[HUGE] Refactoring tokenizers backend - padding - truncation - pre-tokenized pipeline - fast tokenizers - tests (#4510)

* Use tokenizers pre-tokenized pipeline

* failing pretrokenized test

* Fix is_pretokenized in python

* add pretokenized tests

* style and quality

* better tests for batched pretokenized inputs

* tokenizers clean up - new padding_strategy - split the files

* [HUGE] refactoring tokenizers - padding - truncation - tests

* style and quality

* bump up requied tokenizers version to 0.8.0-rc1

* switched padding/truncation API - simpler better backward compat

* updating tests for custom tokenizers

* style and quality - tests on pad

* fix QA pipeline

* fix backward compatibility for max_length only

* style and quality

* Various cleans up - add verbose

* fix tests

* update docstrings

* Fix tests

* Docs reformatted

* __call__ method documented

Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

docs/source/main_classes/tokenizer.rst

@@ -17,12 +17,14 @@ The base classes ``PreTrainedTokenizer`` and ``PreTrainedTokenizerFast`` impleme
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PreTrainedTokenizer
+    :special-members: __call__
     :members:
 
 ``PreTrainedTokenizerFast``
-~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PreTrainedTokenizerFast
+    :special-members: __call__
     :members:
 
 ``BatchEncoding``

examples/summarization/utils.py

@@ -3,8 +3,6 @@ import os
 import torch
 from torch.utils.data import Dataset
 
-from transformers.tokenization_utils import trim_batch
-
 
 def encode_file(tokenizer, data_path, max_length, pad_to_max_length=True, return_tensors="pt"):
     examples = []
@@ -17,6 +15,17 @@ def encode_file(tokenizer, data_path, max_length, pad_to_max_length=True, return
     return examples
 
 
+def trim_batch(
+    input_ids, pad_token_id, attention_mask=None,
+):
+    """Remove columns that are populated exclusively by pad_token_id"""
+    keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)
+    if attention_mask is None:
+        return input_ids[:, keep_column_mask]
+    else:
+        return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
+
+
 class SummarizationDataset(Dataset):
     def __init__(
         self,

setup.py

@@ -108,7 +108,7 @@ setup(
     packages=find_packages("src"),
     install_requires=[
         "numpy",
-        "tokenizers == 0.7.0",
+        "tokenizers == 0.8.0-rc1",
         # dataclasses for Python versions that don't have it
         "dataclasses;python_version<'3.7'",
         # utilities from PyPA to e.g. compare versions

src/transformers/__init__.py

@@ -133,13 +133,16 @@ from .tokenization_reformer import ReformerTokenizer
 from .tokenization_roberta import RobertaTokenizer, RobertaTokenizerFast
 from .tokenization_t5 import T5Tokenizer
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer, TransfoXLTokenizerFast
-from .tokenization_utils import (
+from .tokenization_utils import PreTrainedTokenizer
+from .tokenization_utils_base import (
     BatchEncoding,
-    PreTrainedTokenizer,
-    PreTrainedTokenizerFast,
+    CharSpan,
+    PreTrainedTokenizerBase,
     SpecialTokensMixin,
     TensorType,
+    TokenSpan,
 )
+from .tokenization_utils_fast import PreTrainedTokenizerFast
 from .tokenization_xlm import XLMTokenizer
 from .tokenization_xlm_roberta import XLMRobertaTokenizer
 from .tokenization_xlnet import SPIECE_UNDERLINE, XLNetTokenizer

src/transformers/modeling_tf_albert.py

@@ -1213,7 +1213,7 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
         model = TFAlbertForMultipleChoice.from_pretrained('albert-base-v2')
         choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
 
-        input_ids = tf.constant([tokenizer.encode(s, add_special_tokens=True) for s in choices])[None, :] # Batch size 1, 2 choices
+        input_ids = tokenizer(choices, add_special_tokens=True, return_tensors='tf', truncation=True, padding=True)[None, :] # Batch size 1, 2 choices
         labels = tf.reshape(tf.constant(1), (-1, 1))
         outputs = model(input_ids, labels=labels)
 

src/transformers/tokenization_bert.py

@@ -23,7 +23,8 @@ from typing import List, Optional
 
 from tokenizers import BertWordPieceTokenizer
 
-from .tokenization_utils import PreTrainedTokenizer, PreTrainedTokenizerFast
+from .tokenization_utils import PreTrainedTokenizer
+from .tokenization_utils_fast import PreTrainedTokenizerFast
 
 
 logger = logging.getLogger(__name__)

src/transformers/tokenization_gpt2.py

@@ -23,7 +23,9 @@ from functools import lru_cache
 import regex as re
 from tokenizers import ByteLevelBPETokenizer
 
-from .tokenization_utils import PreTrainedTokenizer, PreTrainedTokenizerFast
+from .tokenization_utils import PreTrainedTokenizer
+from .tokenization_utils_base import BatchEncoding
+from .tokenization_utils_fast import PreTrainedTokenizerFast
 
 
 logger = logging.getLogger(__name__)
@@ -346,3 +348,24 @@ class GPT2TokenizerFast(PreTrainedTokenizerFast):
             unk_token=unk_token,
             **kwargs,
         )
+        self.add_prefix_space = add_prefix_space
+
+    def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
+
+        is_pretokenized = kwargs.get("is_pretokenized", False)
+        assert self.add_prefix_space or not is_pretokenized, (
+            "You need to instantiate GPT2TokenizerFast with add_prefix_space=False "
+            "to use it with pretokenized inputs."
+        )
+
+        return super()._batch_encode_plus(*args, **kwargs)
+
+    def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
+
+        is_pretokenized = kwargs.get("is_pretokenized", False)
+        assert self.add_prefix_space or not is_pretokenized, (
+            "You need to instantiate GPT2TokenizerFast with add_prefix_space=False "
+            "to use it with pretokenized inputs."
+        )
+
+        return super()._encode_plus(*args, **kwargs)

src/transformers/tokenization_openai.py

@@ -23,7 +23,8 @@ import re
 from tokenizers import CharBPETokenizer
 
 from .tokenization_bert import BasicTokenizer
-from .tokenization_utils import PreTrainedTokenizer, PreTrainedTokenizerFast
+from .tokenization_utils import PreTrainedTokenizer
+from .tokenization_utils_fast import PreTrainedTokenizerFast
 
 
 logger = logging.getLogger(__name__)

src/transformers/tokenization_transfo_xl.py

@@ -35,7 +35,8 @@ from tokenizers.pre_tokenizers import CharDelimiterSplit, WhitespaceSplit
 from tokenizers.processors import BertProcessing
 
 from .file_utils import cached_path, is_torch_available
-from .tokenization_utils import PreTrainedTokenizer, PreTrainedTokenizerFast
+from .tokenization_utils import PreTrainedTokenizer
+from .tokenization_utils_fast import PreTrainedTokenizerFast
 
 
 if is_torch_available():

