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 |
|---|---|---|---|---|
def _lowerCAmelCase( __A : Optional[Any] = 1000 ):
UpperCAmelCase = 3
UpperCAmelCase = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(f"{solution() = ... | 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 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerConfig",
],
}
try:
... | 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 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 na... | 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 argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
lo... | 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 inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from ... | 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 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 ... | 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 |
# Copyright (c) 2021-, NVIDIA CORPORATION. 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... | 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
def _lowerCAmelCase( __A , __A ):
UpperCAmelCase = []
UpperCAmelCase = []
UpperCAmelCase = 0
UpperCAmelCase = sum(__A )
create_state_space_tree(__A , __A , __A , __A , __A , ... | 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 math
def _lowerCAmelCase( __A , __A ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(__A )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
r... | 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 re
from filelock import FileLock
try:
import nltk
lowerCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def _lowerCAmelCase( __A ... | 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 typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils imp... | 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'''
lowerCAmelCase__ = "Input must be a string of 8 numbers plus letter"
lowerCAmelCase__ = "TRWAGMYFPDXBNJZSQVHLCKE"
def _lowerCAmelCase( __A ):
if not isinstance(__A , __A ):
UpperCAmelCase = F"Expected string as input, found {type(__A ).__nam... | 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 ):
UpperCAmelCase = hex_num.strip()
if not hex_num:
raise ValueError("No value was passed to the function" )
UpperCAmelCase = hex_num[0] == "-"
if is_negative:
UpperCAmelCase = hex_num[1:]
try:
UpperCAmelCase = int(_... | 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 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
lowerC... | 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 os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __magic_name__ ( _snake_case , ... | 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 __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
lowerCAmelCase__ = 0
lowerCAmelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
... | 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 json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.... | 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 ):
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 ... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
lowerCAmelCase__ = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch_availabl... | 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 ...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... | 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 datasets
from .evaluate import evaluate
lowerCAmelCase__ = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
low... | 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 warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class __magic_name__ ( _snake_case ):
def __init__( self : Any , *lowerCAmelCase__ : Optional[int] , **lowerCAme... | 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 |
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__ = {
"bert-base-uncased": "https://huggingface.co/bert-base... | 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 |
from __future__ import annotations
def _lowerCAmelCase( __A , __A , __A ):
if len(__A ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(__A )
or left < -len(__A )
or right >= len(__A )
or right < -len(__A )
):
rai... | 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 logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
... | 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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ = {
"configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "Perc... | 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 copy import deepcopy
class __magic_name__ :
def __init__( self : List[str] , lowerCAmelCase__ : list[int] | None = None , lowerCAmelCase__ : int | None = None ) -> None:
if arr is None and size is not None:
UpperCAmelCase = size
UpperCAm... | 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 functools import reduce
lowerCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"668966489504452445231617... | 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 |
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__ (... | 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 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__ = {
"facebook/data2vec-text-base": "https://huggingface.co... | 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 |
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__ = {
"... | 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 typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCAmelCase__ = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mik... | 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
def _lowerCAmelCase( __A ):
if not nums:
raise ValueError("List is empty" )
return sum(__A ) / len(__A )
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'''
import math
import sys
def _lowerCAmelCase( __A ):
UpperCAmelCase = ""
try:
with open(__A , "rb" ) as binary_file:
UpperCAmelCase = binary_file.read()
for dat in data:
UpperCAmelCase = F"{dat:08b}"
result += cu... | 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 __future__ import annotations
from functools import lru_cache
from math import ceil
lowerCAmelCase__ = 100
lowerCAmelCase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCAmelCase__ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
... | 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 warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __magic_name__ ( _snake_case ):
UpperCAmelCase = ["""image_processor""", """tokenizer"""]
UpperCAmelCase = """CLIPImageProcessor"""
