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"""
import pprint
import requests
__lowerCAmelCase : Optional[Any] = '''https://zenquotes.io/api'''
def __lowerCAmelCase ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + """/today""" ).j... | 58 |
"""simple docstring"""
def snake_case__ ( _lowerCamelCase ) ->str:
"""simple docstring"""
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7... | 575 | 0 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase=1):
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.')[n_shave_prefix_segments:])
else:
... | 714 |
from abc import ABC, abstractmethod
from typing import List, Optional
class _snake_case ( A__ ):
def __init__( self) -> int:
# test for the above condition
self.test()
def SCREAMING_SNAKE_CASE__ ( self) -> Tuple:
SCREAMING_SNAKE... | 444 | 0 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attenti... | 57 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Union[str, Any] =logging.get_logger(__name__)
A_ : Optional[Any] ={
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.c... | 650 | 0 |
from __future__ import annotations
import math
def lowercase__( A ):
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
... | 705 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
lowerCamelCase : str = 2_9_9_7_9_2_4_5_8
# Symbols
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : Union[str, Any] = symbols('ct x y z')
def lowercase... | 303 | 0 |
'''simple docstring'''
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
snake_case = logging.getLogger(__name__)
def A_ ( ):
_lowerCAmelCase = argparse.ArgumentParser(
description='Prepare TFRecord shard... | 309 | '''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
snake_case = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
snake_case = typing.Union[np.floataa, int, float] # noqa: UP007
def A_ ( _... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def snake_case ( UpperCamelCase__ : float , UpperCamelCase__ : int ) -> float:
lowerCamelCase : Dict = u
for i in range(1 , UpperCamelCase__ ):
lower... | 712 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__lowerCamelCase :Optional[Any] = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
... | 42 | 0 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked... | 284 | """simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Bat... | 434 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def snake_case__ ( UpperCAmelCase : dict ):
r... | 704 |
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
_a : Tuple = logging.get_logger(__name__)
_a : Optional[int] = {
"""fac... | 111 | 0 |
from collections.abc import Sequence
def __lowerCAmelCase ( _A ,_A ):
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(_A ) )
def __lowerCAmelCase ( _A ,_A ):
"""simple docstring"""
_lowercase = 0.0
... | 398 | 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_: Tuple = logging.get_logger(__name__)
... | 398 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
snake_case__ = datasets.utils.logging.get_logger(__name... | 373 |
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,
ConditionalDetrForSegmentation,
... | 373 | 1 |
import math
def _A ( _lowercase , _lowercase ) -> Tuple:
"""simple docstring"""
return math.pow(SCREAMING_SNAKE_CASE_ , 2 ) - a
def _A ( _lowercase ) -> Union[str, Any]:
"""simple docstring"""
return 2 * x
def... | 1 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
__snake_case = "scheduler_config.json"
class UpperCAmelCase ( __snake_case ):
lowercase ... | 386 | 0 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,... | 595 | """simple docstring"""
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 lowercase ( UpperCa... | 595 | 1 |
"""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 import Backbo... | 222 |
'''simple docstring'''
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import T... | 379 | 0 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class UpperCAmelC... | 139 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ : Lis... | 139 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils imp... | 105 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __snake_case ( ):
"""simple docstring"""
raise RuntimeError('''CUDA out o... | 34 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impo... | 703 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase ( __UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = "Speech2TextFeatureExtractor"
_SCREAMING_SNAKE_CASE = "Speech2... | 273 | 0 |
import math
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int ) -> list[int]:
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase : Any = 2
_UpperCAmelCase : Any = int(math.sqrt(lowerCAmelCase ) ) # Size of every segment
_UpperCAmelCase... | 300 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: list[list[int]] ) -> bool:
_UpperCAmelCase : int = len(lowerCAmelCase )
