`` will not. + +See Tutorial: :ref:`constrainedlayout_guide` + +General idea: +------------- + +First, a figure has a gridspec that divides the figure into nrows and ncols, +with heights and widths set by ``height_ratios`` and ``width_ratios``, +often just set to 1 for an equal grid. + +Subplotspecs that are derived from this gridspec can contain either a +``SubPanel``, a ``GridSpecFromSubplotSpec``, or an ``Axes``. The ``SubPanel`` +and ``GridSpecFromSubplotSpec`` are dealt with recursively and each contain an +analogous layout. + +Each ``GridSpec`` has a ``_layoutgrid`` attached to it. The ``_layoutgrid`` +has the same logical layout as the ``GridSpec``. Each row of the grid spec +has a top and bottom "margin" and each column has a left and right "margin". +The "inner" height of each row is constrained to be the same (or as modified +by ``height_ratio``), and the "inner" width of each column is +constrained to be the same (as modified by ``width_ratio``), where "inner" +is the width or height of each column/row minus the size of the margins. + +Then the size of the margins for each row and column are determined as the +max width of the decorators on each Axes that has decorators in that margin. +For instance, a normal Axes would have a left margin that includes the +left ticklabels, and the ylabel if it exists. The right margin may include a +colorbar, the bottom margin the xaxis decorations, and the top margin the +title. + +With these constraints, the solver then finds appropriate bounds for the +columns and rows. It's possible that the margins take up the whole figure, +in which case the algorithm is not applied and a warning is raised. + +See the tutorial :ref:`constrainedlayout_guide` +for more discussion of the algorithm with examples. +""" + +import logging + +import numpy as np + +from matplotlib import _api, artist as martist +import matplotlib.transforms as mtransforms +import matplotlib._layoutgrid as mlayoutgrid + + +_log = logging.getLogger(__name__) + + +###################################################### +def do_constrained_layout(fig, h_pad, w_pad, + hspace=None, wspace=None, rect=(0, 0, 1, 1), + compress=False): + """ + Do the constrained_layout. Called at draw time in + ``figure.constrained_layout()`` + + Parameters + ---------- + fig : `~matplotlib.figure.Figure` + `.Figure` instance to do the layout in. + + h_pad, w_pad : float + Padding around the Axes elements in figure-normalized units. + + hspace, wspace : float + Fraction of the figure to dedicate to space between the + Axes. These are evenly spread between the gaps between the Axes. + A value of 0.2 for a three-column layout would have a space + of 0.1 of the figure width between each column. + If h/wspace < h/w_pad, then the pads are used instead. + + rect : tuple of 4 floats + Rectangle in figure coordinates to perform constrained layout in + [left, bottom, width, height], each from 0-1. + + compress : bool + Whether to shift Axes so that white space in between them is + removed. This is useful for simple grids of fixed-aspect Axes (e.g. + a grid of images). + + Returns + ------- + layoutgrid : private debugging structure + """ + + renderer = fig._get_renderer() + # make layoutgrid tree... + layoutgrids = make_layoutgrids(fig, None, rect=rect) + if not layoutgrids['hasgrids']: + _api.warn_external('There are no gridspecs with layoutgrids. ' + 'Possibly did not call parent GridSpec with the' + ' "figure" keyword') + return + + for _ in range(2): + # do the algorithm twice. This has to be done because decorations + # change size after the first re-position (i.e. x/yticklabels get + # larger/smaller). This second reposition tends to be much milder, + # so doing twice makes things work OK. + + # make margins for all the Axes and subfigures in the + # figure. Add margins for colorbars... + make_layout_margins(layoutgrids, fig, renderer, h_pad=h_pad, + w_pad=w_pad, hspace=hspace, wspace=wspace) + make_margin_suptitles(layoutgrids, fig, renderer, h_pad=h_pad, + w_pad=w_pad) + + # if a layout is such that a columns (or rows) margin has no + # constraints, we need to make all such instances in the grid + # match in margin size. + match_submerged_margins(layoutgrids, fig) + + # update all the variables in the layout. + layoutgrids[fig].update_variables() + + warn_collapsed = ('constrained_layout not applied because ' + 'axes sizes collapsed to zero. Try making ' + 'figure larger or Axes decorations smaller.') + if check_no_collapsed_axes(layoutgrids, fig): + reposition_axes(layoutgrids, fig, renderer, h_pad=h_pad, + w_pad=w_pad, hspace=hspace, wspace=wspace) + if compress: + layoutgrids = compress_fixed_aspect(layoutgrids, fig) + layoutgrids[fig].update_variables() + if