[Flax Generation] Correct inconsistencies PyTorch/Flax
Author: patrickvonplaten
Pull request: yes

# What does this PR do?

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The Flax greedy & beam search generation & Marian model had a couple of issues that are addressed here:

- greedy search now correctly pads **after** the eos token & test against PyTorch is added
- beam search now correctly computes the finished beam scores
- marian correctly makes use of bias
- more beam search tests for marian are added


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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- [ ] Did you write any new necessary tests?


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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @LysandreJik

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @n1t0, @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

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Comments:
The Spanish Marian model should be fixed as well on TPU (cc @gchhablani )

```python
from transformers import FlaxMarianMTModel, MarianTokenizer
import torch

model_name = "Helsinki-NLP/opus-mt-en-es"

model_fx = FlaxMarianMTModel.from_pretrained(model_name)

tokenizer = MarianTokenizer.from_pretrained(model_name)

input_ids = tokenizer("Living Room, The Sheridan House! Your Minneapolis Home!", return_tensors="np").input_ids

sequences_fx = model_fx.generate(input_ids, max_length=64, num_beams=2).sequences

decoded_fx = tokenizer.batch_decode(sequences_fx, skip_special_tokens=True)

print("Out Fx", decoded_fx)
```