Remove task guides auto-update in favor of links towards task pages (#30429)

docs/source/ko/tasks/masked_language_modeling.md

@@ -30,15 +30,8 @@ rendered properly in your Markdown viewer.
 2. 추론 시에 직접 미세 조정한 모델을 사용합니다.
 
 <Tip>
-이번 가이드에서처럼 다른 아키텍처를 미세 조정해 마스킹된 언어 모델링을 할 수 있습니다.
 
-다음 아키텍쳐 중 하나를 선택하세요:
-
-<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
-
-[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [CamemBERT](../model_doc/camembert), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [Perceiver](../model_doc/perceiver), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [TAPAS](../model_doc/tapas), [Wav2Vec2](../model_doc/wav2vec2), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
-
-<!--End of the generated tip-->
+이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/fill-mask)를 확인하는 것이 좋습니다.
 
 </Tip>
 

docs/source/ko/tasks/monocular_depth_estimation.md

@@ -24,13 +24,8 @@ rendered properly in your Markdown viewer.
 
 
 <Tip>
-이 튜토리얼에서 다루는 작업은 다음 모델 아키텍처에서 지원됩니다:
 
-<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
-
-[DPT](../model_doc/dpt), [GLPN](../model_doc/glpn)
-
-<!--End of the generated tip-->
+이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/depth-estimation)를 확인하는 것이 좋습니다.
 
 </Tip>
 

docs/source/ko/tasks/multiple_choice.md

@@ -25,17 +25,6 @@ rendered properly in your Markdown viewer.
 1. [SWAG](https://huggingface.co/datasets/swag) 데이터 세트의 'regular' 구성으로 [BERT](https://huggingface.co/google-bert/bert-base-uncased)를 미세 조정하여 여러 옵션과 일부 컨텍스트가 주어졌을 때 가장 적합한 답을 선택합니다.
 2. 추론에 미세 조정된 모델을 사용합니다.
 
