id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
184,183 | import csv
from pathlib import Path
import zipfile
from functools import reduce
from multiprocessing import cpu_count
from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sentencepiece as sp
from fairseq.data.audio.audio_utils import _get_kaldi_fbank, _get_torchaudio_fbank
f... | null |
184,184 | import csv
from pathlib import Path
import zipfile
from functools import reduce
from multiprocessing import cpu_count
from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sentencepiece as sp
from fairseq.data.audio.audio_utils import _get_kaldi_fbank, _get_torchaudio_fbank
f... | null |
184,185 | import csv
from pathlib import Path
import zipfile
from functools import reduce
from multiprocessing import cpu_count
from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sentencepiece as sp
from fairseq.data.audio.audio_utils import _get_kaldi_fbank, _get_torchaudio_fbank
f... | null |
184,186 | import csv
from pathlib import Path
import zipfile
from functools import reduce
from multiprocessing import cpu_count
from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sentencepiece as sp
from fairseq.data.audio.audio_utils import _get_kaldi_fbank, _get_torchaudio_fbank
f... | null |
184,187 | import csv
from pathlib import Path
import zipfile
from functools import reduce
from multiprocessing import cpu_count
from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sentencepiece as sp
from fairseq.data.audio.audio_utils import _get_kaldi_fbank, _get_torchaudio_fbank
f... | null |
184,188 | import csv
from pathlib import Path
import zipfile
from functools import reduce
from multiprocessing import cpu_count
from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sentencepiece as sp
from fairseq.data.audio.audio_utils import _get_kaldi_fbank, _get_torchaudio_fbank
f... | null |
184,189 | import csv
from pathlib import Path
import zipfile
from functools import reduce
from multiprocessing import cpu_count
from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
import sentencepiece as sp
from fairseq.data.audio.audio_utils import _get_kaldi_fbank, _get_torchaudio_fbank
f... | null |
184,190 | import argparse
import logging
import os
from pathlib import Path
import shutil
from itertools import groupby
from tempfile import NamedTemporaryFile
from typing import Tuple
import pandas as pd
import torchaudio
from examples.speech_to_text.data_utils import (
create_zip,
extract_fbank_features,
filter_man... | null |
184,191 | import argparse
import logging
import os
from pathlib import Path
import shutil
from itertools import groupby
from tempfile import NamedTemporaryFile
from typing import Tuple
import pandas as pd
import torchaudio
from examples.speech_to_text.data_utils import (
create_zip,
extract_fbank_features,
filter_man... | null |
184,192 | import argparse
import logging
from pathlib import Path
import shutil
from tempfile import NamedTemporaryFile
import pandas as pd
from examples.speech_to_text.data_utils import (
create_zip,
extract_fbank_features,
gen_config_yaml,
gen_vocab,
get_zip_manifest,
save_df_to_tsv,
)
from torchaudio.d... | null |
184,193 | import argparse
import logging
from pathlib import Path
import shutil
from tempfile import NamedTemporaryFile
from typing import Optional, Tuple
import pandas as pd
import torchaudio
from examples.speech_to_text.data_utils import (
create_zip,
extract_fbank_features,
filter_manifest_df,
gen_config_yaml,... | null |
184,195 | import logging
from fairseq.models import register_model, register_model_architecture
from fairseq.models.roberta import RobertaEncoder, RobertaModel
from ..modules.linformer_sentence_encoder import LinformerSentenceEncoder
def base_architecture(args):
args.encoder_layers = getattr(args, "encoder_layers", 12)
a... | null |
184,196 | import logging
from fairseq.models import register_model, register_model_architecture
from fairseq.models.roberta import RobertaEncoder, RobertaModel
from ..modules.linformer_sentence_encoder import LinformerSentenceEncoder
def linformer_roberta_large_architecture(args):
args.encoder_layers = getattr(args, "encode... | null |
184,198 | import numpy as np
import torch
from fairseq import checkpoint_utils, options, progress_bar, tasks, utils
from fairseq.sequence_generator import EnsembleModel
class EnsembleModel(nn.Module):
"""A wrapper around an ensemble of models."""
