repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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transition-amr-parser | transition-amr-parser-master/src/transition_amr_parser/parse.py | # Standalone AMR parser from an existing trained APT model
import os
import re
import time
import math
import copy
import signal
import argparse
from datetime import timedelta
import urllib.request
import zipfile
from ipdb import set_trace
import progressbar
from tqdm import tqdm
import torch
from fairseq import chec... | 52,549 | 34.458839 | 174 | py |
transition-amr-parser | transition-amr-parser-master/src/transition_amr_parser/action_pointer/roberta_utils.py | from collections import Counter
import torch
from spacy.tokens import Doc
import copy
from fairseq.models.roberta.alignment_utils import spacy_nlp
from fairseq.data.data_utils import collate_tokens
def get_tokens(roberta, word):
return roberta.task.source_dictionary.encode_line(roberta.bpe.encode(word), append_eo... | 4,040 | 38.617647 | 149 | py |
transition-amr-parser | transition-amr-parser-master/src/transition_amr_parser/action_pointer/amr_parser.py | # Standalone AMR parser
import os
import json
import torch
from transition_amr_parser.model import AMRModel
import transition_amr_parser.utils as utils
from fairseq.models.roberta import RobertaModel
from transition_amr_parser.roberta_utils import extract_features_aligned_to_words
class AMRParser():
def __init_... | 3,749 | 42.103448 | 174 | py |
transition-amr-parser | transition-amr-parser-master/src/transition_amr_parser/action_pointer/parse.py | # Standalone AMR parser from an existing trained APT model
import os
import time
import math
import copy
import signal
import argparse
from datetime import timedelta
from ipdb import set_trace
from tqdm import tqdm
import torch
from fairseq import checkpoint_utils, utils
from fairseq.models.roberta import RobertaMode... | 20,120 | 35.385172 | 127 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/binarize.py | import numpy as np
import torch
from fairseq.data.indexed_dataset import __best_fitting_dtype, MMapIndexedDatasetBuilder, IndexedDatasetBuilder
from fairseq.tokenizer import tokenize_line
# TODO move this file into data folder
def make_builder(out_file, impl, vocab_size=None, dtype=None):
if impl == 'mmap':
... | 1,100 | 31.382353 | 111 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/generate.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 os
from collections import defaultdict
import torc... | 15,450 | 40.872629 | 121 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/options_train.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
from typing import Callable, List, Optional
import torch
from fairseq import utils
from fairseq.data.indexed_dataset import g... | 20,449 | 43.360087 | 123 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/utils.py | import time
import math
from fairseq.tokenizer import tokenize_line
from fairseq.checkpoint_utils import load_checkpoint_to_cpu, torch_persistent_save
from fairseq.file_io import PathManager
from fairseq.utils import move_to_cpu
from fairseq_ext.utils_import import import_user_module
# from fairseq_ext.amr_reform.o1... | 7,661 | 38.90625 | 115 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/average_checkpoints.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 argparse
import collections
import os
import re
import torch
from fairseq.file_io import PathManager
def aver... | 6,018 | 37.583333 | 175 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/sequence_generator_bartsv.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import math
from copy import deepc... | 65,756 | 50.940758 | 124 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/sequence_generator.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import math
from copy import deepc... | 65,291 | 50.942721 | 124 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/options.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
from email.policy import default
import torch
import sys
# from fairseq import utils
from fairseq.data.indexed_dataset impor... | 31,215 | 52.088435 | 123 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/sequence_generator_graph.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import math
from copy import deepc... | 57,973 | 51.228829 | 124 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/generate_sliding.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 os
from collections import defaultdict
import torc... | 16,901 | 43.246073 | 125 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/sequence_generator_graphmp.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import math
from copy import deepc... | 62,664 | 51.615449 | 124 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/train.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.
"""
Train a new model on one or across multiple GPUs.
