python_code stringlengths 0 290k | repo_name stringclasses 30
values | file_path stringlengths 6 125 |
|---|---|---|
from setuptools import find_packages, setup
# 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
# X.Y ... | allennlp-master | setup.py |
allennlp-master | test_fixtures/__init__.py | |
from d.d import D
| allennlp-master | test_fixtures/plugins/d/__init__.py |
import argparse
from overrides import overrides
from allennlp.commands import Subcommand
def do_nothing(_):
pass
@Subcommand.register("d")
class D(Subcommand):
@overrides
def add_subparser(self, parser: argparse._SubParsersAction) -> argparse.ArgumentParser:
subparser = parser.add_parser(self.... | allennlp-master | test_fixtures/plugins/d/d.py |
import os
_MAJOR = "1"
_MINOR = "3"
# On master and in a nightly release the patch should be one ahead of the last
# released build.
_PATCH = "0"
# This is mainly for nightly builds which have the suffix ".dev$DATE". See
# https://semver.org/#is-v123-a-semantic-version for the semantics.
_SUFFIX = os.environ.get("ALLE... | allennlp-master | allennlp/version.py |
# Make sure that allennlp is running on Python 3.6.1 or later
# (to avoid running into this bug: https://bugs.python.org/issue29246)
import sys
if sys.version_info < (3, 6, 1):
raise RuntimeError("AllenNLP requires Python 3.6.1 or later")
# We get a lot of these spurious warnings,
# see https://github.com/Continu... | allennlp-master | allennlp/__init__.py |
#!/usr/bin/env python
import logging
import os
import sys
if os.environ.get("ALLENNLP_DEBUG"):
LEVEL = logging.DEBUG
else:
level_name = os.environ.get("ALLENNLP_LOG_LEVEL", "INFO")
LEVEL = logging._nameToLevel.get(level_name, logging.INFO)
sys.path.insert(0, os.path.dirname(os.path.abspath(os.path.join(__... | allennlp-master | allennlp/__main__.py |
allennlp-master | allennlp/tools/__init__.py | |
import os
from allennlp.common.file_utils import CACHE_DIRECTORY
from allennlp.common.file_utils import filename_to_url
def main():
print(f"Looking for datasets in {CACHE_DIRECTORY}...")
if not os.path.exists(CACHE_DIRECTORY):
print("Directory does not exist.")
print("No cached datasets found... | allennlp-master | allennlp/tools/inspect_cache.py |
#! /usr/bin/env python
"""
Helper script for modifying config.json files that are locked inside
model.tar.gz archives. This is useful if you need to rename things or
add or remove values, usually because of changes to the library.
This script will untar the archive to a temp directory, launch an editor
to modify the c... | allennlp-master | allennlp/tools/archive_surgery.py |
import argparse
import gzip
import os
import torch
from allennlp.common.checks import ConfigurationError
from allennlp.data import Token, Vocabulary
from allennlp.data.token_indexers import ELMoTokenCharactersIndexer
from allennlp.data.vocabulary import DEFAULT_OOV_TOKEN
from allennlp.modules.elmo import _ElmoCharact... | allennlp-master | allennlp/tools/create_elmo_embeddings_from_vocab.py |
"""
Assorted utilities for working with neural networks in AllenNLP.
"""
import copy
import json
import logging
from collections import defaultdict
from typing import Any, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
import math
import numpy
import torch
from allennlp.common.checks import ConfigurationError... | allennlp-master | allennlp/nn/util.py |
"""
An `Activation` is just a function
that takes some parameters and returns an element-wise activation function.
For the most part we just use
[PyTorch activations](https://pytorch.org/docs/master/nn.html#non-linear-activations).
Here we provide a thin wrapper to allow registering them and instantiating them `from_pa... | allennlp-master | allennlp/nn/activations.py |
from allennlp.nn.activations import Activation
from allennlp.nn.initializers import Initializer, InitializerApplicator
from allennlp.nn.regularizers import RegularizerApplicator
| allennlp-master | allennlp/nn/__init__.py |
from inspect import signature
from typing import List, Callable, Tuple, Dict, cast, TypeVar
import warnings
from overrides import overrides
import torch
from allennlp.common import FromParams, Registrable
from allennlp.common.checks import ConfigurationError
from allennlp.nn.util import min_value_of_dtype
StateType... | allennlp-master | allennlp/nn/beam_search.py |
"""
An initializer is just a PyTorch function.