def __init__(self, models):
super().__init__()
self.model... | null |
184,199 | import numpy as np
import torch
from fairseq import checkpoint_utils, options, progress_bar, tasks, utils
from fairseq.sequence_generator import EnsembleModel
def main(args):
assert args.path is not None, "--path required for generation!"
assert (
not args.sampling or args.nbest == args.beam
), "--s... | null |
184,206 | import logging
from typing import Any, Dict, Optional
import torch
import torch.nn as nn
from fairseq import metrics, utils
from fairseq.models import register_model, register_model_architecture
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq.models.transformer import (
DEFAULT_MAX_SOURCE_POSITIO... | null |
184,207 | import logging
from typing import Any, Dict, Optional
import torch
import torch.nn as nn
from fairseq import metrics, utils
from fairseq.models import register_model, register_model_architecture
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq.models.transformer import (
DEFAULT_MAX_SOURCE_POSITIO... | null |
184,208 | import logging
from typing import Any, Dict, Optional
import torch
import torch.nn as nn
from fairseq import metrics, utils
from fairseq.models import register_model, register_model_architecture
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq.models.transformer import (
DEFAULT_MAX_SOURCE_POSITIO... | null |
184,209 | import logging
from typing import Any, Dict, Optional
import torch
import torch.nn as nn
from fairseq import metrics, utils
from fairseq.models import register_model, register_model_architecture
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq.models.transformer import (
DEFAULT_MAX_SOURCE_POSITIO... | null |
184,210 | import logging
from typing import Any, Dict, Optional
import torch
import torch.nn as nn
from fairseq import metrics, utils
from fairseq.models import register_model, register_model_architecture
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq.models.transformer import (
DEFAULT_MAX_SOURCE_POSITIO... | null |
184,226 | from typing import Any, Dict, List, Tuple, Union
import torch
import torch.utils.checkpoint as checkpoint
from fairseq import utils
def pack_kwargs(*args, **kwargs) -> Tuple[List[str], List[Any]]:
"""
Usage::
kwarg_keys, flat_args = pack_kwargs(1, 2, a=3, b=4)
args, kwargs = unpack_kwargs(kwarg_... | A friendlier wrapper for performing activation checkpointing. Compared to the PyTorch version, this version: - wraps an nn.Module, so that all subsequent calls will use checkpointing - handles keyword arguments in the forward - handles non-Tensor outputs from the forward Usage:: checkpointed_module = checkpoint_wrapper... |
184,227 | from typing import Any, Dict, List, Tuple, Union
import torch
import torch.utils.checkpoint as checkpoint
from fairseq import utils
def unpack_kwargs(
kwarg_keys: List[str], flat_args: List[Any]
) -> Tuple[List[Any], Dict[str, Any]]:
if len(kwarg_keys) == 0:
return flat_args, {}
args = flat_args[: ... | null |
184,228 | from typing import Any, Dict, List, Tuple, Union
import torch
import torch.utils.checkpoint as checkpoint
from fairseq import utils
The provided code snippet includes necessary dependencies for implementing the `split_non_tensors` function. Write a Python function `def split_non_tensors( mixed: Union[torch.Tensor,... | Usage:: x = torch.Tensor([1]) y = torch.Tensor([2]) tensors, packed_non_tensors = split_non_tensors((x, y, None, 3)) recon = unpack_non_tensors(tensors, packed_non_tensors) assert recon == (x, y, None, 3) |
184,232 | import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.incremental_decoding_utils import with_incremental_state
from fairseq.modules.fairseq_dropout import FairseqDropout
from .unfold import unfold1d
class DynamicConv1dTBC(nn.Module):
"""Dynamic lightweight convolu... | null |
184,243 | import torch
def quantize(w, scale, zero_point):
return (
torch.clamp(torch.round(w / scale + zero_point), 0, 255) - zero_point
) * scale
def emulate_int8_histogram(w, scale=None, zero_point=None):
if scale is None:
obs = torch.quantization.observer.HistogramObserver()
_ = obs(w.flo... | null |
184,248 | import ast
import collections
import contextlib
import logging
import os
import re
import traceback
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import torch
from fairseq.dataclass.configs import CheckpointConfig, FairseqConfig
from fairseq.dataclass.utils import (
convert_names... | Loads an ensemble of models. Args: filenames (List[str]): checkpoint files to load arg_overrides (Dict[str,Any], optional): override model args that were used during model training task (fairseq.tasks.FairseqTask, optional): task to use for loading |
184,249 | import ast
import collections
import contextlib
import logging
import os
import re
import traceback
from collections import OrderedDict
from typing import Any, Dict, Optional, Union
import torch
from fairseq.dataclass.configs import CheckpointConfig, FairseqConfig
from fairseq.dataclass.utils import (
convert_names... | null |
184,254 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,255 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,256 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,257 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,258 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | Helper for getting incremental state for an nn.Module. |
