id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
185,511 | from fairseq import options, utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
def transforme... | null |
185,512 | from fairseq import options, utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
def transforme... | null |
185,513 | from fairseq import options, utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
def base_lm_ar... | null |
185,517 | from fairseq import options
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.lightconv import (
Embedding,
LightConvDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
def base_lm_architecture(... | null |
185,518 | import sys
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import (
BaseFairseqModel, register_model, register_model_architecture
)
class TransposeLast(nn.Module):
def __init__(self, deconstruct_idx=None):
super().__init__()
self.deconstruct_idx = deconstruc... | null |
185,519 | import sys
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import (
BaseFairseqModel, register_model, register_model_architecture
)
def base_wav2vec_architecture(args):
conv_feature_layers = '[(512, 10, 5)]'
conv_feature_layers += ' + [(512, 8, 4)]'
conv_feature_la... | null |
185,520 | import numpy as np
import torch
import torch.nn.functional as F
from fairseq.models import register_model, register_model_architecture
from fairseq.models.levenshtein_transformer import (
LevenshteinTransformerDecoder,
LevenshteinTransformerModel,
)
from fairseq.models.transformer import Linear, TransformerMode... | null |
185,521 | import numpy as np
import torch
import torch.nn.functional as F
from fairseq.models import register_model, register_model_architecture
from fairseq.models.levenshtein_transformer import (
LevenshteinTransformerDecoder,
LevenshteinTransformerModel,
)
from fairseq.models.transformer import Linear, TransformerMode... | null |
185,522 | import numpy as np
import torch
import torch.nn.functional as F
from fairseq.models import register_model, register_model_architecture
from fairseq.models.levenshtein_transformer import (
LevenshteinTransformerDecoder,
LevenshteinTransformerModel,
)
from fairseq.models.transformer import Linear, TransformerMode... | null |
185,523 | import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
Transfor... | null |
185,524 | import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
Transfor... | null |
185,525 | import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
Transfor... | null |
185,526 | import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
Transfor... | null |
185,527 | import inspect
import torch.nn as nn
from fairseq.legacy_distributed_data_parallel import LegacyDistributedDataParallel
from fairseq.models import BaseFairseqModel
The provided code snippet includes necessary dependencies for implementing the `DistributedFairseqModel` function. Write a Python function `def Distributed... | Wrap a *model* to support distributed data parallel training. This is similar to the built-in DistributedDataParallel, but allows additional configuration of the DistributedDataParallel class to use, and also provides easier access to the wrapped model by forwarding requests for missing attributes to the wrapped model.... |
185,528 | import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.mod... | null |
185,529 | import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.mod... | null |
185,530 | import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.mod... | Weight-normalized Linear layer (input: N x T x C) |
185,531 | import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.mod... | Weight-normalized Conv1d layer optimized for decoding |
185,532 | import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.mod... | Weight-normalized Conv1d layer |
185,533 | import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.mod... | null |
185,534 | import torch
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nonautoregressive_transformer import NATransformerModel
def _sequential_poisoning(s, V, beta=0.33, bos=2, eos=3, pad=1):
# s: input batch
# V: vocabulary size
rand_words = torch.randint(low=4, high=V, si... | null |
185,535 | import torch
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nonautoregressive_transformer import NATransformerModel
def gumbel_noise(input, TINY=1e-8):
return input.new_zeros(*input.size()).uniform_().add_(
TINY).log_().neg_().add_(TINY).log_().neg_() | null |
185,536 | import torch
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nonautoregressive_transformer import NATransformerModel
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_d... | null |
185,541 | from fairseq import options
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.fconv import FConvDecoder
def base_lm_architecture(args):
def fconv_lm_dauphin_wikitext103(args):
layers = '[(850, 6)] * 3'
layers += ' + [(850, 1)] * 1'... | null |
185,543 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerM... | Getting sliced (dim=0) tensor by mask. Supporting tensor and list/dict of tensors. |
185,544 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerM... | null |
185,545 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerM... | Filling tensor x with y at masked positions (dim=0). |
185,546 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerM... | null |
185,547 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerM... | null |
185,548 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerM... | null |
185,549 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerM... | null |
185,550 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerM... | null |
185,551 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerM... | null |
185,552 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerM... | null |
185,553 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerM... | null |
185,561 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftma... | null |
185,562 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftma... | null |
185,564 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
def Em... | null |
185,565 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
def LS... | null |
185,566 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
def LS... | null |
185,567 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
The pr... | Linear layer (input: N x T x C) |
185,568 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
def bas... | null |
185,569 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
def bas... | null |
185,570 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
FairseqEncoder,
register_model,
register_model_architecture,
)
from fairseq.modules import (
LayerNorm,
SinusoidalPositionalEmbedding,
TransformerSenten... | null |
185,571 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
FairseqEncoder,
register_model,
register_model_architecture,
)
from fairseq.modules import (
LayerNorm,
SinusoidalPositionalEmbedding,
TransformerSenten... | null |
185,572 | import argparse
import os
import sys
from fairseq import bleu
from fairseq.data import dictionary
def get_parser():
parser = argparse.ArgumentParser(description='Command-line script for BLEU scoring.')
