code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fro... | 364 |
'''simple docstring'''
import re
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
if len(re.findall("[ATCG]" , _SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("Invalid Strand" )
re... | 67 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
UpperCAmelCase_ : Tuple = 0
UpperCAmelCase_ : Optional[Any] = len(lowerCAmelCase__ ) - 1
UpperCAmel... | 365 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Union[str, Any] =["torch", "torchsde"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["torc... | 67 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTest... | 366 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, Table... | 67 | 0 |
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, BlipaProcessor,... | 367 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowerCamelCase = logging.get_... | 67 | 0 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,... | 368 |
'''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
from ...te... | 67 | 0 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_lowerCamelCase = (
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH"... | 369 |
'''simple docstring'''
from __future__ import annotations
_lowerCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a__ ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING... | 67 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''... | 370 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONF... | 67 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase = {
"configuration... | 371 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.j... | 67 | 0 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] ) -> int:
"""simple docstring"""
UpperCAmelCase_ : Any = FileLock(str(tmpdir / "fo... | 350 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(_SCREAMING_SNAKE_CASE : int , _SCREAMING_SN... | 67 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
UpperCAmelCase_ : str = len(_SCREAMING_SNAKE_CASE )
for _ in range(_SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 ... | 351 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fro... | 67 | 0 |
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Union[str, Any] =["torch", "torchsde"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["torch", "torchsde"] )
... | 352 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import Ba... | 67 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
# See al... | 353 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING... | 67 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=... | 354 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
"""simple docstring"""
UpperCAmelCase_ : Tuple = [randint(-10_00 ... | 67 | 0 |
'''simple docstring'''
import math
from collections.abc import Callable
def a__ ( _SCREAMING_SNAKE_CASE : Callable[[float], float] , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ) -> float:
"""simple docstring"""
UpperCAmelCase_... | 355 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
... | 67 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
"""vocab_file""": """vocab.json""",
"""tokenize... | 356 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_lowerCamelCase = datasets.utils.logging.get_logger(__name__)
class _snake_case (folder_based_builder.FolderBasedBui... | 67 | 0 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartCo... | 357 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
UpperCAmelCase_ : str = len(_SCREAMING_SNAKE_CASE )
for _ in range(_SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 ... | 67 | 0 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class _snake_case :
def __init__( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case=None ,_snake_case=None ):
UpperCAmelCase_ : Tuple = start
UpperCAmelCas... | 358 |
'''simple docstring'''
from collections.abc import Sequence
def a__ ( _SCREAMING_SNAKE_CASE : Sequence[float] , _SCREAMING_SNAKE_CASE : float ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(_SCREAMING_SNAKE_CASE ) )... | 67 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from ... | 359 |
'''simple docstring'''
import os
from pathlib import Path
def a__ ( ) -> Union[str, Any]:
"""simple docstring"""
from torch.utils.cpp_extension import load
UpperCAmelCase_ : Union[str, Any] = Path(_SCREAMING_SNAKE_CASE ).resolve().parent.parent.... | 67 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available... | 360 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def a__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ) -> str | Literal[False]:
"""simple docstring"""
UpperCAmelCase... | 67 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
if not head:
return True
# split the list to two parts
UpperCAmelCase_ : Union[str, Any] = head.next, head
while fast and fast.next:... | 361 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def a__ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 ... | 67 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
... | 362 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def a__ ( _SCREAMING_SNAKE_CASE : str = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ... | 67 | 0 |
'''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_CONFIG_FILE
... | 363 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def a__ ( ) -> None:
"""simple docstring"""
... | 67 | 0 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase = typing.Union[np.floataa, int, float] # noqa... | 364 |
'''simple docstring'''
import re
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
if len(re.findall("[ATCG]" , _SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("Invalid Strand" )
re... | 67 | 0 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
... | 365 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Union[str, Any] =["torch", "torchsde"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["torc... | 67 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_to... | 366 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, Table... | 67 | 0 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a__ ( _... | 367 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowerCamelCase = logging.get_... | 67 | 0 |
'''simple docstring'''
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
_lowerCamelCase = {
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""a... | 368 |
