id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
184,720 | import logging
import numpy as np
import torch
import os
import itertools
from fairseq.data import FairseqDataset, data_utils
from fairseq.data import (
AppendTokenDataset,
ConcatDataset,
PrependTokenDataset,
data_utils,
indexed_dataset,
)
logger = logging.getLogger(__name__)
def collate(
sampl... | null |
184,721 | import itertools
import logging
import io
import os
import sys
import time
from pathlib import Path
from typing import Any, List, Optional, Union, Tuple
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset import FairseqDataset... | Parse data path which is either a path to 1. a .npy/.wav/.flac/.ogg file 2. a stored ZIP file with slicing info: "[zip_path]:[offset]:[length]" Args: path (str): the data path to parse Returns: file_path (str): the file path slice_ptr (list of int): empty in case 1; byte offset and length for the slice in case 2 |
184,722 | import itertools
import logging
import io
import os
import sys
import time
from pathlib import Path
from typing import Any, List, Optional, Union, Tuple
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset import FairseqDataset... | null |
184,723 | import itertools
import logging
import io
import os
import sys
import time
from pathlib import Path
from typing import Any, List, Optional, Union, Tuple
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset import FairseqDataset... | null |
184,724 | import itertools
import logging
import io
import os
import sys
import time
from pathlib import Path
from typing import Any, List, Optional, Union, Tuple
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset import FairseqDataset... | null |
184,725 | import itertools
import logging
import io
import os
import sys
import time
from pathlib import Path
from typing import Any, List, Optional, Union, Tuple
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.data import data_utils, Dictionary
from fairseq.data.fairseq_dataset import FairseqDataset... | null |
184,726 | import itertools
import logging
import os
from fairseq.data import (
AppendTokenDataset,
LanguagePairDataset,
PrependTokenDataset,
StripTokenDataset,
TruncateDataset,
RandomCropDataset,
data_utils,
indexed_dataset,
)
from speechlm.data.concat_dataset import ConcatDataset
logger = logging... | null |
184,727 | import ast
import logging
import math
import os
import sys
from argparse import Namespace
from itertools import chain
import numpy as np
import torch
from omegaconf import DictConfig
from fairseq import checkpoint_utils, options, scoring, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
f... | null |
184,728 | import ast
import logging
import math
import os
import sys
from argparse import Namespace
from itertools import chain
import numpy as np
import torch
from omegaconf import DictConfig
from fairseq import checkpoint_utils, options, scoring, tasks, utils
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
f... | null |
184,730 | import logging
import os
import sys
from typing import Dict, List, Optional, Tuple
from pathlib import Path
import numpy as np
from argparse import Namespace
from collections import OrderedDict
import torch
from dataclasses import dataclass, field
from fairseq.data import (
Dictionary,
encoders,
data_utils,... | Return language token index. |
184,731 | import logging
import os
import sys
from typing import Dict, List, Optional, Tuple
from pathlib import Path
import numpy as np
from argparse import Namespace
from collections import OrderedDict
import torch
from dataclasses import dataclass, field
from fairseq.data import (
Dictionary,
encoders,
data_utils,... | null |
184,732 | import logging
import os
import sys
from typing import Dict, List, Optional, Tuple
from pathlib import Path
import numpy as np
from argparse import Namespace
from collections import OrderedDict
import torch
from dataclasses import dataclass, field
from fairseq.data import (
Dictionary,
encoders,
data_utils,... | tokens: torch.Tensor with repeative tokens |
184,733 | from typing import List, Dict, Any
from dataclasses import dataclass, field
import torch
import torch.nn.functional as F
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
from fairseq.data.data_utils import lengths_to_ma... | null |
184,734 | import argparse
from tqdm import tqdm
from pydub import AudioSegment
import torchaudio
import os
def mp3_convert_wav(mp3_file, wav_file):
try:
sound = AudioSegment.from_mp3(mp3_file)
sound=sound.set_frame_rate(16000)
sound=sound.set_channels(1)
sound=sound.set_sample_width(2)
... | null |
184,735 | import argparse
import logging
