code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _lowerCAmelCase( __A ):
UpperCAmelCase = FileLock(str(tmpdir / "foo.lock" ) )
UpperCAmelCase = FileLock(str(tmpdir / "foo.lock" ) )
UpperCAmelCase = 0.01
with locka.a... | 718 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"facebook/convnextv2-tiny-1k-224": "https://huggi... | 1 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Batch... | 719 |
lowerCAmelCase__ = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerCAmelCase__ = [{"type": "code", "content": INSTALL_CONTENT}]
lowerCAmelCase__ = {
"... | 1 | 0 |
import functools
def _lowerCAmelCase( __A , __A ):
UpperCAmelCase = len(_UpperCamelCase )
UpperCAmelCase = len(_UpperCamelCase )
@functools.cache
def min_distance(__A , __A ) -> int:
# if first word index is overflow - delete all from the second word
... | 720 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import M... | 1 | 0 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def _lowerCAmelCase( __A , __A=() , __A=None , __A="no" , __A="29500" ):
UpperCAmelCase = ... | 721 |
def _lowerCAmelCase( __A , __A ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowerCAmelCase( __A , __A=0 ):
return sorted(__A , key=lambda __A : x[column] )
def _lowerCAmelCase( __A , __A , __A=float("inf" ) ):
for ... | 1 | 0 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __magic_name__ ( _snake_case ):
UpperCAmelCase ... | 700 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __magic_name__ :
def __init__( self : Optional[int] ) -> Optional[Any]:
UpperCAmelCase = ""
UpperCAmelCase = ""
UpperCAmelCase = []
UpperCAmel... | 1 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configu... | 701 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tok... | 1 | 0 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
... | 702 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
lowerCAmelCase__ = ... | 1 | 0 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 703 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class __magic_name__ (... | 1 | 0 |
import numpy
class __magic_name__ :
def __init__( self : Dict , lowerCAmelCase__ : numpy.ndarray , lowerCAmelCase__ : numpy.ndarray ) -> Any:
UpperCAmelCase = input_array
# Random initial weights are assigned where first argument is the
# number ... | 704 |
def _lowerCAmelCase( __A ):
UpperCAmelCase = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _lowerCAmelCase( __A = 100 ):
UpperCAmelCase = 1
UpperCAmelCase = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase ... | 1 | 0 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_configuration_common import ConfigTest... | 705 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbo... | 1 | 0 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERA... | 706 |
import numpy
# List of input, output pairs
lowerCAmelCase__ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowerCAmelCase__ = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__ = [2, 4, 1, 5]
lowerCAmelCase__ = len... | 1 | 0 |
import os
import sys
import unittest
lowerCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read... | 707 |
def _lowerCAmelCase( __A , __A , __A ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__A , n - 1 , __A ) * a) % mod
else:
UpperCAmelCase = binary_exponentiation(__A , n / 2 , __A )
return (b * b) % mod
... | 1 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _lowerCAmelCase( __A : int=None , ... | 708 |
lowerCAmelCase__ = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
... | 1 | 0 |
class __magic_name__ : # Public class to implement a graph
def __init__( self : str , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[int] ) -> None:
UpperCAmelCase = row
UpperCAmelCase = col
U... | 709 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCAmelCase__ = {"UserAgent": UserAgent().random}
def _lowerCAmelCase( __A ):
UpperCAmelCase = script.contents[0]
UpperCAmelCase = json.loads(d... | 1 | 0 |
def _lowerCAmelCase( __A ):
if any(not isinstance(__UpperCamelCase , __UpperCamelCase ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(__UpperCamelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__... | 710 |
import unittest
import numpy as np
def _lowerCAmelCase( __A , __A , __A , __A = None , ):
UpperCAmelCase = np.shape(__A )
UpperCAmelCase = np.shape(__A )
UpperCAmelCase = np.shape(__A )
