code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowercase__ ( ) -> int:
"""simple docstring"""
UpperCAmelCase = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<... | 373 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class __a ( __a ):
''... | 118 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet imp... | 705 |
"""simple docstring"""
def __A ( a_ : int )-> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError('''Input must be a positive integer''' )
SCREAMING_SNAKE_CASE : Optional[int] = [True] * (num + 1)
SCREAMING_SNAKE_CASE : Optiona... | 18 | 0 |
def _snake_case (_snake_case : Any , _snake_case : Any , _snake_case : List[Any] , _snake_case : Optional[Any]) -> Tuple:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_lowercase ... | 181 | '''simple docstring'''
_lowerCAmelCase :Union[str, Any] = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""":... | 251 | 0 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : Optional[Any] ):
__lowerCAmelCase = AutoConfig.from_pretrai... | 552 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Confi... | 552 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import... | 474 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/ma... | 290 | 0 |
"""simple docstring"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __magic_name__ :
_SCREAMING_SNAKE_CASE : float
_SCREAMING_SNAKE_CASE : TreeNode | None = None
_SCREAMING_SNAKE_CASE : TreeNode... | 614 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 1_00 , ) -> float:
"""simple docstring... | 614 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCamelCase ( a_ ):
... | 128 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet... | 652 | 0 |
import requests
from bsa import BeautifulSoup
def A__ ( SCREAMING_SNAKE_CASE_ = "https://www.worldometers.info/coronavirus" ) -> dict:
lowerCamelCase : Tuple =BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_ ).text , '''html.parser''' )
lowerCamelCase :... | 262 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case_ ( _A , _A):
@register_to_config
def __init__( self , ... | 262 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : int = 6 ):
lowerCamelCase__ = None
lowerCamelCase__ = None
... | 129 |
"""simple docstring"""
def _A ( __lowercase = 10 , __lowercase = 22 ):
"""simple docstring"""
lowerCamelCase__ = range(1 , __lowercase )
lowerCamelCase__ = range(1 , __lowercase )
return sum(
... | 129 | 1 |
def lowercase__ ( lowerCAmelCase__ : int = 1_0 , lowerCAmelCase__ : int = 2_2 ) -> int:
'''simple docstring'''
a__ : Tuple = range(1 , lowerCAmelCase__ )
a__ : int = range(1 , lowerCAmelCase__ )
return sum(
1 for power in powers for... | 720 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class __UpperCAmelCase ( _UpperCamelCase ):
def __init__( self : int , *a_ : List[str] ... | 251 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case: Union[str, Any] = logging.get_logger(__name__... | 577 |
def _SCREAMING_SNAKE_CASE ( snake_case = 1_0_0_0 ) -> int:
_UpperCAmelCase , _UpperCAmelCase = 1, 1
_UpperCAmelCase = []
for i in range(1 , n + 1 ):
_UpperCAmelCase = prev_numerator + 2 * prev_denomina... | 518 | 0 |
import gc
import unittest
from transformers import CTRLConfig, 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... | 720 |
'''simple docstring'''
from __future__ import annotations
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argum... | 35 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case ( ... | 675 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, requir... | 184 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
... | 718 |
"""simple docstring"""
import math
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self : Any , __a : list[list[float]] , __a : list[int] ) -> int:
_UpperCamelCase : List[Any] = 0.0
... | 51 | 0 |
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
UpperCAmelCase = "\nArgs:\n predictions (... | 666 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
... | 666 | 1 |
'''simple docstring'''
UpperCamelCase : List[Any] = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
UpperCamelCase : Any = [{"""type""": """code"... | 714 | '''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( snake_case : int = 10**9 ) -> int:
"""simple docstring"""
a : List[str] = 1
a : Any = 2
a : List[Any] = 0
a : Optional[Any] = 0... | 610 | 0 |
"""simple docstring"""
from typing import Any
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ):
'''simple docstring'''
_validation(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , Up... | 389 |
"""simple docstring"""
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_modeli... | 389 | 1 |
'''simple docstring'''
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
... | 710 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__SCREAMING_SNAKE_... | 119 | 0 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCAmelCase ( _A ,_A ,_A ):
"""simple docstring"""
_lowercase = BertConfig.fr... | 398 | import random
class _lowercase :
"""simple docstring"""
@staticmethod
def _UpperCAmelCase ( UpperCAmelCase ):
'''simple docstring'''
_lowercase = [ord(UpperCAmelCase ) for i in text]
_lowercase = ... | 398 | 1 |
'''simple docstring'''
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 58 | '''simple docstring'''
lowercase_ = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/tran... | 58 | 1 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(lowerCamelCase_ , (list, tuple) ) or not all(
