From 35729720ba891c2d9343ab49cfba708f7258e667 Mon Sep 17 00:00:00 2001 From: Martin Krasser Date: Sat, 15 Apr 2023 18:50:46 +0200 Subject: [PATCH] Hugging Face interface for inference - Inference with auto-classes and pipelines - conversion of official models - conversion of training checkpoints - inference examples use Hugging Face interface - major documentation updates --- .pre-commit-config.yaml | 1 - README.md | 179 +- docs/building-blocks.md | 3 +- docs/getting-started.md | 180 - docs/images/library-design-small.jpg | Bin 0 -> 29315 bytes docs/images/library-design.jpg | Bin 0 -> 41771 bytes docs/images/optical-flow.gif | Bin 0 -> 14823821 bytes docs/library-design.md | 78 + docs/model-construction.md | 66 +- docs/pretrained-models.md | 189 +- docs/training-examples.md | 26 +- examples/convert.py | 82 + examples/inference.ipynb | 22963 ++++++++++------ examples/training/clm/train_fsdp.sh | 2 +- examples/training/img_clf/train.py | 10 +- examples/training/mlm/prep.sh | 2 +- examples/training/mlm/train.py | 12 +- examples/training/mlm/train.sh | 4 +- examples/training/txt_clf/prep.sh | 2 +- examples/training/txt_clf/train_all.py | 2 +- examples/training/txt_clf/train_all.sh | 2 +- examples/training/txt_clf/train_dec.py | 5 +- examples/training/txt_clf/train_dec.sh | 2 +- perceiver/data/text/common.py | 5 +- perceiver/data/vision/optical_flow.py | 2 - perceiver/data/vision/video_utils.py | 15 +- perceiver/model/core/__init__.py | 1 - perceiver/model/core/adapter.py | 15 +- perceiver/model/core/classifier.py | 81 +- perceiver/model/core/config.py | 8 +- .../model/core/{convert.py => huggingface.py} | 9 + perceiver/model/core/lightning.py | 45 +- perceiver/model/core/utils.py | 7 - perceiver/model/text/__init__.py | 1 - perceiver/model/text/classifier.py | 75 - perceiver/model/text/classifier/__init__.py | 13 + perceiver/model/text/classifier/backend.py | 42 + .../model/text/classifier/huggingface.py | 120 + perceiver/model/text/classifier/lightning.py | 43 + perceiver/model/text/clm.py | 299 - perceiver/model/text/clm/__init__.py | 12 + perceiver/model/text/clm/backend.py | 106 + perceiver/model/text/clm/huggingface.py | 163 + perceiver/model/text/clm/lightning.py | 163 + perceiver/model/text/common/__init__.py | 1 + .../text/{common.py => common/backend.py} | 26 +- perceiver/model/text/common/huggingface.py | 17 + perceiver/model/text/mlm.py | 255 - perceiver/model/text/mlm/__init__.py | 16 + perceiver/model/text/mlm/backend.py | 88 + perceiver/model/text/mlm/huggingface.py | 165 + perceiver/model/text/mlm/lightning.py | 94 + perceiver/model/text/mlm/utils.py | 27 + perceiver/model/vision/__init__.py | 1 - perceiver/model/vision/image_classifier.py | 171 - .../model/vision/image_classifier/__init__.py | 16 + .../model/vision/image_classifier/backend.py | 92 + .../vision/image_classifier/huggingface.py | 235 + .../vision/image_classifier/lightning.py | 41 + .../model/vision/optical_flow/__init__.py | 13 + .../backend.py} | 90 +- .../model/vision/optical_flow/huggingface.py | 203 + poetry.lock | 38 +- pyproject.toml | 4 +- tests/causal_language_model_generate_test.py | 96 +- tests/causal_language_model_pipeline_test.py | 49 + tests/causal_language_model_test.py | 56 - tests/conftest.py | 22 + tests/image_classifier_convert_test.py | 120 + tests/image_classifier_mnist_test.py | 52 + tests/image_classifier_test.py | 79 - ...ple_filler_test.py => mask_filler_test.py} | 4 +- tests/masked_language_model_convert_test.py | 84 + tests/masked_language_model_filler_test.py | 47 + tests/masked_language_model_test.py | 89 - tests/optical_flow_pipeline_test.py | 54 + tests/optical_flow_test.py | 30 +- tests/text_classifier_imdb_test.py | 45 + tests/text_classifier_test.py | 13 +- 79 files changed, 17267 insertions(+), 10201 deletions(-) delete mode 100644 docs/getting-started.md create mode 100644 docs/images/library-design-small.jpg create mode 100644 