python_code stringlengths 0 290k | repo_name stringclasses 30
values | file_path stringlengths 6 125 |
|---|---|---|
#!/usr/bin/env python
# Copyright 2021 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
#
# Unles... | accelerate-main | src/accelerate/commands/config/cluster.py |
#!/usr/bin/env python
# Copyright 2021 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
#
# Unles... | accelerate-main | src/accelerate/commands/config/sagemaker.py |
#!/usr/bin/env python
# Copyright 2021 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
#
# Unles... | accelerate-main | src/accelerate/commands/config/config_utils.py |
# Copyright 2022 The HuggingFace Team and Brian Chao. 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 requir... | accelerate-main | src/accelerate/commands/menu/selection_menu.py |
from .selection_menu import BulletMenu
| accelerate-main | src/accelerate/commands/menu/__init__.py |
# Copyright 2022 The HuggingFace Team and Brian Chao. 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 requir... | accelerate-main | src/accelerate/commands/menu/keymap.py |
# Copyright 2022 The HuggingFace Team and Brian Chao. 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 requir... | accelerate-main | src/accelerate/commands/menu/input.py |
# Copyright 2022 The HuggingFace Team and Brian Chao. 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 requir... | accelerate-main | src/accelerate/commands/menu/helpers.py |
# Copyright 2022 The HuggingFace Team and Brian Chao. 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 requir... | accelerate-main | src/accelerate/commands/menu/cursor.py |
import sys
from setuptools import setup, find_packages
# PEP0440 compatible formatted version, see:
# https://www.python.org/dev/peps/pep-0440/
#
# release markers:
# X.Y
# X.Y.Z # For bugfix releases
#
# pre-release markers:
# X.YaN # Alpha release
# X.YbN # Beta release
# X.YrcN # Release Candidate
... | adversarialnlp-master | setup.py |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# AdversarialNLP documentation build configuration file, created by
# sphinx-quickstart on Wed Oct 24 11:35:14 2018.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in th... | adversarialnlp-master | docs/conf.py |
from adversarialnlp import Adversarial
from allennlp.data.dataset_readers.reading_comprehension.squad import SquadReader
adversarial = Adversarial(dataset_reader=SquadReader, editor='lstm_lm', num_samples=10)
examples = adversarial.generate() | adversarialnlp-master | tutorials/usage.py |
#!/usr/bin/env python
import logging
import os
import sys
if os.environ.get("ALLENNLP_DEBUG"):
LEVEL = logging.DEBUG
else:
LEVEL = logging.INFO
sys.path.insert(0, os.path.dirname(os.path.abspath(os.path.join(__file__, os.pardir))))
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message... | adversarialnlp-master | adversarialnlp/run.py |
_MAJOR = "0"
_MINOR = "1"
_REVISION = "1-unreleased"
VERSION_SHORT = "{0}.{1}".format(_MAJOR, _MINOR)
VERSION = "{0}.{1}.{2}".format(_MAJOR, _MINOR, _REVISION)
| adversarialnlp-master | adversarialnlp/version.py |
from adversarialnlp.version import VERSION as __version__
| adversarialnlp-master | adversarialnlp/__init__.py |
from .pruner import Pruner | adversarialnlp-master | adversarialnlp/pruners/__init__.py |
from allennlp.common import Registrable
class Pruner(Registrable):
"""
``Pruner`` is used to fil potential adversarial samples
Parameters
----------
dataset_reader : ``DatasetReader``
The ``DatasetReader`` object that will be used to sample training examples.
