text stringlengths 7 318k | id stringlengths 14 166 | metadata dict | __index_level_0__ int64 0 439 |
|---|---|---|---|
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import UNet2DConditionLoadersMixin
from ...utils import BaseOutput, logging
from ..attention_processor import CROSS_AT... | diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py/0 | {
"file_path": "diffusers/src/diffusers/models/unets/unet_spatio_temporal_condition.py",
"repo_id": "diffusers",
"token_count": 9881
} | 115 |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and is... | diffusers/src/diffusers/pipelines/audioldm/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/audioldm/__init__.py",
"repo_id": "diffusers",
"token_count": 581
} | 116 |
# Copyright 2023 Salesforce.com, inc.
# 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/LICENS... | diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py",
"repo_id": "diffusers",
"token_count": 7634
} | 117 |
# 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 applicabl... | diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/mel.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/deprecated/audio_diffusion/mel.py",
"repo_id": "diffusers",
"token_count": 2699
} | 118 |
from typing import TYPE_CHECKING
from ...utils import DIFFUSERS_SLOW_IMPORT, _LazyModule
_import_structure = {"pipeline_dit": ["DiTPipeline"]}
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .pipeline_dit import DiTPipeline
else:
import sys
sys.modules[__name__] = _LazyModule(
__name__,
... | diffusers/src/diffusers/pipelines/dit/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/dit/__init__.py",
"repo_id": "diffusers",
"token_count": 177
} | 119 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. 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.a... | diffusers/src/diffusers/pipelines/onnx_utils.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/onnx_utils.py",
"repo_id": "diffusers",
"token_count": 3623
} | 120 |
# Copyright 2023 Open AI and 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 ... | diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py",
"repo_id": "diffusers",
"token_count": 5666
} | 121 |
# 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 applicabl... | diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py",
"repo_id": "diffusers",
"token_count": 9796
} | 122 |
# 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 applicabl... | diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py",
"repo_id": "diffusers",
"token_count": 5251
} | 123 |
import copy
import inspect
from dataclasses import dataclass
from typing import Callable, List, Optional, Union
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from torch.nn.functional import grid_sample
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from ..... | diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py",
"repo_id": "diffusers",
"token_count": 19885
} | 124 |
# 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 applicabl... | diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py/0 | {
"file_path": "diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py",
"repo_id": "diffusers",
"token_count": 6899
} | 125 |
# Copyright 2023 UC Berkeley Team and 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 ... | diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_ddpm_flax.py",
"repo_id": "diffusers",
"token_count": 5237
} | 126 |
# Copyright 2023 NVIDIA and 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 b... | diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py/0 | {
"file_path": "diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py",
"repo_id": "diffusers",
"token_count": 3955
} | 127 |
# 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 appl... | diffusers/src/diffusers/utils/__init__.py/0 | {
"file_path": "diffusers/src/diffusers/utils/__init__.py",
"repo_id": "diffusers",
"token_count": 1483
} | 128 |
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class SpectrogramDiffusionPipeline(metaclass=DummyObject):
_backends = ["transformers", "torch", "note_seq"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tr... | diffusers/src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py/0 | {
"file_path": "diffusers/src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py",
"repo_id": "diffusers",
"token_count": 236
} | 129 |
# 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 applicabl... | diffusers/tests/fixtures/custom_pipeline/pipeline.py/0 | {
"file_path": "diffusers/tests/fixtures/custom_pipeline/pipeline.py",
"repo_id": "diffusers",
"token_count": 1739
} | 130 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/others/test_ema.py/0 | {
"file_path": "diffusers/tests/others/test_ema.py",
"repo_id": "diffusers",
"token_count": 2816
} | 131 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/dance_diffusion/test_dance_diffusion.py/0 | {
"file_path": "diffusers/tests/pipelines/dance_diffusion/test_dance_diffusion.py",
"repo_id": "diffusers",
"token_count": 2423
} | 132 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py/0 | {
"file_path": "diffusers/tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py",
"repo_id": "diffusers",
"token_count": 9004
} | 133 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py",
"repo_id": "diffusers",
"token_count": 30633
} | 134 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/stable_diffusion_gligen/test_stable_diffusion_gligen.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_gligen/test_stable_diffusion_gligen.py",
"repo_id": "diffusers",
"token_count": 2746
} | 135 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py/0 | {
"file_path": "diffusers/tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py",
"repo_id": "diffusers",
"token_count": 22499
} | 136 |
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# 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 ag... | diffusers/tests/pipelines/test_pipelines_flax.py/0 | {
"file_path": "diffusers/tests/pipelines/test_pipelines_flax.py",
"repo_id": "diffusers",
"token_count": 4560
} | 137 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class IPNDMSchedulerTest(SchedulerCommonTest):
scheduler_classes = (IPNDMScheduler,)
forward_default_kwargs = (("num_inference_steps", 50),)
def get_scheduler_config(self, **kwargs):
... | diffusers/tests/schedulers/test_scheduler_ipndm.py/0 | {
"file_path": "diffusers/tests/schedulers/test_scheduler_ipndm.py",
"repo_id": "diffusers",
"token_count": 3120
} | 138 |
# JAX/Diffusers community sprint
Welcome to the JAX/Diffusers community sprint! The goal of this sprint is to work on fun and creative diffusion models using JAX and Diffusers.
