Maximum sample-based training for Megatron NMT and Text Memmap based Seq2seq Pre-training
# What does this PR do ?

1. Trains Megatron-based NMT models based on maximum number of samples.
2. Added support in `text_memmap` and `csv_memmap` in Megatron encoder-decoder models (T5, BART, UL2)

**Collection**: NLP

# Usage

Add to command line

```
  model.data.data_impl=text_mmap \
  +model.data.data_impl_kwargs.newline_int=10 \
  +model.data.data_impl_kwargs.header_lines=0 \
  +model.data.data_impl_kwargs.workers=null \
  +model.data.data_impl_kwargs.sort_dataset_paths=False
```

```python
# Add a code snippet demonstrating how to use this 
```

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
