Doc fixes in preparation for the docstyle PR (#8061)

* Fixes in preparation for doc styling

* More fixes

* Better syntax

* Fixes

* Style

* More fixes

* More fixes

docs/source/main_classes/processors.rst

@@ -112,7 +112,7 @@ Example usage
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 Here is an example using the processors as well as the conversion method using data files:
 
-Example::
+.. code-block::
 
     # Loading a V2 processor
     processor = SquadV2Processor()
@@ -133,7 +133,7 @@ Example::
 
 Using `tensorflow_datasets` is as easy as using a data file:
 
-Example::
+.. code-block::
 
     # tensorflow_datasets only handle Squad V1.
     tfds_examples = tfds.load("squad")

docs/source/model_doc/bertgeneration.rst

@@ -47,7 +47,7 @@ Usage:
 - Pretrained :class:`~transformers.EncoderDecoderModel` are also directly available in the model hub, e.g.,
 
 
-:: code-block
+.. code-block::
 
   # instantiate sentence fusion model
   sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")

docs/source/model_doc/blenderbot.rst

@@ -28,7 +28,9 @@ Implementation Notes
 Usage
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Model Usage:
+Here is an example of model usage:
+
+.. code-block::
 
         >>> from transformers import BlenderbotSmallTokenizer, BlenderbotForConditionalGeneration
         >>> mname = 'facebook/blenderbot-90M'
@@ -40,7 +42,10 @@ Model Usage:
         >>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in reply_ids])
 
 
-See Config Values:
+Here is how you can check out config values:
+
+.. code-block::
+
 
         >>> from transformers import BlenderbotConfig
         >>> config_90 = BlenderbotConfig.from_pretrained("facebook/blenderbot-90M")

docs/source/model_doc/gpt.rst

@@ -45,6 +45,8 @@ Note:
 If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install 
 ``ftfy`` and ``SpaCy``::
 
+.. code-block:: bash
+
     pip install spacy ftfy==4.4.3
     python -m spacy download en
 

src/transformers/benchmark/benchmark_utils.py

+# This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
+# Copyright by the AllenNLP authors.
 """
 Utilities for working with the local dataset cache.
-This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
-Copyright by the AllenNLP authors.
 """
 
 import copy

src/transformers/commands/convert.py

@@ -8,7 +8,8 @@ from ..utils import logging
 def convert_command_factory(args: Namespace):
     """
     Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint.
-    :return: ServeCommand
+
+    Returns: ServeCommand
     """
     return ConvertCommand(
         args.model_type, args.tf_checkpoint, args.pytorch_dump_output, args.config, args.finetuning_task_name
@@ -26,8 +27,9 @@ class ConvertCommand(BaseTransformersCLICommand):
     def register_subcommand(parser: ArgumentParser):
         """
         Register this command to argparse so it's available for the transformer-cli
-        :param parser: Root parser to register command-specific arguments
-        :return:
+
+        Args:
+            parser: Root parser to register command-specific arguments
         """
         train_parser = parser.add_parser(
             "convert",

src/transformers/commands/serving.py

@@ -31,7 +31,8 @@ logger = logging.get_logger("transformers-cli/serving")
 def serve_command_factory(args: Namespace):
     """
     Factory function used to instantiate serving server from provided command line arguments.
-    :return: ServeCommand
+
+    Returns: ServeCommand
     """
     nlp = pipeline(
         task=args.task,
@@ -81,8 +82,9 @@ class ServeCommand(BaseTransformersCLICommand):
     def register_subcommand(parser: ArgumentParser):
         """
         Register this command to argparse so it's available for the transformer-cli
-        :param parser: Root parser to register command-specific arguments
-        :return:
+
+        Args:
+            parser: Root parser to register command-specific arguments
         """
         serve_parser = parser.add_parser(
             "serve", help="CLI tool to run inference requests through REST and GraphQL endpoints."

src/transformers/commands/train.py

@@ -19,7 +19,8 @@ USE_AMP = False
 def train_command_factory(args: Namespace):
     """
     Factory function used to instantiate training command from provided command line arguments.
-    :return: TrainCommand
+
+    Returns: TrainCommand
     """
     return TrainCommand(args)
 