In this event, we will create various applications with diffusion models in JAX/Flax and Diffusers using free TPU hours generously provided b... | diffusion-models-class/units/en/events/4.mdx/0 | {
"file_path": "diffusion-models-class/units/en/events/4.mdx",
"repo_id": "diffusion-models-class",
"token_count": 11592
} | 139 |
import wandb
import numpy as np
import torch, torchvision
import torch.nn.functional as F
from PIL import Image
from tqdm.auto import tqdm
from fastcore.script import call_parse
from torchvision import transforms
from diffusers import DDPMPipeline
from diffusers import DDIMScheduler
from datasets import load_dataset
fr... | diffusion-models-class/units/fr/unit2/finetune_model.py/0 | {
"file_path": "diffusion-models-class/units/fr/unit2/finetune_model.py",
"repo_id": "diffusion-models-class",
"token_count": 2153
} | 140 |
<jupyter_start><jupyter_text>Diffusion pour l'audio Dans ce *notebook*, nous allons jeter un bref coup d'œil à la génération d'audio avec des modèles de diffusion.Ce que vous allez apprendre :- Comment l'audio est représenté dans un ordinateur- Les méthodes de conversion entre les données audio brutes et les spectrogra... | diffusion-models-class/units/fr/unit4/diffusion_for_audio.ipynb/0 | {
"file_path": "diffusion-models-class/units/fr/unit4/diffusion_for_audio.ipynb",
"repo_id": "diffusion-models-class",
"token_count": 5905
} | 141 |
<jupyter_start><jupyter_text>Tout assembler (PyTorch) Installez la bibliothèque 🤗 *Transformers* pour exécuter ce *notebook*.<jupyter_code>!pip install transformers[sentencepiece]
from transformers import AutoTokenizer
checkpoint = "tblard/tf-allocine"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
sequence =... | notebooks/course/fr/chapter2/section6_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter2/section6_pt.ipynb",
"repo_id": "notebooks",
"token_count": 974
} | 142 |
<jupyter_start><jupyter_text>Création de votre propre jeu de données Installez les bibliothèques 🤗 Transformers et 🤗 Datasets pour exécuter ce *notebook*.<jupyter_code>!pip install datasets evaluate transformers[sentencepiece]
!apt install git-lfs<jupyter_output><empty_output><jupyter_text>Vous aurez besoin de config... | notebooks/course/fr/chapter5/section5.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter5/section5.ipynb",
"repo_id": "notebooks",
"token_count": 1679
} | 143 |
<jupyter_start><jupyter_text>Finetuner un modèle de language masqué (PyTorch) Installez les bibliothèques 🤗 *Datasets*, 🤗 *Transformers* et 🤗 *Accelerate* pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!pip install accelerate
# Pour exécuter l'entraînement sur TPU, vous ... | notebooks/course/fr/chapter7/section3_pt.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter7/section3_pt.ipynb",
"repo_id": "notebooks",
"token_count": 4290
} | 144 |
<jupyter_start><jupyter_text>Construire votre première démo Installez les bibliothèques 🤗 Transformers et 🤗 Gradio pour exécuter ce *notebook*.<jupyter_code>!pip install datasets transformers[sentencepiece]
!pip install gradio
import gradio as gr
def greet(name):
return "Bonjour " + name
demo = gr.Interface(fn=... | notebooks/course/fr/chapter9/section2.ipynb/0 | {
"file_path": "notebooks/course/fr/chapter9/section2.ipynb",
"repo_id": "notebooks",
"token_count": 326
} | 145 |
<jupyter_start><jupyter_text>In-painting pipeline for Stable Diffusion using 🧨 Diffusers This notebook shows how to do text-guided in-painting with Stable Diffusion model using 🤗 Hugging Face [🧨 Diffusers library](https://github.com/huggingface/diffusers). For a general introduction to the Stable Diffusion model pl... | notebooks/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb/0 | {
"file_path": "notebooks/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb",
"repo_id": "notebooks",
"token_count": 1254
} | 146 |
# IDEFICS Demos/examples
## Inference
- [Normal inference](inference.py) (needs ~20GB GPU memory)
- [4bit quantized inference](inference_4bit.py) (needs ~7GB GPU memory)
## Finetuning
The following demos use the Image captioning task:
- [PEFT (LORA) finetuning (notebook)](finetune_image_captioning_peft.ipynb) (fits... | notebooks/examples/idefics/README.md/0 | {
"file_path": "notebooks/examples/idefics/README.md",
"repo_id": "notebooks",
"token_count": 148
} | 147 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers as well as some other libraries. Uncomment the following cell and run it.<jupyter_code># Install
!pip install -q biopython transformers datasets huggingface_hub accelerate<jupyter_output><empty_outpu... | notebooks/examples/nucleotide_transformer_dna_sequence_modelling.ipynb/0 | {
"file_path": "notebooks/examples/nucleotide_transformer_dna_sequence_modelling.ipynb",
