code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def _lowerCamelCase ( lowercase : List[str] ) -> Optional[Any]:
# This defines a "chinese character" as anything... | 63 |
"""simple docstring"""
def __lowerCamelCase ( a_ : int , a_ : str ) -> Optional[int]:
__SCREAMING_SNAKE_CASE :Optional[int] = [1]
for i in range(2 , a_ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < fac... | 191 | 0 |
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 import ModelTesterMi... | 282 |
from collections.abc import Sequence
def _snake_case( SCREAMING_SNAKE_CASE__ : Sequence[int] | None = None ) -> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
A__ ... | 282 | 1 |
import requests
def SCREAMING_SNAKE_CASE_ ( __A : List[Any] , __A : List[Any] ) -> None:
"""simple docstring"""
a_ : Any = {"""Content-Type""": """application/json"""}
a_ : str = requests.post(_lowerCAmelCase , json={... | 32 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
SCREAMING_SNAKE_CASE_:Dict = logging.get_logger(__name__)
SCREAMI... | 116 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-ba... | 365 | """simple docstring"""
import unittest
from transformers import BertGenerationConfig, 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_modelin... | 321 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import fl... | 27 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def UpperCamelCase( __UpperCamelCase : List[str] ):
... | 103 | 0 |
"""simple docstring"""
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_avai... | 356 |
from __future__ import annotations
import math
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if len(__lowerCamelCase ) == 0:
... | 325 | 0 |
'''simple docstring'''
from math import factorial
lowerCamelCase_ = {str(digit): factorial(digit) for digit in range(10)}
def __lowercase ( __lowercase ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
... | 79 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Union[str, Any]= logging.get_logger(__name__)
_a : str= {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}... | 172 | 0 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_SCREAMING_SNAKE_CASE = 2_9_9_7_9_2_4_5_8
# Symbols
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = symbols("""c... | 165 | from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interle... | 165 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase_ : Union[str, Any] = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_... | 63 |
'''simple docstring'''
import math
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def UpperCamelCase__ ( self : List[str] , __a : list[list[float]] , __a : list[int] ):
_a = 0.0
_a = 0.0
fo... | 63 | 1 |
lowercase__ ={
'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-hub>=0... | 90 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 90 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers ... | 202 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class a__ ( a_ ):
def __lt__( self , _a ):
return self[-1] < other[-1]
def __eq__( sel... | 202 | 1 |
'''simple docstring'''
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... | 98 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 98 | 1 |
"""simple docstring"""
import unittest
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_inputs
if is_torch... | 291 | import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import Tensor... | 157 | 0 |
class _lowerCAmelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCamelCase : str ):
'''simple docstring'''
_snake_case : List[Any] = arr.split(',' )
def UpperCamelCase_ ( self : Tuple ):
'''s... | 362 |
from __future__ import annotations
lowerCAmelCase_ = """#"""
class _lowerCAmelCase :
'''simple docstring'''
def __init__( self : Any ):
'''simple docstring'''
_snake_case : dict = {}
def UpperCamelCase_ ( self : Optional[int]... | 260 | 0 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArgument... | 296 |
from pathlib import Path
import fire
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Path(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = ... | 296 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 353 |
'''simple docstring'''
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 VQDiff... | 242 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
__snake_case : Tuple = logging.get_logger(__name__)
__snake_case : Optional[int] = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt... | 134 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoForm... | 134 | 1 |
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,... | 351 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE : Optional[int] = 299792458
# Symbols
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = symbols("c... | 84 | 0 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collat... | 329 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class l... | 176 | 0 |
'''simple docstring'''
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
... | 92 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[int] = {
"edbeeching/decision-transformer-gym-hopper-medium": (... | 92 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_t... | 249 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : str = [1]
__lowercase ,__lowercase ,__lowercase : List[str] = 0, 0, 0
__lowercase : List[str] = ugly_nums[ia] * 2
__lowerca... | 249 | 1 |
'''simple docstring'''
class lowercase :
"""simple docstring"""
def __init__( self ):
'''simple docstring'''