"""
import argparse
import logging
import math
import os
impor... | 23,242 | 39.848858 | 125 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/utils_import.py | import os
import sys
import importlib
# ========== adapted from
# https://github.com/pytorch/fairseq/blob/83e615d66905b8ca7483122a37da1a85f13f4b8e/fairseq/utils.py#L431
# to avoid error in our setup
# ==========
def import_user_module(args):
module_path = getattr(args, "user_dir", None)
if module_path is not ... | 1,748 | 40.642857 | 104 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/modules/multihead_attention.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
from typing import Dict, Optional, Tuple
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.incr... | 23,865 | 40.65096 | 114 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/modules/factored_embeddings.py | import re
from collections import defaultdict
import torch
class FactoredEmbeddings(torch.nn.Module):
def __init__(self, vocabulary, embed_dim, action_factoring='base'):
# register
super(FactoredEmbeddings, self).__init__()
self.full_to_factor_map, factor_voc_by_pos = \
sel... | 6,304 | 41.033333 | 95 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/modules/transformer_layer.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 torch
import torch.nn as nn
from fairseq import utils
# from fairseq.modules import LayerNorm,... | 17,425 | 39.525581 | 88 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/modules/multihead_attention_old.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, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn import Parameter
import torch.nn.functional ... | 23,312 | 45.164356 | 148 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/modules/transformer_layer_old.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.nn as nn
import torch.nn.functional as F
from fairseq import utils
# from fairseq.modules import LayerNorm, MultiheadAttention
fr... | 12,391 | 41.14966 | 93 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/criterions/label_smoothed_cross_entropy_pointer.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
from fairseq import utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.criterio... | 12,919 | 45.642599 | 134 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/criterions/label_smoothed_cross_entropy_pointer_alignment.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
from fairseq import utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.criterio... | 12,636 | 50.369919 | 134 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/models/transformer_tgt_pointer_bart.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
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.mod... | 84,503 | 47.931094 | 159 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/models/transformer_tgt_pointer.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
from packaging import version
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, ut... | 79,650 | 52.709373 | 147 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/models/transformer_tgt_pointer_bartsv_sattn.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
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.mod... | 73,280 | 46.461788 | 159 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/models/transformer_tgt_pointer_graph.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
from packaging import version
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, ut... | 64,589 | 50.343402 | 147 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/models/transformer_tgt_pointer_bart_sattn.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
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.mod... | 79,562 | 47.161622 | 159 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/models/transformer_tgt_pointer_graphmp.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
from packaging import version
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, ut... | 84,683 | 53.38921 | 147 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/models/attention_masks.py | from packaging import version
import torch
def make_bsz_tgt_src_align_mask(tgt_src_cursors, src_max_len, src_pad_mask=None):
"""Get the batched target-source alignment mask.
NOTE
The source batch is **left padded**... should be very careful...
"""
bsz, tgt_max_len = tgt_src_cursors.size()
... | 8,953 | 46.375661 | 119 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/models/transformer_tgt_pointer_bartsv.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
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.mod... | 75,031 | 46.821542 | 159 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/models/graph_attention_masks.py | """Decoder self-attention masks, e.g. graph structure."""
from packaging import version
import torch
def modify_mask_pre_post_softmax(mask):
"""Modify the attention mask to a pre-softmax mask and a post-softmax mask.
The issue is that when one row out of bsz * num_heads (tgt_max_len, src_max_len) masks is fu... | 5,741 | 54.747573 | 120 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/models/graphmp_attention_masks.py | """Decoder self-attention masks, e.g. graph structure for message passing."""