"repo_id": "notebooks",
"token_count": 6637
} | 148 |
<jupyter_start><jupyter_text>If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets as well as other dependencies. Uncomment the following cell and run it.<jupyter_code>#! pip install datasets evaluate transformers rouge-score nltk<jupyter_output><empty_output><jupyt... | notebooks/examples/summarization.ipynb/0 | {
"file_path": "notebooks/examples/summarization.ipynb",
"repo_id": "notebooks",
"token_count": 5127
} | 149 |
<jupyter_start><jupyter_text>Fine-tuning for Video Classification with 🤗 TransformersThis notebook shows how to fine-tune a pre-trained Vision model for Video Classification on a custom dataset. The idea is to add a randomly initialized classification head on top of a pre-trained encoder and fine-tune the model altoge... | notebooks/examples/video_classification.ipynb/0 | {
"file_path": "notebooks/examples/video_classification.ipynb",
"repo_id": "notebooks",
"token_count": 8881
} | 150 |
import argparse
import logging
import os
import random
import sys
from datasets import load_from_disk
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
import torch
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments, AutoTokenizer
if __name__ == "__main_... | notebooks/sagemaker/06_sagemaker_metrics/scripts/train.py/0 | {
"file_path": "notebooks/sagemaker/06_sagemaker_metrics/scripts/train.py",
"repo_id": "notebooks",
"token_count": 1415
} | 151 |
# SageMaker push to hf.co/models example | notebooks/sagemaker/14_train_and_push_to_hub/README.md/0 | {
"file_path": "notebooks/sagemaker/14_train_and_push_to_hub/README.md",
"repo_id": "notebooks",
"token_count": 12
} | 152 |
<!--⚠️ 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.
-->
# DeepSpeed
[DeepSpeed](https://www.deepspeed.ai/) is a library designed for speed and scale for distributed training of large models with billions ... | peft/docs/source/accelerate/deepspeed-zero3-offload.md/0 | {
"file_path": "peft/docs/source/accelerate/deepspeed-zero3-offload.md",
"repo_id": "peft",
"token_count": 2997
} | 153 |
<!--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... | peft/docs/source/package_reference/auto_class.md/0 | {
"file_path": "peft/docs/source/package_reference/auto_class.md",
"repo_id": "peft",
"token_count": 470
} | 154 |
<!--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... | peft/docs/source/quicktour.md/0 | {
"file_path": "peft/docs/source/quicktour.md",
"repo_id": "peft",
"token_count": 2384
} | 155 |
import os
import torch
import torch.nn as nn
import transformers
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import LoraConfig, get_peft_model
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# -*- coding: utf-8 -*-
"""Finetune-opt-bnb-peft.i... | peft/examples/fp4_finetuning/finetune_fp4_opt_bnb_peft.py/0 | {
"file_path": "peft/examples/fp4_finetuning/finetune_fp4_opt_bnb_peft.py",
"repo_id": "peft",
"token_count": 2309
} | 156 |
<jupyter_start><jupyter_code>import argparse
import gc
import hashlib
import itertools
import logging
import math
import os
import threading
import warnings
from pathlib import Path
from typing import Optional
import psutil
import json
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from tor... | peft/examples/lora_dreambooth/lora_dreambooth_inference.ipynb/0 | {
"file_path": "peft/examples/lora_dreambooth/lora_dreambooth_inference.ipynb",
"repo_id": "peft",
"token_count": 2282
} | 157 |
# coding=utf-8
# Copyright 2023-present 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 ap... | peft/src/peft/import_utils.py/0 | {
"file_path": "peft/src/peft/import_utils.py",
"repo_id": "peft",
"token_count": 851
} | 158 |
# coding=utf-8
# Copyright 2023-present 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 ap... | peft/src/peft/tuners/adaption_prompt/utils.py/0 | {
"file_path": "peft/src/peft/tuners/adaption_prompt/utils.py",
"repo_id": "peft",
"token_count": 1532
} | 159 |
# coding=utf-8
# Copyright 2023-present 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 ap... | peft/src/peft/tuners/lora/config.py/0 | {
"file_path": "peft/src/peft/tuners/lora/config.py",
"repo_id": "peft",
"token_count": 5286
} | 160 |
# coding=utf-8
# Copyright 2023-present 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 ap... | peft/src/peft/tuners/p_tuning/config.py/0 | {
"file_path": "peft/src/peft/tuners/p_tuning/config.py",
"repo_id": "peft",
"token_count": 739
} | 161 |
# coding=utf-8
# Copyright 2023-present 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 ap... | peft/src/peft/utils/loftq_utils.py/0 | {
"file_path": "peft/src/peft/utils/loftq_utils.py",
"repo_id": "peft",
"token_count": 4009
} | 162 |
include timm/models/_pruned/*.txt
include timm/data/_info/*.txt
include timm/data/_info/*.json
