id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
11,794 | import json
import logging
import math
import os
import random
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import datasets
import numpy as np
from datasets import load_dataset
from tqdm imp... | Returns batches of size `batch_size` from `eval dataset`. Sharding handled by `pad_shard_unpad` in the eval loop. |
11,797 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Callable, Optional
import datasets
import numpy as np
from datasets import Dataset, load_dataset
from tqd... | Returns batches of size `batch_size` from `dataset`. If `drop_last` is set to `False`, the final batch may be incomplete, and range in size from 1 to `batch_size`. Shuffle batches if `shuffle` is `True`. |
11,798 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Callable, Optional
import datasets
import numpy as np
from datasets import Dataset, load_dataset
from tqd... | null |
11,799 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Callable, Optional
import datasets
import numpy as np
from datasets import Dataset, load_dataset
from tqd... | null |
11,800 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Callable, Optional
import datasets
import numpy as np
from datasets import Dataset, load_dataset
from tqd... | Returns a linear warmup, linear_decay learning rate function. |
11,801 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional
import nltk
import numpy as np
from datasets import load_dataset
from tqdm import tq... | Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned. |
11,802 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional
import nltk
import numpy as np
from datasets import load_dataset
from tqdm import tq... | null |
11,803 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional
import nltk
import numpy as np
from datasets import load_dataset
from tqdm import tq... | null |
11,804 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import fl... | This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2466>`__ . Training parameters to avoid padding with random_spans_noise_mask. When training a model with random_spans_noise_mask, we... |
11,805 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import fl... | Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned. |
11,806 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import fl... | null |
11,807 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import fl... | null |
11,808 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
im... | Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned. |
11,809 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
im... | null |
11,810 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
im... | null |
11,811 | import logging
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from pathlib import Path
from typing import Callable, Optional
import torch
import torchvision
import torchvision.transforms as transforms
from tqdm import tqdm
import jax
import jax.numpy as jnp
impor... | null |
11,812 | import logging
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from pathlib import Path
from typing import Callable, Optional
import torch
import torchvision
import torchvision.transforms as transforms
from tqdm import tqdm
import jax
import jax.numpy as jnp
impor... | Returns a linear warmup, linear_decay learning rate function. |
11,813 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from functools import partial
from pathlib import Path
from typing import Callable, Optional
import datasets
import nltk
import numpy as np
from datasets import Dataset, load_d... | Returns batches of size `batch_size` from `dataset`. If `drop_last` is set to `False`, the final batch may be incomplete, and range in size from 1 to `batch_size`. Shuffle batches if `shuffle` is `True`. |
11,814 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from functools import partial
from pathlib import Path
from typing import Callable, Optional
import datasets
import nltk
import numpy as np
from datasets import Dataset, load_d... | null |
11,815 | import json
import logging
import math
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from functools import partial
from pathlib import Path
from typing import Callable, Optional
import datasets
import nltk
import numpy as np
from datasets import Dataset, load_d... | Returns a linear warmup, linear_decay learning rate function. |
11,816 | import logging
import os
import re
import sys
from collections import OrderedDict, defaultdict
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Union
import datasets
import numpy as np
import torch
from datasets import DatasetDict, load_dataset, load_metric
import transformers
from tran... | null |
11,817 | import logging
import os
import re
import sys
from collections import OrderedDict, defaultdict
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Union
import datasets
import numpy as np
import torch
from datasets import DatasetDict, load_dataset, load_metric
import transformers
from tran... | null |
11,818 | import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import sha256
from io import BytesIO
from pathlib import Path
from urllib.par... | null |
11,819 | import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import sha256
from io import BytesIO
from pathlib import Path
from urllib.par... | null |
11,820 | import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import sha256
from io import BytesIO
from pathlib import Path
from urllib.par... | null |
11,821 | import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import sha256
from io import BytesIO
from pathlib import Path
from urllib.par... | null |
11,822 | import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import sha256
from io import BytesIO
from pathlib import Path
from urllib.par... | null |
11,823 | import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import sha256
from io import BytesIO
from pathlib import Path
from urllib.par... | null |
11,824 | import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import sha256
from io import BytesIO
from pathlib import Path
from urllib.par... | null |
11,825 | import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import sha256
from io import BytesIO
from pathlib import Path
from urllib.par... | null |
11,826 | import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import sha256
from io import BytesIO
from pathlib import Path
from urllib.par... | null |
11,827 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | null |
11,828 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | location will always be cpu for np tensors |
11,829 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | null |
