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 ..utils import DummyObject, requires_backends
class lowerCAmelCase ( metaclass=lowerCamelCase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = ["""note_seq"""]
def __init__( self , *low... | 247 |
"""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 i... | 247 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def snake_case_ ( lowerCAmelCase_ : ... | 649 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
lowerCamelCase : Any = None
try:
import msvcrt
except ImportError:
lowerCamelCase : str = None
try:
import fcntl
except ImportError:
lowerCamelCase : Optional[Any] = ... | 649 | 1 |
from manim import *
class UpperCAmelCase__ ( A_ ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
_lowercase : Tuple = Rectangle(height=0.5 , width=0.5 )
... | 322 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}
class UpperCAme... | 322 | 1 |
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __a ( _SCREAMING_SNAKE_CASE... | 703 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowercase__ = TypeVar('T')
class __snake_case ( Generic[T] ):
def __init__( self , lowercase) -> List[Any]:
'''simple docstring'... | 217 | 0 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple = loggi... | 422 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
_A : int = (DDPMScheduler,)
def UpperCamelCase__ ( self : U... | 578 | 0 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
__magic_name__ = JukeboxTokenizer
__magic_name__ = {
"""artist""": """Zac... | 236 | from __future__ import annotations
from collections.abc import Callable
def a__ ( a , a , a , a = 1_0_0 , ) -> float:
A_ : Any = x_start
A_ : int = fnc(a )
A_ : int = 0.0
for _ ... | 236 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_... | 334 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcesso... | 482 | 0 |
'''simple docstring'''
from math import pi, sqrt, tan
def _SCREAMING_SNAKE_CASE ( A : float ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return ... | 61 |
'''simple docstring'''
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enabl... | 61 | 1 |
"""simple docstring"""
UpperCamelCase_ : List[str] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def A_ ():
'''simple docstring'''
A_ = input("Enter message: " )
A_ = input("Enter key [alphanumeric]: " )
A_ = input("Encrypt/Decrypt [e... | 115 |
lowercase = 8.314_4598
def __lowerCAmelCase ( UpperCAmelCase__ : float , UpperCAmelCase__ : float ) -> float:
if temperature < 0:
raise Exception("""Temperature cannot be less than 0 K""" )
if molar_mass <= 0:
raise Ex... | 272 | 0 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from t... | 719 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InformerConfig""",
],
}
t... | 207 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Any = {"vocab_file": "vocab.json"}
a : Optional[Any] = ... | 679 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...te... | 679 | 1 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowerCamelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCamelCase : list[int] = [ord(letter) for letter in string.ascii_lowercas... | 684 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401... | 684 | 1 |
"""simple docstring"""
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransforme... | 58 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils impo... | 88 | 0 |
from maths.prime_check import is_prime
def __snake_case ( lowercase : int ):
if not isinstance(A__ , A__ ):
snake_case_ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(A__ )
if is_prime(A__ ) and is_prime(num... | 714 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 420 | 0 |
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():
... | 33 |
'''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_funnel import FunnelTokenizer
__snake_case =logging.get_logge... | 133 | 0 |
import numpy
# List of input, output pairs
UpperCamelCase__ =(
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCamelCase__ =(((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCamelCase__ =[2, 4, 1, 5]
UpperCamelCase__ =len(tra... | 381 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
... | 381 | 1 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path... | 43 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCamelCase : int =[
... | 316 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Con... | 140 |
from math import factorial, pi
def lowercase ( a , a = 30 ):
'''simple docstring'''
if not isinstance(a , (int, float) ):
raise ValueError("maclaurin_sin() requires either an int or float for theta" )
if not isinstance(a , a ) or accuracy <= 0:
raise ValueEr... | 140 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversatio... | 31 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common impor... | 512 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)... | 705 | """simple docstring"""
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
UpperCAmelCase : Dict = False
class __SCREAMING_SNAKE_C... | 121 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCAmelCase__ ( metaclass=UpperCAmelCase_ ):
lowercase__ : Any = ["""torch""", """torchsde"""]
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ):
'''simple docstring'''
... | 337 |
"""simple docstring"""
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 ( A ) -> Union[str, Any]:
'''s... | 337 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase ... | 718 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A__ ( UpperCamelCase__ ):
'''simple docstring'''
if (
(cp >= 0x4E00 and cp <= 0x... | 168 | 0 |
"""simple docstring"""
class lowercase_ :
'''simple docstring'''
def __init__( self : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : int ):
_A = None
_A = None
_A = graph... | 7 |
'''simple docstring'''
def UpperCamelCase__ ( __magic_name__ : List[Any] ) -> Tuple:
'''simple docstring'''
if not head:
return True
# split the list to two parts
snake_case__ , snake_case__ : Dict = head.next, head
while fast and fast.next:
snake_... | 38 | 0 |
from __future__ import annotations
def __lowerCamelCase ( __a :list[int | float] , __a :int , __a :int ) -> int | float:
"""simple docstring"""
if len(__a ) == 0:
raise ValueError("""find_max() arg is an empty sequen... | 247 |
import os
