├── faq ├── __init__.py ├── config.py ├── train_val_split.py ├── dataset.py ├── encoder.py ├── train.py ├── model.py ├── serve.py └── baseline.py ├── install_dependencies.sh ├── download_data.sh ├── pyproject.toml ├── README.md ├── .gitignore └── poetry.lock /faq/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /install_dependencies.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | python3 -m venv venv 4 | source venv/bin/activate 5 | pip3 install poetry 6 | poetry install 7 | -------------------------------------------------------------------------------- /faq/config.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | 4 | CODE_DIR = os.path.dirname(__file__) 5 | ROOT_DIR = os.path.dirname(CODE_DIR) 6 | DATA_DIR = os.path.join(ROOT_DIR, "data") 7 | -------------------------------------------------------------------------------- /download_data.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | mkdir data 4 | wget -O data/cloud_faq_dataset.jsonl https://storage.googleapis.com/demo-cloud-faq/dataset/cloud_faq_dataset.jsonl 5 | python3 -m faq.train_val_split -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "quaterion-faq-demo" 3 | version = "0.1.0" 4 | description = "Question-answering via similarity learning and Quaterion." 5 | authors = ["George Panchuk"] 6 | 7 | [tool.poetry.dependencies] 8 | python = ">=3.8,<3.10" 9 | torch = "^1.11.0" 10 | quaterion = "^0.1.22" 11 | sentence-transformers = "^2.2.0" 12 | pandas = "^1.4.2" 13 | scikit-learn = "^1.0.2" 14 | 15 | [tool.poetry.dev-dependencies] 16 | 17 | [build-system] 18 | requires = ["poetry-core>=1.0.0"] 19 | build-backend = "poetry.core.masonry.api" 20 | -------------------------------------------------------------------------------- /faq/train_val_split.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | import pandas as pd 4 | from sklearn.model_selection import train_test_split 5 | 6 | from faq.config import DATA_DIR 7 | 8 | 9 | if __name__ == "__main__": 10 | path = os.path.join(DATA_DIR, "cloud_faq_dataset.jsonl") 11 | df = pd.read_json(path, lines=True) 12 | train_X, val_X = train_test_split(df, stratify=df.source, random_state=42) 13 | 14 | train_X.to_json( 15 | os.path.join(DATA_DIR, "train_cloud_faq_dataset.jsonl"), 16 | orient="records", 17 | lines=True, 18 | ) 19 | val_X.to_json( 20 | os.path.join(DATA_DIR, "val_cloud_faq_dataset.jsonl"), 21 | orient="records", 22 | lines=True, 23 | ) 24 | -------------------------------------------------------------------------------- /faq/dataset.py: -------------------------------------------------------------------------------- 1 | import json 2 | from typing import List, Dict 3 | from torch.utils.data import Dataset 4 | from quaterion.dataset.similarity_samples import SimilarityPairSample 5 | 6 | 7 | class FAQDataset(Dataset): 8 | """Dataset class to process .jsonl files with FAQ from popular cloud providers.""" 9 | 10 | def __init__(self, dataset_path): 11 | self.dataset: List[Dict[str, str]] = self.read_dataset(dataset_path) 12 | 13 | def __getitem__(self, index) -> SimilarityPairSample: 14 | line = self.dataset[index] 15 | question = line["question"] 16 | subgroup = hash(question) 17 | return SimilarityPairSample( 18 | obj_a=question, obj_b=line["answer"], score=1, subgroup=subgroup 19 | ) 20 | 21 | def __len__(self): 22 | return len(self.dataset) 23 | 24 | @staticmethod 25 | def read_dataset(dataset_path) -> List[Dict[str, str]]: 26 | """Read jsonl-file into a memory.""" 27 | with open(dataset_path, "r") as fd: 28 | return [json.loads(json_line) for json_line in fd] 29 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Question Answering tutorial with Quaterion 2 | 3 | ## Intro 4 | 5 | Similarity learning can be handy in a large amount of domains and tasks. 