[Draft] Wav2Vec2 - Save intermediate PR verifying  that implementation matches fairseq ones
Author: patrickvonplaten
Pull request: yes

# What does this PR do?

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Using this branch of fairseq: https://github.com/patrickvonplaten/fairseq/pull/1

running this code should work as expected:

```python
#!/usr/bin/env python3                                                               
import datasets                                                                      
import fairseq                                                                                                                                                             
import torch
                                          
import soundfile as sf
import sys                     
from fairseq.criterions.wav2vec_criterion import Wav2VecCriterionConfig, Wav2vecCriterion
from fairseq.tasks.audio_pretraining import AudioPretrainingConfig, AudioPretrainingTask
                                          
from transformers import Wav2Vec2ForPreTraining, Wav2Vec2FeatureExtractor

hf_path = str(sys.argv[1])
fairseq_wav2vec2_path = str(sys.argv[2])                                             
                                          
model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([fairseq_wav2vec2_path])                                   

feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(hf_path, do_normalize=False)
hf_model = Wav2Vec2ForPreTraining.from_pretrained(hf_path)                   

model = model[0]                                                                     
model.eval()
                                                                                                                                                                           

dummy_speech_data = datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")   
def map_to_array(batch):
    speech_array, _ = sf.read(batch["file"])
    batch["speech"] = speech_array
    return batch


dummy_speech_data = dummy_speech_data.map(map_to_array, remove_columns=["file"])
inputs = feature_extractor(dummy_speech_data[:3]["speech"], return_tensors="pt", padding="longest", return_attention_mask=True)

input_values = inputs.input_values
attention_mask = inputs.attention_mask

audio_cfg = AudioPretrainingConfig(labels="ltr", data="./data")
task = AudioPretrainingTask.setup_task(audio_cfg)
criterion = Wav2vecCriterion(Wav2VecCriterionConfig(infonce=True, log_keys=["prob_perplexity", "code_perplexity", "temp"], loss_weights=[0.1, 10]), task)

sample = {
    "net_input": {
        "source": input_values,
        "padding_mask": attention_mask.ne(1),
    },
    "id": torch.zeros((1,)),
}

torch.manual_seed(0)
loss, sample_size, log, result = criterion(model, sample)
torch.manual_seed(0)
hf_result = hf_model(input_values, attention_mask=attention_mask, mask_time_indices=result["mask_indices"], fsq_negs=result["negs"])

hf_logits = hf_result.logits.permute(1, 2, 0)[result["mask_indices"]]
hf_logits = hf_logits.reshape(result['x'].shape[1:] + (-1,)).permute(2, 0, 1)

assert torch.allclose(hf_logits, result['x'], atol=1e-3), "wrong logits"

print("Loss diff %", 100 * (loss.detach().item() - hf_result.loss.detach().item()) / hf_result.loss.detach())

print("perplexity diff %", 100 * (hf_result.prob_perplexity.detach().item() -result["prob_perplexity"].detach().item()) / hf_result.prob_perplexity.detach())
```

and using [this](https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt) as the fairseq checkpoint and [this](https://huggingface.co/patrickvonplaten/wav2vec2-base) model as the HF model.


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
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      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

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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:
Delete after successful run of Wav2Vec2 PreTraining

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