from packaging import version
import torch
from .graph_attention_masks import modify_mask_pre_post_softmax
def get_graph_self_attn_mask(tgt_actedge_masks,
tgt_actedge_1stnode_masks,
... | 8,485 | 55.573333 | 120 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/tests/test_factored_embeddings.py | import torch
from fairseq.data import Dictionary
from fairseq_ext.modules.factored_embeddings import FactoredEmbeddings
if __name__ == '__main__':
vocab_path = '/n/tata_ddos_ceph/jzhou/transition-amr-parser-o8/EXP/data/graphmp-swaparc-ptrlast_o8.3_act-states/processed/dict.actions_nopos.txt'
embed_dim = 256
... | 546 | 29.388889 | 149 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/tests/test_composite_embeddings.py | import torch
from fairseq.data import Dictionary
from fairseq_ext.extract_bart.composite_embeddings import CompositeEmbeddingBART
if __name__ == '__main__':
vocab_path = '/n/tata_ddos_ceph/jzhou/transition-amr-parser-bart/EXP/data/graphmp-swaparc-ptrlast_o8.3_act-states/processed/dict.actions_nopos.txt'
vocab... | 1,212 | 30.102564 | 151 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/tests/test_composite_embeddings_mapping.py | import os
from tqdm import tqdm
import torch
from fairseq.data import Dictionary
from fairseq_ext.extract_bart.composite_embeddings import CompositeEmbeddingBART, transform_action_symbol
if __name__ == '__main__':
vocab_path = '/n/tata_ddos_ceph/jzhou/transition-amr-parser-bart-o10/EXP/data/o10_act-states/proces... | 1,056 | 34.233333 | 130 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/roberta/binarize_embeddings.py | import numpy as np
import torch
import shutil
import time
from ..data import indexed_dataset
from ..utils import time_since
def dataset_dest_prefix(args, output_prefix, lang):
base = "{}/{}".format(args.embdir, output_prefix)
lang_part = (
".{}-{}.{}".format(args.source_lang, args.target_lang, lang) ... | 4,761 | 32.300699 | 96 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/roberta/pretrained_embeddings_bert.py | import copy
import torch
from transformers import BertTokenizer, BertModel
from ..data.data_utils import collate_tokens
from ..utils_font import yellow_font
def get_average_embeddings(final_layer, word2piece):
# Average worpiece representations to get word representations
num_words = len(word2piece)
ba... | 9,316 | 35.537255 | 106 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/roberta/pretrained_embeddings.py | import copy
import torch
from ..data.data_utils import collate_tokens
from ..utils_font import yellow_font
def get_average_embeddings(final_layer, word2piece):
# Average worpiece representations to get word representations
num_words = len(word2piece)
batch_dim, num_wordpieces, hidden_size = final_layer... | 9,693 | 36.719844 | 118 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/extract_bart/binarize_encodings.py | import numpy as np
import torch
import shutil
import time
from ..data import indexed_dataset
from ..utils import time_since
def dataset_dest_prefix(args, output_prefix, lang):
base = "{}/{}".format(args.embdir, output_prefix)
lang_part = (
".{}-{}.{}".format(args.source_lang, args.target_lang, lang) ... | 6,506 | 35.971591 | 115 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/extract_bart/sentence_encoding.py | import copy
import torch
from ..data.data_utils import collate_tokens
from ..utils_font import yellow_font
from fairseq_ext.roberta.pretrained_embeddings import PretrainedEmbeddings
def get_average_embeddings(final_layer, word2piece):
# Average worpiece representations to get word representations
num_words... | 8,609 | 35.176471 | 118 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/extract_bart/composite_embeddings.py | """Given a base vocabulary and corresponding embeddings, for another vocabulary where elements are composed of
sub-elements of the base vocabulary, construct the new embeddings by pooling over the base vocabulary embeddings.
"""
import torch
import torch.nn as nn
from torch_scatter import scatter_mean
class Composite... | 9,588 | 43.393519 | 119 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/extract_bart/mapavg_embeddings.py | """From a list of symbols, get the average embeddings from some base embeddings and a base vocabulary.