6 | It can be used in CV, NLP, recommendation systems, etc. 7 | 8 | You may need to look at problems you are already used to from a different angle in order to solve them with similarity learning. 9 | At first, it may be unusual and to mitigate "mind-shift" we present our series of [tutorials](https://quaterion.qdrant.tech/tutorials/tutorials.html). 10 | 11 | This tutorial covers one of NLP problems - Q&A. 12 | Here we train a model capable of answering to questions from F.A.Q. pages of popular cloud providers. 13 | 14 | The whole tutorial is available [here](https://quaterion.qdrant.tech/tutorials/nlp_tutorial.html) 15 | 16 | ## Dependencies 17 | 18 | We use [poetry](https://python-poetry.org/) to manage our dependencies. 19 | 20 | All packages required are listed in `pyproject.toml`. 21 | 22 | If you have never used `poetry` earlier, we've collected a bunch of commands to make it seamless in `install_dependencies.sh`. 23 | You can install all the dependencies with only one command: 24 | ```shell 25 | ./install_dependencies.sh 26 | ``` 27 | 28 | Make this file executable with `chmod +x install_dependencies.sh` before usage. 29 | 30 | ## Dataset 31 | 32 | In this tutorial we use our own [dataset](https://github.com/qdrant/dataset-cloud-platform-faq). 33 | 34 | You can download it with 35 | ```shell 36 | ./download_data.sh 37 | ``` 38 | 39 | Make this file executable with `chmod +x download_data.sh` before usage. 40 | 41 | It will fetch `cloud_faq_dataset.jsonl` file from our Google Storage and make a train-val split. 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | -------------------------------------------------------------------------------- /faq/encoder.py: -------------------------------------------------------------------------------- 1 | import os 2 | from torch import Tensor, nn 3 | from sentence_transformers.models import Transformer, Pooling 4 | from quaterion_models.types import TensorInterchange, CollateFnType 5 | from quaterion_models.encoders import Encoder 6 | 7 | 8 | class FAQEncoder(Encoder): 9 | def __init__(self, transformer, pooling): 10 | super().__init__() 11 | self.transformer = transformer 12 | self.pooling = pooling 13 | self.encoder = nn.Sequential(self.transformer, self.pooling) 14 | 15 | @property 16 | def trainable(self) -> bool: 17 | return False 18 | 19 | @property 20 | def embedding_size(self) -> int: 21 | return self.transformer.get_word_embedding_dimension() 22 | 23 | def forward(self, batch: TensorInterchange) -> Tensor: 24 | return self.encoder(batch)["sentence_embedding"] 25 | 26 | def get_collate_fn(self) -> CollateFnType: 27 | return self.transformer.tokenize 28 | 29 | @staticmethod 30 | def _transformer_path(path: str): 31 | return os.path.join(path, "transformer") 32 | 33 | @staticmethod 34 | def _pooling_path(path: str): 35 | return os.path.join(path, "pooling") 36 | 37 | def save(self, output_path: str): 38 | transformer_path = self._transformer_path(output_path) 39 | os.makedirs(transformer_path, exist_ok=True) 40 | pooling_path = self._pooling_path(output_path) 41 | os.makedirs(pooling_path, exist_ok=True) 42 | self.transformer.save(transformer_path) 43 | self.pooling.save(pooling_path) 44 | 45 | @classmethod 46 | def load(cls, input_path: str) -> Encoder: 47 | transformer = Transformer.load(cls._transformer_path(input_path)) 48 | pooling = Pooling.load(cls._pooling_path(input_path)) 49 | return cls(transformer=transformer, pooling=pooling) 50 | -------------------------------------------------------------------------------- /faq/train.