src/transformers/tokenization_utils_fast.py

@@ -0,0 +1,476 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Tokenization classes for fast tokenizers (provided by HuggingFace's tokenizers library).
+    For slow (python) tokenizers see tokenization_utils.py
+"""
+
+import logging
+import os
+from collections import defaultdict
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+from tokenizers import AddedToken as AddedTokenFast
+from tokenizers import Encoding as EncodingFast
+from tokenizers.decoders import Decoder as DecoderFast
+from tokenizers.implementations import BaseTokenizer as BaseTokenizerFast
+
+from .tokenization_utils_base import (
+    BatchEncoding,
+    PaddingStrategy,
+    PreTokenizedInput,
+    PreTokenizedInputPair,
+    PreTrainedTokenizerBase,
+    TextInput,
+    TextInputPair,
+    TruncationStrategy,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
+    """ Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).
+
+    Inherit from PreTrainedTokenizer.
+
+    Handle all the shared methods for tokenization and special tokens as well as methods
+    downloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.
+
+    This class also contain the added tokens in a unified way on top of all tokenizers so we don't
+    have to handle the specific vocabulary augmentation methods of the various underlying
+    dictionary structures (BPE, sentencepiece...).
+
+    Class attributes (overridden by derived classes):
+
+    - ``vocab_files_names``: a python ``dict`` with, as keys, the ``__init__`` keyword name of each vocabulary file
+      required by the model, and as associated values, the filename for saving the associated file (string).
+    - ``pretrained_vocab_files_map``: a python ``dict of dict`` the high-level keys
+      being the ``__init__`` keyword name of each vocabulary file required by the model, the low-level being the
+      `short-cut-names` (string) of the pretrained models with, as associated values, the `url` (string) to the
+      associated pretrained vocabulary file.
+    - ``max_model_input_sizes``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained
+      models, and as associated values, the maximum length of the sequence inputs of this model, or None if the
+      model has no maximum input size.
+    - ``pretrained_init_configuration``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the
+      pretrained models, and as associated values, a dictionnary of specific arguments to pass to the
+      ``__init__``method of the tokenizer class for this pretrained model when loading the tokenizer with the
+      ``from_pretrained()`` method.
+
+    Args:
+        - ``tokenizer`` (`BaseTokenizerFast`): A Fast tokenizer from the HuggingFace tokenizer library (in low level Rust language)
+        - ``model_max_length``: (`Optional`) int: the maximum length in number of tokens for the inputs to the transformer model.
+            When the tokenizer is loaded with `from_pretrained`, this will be set to the value stored for the associated
+            model in ``max_model_input_sizes`` (see above). If no value is provided, will default to VERY_LARGE_INTEGER (`int(1e30)`).
+            no associated max_length can be found in ``max_model_input_sizes``.
+        - ``padding_side``: (`Optional`) string: the side on which the model should have padding applied.
+            Should be selected between ['right', 'left']
+        - ``model_input_names``: (`Optional`) List[string]: the list of the forward pass inputs accepted by the
+            model ("token_type_ids", "attention_mask"...).
+        - ``bos_token``: (`Optional`) string: a beginning of sentence token.
+            Will be associated to ``self.bos_token`` and ``self.bos_token_id``
+        - ``eos_token``: (`Optional`) string: an end of sentence token.
+            Will be associated to ``self.eos_token`` and ``self.eos_token_id``
+        - ``unk_token``: (`Optional`) string: an unknown token.
+            Will be associated to ``self.unk_token`` and ``self.unk_token_id``
+        - ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence).
+            Will be associated to ``self.sep_token`` and ``self.sep_token_id``
+        - ``pad_token``: (`Optional`) string: a padding token.
+            Will be associated to ``self.pad_token`` and ``self.pad_token_id``
+        - ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence
+            leveraging self-attention along the full depth of the model).
+            Will be associated to ``self.cls_token`` and ``self.cls_token_id``
+        - ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language
+            modeling). Will be associated to ``self.mask_token`` and ``self.mask_token_id``
+        - ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens.
+            Adding all special tokens here ensure they won't be split by the tokenization process.
+            Will be associated to ``self.additional_special_tokens`` and ``self.additional_special_tokens_ids``
+
+
+    .. automethod:: __call__
+    """
+
+    def __init__(self, tokenizer: BaseTokenizerFast, **kwargs):
+        if not isinstance(tokenizer, BaseTokenizerFast):
+            raise ValueError(
+                "Tokenizer should be an instance of a Tokenizer " "provided by HuggingFace tokenizers library."
+            )
+        self._tokenizer: BaseTokenizerFast = tokenizer
+
+        # We call this after having initialized the backend tokenizer because we update it.
+        super().__init__(**kwargs)
+
+    @property
+    def is_fast(self) -> bool:
+        return True
+
+    @property
+    def vocab_size(self) -> int:
+        return self._tokenizer.get_vocab_size(with_added_tokens=False)
+
+    def get_vocab(self) -> Dict[str, int]:
+        return self._tokenizer.get_vocab(with_added_tokens=True)
+
+    def __len__(self) -> int:
+        return self._tokenizer.get_vocab_size(with_added_tokens=True)
+
+    @property
+    def backend_tokenizer(self) -> BaseTokenizerFast:
+        return self._tokenizer
+
+    @property
+    def decoder(self) -> DecoderFast:
+        return self._tokenizer._tokenizer.decoder
+
+    def _maybe_update_backend(self, value):
+        """ Update the backend fast tokenizer.
+            Override method from base class SpecialTokensMixin """
+        self._tokenizer.add_special_tokens(value)
+
+    def _convert_encoding(
+        self,
+        encoding: EncodingFast,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        verbose: bool = True,
+    ) -> Dict[str, Any]:
+        """ Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict.
+
+            Overflowing tokens are converted to additional examples (like batches) so the output values of
+            the dict are lists (overflows) of lists (tokens).
+
+            Output shape: (overflows, sequence length)
+        """
+        if return_token_type_ids is None:
+            return_token_type_ids = "token_type_ids" in self.model_input_names
+        if return_attention_mask is None:
+            return_attention_mask = "attention_mask" in self.model_input_names
+
+        if return_overflowing_tokens and encoding.overflowing is not None:
+            encodings = [encoding] + encoding.overflowing
+        else:
+            encodings = [encoding]
+
+        encoding_dict = defaultdict(list)
+        for e in encodings:
+            encoding_dict["input_ids"].append(e.ids)
+
+            if return_token_type_ids:
+                encoding_dict["token_type_ids"].append(e.type_ids)
+            if return_attention_mask:
+                encoding_dict["attention_mask"].append(e.attention_mask)
+            if return_special_tokens_mask:
+                encoding_dict["special_tokens_mask"].append(e.special_tokens_mask)
+            if return_offsets_mapping:
+                encoding_dict["offset_mapping"].append(e.offsets)
+
+        return encoding_dict
+
+    def convert_tokens_to_ids(self, tokens):
+        """ Converts a token string (or a sequence of tokens) in a single integer id
+            (or a sequence of ids), using the vocabulary.
+        """
+        if tokens is None:
+            return None
+
+        if isinstance(tokens, str):
+            return self._convert_token_to_id_with_added_voc(tokens)
+
+        ids = []
+        for token in tokens:
+            ids.append(self._convert_token_to_id_with_added_voc(token))
+        return ids
+
+    def _convert_token_to_id_with_added_voc(self, token: int) -> str:
+        index = self._tokenizer.token_to_id(token)
+        if index is None:
+            return self.unk_token_id
+        return index
+
+    def _convert_id_to_token(self, index: int) -> Optional[str]:
+        return self._tokenizer.id_to_token(int(index))
+
+    def convert_tokens_to_string(self, tokens: List[int], skip_special_tokens: bool = False) -> str:
+        return self._tokenizer.decode(tokens, skip_special_tokens=skip_special_tokens)
+
+    def add_tokens(self, new_tokens: List[Union[str, AddedTokenFast]]) -> int:
+        """
+        Add a list of new tokens to the tokenizer class. If the new tokens are not in the
+        vocabulary, they are added to it with indices starting from length of the current vocabulary.
+
+        Args:
+            new_tokens: string or list of string or :class:`~transformers.AddedTokenFast`. Each string is a token to add.
+                Tokens are only added if they are not already in the vocabulary. AddedTokenFast wrap a string token to
+                let you personnalize it's behavior (Whether this token should only match against single word, whether
+                this token should strip all potential whitespaces on the left side, Whether this token should strip
+                all potential whitespaces on the right side...).
+
+                See details for :class:`~transformers.AddedToken` in HuggingFace tokenizers library.
+
+        Returns:
+            Number of tokens added to the vocabulary.
+
