[breaking|pipelines|tokenizers] Adding slow-fast tokenizers equivalence tests...

[breaking|pipelines|tokenizers] Adding slow-fast tokenizers equivalence tests pipelines - Removing sentencepiece as a required dependency (#8073)

* Fixing roberta for slow-fast tests

* WIP getting equivalence on pipelines

* slow-to-fast equivalence - working on question-answering pipeline

* optional FAISS tests

* Pipeline Q&A

* Move pipeline tests to their own test job again

* update tokenizer to add sequence id methods

* update to tokenizers 0.9.4

* set sentencepiecce as optional

* clean up squad

* clean up pipelines to use sequence_ids

* style/quality

* wording

* Switch to use_fast = True by default

* update tests for use_fast at True by default

* fix rag tokenizer test

* removing protobuf from required dependencies

* fix NER test for use_fast = True by default

* fixing example tests (Q&A examples use slow tokenizers for now)

* protobuf in main deps extras["sentencepiece"] and example deps

* fix protobug install test

* try to fix seq2seq by switching to slow tokenizers for now

* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Update src/transformers/tokenization_utils_base.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

examples/question-answering/run_squad.py

@@ -736,6 +736,7 @@ def main():
         args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
         do_lower_case=args.do_lower_case,
         cache_dir=args.cache_dir if args.cache_dir else None,
+        use_fast=False,  # SquadDataset is not compatible with Fast tokenizers which have a smarter overflow handeling
     )
     model = AutoModelForQuestionAnswering.from_pretrained(
         args.model_name_or_path,
@@ -784,7 +785,10 @@ def main():
 
         # Load a trained model and vocabulary that you have fine-tuned
         model = AutoModelForQuestionAnswering.from_pretrained(args.output_dir)  # , force_download=True)
-        tokenizer = AutoTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
+
+        # SquadDataset is not compatible with Fast tokenizers which have a smarter overflow handeling
+        # So we use use_fast=False here for now until Fast-tokenizer-compatible-examples are out
+        tokenizer = AutoTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case, use_fast=False)
         model.to(args.device)
 
     # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory

examples/question-answering/run_squad_trainer.py

@@ -114,6 +114,7 @@ def main():
     tokenizer = AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
+        use_fast=False,  # SquadDataset is not compatible with Fast tokenizers which have a smarter overflow handeling
     )
     model = AutoModelForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path,

examples/requirements.txt

@@ -18,3 +18,4 @@ fire
 pytest
 conllu
 sentencepiece != 0.1.92
+protobuf

examples/seq2seq/test_datasets.py

@@ -197,7 +197,7 @@ class TestAll(TestCasePlus):
     )
     @require_torch_non_multi_gpu_but_fix_me
     def test_dataset_kwargs(self, tok_name):
-        tokenizer = AutoTokenizer.from_pretrained(tok_name)
+        tokenizer = AutoTokenizer.from_pretrained(tok_name, use_fast=False)
         if tok_name == MBART_TINY:
             train_dataset = Seq2SeqDataset(
                 tokenizer,

setup.py

@@ -96,13 +96,13 @@ else:
     extras["retrieval"] = ["faiss-cpu", "datasets"]
     extras["flax"] = ["jaxlib==0.1.55", "jax>=0.2.0", "flax==0.2.2"]
 
-extras["tokenizers"] = ["tokenizers==0.9.2"]
+extras["tokenizers"] = ["tokenizers==0.9.4"]
 extras["onnxruntime"] = ["onnxruntime>=1.4.0", "onnxruntime-tools>=1.4.2"]
 extras["modelcreation"] = ["cookiecutter==1.7.2"]
 
 extras["serving"] = ["pydantic", "uvicorn", "fastapi", "starlette"]
 
-extras["sentencepiece"] = ["sentencepiece==0.1.91"]
+extras["sentencepiece"] = ["sentencepiece==0.1.91", "protobuf"]
 extras["retrieval"] = ["faiss-cpu", "datasets"]
 extras["testing"] = ["pytest", "pytest-xdist", "timeout-decorator", "parameterized", "psutil"] + extras["retrieval"] + extras["modelcreation"]
 # sphinx-rtd-theme==0.5.0 introduced big changes in the style.
@@ -130,7 +130,7 @@ setup(
     packages=find_packages("src"),
     install_requires=[
         "numpy",
-        "tokenizers == 0.9.3",
+        "tokenizers == 0.9.4",
         # dataclasses for Python versions that don't have it
         "dataclasses;python_version<'3.7'",
         # utilities from PyPA to e.g. compare versions
@@ -143,9 +143,6 @@ setup(
         "tqdm >= 4.27",
         # for OpenAI GPT
         "regex != 2019.12.17",
-        # for SentencePiece models
-        "sentencepiece == 0.1.91",
-        "protobuf",
         # for XLM
         "sacremoses",
     ],

src/transformers/convert_slow_tokenizer.py

@@ -24,10 +24,7 @@ from typing import Dict, List, Tuple
 from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
 from tokenizers.models import BPE, Unigram, WordPiece
 
-# from transformers.tokenization_openai import OpenAIGPTTokenizer
-from transformers.utils import sentencepiece_model_pb2 as model
-
-from .file_utils import requires_sentencepiece
+from .file_utils import requires_protobuf, requires_sentencepiece
 
 
 class SentencePieceExtractor:
@@ -64,12 +61,6 @@ def check_number_comma(piece: str) -> bool:
     return len(piece) < 2 or piece[-1] != "," or not piece[-2].isdigit()
 
 
-def get_proto(filename: str):
-    m = model.ModelProto()
-    m.ParseFromString(open(filename, "rb").read())
-    return m
-
-
 class Converter:
     def __init__(self, original_tokenizer):
         self.original_tokenizer = original_tokenizer
@@ -292,8 +283,15 @@ class RobertaConverter(Converter):
 
 class SpmConverter(Converter):
     def __init__(self, *args):
+        requires_protobuf(self)
+
         super().__init__(*args)
-        self.proto = get_proto(self.original_tokenizer.vocab_file)
+
+        from .utils import sentencepiece_model_pb2 as model_pb2
+
+        m = model_pb2.ModelProto()
+        m.ParseFromString(open(self.original_tokenizer.vocab_file, "rb").read())
+        self.proto = m
 
     def vocab(self, proto):
         return [(piece.piece, piece.score) for piece in proto.pieces]

src/transformers/data/processors/squad.py

@@ -8,7 +8,7 @@ from tqdm import tqdm
 
 from ...file_utils import is_tf_available, is_torch_available
 from ...tokenization_bert import whitespace_tokenize
-from ...tokenization_utils_base import PreTrainedTokenizerBase, TruncationStrategy
+from ...tokenization_utils_base import BatchEncoding, PreTrainedTokenizerBase, TruncationStrategy
 from ...utils import logging
 from .utils import DataProcessor
 
