Revert "[time series] Add PatchTST (#25927)" (#27486)

The model was merged before final review and approval.

This reverts commit 2ac5b932.

README.md

@@ -439,7 +439,6 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
 1. **[OWLv2](https://huggingface.co/docs/transformers/model_doc/owlv2)** (from Google AI) released with the paper [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby.
-1. **[PatchTST](https://huggingface.co/docs/transformers/main/model_doc/patchtst)** (from IBM) released with the paper [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/abs/2211.14730) by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
 1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao, and Peter J. Liu.
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.

README_es.md

@@ -414,7 +414,6 @@ Número actual de puntos de control: ![](https://img.shields.io/endpoint?url=htt
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
 1. **[OWLv2](https://huggingface.co/docs/transformers/model_doc/owlv2)** (from Google AI) released with the paper [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby.
-1. **[PatchTST](https://huggingface.co/docs/transformers/main/model_doc/patchtst)** (from IBM) released with the paper [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/pdf/2211.14730.pdf) by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
 1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao, and Peter J. Liu.
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.

README_hd.md

@@ -388,7 +388,6 @@ conda install -c huggingface transformers
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (Google AI से) साथ में कागज [विज़न ट्रांसफॉर्मर्स के साथ सिंपल ओपन-वोकैबुलरी ऑब्जेक्ट डिटेक्शन](https:/ /arxiv.org/abs/2205.06230) मैथियास मिंडरर, एलेक्सी ग्रिट्सेंको, ऑस्टिन स्टोन, मैक्सिम न्यूमैन, डिर्क वीसेनबोर्न, एलेक्सी डोसोवित्स्की, अरविंद महेंद्रन, अनुराग अर्नब, मुस्तफा देहघानी, ज़ुओरन शेन, जिओ वांग, ज़ियाओहुआ झाई, थॉमस किफ़, और नील हॉल्सबी द्वारा पोस्ट किया गया।
 1. **[OWLv2](https://huggingface.co/docs/transformers/model_doc/owlv2)** (Google AI से) Matthias Minderer, Alexey Gritsenko, Neil Houlsby. द्वाराअनुसंधान पत्र [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) के साथ जारी किया गया
-1. **[PatchTST](https://huggingface.co/docs/transformers/main/model_doc/patchtst)** (IBM से) Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam. द्वाराअनुसंधान पत्र [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/pdf/2211.14730.pdf) के साथ जारी किया गया
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
 1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (Google की ओर से) साथ में दिया गया पेपर [लंबे इनपुट सारांश के लिए ट्रांसफ़ॉर्मरों को बेहतर तरीके से एक्सटेंड करना](https://arxiv .org/abs/2208.04347) जेसन फांग, याओ झाओ, पीटर जे लियू द्वारा।
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (दीपमाइंड से) साथ में पेपर [पर्सीवर आईओ: संरचित इनपुट और आउटपुट के लिए एक सामान्य वास्तुकला] (https://arxiv.org/abs/2107.14795) एंड्रयू जेगल, सेबेस्टियन बोरग्यूड, जीन-बैप्टिस्ट अलायराक, कार्ल डोर्श, कैटलिन इओनेस्कु, डेविड द्वारा डिंग, स्कंद कोप्पुला, डैनियल ज़ोरान, एंड्रयू ब्रॉक, इवान शेलहैमर, ओलिवियर हेनाफ, मैथ्यू एम। बोट्विनिक, एंड्रयू ज़िसरमैन, ओरिओल विनियल्स, जोआओ कैरेरा द्वारा पोस्ट किया गया।

README_ja.md

@@ -448,7 +448,6 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (Meta AI から) Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al から公開された研究論文: [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068)
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (Google AI から) Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby から公開された研究論文: [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230)
 1. **[OWLv2](https://huggingface.co/docs/transformers/model_doc/owlv2)** (Google AI から) Matthias Minderer, Alexey Gritsenko, Neil Houlsby. から公開された研究論文 [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683)
-1. **[PatchTST](https://huggingface.co/docs/transformers/main/model_doc/patchtst)** (IBM から) Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam. から公開された研究論文 [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/pdf/2211.14730.pdf)
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (Google から) Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu から公開された研究論文: [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777)
 1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (Google から) Jason Phang, Yao Zhao, and Peter J. Liu から公開された研究論文: [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347)
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (Deepmind から) Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira から公開された研究論文: [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795)

README_ko.md

@@ -363,7 +363,6 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (Meta AI 에서) Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al 의 [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) 논문과 함께 발표했습니다.
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (Google AI 에서) Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby 의 [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) 논문과 함께 발표했습니다.
 1. **[OWLv2](https://huggingface.co/docs/transformers/model_doc/owlv2)** (Google AI 에서 제공)은 Matthias Minderer, Alexey Gritsenko, Neil Houlsby.의 [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683)논문과 함께 발표했습니다.
-1. **[PatchTST](https://huggingface.co/docs/transformers/main/model_doc/patchtst)** (IBM 에서 제공)은 Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.의 [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/pdf/2211.14730.pdf)논문과 함께 발표했습니다.
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (Google 에서) Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu 의 [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) 논문과 함께 발표했습니다.
 1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (Google 에서) Jason Phang, Yao Zhao, Peter J. Liu 의 [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) 논문과 함께 발표했습니다.
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (Deepmind 에서) Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira 의 [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) 논문과 함께 발표했습니다.

README_zh-hans.md

@@ -387,7 +387,6 @@ conda install -c huggingface transformers
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (来自 Meta AI) 伴随论文 [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) 由 Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al 发布。
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (来自 Google AI) 伴随论文 [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) 由 Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby 发布。
 1. **[OWLv2](https://huggingface.co/docs/transformers/model_doc/owlv2)** (来自 Google AI) 伴随论文 [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) 由 Matthias Minderer, Alexey Gritsenko, Neil Houlsby 发布。
-1. **[PatchTST](https://huggingface.co/docs/transformers/main/model_doc/patchtst)** (来自 IBM) 伴随论文 [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/pdf/2211.14730.pdf) 由 Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam 发布。
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (来自 Google) 伴随论文 [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) 由 Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu 发布。
 1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (来自 Google) 伴随论文 [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) 由 Jason Phang, Yao Zhao, Peter J. Liu 发布。
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (来自 Deepmind) 伴随论文 [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) 由 Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira 发布。

README_zh-hant.md

@@ -399,7 +399,6 @@ conda install -c huggingface transformers
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
 1. **[OWLv2](https://huggingface.co/docs/transformers/model_doc/owlv2)** (from Google AI) released with the paper [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby.
-1. **[PatchTST](https://huggingface.co/docs/transformers/main/model_doc/patchtst)** (from IBM) released with the paper [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/pdf/2211.14730.pdf) by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
 1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao, Peter J. Liu.
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.

docs/source/en/_toctree.yml

@@ -747,8 +747,6 @@
         title: Autoformer
       - local: model_doc/informer
         title: Informer
-      - local: model_doc/patchtst
-        title: PatchTST
       - local: model_doc/time_series_transformer
         title: Time Series Transformer
       title: Time series models

docs/source/en/index.md

@@ -213,7 +213,6 @@ Flax), PyTorch, and/or TensorFlow.
 |                           [OPT](model_doc/opt)                           |       ✅        |         ✅         |      ✅      |
 |                       [OWL-ViT](model_doc/owlvit)                        |       ✅        |         ❌         |      ❌      |
 |                         [OWLv2](model_doc/owlv2)                         |       ✅        |         ❌         |      ❌      |
-|                      [PatchTST](model_doc/patchtst)                      |       ✅        |         ❌         |      ❌      |
 |                       [Pegasus](model_doc/pegasus)                       |       ✅        |         ✅         |      ✅      |
 |                     [PEGASUS-X](model_doc/pegasus_x)                     |       ✅        |         ❌         |      ❌      |
 |                     [Perceiver](model_doc/perceiver)                     |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/patchtst.md

-<!--Copyright 2023 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
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-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.
-
-⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
-rendered properly in your Markdown viewer.
-
--->
-
-# PatchTST
-
-## Overview
-
-The PatchTST model was proposed in [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://arxiv.org/abs/2211.14730) by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.
-
-The abstract from the paper is the following:
-
-*We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; (ii) channel-independence where each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series. Patching design naturally has three-fold benefit: local semantic information is retained in the embedding; computation and memory usage of the attention maps are quadratically reduced given the same look-back window; and the model can attend longer history. Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models. We also apply our model to self-supervised pre-training tasks and attain excellent fine-tuning performance, which outperforms supervised training on large datasets. Transferring of masked pre-trained representation on one dataset to others also produces SOTA forecasting accuracy.*
-
-Tips:
-
-The model can also be used for time series classification and time series regression. See the respective [`PatchTSTForClassification`] and [`PatchTSTForRegression`] classes.
-
-At a high level the model vectorizes time series into patches of a given size and encodes them via a Transformer which then outputs the prediction length forecasts:
-
-![model](https://github.com/namctin/transformers/assets/8100/150af169-29de-419a-8d98-eb78251c21fa)
-
-
-This model was contributed by [namctin](https://huggingface.co/namctin), [gsinthong](https://huggingface.co/gsinthong), [diepi](https://huggingface.co/diepi), [vijaye12](https://huggingface.co/vijaye12), [wmgifford](https://huggingface.co/wmgifford), and [kashif](https://huggingface.co/kashif).
-
-The original code can be found [here](https://github.com/yuqinie98/PatchTST).
-
-
-## PatchTSTConfig
-
-[[autodoc]] PatchTSTConfig
-
-
-## PatchTSTModel
-
-[[autodoc]] PatchTSTModel
-    - forward
-
-
-## PatchTSTForPrediction
-
-[[autodoc]] PatchTSTForPrediction
-    - forward
-
-
-## PatchTSTForClassification
-
-[[autodoc]] PatchTSTForClassification
-    - forward
-
-
-## PatchTSTForPretraining
-
-[[autodoc]] PatchTSTForPretraining
-    - forward
-
-
-## PatchTSTForRegression
-
-[[autodoc]] PatchTSTForRegression
-    - forward

src/transformers/__init__.py

@@ -493,7 +493,6 @@ _import_structure = {
         "OwlViTTextConfig",
         "OwlViTVisionConfig",
     ],
-    "models.patchtst": ["PATCHTST_PRETRAINED_CONFIG_ARCHIVE_MAP", "PatchTSTConfig"],
     "models.pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig", "PegasusTokenizer"],
     "models.pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
     "models.perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "PerceiverTokenizer"],
@@ -1168,8 +1167,6 @@ else:
             "MODEL_FOR_TEXT_ENCODING_MAPPING",
             "MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING",
             "MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING",
-            "MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING",
-            "MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING",
             "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
             "MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING",
             "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING",
@@ -2488,17 +2485,6 @@ else:
             "OwlViTVisionModel",
         ]
     )
-    _import_structure["models.patchtst"].extend(
-        [
-            "PATCHTST_PRETRAINED_MODEL_ARCHIVE_LIST",
-            "PatchTSTForClassification",
-            "PatchTSTForPrediction",
-            "PatchTSTForPretraining",
-            "PatchTSTForRegression",
-            "PatchTSTModel",
-            "PatchTSTPreTrainedModel",
-        ]
-    )
     _import_structure["models.pegasus"].extend(
         ["PegasusForCausalLM", "PegasusForConditionalGeneration", "PegasusModel", "PegasusPreTrainedModel"]
     )
@@ -4711,7 +4697,6 @@ if TYPE_CHECKING:
         OwlViTTextConfig,
         OwlViTVisionConfig,
     )
-    from .models.patchtst import PATCHTST_PRETRAINED_CONFIG_ARCHIVE_MAP, PatchTSTConfig
     from .models.pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig, PegasusTokenizer
     from .models.pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
     from .models.perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverTokenizer
@@ -5318,8 +5303,6 @@ if TYPE_CHECKING:
             MODEL_FOR_TEXT_ENCODING_MAPPING,
             MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING,
             MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING,
-            MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING,
-            MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING,
             MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
             MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
             MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
@@ -6404,15 +6387,6 @@ if TYPE_CHECKING:
             OwlViTTextModel,
             OwlViTVisionModel,
         )
-        from .models.patchtst import (
-            PATCHTST_PRETRAINED_MODEL_ARCHIVE_LIST,
-            PatchTSTForClassification,
-            PatchTSTForPrediction,
-            PatchTSTForPretraining,
-            PatchTSTForRegression,
-            PatchTSTModel,
-            PatchTSTPreTrainedModel,
-        )
         from .models.pegasus import (
             PegasusForCausalLM,
             PegasusForConditionalGeneration,

src/transformers/models/__init__.py

@@ -158,7 +158,6 @@ from . import (
     opt,
     owlv2,
     owlvit,
-    patchtst,
     pegasus,
     pegasus_x,
     perceiver,

