python_code stringlengths 0 4.04M | repo_name stringlengths 8 58 | file_path stringlengths 5 147 |
|---|---|---|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
def calc_mean_invstddev(feature):
if len(feature.size()) != 2:
raise ValueError("We expect the input feature to be ... | data2vec_vision-main | deltalm/src/examples/speech_recognition/data/data_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
from fairseq.data import FairseqDataset
from . import data_utils
from .collaters import Seq2SeqCollater
class... | data2vec_vision-main | deltalm/src/examples/speech_recognition/data/asr_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import math
import torch
import torch.nn.f... | data2vec_vision-main | deltalm/src/examples/speech_recognition/criterions/cross_entropy_acc.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from examples.speech_recognition.data.replabels import pack_replabels
from fairseq import utils
from fair... | data2vec_vision-main | deltalm/src/examples/speech_recognition/criterions/ASG_loss.py |
import importlib
import os
# ASG loss requires wav2letter
files_to_skip = set()
try:
import wav2letter
except ImportError:
files_to_skip.add("ASG_loss.py")
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith(".py") and not file.startswith("_") and file not in files_to_skip:
criter... | data2vec_vision-main | deltalm/src/examples/speech_recognition/criterions/__init__.py |
#!/usr/bin/env python3
#
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
from sacremoses.normalize import MosesPunctNormalizer
def main(args):
normalizer = MosesPunctNormal... | data2vec_vision-main | deltalm/src/examples/constrained_decoding/normalize.py |
#!/usr/bin/env python3
#
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
import sacremoses
def main(args):
"""Tokenizes, preserving tabs"""
mt = sacremoses.MosesTokeniz... | data2vec_vision-main | deltalm/src/examples/constrained_decoding/tok.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import rxf_src # noqa
| data2vec_vision-main | deltalm/src/examples/rxf/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import label_smoothed_cross_entropy_r3f, sentence_prediction_r3f # noqa
| data2vec_vision-main | deltalm/src/examples/rxf/rxf_src/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.criterions import FairseqCriterion, register_... | data2vec_vision-main | deltalm/src/examples/rxf/rxf_src/sentence_prediction_r3f.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn.functional as F
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, ... | data2vec_vision-main | deltalm/src/examples/rxf/rxf_src/label_smoothed_cross_entropy_r3f.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import transformer_xl_model, truncated_bptt_lm_task # noqa
| data2vec_vision-main | deltalm/src/examples/truncated_bptt/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
from dataclasses import dataclass, field
from typing import List, Optional, Tuple
import torch
from fairseq import d... | data2vec_vision-main | deltalm/src/examples/truncated_bptt/truncated_bptt_lm_task.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from dataclasses import dataclass, field
from typing import Dict, List, Optional
import torch
from fairseq.dataclass import Fa... | data2vec_vision-main | deltalm/src/examples/truncated_bptt/transformer_xl_model.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Helper script to pre-compute embeddings for a wav2letter++ dataset
"""
import argparse
import glob
import os
from ... | data2vec_vision-main | deltalm/src/examples/wav2vec/wav2vec_featurize.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Data pre-processing: build vocabularies and binarize training data.
"""
import argparse
import glob
import os
impor... | data2vec_vision-main | deltalm/src/examples/wav2vec/wav2vec_manifest.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Helper script to pre-compute embeddings for a wav2letter++ dataset
"""
import argparse
import os
def main():
... | data2vec_vision-main | deltalm/src/examples/wav2vec/libri_labels.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Helper script to pre-compute embeddings for a wav2letter++ dataset
"""
import argparse
import glob
import os
impor... | data2vec_vision-main | deltalm/src/examples/wav2vec/vq-wav2vec_featurize.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import contextlib
import sys
from collections import Counter
from multiprocessing imp... | data2vec_vision-main | deltalm/src/examples/roberta/multiprocessing_bpe_encoder.py |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import json
import os
import re
class InputExample:
def __init__(self, paragrap... | data2vec_vision-main | deltalm/src/examples/roberta/preprocess_RACE.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
from functools import lru_cache
def convert_sentence_to_json(sentence):
if "_" in sentence:
prefix, rest = sentence.... | data2vec_vision-main | deltalm/src/examples/roberta/wsc/wsc_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.criterions import LegacyFairseqCriterion, reg... | data2vec_vision-main | deltalm/src/examples/roberta/wsc/wsc_criterion.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import wsc_criterion # noqa
from . import wsc_task # noqa
| data2vec_vision-main | deltalm/src/examples/roberta/wsc/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import tempfile
import numpy as np
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.... | data2vec_vision-main | deltalm/src/examples/roberta/wsc/wsc_task.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import commonsense_qa_task # noqa
| data2vec_vision-main | deltalm/src/examples/roberta/commonsense_qa/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import numpy as np
import torch
from fairseq.data import (
Dictionary,
IdDataset,
ListDataset,
NestedDi... | data2vec_vision-main | deltalm/src/examples/roberta/commonsense_qa/commonsense_qa_task.py |
#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import fileinput
import sacremoses
def main():
parser = argparse.ArgumentParser(description="... | data2vec_vision-main | deltalm/src/examples/megatron_11b/detok.py |
#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Translate pre-processed data with a trained model.