@@ -765,6 +765,7 @@ class SquadFeatures:
         token_to_orig_map: mapping between the tokens and the original text, needed in order to identify the answer.
         start_position: start of the answer token index
         end_position: end of the answer token index
+        encoding: optionally store the BatchEncoding with the fast-tokenizer alignement methods.
     """
 
     def __init__(
@@ -784,6 +785,7 @@ class SquadFeatures:
         end_position,
         is_impossible,
         qas_id: str = None,
+        encoding: BatchEncoding = None,
     ):
         self.input_ids = input_ids
         self.attention_mask = attention_mask
@@ -803,6 +805,8 @@ class SquadFeatures:
         self.is_impossible = is_impossible
         self.qas_id = qas_id
 
+        self.encoding = encoding
+
 
 class SquadResult:
     """

src/transformers/file_utils.py

@@ -185,6 +185,15 @@ except ImportError:
     _sentencepiece_available = False
 
 
+try:
+    import google.protobuf  # noqa: F401
+
+    _protobuf_available = True
+
+except ImportError:
+    _protobuf_available = False
+
+
 try:
     import tokenizers  # noqa: F401
 
@@ -270,6 +279,10 @@ def is_sentencepiece_available():
     return _sentencepiece_available
 
 
+def is_protobuf_available():
+    return _protobuf_available
+
+
 def is_tokenizers_available():
     return _tokenizers_available
 
@@ -330,6 +343,14 @@ that match your environment.
 """
 
 
+# docstyle-ignore
+PROTOBUF_IMPORT_ERROR = """
+{0} requires the protobuf library but it was not found in your environment. Checkout the instructions on the
+installation page of its repo: https://github.com/protocolbuffers/protobuf/tree/master/python#installation and follow the ones
+that match your environment.
+"""
+
+
 # docstyle-ignore
 FAISS_IMPORT_ERROR = """
 {0} requires the faiss library but it was not found in your environment. Checkout the instructions on the
@@ -420,6 +441,12 @@ def requires_sentencepiece(obj):
         raise ImportError(SENTENCEPIECE_IMPORT_ERROR.format(name))
 
 
+def requires_protobuf(obj):
+    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
+    if not is_protobuf_available():
+        raise ImportError(PROTOBUF_IMPORT_ERROR.format(name))
+
+
 def add_start_docstrings(*docstr):
     def docstring_decorator(fn):
         fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")

src/transformers/pipelines.py

@@ -32,7 +32,7 @@ import numpy as np
 
 from .configuration_auto import AutoConfig
 from .configuration_utils import PretrainedConfig
-from .data import SquadExample, squad_convert_examples_to_features
+from .data import SquadExample, SquadFeatures, squad_convert_examples_to_features
 from .file_utils import add_end_docstrings, is_tf_available, is_torch_available
 from .modelcard import ModelCard
 from .tokenization_auto import AutoTokenizer
@@ -1758,6 +1758,7 @@ class QuestionAnsweringPipeline(Pipeline):
             - **answer** (:obj:`str`) -- The answer to the question.
         """
         # Set defaults values
+        kwargs.setdefault("padding", "longest")
         kwargs.setdefault("topk", 1)
         kwargs.setdefault("doc_stride", 128)
         kwargs.setdefault("max_answer_len", 15)
@@ -1773,19 +1774,87 @@ class QuestionAnsweringPipeline(Pipeline):
 
         # Convert inputs to features
         examples = self._args_parser(*args, **kwargs)
-        features_list = [
-            squad_convert_examples_to_features(
-                examples=[example],
-                tokenizer=self.tokenizer,
-                max_seq_length=kwargs["max_seq_len"],
-                doc_stride=kwargs["doc_stride"],
-                max_query_length=kwargs["max_question_len"],
-                padding_strategy=PaddingStrategy.MAX_LENGTH.value,
-                is_training=False,
-                tqdm_enabled=False,
-            )
-            for example in examples
-        ]
+        if not self.tokenizer.is_fast:
+            features_list = [
+                squad_convert_examples_to_features(
+                    examples=[example],
+                    tokenizer=self.tokenizer,
+                    max_seq_length=kwargs["max_seq_len"],
+                    doc_stride=kwargs["doc_stride"],
+                    max_query_length=kwargs["max_question_len"],
+                    padding_strategy=PaddingStrategy.MAX_LENGTH.value,
+                    is_training=False,
+                    tqdm_enabled=False,
+                )
+                for example in examples
+            ]
+        else:
+            features_list = []
+            for example in examples:
+                # Define the side we want to truncate / pad and the text/pair sorting
+                question_first = bool(self.tokenizer.padding_side == "right")
+
+                encoded_inputs = self.tokenizer(
+                    text=example.question_text if question_first else example.context_text,
+                    text_pair=example.context_text if question_first else example.question_text,
+                    padding=kwargs["padding"],
+                    truncation="only_second" if question_first else "only_first",
+                    max_length=kwargs["max_seq_len"],
+                    stride=kwargs["doc_stride"],
+                    return_tensors="np",
+                    return_token_type_ids=True,
+                    return_overflowing_tokens=True,
+                    return_offsets_mapping=True,
+                    return_special_tokens_mask=True,
+                )
+
+                # When the input is too long, it's converted in a batch of inputs with overflowing tokens
+                # and a stride of overlap between the inputs. If a batch of inputs is given, a special output
+                # "overflow_to_sample_mapping" indicate which member of the encoded batch belong to which original batch sample.
+                # Here we tokenize examples one-by-one so we don't need to use "overflow_to_sample_mapping".
+                # "num_span" is the number of output samples generated from the overflowing tokens.
+                num_spans = len(encoded_inputs["input_ids"])
+
+                # p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
+                # We put 0 on the tokens from the context and 1 everywhere else (question and special tokens)
+                p_mask = np.asarray(
+                    [
+                        [tok != 1 if question_first else 0 for tok in encoded_inputs.sequence_ids(span_id)]
+                        for span_id in range(num_spans)
+                    ]
+                )
+
+                # keep the cls_token unmasked (some models use it to indicate unanswerable questions)
+                if self.tokenizer.cls_token_id:
+                    cls_index = np.nonzero(encoded_inputs["input_ids"] == self.tokenizer.cls_token_id)
+                    p_mask[cls_index] = 0
+
+                features = []
+                for span_idx in range(num_spans):
+                    features.append(
+                        SquadFeatures(
+                            input_ids=encoded_inputs["input_ids"][span_idx],
+                            attention_mask=encoded_inputs["attention_mask"][span_idx],
+                            token_type_ids=encoded_inputs["token_type_ids"][span_idx],
+                            p_mask=p_mask[span_idx].tolist(),
+                            encoding=encoded_inputs[span_idx],
+                            # We don't use the rest of the values - and actually
+                            # for Fast tokenizer we could totally avoid using SquadFeatures and SquadExample
+                            cls_index=None,
+                            token_to_orig_map={},
+                            example_index=0,
+                            unique_id=0,
+                            paragraph_len=0,
+                            token_is_max_context=0,
+                            tokens=[],
+                            start_position=0,
+                            end_position=0,
+                            is_impossible=False,
+                            qas_id=None,
+                        )
+                    )
+                features_list.append(features)
+
         all_answers = []
         for features, example in zip(features_list, examples):
             model_input_names = self.tokenizer.model_input_names + ["input_ids"]
@@ -1828,20 +1897,56 @@ class QuestionAnsweringPipeline(Pipeline):
                 start_[0] = end_[0] = 0.0
 
