minimal fixes to run DataCollatorForWholeWordMask with return_tensors="np" and return_tensors="tf"
# What does this PR do?

This PR addresses https://github.com/huggingface/transformers/issues/13890 with the minimal fixes to run `DataCollatorForWholeWordMask` with `return_tensors="np"` and `return_tensors="tf"`

Specific problems addressed:
* Renamed `np_call` -> `numpy_call`
* Call `_numpy_collate_batch` instead of` _tf_collate_batch` when returning numpy tensors
* Fix size of `random_words` in `numpy_mask_tokens`
* Clone tensorflow tensors with `tf.identity(tensor)`
* Use tensorflow tensors’ built-in iteration instead of attempting to convert to list
* Change calls to `tf.convert_to_tensor` with `dtype=tf.bool` to `tf.cast` with `dtype=tf.bool`

I've only added a simple test to check for regressions vs all of the padded/unpadded cases as is done for `DataCollatorForLanguageModeling`

Fixes https://github.com/huggingface/transformers/issues/13890


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CC @Rocketknight1 (looks like you did the initial Numpy/TF implementation of these data collators)
Bump @Rocketknight1 @LysandreJik @sgugger since `transformers` releases frequently and it would be nice to have this functionality.

This gives a vetted source for masked language modeling that is not https://github.com/google-research/bert/blob/master/create_pretraining_data.py or https://github.com/tensorflow/models/blob/master/official/nlp/data/create_pretraining_data.py in a package many people are going to be using for modeling (e.g., I could delete my code that is a copy/paste of the former and integrate against this instead for creating a large offline dataset).<|||||>Sorry for the delay, and thank you for the ping! This looks good to me. I've got one question about the `convert_to_tensor` versus `cast` call, but other than that this is a solid and necessary PR.