Inconsistent caching behaviour when using `Dataset.map()` with a `new_fingerprint` and `num_proc>1`

## Describe the bug
Caching does not work when using `Dataset.map()` with:
1. a function that cannot be deterministically fingerprinted 
2. `num_proc>1`
3.  using a custom fingerprint set with the argument `new_fingerprint`. 

This means that the dataset will be mapped with the function for each and every call, which does not happen if `num_proc==1`. In that case (`num_proc==1`) subsequent calls will load the transformed dataset from the cache, which is the  expected behaviour. The example can easily be translated into a unit test.

I have a fix and will submit a pull request asap. 

## Steps to reproduce the bug
```python
import hashlib
import json
import os
from typing import Dict, Any

import numpy as np
from datasets import load_dataset, Dataset

Batch = Dict[str, Any]
filename = 'example.json'


class Transformation():
    """A transformation with a random state that cannot be fingerprinted"""

    def __init__(self):
        self.state = np.random.random()

    def __call__(self, batch: Batch) -> Batch:
        batch['x'] = [np.random.random() for _ in batch['x']]
        return batch


def generate_dataset():
    """generate a simple dataset"""
    rgn = np.random.RandomState(24)
    data = {
        'data': [{'x': float(y), 'y': -float(y)} for y in
                 rgn.random(size=(1000,))]}
    if not os.path.exists(filename):
        with open(filename, 'w') as f:
            f.write(json.dumps(data))

    return filename


def process_dataset_with_cache(num_proc=1, remove_cache=False,
                               cache_expected_to_exist=False):

    # load the generated dataset
    dset: Dataset = next(
        iter(load_dataset('json', data_files=filename, field='data').values()))
    new_fingerprint = hashlib.md5("static-id".encode("utf8")).hexdigest()

    # get the expected cached path
    cache_path = dset._get_cache_file_path(new_fingerprint)
    if remove_cache and os.path.exists(cache_path):
        os.remove(cache_path)

     # check that the cache exists, and print a statement
    # if was actually expected to exist
    cache_exist = os.path.exists(cache_path)
    print(f"> cache file exists={cache_exist}")
    if cache_expected_to_exist and not cache_exist:
        print("=== Cache does not exist! ====")

    # apply the transformation with the new fingerprint
    dset = dset.map(
        Transformation(),
        batched=True,
        num_proc=num_proc,
        new_fingerprint=new_fingerprint,
        desc="mapping dataset with transformation")


generate_dataset()

for num_proc in [1, 2]:
    print(f"# num_proc={num_proc}, first pass")
    # first pass to generate the cache (always create a new cache here)
    process_dataset_with_cache(remove_cache=True,
                               num_proc=num_proc,
                               cache_expected_to_exist=False)
    print(f"# num_proc={num_proc}, second pass")
    # second pass, expects the cache to exist
    process_dataset_with_cache(remove_cache=False,
                               num_proc=num_proc,
                               cache_expected_to_exist=True)

os.remove(filename)

```

## Expected results
In the above python example, with `num_proc=2`, the **cache file should exist in the second call** of `process_dataset_with_cache`  ("=== Cache does not exist! ====" should not be printed). 
When the cache is successfully created, `map()` is called only one time.

## Actual results
In the above python example, with `num_proc=2`, the **cache does not exist in the second call** of `process_dataset_with_cache` (this results in printing "=== Cache does not exist! ===="). 
Because the cache doesn't exist, the `map()` method is executed a second time and the dataset is not loaded from the cache.

## Environment info
- `datasets` version: 1.12.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 5.0.0


Following the discussion in #3045 if would be nice to have a way to let users have a nice experience with caching even if the function is not hashable.

Currently a workaround is to make the function picklable. This can be done by implementing a callable class instead, that can be pickled using by implementing a custom `__getstate__` method for example.

However it sounds pretty complicated for a simple thing. Maybe one idea would be to have something similar to streamlit: they allow users to register the hashing of their own objects.

See the documentation about their `hash_funcs` here: https://docs.streamlit.io/library/advanced-features/caching#the-hash_funcs-parameter

Here is the example they give:

```python
class FileReference:
    def __init__(self, filename):
        self.filename = filename

def hash_file_reference(file_reference):
    filename = file_reference.filename
    return (filename, os.path.getmtime(filename))

@st.cache(hash_funcs={FileReference: hash_file_reference})
def func(file_reference):
    ...
```

My solution was to generate a custom hash, and use the hash as a `new_fingerprint` argument to the `map()` method to enable caching. This works, but is quite hacky.

@lhoestq, this approach is very neat, this would make the whole caching mechanic more explicit. I don't have so much time to look into this right now, but I might give it a try in the future. 

Almost a year later and I'm in a similar boat. Using custom fingerprints and when using multiprocessing the cached datasets are saved with a template at the end of the filename (something like "000001_of_000008" for every process of num_proc). So if in the next time you run the script you set num_proc to a different number, the cache cannot be used.

Is there any way to get around this? I am processing a huge dataset so I do the processing on one machine and then transfer the processed data to another in its cache dir but currently that's not possible due to num_proc mismatch.