                 starts, ends, scores = self.decode(start_, end_, kwargs["topk"], kwargs["max_answer_len"])
-                char_to_word = np.array(example.char_to_word_offset)
-
-                # Convert the answer (tokens) back to the original text
-                answers += [
-                    {
-                        "score": score.item(),
-                        "start": np.where(char_to_word == feature.token_to_orig_map[s])[0][0].item(),
-                        "end": np.where(char_to_word == feature.token_to_orig_map[e])[0][-1].item(),
-                        "answer": " ".join(
-                            example.doc_tokens[feature.token_to_orig_map[s] : feature.token_to_orig_map[e] + 1]
-                        ),
-                    }
-                    for s, e, score in zip(starts, ends, scores)
-                ]
+                if not self.tokenizer.is_fast:
+                    char_to_word = np.array(example.char_to_word_offset)
+
+                    # Convert the answer (tokens) back to the original text
+                    # Score: score from the model
+                    # Start: Index of the first character of the answer in the context string
+                    # End: Index of the character following the last character of the answer in the context string
+                    # Answer: Plain text of the answer
+                    answers += [
+                        {
+                            "score": score.item(),
+                            "start": np.where(char_to_word == feature.token_to_orig_map[s])[0][0].item(),
+                            "end": np.where(char_to_word == feature.token_to_orig_map[e])[0][-1].item(),
+                            "answer": " ".join(
+                                example.doc_tokens[feature.token_to_orig_map[s] : feature.token_to_orig_map[e] + 1]
+                            ),
+                        }
+                        for s, e, score in zip(starts, ends, scores)
+                    ]
+                else:
+                    # Convert the answer (tokens) back to the original text
+                    # Score: score from the model
+                    # Start: Index of the first character of the answer in the context string
+                    # End: Index of the character following the last character of the answer in the context string
+                    # Answer: Plain text of the answer
+                    question_first = bool(self.tokenizer.padding_side == "right")
+                    enc = feature.encoding
+
+                    # Sometimes the max probability token is in the middle of a word so:
+                    # - we start by finding the right word containing the token with `token_to_word`
+                    # - then we convert this word in a character span with `word_to_chars`
+                    answers += [
+                        {
+                            "score": score.item(),
+                            "start": enc.word_to_chars(
+                                enc.token_to_word(s), sequence_index=1 if question_first else 0
+                            )[0],
+                            "end": enc.word_to_chars(enc.token_to_word(e), sequence_index=1 if question_first else 0)[
+                                1
+                            ],
+                            "answer": example.context_text[
+                                enc.word_to_chars(enc.token_to_word(s), sequence_index=1 if question_first else 0)[
+                                    0
+                                ] : enc.word_to_chars(enc.token_to_word(e), sequence_index=1 if question_first else 0)[
+                                    1
+                                ]
+                            ],
+                        }
+                        for s, e, score in zip(starts, ends, scores)
+                    ]
 
             if kwargs["handle_impossible_answer"]:
                 answers.append({"score": min_null_score, "start": 0, "end": 0, "answer": ""})
@@ -2735,7 +2840,7 @@ def pipeline(
     tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None,
     framework: Optional[str] = None,
     revision: Optional[str] = None,
-    use_fast: bool = False,
+    use_fast: bool = True,
     **kwargs
 ) -> Pipeline:
     """
@@ -2795,7 +2900,7 @@ def pipeline(
             When passing a task name or a string model identifier: The specific model version to use. It can be a
             branch name, a tag name, or a commit id, since we use a git-based system for storing models and other
             artifacts on huggingface.co, so ``revision`` can be any identifier allowed by git.
-        use_fast (:obj:`bool`, `optional`, defaults to :obj:`False`):
+        use_fast (:obj:`bool`, `optional`, defaults to :obj:`True`):
             Whether or not to use a Fast tokenizer if possible (a :class:`~transformers.PreTrainedTokenizerFast`).
         kwargs:
             Additional keyword arguments passed along to the specific pipeline init (see the documentation for the

src/transformers/tokenization_auto.py

@@ -280,7 +280,7 @@ class AutoTokenizer:
                 The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                 git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
                 identifier allowed by git.
-            use_fast (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            use_fast (:obj:`bool`, `optional`, defaults to :obj:`True`):
                 Whether or not to try to load the fast version of the tokenizer.
             kwargs (additional keyword arguments, `optional`):
                 Will be passed to the Tokenizer ``__init__()`` method. Can be used to set special tokens like
@@ -308,7 +308,7 @@ class AutoTokenizer:
         if "bert-base-japanese" in str(pretrained_model_name_or_path):
             return BertJapaneseTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
 
-        use_fast = kwargs.pop("use_fast", False)
+        use_fast = kwargs.pop("use_fast", True)
 
         if config.tokenizer_class is not None:
             if use_fast and not config.tokenizer_class.endswith("Fast"):

src/transformers/tokenization_roberta_fast.py

@@ -18,6 +18,7 @@ from typing import List, Optional
 
 from .tokenization_gpt2_fast import GPT2TokenizerFast
 from .tokenization_roberta import RobertaTokenizer
+from .tokenization_utils_base import AddedToken
 from .utils import logging
 
 
@@ -172,6 +173,32 @@ class RobertaTokenizerFast(GPT2TokenizerFast):
             **kwargs,
         )
 
