runing dataset.map, it raises TypeError: can't pickle Tokenizer objects 
 I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.

```
def convert_to_features(example):
    # Tokenize contexts and questions (as pairs of inputs)
    input_pairs = [example['question'], example['context']]
    encodings = tokenizer.encode_plus(input_pairs, pad_to_max_length=True, max_length=512)
    context_encodings = tokenizer.encode_plus(example['context'])
    

    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methodes.
    # this will give us the position of answer span in the context text
    start_idx, end_idx = get_correct_alignement(example['context'], example['answers'])
    start_positions_context = context_encodings.char_to_token(start_idx)
    end_positions_context = context_encodings.char_to_token(end_idx-1)

    # here we will compute the start and end position of the answer in the whole example
    # as the example is encoded like this 
 question

 context

    # and we know the postion of the answer in the context
    # we can just find out the index of the sep token and then add that to position + 1 (+1 because there are two sep tokens)
    # this will give us the position of the answer span in whole example 
    sep_idx = encodings['input_ids'].index(tokenizer.sep_token_id)
    start_positions = start_positions_context + sep_idx + 1
    end_positions = end_positions_context + sep_idx + 1

    if end_positions > 512:
      start_positions, end_positions = 0, 0

    encodings.update({'start_positions': start_positions,
                      'end_positions': end_positions,
                      'attention_mask': encodings['attention_mask']})
    return encodings
```

Then I run `dataset.map(convert_to_features)`, it raise
```
In [59]: a.map(convert_to_features)                                                                                                                        
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-59-c453b508761d> in <module>
----> 1 a.map(convert_to_features)

/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
   1242                 fn_kwargs=fn_kwargs,
   1243                 new_fingerprint=new_fingerprint,
-> 1244                 update_data=update_data,
   1245             )
   1246         else:

/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
    151             "output_all_columns": self._output_all_columns,
    152         }
--> 153         out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
    154         if new_format["columns"] is not None:
    155             new_format["columns"] = list(set(new_format["columns"]) & set(out.column_names))

/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
    156                         kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name
    157                         kwargs[fingerprint_name] = update_fingerprint(
--> 158                             self._fingerprint, transform, kwargs_for_fingerprint
    159                         )
    160 

/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)
    103     for key in sorted(transform_args):
    104         hasher.update(key)
--> 105         hasher.update(transform_args[key])
    106     return hasher.hexdigest()
    107 

/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in update(self, value)
     55     def update(self, value):
     56         self.m.update(f"=={type(value)}==".encode("utf8"))
---> 57         self.m.update(self.hash(value).encode("utf-8"))
     58 
     59     def hexdigest(self):

/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in hash(cls, value)
     51             return cls.dispatch[type(value)](cls, value)
     52         else:
---> 53             return cls.hash_default(value)
     54 
     55     def update(self, value):

/opt/conda/lib/python3.7/site-packages/datasets/fingerprint.py in hash_default(cls, value)
     44     @classmethod
     45     def hash_default(cls, value):
---> 46         return cls.hash_bytes(dumps(value))
     47 
     48     @classmethod

/opt/conda/lib/python3.7/site-packages/datasets/utils/py_utils.py in dumps(obj)
    365     file = StringIO()
    366     with _no_cache_fields(obj):
--> 367         dump(obj, file)
    368     return file.getvalue()
    369 

/opt/conda/lib/python3.7/site-packages/datasets/utils/py_utils.py in dump(obj, file)
    337 def dump(obj, file):
    338     """pickle an object to a file"""
--> 339     Pickler(file, recurse=True).dump(obj)
    340     return
    341 

/opt/conda/lib/python3.7/site-packages/dill/_dill.py in dump(self, obj)
    444             raise PicklingError(msg)
    445         else:
--> 446             StockPickler.dump(self, obj)
    447         stack.clear()  # clear record of 'recursion-sensitive' pickled objects
    448         return

/opt/conda/lib/python3.7/pickle.py in dump(self, obj)
    435         if self.proto >= 4:
    436             self.framer.start_framing()
--> 437         self.save(obj)
    438         self.write(STOP)
    439         self.framer.end_framing()

/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
    502         f = self.dispatch.get(t)
    503         if f is not None:
--> 504             f(self, obj) # Call unbound method with explicit self
    505             return
    506 

/opt/conda/lib/python3.7/site-packages/dill/_dill.py in save_function(pickler, obj)
   1436                                 globs, obj.__name__,
   1437                                 obj.__defaults__, obj.__closure__,
-> 1438                                 obj.__dict__, fkwdefaults), obj=obj)
   1439         else:
   1440             _super = ('super' in getattr(obj.func_code,'co_names',())) and (_byref is not None) and getattr(pickler, '_recurse', False)

/opt/conda/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
    636         else:
    637             save(func)
--> 638             save(args)
    639             write(REDUCE)
    640 

/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
    502         f = self.dispatch.get(t)
    503         if f is not None:
--> 504             f(self, obj) # Call unbound method with explicit self
    505             return
    506 

/opt/conda/lib/python3.7/pickle.py in save_tuple(self, obj)
    787         write(MARK)
    788         for element in obj:
--> 789             save(element)
    790 
    791         if id(obj) in memo:

/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
    502         f = self.dispatch.get(t)
    503         if f is not None:
--> 504             f(self, obj) # Call unbound method with explicit self
    505             return
    506 

/opt/conda/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
    931             # we only care about session the first pass thru
    932             pickler._session = False
--> 933         StockPickler.save_dict(pickler, obj)
    934         log.info("# D2")
    935     return

/opt/conda/lib/python3.7/pickle.py in save_dict(self, obj)
    857 
    858         self.memoize(obj)
--> 859         self._batch_setitems(obj.items())
    860 
    861     dispatch[dict] = save_dict

/opt/conda/lib/python3.7/pickle.py in _batch_setitems(self, items)
    883                 for k, v in tmp:
    884                     save(k)
--> 885                     save(v)
    886                 write(SETITEMS)
    887             elif n:

/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
    547 
    548         # Save the reduce() output and finally memoize the object
--> 549         self.save_reduce(obj=obj, *rv)
    550 
    551     def persistent_id(self, obj):

/opt/conda/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
    660 
    661         if state is not None:
--> 662             save(state)
    663             write(BUILD)
    664 

/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
    502         f = self.dispatch.get(t)
    503         if f is not None:
--> 504             f(self, obj) # Call unbound method with explicit self
    505             return
    506 

/opt/conda/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
    931             # we only care about session the first pass thru
    932             pickler._session = False
--> 933         StockPickler.save_dict(pickler, obj)
    934         log.info("# D2")
    935     return

/opt/conda/lib/python3.7/pickle.py in save_dict(self, obj)
    857 
    858         self.memoize(obj)
--> 859         self._batch_setitems(obj.items())
    860 
    861     dispatch[dict] = save_dict

/opt/conda/lib/python3.7/pickle.py in _batch_setitems(self, items)
    883                 for k, v in tmp:
    884                     save(k)
--> 885                     save(v)
    886                 write(SETITEMS)
    887             elif n:

/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
    547 
    548         # Save the reduce() output and finally memoize the object
--> 549         self.save_reduce(obj=obj, *rv)
    550 
    551     def persistent_id(self, obj):

/opt/conda/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
    660 
    661         if state is not None:
--> 662             save(state)
    663             write(BUILD)
    664 

/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
    502         f = self.dispatch.get(t)
    503         if f is not None:
--> 504             f(self, obj) # Call unbound method with explicit self
    505             return
    506 

/opt/conda/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
    931             # we only care about session the first pass thru
    932             pickler._session = False
--> 933         StockPickler.save_dict(pickler, obj)
    934         log.info("# D2")
    935     return

/opt/conda/lib/python3.7/pickle.py in save_dict(self, obj)
    857 
    858         self.memoize(obj)
--> 859         self._batch_setitems(obj.items())
    860 
    861     dispatch[dict] = save_dict

/opt/conda/lib/python3.7/pickle.py in _batch_setitems(self, items)
    883                 for k, v in tmp:
    884                     save(k)
--> 885                     save(v)
    886                 write(SETITEMS)
    887             elif n:

/opt/conda/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
    522             reduce = getattr(obj, "__reduce_ex__", None)
    523             if reduce is not None:
--> 524                 rv = reduce(self.proto)
    525             else:
    526                 reduce = getattr(obj, "__reduce__", None)

TypeError: can't pickle Tokenizer objects
```