if shape_a[0] != shape_b[0]:
UpperCAmelCas... | 1 | 0 |
def _lowerCAmelCase( __A , __A , __A , __A ):
if height >= 1:
move_tower(height - 1 , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
move_disk(lowerCamelCase_ , lowerCamelCase_ )
move_tower(height - 1 , lowerCamelCase_ ,... | 711 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acceler... | 1 | 0 |
from __future__ import annotations
class __magic_name__ :
def __init__( self : Union[str, Any] , lowerCAmelCase__ : List[Any] ) -> None:
UpperCAmelCase = data
UpperCAmelCase = None
UpperCAmelCase = None
def _lowerCAmelCase( __A... | 712 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowerCAmelCase__ = ""
lowerCAmelCase__ = ""
lowerCAmelCase__ = ""
lowerCAmelCase__ = 1 # (0 is vertical, 1 is horizontal)
def _lowerCAmelCase( ):
UpperCAmelCase , UpperCAmelCase = ... | 1 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowerCAmelCase__ = ... | 713 |
def _lowerCAmelCase( __A ):
if not isinstance(__A , __A ):
raise TypeError("only integers accepted as input" )
else:
UpperCAmelCase = str(abs(__A ) )
UpperCAmelCase = [list(__A ) for char in range(len(__A ) )]
for index in range(len(__A ) ):
... | 1 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_common import ConfigTester
from ...tes... | 714 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCAmelCase__ = logging.getLogger(__name__)
lowerCAmelCase__ = 50 # max width of layer n... | 1 | 0 |
from cva import destroyAllWindows, imread, imshow, waitKey
def _lowerCAmelCase( __A ):
# getting number of pixels in the image
UpperCAmelCase , UpperCAmelCase = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(_UpperCAmelCase ):
for j in r... | 715 |
def _lowerCAmelCase( __A ):
assert column_title.isupper()
UpperCAmelCase = 0
UpperCAmelCase = len(__A ) - 1
UpperCAmelCase = 0
while index >= 0:
UpperCAmelCase = (ord(column_title[index] ) - 64) * pow(26 , __A )
answer += value
po... | 1 | 0 |
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
lowerCAmelCase__ = 1.0_54_57_18_17e-34 # unit of ℏ : J * s
lowerCAmelCase__ = 3e8 # unit of c : m * s^-1
def _lowerCAmelCase( __A , __A ... | 716 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get... | 1 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"xlm-roberta-base": "https://huggingface.co/xlm-robert... | 717 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase__ = "src/diffusers"
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(r"is\_([a-z_]*)_available\(\)")
# M... | 1 | 0 |
from __future__ import annotations
import numpy as np
def _lowerCAmelCase( __A ):
return np.maximum(0 , _lowerCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 718 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"facebook/convnextv2-tiny-1k-224": "https://huggi... | 1 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
... | 719 |
lowerCAmelCase__ = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerCAmelCase__ = [{"type": "code", "content": INSTALL_CONTENT}]
lowerCAmelCase__ = {
"... | 1 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca i... | 720 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import M... | 1 | 0 |
import unittest
from knapsack import greedy_knapsack as kp
class __magic_name__ ( unittest.TestCase ):
def _UpperCamelCase ( self : Optional[int] ) -> Optional[int]:
UpperCAmelCase = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
UpperCAmelCase = [2, 4, 6, 8, 1... | 721 |
def _lowerCAmelCase( __A , __A ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowerCAmelCase( __A , __A=0 ):
return sorted(__A , key=lambda __A : x[column] )
def _lowerCAmelCase( __A , __A , __A=float("inf" ) ):
for ... | 1 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.se... | 700 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __magic_name__ :
def __init__( self : Optional[int] ) -> Optional[Any]:
UpperCAmelCase = ""
UpperCAmelCase = ""
UpperCAmelCase = []
UpperCAmel... | 1 | 0 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configurat... | 701 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tok... | 1 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __magic_name__ ( unittest.... | 702 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
lowerCAmelCase__ = ... | 1 | 0 |
def _lowerCAmelCase( __A , __A ):
UpperCAmelCase = word.split()
def justify(__A , __A , __A ) -> str:
UpperCAmelCase = max_width - width
UpperCAmelCase = len(__lowercase )
if len(__lowercase ) == 1:
# if there is only word in... | 703 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class __magic_name__ (... | 1 | 0 |
from __future__ import annotations
def _lowerCAmelCase( __A ):
UpperCAmelCase = 2
UpperCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__snake_case )
if n > 1:
factors.append(__snake_case )
return factors
if __na... | 704 |
def _lowerCAmelCase( __A ):
UpperCAmelCase = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _lowerCAmelCase( __A = 100 ):
UpperCAmelCase = 1
UpperCAmelCase = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase ... | 1 | 0 |
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,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimensio... | 705 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbo... | 1 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __magic_name__ ( __lowerCamelCase ... | 706 |
import numpy
# List of input, output pairs
lowerCAmelCase__ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowerCAmelCase__ = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__ = [2, 4, 1, 5]
lowerCAmelCase__ = len... | 1 | 0 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class __magic_name__ ( _snake_case ):
Up... | 707 |
def _lowerCAmelCase( __A , __A , __A ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__A , n - 1 , __A ) * a) % mod
else:
UpperCAmelCase = binary_exponentiation(__A , n / 2 , __A )
return (b * b) % mod
... | 1 | 0 |
import json
import sys
def _lowerCAmelCase( __A : Any , __A : Any ):
with open(_A , encoding="utf-8" ) as f:
UpperCAmelCase = json.load(_A )
UpperCAmelCase = ["<details>", "<summary>Show updated benchmarks!</summary>", " "]
for benchmark_name in... | 708 |
lowerCAmelCase__ = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
... | 1 | 0 |
lowerCAmelCase__ = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
lowerCAmelCase__ = [{'type': 'co... | 709 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCAmelCase__ = {"UserAgent": UserAgent().random}
def _lowerCAmelCase( __A ):
UpperCAmelCase = script.contents[0]
UpperCAmelCase = json.loads(d... | 1 | 0 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _lowerCAmelCase( __A ):
UpperCAmelCase = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'decoder.out... | 710 |
import unittest
import numpy as np
def _lowerCAmelCase( __A , __A , __A , __A = None , ):
UpperCAmelCase = np.shape(__A )
UpperCAmelCase = np.shape(__A )
UpperCAmelCase = np.shape(__A )
if shape_a[0] != shape_b[0]:
UpperCAmelCas... | 1 | 0 |
from __future__ import annotations
def _lowerCAmelCase( __A ): # This function is recursive
UpperCAmelCase = len(__snake_case )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
UpperCAmelCas... | 711 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acceler... | 1 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from trans... | 712 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowerCAmelCase__ = ""
lowerCAmelCase__ = ""
lowerCAmelCase__ = ""
lowerCAmelCase__ = 1 # (0 is vertical, 1 is horizontal)
def _lowerCAmelCase( ):
UpperCAmelCase , UpperCAmelCase = ... | 1 | 0 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 713 |
def _lowerCAmelCase( __A ):
if not isinstance(__A , __A ):
raise TypeError("only integers accepted as input" )
else:
UpperCAmelCase = str(abs(__A ) )
UpperCAmelCase = [list(__A ) for char in range(len(__A ) )]
for index in range(len(__A ) ):
... | 1 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_... | 714 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCAmelCase__ = logging.getLogger(__name__)
lowerCAmelCase__ = 50 # max width of layer n... | 1 | 0 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, ... | 715 |
def _lowerCAmelCase( __A ):
assert column_title.isupper()
UpperCAmelCase = 0
UpperCAmelCase = len(__A ) - 1
UpperCAmelCase = 0
while index >= 0:
UpperCAmelCase = (ord(column_title[index] ) - 64) * pow(26 , __A )
answer += value
po... | 1 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configu... | 716 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get... | 1 | 0 |
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,
... | 717 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase__ = "src/diffusers"
