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 |
|---|---|---|---|---|
def __lowerCAmelCase ( __snake_case ):
if len(A__ ) < 2:
return collection
def circle_sort_util(__snake_case , __snake_case , __snake_case ) -> bool:
__lowerCAmelCase = False
if low == high:
retu... | 367 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class UpperCAmelCase__( lowerCamelCase , un... | 622 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transfo... | 709 | '''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : int = logging.get_logger(__name__)
UpperCamelCase__ : Optional[Any] = {'vocab_file': 'vocab.json'}
UpperCamelCase_... | 496 | 0 |
import os
import numpy
import onnx
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
A : int = a.name
A : Optional[int] = b.name
A : Tuple = """"""
A : List[Any] = """"""
... | 662 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 662 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
# Se... | 231 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
_snake_case = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnx... | 231 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : Optional[Any] = logging.get_logger(__name__)
_a : Dict = {
'roberta-base': 'https://hugg... | 479 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def UpperCamelCase__ ( _A: Tuple ):
... | 479 | 1 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
... | 342 | """simple docstring"""
from datetime import datetime as dt
import os
from github import Github
UpperCAmelCase = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def lowercase ( ) -> i... | 342 | 1 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def _low... | 508 |
'''simple docstring'''
from math import isqrt
def __snake_case ( lowercase : int ):
snake_case_ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowercase , lowercase ):
... | 508 | 1 |
"""simple docstring"""
import cva
import numpy as np
class UpperCAmelCase_ :
def __init__( self : int , __UpperCamelCase : float , __UpperCamelCase : int ) -> int:
if k in (0.0_4, 0.0_6):
_UpperCamelCase = k
_UpperCamelC... | 717 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils impor... | 342 | 0 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.confi... | 0 | '''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
UpperCamelCase__ : Optional[Any] = logging.getLogger(__name__)
Uppe... | 614 | 0 |
'''simple docstring'''
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_avail... | 47 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 47 | 1 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...tes... | 81 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
A = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 320 | 0 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a__ (__lowercase :List[Any] , __lowercase :str , ... | 332 |
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 : List[Any] =logging.get_logger(__nam... | 332 | 1 |
from typing import Any
import numpy as np
def UpperCamelCase__ ( lowerCAmelCase__ ):
return np.array_equal(lowercase__ ,matrix.conjugate().T )
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ ):
lowercase = v.conjugate().T
lowercase = v... | 428 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils... | 119 | 0 |
from math import ceil
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = list(range(0 , lowerCamelCase_ ) )
lowercase__ = [item for sublist in list(device_map.values() ) for item in sublist]
... | 708 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
# we need a list not a string, so do something to change the type
lowercase__ = arr.split('''... | 671 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : str = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
... | 568 | """simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import ... | 434 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase_ (_UpperCAmelCase, unittest... | 612 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_t... | 612 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__lowerCAmelCase : Tuple =logging.get_logger(__name__)
class UpperCAmelCase ( UpperCamelCase__ ):
def __init__( self :Un... | 440 |
'''simple docstring'''
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 UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ):
@register_to_c... | 440 | 1 |
'''simple docstring'''
import math
import qiskit
def snake_case_ ( __snake_case : int = 1 , __snake_case : int = 1 , __snake_case : int = 1) -> qiskit.result.counts.Counts:
if (
isinstance(__snake_case , __snake_case)
or isinstance... | 606 | '''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 ... | 606 | 1 |
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
... | 217 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
f... | 217 | 1 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
lowerCAmelCase_ = {
"debug":... | 710 |
def lowerCamelCase_ ( lowerCAmelCase: int )-> list:
_snake_case : List[Any] = int(lowerCAmelCase )
if n_element < 1:
_snake_case : int = ValueError('a should be a positive number' )
raise my_error
_snake_case : Union[str, Any] ... | 669 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""YituTech/conv-bert-base""": """https://huggingface.co/YituTech/c... | 74 | 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
lowercase__ : Optional[int] = ... | 312 | 0 |
'''simple docstring'''
def a_ ( lowerCAmelCase_ : Tuple ):
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def a_ ( lowerCAmelCase_ : Any ):
__lowerCAmelCase = credit_card_number
__lowerCAmelCase = 0
__lower... | 709 |
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
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : List[... | 421 | 0 |
"""simple docstring"""
from collections import deque
def __UpperCAmelCase ( __lowerCamelCase ) -> Tuple:
lowercase__ : Optional[int] = len(__lowerCamelCase )
lowercase__ : str = deque()
lowercase__ : Optional[A... | 560 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Tuple = "SpeechT5FeatureExtractor"
lowerCAmelCase : Optional[Any] = "SpeechT5Toke... | 560 | 1 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from... | 139 |
from __future__ import annotations
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = len(_A )