# fmt: off
parser.add_argument('-s', '--sys', default='-', help='system output')
parser.add_argument(... | null |
185,573 | import numpy as np
import torch
from fairseq import checkpoint_utils, options, progress_bar, tasks, utils
from fairseq.data import LMContextWindowDataset
from fairseq.meters import StopwatchMeter, TimeMeter
from fairseq.sequence_scorer import SequenceScorer
def main(parsed_args):
def cli_main():
parser = options.g... | null |
185,574 | import torch
from fairseq import bleu, checkpoint_utils, options, progress_bar, tasks, utils
from fairseq.meters import StopwatchMeter, TimeMeter
def main(args):
def cli_main():
parser = options.get_generation_parser()
args = options.parse_args_and_arch(parser)
main(args) | null |
185,583 | import collections
import math
import random
import numpy as np
import torch
from fairseq import checkpoint_utils, distributed_utils, options, progress_bar, tasks, utils
from fairseq.data import iterators
from fairseq.trainer import Trainer
from fairseq.meters import AverageMeter, StopwatchMeter
def main(args, init_dis... | null |
185,588 | from collections import Counter
from itertools import zip_longest
from fairseq import options, tasks, utils
from fairseq.data import indexed_dataset
from fairseq.binarizer import Binarizer
from multiprocessing import Pool
import os
import shutil
def dataset_dest_file(args, output_prefix, lang, extension):
class Binari... | null |
185,589 | from collections import Counter
from itertools import zip_longest
from fairseq import options, tasks, utils
from fairseq.data import indexed_dataset
from fairseq.binarizer import Binarizer
from multiprocessing import Pool
import os
import shutil
class Binarizer:
def binarize(
filename,
dict,
... | null |
185,592 | import numpy as np
import torch
from fairseq import checkpoint_utils, options, progress_bar, tasks, utils
from fairseq.data import LMContextWindowDataset
from fairseq.meters import StopwatchMeter, TimeMeter
from fairseq.sequence_scorer import SequenceScorer
def main(parsed_args):
assert parsed_args.path is not None... | null |
185,593 | import torch
from fairseq import bleu, checkpoint_utils, options, progress_bar, tasks, utils
from fairseq.meters import StopwatchMeter, TimeMeter
def main(args):
assert args.path is not None, '--path required for generation!'