Here we implement a proxy class that allows us
to register them and supply any additional function arguments
(for example, the `mean` and `std` of a normal initializer)
as named arguments to the constructor.
The available initialization functions are
* ["normal"](https:/... | allennlp-master | allennlp/nn/initializers.py |
from typing import List, Set, Tuple, Dict
import numpy
from allennlp.common.checks import ConfigurationError
def decode_mst(
energy: numpy.ndarray, length: int, has_labels: bool = True
) -> Tuple[numpy.ndarray, numpy.ndarray]:
"""
Note: Counter to typical intuition, this function decodes the _maximum_
... | allennlp-master | allennlp/nn/chu_liu_edmonds.py |
import re
from typing import List, Tuple
import torch
from allennlp.common import FromParams
from allennlp.nn.regularizers.regularizer import Regularizer
class RegularizerApplicator(FromParams):
"""
Applies regularizers to the parameters of a Module based on regex matches.
"""
def __init__(self, re... | allennlp-master | allennlp/nn/regularizers/regularizer_applicator.py |
"""
This module contains classes representing regularization schemes
as well as a class for applying regularization to parameters.
"""
from allennlp.nn.regularizers.regularizer import Regularizer
from allennlp.nn.regularizers.regularizers import L1Regularizer
from allennlp.nn.regularizers.regularizers import L2Regular... | allennlp-master | allennlp/nn/regularizers/__init__.py |
import torch
from allennlp.nn.regularizers.regularizer import Regularizer
@Regularizer.register("l1")
class L1Regularizer(Regularizer):
"""
Represents a penalty proportional to the sum of the absolute values of the parameters
Registered as a `Regularizer` with name "l1".
"""
def __init__(self, ... | allennlp-master | allennlp/nn/regularizers/regularizers.py |
import torch
from allennlp.common import Registrable
class Regularizer(Registrable):
"""
An abstract class representing a regularizer. It must implement
call, returning a scalar tensor.
"""
default_implementation = "l2"
def __call__(self, parameter: torch.Tensor) -> torch.Tensor:
ra... | allennlp-master | allennlp/nn/regularizers/regularizer.py |
from typing import Optional, Iterable, Dict, Any
from allennlp.common.checks import ConfigurationError
class MetricTracker:
"""
This class tracks a metric during training for the dual purposes of early stopping
and for knowing whether the current value is the best so far. It mimics the PyTorch
`state... | allennlp-master | allennlp/training/metric_tracker.py |
import os
from contextlib import contextmanager
from typing import Any, Dict, Iterator, Tuple
from allennlp.models import Model
from allennlp.training.checkpointer import Checkpointer
from allennlp.training.trainer import Trainer
@Trainer.register("no_op")
class NoOpTrainer(Trainer):
"""
Registered as a `Tra... | allennlp-master | allennlp/training/no_op_trainer.py |
"""
Helper functions for Trainers
"""
import datetime
import logging
import os
import shutil
import json
from os import PathLike
from typing import Any, Dict, Iterable, Optional, Union, Tuple, Set, List, TYPE_CHECKING
from collections import Counter
import torch
# import torch.distributed as dist
from torch.utils.dat... | allennlp-master | allennlp/training/util.py |
from typing import Iterable, Tuple, Optional
import torch
from allennlp.common.registrable import Registrable
NamedParameter = Tuple[str, torch.Tensor]
class MovingAverage(Registrable):
"""
Tracks a moving average of model parameters.
"""
default_implementation = "exponential"
def __init__(se... | allennlp-master | allennlp/training/moving_average.py |
from typing import Union, Dict, Any, List, Tuple, Optional
import logging
import os
import re
import shutil
import time
import torch
import allennlp
from allennlp.common import Registrable
from allennlp.nn import util as nn_util
from allennlp.training import util as training_util
logger = logging.getLogger(__name__... | allennlp-master | allennlp/training/checkpointer.py |
from allennlp.training.checkpointer import Checkpointer
from allennlp.training.tensorboard_writer import TensorboardWriter
from allennlp.training.no_op_trainer import NoOpTrainer
from allennlp.training.trainer import (
Trainer,
GradientDescentTrainer,
BatchCallback,
EpochCallback,
TrainerCallback,
... | allennlp-master | allennlp/training/__init__.py |
"""
AllenNLP just uses
[PyTorch optimizers](https://pytorch.org/docs/master/optim.html),
with a thin wrapper to allow registering them and instantiating them `from_params`.