src/transformers/models/auto/__init__.py

@@ -77,8 +77,6 @@ else:
         "MODEL_WITH_LM_HEAD_MAPPING",
         "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
         "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING",
-        "MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING",
-        "MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING",
         "AutoModel",
         "AutoBackbone",
         "AutoModelForAudioClassification",
@@ -252,8 +250,6 @@ if TYPE_CHECKING:
             MODEL_FOR_TEXT_ENCODING_MAPPING,
             MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING,
             MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING,
-            MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING,
-            MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING,
             MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
             MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
             MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,

src/transformers/models/auto/configuration_auto.py

@@ -164,7 +164,6 @@ CONFIG_MAPPING_NAMES = OrderedDict(
         ("opt", "OPTConfig"),
         ("owlv2", "Owlv2Config"),
         ("owlvit", "OwlViTConfig"),
-        ("patchtst", "PatchTSTConfig"),
         ("pegasus", "PegasusConfig"),
         ("pegasus_x", "PegasusXConfig"),
         ("perceiver", "PerceiverConfig"),
@@ -377,7 +376,6 @@ CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
         ("opt", "OPT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("owlv2", "OWLV2_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("owlvit", "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
-        ("patchtst", "PATCHTST_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("pegasus", "PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("pegasus_x", "PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("perceiver", "PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
@@ -609,7 +607,6 @@ MODEL_NAMES_MAPPING = OrderedDict(
         ("opt", "OPT"),
         ("owlv2", "OWLv2"),
         ("owlvit", "OWL-ViT"),
-        ("patchtst", "PatchTST"),
         ("pegasus", "Pegasus"),
         ("pegasus_x", "PEGASUS-X"),
         ("perceiver", "Perceiver"),

src/transformers/models/auto/modeling_auto.py

@@ -157,7 +157,6 @@ MODEL_MAPPING_NAMES = OrderedDict(
         ("opt", "OPTModel"),
         ("owlv2", "Owlv2Model"),
         ("owlvit", "OwlViTModel"),
-        ("patchtst", "PatchTSTModel"),
         ("pegasus", "PegasusModel"),
         ("pegasus_x", "PegasusXModel"),
         ("perceiver", "PerceiverModel"),
@@ -1131,18 +1130,6 @@ MODEL_FOR_TEXT_ENCODING_MAPPING_NAMES = OrderedDict(
     ]
 )
 
-MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
-    [
-        ("patchtst", "PatchTSTForClassification"),
-    ]
-)
-
-MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING_NAMES = OrderedDict(
-    [
-        ("patchtst", "PatchTSTForRegression"),
-    ]
-)
-
 MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES = OrderedDict(
     [
         ("swin2sr", "Swin2SRForImageSuperResolution"),
@@ -1234,14 +1221,6 @@ MODEL_FOR_MASK_GENERATION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL
 
 MODEL_FOR_TEXT_ENCODING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_TEXT_ENCODING_MAPPING_NAMES)
 
-MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING = _LazyAutoMapping(
-    CONFIG_MAPPING_NAMES, MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING_NAMES
-)
-
-MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING = _LazyAutoMapping(
-    CONFIG_MAPPING_NAMES, MODEL_FOR_TIME_SERIES_REGRESSION_MAPPING_NAMES
-)
-
 MODEL_FOR_IMAGE_TO_IMAGE_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES)
 
 

src/transformers/models/autoformer/modeling_autoformer.py

@@ -208,70 +208,71 @@ class AutoformerFeatureEmbedder(nn.Module):
         )
 
 
-# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesStdScaler with TimeSeriesTransformer->Autoformer,TimeSeries->Autoformer
+# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesStdScaler with TimeSeries->Autoformer
 class AutoformerStdScaler(nn.Module):
     """
-    Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by
-    subtracting from the mean and dividing by the standard deviation.
+    Standardize features by calculating the mean and scaling along some given dimension `dim`, and then normalizes it
+    by subtracting from the mean and dividing by the standard deviation.
+
+    Args:
+        dim (`int`):
+            Dimension along which to calculate the mean and standard deviation.
+        keepdim (`bool`, *optional*, defaults to `False`):
+            Controls whether to retain dimension `dim` (of length 1) in the scale tensor, or suppress it.
+        minimum_scale (`float`, *optional*, defaults to 1e-5):
+            Default scale that is used for elements that are constantly zero along dimension `dim`.
     """
 
-    def __init__(self, config: AutoformerConfig):
+    def __init__(self, dim: int, keepdim: bool = False, minimum_scale: float = 1e-5):
         super().__init__()
-        self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
-        self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
-        self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-10
+        if not dim > 0:
+            raise ValueError("Cannot compute scale along dim = 0 (batch dimension), please provide dim > 0")
+        self.dim = dim
+        self.keepdim = keepdim
+        self.minimum_scale = minimum_scale
 
-    def forward(
-        self, data: torch.Tensor, observed_indicator: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Parameters:
-            data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                input for Batch norm calculation
-            observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                Calculating the scale on the observed indicator.
-        Returns:
-            tuple of `torch.Tensor` of shapes
-                (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
-                `(batch_size, 1, num_input_channels)`)
-        """
-        denominator = observed_indicator.sum(self.dim, keepdim=self.keepdim)
+    @torch.no_grad()
+    def forward(self, data: torch.Tensor, weights: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        denominator = weights.sum(self.dim, keepdim=self.keepdim)
         denominator = denominator.clamp_min(1.0)
-        loc = (data * observed_indicator).sum(self.dim, keepdim=self.keepdim) / denominator
+        loc = (data * weights).sum(self.dim, keepdim=self.keepdim) / denominator
 
-        variance = (((data - loc) * observed_indicator) ** 2).sum(self.dim, keepdim=self.keepdim) / denominator
+        variance = (((data - loc) * weights) ** 2).sum(self.dim, keepdim=self.keepdim) / denominator
         scale = torch.sqrt(variance + self.minimum_scale)
         return (data - loc) / scale, loc, scale
 
 
-# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesMeanScaler with TimeSeriesTransformer->Autoformer,TimeSeries->Autoformer
+# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesMeanScaler with TimeSeries->Autoformer
 class AutoformerMeanScaler(nn.Module):
     """
-    Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data
+    Computes a scaling factor as the weighted average absolute value along dimension `dim`, and scales the data
     accordingly.
+
+    Args:
+        dim (`int`):
+            Dimension along which to compute the scale.
+        keepdim (`bool`, *optional*, defaults to `False`):
+            Controls whether to retain dimension `dim` (of length 1) in the scale tensor, or suppress it.
+        default_scale (`float`, *optional*, defaults to `None`):
+            Default scale that is used for elements that are constantly zero. If `None`, we use the scale of the batch.
+        minimum_scale (`float`, *optional*, defaults to 1e-10):
+            Default minimum possible scale that is used for any item.
     """
 
-    def __init__(self, config: AutoformerConfig):
+    def __init__(
+        self, dim: int = -1, keepdim: bool = True, default_scale: Optional[float] = None, minimum_scale: float = 1e-10
+    ):
         super().__init__()
-        self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
-        self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
-        self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-10
-        self.default_scale = config.default_scale if hasattr(config, "default_scale") else None
+        self.dim = dim
+        self.keepdim = keepdim
+        self.minimum_scale = minimum_scale
+        self.default_scale = default_scale
 
+    @torch.no_grad()
     def forward(
         self, data: torch.Tensor, observed_indicator: torch.Tensor
     ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Parameters:
-            data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                input for Batch norm calculation
-            observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                Calculating the scale on the observed indicator.
-        Returns:
-            tuple of `torch.Tensor` of shapes
-                (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
-                `(batch_size, 1, num_input_channels)`)
-        """
+        # shape: (N, [C], T=1)
         ts_sum = (data * observed_indicator).abs().sum(self.dim, keepdim=True)
         num_observed = observed_indicator.sum(self.dim, keepdim=True)
 
@@ -299,29 +300,26 @@ class AutoformerMeanScaler(nn.Module):
         return scaled_data, torch.zeros_like(scale), scale
 
 
-# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesNOPScaler with TimeSeriesTransformer->Autoformer,TimeSeries->Autoformer
+# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesNOPScaler with TimeSeries->Autoformer
 class AutoformerNOPScaler(nn.Module):
     """
-    Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data.
+    Assigns a scaling factor equal to 1 along dimension `dim`, and therefore applies no scaling to the input data.
+
+    Args:
+        dim (`int`):
+            Dimension along which to compute the scale.
+        keepdim (`bool`, *optional*, defaults to `False`):
+            Controls whether to retain dimension `dim` (of length 1) in the scale tensor, or suppress it.
     """
 
-    def __init__(self, config: AutoformerConfig):
+    def __init__(self, dim: int, keepdim: bool = False):
         super().__init__()
-        self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
-        self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
+        self.dim = dim
+        self.keepdim = keepdim
 
     def forward(
-        self, data: torch.Tensor, observed_indicator: torch.Tensor = None
+        self, data: torch.Tensor, observed_indicator: torch.Tensor
     ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Parameters:
-            data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                input for Batch norm calculation
-        Returns:
-            tuple of `torch.Tensor` of shapes
-                (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
-                `(batch_size, 1, num_input_channels)`)
-        """
         scale = torch.ones_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim)
         loc = torch.zeros_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim)
         return data, loc, scale
@@ -1435,11 +1433,11 @@ class AutoformerModel(AutoformerPreTrainedModel):
         super().__init__(config)
 
         if config.scaling == "mean" or config.scaling is True:
-            self.scaler = AutoformerMeanScaler(config)
+            self.scaler = AutoformerMeanScaler(dim=1, keepdim=True)
         elif config.scaling == "std":
-            self.scaler = AutoformerStdScaler(config)
+            self.scaler = AutoformerStdScaler(dim=1, keepdim=True)
         else:
-            self.scaler = AutoformerNOPScaler(config)
+            self.scaler = AutoformerNOPScaler(dim=1, keepdim=True)
 
         if config.num_static_categorical_features > 0:
             self.embedder = AutoformerFeatureEmbedder(

src/transformers/models/informer/modeling_informer.py

@@ -81,70 +81,71 @@ class InformerFeatureEmbedder(nn.Module):
         )
 
 
-# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesStdScaler with TimeSeriesTransformer->Informer,TimeSeries->Informer
+# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesStdScaler with TimeSeries->Informer
 class InformerStdScaler(nn.Module):
     """
-    Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by
-    subtracting from the mean and dividing by the standard deviation.
+    Standardize features by calculating the mean and scaling along some given dimension `dim`, and then normalizes it
+    by subtracting from the mean and dividing by the standard deviation.
+
+    Args:
+        dim (`int`):
+            Dimension along which to calculate the mean and standard deviation.
+        keepdim (`bool`, *optional*, defaults to `False`):
+            Controls whether to retain dimension `dim` (of length 1) in the scale tensor, or suppress it.
+        minimum_scale (`float`, *optional*, defaults to 1e-5):
+            Default scale that is used for elements that are constantly zero along dimension `dim`.
     """
 
-    def __init__(self, config: InformerConfig):
+    def __init__(self, dim: int, keepdim: bool = False, minimum_scale: float = 1e-5):
         super().__init__()
-        self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
-        self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
-        self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-10
+        if not dim > 0:
+            raise ValueError("Cannot compute scale along dim = 0 (batch dimension), please provide dim > 0")
+        self.dim = dim
+        self.keepdim = keepdim
+        self.minimum_scale = minimum_scale
 
-    def forward(
-        self, data: torch.Tensor, observed_indicator: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Parameters:
-            data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                input for Batch norm calculation
-            observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                Calculating the scale on the observed indicator.
-        Returns:
-            tuple of `torch.Tensor` of shapes
-                (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
-                `(batch_size, 1, num_input_channels)`)
-        """
-        denominator = observed_indicator.sum(self.dim, keepdim=self.keepdim)
+    @torch.no_grad()
+    def forward(self, data: torch.Tensor, weights: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        denominator = weights.sum(self.dim, keepdim=self.keepdim)
         denominator = denominator.clamp_min(1.0)
-        loc = (data * observed_indicator).sum(self.dim, keepdim=self.keepdim) / denominator
+        loc = (data * weights).sum(self.dim, keepdim=self.keepdim) / denominator
 
-        variance = (((data - loc) * observed_indicator) ** 2).sum(self.dim, keepdim=self.keepdim) / denominator
+        variance = (((data - loc) * weights) ** 2).sum(self.dim, keepdim=self.keepdim) / denominator
         scale = torch.sqrt(variance + self.minimum_scale)
         return (data - loc) / scale, loc, scale
 
 
-# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesMeanScaler with TimeSeriesTransformer->Informer,TimeSeries->Informer
+# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesMeanScaler with TimeSeries->Informer
 class InformerMeanScaler(nn.Module):
     """
-    Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data
+    Computes a scaling factor as the weighted average absolute value along dimension `dim`, and scales the data
     accordingly.
+
+    Args:
+        dim (`int`):
+            Dimension along which to compute the scale.
+        keepdim (`bool`, *optional*, defaults to `False`):
+            Controls whether to retain dimension `dim` (of length 1) in the scale tensor, or suppress it.
+        default_scale (`float`, *optional*, defaults to `None`):
+            Default scale that is used for elements that are constantly zero. If `None`, we use the scale of the batch.
+        minimum_scale (`float`, *optional*, defaults to 1e-10):
+            Default minimum possible scale that is used for any item.
     """
 
