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 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Union[str, Any] = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeat... | 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 unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMS... | 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 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 |
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 logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
... | 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.abc import Sequence
def __SCREAMING_SNAKE_CASE ( a__ : Sequence[int] | None = None ) -> int:
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
__A : Dict = nums[0]
for i in range(1 ,len(a__ ) ):
... | 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 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase_ (... | 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 argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
B... | 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 |
def __SCREAMING_SNAKE_CASE ( a__ : str ) -> str:
return " ".join(
"""""".join(word[::-1] ) if len(a__ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sro... | 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 numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
UpperCAmelCase_ : Optional[Any] = models.Sequential()
# S... | 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 contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __SCREAMING_SNAKE_CASE ( a__ : int ) ->... | 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 requests
UpperCAmelCase_ : List[Any] = '''''' # <-- Put your OpenWeatherMap appid here!
UpperCAmelCase_ : List[Any] = '''https://api.openweathermap.org/data/2.5/'''
def __SCREAMING_SNAKE_CASE ( a__ : str = "Chicago" ,a__ : str = APPID ) -> dic... | 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
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from u... | 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 __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
UpperCAmelCase_ : List[Any] = '''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''
def __SCREAMING_SNAKE_CASE ( a__ : str = "mumbai" ) -> Ge... | 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 ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 |
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ) -> int:
return number | (1 << position)
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ) -> int:
return number & ~(1 << position)
def __SCREAMING_SNAKE_CASE ( a__ : in... | 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 |
def __SCREAMING_SNAKE_CASE ( a__ : str ) -> list:
__A : int = [0] * len(a__ )
for i in range(1 ,len(a__ ) ):
# use last results for better performance - dynamic programming
__A : List[Any] = prefix_result[i - 1]
while j > 0 and input_string... | 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 os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart impo... | 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
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from... | 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 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : list[float] ,a__ : list[float] ) -> float:
__A : str = sorted(numsa + numsa )
__A , __A : Any = divmod(len(a__ ) ,2 )
if mod == 1:
return all_numbers[div]
else:... | 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 argparse
from collections import defaultdict
import yaml
UpperCAmelCase_ : List[str] = '''docs/source/en/_toctree.yml'''
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> Union[str, Any]:
__A : Union[str, Any] = defaultdict(a__ )
__A : Dict = ... | 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 torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase_ ( _lowercase ):
_lowercase : Dict = '''M-CLIP'''
def __init__( self : List[str] , __A : str=1024 , __A : Optional[Any]=76... | 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 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax 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 |
def __SCREAMING_SNAKE_CASE ( a__ : str ,a__ : str ) -> Union[str, Any]:
assert x is not None
assert y is not None
__A : str = len(a__ )
__A : Tuple = len(a__ )
# declaring the array for storing the dp values
__A : Dict = [[0] * (n + ... | 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 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 |
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 |
def __SCREAMING_SNAKE_CASE ( a__ : List[Any] ,a__ : Tuple ,a__ : Any ,a__ : Any ) -> Tuple:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
__A : Any = mf_knapsack(i - 1 ,a__ ,a__ ,a__ )
else:
__A : str... | 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 argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( a__ : Any ,a__ : Optional[Any] ) ... | 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 __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCAmelCase_ : Optional[Any] = 1.0_5457_1817e-34 # unit of ℏ : J * s
UpperCAmelCase_ : Union[str, Any] = 3e8 # unit of c : m * s^-... | 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 unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase_ ( unittest.TestCase ):
def lowerCAmelCase_ ( self : Optional[Any] ):
__A : str = [10, 20, 30, 40, 50, 60]
__A : str = [2, 4, 6, 8, 10, 12]
__A : List[Any] ... | 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 |
from __future__ import annotations
import os
from collections.abc import Mapping
UpperCAmelCase_ : List[str] = tuple[int, int]
class lowerCamelCase_ :
def __init__( self : Dict , __A : set[int] , __A : Mapping[EdgeT, int] ):
... | 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 copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/r... | 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 colorsys
from PIL import Image # type: ignore
def __SCREAMING_SNAKE_CASE ( a__ : float ,a__ : float ,a__ : int ) -> float:
__A : List[Any] = x
__A : int = y
for step in range(a__ ): # noqa: B007
__A : List[Any] = a * a - b * ... | 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 typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_i... | 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 inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_conf... | 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
def __SCREAMING_SNAKE_CASE ( ) -> int:
__A : Optional[Any] = os.path.join(os.path.dirname(a__ ) ,"""num.txt""" )
with open(a__ ) as file_hand:
return str(sum(int(a__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(... | 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 argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : str = logging.get_logger(__name__)
def ... | 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 unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase_ : Optional[int] = get_tests... | 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_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Tuple = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 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 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 |
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_torch_available
UpperCAmelCase_ : str = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if not is_torch_available():
... | 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 copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : Dict = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/mask2... | 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__ : str ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
__A : Optional[int] = sorted(string.lower() )
return len(a__ ) == len(set(a__ ... | 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 ) -> bool:
return sum(i for i in range(1 ,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a Perfect number or not...''')
UpperCAmelCase_ : Op... | 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 bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class lowerCamelCase_ ( _lowercase ):
def __lt__( self : str , __A : Tuple ):
return self[-1] < other[-1]
def... | 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 |
def __SCREAMING_SNAKE_CASE ( a__ : Union[str, Any] ) -> int:
stooge(a__ ,0 ,len(a__ ) - 1 )
return arr
def __SCREAMING_SNAKE_CASE ( a__ : Optional[int] ,a__ : Tuple ,a__ : str ) -> Any:
if i >= h:
return
# If first elem... | 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 |
UpperCAmelCase_ : str = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __SCREAMING_SNAKE_CASE ( a__ : Union[str, Any] ,a__ : str ,a__ : Any ,a__ : List[st... | 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 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 |
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 warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowerCamelCase_ ( _lowercase ):
_lowercase : Union[str, Any] ... | 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 |
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 |
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 copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class lowerCamelCase_ ( _lowercase ):
def __init__( self : int , __A : List[str] , __A : Opti... | 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 string
from math import logaa
def __SCREAMING_SNAKE_CASE ( a__ : str ,a__ : str ) -> int:
__A : Optional[int] = document.translate(
str.maketrans("""""" ,"""""" ,string.punctuation ) ).replace("""\n""" ,"""""" )
__A : List[Any] ... | 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 unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase_ : List[Any] = get_tests_dir('''fixtures/test_sentencepiece_with_... | 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 torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __SCREAMING_SNAKE_CASE ( ) -> str:
raise RuntimeError("""CUDA out of memory.""" )
class lowerCamel... | 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
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
... | 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
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCAmelCase_ : int = 10
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ,a__ : list[int]... | 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 |
def __SCREAMING_SNAKE_CASE ( a__ : Tuple ,a__ : List[str] ) -> Tuple:
print("""\nThe shortest path matrix using Floyd Warshall algorithm\n""" )
for i in range(a__ ):
for j in range(a__ ):
if dist[i][j] != float("""inf""" ):
print(int(dist[i][j] ... | 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 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 |
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 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 |
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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Any = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '... | 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
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
UpperCAmelCase_ : Optional[int] = 4
UpperCAmelCase_ : List[str] = 3
class lowerC... | 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
import torch
from torch import nn
from diffusers.models.activations import get_activation
class lowerCamelCase_ ( unittest.TestCase ):
def lowerCAmelCase_ ( self : Optional[int] ):
__A : str = get_activation("""swish""" )
self.... | 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 argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 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 time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = r'''
Args:
i... | 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
from pathlib import Path
def __SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
from torch.utils.cpp_extension import load
__A : int = Path(a__ ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
__A : Any = [
root / filename
for filen... | 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 __future__ import annotations
from random import choice
def __SCREAMING_SNAKE_CASE ( a__ : str ) -> Optional[Any]:
return choice(a__ )
def __SCREAMING_SNAKE_CASE ( a__ : list[int] ,a__ : int ) -> int:
__A : List[str] = random_... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : Optional[Any] = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
... | 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 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention... | 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 ,a__ : int ,a__ : int ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
__A : int = _modexpt(a__ ,exponent // 2 ,a__ ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modex... | 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 math import factorial
def __SCREAMING_SNAKE_CASE ( a__ : int = 100 ) -> int:
return sum(map(a__ ,str(factorial(a__ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 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 gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, lo... | 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 itertools
import string
from collections.abc import Generator, Iterable
def __SCREAMING_SNAKE_CASE ( a__ : Iterable[str] ,a__ : int ) -> Generator[tuple[str, ...], None, None]:
__A : Union[str, Any] = iter(a__ )
while True:
__A : Union[str, Any] =... | 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 tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ ( _lowercase ):
_lowercase : str = (PNDMScheduler,)
_lowercase : List[Any] = (('''num_inference_steps''', 50),)
def low... | 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 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : List[str] = {
'''configuration_bert''... | 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 |
UpperCAmelCase_ : int = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_avai... | 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 math import factorial
def __SCREAMING_SNAKE_CASE ( a__ : int = 100 ) -> int:
return sum(int(a__ ) for x in str(factorial(a__ ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 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 |
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 |
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 gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMS... | 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 pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __SCREAMING_SNAKE_CASE ( a__ : Optional[int] ,a__ : Any ) ... | 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 json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__n... | 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 inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common... | 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 math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_... | 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 unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inp... | 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 json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase_ ( _lowercase , unittest.TestCas... | 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_ : List[str] = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''Gro... | 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 |
class lowerCamelCase_ :
def __init__( self : int ):
__A : int = 0
__A : Union[str, Any] = 0
__A : Any = {}
def lowerCAmelCase_ ( self : List[Any] , __A : Dict ):
if vertex not in self.adjacency:... | 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 |
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 |
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 Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
cla... | 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
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeni... | 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 importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __vers... | 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 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 |
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 json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acce... | 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 __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 |
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 os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCAmelCase_ : Tuple = '''.'''
if __name__ == "__main__":
UpperCAmelCase_ : Any = os.path.join(REPO_PATH, '''utils/documentatio... | 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 Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __SCREAMING_SNAKE_CASE ( a__ : Dict[str, torch.Tensor] ) -> Dict[str, torch.Tensor]:
__A : int = []
__A : str = []
... | 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 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : Union[str, Any] = {'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}
try:
if... | 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 datasets
from .evaluate import evaluate
UpperCAmelCase_ : str = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016... | 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
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, r... | 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 |
from maths.prime_check import is_prime
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> int:
if not isinstance(a__ ,a__ ):
__A : Any = f"""Input value of [number={number}] must be an integer"""
raise TypeError(a__ )
if is_prime(a__ ) and is_prime(number + 2... | 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 gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class ... | 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 warnings
from functools import wraps
from typing import Callable
def __SCREAMING_SNAKE_CASE ( a__ : Callable ) -> Callable:
@wraps(a__ )
def _inner_fn(*a__ : Union[str, Any] ,**a__ : Any ):
warnings.warn(
(f"""'{fn.__name__}' is experimental and... | 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 typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, Flax... | 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 |
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