"""
def __init__(self... | adversarialnlp-master | adversarialnlp/pruners/pruner.py |
adversarialnlp-master | adversarialnlp/tests/__init__.py | |
adversarialnlp-master | adversarialnlp/tests/dataset_readers/__init__.py | |
# pylint: disable=no-self-use,invalid-name
import pytest
from allennlp.common.util import ensure_list
from adversarialnlp.dataset_readers import ActivityNetCaptionsDatasetReader
from adversarialnlp.tests.utils import FIXTURES_ROOT
class TestActivityNetCaptionsReader():
@pytest.mark.parametrize("lazy", (True, Fal... | adversarialnlp-master | adversarialnlp/tests/dataset_readers/activitynet_captions_test.py |
# pylint: disable=no-self-use,invalid-name
from typing import List
import pytest
from allennlp.data.fields import TextField
from allennlp.common.util import ensure_list
from allennlp.common.testing import AllenNlpTestCase
from allennlp.data import Instance, Token, Vocabulary
from allennlp.data.iterators import BasicIt... | adversarialnlp-master | adversarialnlp/tests/generators/swag_generator_test.py |
adversarialnlp-master | adversarialnlp/tests/generators/__init__.py | |
# pylint: disable=no-self-use,invalid-name
from typing import List
import pytest
from adversarialnlp.generators.addsent.addsent_generator import AddSentGenerator
from adversarialnlp.generators.addsent.squad_reader import squad_reader
from adversarialnlp.common.file_utils import FIXTURES_ROOT
# class GeneratorTest(Al... | adversarialnlp-master | adversarialnlp/tests/generators/addsent_generator_test.py |
adversarialnlp-master | adversarialnlp/common/__init__.py | |
# pylint: disable=invalid-name,protected-access
#!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found... | adversarialnlp-master | adversarialnlp/common/file_utils.py |
from typing import Dict
import argparse
import logging
from allennlp.commands.subcommand import Subcommand
from allennlp.common.util import import_submodules
from adversarialnlp import __version__
from adversarialnlp.commands.test_install import TestInstall
logger = logging.getLogger(__name__) # pylint: disable=inv... | adversarialnlp-master | adversarialnlp/commands/__init__.py |
"""
The ``test-install`` subcommand verifies
an installation by running the unit tests.
.. code-block:: bash
$ adversarialnlp test-install --help
usage: adversarialnlp test-install [-h] [--run-all]
[--include-package INCLUDE_PACKAGE]
Test that installation works by running t... | adversarialnlp-master | adversarialnlp/commands/test_install.py |
from .generator import Generator
from .swag import SwagGenerator
from .addsent import AddSentGenerator
| adversarialnlp-master | adversarialnlp/generators/__init__.py |
import logging
from typing import Dict, Union, Iterable, List
from collections import defaultdict
import itertools
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
class Generator():
r"""An abstract ``Generator`` class.
A ``Generator`` takes as inputs an iterable of seeds (for examples
... | adversarialnlp-master | adversarialnlp/generators/generator.py |
# Python wrapper for Stanford CoreNLP
# Copyright (c) 2017 Lynten Guo, 2018 Thomas Wolf
# Extracted and adapted from https://github.com/Lynten/stanford-corenlp
from __future__ import print_function
import glob
import json
import logging
import os
import re
import socket
import subprocess
import sys
import time
impor... | adversarialnlp-master | adversarialnlp/generators/addsent/corenlp.py |
from .addsent_generator import AddSentGenerator
from .squad_reader import squad_reader
| adversarialnlp-master | adversarialnlp/generators/addsent/__init__.py |
"""Utilities for AddSent generator."""
from typing import List, Dict, Tuple, Optional
class ConstituencyParse(object):
"""A CoreNLP constituency parse (or a node in a parse tree).
Word-level constituents have |word| and |index| set and no children.