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(r"is\_([a-z_]*)_available\(\)")
# M... | 1 | 0 |
import math
import qiskit
def _lowerCAmelCase( __A = 1 , __A = 1 , __A = 1 ):
if (
isinstance(__A , __A )
or isinstance(__A , __A )
or isinstance(__A , __A )
):
raise TypeError("inputs must be integers." )
if (input_a < 0) or (inp... | 718 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"facebook/convnextv2-tiny-1k-224": "https://huggi... | 1 | 0 |
'''simple docstring'''
from timeit import timeit
def _lowerCAmelCase( __A ):
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCAmelCase = 0
while number:
number &= number - 1
result += 1
return result
def _lowerCAmelCase( __A ):
... | 719 |
lowerCAmelCase__ = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerCAmelCase__ = [{"type": "code", "content": INSTALL_CONTENT}]
lowerCAmelCase__ = {
"... | 1 | 0 |
def _lowerCAmelCase( __A , __A , __A = 0 , __A = 0 ):
UpperCAmelCase = right or len(__A ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(__A , __A , ... | 720 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import M... | 1 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transforme... | 721 |
def _lowerCAmelCase( __A , __A ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowerCAmelCase( __A , __A=0 ):
return sorted(__A , key=lambda __A : x[column] )
def _lowerCAmelCase( __A , __A , __A=float("inf" ) ):
for ... | 1 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_l... | 700 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __magic_name__ :
def __init__( self : Optional[int] ) -> Optional[Any]:
UpperCAmelCase = ""
UpperCAmelCase = ""
UpperCAmelCase = []
UpperCAmel... | 1 | 0 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __magic_name__ :
UpperCAmelCase = 42
UpperCAmelCase = ... | 701 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tok... | 1 | 0 |
import argparse
import os
import re
import packaging.version
lowerCAmelCase__ = "examples/"
lowerCAmelCase__ = {
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MU... | 702 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
lowerCAmelCase__ = ... | 1 | 0 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __magic_name__ :
def __init__( self : int , lowerCAmelCase__ : Any=2 , lowerCAmelCase__ : List[str]=3 , lowerCAmelCase__ : Union[str, ... | 703 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class __magic_name__ (... | 1 | 0 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICA... | 704 |
def _lowerCAmelCase( __A ):
UpperCAmelCase = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _lowerCAmelCase( __A = 100 ):
UpperCAmelCase = 1
UpperCAmelCase = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase ... | 1 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, requir... | 705 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbo... | 1 | 0 |
import warnings
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 __magic... | 706 |
import numpy
# List of input, output pairs
lowerCAmelCase__ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowerCAmelCase__ = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__ = [2, 4, 1, 5]
lowerCAmelCase__ = len... | 1 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
if not is... | 707 |
def _lowerCAmelCase( __A , __A , __A ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__A , n - 1 , __A ) * a) % mod
else:
UpperCAmelCase = binary_exponentiation(__A , n / 2 , __A )
return (b * b) % mod
... | 1 | 0 |
def _lowerCAmelCase( __A : float , __A : float , __A : int ):
if principal <= 0:
raise Exception("Principal borrowed must be > 0" )
if rate_per_annum < 0:
raise Exception("Rate of interest must be >= 0" )
if years_to_repay <= 0 or not isinstance(lowerC... | 708 |
lowerCAmelCase__ = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
... | 1 | 0 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import Conf... | 709 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCAmelCase__ = {"UserAgent": UserAgent().random}
def _lowerCAmelCase( __A ):
UpperCAmelCase = script.contents[0]
UpperCAmelCase = json.loads(d... | 1 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageV... | 710 |
import unittest
import numpy as np
def _lowerCAmelCase( __A , __A , __A , __A = None , ):
UpperCAmelCase = np.shape(__A )
UpperCAmelCase = np.shape(__A )
UpperCAmelCase = np.shape(__A )
if shape_a[0] != shape_b[0]:
UpperCAmelCas... | 1 | 0 |
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 __magic_name__ ( __UpperCAmelCase , unittest.Te... | 711 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acceler... | 1 | 0 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase( __A , __A , __A ):
# Initialise PyTorch model
UpperCAmelCase = RemBertConfig.... | 712 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowerCAmelCase__ = ""
lowerCAmelCase__ = ""
lowerCAmelCase__ = ""
lowerCAmelCase__ = 1 # (0 is vertical, 1 is horizontal)
def _lowerCAmelCase( ):
UpperCAmelCase , UpperCAmelCase = ... | 1 | 0 |
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 _lowerCAmelCase( __A , __A ):
UpperCAmelCase = args.log_outputs
UpperCAmelCase = "_".... | 713 |
def _lowerCAmelCase( __A ):
if not isinstance(__A , __A ):
raise TypeError("only integers accepted as input" )
else:
UpperCAmelCase = str(abs(__A ) )
UpperCAmelCase = [list(__A ) for char in range(len(__A ) )]
for index in range(len(__A ) ):
... | 1 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
ge... | 714 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCAmelCase__ = logging.getLogger(__name__)
lowerCAmelCase__ = 50 # max width of layer n... | 1 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class __magic_name__ :
def __init__( self : Union[str, Any] , lowerCAmelCase__ : int ) -> Union[str, Any]:
UpperCAmelCase = value
UpperCAmelCase = None
UpperCAmelCase ... | 715 |
def _lowerCAmelCase( __A ):
assert column_title.isupper()
UpperCAmelCase = 0
UpperCAmelCase = len(__A ) - 1
UpperCAmelCase = 0
while index >= 0:
UpperCAmelCase = (ord(column_title[index] ) - 64) * pow(26 , __A )
answer += value
po... | 1 | 0 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
lowerCAmelCase__ = None
try:
import msvcrt
except ImportError:
lowerCAmelCase__ = None
try:
import fcntl
except ImportError:
lowerCAmelCase__ = None
# Backward compatibility
#... | 716 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get... | 1 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowerCAmelCase( __A , __A , __A , __A , ):
UpperCAmelCase = coefficient_matrix.shape
UpperCAmelCase = constant_matrix.shape
if rowsa ... | 717 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase__ = "src/diffusers"
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(r"is\_([a-z_]*)_available\(\)")
# M... | 1 | 0 |
def _lowerCAmelCase( __A , __A ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 718 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"facebook/convnextv2-tiny-1k-224": "https://huggi... | 1 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""xlm-mlm-en-2048""": """... | 719 |
lowerCAmelCase__ = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerCAmelCase__ = [{"type": "code", "content": INSTALL_CONTENT}]
lowerCAmelCase__ = {
"... | 1 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class __magic_name__ ( _snake_case ):
def __init__( self : Optional[int] , *lowerCAmelCase__ : Optional[Any] , *... | 720 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import M... | 1 | 0 |
import torch
from diffusers import StableDiffusionPipeline
lowerCAmelCase__ = "path-to-your-trained-model"
lowerCAmelCase__ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
lowerCAmelCase__ = "A photo of sks dog in a bucket"
lowerCAmelCase__ = pipe(... | 721 |
def _lowerCAmelCase( __A , __A ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowerCAmelCase( __A , __A=0 ):
return sorted(__A , key=lambda __A : x[column] )
def _lowerCAmelCase( __A , __A , __A=float("inf" ) ):
for ... | 1 | 0 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __magic_name__ :
def __init__( self : List[str] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : List[Any] ,... | 700 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __magic_name__ :
def __init__( self : Optional[int] ) -> Optional[Any]:
UpperCAmelCase = ""
UpperCAmelCase = ""
UpperCAmelCase = []
UpperCAmel... | 1 | 0 |
from datetime import datetime
import requests
def _lowerCAmelCase( __A ):
UpperCAmelCase = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
UpperCAmelCase = requests.get(base_url + url ).json()[0]["urls"][0]["src"]
return requests.get(__A ).content
if __... | 701 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tok... | 1 | 0 |
def _lowerCAmelCase( __A ):
UpperCAmelCase = len(__A )