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --... | null |
185,594 | import argparse
import os
import torch
from fairseq.data import (FairseqDataset, PrependTokenDataset,
TokenBlockDataset, TruncateDataset, data_utils, StripTokenDataset, ConcatDataset, PrependTokenDataset, AppendTokenDataset)
from fairseq.data.indexed_dataset import make_builder
from tqdm impor... | null |
185,595 | import argparse
import os
import torch
from fairseq.data import (FairseqDataset, PrependTokenDataset,
TokenBlockDataset, TruncateDataset, data_utils, StripTokenDataset, ConcatDataset, PrependTokenDataset, AppendTokenDataset)
from fairseq.data.indexed_dataset import make_builder
from tqdm impor... | null |
185,596 | import argparse
import os
import torch
from fairseq.data import (FairseqDataset, PrependTokenDataset,
TokenBlockDataset, TruncateDataset, data_utils, StripTokenDataset, ConcatDataset, PrependTokenDataset, AppendTokenDataset)
from fairseq.data.indexed_dataset import make_builder
from tqdm impor... | null |
185,597 | import argparse
import os
import torch
from fairseq.data import (FairseqDataset, PrependTokenDataset,
TokenBlockDataset, TruncateDataset, data_utils, StripTokenDataset, ConcatDataset, PrependTokenDataset, AppendTokenDataset)
from fairseq.data.indexed_dataset import make_builder
from tqdm impor... | null |
185,598 | import argparse
import os
import torch
from fairseq.data import (FairseqDataset, PrependTokenDataset,
TokenBlockDataset, TruncateDataset, data_utils)
from fairseq.data.indexed_dataset import make_builder
from tqdm import tqdm
from transformers import AutoTokenizer
def build_tokenizer(args):
... | null |
185,599 | import argparse
import os
import torch
from fairseq.data import (FairseqDataset, PrependTokenDataset,
TokenBlockDataset, TruncateDataset, data_utils)
from fairseq.data.indexed_dataset import make_builder
from tqdm import tqdm
from transformers import AutoTokenizer
def get_args():
parser = a... | null |
185,600 | import argparse
import os
import torch
from fairseq.data import (FairseqDataset, PrependTokenDataset,
TokenBlockDataset, TruncateDataset, data_utils)
from fairseq.data.indexed_dataset import make_builder
from tqdm import tqdm
from transformers import AutoTokenizer
def make_builder(out_file, i... | null |
185,601 | import argparse
import os
import torch
from fairseq.data import (FairseqDataset, PrependTokenDataset,
TokenBlockDataset, TruncateDataset, data_utils, StripTokenDataset, ConcatDataset)
from fairseq.data.indexed_dataset import make_builder
from tqdm import tqdm
from transformers import AutoToken... | null |
185,602 | import argparse
import os
import torch
from fairseq.data import (FairseqDataset, PrependTokenDataset,
TokenBlockDataset, TruncateDataset, data_utils, StripTokenDataset, ConcatDataset)
from fairseq.data.indexed_dataset import make_builder
from tqdm import tqdm
from transformers import AutoToken... | null |
185,603 | import argparse
import os
import torch
from fairseq.data import (FairseqDataset, PrependTokenDataset,
TokenBlockDataset, TruncateDataset, data_utils, StripTokenDataset, ConcatDataset)
from fairseq.data.indexed_dataset import make_builder
from tqdm import tqdm
from transformers import AutoToken... | null |
185,604 | import argparse
import os
import torch
from fairseq.data import (FairseqDataset, PrependTokenDataset,
TokenBlockDataset, TruncateDataset, data_utils, StripTokenDataset, ConcatDataset)
from fairseq.data.indexed_dataset import make_builder
from tqdm import tqdm
from transformers import AutoToken... | null |
185,605 | import argparse
import importlib._bootstrap
import importlib.machinery
import importlib.util
import logging
import os
import re
import sys
import sysconfig
import warnings
from glob import glob, escape
import _osx_support
def get_platform():
# Cross compiling
if "_PYTHON_HOST_PLATFORM" in os.environ:
r... | null |
185,606 | import argparse
import importlib._bootstrap
import importlib.machinery
import importlib.util
import logging
import os
import re
import sys
import sysconfig
import warnings
from glob import glob, escape
import _osx_support
def run_command(cmd):