Phrase-level constituents have no |word| or |index| and h... | adversarialnlp-master | adversarialnlp/generators/addsent/utils.py |
import json
import logging
from typing import Iterator, List, Tuple
from adversarialnlp.common.file_utils import download_files
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
def squad_reader(file_path: str = None) -> Iterator[List[Tuple[str, str]]]:
r""" Reads a JSON-formatted SQuAD file ... | adversarialnlp-master | adversarialnlp/generators/addsent/squad_reader.py |
import logging
import json
import itertools
from typing import Iterable, Dict, Tuple
from collections import defaultdict
from adversarialnlp.common.file_utils import download_files
from adversarialnlp.generators import Generator
from adversarialnlp.generators.addsent.rules import (ANSWER_RULES, HIGH_CONF_ALTER_RULES, ... | adversarialnlp-master | adversarialnlp/generators/addsent/addsent_generator.py |
import math
from adversarialnlp.generators.addsent.utils import rejoin
MONTHS = ['january', 'february', 'march', 'april', 'may', 'june', 'july',
'august', 'september', 'october', 'november', 'december']
def ans_number(a, tokens, q, **kwargs):
out_toks = []
seen_num = False
for t in to... | adversarialnlp-master | adversarialnlp/generators/addsent/rules/answer_rules.py |
from .answer_rules import ANSWER_RULES
from .alteration_rules import (HIGH_CONF_ALTER_RULES, ALL_ALTER_RULES,
DO_NOT_ALTER, BAD_ALTERATIONS)
from .conversion_rules import CONVERSION_RULES
| adversarialnlp-master | adversarialnlp/generators/addsent/rules/__init__.py |
from pattern import en as patten
CONST_PARSE_MACROS = {
'$Noun': '$NP/$NN/$NNS/$NNP/$NNPS',
'$Verb': '$VB/$VBD/$VBP/$VBZ',
'$Part': '$VBN/$VG',
'$Be': 'is/are/was/were',
'$Do': "do/did/does/don't/didn't/doesn't",
'$WHP': '$WHADJP/$WHADVP/$WHNP/$WHPP',
}
# Map to pattern... | adversarialnlp-master | adversarialnlp/generators/addsent/rules/conversion_rules.py |
import collections
import nltk
nltk.download('wordnet')
from nltk.corpus import wordnet as wn
from nltk.stem.lancaster import LancasterStemmer
STEMMER = LancasterStemmer()
POS_TO_WORDNET = {
'NN': wn.NOUN,
'JJ': wn.ADJ,
'JJR': wn.ADJ,
'JJS': wn.ADJ,
}
def alter_special(token, **kwar... | adversarialnlp-master | adversarialnlp/generators/addsent/rules/alteration_rules.py |
from .swag_generator import SwagGenerator
from .activitynet_captions_reader import ActivityNetCaptionsDatasetReader
| adversarialnlp-master | adversarialnlp/generators/swag/__init__.py |
import re
from itertools import tee
from num2words import num2words
def optimistic_restore(network, state_dict):
mismatch = False
own_state = network.state_dict()
for name, param in state_dict.items():
if name not in own_state:
print("Unexpected key {} in state_dict with size {}".forma... | adversarialnlp-master | adversarialnlp/generators/swag/utils.py |
# pylint: disable=invalid-name,arguments-differ
from typing import List, Iterable, Tuple
import logging
import torch
from allennlp.common.util import JsonDict
from allennlp.common.file_utils import cached_path
from allennlp.data import Instance, Token, Vocabulary
from allennlp.data.fields import TextField
from allenn... | adversarialnlp-master | adversarialnlp/generators/swag/swag_generator.py |
from typing import Dict
import json
import logging
from overrides import overrides
from unidecode import unidecode
from allennlp.common.file_utils import cached_path
from allennlp.data.dataset_readers.dataset_reader import DatasetReader
from allennlp.data.fields import TextField, MetadataField
from allennlp.data.insta... | adversarialnlp-master | adversarialnlp/generators/swag/activitynet_captions_reader.py |
"""
A wrapper around ai2s elmo LM to allow for an lm objective...