"""
import numpy as np
import torch
from fairseq import check... | data2vec_vision-main | deltalm/src/examples/criss/save_encoder.py |
#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import glob
from subprocess import check_call
try:
import faiss
has_faiss = True
except Imp... | data2vec_vision-main | deltalm/src/examples/criss/mining/mine.py |
#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import glob
import numpy as np
DIM = 1024
def compute_dist(source_embs, target_embs, k=5, return... | data2vec_vision-main | deltalm/src/examples/criss/sentence_retrieval/encoder_analysis.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq.search import Search
class NoisyChannelBeamSearch(Search):
def __init__(self, tgt_dict):
super().__in... | data2vec_vision-main | deltalm/src/examples/fast_noisy_channel/noisy_channel_beam_search.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import noisy_channel_translation # noqa
from . import noisy_channel_sequence_generator # noqa
from . import noisy_channel_beam_search... | data2vec_vision-main | deltalm/src/examples/fast_noisy_channel/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional
import math
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor... | data2vec_vision-main | deltalm/src/examples/fast_noisy_channel/noisy_channel_sequence_generator.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.tasks.translation import TranslationTask
from fairseq.tasks.language_modeling import LanguageModelingTask
from fairseq import che... | data2vec_vision-main | deltalm/src/examples/fast_noisy_channel/noisy_channel_translation.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import os.path as op
from collections import namedtuple
from multiprocessing import cpu_count
from typing import Li... | data2vec_vision-main | deltalm/src/examples/byte_level_bpe/get_bitext.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the r... | data2vec_vision-main | deltalm/src/examples/byte_level_bpe/gru_transformer.py |
#!/usr/bin/env python3
from setuptools import find_packages, setup
setup(
name="layoutlmft",
version="0.1",
author="LayoutLM Team",
url="https://github.com/microsoft/unilm/tree/master/layoutlmft",
packages=find_packages(),
python_requires=">=3.7",
extras_require={"dev": ["flake8", "isort", "... | data2vec_vision-main | layoutlmft/setup.py |
import os
import re
import numpy as np
from transformers.utils import logging
logger = logging.get_logger(__name__)
PREFIX_CHECKPOINT_DIR = "checkpoint"
_re_checkpoint = re.compile(r"^" + PREFIX_CHECKPOINT_DIR + r"\-(\d+)$")
def get_last_checkpoint(folder):
content = os.listdir(folder)
checkpoints = [
... | data2vec_vision-main | layoutlmft/layoutlmft/evaluation.py |
from collections import OrderedDict
from transformers import CONFIG_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_NAMES_MAPPING, TOKENIZER_MAPPING
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS, BertConverter, XLMRobertaConverter
from transformers.models.auto.modeling_auto import auto... | data2vec_vision-main | layoutlmft/layoutlmft/__init__.py |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple
import torch
from transformers.file_utils import ModelOutput
@dataclass
class ReOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = No... | data2vec_vision-main | layoutlmft/layoutlmft/utils.py |
data2vec_vision-main | layoutlmft/layoutlmft/models/__init__.py | |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier f... | data2vec_vision-main | layoutlmft/layoutlmft/models/model_args.py |
# coding=utf-8
from transformers.models.layoutlm.tokenization_layoutlm import LayoutLMTokenizer
from transformers.utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"microsoft/layoutlmv2-base-uncased":... | data2vec_vision-main | layoutlmft/layoutlmft/models/layoutlmv2/tokenization_layoutlmv2.py |
from .configuration_layoutlmv2 import LayoutLMv2Config
from .modeling_layoutlmv2 import LayoutLMv2ForRelationExtraction, LayoutLMv2ForTokenClassification, LayoutLMv2Model
from .tokenization_layoutlmv2 import LayoutLMv2Tokenizer
from .tokenization_layoutlmv2_fast import LayoutLMv2TokenizerFast
| data2vec_vision-main | layoutlmft/layoutlmft/models/layoutlmv2/__init__.py |
# -*- coding: utf-8 -*-
def add_layoutlmv2_config(cfg):
_C = cfg
# -----------------------------------------------------------------------------
# Config definition
# -----------------------------------------------------------------------------
_C.MODEL.MASK_ON = True