docs/images/library-design.jpg create mode 100644 docs/images/optical-flow.gif create mode 100644 docs/library-design.md create mode 100644 examples/convert.py rename perceiver/model/core/{convert.py => huggingface.py} (81%) delete mode 100644 perceiver/model/text/classifier.py create mode 100644 perceiver/model/text/classifier/__init__.py create mode 100644 perceiver/model/text/classifier/backend.py create mode 100644 perceiver/model/text/classifier/huggingface.py create mode 100644 perceiver/model/text/classifier/lightning.py delete mode 100644 perceiver/model/text/clm.py create mode 100644 perceiver/model/text/clm/__init__.py create mode 100644 perceiver/model/text/clm/backend.py create mode 100644 perceiver/model/text/clm/huggingface.py create mode 100644 perceiver/model/text/clm/lightning.py create mode 100644 perceiver/model/text/common/__init__.py rename perceiver/model/text/{common.py => common/backend.py} (69%) create mode 100644 perceiver/model/text/common/huggingface.py delete mode 100644 perceiver/model/text/mlm.py create mode 100644 perceiver/model/text/mlm/__init__.py create mode 100644 perceiver/model/text/mlm/backend.py create mode 100644 perceiver/model/text/mlm/huggingface.py create mode 100644 perceiver/model/text/mlm/lightning.py create mode 100644 perceiver/model/text/mlm/utils.py delete mode 100644 perceiver/model/vision/image_classifier.py create mode 100644 perceiver/model/vision/image_classifier/__init__.py create mode 100644 perceiver/model/vision/image_classifier/backend.py create mode 100644 perceiver/model/vision/image_classifier/huggingface.py create mode 100644 perceiver/model/vision/image_classifier/lightning.py create mode 100644 perceiver/model/vision/optical_flow/__init__.py rename perceiver/model/vision/{optical_flow.py => optical_flow/backend.py} (57%) create mode 100644 perceiver/model/vision/optical_flow/huggingface.py create mode 100644 tests/causal_language_model_pipeline_test.py delete mode 100644 tests/causal_language_model_test.py create mode 100644 tests/image_classifier_convert_test.py create mode 100644 tests/image_classifier_mnist_test.py delete mode 100644 tests/image_classifier_test.py rename tests/{masked_sample_filler_test.py => mask_filler_test.py} (94%) create mode 100644 tests/masked_language_model_convert_test.py create mode 100644 tests/masked_language_model_filler_test.py delete mode 100644 tests/masked_language_model_test.py create mode 100644 tests/optical_flow_pipeline_test.py create mode 100644 tests/text_classifier_imdb_test.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d9e8ddf..016e66d 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,7 +16,6 @@ repos: - id: check-toml - id: check-docstring-first - id: check-case-conflict - - id: check-added-large-files - id: detect-private-key - repo: https://github.com/asottile/pyupgrade diff --git a/README.md b/README.md index 3be12a4..9d19934 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ # Perceiver, Perceiver IO and Perceiver AR -This repository is a PyTorch and PyTorch Lightning implementation of +This repository is a PyTorch implementation of Perceiver, Perceiver IO and Perceiver AR, with PyTorch Lightning +interfaces for model training and Hugging Face 🤗 interfaces for inference. @@ -29,13 +30,32 @@ This repository is a PyTorch and PyTorch Lightning implementation of