-    def __init__(self, config: InformerConfig):
+    def __init__(
+        self, dim: int = -1, keepdim: bool = True, default_scale: Optional[float] = None, minimum_scale: float = 1e-10
+    ):
         super().__init__()
-        self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
-        self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
-        self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-10
-        self.default_scale = config.default_scale if hasattr(config, "default_scale") else None
+        self.dim = dim
+        self.keepdim = keepdim
+        self.minimum_scale = minimum_scale
+        self.default_scale = default_scale
 
+    @torch.no_grad()
     def forward(
         self, data: torch.Tensor, observed_indicator: torch.Tensor
     ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Parameters:
-            data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                input for Batch norm calculation
-            observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                Calculating the scale on the observed indicator.
-        Returns:
-            tuple of `torch.Tensor` of shapes
-                (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
-                `(batch_size, 1, num_input_channels)`)
-        """
+        # shape: (N, [C], T=1)
         ts_sum = (data * observed_indicator).abs().sum(self.dim, keepdim=True)
         num_observed = observed_indicator.sum(self.dim, keepdim=True)
 
@@ -172,29 +173,26 @@ class InformerMeanScaler(nn.Module):
         return scaled_data, torch.zeros_like(scale), scale
 
 
-# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesNOPScaler with TimeSeriesTransformer->Informer,TimeSeries->Informer
+# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesNOPScaler with TimeSeries->Informer
 class InformerNOPScaler(nn.Module):
     """
-    Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data.
+    Assigns a scaling factor equal to 1 along dimension `dim`, and therefore applies no scaling to the input data.
+
+    Args:
+        dim (`int`):
+            Dimension along which to compute the scale.
+        keepdim (`bool`, *optional*, defaults to `False`):
+            Controls whether to retain dimension `dim` (of length 1) in the scale tensor, or suppress it.
     """
 
-    def __init__(self, config: InformerConfig):
+    def __init__(self, dim: int, keepdim: bool = False):
         super().__init__()
-        self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
-        self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
+        self.dim = dim
+        self.keepdim = keepdim
 
     def forward(
-        self, data: torch.Tensor, observed_indicator: torch.Tensor = None
+        self, data: torch.Tensor, observed_indicator: torch.Tensor
     ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Parameters:
-            data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                input for Batch norm calculation
-        Returns:
-            tuple of `torch.Tensor` of shapes
-                (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
-                `(batch_size, 1, num_input_channels)`)
-        """
         scale = torch.ones_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim)
         loc = torch.zeros_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim)
         return data, loc, scale
@@ -1448,11 +1446,11 @@ class InformerModel(InformerPreTrainedModel):
         super().__init__(config)
 
         if config.scaling == "mean" or config.scaling is True:
-            self.scaler = InformerMeanScaler(config)
+            self.scaler = InformerMeanScaler(dim=1, keepdim=True)
         elif config.scaling == "std":
-            self.scaler = InformerStdScaler(config)
+            self.scaler = InformerStdScaler(dim=1, keepdim=True)
         else:
-            self.scaler = InformerNOPScaler(config)
+            self.scaler = InformerNOPScaler(dim=1, keepdim=True)
 
         if config.num_static_categorical_features > 0:
             self.embedder = InformerFeatureEmbedder(

src/transformers/models/patchtst/__init__.py

-# Copyright 2023 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.
-from typing import TYPE_CHECKING
-
-# rely on isort to merge the imports
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
-
-
-_import_structure = {
-    "configuration_patchtst": [
-        "PATCHTST_PRETRAINED_CONFIG_ARCHIVE_MAP",
-        "PatchTSTConfig",
-    ],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_patchtst"] = [
-        "PATCHTST_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "PatchTSTModel",
-        "PatchTSTPreTrainedModel",
-        "PatchTSTForPrediction",
-        "PatchTSTForPretraining",
-        "PatchTSTForRegression",
-        "PatchTSTForClassification",
-    ]
-
-
-if TYPE_CHECKING:
-    from .configuration_patchtst import PATCHTST_PRETRAINED_CONFIG_ARCHIVE_MAP, PatchTSTConfig
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_patchtst import (
-            PATCHTST_PRETRAINED_MODEL_ARCHIVE_LIST,
-            PatchTSTForClassification,
-            PatchTSTForPrediction,
-            PatchTSTForPretraining,
-            PatchTSTForRegression,
-            PatchTSTModel,
-            PatchTSTPreTrainedModel,
-        )
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