The available optimizers are
* [adadelta](https://pytorch.org/docs/master/optim.html#torch.optim.Adadelta)
* [adagrad](https://pytorch.org/docs/m... | allennlp-master | allennlp/training/optimizers.py |
from typing import Dict, Any
import torch
class Scheduler:
"""
A `Scheduler` is a generalization of PyTorch learning rate schedulers.
A scheduler can be used to update any field in an optimizer's parameter groups,
not just the learning rate.
During training using the AllenNLP `Trainer`, this is... | allennlp-master | allennlp/training/scheduler.py |
import datetime
import logging
import math
import os
import re
import time
import traceback
from contextlib import contextmanager
from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Type, Union
from allennlp.common.util import int_to_device
import torch
import torch.distributed as dist
from torch... | allennlp-master | allennlp/training/trainer.py |
from typing import Any, Callable, Dict, List, Optional, Set
import logging
import os
from tensorboardX import SummaryWriter
import torch
from allennlp.common.from_params import FromParams
from allennlp.data.dataloader import TensorDict
from allennlp.nn import util as nn_util
from allennlp.training.optimizers import O... | allennlp-master | allennlp/training/tensorboard_writer.py |
from typing import Optional
from overrides import overrides
import torch
import torch.distributed as dist
import scipy.stats as stats
from allennlp.common.util import is_distributed
from allennlp.training.metrics.metric import Metric
@Metric.register("spearman_correlation")
class SpearmanCorrelation(Metric):
""... | allennlp-master | allennlp/training/metrics/spearman_correlation.py |
from typing import Optional
from overrides import overrides
import torch
import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.training.metrics.metric import Metric
@Metric.register("mean_absolute_error")
class MeanAbsoluteError(Metric):
"""
This `Metric` calculates ... | allennlp-master | allennlp/training/metrics/mean_absolute_error.py |
from typing import Dict
from allennlp.training.metrics.metric import Metric
from allennlp.training.metrics.fbeta_measure import FBetaMeasure
@Metric.register("f1")
class F1Measure(FBetaMeasure):
"""
Computes Precision, Recall and F1 with respect to a given `positive_label`.
For example, for a BIO tagging... | allennlp-master | allennlp/training/metrics/f1_measure.py |
import logging
from typing import Optional
import math
import numpy as np
from overrides import overrides
import torch
# import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.training.metrics.covariance import Covariance
from allennlp.training.metrics.metric import Metric
l... | allennlp-master | allennlp/training/metrics/pearson_correlation.py |
from collections import Counter
import math
from typing import Iterable, Tuple, Dict, Set
from overrides import overrides
import torch
import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.training.metrics.metric import Metric
@Metric.register("bleu")
class BLEU(Metric):
... | allennlp-master | allennlp/training/metrics/bleu.py |
from typing import Optional
from overrides import overrides
import torch
import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.training.metrics.metric import Metric
@Metric.register("boolean_accuracy")
class BooleanAccuracy(Metric):
"""
Just checks batch-equality of ... | allennlp-master | allennlp/training/metrics/boolean_accuracy.py |
from typing import List, Optional, Union
import torch
import torch.distributed as dist
from overrides import overrides
from allennlp.common.util import is_distributed
from allennlp.common.checks import ConfigurationError
from allennlp.training.metrics.metric import Metric
@Metric.register("fbeta")
class FBetaMeasur... | allennlp-master | allennlp/training/metrics/fbeta_measure.py |
from overrides import overrides
import math
from allennlp.training.metrics.average import Average
from allennlp.training.metrics.metric import Metric
@Metric.register("perplexity")
class Perplexity(Average):
"""
Perplexity is a common metric used for evaluating how well a language model
predicts a sample... | allennlp-master | allennlp/training/metrics/perplexity.py |
import logging
from typing import Optional
from overrides import overrides
import torch
# import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.training.metrics.metric import Metric
logger = logging.getLogger(__name__)
@Metric.register("covariance")
class Covariance(Metric... | allennlp-master | allennlp/training/metrics/covariance.py |
from collections import defaultdict
from typing import Tuple, Dict, Set
from overrides import overrides
import torch
import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.training.metrics.metric import Metric
@Metric.register("rouge")
class ROUGE(Metric):
"""
Recall-... | allennlp-master | allennlp/training/metrics/rouge.py |
"""
A `~allennlp.training.metrics.metric.Metric` is some quantity or quantities
that can be accumulated during training or evaluation; for example,
accuracy or F1 score.
