code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case : Optional[int] = {
'configuration_mobilebert': [
... | 22 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_p... | 22 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.uti... | 22 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from trans... | 22 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num... | 22 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorTyp... | 22 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_c... | 22 | 1 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def snake_c... | 22 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 22 | 1 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_snake_case : Any = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 22 |
'''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,
r... | 22 | 1 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case_ (UpperCamelCase : ... | 22 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_layou... | 22 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Union[str, Any] = logging.get_logger(__name__)
_snake_case : Optional[int] = {
'asapp/sew-d-tiny-100k': 'https://hug... | 22 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_tor... | 22 |
'''simple docstring'''
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 snake_case_ (UpperCamelCase : ... | 22 | 1 |
'''simple docstring'''
_snake_case : Union[str, Any] = [0, 2, 4, 6, 8]
_snake_case : Optional[int] = [1, 3, 5, 7, 9]
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : list[int] , UpperCamelCase : int ):
... | 22 |
'''simple docstring'''
import qiskit
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = qiskit.Aer.get_backend('''aer_simulator''' )
_a = qiskit.QuantumCircuit(4 , 2 )... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (UpperCamelCase : dict , UpperCamelCase : str ):
'''simple docstring'''
_a , _a = set(UpperCamelCase ), [start]
while stack:
_a ... | 22 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
... | 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_im... | 22 | 1 |
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo... | 22 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
... | 22 | 1 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def snake_case_ ():
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import ... | 22 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
... | 22 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Any =... | 22 | 1 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutt... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : str , UpperCamelCase : list[str] ):
'''simple docstring'''
_a = ''''''
for word_or_phrase in separated:
if not isinstance(UpperCamelCase , UpperCamelCa... | 22 |
'''simple docstring'''
from math import pi, sqrt
def snake_case_ (UpperCamelCase : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math rang... | 22 | 1 |
'''simple docstring'''
import numpy as np
from PIL import Image
def snake_case_ (UpperCamelCase : np.ndarray , UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = np.array(UpperCamelCase )
if a... | 22 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 22 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_comm... | 22 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_snake_case : Any = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
_a = abs(UpperCamelCase )
_a = 0
while n > 0:
res += n % 10
n //= 10
return res
def sn... | 22 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
_snake_case : Tuple = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : Optional[int] , UpperCamelCase : List[Any] ):
'''simple docstring'''
_a = 0
_a = len(UpperCamelCase ) - 1
while left <= right:
# avoid... | 22 |
'''simple docstring'''
import requests
def snake_case_ (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(UpperCamelCase ,... | 22 | 1 |
'''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 pack... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,... | 22 | 1 |
'''simple docstring'''
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_snake_case : Any = {
'facebook/mask2former-swin-small-coco-instance': (
'https:... | 22 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 22 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_f... | 22 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.uti... | 22 | 1 |
'''simple docstring'''
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_ti... | 22 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num... | 22 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
excep... | 22 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_c... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
_a = int(UpperCamelCase )
if n_element < 1:
_a = ValueError('''a should be a positive number''' )
raise my_er... | 22 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 22 | 1 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require... | 22 |
'''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,
r... | 22 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diff... | 22 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_layou... | 22 | 1 |
'''simple docstring'''
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 A ... | 22 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 1 |
'''simple docstring'''
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
fr... | 22 |
'''simple docstring'''
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 snake_case_ (UpperCamelCase : ... | 22 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import Gene... | 22 |
'''simple docstring'''
import qiskit
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = qiskit.Aer.get_backend('''aer_simulator''' )
_a = qiskit.QuantumCircuit(4 , 2 )... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
... | 22 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(UpperCamelCase , (list, tuple) ) or not all(
isinstance(UpperCamelCase , Up... | 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_im... | 22 | 1 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class A :
def __init__( self : Tuple , lowerCAmelCase_ : ... | 22 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
... | 22 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
... | 22 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : Optional[Any] ... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Any =... | 22 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class A :
lowercase_ = field(
... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 1 |
'''simple docstring'''
from datetime import datetime