status = os.system(cmd)
return os.waitstatus_to_exitcode(status) | null |
185,607 | import argparse
import importlib._bootstrap
import importlib.machinery
import importlib.util
import logging
import os
import re
import sys
import sysconfig
import warnings
from glob import glob, escape
import _osx_support
def set_compiler_flags(compiler_flags, compiler_py_flags_nodist):
flags = sysconfig.get_confi... | null |
185,608 | import argparse
import importlib._bootstrap
import importlib.machinery
import importlib.util
import logging
import os
import re
import sys
import sysconfig
import warnings
from glob import glob, escape
import _osx_support
The provided code snippet includes necessary dependencies for implementing the `add_dir_to_list` ... | Add the directory 'dir' to the list 'dirlist' (after any relative directories) if: 1) 'dir' is not already in 'dirlist' 2) 'dir' actually exists, and is a directory. |
185,609 | import argparse
import importlib._bootstrap
import importlib.machinery
import importlib.util
import logging
import os
import re
import sys
import sysconfig
import warnings
from glob import glob, escape
import _osx_support
The provided code snippet includes necessary dependencies for implementing the `sysroot_paths` fu... | Get the paths of sysroot sub-directories. * make_vars: a sequence of names of variables of the Makefile where sysroot may be set. * subdirs: a sequence of names of subdirectories used as the location for headers or libraries. |
185,610 | import argparse
import importlib._bootstrap
import importlib.machinery
import importlib.util
import logging
import os
import re
import sys
import sysconfig
import warnings
from glob import glob, escape
import _osx_support
MACOS_SDK_SPECIFIED = None
def macosx_sdk_root():
"""Return the directory of the current macOS... | Returns true if an SDK was explicitly configured. True if an SDK was selected at configure time, either by specifying --enable-universalsdk=(something other than no or /) or by adding a -isysroot option to CFLAGS. In some cases, like when making decisions about macOS Tk framework paths, we need to be able to know wheth... |
185,611 | import argparse
import importlib._bootstrap
import importlib.machinery
import importlib.util
import logging
import os
import re
import sys
import sysconfig
import warnings
from glob import glob, escape
import _osx_support
def grep_headers_for(function, headers):
for header in headers:
with open(header, 'r'... | null |
185,612 | import argparse
import importlib._bootstrap
import importlib.machinery
import importlib.util
import logging
import os
import re
import sys
import sysconfig
import warnings
from glob import glob, escape
import _osx_support
MACOS = (HOST_PLATFORM == 'darwin')
def macosx_sdk_root():
"""Return the directory of the curr... | null |
185,613 | import argparse
import importlib._bootstrap
import importlib.machinery
import importlib.util
import logging
import os
import re
import sys
import sysconfig
import warnings
from glob import glob, escape
import _osx_support
def validate_tzpath():
base_tzpath = sysconfig.get_config_var('TZPATH')
if not base_tzpat... | null |
185,614 | import argparse
import importlib._bootstrap
import importlib.machinery
import importlib.util
import logging
import os
import re
import sys
import sysconfig
import warnings
from glob import glob, escape
import _osx_support
log = logging.getLogger('setup')
def find_file(filename, std_dirs, paths):
"""Searches for the... | Find a module in a set of possible folders. If it is not found return the unadorned filename |
185,615 | import argparse
import functools
import os
import re
import shutil
import subprocess
import sys
import tempfile
import zipfile
from pathlib import Path
def get_appx_layout(ns):
VER_DOT = "{}.{}".format(VER_MAJOR, VER_MINOR)
PYTHON_DLL_NAME = "python{}{}.dll".format(VER_MAJOR, VER_MINOR)
PYTHON_STABLE_DLL_NAME = "pyt... | null |
185,617 | import argparse
import functools
import os
import re
import shutil
import subprocess
import sys
import tempfile
import zipfile
from pathlib import Path
class Path(PurePath):
"""PurePath subclass that can make system calls.