from pathlib import Path
from tempfile import NamedTemporaryFile
from typing import Optional, Tuple
import pandas as pd
import torchaudio
from examples.speech_to_text.data_utils import (
filter_manifest_df,
gen_config_yaml,
gen_vocab,
load_df_from_tsv,
save_df_to_tsv,
... | null |
184,736 | import argparse
import logging
from pathlib import Path
from tempfile import NamedTemporaryFile
from typing import Optional, Tuple
import pandas as pd
import torchaudio
from examples.speech_to_text.data_utils import (
filter_manifest_df,
gen_config_yaml,
gen_vocab,
load_df_from_tsv,
save_df_to_tsv,
... | null |
184,737 | import os
import argparse
from tqdm import tqdm
import numpy as np
def writefile(filename, lines):
with open(filename, 'w', encoding='utf-8') as f:
f.writelines(lines) | null |
184,738 | import argparse
import logging
from pathlib import Path
from collections import defaultdict
import pandas as pd
import torchaudio
from tqdm import tqdm
import numpy as np
import torch
from fairseq.data.audio.audio_utils import convert_waveform
from examples.speech_to_text.data_utils import save_df_to_tsv
from examples.... | null |
184,739 | import argparse
import numpy as np
import sys
from g2p_en import G2p
from tqdm import tqdm
import logging
def get_parser():
parser = argparse.ArgumentParser(
description="converts words to phones adding optional silences around in between words"
)
parser.add_argument(
"--sil-prob",
... | null |
184,740 | import argparse
import numpy as np
import sys
from g2p_en import G2p
from tqdm import tqdm
import logging
The provided code snippet includes necessary dependencies for implementing the `normalize_phn` function. Write a Python function `def normalize_phn(phons)` to solve the following problem:
convert g2p style phone t... | convert g2p style phone to 39-phone set |
184,741 | import argparse
import logging
from pathlib import Path
from collections import defaultdict
import pandas as pd
from tqdm import tqdm
import numpy as np
from examples.speech_to_text.data_utils import save_df_to_tsv
The provided code snippet includes necessary dependencies for implementing the `get_duration` function. ... | fa_phone: force-aligned phone, 1-D numpy |
184,742 | import argparse
import logging
from pathlib import Path
from collections import defaultdict
import pandas as pd
from tqdm import tqdm
import numpy as np
from examples.speech_to_text.data_utils import save_df_to_tsv
def save_df_to_tsv(dataframe, path: Union[str, Path]):
_path = path if isinstance(path, str) else pa... | null |
184,743 | import ast
import hashlib
import logging
import os
import shutil
import sys
from dataclasses import dataclass, field, is_dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
import editdistance
import torch
import torch.distributed as dist
import examples
from examples.speech_re... | null |
184,744 | import logging
import torch
from fairseq import utils
from fairseq.models import (
FairseqEncoderModel,
register_model,
register_model_architecture,
)
from fairseq.models.text_to_speech import fastspeech2
def base_architecture(args):
args.dropout = getattr(args, "dropout", 0.2)
args.output_frame_di... | null |
184,745 | import logging
import torch
from fairseq import utils
from fairseq.models import (
FairseqEncoderModel,
register_model,
register_model_architecture,
)
from fairseq.models.text_to_speech import fastspeech2
def base_architecture(args):
args.dropout = getattr(args, "dropout", 0.2)
args.output_frame_di... | null |
184,746 | import logging
import torch
from fairseq import utils
from fairseq.models import (
FairseqEncoderModel,
register_model,
register_model_architecture,
)
from fairseq.models.text_to_speech import fastspeech2
def base_architecture(args):
args.dropout = getattr(args, "dropout", 0.2)
args.output_frame_di... | null |
184,747 | import contextlib
import torch
import torch.nn as nn
from argparse import Namespace
from dataclasses import dataclass, field
from typing import Any
from fairseq import checkpoint_utils, tasks, utils
from fairseq.models import FairseqEncoderDecoderModel, register_model
from fairseq.models.fairseq_decoder import FairseqD... | null |
184,748 | import contextlib
import torch
import torch.nn as nn
from argparse import Namespace
from dataclasses import dataclass, field
from typing import Any
from fairseq import checkpoint_utils, tasks, utils
from fairseq.models import FairseqEncoderDecoderModel, register_model
from fairseq.models.fairseq_decoder import FairseqD... | null |
184,749 | import logging
import os
import torch
from transformers.data.processors.utils import (