+        Examples::
+
+            # Let's see how to increase the vocabulary of Bert model and tokenizer
+            tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
+            model = BertModel.from_pretrained('bert-base-uncased')
+
+            num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
+            print('We have added', num_added_toks, 'tokens')
+            model.resize_token_embeddings(len(tokenizer))  # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
+        """
+        if isinstance(new_tokens, str):
+            new_tokens = [new_tokens]
+        # TODO This should be done in tokenizers to be really clean.
+        # Removing for now
+        # tokens = []
+        # for token in new_tokens:
+        #     if self.init_kwargs.get("do_lower_case", False) and token not in self.all_special_tokens:
+        #         token = token.lower()
+        #     if token not in tokens:
+        #         tokens.append(token)
+        return self._tokenizer.add_tokens(new_tokens)
+
+    def num_special_tokens_to_add(self, pair: bool = False) -> int:
+        return self._tokenizer.num_special_tokens_to_add(pair)
+
+    def convert_ids_to_tokens(
+        self, ids: Union[int, List[int]], skip_special_tokens: bool = False
+    ) -> Union[int, List[int]]:
+        """ Converts a single index or a sequence of indices (integers) in a token "
+            (resp.) a sequence of tokens (str), using the vocabulary and added tokens.
+
+            Args:
+                skip_special_tokens: Don't decode special tokens (self.all_special_tokens). Default: False
+        """
+        if isinstance(ids, int):
+            return self._tokenizer.id_to_token(ids)
+        tokens = []
+        for index in ids:
+            index = int(index)
+            if skip_special_tokens and index in self.all_special_ids:
+                continue
+            tokens.append(self._tokenizer.id_to_token(index))
+        return tokens
+
+    def tokenize(
+        self, text: TextInput, pair: Optional[TextInput] = None, add_special_tokens: bool = False
+    ) -> List[str]:
+        return self._tokenizer.encode(text, pair, add_special_tokens=add_special_tokens).tokens
+
+    def set_truncation_and_padding(
+        self, padding_strategy: PaddingStrategy, truncation_strategy: TruncationStrategy, max_length: int, stride: int,
+    ):
+        """ This contextmanager is in charge of defining the truncation and the padding strategies for fast tokenizers
+            (provided by HuggingFace tokenizers library) and restore the tokenizer settings afterwards.
+
+            This contextmanager assumes the provider tokenizer has no padding / truncation strategy
+            before the managed section. If your tokenizer set a padding / truncation strategy before,
+            then it will be reset to no padding/truncation when exiting the managed section.
+
+            Args:
+                tokenizer (BaseTokenizerFast): The tokenizer which will be used
+                max_length (int): The maximum size of the sequence
+                stride (int): The stride to use when handling overflow
+                strategy (str): Overflowing logic to use
+                pad_to_max_length (bool): Boolean indicating if the output needs to be padded up to max_length
+                padding_side (str): "left" or "right" indicating the direction the output sequence will be padded
+                pad_token_id (int): The integer representation of the padding token to use
+                pad_token_type_id (int): The integer representation of the padding token type to use
+                pad_token (str): The string representation of the padding token to use
+
+        """
+        # Set truncation and padding on the backend tokenizer
+        if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE:
+            self._tokenizer.enable_truncation(max_length, stride=stride, strategy=truncation_strategy.value)
+        else:
+            self._tokenizer.no_truncation()
+
+        if padding_strategy != PaddingStrategy.DO_NOT_PAD:
+            self._tokenizer.enable_padding(
+                length=max_length if padding_strategy == PaddingStrategy.MAX_LENGTH else None,
+                direction=self.padding_side,
+                pad_id=self.pad_token_id,
+                pad_type_id=self.pad_token_type_id,
+                pad_token=self.pad_token,
+            )
+        else:
+            self._tokenizer.no_padding()
+
+    def _batch_encode_plus(
+        self,
+        batch_text_or_text_pairs: Union[
+            List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair]
+        ],
+        add_special_tokens: bool = True,
+        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        is_pretokenized: bool = False,
+        return_tensors: Optional[str] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        return_lengths: bool = False,
+        verbose: bool = True,
+        **kwargs
+    ) -> BatchEncoding:
+
+        if not isinstance(batch_text_or_text_pairs, list):
+            raise ValueError(
+                "batch_text_or_text_pairs has to be a list (got {})".format(type(batch_text_or_text_pairs))
+            )
+
+        # Set the truncation and padding strategy and restore the initial configuration
+        self.set_truncation_and_padding(
+            padding_strategy=padding_strategy,
+            truncation_strategy=truncation_strategy,
+            max_length=max_length,
+            stride=stride,
+        )
+
+        # Avoid thread overhead if only one example.
+        if len(batch_text_or_text_pairs) == 1:
+            if isinstance(batch_text_or_text_pairs[0], tuple):
+                # We got a Tuple with a pair of sequences
+                encodings = self._tokenizer.encode(
+                    *batch_text_or_text_pairs[0],
+                    add_special_tokens=add_special_tokens,
+                    is_pretokenized=is_pretokenized,
+                )
+            else:
+                # We got a single sequence
+                encodings = self._tokenizer.encode(
+                    batch_text_or_text_pairs[0],
+                    add_special_tokens=add_special_tokens,
+                    is_pretokenized=is_pretokenized,
+                )
+            encodings = [encodings]
+        else:
+            encodings = self._tokenizer.encode_batch(
+                batch_text_or_text_pairs, add_special_tokens=add_special_tokens, is_pretokenized=is_pretokenized
+            )
+
+        # Convert encoding to dict
+        # `Tokens` has type: List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]]
+        # with nested dimensions corresponding to batch, overflows, sequence length
+        tokens = [
+            self._convert_encoding(
+                encoding=encoding,
+                return_token_type_ids=return_token_type_ids,
+                return_attention_mask=return_attention_mask,
+                return_overflowing_tokens=return_overflowing_tokens,
+                return_special_tokens_mask=return_special_tokens_mask,
+                return_offsets_mapping=return_offsets_mapping,
+                verbose=verbose,
+            )
+            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
+
+        # 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
+
+        return BatchEncoding(sanitized, encodings, tensor_type=return_tensors)
+
+    def _encode_plus(
+        self,
+        text: Union[TextInput, PreTokenizedInput],
+        text_pair: Optional[Union[TextInput, PreTokenizedInput]] = None,
+        add_special_tokens: bool = True,
+        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        is_pretokenized: bool = False,
+        return_tensors: Optional[bool] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        verbose: bool = True,
+        **kwargs
+    ) -> BatchEncoding:
+
+        batched_input = [(text, text_pair)] if text_pair else [text]
+        batched_output = self._batch_encode_plus(
+            batched_input,
+            is_pretokenized=is_pretokenized,
+            add_special_tokens=add_special_tokens,
+            padding_strategy=padding_strategy,
+            truncation_strategy=truncation_strategy,
+            max_length=max_length,
+            stride=stride,
+            return_tensors=return_tensors,
+            return_token_type_ids=return_token_type_ids,
+            return_attention_mask=return_attention_mask,
+            return_overflowing_tokens=return_overflowing_tokens,
+            return_special_tokens_mask=return_special_tokens_mask,
+            return_offsets_mapping=return_offsets_mapping,
+            verbose=verbose,
+            **kwargs,
+        )
+
+        # Return tensor is None, then we can remove the leading batch axis
+        # Overfolwing tokens are returned as a batch of output so we keep them in this case
+        if return_tensors is None and not return_overflowing_tokens:
+            batched_output = BatchEncoding(
+                {
+                    key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
+                    for key, value in batched_output.items()
+                },
+                batched_output.encodings,
+            )
+
+        return batched_output
+
+    def decode(
+        self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True
+    ) -> str:
+        text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
+
+        if clean_up_tokenization_spaces:
+            clean_text = self.clean_up_tokenization(text)
+            return clean_text
+        else:
+            return text
+
+    def save_vocabulary(self, save_directory: str) -> Tuple[str]:
+        if os.path.isdir(save_directory):
+            files = self._tokenizer.save_model(save_directory)
+        else:
+            folder, file = os.path.split(os.path.abspath(save_directory))
+            files = self._tokenizer.save_model(folder, name=file)
+
+        return tuple(files)