184,259 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | Helper for setting incremental state for an nn.Module. |
184,260 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,261 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,262 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | Parse embedding text file into a dictionary of word and embedding tensors. The first line can have vocabulary size and dimension. The following lines should contain word and embedding separated by spaces. Example: 2 5 the -0.0230 -0.0264 0.0287 0.0171 0.1403 at -0.0395 -0.1286 0.0275 0.0254 -0.0932 |
184,263 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,264 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,265 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. |
184,266 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,267 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,268 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,269 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,270 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | FP16-compatible function that fills a tensor with -inf. |
184,271 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | Resolve max position constraints from multiple sources. |
184,272 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,273 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,274 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,275 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,276 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | Returns the activation function corresponding to `activation` |
184,277 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,278 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,279 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,280 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,281 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,282 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | Parses a single line from the alingment file. Args: line (str): String containing the alignment of the format: <src_idx_1>-<tgt_idx_1> <src_idx_2>-<tgt_idx_2> .. <src_idx_m>-<tgt_idx_m>. All indices are 0 indexed. Returns: torch.IntTensor: packed alignments of shape (2 * m). |
184,283 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,284 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,285 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | Return a Tensor of `size` filled with a range function on the device of x. If size is empty, using the size of the variable x. |
184,286 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,287 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,288 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,289 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,290 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,291 | import argparse
import contextlib
import copy
import importlib
import logging
import os
import sys
import tempfile
import warnings
from itertools import accumulate
from typing import Callable, Dict, List, Optional
import torch
import torch.nn.functional as F
from fairseq.data import iterators
from fairseq.file_io impor... | null |
184,313 | import contextlib
import logging
import sys
import time
from argparse import Namespace
from itertools import chain
from typing import Any, Dict, List
import torch
from fairseq import checkpoint_utils, distributed_utils, models, optim, utils
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.util... | null |
184,314 | import contextlib
import logging
import sys
import time
from argparse import Namespace
from itertools import chain
from typing import Any, Dict, List
import torch
from fairseq import checkpoint_utils, distributed_utils, models, optim, utils
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.util... | null |
184,315 | import contextlib
import logging
import sys
import time
from argparse import Namespace
from itertools import chain
from typing import Any, Dict, List
import torch
from fairseq import checkpoint_utils, distributed_utils, models, optim, utils
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.util... | null |
184,321 | import io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse import Namespace
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional
import torch
import torch.distributed as... | null |
184,322 | import io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse import Namespace
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional
import torch
import torch.distributed as... | Return my rank for the data parallel group. |
184,323 | import io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse import Namespace
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional
import torch
import torch.distributed as... | Return world size for the data parallel group. |
184,324 | import io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse import Namespace
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional
import torch