UpperCamelCase__ :dict[str, TrieNode] = {} # Mapping from char to TrieNode
UpperCamelCase__ :int = False
def lowerCAmelC... | 219 |
'''simple docstring'''
from __future__ import annotations
__snake_case = list[list[int]]
# assigning initial values to the grid
__snake_case = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0,... | 219 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBart... | 35 |
'''simple docstring'''
from __future__ import annotations
class snake_case :
"""simple docstring"""
def __init__( self : Optional[int] , __A : list[list[int]] ):
__UpperCamelCase = TypeError(
'Matrices must be formed from a list of ... | 53 | 0 |
'''simple docstring'''
import numpy as np
import datasets
UpperCamelCase = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was i... | 369 |
'''simple docstring'''
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] ) -> ... | 334 | 0 |
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : Union[str, Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : int = 0
while number > 0:
snake_case_ : Un... | 327 |
def SCREAMING_SNAKE_CASE__ ( __a ):
if not isinstance(__a , __a ):
snake_case_ : int = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__a )
if number < 0:
return False
snake_case_ : Dict = number * number
... | 327 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def lowerCamelCase ( a_ , a_ ) -> int:
lowerCAmelCase_ = int(a_ )
assert noofclusters < len(a_ )
# Find out the dimensionality
lowerCAmelCase... | 368 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowerCamelCase ( a_ ) -> Any:
lowerCAmelCase_ ... | 14 | 0 |
"""simple docstring"""
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.util... | 64 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available()... | 64 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Optional[int] = logging.get_logger(__name__)
lowercase__ : List[str] = {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/g... | 352 |
"""simple docstring"""
import os
def __lowercase ( _a ):
snake_case_ : Tuple = len(grid[0] )
snake_case_ : Optional[int] = len(_a )
snake_case_ : Union[str, Any] = 0
snake_case_ : Union[str, Any] = 0
snake_case_ : L... | 155 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase (... | 339 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Dict = {
'configuration_xlm_roberta': [
'XLM_ROBERTA_... | 6 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
A_ : Tuple = ... | 141 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test... | 141 | 1 |
"""simple docstring"""
import random
class A_ :
"""simple docstring"""
@staticmethod
def UpperCAmelCase__ ( lowercase_ :str ) -> tuple[list[int], list[int]]:
UpperCAmelCase = [ord(lowercase_ ) for i... | 78 |
from __future__ import annotations
import time
a =list[tuple[int, int]]
a =[
[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],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, ... | 73 | 0 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTraini... | 157 |
"""simple docstring"""
import argparse
import os
import re
_SCREAMING_SNAKE_CASE : List[str] = """src/diffusers"""
# Pattern that looks at the indentation in a line.
_SCREAMING_SNAKE_CASE : Optional[int] = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in grou... | 157 | 1 |
'''simple docstring'''
__snake_case =[
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_man... | 4 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/f... | 4 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch... | 370 |
from math import isqrt
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , _UpperCAmelCase , _UpperCAmelCase):
SCREAMI... | 327 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, 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 M... | 63 |
'''simple docstring'''
from ....utils import logging
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , __a : int , ... | 63 | 1 |
"""simple docstring"""
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
... | 86 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : List[Any] = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
... | 86 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE_ ( ) -> str:
"""simple docstring"""
UpperCamelCase :Optional[Any] = HfArgumentParser(__magic_name__ )
UpperCamelCase :Any = parser.par... | 38 |
import unittest
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_inputs
if is_torch_available():
import tor... | 38 | 1 |
"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..st... | 108 |
"""simple docstring"""
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 a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> st... | 108 | 1 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def _UpperCamelCase ( lowercase__ ):
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.c... | 9 |
def _a ( lowerCamelCase: dict ) -> bool:
'''simple docstring'''
__A = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
__A = set()
return any(
node not in visited a... | 117 | 0 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
snake_case : Optional[Any] = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def lowerCAmelCase_ ( _snake_case : str = "mumbai" ) -> Generator[t... | 352 |
def lowerCAmelCase_ ( _snake_case : int ) -> int:
'''simple docstring'''
assert isinstance(_snake_case , _snake_case ), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