+    @property
+    def mask_token(self) -> str:
+        """
+        :obj:`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while
+        not having been set.
+
+        Roberta tokenizer has a special mask token to be usble in the fill-mask pipeline. The mask token will greedily
+        comprise the space before the `<mask>`.
+        """
+        if self._mask_token is None and self.verbose:
+            logger.error("Using mask_token, but it is not set yet.")
+            return None
+        return str(self._mask_token)
+
+    @mask_token.setter
+    def mask_token(self, value):
+        """
+        Overriding the default behavior of the mask token to have it eat the space before it.
+
+        This is needed to preserve backward compatibility with all the previously used models based on Roberta.
+        """
+        # Mask token behave like a normal word, i.e. include the space before it
+        # So we set lstrip to True
+        value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value
+        self._mask_token = value
+
     def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
         output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
         if token_ids_1 is None:

src/transformers/tokenization_utils_base.py

@@ -182,7 +182,9 @@ def to_py_obj(obj):
     """
     Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a python list.
     """
-    if isinstance(obj, (list, tuple)):
+    if isinstance(obj, (dict, BatchEncoding)):
+        return {k: to_py_obj(v) for k, v in obj.items()}
+    elif isinstance(obj, (list, tuple)):
         return [to_py_obj(o) for o in obj]
     elif is_tf_available() and isinstance(obj, tf.Tensor):
         return obj.numpy().tolist()
@@ -216,6 +218,9 @@ class BatchEncoding(UserDict):
             initialization.
         prepend_batch_axis (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether or not to add a batch axis when converting to tensors (see :obj:`tensor_type` above).
+        n_sequences (:obj:`Optional[int]`, `optional`):
+            You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at
+            initialization.
     """
 
     def __init__(
@@ -224,6 +229,7 @@ class BatchEncoding(UserDict):
         encoding: Optional[Union[EncodingFast, Sequence[EncodingFast]]] = None,
         tensor_type: Union[None, str, TensorType] = None,
         prepend_batch_axis: bool = False,
+        n_sequences: Optional[int] = None,
     ):
         super().__init__(data)
 
@@ -232,8 +238,22 @@ class BatchEncoding(UserDict):
 
         self._encodings = encoding
 
+        if n_sequences is None and encoding is not None and len(encoding):
+            n_sequences = encoding[0].n_sequences
+
+        self._n_sequences = n_sequences
+
         self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
 
+    @property
+    def n_sequences(self) -> Optional[int]:
+        """
+        :obj:`Optional[int]`: The number of sequences used to generate each sample from the batch encoded in this
+        :class:`~transformers.BatchEncoding`. Currently can be one of :obj:`None` (unknown), :obj:`1` (a single
+        sentence) or :obj:`2` (a pair of sentences)
+        """
+        return self.n_sequences
+
     @property
     def is_fast(self) -> bool:
         """
@@ -311,6 +331,27 @@ class BatchEncoding(UserDict):
             raise ValueError("tokens() is not available when using Python-based tokenizers")
         return self._encodings[batch_index].tokens
 
+    def sequence_ids(self, batch_index: int = 0) -> List[Optional[int]]:
+        """
+        Return a list mapping the tokens to the id of their original sentences:
+
+            - :obj:`None` for special tokens added around or between sequences,
+            - :obj:`0` for tokens corresponding to words in the first sequence,
+            - :obj:`1` for tokens corresponding to words in the second sequence when a pair of sequences was jointly
+              encoded.
+
+        Args:
+            batch_index (:obj:`int`, `optional`, defaults to 0): The index to access in the batch.
+
+        Returns:
+            :obj:`List[Optional[int]]`: A list indicating the sequence id corresponding to each token. Special tokens
+            added by the tokenizer are mapped to :obj:`None` and other tokens are mapped to the index of their
+            corresponding sequence.
+        """
+        if not self._encodings:
+            raise ValueError("sequence_ids() is not available when using Python-based tokenizers")
+        return self._encodings[batch_index].sequence_ids
+
     def words(self, batch_index: int = 0) -> List[Optional[int]]:
         """
         Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer.
@@ -325,7 +366,67 @@ class BatchEncoding(UserDict):
         """
         if not self._encodings:
             raise ValueError("words() is not available when using Python-based tokenizers")
-        return self._encodings[batch_index].words
+        warnings.warn(
+            "`BatchEncoding.words()` property is deprecated and should be replaced with the identical, "
+            "but more self-explanatory `BatchEncoding.word_ids()` property.",
+            FutureWarning,
+        )
+        return self.word_ids(batch_index)
+
+    def word_ids(self, batch_index: int = 0) -> List[Optional[int]]:
+        """
+        Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer.
+
+        Args:
+            batch_index (:obj:`int`, `optional`, defaults to 0): The index to access in the batch.
+
+        Returns:
+            :obj:`List[Optional[int]]`: A list indicating the word corresponding to each token. Special tokens added by
+            the tokenizer are mapped to :obj:`None` and other tokens are mapped to the index of their corresponding
+            word (several tokens will be mapped to the same word index if they are parts of that word).
+        """
+        if not self._encodings:
+            raise ValueError("word_ids() is not available when using Python-based tokenizers")
+        return self._encodings[batch_index].word_ids
+
+    def token_to_sequence(self, batch_or_token_index: int, token_index: Optional[int] = None) -> int:
+        """
+        Get the index of the sequence represented by the given token. In the general use case, this method returns
+        :obj:`0` for a single sequence or the first sequence of a pair, and :obj:`1` for the second sequence of a pair
+
+        Can be called as:
+
+        - ``self.token_to_sequence(token_index)`` if batch size is 1
+        - ``self.token_to_sequence(batch_index, token_index)`` if batch size is greater than 1
+
+        This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e.,
+        words are defined by the user). In this case it allows to easily associate encoded tokens with provided
+        tokenized words.
+
+        Args:
+            batch_or_token_index (:obj:`int`):
+                Index of the sequence in the batch. If the batch only comprises one sequence, this can be the index of
+                the token in the sequence.
+            token_index (:obj:`int`, `optional`):
+                If a batch index is provided in `batch_or_token_index`, this can be the index of the token in the
+                sequence.
+
+        Returns:
+            :obj:`int`: Index of the word in the input sequence.
+        """
+
+        if not self._encodings:
+            raise ValueError("token_to_sequence() is not available when using Python based tokenizers")
+        if token_index is not None:
+            batch_index = batch_or_token_index
+        else:
+            batch_index = 0
+            token_index = batch_or_token_index
+        if batch_index < 0:
+            batch_index = self._batch_size + batch_index
+        if token_index < 0:
+            token_index = self._seq_len + token_index
+        return self._encodings[batch_index].token_to_sequence(token_index)
 
     def token_to_word(self, batch_or_token_index: int, token_index: Optional[int] = None) -> int:
         """
@@ -365,9 +466,11 @@ class BatchEncoding(UserDict):
             token_index = self._seq_len + token_index
         return self._encodings[batch_index].token_to_word(token_index)
 
-    def word_to_tokens(self, batch_or_word_index: int, word_index: Optional[int] = None) -> Optional[TokenSpan]:
+    def word_to_tokens(
+        self, batch_or_word_index: int, word_index: Optional[int] = None, sequence_index: int = 0
+    ) -> Optional[TokenSpan]:
         """
-        Get the encoded token span corresponding to a word in the sequence of the batch.
+        Get the encoded token span corresponding to a word in a sequence of the batch.
 