"""
from typing import Optional, Tuple
from typing import Union, List, Dict
import numpy as np
import torch
from allennlp.common.checks import ConfigurationError
from allennlp.data import Token, Vocabulary, Instance
from allennlp.data.dataset import Ba... | adversarialnlp-master | adversarialnlp/generators/swag/simple_bilm.py |
from typing import Dict, List, Tuple, Union, Optional
import torch
import numpy as np
from allennlp.common.checks import ConfigurationError
from allennlp.data.vocabulary import Vocabulary
from allennlp.models.model import Model
from allennlp.modules.openai_transformer import OpenaiTransformer
from allennlp.modules.to... | adversarialnlp-master | adversarialnlp/generators/swag/openai_transformer_model.py |
# coding=utf-8
from pytorch_pretrained_bert import BertForMaskedLM,tokenization
import torch
import sys
import csv
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = 'bert-large-uncased'
if 'base' in sys.argv: model_name = 'bert-base-uncased'
print("using model:",model_name,file=sys.st... | bert-syntax-master | eval_bert.py |
import sys
from collections import *
files=[("base","results/marvin_results_base.txt"),("large","results/marvin_results_large.txt")]
if "with_only_prefix" in sys.argv:
files += [("base_only_prefix","results/marvin_results_base_only_prefix.txt"),("large_only_prefix","results/marvin_results_large_only_prefix.txt")]... | bert-syntax-master | gen_marvin_tbl_openai_gpt.py |
import sys
from collections import *
files=[("base","results/gulordava_results_base.txt"),("large","results/gulordava_results_large.txt")]
by_model={}
conditions=set()
nskipped=0
for title,fname in files:
lines = open(fname)
results=defaultdict(Counter)
by_model[title]=results
skipped = set()
for ... | bert-syntax-master | gen_gul_tbl.py |
import sys
from collections import *
files=[("base","results/lgd_results_base.txt"),("large","results/lgd_results_large.txt")]
if "with_only_prefix" in sys.argv:
files+=[("base_only_prefix","results/lgd_results_base_only_prefix.txt"),("large_only_prefix","results/lgd_results_large_only_prefix.txt")]
if "no_split"... | bert-syntax-master | gen_lgd_tbl_openai_gpt.py |
'''
inflect.py: correctly generate plurals, ordinals, indefinite articles;
convert numbers to words
Copyright (C) 2010 Paul Dyson
Based upon the Perl module Lingua::EN::Inflect by Damian Conway.
This program is free software: you can redistribute it and/or modify
it under the terms... | bert-syntax-master | inflect.py |
import sys
from collections import *
files=[("base","results/gulordava_results_base.txt"),("large","results/gulordava_results_large.txt")]
if "with_only_prefix" in sys.argv:
files+=[("base_only_prefix","results/gulordava_results_base_only_prefix.txt"),("large_only_prefix","results/gulordava_results_large_only_pref... | bert-syntax-master | gen_gul_tbl_openai_gpt.py |
import csv
cases_we_care_about=['1','2','3','4']
from utils import vinfl
def inflect(verb):
return vinfl[verb]
for record in csv.DictReader(open('agr_50_mostcommon_10K.tsv','r'), delimiter='\t'):
orig = record['orig_sentence']
n_i = record['n_intervening']
n_di = record['n_diff_intervening']
vind... | bert-syntax-master | make_linzen_goldberg_testset.py |
# coding=utf-8
from pytorch_pretrained_bert import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, BertTokenizer
import torch
import sys
import csv
import logging
import itertools
logging.basicConfig(level=logging.INFO)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = "openai-gpt"
print("... | bert-syntax-master | eval_openai_gpt.py |
# from Linzen's code repo
import inflect
infl_eng = inflect.engine()
def gen_inflect_from_vocab(vocab_file, freq_threshold=1000):
vbp = {}
vbz = {}
nn = {}
nns = {}
from_pos = {'NNS': nns, 'NN': nn, 'VBP': vbp, 'VBZ': vbz}
for line in file(vocab_file):