__magic_name__ : Dict = F'''T... | 41 | 0 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowerCAmelCase_ = """<<<<<<< This should probably be modified because it mentions: """
lowerCAmelCase_ = """=======
>>... | 279 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class A__(a_ ):
"""simple docstring"""
_A : ... | 248 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set... | 183 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger('transformers.models.speecht5')
def lowercase... | 183 | 1 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
... | 169 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowerCAmelCase = '<<<<<<< This should probably be modified because it mentions: '
lowerCAmelCase = ... | 110 | 0 |
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __magic_name__ ( TensorFormatt... | 352 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_lowerCAmelCase : Dict = '''scheduler_config.json'''
class __magic_name__ (... | 70 | 0 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def snake_case ( ):
UpperCAmelCase_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" ,type=A__ ,default="biencoder-nq-dev.json" ,help="Path to raw DPR tra... | 268 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
... | 268 | 1 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase__ : Optional[int] = TypeVar("""T""")
class UpperCamelCase__ ( Generic[T] ):
"""simple doc... | 366 |
"""simple docstring"""
import re
def UpperCamelCase_ ( lowerCAmelCase__ : str ) -> bool:
"""simple docstring"""
lowerCAmelCase_ : str = re.compile(
R'^(?:0|94|\+94|0{2}94)' R'7(0|1|2|4|5|6|7|8)' R'(-| |)' R'\d{7}$' )
... | 289 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.du... | 259 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
__snake_case = """__DUMMY_TRANSFORMERS_USER__"""
__snake_case = """Dummy User"""
__snake_case = """hf_hZEmnoOEYISjraJt... | 259 | 1 |
'''simple docstring'''
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_SCREAMING_SNAKE_CASE ... | 368 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_SCREAMING_SNAKE_CASE = ["a", "b", "c", "d", "e"]
def __lowerCamelCase ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : str , __... | 3 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ ):
assert (
isinstance(lowerCamelCase__ , lowerCamelCase__ ) and number_of_steps > 0
), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
if number_of_steps == 1:
return 1
lowerCamelCase_ , lowerCamelCase_... | 19 |
def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
"""simple docstring"""
while b:
a , a :Optional[Any] = b, a % b
return a
def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ ... | 94 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils... | 351 |
def _lowerCAmelCase ( A__: list[int] , A__: list[int] ):
'''simple docstring'''
UpperCAmelCase = len(A__ )
print('''The following activities are selected:''' )
# The first activity is always selected
UpperCAmelCase = 0
print... | 152 | 0 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
... | 111 |
UpperCAmelCase__ = 8.31_44_62 # Unit - J mol-1 K-1
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.'... | 5 | 0 |
'''simple docstring'''
from collections import namedtuple
import requests
from lxml import html # type: ignore
lowerCAmelCase: Optional[Any] = namedtuple('covid_data', 'cases deaths recovered')
def lowerCamelCase__ ( _A = "https://www.worldometers.info/coronavirus/" ):
a : ... | 352 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class a__( tf.keras.optimizers.schedules.LearningRateSchedu... | 96 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
... | 176 |
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
a =logging.get_logger(__name__)
a ={"""vocab_file""": """spiece.mo... | 73 | 0 |
'''simple docstring'''
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,
bi... | 174 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_ut... | 174 | 1 |
from itertools import count
def a ( snake_case__: List[Any] = 50 ):
'''simple docstring'''
lowercase_ = [1] * min_block_length
for n in count(_lowercase ):
fill_count_functions.append(1 )
for block_length in range(_lowercase ... | 30 |
'''simple docstring'''
from collections import UserDict
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_availabl... | 265 | 0 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __... | 365 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
A... | 279 | 0 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
snake_case : str = datasets.utils.loggi... | 240 |
import argparse
snake_case : int = '''docs/source/_static/js/custom.js'''
def __lowercase ( __lowerCAmelCase : Optional[Any] ):
with open(__lowerCAmelCase , encoding='utf-8' , newline='\n' ) as f:
a__ = f.readlin... | 240 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase : Any ={"confi... | 123 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokeniz... | 123 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUps... | 183 |
"""simple docstring"""
_UpperCamelCase : List[str] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
_UpperCame... | 220 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...ut... | 169 |
import unittest
from transformers import 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 ModelTesterMixin, ids_tensor
from .... | 169 | 1 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_lowercase: Dict = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
_lowercase: Any ... | 227 |
'''simple docstring'''
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 __SCREAMING_SNAKE_CASE (lowerCamelCa... | 63 | 0 |