-<Tip>
-이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다:
-
-<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
-
-[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [I-BERT](../model_doc/ibert), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
-
-<!--End of the generated tip-->
-
-</Tip>
-
 시작하기 전에 필요한 라이브러리가 모두 설치되어 있는지 확인하세요:
 
 ```bash

docs/source/ko/tasks/object_detection.md

@@ -30,13 +30,8 @@ rendered properly in your Markdown viewer.
  2. 미세조정 한 모델을 추론에 사용하기.
 
 <Tip>
-이 튜토리얼의 태스크는 다음 모델 아키텍처에서 지원됩니다:
 
-<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
-
-[Conditional DETR](../model_doc/conditional_detr), [Deformable DETR](../model_doc/deformable_detr), [DETA](../model_doc/deta), [DETR](../model_doc/detr), [Table Transformer](../model_doc/table-transformer), [YOLOS](../model_doc/yolos)
-
-<!--End of the generated tip-->
+이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/object-detection)를 확인하는 것이 좋습니다.
 
 </Tip>
 

docs/source/ko/tasks/question_answering.md

@@ -31,14 +31,8 @@ rendered properly in your Markdown viewer.
 2. 추론에 미세 조정된 모델 사용하기
 
 <Tip>
-이 튜토리얼에서 설명하는 태스크는 다음과 같은 모델 아키텍처에서 지원됩니다.
 
-<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
-
-[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [LXMERT](../model_doc/lxmert), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OPT](../model_doc/opt), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [Splinter](../model_doc/splinter), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
-
-
-<!--End of the generated tip-->
+이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/question-answering)를 확인하는 것이 좋습니다.
 
 </Tip>
 

docs/source/ko/tasks/semantic_segmentation.md

@@ -29,13 +29,8 @@ rendered properly in your Markdown viewer.
 2. 미세 조정된 모델을 추론에 사용하기.
 
 <Tip>
-이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다:
 
-<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
-
-[BEiT](../model_doc/beit), [Data2VecVision](../model_doc/data2vec-vision), [DPT](../model_doc/dpt), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [SegFormer](../model_doc/segformer), [UPerNet](../model_doc/upernet)
-
-<!--End of the generated tip-->
+이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/image-segmentation)를 확인하는 것이 좋습니다.
 
 </Tip>
 

docs/source/ko/tasks/sequence_classification.md

@@ -28,14 +28,8 @@ rendered properly in your Markdown viewer.
 2. 추론을 위해 파인 튜닝 모델을 사용합니다.
 
 <Tip>
-이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에 의해 지원됩니다:
 
-<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
-
-[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPT Neo](../model_doc/gpt_neo), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [LLaMA](../model_doc/llama), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Perceiver](../model_doc/perceiver), [PLBart](../model_doc/plbart), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [TAPAS](../model_doc/tapas), [Transformer-XL](../model_doc/transfo-xl), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
-
-
-<!--End of the generated tip-->
+이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/text-classification)를 확인하는 것이 좋습니다.
 
 </Tip>
 

docs/source/ko/tasks/summarization.md

@@ -33,13 +33,8 @@ rendered properly in your Markdown viewer.
 2. 파인튜닝된 모델을 사용하여 추론합니다.
 
 <Tip>
-이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다:
 
-<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
-
-[BART](../model_doc/bart), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [Encoder decoder](../model_doc/encoder-decoder), [FairSeq Machine-Translation](../model_doc/fsmt), [GPTSAN-japanese](../model_doc/gptsan-japanese), [LED](../model_doc/led), [LongT5](../model_doc/longt5), [M2M100](../model_doc/m2m_100), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [NLLB](../model_doc/nllb), [NLLB-MOE](../model_doc/nllb-moe), [Pegasus](../model_doc/pegasus), [PEGASUS-X](../model_doc/pegasus_x), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [SwitchTransformers](../model_doc/switch_transformers), [T5](../model_doc/t5), [XLM-ProphetNet](../model_doc/xlm-prophetnet)
-
-<!--End of the generated tip-->
+이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/summarization)를 확인하는 것이 좋습니다.
 
 </Tip>
 

docs/source/ko/tasks/token_classification.md

@@ -28,13 +28,8 @@ rendered properly in your Markdown viewer.
 2. 추론을 위해 파인 튜닝 모델을 사용합니다.
 
 <Tip>
-이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에 의해 지원됩니다:
 
-<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
-
-[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BioGpt](../model_doc/biogpt), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
-
-<!--End of the generated tip-->
+이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/token-classification)를 확인하는 것이 좋습니다.
 
 </Tip>
 

docs/source/ko/tasks/translation.md

@@ -28,13 +28,8 @@ rendered properly in your Markdown viewer.
 2. 파인튜닝된 모델을 추론에 사용하는 방법입니다.
 
 <Tip>
-이 태스크 가이드는 아래 모델 아키텍처에도 응용할 수 있습니다.
 
-<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
-
-[BART](../model_doc/bart), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [Encoder decoder](../model_doc/encoder-decoder), [FairSeq Machine-Translation](../model_doc/fsmt), [GPTSAN-japanese](../model_doc/gptsan-japanese), [LED](../model_doc/led), [LongT5](../model_doc/longt5), [M2M100](../model_doc/m2m_100), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [NLLB](../model_doc/nllb), [NLLB-MOE](../model_doc/nllb-moe), [Pegasus](../model_doc/pegasus), [PEGASUS-X](../model_doc/pegasus_x), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [SwitchTransformers](../model_doc/switch_transformers), [T5](../model_doc/t5), [XLM-ProphetNet](../model_doc/xlm-prophetnet)
-
-<!--End of the generated tip-->
+이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/translation)를 확인하는 것이 좋습니다.
 
 </Tip>
 

docs/source/ko/tasks/video_classification.md

@@ -28,13 +28,7 @@ rendered properly in your Markdown viewer.
 
 <Tip>
 
-이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다:
-
-<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
-
-[TimeSformer](../model_doc/timesformer), [VideoMAE](../model_doc/videomae)
-
-<!--End of the generated tip-->
+이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/video-classification)를 확인하는 것이 좋습니다.
 
 </Tip>
 

docs/source/zh/tasks/asr.md