# When using pre-trained m... | data2vec_vision-main | layoutlmft/layoutlmft/models/layoutlmv2/detectron2_config.py |
# coding=utf-8
import math
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
import detectron2
from detectron2.modeling import META_ARCH_REGISTRY
from transformers import PreTrainedModel
from transformers.modeling_outputs import (
... | data2vec_vision-main | layoutlmft/layoutlmft/models/layoutlmv2/modeling_layoutlmv2.py |
# coding=utf-8
from transformers.models.layoutlm.tokenization_layoutlm_fast import LayoutLMTokenizerFast
from transformers.utils import logging
from .tokenization_layoutlmv2 import LayoutLMv2Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.... | data2vec_vision-main | layoutlmft/layoutlmft/models/layoutlmv2/tokenization_layoutlmv2_fast.py |
# coding=utf-8
from transformers.models.layoutlm.configuration_layoutlm import LayoutLMConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main... | data2vec_vision-main | layoutlmft/layoutlmft/models/layoutlmv2/configuration_layoutlmv2.py |
# coding=utf-8
from transformers.utils import logging
from ..layoutlmv2 import LayoutLMv2Config
logger = logging.get_logger(__name__)
LAYOUTXLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"layoutxlm-base": "https://huggingface.co/layoutxlm-base/resolve/main/config.json",
"layoutxlm-large": "https://huggingface.co/lay... | data2vec_vision-main | layoutlmft/layoutlmft/models/layoutxlm/configuration_layoutxlm.py |
# coding=utf-8
from transformers import XLMRobertaTokenizerFast
from transformers.file_utils import is_sentencepiece_available
from transformers.utils import logging
if is_sentencepiece_available():
from .tokenization_layoutxlm import LayoutXLMTokenizer
else:
LayoutXLMTokenizer = None
logger = logging.get_l... | data2vec_vision-main | layoutlmft/layoutlmft/models/layoutxlm/tokenization_layoutxlm_fast.py |
# coding=utf-8
from transformers import XLMRobertaTokenizer
from transformers.utils import logging
logger = logging.get_logger(__name__)
SPIECE_UNDERLINE = "▁"
VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"layoutxlm-base": "https://huggin... | data2vec_vision-main | layoutlmft/layoutlmft/models/layoutxlm/tokenization_layoutxlm.py |
from .configuration_layoutxlm import LayoutXLMConfig
from .modeling_layoutxlm import LayoutXLMForRelationExtraction, LayoutXLMForTokenClassification, LayoutXLMModel
from .tokenization_layoutxlm import LayoutXLMTokenizer
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
| data2vec_vision-main | layoutlmft/layoutlmft/models/layoutxlm/__init__.py |
# coding=utf-8
from transformers.utils import logging
from ..layoutlmv2 import LayoutLMv2ForRelationExtraction, LayoutLMv2ForTokenClassification, LayoutLMv2Model
from .configuration_layoutxlm import LayoutXLMConfig
logger = logging.get_logger(__name__)
LAYOUTXLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
"layoutxlm-base... | data2vec_vision-main | layoutlmft/layoutlmft/models/layoutxlm/modeling_layoutxlm.py |
from transformers.models.layoutlm import *
| data2vec_vision-main | layoutlmft/layoutlmft/models/layoutlm/__init__.py |
import collections
import time
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from packaging import version
from torch import nn
from torch.utils.data import DataLoader, Dataset
from transformers.trainer_utils import EvalPrediction, PredictionOutput, speed_metrics
from transformers.utils impo... | data2vec_vision-main | layoutlmft/layoutlmft/trainers/xfun_trainer.py |
from .funsd_trainer import FunsdTrainer
from .xfun_trainer import XfunReTrainer, XfunSerTrainer
| data2vec_vision-main | layoutlmft/layoutlmft/trainers/__init__.py |
from typing import Any, Dict, Union
import torch
from transformers import Trainer
class FunsdTrainer(Trainer):
def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]:
"""
Prepare :obj:`inputs` before feeding them to the model, converting the... | data2vec_vision-main | layoutlmft/layoutlmft/trainers/funsd_trainer.py |
data2vec_vision-main | layoutlmft/layoutlmft/modules/__init__.py | |
data2vec_vision-main | layoutlmft/layoutlmft/modules/decoders/__init__.py | |
import copy
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
class BiaffineAttention(torch.nn.Module):
"""Implements a biaffine attention operator for binary relation classification.