# We need to create solution object to save path.
snake_case_ = [[0 for _ in range(_A )] for _ in range(_A )]
snake_cas... | 139 | 1 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )-> Any:
"""simple docstring"""
... | 554 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, s... | 554 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutp... | 705 | 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,
StableDiffusionPipeline,
UNetaDConditi... | 580 | 0 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : Tuple = int(UpperCAmel... | 152 | '''simple docstring'''
import argparse
import json
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 ... | 152 | 1 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__lowerCamelCase = TypeVar('KT')
__lowerCamelCase = TypeVar('VT')
class _UpperCamelCase( Generic[KT, VT] ):
def __init__( self : str , _lowerCamelCase : KT | str = "root"... | 328 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False... | 328 | 1 |
import operator as op
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : Any = lambda lowercase , lowercase : int(x / y ) # noqa: E731 integer division operation
SCREA... | 62 |
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,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMSched... | 272 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Con... | 548 |
def _UpperCamelCase (a__ :int ):
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase__ = 1
UpperCamelCase__ = 1
while repunit:
UpperCamelCase__ = (10 * repuni... | 548 | 1 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lo... | 17 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default... | 128 | 0 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
C... | 703 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
lowercase ='docs/source/en/_toctree.yml'
def lowerCamelCase__ ( __lowerCamelCase : List[Any] ):
'''simple docstring'''
_UpperCAmelCase : str =defaultdict(__lo... | 331 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_... | 54 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def a__ ( lowercase__ , lowercase__ , lowercase__=1_0_2_4 , lowercase__=1_0_2_4 , lowercase__=False , ... | 54 | 1 |
"""simple docstring"""
import copy
import re
class lowerCAmelCase_ :
'''simple docstring'''
_lowerCamelCase: Tuple = '''hp'''
_lowerCamelCase: Optional[Any] = {}
_lowerCamelCase: Optional[Any] = None
@classmethod
def _SCR... | 22 |
"""simple docstring"""
def _snake_case ( snake_case__ : list , snake_case__ : list , snake_case__ : int ):
A = len(snake_case__ )
A = [[0] * n for i in range(snake_case__ )]
for i in range(snake_case__ ):
A = y_points[i]
for i... | 22 | 1 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case__ ( ) -> List[str]:
'''simple docstring'''
lowerCAmelCase = ArgumentParser(
... | 370 |
'''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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feat... | 582 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( __lowercase : float , __lowercase : float , __lowercase : float ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
... | 119 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNet... | 119 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
snake_case_ : Optional[int] = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRA... | 595 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 595 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, 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, ran... | 552 |
def _a ( SCREAMING_SNAKE_CASE_ : int ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
__lowerCAmelCase = 1
__lowerCAmelCase = 1
while repunit:
__lowerCAmelCase = (10 * repunit + 1) % divisor
repunit_index += 1... | 552 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_UpperCAmelCase : Dict = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],
}
try:
if not is_... | 362 |
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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_UpperCAm... | 362 | 1 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_av... | 476 | import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTeste... | 476 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowerCamelCase (unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self : Op... | 196 |
"""simple docstring"""
def A ( snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 2
while i * i <= n:
SCREAMING_SNAKE_CASE__ = 0
while n % i == 0:
n //= i
... | 196 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( _lowerCamelCase: dict , _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = set(__a ), [start]
while stack:
__SCREAMING_SNAKE_CASE : Optional[Any] = stac... | 720 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
UpperCamelCase__ : Optional[Any] = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thiri... | 178 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def _SCREAMING_SNAKE_CASE ( ):
_A , _A = 9, 1_4 # noqa: F841
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, ... | 107 |
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_utils import BatchFeature
... | 487 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...uti... | 718 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