@@ -29,8 +30,9 @@ class TrainCommand(BaseTransformersCLICommand):
     def register_subcommand(parser: ArgumentParser):
         """
         Register this command to argparse so it's available for the transformer-cli
-        :param parser: Root parser to register command-specific arguments
-        :return:
+
+        Args:
+            parser: Root parser to register command-specific arguments
         """
         train_parser = parser.add_parser("train", help="CLI tool to train a model on a task.")
 

src/transformers/commands/user.py

@@ -70,7 +70,7 @@ class BaseUserCommand:
 
 class LoginCommand(BaseUserCommand):
     def run(self):
-        print(
+        print(  # docstyle-ignore
             """
         _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|
         _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|
@@ -127,8 +127,9 @@ class ListObjsCommand(BaseUserCommand):
     def tabulate(self, rows: List[List[Union[str, int]]], headers: List[str]) -> str:
         """
         Inspired by:
-        stackoverflow.com/a/8356620/593036
-        stackoverflow.com/questions/9535954/printing-lists-as-tabular-data
+
+        - stackoverflow.com/a/8356620/593036
+        - stackoverflow.com/questions/9535954/printing-lists-as-tabular-data
         """
         col_widths = [max(len(str(x)) for x in col) for col in zip(*rows, headers)]
         row_format = ("{{:{}}} " * len(headers)).format(*col_widths)

src/transformers/convert_bert_pytorch_checkpoint_to_original_tf.py

@@ -28,19 +28,19 @@ from transformers import BertModel
 def convert_pytorch_checkpoint_to_tf(model: BertModel, ckpt_dir: str, model_name: str):
 
     """
-    :param model:BertModel Pytorch model instance to be converted
-    :param ckpt_dir: Tensorflow model directory
-    :param model_name: model name
-    :return:
+    Args
+        model: BertModel Pytorch model instance to be converted
+        ckpt_dir: Tensorflow model directory
+        model_name: model name
 
     Currently supported HF models:
-        Y BertModel
-        N BertForMaskedLM
-        N BertForPreTraining
-        N BertForMultipleChoice
-        N BertForNextSentencePrediction
-        N BertForSequenceClassification
-        N BertForQuestionAnswering
+        - Y BertModel
+        - N BertForMaskedLM
+        - N BertForPreTraining
+        - N BertForMultipleChoice
+        - N BertForNextSentencePrediction
+        - N BertForSequenceClassification
+        - N BertForQuestionAnswering
     """
 
     tensors_to_transpose = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

src/transformers/convert_marian_tatoeba_to_pytorch.py

@@ -28,11 +28,13 @@ LANG_CODE_PATH = "lang_code_data/language-codes-3b2.csv"
 
 class TatoebaConverter:
     """Convert Tatoeba-Challenge models to huggingface format.
+
     Steps:
-        (1) convert numpy state dict to hf format (same code as OPUS-MT-Train conversion).
-        (2) rename opus model to huggingface format. This means replace each alpha3 code with an alpha2 code if a unique one existes.
-         e.g. aav-eng -> aav-en, heb-eng -> he-en
-        (3) write a model card containing the original Tatoeba-Challenge/README.md and extra info about alpha3 group members.
+
+        1. convert numpy state dict to hf format (same code as OPUS-MT-Train conversion).
+        2. rename opus model to huggingface format. This means replace each alpha3 code with an alpha2 code if a unique one existes.
+           e.g. aav-eng -> aav-en, heb-eng -> he-en
+        3. write a model card containing the original Tatoeba-Challenge/README.md and extra info about alpha3 group members.
     """
 
     def __init__(self, save_dir="marian_converted"):

src/transformers/data/data_collator.py

@@ -19,14 +19,12 @@ DataCollator = NewType("DataCollator", Callable[[List[InputDataClass]], Dict[str
 
 def default_data_collator(features: List[InputDataClass]) -> Dict[str, torch.Tensor]:
     """
-    Very simple data collator that:
-    - simply collates batches of dict-like objects
-    - Performs special handling for potential keys named:
+    Very simple data collator that simply collates batches of dict-like objects and erforms special handling for potential keys named:
+
         - ``label``: handles a single value (int or float) per object
         - ``label_ids``: handles a list of values per object
-    - does not do any additional preprocessing
 
-    i.e., Property names of the input object will be used as corresponding inputs to the model.
+    Des not do any additional preprocessing: property names of the input object will be used as corresponding inputs to the model.
     See glue and ner for example of how it's useful.
     """
 
