Nezha Pytorch implementation (#17776)

* wip * rebase * all tests pass * rebase * ready for PR * address comments * fix styles * add require_torch to pipeline test * remove remote image to improve CI consistency * address comments; fix tf/flax tests * address comments; fix tf/flax tests * fix tests; add alias * repo consistency tests * Update src/transformers/pipelines/visual_question_answering.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * address comments * Update src/transformers/pipelines/visual_question_answering.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * merge * wip * wip * wip * most basic tests passes * all tests pass now * relative embedding * wip * running make fixup * remove bert changes * fix doc * fix doc * fix issues * fix doc * address comments * fix CI * remove redundant copied from * address comments * fix broken test Co-authored-by: Sijun He <sijunhe@Sijuns-MacBook-Pro.local> Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
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+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Nezha
+
+## Overview
+
+The Nezha model was proposed in [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) by Junqiu Wei et al.
+
+The abstract from the paper is the following:
+
+*The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks
+due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
+In this technical report, we present our practice of pre-training language models named NEZHA (NEural contextualiZed
+representation for CHinese lAnguage understanding) on Chinese corpora and finetuning for the Chinese NLU tasks. 
+The current version of NEZHA is based on BERT with a collection of proven improvements, which include Functional 
+Relative Positional Encoding as an effective positional encoding scheme, Whole Word Masking strategy,
+Mixed Precision Training and the LAMB Optimizer in training the models. The experimental results show that NEZHA
+achieves the state-of-the-art performances when finetuned on several representative Chinese tasks, including
+named entity recognition (People's Daily NER), sentence matching (LCQMC), Chinese sentiment classification (ChnSenti)
+and natural language inference (XNLI).*
+
+This model was contributed by [sijunhe](https://huggingface.co/sijunhe). The original code can be found [here](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/NEZHA-PyTorch).
+
+## NezhaConfig
+
+[[autodoc]] NezhaConfig
+
+## NezhaModel
+
+[[autodoc]] NezhaModel
+    - forward
+
+## NezhaForPreTraining
+
+[[autodoc]] NezhaForPreTraining
+    - forward
+
+## NezhaForMaskedLM
+
+[[autodoc]] NezhaForMaskedLM
+    - forward
+
+## NezhaForNextSentencePrediction
+
+[[autodoc]] NezhaForNextSentencePrediction
+    - forward
+
+## NezhaForSequenceClassification
+
+[[autodoc]] NezhaForSequenceClassification
+    - forward
+
+## NezhaForMultipleChoice
+
+[[autodoc]] NezhaForMultipleChoice
+    - forward
+
+## NezhaForTokenClassification
+
+[[autodoc]] NezhaForTokenClassification
+    - forward
+
+## NezhaForQuestionAnswering
+
+[[autodoc]] NezhaForQuestionAnswering
+    - forward