templates/adding_a_new_model/tests/test_tokenization_xxx.py

@@ -51,7 +51,7 @@ class XxxTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     def get_tokenizer(self, **kwargs):
         return XxxTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
-    def get_input_output_texts(self):
+    def get_input_output_texts(self, tokenizer):
         input_text = "UNwant\u00E9d,running"
         output_text = "unwanted, running"
         return input_text, output_text

tests/test_tokenization_albert.py

@@ -36,7 +36,7 @@ class AlbertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         tokenizer = AlbertTokenizer(SAMPLE_VOCAB)
         tokenizer.save_pretrained(self.tmpdirname)
 
-    def get_input_output_texts(self):
+    def get_input_output_texts(self, tokenizer):
         input_text = "this is a test"
         output_text = "this is a test"
         return input_text, output_text

tests/test_tokenization_bert.py

@@ -44,6 +44,8 @@ class BertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
             "[UNK]",
             "[CLS]",
             "[SEP]",
+            "[PAD]",
+            "[MASK]",
             "want",
             "##want",
             "##ed",
@@ -62,7 +64,7 @@ class BertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     def get_rust_tokenizer(self, **kwargs):
         return BertTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
 
-    def get_input_output_texts(self):
+    def get_input_output_texts(self, tokenizer):
         input_text = "UNwant\u00E9d,running"
         output_text = "unwanted, running"
         return input_text, output_text
@@ -72,7 +74,7 @@ class BertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
 
         tokens = tokenizer.tokenize("UNwant\u00E9d,running")
         self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
-        self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])
+        self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [9, 6, 7, 12, 10, 11])
 
     def test_rust_and_python_full_tokenizers(self):
         if not self.test_rust_tokenizer:
@@ -96,6 +98,25 @@ class BertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         rust_ids = rust_tokenizer.encode(sequence)
         self.assertListEqual(ids, rust_ids)
 
+        # With lower casing
+        tokenizer = self.get_tokenizer(do_lower_case=True)
+        rust_tokenizer = self.get_rust_tokenizer(do_lower_case=True)
+
+        sequence = "UNwant\u00E9d,running"
+
+        tokens = tokenizer.tokenize(sequence)
+        rust_tokens = rust_tokenizer.tokenize(sequence)
+        self.assertListEqual(tokens, rust_tokens)
+
+        ids = tokenizer.encode(sequence, add_special_tokens=False)
+        rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
+        self.assertListEqual(ids, rust_ids)
+
+        rust_tokenizer = self.get_rust_tokenizer()
+        ids = tokenizer.encode(sequence)
+        rust_ids = rust_tokenizer.encode(sequence)
+        self.assertListEqual(ids, rust_ids)
+
     def test_chinese(self):
         tokenizer = BasicTokenizer()
 

tests/test_tokenization_bert_japanese.py

@@ -60,11 +60,26 @@ class BertJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
 
-    def get_input_output_texts(self):
+    def get_input_output_texts(self, tokenizer):
         input_text = "こんにちは、世界。 \nこんばんは、世界。"
         output_text = "こんにちは 、 世界 。 こんばんは 、 世界 。"
         return input_text, output_text
 
+    def get_clean_sequence(self, tokenizer):
+        input_text, output_text = self.get_input_output_texts(tokenizer)
+        ids = tokenizer.encode(output_text, add_special_tokens=False)
+        text = tokenizer.decode(ids, clean_up_tokenization_spaces=False)
+        return text, ids
+
+    def test_pretokenized_inputs(self):
+        pass  # TODO add if relevant
+
+    def test_maximum_encoding_length_pair_input(self):
+        pass  # TODO add if relevant
+
+    def test_maximum_encoding_length_single_input(self):
+        pass  # TODO add if relevant
+
     def test_full_tokenizer(self):
         tokenizer = self.tokenizer_class(self.vocab_file)
 
@@ -157,11 +172,20 @@ class BertJapaneseCharacterTokenizationTest(TokenizerTesterMixin, unittest.TestC
     def get_tokenizer(self, **kwargs):
         return BertJapaneseTokenizer.from_pretrained(self.tmpdirname, subword_tokenizer_type="character", **kwargs)
 