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import pytorch_lightning as pl 3 | 4 | from quaterion import Quaterion 5 | from quaterion.dataset import PairsSimilarityDataLoader 6 | from quaterion.eval.evaluator import Evaluator 7 | from quaterion.eval.pair import RetrievalReciprocalRank, RetrievalPrecision 8 | from quaterion.eval.samplers.pair_sampler import PairSampler 9 | 10 | from faq.dataset import FAQDataset 11 | 12 | 13 | def train(model, train_dataset_path, val_dataset_path, params): 14 | use_gpu = params.get("cuda", torch.cuda.is_available()) 15 | 16 | trainer = pl.Trainer( 17 | min_epochs=params.get("min_epochs", 1), 18 | max_epochs=params.get("max_epochs", 300), 19 | auto_select_gpus=use_gpu, 20 | log_every_n_steps=params.get("log_every_n_steps", 10), 21 | gpus=int(use_gpu), 22 | num_sanity_val_steps=2, 23 | ) 24 | train_dataset = FAQDataset(train_dataset_path) 25 | val_dataset = FAQDataset(val_dataset_path) 26 | train_dataloader = PairsSimilarityDataLoader(train_dataset, batch_size=1024) 27 | val_dataloader = PairsSimilarityDataLoader(val_dataset, batch_size=1024) 28 | Quaterion.fit(model, trainer, train_dataloader, val_dataloader) 29 | 30 | metrics = { 31 | "rrk": RetrievalReciprocalRank(), 32 | "rp@1": RetrievalPrecision(k=1) 33 | } 34 | sampler = PairSampler() 35 | evaluator = Evaluator(metrics, sampler) 36 | results = Quaterion.evaluate(evaluator, val_dataset, model.model) 37 | print(f"results: {results}") 38 | 39 | 40 | if __name__ == "__main__": 41 | import os 42 | from pytorch_lightning import seed_everything 43 | from faq.model import FAQModel 44 | from faq.config import DATA_DIR, ROOT_DIR 45 | 46 | seed_everything(42, workers=True) 47 | faq_model = FAQModel() 48 | train_path = os.path.join(DATA_DIR, "train_part.jsonl") 49 | val_path = os.path.join(DATA_DIR, "val_part.jsonl") 50 | train(faq_model, train_path, val_path, {}) 51 | faq_model.save_servable(os.path.join(ROOT_DIR, "servable")) 52 | -------------------------------------------------------------------------------- /faq/model.py: -------------------------------------------------------------------------------- 1 | from typing import Union, Dict, Optional 2 | 3 | from quaterion.eval.attached_metric import AttachedMetric 4 | from torch.optim import Adam 5 | from quaterion import TrainableModel 6 | from quaterion.train.cache import CacheConfig, CacheType 7 | from quaterion.loss import MultipleNegativesRankingLoss, SimilarityLoss 8 | from sentence_transformers import SentenceTransformer 9 | from quaterion.eval.pair import RetrievalPrecision, RetrievalReciprocalRank 10 | from sentence_transformers.models import Transformer, Pooling 11 | from quaterion_models.encoders import Encoder 12 | from quaterion_models.heads import EncoderHead 13 | from quaterion_models.heads.skip_connection_head import SkipConnectionHead 14 | from faq.encoder import FAQEncoder 15 | 16 | 17 | class FAQModel(TrainableModel): 18 | def __init__(self, lr=10e-5, *args, **kwargs): 19 | self.lr = lr 20 | super().__init__(*args, **kwargs) 21 | 22 | def configure_metrics(self): 23 | return [ 24 | AttachedMetric( 25 | "RetrievalPrecision", 26 | RetrievalPrecision(k=1), 27 | prog_bar=True, 28 | on_epoch=True, 29 | ), 30 | AttachedMetric( 31 | "RetrievalReciprocalRank", 32 | RetrievalReciprocalRank(), 33 | prog_bar=True, 34 | on_epoch=True 35 | ), 36 | ] 37 | 38 | def configure_optimizers(self): 39 | return Adam(self.model.parameters(), lr=self.lr) 40 | 41 | def configure_loss(self) -> SimilarityLoss: 42 | return MultipleNegativesRankingLoss(symmetric=True) 43 | 44 | def configure_encoders(self) -> Union[Encoder, Dict[str, Encoder]]: 45 | pre_trained_model = SentenceTransformer("all-MiniLM-L6-v2") 46 | transformer: Transformer = pre_trained_model[0] 47 | pooling: Pooling = pre_trained_model[1] 48 | encoder = FAQEncoder(transformer, pooling) 49 | return encoder 50 | 51 | def configure_head(self, input_embedding_size: int) -> EncoderHead: 52 | return SkipConnectionHead(input_embedding_size) 53 | 54 | def configure_caches(self) -> Optional[CacheConfig]: 55 | return CacheConfig(CacheType.AUTO, batch_size=256) 56 | -------------------------------------------------------------------------------- /faq/serve.