11,830 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | null |
11,831 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | null |
11,832 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | null |
11,833 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | Args: proposals (list[Tensor]): (L, N, Hi*Wi*A, 4). pred_objectness_logits: tensors of length L. nms_thresh (float): IoU threshold to use for NMS pre_nms_topk (int): before nms post_nms_topk (int): after nms min_box_side_len (float): minimum proposal box side training (bool): True if proposals are to be used in trainin... |
11,834 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | Returns: pos_idx, neg_idx (Tensor): 1D vector of indices. The total length of both is `num_samples` or fewer. |
11,835 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | null |
11,836 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | null |
11,837 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | null |
11,838 | import itertools
import math
import os
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, namedtuple
from typing import Dict, List, Tuple
import numpy as np
import torch
from torch import nn
from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops import RoIPool
from torchvision... | null |
11,839 | import getopt
import json
import os
import sys
from collections import OrderedDict
import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess
from utils import Config
def tryload(stream):
try:
data = json.load(stream)
try:
... | null |
11,840 | import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
def _scale_box(boxes, scale_yx):
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return bo... | null |
11,841 | import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
def _clip_box(tensor, box_size: Tuple[int, int]):
assert torch.isfinite(tensor).all(), "Box tensor contains infinite... | null |
11,842 | import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTo... | A bunch of args sanity checks to perform even starting... |
11,843 | import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTo... | null |
11,844 | import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTo... | null |
11,845 | import json
import logging
import os
import socket
import git
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `git_log` function. Write a Python function `def git_log(folder_path: str)` to solve the following problem:
Log commit info.
Here is the function... | Log commit info. |
11,846 | import json
import logging
import os
import socket
import git
import numpy as np
import torch
logger = logging.getLogger(__name__)
The provided code snippet includes necessary dependencies for implementing the `init_gpu_params` function. Write a Python function `def init_gpu_params(params)` to solve the following prob... | Handle single and multi-GPU / multi-node. |
11,847 | import json
import logging
import os
import socket
import git
import numpy as np
import torch
The provided code snippet includes necessary dependencies for implementing the `set_seed` function. Write a Python function `def set_seed(args)` to solve the following problem:
Set the random seed.
Here is the function:
def... | Set the random seed. |
11,848 | import argparse
import glob
import logging
import os
import random
import timeit
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
import transformers
fr... | Train the model |
11,849 | import bisect
import copy
from collections import defaultdict
import numpy as np
from torch.utils.data import BatchSampler, Sampler
from utils import logger
def _quantize(x, bins):
bins = copy.deepcopy(bins)
bins = sorted(bins)
quantized = list(map(lambda y: bisect.bisect_right(bins, y), x))
return quan... | null |
11,850 | import datasets
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
import faiss
import transformers
from eli5_utils import (
embed_questions_for_retrieval,
make_qa_s2s_model,
qa_s2s_generate,
query_es_index,
query_qa_dense_index,
)
from transformers import... | null |
11,851 | import datasets
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
import faiss
import transformers
from eli5_utils import (
embed_questions_for_retrieval,
make_qa_s2s_model,
qa_s2s_generate,
query_es_index,
query_qa_dense_index,
)
from transformers import... | null |
11,852 | import datasets
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
import faiss
import transformers
from eli5_utils import (
embed_questions_for_retrieval,
make_qa_s2s_model,
qa_s2s_generate,
query_es_index,
query_qa_dense_index,
)
from transformers import... | null |
11,853 | import datasets
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
import faiss
import transformers
from eli5_utils import (
embed_questions_for_retrieval,
make_qa_s2s_model,
qa_s2s_generate,
query_es_index,
query_qa_dense_index,
)
from transformers import... | null |
11,854 | import datasets
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
import faiss
import transformers
from eli5_utils import (
embed_questions_for_retrieval,
make_qa_s2s_model,
qa_s2s_generate,
query_es_index,
query_qa_dense_index,
)
from transformers import... | null |
11,855 | import datasets
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
import faiss
import transformers
from eli5_utils import (
embed_questions_for_retrieval,
make_qa_s2s_model,
qa_s2s_generate,
query_es_index,
query_qa_dense_index,
)
from transformers import... | null |
11,856 | import functools
import math
import os
from random import choice, randint
from time import time
import datasets
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk, streaming_bulk
from torch im... | null |
11,857 | import functools
import math
import os
from random import choice, randint
from time import time
import datasets
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk, streaming_bulk
from torch im... | null |
11,858 | import functools
import math
import os
from random import choice, randint
from time import time
import datasets
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk, streaming_bulk
from torch im... | null |
11,859 | import functools
import math
import os
from random import choice, randint
from time import time
import datasets
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk, streaming_bulk
from torch im... | null |
11,860 | import functools
import math
import os
from random import choice, randint
from time import time
import datasets
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk, streaming_bulk
from torch im... | null |
11,861 | import functools
import math
import os
from random import choice, randint
from time import time
import datasets
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk, streaming_bulk
from torch im... | null |
11,862 | import functools