# We need to create solution object to save path.
_UpperCAmelCase : List[Any] = [[0 for _ in range(low... | 300 | 1 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from... | 220 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def SCREAMING_SNAKE_CASE_ ( snake_case_ : np.ndarray ) -> np.ndarray:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] ... | 220 | 1 |
"""simple docstring"""
import sys
import turtle
def UpperCamelCase ( _lowerCAmelCase : tuple[float, float], _lowerCAmelCase : tuple[float, float] ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def UpperCamelCase ( _lowerCAme... | 238 |
"""simple docstring"""
import argparse
import json
import subprocess
def UpperCamelCase ( _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : Dict ... | 238 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTenso... | 74 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTenso... | 74 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Tuple = {
"xl... | 419 |
class snake_case__ :
def __init__( self , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[int]:
"""simple docstring"""
a_ : Optional[Any] = name
a_ : Union[str, Any] = val
def __str__( self ) -> Tup... | 419 | 1 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def __lowerCAmelCase ( snake_case : dict ) -> tuple:
r... | 189 |
from sklearn.metrics import fa_score
import datasets
_A : Any = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
_A : Dict = '''
Args:
predictions (`list` of `... | 189 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case = {
"""configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}
tr... | 378 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
snake_case = get_tests_dir("""fixtures/... | 378 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=_UpperCAmelCase ):
__lowerCAmelCase : List[Any] = ['torch']
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> List[Any]:
... | 718 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A: List[Any] = (3, 9, -1_1, 0, 7, 5, 1, -1)
A: Union[str, Any] = (4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class SCREAMING_SNAKE_CASE__ :
... | 359 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : List[str] = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnx... | 675 |
'''simple docstring'''
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 snake_case ( lowercase ):
"""... | 675 | 1 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase_ : Any = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase_ : List[str] = False
if NLTK_AVAILABLE:
... | 713 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _lowerCAmelCase(a : List[str] , a : str , a : Optional[int] ) -> ... | 165 | 0 |
"""simple docstring"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class a__ :
def __init__( self , _a , _a , _a , _a , _a , _a=0.2 , _a=0.2 ):
lowercase : ... | 361 | '''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__lowerCAmelCase : List[Any] = logging.get_logger(__name__) # pylint: disable=inv... | 262 | 0 |
def _lowerCAmelCase ( _a : str ) -> bool:
lowerCAmelCase_ : str = [int(_a ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(_a ) == 4 and all(0 <= int(_a ) <= 2_54 for octet in octets )
if __name__ == "__main__":
UpperCAmelCase_ : ... | 716 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""... | 440 | 0 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _lowercase ( unittest.TestCase ):
def a ( self : Optional[Any] ) -> None:
__snake_case ... | 56 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 471 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.jso... | 703 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'google/bigbird-roberta-base': 'https:/... | 257 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Tuple = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
if not is... | 679 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCST... | 679 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ : List[Any] = {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_... | 446 |
from math import ceil
def __lowercase ( _A = 1001 ) -> int:
SCREAMING_SNAKE_CASE : Union[str, Any] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
SCREAMING_SNAKE_CASE : Dict = 2 * i + 1
SCREAMING_... | 446 | 1 |
def UpperCAmelCase__ ( lowerCamelCase_ : Tuple , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : int=False ):
if isinstance(lowerCamelCase_ , lowerCamelCase_ ) and isinstance(lowerCamelCase_ , lowerCamelCase_ ):
__a ... | 47 |
from typing import Any
import numpy as np
def SCREAMING_SNAKE_CASE__ ( snake_case__ :np.ndarray ) -> bool:
return np.array_equal(snake_case__ , matrix.conjugate().T )
def SCREAMING_SNAKE_CASE__ ( snake_case__ :np.ndarray , snake_case__ :np.ndarray ) ... | 67 | 0 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
__UpperCAmelCase :Tuple = "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation ... | 702 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
... | 266 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__UpperCamelCase = logging.get_logger(__name__)
class _A ( __lowercase ):
def __init__( self : List[str] , *__... | 26 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class lowercase_ :
def __init__( self : Optional[Any] , _lowercase : O... | 308 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from... | 716 |
UpperCamelCase = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
UpperCamelCase = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperC... | 152 | 0 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