DataProcessor, InputExample, InputFeatures)
from torch.utils.data import (
DataLoader, RandomSampler, SequentialSampler, TensorDataset)
logger = logging.getLogger(__name__)
class TatoebaProcesser(DataProcessor):
def convert_... | null |
184,750 | import logging
import os
import torch
from transformers.data.processors.utils import (DataProcessor,
InputExample, InputFeatures)
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from src.data import convert_examples_to_features
from src.io impor... | null |
184,751 | import os
import logging
import torch
from torch.utils.data import TensorDataset
from src.pequod.data.utils_squad import (read_squad_examples,
convert_examples_to_features)
logger = logging.getLogger(__name__)
def read_squad_examples(input_file, is_training, version_2_with_negative):
"""Read a SQuAD json file in... | null |
184,755 | import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
def merge_eval(main_eval, new_eval, prefix):
def make_precision_recall_eval(scores, na_probs, num_true_pos, qid_to_has_ans,
out_image=None, title=None):
def run_precision_recall... | null |
184,758 | from __future__ import absolute_import, division, print_function
import json
import logging
import math
import collections
from io import open
from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
from src.pequod.data.utils_squad_evaluate import find_all_best_thresh_v2, make_qid_to_has_ans, get... | Write final predictions to the json file and log-odds of null if needed. |
184,759 | from __future__ import absolute_import, division, print_function
import json
import logging
import math
import collections
from io import open
from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
from src.pequod.data.utils_squad_evaluate import find_all_best_thresh_v2, make_qid_to_has_ans, get... | XLNet write prediction logic (more complex than Bert's). Write final predictions to the json file and log-odds of null if needed. Requires utils_squad_evaluate.py |
184,760 | import logging
import os
import torch
from transformers.data.processors.utils import (DataProcessor,
InputExample, InputFeatures)
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
class MLDocProcessor(XDocProcessor):
def get_labels(self): return... | null |
184,761 | import logging
import os
import torch
from transformers.data.processors.utils import (DataProcessor,
InputExample, InputFeatures)
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
logger = logging.getLogger(__name__)
def xdoc_convert_examples_to_f... | null |
184,762 | import logging
import numpy as np
import os
import torch
import random
from torch.autograd import Variable
from torch.utils.data import DataLoader, TensorDataset
from src.pequod.trainer import (Trainer,
XClassificationTrainer, XQATrainer, SelfTrainer)
from transformers import AdamW, ConstantLRSchedule, WarmupLinearSc... | null |
184,763 | def _lines_gen_from_single_file(filename):
with open(filename) as fp:
for line in fp: yield line.strip()
def lines_gen(*filenames):
for ret in zip(*map(_lines_gen_from_single_file, filenames)): yield ret | null |
184,764 | import logging
import torch
from transformers.modeling_bert import (BertConfig, BertEncoder,
BertIntermediate, BertLayer,
BertModel, BertOutput,
BertSelfAttention,
... | null |
184,765 | import logging
import torch
from transformers.modeling_bert import (BertConfig, BertEncoder,
BertIntermediate, BertLayer,
BertModel, BertOutput,
BertSelfAttention,
... | null |
184,766 | import os
import sys
import faiss
import tempfile
import numpy as np
def knn(x, y, k, use_gpu, dist='cosine'):
def score(x, y, fwd_mean, bwd_mean, margin, dist='cosine'):
def score_candidates(x, y, candidate_inds, fwd_mean, bwd_mean, margin, dist='cosine'):
def text_load_unify(fname, encoding, unify=True):
def unique_e... | null |
184,767 | import os
import sys
import faiss
import tempfile
import numpy as np
def bucc_optimize(candidate2score, gold):
def bucc_extract(cand2score, th, fname):
def read_candidate2score(candidates_file, src_text_file, trg_text_file, src_id_file, trg_id_file, encoding='utf-8'):
def bucc_eval(candidates_file, gold_file, src_file... | null |
184,768 | import os
import sys
import faiss
import tempfile
import numpy as np
def similarity_search(x, y, dim, normalize=False):
num = x.shape[0]
idx = faiss.IndexFlatL2(dim)
if normalize:
faiss.normalize_L2(x)
faiss.normalize_L2(y)
idx.add(x)
scores, prediction = idx.search(y, 1)
return prediction | null |
184,769 | import faiss
import json
import logging
import numpy as np
import os
import torch
from src.pequod.data.xretrieval import load_and_cache_examples