if line.startswith(' '): # emp... | bert-syntax-master | utils.py |
import sys
from collections import *
files=[("base","results/marvin_results_base.txt"),("large","results/marvin_results_large.txt")]
by_model={}
conditions=set()
for title,fname in files:
lines = open(fname)
results=defaultdict(Counter)
by_model[title]=results
skipped = set()
for line in lines:
... | bert-syntax-master | gen_marvin_tbl.py |
import sys
from collections import *
files=[("base","results/lgd_results_base.txt"),("large","results/lgd_results_large.txt")]
by_model={}
conditions=set()
nskipped=0
for title,fname in files:
lines = open(fname)
results=defaultdict(Counter)
by_model[title]=results
skipped = set()
for line in line... | bert-syntax-master | gen_lgd_tbl.py |
# Lint as: python3
"""
HuggingFace / AutoTrain Advanced
"""
import os
from setuptools import find_packages, setup
DOCLINES = __doc__.split("\n")
this_directory = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(this_directory, "README.md"), encoding="utf-8") as f:
LONG_DESCRIPTION = f.read()
#... | autotrain-advanced-main | setup.py |
import os
from uuid import uuid4
from datasets import load_dataset
from autotrain.dataset import AutoTrainDataset
from autotrain.project import Project
RANDOM_ID = str(uuid4())
DATASET = "amazon_reviews_multi"
PROJECT_NAME = f"amazon_reviews_multi_{RANDOM_ID}"
TASK = "text_multi_class_classification"
MODEL = "bert-... | autotrain-advanced-main | examples/text_classification_multiclass.py |
import os
from uuid import uuid4
from datasets import load_dataset
from autotrain.dataset import AutoTrainDataset
from autotrain.project import Project
RANDOM_ID = str(uuid4())
DATASET = "imdb"
PROJECT_NAME = f"imdb_{RANDOM_ID}"
TASK = "text_binary_classification"
MODEL = "bert-base-uncased"
USERNAME = os.environ[... | autotrain-advanced-main | examples/text_classification_binary.py |
import sys
from accelerate.state import PartialState
from loguru import logger
emojis = {
"TRACE": "🔍",
"DEBUG": "🐛",
"INFO": "🚀",
"SUCCESS": "✅",
"WARNING": "⚠️",
"ERROR": "❌",
"CRITICAL": "🚨",
}
def should_log(record):
return PartialState().is_main_process
def emoji_filter(r... | autotrain-advanced-main | src/autotrain/logging.py |
from dataclasses import dataclass
from typing import Literal
import gradio as gr
from pydantic import BaseModel, Field
from autotrain.languages import SUPPORTED_LANGUAGES
from autotrain.tasks import TASKS
class LoraR:
TYPE = "int"
MIN_VALUE = 1
MAX_VALUE = 100
DEFAULT = 16
STEP = 1
STREAMLIT... | autotrain-advanced-main | src/autotrain/params.py |
import io
import json
import os
from dataclasses import dataclass
from typing import Union
import requests
from huggingface_hub import HfApi
from autotrain import logger
from autotrain.dataset import AutoTrainDataset, AutoTrainDreamboothDataset
from autotrain.trainers.clm.params import LLMTrainingParams
from autotrai... | autotrain-advanced-main | src/autotrain/backend.py |
NLP_TASKS = {
"text_binary_classification": 1,
"text_multi_class_classification": 2,
"text_entity_extraction": 4,
"text_extractive_question_answering": 5,
"text_summarization": 8,
"text_single_column_regression": 10,
"speech_recognition": 11,
"natural_language_inference": 22,
"lm_tra... | autotrain-advanced-main | src/autotrain/tasks.py |
import os
import sys
from autotrain import logger
AUTOTRAIN_BACKEND_API = os.getenv("AUTOTRAIN_BACKEND_API", "https://api.autotrain.huggingface.co")
HF_API = os.getenv("HF_API", "https://huggingface.co")
logger.configure(handlers=[dict(sink=sys.stderr, format="> <level>{level:<7} {message}</level>")])
| autotrain-advanced-main | src/autotrain/config.py |
TEXT_CLASSIFICATION = [
".csv",
".jsonl",
]
| autotrain-advanced-main | src/autotrain/allowed_file_types.py |
