Doc styling (#8067)
* Important files * Styling them all * Revert "Styling them all" This reverts commit 7d029395fdae8513b8281cbc2a6c239f8093503e. * Syling them for realsies * Fix syntax error * Fix benchmark_utils * More fixes * Fix modeling auto and script * Remove new line * Fixes * More fixes * Fix more files * Style * Add FSMT * More fixes * More fixes * More fixes * More fixes * Fixes * More fixes * More fixes * Last fixes * Make sphinx happy
This commit is contained in:
Sylvain Gugger
@@ -1,7 +1,6 @@
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"""
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Utilities for working with the local dataset cache.
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This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
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Copyright by the AllenNLP authors.
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Utilities for working with the local dataset cache. This file is adapted from the AllenNLP library at
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https://github.com/allenai/allennlp Copyright by the AllenNLP authors.
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"""
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import fnmatch
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@@ -433,10 +432,9 @@ def add_start_docstrings_to_callable(*docstr):
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note = r"""
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.. note::
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Although the recipe for forward pass needs to be defined within
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this function, one should call the :class:`Module` instance afterwards
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instead of this since the former takes care of running the
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pre and post processing steps while the latter silently ignores them.
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Although the recipe for forward pass needs to be defined within this function, one should call the
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:class:`Module` instance afterwards instead of this since the former takes care of running the pre and post
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processing steps while the latter silently ignores them.
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"""
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fn.__doc__ = intro + note + "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")
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return fn
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@@ -454,20 +452,18 @@ def add_end_docstrings(*docstr):
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PT_RETURN_INTRODUCTION = r"""
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Returns:
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:class:`~{full_output_type}` or :obj:`tuple(torch.FloatTensor)`:
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A :class:`~{full_output_type}` (if ``return_dict=True`` is passed or when ``config.return_dict=True``) or a
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tuple of :obj:`torch.FloatTensor` comprising various elements depending on the configuration
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(:class:`~transformers.{config_class}`) and inputs.
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:class:`~{full_output_type}` or :obj:`tuple(torch.FloatTensor)`: A :class:`~{full_output_type}` (if
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``return_dict=True`` is passed or when ``config.return_dict=True``) or a tuple of :obj:`torch.FloatTensor`
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comprising various elements depending on the configuration (:class:`~transformers.{config_class}`) and inputs.
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"""
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TF_RETURN_INTRODUCTION = r"""
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Returns:
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:class:`~{full_output_type}` or :obj:`tuple(tf.Tensor)`:
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A :class:`~{full_output_type}` (if ``return_dict=True`` is passed or when ``config.return_dict=True``) or a
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tuple of :obj:`tf.Tensor` comprising various elements depending on the configuration
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(:class:`~transformers.{config_class}`) and inputs.
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:class:`~{full_output_type}` or :obj:`tuple(tf.Tensor)`: A :class:`~{full_output_type}` (if
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``return_dict=True`` is passed or when ``config.return_dict=True``) or a tuple of :obj:`tf.Tensor` comprising
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various elements depending on the configuration (:class:`~transformers.{config_class}`) and inputs.
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"""
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@@ -831,19 +827,16 @@ def is_remote_url(url_or_filename):
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def hf_bucket_url(model_id: str, filename: str, use_cdn=True, mirror=None) -> str:
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"""
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Resolve a model identifier, and a file name, to a HF-hosted url
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on either S3 or Cloudfront (a Content Delivery Network, or CDN).
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Resolve a model identifier, and a file name, to a HF-hosted url on either S3 or Cloudfront (a Content Delivery
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Network, or CDN).
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Cloudfront is replicated over the globe so downloads are way faster
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for the end user (and it also lowers our bandwidth costs). However, it
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is more aggressively cached by default, so may not always reflect the
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latest changes to the underlying file (default TTL is 24 hours).
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Cloudfront is replicated over the globe so downloads are way faster for the end user (and it also lowers our
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bandwidth costs). However, it is more aggressively cached by default, so may not always reflect the latest changes
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to the underlying file (default TTL is 24 hours).
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In terms of client-side caching from this library, even though
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Cloudfront relays the ETags from S3, using one or the other
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(or switching from one to the other) will affect caching: cached files
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are not shared between the two because the cached file's name contains
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a hash of the url.
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In terms of client-side caching from this library, even though Cloudfront relays the ETags from S3, using one or
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the other (or switching from one to the other) will affect caching: cached files are not shared between the two
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because the cached file's name contains a hash of the url.
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"""
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endpoint = (
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PRESET_MIRROR_DICT.get(mirror, mirror)
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@@ -861,12 +854,10 @@ def hf_bucket_url(model_id: str, filename: str, use_cdn=True, mirror=None) -> st
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def url_to_filename(url, etag=None):
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"""
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Convert `url` into a hashed filename in a repeatable way.
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If `etag` is specified, append its hash to the url's, delimited
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by a period.
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If the url ends with .h5 (Keras HDF5 weights) adds '.h5' to the name
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so that TF 2.0 can identify it as a HDF5 file
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(see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380)
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Convert `url` into a hashed filename in a repeatable way. If `etag` is specified, append its hash to the url's,
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delimited by a period. If the url ends with .h5 (Keras HDF5 weights) adds '.h5' to the name so that TF 2.0 can
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identify it as a HDF5 file (see
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https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1380)
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"""
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url_bytes = url.encode("utf-8")
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url_hash = sha256(url_bytes)
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@@ -885,8 +876,8 @@ def url_to_filename(url, etag=None):
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def filename_to_url(filename, cache_dir=None):
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"""
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Return the url and etag (which may be ``None``) stored for `filename`.
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Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
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Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or
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its stored metadata do not exist.
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"""
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if cache_dir is None:
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cache_dir = TRANSFORMERS_CACHE
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@@ -921,10 +912,10 @@ def cached_path(
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local_files_only=False,
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) -> Optional[str]:
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"""
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Given something that might be a URL (or might be a local path),
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determine which. If it's a URL, download the file and cache it, and
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return the path to the cached file. If it's already a local path,
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make sure the file exists and then return the path.
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Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file
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and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and
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then return the path
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Args:
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cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
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force_download: if True, re-dowload the file even if it's already cached in the cache dir.
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@@ -936,8 +927,8 @@ def cached_path(
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re-extract the archive and overide the folder where it was extracted.
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Return:
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None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
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Local path (string) otherwise
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None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk). Local path (string)
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otherwise
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"""
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if cache_dir is None:
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cache_dir = TRANSFORMERS_CACHE
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@@ -1045,12 +1036,12 @@ def get_from_cache(
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local_files_only=False,
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) -> Optional[str]:
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"""
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Given a URL, look for the corresponding file in the local cache.
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If it's not there, download it. Then return the path to the cached file.
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Given a URL, look for the corresponding file in the local cache. If it's not there, download it. Then return the
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path to the cached file.
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Return:
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None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk).
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Local path (string) otherwise
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None in case of non-recoverable file (non-existent or inaccessible url + no cache on disk). Local path (string)
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otherwise
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"""
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if cache_dir is None:
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cache_dir = TRANSFORMERS_CACHE
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@@ -1213,8 +1204,8 @@ def is_tensor(x):
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class ModelOutput(OrderedDict):
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"""
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Base class for all model outputs as dataclass. Has a ``__getitem__`` that allows indexing by integer or slice (like
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a tuple) or strings (like a dictionary) that will ignore the ``None`` attributes. Otherwise behaves like a
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regular python dictionary.
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a tuple) or strings (like a dictionary) that will ignore the ``None`` attributes. Otherwise behaves like a regular
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python dictionary.
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.. warning::
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You can't unpack a :obj:`ModelOutput` directly. Use the :meth:`~transformers.file_utils.ModelOutput.to_tuple`
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