from src.pequod.eval.evaluator import Evaluator
def similarity_search(x, y, dim, normalize=False, dist='L2'):
top_k = 10
num = x.shape[0]
if dist == 'cosine':
idx =... | null |
184,770 | import faiss
import json
import logging
import numpy as np
import os
import torch
from src.pequod.data.xretrieval import load_and_cache_examples
from src.pequod.eval.evaluator import Evaluator
from src.pequod.eval.utils_retrieve import mine_bitext, bucc_eval
logger = logging.getLogger(__name__)
def load_embeddings(emb... | null |
184,771 | import argparse
import glob
import logging
import os
import random
import json
import copy
import math
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset, ConcatDataset, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm imp... | null |
184,772 | import argparse
import glob
import logging
import os
import random
import json
import copy
import math
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset, ConcatDataset, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm imp... | Train the model |
184,773 | import argparse
import glob
import logging
import os
import random
import json
import copy
import math
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset, ConcatDataset, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm imp... | null |
184,774 | import argparse
import glob
import logging
import os
import random
import timeit
import itertools
import json
import copy
import math
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from ... | Train the model |
184,775 | from __future__ import absolute_import, division, print_function
import logging
import os
import random
from io import open
from transformers import XLMTokenizer
def get_labels(path):
with open(path, "r") as f:
labels = f.read().splitlines()
if "O" not in labels:
labels = ["O"] + labels
ret... | null |
184,776 | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import copy
import json
import random
import math
import numpy as np
import torch
from seqeval.metrics import precision_score, recall_score, f1_score
from tensorboardX import SummaryWriter
from torch.nn... | null |
184,777 | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import copy
import json
import random
import math
import numpy as np
import torch
from seqeval.metrics import precision_score, recall_score, f1_score
from tensorboardX import SummaryWriter
from torch.nn... | Train the model. |
184,778 | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import copy
import json
import random
import math
import numpy as np
import torch
from seqeval.metrics import precision_score, recall_score, f1_score
from tensorboardX import SummaryWriter
from torch.nn... | null |
184,779 | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import copy
import json
import random
import math
import numpy as np
import torch
from seqeval.metrics import precision_score, recall_score, f1_score
from tensorboardX import SummaryWriter
from torch.nn... | null |
184,780 | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import copy
import json
import random
import math
import numpy as np
import torch
from seqeval.metrics import precision_score, recall_score, f1_score
from tensorboardX import SummaryWriter
from torch.nn... | null |
184,781 | import logging
import torch
from collections import OrderedDict
from transformers.modeling_bert import (BertConfig, BertEncoder,
BertIntermediate, BertLayer,
BertModel, BertOutput,
BertSelfAttention,
... | null |
184,782 | import logging
import os
import h5py
import numpy as np
import tensorflow as tf
from tensorflow.python.keras.saving import hdf5_format
from .configuration_utils import PretrainedConfig
from .file_utils import DUMMY_INPUTS, TF2_WEIGHTS_NAME, WEIGHTS_NAME, cached_path, hf_bucket_url, is_remote_url
from .modeling_tf_pytor... | null |
184,783 | import logging
import os
import h5py
import numpy as np
import tensorflow as tf
from tensorflow.python.keras.saving import hdf5_format
from .configuration_utils import PretrainedConfig
from .file_utils import DUMMY_INPUTS, TF2_WEIGHTS_NAME, WEIGHTS_NAME, cached_path, hf_bucket_url, is_remote_url
from .modeling_tf_pytor... | Filter a distribution of logits using top-k and/or nucleus (top-p) filtering Args: logits: logits distribution shape (batch size, vocabulary size) if top_k > 0: keep only top k tokens with highest probability (top-k filtering). if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering)... |
184,784 | import logging
import os
import h5py
import numpy as np
import tensorflow as tf
from tensorflow.python.keras.saving import hdf5_format
from .configuration_utils import PretrainedConfig