check_no_collapsed_axes(layoutgrids, fig): + reposition_axes(layoutgrids, fig, renderer, h_pad=h_pad, + w_pad=w_pad, hspace=hspace, wspace=wspace) + else: + _api.warn_external(warn_collapsed) + + if ((suptitle := fig._suptitle) is not None and + suptitle.get_in_layout() and suptitle._autopos): + x, _ = suptitle.get_position() + suptitle.set_position( + (x, layoutgrids[fig].get_inner_bbox().y1 + h_pad)) + suptitle.set_verticalalignment('bottom') + else: + _api.warn_external(warn_collapsed) + reset_margins(layoutgrids, fig) + return layoutgrids + + +def make_layoutgrids(fig, layoutgrids, rect=(0, 0, 1, 1)): + """ + Make the layoutgrid tree. + + (Sub)Figures get a layoutgrid so we can have figure margins. + + Gridspecs that are attached to Axes get a layoutgrid so Axes + can have margins. + """ + + if layoutgrids is None: + layoutgrids = dict() + layoutgrids['hasgrids'] = False + if not hasattr(fig, '_parent'): + # top figure; pass rect as parent to allow user-specified + # margins + layoutgrids[fig] = mlayoutgrid.LayoutGrid(parent=rect, name='figlb') + else: + # subfigure + gs = fig._subplotspec.get_gridspec() + # it is possible the gridspec containing this subfigure hasn't + # been added to the tree yet: + layoutgrids = make_layoutgrids_gs(layoutgrids, gs) + # add the layoutgrid for the subfigure: + parentlb = layoutgrids[gs] + layoutgrids[fig] = mlayoutgrid.LayoutGrid( + parent=parentlb, + name='panellb', + parent_inner=True, + nrows=1, ncols=1, + parent_pos=(fig._subplotspec.rowspan, + fig._subplotspec.colspan)) + # recursively do all subfigures in this figure... + for sfig in fig.subfigs: + layoutgrids = make_layoutgrids(sfig, layoutgrids) + + # for each Axes at the local level add its gridspec: + for ax in fig._localaxes: + gs = ax.get_gridspec() + if gs is not None: + layoutgrids = make_layoutgrids_gs(layoutgrids, gs) + + return layoutgrids + + +def make_layoutgrids_gs(layoutgrids, gs): + """ + Make the layoutgrid for a gridspec (and anything nested in the gridspec) + """ + + if gs in layoutgrids or gs.figure is None: + return layoutgrids + # in order to do constrained_layout there has to be at least *one* + # gridspec in the tree: + layoutgrids['hasgrids'] = True + if not hasattr(gs, '_subplot_spec'): + # normal gridspec + parent = layoutgrids[gs.figure] + layoutgrids[gs] = mlayoutgrid.LayoutGrid( + parent=parent, + parent_inner=True, + name='gridspec', + ncols=gs._ncols, nrows=gs._nrows, + width_ratios=gs.get_width_ratios(), + height_ratios=gs.get_height_ratios()) + else: + # this is a gridspecfromsubplotspec: + subplot_spec = gs._subplot_spec + parentgs = subplot_spec.get_gridspec() + # if a nested gridspec it is possible the parent is not in there yet: + if parentgs not in layoutgrids: + layoutgrids = make_layoutgrids_gs(layoutgrids, parentgs) + subspeclb = layoutgrids[parentgs] + # gridspecfromsubplotspec need an outer container: + # get a unique representation: + rep = (gs, 'top') + if rep not in layoutgrids: + layoutgrids[rep] = mlayoutgrid.LayoutGrid( + parent=subspeclb, + name='top', + nrows=1, ncols=1, + parent_pos=(subplot_spec.rowspan, subplot_spec.colspan)) + layoutgrids[gs] = mlayoutgrid.LayoutGrid( + parent=layoutgrids[rep], + name='gridspec', + nrows=gs._nrows, ncols=gs._ncols, + width_ratios=gs.get_width_ratios(), + height_ratios=gs.get_height_ratios()) + return layoutgrids + + +def check_no_collapsed_axes(layoutgrids, fig): + """ + Check that no Axes have collapsed to zero size. + """ + for sfig in fig.subfigs: + ok = check_no_collapsed_axes(layoutgrids, sfig) + if not ok: + return False + for ax in fig.axes: + gs = ax.get_gridspec() + if gs in layoutgrids: # also implies gs is not None. + lg = layoutgrids[gs] + for i in range(gs.nrows): + for j in range(gs.ncols): + bb = lg.get_inner_bbox(i, j) + if bb.width <= 0 or bb.height <= 0: + return False + return True + + +def compress_fixed_aspect(layoutgrids, fig): + gs = None + for ax in fig.axes: + if ax.get_subplotspec() is None: + continue + ax.apply_aspect() + sub = ax.get_subplotspec() + _gs = sub.get_gridspec() + if gs is None: + gs = _gs + extraw = np.zeros(gs.ncols) + extrah = np.zeros(gs.nrows) + elif _gs != gs: + raise ValueError('Cannot do compressed layout if Axes are not' + 'all from the same gridspec') + orig = ax.get_position(original=True) + actual = ax.get_position(original=False) + dw = orig.width - actual.width + if dw > 0: + extraw[sub.colspan] = np.maximum(extraw[sub.colspan], dw) + dh = orig.height - actual.height + if dh > 0: + extrah[sub.rowspan] = np.maximum(extrah[sub.rowspan], dh) + + if gs is None: + raise ValueError('Cannot do compressed layout if no Axes ' + 'are part of a gridspec.') + w = np.sum(extraw) / 2 + layoutgrids[fig].edit_margin_min('left', w) + layoutgrids[fig].edit_margin_min('right', w) + + h = np.sum(extrah) / 2 + layoutgrids[fig].edit_margin_min('top', h) + layoutgrids[fig].edit_margin_min('bottom', h) + return layoutgrids + + +def get_margin_from_padding(obj, *, w_pad=0, h_pad=0, + hspace=0, wspace=0): + + ss = obj._subplotspec + gs = ss.get_gridspec() + + if hasattr(gs, 'hspace'): + _hspace = (gs.hspace if gs.hspace is not None else hspace) + _wspace = (gs.wspace if gs.wspace is not None else wspace) + else: + _hspace = (gs._hspace if gs._hspace is not None else hspace) + _wspace = (gs._wspace if gs._wspace is not None else wspace) + + _wspace = _wspace / 2 + _hspace = _hspace / 2 + + nrows, ncols = gs.get_geometry() + # there are two margins for each direction. The "cb" + # margins are for pads and colorbars, the non-"cb" are + # for the Axes decorations (labels etc). + margin = {'leftcb': w_pad, 'rightcb': w_pad, + 'bottomcb': h_pad, 'topcb': h_pad, + 'left': 0, 'right': 0, + 'top': 0, 'bottom': 0} + if _wspace / ncols > w_pad: + if ss.colspan.start > 0: + margin['leftcb'] = _wspace / ncols + if ss.colspan.stop < ncols: + margin['rightcb'] = _wspace / ncols + if _hspace / nrows > h_pad: + if ss.rowspan.stop < nrows: + margin['bottomcb'] = _hspace / nrows + if ss.rowspan.start > 0: + margin['topcb'] = _hspace / nrows + + return margin + + +def make_layout_margins(layoutgrids, fig, renderer, *, w_pad=0, h_pad=0, + hspace=0, wspace=0): + """ + For each Axes, make a margin between the *pos* layoutbox and the + *axes* layoutbox be a minimum size that can accommodate the + decorations on the axis. + + Then make room for colorbars. + + Parameters + ---------- + layoutgrids : dict + fig : `~matplotlib.figure.Figure` + `.Figure` instance to do the layout in. + renderer : `~matplotlib.backend_bases.RendererBase` subclass. + The renderer to use. + w_pad, h_pad : float, default: 0 + Width and height padding (in fraction of figure). + hspace, wspace : float, default: 0 + Width and height padding as fraction of figure size divided by + number of columns or rows. + """ + for sfig in fig.subfigs: # recursively make child panel margins + ss = sfig._subplotspec + gs = ss.get_gridspec() + + make_layout_margins(layoutgrids, sfig, renderer, + w_pad=w_pad, h_pad=h_pad, + hspace=hspace, wspace=wspace) + + margins = get_margin_from_padding(sfig, w_pad=0, h_pad=0, + hspace=hspace, wspace=wspace) + layoutgrids[gs].edit_outer_margin_mins(margins, ss) + + for ax in fig._localaxes: + if not ax.get_subplotspec() or not ax.get_in_layout(): + continue + + ss = ax.get_subplotspec() + gs = ss.get_gridspec() + + if gs not in layoutgrids: + return + + margin = get_margin_from_padding(ax, w_pad=w_pad, h_pad=h_pad, + hspace=hspace, wspace=wspace) + pos, bbox = get_pos_and_bbox(ax, renderer) + # the margin is the distance between the bounding box of the Axes + # and its position (plus the padding from above) + margin['left'] += pos.x0 - bbox.x0 + margin['right'] += bbox.x1 - pos.x1 + # remember that rows are ordered from top: + margin['bottom'] += pos.y0 - bbox.y0 + margin['top'] += bbox.y1 - pos.y1 + + # make margin for colorbars. These margins go in the + # padding margin, versus the margin for Axes decorators. + for cbax in ax._colorbars: + # note pad is a fraction of the parent width... + pad = colorbar_get_pad(layoutgrids, cbax) + # colorbars can be child of more than one subplot spec: + cbp_rspan, cbp_cspan = get_cb_parent_spans(cbax) + loc = cbax._colorbar_info['location'] + cbpos, cbbbox = get_pos_and_bbox(cbax, renderer) + if loc == 'right': + if cbp_cspan.stop == ss.colspan.stop: + # only increase if the colorbar is on the right edge + margin['rightcb'] += cbbbox.width + pad + elif loc == 'left': + if cbp_cspan.start == ss.colspan.start: + # only increase if the colorbar is on the left edge + margin['leftcb'] += cbbbox.width + pad + elif loc == 'top': + if cbp_rspan.start == ss.rowspan.start: + margin['topcb'] += cbbbox.height + pad + else: + if cbp_rspan.stop == ss.rowspan.stop: + margin['bottomcb'] += cbbbox.height + pad + # If the colorbars are wider than the parent box in the + # cross direction + if loc in ['top', 'bottom']: + if (cbp_cspan.start == ss.colspan.start and + cbbbox.x0 < bbox.x0): + margin['left'] += bbox.x0 - cbbbox.x0 + if (cbp_cspan.stop == ss.colspan.stop and + cbbbox.x1 > bbox.x1): + margin['right'] += cbbbox.x1 - bbox.x1 + # or taller: + if loc in ['left', 'right']: + if (cbp_rspan.stop == ss.rowspan.stop and + cbbbox.y0 < bbox.y0): + margin['bottom'] += bbox.y0 - cbbbox.y0 + if (cbp_rspan.start == ss.rowspan.start and + cbbbox.y1 > bbox.y1): + margin['top'] += cbbbox.y1 - bbox.y1 + # pass