| pytorch-image-models/MANIFEST.in/0 | {
"file_path": "pytorch-image-models/MANIFEST.in",
"repo_id": "pytorch-image-models",
"token_count": 34
} | 163 |
# FBNet
**FBNet** is a type of convolutional neural architectures discovered through [DNAS](https://paperswithcode.com/method/dnas) neural architecture search. It utilises a basic type of image model block inspired by [MobileNetv2](https://paperswithcode.com/method/mobilenetv2) that utilises depthwise convolutions and... | pytorch-image-models/docs/models/.templates/models/fbnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/fbnet.md",
"repo_id": "pytorch-image-models",
"token_count": 896
} | 164 |
# MnasNet
**MnasNet** is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and late... | pytorch-image-models/docs/models/.templates/models/mnasnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/mnasnet.md",
"repo_id": "pytorch-image-models",
"token_count": 1292
} | 165 |
# SelecSLS
**SelecSLS** uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github... | pytorch-image-models/docs/models/.templates/models/selecsls.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/selecsls.md",
"repo_id": "pytorch-image-models",
"token_count": 1608
} | 166 |
# Vision Transformer (ViT)
The **Vision Transformer** is a model for image classification that employs a Transformer-like architecture over patches of the image. This includes the use of [Multi-Head Attention](https://paperswithcode.com/method/multi-head-attention), [Scaled Dot-Product Attention](https://paperswithcod... | pytorch-image-models/docs/models/.templates/models/vision-transformer.md/0 | {
"file_path": "pytorch-image-models/docs/models/.templates/models/vision-transformer.md",
"repo_id": "pytorch-image-models",
"token_count": 3834
} | 167 |
# TResNet
A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency. They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https://paperswithcode.com/method/anti-alias-down... | pytorch-image-models/docs/models/tresnet.md/0 | {
"file_path": "pytorch-image-models/docs/models/tresnet.md",
"repo_id": "pytorch-image-models",
"token_count": 4197
} | 168 |
# Big Transfer (BiT)
**Big Transfer (BiT)** is a type of pretraining recipe that pre-trains on a large supervised source dataset, and fine-tunes the weights on the target task. Models are trained on the JFT-300M dataset. The finetuned models contained in this collection are finetuned on ImageNet.
## How do I use thi... | pytorch-image-models/hfdocs/source/models/big-transfer.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/big-transfer.mdx",
"repo_id": "pytorch-image-models",
"token_count": 4101
} | 169 |
# Noisy Student (EfficientNet)
**Noisy Student Training** is a semi-supervised learning approach. It extends the idea of self-training
and distillation with the use of equal-or-larger student models and noise added to the student during learning. It has three main steps:
1. train a teacher model on labeled images
2.... | pytorch-image-models/hfdocs/source/models/noisy-student.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/models/noisy-student.mdx",
"repo_id": "pytorch-image-models",
"token_count": 6683
} | 170 |
# Optimization
This page contains the API reference documentation for learning rate optimizers included in `timm`.
## Optimizers
### Factory functions
[[autodoc]] timm.optim.optim_factory.create_optimizer
[[autodoc]] timm.optim.optim_factory.create_optimizer_v2
### Optimizer Classes
[[autodoc]] timm.optim.adabeli... | pytorch-image-models/hfdocs/source/reference/optimizers.mdx/0 | {
"file_path": "pytorch-image-models/hfdocs/source/reference/optimizers.mdx",
"repo_id": "pytorch-image-models",
"token_count": 333
} | 171 |
import os
from typing import Optional
from .reader_image_folder import ReaderImageFolder
from .reader_image_in_tar import ReaderImageInTar
def create_reader(
name: str,
root: Optional[str] = None,
split: str = 'train',
**kwargs,
):
kwargs = {k: v for k, v in kwargs.items() if v is... | pytorch-image-models/timm/data/readers/reader_factory.py/0 | {
"file_path": "pytorch-image-models/timm/data/readers/reader_factory.py",
"repo_id": "pytorch-image-models",
"token_count": 694
} | 172 |
""" Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or bas... | pytorch-image-models/timm/layers/activations_me.py/0 | {
"file_path": "pytorch-image-models/timm/layers/activations_me.py",
"repo_id": "pytorch-image-models",
"token_count": 2598
} | 173 |
""" NormAct (Normalizaiton + Activation Layer) Factory
Create norm + act combo modules that attempt to be backwards compatible with separate norm + act
isntances in models. Where these are used it will be possible to swap separate BN + act layers with
combined modules like IABN or EvoNorms.