-All model classes are written in plain PyTorch and can be wrapped into [PyTorch Lightning](https://pytorch-lightning.readthedocs.io/en/stable/) -modules for training at scale. The command line interface is implemented with the [Lightning CLI](https://pytorch-lightning.readthedocs.io/en/stable/cli/lightning_cli.html). -[Pretrained weights](docs/pretrained-models.md) can be imported for [official models](docs/pretrained-models.md#official-models) -from the 🤗 Hub, [training checkpoints](docs/pretrained-models.md#training-checkpoints) from [training examples](docs/training-examples.md) -are available for download too. Datasets used in the training examples are 🤗 [datasets](https://huggingface.co/docs/datasets) -wrapped into PyTorch Lightning [data modules](perceiver/data). For NLP tasks, this library supports all 🤗 -[fast tokenizers](https://huggingface.co/docs/transformers/fast_tokenizers) and the 🤗 Perceiver UTF-8 bytes tokenizer. +## Overview + +Core of the `perceiver-io` library are *backend models*, lightweight PyTorch implementations of Perceiver, +Perceiver IO and Perceiver AR. They can be wrapped into [PyTorch Lightning](https://pytorch-lightning.readthedocs.io/en/stable/) +modules for training (*Lightning interface*) and 🤗 modules for inference (*Hugging Face interface*). See +[library design](docs/library-design.md) for details. + +

+ library-design +

+ +The command line interface for training is implemented with [Lightning CLI](https://pytorch-lightning.readthedocs.io/en/stable/cli/lightning_cli.html). +Training datasets are 🤗 [datasets](https://huggingface.co/docs/datasets) wrapped into PyTorch Lightning data modules. +For NLP tasks, `perceiver-io` supports all 🤗 [fast tokenizers](https://huggingface.co/docs/transformers/fast_tokenizers) +and the 🤗 Perceiver UTF-8 bytes tokenizer. + +## Documentation + +- [Installation](#installation) +- [Getting started](#getting-started) +- [Library design](docs/library-design.md) +- [Pretrained models](docs/pretrained-models.md) +- [Training examples](docs/training-examples.md) +- [Inference examples](examples/inference.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/perceiver-io/blob/main/examples/inference.ipynb) +- [Model construction](docs/model-construction.md) +- [Building blocks](docs/building-blocks.md) ## Installation @@ -78,14 +98,143 @@ docker pull ghcr.io/krasserm/perceiver-io:latest See [Docker image](docs/docker-image.md) for details. -## Documentation +## Getting started -- [Getting started](docs/getting-started.md) -- [Model construction](docs/model-construction.md) -- [Pretrained models](docs/pretrained-models.md) -- [Training examples](docs/training-examples.md) -- [Inference examples](examples/inference.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/perceiver-io/blob/0.8.2/examples/inference.ipynb) -- [Building blocks](docs/building-blocks.md) +### Inference + +Compute the optical flow between consecutive frames of an input video and write the rendered results to an output +video: + +```python +from urllib.request import urlretrieve +from transformers import pipeline + +from perceiver.data.vision import video_utils +from perceiver.model.vision import optical_flow # register auto-classes and pipeline + +urlretrieve( + url="https://martin-krasser.com/perceiver/flow/sintel_clip_cave_dragon_fight.mp4", + filename="sintel_clip_cave_dragon_fight.mp4", +) + +# Create optical flow pipeline +optical_flow_pipeline = pipeline("optical-flow", model="krasserm/perceiver-io-optical-flow", device="cuda:0") + +# load consecutive video frame pairs +frame_pairs = video_utils.read_video_frame_pairs("sintel_clip_cave_dragon_fight.mp4") + +# create and render optical flow for all frame pairs +optical_flows = optical_flow_pipeline(frame_pairs, render=True, device="cuda:0") + +# create video with rendered optical flows +video_utils.write_video("sintel_clip_cave_dragon_fight_output.mp4", optical_flows, fps=24) +``` + +Here is a side-by-side comparison of the input and output video: + +

+ optical-flow-sbs +