import string
import sys
A : Dict = 1 << 8
A : Dict = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 2_7,
'''up''': 6_5 + ARROW_KEY_FLAG,
'''down''': 6_6 + ARROW_KEY_FLAG,
'''right''': 6_7 + ARROW_KEY_FLAG,
... | 247 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
l... | 18 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __a(SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Tuple=None ):
'''simple docstring'''
... | 18 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ : Optional[Any] = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED... | 295 |
"""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,
StableDiffusionLatentUpscalePipeline,
Sta... | 295 | 1 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
UpperCamelCase__ : str =... | 105 |
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_dataset... | 570 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constan... | 70 | from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def UpperCamelCase ( ):
'''simple docstring'''
A_ , A_ : Any = 9, 14 # noqa: F841
A_ : str = [
[0, 1, 4],
[0, 7, 8],
... | 70 | 1 |
'''simple docstring'''
def lowerCAmelCase ( UpperCamelCase__ : int ):
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
__UpperCAmelCase = ... | 262 | '''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def lowerCAmelCase ( UpperCamelCase__ : Dict ):
"""simple docstring"""
_... | 262 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Option... | 721 |
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,
)
from torch.utils.data import... | 380 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase : int = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_A... | 284 | import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.valu... | 140 | 0 |
from __future__ import annotations
def UpperCamelCase_( __magic_name__ : Optional[Any] , __magic_name__ : str , __magic_name__ : Any , __magic_name__ : List[Any] ): # noqa: E741
"""simple docstring"""
while r - l > 1:
... | 703 |
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 impor... | 382 | 0 |
'''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.checkpo... | 116 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_... | 410 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transforme... | 183 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 183 | 1 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase, ... | 154 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_... | 144 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCamelCase = {
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
'tokenization_m2m_100': ['M2M100... | 307 |
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 transformers import DPRC... | 307 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
snake_case = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SP... | 309 | '''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pi... | 309 | 1 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Dict = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''snap-research/efficientformer-l1-300'... | 338 |
'''simple docstring'''
import os
import re
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
lowercase__ : Optional[int] ... | 338 | 1 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneT... | 122 |
from ..utils import DummyObject, requires_backends
class __magic_name__ (metaclass=__lowercase ):
lowerCamelCase__ = ['''speech''']
def __init__( self , *_a , **_a ) -> str:
requires_backends(self , ["speech"] )
class __magic_name__ (metacl... | 122 | 1 |
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 A ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
... | 433 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.... | 433 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( A__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = (IPNDMScheduler,)
SCREAMING_SNAKE_CA... | 174 |
"""simple docstring"""
def lowerCAmelCase_ ( snake_case_ : int = 1_0_0_0 ) ->int:
lowerCamelCase__ : Any =2**power
lowerCamelCase__ : Dict =0
while n:
lowerCamelCase__ , lowerCamelCase__ : Any =r + n % 1_0, n // 1_0
return r
... | 174 | 1 |
"""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, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditio... | 100 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
UpperCAmelCase : Optional[Any] = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resol... | 100 | 1 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import C... | 81 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : Optional[int] = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_... | 637 | 0 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowerCamelCase : List[Any] = logging.get_logger(__name__)
class snake_case__ ( UpperCamelCase_ ):
_lowerCAmelCase ... | 303 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : Optional[int] = logging.get_logger(__name__)
lowerCamelCase : List[Any] = {
'facebook/xmod-ba... | 303 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : str = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_availabl... | 675 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ : Optional[int] = logging.get_logger(__name__)
a_ : Dict = {
"""SenseTime/deformable-detr""": """h... | 675 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class __lowerCamelCase :
"... | 149 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__SCREAMING_SNAKE_CASE : Dict = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 E... | 149 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( _lowerCamelCase : int ):
A__ = [True] * limit
A__ = False
A__ = False
A__ = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
A__ ... | 440 |
'''simple docstring'''
# Copyright 2023 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
#
... | 440 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase__ : Union[str, Any] ... | 709 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelO... | 495 | 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
UpperCamelCase ... | 37 |
from __future__ import annotations
def __UpperCAmelCase( lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = False , ):
_lowerCamelCase : Tuple = cipher_alphabet or [chr(lowercase_ ) for i in range(97 , 1_23 )]