@@ -31,13 +31,7 @@ Siri 和 Alexa 这类虚拟助手使用 ASR 模型来帮助用户日常生活，
 
 <Tip>
 
-本教程中展示的任务受以下模型架构的支持：
-
-<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
-
-[Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [M-CTC-T](../model_doc/mctct), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-BERT](../model_doc/wav2vec2-bert), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm)
-
-<!--End of the generated tip-->
+如果您想查看所有与本任务兼容的架构和检查点，最好查看[任务页](https://huggingface.co/tasks/automatic-speech-recognition)。
 
 </Tip>
 

utils/check_task_guides.py

-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# 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.
-"""
-Utility that checks the list of models in the tips in the task-specific pages of the doc is up to date and potentially
-fixes it.
-
-Use from the root of the repo with:
-
-```bash
-python utils/check_task_guides.py
-```
-
-for a check that will error in case of inconsistencies (used by `make repo-consistency`).
-
-To auto-fix issues run:
-
-```bash
-python utils/check_task_guides.py --fix_and_overwrite
-```
-
-which is used by `make fix-copies`.
-"""
-import argparse
-import os
-
-from transformers.utils import direct_transformers_import
-
-
-# All paths are set with the intent you should run this script from the root of the repo with the command
-# python utils/check_task_guides.py
-TRANSFORMERS_PATH = "src/transformers"
-PATH_TO_TASK_GUIDES = "docs/source/en/tasks"
-
-
-def _find_text_in_file(filename: str, start_prompt: str, end_prompt: str) -> str:
-    """
-    Find the text in filename between two prompts.
-
-    Args:
-        filename (`str`): The file to search into.
-        start_prompt (`str`): A string to look for at the start of the content searched.
-        end_prompt (`str`): A string that will mark the end of the content to look for.
-
-    Returns:
-        `str`: The content between the prompts.
-    """
-    with open(filename, "r", encoding="utf-8", newline="\n") as f:
-        lines = f.readlines()
-    # Find the start prompt.
-    start_index = 0
-    while not lines[start_index].startswith(start_prompt):
-        start_index += 1
-    start_index += 1
-
-    # Now go until the end prompt.
-    end_index = start_index
-    while not lines[end_index].startswith(end_prompt):
-        end_index += 1
-    end_index -= 1
-
-    while len(lines[start_index]) <= 1:
-        start_index += 1
-    while len(lines[end_index]) <= 1:
-        end_index -= 1
-    end_index += 1
-    return "".join(lines[start_index:end_index]), start_index, end_index, lines
-
-
-# This is to make sure the transformers module imported is the one in the repo.
-transformers_module = direct_transformers_import(TRANSFORMERS_PATH)
-
-# Map between a task guide and the corresponding auto class.
-TASK_GUIDE_TO_MODELS = {
-    "asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
-    "audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
-    "language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
-    "image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
-    "masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
-    "multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
-    "object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
-    "question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
-    "semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
-    "sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
-    "summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
-    "token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
-    "translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
-    "video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
-    "document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
-    "monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
-}
-
-# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
-# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
-SPECIAL_TASK_GUIDE_TO_MODEL_TYPES = {
-    "summarization.md": ("nllb",),
-    "translation.md": ("nllb",),
-}
-
-
-def get_model_list_for_task(task_guide: str) -> str:
-    """
-    Return the list of models supporting a given task.
-
-    Args:
-        task_guide (`str`): The name of the task guide to check.
-
-    Returns:
-        `str`: The list of models supporting this task, as links to their respective doc pages separated by commas.
-    """
-    model_maping_names = TASK_GUIDE_TO_MODELS[task_guide]
-    special_model_types = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(task_guide, set())
-    model_names = {
-        code: name
-        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
-        if (code in model_maping_names or code in special_model_types)
-    }
-    return ", ".join([f"[{name}](../model_doc/{code})" for code, name in model_names.items()]) + "\n"
-
-
-def check_model_list_for_task(task_guide: str, overwrite: bool = False):
-    """
-    For a given task guide, checks the model list in the generated tip for consistency with the state of the lib and
-    updates it if needed.
-
-    Args:
-        task_guide (`str`):
-            The name of the task guide to check.
-        overwrite (`bool`, *optional*, defaults to `False`):
-            Whether or not to overwrite the table when it's not up to date.
-    """
-    current_list, start_index, end_index, lines = _find_text_in_file(
-        filename=os.path.join(PATH_TO_TASK_GUIDES, task_guide),
-        start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->",
-        end_prompt="<!--End of the generated tip-->",
-    )
-
-    new_list = get_model_list_for_task(task_guide)
-
-    if current_list != new_list:
-        if overwrite:
-            with open(os.path.join(PATH_TO_TASK_GUIDES, task_guide), "w", encoding="utf-8", newline="\n") as f:
-                f.writelines(lines[:start_index] + [new_list] + lines[end_index:])
-        else:
-            raise ValueError(
-                f"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
-                " to fix this."
-            )
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
-    args = parser.parse_args()
-
-    for task_guide in TASK_GUIDE_TO_MODELS.keys():
-        check_model_list_for_task(task_guide, args.fix_and_overwrite)