"""
import torch
from .composite_embeddings import CompositeEmbedding
class MapAvgEmbeddingBART(CompositeEmbedding):
def __init__(self, bart, bart_embeddings):
super().__init__(bart.task.target_diction... | 5,253 | 38.503759 | 119 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/amr_spec/action_info_binarize_bartsv.py | import os
import itertools
from multiprocessing import Pool
import time
from collections import Counter
import torch
import numpy as np
from tqdm import tqdm
from ..amr_reform.o10_action_reformer_subtok import AMRStateMachineSubtoken
from .action_info_bartsv import get_actions_states
from ..tokenizer import tokenize_... | 15,835 | 40.783641 | 117 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/amr_spec/action_info_binarize_graphmp_amr1.py | import os
import itertools
from multiprocessing import Pool
import time
from collections import Counter
import torch
import numpy as np
from transition_amr_parser.action_pointer.o8_state_machine import AMRStateMachine
from .action_info_graphmp_amr1 import get_actions_states
from ..tokenizer import tokenize_line_tab
f... | 21,411 | 43.888889 | 117 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/amr_spec/action_info_binarize_graphmp.py | import os
import itertools
from multiprocessing import Pool
import time
from collections import Counter
import torch
import numpy as np
from transition_amr_parser.action_pointer.o8_state_machine import AMRStateMachine
from .action_info_graphmp import get_actions_states
from ..tokenizer import tokenize_line_tab
from .... | 21,406 | 43.878407 | 117 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/amr_spec/action_info_binarize.py | import os
import itertools
from multiprocessing import Pool
import time
from collections import Counter
import torch
import numpy as np
from tqdm import tqdm
from transition_amr_parser.amr_machine import AMRStateMachine
from .action_info import get_actions_states
from ..tokenizer import tokenize_line_tab
from ..binar... | 15,698 | 40.531746 | 117 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/amr_spec/old_action_info_binarize.py | import os
import itertools
from multiprocessing import Pool
import time
import torch
import numpy as np
from tqdm import tqdm
from transition_amr_parser.action_pointer.o8_state_machine import AMRStateMachine
from .action_info import get_actions_states
from ..tokenizer import tokenize_line_tab
from ..binarize import m... | 28,857 | 49.451049 | 120 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/data/language_pair_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.
import numpy as np
import torch
from fairseq.data import FairseqDataset
from fairseq_ext.data import data_utils
from fairseq_ext.data.data_u... | 18,120 | 40.466819 | 106 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/data/amr_bpe.py | """BART GPT2 BPE encoder for tokenization with added AMR special action symbols.
"""
import json
from pathlib import Path
from typing import List, Union, Tuple
import regex as re
from fairseq import file_utils
from fairseq.data.encoders.gpt2_bpe_utils import Encoder
from fairseq.data.dictionary import Dictionary
impor... | 10,917 | 41.15444 | 120 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/data/amr_action_pointer_graphmp_dataset.py | import numpy as np
import torch
from fairseq.data import FairseqDataset
from fairseq_ext.data.data_utils import (
collate_embeddings,
collate_wp_idx,
# collate_target_masks,
# collate_masks
)
from fairseq_ext.data import data_utils
def collate(
samples, pad_idx, eos_idx, left_pad_source=True, lef... | 30,770 | 46.855365 | 120 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/data/data_utils.py | import itertools
import torch
def collate_embeddings(values, pad_idx, eos_idx=None, left_pad=False, move_eos_to_beginning=False):
"""Convert a list of 1d tensors into a padded 2d tensor."""
# longest sentence size
size = max(v.size(0) for v in values)
# embedding size
emb_dim = list(set(v.size(1... | 9,147 | 34.320463 | 129 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/data/indexed_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 functools import lru_cache
import os
import shutil
import struct