"""
from allennlp.training.metrics.attachment_scores import AttachmentScores
from allennlp.training.metrics.average import Average
from allennlp.trai... | allennlp-master | allennlp/training/metrics/__init__.py |
from typing import Optional
import sys
from overrides import overrides
import torch
import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.common.checks import ConfigurationError
from allennlp.training.metrics.metric import Metric
@Metric.register("unigram_recall")
class Uni... | allennlp-master | allennlp/training/metrics/unigram_recall.py |
from typing import List, Optional
import torch
import torch.distributed as dist
from overrides import overrides
from allennlp.common.checks import ConfigurationError
from allennlp.common.util import is_distributed
from allennlp.training.metrics import FBetaMeasure
from allennlp.training.metrics.metric import Metric
... | allennlp-master | allennlp/training/metrics/fbeta_multi_label_measure.py |
from typing import Optional, List
from overrides import overrides
import torch
import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.training.metrics.metric import Metric
@Metric.register("attachment_scores")
class AttachmentScores(Metric):
"""
Computes labeled and u... | allennlp-master | allennlp/training/metrics/attachment_scores.py |
from typing import Dict, Iterable, Optional, Any
import torch
from allennlp.common.registrable import Registrable
class Metric(Registrable):
"""
A very general abstract class representing a metric which can be
accumulated.
"""
supports_distributed = False
def __call__(
self, predic... | allennlp-master | allennlp/training/metrics/metric.py |
from overrides import overrides
import torch
import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.training.metrics.metric import Metric
@Metric.register("average")
class Average(Metric):
"""
This [`Metric`](./metric.md) breaks with the typical `Metric` API and just ... | allennlp-master | allennlp/training/metrics/average.py |
from typing import Optional
from overrides import overrides
import torch
import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.common.checks import ConfigurationError
from allennlp.training.metrics.metric import Metric
@Metric.register("categorical_accuracy")
class Categoric... | allennlp-master | allennlp/training/metrics/categorical_accuracy.py |
from typing import List
import logging
import os
import tempfile
import subprocess
import shutil
from overrides import overrides
from nltk import Tree
import torch
import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.common.checks import ConfigurationError
from allennlp.trai... | allennlp-master | allennlp/training/metrics/evalb_bracketing_scorer.py |
from typing import Optional
from overrides import overrides
import torch
import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.training.metrics.metric import Metric
@Metric.register("entropy")
class Entropy(Metric):
def __init__(self) -> None:
self._entropy = 0.0... | allennlp-master | allennlp/training/metrics/entropy.py |
from typing import Optional
from overrides import overrides
import torch
import torch.distributed as dist
from allennlp.common.util import is_distributed
from allennlp.common.checks import ConfigurationError
from allennlp.training.metrics.metric import Metric
@Metric.register("sequence_accuracy")
class SequenceAccu... | allennlp-master | allennlp/training/metrics/sequence_accuracy.py |
from typing import Dict, List, Optional, Set, Callable
from collections import defaultdict
import torch
from allennlp.common.util import is_distributed
from allennlp.common.checks import ConfigurationError
from allennlp.nn.util import get_lengths_from_binary_sequence_mask
from allennlp.data.vocabulary import Vocabula... | allennlp-master | allennlp/training/metrics/span_based_f1_measure.py |
from typing import Optional
from overrides import overrides
import torch
import torch.distributed as dist
from sklearn import metrics
from allennlp.common.util import is_distributed
from allennlp.common.checks import ConfigurationError
from allennlp.training.metrics.metric import Metric
@Metric.register("auc")
clas... | allennlp-master | allennlp/training/metrics/auc.py |
from overrides import overrides
import torch
from allennlp.training.learning_rate_schedulers.learning_rate_scheduler import LearningRateScheduler
@LearningRateScheduler.register("polynomial_decay")
class PolynomialDecay(LearningRateScheduler):
"""