the new margins down to the layout grid for the solution... + layoutgrids[gs].edit_outer_margin_mins(margin, ss) + + # make margins for figure-level legends: + for leg in fig.legends: + inv_trans_fig = None + if leg._outside_loc and leg._bbox_to_anchor is None: + if inv_trans_fig is None: + inv_trans_fig = fig.transFigure.inverted().transform_bbox + bbox = inv_trans_fig(leg.get_tightbbox(renderer)) + w = bbox.width + 2 * w_pad + h = bbox.height + 2 * h_pad + legendloc = leg._outside_loc + if legendloc == 'lower': + layoutgrids[fig].edit_margin_min('bottom', h) + elif legendloc == 'upper': + layoutgrids[fig].edit_margin_min('top', h) + if legendloc == 'right': + layoutgrids[fig].edit_margin_min('right', w) + elif legendloc == 'left': + layoutgrids[fig].edit_margin_min('left', w) + + +def make_margin_suptitles(layoutgrids, fig, renderer, *, w_pad=0, h_pad=0): + # Figure out how large the suptitle is and make the + # top level figure margin larger. + + inv_trans_fig = fig.transFigure.inverted().transform_bbox + # get the h_pad and w_pad as distances in the local subfigure coordinates: + padbox = mtransforms.Bbox([[0, 0], [w_pad, h_pad]]) + padbox = (fig.transFigure - + fig.transSubfigure).transform_bbox(padbox) + h_pad_local = padbox.height + w_pad_local = padbox.width + + for sfig in fig.subfigs: + make_margin_suptitles(layoutgrids, sfig, renderer, + w_pad=w_pad, h_pad=h_pad) + + if fig._suptitle is not None and fig._suptitle.get_in_layout(): + p = fig._suptitle.get_position() + if getattr(fig._suptitle, '_autopos', False): + fig._suptitle.set_position((p[0], 1 - h_pad_local)) + bbox = inv_trans_fig(fig._suptitle.get_tightbbox(renderer)) + layoutgrids[fig].edit_margin_min('top', bbox.height + 2 * h_pad) + + if fig._supxlabel is not None and fig._supxlabel.get_in_layout(): + p = fig._supxlabel.get_position() + if getattr(fig._supxlabel, '_autopos', False): + fig._supxlabel.set_position((p[0], h_pad_local)) + bbox = inv_trans_fig(fig._supxlabel.get_tightbbox(renderer)) + layoutgrids[fig].edit_margin_min('bottom', + bbox.height + 2 * h_pad) + + if fig._supylabel is not None and fig._supylabel.get_in_layout(): + p = fig._supylabel.get_position() + if getattr(fig._supylabel, '_autopos', False): + fig._supylabel.set_position((w_pad_local, p[1])) + bbox = inv_trans_fig(fig._supylabel.get_tightbbox(renderer)) + layoutgrids[fig].edit_margin_min('left', bbox.width + 2 * w_pad) + + +def match_submerged_margins(layoutgrids, fig): + """ + Make the margins that are submerged inside an Axes the same size. + + This allows Axes that span two columns (or rows) that are offset + from one another to have the same size. + + This gives the proper layout for something like:: + fig = plt.figure(constrained_layout=True) + axs = fig.subplot_mosaic("AAAB\nCCDD") + + Without this routine, the Axes D will be wider than C, because the + margin width between the two columns in C has no width by default, + whereas the margins between the two columns of D are set by the + width of the margin between A and B. However, obviously the user would + like C and D to be the same size, so we need to add constraints to these + "submerged" margins. + + This routine makes all the interior margins the same, and the spacing + between the three columns in A and the two column in C are all set to the + margins between the two columns of D. + + See test_constrained_layout::test_constrained_layout12 for an example. + """ + + axsdone = [] + for sfig in fig.subfigs: + axsdone += match_submerged_margins(layoutgrids, sfig) + + axs = [a for a in fig.get_axes() + if (a.get_subplotspec() is not None and a.get_in_layout() and + a not in axsdone)] + + for ax1 in axs: + ss1 = ax1.get_subplotspec() + if ss1.get_gridspec() not in layoutgrids: + axs.remove(ax1) + continue + lg1 = layoutgrids[ss1.get_gridspec()] + + # interior columns: + if len(ss1.colspan) > 1: + maxsubl = np.max( + lg1.margin_vals['left'][ss1.colspan[1:]] + + lg1.margin_vals['leftcb'][ss1.colspan[1:]] + ) + maxsubr = np.max( + lg1.margin_vals['right'][ss1.colspan[:-1]] + + lg1.margin_vals['rightcb'][ss1.colspan[:-1]] + ) + for ax2 in axs: + ss2 = ax2.get_subplotspec() + lg2 = layoutgrids[ss2.get_gridspec()] + if lg2 is not None and len(ss2.colspan) > 1: + maxsubl2 = np.max( + lg2.margin_vals['left'][ss2.colspan[1:]] + + lg2.margin_vals['leftcb'][ss2.colspan[1:]]) + if maxsubl2 > maxsubl: + maxsubl = maxsubl2 + maxsubr2 = np.max( + lg2.margin_vals['right'][ss2.colspan[:-1]] + + lg2.margin_vals['rightcb'][ss2.colspan[:-1]]) + if maxsubr2 > maxsubr: + maxsubr = maxsubr2 + for i in ss1.colspan[1:]: + lg1.edit_margin_min('left', maxsubl, cell=i) + for i in ss1.colspan[:-1]: + lg1.edit_margin_min('right', maxsubr, cell=i) + + # interior