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | import torch 5 | from quaterion_models.model import SimilarityModel 6 | from quaterion.distances import Distance 7 | 8 | from faq.config import DATA_DIR, ROOT_DIR 9 | 10 | 11 | if __name__ == "__main__": 12 | device = "cuda:0" if torch.cuda.is_available() else "cpu" 13 | model = SimilarityModel.load(os.path.join(ROOT_DIR, "servable")) 14 | model.to(device) 15 | dataset_path = os.path.join(DATA_DIR, "val_cloud_faq_dataset.jsonl") 16 | 17 | with open(dataset_path) as fd: 18 | answers = [json.loads(json_line)["answer"] for json_line in fd] 19 | 20 | # everything is ready, let's encode our answers 21 | answer_embeddings = model.encode(answers, to_numpy=False) 22 | 23 | # Some prepared questions and answers to ensure that our model works as intended 24 | questions = [ 25 | "what is the pricing of aws lambda functions powered by aws graviton2 processors?", 26 | "can i run a cluster or job for a long time?", 27 | "what is the dell open manage system administrator suite (omsa)?", 28 | "what are the differences between the event streams standard and event streams enterprise plans?", 29 | ] 30 | ground_truth_answers = [ 31 | "aws lambda functions powered by aws graviton2 processors are 20% cheaper compared to x86-based lambda functions", 32 | "yes, you can run a cluster for as long as is required", 33 | "omsa enables you to perform certain hardware configuration tasks and to monitor the hardware directly via the operating system", 34 | "to find out more information about the different event streams plans, see choosing your plan", 35 | ] 36 | 37 | # encode our questions and find the closest to them answer embeddings 38 | question_embeddings = model.encode(questions, to_numpy=False) 39 | distance = Distance.get_by_name(Distance.COSINE) 40 | question_answers_distances = distance.distance_matrix( 41 | question_embeddings, answer_embeddings 42 | ) 43 | answers_indices = question_answers_distances.min(dim=1)[1] 44 | for q_ind, a_ind in enumerate(answers_indices): 45 | print("Q:", questions[q_ind]) 46 | print("A:", answers[a_ind], end="\n\n") 47 | assert ( 48 | answers[a_ind] == ground_truth_answers[q_ind] 49 | ), f"<{answers[a_ind]}> != <{ground_truth_answers[q_ind]}>" 50 | -------------------------------------------------------------------------------- /faq/baseline.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | from typing import List, Dict 4 | 5 | import torch 6 | from sentence_transformers import SentenceTransformer 7 | 8 | from quaterion.distances import Distance 9 | from quaterion.eval.pair import retrieval_precision, retrieval_reciprocal_rank 10 | 11 | from faq.config import DATA_DIR 12 | 13 | 14 | def load_data(file_path): 15 | questions = [] 16 | answers = [] 17 | with open(file_path, "r") as f: 18 | for j_line in f: 19 | line = json.loads(j_line) 20 | questions.append(line["question"]) 21 | answers.append(line["answer"]) 22 | return questions + answers 23 | 24 | 25 | def process(texts: List[str]) -> Dict[str, float]: 26 | """ 27 | 28 | Args: 29 | texts: list of questions and answers, first half contains questions, second - answers 30 | 31 | Returns: 32 | metrics 33 | """ 34 | embeddings = model.encode(texts, convert_to_tensor=True) 35 | distance_matrix = Distance.get_by_name(Distance.COSINE).distance_matrix( 36 | embeddings 37 | ) 38 | distance_matrix[ 39 | torch.eye(embeddings.shape[0], dtype=torch.bool) 40 | ] = 1.0 # max cosine distance is 1.0. 