from math import factorial, pi
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase = 30 )-> float:
'''simple docstring'''
if not isinstance(__lowerCAmelCase , (int, float) ):
raise ValueError('''maclaurin_sin() requires either an int or float for ... | 78 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = ["""image_processor""", """tokenizer"""]
__lowerCamelCase : Union[str, Any]... | 78 | 1 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__snake_case : int =... | 248 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'p... | 298 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
'studio-ous... | 363 |
"""simple docstring"""
from math import factorial
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if successes > trials:
raise ValueError("""successes must be lower or equal to trials"... | 324 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAmelCase_ : List[str] ... | 91 |
'''simple docstring'''
import os
_SCREAMING_SNAKE_CASE : int = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
snake_case_ = 0
snake_case_ = ... | 85 | 0 |
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : str , __A : int ):
snake_case__ : List[Any] = size
snake_case__ : List[Any] = [0] * size
snake_case__ : Any = [0] * size
@stat... | 286 |
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_torc... | 286 | 1 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
__snake_case = logging.get_logger(__name__)
def _A ( SCREAMING_SNAKE_CASE__ : Option... | 259 |
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,
)... | 259 | 1 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_ut... | 363 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
A__ : Optional[int] = {
'configuration_speech_to_text': ['SPE... | 209 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSav... | 4 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ : Union[str, Any] = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHI... | 81 | 0 |
'''simple docstring'''
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... | 280 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ ... | 280 | 1 |
"""simple docstring"""
import random
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = a[left_index]
lowercase_ = left_index + 1
fo... | 136 |
"""simple docstring"""
from math import pi, sqrt, tan
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""" )
return 6 * side_length... | 136 | 1 |
def snake_case (UpperCAmelCase__ = 1_0_0 ) -> int:
UpperCamelCase_: Optional[Any] = n * (n + 1) * (2 * n + 1) / 6
UpperCamelCase_: Optional[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'''{solution() ... | 292 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS... | 292 | 1 |
lowerCAmelCase = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
lowercase__ = f'a bytes-like obje... | 110 | """simple docstring"""
from math import ceil
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ,_lowerCamelCase : Union[str, Any] ) -> int:
_lowerCAmelCase : Dict = list(range(0 ,_lowerCamelCase ) )
_lowerCAmelCase : ... | 44 | 0 |
"""simple docstring"""
from collections import namedtuple
lowercase_ = namedtuple("from_to", "from_ to")
lowercase_ = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.001, 1_0_0_0),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.00454, 264.172),
"cub... | 11 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
... | 11 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__lowerCamelCase : List[str] = logging.get_logger(__name_... | 52 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __magic_name__ ( datasets.BeamBasedBuilder):
def UpperCAmelCase__ ( self : Tuple ) -... | 146 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCAmelCase: List[str] = logging.get_logger(__name__)
class a__( lowerCamelCase__ ):
def __init__( self : Optional[int] , *__snake_case : Optional... | 358 |
'''simple docstring'''
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__... | 96 | 0 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lo... | 289 |
"""simple docstring"""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXL... | 25 | 0 |
from random import shuffle
import tensorflow as tf
from numpy import array
def __magic_name__ ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str ) -> Optional[Any]:
__lowerCamelCase = int(__lowerCAmelCase )
assert noofclusters < len(__l... | 339 |
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE__ : Any = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __magic_name__ ( ) -> Any:
... | 339 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
_a = R'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model ou... | 17 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
a_ : Optional[Any] = logging.get_logger(__n... | 137 | 0 |
'''simple docstring'''
import os
import string
import sys
lowerCAmelCase :str = 1 << 8
lowerCAmelCase :Optional[Any] = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 2_7,
'''up''': 6_5 + ARROW_KEY_FLAG,
'''down''': 6_6 + ARROW_KEY_FLAG,
... | 354 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
lowerCAmelCase :Tuple = logging.get_logger(__name__)
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self : Any , ... | 275 | 0 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def A_ ( _lowercase = "isbn/0140328726" ):
'''simple docstring'''
snake_case_ :str = olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & s... | 66 |
"""simple docstring"""