'''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
from ...te... | 67 | 0 |
'''simple docstring'''
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a__ ( ... | 369 |
'''simple docstring'''
from __future__ import annotations
_lowerCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a__ ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING... | 67 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformer... | 370 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONF... | 67 | 0 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 371 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.j... | 67 | 0 |
'''simple docstring'''
class _snake_case :
def __init__( self ,_snake_case ):
UpperCAmelCase_ : Tuple = set_counts
UpperCAmelCase_ : Optional[Any] = max(_snake_case )
UpperCAmelCase_ : str = len(_... | 350 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(_SCREAMING_SNAKE_CASE : int , _SCREAMING_SN... | 67 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""... | 351 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fro... | 67 | 0 |
def a__ ( _SCREAMING_SNAKE_CASE : int = 1_00_00_00 ) -> int:
"""simple docstring"""
UpperCAmelCase_ : List[Any] = set(range(3 , _SCREAMING_SNAKE_CASE , 2 ) )
primes.add(2 )
for p in range(3 , _SCREAMING... | 352 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import Ba... | 67 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common im... | 353 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING... | 67 | 0 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
_lowerCamelCase = [
"""kernels/rwkv/wkv_cuda.cu""",
"""kernels/rwkv/wkv_op.cpp""",
"""kernels/deformable_detr/ms_deform_attn.h""",
"""kernels/deformable_detr/cuda/ms_deform_... | 354 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
"""simple docstring"""
UpperCAmelCase_ : Tuple = [randint(-10_00 ... | 67 | 0 |
'''simple docstring'''
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .confi... | 355 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
... | 67 | 0 |
import numpy as np
def a__ ( _SCREAMING_SNAKE_CASE : np.array ) -> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 356 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_lowerCamelCase = datasets.utils.logging.get_logger(__name__)
class _snake_case (folder_based_builder.FolderBasedBui... | 67 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _snake_case (__SCREAMING_SNAKE_CASE):
__A : List[Any] =["image_processor", "feature_extractor"]
__A : List[str] ="TvltImageProcessor"
__A : Any ="TvltFeatureExtractor"
... | 357 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
UpperCAmelCase_ : str = len(_SCREAMING_SNAKE_CASE )
for _ in range(_SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 ... | 67 | 0 |
'''simple docstring'''
import qiskit
def a__ ( _SCREAMING_SNAKE_CASE : int = 2 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = qubits
# Using Aer's simulator
UpperCAmelCase_ : Optional[int] ... | 358 |
'''simple docstring'''
from collections.abc import Sequence
def a__ ( _SCREAMING_SNAKE_CASE : Sequence[float] , _SCREAMING_SNAKE_CASE : float ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(_SCREAMING_SNAKE_CASE ) )... | 67 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProc... | 359 |
'''simple docstring'''
import os
from pathlib import Path
def a__ ( ) -> Union[str, Any]:
"""simple docstring"""
from torch.utils.cpp_extension import load
UpperCAmelCase_ : Union[str, Any] = Path(_SCREAMING_SNAKE_CASE ).resolve().parent.parent.... | 67 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Any ) -> Union[str, Any]:
"""simple docstring"""
global f # a global ... | 360 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def a__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ) -> str | Literal[False]:
"""simple docstring"""
UpperCAmelCase... | 67 | 0 |
'''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,
... | 361 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def a__ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 ... | 67 | 0 |
'''simple docstring'''
class _snake_case (__SCREAMING_SNAKE_CASE):
pass
class _snake_case (__SCREAMING_SNAKE_CASE):
pass
class _snake_case :
def __init__( self ):
UpperCAmelCase_ : Tuple = [
[],
... | 362 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def a__ ( _SCREAMING_SNAKE_CASE : str = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ... | 67 | 0 |
'''simple docstring'''
import numpy as np
import datasets
_lowerCamelCase = """
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
... | 363 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def a__ ( ) -> None:
"""simple docstring"""
... | 67 | 0 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : list[str] | None = None , _SCREAMING_SNAKE_CASE : dict[str, float] | None = None , _SCREAMING_SNAKE_CASE : bool = False , ) -> ... | 364 |
'''simple docstring'''
import re
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
if len(re.findall("[ATCG]" , _SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("Invalid Strand" )
re... | 67 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _snake_case (unitt... | 365 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Union[str, Any] =["torch", "torchsde"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["torc... | 67 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch impo... | 366 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, Table... | 67 | 0 |
def a__ ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : int ) -> float:
"""simple docstring"""
if digit_amount > 0:
return round(number - int(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
return number - int(_SCREAMING_SNAK... | 367 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowerCamelCase = logging.get_... | 67 | 0 |
'''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
from transformers... | 368 |
'''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
from ...te... | 67 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.... | 369 |
'''simple docstring'''
from __future__ import annotations
_lowerCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a__ ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING... | 67 | 0 |
'''simple docstring'''
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
"""stable diffusion controlnet""",
"""0.22.0""",