Implements polynomial decay Learning rate scheduling. The lea... | allennlp-master | allennlp/training/learning_rate_schedulers/polynomial_decay.py |
import logging
from overrides import overrides
import numpy as np
import torch
from allennlp.training.learning_rate_schedulers.learning_rate_scheduler import LearningRateScheduler
logger = logging.getLogger(__name__)
@LearningRateScheduler.register("cosine")
class CosineWithRestarts(LearningRateScheduler):
""... | allennlp-master | allennlp/training/learning_rate_schedulers/cosine.py |
import logging
from typing import List, Optional
from overrides import overrides
import torch
from allennlp.training.learning_rate_schedulers.learning_rate_scheduler import LearningRateScheduler
logger = logging.getLogger(__name__)
@LearningRateScheduler.register("slanted_triangular")
class SlantedTriangular(Lear... | allennlp-master | allennlp/training/learning_rate_schedulers/slanted_triangular.py |
"""
AllenNLP uses most
`PyTorch learning rate schedulers <https://pytorch.org/docs/master/optim.html#how-to-adjust-learning-rate>`_,
with a thin wrapper to allow registering them and instantiating them `from_params`.
The available learning rate schedulers from PyTorch are
* `"step" <https://pytorch.org/docs/master/op... | allennlp-master | allennlp/training/learning_rate_schedulers/__init__.py |
from overrides import overrides
import torch
from allennlp.training.learning_rate_schedulers.learning_rate_scheduler import LearningRateScheduler
@LearningRateScheduler.register("noam")
class NoamLR(LearningRateScheduler):
"""
Implements the Noam Learning rate schedule. This corresponds to increasing the lea... | allennlp-master | allennlp/training/learning_rate_schedulers/noam.py |
from typing import Any, Dict, List, Union
from overrides import overrides
import torch
from allennlp.common.checks import ConfigurationError
from allennlp.common.registrable import Registrable
from allennlp.training.scheduler import Scheduler
from allennlp.training.optimizers import Optimizer
class LearningRateSche... | allennlp-master | allennlp/training/learning_rate_schedulers/learning_rate_scheduler.py |
from typing import Dict, Any, List, Tuple, Optional
from overrides import overrides
import torch
from allennlp.common.lazy import Lazy
from allennlp.training.learning_rate_schedulers.learning_rate_scheduler import LearningRateScheduler
@LearningRateScheduler.register("combined")
class CombinedLearningRateScheduler(... | allennlp-master | allennlp/training/learning_rate_schedulers/combined.py |
import torch
from allennlp.training.learning_rate_schedulers import PolynomialDecay
from allennlp.training.learning_rate_schedulers.learning_rate_scheduler import LearningRateScheduler
@LearningRateScheduler.register("linear_with_warmup")
class LinearWithWarmup(PolynomialDecay):
"""
Implements a learning rat... | allennlp-master | allennlp/training/learning_rate_schedulers/linear_with_warmup.py |
import torch
from allennlp.training.momentum_schedulers.momentum_scheduler import MomentumScheduler
@MomentumScheduler.register("inverted_triangular")
class InvertedTriangular(MomentumScheduler):
"""
Adjust momentum during training according to an inverted triangle-like schedule.
The momentum starts off... | allennlp-master | allennlp/training/momentum_schedulers/inverted_triangular.py |
import torch
from allennlp.common.registrable import Registrable
from allennlp.training.scheduler import Scheduler
class MomentumScheduler(Scheduler, Registrable):
def __init__(self, optimizer: torch.optim.Optimizer, last_epoch: int = -1) -> None:
super().__init__(optimizer, "momentum", last_epoch)
... | allennlp-master | allennlp/training/momentum_schedulers/momentum_scheduler.py |
from allennlp.training.momentum_schedulers.momentum_scheduler import MomentumScheduler
from allennlp.training.momentum_schedulers.inverted_triangular import InvertedTriangular
| allennlp-master | allennlp/training/momentum_schedulers/__init__.py |
from typing import List, Iterator, Dict, Tuple, Any, Type, Union
import logging
import json
import re
from contextlib import contextmanager
from pathlib import Path
import numpy
import torch
from torch.utils.hooks import RemovableHandle
from torch import Tensor
from torch import backends
from allennlp.common import R... | allennlp-master | allennlp/predictors/predictor.py |
"""
A `Predictor` is
a wrapper for an AllenNLP `Model`
that makes JSON predictions using JSON inputs. If you
want to serve up a model through the web service
(or using `allennlp.commands.predict`), you'll need
a `Predictor` that wraps it.