import numpy as np
import torch
from fairseq.data import FairseqDatase... | 16,218 | 29.776091 | 108 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/data/amr_action_pointer_goldamr_dataset.py | import numpy as np
import torch
from fairseq.data import FairseqDataset
from fairseq_ext.data.data_utils import (
collate_embeddings,
collate_wp_idx,
# collate_target_masks,
# collate_masks
)
from fairseq_ext.data import data_utils
def collate(
samples, pad_idx, eos_idx, left_pad_source=True, lef... | 30,695 | 45.159398 | 124 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/data/amr_action_pointer_dataset.py | import numpy as np
import torch
from fairseq.data import FairseqDataset
from fairseq_ext.data.data_utils import (
collate_embeddings,
collate_wp_idx,
# collate_target_masks,
# collate_masks
)
from fairseq_ext.data import data_utils
def collate(
samples, pad_idx, eos_idx, left_pad_source=True, lef... | 29,892 | 45.345736 | 124 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/data/amr_action_pointer_bartsv_dataset.py | import numpy as np
import torch
from fairseq.data import FairseqDataset
from fairseq_ext.data.data_utils import (
collate_embeddings,
collate_wp_idx,
# collate_target_masks,
# collate_masks
)
from fairseq_ext.data import data_utils
def collate(
samples, pad_idx, eos_idx, left_pad_source=True, lef... | 29,683 | 45.453834 | 124 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/tasks/amr_action_pointer_graphmp_amr1.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import itertools
import os
import... | 24,616 | 48.731313 | 120 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/tasks/amr_action_pointer_bart_dyo.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import itertools
import os
import ... | 55,372 | 44.687294 | 128 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/tasks/amr_action_pointer_graphmp.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import itertools
import os
import... | 24,602 | 48.70303 | 120 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/tasks/amr_action_pointer_bartsv.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import itertools
import os
import... | 28,086 | 47.932056 | 128 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/tasks/amr_action_pointer.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import itertools
import os
import... | 22,251 | 46.853763 | 118 | py |
transition-amr-parser | transition-amr-parser-master/src/fairseq_ext/tasks/amr_action_pointer_bart.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import itertools
import os
import... | 27,439 | 47.480565 | 128 | py |
transition-amr-parser | transition-amr-parser-master/scripts/Blinker.py |
import sys
import os
import glob
import argparse
import torch.cuda
import importlib.util
import logging
import string
import json
import numpy
import random
import hashlib
MENTIONSTART = '[unused1]'
MENTIONEND = '[unused2]'
CONTEXTLEFTKEY = 'context_left'
CONTEXTRIGHTKEY = 'context_right'
MENTIONKEY = 'mention'
CACHE... | 18,796 | 31.690435 | 150 | py |
transition-amr-parser | transition-amr-parser-master/tests/correctly_installed.py | import torch
import subprocess
# from ipdb import set_trace
def main():
# Pytorch and CUDA
passed = True
print()
import torch
print(f'pytorch {torch.__version__}')
if torch.cuda.is_available():
print(f'cuda {torch.version.cuda}')
# happens when CUDA missconfigured
asse... | 1,918 | 24.932432 | 65 | py |
transition-amr-parser | transition-amr-parser-master/run/status.py | import sys
import shutil
from time import sleep
import numpy as np
from glob import glob
import signal
import re
import os
from datetime import datetime
import argparse
from collections import defaultdict, Counter
from statistics import mean
from transition_amr_parser.io import read_config_variables
from transition_amr... | 41,853 | 32.323248 | 82 | py |
nfm | nfm-master/experiment_synthesis.py | import torch
import numpy as np
import torch.nn as nn
import matplotlib.pyplot as plt
from tqdm import tqdm
from torch.utils.data import DataLoader
from nfm.base import TransNLL, MonotoneNLL
from nfm.eps_config import ParetoEps
from nfm.synthesis import *
torch.manual_seed(88888888)
sample_size = 5000
