id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
27,727 | from datetime import date, datetime, timedelta
from functools import reduce
from typing import Optional
The provided code snippet includes necessary dependencies for implementing the `clear_currently_syncing` function. Write a Python function `def clear_currently_syncing(state: dict) -> dict` to solve the following pr... | Clear the currently syncing from the state. Arguments: state (dict) -- State file Returns: dict -- New state file |
27,728 | from datetime import date, datetime, timedelta
from functools import reduce
from typing import Optional
The provided code snippet includes necessary dependencies for implementing the `get_stream_state` function. Write a Python function `def get_stream_state(state: dict, tap_stream_id: str) -> dict` to solve the follow... | Return the state of the stream. Arguments: state {dict} -- The state tap_stream_id {str} -- The id of the stream Returns: dict -- The state of the stream |
27,729 | from datetime import date, datetime, timedelta
from functools import reduce
from typing import Optional
The provided code snippet includes necessary dependencies for implementing the `retrieve_bookmark_with_path` function. Write a Python function `def retrieve_bookmark_with_path(path: str, row: dict) -> Optional[str]`... | Bookmark exists in the row of data which is an dictionary. The bookmark can either be a key such as row[key] but also a subkey such as row[key][subkey]. In the streams definition file, the key can be saved as a string, but [key][subkey] cannot. Therefore, in the streams file, if we want to use a subkey as bookmark, we ... |
27,730 | from datetime import date, datetime, timedelta
from functools import reduce
from typing import Optional
The provided code snippet includes necessary dependencies for implementing the `create_bookmark` function. Write a Python function `def create_bookmark(stream_name: str, bookmark_value: str) -> str` to solve the fol... | Create bookmark. Arguments: stream_name {str} -- Name of stream bookmark_value {str} -- Bookmark value Returns: str -- Created bookmark |
27,731 | import collections
import sys
from types import MappingProxyType
from typing import Any, Optional
from mage_integrations.sources.postmark.tap_postmark.streams import STREAMS
def clean_row(row: dict, mapping: dict) -> dict:
"""Clean the row according to the mapping.
The mapping is a dictionary with optional keys... | Clean postmark bounces response_data. Arguments: response_data {dict} -- input response_data Returns: dict -- cleaned response_data |
27,732 | import collections
import sys
from types import MappingProxyType
from typing import Any, Optional
from mage_integrations.sources.postmark.tap_postmark.streams import STREAMS
def clean_row(row: dict, mapping: dict) -> dict:
"""Clean the row according to the mapping.
The mapping is a dictionary with optional keys... | Clean postmark overview response_data. Arguments: response_data {dict} -- input response_data Returns: dict -- cleaned response_data |
27,733 | import collections
import sys
from types import MappingProxyType
from typing import Any, Optional
from mage_integrations.sources.postmark.tap_postmark.streams import STREAMS
def clean_row(row: dict, mapping: dict) -> dict:
"""Clean the row according to the mapping.
The mapping is a dictionary with optional keys... | Clean postmark platform opens response_data. Arguments: response_data {dict} -- input response_data Returns: dict -- cleaned response_data |
27,734 | import collections
import sys
from types import MappingProxyType
from typing import Any, Optional
from mage_integrations.sources.postmark.tap_postmark.streams import STREAMS
def clean_row(row: dict, mapping: dict) -> dict:
"""Clean the row according to the mapping.
The mapping is a dictionary with optional keys... | Clean postmark messages outbound response_data. Arguments: date_day {str} -- Date response_data {dict} -- Input response_data Returns: dict -- cleaned response_data |
27,735 | import collections
import sys
from types import MappingProxyType
from typing import Any, Optional
from mage_integrations.sources.postmark.tap_postmark.streams import STREAMS
def clean_row(row: dict, mapping: dict) -> dict:
"""Clean the row according to the mapping.
The mapping is a dictionary with optional keys... | Clean postmark messages clients response_data. Arguments: date_day {str} -- Date response_data {dict} -- Input response_data Returns: dict -- cleaned response_data |
27,736 | import collections
import sys
from types import MappingProxyType
from typing import Any, Optional
from mage_integrations.sources.postmark.tap_postmark.streams import STREAMS
def clean_row(row: dict, mapping: dict) -> dict:
"""Clean the row according to the mapping.
The mapping is a dictionary with optional keys... | Clean postmark messages opens response_data. Arguments: date_day {str} -- Date response_data {dict} -- Input response_data Returns: dict -- cleaned response_data |
27,737 | import json
import os
from singer.schema import Schema
def get_abs_path(path: str) -> str:
"""Help function to get the absolute path.
Arguments:
path {str} -- Path to directory
Returns:
str -- The absolute path
"""
return os.path.join(
os.path.dirname(os.path.realpath(__file_... | Load schemas from schemas folder. Returns: dict -- Scemas |
27,738 | from datetime import datetime
from types import MappingProxyType
from dateutil.parser import parse as parse_date
TIMEZONES: MappingProxyType = MappingProxyType({
'A': HOUR,
'ACDT': 10.5 * HOUR, # noqa: WPS432
'ACST': 9.5 * HOUR, # noqa: WPS432
'ACT': -5 * HOUR, # noqa: WPS432
'ACWST': 8.75 * HOUR... | Help function to parse timezones correctly in strings. Arguments: input_date {str} -- Input date as string Returns: {str} -- Date in isoformat |
27,739 | from mage_integrations.sources.constants import COLUMN_TYPE_NUMBER, COLUMN_TYPE_STRING, COLUMN_TYPES
from pandas.api.types import infer_dtype
from typing import Dict
import pandas as pd
def write_parquet_file(file_path: str, df: pd.DataFrame) -> None:
df_output = df.copy()
# Clean up data types since parquet d... | null |
27,740 | from mage_integrations.sources.constants import COLUMN_TYPE_NUMBER, COLUMN_TYPE_STRING, COLUMN_TYPES
from pandas.api.types import infer_dtype
from typing import Dict
import pandas as pd
def infer_dtypes(df: pd.DataFrame) -> Dict[str, str]:
return {column: infer_dtype(df[column], skipna=True) for column in df.colum... | null |
27,741 | from mage_integrations.sources.constants import COLUMN_TYPE_NUMBER, COLUMN_TYPE_STRING, COLUMN_TYPES
from pandas.api.types import infer_dtype
from typing import Dict
import pandas as pd
INVALID_DATA_TYPES = [
'empty',
'mixed',
]
def convert_data_type(v) -> str:
if v in INVALID_DATA_TYPES:
return CO... | null |
27,742 | import datetime
import math
def date_intervals(start_date, end_date, timedelta):
tzinfos = list(filter(lambda x: x, [end_date.tzinfo, start_date.tzinfo]))
if len(tzinfos) == 1:
tzinfo = tzinfos[0]
if not end_date.tzinfo:
end_date = end_date.replace(tzinfo=tzinfo)
if not star... | null |
27,743 | import os
def get_abs_path(path):
return os.path.join(os.path.dirname(os.path.realpath(__file__)), path) | null |
27,744 | import random
def batch(iterable, n=1):
l = len(iterable)
for ndx in range(0, l, n):
yield iterable[ndx:min(ndx + n, l)] | null |
27,747 | import random
def find(condition, arr, map=None):
try:
return next(map(x) if map else x for x in arr if condition(x))
except StopIteration:
return None | null |
27,748 | import random
def find_index(condition, arr):
for idx, item in enumerate(arr):
if condition(item):
return idx
return -1 | null |
27,750 | import random
def subtract(arr1, arr2):
arr2_lookup = set(arr2)
return [i for i in arr1 if i not in arr2_lookup] | null |
27,751 | import uuid
from datetime import datetime, timedelta
def encode_complex(obj):
if hasattr(obj, 'isoformat') and 'method' in type(obj.isoformat).__name__:
return obj.isoformat()
elif isinstance(obj, datetime):
return obj.isoformat()
elif isinstance(obj, timedelta):
# Used to encode th... | null |
27,752 |
def is_number(s):
try:
float(s)
return True
except ValueError:
return False | null |
27,753 | from mage_integrations.sources.constants import (
INCLUSION_AUTOMATIC,
INCLUSION_UNSUPPORTED,
METADATA_KEY_INCLUSION,
METADATA_KEY_SELECTED,
)
from typing import Dict, List
def extract_selected_columns(metadata_array: List[dict]) -> List[str]:
columns = []
for d in metadata_array:
brea... | null |
27,754 | from mage_integrations.sources.constants import (
INCLUSION_AUTOMATIC,
INCLUSION_UNSUPPORTED,
METADATA_KEY_INCLUSION,
METADATA_KEY_SELECTED,
)
from typing import Dict, List
def filter_columns(columns: List[str], properties: Dict, column_types: List[str]):
filtered_columns = []
for col in column... | null |
27,755 | import math
import re
from functools import reduce
def dig(obj_arg, arr_or_string):
if type(arr_or_string) is str:
arr_or_string = arr_or_string.split('.')
arr = list(map(str.strip, arr_or_string))
def _build(obj, key):
tup = re.split(r'\[(\d+)\]$', key)
if len(tup) >= 2:
... | null |
27,756 | import math
import re
from functools import reduce
def flatten(input_data):
final_data = {}
for k1, v1 in input_data.items():
if type(v1) is dict:
for k2, v2 in v1.items():
if type(v2) is dict:
for k3, v3 in v2.items():
final_data... | null |
27,757 | import math
import re
from functools import reduce
def ignore_keys(d, keys):
d_keys = d.keys()
d2 = d.copy()
for key in keys:
if key in d_keys:
d2.pop(key)
return d2 | null |
27,758 | import math
import re
from functools import reduce
def ignore_keys_with_blank_values(d):
d2 = d.copy()
for key, value in d.items():
if not value:
d2.pop(key)
return d2 | null |
27,759 | import math
import re
from functools import reduce
def extract(d, keys):
def _build(obj, key):
val = None
if key in d:
val = d[key]
if val is not None:
obj[key] = val
return obj
return reduce(_build, keys, {}) | null |
27,760 | import math
import re
from functools import reduce
def extract_arrays(input_data):
arr = []
for k, v in input_data.items():
if type(v) is list:
arr.append(v)
return arr | null |
27,761 | import math
import re
from functools import reduce
def group_by(func, arr):
def _build(obj, item):
val = func(item)
if not obj.get(val):
obj[val] = []
obj[val].append(item)
return obj
return reduce(_build, arr, {}) | null |
27,762 | import math
import re
from functools import reduce
def index_by(func, arr):
obj = {}
for item in arr:
key = func(item)
obj[key] = item
return obj | null |
27,763 | import math
import re
from functools import reduce
def merge_dict(a, b):
c = a.copy()
c.update(b)
return c | null |
27,764 | import math
import re
from functools import reduce
def replace_dict_nan_value(d):
def _replace_nan_value(v):
if type(v) == float and math.isnan(v):
return None
return v
return {k: _replace_nan_value(v) for k, v in d.items()} | null |
27,765 | import argparse
import json
from typing import Dict
from kubernetes import client, config
The provided code snippet includes necessary dependencies for implementing the `update_kubernetes_config` function. Write a Python function `def update_kubernetes_config( stateful_set_name: str, initial_container_config: Dict... | Updates the Kubernetes configuration. Args: config (Dict): Kubernetes configuration. |
27,766 | import argparse
import datetime
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
import os
from pathlib import Path
from timm.models import create_model
from timm.utils import ModelEma
from optim_factory import create_optimizer, get_parameter_groups, \
LayerDecayValueAssi... | null |
27,767 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | Parse boolean arguments from the command line. |
27,768 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
27,769 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
27,770 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
27,771 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
27,772 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
27,773 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
27,774 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
27,775 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
27,776 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
27,777 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
27,778 | import datetime
import io
import os
import math
import time
import json
import argparse
import numpy as np
from pathlib import Path
from collections import defaultdict, deque
from timm.utils import get_state_dict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from to... | null |
27,779 | import math
from functools import partial
import utils
import torch
import torch.nn as nn
from torchscale.architecture.encoder import Encoder
from torchscale.model.LongNet import LongNetEncoder
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_trunc_normal_
... | null |
27,780 | import math
from functools import partial
import utils
import torch
import torch.nn as nn
from torchscale.architecture.encoder import Encoder
from torchscale.model.LongNet import LongNetEncoder
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_trunc_normal_
... | null |
27,781 | from torch import optim as optim
from timm.optim.lookahead import Lookahead
import json
def get_num_layer_for_vit(var_name, num_max_layer):
if "embed" in var_name:
return 0
elif var_name in (
"cls_token", "mask_token", "pos_embed", "model.pos_embed", "language_pos_embed",
"word_embeddi... | null |
27,782 | from torch import optim as optim
from timm.optim.lookahead import Lookahead
import json
def get_is_head_flag_for_vit(var_name, num_max_layer):
if var_name.startswith("head"):
return 1
# elif var_name.startswith("pooler"):
# return 1
else:
return 0 | null |
27,783 | from torch import optim as optim
from timm.optim.lookahead import Lookahead
import json
def get_parameter_groups(model, weight_decay=1e-5, skip_list=(), get_num_layer=None, get_layer_scale=None):
parameter_group_names = {}
parameter_group_vars = {}
for name, param in model.named_parameters():
if not... | null |
27,784 | import utils
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.registry import register_model
from functools import partial
from longvit import LongViT
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_tru... | null |
27,785 | import utils
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.registry import register_model
from functools import partial
from longvit import LongViT
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_tru... | null |
27,786 | import utils
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.registry import register_model
from functools import partial
from longvit import LongViT
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_tru... | null |
27,787 | import utils
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.registry import register_model
from functools import partial
from longvit import LongViT
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_tru... | null |
27,788 | import utils
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.registry import register_model
from functools import partial
from longvit import LongViT
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_tru... | null |
27,789 | import utils
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.registry import register_model
from functools import partial
from longvit import LongViT
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_tru... | null |
27,790 | import utils
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.registry import register_model
from functools import partial
from longvit import LongViT
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_tru... | null |
27,791 | import utils
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.registry import register_model
from functools import partial
from longvit import LongViT
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_tru... | null |
27,792 | import utils
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.registry import register_model
from functools import partial
from longvit import LongViT
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_tru... | null |
27,793 | import utils
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.registry import register_model
from functools import partial
from longvit import LongViT
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_tru... | null |
27,794 | import utils
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from timm.models.registry import register_model
from functools import partial
from longvit import LongViT
from torchscale.architecture.config import EncoderConfig
from timm.models.layers import trunc_normal_ as __call_tru... | null |
27,795 | import os
import sys
import cv2
import json
import numpy as np
import openslide
import time
import torch
import openslide
import argparse
import random
import shutil
import glob
from concurrent.futures import ProcessPoolExecutor
from PIL import Image
from torchvision import transforms
from torchvision.transforms impo... | null |
27,796 | import os
import glob
import argparse
import openslide
from PIL import Image
from concurrent.futures import ProcessPoolExecutor
def convert_wsi_to_images(slide_path, image_path, target_size, level=0):
slide = openslide.open_slide(slide_path)
level_dims = slide.level_dimensions
region = slide.read_region((0,... | null |
27,797 | import os
import json
import shutil
import argparse
from PIL import Image
from concurrent.futures import ProcessPoolExecutor
def find_images(input_folder):
image_files = []
for root, _, files in os.walk(input_folder):
for f in files:
if f.lower().endswith(('.png', '.jpg', '.jp... | null |
27,798 | import os
import json
import shutil
import argparse
from PIL import Image
from concurrent.futures import ProcessPoolExecutor
def split_image(image_path, input_folder, output_folder, num_splits):
def process_images(image_files, input_folder, output_folder, num_splits, num_processes):
with ProcessPoolExecuto... | null |
27,799 | import os
import sys
import torch
import random
import argparse
from PIL import Image, ImageFilter, ImageOps
from multiprocessing import Pool, cpu_count
from timm.data.transforms import RandomResizedCropAndInterpolation
import torchvision.transforms as transforms
def build_transform(input_size):
train_interpolatio... | null |
27,800 | import os
import sys
import torch
import random
import argparse
from PIL import Image, ImageFilter, ImageOps
from multiprocessing import Pool, cpu_count
from timm.data.transforms import RandomResizedCropAndInterpolation
import torchvision.transforms as transforms
def get_image_files(input_dir):
for root, _, file... | null |
27,801 | import os
import sys
import torch
import random
import argparse
from PIL import Image, ImageFilter, ImageOps
from multiprocessing import Pool, cpu_count
from timm.data.transforms import RandomResizedCropAndInterpolation
import torchvision.transforms as transforms
def pil_loader(path):
with open(path, "rb") as f:
... | null |
27,802 | import os
import json
import random
import torch
import glob
from collections import defaultdict, Counter
from torchvision import transforms
from torchvision.datasets.folder import default_loader
from torchvision.transforms import InterpolationMode
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT... | null |
27,803 | import os
import json
import random
import torch
import glob
from collections import defaultdict, Counter
from torchvision import transforms
from torchvision.datasets.folder import default_loader
from torchvision.transforms import InterpolationMode
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT... | null |
27,804 | import os
import json
import random
import torch
import glob
from collections import defaultdict, Counter
from torchvision import transforms
from torchvision.datasets.folder import default_loader
from torchvision.transforms import InterpolationMode
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT... | null |
27,805 | import os
import json
import random
import torch
import glob
from collections import defaultdict, Counter
from torchvision import transforms
from torchvision.datasets.folder import default_loader
from torchvision.transforms import InterpolationMode
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT... | null |
27,806 | import os
import json
import random
import torch
import glob
from collections import defaultdict, Counter
from torchvision import transforms
from torchvision.datasets.folder import default_loader
from torchvision.transforms import InterpolationMode
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT... | null |
27,807 | import math
from functools import partial
import torch
import torch.nn as nn
from utils import trunc_normal_
from torchscale.architecture.encoder import Encoder
from torchscale.model.LongNet import LongNetEncoder
from torchscale.architecture.config import EncoderConfig
def drop_path(x, drop_prob: float = 0., training:... | null |
27,808 | import math
from functools import partial
import torch
import torch.nn as nn
from utils import trunc_normal_
from torchscale.architecture.encoder import Encoder
from torchscale.model.LongNet import LongNetEncoder
from torchscale.architecture.config import EncoderConfig
class VisionTransformer(nn.Module):
""" Vision... | null |
27,809 | import math
import sys
import json
import numpy as np
from typing import Iterable, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.utils import ModelEma
from timm.utils import accuracy, ModelEma
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from sklearn.p... | null |
27,810 | import math
import sys
import json
import numpy as np
from typing import Iterable, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.utils import ModelEma
from timm.utils import accuracy, ModelEma
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from sklearn.p... | null |
27,811 | import math
import sys
import json
import numpy as np
from typing import Iterable, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.utils import ModelEma
from timm.utils import accuracy, ModelEma
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from sklearn.p... | null |
27,812 | import collections
from random import Random
from typing import Dict, Iterable, Optional
import numpy as np
from infinibatch import iterators
def apply_to_sample(f, sample):
if hasattr(sample, "__len__") and len(sample) == 0:
return {}
def _apply(x):
if isinstance(x, np.ndarray):
r... | null |
27,813 | import logging
from typing import Dict, List, Optional, Tuple
import torch