"""
from allennlp.predictors.predictor import Predictor
from allennlp.predictors.... | allennlp-master | allennlp/predictors/__init__.py |
from typing import List, Dict
from overrides import overrides
import numpy
from allennlp.common.util import JsonDict
from allennlp.data import DatasetReader, Instance
from allennlp.data.fields import FlagField, TextField, SequenceLabelField
from allennlp.data.tokenizers.spacy_tokenizer import SpacyTokenizer
from alle... | allennlp-master | allennlp/predictors/sentence_tagger.py |
from typing import List, Dict
from overrides import overrides
import numpy
from allennlp.common.util import JsonDict
from allennlp.data import Instance
from allennlp.predictors.predictor import Predictor
from allennlp.data.fields import LabelField
from allennlp.data.tokenizers.spacy_tokenizer import SpacyTokenizer
... | allennlp-master | allennlp/predictors/text_classifier.py |
from typing import Dict, Optional, List, Any
import numpy
from overrides import overrides
import torch
from torch.nn.modules.linear import Linear
import torch.nn.functional as F
from allennlp.common.checks import check_dimensions_match, ConfigurationError
from allennlp.data import TextFieldTensors, Vocabulary
from al... | allennlp-master | allennlp/models/simple_tagger.py |
"""
These submodules contain the classes for AllenNLP models,
all of which are subclasses of `Model`.
"""
from allennlp.models.model import Model
from allennlp.models.archival import archive_model, load_archive, Archive
from allennlp.models.simple_tagger import SimpleTagger
from allennlp.models.basic_classifier import... | allennlp-master | allennlp/models/__init__.py |
"""
`Model` is an abstract class representing
an AllenNLP model.
"""
import logging
import os
from os import PathLike
import re
from typing import Dict, List, Set, Type, Optional, Union
import numpy
import torch
from allennlp.common.checks import ConfigurationError
from allennlp.common.params import Params, remove_k... | allennlp-master | allennlp/models/model.py |
from typing import Dict, Optional
from overrides import overrides
import torch
from allennlp.data import TextFieldTensors, Vocabulary
from allennlp.models.model import Model
from allennlp.modules import FeedForward, Seq2SeqEncoder, Seq2VecEncoder, TextFieldEmbedder
from allennlp.nn import InitializerApplicator, util
... | allennlp-master | allennlp/models/basic_classifier.py |
"""
Helper functions for archiving models and restoring archived models.
"""
from os import PathLike
from typing import NamedTuple, Union, Dict, Any, List, Optional
import logging
import os
import tempfile
import tarfile
import shutil
from pathlib import Path
from contextlib import contextmanager
import glob
from torc... | allennlp-master | allennlp/models/archival.py |
"""
# Plugin management.
AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that
provides custom registered classes or additional `allennlp` subcommands.
In order for AllenNLP to find your plugins, you have to create either a local plugins
file named `.allennlp_plugins` in the director... | allennlp-master | allennlp/common/plugins.py |
import logging
from logging import Filter
import os
from os import PathLike
from typing import Union
import sys
class AllenNlpLogger(logging.Logger):
"""
A custom subclass of 'logging.Logger' that keeps a set of messages to
implement {debug,info,etc.}_once() methods.
"""
def __init__(self, name)... | allennlp-master | allennlp/common/logging.py |
import copy
import json
import logging
import os
import zlib
from collections import OrderedDict
from collections.abc import MutableMapping
from os import PathLike
from typing import Any, Dict, List, Union, Optional
from overrides import overrides
# _jsonnet doesn't work on Windows, so we have to use fakes.
try:
... | allennlp-master | allennlp/common/params.py |
import logging
from typing import NamedTuple, Optional, Dict, Tuple
import transformers
from transformers import AutoModel
logger = logging.getLogger(__name__)
class TransformerSpec(NamedTuple):
model_name: str
override_weights_file: Optional[str] = None
override_weights_strip_prefix: Optional[str] = No... | allennlp-master | allennlp/common/cached_transformers.py |
"""
Various utilities that don't fit anywhere else.