from .file_utils import DUMMY_INPUTS, TF2_WEIGHTS_NAME, WEIGHTS_NAME, cached_path, hf_bucket_url, is_remote_url
from .modeling_tf_pytor... | Creates a `tf.initializers.truncated_normal` with the given range. Args: initializer_range: float, initializer range for stddev. Returns: TruncatedNormal initializer with stddev = `initializer_range`. |
184,785 | import logging
import numpy as np
import tensorflow as tf
from .configuration_ctrl import CTRLConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list
def angle_defn(pos, i, d_model_size):
angle_rates = 1 /... | null |
184,786 | import logging
import numpy as np
import tensorflow as tf
from .configuration_ctrl import CTRLConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list
def shape_list(x):
"""Deal with dynamic shape in tenso... | null |
184,787 | import logging
import numpy as np
import tensorflow as tf
from .configuration_ctrl import CTRLConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list
def point_wise_feed_forward_network(d_model_size, dff, nam... | null |
184,788 | import os
from argparse import ArgumentParser, Namespace
from logging import getLogger
from transformers import SingleSentenceClassificationProcessor as Processor
from transformers import TextClassificationPipeline, is_tf_available, is_torch_available
from transformers.commands import BaseTransformersCLICommand
class T... | Factory function used to instantiate serving server from provided command line arguments. :return: ServeCommand |
184,789 | from argparse import ArgumentParser, Namespace
from logging import getLogger
from transformers.commands import BaseTransformersCLICommand
class ConvertCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the... | Factory function used to convert a model TF 1.0 checkpoint in a PyTorch checkpoint. :return: ServeCommand |
184,790 | import logging
from argparse import ArgumentParser
from transformers.commands import BaseTransformersCLICommand
from transformers.pipelines import SUPPORTED_TASKS, Pipeline, PipelineDataFormat, pipeline
def try_infer_format_from_ext(path: str):
if not path:
return "pipe"
for ext in PipelineDataFormat.SU... | null |
184,791 | import logging
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from transformers import Pipeline
from transformers.commands import BaseTransformersCLICommand
from transformers.pipelines import SUPPORTED_TASKS, pipeline
def Body(*x, **y):
pass | null |
184,792 | import logging
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from transformers import Pipeline
from transformers.commands import BaseTransformersCLICommand
from transformers.pipelines import SUPPORTED_TASKS, pipeline
class ServeCommand(BaseTransformersCLICommand):
def registe... | Factory function used to instantiate serving server from provided command line arguments. :return: ServeCommand |
184,793 | from argparse import ArgumentParser
from transformers.commands import BaseTransformersCLICommand
class DownloadCommand(BaseTransformersCLICommand):
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("download")
download_parser.add_argument(
"--cache-dir"... | null |
184,794 | import platform
from argparse import ArgumentParser
from transformers import __version__ as version
from transformers import is_tf_available, is_torch_available
from transformers.commands import BaseTransformersCLICommand
class EnvironmentCommand(BaseTransformersCLICommand):
def register_subcommand(parser: Argument... | null |
184,795 | import argparse
import logging
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
# Initialise PyTorch model
config = BertConfig.from_json_file(bert_config_file)
print("... | null |
184,796 | import logging
import numpy as np
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from .configuration_ctrl import CTRLConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_utils import Conv1D, PreTrainedModel
def angle_defn(pos, i, d_model_size):
... | null |
184,797 | import logging
import numpy as np
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from .configuration_ctrl import CTRLConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_utils import Conv1D, PreTrainedModel
def scaled_dot_product_attention(q, k,... | null |
184,798 | import logging
import numpy as np
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from .configuration_ctrl import CTRLConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_utils import Conv1D, PreTrainedModel
def point_wise_feed_forward_network(d_... | null |
184,799 | import logging
import math
import random