UpperC... | 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 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... | 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 ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=_snake_case ):
UpperCAmelCase = ["""flax""", """transformers"""]
def __init__( self : str , *lowerCAmelCase__ : Optional[Any] , **lowerCAmelCase__ : Any ) -> ... | 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 gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determini... | 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 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join ... | 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 argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenize... | 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 (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase( __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 copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regress... | 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 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_i... | 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 os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import ... | 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 math
def _lowerCAmelCase( __A , __A ):
return math.pow(__A , 2 ) - a
def _lowerCAmelCase( __A ):
return 2 * x
def _lowerCAmelCase( __A ):
UpperCAmelCase = 2.0
while start <= a:
UpperCAmelCase = math.pow(__A , 2 )
return star... | 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 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ = {"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig", "BeitOnnxConfig"]}
try:
i... | 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
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
... | 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 typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENA... | 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 json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
lowerCAmelCase__ = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"text-classification",
"l... | 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 math
def _lowerCAmelCase( __A ):
return math.sqrt(__A ) * math.sqrt(__A ) == num
def _lowerCAmelCase( __A ):
UpperCAmelCase = 0
UpperCAmelCase = n
while left <= right:
UpperCAmelCase = (left + right) // 2
if mid**2 == n:
return True
e... | 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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = "▁"
lowerCAmelCase__ = {"vocab_file": "spiece.model"}
lowerCAmelCase__ = {... | 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 |
# flake8: noqa
# Lint as: python3
lowerCAmelCase__ = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_p... | 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 pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _lowerCAmelCase( __A ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def _lowerCAmelCase( __A ):
class __magic_nam... | 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 copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class ... | 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 json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ ... | 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 pytest
import datasets
# Import fixture modules as plugins
lowerCAmelCase__ = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def _lowerCAmelCase( __A , __A ):
# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
for ... | 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 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils i... | 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 |
from typing import Any
class __magic_name__ :
def __init__( self : int , lowerCAmelCase__ : Any ) -> Tuple:
UpperCAmelCase = data
UpperCAmelCase = None
class __magic_name__ :
def __init__( self : Tuple ) -> Tuple... | 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
class __magic_name__ ( unittest.TestCase ):
def _UpperCamelCase ( self : Dict ) -> Tuple:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def _UpperCamelCase ( self : Tuple ) -> List[Any... | 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 AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 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 __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowerCAmelCase__ : List[str] = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCAmelCase__ : Tuple = [ord(letter) for letter in string.asc... | 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
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
... | 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
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..util... | 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 numpy as np
from PIL import Image
def _lowerCAmelCase( __A , __A , __A ):
UpperCAmelCase = np.array(__A )
if arr.shape[0] != arr.shape[1]:
raise ValueError("The input array is not a square matrix" )
UpperCAmelCase = 0
UpperCAmelCase = 0
Up... | 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 torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __magic_name__ ( _snake_case , _snake_case ):
@register_to_config
def __init__( self : Optional[Any] , *,
lowerCAmelCase__ ... | 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 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
if not ... | 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 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/google/fnet-... | 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 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 _lowerCAmelCase( __A , __A ):
# Load checkpoint
UpperCAmelCase ... | 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 copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available()... | 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 typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
"configuration_autoformer": [
"AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AutoformerConfig",
],
}
try:
... | 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 os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
lowerCAmel... | 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 |
from math import pi
def _lowerCAmelCase( __A , __A ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 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 string
def _lowerCAmelCase( __A ):
for key in range(len(string.ascii_uppercase ) ):
UpperCAmelCase = ""
for symbol in message:
if symbol in string.ascii_uppercase:
UpperCAmelCase = string.ascii_uppercase.find(__A )
UpperCAmelCase = n... | 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
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common ... | 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 ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_... | 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 argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _lowerCAmelCase( __A , __A , __A ):
# Construct model
if openai_config_... | 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 unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 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 |
lowerCAmelCase__ = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hu... | 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 requests
from bsa import BeautifulSoup
def _lowerCAmelCase( __A , __A ):
UpperCAmelCase = BeautifulSoup(requests.get(__A , params=__A ).content , "html.parser" )
UpperCAmelCase = soup.find("div" , attrs={"class": "gs_ri"} )
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 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_e... | 2 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__)
class __A (snake_case__):
'''simple docstring'''
__lowercase: ... | 2 | 1 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class __A (logging.LoggerAdapter):
'''simple docstring'''
@staticmethod
def lowerCAmelCase ( UpperCAmelCase_ : List[str] ) ->List[Any]:
"""simple ... | 2 |
"""simple docstring"""
from functools import reduce
__SCREAMING_SNAKE_CASE : Tuple = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'1254069874715852386305071569329096329522... | 2 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : int = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
exc... | 2 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : str = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolv... | 2 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : str = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
... | 2 |
"""simple docstring"""
from math import factorial
def _a ( _SCREAMING_SNAKE_CASE = 100 ) -> int:
return sum(int(_SCREAMING_SNAKE_CASE ) for x in str(factorial(_SCREAMING_SNAKE_CASE ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').s... | 2 | 1 |
"""simple docstring"""
import pprint
import requests
__SCREAMING_SNAKE_CASE : Tuple = 'https://zenquotes.io/api'
def _a ( ) -> list:
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def _a ( ) -> list:
return requests.g... | 2 |
"""simple docstring"""
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class ... | 2 | 1 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if index == number_of_items:
return 0
snake_case_ = 0
snake_case_ ... | 2 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, sl... | 2 | 1 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> Dict:
snake_case_ = (path or []) + [u]
for v in graph[u]:
... | 2 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
class __A :
'''simple docstring'''
def __init__( self : List[Any] , UpperCAmelCase_ : list[str] ) ->List[Any]:
"""simple docstring"""
... | 2 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__SCREAMING_SNAKE_CASE : Optional[int] = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 2 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_visio... | 2 | 1 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __A (ctypes.Structure):
'''simple docstring'''
__lowercase: Optional[Any] = [("""size"... | 2 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 2 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from... | 2 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if index == number_of_items:
return 0
snake_case_ = 0
snake_case_ ... | 2 | 1 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__SCREAMING_SNAKE_CASE : List[str] = '\\n@misc{chen2021evaluating,... | 2 |
"""simple docstring"""
from math import factorial
def _a ( _SCREAMING_SNAKE_CASE = 20 ) -> int:
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n // 2
return int(fact... | 2 | 1 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _a ( _SCREAMING_SNAKE_CASE ) -> str:
for param in module.parameters():
snake_case_ = False
def _a ( ) -> Any:
snake_ca... | 2 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _a ( _SCREAMING_SNAKE_CASE = 8 ) -> str:
snake_case_ = ascii_letters + digits + punctuation
return "".joi... | 2 | 1 |
"""simple docstring"""
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( _SCREAMING_SNAKE_CASE ) -> Any:
snake_case_ = int(... | 2 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
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 i... | 2 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSe... | 2 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impor... | 2 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __A (tf.keras.layers.Layer):
'''simple docstring... | 2 |
"""simple docstring"""
from __future__ import annotations
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> list[int]:
snake_case_ = 0
snake_case_ = len(_SCREAMING_SNAKE_CASE ) - 1
while i < j:
if nums[i] + nums[j... | 2 | 1 |
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _a ( _SCREAMING_SNAKE_CASE ) -> str:
return getitem, k
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 2 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
't... | 2 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 2 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = 'Input must be a string of 8 numbers plus letter'
__SCREAMING_SNAKE_CASE : Dict = 'TRWAGMYFPDXBNJZSQVHLCKE'
def _a ( _SCREAMING_SNAKE_CASE ) -> bool:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCR... | 2 | 1 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Tuple = {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cas... | 2 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[str] = logging.... | 2 | 1 |
"""simple docstring"""
from math import factorial
def _a ( _SCREAMING_SNAKE_CASE = 20 ) -> int:
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n // 2
return int(fact... | 2 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("""The given input must be positive""" )
# get the generated string sequence
snake_case_ ... | 2 | 1 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> int:
snake_case_ = len(_SCREAMING_SNAKE_CASE )
snake_case_ = len(matrix[0] )
snake_case_ = min(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for row in range(_... | 2 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
... | 2 | 1 |
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