from __future__ import annotations
__a = 10
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = 1
snake_case_ :List[str] = max(_lowercase )
while placement <= max_digit:
# declare and initialize... | 66 | 1 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
... | 367 | """simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__lowerCAmelCase : List[Any] =numpy.array([0, 0])
__lowerCAmelCase : List[str] =numpy.array([0.5, 0.866_0254])
__lowerCAmelCase... | 32 | 0 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
re... | 12 |
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 ...util... | 12 | 1 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 235 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowerCamelCase__ ( _lowercase , _lowercase=0 ):
'''simple docstring'''
return sorted(_lowercase ... | 235 | 1 |
def lowerCAmelCase_ ( __a , __a ) -> list:
"""simple docstring"""
lowerCamelCase__: int =word.split()
def justify(__a , __a , __a ) -> str:
lowerCamelCase__: Tuple =max_width - width
lowerCamelCase__: str =len(__a )
if len(__a ... | 10 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from tra... | 172 | 0 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : dict ,__UpperCamelCase : str ):
"""simple docstring"""
A_ , A_ = set(__UpperCamelCase ), [start]
while stack:
A_ = stack.pop()... | 361 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_... | 329 | 0 |
def UpperCamelCase ( lowerCAmelCase__ = 1000 ):
'''simple docstring'''
return sum(e for e in range(3 , lowerCAmelCase__ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F'{solution() = }')
| 101 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
... | 101 | 1 |
"""simple docstring"""
import math
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = len(_SCREAMING_SNAKE_CASE )
UpperCamelCase = int(math.floor(math.sqrt(_SCREAMING_SNAKE_CASE ) ) )
UpperCamelCase ... | 244 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowerCAmelCase__ = '''\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding ... | 244 | 1 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 265 |
'''simple docstring'''
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, requ... | 265 | 1 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :list ) -> list:
__lowerCAmelCase : List[str] = len(SCREAMING_SNAKE_CASE )
for _ in range(SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
__lowerCAmelCase , __lo... | 232 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :list[list[int | float]] ) -> int:
__lowerCAmelCase : str = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = len(matrix[0] )
__lowerCAmelCase : List[Any] = min(SCREAMING_SNAKE_CASE ... | 232 | 1 |
'''simple docstring'''
def a__ ( a__ ):
"""simple docstring"""
if n == 1 or not isinstance(a__ , a__ ):
return 0
elif n == 2:
return 1
else:
__SCREAMING_SNAKE_CASE = [0, 1]
for i in range(2 , n + 1 )... | 267 | """simple docstring"""
def a_ ( lowerCamelCase , lowerCamelCase ):
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCAmelCase__ = str(bin(lowerCamelCase ) )[2:] # remove the leading "0b"
UpperCAm... | 98 | 0 |
'''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 SPIECE_UNDERLINE, logging
_lowerCamelCase ... | 249 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Tuple = {
"configuration_convnext": ["CONVNEXT_PRE... | 249 | 1 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_a : Any = logging.getLogger(__name__)
class __A :
def __init__( self ):
... | 44 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class lo... | 26 | 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 transformer... | 361 |
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE__ : Any = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __magic_name__ ( ) -> Any:
... | 339 | 0 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,... | 32 |
# 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 re... | 32 | 1 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class _A ( __UpperCAmelCase ):
def __init__( self : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CAS... | 131 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow,... | 131 | 1 |
from __future__ import annotations
from math import gcd
def A ( _lowercase , _lowercase = 2 , _lowercase = 1 , _lowercase = 3 , ):
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError('''The input value cannot be less than 2''' ... | 182 | from PIL import Image
def lowercase_ ( _lowerCamelCase : Image , _lowerCamelCase : int):
lowercase__ : List[str] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamelCase : int) -> int:
return int(128 + factor * (c - 12... | 87 | 0 |
def __UpperCamelCase ( lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : Any = 0
for ch in input_str:
lowerCAmelCase_ : Any = ord(lowercase__ )
lowerCAmelCase_ : Dict = pow(2 , lowercas... | 368 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __a ( unittest.TestCase ):
def A ( self : List[Any] ):
lowerCAmelCase_ : Dict = Vector([1, 2, 3] )... | 28 | 0 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
... | 105 | """simple docstring"""
def a_ ( lowerCamelCase ):
return str(lowerCamelCase ) == str(lowerCamelCase )[::-1]
def a_ ( lowerCamelCase ):
return int(lowerCamelCase ) + int(str(lowerCamelCase )[::-1] )
def a_ ( lowerCamelCase = 1... | 98 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_te... | 368 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start... | 96 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.