Download code/test/Python/0028990_init__.py from Variable-role/sajaniemi_variable_dataset_large: direct link, hf CLI and curl.
- Browser
- Download file 7.45 kB
-
https://huggingface.co/datasets/Variable-role/sajaniemi_variable_dataset_large/resolve/main/code/test/Python/0028990_init__.py
- Command line
-
hf download hf://datasets/Variable-role/sajaniemi_variable_dataset_large/code/test/Python/0028990_init__.py
-
curl -L -o 0028990_init__.py https://huggingface.co/datasets/Variable-role/sajaniemi_variable_dataset_large/resolve/main/code/test/Python/0028990_init__.py
7.45 kB
| # Authors: David Goodger, Ueli Schlaepfer | |
| # Contact: goodger@users.sourceforge.net | |
| # Revision: $Revision: 21817 $ | |
| # Date: $Date: 2005-07-21 13:39:57 -0700 (Thu, 21 Jul 2005) $ | |
| # Copyright: This module has been placed in the public domain. | |
| """ | |
| This package contains modules for standard tree transforms available | |
| to Docutils components. Tree transforms serve a variety of purposes: | |
| - To tie up certain syntax-specific "loose ends" that remain after the | |
| initial parsing of the input plaintext. These transforms are used to | |
| supplement a limited syntax. | |
| - To automate the internal linking of the document tree (hyperlink | |
| references, footnote references, etc.). | |
| - To extract useful information from the document tree. These | |
| transforms may be used to construct (for example) indexes and tables | |
| of contents. | |
| Each transform is an optional step that a Docutils Reader may choose to | |
| perform on the parsed document, depending on the input context. A Docutils | |
| Reader may also perform Reader-specific transforms before or after performing | |
| these standard transforms. | |
| """ | |
| __docformat__ = 'reStructuredText' | |
| from docutils import languages, ApplicationError, TransformSpec | |
| class TransformError(ApplicationError): pass | |
| class Transform: | |
| """ | |
| Docutils transform component abstract base class. | |
| """ | |
| default_priority = None | |
| """Numerical priority of this transform, 0 through 999 (override).""" | |
| def __init__(self, document, startnode=None): | |
| """ | |
| Initial setup for in-place document transforms. | |
| """ | |
| self.document = document | |
| """The document tree to transform.""" | |
| self.startnode = startnode | |
| """Node from which to begin the transform. For many transforms which | |
| apply to the document as a whole, `startnode` is not set (i.e. its | |
| value is `None`).""" | |
| self.language = languages.get_language( | |
| document.settings.language_code) | |
| """Language module local to this document.""" | |
| def apply(self, **kwargs): | |
| """Override to apply the transform to the document tree.""" | |
| raise NotImplementedError('subclass must override this method') | |
| class Transformer(TransformSpec): | |
| """ | |
| Stores transforms (`Transform` classes) and applies them to document | |
| trees. Also keeps track of components by component type name. | |
| """ | |
| from docutils.transforms import universal | |
| stage1_transforms = (universal.Decorations, | |
| universal.ExposeInternals) | |
| """Suggested replacement for `default_transforms` when generating | |
| a document tree without writing it.""" | |
| stage2_transforms = (universal.Messages, | |
| universal.FilterMessages) | |
| """Suggested replacement for `default_transforms` when writing a | |
| previously-parsed document tree. Only transforms which *must* be applied | |
| after writer-specific transforms should be added to this list.""" | |
| default_transforms = stage1_transforms + stage2_transforms | |
| """These transforms are applied to all document trees.""" | |
| def __init__(self, document): | |
| self.transforms = [] | |
| """List of transforms to apply. Each item is a 3-tuple: | |
| ``(priority string, transform class, pending node or None)``.""" | |
| self.unknown_reference_resolvers = [] | |
| """List of hook functions which assist in resolving references""" | |
| self.document = document | |
| """The `nodes.document` object this Transformer is attached to.""" | |
| self.applied = [] | |
| """Transforms already applied, in order.""" | |
| self.sorted = 0 | |
| """Boolean: is `self.tranforms` sorted?""" | |
| self.components = {} | |
| """Mapping of component type name to component object. Set by | |
| `self.populate_from_components()`.""" | |
| self.serialno = 0 | |
| """Internal serial number to keep track of the add order of | |
| transforms.""" | |
| def add_transform(self, transform_class, priority=None, **kwargs): | |
| """ | |
| Store a single transform. Use `priority` to override the default. | |
| `kwargs` is a dictionary whose contents are passed as keyword | |
| arguments to the `apply` method of the transform. This can be used to | |
| pass application-specific data to the transform instance. | |
| """ | |
| if priority is None: | |
| priority = transform_class.default_priority | |
| priority_string = self.get_priority_string(priority) | |
| self.transforms.append( | |
| (priority_string, transform_class, None, kwargs)) | |
| self.sorted = 0 | |
| def add_transforms(self, transform_list): | |
| """Store multiple transforms, with default priorities.""" | |
| for transform_class in transform_list: | |
| priority_string = self.get_priority_string( | |
| transform_class.default_priority) | |
| self.transforms.append( | |
| (priority_string, transform_class, None, {})) | |
| self.sorted = 0 | |
| def add_pending(self, pending, priority=None): | |
| """Store a transform with an associated `pending` node.""" | |
| transform_class = pending.transform | |
| if priority is None: | |
| priority = transform_class.default_priority | |
| priority_string = self.get_priority_string(priority) | |
| self.transforms.append( | |
| (priority_string, transform_class, pending, {})) | |
| self.sorted = 0 | |
| def get_priority_string(self, priority): | |
| """ | |
| Return a string, `priority` combined with `self.serialno`. | |
| This ensures FIFO order on transforms with identical priority. | |
| """ | |
| self.serialno += 1 | |
| return '%03d-%03d' % (priority, self.serialno) | |
| def populate_from_components(self, components): | |
| """ | |
| Store each component's default transforms, with default priorities. | |
| Also, store components by type name in a mapping for later lookup. | |
| """ | |
| self.add_transforms(self.default_transforms) | |
| for component in components: | |
| if component is None: | |
| continue | |
| self.add_transforms(component.default_transforms) | |
| self.components[component.component_type] = component | |
| self.sorted = 0 | |
| # Set up all of the reference resolvers for this transformer. Each | |
| # component of this transformer is able to register its own helper | |
| # functions to help resolve references. | |
| unknown_reference_resolvers = [] | |
| for i in components: | |
| unknown_reference_resolvers.extend(i.unknown_reference_resolvers) | |
| decorated_list = [(f.priority, f) for f in unknown_reference_resolvers] | |
| decorated_list.sort() | |
| self.unknown_reference_resolvers.extend([f[1] for f in decorated_list]) | |
| def apply_transforms(self): | |
| """Apply all of the stored transforms, in priority order.""" | |
| self.document.reporter.attach_observer( | |
| self.document.note_transform_message) | |
| while self.transforms: | |
| if not self.sorted: | |
| # Unsorted initially, and whenever a transform is added. | |
| self.transforms.sort() | |
| self.transforms.reverse() | |
| self.sorted = 1 | |
| priority, transform_class, pending, kwargs = self.transforms.pop() | |
| transform = transform_class(self.document, startnode=pending) | |
| transform.apply(**kwargs) | |
| self.applied.append((priority, transform_class, pending, kwargs)) | |