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from .configuration_bart import BartConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_utils import BeamHypo... | Prepare masks that ignore padding tokens decoder and a causal lm mask for the decoder if none are provided. This mimics the default behavior in fairseq. To override it pass in masks. |
184,800 | import logging
import math
import random
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from .configuration_bart import BartConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_utils import BeamHypo... | null |
184,801 | import logging
import math
import random
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from .configuration_bart import BartConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_utils import BeamHypo... | Reorder buffered internal state (for incremental generation). |
184,802 | import logging
import math
import random
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from .configuration_bart import BartConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_utils import BeamHypo... | null |
184,803 | import logging
import math
import random
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from .configuration_bart import BartConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_utils import BeamHypo... | Remove entries that are None or [] from an iterable. |
184,804 | import argparse
import logging
import torch
from transformers import AlbertConfig, AlbertForMaskedLM, load_tf_weights_in_albert
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, albert_config_file, pytorch_dump_path):
# Initialise PyTorch model
config = AlbertConfig.from_json_file(albert_config_file)
... | null |
184,805 | import re
import tensorflow as tf
class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
"""Applys a warmup schedule on a given learning rate decay schedule."""
def __init__(self, initial_learning_rate, decay_schedule_fn, warmup_steps, power=1.0, name=None):
super().__init__()
self.in... | Creates an optimizer with learning rate schedule. |
184,806 | import json
import logging
import os
import re
from typing import List, Optional, Union
from tokenizers import Tokenizer
from tokenizers.decoders import BPEDecoder
from tokenizers.implementations import BaseTokenizer
from tokenizers.models import BPE
from tokenizers.normalizers import BertNormalizer, Sequence, unicode_... | Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length strings) |
184,807 | import json
import logging
import os
import re
from typing import List, Optional, Union
from tokenizers import Tokenizer
from tokenizers.decoders import BPEDecoder
from tokenizers.implementations import BaseTokenizer
from tokenizers.models import BPE
from tokenizers.normalizers import BertNormalizer, Sequence, unicode_... | fixes some issues the spacy tokenizer had on books corpus also does some whitespace standardization |
184,808 | import argparse
import logging
import torch
from transformers import T5Config, T5Model, load_tf_weights_in_t5
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
# Initialise PyTorch model
config = T5Config.from_json_file(config_file)
print("Building PyTorch model from... | null |
184,809 | import logging
import numpy as np
import tensorflow as tf
from .configuration_openai import OpenAIGPTConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_utils import (
TFConv1D,
TFPreTrainedModel,
TFSequenceSummary,
TFSharedEmbeddings,
get_initiali... | Gaussian Error Linear Unit. This is a smoother version of the RELU. Original paper: https://arxiv.org/abs/1606.08415 Args: x: float Tensor to perform activation. Returns: `x` with the GELU activation applied. |
184,810 | import logging
import numpy as np
import tensorflow as tf
from .configuration_openai import OpenAIGPTConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_utils import (
TFConv1D,
TFPreTrainedModel,
TFSequenceSummary,
TFSharedEmbeddings,
get_initiali... | null |
184,811 | import argparse
import logging
import torch
from transformers import CONFIG_NAME, WEIGHTS_NAME, GPT2Config, GPT2Model, load_tf_weights_in_gpt2
def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):
# Construct model
if gpt2_config_file == "":
config = ... | null |
184,812 | import argparse
import logging
import torch
from transformers import CONFIG_NAME, WEIGHTS_NAME, OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_config_file, pytorch_dump_folder_path):
# Construct model
if openai_confi... | null |
184,813 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from .configuration_transfo_xl import TransfoXLConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .modeling_transfo_xl_utilities import LogUniformSampler, ProjectedAdaptiveLogSoftmax, sample_logits