UpperCAmelCase = len(matrix[0] )
UpperCAmelCase = min(__A , __A )
for row in range(__A ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements... | 702 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
lowerCAmelCase__ = ... | 1 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase__ : Optional[int] = {
"junnyu/ro... | 703 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class __magic_name__ (... | 1 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowerCAmelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
lowerCAmelCase__ =... | 704 |
def _lowerCAmelCase( __A ):
UpperCAmelCase = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _lowerCAmelCase( __A = 100 ):
UpperCAmelCase = 1
UpperCAmelCase = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase ... | 1 | 0 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=T... | 705 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbo... | 1 | 0 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_util... | 706 |
import numpy
# List of input, output pairs
lowerCAmelCase__ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowerCAmelCase__ = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__ = [2, 4, 1, 5]
lowerCAmelCase__ = len... | 1 | 0 |
from __future__ import annotations
class __magic_name__ :
def __init__( self : List[str] , lowerCAmelCase__ : str , lowerCAmelCase__ : str ) -> Tuple:
UpperCAmelCase , UpperCAmelCase = text, pattern
UpperCAmelCase , UpperCAmelCase ... | 707 |
def _lowerCAmelCase( __A , __A , __A ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__A , n - 1 , __A ) * a) % mod
else:
UpperCAmelCase = binary_exponentiation(__A , n / 2 , __A )
return (b * b) % mod
... | 1 | 0 |
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
lowerCAmelCase__ = (
"This metric will be removed from the library soon, metrics should... | 708 |
lowerCAmelCase__ = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
... | 1 | 0 |
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... | 709 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCAmelCase__ = {"UserAgent": UserAgent().random}
def _lowerCAmelCase( __A ):
UpperCAmelCase = script.contents[0]
UpperCAmelCase = json.loads(d... | 1 | 0 |
def _lowerCAmelCase( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCAmelCase__ = generate_large_matrix()
lowerCAmelCase__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7... | 710 |
import unittest
import numpy as np
def _lowerCAmelCase( __A , __A , __A , __A = None , ):
UpperCAmelCase = np.shape(__A )
UpperCAmelCase = np.shape(__A )
UpperCAmelCase = np.shape(__A )
if shape_a[0] != shape_b[0]:
UpperCAmelCas... | 1 | 0 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
lowerCAmelCase__ = ... | 711 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acceler... | 1 | 0 |
def _lowerCAmelCase( __A , __A , __A ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__A , n - 1 , __A ) * a) % mod
else:
UpperCAmelCase = binary_exponentiation(__A , n / 2 , __A )
return (b * b) % mod
... | 712 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowerCAmelCase__ = ""
lowerCAmelCase__ = ""
lowerCAmelCase__ = ""
lowerCAmelCase__ = 1 # (0 is vertical, 1 is horizontal)
def _lowerCAmelCase( ):
UpperCAmelCase , UpperCAmelCase = ... | 1 | 0 |
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
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ ... | 713 |
def _lowerCAmelCase( __A ):
if not isinstance(__A , __A ):
raise TypeError("only integers accepted as input" )
else:
UpperCAmelCase = str(abs(__A ) )
UpperCAmelCase = [list(__A ) for char in range(len(__A ) )]
for index in range(len(__A ) ):
... | 1 | 0 |
import torch
from diffusers import StableDiffusionPipeline
lowerCAmelCase__ = "path-to-your-trained-model"
lowerCAmelCase__ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
lowerCAmelCase__ = "A photo of sks dog in a bucket"
lowerCAmelCase__ = pipe(... | 714 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCAmelCase__ = logging.getLogger(__name__)
lowerCAmelCase__ = 50 # max width of layer n... | 1 | 0 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __magic_name__ :
def __init__( self : Optional[int] ) -> Optional[Any]:
UpperCAmelCase = ""
UpperCAmelCase = ""
UpperCAmelCase = []
UpperCAmel... | 715 |
def _lowerCAmelCase( __A ):
assert column_title.isupper()
UpperCAmelCase = 0