Path represents a filesystem path but unlike PurePath, also offers
methods to do sy... | null |
185,620 | import logging
import sys
LOG = None
import logging
import sys
if sys.platform == 'win32' and ' 32 bit (ARM)' in sys.version:
# bpo-37553: test_socket.SendfileUsingSendTest is taking longer than 2
# seconds on Windows ARM32 buildbot
LOOPBACK_TIMEOUT = 10
elif sys.platform == 'vxworks':
LOOPBACK_... | null |
185,623 | OPTIONS = {
"stable": {"help": "stable ABI stub"},
"pip": {"help": "pip"},
"pip-user": {"help": "pip.ini file for default --user"},
"distutils": {"help": "distutils"},
"tcltk": {"help": "Tcl, Tk and tkinter"},
"idle": {"help": "Idle"},
"tests": {"help": "test suite"},
"tools": {"help": "... | null |
185,624 | OPTIONS = {
"stable": {"help": "stable ABI stub"},
"pip": {"help": "pip"},
"pip-user": {"help": "pip.ini file for default --user"},
"distutils": {"help": "distutils"},
"tcltk": {"help": "Tcl, Tk and tkinter"},
"idle": {"help": "Idle"},
"tests": {"help": "test suite"},
"tools": {"help": "... | null |
185,625 | import os
import re
import struct
import sys
import sys
if sys.platform == 'win32' and ' 32 bit (ARM)' in sys.version:
# bpo-37553: test_socket.SendfileUsingSendTest is taking longer than 2
# seconds on Windows ARM32 buildbot
LOOPBACK_TIMEOUT = 10
elif sys.platform == 'vxworks':
LOOPBACK_TIMEOUT ... | null |
185,629 | import platform, os, sys, getopt, textwrap, shutil, stat, time, pwd, grp
def getVersion():
global _cache_getVersion
if _cache_getVersion is None:
_cache_getVersion = grepValue(
os.path.join(SRCDIR, 'configure'), 'PACKAGE_VERSION')
return _cache_getVersion
def getFullVersion():
global... | Parse arguments and update global settings. |
185,630 | import platform, os, sys, getopt, textwrap, shutil, stat, time, pwd, grp
WORKDIR = "/tmp/_py"
ARCHLIST = universal_opts_map[UNIVERSALARCHS]
def library_recipes():
result = []
# Since Apple removed the header files for the deprecated system
# OpenSSL as of the Xcode 7 release (for OS X 10.10+), we do not
... | Build our dependencies into $WORKDIR/libraries/usr/local |
185,632 | import platform, os, sys, getopt, textwrap, shutil, stat, time, pwd, grp
STAT_0o775 = ( stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR
| stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP
| stat.S_IROTH | stat.S_IXOTH )
RUNNING_ON_PYTHON2 = sys.version_info.major == 2
if RUNNING_ON_PYTHON2:... | null |
185,635 | import os
import setuptools
from typing import List
from distutils.command.build_ext import build_ext
from concurrent.futures import ThreadPoolExecutor
List = _alias(list, 1, inst=False, name='List')
def find_header_files(directories: List[str]) -> List[str]:
header_files = []
for directory in directories:
... | null |
185,636 | import lldb
import os
import sys
import re
The provided code snippet includes necessary dependencies for implementing the `_get_function_name` function. Write a Python function `def _get_function_name(instance=None)` to solve the following problem:
Return the name of the calling function
Here is the function:
def _g... | Return the name of the calling function |
185,637 | import lldb
import os
import sys
import re
_MODULE_NAME = os.path.basename(__file__).split(".")[0]
class flat_map_slot_type:
CLASS_PATTERN = "^phmap::priv::raw_hash_set<phmap::priv::FlatHashMapPolicy.*>::slot_type$"
HAS_SUMMARY = True
IS_SYNTHETIC_PROVIDER = False
def summary(valobj, _):
try:
... | null |
185,638 | import argparse
import math
import operator
import sys
from enum import Enum
from functools import reduce
from typing import Dict, List, NamedTuple, Optional, Set, TextIO, Tuple
class Lifetime(Enum):
class TypeFlag(Enum):