         Token spans are returned as a :class:`~transformers.tokenization_utils_base.TokenSpan` with:
 
@@ -376,8 +479,9 @@ class BatchEncoding(UserDict):
 
         Can be called as:
 
-        - ``self.word_to_tokens(word_index)`` if batch size is 1
-        - ``self.word_to_tokens(batch_index, word_index)`` if batch size is greater or equal to 1
+        - ``self.word_to_tokens(word_index, sequence_index: int = 0)`` if batch size is 1
+        - ``self.word_to_tokens(batch_index, word_index, sequence_index: int = 0)`` if batch size is greater or equal
+          to 1
 
         This method is particularly suited when the input sequences are provided as pre-tokenized sequences (i.e. words
         are defined by the user). In this case it allows to easily associate encoded tokens with provided tokenized
@@ -390,6 +494,9 @@ class BatchEncoding(UserDict):
             word_index (:obj:`int`, `optional`):
                 If a batch index is provided in `batch_or_token_index`, this can be the index of the word in the
                 sequence.
+            sequence_index (:obj:`int`, `optional`, defaults to 0):
+                If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0
+                or 1) the provided word index belongs to.
 
         Returns:
             Optional :class:`~transformers.tokenization_utils_base.TokenSpan` Span of tokens in the encoded sequence.
@@ -407,7 +514,7 @@ class BatchEncoding(UserDict):
             batch_index = self._batch_size + batch_index
         if word_index < 0:
             word_index = self._seq_len + word_index
-        span = self._encodings[batch_index].word_to_tokens(word_index)
+        span = self._encodings[batch_index].word_to_tokens(word_index, sequence_index)
         return TokenSpan(*span) if span is not None else None
 
     def token_to_chars(self, batch_or_token_index: int, token_index: Optional[int] = None) -> CharSpan:
@@ -446,7 +553,9 @@ class BatchEncoding(UserDict):
             token_index = batch_or_token_index
         return CharSpan(*(self._encodings[batch_index].token_to_chars(token_index)))
 
-    def char_to_token(self, batch_or_char_index: int, char_index: Optional[int] = None) -> int:
+    def char_to_token(
+        self, batch_or_char_index: int, char_index: Optional[int] = None, sequence_index: int = 0
+    ) -> int:
         """
         Get the index of the token in the encoded output comprising a character in the original string for a sequence
         of the batch.
@@ -467,6 +576,9 @@ class BatchEncoding(UserDict):
             char_index (:obj:`int`, `optional`):
                 If a batch index is provided in `batch_or_token_index`, this can be the index of the word in the
                 sequence.
+            sequence_index (:obj:`int`, `optional`, defaults to 0):
+                If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0
+                or 1) the provided character index belongs to.
 
 
         Returns:
@@ -480,9 +592,11 @@ class BatchEncoding(UserDict):
         else:
             batch_index = 0
             char_index = batch_or_char_index
-        return self._encodings[batch_index].char_to_token(char_index)
+        return self._encodings[batch_index].char_to_token(char_index, sequence_index)
 
-    def word_to_chars(self, batch_or_word_index: int, word_index: Optional[int] = None) -> CharSpan:
+    def word_to_chars(
+        self, batch_or_word_index: int, word_index: Optional[int] = None, sequence_index: int = 0
+    ) -> CharSpan:
         """
         Get the character span in the original string corresponding to given word in a sequence of the batch.
 
@@ -503,6 +617,9 @@ class BatchEncoding(UserDict):
             word_index (:obj:`int`, `optional`):
                 If a batch index is provided in `batch_or_token_index`, this can be the index of the word in the
                 sequence.
+            sequence_index (:obj:`int`, `optional`, defaults to 0):
+                If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0
+                or 1) the provided word index belongs to.
 
         Returns:
             :obj:`CharSpan` or :obj:`List[CharSpan]`: Span(s) of the associated character or characters in the string.
@@ -520,9 +637,9 @@ class BatchEncoding(UserDict):
         else:
             batch_index = 0
             word_index = batch_or_word_index
-        return CharSpan(*(self._encodings[batch_index].word_to_chars(word_index)))
+        return CharSpan(*(self._encodings[batch_index].word_to_chars(word_index, sequence_index)))
 
-    def char_to_word(self, batch_or_char_index: int, char_index: Optional[int] = None) -> int:
+    def char_to_word(self, batch_or_char_index: int, char_index: Optional[int] = None, sequence_index: int = 0) -> int:
         """
         Get the word in the original string corresponding to a character in the original string of a sequence of the
         batch.
@@ -543,6 +660,9 @@ class BatchEncoding(UserDict):
             char_index (:obj:`int`, `optional`):
                 If a batch index is provided in `batch_or_token_index`, this can be the index of the character in the
                 original string.
+            sequence_index (:obj:`int`, `optional`, defaults to 0):
+                If pair of sequences are encoded in the batch this can be used to specify which sequence in the pair (0
+                or 1) the provided character index belongs to.
 
 
         Returns:
@@ -556,7 +676,7 @@ class BatchEncoding(UserDict):
         else:
             batch_index = 0
             char_index = batch_or_char_index
-        return self._encodings[batch_index].char_to_word(char_index)
+        return self._encodings[batch_index].char_to_word(char_index, sequence_index)
 
     def convert_to_tensors(
         self, tensor_type: Optional[Union[str, TensorType]] = None, prepend_batch_axis: bool = False
@@ -1872,6 +1992,8 @@ class PreTrainedTokenizerBase(SpecialTokensMixin):
                 "Only fast tokenizers (instances of PretrainedTokenizerFast) can be saved in non legacy format."
             )
 
+        save_directory = str(save_directory)
+
         added_tokens_file = os.path.join(
             save_directory, (filename_prefix + "-" if filename_prefix else "") + ADDED_TOKENS_FILE
         )

src/transformers/tokenization_utils_fast.py

@@ -169,9 +169,10 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
         return_offsets_mapping: bool = False,
         return_length: bool = False,
         verbose: bool = True,
-    ) -> Dict[str, Any]:
+    ) -> Tuple[Dict[str, Any], List[EncodingFast]]:
         """
-        Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict.
+        Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict and a list
+        of encodings, take care of building a batch from overflowing tokens.
 