import torch.distributed as... | Return world size for the model parallel group. |
184,325 | import io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse import Namespace
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional
import torch
import torch.distributed as... | Perform an all-to-all operation on a 1D Tensor. |
184,326 | import io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse import Namespace
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional
import torch
import torch.distributed as... | Perform an all-gather operation. |
184,327 | import io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse import Namespace
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional
import torch
import torch.distributed as... | Gathers arbitrary data from all nodes into a list. Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python data. Note that *data* must be picklable. Args: data (Any): data from the local worker to be gathered on other workers group: group of the collective max_size (int, optional): maximum size of the... |
184,328 | import io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse import Namespace
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional
import torch
import torch.distributed as... | AllReduce a dictionary of values across workers. We separately reduce items that are already on the device and items on CPU for better performance. Args: data (Mapping[str, Any]): dictionary of data to all-reduce, but cannot be a nested dictionary device (torch.device): device for the reduction group: group of the coll... |
184,329 | import io
import logging
import os
import pickle
import random
import socket
import struct
import subprocess
import warnings
from argparse import Namespace
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional
import torch
import torch.distributed as... | Broadcast an arbitrary Python object to other workers. |
184,334 | import contextlib
import itertools
import logging
import os
import warnings
from typing import Optional, Tuple
import numpy as np
import torch
logger = logging.getLogger(__name__)
class ConcatDataset(FairseqDataset):
def cumsum(sequence, sample_ratios):
r, s = [], 0
for e, ratio in zip(sequence, sa... | A helper function for loading indexed datasets. Args: path (str): path to indexed dataset (e.g., 'data-bin/train') dictionary (~fairseq.data.Dictionary): data dictionary dataset_impl (str, optional): which dataset implementation to use. If not provided, it will be inferred automatically. For legacy indexed data we use ... |
184,338 | import contextlib
import itertools
import logging
import os
import warnings
from typing import Optional, Tuple
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `batch_by_size` function. Write a Python function `def batch_by_size( indices, num_tokens... | Yield mini-batches of indices bucketed by size. Batches may contain sequences of different lengths. Args: indices (List[int]): ordered list of dataset indices num_tokens_fn (callable): function that returns the number of tokens at a given index num_tokens_vec (List[int], optional): precomputed vector of the number of t... |
184,339 | import contextlib
import itertools
import logging
import os
import warnings
from typing import Optional, Tuple
import numpy as np
import torch
def post_process(sentence: str, symbol: str):
if symbol == "sentencepiece":
sentence = sentence.replace(" ", "").replace("\u2581", " ").strip()
elif symbol == "... | null |
184,343 | import os.path as op
from typing import BinaryIO, Optional, Tuple, Union
import numpy as np
def get_waveform(
path_or_fp: Union[str, BinaryIO], normalization=True
) -> Tuple[np.ndarray, int]:
"""Get the waveform and sample rate of a 16-bit mono-channel WAV or FLAC.
Args:
path_or_fp (str or BinaryIO)... | Get mel-filter bank features via PyKaldi or TorchAudio. Prefer PyKaldi (faster CPP implementation) to TorchAudio (Python implementation). Note that Kaldi/TorchAudio requires 16-bit signed integers as inputs and hence the waveform should not be normalized. |
184,348 | import datetime
import hashlib
import logging
import time
from bisect import bisect_right
from collections import OrderedDict, defaultdict
from enum import Enum
from typing import List
import numpy as np
import torch
from fairseq import distributed_utils
from fairseq.data import FairseqDataset, data_utils
def get_time... | null |
184,349 | import datetime
import hashlib
import logging
import time
from bisect import bisect_right
from collections import OrderedDict, defaultdict
from enum import Enum
from typing import List
import numpy as np
import torch
from fairseq import distributed_utils
from fairseq.data import FairseqDataset, data_utils
def default_... | null |
184,356 | import logging
import numpy as np
import torch
from fairseq.data import FairseqDataset, data_utils
logger = logging.getLogger(__name__)
def collate(
samples,
pad_idx,
eos_idx,
left_pad_source=True,
left_pad_target=False,
input_feeding=True,
pad_to_length=None,
pad_to_multiple=1,
):
... | null |
184,365 | import re
def byte_decode(x: str) -> str:
def smart_byte_decode(x: str) -> str:
output = byte_decode(x)
if output == "":
# DP the best recovery (max valid chars) if it's broken
n_bytes = len(x)