41 | # Set max distance between element and itself to ignore 42 | 43 | num_of_pairs = distance_matrix.shape[0] // 2 44 | ones_vec = torch.tensor([1]).repeat(num_of_pairs).to(embeddings.device) 45 | 46 | question_to_answer_labels = torch.diag_embed(ones_vec, num_of_pairs).to( 47 | embeddings.device 48 | ) # matrix: shape (embeddings_num, embeddings_num) - with shifted by `num_of_pairs` diagonal 49 | # to the right. 50 | # For example: 51 | # If a question's index is 5, then answer's index (where we just set 1 ) is `num_of_pairs` + 5 52 | answer_to_question_labels = torch.diag_embed(ones_vec, -num_of_pairs).to( 53 | embeddings.device 54 | ) # matrix: shape (embeddings_num, embeddings_num) - with shifted by `num_of_pairs` diagonal 55 | # to the bottom. 56 | # For example: 57 | # If an answer's index is `num_of_pairs` + 5, then question's index (where we just set 1 ) is 5 58 | labels = question_to_answer_labels + answer_to_question_labels 59 | 60 | metrics = { 61 | "rrk": retrieval_precision(distance_matrix, labels, k=1).mean().item(), 62 | "rp@1": retrieval_reciprocal_rank(distance_matrix, labels).mean().item(), 63 | } 64 | 65 | return metrics 66 | 67 | 68 | if __name__ == "__main__": 69 | model = SentenceTransformer("all-MiniLM-L6-v2") # load model 70 | 71 | if torch.cuda.is_available(): 72 | model.cuda() 73 | 74 | filename = "val_cloud_faq_dataset.jsonl" 75 | filepath = os.path.join(DATA_DIR, filename) 76 | data = load_data(filepath) 77 | print(process(data)) 78 | 79 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 98 | __pypackages__/ 99 | 100 | # Celery stuff 101 | celerybeat-schedule 102 | celerybeat.pid 103 | 104 | # SageMath parsed files 105 | *.sage.py 106 | 107 | # Environments 108 | .env 109 | .venv 110 | env/ 111 | venv/ 112 | ENV/ 113 | env.bak/ 114 | venv.bak/ 115 | 116 | # Spyder project settings 117 | .spyderproject 118 | .spyproject 119 | 120 | # Rope project settings 121 | .ropeproject 122 | 123 | # mkdocs documentation 124 | /site 125 | 126 | # mypy 127 | .mypy_cache/ 128 | .dmypy.json 129 | dmypy.json 130 | 131 | # Pyre type checker 132 | .pyre/ 133 | 134 | # pytype static type analyzer 135 | .pytype/ 136 | 137 | # Cython debug symbols 138 | cython_debug/ 139 | 140 | # logs 141 | lightning_logs/ 142 | 143 | # data 144 | data/*.jsonl -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | [[package]] 2 | name = "absl-py" 3 | version = "1.1.0" 4 | description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." 5 | category = "main" 6 | optional = false 7 | python-versions = ">=3.6" 8 | 9 | [[package]] 10 | name = "aiohttp" 11 | version = "3.8.1" 12 | description = "Async http client/server framework (asyncio)" 13 | category = "main" 14 | optional = false 15 | python-versions = ">=3.6" 16 | 17 | [package.dependencies] 18 | aiosignal = ">=1.1.2" 19 | async-timeout = ">=4.0.0a3,<5.0" 20 | attrs = ">=17.3.0" 21 | charset-normalizer = ">=2.0,<3.0" 22 | frozenlist = ">=1.1.1" 23 | multidict = ">=4.5,<7.0" 24 | yarl = ">=1.0,<2.0" 25 | 26 | [package.extras] 27 | speedups = ["aiodns", "brotli", "cchardet"] 28 | 29 | [[package]] 30 | name = "aiosignal" 31 | version = "1.2.0" 32 | description = "aiosignal: a list of registered asynchronous callbacks" 33 | category = "main" 34 | optional = false 35 | python-versions = ">=3.6" 36 | 37 | [package.dependencies] 38 | frozenlist = ">=1.1.0" 39 | 40 | [[package]] 41 | name = "async-timeout" 42 | version = "4.0.2" 43 | description = "Timeout context manager for asyncio programs" 44 | category = "main" 45 | optional = false 46 | python-versions = ">=3.6" 47 | 48 | [[package]] 49 | name = "attrs" 50 | version = "21.4.0" 51 | description = "Classes Without Boilerplate" 52 | category = "main" 53 | optional = false 54 | python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" 55 | 56 | [package.extras] 57 | dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] 58 | docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] 59 | tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"] 60 | tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"] 61 | 62 | [[package]] 63 | name = "cachetools" 64 | version = "5.2.0" 65 | description = "Extensible memoizing collections and decorators" 66 | category = "main" 67 | optional = false 68 | python-versions = "~=3.7" 69 | 70 | [[package]] 71 | name = "certifi" 72 | version = "2022.5.18.1" 73 | description = "Python package for providing Mozilla's CA Bundle." 