Hacked together by / Copyr... | pytorch-image-models/timm/layers/create_norm_act.py/0 | {
"file_path": "pytorch-image-models/timm/layers/create_norm_act.py",
"repo_id": "pytorch-image-models",
"token_count": 1594
} | 174 |
""" Linear layer (alternate definition)
"""
import torch
import torch.nn.functional as F
from torch import nn as nn
class Linear(nn.Linear):
r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
Wraps torch.nn.Linear to support AMP + torchscript usage by manually casting
weight &... | pytorch-image-models/timm/layers/linear.py/0 | {
"file_path": "pytorch-image-models/timm/layers/linear.py",
"repo_id": "pytorch-image-models",
"token_count": 282
} | 175 |
""" Depthwise Separable Conv Modules
Basic DWS convs. Other variations of DWS exist with batch norm or activations between the
DW and PW convs such as the Depthwise modules in MobileNetV2 / EfficientNet and Xception.
Hacked together by / Copyright 2020 Ross Wightman
"""
from torch import nn as nn
from .create_conv2d... | pytorch-image-models/timm/layers/separable_conv.py/0 | {
"file_path": "pytorch-image-models/timm/layers/separable_conv.py",
"repo_id": "pytorch-image-models",
"token_count": 1138
} | 176 |
import dataclasses
import logging
import os
from copy import deepcopy
from typing import Optional, Dict, Callable, Any, Tuple
from torch import nn as nn
from torch.hub import load_state_dict_from_url
from timm.models._features import FeatureListNet, FeatureHookNet
from timm.models._features_fx import FeatureGraphNet
... | pytorch-image-models/timm/models/_builder.py/0 | {
"file_path": "pytorch-image-models/timm/models/_builder.py",
"repo_id": "pytorch-image-models",
"token_count": 7677
} | 177 |
""" Model Registry
Hacked together by / Copyright 2020 Ross Wightman
"""
import fnmatch
import re
import sys
import warnings
from collections import defaultdict, deque
from copy import deepcopy
from dataclasses import replace
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Sequence, Union, Tuple... | pytorch-image-models/timm/models/_registry.py/0 | {
"file_path": "pytorch-image-models/timm/models/_registry.py",
"repo_id": "pytorch-image-models",
"token_count": 5428
} | 178 |
""" EdgeNeXt
Paper: `EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications`
- https://arxiv.org/abs/2206.10589
Original code and weights from https://github.com/mmaaz60/EdgeNeXt
Modifications and additions for timm by / Copyright 2022, Ross Wightman
"""
import math
from colle... | pytorch-image-models/timm/models/edgenext.py/0 | {
"file_path": "pytorch-image-models/timm/models/edgenext.py",
"repo_id": "pytorch-image-models",
"token_count": 11040
} | 179 |
""" HRNet
Copied from https://github.com/HRNet/HRNet-Image-Classification
Original header:
Copyright (c) Microsoft
Licensed under the MIT License.
Written by Bin Xiao (Bin.Xiao@microsoft.com)
Modified by Ke Sun (sunk@mail.ustc.edu.cn)
"""
import logging
from typing import List
import torch
import torch.nn as... | pytorch-image-models/timm/models/hrnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/hrnet.py",
"repo_id": "pytorch-image-models",
"token_count": 17584
} | 180 |
""" Normalization Free Nets. NFNet, NF-RegNet, NF-ResNet (pre-activation) Models
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Paper: `High-Performance Large-Scale Image Recognition Without Normalization`
- https://arxiv.org/... | pytorch-image-models/timm/models/nfnet.py/0 | {
"file_path": "pytorch-image-models/timm/models/nfnet.py",
"repo_id": "pytorch-image-models",
"token_count": 19131
} | 181 |
""" Selective Kernel Networks (ResNet base)
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
This was inspired by reading 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268)
and a streamlined impl at https://github.com/clovaai/assembled-cnn but I ended up building somet... | pytorch-image-models/timm/models/sknet.py/0 | {
"file_path": "pytorch-image-models/timm/models/sknet.py",
"repo_id": "pytorch-image-models",
"token_count": 3801
} | 182 |
"""
Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Ke... | pytorch-image-models/timm/models/xception.py/0 | {
"file_path": "pytorch-image-models/timm/models/xception.py",
"repo_id": "pytorch-image-models",
"token_count": 3973
} | 183 |
""" NAdamW Optimizer
Based on simplified algorithm in https://github.com/mlcommons/algorithmic-efficiency/tree/main/baselines/nadamw
Added multi-tensor (foreach) path.