... | Load tf checkpoints in a pytorch model |
184,814 | import json
import logging
import os
import regex as re
from .tokenization_utils import PreTrainedTokenizer
The provided code snippet includes necessary dependencies for implementing the `get_pairs` function. Write a Python function `def get_pairs(word)` to solve the following problem:
Return set of symbol pairs in a ... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
184,815 | import glob
import logging
import os
import pickle
import re
from collections import Counter, OrderedDict
from typing import List, Optional, Tuple, Union
import numpy as np
from tokenizers import Encoding, Tokenizer
from tokenizers.implementations import BaseTokenizer
from tokenizers.models import WordLevel
from tokeni... | null |
184,816 | import fnmatch
import json
import logging
import os
import shutil
import sys
import tarfile
import tempfile
from contextlib import contextmanager
from functools import partial, wraps
from hashlib import sha256
from typing import Optional
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import b... | null |
184,817 | import fnmatch
import json
import logging
import os
import shutil
import sys
import tarfile
import tempfile
from contextlib import contextmanager
from functools import partial, wraps
from hashlib import sha256
from typing import Optional
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import b... | null |
184,818 | import fnmatch
import json
import logging
import os
import shutil
import sys
import tarfile
import tempfile
from contextlib import contextmanager
from functools import partial, wraps
from hashlib import sha256
from typing import Optional
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import b... | null |
184,819 | import fnmatch
import json
import logging
import os
import shutil
import sys
import tarfile
import tempfile
from contextlib import contextmanager
from functools import partial, wraps
from hashlib import sha256
from typing import Optional
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import b... | null |
184,820 | import fnmatch
import json
import logging
import os
import shutil
import sys
import tarfile
import tempfile
from contextlib import contextmanager
from functools import partial, wraps
from hashlib import sha256
from typing import Optional
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import b... | Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist. |
184,821 | import fnmatch
import json
import logging
import os
import shutil
import sys
import tarfile
import tempfile
from contextlib import contextmanager
from functools import partial, wraps
from hashlib import sha256
from typing import Optional
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import b... | Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and then return the path. Args: cache_dir: specify a cache directory to save the file to (overwr... |
184,822 | import fnmatch
import json
import logging
import os
import shutil
import sys
import tarfile
import tempfile
from contextlib import contextmanager
from functools import partial, wraps
from hashlib import sha256
from typing import Optional
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import b... | Wrapper function for s3 requests in order to create more helpful error messages. |
184,823 | import torch
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `sample_logits` function. Write a Python function `def sample_logits(embedding, bias, labels, inputs, sampler)` to solve the following problem:
embedding: an nn.Embedding la... | embedding: an nn.Embedding layer bias: [n_vocab] labels: [b1, b2] inputs: [b1, b2, n_emb] sampler: you may use a LogUniformSampler Return logits: [b1, b2, 1 + n_sample] |
184,824 | import logging
import math
import torch
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
The provided code snippet includes necessary dependencies for implementing the `get_constant_schedule` function. Write a Python function `def get_constant_schedule(optimizer, last_epoch=-1)` to solve... | Create a schedule with a constant learning rate. |
184,825 | import logging
import math
import torch
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
The provided code snippet includes necessary dependencies for implementing the `get_constant_schedule_with_warmup` function. Write a Python function `def get_constant_schedule_with_warmup(optimizer, ... | Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate increases linearly between 0 and 1. |
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