-    def get_input_output_texts(self):
+    def get_input_output_texts(self, tokenizer):
         input_text = "こんにちは、世界。 \nこんばんは、世界。"
         output_text = "こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"
         return input_text, output_text
 
+    def test_pretokenized_inputs(self):
+        pass  # TODO add if relevant
+
+    def test_maximum_encoding_length_pair_input(self):
+        pass  # TODO add if relevant
+
+    def test_maximum_encoding_length_single_input(self):
+        pass  # TODO add if relevant
+
     def test_full_tokenizer(self):
         tokenizer = self.tokenizer_class(self.vocab_file, subword_tokenizer_type="character")
 

tests/test_tokenization_ctrl.py

@@ -46,7 +46,7 @@ class CTRLTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         kwargs.update(self.special_tokens_map)
         return CTRLTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
-    def get_input_output_texts(self):
+    def get_input_output_texts(self, tokenizer):
         input_text = "adapt react readapt apt"
         output_text = "adapt react readapt apt"
         return input_text, output_text

tests/test_tokenization_fast.py

@@ -27,7 +27,7 @@ logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 NON_ENGLISH_TAGS = ["chinese", "dutch", "french", "finnish", "german", "multilingual"]
-Tokenizer = namedtuple("Tokenizer", ["name", "rust_cls", "python_cls", "vocab_key", "filter"])
+Tokenizer = namedtuple("Tokenizer", ["name", "rust_cls", "python_cls", "vocab_key", "filter", "kwargs"])
 
 
 def filter_non_english(_: Tokenizer, pretrained_name: str):
@@ -60,10 +60,10 @@ class CommonFastTokenizerTest(unittest.TestCase):
                         tokenizer_r = tok_case.rust_cls.from_pretrained(pretrained_name)
                         tokenizer_p = tok_case.python_cls.from_pretrained(pretrained_name)
 
-                        self.fast_align_python(tokenizer_r, tokenizer_p)
+                        self.fast_align_python(tokenizer_r, tokenizer_p, tok_case, pretrained_name)
                         self.fast_only(tokenizer_r)
 
-    def fast_align_python(self, tokenizer_r, tokenizer_p):
+    def fast_align_python(self, tokenizer_r, tokenizer_p, tok_case, pretrained_name):
         # Check is_fast is set correctly
         self.assertFalse(tokenizer_p.is_fast)
         self.assertTrue(tokenizer_r.is_fast)
@@ -75,6 +75,7 @@ class CommonFastTokenizerTest(unittest.TestCase):
         self.assert_special_tokens_map_equal(tokenizer_r, tokenizer_p)
         self.assert_embeded_special_tokens(tokenizer_r, tokenizer_p)
         self.assert_padding(tokenizer_r, tokenizer_p)
+        self.assert_pretokenized_inputs(tokenizer_r, tokenizer_p)
         self.assert_create_token_type_ids(tokenizer_r, tokenizer_p)
         # TODO: enable for v3.0.0
         # self.assert_empty_output_no_special_tokens(tokenizer_r, tokenizer_p)
@@ -90,6 +91,7 @@ class CommonFastTokenizerTest(unittest.TestCase):
         self.assert_offsets_mapping(tokenizer_r)
         self.assert_add_special_tokens(tokenizer_r)
         self.assert_alignement_methods(tokenizer_r)
+        self.assert_batch_encode_dynamic_overflowing(tokenizer_r)
 
     def assert_alignement_methods(self, tokenizer_r):
         words = ["Wonderful", "no", "inspiration", "example", "with", "subtoken"]
@@ -169,7 +171,7 @@ class CommonFastTokenizerTest(unittest.TestCase):
         self.assertEqual(batch_encoding.word_to_chars(0, last_word_index).end, last_char_index + 1)
         self.assertEqual(batch_encoding.word_to_chars(last_batch_index, last_word_index).end, last_char_index + 1)
 
-    def assert_tokenization_python_rust_equals(self, tokenizer_p, tokenizer_r):
+    def assert_tokenization_python_rust_equals(self, tokenizer_r, tokenizer_p):
         # Ensure basic input match
         input_p = tokenizer_p.encode_plus(self._data)
         input_r = tokenizer_r.encode_plus(self._data)
@@ -184,18 +186,22 @@ class CommonFastTokenizerTest(unittest.TestCase):
             self.assertSequenceEqual(input_pairs_p[key], input_pairs_r[key])
 
         # Ensure truncation match
-        input_p = tokenizer_p.encode_plus(self._data, max_length=512)
-        input_r = tokenizer_r.encode_plus(self._data, max_length=512)
+        input_p = tokenizer_p.encode_plus(self._data, max_length=512, truncation=True)
+        input_r = tokenizer_r.encode_plus(self._data, max_length=512, truncation=True)
 
         for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
             self.assertSequenceEqual(input_p[key], input_r[key])
 
         # Ensure truncation with stride match
-        input_p = tokenizer_p.encode_plus(self._data, max_length=512, stride=3, return_overflowing_tokens=True)
-        input_r = tokenizer_r.encode_plus(self._data, max_length=512, stride=3, return_overflowing_tokens=True)
+        input_p = tokenizer_p.encode_plus(
+            self._data, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
+        )
+        input_r = tokenizer_r.encode_plus(
+            self._data, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
+        )
 
         for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
-            self.assertSequenceEqual(input_p[key], input_r[key])
+            self.assertSequenceEqual(input_p[key], input_r[key][0])
 
     def assert_num_special_tokens_to_add_equal(self, tokenizer_r, tokenizer_p):
         # Check we have the same number of added_tokens for both pair and non-pair inputs.
@@ -274,9 +280,14 @@ class CommonFastTokenizerTest(unittest.TestCase):
         """
         returned_tensor = "pt" if is_torch_available() else "tf"
 
+        if not tokenizer.pad_token or tokenizer.pad_token_id < 0:
+            return
+
         tokens = tokenizer.encode_plus(
             "HuggingFace is solving NLP one commit at a time",
             max_length=6,
+            padding=True,
+            truncation=True,
             return_tensors=returned_tensor,
             return_overflowing_tokens=True,
         )
@@ -288,7 +299,8 @@ class CommonFastTokenizerTest(unittest.TestCase):
         tokens = tokenizer.batch_encode_plus(
             ["HuggingFace is solving NLP one commit at a time"],
             max_length=6,
-            pad_to_max_len=True,
+            padding=True,
+            truncation="only_first",
             return_tensors=returned_tensor,
             return_overflowing_tokens=True,
         )
@@ -301,7 +313,8 @@ class CommonFastTokenizerTest(unittest.TestCase):
         tokens = tokenizer.batch_encode_plus(
             ["HuggingFace is solving NLP one commit at a time", "Very tiny input"],
             max_length=6,
-            pad_to_max_len=True,
+            padding=True,
+            truncation="only_first",
             return_tensors=returned_tensor,
             return_overflowing_tokens=True,
         )
@@ -310,6 +323,58 @@ class CommonFastTokenizerTest(unittest.TestCase):
             self.assertEqual(len(tokens[key].shape), 2)
             self.assertEqual(tokens[key].shape[-1], 6)
 