PyTorch implementation of the biaffine attention operator from "End-to-end neural relation
extract... | data2vec_vision-main | layoutlmft/layoutlmft/modules/decoders/re.py |
# flake8: noqa
from .data_collator import DataCollatorForKeyValueExtraction
from .datasets import *
| data2vec_vision-main | layoutlmft/layoutlmft/data/__init__.py |
import torch
from detectron2.data.detection_utils import read_image
from detectron2.data.transforms import ResizeTransform, TransformList
def normalize_bbox(bbox, size):
return [
int(1000 * bbox[0] / size[0]),
int(1000 * bbox[1] / size[1]),
int(1000 * bbox[2] / size[0]),
int(1000 ... | data2vec_vision-main | layoutlmft/layoutlmft/data/utils.py |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
task_name: Optional[str] = field(default="ner", metadata={"help": "The name of the task (ner, p... | data2vec_vision-main | layoutlmft/layoutlmft/data/data_args.py |
from dataclasses import dataclass
from typing import Optional, Union
import torch
from detectron2.structures import ImageList
from transformers import PreTrainedTokenizerBase
from transformers.file_utils import PaddingStrategy
@dataclass
class DataCollatorForKeyValueExtraction:
"""
Data collator that will d... | data2vec_vision-main | layoutlmft/layoutlmft/data/data_collator.py |
data2vec_vision-main | layoutlmft/layoutlmft/data/datasets/__init__.py | |
# Lint as: python3
import json
import logging
import os
import datasets
from layoutlmft.data.utils import load_image, merge_bbox, normalize_bbox, simplify_bbox
from transformers import AutoTokenizer
_URL = "https://github.com/doc-analysis/XFUN/releases/download/v1.0/"
_LANG = ["zh", "de", "es", "fr", "en", "it", "... | data2vec_vision-main | layoutlmft/layoutlmft/data/datasets/xfun.py |
# coding=utf-8
import json
import os
import datasets
from layoutlmft.data.utils import load_image, normalize_bbox
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{Jaume2019FUNSDAD,
title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
author={Guillaume Jaume and ... | data2vec_vision-main | layoutlmft/layoutlmft/data/datasets/funsd.py |
#!/usr/bin/env python
# coding=utf-8
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
from datasets import ClassLabel, load_dataset, load_metric
import layoutlmft.data.datasets.xfun
import transformers
from layoutlmft.data import DataCollator... | data2vec_vision-main | layoutlmft/examples/run_xfun_ser.py |
#!/usr/bin/env python
# coding=utf-8
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
from datasets import ClassLabel, load_dataset, load_metric
import layoutlmft.data.datasets.funsd
import transformers
from layoutlmft.data import DataCollato... | data2vec_vision-main | layoutlmft/examples/run_funsd.py |
#!/usr/bin/env python
# coding=utf-8
import logging
import os
import sys
import numpy as np
from datasets import ClassLabel, load_dataset
import layoutlmft.data.datasets.xfun
import transformers
from layoutlmft import AutoModelForRelationExtraction
from layoutlmft.data.data_args import XFUNDataTrainingArguments
from... | data2vec_vision-main | layoutlmft/examples/run_xfun_re.py |
"""
Simple check list from AllenNLP repo: https://github.com/allenai/allennlp/blob/master/setup.py
To create the package for pypi.
1. Change the version in __init__.py and setup.py.
2. Commit these changes with the message: "Release: VERSION"
3. Add a tag in git to mark the release: "git tag VERSION -m'Adds tag VER... | data2vec_vision-main | unilm-v1/src/setup.py |
import torch
from torch.nn import DataParallel
from torch.cuda._utils import _get_device_index
from torch.nn.parallel._functions import Scatter
from itertools import chain
def scatter_imbalance(inputs, target_gpus, dim=0):
r"""
Slices tensors into approximately equal chunks and
distributes them across giv... | data2vec_vision-main | unilm-v1/src/nn/data_parallel.py |
data2vec_vision-main | unilm-v1/src/nn/__init__.py | |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | data2vec_vision-main | unilm-v1/src/pytorch_pretrained_bert/optimization.py |
__version__ = "0.4.0"
from .tokenization import BertTokenizer, BasicTokenizer, WordpieceTokenizer
from .modeling import (BertConfig, BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction, BertForSequenceClassification,
BertForMultipleChoice, BertForTokenClassification, Ber... | data2vec_vision-main | unilm-v1/src/pytorch_pretrained_bert/__init__.py |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | data2vec_vision-main | unilm-v1/src/pytorch_pretrained_bert/tokenization.py |
# coding=utf-8
"""PyTorch optimization for BERT model."""
from apex.optimizers import FP16_Optimizer
class FP16_Optimizer_State(FP16_Optimizer):
def __init__(self,
init_optimizer,
static_loss_scale=1.0,
dynamic_loss_scale=False,
dynamic_loss_arg... | data2vec_vision-main | unilm-v1/src/pytorch_pretrained_bert/optimization_fp16.py |
# coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
class LabelSmoothingLoss(_Loss):
"""
With label smoothing,
KL-divergence between q_{smoothed gr... | data2vec_vision-main | unilm-v1/src/pytorch_pretrained_bert/loss.py |
"""
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
import os
import logging
import shutil
import tempfile
import json
from urllib.parse import urlparse
from pathlib import Path
from typing ... | data2vec_vision-main | unilm-v1/src/pytorch_pretrained_bert/file_utils.py |
# coding=utf-8
"""PyTorch BERT model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import copy
import json
import math
import logging
import tarfile
import tempfile
import shutil
import numpy as np
from scipy.stats import truncnorm
import t... | data2vec_vision-main | unilm-v1/src/pytorch_pretrained_bert/modeling.py |
# coding: utf8
def main():
import sys
try:
from .convert_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ModuleNotFoundError:
print("pytorch_pretrained_bert can only be used from the commandline to convert TensorFlow models in PyTorch, "
"In that case, i... | data2vec_vision-main | unilm-v1/src/pytorch_pretrained_bert/__main__.py |
#!/usr/bin/env python
from __future__ import print_function
__author__ = 'xinya'
from bleu.bleu import Bleu
from meteor.meteor import Meteor
from rouge.rouge import Rouge
from cider.cider import Cider
from collections import defaultdict
from argparse import ArgumentParser
import string
import sys
reload(sys)
sys.setd... | data2vec_vision-main | unilm-v1/src/qg/eval_on_unilm_tokenized_ref.py |
#!/usr/bin/env python
from __future__ import print_function
__author__ = 'xinya'
from bleu.bleu import Bleu
from meteor.meteor import Meteor
from rouge.rouge import Rouge
from cider.cider import Cider
from collections import defaultdict
from argparse import ArgumentParser
import string
import sys
reload(sys)
sys.setd... | data2vec_vision-main | unilm-v1/src/qg/eval.py |
data2vec_vision-main | unilm-v1/src/gigaword/__init__.py | |
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import json
import argparse
import math
import string
from multiprocessing import Pool, cpu_count
from tqdm import tqdm, trange
from pathlib i... | data2vec_vision-main | unilm-v1/src/gigaword/eval.py |
from __future__ import print_function, unicode_literals, division
import os
import re
import codecs
import platform
from subprocess import check_output
from tempfile import mkdtemp
from functools import partial
try:
from configparser import ConfigParser
except ImportError:
from ConfigParser import ConfigPars... | data2vec_vision-main | unilm-v1/src/gigaword/bs_pyrouge.py |
data2vec_vision-main | unilm-v1/src/cnndm/__init__.py | |
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import json
import argparse
import math
import string
from multiprocessing import Pool, cpu_count
from tqdm import tqdm, trange
from pathlib i... | data2vec_vision-main | unilm-v1/src/cnndm/eval.py |
from __future__ import print_function, unicode_literals, division
import os
import re
import codecs
import platform
from subprocess import check_output
from tempfile import mkdtemp
from functools import partial
try:
from configparser import ConfigParser
except ImportError:
from ConfigParser import ConfigPars... | data2vec_vision-main | unilm-v1/src/cnndm/bs_pyrouge.py |
from random import randint, shuffle
from random import random as rand
import numpy as np
import torch
import torch.utils.data
def get_random_word(vocab_words):
i = randint(0, len(vocab_words)-1)
return vocab_words[i]
def batch_list_to_batch_tensors(batch):
batch_tensors = []
for x in zip(*batch):
... | data2vec_vision-main | unilm-v1/src/biunilm/loader_utils.py |
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import argparse
import math
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampl... | data2vec_vision-main | unilm-v1/src/biunilm/decode_seq2seq.py |
data2vec_vision-main | unilm-v1/src/biunilm/__init__.py | |
from random import randint, shuffle, choice
from random import random as rand
import math
import torch
from biunilm.loader_utils import get_random_word, batch_list_to_batch_tensors, Pipeline
# Input file format :
# 1. One sentence per line. These should ideally be actual sentences,
# not entire paragraphs or arbit... | data2vec_vision-main | unilm-v1/src/biunilm/seq2seq_loader.py |
import pickle
import math
import argparse
import glob
from pathlib import Path
from tqdm import tqdm
import unicodedata
from pytorch_pretrained_bert.tokenization import BertTokenizer
def read_traces_from_file(file_name):
with open(file_name, "rb") as fin:
meta = pickle.load(fin)
num_samples = met... | data2vec_vision-main | unilm-v1/src/biunilm/gen_seq_from_trace.py |
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import math
import json
import argparse
import random
from pathlib import Path
from tqdm import tqdm, trange
import numpy as np
import torch
f... | data2vec_vision-main | unilm-v1/src/biunilm/run_seq2seq.py |
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import json
import logging
import argparse
import math
from tqdm import tqdm, trange
import numpy as np
import torch
import random
import pickle
from transformers impor... | data2vec_vision-main | s2s-ft/decode_seq2seq.py |
from io import open
from setuptools import find_packages, setup
extras = {
'serving': ['pydantic', 'uvicorn', 'fastapi'],
'serving-tf': ['pydantic', 'uvicorn', 'fastapi'],
'serving-torch': ['pydantic', 'uvicorn', 'fastapi', 'torch']
}
extras['all'] = [package for package in extras.values()]
setup(
na... | data2vec_vision-main | s2s-ft/setup.py |
import pickle
import math
import argparse
import glob
import logging
from pathlib import Path
from tqdm import tqdm
import unicodedata
from transformers import BertTokenizer, RobertaTokenizer
from s2s_ft.tokenization_unilm import UnilmTokenizer
from s2s_ft.tokenization_minilm import MinilmTokenizer
logging.basicConf... | data2vec_vision-main | s2s-ft/gen_seq_from_trace.py |
from __future__ import absolute_import, division, print_function
import argparse
import logging
import os
import json
import random
import numpy as np
import torch
from torch.utils.data import (DataLoader, SequentialSampler)
from torch.utils.data.distributed import DistributedSampler
try:
from torch.utils.tensor... | data2vec_vision-main | s2s-ft/run_seq2seq.py |
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import json
import argparse
import math
import string
from multiprocessing import Pool, cpu_count
from tqdm import tqdm, trange
from pathlib i... | data2vec_vision-main | s2s-ft/evaluations/eval_for_xsum.py |
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