         Overflowing tokens are converted to additional examples (like batches) so the output values of the dict are
         lists (overflows) of lists (tokens).
@@ -203,7 +204,7 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
             if return_length:
                 encoding_dict["length"].append(len(e.ids))
 
-        return encoding_dict
+        return encoding_dict, encodings
 
     def convert_tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]:
         """
@@ -390,9 +391,12 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
         )
 
         # Convert encoding to dict
-        # `Tokens` has type: List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]]
+        # `Tokens` has type: Tuple[
+        #                       List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
+        #                       List[EncodingFast]
+        #                    ]
         # with nested dimensions corresponding to batch, overflows, sequence length
-        tokens = [
+        tokens_and_encodings = [
             self._convert_encoding(
                 encoding=encoding,
                 return_token_type_ids=return_token_type_ids,
@@ -406,22 +410,27 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
             for encoding in encodings
         ]
 
-        # Convert the output to have dict[list] from list[dict]
-        sanitized = {}
-        for key in tokens[0].keys():
-            # To List[List[List[int]]] of shape (batch, overflows, sequence length)
-            stack = [e for item in tokens for e in item[key]]
-            sanitized[key] = stack
+        # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
+        # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
+        # (we say ~ because the number of overflow varies with the example in the batch)
+        #
+        # To match each overflowing sample with the original sample in the batch
+        # we add an overflow_to_sample_mapping array (see below)
+        sanitized_tokens = {}
+        for key in tokens_and_encodings[0][0].keys():
+            stack = [e for item, _ in tokens_and_encodings for e in item[key]]
+            sanitized_tokens[key] = stack
+        sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
 
         # If returning overflowing tokens, we need to return a mapping
         # from the batch idx to the original sample
         if return_overflowing_tokens:
             overflow_to_sample_mapping = []
-            for i, enc in enumerate(tokens):
-                overflow_to_sample_mapping += [i] * len(enc["input_ids"])
-            sanitized["overflow_to_sample_mapping"] = overflow_to_sample_mapping
+            for i, (toks, _) in enumerate(tokens_and_encodings):
+                overflow_to_sample_mapping += [i] * len(toks["input_ids"])
+            sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
 
-        return BatchEncoding(sanitized, encodings, tensor_type=return_tensors)
+        return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
 
     def _encode_plus(
         self,
@@ -518,6 +527,8 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
         Fast tokenizers can also be saved in a unique JSON file containing {config + vocab + added-tokens} using the
         specific :meth:`~transformers.PreTrainedTokenizerFast._save_pretrained`
         """
+        save_directory = str(save_directory)
+
         if legacy_format:
             added_tokens_file = os.path.join(
                 save_directory, (filename_prefix + "-" if filename_prefix else "") + ADDED_TOKENS_FILE

tests/test_pipelines_common.py

 from typing import List, Optional
+from unittest import mock
 
 from transformers import is_tf_available, is_torch_available, pipeline
-
-# from transformers.pipelines import DefaultArgumentHandler, Pipeline
 from transformers.pipelines import Pipeline
 from transformers.testing_utils import _run_slow_tests, is_pipeline_test, require_tf, require_torch, slow
+from transformers.tokenization_utils_base import to_py_obj
 
 
 VALID_INPUTS = ["A simple string", ["list of strings"]]
@@ -13,9 +13,11 @@ VALID_INPUTS = ["A simple string", ["list of strings"]]
 @is_pipeline_test
 class CustomInputPipelineCommonMixin:
     pipeline_task = None
-    pipeline_loading_kwargs = {}
-    small_models = None  # Models tested without the @slow decorator
-    large_models = None  # Models tested with the @slow decorator
+    pipeline_loading_kwargs = {}  # Additional kwargs to load the pipeline with
+    pipeline_running_kwargs = {}  # Additional kwargs to run the pipeline with
+    small_models = []  # Models tested without the @slow decorator
+    large_models = []  # Models tested with the @slow decorator
+    valid_inputs = VALID_INPUTS  # Some inputs which are valid to compare fast and slow tokenizers
 
     def setUp(self) -> None:
         if not is_tf_available() and not is_torch_available():
@@ -47,73 +49,11 @@ class CustomInputPipelineCommonMixin:
     @require_torch
     @slow
     def test_pt_defaults(self):
-        pipeline(self.pipeline_task, framework="pt")
-
-    @require_tf
-    @slow
-    def test_tf_defaults(self):
-        pipeline(self.pipeline_task, framework="tf")
-
-    @require_torch
-    def test_torch_small(self):
-        for model_name in self.small_models:
-            nlp = pipeline(task=self.pipeline_task, model=model_name, tokenizer=model_name, framework="pt")
-            self._test_pipeline(nlp)
-
-    @require_tf
-    def test_tf_small(self):
-        for model_name in self.small_models:
-            nlp = pipeline(task=self.pipeline_task, model=model_name, tokenizer=model_name, framework="tf")
-            self._test_pipeline(nlp)
-
-    @require_torch
-    @slow
-    def test_torch_large(self):
-        for model_name in self.large_models:
-            nlp = pipeline(task=self.pipeline_task, model=model_name, tokenizer=model_name, framework="pt")
-            self._test_pipeline(nlp)
-
-    @require_tf
-    @slow
-    def test_tf_large(self):
-        for model_name in self.large_models:
-            nlp = pipeline(task=self.pipeline_task, model=model_name, tokenizer=model_name, framework="tf")
-            self._test_pipeline(nlp)
-
-    def _test_pipeline(self, nlp: Pipeline):
-        raise NotImplementedError
-
-
-@is_pipeline_test
-class MonoInputPipelineCommonMixin:
-    pipeline_task = None
-    pipeline_loading_kwargs = {}  # Additional kwargs to load the pipeline with
-    pipeline_running_kwargs = {}  # Additional kwargs to run the pipeline with
-    small_models = []  # Models tested without the @slow decorator
-    large_models = []  # Models tested with the @slow decorator
-    mandatory_keys = {}  # Keys which should be in the output
-    valid_inputs = VALID_INPUTS  # inputs which are valid
-    invalid_inputs = [None]  # inputs which are not allowed
-    expected_multi_result: Optional[List] = None
-    expected_check_keys: Optional[List[str]] = None
-
-    def setUp(self) -> None:
-        if not is_tf_available() and not is_torch_available():
-            return  # Currently no JAX pipelines
-
-        for model_name in self.small_models:
-            pipeline(self.pipeline_task, model=model_name, tokenizer=model_name, **self.pipeline_loading_kwargs)
-        for model_name in self.large_models:
-            pipeline(self.pipeline_task, model=model_name, tokenizer=model_name, **self.pipeline_loading_kwargs)
-
-    @require_torch
-    @slow
-    def test_pt_defaults_loads(self):
         pipeline(self.pipeline_task, framework="pt", **self.pipeline_loading_kwargs)
 