# coding=utf-8
# Copyright 2020-2023 The HuggingFace AutoTrain Authors
#
# 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... | autotrain-advanced-main | src/autotrain/__init__.py |
import os
import uuid
import zipfile
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
import pandas as pd
from autotrain import logger
from autotrain.preprocessor.dreambooth import DreamboothPreprocessor
from autotrain.preprocessor.tabular import (
TabularBinaryClassificationPreproce... | autotrain-advanced-main | src/autotrain/dataset.py |
import json
import os
import subprocess
import psutil
from fastapi import FastAPI
from autotrain import logger
from autotrain.trainers.clm.params import LLMTrainingParams
from autotrain.trainers.dreambooth.params import DreamBoothTrainingParams
from autotrain.trainers.generic.params import GenericParams
from autotrai... | autotrain-advanced-main | src/autotrain/api.py |
import glob
import json
import os
import re
import shutil
import subprocess
import traceback
from typing import Dict, Optional
import requests
from accelerate.state import PartialState
from huggingface_hub import HfApi, HfFolder
from huggingface_hub.repository import Repository
from transformers import AutoConfig
fro... | autotrain-advanced-main | src/autotrain/utils.py |
import json
import os
import random
import string
import zipfile
import gradio as gr
import pandas as pd
from huggingface_hub import list_models
from autotrain import logger
from autotrain.dataset import AutoTrainDataset, AutoTrainDreamboothDataset, AutoTrainImageClassificationDataset
from autotrain.languages import ... | autotrain-advanced-main | src/autotrain/app.py |
TRAIN_SPLIT = "train"
VALID_SPLIT = "valid"
TEST_SPLIT = "test"
| autotrain-advanced-main | src/autotrain/splits.py |
import os
import pty
import random
import shutil
import string
import subprocess
import gradio as gr
from huggingface_hub import HfApi, whoami
# ❯ autotrain dreambooth --help
# usage: autotrain <command> [<args>] dreambooth [-h] --model MODEL [--revision REVISION] [--tokenizer TOKENIZER] --image-path IMAGE_PATH
# ... | autotrain-advanced-main | src/autotrain/dreambooth_app.py |
APP_AUTOTRAIN_USERNAME = """Please choose the user or organization who is creating the AutoTrain Project.
In case of non-free tier, this user or organization will be billed.
"""
APP_PROJECT_NAME = """A unique name for the AutoTrain Project.
This name will be used to identify the project in the AutoTrain dashboard."""
... | autotrain-advanced-main | src/autotrain/help.py |
SUPPORTED_LANGUAGES = [
"en",
"ar",
"bn",
"de",
"es",
"fi",
"fr",
"hi",
"it",
"ja",
"ko",
"nl",
"pt",
"sv",
"tr",
"zh",
"unk",
]
| autotrain-advanced-main | src/autotrain/languages.py |
"""
Copyright 2023 The HuggingFace Team
"""
import json
import os
import time
from dataclasses import dataclass
from typing import Dict, List, Optional, Union
import pandas as pd
from codecarbon import EmissionsTracker
from autotrain import logger
from autotrain.backend import SpaceRunner
from autotrain.dataset impo... | autotrain-advanced-main | src/autotrain/project.py |
def test_dummy():
assert 1 + 1 == 2
| autotrain-advanced-main | src/autotrain/tests/test_dummy.py |
import os
from argparse import ArgumentParser
from . import BaseAutoTrainCommand
def run_app_command_factory(args):
return RunAutoTrainAppCommand(
args.port,
args.host,
args.task,
)
class RunAutoTrainAppCommand(BaseAutoTrainCommand):
@staticmethod
def register_subcommand(par... | autotrain-advanced-main | src/autotrain/cli/run_app.py |
from argparse import ArgumentParser
from autotrain.backend import SpaceRunner
from autotrain.trainers.generic.params import GenericParams
from autotrain.trainers.generic.utils import create_dataset_repo
from . import BaseAutoTrainCommand
BACKEND_CHOICES = [
"spaces-a10gl",
"spaces-a10gs",