src/transformers/models/patchtst/configuration_patchtst.py

-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. 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.
-"""PatchTST model configuration"""
-
-from typing import List, Optional, Union
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-PATCHTST_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "ibm/patchtst-base": "https://huggingface.co/ibm/patchtst-base/resolve/main/config.json",
-    # See all PatchTST models at https://huggingface.co/ibm/models?filter=patchtst
-}
-
-
-class PatchTSTConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of an [`PatchTSTModel`]. It is used to instantiate an
-    PatchTST model according to the specified arguments, defining the model architecture.
-    [ibm/patchtst](https://huggingface.co/ibm/patchtst) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        num_input_channels (`int`, *optional*, defaults to 1):
-            The size of the target variable which by default is 1 for univariate targets. Would be > 1 in case of
-            multivariate targets.
-        context_length (`int`, *optional*, defaults to 32):
-            The context length for the encoder.
-        distribution_output (`str`, *optional*, defaults to `"student_t"`):
-            The distribution emission head for the model when loss is "nll". Could be either "student_t", "normal" or
-            "negative_binomial".
-        loss (`str`, *optional*, defaults to `"mse"`):
-            The loss function for the model corresponding to the `distribution_output` head. For parametric
-            distributions it is the negative log likelihood ("nll") and for point estimates it is the mean squared
-            error "mse".
-        patch_length (`int`, *optional*, defaults to 1):
-            Define the patch length of the patchification process.
-        patch_stride (`int`, *optional*, defaults to 1):
-            define the stride of the patchification process.
-        encoder_layers (`int`, *optional*, defaults to 3):
-            Number of encoder layers.
-        d_model (`int`, *optional*, defaults to 64):
-            Dimensionality of the transformer layers.
-        encoder_attention_heads (`int`, *optional*, defaults to 4):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        shared_embedding (`bool`, *optional*, defaults to `True`):
-            Sharing the input embedding across all channels.
-        channel_attention (`bool`, *optional*, defaults to `False`):
-            Activate channel attention block in the Transformer to allow channels to attend each other.
-        encoder_ffn_dim (`int`, *optional*, defaults to 256):
-            Dimension of the "intermediate" (often named feed-forward) layer in encoder.
-        norm (`str` , *optional*, defaults to `"BatchNorm"`):
-            Normalization at each Transformer layer. Can be `"BatchNorm"` or `"LayerNorm"`.
-        norm_eps (`float`, *optional*, defaults to 1e-05):
-            A value added to the denominator for numerical stability of normalization.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout probability for the attention probabilities.
-        dropout (`float`, *optional*, defaults to 0.0):
-            The dropout probability for all fully connected layers in the encoder, and decoder.
-        positional_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout probability in the positional embedding layer.
-        dropout_path (`float`, *optional*, defaults to 0.0):
-            The dropout path in the residual block.
-        ff_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout probability used between the two layers of the feed-forward networks.
-        bias (`bool`, *optional*, defaults to `True`):
-            Consider bias in the feed-forward networks.
-        activation_function (`str`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function (string) in the encoder.`"gelu"` and `"relu"` are supported.
-        pre_norm (`bool`, *optional*, defaults to `True`):
-            Normalization is applied before self-attention if pre_norm is set to `True`. Otherwise, normalization is
-            applied after residual block.
-        positional_encoding_type (`str`, *optional*, defaults to `"sincos"`):
-            Positional encodings. `"zeros"`, `"normal"`, `"uniform"' and `"sincos"` are supported.
-        learn_pe (`bool`, *optional*, defaults to `False`):
-            Whether the positional encoding is updated during training.
-        use_cls_token (`bool`, *optional*, defaults to `False`):
-            Whether cls token is used.
-        init_std (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated normal weight initialization distribution.
-        shared_projection (`bool`, *optional*, defaults to `True`):
-            Sharing the projection layer across different channels in the forecast head.
-        seed_number (`Optional`, *optional*):
-            Seed number used for random masking. If unset, no seed is set.
-        scaling (`Union`, *optional*, defaults to `"mean"`):
-            Whether to scale the input targets via "mean" scaler, "std" scaler or no scaler if `None`. If `True`, the
-            scaler is set to "mean".
-        mask_input (`bool`, *optional*, defaults to `False`):
-            Apply masking during the pretraining.
-        mask_type (`str`, *optional*, defaults to `"random"`):
-            Masking type. Only `"random"` and `"forecast"` are currently supported.
-        random_mask_ratio (`float`, *optional*, defaults to 0.5):
-            Masking ratio is applied to mask the input data during random pretraining.
-        forecast_mask_patches (`List`, *optional*, defaults to `[2, 3]`):
-            List of patch lengths to mask in the end of the data.
-        forecast_mask_ratios (`List`, *optional*, defaults to `[1, 1]`):
-            List of weights to use for each patch length. For Ex. if patch_lengths is [5,4] and mix_ratio is [1,1],
-            then equal weights to both patch lengths. Defaults to None.
-        channel_consistent_masking (`bool`, *optional*, defaults to `False`):
-            If channel consistent masking is True, all the channels will have the same masking.
-        unmasked_channel_indices (`list`, *optional*):
-            Channels that are not masked during pretraining.
-        mask_value (`int`, *optional*, defaults to 0):
-            Define the value of entries to be masked when pretraining.
-        pooling_type (`str`, *optional*, defaults to `"mean"`):
-            Pooling of the embedding. `"mean"`, `"max"` and `None` are supported.
-        head_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout probability for head.
-        prediction_length (`int`, *optional*, defaults to 24):
-            The prediction length for the encoder. In other words, the prediction horizon of the model.
-        num_targets (`int`, *optional*, defaults to 1):
-            Number of targets for regression and classificastion tasks. For classification, it is the number of
-            classes.
-        output_range (`list`, *optional*):
-            Output range for regression task. The range of output values can be set to enforce the model to produce
-            values within a range.
-        num_parallel_samples (`int`, *optional*, defaults to 100):
-            The number of samples is generated in parallel for probablistic prediction.
-
-
-    ```python
-    >>> from transformers import PatchTSTConfig, PatchTSTModel
-
-    >>> # Initializing an PatchTST configuration with 12 time steps for prediction
-    >>> configuration = PatchTSTConfig(prediction_length=12)
-
-    >>> # Randomly initializing a model (with random weights) from the configuration
-    >>> model = PatchTSTModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-    model_type = "patchtst"
-    attribute_map = {
-        "hidden_size": "d_model",
-        "num_attention_heads": "encoder_attention_heads",
-        "num_hidden_layers": "encoder_layers",
-    }
-
-    def __init__(
-        self,
-        # time series specific configuration
-        num_input_channels: int = 1,
-        context_length: int = 32,
-        distribution_output: str = "student_t",
-        loss: str = "mse",
-        # PatchTST arguments
-        patch_length: int = 1,
-        patch_stride: int = 1,
-        # Transformer architecture configuration
-        encoder_layers: int = 3,
-        d_model: int = 64,
-        encoder_attention_heads: int = 4,
-        shared_embedding: bool = True,
-        channel_attention: bool = False,
-        encoder_ffn_dim: int = 256,
-        norm: str = "BatchNorm",
-        norm_eps: float = 1e-5,
-        attention_dropout: float = 0.0,
-        dropout: float = 0.0,
-        positional_dropout: float = 0.0,
-        dropout_path: float = 0.0,
-        ff_dropout: float = 0.0,
-        bias: bool = True,
-        activation_function: str = "gelu",
-        pre_norm: bool = True,
-        positional_encoding_type: str = "sincos",
-        learn_pe: bool = False,
-        use_cls_token: bool = False,
-        init_std: float = 0.02,
-        shared_projection: bool = True,
-        seed_number: Optional[int] = None,
-        scaling: Optional[Union[str, bool]] = "mean",
-        # mask pretraining
-        mask_input: Optional[bool] = None,
-        mask_type: str = "random",
-        random_mask_ratio: float = 0.5,
-        forecast_mask_patches: List[int] = [2, 3],
-        forecast_mask_ratios: List[int] = [1, 1],
-        channel_consistent_masking: bool = False,
-        unmasked_channel_indices: Optional[List[int]] = None,
-        mask_value=0,
-        # head
-        pooling_type: str = "mean",
-        head_dropout: float = 0.0,
-        prediction_length: int = 24,
-        num_targets: int = 1,
-        output_range: List = None,
-        # distribution head
-        num_parallel_samples: int = 100,
-        **kwargs,
-    ):
-        # time series specific configuration
-        self.context_length = context_length
-        self.num_input_channels = num_input_channels  # n_vars
-        self.loss = loss
-        self.distribution_output = distribution_output
-        self.num_parallel_samples = num_parallel_samples
-
-        # Transformer architecture configuration
-        self.d_model = d_model
-        self.encoder_attention_heads = encoder_attention_heads
-        self.encoder_ffn_dim = encoder_ffn_dim
-        self.encoder_layers = encoder_layers
-        self.dropout = dropout
-        self.attention_dropout = attention_dropout
-        self.shared_embedding = shared_embedding
-        self.channel_attention = channel_attention
-        self.norm = norm
-        self.norm_eps = norm_eps
-        self.positional_dropout = positional_dropout
-        self.dropout_path = dropout_path
-        self.ff_dropout = ff_dropout
-        self.bias = bias
-        self.activation_function = activation_function
-        self.pre_norm = pre_norm
-        self.positional_encoding_type = positional_encoding_type
-        self.learn_pe = learn_pe
-        self.use_cls_token = use_cls_token
-        self.init_std = init_std
-        self.scaling = scaling
-
-        # PatchTST parameters
-        self.patch_length = patch_length
-        self.patch_stride = patch_stride
-        self.num_patches = self._num_patches()
-
-        # Mask pretraining
-        self.seed_number = seed_number
-        self.mask_input = mask_input
-        self.mask_type = mask_type
-        self.random_mask_ratio = random_mask_ratio  # for random masking
-        self.forecast_mask_patches = forecast_mask_patches  # for forecast masking
-        self.forecast_mask_ratios = forecast_mask_ratios
-        self.channel_consistent_masking = channel_consistent_masking
-        self.unmasked_channel_indices = unmasked_channel_indices
-        self.mask_value = mask_value
-
-        # general head params
-        self.pooling_type = pooling_type
-        self.head_dropout = head_dropout
-
-        # For prediction head
-        self.shared_projection = shared_projection
-        self.prediction_length = prediction_length
-
-        # For prediction and regression head
-        self.num_parallel_samples = num_parallel_samples
-
-        # Regression
-        self.num_targets = num_targets
-        self.output_range = output_range
-
-        super().__init__(**kwargs)
-
-    def _num_patches(self):
-        return (max(self.context_length, self.patch_length) - self.patch_length) // self.patch_stride + 1

src/transformers/models/patchtst/modeling_patchtst.py

-# coding=utf-8
-# Copyright 2023 IBM & Hugging Face. 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.
-""" PyTorch PatchTST model."""
-
-import math
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-
-import torch
-from torch import nn
-
-from ...activations import ACT2CLS
-from ...modeling_outputs import BaseModelOutput
-from ...modeling_utils import PreTrainedModel
-from ...time_series_utils import NegativeBinomialOutput, NormalOutput, StudentTOutput
-from ...trainer_utils import set_seed
-from ...utils import ModelOutput, add_start_docstrings, logging
-from .configuration_patchtst import PatchTSTConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "PatchTSTConfig"
-
-PATCHTST_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "ibm/patchtst-etth1-pretrain",
-    # See all PatchTST models at https://huggingface.co/models?filter=patchtst
-]
-
-
-# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->PatchTST
-class PatchTSTAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(
-        self,
-        embed_dim: int,
-        num_heads: int,
-        dropout: float = 0.0,
-        is_decoder: bool = False,
-        bias: bool = True,
-        is_causal: bool = False,
-        config: Optional[PatchTSTConfig] = None,
-    ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-        self.config = config
-
-        if (self.head_dim * num_heads) != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
-                f" and `num_heads`: {num_heads})."
-            )
-        self.scaling = self.head_dim**-0.5
-        self.is_decoder = is_decoder
-        self.is_causal = is_causal
-
-        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        key_value_states: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        layer_head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        # if key_value_states are provided this layer is used as a cross-attention layer
-        # for the decoder
-        is_cross_attention = key_value_states is not None
-
-        bsz, tgt_len, _ = hidden_states.size()
-
-        # get query proj
-        query_states = self.q_proj(hidden_states) * self.scaling
-        # get key, value proj
-        # `past_key_value[0].shape[2] == key_value_states.shape[1]`
-        # is checking that the `sequence_length` of the `past_key_value` is the same as
-        # the provided `key_value_states` to support prefix tuning
-        if (
-            is_cross_attention
-            and past_key_value is not None
-            and past_key_value[0].shape[2] == key_value_states.shape[1]
-        ):
-            # reuse k,v, cross_attentions
-            key_states = past_key_value[0]
-            value_states = past_key_value[1]
-        elif is_cross_attention:
-            # cross_attentions
-            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
-            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
-        elif past_key_value is not None:
-            # reuse k, v, self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-        else:
-            # self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-
-        if self.is_decoder:
-            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
-            # Further calls to cross_attention layer can then reuse all cross-attention
-            # key/value_states (first "if" case)
-            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
-            # all previous decoder key/value_states. Further calls to uni-directional self-attention
-            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
-            # if encoder bi-directional self-attention `past_key_value` is always `None`
-            past_key_value = (key_states, value_states)
-
-        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
-        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
-        key_states = key_states.reshape(*proj_shape)
-        value_states = value_states.reshape(*proj_shape)
-
-        src_len = key_states.size(1)
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
-
-        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        if layer_head_mask is not None:
-            if layer_head_mask.size() != (self.num_heads,):
-                raise ValueError(
-                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
-                    f" {layer_head_mask.size()}"
-                )
-            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        if output_attentions:
-            # this operation is a bit awkward, but it's required to
-            # make sure that attn_weights keeps its gradient.
-            # In order to do so, attn_weights have to be reshaped