import requests
def snake_case_ (UpperCamelCase : str ):
'''simple docstring'''
_a = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
_a = requ... | 22 |
'''simple docstring'''
from math import pi, sqrt
def snake_case_ (UpperCamelCase : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math rang... | 22 | 1 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
_snake_case : Tuple = 637_8137.0
_snake_case : Optional[int] = 635_6752.31_4245
_snake_case : int = 6378137
def snake_case_ (UpperCame... | 22 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def snake_case_ (UpperCamelCase : Callable[[int | float], int | float] , UpperCamelCase : int | float , UpperCamelCase : int | float , UpperCamelCase ... | 22 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_snake_case : Any = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 22 | 1 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
... | 22 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
_snake_case : Tuple = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 22 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def snake_case_ (UpperCamelCase : Optional[int] ):
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.force , ... | 22 |
'''simple docstring'''
import requests
def snake_case_ (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(UpperCamelCase ,... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : list , UpperCamelCase : int , UpperCamelCase : int = 0 , UpperCamelCase : int = 0 ):
'''simple docstring'''
_a = right or len(UpperCamelCase ) - 1
if l... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def snake_case_ (UpperCamelCase : list[Any] ):
'''simple docstring'''
create_state_space_tree(UpperCamelCase , [] , 0 )
def snake_case_ (UpperCamelCas... | 22 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 22 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.uti... | 22 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.uti... | 22 | 1 |
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelo... | 22 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num... | 22 | 1 |
'''simple docstring'''
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If u... | 22 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_c... | 22 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : int = logging.get_logger(__name__)
_snake_case : Optional[Any] =... | 22 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 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_ ():
'''simple docstring'''
_a = ArgumentParser(
... | 22 |
'''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,
r... | 22 | 1 |
'''simple docstring'''
import math
def snake_case_ (UpperCamelCase : list , UpperCamelCase : int ):
'''simple docstring'''
_a = len(UpperCamelCase )
_a = int(math.floor(math.sqrt(UpperCamelCase ) ) )
_a ... | 22 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_layou... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case : Any = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokeni... | 22 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 1 |
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ... | 22 |
'''simple docstring'''
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 snake_case_ (UpperCamelCase : ... | 22 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a )
class A ( _a ):
lowercase_ = field(default='language... | 22 |
'''simple docstring'''
import qiskit
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = qiskit.Aer.get_backend('''aer_simulator''' )
_a = qiskit.QuantumCircuit(4 , 2 )... | 22 | 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
_snake_case : Optional[int] = logging.get_logger(__na... | 22 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : list[int] , UpperCamelCase : str ):
'''simple docstring'''
_a = int(UpperCamelCase )
# Initialize Result
_a = []
# Traverse through all den... | 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_im... | 22 | 1 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : int = logging.get_logg... | 22 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
... | 22 | 1 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
_a... | 22 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
... | 22 | 1 |
'''simple docstring'''
import copy
import re
class A :
lowercase_ = 'hp'
lowercase_ = {}
lowercase_ = None
@classmethod
def __lowerCAmelCase ( cls : List[Any] , low... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Any =... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_snake_case : Optional[int] = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 |
'''simple docstring'''
from math import pi, sqrt
def snake_case_ (UpperCamelCase : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math rang... | 22 | 1 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def snake_case_ (UpperCamelCase : Optional[int] ):
'''simple docstring'''
_a = [
'''e... | 22 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 22 | 1 |
'''simple docstring'''
import logging
import os
from .state import PartialState
class A ( logging.LoggerAdapter ):
@staticmethod
def __lowerCAmelCase ( lowerCAmelCase_ : Union[str, Any] ) -> Dict:
"""simp... | 22 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_snake_case : Any = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (UpperCamelCase : list[int] ):
'''simple docstring'''
if len(UpperCamelCase ) == 0:
return array
_a , _a = min(UpperCamelCase ), max(UpperCamel... | 22 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
_snake_case : Tuple = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 22 | 1 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_... | 22 |
'''simple docstring'''
import requests
def snake_case_ (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(UpperCamelCase ,... | 22 | 1 |
'''simple docstring'''
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 : List[Any] = logging.get_logger(__name__)
... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,... | 22 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get... | 22 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 22 | 1 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTe... | 22 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.uti... | 22 | 1 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly forma... | 22 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num... | 22 | 1 |
'''simple docstring'''
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
@data... | 22 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_c... | 22 | 1 |
'''simple docstring'''