# If the argument is No... | 114 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def UpperCamelCase__ ( __magic_name__ : int ) -> List[str]:
'''simple docstring'''
if "img_encoder.pos_embed" in... | 714 |
'''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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Co... | 419 | 0 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ):
'''simple docstring'''
if len(lowercase_ ) == 0:
return []
UpperCAmelCase__ = min(lowercase_ ), max(lowercase_ )
UpperCAmelCase__ ... | 603 |
def __SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : str = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
__UpperCAmelCase : Union[str, Any] = 6
__UpperCAmelCase : Optional[Any] = 1
... | 462 | 0 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vi... | 14 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ ):
if not head:
return True
# split the list to two parts
UpperCAmelCase_ , UpperCAmelCase_ = head.next, head
while fast and fast.next:
UpperCAmelCase_ = ... | 14 | 1 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def lowercase (SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : int ) -> float:
SCREAMING_SNAKE_CASE = ... | 247 | '''simple docstring'''
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_util... | 274 | 0 |
"""simple docstring"""
from __future__ import annotations
def snake_case__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) ->Dict:
UpperCAmelCase__ = len(__snake_case )
# If row is ... | 705 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a ... | 422 | 0 |
"""simple docstring"""
import heapq
def lowerCamelCase__ ( __snake_case ) -> set[int]:
"""simple docstring"""
_UpperCamelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq... | 19 | import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes... | 216 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = "▁"
__UpperCAme... | 259 |
import random
def A_ ( lowercase_ ) ->bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE = num - 1
SCREAMING_SNAKE_CASE = 0
while s % 2 == 0:
SCREAMING_SNAKE_CASE = s // 2
t += 1
for _ in range(5 ):
SCREAMING_SNAKE_CASE = rand... | 259 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def snake_case_ (UpperCamelCase : Dict[str, torch.Tensor] ):
'''simple docstring'''
... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 1 |
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
... | 485 |
"""simple docstring"""
def __lowercase ( _a ):
snake_case_ : Optional[Any] = int(_a )
if decimal in (0, 1): # Exit cases for the recursion
return str(_a )
snake_case_, snake_case_ : Optional[int] = divmod(_a , 2 )
return binary_recursiv... | 485 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : list , UpperCAmelCase_ : list , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
if index == number_of_items:
... | 13 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dime... | 242 | 0 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingS... | 686 |
'''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/facebook/musicgen-smal... | 686 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wa... | 398 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Optional[Any] = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MA... | 239 | 0 |
"""simple docstring"""
import sys
import turtle
def _lowerCamelCase ( lowerCamelCase__ : tuple[float, float] , lowerCamelCase__ : tuple[float, float] ):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def _lowerCamelCase ( lowerCamelCase__ : tuple[floa... | 720 |
"""simple docstring"""
from math import sqrt
def _lowerCamelCase ( lowerCamelCase__ : int ):
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... | 128 | 0 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: float = 1 / sqrt(2 ) )-> IIRFilter:
_snake_case : List[Any] = tau * frequency... | 411 |
import string
def lowerCamelCase_ ( lowerCAmelCase: str )-> str:
_snake_case : str = ''
for i in sequence:
_snake_case : Tuple = ord(lowerCAmelCase )
if 65 <= extract <= 90:
output += chr(1_55 - extract )
elif 97 <= extract <= 1... | 411 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 4 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.... | 4 | 1 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertToke... | 244 | '''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_... | 244 | 1 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Union[str, Any] ):
"... | 720 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( lowercase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ["image_processor", "tokenizer"]
SCREAMING_SNAKE_CASE = "AutoImageProcessor... | 199 | 0 |
_UpperCamelCase = 8.3_14_45_98
def lowerCAmelCase__( lowercase : float , lowercase : float ) -> float:
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less th... | 243 |
import tensorflow as tf
from ...tf_utils import shape_list
class _lowerCamelCase ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=1... | 243 | 1 |
"""simple docstring"""
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_snake_case = logging.get_logger(__name__)
class _a :
a_ : int = None
@experiment... | 703 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def snake_case ( _a: int , _a: int = 2 , _a: int = 1 , _a: int = 3 , )-> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('The input va... | 659 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipeli... | 162 |
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
A_ : List[Any] = logging.get_logg... | 456 | 0 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
SCREAMING_SNAKE_CASE_ = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghu... | 715 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class a ( UpperCAmelCas... | 467 | 0 |
'''simple docstring'''
import numpy as np
import datasets
__A : Optional[int] = '''
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 distanc... | 334 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 8 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': ... | 102 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 102 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowerCAmelCase__ ( __lowerCamelCase ):
"""simple docstring"""
def _UpperCamelCase ( self ):
return [
... | 250 |
from __future__ import annotations
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