-            # twice and have to be reused in the following
-            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
-        else:
-            attn_weights_reshaped = None
-
-        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-
-        attn_output = torch.bmm(attn_probs, value_states)
-
-        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-        attn_output = attn_output.transpose(1, 2)
-
-        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
-        # partitioned across GPUs when using tensor-parallelism.
-        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped, past_key_value
-
-
-class PatchTSTBatchNorm(nn.Module):
-    """
-    Parameters:
-    Compute batch normalization
-        d_model (`int`): model dimension
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__()
-        self.batchnorm = nn.BatchNorm1d(config.d_model, eps=config.norm_eps)
-
-    def forward(self, inputs: torch.Tensor):
-        """
-        Parameters:
-            inputs (`torch.Tensor` of shape `(batch_size, sequence_length, d_model)`):
-                input for Batch norm calculation
-        Returns:
-            `torch.Tensor` of shape `(batch_size, sequence_length, d_model)`
-        """
-        output = inputs.transpose(1, 2)  # output: (batch_size, d_model, sequence_length)
-        output = self.batchnorm(output)
-        return output.transpose(1, 2)
-
-
-def positional_encoding(positional_encoding_type, learned, q_len, d_model):
-    # Positional encoding
-    if positional_encoding_type is None:
-        # positional_encoding_type = None and learned = False can be used to measure impact of positional encoding
-        position_enc = torch.empty((q_len, d_model))
-        nn.init.uniform_(position_enc, -0.02, 0.02)
-        learned = False
-    elif positional_encoding_type == "zeros":
-        position_enc = torch.empty((q_len, d_model))
-        nn.init.uniform_(position_enc, -0.02, 0.02)
-    elif positional_encoding_type == "normal":
-        position_enc = torch.zeros((q_len, 1))
-        nn.init.normal_(position_enc, mean=0.0, std=0.1)
-    elif positional_encoding_type == "uniform":
-        position_enc = torch.zeros((q_len, 1))
-        nn.init.uniform_(position_enc, a=0.0, b=0.1)
-    elif positional_encoding_type == "sincos":
-        position_enc = torch.zeros(q_len, d_model)
-        position = torch.arange(0, q_len).unsqueeze(1)
-        div_term = torch.exp(torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model))
-        position_enc[:, 0::2] = torch.sin(position * div_term)
-        position_enc[:, 1::2] = torch.cos(position * div_term)
-        position_enc = position_enc - position_enc.mean()
-        position_enc = position_enc / (position_enc.std() * 10)
-    else:
-        raise ValueError(
-            f"{positional_encoding_type} is not a valid positional encoder. Available types are 'normal', 'zeros', 'zero', uniform', 'sincos', None."
-        )
-    return nn.Parameter(position_enc, requires_grad=learned)
-
-
-def random_masking(
-    inputs: torch.Tensor,
-    mask_ratio: float,
-    unmasked_channel_indices: list = None,
-    channel_consistent_masking: bool = False,
-    mask_value: int = 0,
-    seed_number: Optional[int] = None,
-):
-    """random_masking: Mask the input considering the control variables.
-
-    Args:
-        inputs (`torch.Tensor` of shape `(batch_size, num_channels, sequence_length, num_features)`):
-            The input tensor to mask.
-        mask_ratio (`float`):
-            Mask ratio.
-        unmasked_channel_indices (list, *optional*):
-            indices of unmasked channels. These channels will not be masked.
-        channel_consistent_masking (bool, *optional* defaults to False):
-            When true, masking will be same across all channels of a timeseries. Otherwise, masking positions will vary
-            across channels.
-        mask_value (int, *optional*, defaults to 0):
-            Value to use for masking.
-        seed_number (int, *optional*):
-            Value to set for the random seed.
-
-    Returns:
-        `tuple(torch.Tensor)`: inputs_mask, masked input, same shape as input Tensor and mask tensor of shape [bs x c x
-        n]
-    """
-    if seed_number:
-        set_seed(seed_number)
-
-    batch_size, num_channels, sequence_length, num_features = inputs.shape
-    device = inputs.device
-
-    len_keep = int(sequence_length * (1 - mask_ratio))
-
-    if channel_consistent_masking:
-        noise = torch.rand(batch_size, 1, sequence_length, device=device)  # noise in [0, 1], bs x 1 x  L
-        noise = noise.repeat(1, num_channels, 1)  # bs x num_channels x time
-    else:
-        # noise in [0, 1], bs x num_channels x L
-        noise = torch.rand(batch_size, num_channels, sequence_length, device=device)
-
-    # mask: [bs x num_channels x num_patch]
-    mask = torch.ones(batch_size, num_channels, sequence_length, device=device)
-    mask[:, :, :len_keep] = 0
-
-    # sort noise for each sample
-    ids_shuffle = torch.argsort(noise, dim=-1)  # ascend: small is keep, large is remove
-    ids_restore = torch.argsort(ids_shuffle, dim=-1)  # ids_restore: [bs x num_channels x L]
-
-    mask = torch.gather(mask, dim=-1, index=ids_restore)
-    mask = mask.unsqueeze(-1).repeat(1, 1, 1, num_features)  # mask: [bs x num_channels x num_patches x patch_length]
-    if unmasked_channel_indices is not None:
-        mask[:, unmasked_channel_indices, :, :] = 0
-
-    inputs_mask = inputs.masked_fill(mask.bool(), mask_value)
-    return inputs_mask, mask[..., 0]
-
-
-def forecast_masking(
-    inputs: torch.Tensor,
-    forecast_mask_patches: list,
-    forecast_mask_ratios: list = None,
-    unmasked_channel_indices: list = None,
-    mask_value: int = 0,
-    seed_number: Optional[int] = None,
-):
-    """Forecast masking that masks the last K patches where K is from the forecast_mask_patches list.
-    For every batch, distribute the patch lengths based on forecast_mask_ratios and ignore masks for column indices
-    mentioned in unmasked_channel_indices.
-
-    Parameters:
-        inputs (`torch.Tensor`):
-            Input of shape `(bs, num_channels, num_patch, patch_len)` or `(bs, tsg1, tag2, num_channels, num_patch,
-            patch_len)`
-        forecast_mask_patches (`list`):
-            List of patch lengths to mask at the end of the data e.g. [2, 4].
-        forecast_mask_ratios (`list`, *optional*):
-            List of weights to use for each patch length. For example if forecast_mask_patches is [5,4] and
-            forecast_mask_ratios is [1,1], then equal weights to both patch lengths.
-        unmasked_channel_indices (`list`, *optional*):
-            Control Variable channel indices. These channels will not be masked.
-        mask_value (`int`, *optional*, defaults to 0):
-            Value to use for masking.
-        seed_number (`int`, *optional*):
-            Value to set for the random seed.
-
-    Returns:
-        `tuple(torch.Tensor)`: inputs_mask, masked input, same shape as inputs Tensor and Mask tensor of shape `(bs,
-        num_channels , num_patch)` or `(bs, tsg1, tsg2, num_channels, num_patch)`
-    """
-    if seed_number:
-        set_seed(seed_number)
-
-    if forecast_mask_ratios is None:
-        forecast_mask_ratios = [1 for _ in forecast_mask_patches]
-
-    batch_size, num_channels, sequence_length, num_features = inputs.shape
-    mask = torch.zeros(batch_size, num_channels, sequence_length, device=inputs.device)
-
-    t_list = []
-    total_length = 0
-    total_ratio = sum(forecast_mask_ratios)
-
-    for patch_length, ratio in zip(forecast_mask_patches, forecast_mask_ratios):
-        if patch_length <= 0 or patch_length >= sequence_length:
-            raise Exception("masked_patch_len should be greater than 0 and less than total patches.")
-        temp_len = int(batch_size * ratio / total_ratio)
-        t_list.append([patch_length, ratio, temp_len])
-        total_length += temp_len
-
-    t_list = sorted(t_list, key=lambda x: x[2])
-
-    if total_length < batch_size:
-        t_list[0][2] = t_list[0][2] + (batch_size - total_length)
-    elif total_length > batch_size:
-        t_list[-1][2] = t_list[-1][2] + (total_length - batch_size)
-
-    batch1 = 0
-    for patch_len, _, temp_len in t_list:
-        batch2 = batch1 + temp_len
-        mask[batch1:batch2, :, -patch_len:] = 1
-        batch1 = batch2
-
-    perm = torch.randperm(mask.shape[0])
-    mask = mask[perm]
-
-    mask = mask.unsqueeze(-1).repeat(1, 1, 1, num_features)  # mask: [bs x num_channels x num_patch x patch_len]
-    if unmasked_channel_indices is not None:
-        mask[:, unmasked_channel_indices, :, :] = 0
-
-    inputs_mask = inputs.masked_fill(mask.bool(), mask_value)
-    return inputs_mask, mask[..., 0]
-
-
-class PatchTSTPatchify(nn.Module):
-    """
-    A class to patchify the time series sequence into different patches
-
-    Returns:
-        `torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__()
-
-        self.sequence_length = config.context_length
-        self.patch_length = config.patch_length
-        self.patch_stride = config.patch_stride
-
-        if self.sequence_length <= self.patch_length:
-            raise ValueError(
-                f"Sequence length ({self.sequence_length}) has to be greater than the patch length ({self.patch_length})"
-            )
-
-        # get the number of patches
-        num_patches = (max(self.sequence_length, self.patch_length) - self.patch_length) // self.patch_stride + 1
-        new_sequence_length = self.patch_length + self.patch_stride * (num_patches - 1)
-        self.sequence_start = self.sequence_length - new_sequence_length
-
-    def forward(self, past_values: torch.Tensor):
-        """
-        Parameters:
-            past_values (`torch.Tensor` of shape `(batch_size, sequence_length, num_channels)`, *required*):
-                Input to be patchified
-
-        Returns:
-            `torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`
-        """
-        sequence_length = past_values.shape[-2]
-        if sequence_length != self.sequence_length:
-            raise ValueError(
-                f"Input sequence length ({sequence_length}) doesn't match model configuration ({self.sequence_length})."
-            )
-        # output: [bs x new_sequence_length x num_channels]
-        output = past_values[:, self.sequence_start :, :]
-        # output: [bs x num_patches x num_input_channels x patch_length]
-        output = output.unfold(dimension=-2, size=self.patch_length, step=self.patch_stride)
-        # output: [bs x num_input_channels x num_patches x patch_length]
-        output = output.transpose(-2, -3).contiguous()
-        return output
-
-
-class PatchTSTMasking(nn.Module):
-    """
-    Class to perform random or forecast masking.
-
-    Parameters:
-        config (`PatchTSTConfig`): model config
-
-    Returns:
-        x_mask (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`)
-                Masked patched input
-        mask (`torch.Tensor` of shape `(batch_size, num_channels, num_patches)`)
-            Bool tensor indicating True on masked points
-
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__()
-        self.random_mask_ratio = config.random_mask_ratio
-        self.channel_consistent_masking = config.channel_consistent_masking
-        self.mask_type = config.mask_type
-        self.forecast_mask_patches = config.forecast_mask_patches
-        self.forecast_mask_ratios = config.forecast_mask_ratios
-        self.unmasked_channel_indices = config.unmasked_channel_indices
-        self.mask_value = config.mask_value
-        if self.unmasked_channel_indices is not None:
-            self.unmasked_channel_indices.sort()
-        self.seed_number = config.seed_number
-
-    def forward(self, patch_input: torch.Tensor):
-        """
-        Parameters:
-            patch_input (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`, *required*):
-                Patch input
-
-        Return:
-            masked_input (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`)
-                Masked patched input
-            mask (`torch.Tensor` of shape `(batch_size, num_channels, num_patches)`)
-                Bool tensor indicating True on masked points
-
-        """
-
-        if self.mask_type == "random":
-            masked_input, mask = random_masking(
-                inputs=patch_input,
-                mask_ratio=self.random_mask_ratio,
-                unmasked_channel_indices=self.unmasked_channel_indices,
-                channel_consistent_masking=self.channel_consistent_masking,
-                mask_value=self.mask_value,
-                seed_number=self.seed_number,
-            )
-        elif self.mask_type == "forecast":
-            masked_input, mask = forecast_masking(
-                inputs=patch_input,
-                forecast_mask_patches=self.forecast_mask_patches,
-                forecast_mask_ratios=self.forecast_mask_ratios,
-                unmasked_channel_indices=self.unmasked_channel_indices,
-                mask_value=self.mask_value,
-                seed_number=self.seed_number,
-            )
-        else:
-            raise Exception("Invalid mask type")
-
-        mask = mask.bool()  # mask: [bs x num_input_channels x num_patch]
-
-        return masked_input, mask
-
-
-class PatchTSTEncoderLayer(nn.Module):
-    """
-    PatchTST encoder layer
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__()
-
-        self.channel_attention = config.channel_attention
-
-        # Multi-Head attention
-        self.self_attn = PatchTSTAttention(
-            embed_dim=config.d_model,
-            num_heads=config.encoder_attention_heads,
-            dropout=config.attention_dropout,
-        )
-
-        # Add & Norm of the sublayer 1
-        self.dropout_path1 = nn.Dropout(config.dropout_path) if config.dropout_path > 0 else nn.Identity()
-        if "batch" in config.norm.lower():
-            self.norm_sublayer1 = PatchTSTBatchNorm(config)
-        else:
-            self.norm_sublayer1 = nn.LayerNorm(config.d_model, eps=config.norm_eps)
-
-        # Add & Norm of the sublayer 2
-        if self.channel_attention:
-            self.dropout_path2 = nn.Dropout(config.dropout_path) if config.dropout_path > 0 else nn.Identity()
-            if "batch" in config.norm.lower():
-                self.norm_sublayer2 = PatchTSTBatchNorm(config)
-            else:
-                self.norm_sublayer2 = nn.LayerNorm(config.d_model, eps=config.norm_eps)
-
-        # Position-wise Feed-Forward
-        self.ff = nn.Sequential(
-            nn.Linear(config.d_model, config.encoder_ffn_dim, bias=config.bias),
-            ACT2CLS[config.activation_function](),
-            nn.Dropout(config.ff_dropout) if config.ff_dropout > 0 else nn.Identity(),
-            nn.Linear(config.encoder_ffn_dim, config.d_model, bias=config.bias),
-        )
-
-        # Add & Norm of sublayer 3
-        self.dropout_path3 = nn.Dropout(config.dropout_path) if config.dropout_path > 0 else nn.Identity()
-        if "batch" in config.norm.lower():
-            self.norm_sublayer3 = PatchTSTBatchNorm(config)
-        else:
-            self.norm_sublayer3 = nn.LayerNorm(config.d_model, eps=config.norm_eps)
-
-        self.pre_norm = config.pre_norm
-
-    def forward(self, hidden_state: torch.Tensor, output_attentions: Optional[bool] = None):
-        """
-        Parameters:
-            hidden_state (`torch.Tensor` of shape `(batch_size, num_channels, sequence_length, d_model)`, *required*):
-                Past values of the time series
-        Return:
-            `torch.Tensor` of shape `(batch_size, num_channels, sequence_length, d_model)`
-
-        """
-        batch_size, num_input_channels, sequence_length, d_model = hidden_state.shape
-
-        # First sublayer: attention across time
-        # hidden_states: [(bs*num_channels) x sequence_length x d_model]
-        hidden_state = hidden_state.view(batch_size * num_input_channels, sequence_length, d_model)
-
-        if self.pre_norm:
-            ## Norm and Multi-Head attention and Add residual connection
-            attn_output, attn_weights, _ = self.self_attn(
-                hidden_states=self.norm_sublayer1(hidden_state), output_attentions=output_attentions
-            )
-            # Add: residual connection with residual dropout
-            hidden_state = hidden_state + self.dropout_path1(attn_output)
-        else:
-            ## Multi-Head attention and Add residual connection and Norm - Standard Transformer from BERT
-            attn_output, attn_weights, _ = self.self_attn(
-                hidden_states=hidden_state, output_attentions=output_attentions
-            )
-            # hidden_states: [(bs*num_channels) x sequence_length x d_model]
-            hidden_state = self.norm_sublayer1(hidden_state + self.dropout_path1(attn_output))
-
-        # hidden_state: [bs x num_channels x sequence_length x d_model]
-        hidden_state = hidden_state.reshape(batch_size, num_input_channels, sequence_length, d_model)
-
-        # second sublayer: attention across variable at any given time
-        if self.channel_attention:
-            # hidden_state: [bs x sequence_length x num_channels x d_model]