isinstance(lowerCamelCase_ , lowerCamelCase_ ) for number in numbers ):
raise ValueError("""numbers must be ... | 379 |
'''simple docstring'''
import random
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = num - 1
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
while s % 2 == 0:
SCREAMING_SNAKE_CASE : List[str] = s // 2
t += 1
for _ in range(5 ):
... | 379 | 1 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__UpperCamelCase : Tuple = re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
class ... | 34 | import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils i... | 34 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import ... | 673 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase_ : Any = {
'''configuration_encodec''': [
'''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''E... | 673 | 1 |
import argparse
import json
import subprocess
def SCREAMING_SNAKE_CASE ( lowerCAmelCase , lowerCAmelCase ):
_UpperCamelCase = []
_UpperCamelCase = (
f'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {... | 105 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : str = logging.get_logger(__name__)
lowercase : Tuple = {"""vocab_file""": """vocab.json"""}
lowercase : int = {
"""vocab_file""":... | 105 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase ( __snake_case ):
lowercase : str = ["image_processor", "tokenizer"]
lowercase : Union[str, Any] = "AutoImageProcessor"
lower... | 499 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> List[An... | 66 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( lowerCamelCase : list[int] , lowerCamelCase : list[int] , lowerCamelCase : int ):
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(l... | 39 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
lowerCAmelCase : Optional[Any] = logging.... | 39 | 1 |
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 torch
... | 290 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ : list[float] ) -> float:
lowercase : Any =0.0_0
lowercase : Tuple =0
for resistor in resistors:
if resistor <= 0:
l... | 92 | 0 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
_lowerCamelCase = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), ga... | 59 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 59 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
A : Dict = ... | 15 |
"""simple docstring"""
import sys
def __lowerCamelCase ( a_ : List[Any] ) -> str:
__SCREAMING_SNAKE_CASE :str = len(a_ )
__SCREAMING_SNAKE_CASE :List[Any] = [[0 for x in range(a_ )] for x in range(a_ )]
__SCREAMING_SNAKE_... | 498 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class UpperCamelCase_ :
lowercase = None
lowercase = False
lowercase = False
lowercase = False
lowercase = None
lowercase ... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Any = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
... | 672 | 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
_a = """\
@misc{chen2021evaluating,
tit... | 19 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from .... | 19 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
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... | 134 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase : List[str] = logging.get_logger(__name__)
_UpperCamelC... | 134 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 42 |
_lowercase : Any =[0, 2, 4, 6, 8]
_lowercase : List[Any] =[1, 3, 5, 7, 9]
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
... | 364 | 0 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
UpperCamelCase_ : List[Any] = 3
def __lowercase ( a : int ) -> int:
print('''Generating primitive root of p''' )
while True:
... | 497 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffus... | 497 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
"""configuration_rembert""": ["... | 92 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from t... | 92 | 1 |
'''simple docstring'''
def _a ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] ):
# Check if the input is valid
if not len(_SCREAMING_SNAKE_CASE ) == len(_SCREAMING_SNAKE_CASE ) == 3:
raise ValueError("Please enter a valid equation." )
if equ... | 493 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _a ( _SCREAMING_SNAKE_CASE : int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(_SCREAMING_SNAKE_CASE : float ,... | 493 | 1 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
__A : Tuple = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
__A : str = '\nArgs:\n prediction... | 575 |
"""simple docstring"""
from math import factorial, pi
def snake_case__ ( _lowerCamelCase, _lowerCamelCase = 30 ) ->float:
"""simple docstring"""
if not isinstance(_lowerCamelCase, (int, float) ):
raise ValueError("maclaurin_sin() requires either an int or float ... | 575 | 1 |
'''simple docstring'''
# 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 Schedu... | 537 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if i... | 537 | 1 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def _a ( _lowerCamelCase ) -> List[Any]:
"""simple docstring"""
__snake_case , __snake_case , __snake_case : Optional[int] ... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise Op... | 39 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Con... | 720 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
... | 588 | 0 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