# See all WavLM models at htt... | 193 | 0 |
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_avai... | 637 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 407 | 0 |
def UpperCamelCase_( _A :int )-> str:
if upper_limit < 0:
raise ValueError("Limit for the Catalan sequence must be ≥ 0" )
UpperCamelCase__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
UpperCamelCase__ = 1
if upper_limit > 0:
UpperCamelCase__ ... | 715 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig'... | 185 | 0 |
'''simple docstring'''
_UpperCAmelCase : dict[str, float] = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_... | 72 | from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
A : Optional[int] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
A : List[str] = typing.Union[np.floataa, int, float] # noqa: UP007
def a__ ( __Upp... | 140 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[int] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CON... | 709 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoCo... | 486 | 0 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__lowerCAmelCase = "."
if __name__ == "__main__":
__lowerCAmelCase = os.path.join(REPO_PATH, "utils/documentation_test... | 684 |
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
# Check if the input is valid
if not len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equatio... | 684 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __A ( _A , _A , **_A ):
"""simple docstring"""
__a = AutoConfig.from_pretrained(_A , **_A )
__a = AutoModelForSeqaSeqLM.from_config(_A )
model.s... | 701 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"""fac... | 525 | 0 |
from scipy.stats import spearmanr
import datasets
_lowercase = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that ... | 632 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
_lowercase = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
raise ImportWarning(
... | 632 | 1 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, Sw... | 508 |
'''simple docstring'''
class _a :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : dict[str, TrieNode] = {} # Mapping from char to ... | 508 | 1 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
def UpperCAmelCase__( _SCREAMING_SNAKE_CASE : Union[str, A... | 186 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...util... | 290 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta... | 267 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaP... | 267 | 1 |
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __lowercase ( snake_cas... | 313 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if "cls_toke... | 207 | 0 |
'''simple docstring'''
_snake_case : List[str] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
_a = 0
while number:
# Increased S... | 377 |
'''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, ... | 377 | 1 |
'''simple docstring'''
import random
def A ( UpperCamelCase_ : int ) -> bool:
'''simple docstring'''
lowerCAmelCase__ = num - 1
lowerCAmelCase__ = 0
while s % 2 == 0:
lowerCAmelCase__ = s // 2
t += 1
for _ in ra... | 48 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__( unittest.TestCa... | 210 | 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, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import loa... | 553 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
f... | 553 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_... | 93 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Dict = logging.get_logger(__name__)
__snake_case : int = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/c... | 293 | 0 |
def __UpperCamelCase ( lowerCAmelCase__ : int = 4_0_0_0_0_0_0 ):
__a : Dict = []
__a , __a : Dict = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(lowerCAmelCase__ )
__a , __a : List[Any] = b, a + b
... | 326 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __UpperCamelCase ( lowerCAmelCase__ ... | 326 | 1 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
A : Tuple = HfArgumentParser(InitializationArguments)
A : str = parser.parse_args()
# Load codeparrot tokenizer traine... | 15 |
def __snake_case ( __magic_name__ ):
'''simple docstring'''
lowercase , lowercase = [], []
while len(__magic_name__ ) > 1:
lowercase , lowercase = min(__magic_name__ ), max(__magic_name__ )
start.append(__m... | 441 | 0 |
import math
def lowercase_ ( A__ , A__ = 0 , A__ = 0 ) -> list:
"""simple docstring"""
snake_case = end or len(A__ )
for i in range(A__ , A__ ):
snake_case = i
snake_case = array[i]
while temp_index != start and temp_in... | 715 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_se... | 294 | 0 |
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = 9.8_0_6_6_5
def __A ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = g ):
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
... | 414 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : str = {}
_UpperCAmelCase : Optional[Any] = job["""started_at"""]
... | 414 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__a = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
try:
if not is_torch_available():
ra... | 409 |
import numpy as np
import qiskit
def a ( snake_case__: int = 8 , snake_case__: int | None = None ):
'''simple docstring'''
lowercase_ = np.random.default_rng(seed=snake_case__ )