-            hidden_state = hidden_state.transpose(2, 1).contiguous()
-            # hidden_state: [(bs*sequence_length) x num_channels x d_model]
-            hidden_state = hidden_state.view(batch_size * sequence_length, num_input_channels, d_model)
-            if self.pre_norm:
-                ## Norm and Multi-Head attention and Add residual connection
-                attn_output, channel_attn_weights, _ = self.self_attn(
-                    hidden_states=self.norm_sublayer2(hidden_state), output_attentions=output_attentions
-                )
-                # Add: residual connection with residual dropout
-                hidden_state = hidden_state + self.dropout_path2(attn_output)
-            else:
-                ## Multi-Head attention and Add residual connection and Norm
-                attn_output, channel_attn_weights, _ = self.self_attn(
-                    hidden_states=hidden_state, output_attentions=output_attentions
-                )
-                # hidden_states: [(bs*sequence_length) x num_channels x d_model]
-                hidden_state = self.norm_sublayer2(hidden_state + self.dropout_path2(attn_output))
-
-            # Reshape hidden state
-            # hidden_state: [bs x sequence_length x num_channels x d_model]
-            hidden_state = hidden_state.reshape(batch_size, sequence_length, num_input_channels, d_model)
-            # hidden_state: [bs x num_channels x sequence_length x d_model]
-            hidden_state = hidden_state.transpose(1, 2).contiguous()
-
-        # Third sublayer: mixing across hidden
-        # hidden_state: [(batch_size*num_channels) x sequence_length x d_model]
-        hidden_state = hidden_state.view(batch_size * num_input_channels, sequence_length, d_model)
-        if self.pre_norm:
-            ## Norm and Position-wise Feed-Forward and Add residual connection
-            # Add: residual connection with residual dropout
-            hidden_state = hidden_state + self.dropout_path3(self.ff(self.norm_sublayer3(hidden_state)))
-        else:
-            ## Position-wise Feed-Forward and Add residual connection and Norm
-            # Add: residual connection with residual dropout
-            hidden_state = self.norm_sublayer3(hidden_state + self.dropout_path3(self.ff(hidden_state)))
-
-        # [bs x num_channels x sequence_length x d_model]
-        hidden_state = hidden_state.reshape(batch_size, num_input_channels, sequence_length, d_model)
-
-        outputs = (hidden_state,)
-        if output_attentions:
-            outputs += (attn_weights, channel_attn_weights) if self.channel_attention else (attn_weights,)
-
-        return outputs
-
-
-class PatchTSTPreTrainedModel(PreTrainedModel):
-    config_class = PatchTSTConfig
-    base_model_prefix = "model"
-    main_input_name = "past_values"
-    supports_gradient_checkpointing = False
-
-    def _init_weights(self, module):
-        """Initialize weights"""
-        if self.config.use_cls_token:
-            nn.init.normal_(self.config.cls_token, std=0.02)
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        elif isinstance(module, (nn.Linear, nn.Conv1d)):
-            module.weight.data.normal_(mean=0.0, std=self.config.init_std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (PatchTSTEncoder)):
-            module.gradient_checkpointing = value
-
-
-class PatchTSTEmbedding(nn.Module):
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__()
-        # Input encoding: projection of feature vectors onto a d-dim vector space
-        if not config.shared_embedding:
-            self.input_embedding = nn.ModuleList()
-            for _ in range(config.num_input_channels):
-                self.input_embedding.append(nn.Linear(config.patch_length, config.d_model))
-        else:
-            self.input_embedding = nn.Linear(config.patch_length, config.d_model)
-
-    def forward(self, patch_input: torch.Tensor):
-        """
-        Parameters:
-            patch_input (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`, *required*):
-                Patch input for embedding
-        return:
-            `torch.Tensor` of shape `(batch_size, num_channels, num_patches, d_model)`
-        """
-        # Input encoding
-        num_input_channels = patch_input.shape[1]
-        if isinstance(self.input_embedding, nn.ModuleList):
-            embeddings = [self.input_embedding[i](patch_input[:, i, :, :]) for i in range(num_input_channels)]
-            embeddings = torch.stack(embeddings, dim=1)
-        else:
-            embeddings = self.input_embedding(patch_input)  # x: [bs x num_channels  x num_patches x d_model]
-        return embeddings
-
-
-class PatchTSTPositionalEncoding(nn.Module):
-    """
-    Class for positional encoding
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__()
-        self.use_cls_token = config.use_cls_token
-        if config.use_cls_token:
-            self.cls_token = nn.Parameter(torch.zeros(1, 1, 1, config.d_model))
-            num_patches = config.num_patches + 1
-        else:
-            num_patches = config.num_patches
-        # postional encoding
-        self.position_enc = positional_encoding(
-            config.positional_encoding_type, config.learn_pe, num_patches, config.d_model
-        )
-        # Positional dropout
-        self.positional_dropout = (
-            nn.Dropout(config.positional_dropout) if config.positional_dropout > 0 else nn.Identity()
-        )
-
-    def forward(self, patch_input: torch.Tensor):
-        if self.use_cls_token:
-            # patch_input: [bs x num_channels x num_patches x d_model]
-            patch_input = self.positional_dropout(patch_input + self.position_enc[1:, :])
-            # append cls token where cls_token: [1 x 1 x 1 x d_model]
-            cls_token = self.cls_token + self.position_enc[:1, :]
-            # get the same copy of cls_token for all the samples in batch
-            cls_tokens = cls_token.expand(patch_input.shape[0], -1, -1)
-            # hidden_state: [bs x num_channels x (num_patches+1) x d_model]
-            hidden_state = torch.cat((cls_tokens, patch_input), dim=1)
-        else:
-            # hidden_state: [bs x num_channels x num_patches x d_model]
-            hidden_state = self.positional_dropout(patch_input + self.position_enc)
-        return hidden_state
-
-
-class PatchTSTEncoder(PatchTSTPreTrainedModel):
-    """
-    PatchTST Encoder
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__(config)
-        self.num_input_channels = config.num_input_channels
-        self.num_patches = config.num_patches
-        self.patch_length = config.patch_length
-        self.d_model = config.d_model
-        self.shared_embedding = config.shared_embedding
-        self.use_cls_token = config.use_cls_token
-        self.gradient_checkpointing = False
-
-        # Input embedding: projection of feature vectors onto a d-dim vector space
-        self.embedder = PatchTSTEmbedding(config)
-        # Positional encoding
-        self.positional_encoder = PatchTSTPositionalEncoding(config)
-        # Encoder
-        self.layers = nn.ModuleList([PatchTSTEncoderLayer(config) for i in range(config.encoder_layers)])
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        patch_input: torch.Tensor,
-        output_hidden_states: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-    ) -> BaseModelOutput:
-        """
-        Parameters:
-            patch_input (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`, *required*):
-                Past values of the time series
-            output_hidden_states (bool, optional): Indicates if hidden states should be outputted.
-            output_attentions (bool, optional): Indicates if attentions should be outputted.
-
-        return:
-            `BaseModelOutput`
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-
-        # Input embedding
-        patch_input = self.embedder(patch_input)
-        # Positional encoding
-        hidden_state = self.positional_encoder(patch_input)
-
-        encoder_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-        for encoder_layer in self.layers:
-            if output_hidden_states:
-                encoder_states = encoder_states + (hidden_state,)
-
-            layer_outputs = encoder_layer(hidden_state=hidden_state, output_attentions=output_attentions)
-            # get hidden state. hidden_state shape is [bs x num_channels x num_patches x d_model]
-            # or [bs x num_channels x (num_patches+1) x d_model] if use cls_token
-            hidden_state = layer_outputs[0]
-            # append attention matrix at each layer
-            if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-        # return past_values, hidden_states
-        return BaseModelOutput(last_hidden_state=hidden_state, hidden_states=encoder_states, attentions=all_attentions)
-
-
-PATCHTST_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`PatchTSTConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-PATCHTST_INPUTS_DOCSTRING = r"""
-    Parameters:
-        past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, num_input_channels)`):
-            Past values of the time series, that serve as context in order to predict the future. The sequence size of
-            this tensor must be larger than the `context_length` of the model, since the model will use the larger size
-            to construct lag features, i.e. additional values from the past which are added in order to serve as "extra
-            context".
-
-            The `sequence_length` here is equal to `config.context_length`
-
-            The `past_values` is what the Transformer encoder gets as input (with optional additional features, such as
-            `static_categorical_features`, `static_real_features`).
-
-            For multivariate time series, the `num_input_channels` > 1 dimension is required and corresponds to the
-            number of variates in the time series per time step.
-
-        future_values (`torch.FloatTensor` of shape `(batch_size, prediction_length)` or `(batch_size, prediction_length, num_input_channels)`, *optional*):
-            Future values of the time series, that serve as labels for the model. The `future_values` is what the
-            Transformer needs during training to learn to output, given the `past_values`.
-
-            The sequence length here is equal to `prediction_length`.
-
-            See the demo notebook and code snippets for details.
-
-            For multivariate time series, the `num_input_channels` > 1 dimension is required and corresponds to the
-            number of variates in the time series per time step.
-
-        output_hidden_states (`bool`, *optional*, default to False):
-            Whether or not to return the hidden states of all layers.
-"""
-
-
-@dataclass
-class PatchTSTModelOutput(ModelOutput):
-    """
-    Base class for model's outputs, with potential hidden states.
-
-    Parameters:
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, patch_length)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of
-            the model at the output of each layer plus the optional initial embedding outputs.
-        patch_input (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, patch_length)`):
-            patched input to the Transformer
-        mask: (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches)`,*optional*)
-            Bool masked tensor indicating which patches are masked
-        loc: (`torch.FloatTensor` of shape `(batch_size, 1, num_channels)`,*optional*)
-            mean of the input data (batch_size, sequence_length, num_channels) over the sequence_length
-        scale: (`torch.FloatTensor` of shape `(batch_size, 1, num_channels)`,*optional*)
-            std of the input data (batch_size, sequence_length, num_channels) over the sequence_length
-    """
-
-    last_hidden_state: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-    patch_input: torch.FloatTensor = None
-    mask: torch.FloatTensor = None
-    loc: torch.FloatTensor = None
-    scale: torch.FloatTensor = None
-
-
-@dataclass
-class PatchTSTForPretrainingOutput(ModelOutput):
-    """
-    Output type of [`PatchTSTForPretraining`].
-
-    Parameters:
-        loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
-            MSE loss.
-        prediction_outputs (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction outputs of the time series modeling heads.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    prediction_output: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class PatchTSTForRegressionOutput(ModelOutput):
-    """
-    Output type of [`PatchTSTForRegression`].
-
-    Parameters:
-        loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
-            MSE loss.
-        forecast_outputs (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction outputs of the time series modeling heads.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    forecast_outputs: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class PatchTSTForPredictionOutput(ModelOutput):
-    """
-    Output type of [`PatchTSTForPrediction`].
-
-    Parameters:
-        loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
-            MSE loss.
-        prediction_outputs (`torch.FloatTensor` of shape `(batch_size, sequence_length, -1)`):
-            Prediction outputs of the time series modeling heads.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    prediction_outputs: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-    loc: torch.FloatTensor = None
-    scale: torch.FloatTensor = None
-
-
-@dataclass
-class PatchTSTForClassificationOutput(ModelOutput):
-    """
-    Output type of [`PatchTSTForClassification`].
-
-    Parameters:
-        loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
-            Total loss as the sum of the masked language modeling loss and the next sequence prediction
-            (classification) loss.
-        prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    prediction_logits: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class SamplePatchTSTPredictionOutput(ModelOutput):
-    """
-    Base class for time series model's predictions outputs that contains the sampled values from the chosen
-    distribution.
-
-    Parameters:
-        sequences `(batch_size, num_samples, prediction_length, num_targets)`):
-                Sampled values from the chosen distribution.
-    """
-
-    sequences: torch.FloatTensor = None
-
-
-@dataclass
-class SamplePatchTSTRegressionOutput(ModelOutput):
-    """
-    Base class for time series model's predictions outputs that contains the sampled values from the chosen
-    distribution.
-
-    Parameters:
-        sequences (`torch.FloatTensor` of shape `(batch_size, num_samples, num_targets)`
-                Sampled values from the chosen distribution.
-    """
-
-    sequences: torch.FloatTensor = None
-
-
-# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.nll
-def nll(input: torch.distributions.Distribution, target: torch.Tensor) -> torch.Tensor:
-    """
-    Computes the negative log likelihood loss from input distribution with respect to target.
-    """
-    return -input.log_prob(target)
-
-
-# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.weighted_average
-def weighted_average(input_tensor: torch.Tensor, weights: Optional[torch.Tensor] = None, dim=None) -> torch.Tensor:
-    """
-    Computes the weighted average of a given tensor across a given `dim`, masking values associated with weight zero,
-    meaning instead of `nan * 0 = nan` you will get `0 * 0 = 0`.
-
-    Args:
-        input_tensor (`torch.FloatTensor`):
-            Input tensor, of which the average must be computed.
-        weights (`torch.FloatTensor`, *optional*):
-            Weights tensor, of the same shape as `input_tensor`.
-        dim (`int`, *optional*):
-            The dim along which to average `input_tensor`.
-
-    Returns:
-        `torch.FloatTensor`: The tensor with values averaged along the specified `dim`.
-    """
-    if weights is not None:
-        weighted_tensor = torch.where(weights != 0, input_tensor * weights, torch.zeros_like(input_tensor))
-        sum_weights = torch.clamp(weights.sum(dim=dim) if dim else weights.sum(), min=1.0)
-        return (weighted_tensor.sum(dim=dim) if dim else weighted_tensor.sum()) / sum_weights
-    else:
-        return input_tensor.mean(dim=dim)
-
-
-# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesStdScaler with TimeSeriesTransformer->PatchTST,TimeSeries->PatchTST
-class PatchTSTStdScaler(nn.Module):
-    """
-    Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by
-    subtracting from the mean and dividing by the standard deviation.
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__()
-        self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
-        self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
-        self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-10
-
-    def forward(
-        self, data: torch.Tensor, observed_indicator: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Parameters:
-            data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                input for Batch norm calculation