74 | category = "main" 75 | optional = false 76 | python-versions = ">=3.6" 77 | 78 | [[package]] 79 | name = "charset-normalizer" 80 | version = "2.0.12" 81 | description = "The Real First Universal Charset Detector. 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116 | [package.extras] 117 | test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"] 118 | 119 | [[package]] 120 | name = "filelock" 121 | version = "3.7.1" 122 | description = "A platform independent file lock." 123 | category = "main" 124 | optional = false 125 | python-versions = ">=3.7" 126 | 127 | [package.extras] 128 | docs = ["furo (>=2021.8.17b43)", "sphinx (>=4.1)", "sphinx-autodoc-typehints (>=1.12)"] 129 | testing = ["covdefaults (>=1.2.0)", "coverage (>=4)", "pytest (>=4)", "pytest-cov", "pytest-timeout (>=1.4.2)"] 130 | 131 | [[package]] 132 | name = "frozenlist" 133 | version = "1.3.0" 134 | description = "A list-like structure which implements collections.abc.MutableSequence" 135 | category = "main" 136 | optional = false 137 | python-versions = ">=3.7" 138 | 139 | [[package]] 140 | name = "fsspec" 141 | version = "2022.5.0" 142 | description = "File-system specification" 143 | category = "main" 144 | optional = false 145 | python-versions = ">=3.7" 146 | 147 | [package.dependencies] 148 | aiohttp = {version = "*", optional = true, markers = "extra == \"http\""} 149 | requests = {version = "*", optional = true, markers = "extra == \"http\""} 150 | 151 | [package.extras] 152 | abfs = ["adlfs"] 153 | adl = ["adlfs"] 154 | arrow = ["pyarrow (>=1)"] 155 | dask = ["dask", "distributed"] 156 | dropbox = ["dropboxdrivefs", "requests", "dropbox"] 157 | entrypoints = ["importlib-metadata"] 158 | fuse = ["fusepy"] 159 | gcs = ["gcsfs"] 160 | git = ["pygit2"] 161 | github = ["requests"] 162 | gs = ["gcsfs"] 163 | gui = ["panel"] 164 | hdfs = ["pyarrow (>=1)"] 165 | http = ["requests", "aiohttp"] 166 | libarchive = ["libarchive-c"] 167 | oci = ["ocifs"] 168 | s3 = ["s3fs"] 169 | sftp = ["paramiko"] 170 | smb = ["smbprotocol"] 171 | ssh = ["paramiko"] 172 | tqdm = ["tqdm"] 173 | 174 | [[package]] 175 | name = "google-auth" 176 | version = "2.7.0" 177 | description = "Google Authentication Library" 178 | category = "main" 179 | optional = false 180 | 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415 | category = "main" 416 | optional = false 417 | python-versions = ">=3.5" 418 | 419 | [[package]] 420 | name = "pyasn1" 421 | version = "0.4.8" 422 | description = "ASN.1 types and codecs" 423 | category = "main" 424 | optional = false 425 | python-versions = "*" 426 | 427 | [[package]] 428 | name = "pyasn1-modules" 429 | version = "0.2.8" 430 | description = "A collection of ASN.1-based protocols modules." 431 | category = "main" 432 | optional = false 433 | python-versions = "*" 434 | 435 | [package.dependencies] 436 | pyasn1 = ">=0.4.6,<0.5.0" 437 | 438 | [[package]] 439 | name = "pydeprecate" 440 | version = "0.3.2" 441 | description = "Deprecation tooling" 442 | category = "main" 443 | optional = false 444 | python-versions = ">=3.6" 445 | 446 | [[package]] 447 | name = "pygments" 448 | version = "2.12.0" 449 | description = "Pygments is a syntax highlighting package written in Python." 450 | category = "main" 451 | optional = false 452 | python-versions = ">=3.6" 453 | 454 | 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