"spaces-a100",... | autotrain-advanced-main | src/autotrain/cli/run_spacerunner.py |
from argparse import ArgumentParser
from . import BaseAutoTrainCommand
def run_api_command_factory(args):
return RunAutoTrainAPICommand(
args.port,
args.host,
args.task,
)
class RunAutoTrainAPICommand(BaseAutoTrainCommand):
@staticmethod
def register_subcommand(parser: Argum... | autotrain-advanced-main | src/autotrain/cli/run_api.py |
import os
import sys
from argparse import ArgumentParser
import torch
from autotrain import logger
from autotrain.backend import EndpointsRunner, SpaceRunner
from . import BaseAutoTrainCommand
def run_tabular_command_factory(args):
return RunAutoTrainTabularCommand(args)
class RunAutoTrainTabularCommand(Base... | autotrain-advanced-main | src/autotrain/cli/run_tabular.py |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class BaseAutoTrainCommand(ABC):
@staticmethod
@abstractmethod
def register_subcommand(parser: ArgumentParser):
raise NotImplementedError()
@abstractmethod
def run(self):
raise NotImplementedError()
| autotrain-advanced-main | src/autotrain/cli/__init__.py |
import subprocess
from argparse import ArgumentParser
from autotrain import logger
from . import BaseAutoTrainCommand
def run_app_command_factory(args):
return RunSetupCommand(args.update_torch)
class RunSetupCommand(BaseAutoTrainCommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):... | autotrain-advanced-main | src/autotrain/cli/run_setup.py |
import argparse
from .. import __version__
from .run_api import RunAutoTrainAPICommand
from .run_app import RunAutoTrainAppCommand
from .run_dreambooth import RunAutoTrainDreamboothCommand
from .run_image_classification import RunAutoTrainImageClassificationCommand
from .run_llm import RunAutoTrainLLMCommand
from .run... | autotrain-advanced-main | src/autotrain/cli/autotrain.py |
import os
import subprocess
from argparse import ArgumentParser
import torch
from autotrain import logger
from . import BaseAutoTrainCommand
def run_image_classification_command_factory(args):
return RunAutoTrainImageClassificationCommand(args)
class RunAutoTrainImageClassificationCommand(BaseAutoTrainComman... | autotrain-advanced-main | src/autotrain/cli/run_image_classification.py |
import os
import subprocess
import sys
from argparse import ArgumentParser
import torch
from autotrain import logger
from autotrain.backend import EndpointsRunner, SpaceRunner
from . import BaseAutoTrainCommand
def run_text_classification_command_factory(args):
return RunAutoTrainTextClassificationCommand(args... | autotrain-advanced-main | src/autotrain/cli/run_text_classification.py |
import os
import subprocess
import sys
from argparse import ArgumentParser
import torch
from autotrain import logger
from . import BaseAutoTrainCommand
def run_llm_command_factory(args):
return RunAutoTrainLLMCommand(args)
class RunAutoTrainLLMCommand(BaseAutoTrainCommand):
@staticmethod
def register... | autotrain-advanced-main | src/autotrain/cli/run_llm.py |
import glob
import os
from argparse import ArgumentParser
from autotrain import logger
from autotrain.cli import BaseAutoTrainCommand
try:
from autotrain.trainers.dreambooth.__main__ import train as train_dreambooth
from autotrain.trainers.dreambooth.params import DreamBoothTrainingParams
from autotrain.... | autotrain-advanced-main | src/autotrain/cli/run_dreambooth.py |
import os
import albumentations as A
import numpy as np
import torch
from datasets import load_dataset
from sklearn import metrics
from transformers import (
AutoConfig,
AutoImageProcessor,
AutoModelForImageClassification,
EarlyStoppingCallback,
Trainer,
TrainingArguments,
)
from autotrain imp... | autotrain-advanced-main | src/autotrain/trainers/image_classification.py |
autotrain-advanced-main | src/autotrain/trainers/__init__.py | |
import os
import numpy as np
import torch