+ +See [inference examples](https://colab.research.google.com/github/krasserm/perceiver-io/blob/main/examples/inference.ipynb) +for more examples. + +### Training + +Train a small Perceiver IO image classifier (907K parameters) on MNIST from the command line. The classifier +cross-attends to individual pixels of input images with [repeated cross-attention](docs/building-blocks.md). +See [image classification](docs/training-examples.md#image-classification) training example for more details. + +```shell +python -m perceiver.scripts.vision.image_classifier fit \ + --model.num_latents=32 \ + --model.num_latent_channels=128 \ + --model.encoder.num_frequency_bands=32 \ + --model.encoder.num_cross_attention_layers=2 \ + --model.encoder.num_self_attention_blocks=3 \ + --model.encoder.num_self_attention_layers_per_block=3 \ + --model.encoder.first_self_attention_block_shared=false \ + --model.encoder.dropout=0.1 \ + --model.encoder.init_scale=0.1 \ + --model.decoder.num_output_query_channels=128 \ + --model.decoder.dropout=0.1 \ + --model.decoder.init_scale=0.1 \ + --data=MNISTDataModule \ + --data.batch_size=64 \ + --optimizer=AdamW \ + --optimizer.lr=1e-3 \ + --lr_scheduler.warmup_steps=500 \ + --trainer.accelerator=gpu \ + --trainer.devices=1 \ + --trainer.max_epochs=30 \ + --trainer.logger=TensorBoardLogger \ + --trainer.logger.save_dir=logs \ + --trainer.logger.name=logs +``` + +[Model construction](docs/model-construction.md) describes how to implement model-specific command line interfaces +with the Lightning CLI. Training checkpoints are written to the `logs/img_clf/version_0/checkpoints` directory. Assuming +a checkpoint with filename `epoch=025-val_loss=0.065.ckpt` exists, it can be converted to a `perceiver-io` 🤗 model with + +```python +from perceiver.model.vision.image_classifier import convert_mnist_classifier_checkpoint + +convert_mnist_classifier_checkpoint( + save_dir="example/mnist-classifier", + ckpt_url="logs/img_clf/version_0/checkpoints/epoch=025-val_loss=0.065.ckpt", +) +``` + +so that it can be used in a 🤗 image classification pipeline + +```python +from datasets import load_dataset +from transformers import pipeline + +mnist_dataset = load_dataset("mnist", split="test")[:9] + +images = mnist_dataset["image"] +labels = mnist_dataset["label"] + +classifier = pipeline("image-classification", model="example/mnist-classifier") +predictions = [int(pred[0]["label"]) for pred in classifier(images)] + +print(f"Labels: {labels}") +print(f"Predictions: {predictions}") +``` +``` +Labels: [7, 2, 1, 0, 4, 1, 4, 9, 5] +Predictions: [7, 2, 1, 0, 4, 1, 4, 9, 5] +``` + +or loaded directly: + +```python +import torch +from transformers import AutoModelForImageClassification, AutoImageProcessor + +model = AutoModelForImageClassification.from_pretrained("example/mnist-classifier") +processor = AutoImageProcessor.from_pretrained("example/mnist-classifier") + +inputs = processor(images, return_tensors="pt") + +with torch.no_grad(): + # use perceiver-io Hugging Face model + output_1 = model(**inputs).logits + +with torch.no_grad(): + # or use perceiver-io backend model directly + output_2 = model.backend_model(inputs.pixel_values) + +print(f"Predictions: {output_1.argmax(dim=-1).numpy().tolist()}") +print(f"Predictions: {output_2.argmax(dim=-1).numpy().tolist()}") +``` +``` +Predictions: [7, 2, 1, 0, 4, 1, 4, 9, 5] +Predictions: [7, 2, 1, 0, 4, 1, 4, 9, 5] +``` + +See [training examples](docs/training-examples.md) for more examples. ## Articles diff --git a/docs/building-blocks.md b/docs/building-blocks.md index 7199d8a..b49c6ce 100644 --- a/docs/building-blocks.md +++ b/docs/building-blocks.md @@ -1,7 +1,8 @@ # Building blocks The following subsections map Perceiver, Perceiver IO and Perceiver AR concepts to the [core modules](../perceiver/model/core/modules.py) -of this library. Core modules are the building blocks of concrete PyTorch models (see also [model construction](model-construction.md)). +of this library. Core modules are the building blocks of concrete backend models (see also [library-design](library-design.md) +and [model construction](model-construction.md)). ## Perceiver IO diff --git a/docs/getting-started.md b/docs/getting-started.md deleted file mode 100644 index 54ad9ff..0000000 --- a/docs/getting-started.md +++ /dev/null @@ -1,180 +0,0 @@ -# Getting started - -This is a minimal example for autoregressive