+    def assert_pretokenized_inputs(self, tokenizer_r, tokenizer_p):
+        # Input string
+        pretokenized_input_simple = "This is a sample input".split()
+        pretokenized_input_pair = "This is a sample pair".split()
+
+        # Test encode for pretokenized inputs
+        output_r = tokenizer_r.encode(pretokenized_input_simple, is_pretokenized=True)
+        output_p = tokenizer_p.encode(pretokenized_input_simple, is_pretokenized=True)
+        self.assertEqual(output_p, output_r)
+
+        kwargs = {
+            "is_pretokenized": True,
+            "return_token_type_ids": True,
+            "return_attention_mask": True,
+            "return_overflowing_tokens": False,
+            "return_special_tokens_mask": True,
+            "return_offsets_mapping": False,  # Not implemented in python tokenizers
+        }
+        # Test encode_plus for pretokenized inputs
+        output_r = tokenizer_r.encode_plus(pretokenized_input_simple, **kwargs)
+        output_p = tokenizer_p.encode_plus(pretokenized_input_simple, **kwargs)
+        for key in output_p.keys():
+            self.assertEqual(output_p[key], output_r[key])
+
+        # Test batch_encode_plus for pretokenized inputs
+        input_batch = ([pretokenized_input_simple] * 2) + [pretokenized_input_simple + pretokenized_input_pair]
+        output_r = tokenizer_r.batch_encode_plus(input_batch, **kwargs)
+        output_p = tokenizer_p.batch_encode_plus(input_batch, **kwargs)
+        for key in output_p.keys():
+            self.assertEqual(output_p[key], output_r[key])
+
+        # Test encode for pretokenized inputs pairs
+        output_r = tokenizer_r.encode(pretokenized_input_simple, pretokenized_input_pair, is_pretokenized=True)
+        output_p = tokenizer_p.encode(pretokenized_input_simple, pretokenized_input_pair, is_pretokenized=True)
+        self.assertEqual(output_p, output_r)
+
+        # Test encode_plus for pretokenized inputs
+        output_r = tokenizer_r.encode_plus(pretokenized_input_simple, pretokenized_input_pair, **kwargs)
+        output_p = tokenizer_p.encode_plus(pretokenized_input_simple, pretokenized_input_pair, **kwargs)
+        for key in output_p.keys():
+            self.assertEqual(output_p[key], output_r[key])
+
+        # Test batch_encode_plus for pretokenized inputs
+        input_batch_pair = ([pretokenized_input_simple, pretokenized_input_pair] * 2) + [
+            pretokenized_input_simple + pretokenized_input_pair,
+            pretokenized_input_pair,
+        ]
+        output_r = tokenizer_r.batch_encode_plus(input_batch_pair, **kwargs)
+        output_p = tokenizer_p.batch_encode_plus(input_batch_pair, **kwargs)
+        for key in output_p.keys():
+            self.assertEqual(output_p[key], output_r[key])
+
     def assert_create_token_type_ids(self, tokenizer_r, tokenizer_p):
         input_simple = [1, 2, 3]
         input_pair = [1, 2, 3]
@@ -357,17 +422,22 @@ class CommonFastTokenizerTest(unittest.TestCase):
         def assert_padded_input_match(input_r: list, input_p: list, max_length: int):
 
             # Ensure we match max_length
-            self.assertEqual(len(input_r), max_length), self.assertEqual(len(input_p), max_length)
+            self.assertEqual(len(input_r), max_length)
+            self.assertEqual(len(input_p), max_length)
 
             # Ensure the number of padded tokens is the same
             padded_tokens_r = list(takewhile(lambda i: i == tokenizer_r.pad_token_id, reversed(input_r)))
             padded_tokens_p = list(takewhile(lambda i: i == tokenizer_p.pad_token_id, reversed(input_p)))
             self.assertSequenceEqual(padded_tokens_r, padded_tokens_p)
 
-        def assert_batch_padded_input_match(input_r: dict, input_p: dict):
+        def assert_batch_padded_input_match(input_r: dict, input_p: dict, max_length: int):
             for i_r in input_r.values():
-                self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), 15), self.assertEqual(len(i_r[1]), 15)
-                self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), 15), self.assertEqual(len(i_r[1]), 15)
+                self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
+                    len(i_r[1]), max_length
+                )
+                self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
+                    len(i_r[1]), max_length
+                )
 
             for i_r, i_p in zip(input_r["input_ids"], input_p["input_ids"]):
                 assert_padded_input_match(i_r, i_p, max_length)
@@ -375,12 +445,19 @@ class CommonFastTokenizerTest(unittest.TestCase):
             for i_r, i_p in zip(input_r["attention_mask"], input_p["attention_mask"]):
                 self.assertSequenceEqual(i_r, i_p)
 
-        # Simple input
+        # Encode - Simple input
         input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
         input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
         assert_padded_input_match(input_r, input_p, max_length)
+        input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, padding="max_length")
+        input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, padding="max_length")
+        assert_padded_input_match(input_r, input_p, max_length)
 
-        # Pair input
+        input_r = tokenizer_r.encode("This is a simple input", padding="longest")
+        input_p = tokenizer_p.encode("This is a simple input", padding=True)
+        assert_padded_input_match(input_r, input_p, len(input_r))
+
+        # Encode - Pair input
         input_r = tokenizer_r.encode(
             "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
         )
@@ -388,14 +465,34 @@ class CommonFastTokenizerTest(unittest.TestCase):
             "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
         )
         assert_padded_input_match(input_r, input_p, max_length)
+        input_r = tokenizer_r.encode(
+            "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
+        )
+        input_p = tokenizer_p.encode(
+            "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
+        )
+        assert_padded_input_match(input_r, input_p, max_length)
+        input_r = tokenizer_r.encode("This is a simple input", "This is a pair", padding=True)
+        input_p = tokenizer_p.encode("This is a simple input", "This is a pair", padding="longest")
+        assert_padded_input_match(input_r, input_p, len(input_r))
 
-        # Simple input
+        # Encode_plus - Simple input
         input_r = tokenizer_r.encode_plus("This is a simple input", max_length=max_length, pad_to_max_length=True)
         input_p = tokenizer_p.encode_plus("This is a simple input", max_length=max_length, pad_to_max_length=True)
         assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
         self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
+        input_r = tokenizer_r.encode_plus("This is a simple input", max_length=max_length, padding="max_length")
+        input_p = tokenizer_p.encode_plus("This is a simple input", max_length=max_length, padding="max_length")
+        assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
+        self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
 