UpperCAmelCase = len(__A ) - 1
UpperCAmelCase = 0
while index >= 0:
UpperCAmelCase = (ord(column_title[index] ) - 64) * pow(26 , __A )
answer += value
po... | 1 | 0 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils impor... | 716 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get... | 1 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 717 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase__ = "src/diffusers"
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(r"is\_([a-z_]*)_available\(\)")
# M... | 1 | 0 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
lowerCAmelCase__ = "Usage of script: script_name <size_of_canvas:int>"
lowerCAmelCase__ = [0] * 100 + [1] * 10
random.shuffle(choice)
def _lowerCAmelCase( __A ):
UpperC... | 718 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"facebook/convnextv2-tiny-1k-224": "https://huggi... | 1 | 0 |
'''simple docstring'''
def _lowerCAmelCase( __A = 4000000 ):
UpperCAmelCase = []
UpperCAmelCase , UpperCAmelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__A )
UpperCAmelCase , UpperCAmelCase = b, a + b
return sum(__A )
if ... | 719 |
lowerCAmelCase__ = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerCAmelCase__ = [{"type": "code", "content": INSTALL_CONTENT}]
lowerCAmelCase__ = {
"... | 1 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
def _lowerCAmelCase( __A ):
if isinstance(__A , np.ndarray ):
return list(tensor.shape )
UpperCAmelCase = tf.s... | 720 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import M... | 1 | 0 |
def _lowerCAmelCase( __A ):
if number > 0:
raise ValueError("input must be a negative integer" )
UpperCAmelCase = len(bin(__A )[3:] )
UpperCAmelCase = bin(abs(__A ) - (1 << binary_number_length) )[3:]
UpperCAmelCase = (
(
"1"
+ "0... | 721 |
def _lowerCAmelCase( __A , __A ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowerCAmelCase( __A , __A=0 ):
return sorted(__A , key=lambda __A : x[column] )
def _lowerCAmelCase( __A , __A , __A=float("inf" ) ):
for ... | 1 | 0 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, ... | 700 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __magic_name__ :
def __init__( self : Optional[int] ) -> Optional[Any]:
UpperCAmelCase = ""
UpperCAmelCase = ""
UpperCAmelCase = []
UpperCAmel... | 1 | 0 |
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():
... | 701 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tok... | 1 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get... | 702 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
lowerCAmelCase__ = ... | 1 | 0 |
from typing import Dict, List, Optional, 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,
)
from... | 703 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class __magic_name__ (... | 1 | 0 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
lowerCAmelCase__ = Mapping[str, np.ndarray]
lowerCAmelCase__ = Mapping[str, Any] # Is a nested dict.
lowerCAmelCase__ = ... | 704 |
def _lowerCAmelCase( __A ):
UpperCAmelCase = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _lowerCAmelCase( __A = 100 ):
UpperCAmelCase = 1
UpperCAmelCase = 2
for i in range(2 , max_n + 1 ):
UpperCAmelCase ... | 1 | 0 |
from math import pow, sqrt
def _lowerCAmelCase( *__A ):
UpperCAmelCase = len(__A ) > 0 and all(value > 0.0 for value in values )
return result
def _lowerCAmelCase( __A , __A ):
return (
round(sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(__A ... | 705 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbo... | 1 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtract... | 706 |
import numpy
# List of input, output pairs
lowerCAmelCase__ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowerCAmelCase__ = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__ = [2, 4, 1, 5]
lowerCAmelCase__ = len... | 1 | 0 |
def _lowerCAmelCase( __A ):
UpperCAmelCase = []
UpperCAmelCase = set({"(", "[", "{"} )
UpperCAmelCase = set({")", "]", "}"} )
UpperCAmelCase = {"{": "}", "[": "]", "(": ")"}
for i in range(len(__A ) ):
if s[i] in open_brackets:
stack.ap... | 707 |
def _lowerCAmelCase( __A , __A , __A ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__A , n - 1 , __A ) * a) % mod
else:
UpperCAmelCase = binary_exponentiation(__A , n / 2 , __A )
return (b * b) % mod
... | 1 | 0 |
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