lo... | 439 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_... | 439 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_a : List[Any]= {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["Tap... | 192 | """simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_... | 192 | 1 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __lowercase :
def __init__( self , lowerca... | 313 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __lowercase :
def __init__( self , lowerca... | 313 | 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
from ...... | 229 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _snake_case : float , _snake_case : float , _snake_case : float , ) ->tuple[str, float]:
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot... | 229 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_avail... | 280 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ : str = logging.get_logger(__name__)
def lowercase__ ( _UpperCamelCase) -> int:
... | 280 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
fr... | 711 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [1]
for i in range(2 , SCREAMING... | 68 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, At... | 586 |
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_common import SequenceFeatu... | 445 | 0 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCamelCase__ = logging.getLogger(__name__)
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
def __init__(... | 82 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load... | 82 | 1 |
from __future__ import annotations
import math
def _lowerCamelCase ( __A : int ) -> list[int]:
if num <= 0:
_UpperCAmelCase : Tuple = f'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(__A )
_Upp... | 485 |
def _lowerCamelCase ( __A : int ) -> str:
_UpperCAmelCase : Tuple = int(__A )
if decimal in (0, 1): # Exit cases for the recursion
return str(__A )
_UpperCAmelCase , _UpperCAmelCase : int = divmod(__A , 2 )
... | 485 | 1 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
... | 709 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def UpperCAmelCase ( A : Union[str, Any] , A : Optional[int] ... | 464 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDis... | 427 | '''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( snake_case_ : list[int] ) -> bool:
'''simple docstring'''
return len(set(snake_case_ ) ) == len(snake_case_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 427 | 1 |
'''simple docstring'''
import re
def a_ ( _UpperCAmelCase : str ) -> list:
return [char.split() for char in re.split(r'[^ a-z A-Z 0-9 \s]' ,str_ )]
def a_ ( _UpperCAmelCase : str ) -> str:
__snake_case : Any ... | 124 |
'''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def a_ ( _UpperCAmelCase : str ,_UpperCAmelCase : Optional[Any]=10_00 ) -> List[str]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# th... | 124 | 1 |
'''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 (
HPSear... | 8 |
'''simple docstring'''
from __future__ import annotations
import math
class SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCAmelCase):
'''simple docstring'''
__A : int = size
# approximate the overall ... | 8 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "https://hu... | 707 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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 r... | 262 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class a__ ( ... | 77 |
from math import isqrt
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowerCamelCase__ , lower... | 463 | 0 |
'''simple docstring'''
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCAmelCase = {
"tiny.en": "https://openaipublic.... | 702 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
... | 318 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
UpperCAmelCase_ : str = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Langua... | 255 | '''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : List[Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-... | 168 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__a : Optional[int] = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
__a : int = _LazyModule(__name__, globa... | 199 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def _SCREAMING_SNAKE_CASE ( __lower... | 199 | 1 |
def lowerCAmelCase_ ( snake_case_ ):
if n == 1 or not isinstance(snake_case_,snake_case_ ):
return 0
elif n == 2:
return 1
else:
_A : Union[str, Any] = [0, 1]
for i in range(2,n + 1 ):
sequence.append(sequence[i... | 307 |
from __future__ import annotations
_snake_case = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_snake_case = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowerCAmelCase_ ( snake_case_ ):
_A : str = []
_A : int = len(snake... | 307 | 1 |
"""simple docstring"""
from math import factorial
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> float:
"""simple docstring"""
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or succ... | 700 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase : Any = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG... | 91 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_... | 661 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__SCREAMING_SNAKE_CASE : Optional[int] = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translatio... | 661 | 1 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( snake_case__ , snake_case__ ) -> List[str]:
__UpperCAmelCase ... | 710 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def A__ (self):
'''simple ... | 142 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_... | 28 | 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
_SCREAMING_SNAKE_CASE = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])
def snake_case ( snake_... | 401 | 0 |
from __future__ import annotations
def A__ ( __A : list[int] ) ->int:
if not nums:
return 0
__A =nums[0]
__A =0
for num in nums[1:]:
__A , __A =(
max_excluding + num,
max(__A , __A ),
... | 516 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
# See all Bio... | 516 | 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 lowerCamelCase (unittest.TestCase ):
def SCREAMING_S... | 196 |
"""simple docstring"""
import numpy as np
def A ( snake_case__ ):
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 196 | 1 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision... | 706 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
a__ : Any = parse(importlib.metadata.version('torch'))
def __snake_case ( SCREAMING_SNAKE_CASE_ : Union[str, Version] , SCREAMI... | 570 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase : List[str] = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 663 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCamelCase : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 663 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a ( nn.Module ):
def __in... | 467 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 467 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from t... | 49 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONF... | 407 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _snake_case ( snake_case__ : Op... | 22 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common i... | 22 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class snake_case_ ( _lowerCamelCase ):
... | 260 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
class snake_case_ ( _lowerCamelCase ):
... | 260 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mobile... | 142 |
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=_lowerCAmelCase ):
a_ : Dict = ['''torch''', '''transformers''', '''onnx''']
def __init__(self , *UpperCAmelCase , **UpperCAmelCase):
'''simple docstring'''
... | 142 | 1 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
__lowerCamelCase = 3
def a ( __UpperCAmelCase : List[Any] ) -> int:
print("""Generating primitive root of p... | 96 |
from bisect import bisect
from itertools import accumulate
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Dict:
a = sorted(zip(__lowerCamelCase , __lowerCamelCase ) , key=lambda ... | 468 | 0 |
"""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
... | 710 |
"""simple docstring"""
def _a ( UpperCAmelCase__ = 10**9 ) -> int:
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
... | 690 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
def UpperCamelCase__ (self : int , UpperCamelCas... | 460 |
'''simple docstring'''
import unittest
import numpy as np
def _SCREAMING_SNAKE_CASE (A , A , A , A = None , ) -> np.ndarray:
"""simple docstring"""
lowercase__ = np.shape(A )
lowercase__ = np.shape(A )
lowercase__ ... | 460 | 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.pipe... | 369 |
# Lint as: python3
import itertools
import os
import re
UpperCAmelCase_ = re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCAmelCase_ = re.compile(R'([a-z\d])([A-Z])')
UpperCAmelCase_ = re.compile(R'(?<!_)_(?!_)')
UpperCAmelCase_ = re.compile(R'(_{2,})')
U... | 369 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeat... | 363 | """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
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {"vocab_file": "se... | 363 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ : Union[str, Any] = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
if not is_torch_availabl... | 703 | import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
im... | 107 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline | 623 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""... | 623 | 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_unispeech": ["UNISPEECH_PRETRAINED_CONFI... | 701 | '''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import... | 43 | 0 |
"""simple docstring"""
import string
from math import logaa
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCamelCase_ = document.translate(
str.maketrans('''''' ,'''''' ,string.punctuation ) ).replace('''\n''' ,'''''' )
lowerCamelCase_ = document_wit... | 29 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils imp... | 702 |
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 __magic_name__ ( _a):
_UpperCAmelCase : Optional[int] =... | 405 | 0 |
'''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_bert import BertTokenizer
__A : List[Any] = logging.get_logger(__nam... | 394 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Accele... | 394 | 1 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import ... | 19 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_t... | 19 | 1 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case = get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.mo... | 658 |
def _A ( SCREAMING_SNAKE_CASE__ : int ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise ValueError('''check_bouncy() accepts only integer arguments''' )
