[PretrainedFeatureExtractor] + Wav2Vec2FeatureExtractor, Wav2Vec2Processor, Wav2Vec2Tokenizer (#10324)

* push to show

* small improvement

* small improvement

* Update src/transformers/feature_extraction_utils.py

* Update src/transformers/feature_extraction_utils.py

* implement base

* add common tests

* make all tests pass for wav2vec2

* make padding work & add more tests

* finalize feature extractor utils

* add call method to feature extraction

* finalize feature processor

* finish tokenizer

* finish general processor design

* finish tests

* typo

* remove bogus file

* finish docstring

* add docs

* finish docs

* small fix

* correct docs

* save intermediate

* load changes

* apply changes

* apply changes to doc

* change tests

* apply surajs recommend

* final changes

* Apply suggestions from code review

* fix typo

* fix import

* correct docstring

examples/multiple-choice/run_swag.py

@@ -39,7 +39,8 @@ from transformers import (
     default_data_collator,
     set_seed,
 )
-from transformers.tokenization_utils_base import PaddingStrategy, PreTrainedTokenizerBase
+from transformers.file_utils import PaddingStrategy
+from transformers.tokenization_utils_base import PreTrainedTokenizerBase
 from transformers.trainer_utils import get_last_checkpoint, is_main_process
 
 
@@ -133,7 +134,7 @@ class DataCollatorForMultipleChoice:
     Args:
         tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
             The tokenizer used for encoding the data.
-        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
             Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
             among: