code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import sys
def __SCREAMING_SNAKE_CASE ( a__ : List[Any] ) -> int:
__A : Union[str, Any] = len(a__ )
__A : Any = [[0 for x in range(a__ )] for x in range(a__ )]
__A : Optional[Any] = [[0 for x in range(a__ )] for x in range(a__ ... | 17 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase_ ( _lowercase ):
_lowercase : Union[str, Any] = '''EncodecFeatureExtractor'''
_lowercase : Any = ('''T5Tokenizer''', ... | 17 | 1 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
UpperCAmelCase_ : str = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str... | 17 |
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> int:
if not isinstance(a__ ,a__ ):
raise TypeError("""Input value must be an 'int' type""" )
__A : Union[str, Any] = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
... | 17 | 1 |
import fire
from utils import calculate_rouge, save_json
def __SCREAMING_SNAKE_CASE ( a__ : Any ,a__ : Tuple ,a__ : Any=None ,**a__ : Dict ) -> Optional[Any]:
__A : int = [x.strip() for x in open(a__ ).readlines()]
__A : List[str] = [x.str... | 17 |
UpperCAmelCase_ : dict[tuple[int, int, int], int] = {}
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ,a__ : int ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
retu... | 17 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class lowerCamelCase_ ( unittest.TestCase ):
def lowerCAmelCase_ ( self : Optional[Any] ):
debug_launcher(test_script.main ... | 17 |
class lowerCamelCase_ :
def __init__( self : Dict , __A : int , __A : Tuple , __A : List[Any] ):
__A : Optional[int] = None
__A : Any = None
__A : int = graph
self._normalize_graph(__A , ... | 17 | 1 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCAmelCase_ : int = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'''... | 17 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __SCREAMING_SNAKE_CASE ( a__ : str ,a__ : str ) -> str | Literal[False]:
__A : Tuple = list(a__ )
__A : Optional[int] = list(a__ )
__A : int ... | 17 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
... | 17 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : List[str] ,a__ : Dict ,a__ : Union[str, Any] ,a__ : Any ) -> Optional[int]: # noqa: E741
while r - l > 1:
__A : Any = (l + r) // 2
if v[m] >= key:
__A : Optional[int] = ... | 17 | 1 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class lowerCamelCase_ ( datasets.BuilderConfig ):
_lowercase : Optional[datasets.Features] = None
... | 17 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 17 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : str ... | 17 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/micro... | 17 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __SCREAMING_SNAKE_CASE ( a__ : str ) -> List[s... | 17 |
import fire
from utils import calculate_rouge, save_json
def __SCREAMING_SNAKE_CASE ( a__ : Any ,a__ : Tuple ,a__ : Any=None ,**a__ : Dict ) -> Optional[Any]:
__A : int = [x.strip() for x in open(a__ ).readlines()]
__A : List[str] = [x.str... | 17 | 1 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..util... | 17 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( a__ : Optional[Any] ,a__ : Union[str, Any] ,a__ : Optiona... | 17 | 1 |
def __SCREAMING_SNAKE_CASE ( a__ : int = 1000000 ) -> int:
__A : Optional[Any] = [i - 1 for i in range(limit + 1 )]
for i in range(2 ,limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i ,limit + 1 ,a__ ):
phi[j] -= phi[j] // i
return sum(... | 17 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : int = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
... | 17 | 1 |
from __future__ import annotations
from cmath import sqrt
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ,a__ : int ) -> tuple[complex, complex]:
if a == 0:
raise ValueError("""Coefficient 'a' must not be zero.""" )
__A : str = b * b - 4 * a * c
_... | 17 |
import math
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , __A : List[str]=0 ): # a graph with Node 0,1,...,N-1
__A : List[str] = n
__A : List[str] = [
[math.inf for j in range(0 , __A )] for i in ran... | 17 | 1 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from tra... | 17 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __SCREAMING_SNAKE_CASE ( a__ : str ,a__ : complex ,a__ : str = "x" ,a__ : float = 10**-10 ,a__ : int = 1 ,) -> complex:
__A : Tuple = symbols(a__ )
__A : ... | 17 | 1 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : List[str] ,a__ : Dict ,a__ : Union[str, Any] ,a__ : Any ) -> Optional[int]: # noqa: E741
while r - l > 1:
__A : Any = (l + r) // 2
if v[m] >= key:
__A : Optional[int] = ... | 17 |
from math import sqrt
def __SCREAMING_SNAKE_CASE ( a__ : int = 1000000 ) -> int:
__A : int = 0
__A : int = 0
__A : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 ,2 * max_cuboid_size + 1 ):
if sqrt(sum_short... | 17 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class lowerCamelCase_ ( unittest.TestCase ):
_lowercase : List[str] = JukeboxTokenizer
_lowercase : Any = {
'''artist''': '''Zac Brown Band''',
'''g... | 17 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,... | 17 | 1 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_mu... | 17 |
class lowerCamelCase_ :
def __init__( self : Dict , __A : Tuple , __A : Optional[int] , __A : int ):
__A : List[str] = name
__A : Optional[int] = value
__A : Optional[Any] = weight
def __repr_... | 17 | 1 |
def __SCREAMING_SNAKE_CASE ( a__ : list ,a__ : list ,a__ : int ,a__ : int ,a__ : int ) -> int:
if index == number_of_items:
return 0
__A : Optional[int] = 0
__A : List[Any] = 0
__A : int = knapsack(a__ ,a__ ,a__ ,a__ ,... | 17 |
UpperCAmelCase_ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_186_800... | 17 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
imp... | 17 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : Optional[Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 17 | 1 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimize... | 17 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from da... | 17 | 1 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from da... | 17 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 17 | 1 |
from __future__ import annotations
import typing
from collections import Counter
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> typing.Counter[int]:
__A : typing.Counter[int] = Counter()
for base in range(1 ,max_perimeter + 1 ):
for perpendicular in range(a__ ,max_p... | 17 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __SCREAMING_SNAKE_CASE ( ) -> Tuple:
__A : List[Any] = ArgumentParser(
description=(
"""PyTorch TPU distributed t... | 17 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowerCamelCase_ ( unittest.TestCase ):
def lowerCAmelCase_ ( self ... | 17 |
from collections.abc import Sequence
def __SCREAMING_SNAKE_CASE ( a__ : Sequence[float] ,a__ : float ) -> float:
return sum(c * (x**i) for i, c in enumerate(a__ ) )
def __SCREAMING_SNAKE_CASE ( a__ : Sequence[float] ,a__ : float ) -... | 17 | 1 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCamelCase_ ( tf.keras.layers.Layer ):
def __init__( self : ... | 17 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase_ ( _lowercase ):
_lowercase : Union[str, Any] = '''EncodecFeatureExtractor'''
_lowercase : Any = ('''T5Tokenizer''', ... | 17 | 1 |
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> list[int]:
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__A : Any = [True] * (num + 1)
__A : Optional[int] = 2
while p * p <= num:
if primes[p]:
for i in range(p * p ,num + 1 ... | 17 |
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> int:
if not isinstance(a__ ,a__ ):
raise TypeError("""Input value must be an 'int' type""" )
__A : Union[str, Any] = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
... | 17 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
UpperCAmelCase_ : Dict = logging.get_logger(__name... | 17 |
UpperCAmelCase_ : dict[tuple[int, int, int], int] = {}
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ,a__ : int ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
retu... | 17 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
UpperCAme... | 17 |
class lowerCamelCase_ :
def __init__( self : Dict , __A : int , __A : Tuple , __A : List[Any] ):
__A : Optional[int] = None
__A : Any = None
__A : int = graph
self._normalize_graph(__A , ... | 17 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ : Any = {
'''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
'''configuratio... | 17 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __SCREAMING_SNAKE_CASE ( a__ : str ,a__ : str ) -> str | Literal[False]:
__A : Tuple = list(a__ )
__A : Optional[int] = list(a__ )
__A : int ... | 17 | 1 |
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ,a__ : int ) -> float:
__A : List[Any] = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def __SCREAMING_SNAKE_CASE ( ) -> in... | 17 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : List[str] ,a__ : Dict ,a__ : Union[str, Any] ,a__ : Any ) -> Optional[int]: # noqa: E741
while r - l > 1:
__A : Any = (l + r) // 2
if v[m] >= key:
__A : Optional[int] = ... | 17 | 1 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 17 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 17 | 1 |
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ) -> int:
while a != 0:
__A , __A : Tuple = b % a, a
return b
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ) -> int:
if gcd(a__ ,a__ ) != 1:
__A : U... | 17 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/micro... | 17 | 1 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __SCREAMING_SNAKE_CASE ( a__ : Any ) -> Any:
__A : Union[str, Any] = FileLock(str(tmpdir / """foo.lock""" ) )
__A : Optional[int] = FileLock(str(tmpdir / """fo... | 17 |
import fire
from utils import calculate_rouge, save_json
def __SCREAMING_SNAKE_CASE ( a__ : Any ,a__ : Tuple ,a__ : Any=None ,**a__ : Dict ) -> Optional[Any]:
__A : int = [x.strip() for x in open(a__ ).readlines()]
__A : List[str] = [x.str... | 17 | 1 |
import operator as op
UpperCAmelCase_ : Union[str, Any] = '''scaler.pt'''
UpperCAmelCase_ : int = '''pytorch_model'''
UpperCAmelCase_ : Optional[Any] = '''random_states'''
UpperCAmelCase_ : Dict = '''optimizer'''
UpperCAmelCase_ : Dict = '''sched... | 17 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( a__ : Optional[Any] ,a__ : Union[str, Any] ,a__ : Optiona... | 17 | 1 |
class lowerCamelCase_ :
def __init__( self : Dict , __A : int , __A : Tuple , __A : List[Any] ):
__A : Optional[int] = None
__A : Any = None
__A : int = graph
self._normalize_graph(__A , ... | 17 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : int = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
... | 17 | 1 |
import math
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , __A : List[str]=0 ): # a graph with Node 0,1,...,N-1
__A : List[str] = n
__A : List[str] = [
[math.inf for j in range(0 , __A )] for i in ran... | 17 |
import math
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , __A : List[str]=0 ): # a graph with Node 0,1,...,N-1
__A : List[str] = n
__A : List[str] = [
[math.inf for j in range(0 , __A )] for i in ran... | 17 | 1 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __SCREAMING_SNAKE_CASE ( a__ : Any ,a__ : Optional[int]=False ) -> Optional[Any]:
__A : Tuple = OmegaConf.load(a__ )
if display:
print(yaml.du... | 17 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __SCREAMING_SNAKE_CASE ( a__ : str ,a__ : complex ,a__ : str = "x" ,a__ : float = 10**-10 ,a__ : int = 1 ,) -> complex:
__A : Tuple = symbols(a__ )
__A : ... | 17 | 1 |
def __SCREAMING_SNAKE_CASE ( a__ : list ,a__ : int = 0 ) -> list:
__A : Optional[Any] = length or len(a__ )
__A : Dict = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
__A , __A : Dict = list_data[i + 1]... | 17 |
from math import sqrt
def __SCREAMING_SNAKE_CASE ( a__ : int = 1000000 ) -> int:
__A : int = 0
__A : int = 0
__A : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 ,2 * max_cuboid_size + 1 ):
if sqrt(sum_short... | 17 | 1 |
from collections import deque
from .hash_table import HashTable
class lowerCamelCase_ ( _lowercase ):
def __init__( self : List[Any] , *__A : str , **__A : Union[str, Any] ):
super().__init__(*__A , **__A )
def l... | 17 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,... | 17 | 1 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokeni... | 17 |
class lowerCamelCase_ :
def __init__( self : Dict , __A : Tuple , __A : Optional[int] , __A : int ):
__A : List[str] = name
__A : Optional[int] = value
__A : Optional[Any] = weight
def __repr_... | 17 | 1 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ ( _lowercase ):
_lowercase : str = (DDPMParallelScheduler,)
def lowerCAmelCase_ ( self : List[Any] , **__A : ... | 17 |
UpperCAmelCase_ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_186_800... | 17 | 1 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def __SCREAMING_SNAKE_CASE ( a__ : dict ,a__ : str ,a__ : set ,a__ : set ,a__ : dict ,a__ : dict ,a__ : PriorityQueue ,a__ : dict ,a__ : float | int ,) -> float... | 17 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : Optional[Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 17 | 1 |
import pytest
UpperCAmelCase_ : Optional[int] = '''__dummy_dataset1__'''
UpperCAmelCase_ : str = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", ... | 17 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from da... | 17 | 1 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowerCamelCase_ :
@property
def lowerCAmelCase_ ( self ... | 17 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 17 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sense... | 17 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __SCREAMING_SNAKE_CASE ( ) -> Tuple:
__A : List[Any] = ArgumentParser(
description=(
"""PyTorch TPU distributed t... | 17 | 1 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to hav... | 17 |
from collections.abc import Sequence
def __SCREAMING_SNAKE_CASE ( a__ : Sequence[float] ,a__ : float ) -> float:
return sum(c * (x**i) for i, c in enumerate(a__ ) )
def __SCREAMING_SNAKE_CASE ( a__ : Sequence[float] ,a__ : float ) -... | 17 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Any = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCH... | 17 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase_ ( _lowercase ):
_lowercase : Union[str, Any] = '''EncodecFeatureExtractor'''
_lowercase : Any = ('''T5Tokenizer''', ... | 17 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
'''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/res... | 17 |
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> int:
if not isinstance(a__ ,a__ ):
raise TypeError("""Input value must be an 'int' type""" )
__A : Union[str, Any] = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
... | 17 | 1 |
import math
def __SCREAMING_SNAKE_CASE ( a__ : list ,a__ : int = 0 ,a__ : int = 0 ) -> list:
__A : Optional[int] = end or len(a__ )
for i in range(a__ ,a__ ):
__A : List[Any] = i
__A : Optional[int] = array[i]
while temp_ind... | 17 |
UpperCAmelCase_ : dict[tuple[int, int, int], int] = {}
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ,a__ : int ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
retu... | 17 | 1 |
from sklearn.metrics import recall_score
import datasets
UpperCAmelCase_ : Dict = '''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false ... | 17 |
class lowerCamelCase_ :
def __init__( self : Dict , __A : int , __A : Tuple , __A : List[Any] ):
__A : Optional[int] = None
__A : Any = None
__A : int = graph
self._normalize_graph(__A , ... | 17 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,... | 17 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __SCREAMING_SNAKE_CASE ( a__ : str ,a__ : str ) -> str | Literal[False]:
__A : Tuple = list(a__ )
__A : Optional[int] = list(a__ )
__A : int ... | 17 | 1 |
import math
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> str:
__A : Optional[int] = 0
__A : List[str] = 0
while num > 0:
__A : Optional[int] = num % 8
__A : List[Any] = octal + (remainder * math.floor(math.pow(10 ,a__ ) ))
counte... | 17 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : List[str] ,a__ : Dict ,a__ : Union[str, Any] ,a__ : Any ) -> Optional[int]: # noqa: E741
while r - l > 1:
__A : Any = (l + r) // 2
if v[m] >= key:
__A : Optional[int] = ... | 17 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
class lowerCamelCase_ ( _lowercase ):
_lowercase : Optional[int] = '''encoder-decoder'''
_lowercase : str = ... | 17 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 17 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : Dict = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''V... | 17 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/micro... | 17 | 1 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase_ ( _lowercase , unittest.TestCase ):
_lo... | 17 |
import fire
from utils import calculate_rouge, save_json
def __SCREAMING_SNAKE_CASE ( a__ : Any ,a__ : Tuple ,a__ : Any=None ,**a__ : Dict ) -> Optional[Any]:
__A : int = [x.strip() for x in open(a__ ).readlines()]
__A : List[str] = [x.str... | 17 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/micro... | 17 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( a__ : Optional[Any] ,a__ : Union[str, Any] ,a__ : Optiona... | 17 | 1 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase_ : Any = loggin... | 17 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : int = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
... | 17 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
... | 17 |
import math
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , __A : List[str]=0 ): # a graph with Node 0,1,...,N-1
__A : List[str] = n
__A : List[str] = [
[math.inf for j in range(0 , __A )] for i in ran... | 17 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[int] = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.... | 17 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __SCREAMING_SNAKE_CASE ( a__ : str ,a__ : complex ,a__ : str = "x" ,a__ : float = 10**-10 ,a__ : int = 1 ,) -> complex:
__A : Tuple = symbols(a__ )
__A : ... | 17 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
cla... | 17 |
from math import sqrt
def __SCREAMING_SNAKE_CASE ( a__ : int = 1000000 ) -> int:
__A : int = 0
__A : int = 0
__A : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 ,2 * max_cuboid_size + 1 ):
if sqrt(sum_short... | 17 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase_ : Any = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
U... | 17 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,... | 17 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 17 |
class lowerCamelCase_ :
def __init__( self : Dict , __A : Tuple , __A : Optional[int] , __A : int ):
__A : List[str] = name
__A : Optional[int] = value
__A : Optional[Any] = weight
def __repr_... | 17 | 1 |
def __SCREAMING_SNAKE_CASE ( a__ : list ) -> list:
if len(a__ ) <= 1:
return [tuple(a__ )]
__A : int = []
def generate(a__ : int ,a__ : list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 ,a__ ... | 17 |
UpperCAmelCase_ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_186_800... | 17 | 1 |
from __future__ import annotations
import requests
UpperCAmelCase_ : Optional[Any] = set(
'''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categori... | 17 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : Optional[Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 17 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
... | 17 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from da... | 17 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 17 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 17 | 1 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
def __init__( self : Dict , __A : Optional[Any]=2 , __A : Union[str, Any]=3 , __A ... | 17 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __SCREAMING_SNAKE_CASE ( ) -> Tuple:
__A : List[Any] = ArgumentParser(
description=(
"""PyTorch TPU distributed t... | 17 | 1 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : list[list[int]] ) -> int:
# preprocessing the first row
for i in range(1 ,len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 ,len(a__ ) ... | 17 |
from collections.abc import Sequence
def __SCREAMING_SNAKE_CASE ( a__ : Sequence[float] ,a__ : float ) -> float:
return sum(c * (x**i) for i, c in enumerate(a__ ) )
def __SCREAMING_SNAKE_CASE ( a__ : Sequence[float] ,a__ : float ) -... | 17 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : Dict = {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']... | 17 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase_ ( _lowercase ):
_lowercase : Union[str, Any] = '''EncodecFeatureExtractor'''
_lowercase : Any = ('''T5Tokenizer''', ... | 17 | 1 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( a__ : Optional[int] ) -> int:
monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings""" ,set() )
@pytest.fixture
def ... | 17 |
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> int:
if not isinstance(a__ ,a__ ):
raise TypeError("""Input value must be an 'int' type""" )
__A : Union[str, Any] = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
... | 17 | 1 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
... | 17 |
UpperCAmelCase_ : dict[tuple[int, int, int], int] = {}
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ,a__ : int ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
retu... | 17 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class lowerCamelCase_ :
... | 17 |
class lowerCamelCase_ :
def __init__( self : Dict , __A : int , __A : Tuple , __A : List[Any] ):
__A : Optional[int] = None
__A : Any = None
__A : int = graph
self._normalize_graph(__A , ... | 17 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)... | 17 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __SCREAMING_SNAKE_CASE ( a__ : str ,a__ : str ) -> str | Literal[False]:
__A : Tuple = list(a__ )
__A : Optional[int] = list(a__ )
__A : int ... | 17 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __SCREAMING_SNAKE_CASE ( a__ : str ,a__ : complex ,a__ : str = "x" ,a__ : float = 10**-10 ,a__ : int = 1 ,) -> complex:
__A : Tuple = symbols(a__ )
__A : ... | 17 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : List[str] ,a__ : Dict ,a__ : Union[str, Any] ,a__ : Any ) -> Optional[int]: # noqa: E741
while r - l > 1:
__A : Any = (l + r) // 2
if v[m] >= key:
__A : Optional[int] = ... | 17 | 1 |
def __SCREAMING_SNAKE_CASE ( a__ : int = 100 ) -> int:
__A : Union[str, Any] = n * (n + 1) * (2 * n + 1) / 6
__A : List[str] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f"""{solution() = }""")
... | 17 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 17 | 1 |
from manim import *
class lowerCamelCase_ ( _lowercase ):
def lowerCAmelCase_ ( self : str ):
__A : Optional[Any] = Rectangle(height=0.5 , width=0.5 )
__A : Any = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(... | 17 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/micro... | 17 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> str:
def wrapper(*a__ : List[Any] ,**a__ : str ):
__A : List[Any] = ... | 17 |
import fire
from utils import calculate_rouge, save_json
def __SCREAMING_SNAKE_CASE ( a__ : Any ,a__ : Tuple ,a__ : Any=None ,**a__ : Dict ) -> Optional[Any]:
__A : int = [x.strip() for x in open(a__ ).readlines()]
__A : List[str] = [x.str... | 17 | 1 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import ... | 17 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( a__ : Optional[Any] ,a__ : Union[str, Any] ,a__ : Optiona... | 17 | 1 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 17 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : int = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
... | 17 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
Stab... | 17 |
import math
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , __A : List[str]=0 ): # a graph with Node 0,1,...,N-1
__A : List[str] = n
__A : List[str] = [
[math.inf for j in range(0 , __A )] for i in ran... | 17 | 1 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __SCREAMING_SNAKE_CASE ( a__ : Dataset ,a__ : Dict[str, str] ) -> Union[str, Any]:
__A : ... | 17 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __SCREAMING_SNAKE_CASE ( a__ : str ,a__ : complex ,a__ : str = "x" ,a__ : float = 10**-10 ,a__ : int = 1 ,) -> complex:
__A : Tuple = symbols(a__ )
__A : ... | 17 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __SCREAMING_SNAKE_CASE ( a__ : int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(a__ ,a__ ):
raise TypeError("""number of qubits must be... | 17 |
from math import sqrt
def __SCREAMING_SNAKE_CASE ( a__ : int = 1000000 ) -> int:
__A : int = 0
__A : int = 0
__A : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 ,2 * max_cuboid_size + 1 ):
if sqrt(sum_short... | 17 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
UpperCAmelCase_ : List[str] = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle = "Proc... | 17 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,... | 17 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace... | 17 |
class lowerCamelCase_ :
def __init__( self : Dict , __A : Tuple , __A : Optional[int] , __A : int ):
__A : List[str] = name
__A : Optional[int] = value
__A : Optional[Any] = weight
def __repr_... | 17 | 1 |
class lowerCamelCase_ :
def __init__( self : Dict , __A : Tuple , __A : Optional[int] , __A : int ):
__A : List[str] = name
__A : Optional[int] = value
__A : Optional[Any] = weight
def __repr_... | 17 |
UpperCAmelCase_ : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_186_800... | 17 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv... | 17 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : Optional[Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 17 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_lowercase )
class lowerCamelCase_ ( _lowercase ):
_lowercase : str = field(default='''image-class... | 17 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from da... | 17 | 1 |
from __future__ import annotations
from fractions import Fraction
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def __SCREAMING_SNAKE_CASE ( a__ : in... | 17 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 17 | 1 |