UpperCamelCase :int = str(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :O... | 658 | 1 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class SCREAMING_SNAKE_CASE_ :
'''simple docstring'''
_a = field(
default="codeparrot/codeparrot" , metadata={"help": "Model name or path of model to be trained."} )
_... | 171 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNA... | 171 | 1 |
from ..utils import DummyObject, requires_backends
class a ( metaclass=__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase_ : Tuple = ['flax', 'transformers']
def __init__( self : str , *lowerCamelCase__ : int , **lowerCam... | 332 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment... | 332 | 1 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCAmelCase__ =logging.getLogger(__name__)
class lowerCamelCase__ ( _a ):
a : Union[str, Any] = """masked_bert"""
def __init__( self : List... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ ={"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torch_availa... | 442 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHea... | 389 |
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .token... | 389 | 1 |
'''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
SCREAMING_SNAKE_CASE_: Union[str, Any] =logging.get_logger(__name__)
SCREAMI... | 718 | '''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
... | 415 | 0 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowercase_ ( tf.keras.layers.Layer):
"""sim... | 483 |
"""simple docstring"""
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return " ".join(
''.join(word[::-1] ) if len(_snake_case ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
... | 7 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
fro... | 271 |
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
__lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
class UpperCAmelCase :
"""simple docstring"""
lowerCAmel... | 271 | 1 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
f... | 528 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self , __snake_case="" , __snake_case="train" ) -> Optional[Any]:
'''simple docstring... | 242 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
el... | 700 | """simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCAmelCase_ :
snake_case__ = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or path of model to be trained.'''})
snake_case... | 342 | 0 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tok... | 37 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a ):
'''simple docstring'''
A_ = 0.00
A_ = 0
for resistor in resistors:
if resistor <= 0:
A_ = f'Resistor at index {index} has a negative or zero value!'
... | 115 | 0 |
from __future__ import annotations
def _lowerCamelCase ( snake_case , snake_case , snake_case ):
_lowerCAmelCase = list(range(len(__lowerCAmelCase ) ) )
_lowerCAmelCase = [v / w for v, w in zip(__lowerCAmelCase , __lowerCAmelCase )]
... | 709 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase: List[str] = logging.get_logger(__name__)
_lowercase: Optional[Any] = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''https:... | 225 | 0 |
import requests
A : str = 'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='
def UpperCamelCase ( __magic_name__ : str ) -> None:
"""simple docstring"""
lowercase__ = requests.get(_NEWS_API + bbc_news_api_key ).json()
#... | 15 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class A_ (a_ ):
"""simple docstring"""
def __init__( self :List[str] , *lowerCAmelCase__ :Optional[Any] , **lowerCAmelCase__ ... | 653 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : str = {
"google/pix2struct-textcaps-base": (
"https://huggingface.co/google/... | 711 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
_UpperCAmelCase : List[Any] = logging.getLogger()
@unittest.skip('Temporarily disable the doc t... | 288 | 0 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCAmelCase = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui ... | 43 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return len(set(SCREAMING_SNAKE_CASE ) ) == len(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43 | 1 |
'''simple docstring'''
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_... | 719 |
'''simple docstring'''
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.u... | 425 | 0 |
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.numpy as jnp
from transfo... | 464 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'vocab_file': 'vocab.json',
... | 76 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( A__ = 1_00 ):
a_ = 0
a_ = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F"""{solution() = }""")
| 511 |
'''simple docstring'''
from maths.prime_factors import prime_factors
def UpperCamelCase_ ( A__ ):
if not isinstance(A__ , A__ ):
a_ = F'''Input value of [number={number}] must be an integer'''
raise TypeError(A__ )
if number < 1:
raise ValueError("""Input must be a positive int... | 511 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProc... | 299 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils... | 347 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__lowerCamelCase : int = logging.get_logger(__name__)
class A_ (_UpperCAmelCase ):
"""simple docstring"""
def __init__( self ... | 704 |
'''simple docstring'''
import inspect
import re
from transformers.utils import direct_transformers_import
# 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
__lowerCamelCase : Any ... | 656 | 0 |
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