@@ -425,6 +423,7 @@ class DataCollatorForPermutationLanguageModeling:
     def mask_tokens(self, inputs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         """
         The masked tokens to be predicted for a particular sequence are determined by the following algorithm:
+
             0. Start from the beginning of the sequence by setting ``cur_len = 0`` (number of tokens processed so far).
             1. Sample a ``span_length`` from the interval ``[1, max_span_length]`` (length of span of tokens to be masked)
             2. Reserve a context of length ``context_length = span_length / plm_probability`` to surround span to be masked

src/transformers/file_utils.py

@@ -289,6 +289,7 @@ def torch_only_method(fn):
     return wrapper
 
 
+# docstyle-ignore
 DATASETS_IMPORT_ERROR = """
 {0} requires the 🤗 Datasets library but it was not found in your enviromnent. You can install it with:
 ```
@@ -306,6 +307,7 @@ that python file if that's the case.
 """
 
 
+# docstyle-ignore
 TOKENIZERS_IMPORT_ERROR = """
 {0} requires the 🤗 Tokenizers library but it was not found in your enviromnent. You can install it with:
 ```
@@ -318,6 +320,7 @@ In a notebook or a colab, you can install it by executing a cell with
 """
 
 
+# docstyle-ignore
 SENTENCEPIECE_IMPORT_ERROR = """
 {0} requires the SentencePiece library but it was not found in your enviromnent. Checkout the instructions on the
 installation page of its repo: https://github.com/google/sentencepiece#installation and follow the ones
@@ -325,6 +328,7 @@ that match your enviromnent.
 """
 
 
+# docstyle-ignore
 FAISS_IMPORT_ERROR = """
 {0} requires the faiss library but it was not found in your enviromnent. Checkout the instructions on the
 installation page of its repo: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md and follow the ones
@@ -332,12 +336,14 @@ that match your enviromnent.
 """
 
 
+# docstyle-ignore
 PYTORCH_IMPORT_ERROR = """
 {0} requires the PyTorch library but it was not found in your enviromnent. Checkout the instructions on the
 installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your enviromnent.
 """
 