rows: + if len(ss1.rowspan) > 1: + maxsubt = np.max( + lg1.margin_vals['top'][ss1.rowspan[1:]] + + lg1.margin_vals['topcb'][ss1.rowspan[1:]] + ) + maxsubb = np.max( + lg1.margin_vals['bottom'][ss1.rowspan[:-1]] + + lg1.margin_vals['bottomcb'][ss1.rowspan[:-1]] + ) + + for ax2 in axs: + ss2 = ax2.get_subplotspec() + lg2 = layoutgrids[ss2.get_gridspec()] + if lg2 is not None: + if len(ss2.rowspan) > 1: + maxsubt = np.max([np.max( + lg2.margin_vals['top'][ss2.rowspan[1:]] + + lg2.margin_vals['topcb'][ss2.rowspan[1:]] + ), maxsubt]) + maxsubb = np.max([np.max( + lg2.margin_vals['bottom'][ss2.rowspan[:-1]] + + lg2.margin_vals['bottomcb'][ss2.rowspan[:-1]] + ), maxsubb]) + for i in ss1.rowspan[1:]: + lg1.edit_margin_min('top', maxsubt, cell=i) + for i in ss1.rowspan[:-1]: + lg1.edit_margin_min('bottom', maxsubb, cell=i) + + return axs + + +def get_cb_parent_spans(cbax): + """ + Figure out which subplotspecs this colorbar belongs to. + + Parameters + ---------- + cbax : `~matplotlib.axes.Axes` + Axes for the colorbar. + """ + rowstart = np.inf + rowstop = -np.inf + colstart = np.inf + colstop = -np.inf + for parent in cbax._colorbar_info['parents']: + ss = parent.get_subplotspec() + rowstart = min(ss.rowspan.start, rowstart) + rowstop = max(ss.rowspan.stop, rowstop) + colstart = min(ss.colspan.start, colstart) + colstop = max(ss.colspan.stop, colstop) + + rowspan = range(rowstart, rowstop) + colspan = range(colstart, colstop) + return rowspan, colspan + + +def get_pos_and_bbox(ax, renderer): + """ + Get the position and the bbox for the Axes. + + Parameters + ---------- + ax : `~matplotlib.axes.Axes` + renderer : `~matplotlib.backend_bases.RendererBase` subclass. + + Returns + ------- + pos : `~matplotlib.transforms.Bbox` + Position in figure coordinates. + bbox : `~matplotlib.transforms.Bbox` + Tight bounding box in figure coordinates. + """ + fig = ax.get_figure(root=False) + pos = ax.get_position(original=True) + # pos is in panel co-ords, but we need in figure for the layout + pos = pos.transformed(fig.transSubfigure - fig.transFigure) + tightbbox = martist._get_tightbbox_for_layout_only(ax, renderer) + if tightbbox is None: + bbox = pos + else: + bbox = tightbbox.transformed(fig.transFigure.inverted()) + return pos, bbox + + +def reposition_axes(layoutgrids, fig, renderer, *, + w_pad=0, h_pad=0, hspace=0, wspace=0): + """ + Reposition all the Axes based on the new inner bounding box. + """ + trans_fig_to_subfig = fig.transFigure - fig.transSubfigure + for sfig in fig.subfigs: + bbox = layoutgrids[sfig].get_outer_bbox() + sfig._redo_transform_rel_fig( + bbox=bbox.transformed(trans_fig_to_subfig)) + reposition_axes(layoutgrids, sfig, renderer, + w_pad=w_pad, h_pad=h_pad, + wspace=wspace, hspace=hspace) + + for ax in fig._localaxes: + if ax.get_subplotspec() is None or not ax.get_in_layout(): + continue + + # grid bbox is in Figure coordinates, but we specify in panel + # coordinates... + ss = ax.get_subplotspec() + gs = ss.get_gridspec() + if gs not in layoutgrids: + return + + bbox = layoutgrids[gs].get_inner_bbox(rows=ss.rowspan, + cols=ss.colspan) + + # transform from figure to panel for set_position: + newbbox = trans_fig_to_subfig.transform_bbox(bbox) + ax._set_position(newbbox) + + # move the colorbars: + # we need to keep track of oldw and oldh if there is more than + # one colorbar: + offset = {'left': 0, 'right': 0, 'bottom': 0, 'top': 0} + for nn, cbax in enumerate(ax._colorbars[::-1]): + if ax == cbax._colorbar_info['parents'][0]: + reposition_colorbar(layoutgrids, cbax, renderer, + offset=offset) + + +def reposition_colorbar(layoutgrids, cbax, renderer, *, offset=None): + """ + Place the colorbar in its new place. + + Parameters + ---------- + layoutgrids : dict + cbax : `~matplotlib.axes.Axes` + Axes for the colorbar. + renderer : `~matplotlib.backend_bases.RendererBase` subclass. + The renderer to use. + offset : array-like + Offset the colorbar needs to be pushed to in order to + account for multiple colorbars. + """ + + parents = cbax._colorbar_info['parents'] + gs = parents[0].get_gridspec() + fig = cbax.get_figure(root=False) + trans_fig_to_subfig = fig.transFigure - fig.transSubfigure + + cb_rspans, cb_cspans = get_cb_parent_spans(cbax) + bboxparent = layoutgrids[gs].get_bbox_for_cb(rows=cb_rspans, + cols=cb_cspans) + pb = layoutgrids[gs].get_inner_bbox(rows=cb_rspans, cols=cb_cspans) + + location = cbax._colorbar_info['location'] + anchor = cbax._colorbar_info['anchor'] + fraction = cbax._colorbar_info['fraction'] + aspect = cbax._colorbar_info['aspect'] + shrink = cbax._colorbar_info['shrink'] + + cbpos, cbbbox = get_pos_and_bbox(cbax, renderer) + + # Colorbar gets put at extreme