"""
import math
from typing import List, Optional
import torch
from torch import Tensor
# Modified from github.com/pytorch/pytorch/blob/v1.12.1/tor... | pytorch-image-models/timm/optim/nadamw.py/0 | {
"file_path": "pytorch-image-models/timm/optim/nadamw.py",
"repo_id": "pytorch-image-models",
"token_count": 5958
} | 184 |
from .agc import adaptive_clip_grad
from .checkpoint_saver import CheckpointSaver
from .clip_grad import dispatch_clip_grad
from .cuda import ApexScaler, NativeScaler
from .decay_batch import decay_batch_step, check_batch_size_retry
from .distributed import distribute_bn, reduce_tensor, init_distributed_device,\
wo... | pytorch-image-models/timm/utils/__init__.py/0 | {
"file_path": "pytorch-image-models/timm/utils/__init__.py",
"repo_id": "pytorch-image-models",
"token_count": 246
} | 185 |
__version__ = '0.9.14dev0'
| pytorch-image-models/timm/version.py/0 | {
"file_path": "pytorch-image-models/timm/version.py",
"repo_id": "pytorch-image-models",
"token_count": 14
} | 186 |
Hugging Face Optimized Inference License 1.0 (HFOILv1.0)
This License Agreement governs the use of the Software and its Modifications. It is a
binding agreement between the Licensor and You.
This License Agreement shall be referred to as Hugging Face Optimized Inference License
1.0 or HFOILv1.0. We may publish revis... | text-generation-inference/LICENSE/0 | {
"file_path": "text-generation-inference/LICENSE",
"repo_id": "text-generation-inference",
"token_count": 2207
} | 187 |
# Text Generation
The Hugging Face Text Generation Python library provides a convenient way of interfacing with a
`text-generation-inference` instance running on
[Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints) or on the Hugging Face Hub.
## Get Started
### Install
```shell
pip install... | text-generation-inference/clients/python/README.md/0 | {
"file_path": "text-generation-inference/clients/python/README.md",
"repo_id": "text-generation-inference",
"token_count": 2195
} | 188 |
# Consuming Text Generation Inference
There are many ways you can consume Text Generation Inference server in your applications. After launching, you can use the `/generate` route and make a `POST` request to get results from the server. You can also use the `/generate_stream` route if you want TGI to return a stream ... | text-generation-inference/docs/source/basic_tutorials/consuming_tgi.md/0 | {
"file_path": "text-generation-inference/docs/source/basic_tutorials/consuming_tgi.md",
"repo_id": "text-generation-inference",
"token_count": 2274
} | 189 |
# Supported Models and Hardware
Text Generation Inference enables serving optimized models on specific hardware for the highest performance. The following sections list which models are hardware are supported.
## Supported Models
The following models are optimized and can be served with TGI, which uses custom CUDA k... | text-generation-inference/docs/source/supported_models.md/0 | {
"file_path": "text-generation-inference/docs/source/supported_models.md",
"repo_id": "text-generation-inference",
"token_count": 1170
} | 190 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "stop_sequence",
"generated_tokens": 5,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -8.6875,
"text": "Test"
},
{
"id":... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 669
} | 191 |
{
"details": {
"best_of_sequences": null,
"finish_reason": "stop_sequence",
"generated_tokens": 6,
"prefill": [
{
"id": 14402,
"logprob": null,
"text": "Test"
},
{
"id": 2581,
"logprob": -11.6171875,
"text": " request"
}
],
... | text-generation-inference/integration-tests/models/__snapshots__/test_flash_phi/test_flash_phi_all_params.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_flash_phi/test_flash_phi_all_params.json",
"repo_id": "text-generation-inference",
"token_count": 690
} | 192 |
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 6,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": null,
"tokens": [
{
"id": 259,
... | text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_load.json/0 | {
"file_path": "text-generation-inference/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_load.json",
"repo_id": "text-generation-inference",
"token_count": 2874
} | 193 |
import pytest
@pytest.fixture(scope="module")
def flash_neox_handle(launcher):
with launcher("stabilityai/stablelm-tuned-alpha-3b", num_shard=1) as handle:
yield handle
@pytest.fixture(scope="module")
async def flash_neox(flash_neox_handle):
await flash_neox_handle.health(300)
return flash_neox_... | text-generation-inference/integration-tests/models/test_flash_neox.py/0 | {
"file_path": "text-generation-inference/integration-tests/models/test_flash_neox.py",
"repo_id": "text-generation-inference",
"token_count": 498
} | 194 |
[package]
name = "text-generation-launcher"
description = "Text Generation Launcher"
version.workspace = true
edition.workspace = true
authors.workspace = true
homepage.workspace = true
[dependencies]
clap = { version = "4.4.5", features = ["derive", "env"] }
ctrlc = { version = "3.4.1", features = ["termination"] }
n... | text-generation-inference/launcher/Cargo.toml/0 | {
"file_path": "text-generation-inference/launcher/Cargo.toml",
"repo_id": "text-generation-inference",
"token_count": 277
} | 195 |
eetq_commit := 71adb5e191bb8290069a580abff0355d7b2dd5c9
eetq:
# Clone eetq
pip install packaging
git clone https://github.com/NetEase-FuXi/EETQ.git eetq
build-eetq: eetq
cd eetq && git fetch && git checkout $(eetq_commit) && git submodule update --init --recursive
cd eetq && python setup.py build