     @require_tf
     @slow
-    def test_tf_defaults_loads(self):
+    def test_tf_defaults(self):
         pipeline(self.pipeline_task, framework="tf", **self.pipeline_loading_kwargs)
 
     @require_torch
@@ -166,6 +106,95 @@ class MonoInputPipelineCommonMixin:
             )
             self._test_pipeline(nlp)
 
+    def _test_pipeline(self, nlp: Pipeline):
+        raise NotImplementedError
+
+    @require_torch
+    def test_compare_slow_fast_torch(self):
+        for model_name in self.small_models:
+            nlp_slow = pipeline(
+                task=self.pipeline_task,
+                model=model_name,
+                tokenizer=model_name,
+                framework="pt",
+                use_fast=False,
+                **self.pipeline_loading_kwargs,
+            )
+            nlp_fast = pipeline(
+                task=self.pipeline_task,
+                model=model_name,
+                tokenizer=model_name,
+                framework="pt",
+                use_fast=True,
+                **self.pipeline_loading_kwargs,
+            )
+            self._compare_slow_fast_pipelines(nlp_slow, nlp_fast, method="forward")
+
+    @require_tf
+    def test_compare_slow_fast_tf(self):
+        for model_name in self.small_models:
+            nlp_slow = pipeline(
+                task=self.pipeline_task,
+                model=model_name,
+                tokenizer=model_name,
+                framework="tf",
+                use_fast=False,
+                **self.pipeline_loading_kwargs,
+            )
+            nlp_fast = pipeline(
+                task=self.pipeline_task,
+                model=model_name,
+                tokenizer=model_name,
+                framework="tf",
+                use_fast=True,
+                **self.pipeline_loading_kwargs,
+            )
+            self._compare_slow_fast_pipelines(nlp_slow, nlp_fast, method="call")
+
+    def _compare_slow_fast_pipelines(self, nlp_slow: Pipeline, nlp_fast: Pipeline, method: str):
+        """We check that the inputs to the models forward passes are identical for
+        slow and fast tokenizers.
+        """
+        with mock.patch.object(
+            nlp_slow.model, method, wraps=getattr(nlp_slow.model, method)
+        ) as mock_slow, mock.patch.object(nlp_fast.model, method, wraps=getattr(nlp_fast.model, method)) as mock_fast:
+            for inputs in self.valid_inputs:
+                if isinstance(inputs, dict):
+                    inputs.update(self.pipeline_running_kwargs)
+                    _ = nlp_slow(**inputs)
+                    _ = nlp_fast(**inputs)
+                else:
+                    _ = nlp_slow(inputs, **self.pipeline_running_kwargs)
+                    _ = nlp_fast(inputs, **self.pipeline_running_kwargs)
+
+                mock_slow.assert_called()
+                mock_fast.assert_called()
+
+                self.assertEqual(len(mock_slow.call_args_list), len(mock_fast.call_args_list))
+                for mock_slow_call_args, mock_fast_call_args in zip(
+                    mock_slow.call_args_list, mock_slow.call_args_list
+                ):
+                    slow_call_args, slow_call_kwargs = mock_slow_call_args
+                    fast_call_args, fast_call_kwargs = mock_fast_call_args
+
+                    slow_call_args, slow_call_kwargs = to_py_obj(slow_call_args), to_py_obj(slow_call_kwargs)
+                    fast_call_args, fast_call_kwargs = to_py_obj(fast_call_args), to_py_obj(fast_call_kwargs)
+
+                    self.assertEqual(slow_call_args, fast_call_args)
+                    self.assertDictEqual(slow_call_kwargs, fast_call_kwargs)
+
+
+@is_pipeline_test
+class MonoInputPipelineCommonMixin(CustomInputPipelineCommonMixin):
+    """A version of the CustomInputPipelineCommonMixin
+    with a predefined `_test_pipeline` method.
+    """
+
+    mandatory_keys = {}  # Keys which should be in the output
+    invalid_inputs = [None]  # inputs which are not allowed
+    expected_multi_result: Optional[List] = None
+    expected_check_keys: Optional[List[str]] = None
+
     def _test_pipeline(self, nlp: Pipeline):
         self.assertIsNotNone(nlp)
 
@@ -199,76 +228,3 @@ class MonoInputPipelineCommonMixin:
                 self.assertIn(key, result)
 
         self.assertRaises(Exception, nlp, self.invalid_inputs)
-
-
-# @is_pipeline_test
-# class DefaultArgumentHandlerTestCase(unittest.TestCase):
-#     def setUp(self) -> None:
-#         self.handler = DefaultArgumentHandler()
-#
-#     def test_kwargs_x(self):
-#         mono_data = {"X": "This is a sample input"}
-#         mono_args = self.handler(**mono_data)
-#
-#         self.assertTrue(isinstance(mono_args, list))
-#         self.assertEqual(len(mono_args), 1)
-#
-#         multi_data = {"x": ["This is a sample input", "This is a second sample input"]}
-#         multi_args = self.handler(**multi_data)
-#
-#         self.assertTrue(isinstance(multi_args, list))
-#         self.assertEqual(len(multi_args), 2)
-#
-#     def test_kwargs_data(self):
-#         mono_data = {"data": "This is a sample input"}
-#         mono_args = self.handler(**mono_data)
-#
-#         self.assertTrue(isinstance(mono_args, list))
-#         self.assertEqual(len(mono_args), 1)
-#
-#         multi_data = {"data": ["This is a sample input", "This is a second sample input"]}
-#         multi_args = self.handler(**multi_data)
-#
-#         self.assertTrue(isinstance(multi_args, list))
-#         self.assertEqual(len(multi_args), 2)
-#
-#     def test_multi_kwargs(self):
-#         mono_data = {"data": "This is a sample input", "X": "This is a sample input 2"}
-#         mono_args = self.handler(**mono_data)
-#
-#         self.assertTrue(isinstance(mono_args, list))
-#         self.assertEqual(len(mono_args), 2)
-#
-#         multi_data = {
-#             "data": ["This is a sample input", "This is a second sample input"],
-#             "test": ["This is a sample input 2", "This is a second sample input 2"],
-#         }
-#         multi_args = self.handler(**multi_data)
-#
-#         self.assertTrue(isinstance(multi_args, list))
-#         self.assertEqual(len(multi_args), 4)
-#
-#     def test_args(self):
-#         mono_data = "This is a sample input"
-#         mono_args = self.handler(mono_data)
-#
-#         self.assertTrue(isinstance(mono_args, list))
-#         self.assertEqual(len(mono_args), 1)
-#
-#         mono_data = ["This is a sample input"]
-#         mono_args = self.handler(mono_data)
-#
-#         self.assertTrue(isinstance(mono_args, list))
-#         self.assertEqual(len(mono_args), 1)
-#
-#         multi_data = ["This is a sample input", "This is a second sample input"]
-#         multi_args = self.handler(multi_data)
-#
-#         self.assertTrue(isinstance(multi_args, list))
-#         self.assertEqual(len(multi_args), 2)
-#
-#         multi_data = ["This is a sample input", "This is a second sample input"]
-#         multi_args = self.handler(*multi_data)
-#
-#         self.assertTrue(isinstance(multi_args, list))
-#         self.assertEqual(len(multi_args), 2)