d = 5
censor_... | 2,775 | 31.658824 | 109 | py |
nfm | nfm-master/experiment_metabric_pf.py | import torch
import numpy as np
import pandas as pd
import torch.nn as nn
from tqdm import tqdm
from torch.utils.data import DataLoader
from nfm.datasets import SurvivalDataset
from nfm.base import MonotoneNLL
from nfm.eps_config import IGGEps
from nfm.metric import c_index
from nfm.utils import default_device
from pyc... | 3,577 | 38.755556 | 118 | py |
nfm | nfm-master/experiment_metabric_fn.py | import torch
import numpy as np
import pandas as pd
import torch.nn as nn
from tqdm import tqdm
from torch.utils.data import DataLoader
from nfm.datasets import SurvivalDataset
from nfm.base import FullyNeuralNLL
from nfm.eps_config import ParetoEps
from pycox.evaluation.eval_surv import EvalSurv
class Net(nn.Module)... | 3,461 | 37.898876 | 98 | py |
nfm | nfm-master/nfm/base.py | import torch
import torch.nn as nn
from .umnn import UMNN
from .umnn_v2 import ParallelNeuralIntegral, _flatten
from .utils import default_device
class EpsDistribution(object):
"""Abstraction of (univariate) distribution of epsilon, shall be differentiable and vectorized
Currently we don't allow the configura... | 7,405 | 39.917127 | 103 | py |
nfm | nfm-master/nfm/umnn_v2.py | # Actually the original impl of UMNN
import torch
import numpy as np
import math
def _flatten(sequence):
flat = [p.contiguous().view(-1) for p in sequence]
return torch.cat(flat) if len(flat) > 0 else torch.tensor([])
def compute_cc_weights(nb_steps):
lam = np.arange(0, nb_steps + 1, 1).reshape(-1, 1)
... | 3,963 | 38.247525 | 138 | py |
nfm | nfm-master/nfm/synthesis.py | import torch
import torch.utils.data as data
import torch.nn.functional as F
__all__ = ('IdentityH', 'ExponentialH', 'M', 'Z', 'Noise', 'SyntheticData', 'PFSyntheticData')
class GenericH(object):
"""Configuration for oracle H, used in proportional frailty models"""
def __call__(self, x: torch.Tensor) -> to... | 4,256 | 27.192053 | 94 | py |
nfm | nfm-master/nfm/umnn.py | # Implemntations adapted from UMNN's official libray at
# https://github.com/AWehenkel/UMNN
import torch
import math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from .utils import default_device
def _flatten(sequence):
flat = [p.contiguous().view(-1) for p in... | 5,085 | 35.070922 | 119 | py |
nfm | nfm-master/nfm/utils.py | import torch
_ALLOW_MPS = False
def get_default_device():
if not torch.cuda.is_available():
if _ALLOW_MPS:
try:
device = torch.device('mps')
except BaseException as inst:
device = torch.device('cpu')
else:
device = torch.device(... | 436 | 18 | 44 | py |
nfm | nfm-master/nfm/datasets.py | import os
import torch
import h5py
import numpy as np
import pandas as pd
import torch.nn.functional as F
from torch.utils.data import Dataset
from .utils import default_device
from pycox.datasets import kkbox_v1 as kkbox
class SurvivalDataset(Dataset):
"""Interface of survival datasets
"""
@classmethod
... | 11,320 | 43.396078 | 136 | py |
nfm | nfm-master/nfm/eps_config.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .base import EpsDistribution
class GaussianEps(EpsDistribution):
"""As-is"""
def __init__(self):
self._gaussian_rv = torch.distributions.Normal(0., 1.)
def hazard(self, x):
return torch.exp(self._gaussian_rv.log_prob... | 5,660 | 30.983051 | 110 | py |
nfm | nfm-master/nfm/metric.py | import torch
import numba
import numpy as np
def c_index(y_pred, y_true, delta):
"""implementation of the concordance index, essentially a censoring-compatible version of Kendall's tau
Args:
y_pred(torch.Tensor): predicted survival upto monotone transforms, with shape [batch_size, 1]
y_true(t... | 1,333 | 32.35 | 107 | py |
MTFuzz | MTFuzz-master/mtfuzz_wrapper.py | import subprocess
import sys
import math
import shutil
import subprocess
import glob
import ipdb
import pickle
import os
import numpy as np
import struct
import time
FNULL = open(os.devnull, 'w')
mut_cnt = 0
'''
def train(x, y):
model = Sequential()
model.add(Dense(8, input_dim=x.shape[1]))
#model.add(Dense... | 74,598 | 50.130226 | 240 | py |
MTFuzz | MTFuzz-master/nn.py | import pickle
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import glob
import math