install-eet... | text-generation-inference/server/Makefile-eetq/0 | {
"file_path": "text-generation-inference/server/Makefile-eetq",
"repo_id": "text-generation-inference",
"token_count": 155
} | 196 |
// Adapted from turboderp exllama: https://github.com/turboderp/exllama
#ifndef _q4_matrix_cuh
#define _q4_matrix_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
class Q4Matrix
{
public:
int device;
int height;
int width;
int groups;
int groupsize;
uint32_t* cuda_qw... | text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cuh/0 | {
"file_path": "text-generation-inference/server/exllama_kernels/exllama_kernels/cuda_func/q4_matrix.cuh",
"repo_id": "text-generation-inference",
"token_count": 419
} | 197 |
#ifndef _q_matrix_cuh
#define _q_matrix_cuh
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <cstdint>
#include <cstdio>
#define MAX_SUPERGROUPS 16
class QMatrix
{
public:
int device;
bool is_gptq;
int height;
int width;
int groups;
int gptq_groupsize;
int rows_8;
int rows... | text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cuh/0 | {
"file_path": "text-generation-inference/server/exllamav2_kernels/exllamav2_kernels/cuda/q_matrix.cuh",
"repo_id": "text-generation-inference",
"token_count": 702
} | 198 |
import pytest
from text_generation_server.pb import generate_pb2
@pytest.fixture
def default_pb_parameters():
return generate_pb2.NextTokenChooserParameters(
temperature=1.0,
repetition_penalty=1.0,
top_k=0,
top_p=1.0,
typical_p=1.0,
do_sample=False,
)
@pytes... | text-generation-inference/server/tests/conftest.py/0 | {
"file_path": "text-generation-inference/server/tests/conftest.py",
"repo_id": "text-generation-inference",
"token_count": 199
} | 199 |
import torch
import torch.distributed
from typing import Optional, Type
from transformers import (
AutoTokenizer,
AutoConfig,
PreTrainedTokenizerBase,
)
from text_generation_server.models.custom_modeling.bloom_modeling import (
BloomForCausalLM,
)
from text_generation_server.models import CausalLM
fr... | text-generation-inference/server/text_generation_server/models/bloom.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/bloom.py",
"repo_id": "text-generation-inference",
"token_count": 1581
} | 200 |
# coding=utf-8
# Copyright 2022 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... | text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_processing.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/custom_modeling/idefics_processing.py",
"repo_id": "text-generation-inference",
"token_count": 8157
} | 201 |
import torch
import torch.distributed
from typing import Optional
from transformers import (
AutoTokenizer,
AutoConfig,
)
from text_generation_server.models import CausalLM
from text_generation_server.models.custom_modeling.neox_modeling import (
GPTNeoxForCausalLM,
)
from text_generation_server.utils imp... | text-generation-inference/server/text_generation_server/models/gpt_neox.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/models/gpt_neox.py",
"repo_id": "text-generation-inference",
"token_count": 1220
} | 202 |
# Copied logic from https://github.com/mit-han-lab/llm-awq/blob/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa/awq/quantize/qmodule.py
import math
import torch
import torch.nn as nn
import awq_inference_engine # with CUDA kernels
# class ScaledActivation(nn.Module):
# def __init__(self, module, scales):
# sup... | text-generation-inference/server/text_generation_server/utils/awq/quantize/qmodule.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/awq/quantize/qmodule.py",
"repo_id": "text-generation-inference",
"token_count": 770
} | 203 |
import os
import json
from loguru import logger
import torch
from transformers import AutoTokenizer
from peft import AutoPeftModelForCausalLM, AutoPeftModelForSeq2SeqLM
def download_and_unload_peft(model_id, revision, trust_remote_code):
torch_dtype = torch.float16
logger.info("Trying to load a Peft model. ... | text-generation-inference/server/text_generation_server/utils/peft.py/0 | {
"file_path": "text-generation-inference/server/text_generation_server/utils/peft.py",
"repo_id": "text-generation-inference",
"token_count": 629
} | 204 |
/* eslint-disable @typescript-eslint/no-explicit-any */
import { bertProcessing, byteLevelProcessing, robertaProcessing, sequenceProcessing, templateProcessing } from '../../'
describe('bertProcessing', () => {
it('instantiates correctly with only two parameters', () => {
const processor = bertProcessing(['sep'... | tokenizers/bindings/node/lib/bindings/post-processors.test.ts/0 | {
"file_path": "tokenizers/bindings/node/lib/bindings/post-processors.test.ts",
"repo_id": "tokenizers",
"token_count": 1022
} | 205 |
# `tokenizers-linux-arm64-gnu`
This is the **aarch64-unknown-linux-gnu** binary for `tokenizers`
| tokenizers/bindings/node/npm/linux-arm64-gnu/README.md/0 | {
"file_path": "tokenizers/bindings/node/npm/linux-arm64-gnu/README.md",
"repo_id": "tokenizers",
"token_count": 35
} | 206 |
use serde::de::Deserializer;
use serde::ser::Serializer;
use serde::{Deserialize, Serialize};
use std::sync::{Arc, RwLock};
pub fn serialize<S, T>(val: &Option<Arc<RwLock<T>>>, s: S) -> Result<S::Ok, S::Error>
where
S: Serializer,
T: Serialize,
{
T::serialize(&*(val.clone().unwrap()).read().unwrap(), s)
}
pub f... | tokenizers/bindings/node/src/arc_rwlock_serde.rs/0 | {
"file_path": "tokenizers/bindings/node/src/arc_rwlock_serde.rs",
"repo_id": "tokenizers",
"token_count": 220
} | 207 |
# Generated content DO NOT EDIT
class AddedToken:
"""
Represents a token that can be be added to a :class:`~tokenizers.Tokenizer`.