"""
import hashlib
import io
import pickle
from datetime import timedelta
import importlib
import json
import logging
import os
import pkgutil
import random
import sys
from contextlib import contextmanager
from itertools import islice, zip_longest
from pathlib import ... | allennlp-master | allennlp/common/util.py |
import collections.abc
from copy import deepcopy
from pathlib import Path
from typing import (
Any,
Callable,
cast,
Dict,
Iterable,
List,
Mapping,
Set,
Tuple,
Type,
TypeVar,
Union,
)
import inspect
import logging
from allennlp.common.checks import ConfigurationError
from... | allennlp-master | allennlp/common/from_params.py |
"""
Functions and exceptions for checking that
AllenNLP and its models are configured correctly.
"""
import logging
import re
import subprocess
from typing import List, Union
import torch
from torch import cuda
logger = logging.getLogger(__name__)
class ConfigurationError(Exception):
"""
The exception raise... | allennlp-master | allennlp/common/checks.py |
from allennlp.common.from_params import FromParams
from allennlp.common.lazy import Lazy
from allennlp.common.params import Params
from allennlp.common.registrable import Registrable
from allennlp.common.tqdm import Tqdm
from allennlp.common.util import JsonDict
| allennlp-master | allennlp/common/__init__.py |
"""
`allennlp.common.registrable.Registrable` is a "mixin" for endowing
any base class with a named registry for its subclasses and a decorator
for registering them.
"""
from collections import defaultdict
from typing import TypeVar, Type, Callable, Dict, List, Optional, Tuple
import importlib
import logging
from alle... | allennlp-master | allennlp/common/registrable.py |
"""
`allennlp.common.tqdm.Tqdm` wraps tqdm so we can add configurable
global defaults for certain tqdm parameters.
"""
import logging
from allennlp.common import logging as common_logging
import sys
from time import time
from typing import Optional
try:
SHELL = str(type(get_ipython())) # type:ignore # noqa: F821
... | allennlp-master | allennlp/common/tqdm.py |
"""
Utilities for working with the local dataset cache.
"""
import glob
import os
import logging
import tempfile
import json
from collections import defaultdict
from dataclasses import dataclass, asdict
from datetime import timedelta
from fnmatch import fnmatch
from os import PathLike
from urllib.parse import urlparse... | allennlp-master | allennlp/common/file_utils.py |
import inspect
from typing import Callable, Generic, TypeVar, Type, Union
from allennlp.common.params import Params
T = TypeVar("T")
class Lazy(Generic[T]):
"""
This class is for use when constructing objects using `FromParams`, when an argument to a
constructor has a _sequential dependency_ with anoth... | allennlp-master | allennlp/common/lazy.py |
import copy
import json
from os import PathLike
import random
from typing import Any, Dict, Iterable, Set, Union
import torch
import numpy
from numpy.testing import assert_allclose
from allennlp.commands.train import train_model_from_file
from allennlp.common import Params
from allennlp.common.testing.test_case impor... | allennlp-master | allennlp/common/testing/model_test_case.py |
"""
Utilities and helpers for writing tests.