import math
import os
from random import choice, randint
from time import time
import datasets
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk, streaming_bulk
from torch im... | null |
11,863 | import functools
import math
import os
from random import choice, randint
from time import time
import datasets
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk, streaming_bulk
from torch im... | null |
11,864 | import functools
import math
import os
from random import choice, randint
from time import time
import datasets
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk, streaming_bulk
from torch im... | null |
11,865 | import functools
import math
import os
from random import choice, randint
from time import time
import datasets
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk, streaming_bulk
from torch im... | null |
11,866 | import os
from pathlib import Path
from typing import Dict, List
import fire
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers.utils.logging import get_logger
logger = get_logger(__name__)
def sanitize(sd):
return {remove_prefix(k, "model."): v for k, v in sd.items()}
def ... | Cleanup a pytorch-lightning .ckpt file or experiment dir and save a huggingface model with that state dict. Silently allows extra pl keys (like teacher.) Puts all ckpt models into CPU RAM at once! Args: pl_ckpt_path (:obj:`str`): Path to a .ckpt file saved by pytorch_lightning or dir containing ckpt files. If a directo... |
11,867 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | From fairseq |
11,868 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | null |
11,869 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | Remove columns that are populated exclusively by pad_token_id |
11,870 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | Go through the text data by order of src length with a bit of randomness. From fastai repo. |
11,871 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | Update config with summarization specific params. |
11,872 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | pickle.load(path) |
11,873 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | pickle.dump(obj, path) |
11,874 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | null |
11,875 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | Save git information to output_dir/git_log.json |
11,876 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | null |
11,877 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | Freeze token embeddings and positional embeddings for bart, just token embeddings for t5. |
11,878 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | null |
11,879 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | null |
11,880 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | null |
11,881 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | Parse an argv list of unspecified command line args to a dict. Assumes all values are either numeric or boolean in the form of true/false. |
11,882 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | null |
11,883 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | Yield successive n-sized chunks from lst. |
11,884 | import itertools
import json
import linecache
import math
import os
import pickle
import socket
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List, Tuple, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge... | Checks whether to bail out if output_dir already exists and has more than expected_items in it `args`: needs to have the following attributes of `args`: - output_dir - do_train - overwrite_output_dir `expected_items`: normally 0 (default) - i.e. empty dir, but in some cases a few files are expected (e.g. recovery from ... |
11,885 | import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
LAYERS_TO_SUPERVISE = {
# maps num layers in student -> which teacher layers to copy.... | Used or the --supervise_forward kwarg |
11,886 | import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
logger = logging.get_logger(__name__)
def copy_layers(src_layers: nn.ModuleList, dest_laye... | Make a student by copying alternating layers from a teacher, save it to save_path. Args: teacher: str or PreTrainedModel if str, this will call AutoModelForSeq2SeqLM.from_pretrained(teacher) before copying layers save_path: where to save the student, defaults to student directory. e: how many Encoder layers should the ... |
11,887 | import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_n... | Takes input text, generates output, and then using reference calculates the BLEU scores. The results are saved to a file and returned to the caller, and printed out unless ``verbose=False`` is passed. Args: verbose (:obj:`bool`, `optional`, defaults to :obj:`True`): print results to stdout Returns: a tuple: ``(scores, ... |
11,888 | import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils import save_json
def count_trainable_parameters(model):
model_parameters = ... | null |
11,889 | import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils import save_json
The provided code snippet includes necessary dependencies for ... | Saves the best model by validation ROUGE2 score. |
11,890 | import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils import save_json
def get_early_stopping_callback(metric, patience):
return ... | null |
11,891 | import re
from filelock import FileLock
try:
import nltk
NLTK_AVAILABLE = True
except (ImportError, ModuleNotFoundError):
NLTK_AVAILABLE = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
The provided code snippet includes necessary dependencies for im... | This was added to get rougeLsum scores matching published rougeL scores for BART and PEGASUS. |
11,892 | import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List
import pytorch_lightning as pl
import torch
from torch import nn
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating... | null |
11,893 | import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List
import pytorch_lightning as pl
import torch
from torch import nn
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating... | null |
11,896 | import logging
import re
import torch
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
The provided code snippet includes necessary dependencies for implementing the `add_arguments` function. Writ... | Add arguments to parser for functions defined in quant_trainer. |
11,897 | import logging
import re
import torch
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
The provided code snippet includes necessary dependencies for implementing the `set_default_quantizers` funct... | Set default quantizers before creating the model. |
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