from __future__ import annotations
import math
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ,a__ : bool ,a__ : list[int] ,a__ : float ) -> int:
if depth < 0:
raise ValueError("""Depth cannot be less than 0""" )
if not scores:
raise ValueEr... | 17 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __SCREAMING_SNAKE_CASE ( ) -> Tuple:
__A : List[Any] = ArgumentParser(
description=(
"""PyTorch TPU distributed t... | 17 | 1 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase_ : int = ... | 17 |
from collections.abc import Sequence
def __SCREAMING_SNAKE_CASE ( a__ : Sequence[float] ,a__ : float ) -> float:
return sum(c * (x**i) for i, c in enumerate(a__ ) )
def __SCREAMING_SNAKE_CASE ( a__ : Sequence[float] ,a__ : float ) -... | 17 | 1 |
import datasets
UpperCAmelCase_ : str = '''\
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
... | 17 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase_ ( _lowercase ):
_lowercase : Union[str, Any] = '''EncodecFeatureExtractor'''
_lowercase : Any = ('''T5Tokenizer''', ... | 17 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_lowercase )
class lowerCamelCase_ ( _lowercase ):
# `task` is not a ClassVar since we want it to be part of the `asdict` outpu... | 17 |
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> int:
if not isinstance(a__ ,a__ ):
raise TypeError("""Input value must be an 'int' type""" )
__A : Union[str, Any] = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
... | 17 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : int = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/co... | 17 |
UpperCAmelCase_ : dict[tuple[int, int, int], int] = {}
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ,a__ : int ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
retu... | 17 | 1 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( a__ ... | 17 |
class lowerCamelCase_ :
def __init__( self : Dict , __A : int , __A : Tuple , __A : List[Any] ):
__A : Optional[int] = None
__A : Any = None
__A : int = graph
self._normalize_graph(__A , ... | 17 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : Optional[Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 17 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __SCREAMING_SNAKE_CASE ( a__ : str ,a__ : str ) -> str | Literal[False]:
__A : Tuple = list(a__ )
__A : Optional[int] = list(a__ )
__A : int ... | 17 | 1 |
from __future__ import annotations
UpperCAmelCase_ : Any = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase_ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0,... | 17 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : List[str] ,a__ : Dict ,a__ : Union[str, Any] ,a__ : Any ) -> Optional[int]: # noqa: E741
while r - l > 1:
__A : Any = (l + r) // 2
if v[m] >= key:
__A : Optional[int] = ... | 17 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impor... | 17 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 17 | 1 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
class lowerCamelCase_ ( _lowercase ):
def __init__( self : Union[str, Any] , *__A :... | 17 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/micro... | 17 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCamelCase_ :
_lowercase : int
_lowercase : int
class lowerCamelCase_ :
def __init__( self : Tuple ... | 17 |
import fire
from utils import calculate_rouge, save_json
def __SCREAMING_SNAKE_CASE ( a__ : Any ,a__ : Tuple ,a__ : Any=None ,**a__ : Dict ) -> Optional[Any]:
__A : int = [x.strip() for x in open(a__ ).readlines()]
__A : List[str] = [x.str... | 17 | 1 |
UpperCAmelCase_ : Optional[int] = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def ... | 17 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( a__ : Optional[Any] ,a__ : Union[str, Any] ,a__ : Optiona... | 17 | 1 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
# TODO Update this
UpperCAmelCase_ : Union[str, Any] = {
'''facebook/esm-1b''... | 17 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : int = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
... | 17 | 1 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAM... | 17 |
import math
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , __A : List[str]=0 ): # a graph with Node 0,1,...,N-1
__A : List[str] = n
__A : List[str] = [
[math.inf for j in range(0 , __A )] for i in ran... | 17 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.