It can have special options that defines the way it should behave.
Args:
content (:obj:`str`): The content of the token
single_word (:obj:`bool`, defaults ... | tokenizers/bindings/python/py_src/tokenizers/__init__.pyi/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/__init__.pyi",
"repo_id": "tokenizers",
"token_count": 16502
} | 208 |
# Generated content DO NOT EDIT
from .. import processors
PostProcessor = processors.PostProcessor
BertProcessing = processors.BertProcessing
ByteLevel = processors.ByteLevel
RobertaProcessing = processors.RobertaProcessing
Sequence = processors.Sequence
TemplateProcessing = processors.TemplateProcessing
| tokenizers/bindings/python/py_src/tokenizers/processors/__init__.py/0 | {
"file_path": "tokenizers/bindings/python/py_src/tokenizers/processors/__init__.py",
"repo_id": "tokenizers",
"token_count": 74
} | 209 |
#![warn(clippy::all)]
#![allow(clippy::upper_case_acronyms)]
// Many false positives with pyo3 it seems &str, and &PyAny get flagged
#![allow(clippy::borrow_deref_ref)]
extern crate tokenizers as tk;
mod decoders;
mod encoding;
mod error;
mod models;
mod normalizers;
mod pre_tokenizers;
mod processors;
mod token;
mod... | tokenizers/bindings/python/src/lib.rs/0 | {
"file_path": "tokenizers/bindings/python/src/lib.rs",
"repo_id": "tokenizers",
"token_count": 1086
} | 210 |
import pytest
from tokenizers import ByteLevelBPETokenizer
from ..utils import data_dir, multiprocessing_with_parallelism, roberta_files
class TestByteLevelBPE:
def test_basic_encode(self, roberta_files):
tokenizer = ByteLevelBPETokenizer.from_file(roberta_files["vocab"], roberta_files["merges"])
... | tokenizers/bindings/python/tests/implementations/test_byte_level_bpe.py/0 | {
"file_path": "tokenizers/bindings/python/tests/implementations/test_byte_level_bpe.py",
"repo_id": "tokenizers",
"token_count": 1658
} | 211 |
# Pre-tokenizers
<tokenizerslangcontent>
<python>
## BertPreTokenizer
[[autodoc]] tokenizers.pre_tokenizers.BertPreTokenizer
## ByteLevel
[[autodoc]] tokenizers.pre_tokenizers.ByteLevel
## CharDelimiterSplit
[[autodoc]] tokenizers.pre_tokenizers.CharDelimiterSplit
## Digits
[[autodoc]] tokenizers.pre_tokenizers... | tokenizers/docs/source-doc-builder/api/pre-tokenizers.mdx/0 | {
"file_path": "tokenizers/docs/source-doc-builder/api/pre-tokenizers.mdx",
"repo_id": "tokenizers",
"token_count": 371
} | 212 |
The tokenization pipeline
====================================================================================================
When calling :entity:`Tokenizer.encode` or :entity:`Tokenizer.encode_batch`, the input text(s) go
through the following pipeline:
- :ref:`normalization`
- :ref:`pre-tokenization`
- :ref:`mode... | tokenizers/docs/source/pipeline.rst/0 | {
"file_path": "tokenizers/docs/source/pipeline.rst",
"repo_id": "tokenizers",
"token_count": 6323
} | 213 |
[package]
name = "unstable_wasm"
version = "0.1.0"
authors = ["Nicolas Patry"]
edition = "2018"
[lib]
crate-type = ["cdylib", "rlib"]
[features]
default = ["console_error_panic_hook"]
[dependencies]
wasm-bindgen = "0.2.63"
# The `console_error_panic_hook` crate provides better debugging of panics by
# logging them ... | tokenizers/tokenizers/examples/unstable_wasm/Cargo.toml/0 | {
"file_path": "tokenizers/tokenizers/examples/unstable_wasm/Cargo.toml",
"repo_id": "tokenizers",
"token_count": 364
} | 214 |
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