"""
from typing import Dict, Any, Optional, Union, Tuple, List
import torch
from torch.testing import assert_allclose
import pytest
from allennlp.common.testing.test_case import AllenNlpTestCase
from allennlp.common.testing.model_test_case import ModelTestCase
from allennlp... | allennlp-master | allennlp/common/testing/__init__.py |
import logging
import os
import pathlib
import shutil
import tempfile
from allennlp.common.checks import log_pytorch_version_info
TEST_DIR = tempfile.mkdtemp(prefix="allennlp_tests")
class AllenNlpTestCase:
"""
A custom testing class that disables some of the more verbose AllenNLP
logging and that creat... | allennlp-master | allennlp/common/testing/test_case.py |
from allennlp.predictors import TextClassifierPredictor
from allennlp.models.model import Model
import torch
class FakeModelForTestingInterpret(Model):
def __init__(self, vocab, max_tokens=7, num_labels=2):
super().__init__(vocab)
self._max_tokens = max_tokens
self.embedder = torch.nn.Embe... | allennlp-master | allennlp/common/testing/interpret_test.py |
import datetime
from typing import List, Dict, Any, Tuple, Callable
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from allennlp.common.checks import check_for_gpu
def init_process(
process_rank: int,
world_size: int,
distributed_device_ids: List[int],
func: Callable... | allennlp-master | allennlp/common/testing/distributed_test.py |
from allennlp.interpret.attackers.attacker import Attacker
from allennlp.interpret.saliency_interpreters.saliency_interpreter import SaliencyInterpreter
| allennlp-master | allennlp/interpret/__init__.py |
import math
from typing import List, Dict, Any
import numpy
import torch
from allennlp.common.util import JsonDict, sanitize
from allennlp.data import Instance
from allennlp.interpret.saliency_interpreters.saliency_interpreter import SaliencyInterpreter
from allennlp.nn import util
@SaliencyInterpreter.register("in... | allennlp-master | allennlp/interpret/saliency_interpreters/integrated_gradient.py |
from typing import List
import numpy
import torch
from allennlp.common import Registrable
from allennlp.common.util import JsonDict
from allennlp.nn import util
from allennlp.predictors import Predictor
class SaliencyInterpreter(Registrable):
"""
A `SaliencyInterpreter` interprets an AllenNLP Predictor's ou... | allennlp-master | allennlp/interpret/saliency_interpreters/saliency_interpreter.py |
import math
from typing import Dict, Any
import numpy
import torch
from allennlp.common.util import JsonDict, sanitize
from allennlp.data import Instance
from allennlp.interpret.saliency_interpreters.saliency_interpreter import SaliencyInterpreter
from allennlp.predictors import Predictor
@SaliencyInterpreter.regis... | allennlp-master | allennlp/interpret/saliency_interpreters/smooth_gradient.py |
import math
from typing import List
import numpy
import torch
from allennlp.common.util import JsonDict, sanitize
from allennlp.interpret.saliency_interpreters.saliency_interpreter import SaliencyInterpreter
from allennlp.nn import util
@SaliencyInterpreter.register("simple-gradient")
class SimpleGradient(SaliencyI... | allennlp-master | allennlp/interpret/saliency_interpreters/simple_gradient.py |
from allennlp.interpret.saliency_interpreters.saliency_interpreter import SaliencyInterpreter
from allennlp.interpret.saliency_interpreters.simple_gradient import SimpleGradient
from allennlp.interpret.saliency_interpreters.integrated_gradient import IntegratedGradient
from allennlp.interpret.saliency_interpreters.smoo... | allennlp-master | allennlp/interpret/saliency_interpreters/__init__.py |
from allennlp.interpret.attackers.attacker import Attacker
from allennlp.interpret.attackers.input_reduction import InputReduction
from allennlp.interpret.attackers.hotflip import Hotflip
| allennlp-master | allennlp/interpret/attackers/__init__.py |
from allennlp.common.util import JsonDict
from allennlp.data import Instance
def get_fields_to_compare(
inputs: JsonDict, instance: Instance, input_field_to_attack: str
) -> JsonDict:
"""
Gets a list of the fields that should be checked for equality after an attack is performed.
# Parameters
inp... | allennlp-master | allennlp/interpret/attackers/utils.py |
from copy import deepcopy
from typing import Dict, List, Tuple
import numpy
import torch
from allennlp.common.util import JsonDict, sanitize
from allennlp.data import Instance, Token
from allennlp.data.fields import TextField
from allennlp.data.token_indexers import (
ELMoTokenCharactersIndexer,
TokenCharacte... | allennlp-master | allennlp/interpret/attackers/hotflip.py |
from copy import deepcopy
from typing import List, Tuple
import heapq
import numpy as np
import torch
from allennlp.common.util import JsonDict, sanitize
from allennlp.data import Instance
from allennlp.data.fields import TextField, SequenceLabelField
from allennlp.interpret.attackers import utils
from allennlp.inter... | allennlp-master | allennlp/interpret/attackers/input_reduction.py |
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