a_ : Union[str, Any] = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models... | 439 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTest... | 439 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : int = {
"""huggingface/informer-tourism-monthly""": (
... | 703 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class lowercase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : in... | 187 | 0 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_avail... | 409 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCAmelCase = logging.get_lo... | 409 | 1 |
"""simple docstring"""
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
return int((input_a, input_a).count(0 ) == 0 )
def __lowerCamelCase ( ):
assert and_gate(0 ,0 ) == 0
assert and_gate(0 ,1 ) == 0
as... | 554 |
"""simple docstring"""
import os
import numpy
import onnx
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
A__ = a.name
A__ = b.name
A__ = ''
A__ = ''
A__ = a... | 554 | 1 |
'''simple docstring'''
import cva
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase )-> List[str]:
if k in (0.0_4, 0.0_6):
lowercase__ = k
low... | 161 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransfo... | 337 | 0 |
'''simple docstring'''
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, r... | 701 |
'''simple docstring'''
UpperCamelCase__ : Dict = {
"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": "A... | 0 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
UpperCamelCase = {
"configuration_audio_spectrogram_transformer": [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ASTConfig... | 45 |
from __future__ import annotations
def A ( lowercase__ : list[int] ) -> bool:
return len(set(lowercase__ ) ) == len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 45 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion i... | 220 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__UpperCAmelCase = 'http://... | 220 | 1 |
"""simple docstring"""
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
... | 29 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGEN... | 29 | 1 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STAN... | 717 |
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 650 | 0 |
def lowerCamelCase__ (__lowerCamelCase ):
return 10 - x * x
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
# Bolzano theory in order to find if there is a root between a and b
if equation(A__ ) * equation(A__ ) >= 0:
raise V... | 249 |
'''simple docstring'''
def UpperCamelCase_ ( A__ , A__ , A__ ):
if len(A__ ) != len(A__ ):
raise ValueError("""The length of profit and weight must be same.""" )
if max_weight <= 0:
raise ValueError("""max_weight must greater than zero.""" )
if any(p < 0 for p i... | 263 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __a ( __A ):
'''simple docstring'''
UpperCAmelCase__ : Any = """ClapFeatureExtractor"""
UpperCAmelCase__ : Any ... | 709 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_... | 97 | 0 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available(... | 594 |
"""simple docstring"""
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
cla... | 594 | 1 |
# Imports
import numpy as np
class _lowerCAmelCase :
def __init__( self : List[Any] , __snake_case : List[Any]=None , __snake_case : Optional[int]=None , __snake_case : Optional[Any]=None , __snake_case : int=None , __snake_case : ... | 49 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""andreasmadsen/efficient_mlm_m0.40""": (
... | 49 | 1 |
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ ) -> Optional[int]:
"""simple docstring"""
def update_area_of_max_square(snake_case__ , snake_case__ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
... | 193 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration... | 223 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'SCUT-DLVCLab/lilt-roberta-en-base': (
'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/c... | 710 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class __snake_case( _lowerCAmelCase ):
'''simple docstring'''
def __init__( ... | 344 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
}
lo... | 74 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : List[Any] = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
... | 323 | 0 |
"""simple docstring"""
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_commo... | 612 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_... | 612 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {}
class lowerCAmelCase_ ( _lowerCAmelCase ):
'''simple docstring'''
_lowercase ... | 220 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( snake_case__ , snake_case__ , snake_case__ , snake_ca... | 132 | 0 |
import math
from numpy import inf
from scipy.integrate import quad
def __lowerCamelCase ( snake_case__ ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
return quad(snake_case__ ,0 ,sna... | 717 |
import math
from numpy import inf
from scipy.integrate import quad
def __lowerCamelCase ( snake_case__ ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
return quad(snake_case__ ,0 ,sna... | 569 | 0 |
'''simple docstring'''
import math
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> List[str]:
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
ra... | 358 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCamelCase = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.m... | 492 | 0 |
