* [Auto Parallel] Reorganize the fold structure

* [Auto Parallel] Fix some import errors

* mv ps distributed dir

* fix

* add del auto_parallel

* add auto_parallel

* fix ps

* fix bug

* fix test bug

* fix test bug

* merge develop fix error

* merge develop fix error

* merge develop fix error

* add isort config

* isort all files

* update config

* re-blacken python code

* temporarily disable date and diff_py_file

* skip a format

* [AutoParallel] add callbacks

* fix unittest

* fix dist_context

* fix engine

* fix cmakelist

* fix unittest's returns

* fix cmakelist

[CodeStyle][F401] remove unused imports in python_paddle/nn_tensor_reader_tests_distributed_distribution_dataset_utils. (#46721)

* use yapf to format all python file

* yapf exclude two unittests file for they rely on writing and reading file, and format will break them

* disable diff_py_file because too many diff files cause command following failed

* split callback unittest

* rm test_callback from timeout list

* enhance logger callback for benchmark

* add early stopping

* add doc for early stopping

* fix sample code bugs

* update infer of mode, update doc, add unittests to increase coverage rate

* fix sample code for early stopping

* update sample code and unittests

* reduce time cost of test_callbacks unittest

* fix model.py code style error

* return pil image as default according backend

* add pil backend

* add visualdl callback

* Move hapi form paddle/incubate to paddle

* Remove vision/datasets/utils.py and clean code

* Add sample code for conll05

* Print pull path when saving model

* Fix sample code after paramter_list of SGD is changed to parameters

* Fix bug in wmt16 datase

* Rename `Input` into `InputSpec`

* fix argument place of Input api

* Refine Model

1. Take the network (instance of Layer) as the input of Model.
2. Refine set_dict/load_dict of Layer.
3. Refine Input interface, so update code sample about Input

* Merge hapi into Paddle

Hapi is a high level API for training and inference.
The main modules include Model, Loss, Metrics, Dataset.
Also includes common modules and models in NLP and computer vision, such as BERT, ResNet.

These modules are developed by:
0YuanZhang0, guoshengCS heavengate, LielinJiang, qingqing01, xyzhou-puck huangjun12, wangxiao1021, zhangyang.