tests/test_pipelines_dialog.py

-import unittest
-
-from transformers.pipelines import Conversation, Pipeline
-
-from .test_pipelines_common import CustomInputPipelineCommonMixin
-
-
-class DialoguePipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
-    pipeline_task = "conversational"
-    small_models = []  # Default model - Models tested without the @slow decorator
-    large_models = ["microsoft/DialoGPT-medium"]  # Models tested with the @slow decorator
-
-    def _test_pipeline(self, nlp: Pipeline):
-        valid_inputs = [Conversation("Hi there!"), [Conversation("Hi there!"), Conversation("How are you?")]]
-        invalid_inputs = ["Hi there!", Conversation()]
-        self.assertIsNotNone(nlp)
-
-        mono_result = nlp(valid_inputs[0])
-        self.assertIsInstance(mono_result, Conversation)
-
-        multi_result = nlp(valid_inputs[1])
-        self.assertIsInstance(multi_result, list)
-        self.assertIsInstance(multi_result[0], Conversation)
-        # Inactive conversations passed to the pipeline raise a ValueError
-        self.assertRaises(ValueError, nlp, valid_inputs[1])
-
-        for bad_input in invalid_inputs:
-            self.assertRaises(Exception, nlp, bad_input)
-        self.assertRaises(Exception, nlp, invalid_inputs)

tests/test_pipelines_ner.py

@@ -146,10 +146,10 @@ class NerPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
     @require_torch
     def test_pt_ignore_subwords_slow_tokenizer_raises(self):
         for model_name in self.small_models:
-            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
 
             with self.assertRaises(ValueError):
-                pipeline(task="ner", model=model_name, tokenizer=tokenizer, ignore_subwords=True)
+                pipeline(task="ner", model=model_name, tokenizer=tokenizer, ignore_subwords=True, use_fast=False)
 
     @require_torch
     def test_pt_defaults_slow_tokenizer(self):

tests/test_pipelines_question_answering.py

@@ -8,10 +8,22 @@ from .test_pipelines_common import CustomInputPipelineCommonMixin
 
 class QAPipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
     pipeline_task = "question-answering"
+    pipeline_running_kwargs = {
+        "padding": "max_length",
+        "max_seq_len": 25,
+        "doc_stride": 5,
+    }  # Default is 'longest' but we use 'max_length' to test equivalence between slow/fast tokenizers
     small_models = [
         "sshleifer/tiny-distilbert-base-cased-distilled-squad"
     ]  # Models tested without the @slow decorator
     large_models = []  # Models tested with the @slow decorator
+    valid_inputs = [
+        {"question": "Where was HuggingFace founded ?", "context": "HuggingFace was founded in Paris."},
+        {
+            "question": "In what field is HuggingFace working ?",
+            "context": "HuggingFace is a startup based in New-York founded in Paris which is trying to solve NLP.",
+        },
+    ]
 
     def _test_pipeline(self, nlp: Pipeline):
         output_keys = {"score", "answer", "start", "end"}

tests/test_pipelines_zero_shot.py

@@ -12,6 +12,18 @@ class ZeroShotClassificationPipelineTests(CustomInputPipelineCommonMixin, unitte
         "sshleifer/tiny-distilbert-base-uncased-finetuned-sst-2-english"
     ]  # Models tested without the @slow decorator
     large_models = ["roberta-large-mnli"]  # Models tested with the @slow decorator
+    valid_inputs = [
+        {"sequences": "Who are you voting for in 2020?", "candidate_labels": "politics"},
+        {"sequences": "Who are you voting for in 2020?", "candidate_labels": ["politics"]},
+        {"sequences": "Who are you voting for in 2020?", "candidate_labels": "politics, public health"},
+        {"sequences": "Who are you voting for in 2020?", "candidate_labels": ["politics", "public health"]},
+        {"sequences": ["Who are you voting for in 2020?"], "candidate_labels": "politics"},
+        {
+            "sequences": "Who are you voting for in 2020?",
+            "candidate_labels": "politics",
+            "hypothesis_template": "This text is about {}",
+        },
+    ]
 
     def _test_scores_sum_to_one(self, result):
         sum = 0.0

tests/test_retrieval_rag.py

@@ -9,7 +9,7 @@ from unittest.mock import patch
 import numpy as np
 from datasets import Dataset
 
-import faiss
+from transformers import is_faiss_available
 from transformers.configuration_bart import BartConfig
 from transformers.configuration_dpr import DPRConfig
 from transformers.configuration_rag import RagConfig
@@ -27,6 +27,10 @@ from transformers.tokenization_dpr import DPRQuestionEncoderTokenizer
 from transformers.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
 
 
+if is_faiss_available():
+    import faiss
+
+
 @require_faiss
 @require_datasets
 class RagRetrieverTest(TestCase):

tests/test_tokenization_auto.py

@@ -116,5 +116,5 @@ class AutoTokenizerTest(unittest.TestCase):
 
     @require_tokenizers
     def test_from_pretrained_use_fast_toggle(self):
-        self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"), BertTokenizer)
-        self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased", use_fast=True), BertTokenizerFast)
+        self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased", use_fast=False), BertTokenizer)
+        self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"), BertTokenizerFast)