from fairseq import distributed_utils, utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.models.tr... | null |
27,814 | import logging
from dataclasses import dataclass, field
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import BaseFairseqModel, register_model, register_model_architec... | null |
27,815 | import logging
from dataclasses import dataclass, field
from typing import Optional
import torch
from fairseq import distributed_utils, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqIncrementalDecoder,
FairseqLanguageModel,
register_model,
register_... | null |
27,816 | import logging
from dataclasses import dataclass, field
from typing import Optional
import torch
from fairseq import distributed_utils, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqIncrementalDecoder,
FairseqLanguageModel,
register_model,
register_... | null |
27,817 | import logging
from dataclasses import dataclass, field
from typing import Optional
import torch
from fairseq import distributed_utils, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqIncrementalDecoder,
FairseqLanguageModel,
register_model,
register_... | null |
27,818 | import logging
from dataclasses import dataclass, field
from typing import Optional
import torch
from fairseq import distributed_utils, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqIncrementalDecoder,
FairseqLanguageModel,
register_model,
register_... | null |
27,819 | import logging
from dataclasses import dataclass, field
from typing import Optional
import torch
from fairseq import distributed_utils, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqIncrementalDecoder,
FairseqLanguageModel,
register_model,
register_... | null |
27,820 | import logging
from dataclasses import dataclass, field
from typing import Optional
import torch
from fairseq import distributed_utils, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqIncrementalDecoder,
FairseqLanguageModel,
register_model,
register_... | null |
27,821 | import logging
from dataclasses import dataclass, field
from typing import Optional
import torch
from fairseq import distributed_utils, utils
from fairseq.dataclass import ChoiceEnum, FairseqDataclass
from fairseq.models import (
FairseqIncrementalDecoder,
FairseqLanguageModel,
register_model,
register_... | null |
27,822 | import math
import warnings
import torch
import torch.distributed as dist
from fairseq.utils import multi_tensor_l2norm_available, multi_tensor_total_norm
def clip_grad_norm_(
params, max_norm, moe_expert_count, aggregate_norm_fn=None
) -> torch.Tensor:
def grad_exists(p):
return p is not None and geta... | null |
27,823 | import numpy as np
import torch
import torch.nn as nn
def fixed_pos_embedding(x):
seq_len, dim = x.shape
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim) / dim))
sinusoid_inp = (
torch.einsum("i , j -> i j", torch.arange(0, seq_len, dtype=torch.float), inv_freq).to(x)
)
return torch.sin(sin... | null |
27,824 | import numpy as np
import torch
import torch.nn as nn
def rotate_every_two(x):
x1 = x[:, :, ::2]
x2 = x[:, :, 1::2]
x = torch.stack((-x2, x1), dim=-1)
return x.flatten(-2) # in einsum notation: rearrange(x, '... d j -> ... (d j)')\
def duplicate_interleave(m):
"""
A simple version of `torch.rep... | null |
27,825 | import torch
import torch.distributed as dist
def padding_to_multiple_of(n, mult):
remainder = n % mult
if remainder == 0:
return 0
return mult - remainder | null |
27,826 | import torch
import torch.distributed as dist
def get_data_parallel_group():
def get_rank(group):
def get_data_parallel_rank():
return get_rank(get_data_parallel_group()) | null |
27,827 | import torch
import torch.distributed as dist
def get_data_parallel_group():
if torch.distributed.is_initialized():
if not hasattr(get_data_parallel_group, "_global_group"):
get_data_parallel_group._global_group = dist.new_group()
return get_data_parallel_group._global_group
else:
... | null |
27,828 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .xmoe.global_groups import get_moe_group
class set_torch_seed(object):
def __init__(self, seed):
def get_rng_state(self):
def set_rng_state(self, state):
def __enter__(self):
def __exit__(self, *exc):
class FeedForwardNetwo... | null |
27,829 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .xmoe.global_groups import get_moe_group
def get_activation_fn(activation):
if activation == "relu":
return F.relu
elif activation == "gelu":
return F.gelu
elif activation == "swish":
return F.silu
else:
... | null |
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