from datasets import load_dataset
from sklearn import metrics
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
EarlyStoppingCallback,
Trainer,
TrainingArguments,
)
from autotrain import logger, utils
from autotr... | autotrain-advanced-main | src/autotrain/trainers/text_classification.py |
import os
from pydantic import BaseModel
from autotrain import logger
class AutoTrainParams(BaseModel):
def save(self, output_dir):
os.makedirs(output_dir, exist_ok=True)
path = os.path.join(output_dir, "training_params.json")
# save formatted json
with open(path, "w") as f:
... | autotrain-advanced-main | src/autotrain/trainers/common.py |
import os
from itertools import chain
import torch
from datasets import Dataset, load_dataset
from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
Trainer,
TrainingArguments,
default_data_collato... | autotrain-advanced-main | src/autotrain/trainers/lm_trainer.py |
from typing import List, Union
from pydantic import Field
from autotrain.trainers.common import AutoTrainParams
class TabularParams(AutoTrainParams):
data_path: str = Field(None, title="Data path")
model: str = Field("xgboost", title="Model name")
username: str = Field(None, title="Hugging Face Username... | autotrain-advanced-main | src/autotrain/trainers/tabular/params.py |
autotrain-advanced-main | src/autotrain/trainers/tabular/__init__.py | |
import copy
from collections import defaultdict
from dataclasses import dataclass
from functools import partial
from typing import List, Optional
import numpy as np
from sklearn import ensemble, impute, linear_model
from sklearn import metrics as skmetrics
from sklearn import naive_bayes, neighbors, pipeline, preproce... | autotrain-advanced-main | src/autotrain/trainers/tabular/utils.py |
import argparse
import json
import os
from functools import partial
import joblib
import numpy as np
import optuna
import pandas as pd
from datasets import load_dataset
from huggingface_hub import HfApi
from sklearn import pipeline, preprocessing
from sklearn.compose import ColumnTransformer
from autotrain import log... | autotrain-advanced-main | src/autotrain/trainers/tabular/__main__.py |
from typing import Dict
from pydantic import Field
from autotrain.trainers.common import AutoTrainParams
class GenericParams(AutoTrainParams):
username: str = Field(None, title="Hugging Face Username")
project_name: str = Field(None, title="Output directory")
data_path: str = Field(None, title="Data pat... | autotrain-advanced-main | src/autotrain/trainers/generic/params.py |
autotrain-advanced-main | src/autotrain/trainers/generic/__init__.py | |
import os
import subprocess
import requests
from huggingface_hub import HfApi, snapshot_download
from loguru import logger
def create_dataset_repo(username, project_name, script_path, token):
logger.info("Creating dataset repo...")
api = HfApi(token=token)
repo_id = f"{username}/autotrain-{project_name}"... | autotrain-advanced-main | src/autotrain/trainers/generic/utils.py |
import argparse
import json
import os
from huggingface_hub import HfApi
from autotrain import logger
from autotrain.trainers.generic import utils
from autotrain.trainers.generic.params import GenericParams
from autotrain.utils import monitor
def parse_args():
# get training_config.json from the end user
par... | autotrain-advanced-main | src/autotrain/trainers/generic/__main__.py |
from pydantic import Field
from autotrain.trainers.common import AutoTrainParams
class DreamBoothTrainingParams(AutoTrainParams):
model: str = Field(None, title="Model name")
revision: str = Field(None, title="Revision")
tokenizer: str = Field(None, title="Tokenizer, if different from model")
image_p... | autotrain-advanced-main | src/autotrain/trainers/dreambooth/params.py |
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