-        # Pair input
+        input_r = tokenizer_r.encode_plus("This is a simple input", padding="longest")
+        input_p = tokenizer_p.encode_plus("This is a simple input", padding=True)
+        assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]))
+
+        self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
+
+        # Encode_plus - Pair input
         input_r = tokenizer_r.encode_plus(
             "This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
         )
@@ -404,34 +501,130 @@ class CommonFastTokenizerTest(unittest.TestCase):
         )
         assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
         self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
+        input_r = tokenizer_r.encode_plus(
+            "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
+        )
+        input_p = tokenizer_p.encode_plus(
+            "This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
+        )
+        assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
+        self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
+        input_r = tokenizer_r.encode_plus("This is a simple input", "This is a pair", padding="longest")
+        input_p = tokenizer_p.encode_plus("This is a simple input", "This is a pair", padding=True)
+        assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]))
+        self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
 
-        # Simple input
+        # Batch_encode_plus - Simple input
         input_r = tokenizer_r.batch_encode_plus(
             ["This is a simple input 1", "This is a simple input 2"], max_length=max_length, pad_to_max_length=True
         )
         input_p = tokenizer_p.batch_encode_plus(
             ["This is a simple input 1", "This is a simple input 2"], max_length=max_length, pad_to_max_length=True
         )
-        assert_batch_padded_input_match(input_r, input_p)
+        assert_batch_padded_input_match(input_r, input_p, max_length)
 
-        # Pair input
+        input_r = tokenizer_r.batch_encode_plus(
+            ["This is a simple input 1", "This is a simple input 2"], max_length=max_length, padding="max_length",
+        )
+        input_p = tokenizer_p.batch_encode_plus(
+            ["This is a simple input 1", "This is a simple input 2"], max_length=max_length, padding="max_length",
+        )
+        assert_batch_padded_input_match(input_r, input_p, max_length)
+
+        input_r = tokenizer_r.batch_encode_plus(
+            ["This is a simple input 1", "This is a simple input 2"], max_length=max_length, padding="longest",
+        )
+        input_p = tokenizer_p.batch_encode_plus(
+            ["This is a simple input 1", "This is a simple input 2"], max_length=max_length, padding=True,
+        )
+        assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]))
+
+        input_r = tokenizer_r.batch_encode_plus(
+            ["This is a simple input 1", "This is a simple input 2"], padding="longest"
+        )
+        input_p = tokenizer_p.batch_encode_plus(["This is a simple input 1", "This is a simple input 2"], padding=True)
+        assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]))
+
+        # Batch_encode_plus - Pair input
         input_r = tokenizer_r.batch_encode_plus(
             [
                 ("This is a simple input 1", "This is a simple input 2"),
                 ("This is a simple pair 1", "This is a simple pair 2"),
             ],
-            max_length=15,
-            pad_to_max_length=True,
+            max_length=max_length,
+            truncation=True,
+            padding="max_length",
         )
         input_p = tokenizer_p.batch_encode_plus(
             [
                 ("This is a simple input 1", "This is a simple input 2"),
                 ("This is a simple pair 1", "This is a simple pair 2"),
             ],
-            max_length=15,
-            pad_to_max_length=True,
+            max_length=max_length,
+            truncation=True,
+            padding="max_length",
         )
-        assert_batch_padded_input_match(input_r, input_p)
+        assert_batch_padded_input_match(input_r, input_p, max_length)
+
+        input_r = tokenizer_r.batch_encode_plus(
+            [
+                ("This is a simple input 1", "This is a simple input 2"),
+                ("This is a simple pair 1", "This is a simple pair 2"),
+            ],
+            padding=True,
+        )
+        input_p = tokenizer_p.batch_encode_plus(
+            [
+                ("This is a simple input 1", "This is a simple input 2"),
+                ("This is a simple pair 1", "This is a simple pair 2"),
+            ],
+            padding="longest",
+        )
+        assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]))
+
+        # Using pad on single examples after tokenization
+        input_r = tokenizer_r.encode_plus("This is a input 1")
+        input_r = tokenizer_r.pad(input_r)
+
+        input_p = tokenizer_r.encode_plus("This is a input 1")
+        input_p = tokenizer_r.pad(input_p)
+
+        assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]))
+
+        # Using pad on single examples after tokenization
+        input_r = tokenizer_r.encode_plus("This is a input 1")
+        input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
+
+        input_p = tokenizer_r.encode_plus("This is a input 1")
+        input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
+
+        assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
+
+        # Using pad after tokenization
+        input_r = tokenizer_r.batch_encode_plus(
+            ["This is a input 1", "This is a much longer input whilch should be padded"]
+        )
+        input_r = tokenizer_r.pad(input_r)
+
+        input_p = tokenizer_r.batch_encode_plus(
+            ["This is a input 1", "This is a much longer input whilch should be padded"]
+        )
+        input_p = tokenizer_r.pad(input_p)
+
+        assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]))
+
+        # Using pad after tokenization
+        input_r = tokenizer_r.batch_encode_plus(
+            ["This is a input 1", "This is a much longer input whilch should be padded"]
+        )
+        input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
+
+        input_p = tokenizer_r.batch_encode_plus(
+            ["This is a input 1", "This is a much longer input whilch should be padded"]
+        )
+        input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
+
+        assert_batch_padded_input_match(input_r, input_p, max_length)
 
     def assert_save_pretrained(self, tokenizer_r, tokenizer_p):
         # Checks it save with the same files
@@ -503,8 +696,10 @@ class WordPieceFastTokenizerTest(CommonFastTokenizerTest):
 
     TOKENIZERS_CLASSES = frozenset(
         [
-            Tokenizer("Bert", BertTokenizerFast, BertTokenizer, "vocab_file", filter_non_english),
-            Tokenizer("DistilBert", DistilBertTokenizerFast, DistilBertTokenizer, "vocab_file", filter_non_english),
+            Tokenizer("Bert", BertTokenizerFast, BertTokenizer, "vocab_file", filter_non_english, None),
+            Tokenizer(
+                "DistilBert", DistilBertTokenizerFast, DistilBertTokenizer, "vocab_file", filter_non_english, None
+            ),
         ]
     )
 
@@ -552,7 +747,7 @@ class WordPieceFastTokenizerTest(CommonFastTokenizerTest):
 
 class RobertaFastTokenizerTest(CommonFastTokenizerTest):
     TOKENIZERS_CLASSES = frozenset(
-        [Tokenizer("Roberta", RobertaTokenizerFast, RobertaTokenizer, "vocab_file", filter_roberta_detectors)]
+        [Tokenizer("Roberta", RobertaTokenizerFast, RobertaTokenizer, "vocab_file", filter_roberta_detectors, None)]
     )
 
     def assert_embeded_special_tokens(self, tokenizer_r, tokenizer_p):
@@ -580,10 +775,30 @@ class RobertaFastTokenizerTest(CommonFastTokenizerTest):
 
 class NoPaddingTokenFastTokenizerMatchingTest(CommonFastTokenizerTest):
     TOKENIZERS_CLASSES = [
-        Tokenizer("OpenAI GPT", OpenAIGPTTokenizerFast, OpenAIGPTTokenizer, "vocab_file", None),
-        Tokenizer("GPT2", GPT2TokenizerFast, GPT2Tokenizer, "vocab_file", None),
+        Tokenizer("OpenAI GPT", OpenAIGPTTokenizerFast, OpenAIGPTTokenizer, "vocab_file", None, None),
+        Tokenizer("GPT2", GPT2TokenizerFast, GPT2Tokenizer, "vocab_file", None, [("add_prefix_space", True)]),
     ]
 