... | 250 | 1 |
from math import ceil, sqrt
def __lowerCamelCase ( _lowerCAmelCase = 1_000_000 ) -> int:
_UpperCAmelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_UpperCAmelCase = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
... | 129 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class ... | 129 | 1 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class a__ ( nn.Module ):
'''simple docstring'''
A : Optional[Any] = 42
A : List[str] = jnp.floataa
def lowerCAmelCase ( self : Dict ) ... | 186 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 665 | 0 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
A__ = 10
def _lowercase ( a_ : int ,a_ : int ,a_ : list[int] ,a_ : int ... | 184 |
from math import factorial, radians
def _lowercase ( a_ : float ,a_ : int = 1_8 ,a_ : int = 1_0 ) -> float:
'''simple docstring'''
__magic_name__ = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees ... | 184 | 1 |
'''simple docstring'''
lowerCAmelCase : List[Any] ={
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''f... | 172 |
'''simple docstring'''
from itertools import permutations
def UpperCAmelCase_ ( __lowerCamelCase : tuple ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
lowercase_ :... | 172 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
fro... | 672 |
'''simple docstring'''
import math
def __lowerCamelCase ( _lowercase ) -> bool:
assert isinstance(_lowercase , _lowercase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 672 | 1 |
'''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 IM... | 310 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import lo... | 310 | 1 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
a__: List[str] = {
... | 718 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
a__: Optional[int] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
def __init__( self,*__lowerCamelCase,**__lowerCamelCa... | 212 | 0 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from d... | 119 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils imp... | 119 | 1 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTest... | 711 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A ):
def __init__( self : int,__A : Any=None,**__A : O... | 11 | 0 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_avail... | 54 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...te... | 258 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def _A ( __lowercase , __lowercase , __lowercase , __lowercase=None ):
"""simple docstring"""
lowerCamelCase__ = (path or []) + [u]
for v in graph[u]:
... | 258 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerC... | 1 | '''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchma... | 251 | 0 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _lowerCAmelCase(a ... | 712 |
"""simple docstring"""
def _lowerCAmelCase(a : int ) -> bool:
if number < 0:
raise ValueError('''number must not be negative''' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 165 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCamelCase__ : Any = datasets.utils.logging.get_logger(__name__)
class _snake_case ( folder_based_builder.FolderBasedBuilderConfig ... | 12 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : int ... | 560 | 0 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteri... | 604 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ = "x" , snake_case__ = 10**-10 , snake_case__ = 1 , ) -> complex:
"""simple docstring"""
lowerCAmelCase... | 604 | 1 |
import copy
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
from ..auto import CONFIG_MAPPING
_snake_case = logging.get_logger(__name__)
_... | 655 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.namedtuple("""_Datasets""", ["""train""",... | 655 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
_snake_case : Union[str, Any] = 'WhisperFeatureExtractor'
_snake_case : int = 'WhisperTokenizer'
def __init__( ... | 228 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_com... | 228 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
... | 14 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={
'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-20... | 249 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : str = {
"""configuration_blenderb... | 176 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers... | 176 | 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 SCREAMING_SNAKE_CASE__ ( s... | 3 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import... | 383 | 0 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
c... | 708 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerat... | 336 | 0 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_atte... | 149 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared ... | 149 | 1 |
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 VOCAB_FILES_NAMES... | 154 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=a )
class __A ( a ):
"""simple docstring"""
UpperCam... | 154 | 1 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
_lowercase : Optional[int] = TypeVar('KT')
_lowercase : Optional[int] = TypeVar('VT')
class _UpperCAmelCase ( Generic[KT, VT] ... | 49 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase : Any = 'src/transformers'
# This is to make sure the transfo... | 627 | 0 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def A_ ( snake_case ):
if num <= 0:
raise ValueError("math domain error" )
return quad(snake_case , 0 , snake_case , args=(snake_case) )[0]
def ... | 465 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=_a ):
_A : Any = ['''torch''', '''torchsde''']
def __init__( self : Any ,*SCREAMING_SNAKE_CASE__ : int ,**SCREAMING_SNAKE_CASE__ ... | 465 | 1 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availab... | 106 |
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,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaToke... | 395 | 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_seed
from accele... | 47 |
'''simple docstring'''
def _a ( lowerCAmelCase_ ):
"""simple docstring"""
if n == 1 or not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return 0
elif n == 2:
return 1
else:
_snake_case : Union[str, Any] = [0, ... | 47 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.