import time
from functools import partial
import keras
import random
import socket
import subprocess
import numpy as np
import tensorflow as tf
import keras.backend as K
from collec... | 19,020 | 34.420857 | 163 | py |
MTFuzz | MTFuzz-master/programs/hb-fuzzer/mtfuzz_wrapper.py | import subprocess
import sys
import math
import shutil
import subprocess
import glob
import ipdb
import pickle
import os
import numpy as np
import struct
import time
FNULL = open(os.devnull, 'w')
mut_cnt = 0
'''
def train(x, y):
model = Sequential()
model.add(Dense(8, input_dim=x.shape[1]))
#model.add(Dense... | 74,268 | 50.114246 | 240 | py |
MTFuzz | MTFuzz-master/programs/hb-fuzzer/nn.py | import pickle
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import glob
import math
import time
from functools import partial
import keras
import random
import socket
import subprocess
import numpy as np
import tensorflow as tf
import keras.backend as K
from collec... | 19,645 | 34.398198 | 163 | py |
MTFuzz | MTFuzz-master/programs/djpeg/mtfuzz_wrapper.py | import subprocess
import sys
import math
import shutil
import subprocess
import glob
import ipdb
import pickle
import os
import numpy as np
import struct
import time
FNULL = open(os.devnull, 'w')
mut_cnt = 0
'''
def train(x, y):
model = Sequential()
model.add(Dense(8, input_dim=x.shape[1]))
#model.add(Dense... | 74,273 | 50.117688 | 240 | py |
MTFuzz | MTFuzz-master/programs/djpeg/nn.py | import pickle
import os
os.environ["CUDA_VISIBLE_DEVICES"]="2"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import shutil
import glob
import math
import time
from functools import partial
import keras
import random
import socket
import subprocess
import numpy as np
import tensorflow as tf
import keras.backend as... | 19,702 | 34.310036 | 174 | py |
MTFuzz | MTFuzz-master/programs/readelf/mtfuzz_wrapper.py | import subprocess
import sys
import math
import shutil
import subprocess
import glob
import ipdb
import pickle
import os
import numpy as np
import struct
import time
FNULL = open(os.devnull, 'w')
mut_cnt = 0
'''
def train(x, y):
model = Sequential()
model.add(Dense(8, input_dim=x.shape[1]))
#model.add(Dense... | 74,267 | 50.113558 | 240 | py |
MTFuzz | MTFuzz-master/programs/readelf/nn.py | import pickle
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import glob
import math
import time
from functools import partial
import keras
import random
import socket
import subprocess
import numpy as np
import tensorflow as tf
import keras.backend as K
from collec... | 19,328 | 34.20765 | 163 | py |
MTFuzz | MTFuzz-master/programs/nm/mtfuzz_wrapper.py | import subprocess
import sys
import math
import shutil
import subprocess
import glob
import ipdb
import pickle
import os
import numpy as np
import struct
import time
FNULL = open(os.devnull, 'w')
mut_cnt = 0
'''
def train(x, y):
model = Sequential()
model.add(Dense(8, input_dim=x.shape[1]))
#model.add(Dense... | 74,268 | 50.114246 | 240 | py |
MTFuzz | MTFuzz-master/programs/nm/nn.py | import pickle
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import glob
import math
import time
from functools import partial
import keras
import random
import socket
import subprocess
import numpy as np
import tensorflow as tf
import keras.backend as K
from collec... | 19,328 | 34.20765 | 163 | py |
MTFuzz | MTFuzz-master/programs/mutool/mtfuzz_wrapper.py | import subprocess
import sys
import math
import shutil
import subprocess
import glob
import ipdb
import pickle
import os
import numpy as np
import struct
import time
FNULL = open(os.devnull, 'w')
mut_cnt = 0
'''
def train(x, y):
model = Sequential()
model.add(Dense(8, input_dim=x.shape[1]))
#model.add(Dense... | 74,272 | 50.116999 | 240 | py |
MTFuzz | MTFuzz-master/programs/mutool/nn.py | import pickle
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import glob
import math
import time
from functools import partial
import keras
import random
import socket
import subprocess
import numpy as np
import tensorflow as tf
import keras.backend as K
from collec... | 15,743 | 33.151844 | 163 | py |
MTFuzz | MTFuzz-master/programs/xmllint/mtfuzz_wrapper.py | import subprocess
import sys
import math
import shutil