edge of outer bbox of the subplotspec + # It needs to be moved in by: 1) a pad 2) its "margin" 3) by + # any colorbars already added at this location: + cbpad = colorbar_get_pad(layoutgrids, cbax) + if location in ('left', 'right'): + # fraction and shrink are fractions of parent + pbcb = pb.shrunk(fraction, shrink).anchored(anchor, pb) + # The colorbar is at the left side of the parent. Need + # to translate to right (or left) + if location == 'right': + lmargin = cbpos.x0 - cbbbox.x0 + dx = bboxparent.x1 - pbcb.x0 + offset['right'] + dx += cbpad + lmargin + offset['right'] += cbbbox.width + cbpad + pbcb = pbcb.translated(dx, 0) + else: + lmargin = cbpos.x0 - cbbbox.x0 + dx = bboxparent.x0 - pbcb.x0 # edge of parent + dx += -cbbbox.width - cbpad + lmargin - offset['left'] + offset['left'] += cbbbox.width + cbpad + pbcb = pbcb.translated(dx, 0) + else: # horizontal axes: + pbcb = pb.shrunk(shrink, fraction).anchored(anchor, pb) + if location == 'top': + bmargin = cbpos.y0 - cbbbox.y0 + dy = bboxparent.y1 - pbcb.y0 + offset['top'] + dy += cbpad + bmargin + offset['top'] += cbbbox.height + cbpad + pbcb = pbcb.translated(0, dy) + else: + bmargin = cbpos.y0 - cbbbox.y0 + dy = bboxparent.y0 - pbcb.y0 + dy += -cbbbox.height - cbpad + bmargin - offset['bottom'] + offset['bottom'] += cbbbox.height + cbpad + pbcb = pbcb.translated(0, dy) + + pbcb = trans_fig_to_subfig.transform_bbox(pbcb) + cbax.set_transform(fig.transSubfigure) + cbax._set_position(pbcb) + cbax.set_anchor(anchor) + if location in ['bottom', 'top']: + aspect = 1 / aspect + cbax.set_box_aspect(aspect) + cbax.set_aspect('auto') + return offset + + +def reset_margins(layoutgrids, fig): + """ + Reset the margins in the layoutboxes of *fig*. + + Margins are usually set as a minimum, so if the figure gets smaller + the minimum needs to be zero in order for it to grow again. + """ + for sfig in fig.subfigs: + reset_margins(layoutgrids, sfig) + for ax in fig.axes: + if ax.get_in_layout(): + gs = ax.get_gridspec() + if gs in layoutgrids: # also implies gs is not None. + layoutgrids[gs].reset_margins() + layoutgrids[fig].reset_margins() + + +def colorbar_get_pad(layoutgrids, cax): + parents = cax._colorbar_info['parents'] + gs = parents[0].get_gridspec() + + cb_rspans, cb_cspans = get_cb_parent_spans(cax) + bboxouter = layoutgrids[gs].get_inner_bbox(rows=cb_rspans, cols=cb_cspans) + + if cax._colorbar_info['location'] in ['right', 'left']: + size = bboxouter.width + else: + size = bboxouter.height + + return cax._colorbar_info['pad'] * size diff --git a/shared/package-files/5d/5d817418b5612568d2f27a04c784c9ef4e04aadf89181a2545910e93080d22d1/blob b/shared/package-files/5d/5d817418b5612568d2f27a04c784c9ef4e04aadf89181a2545910e93080d22d1/blob new file mode 100644 index 0000000000000000000000000000000000000000..1d606590411048e9bebb2dc04d28e56be89783b3 --- /dev/null +++ b/shared/package-files/5d/5d817418b5612568d2f27a04c784c9ef4e04aadf89181a2545910e93080d22d1/blob @@ -0,0 +1,211 @@ +# http://developer.intel.com/software/products/compilers/flin/ +import sys + +from numpy.distutils.ccompiler import simple_version_match +from numpy.distutils.fcompiler import FCompiler, dummy_fortran_file + +compilers = ['IntelFCompiler', 'IntelVisualFCompiler', + 'IntelItaniumFCompiler', 'IntelItaniumVisualFCompiler', + 'IntelEM64VisualFCompiler', 'IntelEM64TFCompiler'] + + +def intel_version_match(type): + # Match against the important stuff in the version string + return simple_version_match(start=r'Intel.*?Fortran.*?(?:%s).*?Version' % (type,)) + + +class BaseIntelFCompiler(FCompiler): + def update_executables(self): + f = dummy_fortran_file() + self.executables['version_cmd'] = ['', '-FI', '-V', '-c', + f + '.f', '-o', f + '.o'] + + def runtime_library_dir_option(self, dir): + # TODO: could use -Xlinker here, if it's supported + assert "," not in dir + + return '-Wl,-rpath=%s' % dir + + +class IntelFCompiler(BaseIntelFCompiler): + + compiler_type = 'intel' + compiler_aliases = ('ifort',) + description = 'Intel Fortran Compiler for 32-bit apps' + version_match = intel_version_match('32-bit|IA-32') + + possible_executables = ['ifort', 'ifc'] + + executables = { + 'version_cmd' : None, # set by update_executables + 'compiler_f77' : [None, "-72", "-w90", "-w95"], + 'compiler_f90' : [None], + 'compiler_fix' : [None, "-FI"], + 'linker_so' : ["", "-shared"], + 'archiver' : ["ar", "-cr"], + 'ranlib' : ["ranlib"] + } + + pic_flags = ['-fPIC'] + module_dir_switch = '-module ' # Don't remove ending space! + module_include_switch = '-I' + + def get_flags_free(self): + return ['-FR'] + + def get_flags(self): + return ['-fPIC'] + + def get_flags_opt(self): # Scipy test failures with -O2 + v = self.get_version() + mpopt = 'openmp' if v and v < '15' else 'qopenmp' + return ['-fp-model', 'strict', '-O1', + '-assume', 'minus0', '-{}'.format(mpopt)] + + def get_flags_arch(self): + return [] + + def get_flags_linker_so(self): + opt = FCompiler.get_flags_linker_so(self) + v = self.get_version() + if v and v >= '8.0': + opt.append('-nofor_main') + if sys.platform == 'darwin': + # Here, it's -dynamiclib + try: + idx = opt.index('-shared') + opt.remove('-shared') + except ValueError: + idx = 0 + opt[idx:idx] = ['-dynamiclib', '-Wl,-undefined,dynamic_lookup'] + return opt + + +class IntelItaniumFCompiler(IntelFCompiler): + compiler_type = 'intele' + compiler_aliases = () + description = 'Intel Fortran Compiler for Itanium apps' + + version_match = intel_version_match('Itanium|IA-64') + + possible_executables = ['ifort', 'efort', 'efc'] + + executables = { + 'version_cmd' : None, + 'compiler_f77' : [None, "-FI", "-w90", "-w95"], + 'compiler_fix' : [None, "-FI"], + 'compiler_f90' : [None], + 'linker_so' : ['', "-shared"], + 'archiver' : ["ar", "-cr"], + 'ranlib' : ["ranlib"] + } + + +class IntelEM64TFCompiler(IntelFCompiler): + compiler_type = 'intelem' + compiler_aliases = () + description = 'Intel Fortran Compiler for 64-bit apps' + + version_match = intel_version_match('EM64T-based|Intel\\(R\\) 64|64|IA-64|64-bit') + + possible_executables = ['ifort', 'efort', 'efc'] + + executables = { + 'version_cmd' : None, + 'compiler_f77' : [None, "-FI"], + 'compiler_fix' : [None, "-FI"], + 'compiler_f90' : [None], + 'linker_so' : ['', "-shared"], + 'archiver' : ["ar", "-cr"], + 'ranlib' : ["ranlib"] + } + +# Is there no difference in the version string between the above compilers +# and the Visual compilers? + + +class IntelVisualFCompiler(BaseIntelFCompiler): + compiler_type = 'intelv' + description = 'Intel Visual Fortran Compiler for 32-bit apps' + version_match = intel_version_match('32-bit|IA-32') + + def update_executables(self): + f = dummy_fortran_file() + self.executables['version_cmd'] = ['', '/FI', '/c', + f + '.f', '/o', f + '.o'] + + ar_exe = 'lib.exe' + possible_executables = ['ifort', 'ifl'] + + executables = { + 'version_cmd' : None, + 'compiler_f77' : [None], + 'compiler_fix' : [None], + 'compiler_f90' : [None], + 'linker_so' : [None], + 'archiver' : [ar_exe, "/verbose", "/OUT:"], + 'ranlib' : None + } + + compile_switch = '/c ' + object_switch = '/Fo' # No space after /Fo! + library_switch = '/OUT:' # No space after /OUT:! + module_dir_switch = '/module:' # No space after /module: + module_include_switch = '/I' + + def get_flags(self): + opt = ['/nologo', '/MD', '/nbs', '/names:lowercase', + '/assume:underscore', '/fpp'] + return opt + + def get_flags_free(self): + return [] + + def get_flags_debug(self): + return ['/4Yb', '/d2'] + + def get_flags_opt(self): + return ['/O1', '/assume:minus0'] # Scipy test failures with /O2 + + def get_flags_arch(self): + return ["/arch:IA32", "/QaxSSE3"] + + def runtime_library_dir_option(self, dir): + raise NotImplementedError + + +class IntelItaniumVisualFCompiler(IntelVisualFCompiler): + compiler_type = 'intelev' + description = 'Intel Visual Fortran Compiler for Itanium apps' + + version_match = intel_version_match('Itanium') + + possible_executables = ['efl'] # XXX this is a wild guess + ar_exe = IntelVisualFCompiler.ar_exe + + executables = { + 'version_cmd' : None, + 'compiler_f77' : [None, "-FI", "-w90", "-w95"], + 'compiler_fix' : [None, "-FI", "-4L72", "-w"], + 'compiler_f90' : [None], + 'linker_so' : ['', "-shared"], + 'archiver' : [ar_exe, "/verbose", "/OUT:"], + 'ranlib' : None + } + + +class IntelEM64VisualFCompiler(IntelVisualFCompiler): + compiler_type = 'intelvem' + description = 'Intel Visual Fortran Compiler for 64-bit apps' + + version_match = simple_version_match(start=r'Intel\(R\).*?64,') + + def get_flags_arch(self): + return [] + + +if __name__ == '__main__': + from distutils import log + log.set_verbosity(2) + from numpy.distutils import customized_fcompiler + print(customized_fcompiler(compiler='intel').get_version()) diff --git a/shared/package-files/5d/5d8699fd446b02d56a1ccabeff662d5e4fc44de438c9958b5409ec8be93631a7/blob b/shared/package-files/5d/5d8699fd446b02d56a1ccabeff662d5e4fc44de438c9958b5409ec8be93631a7/blob new file mode 100644 index 0000000000000000000000000000000000000000..4a88e7a81125cbed89d78d0e67288075ed9295f8 --- /dev/null +++ b/shared/package-files/5d/5d8699fd446b02d56a1ccabeff662d5e4fc44de438c9958b5409ec8be93631a7/blob @@ -0,0 +1,314 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#include "arrow/array/data.h" +#include "arrow/device_allocation_type_set.h" +#include "arrow/scalar.h" +#include "arrow/type.h" +#include "arrow/type_traits.h" +#include "arrow/util/checked_cast.h" +#include "arrow/util/macros.h" +#include "arrow/util/visibility.h" + +namespace arrow { + +class Array; +class ChunkedArray; +class RecordBatch; +class Table; + +/// \class Datum +/// \brief Variant type for various Arrow C++ data structures +struct ARROW_EXPORT Datum { + /// \brief The kind of datum stored + enum Kind { NONE, SCALAR, ARRAY, CHUNKED_ARRAY, RECORD_BATCH, TABLE }; + + /// \brief A placeholder type to represent empty datum + struct Empty {}; + + /// \brief Datums variants may have a length. This special value indicate that the + /// current variant does not have a length. + static constexpr int64_t kUnknownLength = -1; + + /// \brief Storage of the actual datum. + /// + /// Note: For arrays, ArrayData is stored instead of Array for easier processing + std::variant, std::shared_ptr, + std::shared_ptr, std::shared_ptr, + std::shared_ptr> + value; + + /// \brief Empty datum, to be populated elsewhere + Datum() = default; + + Datum(const Datum& other) = default; + Datum& operator=(const Datum& other) = default; + Datum(Datum&& other) = default; + Datum& operator=(Datum&& other) = default; + + /// \brief Construct from a Scalar + Datum(std::shared_ptr value) // NOLINT implicit conversion + : value(std::move(value)) {} + + /// \brief Construct from an ArrayData + Datum(std::shared_ptr value) // NOLINT implicit conversion + : value(std::move(value)) {} + + /// \brief Construct from an ArrayData + Datum(ArrayData arg) // NOLINT implicit conversion + : value(std::make_shared(std::move(arg))) {} + + /// \brief Construct from an Array + Datum(const Array& value); // NOLINT implicit conversion + + /// \brief Construct from an Array + Datum(const std::shared_ptr& value); // NOLINT implicit conversion + + /// \brief Construct from a ChunkedArray + Datum(std::shared_ptr value); // NOLINT implicit conversion + + /// \brief Construct from a RecordBatch + Datum(std::shared_ptr value); // NOLINT implicit conversion + + /// \brief Construct from a Table + Datum(std::shared_ptr
value); // NOLINT implicit conversion + + /// \brief Construct from a ChunkedArray. + /// + /// This can be expensive, prefer the shared_ptr constructor + explicit Datum(const ChunkedArray& value); + + /// \brief Construct from a RecordBatch. + /// + /// This can be expensive, prefer the shared_ptr constructor + explicit Datum(const RecordBatch& value); + + /// \brief Construct from a Table. + /// + /// This can be expensive, prefer the shared_ptr
constructor + explicit Datum(const Table& value); + + /// \brief Cast from concrete subtypes of Array or Scalar to Datum + template , + bool IsScalar = std::is_base_of_v, + typename = enable_if_t> + Datum(std::shared_ptr value) // NOLINT implicit conversion + : Datum(std::shared_ptr::type>( + std::move(value))) {} + + /// \brief Cast from concrete subtypes of Array or Scalar to Datum + template , + bool IsArray = std::is_base_of_v, + bool IsScalar = std::is_base_of_v, + typename = enable_if_t> + Datum(T&& value) // NOLINT implicit conversion + : Datum(std::make_shared(std::forward(value))) {} + + /// \brief Copy from concrete subtypes of Scalar. + /// + /// The concrete scalar type must be copyable (not all of them are). + template >> + Datum(const T& value) // NOLINT implicit conversion + : Datum(std::make_shared(value)) {} + + // Convenience constructors + /// \brief Convenience constructor storing a bool scalar. + explicit Datum(bool value); + /// \brief Convenience constructor storing an int8 scalar. + explicit Datum(int8_t value); + /// \brief Convenience constructor storing a uint8 scalar. + explicit Datum(uint8_t value); + /// \brief Convenience constructor storing an int16 scalar. + explicit Datum(int16_t value); + /// \brief Convenience constructor storing a uint16 scalar. + explicit Datum(uint16_t value); + /// \brief Convenience constructor storing an int32 scalar. + explicit Datum(int32_t value); + /// \brief Convenience constructor storing a uint32 scalar. + explicit Datum(uint32_t value); + /// \brief Convenience constructor storing an int64 scalar. + explicit Datum(int64_t value); + /// \brief Convenience constructor storing a uint64 scalar. + explicit Datum(uint64_t value); + /// \brief Convenience constructor storing a float scalar. + explicit Datum(float value); + /// \brief Convenience constructor storing a double scalar. + explicit Datum(double value); + /// \brief Convenience constructor storing a string scalar. + explicit Datum(std::string value); + /// \brief Convenience constructor storing a string scalar. + explicit Datum(const char* value); + + /// \brief Convenience constructor for a DurationScalar from std::chrono::duration + template