 
 > I have the same issue with `transformers/BertJapaneseTokenizer`.

It looks like it this tokenizer is not supported unfortunately.
This is because `t.word_tokenizer.mecab` is a `fugashi.fugashi.GenericTagger` which is not compatible with pickle nor dill.

We need objects passes to `map` to be picklable for our caching system to work properly.
Here it crashes because the caching system is not able to pickle the GenericTagger.

\> Maybe you can create an issue on [fugashi](https://github.com/polm/fugashi/issues) 's repo and ask to make `fugashi.fugashi.GenericTagger` compatible with pickle ?

What you can do in the meantime is use a picklable wrapper of the tokenizer:


```python
from transformers import BertJapaneseTokenizer, MecabTokenizer

class PicklableTokenizer(BertJapaneseTokenizer):

    def __getstate__(self):
        state = dict(self.__dict__)
        state["do_lower_case"] = self.word_tokenizer.do_lower_case
        state["never_split"] = self.word_tokenizer.never_split 
        del state["word_tokenizer"]
        return state

    def __setstate__(self, state):
        do_lower_case = state.pop("do_lower_case")
        never_split = state.pop("never_split")
        self.__dict__ = state
        self.word_tokenizer = MecabTokenizer(
            do_lower_case=do_lower_case, never_split=never_split)
        )

t = PicklableTokenizer.from_pretrained("cl-tohoku/bert-base-japanese-whole-word-masking")
encoded = train_ds.map(lambda examples: {'tokens': t.encode(examples['title'])}, batched=True)  # it works
```