import subprocess
import glob
import ipdb
import pickle
import os
import numpy as np
import struct
import time
FNULL = open(os.devnull, 'w')
mut_cnt = 0
'''
def train(x, y):
model = Sequential()
model.add(Dense(8, input_dim=x.shape[1]))
#model.add(Dense... | 74,268 | 50.114246 | 240 | py |
MTFuzz | MTFuzz-master/programs/xmllint/nn.py | import pickle
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import glob
import math
import time
from functools import partial
import keras
import random
import socket
import subprocess
import numpy as np
import tensorflow as tf
import keras.backend as K
from collec... | 19,679 | 34.332136 | 163 | py |
MTFuzz | MTFuzz-master/programs/miniunz/mtfuzz_wrapper.py | import subprocess
import sys
import math
import shutil
import subprocess
import glob
import ipdb
import pickle
import os
import numpy as np
import struct
import time
FNULL = open(os.devnull, 'w')
mut_cnt = 0
'''
def train(x, y):
model = Sequential()
model.add(Dense(8, input_dim=x.shape[1]))
#model.add(Dense... | 74,272 | 50.116999 | 240 | py |
MTFuzz | MTFuzz-master/programs/miniunz/nn.py | import pickle
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import glob
import math
import time
from functools import partial
import keras
import random
import socket
import subprocess
import numpy as np
import tensorflow as tf
import keras.backend as K
from collec... | 15,787 | 33.17316 | 175 | py |
MTFuzz | MTFuzz-master/programs/objdump/mtfuzz_wrapper.py | import subprocess
import sys
import math
import shutil
import subprocess
import glob
import ipdb
import pickle
import os
import numpy as np
import struct
import time
FNULL = open(os.devnull, 'w')
mut_cnt = 0
'''
def train(x, y):
model = Sequential()
model.add(Dense(8, input_dim=x.shape[1]))
#model.add(Dense... | 74,278 | 50.121129 | 240 | py |
MTFuzz | MTFuzz-master/programs/objdump/nn.py | import pickle
import os
os.environ["CUDA_VISIBLE_DEVICES"]="2"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import shutil
import glob
import math
import time
from functools import partial
import keras
import random
import socket
import subprocess
import numpy as np
import tensorflow as tf
import keras.backend as... | 19,698 | 34.302867 | 174 | py |
MTFuzz | MTFuzz-master/programs/size/mtfuzz_wrapper.py | import subprocess
import sys
import math
import shutil
import subprocess
import glob
import ipdb
import pickle
import os
import numpy as np
import struct
import time
FNULL = open(os.devnull, 'w')
mut_cnt = 0
'''
def train(x, y):
model = Sequential()
model.add(Dense(8, input_dim=x.shape[1]))
#model.add(Dense... | 74,268 | 50.114246 | 240 | py |
MTFuzz | MTFuzz-master/programs/size/nn.py | import pickle
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import glob
import math
import time
from functools import partial
import keras
import random
import socket
import subprocess
import numpy as np
import tensorflow as tf
import keras.backend as K
from collec... | 19,020 | 34.420857 | 163 | py |
MTFuzz | MTFuzz-master/programs/strip/mtfuzz_wrapper.py | import subprocess
import sys
import math
import shutil
import subprocess
import glob
import ipdb
import pickle
import os
import numpy as np
import struct
import time
FNULL = open(os.devnull, 'w')
mut_cnt = 0
'''
def train(x, y):
model = Sequential()
model.add(Dense(8, input_dim=x.shape[1]))
#model.add(Dense... | 74,268 | 50.114246 | 240 | py |
MTFuzz | MTFuzz-master/programs/strip/nn.py | import pickle
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import glob
import math
import time
from functools import partial
import keras
import random
import socket
import subprocess
import numpy as np
import tensorflow as tf
import keras.backend as K
from collec... | 19,328 | 34.20765 | 163 | py |
DPSN | DPSN-master/time_series_proto/lib/models/feature_transformer.py | import torch
import torch.nn as nn
from .initialization import init_kaiming
class linear_transform(nn.Module):
def __init__(self, fea_dim, hidden_dim=2048, out_dim=2048):
super(linear_transform, self).__init__()
self.fc1 = nn.Linear(fea_dim, hidden_dim)
self.dp = nn.Dropout(p=0.5)
self.fc2 = nn.Linea... | 1,001 | 26.833333 | 62 | py |
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