+    def fast_align_python(self, tokenizer_r, tokenizer_p, tok_case, pretrained_name):
+        # Check is_fast is set correctly
+        self.assertFalse(tokenizer_p.is_fast)
+        self.assertTrue(tokenizer_r.is_fast)
+
+        # Check that Rust and Python align
+        self.assert_tokenization_python_rust_equals(tokenizer_r, tokenizer_p)
+        self.assert_num_special_tokens_to_add_equal(tokenizer_r, tokenizer_p)
+        self.assert_max_length_equal(tokenizer_r, tokenizer_p)
+        self.assert_special_tokens_map_equal(tokenizer_r, tokenizer_p)
+        self.assert_embeded_special_tokens(tokenizer_r, tokenizer_p)
+        self.assert_padding(tokenizer_r, tokenizer_p)
+
+        # Specific for
+        kwargs = {}
+        if tok_case.kwargs is not None:
+            kwargs = dict(tok_case.kwargs)
+        tokenizer_r = tok_case.rust_cls.from_pretrained(pretrained_name, **kwargs)
+        self.assert_pretokenized_inputs(tokenizer_r, tokenizer_p)
+
     def assert_padding(self, tokenizer_r, tokenizer_p, max_length=15):
         # Simple input
         s = "This is a simple input"
@@ -595,27 +810,31 @@ class NoPaddingTokenFastTokenizerMatchingTest(CommonFastTokenizerTest):
         ]
 
         # Simple input tests
-        self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, pad_to_max_length=True)
+        self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
 
         # Simple input
-        self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, pad_to_max_length=True)
+        self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
 
         # Simple input
-        self.assertRaises(ValueError, tokenizer_r.batch_encode_plus, s2, max_length=max_length, pad_to_max_length=True)
+        self.assertRaises(
+            ValueError, tokenizer_r.batch_encode_plus, s2, max_length=max_length, padding="max_length",
+        )
 
         # Pair input
-        self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, pad_to_max_length=True)
+        self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
 
         # Pair input
-        self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, pad_to_max_length=True)
+        self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
 
         # Pair input
-        self.assertRaises(ValueError, tokenizer_r.batch_encode_plus, p2, max_length=max_length, pad_to_max_length=True)
+        self.assertRaises(
+            ValueError, tokenizer_r.batch_encode_plus, p2, max_length=max_length, padding="max_length",
+        )
 
 
 class TransfoXLFastTokenizerTest(NoPaddingTokenFastTokenizerMatchingTest):
     TOKENIZERS_CLASSES = frozenset(
-        [Tokenizer("TransfoXL", TransfoXLTokenizerFast, TransfoXLTokenizer, "pretrained_vocab_file", None)]
+        [Tokenizer("TransfoXL", TransfoXLTokenizerFast, TransfoXLTokenizer, "pretrained_vocab_file", None, None)]
     )
 
     @require_torch

tests/test_tokenization_gpt2.py

@@ -53,6 +53,7 @@ class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
             "\u0120newer",
             "\u0120wider",
             "<unk>",
+            "<|endoftext|>",
         ]
         vocab_tokens = dict(zip(vocab, range(len(vocab))))
         merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
@@ -73,7 +74,7 @@ class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         kwargs.update(self.special_tokens_map)
         return GPT2TokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
 
-    def get_input_output_texts(self):
+    def get_input_output_texts(self, tokenizer):
         input_text = "lower newer"
         output_text = "lower newer"
         return input_text, output_text
@@ -118,3 +119,8 @@ class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         input_tokens = tokens + [rust_tokenizer.unk_token]
         input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
+
+    def test_pretokenized_inputs(self, *args, **kwargs):
+        # It's very difficult to mix/test pretokenization with byte-level
+        # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
+        pass

tests/test_tokenization_marian.py

@@ -51,10 +51,10 @@ class MarianTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         tokenizer = MarianTokenizer.from_pretrained(self.tmpdirname)
         tokenizer.save_pretrained(self.tmpdirname)
 
-    def get_tokenizer(self, max_len=None, **kwargs) -> MarianTokenizer:
-        return MarianTokenizer.from_pretrained(self.tmpdirname, model_max_length=max_len, **kwargs)
+    def get_tokenizer(self, **kwargs) -> MarianTokenizer:
+        return MarianTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
-    def get_input_output_texts(self):
+    def get_input_output_texts(self, tokenizer):
         return (
             "This is a test",
             "This is a test",

tests/test_tokenization_openai.py

@@ -64,7 +64,7 @@ class OpenAIGPTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         with open(self.merges_file, "w") as fp:
             fp.write("\n".join(merges))
 
-    def get_input_output_texts(self):
+    def get_input_output_texts(self, tokenizer):
         return "lower newer", "lower newer"
 
     def test_full_tokenizer(self):

tests/test_tokenization_roberta.py

@@ -18,7 +18,7 @@ import json
 import os
 import unittest
 
-from transformers.tokenization_roberta import VOCAB_FILES_NAMES, RobertaTokenizer
+from transformers.tokenization_roberta import VOCAB_FILES_NAMES, RobertaTokenizer, RobertaTokenizerFast
 
 from .test_tokenization_common import TokenizerTesterMixin
 from .utils import slow
@@ -68,7 +68,11 @@ class RobertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         kwargs.update(self.special_tokens_map)
         return RobertaTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
-    def get_input_output_texts(self):
+    def get_rust_tokenizer(self, **kwargs):
+        kwargs.update(self.special_tokens_map)
+        return RobertaTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_input_output_texts(self, tokenizer):
         input_text = "lower newer"
         output_text = "lower newer"
         return input_text, output_text

tests/test_tokenization_transfo_xl.py

@@ -56,7 +56,7 @@ class TransfoXLTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         kwargs["lower_case"] = True
         return TransfoXLTokenizer.from_pretrained(self.tmpdirname, **kwargs)
 
-    def get_input_output_texts(self):
+    def get_input_output_texts(self, tokenizer):
         input_text = "<unk> UNwanted , running"
         output_text = "<unk> unwanted, running"
         return input_text, output_text

tests/test_tokenization_xlm.py

@@ -65,7 +65,7 @@ class XLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
         with open(self.merges_file, "w") as fp:
             fp.write("\n".join(merges))
 
-    def get_input_output_texts(self):
+    def get_input_output_texts(self, tokenizer):
         input_text = "lower newer"
         output_text = "lower newer"
         return input_text, output_text