language modeling with Perceiver AR. A small language model (30.6M parameters) -is trained on the WikiText-103-raw dataset and then used to generate text from a prompt. Input text is tokenized into raw -UTF-8 bytes. Generated tokens are also raw UTF-8 bytes. - -The PyTorch model class (`CausalLanguageModel`) and the corresponding PyTorch Lightning wrapper class -(`LitCausalLanguageModel`) are defined in [perceiver/model/text/clm.py](../perceiver/model/text/clm.py) (see also -[model construction](model-construction.md) for further details). The PyTorch Lightning data module -(`WikiTextDataModule`) is defined in [perceiver/data/text/wikitext.py](../perceiver/data/text/wikitext.py). - -### Training - -#### Command line - -The script for training a `CausalLanguageModel` on the command line is [perceiver/scripts/text/clm.py](../perceiver/scripts/text/clm.py). -The constructor signatures of `LitCausalLanguageModel` and `WikiTextDataModule` determine the available `--model.*` and -`--data.*` command line options. Command line options `--optimizer.*`, `--lr_scheduler.*` and `--trainer.*` configure -the optimizer, learning rate scheduler and the PyTorch Lightning [Trainer](https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html), -respectively. - -```shell -python -m perceiver.scripts.text.clm fit \ - --model.max_latents=512 \ - --model.num_channels=512 \ - --model.num_self_attention_layers=8 \ - --model.cross_attention_dropout=0.5 \ - --data=WikiTextDataModule \ - --data.tokenizer=deepmind/language-perceiver \ - --data.random_train_shift=true \ - --data.padding_side=left \ - --data.max_seq_len=4096 \ - --data.task=clm \ - --data.batch_size=16 \ - --optimizer=Adam \ - --optimizer.lr=2e-4 \ - --lr_scheduler.warmup_steps=200 \ - --trainer.accelerator=gpu \ - --trainer.devices=1 \ - --trainer.max_steps=5000 \ - --trainer.accumulate_grad_batches=4 -``` - -Supported optimizers are those packaged with PyTorch and [pytorch-optimizer](https://github.com/jettify/pytorch-optimizer). -The `--data.task=clm` option configures the data module to produce data compatible with causal language modeling (other -possible values are `mlm` for masked language modeling and `clf` for sequence classification). When running this command -for the first time, the WikiText dataset is downloaded and preprocessed. A faster alternative is to download and preprocess -the dataset prior to training with: - -```shell -python -m perceiver.scripts.text.preproc wikitext \ - --tokenizer=deepmind/language-perceiver \ - --max_seq_len=4096 \ - --task=clm -``` - -#### Python code - -Training on the command line uses the PyTorch Lightning `Trainer` under the hood. To run the `Trainer` directly from -a Python script, dynamically add a `configure_optimizers` method to `LitCausalLanguageModel`, create instances of -`LitCausalLanguageModel` and `WikiTextDataModule` and then call `trainer.fit()` with the model and data module as -arguments: - -```python -from torch.optim import Adam - -from perceiver.data.text import WikiTextDataModule, Task -from perceiver.model.text.clm import LitCausalLanguageModel, CausalLanguageModelConfig -from perceiver.scripts.lrs import CosineWithWarmupLR - -import pytorch_lightning as pl - - -def configure_optimizers(self): - optimizer = Adam(self.parameters(), lr=2e-4) - scheduler = CosineWithWarmupLR(optimizer, training_steps=5000, warmup_steps=200) - return { - "optimizer": optimizer, - "lr_scheduler": {"scheduler": scheduler, "interval": "step", "frequency": 1}, - } - - -# Add configure_optimizers method to LitCausalLanguageModel -setattr(LitCausalLanguageModel, "configure_optimizers", configure_optimizers), - - -if __name__ == '__main__': - data = WikiTextDataModule( - tokenizer="deepmind/language-perceiver", - padding_side="left", - max_seq_len=4096, - task=Task.clm, - batch_size=16, - ) - - config = CausalLanguageModelConfig( - vocab_size=data.vocab_size, - max_seq_len=data.max_seq_len, - max_latents=512, - num_channels=512, - num_self_attention_layers=8, - cross_attention_dropout=0.5, - ) - - # Create Lightning module of CausalLanguageModel from configuration object - lit_model = LitCausalLanguageModel.create(config) - - # Instantiate PyTorch Lightning Trainer - trainer = pl.Trainer(accelerator="gpu", devices=1, max_steps=5000, accumulate_grad_batches=4) - - # Train model (will also preprocess dataset if not already done yet) - trainer.fit(lit_model, datamodule=data) -``` - -The trained PyTorch model can be accessed with `lit_model.model`. If you prefer to use a custom training loop without -using the PyTorch Lightning Trainer, create a plain PyTorch model with `CausalLanguageModel.create(config=...)` and -train it directly as shown in the following simplified example: - -```python -from perceiver.model.text.clm import CausalLanguageModel - -import torch -import torch.nn.functional as F -from torch.optim import Adam - -data = ... -data.prepare_data() -data.setup() - -model_config = ... -model = CausalLanguageModel(config=model_config) -model.train() - -optim = Adam(model.parameters(), lr=2e-4) - -# Simplified training loop compared to previous examples -# (no gradient accumulation, ...) -for epoch in range(5): - for labels_ids, input_ids, pad_mask in data.train_dataloader(): - logits = model(input_ids, prefix_len=input_ids.shape[1] - model_config.max_latents, pad_mask=pad_mask) - loss = F.cross_entropy(logits.permute(0, 2, 1), labels_ids[:, -model_config.max_latents:]) - print(loss.item()) - loss.backward() - optim.step() - optim.zero_grad() - -# Save trained model -torch.save(model.state_dict(), "/path/to/model.pt") -``` - -### Inference - -For generating text from a prompt via top-k sampling, `CausalLanguageModel` provides a `generate()` method. The following -example first loads a trained model from a checkpoint and then generates text from a short sample prompt. An interactive -demo is also available in the [Colab notebook](https://colab.research.google.com/github/krasserm/perceiver-io/blob/0.8.2/examples/inference.ipynb). - -```python -from perceiver.data.text import TextPreprocessor -from perceiver.model.text.clm import LitCausalLanguageModel - -# Load model from a checkpoint that has been written by the PyTorch Lightning Trainer -model = LitCausalLanguageModel.load_from_checkpoint("/path/to/checkpoint").model.eval() - -# Alternatively, load the model's state_dict directly -# model = CausalLanguageModel(config=model_config).eval() -# model.load_state_dict(torch.load("/path/to/model.pt")) - -# Create a text preprocessor and configure padding on the left (required by Perceiver AR) -preproc = TextPreprocessor(tokenizer="deepmind/language-perceiver", max_seq_len=4096, add_special_tokens=False) -preproc.tokenizer.padding_side = "left" - -# Convert text to model input -prompt, pad_mask = preproc.preprocess_batch(["A man was reading a book on a sunny day until he sudden"]) - -# Continue prompt via top-k sampling, starting with 7 latent tokens -generated = model.generate(prompt=prompt, pad_mask=pad_mask, num_tokens=256, num_latents=7, top_k=10) - -# Decode model output using preprocessor's tokenizer -generated_text = preproc.tokenizer.decode(generated[0]) -``` diff --git a/docs/images/library-design-small.jpg b/docs/images/library-design-small.jpg new file mode 100644 index 0000000000000000000000000000000000000000..87ee151839783a806e333927d6bb42746533d402 GIT binary patch literal 29315 zcmdSB1wb83x-Qzd2X}(Iy9IZ55AN;~BoN%)CAhmokdOp-hY%#VyC>LNBzw>7c{6j~ zIp^Ny8iFC`m4ISyV};%*QZqgMMgqe0ssR81EfF;@U#wID=jK&proQGAuT5k z5&!^ruC0lk3nViD*x9=}t4N9vYia8c!|VW%06KsG5CEh`CN7S`N=kCTzfIS30LT*n zOfx+D`nPKTT7YC~=3)W>V8kG?kcp$SD~L7((VQNxj?Z)&h{iCnG%^Lzl^~kl85AIh z{`$-}{!Je~(-yyIa1aS#IIF3Mg4%`wqKPg3LL2{uHnDWJ1L?4XbjVHZ>_Ps)wSLp4 z&$Q1oZD;ESitSf=ZXu$Xy}Bx>BnSNn07*a^kO!0iV!#-11FQgBz!hKsmG&Tx3!nm$ 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