BUILTIN_PYTYPES: Set[str] = {
"Long",
"Object",
*BASIC_FINAL_TYPES,
*BASIC_BASE_TY... | null |
185,639 | import argparse
import math
import operator
import sys
from enum import Enum
from functools import reduce
from typing import Dict, List, NamedTuple, Optional, Set, TextIO, Tuple
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Generate type_generated.h")
parser.add_argument... | null |
185,640 | try:
from cinderx.static import (
set_type_code,
TYPED_BOOL,
TYPED_CHAR,
TYPED_DOUBLE,
TYPED_INT16,
TYPED_INT32,
TYPED_INT64,
TYPED_INT8,
TYPED_SINGLE,
TYPED_UINT16,
TYPED_UINT32,
TYPED_UINT64,
TYPED_UINT8,
)... | null |
185,641 | from __future__ import annotations
import inspect
from typing import Iterable, Mapping, Sequence, Tuple, Type
from .type_code import set_type_code, TYPED_INT64
class Enum(metaclass=EnumMeta):
def __init__(self, value: object) -> None:
self.value = value
def __dir__(self) -> Sequence[str]:
return... | null |
185,642 | from __future__ import annotations
import inspect
from typing import Iterable, Mapping, Sequence, Tuple, Type
from .type_code import set_type_code, TYPED_INT64
class Enum(metaclass=EnumMeta):
def __init__(self, value: object) -> None:
self.value = value
def __dir__(self) -> Sequence[str]:
return... | null |
185,643 | from __future__ import annotations
import inspect
from typing import Iterable, Mapping, Sequence, Tuple, Type
from .type_code import set_type_code, TYPED_INT64
class Enum(metaclass=EnumMeta):
def __init__(self, value: object) -> None:
self.value = value
def __dir__(self) -> Sequence[str]:
return... | Class decorator for enumerations ensuring unique member values |
185,644 | from __future__ import annotations
import ctypes
from typing import Tuple
from cinderx.static import (
resolve_primitive_descr,
TYPED_BOOL,
TYPED_CHAR,
TYPED_DOUBLE,
TYPED_INT16,
TYPED_INT32,
TYPED_INT64,
TYPED_INT8,
TYPED_UINT16,
TYPED_UINT32,
TYPED_UINT64,
TYPED_UINT8,
... | null |
185,645 | shadowop = set()
cinderxop = set()
def init(opname, opmap, hasname, hasjrel, hasjabs, hasconst):
def def_op(name, op):
opname[op] = name
opmap[name] = op
cinderxop.add(name)
def name_op(name, op):
def_op(name, op)
hasname.append(op)
def jrel_op(name, op):
d... | null |
185,646 | from __future__ import print_function
import ast
import os
import sys
from .consts import (
CO_FUTURE_ANNOTATIONS,
SC_CELL,
SC_FREE,
SC_GLOBAL_EXPLICIT,
SC_GLOBAL_IMPLICIT,
SC_LOCAL,
SC_UNKNOWN,
)
from .misc import mangle
from .visitor import ASTVisitor
def list_eq(l1, l2):
return sorte... | null |
185,647 | import ast
from typing import Any, Callable, Dict, List, Optional, Type
def _format_name(node: ast.Name, level: int) -> str:
return node.id | null |
185,648 | import ast
from typing import Any, Callable, Dict, List, Optional, Type
PR_CMP = 5
def get_op(node: ast.cmpop) -> str:
if isinstance(node, ast.Is):
return " is "
elif isinstance(node, ast.IsNot):
return " is not "
elif isinstance(node, ast.In):
return " in "
elif isinst... | null |
185,649 | import ast
from typing import Any, Callable, Dict, List, Optional, Type
def _format_nameconstant(node: ast.NameConstant, level: int) -> str:
if node.value is None:
return "None"
elif node.value is True:
return "True"
elif node.value is False:
return "False"
return "<unknown cons... | null |
185,650 | import ast
from typing import Any, Callable, Dict, List, Optional, Type
def _format_num(node: ast.Num, level: int) -> str:
return repr(node.n) | null |
185,651 | import ast
from typing import Any, Callable, Dict, List, Optional, Type
def _format_str(node: ast.Str, level: int) -> str:
return repr(node.s) | null |
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