# Roughly 25% of the qubits will contribute to the key.
... | 409 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import ... | 195 |
'''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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)... | 330 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a : Optional[Any] = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}
try:
... | 414 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__a : int = 2_0_4_8
__a : Optional[int] = 4_0_9_6
__a : Optional[int] = 4_2
__a : Optional[Any] = os.environ.pop("""PROCESS_TRAIN""", """false""")
__a... | 414 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
... | 549 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class a ( unittest.TestCa... | 549 | 1 |
from __future__ import annotations
import queue
class UpperCAmelCase :
def __init__( self , _lowerCAmelCase ):
_lowerCAmelCase = data
_lowerCAmelCase = None
_lowerCAmelCase = None
def UpperCAmelCase__ ( )->TreeNode:
print... | 664 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase ( snake_case_ ):
SCREAMING_SNAKE_CASE__ = '''ClapFeatureExtractor'''
SCREAMING_SNAKE_CASE__ = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
... | 664 | 1 |
import math
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> str:
__A : Optional[int] = 0
__A : List[str] = 0
while num > 0:
__A : Optional[int] = num % 8
__A : List[Any] = octal + (remainder * math.floor(math.pow(10 ,a__ ) ))
counte... | 17 |
'''simple docstring'''
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils impo... | 173 | 0 |
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
lowerCAmelCase__ = 4
lowerCAmelCase__ = 3
class lowercase ( _lowercase ):
"""si... | 648 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTes... | 648 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( __snake_case : str , __snake_case : str , __snake_case : List[Any]=False ):
if isinstance(__snake_case , __snake_case ) and isinstance(__snake_case , __snake_case ):
_A = len(set_a.intersec... | 107 |
'''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, Seque... | 466 | 0 |
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_availabl... | 19 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a_ ( nn.Module ):
def __init__( self : List[str] , __UpperCamelCase : int = 16 , __UpperCamelCase : ... | 19 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diff... | 652 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Da... | 652 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}
class lowercase__( UpperCAmelCase ... | 409 |
def a ( snake_case__: int ):
'''simple docstring'''
lowercase_ = [0] * len(snake_case__ )
lowercase_ = []
lowercase_ = []
lowercase_ = 0
for values in graph.values():
for i in values:
... | 409 | 1 |
def SCREAMING_SNAKE_CASE ( snake_case ) -> bool:
if not isinstance(snake_case , snake_case ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(snake_case ) == 0:
raise ValueError('Input list must be a non empty list' )
... | 375 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
SCREAMING_SNAKE_CASE_ : str = {
'''... | 375 | 1 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoencode... | 702 | """simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : List[Any] ... | 645 | 0 |
import torch
from torch import nn
class UpperCamelCase ( nn.Module ):
def __init__(self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=1 , __UpperCamelCase=False ) -> Optional[int]:
... | 635 | import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__na... | 635 | 1 |
"""simple docstring"""
def __A ( a_ :int) -> List[Any]:
__a : Dict = abs(snake_case__)
__a : Optional[int] = 0
while n > 0:
res += n % 10
n //= 10
return res
def __A ( a_ :int) -> in... | 700 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
... | 101 | 0 |
"""simple docstring"""
def a_ ( ):
return 1
def a_ ( lowercase__ :Optional[Any] ):
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def a_ ( lowercase__ :int ):
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(a_ )
... | 281 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a_ ( unittest.TestC... | 318 | 0 |
from __future__ import annotations
import requests
A = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded g... | 711 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
A = logging.get_logger(__name__)
def lowerCAmelCase__ ( lowerCamelCase__=None , lowerCamelCase__=No... | 109 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''multiplicative_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''multiplicative_persistence() does not accep... | 11 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCame... | 11 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {"""vocab_fi... | 78 |
"""simple docstring"""
def lowerCamelCase__ ( ) -> list[list[int]]:
