Datasets.map causes incorrect overflow_to_sample_mapping when used with tokenizers and small batch size
State: open
Labels: description: Something isn't working
name: bug

## Describe the bug
When using `tokenizer`, we can retrieve the field `overflow_to_sample_mapping`, since long samples will be overflown into multiple token sequences.

However, when tokenizing is done via `Dataset.map`, with `n_proc > 1`, the `overflow_to_sample_mapping` field is wrong.  This seems to be because each tokenizer only looks at its share of the samples, and maps to the index _within its share_, but then `Dataset.map` collates them together.

## Steps to reproduce the bug

1. Make a dataset of 3 strings.
2. Tokenize via Dataset.map with n_proc = 8
3. Inspect the `overflow_to_sample_mapping` field


## Expected results
`[0, 1, 2]`

## Actual results
`[0, 0, 0]`

Notes:

1. I have not yet extracted a minimal example, but the above works reliably
2. If the dataset is large, I've yet to determine if this bug still happens a. not at all b. always c. on the small, leftover batch at the end.


Comments:
I've built a minimal example that shows this bug without `n_proc`. It seems like it's a problem any way of using **tokenizers, `overflow_to_sample_mapping`, and Dataset.map, with a small batch size**:

```
import datasets
import transformers
pretrained = 'deepset/tinyroberta-squad2'
tokenizer = transformers.AutoTokenizer.from_pretrained(pretrained)

questions = ['Can you tell me why?', 'What time is it?']
contexts = ['This is context zero', 'Another paragraph goes here']   

def tok(questions, contexts):
    return tokenizer(text=questions,
            text_pair=contexts,
            truncation='only_second',
            return_overflowing_tokens=True,
            )
print(tok(questions, contexts)['overflow_to_sample_mapping'])
assert tok(questions, contexts)['overflow_to_sample_mapping'] == [0, 1] # PASSES

def tok2(d):
    return tok(d['question'], d['context'])

def tok2(d):
    return tok(d['question'], d['context'])

ds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})
tokens = ds.map(tok2, batched=True, batch_size=1)
print(tokens['overflow_to_sample_mapping'])
assert tokens['overflow_to_sample_mapping'] == [0, 1] # FAILS produces [0,0]
```

Note that even if the batch size would be larger, there will be instances where we will not have a lot of data, and end up using small batches.  This can occur e.g. if `n_proc` causes batches to be underfill.  I imagine it can also occur in other ways, e.g. the final leftover batch at the end.

A larger batch size does _not_ have this behavior:

```
def tok2(d):
    return tok(d['question'], d['context'])

ds = datasets.Dataset.from_dict({'question': questions, 'context': contexts})
tokens = ds.map(tok2, batched=True, batch_size=2)
print(tokens['overflow_to_sample_mapping'])
assert tokens['overflow_to_sample_mapping'] == [0, 1] # PASSES
```

I was trying the [Question answering](https://huggingface.co/learn/nlp-course/chapter7/7#question-answering) tutorial on Hugging face when i faced the same problem.  The preprocessing step is [here](https://huggingface.co/learn/nlp-course/chapter7/7#processing-the-validation-data). i have changed ```max_length=200, stride=50```,

```
validation_dataset = raw_datasets['validation'].select(range(8)).map(
    preprocess_validation_examples,
    batched=True,
    remove_columns=raw_datasets["validation"].column_names,
    num_proc=1
)
print(validation_dataset['overflow_to_sample_mapping'])
print(validation_dataset['example_id'])
```
result

```
[0, 1, 2, 3, 4, 5, 6, 7]
['56be4db0acb8001400a502ec', '56be4db0acb8001400a502ed', '56be4db0acb8001400a502ee', 
'56be4db0acb8001400a502ef', '56be4db0acb8001400a502f0', '56be8e613aeaaa14008c90d1', 
'56be8e613aeaaa14008c90d2', '56be8e613aeaaa14008c90d3']
```
when ```num_proc=2```, result - 

```
[0, 1, 2, 3, 0, 1, 2, 3]
['56be4db0acb8001400a502ec', '56be4db0acb8001400a502ed', '56be4db0acb8001400a502ee', 
'56be4db0acb8001400a502ef', '56be4db0acb8001400a502f0', '56be8e613aeaaa14008c90d1', 
'56be8e613aeaaa14008c90d2', '56be8e613aeaaa14008c90d3']
```

when ```num_proc=3```, result - 

```
[0, 1, 2, 0, 1, 2, 0, 1]
['56be4db0acb8001400a502ec', '56be4db0acb8001400a502ed', '56be4db0acb8001400a502ee', 
'56be4db0acb8001400a502ef', '56be4db0acb8001400a502f0', '56be8e613aeaaa14008c90d1', 
'56be8e613aeaaa14008c90d2', '56be8e613aeaaa14008c90d3']
```

The```overflow_to_sample_mapping``` changes with ```num_proc```, but ```example_id``` field remains the same . It seems that each process in ```map``` has its own counter for overflow_to_sample_mapping. If you are using ```overflow_to_sample_mapping``` inside the ```preprocess_validation_examples``` function, then there is no issue.