from __future__ import annotations
def lowercase ( SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> str:
return [ord(_UpperCAmelCase ) - 96 for elem in plain]
def lowercase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Tuple:
return "".jo... | 716 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
C... | 198 | 0 |
'''simple docstring'''
def lowercase (_A , _A ):
"""simple docstring"""
_lowerCAmelCase : List[Any] = len(_A )
_lowerCAmelCase : List[str] = []
for i in range(len(_A ) - ... | 444 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase : Any = {
"""configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """C... | 444 | 1 |
'''simple docstring'''
import os
from math import logaa
def _UpperCamelCase ( UpperCamelCase__ = "base_exp.txt" ):
UpperCAmelCase__ : float = 0
UpperCAmelCase__ : int = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(Upp... | 113 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__A =logging.getLogger()
... | 113 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class A__ ( u... | 222 |
"""simple docstring"""
from __future__ import annotations
def snake_case ( UpperCamelCase__ : list[int] , UpperCamelCase__ : int ) -> list[list[int]]:
lowerCamelCase : list[list[int]] = []
lowerCamelCase : list[int] = []
... | 222 | 1 |
"""simple docstring"""
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_subpro... | 708 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from to... | 215 | 0 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.mod... | 681 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__a : Optional[Any] = TypeVar("""_T""")
class __UpperCAmelCase ( Generic[_T] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE = None ) ... | 606 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from ... | 19 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 19 | 1 |
from __future__ import annotations
from math import gcd
def A_ ( _lowerCAmelCase , _lowerCAmelCase = 2 , _lowerCAmelCase = 1 , _lowerCAmelCase = 3 , ) -> int | None:
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError("The input ... | 629 |
import os
def A_ ( ) -> Union[str, Any]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCamelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
UpperCamelCase : ... | 629 | 1 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
__SCREAMING_SNAKE_CASE : Optional[int] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets... | 697 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
'''simple docst... | 697 | 1 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
... | 85 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processi... | 85 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTeste... | 359 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( UpperCamelCase : list[list[float]] ):
UpperCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works... | 359 | 1 |
"""simple docstring"""
from typing import List
import numpy as np
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: str ={key: len(lowercase ) for key, value in gen_kwargs.items() if isinstance(lowercase , lowercase )}
if len(set(lists_lengths.values... | 409 |
"""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,
)
... | 409 | 1 |
"""simple docstring"""
def __UpperCamelCase ( SCREAMING_SNAKE_CASE = 10 ) -> str:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) or n < 0:
raise ValueError("Invalid input" )
__snake_case = 10**... | 614 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConf... | 614 | 1 |
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
from transformers import AutoImageProcessor, ResNet... | 226 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common impor... | 336 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( A__ ) -> List[str]:
"""simple docstring"""
if number > 0:
raise ValueError("input must be a negative integer" )
lowerCamelCase__ = len(bin(__A )[3:] )
lowerCamelCase__ = bin(abs(__A ) - (1 << binary_number_... | 701 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy ... | 274 | 0 |
import math
import os
import sys
def A ( __UpperCamelCase ) -> str:
A__ = ''
try:
with open(snake_case__ , 'rb' ) as binary_file:
A__ = binary_file.read()
for dat in data:
A__ = f'''{dat:08b}'''
result += curr_byte
... | 9 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''',... | 40 | 0 |
import math
def snake_case (UpperCamelCase : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# ... | 235 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def snake_case (UpperCamelCase : Optional[Any] ):
'''simple docstring'''
lowerCamelCase__ = FileLock(str(tmpdir / """foo.lock""" ) )
lowerCamelCase__ = FileLock... | 235 | 1 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transform... | 306 |
# 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
# 'pip install -e .[dev]' when switching bet... | 306 | 1 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__SCREAMING_SNAKE_CASE = logging.getLogger()
@unittest.skip('''Tempor... | 395 |
"""simple docstring"""
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def A_ ( __lowercase ):
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set() )
@pytest.fixture
def A_ ( __lowercase )... | 395 | 1 |
"""simple docstring"""
from math import pi, sqrt, tan
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->float:
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def UpperCamelCase ( ... | 434 | """simple docstring"""
import argparse
import json
import subprocess
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->Any:
_lowerCamelCase : List[str] = []
_lowerCamelCase : Optional[int] = (
F'''curl -H "Accept: appli... | 434 | 1 |
from __future__ import annotations
snake_case__ : str = '#'
class SCREAMING_SNAKE_CASE_ :
'''simple docstring'''
def __init__( self : Optional[int] ) ->None:
lowerCamelCase_ : dict = {}
def _lowerCAmelCase ... | 717 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNA... | 171 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils... | 261 |
'''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_seed
from accelerat... | 261 | 1 |
'''simple docstring'''
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 Re... | 717 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCAmelCase__:
'''simple docstring'''
def __init__( self , __lowerCamelCase ) -> Tuple:
_SCREAMING_SNAKE_CASE : List[str] = list_of_po... | 381 | 0 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class A ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self , *,
lowerCam... | 325 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
f... | 325 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils ... | 717 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggi... | 48 | 0 |
def SCREAMING_SNAKE_CASE__ ( ) -> Optional[int]:
_lowercase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_lowercase = 6
_lowercase = 1
_lowercase = 1901
_lowercase = 0
while year < 2001:
day += 7
... | 67 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
... | 67 | 1 |
'''simple docstring'''
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_A : str = 2048
_A : str = 4096
_A : Tuple = 42
_A : Optional[Any] = os.environ.pop('''PROCESS_TRAIN''', '''false''')
_A : str ... | 719 | '''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extracti... | 330 | 0 |
"""simple docstring"""
def A ( snake_case__ , snake_case__ = " " ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = 0
for index, char in enumerate(_UpperCAmelCase ):
if char == separator:
... | 196 |
'''simple docstring'''
import os
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__: Optional[int] = logging.get... | 694 | 0 |
'''simple docstring'''
lowerCAmelCase : Any = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def __lowerCAmelCase ( ):
'''simple docstring'''
__lowerCAmelCase = input("Enter message: " )
__lowerCAmelCase = input("Enter key [alphanumeric]: " )
__lowerCAmelCase ... | 710 |
'''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,
... | 39 | 0 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A_ : Dict = logging.getLogger(__name__)
@dataclass
class _a (__magic_name__ ):
'''simple docstring'''
... | 456 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
A_ : Any = logging.get_logger(__name__)
class _a :
'''simple docstring'''
UpperCAmelCase__: str = None
@experimental
def UpperC... | 456 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
__lowerCAmelCase : int = TypeVar('''T''')
__lowerCAmelCase : Optional[int] = Union[List[T], Tuple[T, ...]]
__lowerCAmelCase : List[Any] =... | 21 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
... | 21 | 1 |
import math
def UpperCAmelCase__ ( __magic_name__ : float , __magic_name__ : float ):
'''simple docstring'''
if (
not isinstance(__magic_name__ , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('''power_facto... | 348 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def UpperCAmelCase__ ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : bool = False ):
'''simple docstring'''
if radian_mode:
... | 348 | 1 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
_snake_case : Optional[Any] = 4
_snake_case : Dict = 3
class _UpperC... | 704 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_u... | 524 | 0 |
from __future__ import annotations
import numpy as np
def a_ ( _A ) -> List[str]:
"""simple docstring"""
return np.maximum(0 , _A )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 328 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute... | 328 | 1 |
def UpperCamelCase__( UpperCamelCase__ : int , UpperCamelCase__ : int )->int:
return number | (1 << position)
def UpperCamelCase__( UpperCamelCase__ : int , UpperCamelCase__ : int )->int:
return number & ~(1 << position)
def UpperCamelCase__( ... | 212 |
def UpperCamelCase__( UpperCamelCase__ : str )->str:
A__ = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def UpperCamelCase__( UpperCamelCase__ : st... | 212 | 1 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def A_ ():
'''simple docstring'''
A_ , A_ = 9, 14 # noqa: F841
A_ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
... | 115 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase = 100 , ) -> Dict:
_A = x_start
_A = fnc(__lowercase )
_A ... | 709 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/bli... | 621 | 0 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 62 |
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
cla... | 14 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowercase__ : List[str] = datasets.utils.logging.get_logger(__name__)
class UpperCAmelCase ( folder_based_builder.FolderBasedBuilde... | 707 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
lowercase__ : Optional[int] = False
class UpperCAmelCase ( ... | 139 | 0 |
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