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 snake_case_ (UpperCamelCase : ... | 22 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 22 | 1 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_snake_case : Optional[int] = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D... | 22 |
'''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,
r... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : str ):
'''simple docstring'''
_a = [int(UpperCamelCase ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(UpperCamelCase ) == 4 and all(0 <= int(UpperCamelCase ) <=... | 22 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_layou... | 22 | 1 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim im... | 22 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 1 |
'''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 Runtim... | 22 |
'''simple docstring'''
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 snake_case_ (UpperCamelCase : ... | 22 | 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 impor... | 22 |
'''simple docstring'''
import qiskit
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = qiskit.Aer.get_backend('''aer_simulator''' )
_a = qiskit.QuantumCircuit(4 , 2 )... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case : List[str] = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 22 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
... | 22 | 1 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def snake_case_ (UpperCamelCase : str = "isbn/0140328726" ):
'''simple docstring'''
_a = olid.strip().strip('''/''' ... | 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_im... | 22 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
f... | 22 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
... | 22 | 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 impor... | 22 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
... | 22 | 1 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def snake_case_ ():
'''simple docstring'''
_a = {
'''repo_name''': ['''te... | 22 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Any =... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
import bisect
def snake_case_ (UpperCamelCase : list[int] , UpperCamelCase : int , UpperCamelCase : int = 0 , UpperCamelCase : int = -1 ):
'''simple docstring'''
if h... | 22 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
if num <= 0:
raise ValueError('''Input must be a positive integer''' )
_a = [True] * (num + 1)
_a = 2
wh... | 22 |
'''simple docstring'''
from math import pi, sqrt
def snake_case_ (UpperCamelCase : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math rang... | 22 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
_snake_case : Optional[int] ... | 22 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determini... | 22 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : list[int] , UpperCamelCase : list[int] ):
'''simple docstring'''
_a = len(UpperCamelCase )
print('''The following activities are selected:''' )
# The first activ... | 22 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_snake_case : Any = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 22 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
_snake_case : Tuple = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 22 | 1 |
'''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_... | 22 |
'''simple docstring'''
import requests
def snake_case_ (UpperCamelCase : str , UpperCamelCase : str ):
'''simple docstring'''
_a = {'''Content-Type''': '''application/json'''}
_a = requests.post(UpperCamelCase ,... | 22 | 1 |
'''simple docstring'''
import random
def snake_case_ (UpperCamelCase : Dict , UpperCamelCase : List[Any] , UpperCamelCase : Any ):
'''simple docstring'''
_a = a[left_index]
_a = left_index + 1
for ... | 22 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,... | 22 | 1 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A ( nn.Module ):
lowercase_ = 42
lowe... | 22 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
... | 22 | 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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTex... | 22 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.uti... | 22 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from to... | 22 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , num... | 22 | 1 |
'''simple docstring'''
import argparse
import copy
def snake_case_ (UpperCamelCase : Tuple ):
'''simple docstring'''
_a = {}
with open(UpperCamelCase ) as f:
for line in f:
if line.split()[0] not i... | 22 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_c... | 22 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case : str = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available()... | 22 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 22 | 1 |
'''simple docstring'''
from decimal import Decimal, getcontext
from math import ceil, factorial
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise TypeError('''Unde... | 22 |
'''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,
r... | 22 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_c... | 22 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : str = {
'configuration_layou... | 22 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def snake_case_... | 22 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( _a ):
lowercase_ = (DDPMParallelScheduler,)
def __lowerCAmelCase ( self : ... | 22 | 1 |
'''simple docstring'''
import pytest
_snake_case : List[Any] = '__dummy_dataset1__'
_snake_case : Optional[int] = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO... | 22 |
'''simple docstring'''
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 snake_case_ (UpperCamelCase : ... | 22 | 1 |
'''simple docstring'''
import math
import qiskit
def snake_case_ (UpperCamelCase : int = 1 , UpperCamelCase : int = 1 , UpperCamelCase : int = 1 ):
'''simple docstring'''
if (
isinstance(UpperCamelCase , UpperCamelCas... | 22 |
'''simple docstring'''
import qiskit
def snake_case_ (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
_a = qiskit.Aer.get_backend('''aer_simulator''' )
_a = qiskit.QuantumCircuit(4 , 2 )... | 22 | 1 |
'''simple docstring'''
import argparse
import collections
import os
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_table.py
_snake_case : Option... | 22 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
... | 22 | 1 |
'''simple docstring'''
import math
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 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_im... | 22 | 1 |
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