f = [0 for _ in range(n_bytes + 1)]
pt = [0 for _ in range(n_bytes + 1)]
for ... | null |
184,367 | import shutil
import struct
from functools import lru_cache
import numpy as np
import torch
from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
from fairseq.data.fasta_dataset import FastaDataset
from fairseq.file_io import PathManager
from . import FairseqDataset
DATASET_IMPL_CHOICES = ChoiceEnum(["raw", "la... | null |
184,368 | import shutil
import struct
from functools import lru_cache
import numpy as np
import torch
from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
from fairseq.data.fasta_dataset import FastaDataset
from fairseq.file_io import PathManager
from . import FairseqDataset
def index_file_path(prefix_path):
return p... | null |
184,369 | import shutil
import struct
from functools import lru_cache
import numpy as np
import torch
from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
from fairseq.data.fasta_dataset import FastaDataset
from fairseq.file_io import PathManager
from . import FairseqDataset
def __best_fitting_dtype(vocab_size=None):
... | null |
184,370 | import shutil
import struct
from functools import lru_cache
import numpy as np
import torch
from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
from fairseq.data.fasta_dataset import FastaDataset
from fairseq.file_io import PathManager
from . import FairseqDataset
class IndexedDataset(FairseqDataset):
"""L... | null |
184,371 | import shutil
import struct
from functools import lru_cache
import numpy as np
import torch
from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
from fairseq.data.fasta_dataset import FastaDataset
from fairseq.file_io import PathManager
from . import FairseqDataset
class IndexedDataset(FairseqDataset):
"""L... | null |
184,372 | import shutil
import struct
from functools import lru_cache
import numpy as np
import torch
from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
from fairseq.data.fasta_dataset import FastaDataset
from fairseq.file_io import PathManager
from . import FairseqDataset
def read_longs(f, n):
a = np.empty(n, dty... | null |
184,373 | import shutil
import struct
from functools import lru_cache
import numpy as np
import torch
from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
from fairseq.data.fasta_dataset import FastaDataset
from fairseq.file_io import PathManager
from . import FairseqDataset
def write_longs(f, a):
f.write(np.array(a... | null |
184,374 | import shutil
import struct
from functools import lru_cache
import numpy as np
import torch
from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
from fairseq.data.fasta_dataset import FastaDataset
from fairseq.file_io import PathManager
from . import FairseqDataset
dtypes = {
1: np.uint8,
2: np.int8,
... | null |
184,375 | import shutil
import struct
from functools import lru_cache
import numpy as np
import torch
from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
from fairseq.data.fasta_dataset import FastaDataset
from fairseq.file_io import PathManager
from . import FairseqDataset
def _warmup_mmap_file(path):
with open(pa... | null |
184,376 | import shutil
import struct
from functools import lru_cache
import numpy as np
import torch
from fairseq.dataclass.constants import DATASET_IMPL_CHOICES
from fairseq.data.fasta_dataset import FastaDataset
from fairseq.file_io import PathManager
from . import FairseqDataset
def index_file_path(prefix_path):
return p... | null |
184,379 | import torch
from torch import nn
import math
from typing import Dict, List, Optional
import warnings
The provided code snippet includes necessary dependencies for implementing the `is_cuda_extension_usable` function. Write a Python function `def is_cuda_extension_usable() -> bool` to solve the following problem:
Chec... | Check whether ngram_repeat_block_cuda is built properly |
184,380 | import itertools
import json
import logging
import os
from argparse import Namespace
import numpy as np
from fairseq import metrics, options, utils
from fairseq.data import (
AppendTokenDataset,
ConcatDataset,
LanguagePairDataset,
PrependTokenDataset,
StripTokenDataset,
TruncateDataset,
data... | null |
184,387 | import argparse
from typing import Callable, List, Optional
import torch
from fairseq import utils
from fairseq.data.indexed_dataset import get_available_dataset_impl
from fairseq.dataclass.configs import (
CheckpointConfig,
CommonConfig,
CommonEvalConfig,
DatasetConfig,
DistributedTrainingConfig,
... | null |
184,388 | import argparse
from typing import Callable, List, Optional
import torch
from fairseq import utils
from fairseq.data.indexed_dataset import get_available_dataset_impl
from fairseq.dataclass.configs import (
CheckpointConfig,
CommonConfig,
CommonEvalConfig,
DatasetConfig,
DistributedTrainingConfig,
... | null |
184,390 | import argparse
from typing import Callable, List, Optional
import torch
from fairseq import utils
from fairseq.data.indexed_dataset import get_available_dataset_impl
from fairseq.dataclass.configs import (
CheckpointConfig,
CommonConfig,
CommonEvalConfig,
DatasetConfig,
DistributedTrainingConfig,
... | null |
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