-            observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                Calculating the scale on the observed indicator.
-        Returns:
-            tuple of `torch.Tensor` of shapes
-                (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
-                `(batch_size, 1, num_input_channels)`)
-        """
-        denominator = observed_indicator.sum(self.dim, keepdim=self.keepdim)
-        denominator = denominator.clamp_min(1.0)
-        loc = (data * observed_indicator).sum(self.dim, keepdim=self.keepdim) / denominator
-
-        variance = (((data - loc) * observed_indicator) ** 2).sum(self.dim, keepdim=self.keepdim) / denominator
-        scale = torch.sqrt(variance + self.minimum_scale)
-        return (data - loc) / scale, loc, scale
-
-
-# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesMeanScaler with TimeSeriesTransformer->PatchTST,TimeSeries->PatchTST
-class PatchTSTMeanScaler(nn.Module):
-    """
-    Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data
-    accordingly.
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__()
-        self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
-        self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
-        self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-10
-        self.default_scale = config.default_scale if hasattr(config, "default_scale") else None
-
-    def forward(
-        self, data: torch.Tensor, observed_indicator: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Parameters:
-            data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                input for Batch norm calculation
-            observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                Calculating the scale on the observed indicator.
-        Returns:
-            tuple of `torch.Tensor` of shapes
-                (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
-                `(batch_size, 1, num_input_channels)`)
-        """
-        ts_sum = (data * observed_indicator).abs().sum(self.dim, keepdim=True)
-        num_observed = observed_indicator.sum(self.dim, keepdim=True)
-
-        scale = ts_sum / torch.clamp(num_observed, min=1)
-
-        # If `default_scale` is provided, we use it, otherwise we use the scale
-        # of the batch.
-        if self.default_scale is None:
-            batch_sum = ts_sum.sum(dim=0)
-            batch_observations = torch.clamp(num_observed.sum(0), min=1)
-            default_scale = torch.squeeze(batch_sum / batch_observations)
-        else:
-            default_scale = self.default_scale * torch.ones_like(scale)
-
-        # apply default scale where there are no observations
-        scale = torch.where(num_observed > 0, scale, default_scale)
-
-        # ensure the scale is at least `self.minimum_scale`
-        scale = torch.clamp(scale, min=self.minimum_scale)
-        scaled_data = data / scale
-
-        if not self.keepdim:
-            scale = scale.squeeze(dim=self.dim)
-
-        return scaled_data, torch.zeros_like(scale), scale
-
-
-# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesNOPScaler with TimeSeriesTransformer->PatchTST,TimeSeries->PatchTST
-class PatchTSTNOPScaler(nn.Module):
-    """
-    Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data.
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__()
-        self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1
-        self.keepdim = config.keepdim if hasattr(config, "keepdim") else True
-
-    def forward(
-        self, data: torch.Tensor, observed_indicator: torch.Tensor = None
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Parameters:
-            data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                input for Batch norm calculation
-        Returns:
-            tuple of `torch.Tensor` of shapes
-                (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
-                `(batch_size, 1, num_input_channels)`)
-        """
-        scale = torch.ones_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim)
-        loc = torch.zeros_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim)
-        return data, loc, scale
-
-
-class PatchTSTScaler(nn.Module):
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__()
-        if config.scaling == "mean" or config.scaling is True:
-            self.scaler = PatchTSTMeanScaler(config)
-        elif config.scaling == "std":
-            self.scaler = PatchTSTStdScaler(config)
-        else:
-            self.scaler = PatchTSTNOPScaler(config)
-
-    def forward(
-        self, data: torch.Tensor, observed_indicator: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Parameters:
-            data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                input for Batch norm calculation
-            observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                Calculating the scale on the observed indicator.
-        Returns:
-            tuple of `torch.Tensor` of shapes
-                (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`,
-                `(batch_size, 1, um_input_channels)`)
-        """
-        data, loc, scale = self.scaler(data, observed_indicator)
-        return data, loc, scale
-
-
-@add_start_docstrings(
-    "The bare PatchTST Model outputting raw hidden-states without any specific head.",
-    PATCHTST_START_DOCSTRING,
-)
-class PatchTSTModel(PatchTSTPreTrainedModel):
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__(config)
-
-        self.scaler = PatchTSTScaler(config)
-        self.patchifier = PatchTSTPatchify(config)
-        self.mask_input = config.mask_input
-
-        if self.mask_input:
-            self.masking = PatchTSTMasking(config)
-        else:
-            self.masking = nn.Identity()
-        self.encoder = PatchTSTEncoder(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        past_values: torch.Tensor,
-        past_observed_mask: Optional[torch.Tensor] = None,
-        future_values: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, PatchTSTModelOutput]:
-        """
-        Parameters:
-            past_values (`torch.Tensor` of shape `(bs, sequence_length, num_input_channels)`, *required*):
-                Input sequence to the model
-            past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*):
-                Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected
-                in `[0, 1]`:
-
-                - 1 for values that are **observed**,
-                - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros).
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the output attention of all layers
-            return_dict (`bool`, *optional*): Whether or not to return a `ModelOutput` instead of a plain tuple.
-
-        Returns:
-            `PatchTSTModelOutput` or tuple of `torch.Tensor` (if `return_dict`=False or `config.return_dict`=False)
-
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-
-        if past_observed_mask is None:
-            past_observed_mask = torch.ones_like(past_values)
-
-        # x: tensor [bs x sequence_length x num_input_channels]
-        scaled_past_values, loc, scale = self.scaler(past_values, past_observed_mask)
-
-        # patched_values: [bs x num_input_channels x num_patches x patch_length] for pretrain
-        patched_values = self.patchifier(scaled_past_values)
-        if self.mask_input:
-            masked_values, mask = self.masking(patched_values)
-        else:
-            masked_values, mask = self.masking(patched_values), None
-
-        encoder_output = self.encoder(
-            patch_input=masked_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions
-        )
-
-        if not return_dict:
-            outputs = (encoder_output.last_hidden_state, encoder_output.hidden_states, encoder_output.attentions)
-            outputs = outputs + (patched_values, mask, loc, scale)
-            return tuple(v for v in outputs if v is not None)
-
-        return PatchTSTModelOutput(
-            last_hidden_state=encoder_output.last_hidden_state,
-            hidden_states=encoder_output.hidden_states,
-            attentions=encoder_output.attentions,
-            patch_input=patched_values,
-            mask=mask,
-            loc=loc,
-            scale=scale,
-        )
-
-
-class PatchTSTMaskPretrainHead(nn.Module):
-    """
-    Pretraining head for mask modelling
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__()
-        self.dropout = nn.Dropout(config.dropout)
-        self.linear = nn.Linear(config.d_model, config.patch_length)
-        self.use_cls_token = config.use_cls_token
-
-    def forward(self, embedding: torch.Tensor) -> torch.Tensor:
-        """
-        Parameters:
-            embedding (`torch.Tensor` of shape `(bs, num_channels, num_patches, d_model)`
-                    or `(bs, num_channels, num_patches+1, d_model)` if `cls_token` is set to True, *required*):
-                    Embedding from the model
-        Returns:
-            `torch.Tensor` of shape `(bs, num_channels, num_patches, d_model)` or
-                            `(bs, num_channels, num_patches+1, d_model)` if `cls_token` is set to True
-
-        """
-        embedding = self.linear(self.dropout(embedding))  # [bs x num_channels x num_patches x patch_length]
-        if self.use_cls_token:
-            embedding = embedding[:, :, 1:, :]  # remove the first cls token
-        return embedding
-
-
-class PatchTSTForPretraining(PatchTSTPreTrainedModel):
-    """
-    Mask pretrain model: PatchTST model + pretrain head
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__(config)
-
-        config.mask_input = True
-        self.model = PatchTSTModel(config=config)
-        self.head = PatchTSTMaskPretrainHead(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        past_values: torch.Tensor,
-        past_observed_mask: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, PatchTSTForPretrainingOutput]:
-        """
-        Parameters:
-            past_values (`torch.Tensor` of shape `(bs, sequence_length, num_input_channels)`, *required*):
-                Input sequence to the model
-            past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*):
-                Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected
-                in `[0, 1]`:
-
-                - 1 for values that are **observed**,
-                - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros).
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers
-            return_dict (`bool`, *optional*): Whether or not to return a `ModelOutput` instead of a plain tuple.
-
-        Returns:
-            `PatchTSTForPretrainingOutput` or tuple of `torch.Tensor` (if `return_dict`=False or
-            `config.return_dict`=False)
-
-        """
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # past_values: [bs x num_channels x num_patches x d_model] or
-        # [bs x num_channels x (num_patches+1) x d_model] if use cls_token
-        model_output = self.model(
-            past_values=past_values,
-            past_observed_mask=past_observed_mask,
-            output_hidden_states=output_hidden_states,
-            output_attentions=output_attentions,
-        )
-
-        # model_output[0]: [bs x num_channels x num_patches x patch_length] or
-        # [bs x num_channels x (num_patches+1) x patch_length] if use cls_token
-        x_hat = self.head(model_output[0])
-
-        # calculate masked_loss
-        loss = nn.MSELoss(reduction="none")
-        loss_val = loss(x_hat, model_output.patch_input)
-        masked_loss = (loss_val.mean(dim=-1) * model_output.mask).sum() / (model_output.mask.sum() + 1e-10)
-
-        encoder_states = model_output.hidden_states
-        if not return_dict:
-            outputs = (masked_loss, x_hat, model_output.hidden_states, model_output.attentions)
-            return tuple(v for v in outputs if v is not None)
-        return PatchTSTForPretrainingOutput(
-            loss=masked_loss, prediction_output=x_hat, hidden_states=encoder_states, attentions=model_output.attentions
-        )
-
-
-class PatchTSTForClassification(PatchTSTPreTrainedModel):
-    """
-    PatchTST model for classification. The model contains PatchTST model + classification head
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__(config)
-
-        self.model = PatchTSTModel(config)
-        self.head = PatchTSTClassificationHead(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        past_values: torch.Tensor,
-        target_values: torch.Tensor = None,
-        past_observed_mask: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[tuple, PatchTSTForClassificationOutput]:
-        """
-        Parameters:
-            past_values (`torch.Tensor` of shape `(bs, sequence_length, num_input_channels)`, *required*):
-                Input sequence to the model
-            target_values (`torch.Tensor`, *optional*): labels associates with the `past_values`
-            past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*):
-                Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected
-                in `[0, 1]`:
-
-                - 1 for values that are **observed**,
-                - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros).
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers
-            return_dict (`bool`, *optional*): Whether or not to return a `ModelOutput` instead of a plain tuple.
-
-        Returns:
-            `PatchTSTForClassificationOutput` or tuple of `torch.Tensor` (if `return_dict`=False or
-            `config.return_dict`=False)
-
-        """
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        model_output = self.model(
-            past_values=past_values,
-            past_observed_mask=past_observed_mask,
-            output_hidden_states=output_hidden_states,
-            output_attentions=output_attentions,
-        )
-        y_hat = self.head(model_output[0])
-
-        loss_val = None
-        if target_values is not None:
-            loss = nn.CrossEntropyLoss()
-            loss_val = loss(y_hat, target_values)
-
-        if not return_dict:
-            outputs = (loss_val, y_hat, model_output.hidden_states, model_output.attentions)
-            return tuple(v for v in outputs if v is not None)
-        return PatchTSTForClassificationOutput(
-            loss=loss_val,
-            prediction_logits=y_hat,
-            hidden_states=model_output.hidden_states,
-            attentions=model_output.attentions,
-        )
-
-
-class PatchTSTClassificationHead(nn.Module):
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__()
-        self.use_cls_token = config.use_cls_token
-        self.pooling_type = config.pooling_type
-        self.flatten = nn.Flatten(start_dim=1)
-        self.dropout = nn.Dropout(config.head_dropout) if config.head_dropout > 0 else nn.Identity()
-        self.linear = nn.Linear(config.num_input_channels * config.d_model, config.num_targets)
-
-    def forward(self, embedding: torch.Tensor):
-        """
-        Parameters:
-            embedding (`torch.Tensor` of shape `(bs, num_channels, num_patches, d_model)`
-                    or `(bs, num_channels, num_patches+1, d_model)` if `cls_token` is set to True, *required*):
-                    Embedding from the model
-        Returns:
-            `torch.Tensor` of shape `(bs, num_targets)`
-
-        """
-        if self.use_cls_token:
-            # use the first output token, pooled_embedding: bs x num_channels x d_model
-            pooled_embedding = embedding[:, :, 0, :]
-        elif self.pooling_type == "mean":
-            # pooled_embedding: [bs x num_channels x d_model]
-            pooled_embedding = embedding.mean(dim=2)
-        elif self.pooling_type == "max":
-            # pooled_embedding: [bs x num_channels x d_model]
-            pooled_embedding = embedding.max(dim=2)
-        else:
-            raise Exception(f"pooling operator {self.pooling_type} is not implemented yet")
-        # pooled_embedding: bs x num_channels * d_model
-        pooled_embedding = self.flatten(pooled_embedding)
-        # output: bs x n_classes
-        output = self.linear(self.dropout(pooled_embedding))
-        return output
-
-
-class PatchTSTPredictionHead(nn.Module):
-    def __init__(self, config: PatchTSTConfig, distribution_output=None):
-        super().__init__()
-
-        self.shared_projection = config.shared_projection
-        self.num_input_channels = config.num_input_channels
-        self.use_cls_token = config.use_cls_token
-        self.pooling_type = config.pooling_type
-        head_dim = config.d_model if self.pooling_type else config.d_model * config.num_patches
-
-        if not self.shared_projection:
-            # if each channel has its own head
-            self.projections = nn.ModuleList()
-            self.dropouts = nn.ModuleList()
-            self.flattens = nn.ModuleList()
-            for i in range(self.num_input_channels):
-                self.flattens.append(nn.Flatten(start_dim=2))
-                if distribution_output is None:
-                    # use linear head
-                    self.projections.append(nn.Linear(head_dim, config.prediction_length))