 
+# docstyle-ignore
 SKLEARN_IMPORT_ERROR = """
 {0} requires the scikit-learn library but it was not found in your enviromnent. You can install it with:
 ```
@@ -350,12 +356,14 @@ In a notebook or a colab, you can install it by executing a cell with
 """
 
 
+# docstyle-ignore
 TENSORFLOW_IMPORT_ERROR = """
 {0} requires the TensorFlow library but it was not found in your enviromnent. Checkout the instructions on the
 installation page: https://www.tensorflow.org/install and follow the ones that match your enviromnent.
 """
 
 
+# docstyle-ignore
 FLAX_IMPORT_ERROR = """
 {0} requires the FLAX library but it was not found in your enviromnent. Checkout the instructions on the
 installation page: https://github.com/google/flax and follow the ones that match your enviromnent.

src/transformers/generation_tf_utils.py

@@ -917,7 +917,7 @@ def _create_next_token_logits_penalties(input_ids, logits, repetition_penalty):
 
 
 def calc_banned_ngram_tokens(prev_input_ids, num_hypos, no_repeat_ngram_size, cur_len):
-    # Copied from fairseq for no_repeat_ngram in beam_search"""
+    # Copied from fairseq for no_repeat_ngram in beam_search
     if cur_len + 1 < no_repeat_ngram_size:
         # return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
         return [[] for _ in range(num_hypos)]

src/transformers/modeling_gpt2.py

@@ -857,16 +857,16 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
         **kwargs,
     ):
         r"""
-        mc_token_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input)
+        mc_token_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input):
             Index of the classification token in each input sequence.
             Selected in the range ``[0, input_ids.size(-1) - 1[``.
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`)
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
             Labels for language modeling.
             Note that the labels **are shifted** inside the model, i.e. you can set ``labels = input_ids``
             Indices are selected in ``[-1, 0, ..., config.vocab_size]``
             All labels set to ``-100`` are ignored (masked), the loss is only
             computed for labels in ``[0, ..., config.vocab_size]``
-        mc_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size)`, `optional`)
+        mc_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size)`, `optional`):
             Labels for computing the multiple choice classification loss.
             Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
             of the input tensors. (see `input_ids` above)

src/transformers/modeling_longformer.py

@@ -105,8 +105,10 @@ def create_position_ids_from_input_ids(input_ids, padding_idx):
     padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
     `utils.make_positions`.
 
-    :param torch.Tensor x:
-    :return torch.Tensor:
+    Args:
+        x: torch.Tensor x:
+
+    Returns: torch.Tensor
     """
     # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
     mask = input_ids.ne(padding_idx).int()
@@ -176,8 +178,10 @@ class LongformerEmbeddings(nn.Module):
         """We are provided embeddings directly. We cannot infer which are padded so just generate
         sequential position ids.
 
-        :param torch.Tensor inputs_embeds:
-        :return torch.Tensor:
+        Args:
+            inputs_embeds: torch.Tensor inputs_embeds:
+
+        Returns: torch.Tensor
         """
         input_shape = inputs_embeds.size()[:-1]
         sequence_length = input_shape[1]

src/transformers/modeling_openai.py

@@ -647,16 +647,16 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
         **kwargs
     ):
         r"""
-        mc_token_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input)
+        mc_token_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input):
             Index of the classification token in each input sequence.
             Selected in the range ``[0, input_ids.size(-1) - 1]``.
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`)
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
             Labels for language modeling.
             Note that the labels **are shifted** inside the model, i.e. you can set ``labels = input_ids``
             Indices are selected in ``[-1, 0, ..., config.vocab_size]``
             All labels set to ``-100`` are ignored (masked), the loss is only
             computed for labels in ``[0, ..., config.vocab_size]``
-        mc_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size)`, `optional`)
+        mc_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size)`, `optional`):
             Labels for computing the multiple choice classification loss.
             Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
             of the input tensors. (see `input_ids` above)

src/transformers/modeling_roberta.py

@@ -127,8 +127,10 @@ class RobertaEmbeddings(nn.Module):
         """We are provided embeddings directly. We cannot infer which are padded so just generate
         sequential position ids.
 
-        :param torch.Tensor inputs_embeds:
-        :return torch.Tensor:
+        Args:
+            inputs_embeds: torch.Tensor
+
+        Returns: torch.Tensor
         """
         input_shape = inputs_embeds.size()[:-1]
         sequence_length = input_shape[1]
@@ -1326,8 +1328,10 @@ def create_position_ids_from_input_ids(input_ids, padding_idx):
     padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
     `utils.make_positions`.
 
-    :param torch.Tensor x:
-    :return torch.Tensor:
+    Args:
+        x: torch.Tensor x:
+
+    Returns: torch.Tensor
     """
     # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
     mask = input_ids.ne(padding_idx).int()

src/transformers/modeling_tf_gpt2.py

@@ -704,7 +704,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
         training=False,
     ):
         r"""
-        mc_token_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input)
+        mc_token_ids (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, num_choices)`, `optional`, default to index of the last token of the input):
             Index of the classification token in each input sequence.
             Selected in the range ``[0, input_ids.size(-1) - 1[``.
 

src/transformers/modeling_tf_longformer.py

@@ -166,8 +166,11 @@ class TFLongformerEmbeddings(tf.keras.layers.Layer):
         """Replace non-padding symbols with their position numbers. Position numbers begin at
         padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
         `utils.make_positions`.
-        :param tf.Tensor x:
-        :return tf.Tensor:
+
+        Args:
+            x: tf.Tensor
+
+        Returns: tf.Tensor
         """
         mask = tf.cast(tf.math.not_equal(x, self.padding_idx), dtype=tf.int32)
         incremental_indicies = tf.math.cumsum(mask, axis=1) * mask
@@ -177,8 +180,11 @@ class TFLongformerEmbeddings(tf.keras.layers.Layer):
     def create_position_ids_from_inputs_embeds(self, inputs_embeds):
         """We are provided embeddings directly. We cannot infer which are padded so just generate
         sequential position ids.
-        :param tf.Tensor inputs_embeds:
-        :return tf.Tensor:
+
+        Args:
+            inputs_embeds: tf.Tensor
+
+        Returns: tf.Tensor
         """
         seq_length = shape_list(inputs_embeds)[1]
         position_ids = tf.range(self.padding_idx + 1, seq_length + self.padding_idx + 1, dtype=tf.int32)[tf.newaxis, :]