"""simple docstring"""
return [list(range(10_00 - i, -10_00 - i, -1 ) ) for i in range(10_00 )]
_a = generate_large_matrix()
_a = (
[[4, 3, 2, -1], [3, 2, 1... | 78 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def lowercase (_lowerCAmelCase ):
return np.maximum(0 , _lowerCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 465 |
"""simple docstring"""
def __magic_name__ ( UpperCamelCase : int , UpperCamelCase : list[int] , UpperCamelCase : int ) -> int:
def count_of_possible_combinations(UpperCamelCase : int ) -> int:
if target < 0:
return 0
if ta... | 273 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ ={
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConfig""",
"""XCLIPTextConfig""",
"""X... | 33 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __UpperCamelCase ( __UpperCAmelCase ):
'''simple docstring'''
... | 33 | 1 |
import math
import unittest
def UpperCamelCase__( UpperCamelCase__ : Tuple )->bool:
assert isinstance(UpperCamelCase__ , UpperCamelCase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 ... | 190 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
... | 604 | 0 |
"""simple docstring"""
def lowercase ( _snake_case : int ) ->bool:
"""simple docstring"""
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 229 |
"""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
#
# Un... | 229 | 1 |
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 __UpperCamelCase ( A__ ):
__A : Dict ... | 32 | """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
from a... | 363 | 0 |
from __future__ import annotations
from collections import namedtuple
def lowerCamelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
_a : Optional[Any] = namedtuple('''result''' , '''name value''' )
if (voltage, current, power).count(0 ) != 1:
... | 249 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ... | 249 | 1 |
'''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
_snake_case = logging.get_logger(__name__)
_sn... | 245 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
fr... | 245 | 1 |
'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_mod... | 713 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowerCamelCase__( nn.Module):
UpperCAmelCase__ : int
UpperCAmelCase__ : int
UpperCAmelCase_... | 80 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 380 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_to... | 459 | 0 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase : Any = logging.getLogger(__name__)
if __name__ == "__main__":
... | 392 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Optional[int] = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# S... | 392 | 1 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class A__ :
"""simple docstring"""
pass
| 37 |
"""simple docstring"""
import operator
def A_ (__a , __a = False , __a = None ):
'''simple docstring'''
A_ = operator.lt if reverse else operator.gt
A_ = solution or []
if not arr:
return solution
A_ = [arr.pop(0 )]
... | 115 | 0 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import Iter... | 711 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> int:
"""simple docstring"""
if not nums:
return 0
A = nums[0]
A = 0
for num in nums[1:]:
A , A = (
max_excluding + nu... | 91 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__UpperCamelCase : str =... | 328 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __SCREAMING_SNAKE_CASE( a_... | 328 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase :Any = {
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://hu... | 686 |
'''simple docstring'''
import pytest
lowerCamelCase :Optional[Any] = '''__dummy_dataset1__'''
lowerCamelCase :List[Any] = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = ... | 686 | 1 |
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
UpperCamelCase_: int = len(UpperCAmelCase__ )
UpperCamelCase_: int = len(UpperCAmelCase__ )
UpperCamelCase_: int = (
first_str_length if first_str_le... | 57 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class ... | 658 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Any = logging.get_logger(__name__)
__lowerCamelCase : Any = {
'''microsoft/unispeech-... | 656 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__=None ,**__magic_name__ )-> Optional[Any]:
"""simple docstring"""
snake_case_ : int = ... | 656 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A__ :
A__ = 42
A__ = 42
class A__ :
def __init__( self : ... | 405 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelF... | 405 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Union[str, Any] = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegT... | 188 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class lowerCAmelCase ( unittest.TestCase ... | 188 | 1 |
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