-                else:
-                    # use distribution head
-                    self.projections.append(distribution_output.get_parameter_projection(head_dim))
-                self.dropouts.append(nn.Dropout(config.head_dropout) if config.head_dropout > 0 else nn.Identity())
-        else:
-            # all the channels share the same head
-            self.flatten = nn.Flatten(start_dim=2)
-            if distribution_output is None:
-                # use linear head
-                self.projection = nn.Linear(head_dim, config.prediction_length)
-            else:
-                # use distribution head
-                self.projection = distribution_output.get_parameter_projection(head_dim)
-            self.dropout = nn.Dropout(config.head_dropout) if config.head_dropout > 0 else nn.Identity()
-
-    def forward(self, embedding: torch.Tensor):
-        """
-        Parameters:
-            embedding (`torch.Tensor` of shape `(bs, num_channels, num_patches, d_model)`
-                    or `(bs, num_channels, num_patches+1, d_model)` if `cls_token` is set to True, *required*):
-                    Embedding from the model
-        Returns:
-            `torch.Tensor` of shape `(bs, forecast_len, num_channels)`
-
-        """
-        if self.use_cls_token:
-            # pooled_embedding: [bs x num_channels x d_model]
-            pooled_embedding = embedding[:, :, 0, :]
-        else:
-            if self.pooling_type == "mean":
-                # pooled_embedding: [bs x num_channels x d_model]
-                pooled_embedding = embedding.mean(dim=2)
-            elif self.pooling_type == "max":
-                # pooled_embedding: [bs x num_channels x d_model]
-                pooled_embedding = embedding.max(dim=2)
-            else:
-                # pooled_embedding: [bs x num_channels x num_patches x d_model]
-                pooled_embedding = embedding
-
-        if not self.shared_projection:
-            output = []
-            for i in range(self.num_input_channels):
-                # pooled_embedding: [bs x (d_model * num_patches)] or [bs x d_model)]
-                pooled_embedding = self.flattens[i](pooled_embedding[:, i, :])
-                pooled_embedding = self.dropouts[i](pooled_embedding)
-                # pooled_embedding: [bs x forecast_len]
-                #  or tuple ([bs x forecast_len], [bs x forecast_len]) if using distribution head
-                pooled_embedding = self.projections[i](pooled_embedding)
-                output.append(pooled_embedding)
-            # output: [bs x num_channels x forecast_len]
-            output = torch.stack(output, dim=1)
-        else:
-            # pooled_embedding: [bs x num_channels x (d_model * num_patches)] or [bs x num_channels x d_model)]
-            pooled_embedding = self.flatten(pooled_embedding)
-            pooled_embedding = self.dropout(pooled_embedding)
-            # output: [bs x num_channels x forecast_len] or
-            # tuple ([bs x num_channels x forecast_len], [bs x num_channels x forecast_len]) if using distribution head
-            output = self.projection(pooled_embedding)
-
-        if isinstance(output, tuple):
-            # output: ([bs x forecast_len x num_channels], [bs x forecast_len x num_channels])
-            output = tuple(z.transpose(2, 1) for z in output)
-        else:
-            output = output.transpose(2, 1)  # [bs x forecast_len x num_channels]
-        return output
-
-
-class PatchTSTForPrediction(PatchTSTPreTrainedModel):
-    """
-    PatchTST for forecasting. The model contains PatchTST model + Forecasting head
-    """
-
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__(config)
-        self.model = PatchTSTModel(config)
-
-        if config.loss == "mse":
-            self.distribution_output = None
-        else:
-            if config.distribution_output == "student_t":
-                self.distribution_output = StudentTOutput(dim=config.prediction_length)
-            elif config.distribution_output == "normal":
-                self.distribution_output = NormalOutput(dim=config.prediction_length)
-            elif config.distribution_output == "negative_binomial":
-                self.distribution_output = NegativeBinomialOutput(dim=config.prediction_length)
-            else:
-                raise ValueError(f"Unknown distribution output {config.distribution_output}")
-
-        self.head = PatchTSTPredictionHead(config, self.distribution_output)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        past_values: torch.Tensor,
-        past_observed_mask: Optional[torch.Tensor] = None,
-        future_values: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, PatchTSTForPredictionOutput]:
-        """
-        Parameters:
-            past_values (`torch.Tensor` of shape `(bs, sequence_length, num_input_channels)`, *required*):
-                Input sequence to the model
-            past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*):
-                Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected
-                in `[0, 1]`:
-
-                - 1 for values that are **observed**,
-                - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros).
-            future_values (`torch.Tensor` of shape `(bs, forecast_len, num_input_channels)`, *optional*):
-                future target values associated with the `past_values`
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers
-            return_dict (`bool`, *optional*): Whether or not to return a `ModelOutput` instead of a plain tuple.
-
-        Returns:
-            `PatchTSTForPredictionOutput` or tuple of `torch.Tensor` (if `return_dict`=False or
-            `config.return_dict`=False)
-
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # get model output
-        model_output = self.model(
-            past_values=past_values,
-            past_observed_mask=past_observed_mask,
-            output_hidden_states=output_hidden_states,
-            output_attentions=output_attentions,
-        )
-        # get output head
-        y_hat = self.head(model_output.last_hidden_state)
-
-        loss_val = None
-
-        if future_values is not None:
-            if self.distribution_output:
-                distribution = self.distribution_output.distribution(
-                    y_hat, loc=model_output.loc, scale=model_output.scale
-                )
-                loss_val = nll(distribution, future_values)
-                # take average of the loss
-                loss_val = weighted_average(loss_val)
-                # for testing
-                # loss_val = nn.MSELoss(reduction='none')(distribution.mean, future_values)
-                # loss_val = weighted_average(loss_val)
-            else:
-                y_hat = y_hat * model_output.scale + model_output.loc
-                loss = nn.MSELoss(reduction="mean")
-                loss_val = loss(y_hat, future_values)
-
-        loc = model_output.loc
-        scale = model_output.scale
-
-        if not return_dict:
-            outputs = (loss_val, y_hat, model_output.hidden_states, model_output.attentions, loc, scale)
-            return tuple(v for v in outputs if v is not None)
-        return PatchTSTForPredictionOutput(
-            loss=loss_val,
-            prediction_outputs=y_hat,
-            hidden_states=model_output.hidden_states,
-            attentions=model_output.attentions,
-            loc=loc,
-            scale=scale,
-        )
-
-    def generate(
-        self,
-        past_values: torch.Tensor,
-        past_observed_mask: Optional[torch.Tensor] = None,
-    ) -> SamplePatchTSTPredictionOutput:
-        """
-        Generate sequences of sample predictions from a model with a probability distribution head.
-
-        Parameters:
-            past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                Past values of the time series that serves as context in order to predict the future.
-
-            past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*):
-                Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected
-                in `[0, 1]`:
-
-                - 1 for values that are **observed**,
-                - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros).
-
-        Return:
-            [`SamplePatchTSTPredictionOutput`] where the outputs `sequences` tensor will have shape `(batch_size,
-            number of samples, prediction_length, 1)` or `(batch_size, number of samples, prediction_length,
-            num_input_channels)` for multivariate predictions.
-        """
-        # get number of samples
-        num_parallel_samples = self.config.num_parallel_samples
-
-        # get model output
-        outputs = self(
-            past_values=past_values,
-            future_values=None,
-            past_observed_mask=past_observed_mask,
-            output_hidden_states=False,
-        )
-
-        # get distribution
-        distribution = self.distribution_output.distribution(
-            outputs.prediction_outputs, loc=outputs.loc, scale=outputs.scale
-        )
-        # get samples: list of [bs x forecast_len x num_channels]
-        samples = [distribution.sample() for _ in range(num_parallel_samples)]
-        # stack tensors
-        samples = torch.stack(samples, dim=1)  # [bs x num_samples x forecast_len x num_channels]
-        return SamplePatchTSTPredictionOutput(sequences=samples)
-
-
-class PatchTSTRegressionHead(nn.Module):
-    """
-    Regression head
-    """
-
-    def __init__(self, config: PatchTSTConfig, distribution_output=None):
-        super().__init__()
-        self.y_range = config.output_range
-        self.use_cls_token = config.use_cls_token
-        self.pooling_type = config.pooling_type
-        self.distribution_output = distribution_output
-
-        head_dim = config.num_input_channels * config.d_model
-
-        self.flatten = nn.Flatten(start_dim=1)
-        self.dropout = nn.Dropout(config.head_dropout) if config.head_dropout > 0 else nn.Identity()
-
-        if distribution_output is None:
-            self.projection = nn.Linear(head_dim, config.num_targets)
-        else:
-            self.projection = distribution_output.get_parameter_projection(head_dim)
-
-    def forward(self, embedding: torch.Tensor):
-        """
-        Parameters:
-            embedding (`torch.Tensor` of shape `(bs, num_channels, num_patches, d_model)`
-                    or `(bs, num_channels, num_patches+1, d_model)` if `cls_token` is set to True, *required*):
-                    Embedding from the model
-        Returns:
-            `torch.Tensor` of shape `(bs, output_dim)`
-
-        """
-        if self.use_cls_token:
-            # use the first output token, pooled_embedding: [bs x num_channels x d_model]
-            pooled_embedding = embedding[:, :, 0, :]
-        elif self.pooling_type == "mean":
-            # pooled_embedding: [bs x num_channels x d_model]
-            pooled_embedding = embedding.mean(dim=2)
-        elif self.pooling_type == "max":
-            # pooled_embedding: [bs x num_channels x d_model]
-            pooled_embedding = embedding.max(dim=2)
-        else:
-            raise Exception(f"pooling operator {self.pooling_type} is not implemented yet")
-        # flatten the input
-        # pooled_embedding: bs x (num_channels * d_model)
-        pooled_embedding = self.dropout(self.flatten(pooled_embedding))
-        # projection
-        # output: bs x output_dim or a tuple of this shape for distribution head
-        output = self.projection(pooled_embedding)
-        #
-        if (self.distribution_output is None) & (self.y_range is not None):  # linear head
-            output = torch.sigmoid(output) * (self.y_range[1] - self.y_range[0]) + self.y_range[0]
-        return output
-
-
-class PatchTSTForRegression(PatchTSTPreTrainedModel):
-    # PatchTST model + Regression head
-    def __init__(self, config: PatchTSTConfig):
-        super().__init__(config)
-        self.model = PatchTSTModel(config)
-
-        self.model = PatchTSTModel(config)
-        if config.loss == "mse":
-            self.distribution_output = None
-        else:
-            if config.distribution_output == "student_t":
-                self.distribution_output = StudentTOutput(dim=config.prediction_length * config.num_targets)
-            elif config.distribution_output == "normal":
-                self.distribution_output = NormalOutput(dim=config.prediction_length * config.num_targets)
-            elif config.distribution_output == "negative_binomial":
-                self.distribution_output = NegativeBinomialOutput(dim=config.prediction_length * config.num_targets)
-            else:
-                raise ValueError(f"Unknown distribution output {config.distribution_output}")
-
-        self.head = PatchTSTRegressionHead(config, self.distribution_output)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        past_values: torch.Tensor,
-        target_values: torch.Tensor,
-        past_observed_mask: Optional[torch.Tensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[tuple, PatchTSTForRegressionOutput]:
-        """
-        Parameters:
-            past_values (`torch.Tensor` of shape `(bs, sequence_length, num_input_channels)`, *required*):
-                Input sequence to the model
-            past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*):
-                Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected
-                in `[0, 1]`:
-
-                - 1 for values that are **observed**,
-                - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros).
-            target_values (`torch.Tensor` of shape `(bs, num_input_channels)`):
-                target values associates with the `past_values`
-            output_hidden_states (`bool`, *optional*):
-                Whether or not to return the hidden states of all layers
-            return_dict (`bool`, *optional*): Whether or not to return a `ModelOutput` instead of a plain tuple.
-
-        Returns:
-            `PatchTSTForRegressionOutput` or tuple of `torch.Tensor` (if `return_dict`=False or
-            `config.return_dict`=False)
-
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        model_output = self.model(
-            past_values=past_values,
-            past_observed_mask=past_observed_mask,
-            output_hidden_states=output_hidden_states,
-            output_attentions=output_attentions,
-        )
-        # get output head. y_hat is of shape [bs x num_targets] or tuple of this shape
-        y_hat = self.head(model_output.last_hidden_state)
-
-        loss_val = None
-        if target_values is not None:
-            if self.distribution_output:
-                distribution = self.distribution_output.distribution(y_hat)
-                loss_val = nll(distribution, target_values)
-                # take average of the loss
-                loss_val = weighted_average(loss_val)
-            else:
-                loss = nn.MSELoss(reduction="mean")
-                loss_val = loss(y_hat, target_values)
-
-        if not return_dict:
-            outputs = (loss_val, y_hat, model_output.hidden_states, model_output.attentions)
-            return tuple(v for v in outputs if v is not None)
-        return PatchTSTForRegressionOutput(
-            loss=loss_val,
-            forecast_outputs=y_hat,
-            hidden_states=model_output.hidden_states,
-            attentions=model_output.attentions,
-        )
-
-    def generate(
-        self,
-        past_values: torch.Tensor,
-        past_observed_mask: Optional[torch.Tensor] = None,
-    ) -> SamplePatchTSTRegressionOutput:
-        """
-        Generate sequences of sample predictions from a model with a probability distribution head.
-
-        Parameters:
-            past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_input_channels)`):
-                Past values of the time series that serves as context in order to predict the future.
-
-            past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*):
-                Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected
-                in `[0, 1]`:
-
-                - 1 for values that are **observed**,
-                - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros).
-
-        Return:
-            [`SamplePatchTSTRegressionOutput`] where the outputs `sequences` tensor will have shape `(batch_size,
-            number of samples, num_targets)`.
-        """
-        # get number of samples
-        num_parallel_samples = self.config.num_parallel_samples
-
-        # get model output
-        outputs = self(
-            past_values=past_values,
-            target_values=None,
-            past_observed_mask=past_observed_mask,
-            output_hidden_states=False,
-        )
-
-        # get distribution
-        distribution = self.distribution_output.distribution(outputs.forecast_outputs)
-        # get samples: list of [bs x num_targets]
-        samples = [distribution.sample() for _ in range(num_parallel_samples)]
-        # stack tensors
-        samples = torch.stack(samples, dim=1)  # [bs x num_samples x num_targets]
-        return SamplePatchTSTRegressionOutput(sequences=samples)