Improve the speed of adding tokens from added_tokens.json
# What does this PR do?

This PR significantly improves the speed of adding tokens from `added_tokens.json`, when it contains a large number of tokens (e.g., 20,000+). When adding one token at a time, it uses `bisect` to insert the token into `PreTrainedTokenizer.unique_no_split_tokens`. Please see a detailed description and motivation in this issue: https://github.com/huggingface/transformers/issues/10676

This change relies on the requirement that `unique_no_split_tokens` is sorted. (I'm not sure if this is a fair assumption, otherwise I can check if it's already sorted before the insertion.)

Fixes #10676


## Benchmark
MacOS Mojave (CPU: 2.6 GHz Intel Core i7)
python==3.7.9
torch==1.7.1+cpu
transformers==3.5.1 or transformers==4.4.2 (similar results)

```python
import json
from timeit import default_timer as timer
from transformers import DistilBertTokenizer

model_dir = '/home/username/saved_model'

# Load a pretrained model's tokenizer, save it to {model_dir}
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
tokenizer.save_pretrained(model_dir)
print('len(tokenizer) of a pretrained model distilbert-base-uncased:', len(tokenizer))

# Generate n values as token suffix, to randomize the insertion position of added tokens
low = 30522
high = 82181   # exclusive
random_suffix = list(range(low, high))
random.shuffle(random_suffix)

# Save the n new tokens with correct indices to added_tokens.json
added_tokens = {f'addedtoken{val}': low + idx for idx, val in enumerate(random_suffix)}
with open(f'{model_dir}/added_tokens.json', 'w') as f:
    json.dump(added_tokens, f)
print(f'saved {len(added_tokens)} tokens to added_tokens.json')

# Load the tokenizer from {model_dir}, and print the elapsed time
start = timer()
tokenizer = DistilBertTokenizer.from_pretrained(model_dir)
print('len(tokenizer) after loading from saved model:', len(tokenizer))
end = timer()
print('Elapsed (seconds):', round(end - start, 3))

# Make sure tokenizer.unique_no_split_tokens remains sorted
all_values = tokenizer.unique_no_split_tokens
assert all(all_values[i+1] > all_values[i] for i in range(len(all_values) - 1))
```

**If we save 21659 tokens in added_tokens.json**, output before the change:
```bash
len(tokenizer) of a pretrained model distilbert-base-uncased: 30522
saved 21659 tokens to added_tokens.json
len(tokenizer) after loading from saved model: 52181
*** Elapsed (seconds): 76.95
```

output after the change:
```bash
len(tokenizer) of a pretrained model distilbert-base-uncased: 30522
saved 21659 tokens to added_tokens.json
len(tokenizer) after loading from saved model: 52181
*** Elapsed (seconds): 0.308
```

**If we save 51659 tokens in added_tokens.json**, output before the change:
```bash
len(tokenizer) of a pretrained model distilbert-base-uncased: 30522
saved 51659 tokens to added_tokens.json
len(tokenizer) after loading from saved model: 82181
*** Elapsed (seconds): 527.795
```

output after the change:
```bash
len(tokenizer) of a pretrained model distilbert-base-uncased: 30522
saved 51659 tokens to added_tokens.json
len(tokenizer) after loading from saved model: 82181
*** Elapsed (seconds): 0.83
```

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## Who can review?

@lhoestq @LysandreJik 

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- albert, bert, xlm: @LysandreJik
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Library:

- benchmarks: @patrickvonplaten
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Documentation: @sgugger

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Hi! Could you provide a way to benchmark the change so that we can see in which situations is the speedup visible? Thank you!<|||||>> Hi! Could you provide a way to benchmark the change so that we can see in which situations is the speedup visible? Thank you!

Hi @LysandreJik , I just updated the benchmark code snippets and sample output in the description. Hopefully it can validate the change.<|||||>Hi @LysandreJik , just wanted to follow up on this, is there anything else you would like to see on this PR? I also tried to run the slow test. It looked ok, but with a few connection errors: `ValueError: Connection error, and we cannot find the requested files in the cached path. Please try again or make sure your Internet connection is on`, and re-run didn't fix it. I was wondering how the slow tests look like on your end. Thanks!<|||||>Awesome, thanks to both of you!