Fix prepare_for_model error inconsistency
# What does this PR do?
The model default parameters in the `tokenization_utils_base.prepare_for_model` function are fetched after the parameter sanity check. This causes the sanity check to not recognize wrong parameter combinations when using model defaults.
This PR swaps those two.

Fixes #15679

- tokenizers: @n1t0, @LysandreJik

## Code to test:
Prepare:
```
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
sentences = ['Sentence number one.',
             'Sentence number two is longer to trigger padding.']

token_ids = tokenizer.batch_encode_plus(
    sentences,
    add_special_tokens=True,
    truncation=False,
    padding=False)
token_ids = token_ids['input_ids']
```

Test:
```
try:
	  tokenizer.prepare_for_model(
	      token_ids,
	      add_special_tokens=False,
	      padding='longest',
	      truncation=False,
	      return_token_type_ids=None) # Uses the model default
except Exception as e:
	e1 = e	

try: 
	  tokenizer.prepare_for_model(
	      token_ids,
	      add_special_tokens=False,
	      padding='longest',
	      truncation=False,
	      return_token_type_ids=True) # Explicitly set to true
except Exception as e:
	e2 = e

assert type(e1) is type(e2) and e1.args == e2.args
```

The error code of Test_01 and Test_02 should be the same.
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_15681). All of your documentation changes will be reflected on that endpoint.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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