"""Importi... | 370 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONF... | 67 | 0 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _snake_case (nn.Module):
def __init__( self ,_snake_case = 16 ,_snake_case = 88 ,_snake_case = None ,_snake_case = 1 ,_snake_case = 0.0 ,_snak... | 371 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.j... | 67 | 0 |
'''simple docstring'''
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE : int | float | str , _SCREAMING_SNAKE_CASE : int | float | str ) -> list[str]:
"""simple docstring"""
if nth_term == "":
return [""]
UpperCAmelCase_ : Tupl... | 350 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(_SCREAMING_SNAKE_CASE : int , _SCREAMING_SN... | 67 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> int:
"""simple docstring"""
UpperCAmelCase_ : List[str] = hex_num.strip()
if not hex_num:
raise ValueError("No value was passed to the function" )
UpperCAmelCase_ : ... | 351 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fro... | 67 | 0 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffu... | 352 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import Ba... | 67 | 0 |
'''simple docstring'''
import numpy as np
def a__ ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : float ) -> np.ndarray:
"""simple docstring"""
return np.where(vector > 0 , _SCREAMING_SNAKE_CASE , (alpha * (np.exp(_SCREAMING_SNAKE_CASE ... | 353 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING... | 67 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCamelCase = logging.get_logger(__name__)
def a__ ( _SCREAMING_SNAKE_CASE : Union[str, Any]=None , _... | 354 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
"""simple docstring"""
UpperCAmelCase_ : Tuple = [randint(-10_00 ... | 67 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {"""vocab_file""": """vocab.json"""}
_lowerCam... | 355 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
... | 67 | 0 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_b... | 356 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_lowerCamelCase = datasets.utils.logging.get_logger(__name__)
class _snake_case (folder_based_builder.FolderBasedBui... | 67 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
... | 357 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
UpperCAmelCase_ : str = len(_SCREAMING_SNAKE_CASE )
for _ in range(_SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 ... | 67 | 0 |
'''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"""):
_lowerCamelCase = {
"""linear""": PIL.Image.Resampling.BILINEAR,
""... | 358 |
'''simple docstring'''
from collections.abc import Sequence
def a__ ( _SCREAMING_SNAKE_CASE : Sequence[float] , _SCREAMING_SNAKE_CASE : float ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(_SCREAMING_SNAKE_CASE ) )... | 67 | 0 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def a__ ( _SCREAMING_SNAKE_CASE : str = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ... | 359 |
'''simple docstring'''
import os
from pathlib import Path
def a__ ( ) -> Union[str, Any]:
"""simple docstring"""
from torch.utils.cpp_extension import load
UpperCAmelCase_ : Union[str, Any] = Path(_SCREAMING_SNAKE_CASE ).resolve().parent.parent.... | 67 | 0 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def a__ (... | 360 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def a__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ) -> str | Literal[False]:
"""simple docstring"""
UpperCAmelCase... | 67 | 0 |
'''simple docstring'''
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_lowerCamelCase = re.compile(R"""^(?P<major>\d+)""" R"""\.(?P<minor>\d+)""" R"""\.(?P<patch>\d+)$""")
@total_ordering
@dataclass... | 361 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def a__ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 ... | 67 | 0 |
'''simple docstring'''
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 ... | 362 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def a__ ( _SCREAMING_SNAKE_CASE : str = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ... | 67 | 0 |
'''simple docstring'''
from __future__ import annotations
class _snake_case :
def __init__( self ,_snake_case ,_snake_case ):
UpperCAmelCase_ : List[Any] = text, pattern
UpperCAmelCase_ : Dict = len(_snake_case ... | 363 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def a__ ( ) -> None:
"""simple docstring"""
... | 67 | 0 |
'''simple docstring'''
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_... | 364 |
'''simple docstring'''
import re
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
if len(re.findall("[ATCG]" , _SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("Invalid Strand" )
re... | 67 | 0 |
'''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
from ...te... | 365 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Union[str, Any] =["torch", "torchsde"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["torc... | 67 | 0 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
fr... | 366 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, Table... | 67 | 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 impo... | 367 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowerCamelCase = logging.get_... | 67 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, 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, to_pil_image
from ...image_utils import... | 368 |
'''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
from ...te... | 67 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a__ ( _SCREAMING_SNAKE_CASE : List[Any] , _SCR... | 369 |
'''simple docstring'''
from __future__ import annotations
_lowerCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a__ ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING... | 67 | 0 |
'''simple docstring'''
def a__ ( ) -> int:
"""simple docstring"""
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(_SCREAMING_SNAKE_CASE , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
i... | 370 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONF... | 67 | 0 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
fro... | 371 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.j... | 67 | 0 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class _snake_case (tf.keras.optimizers.schedules.Learnin... | 350 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(_SCREAMING_SNAKE_CASE : int , _SCREAMING_SN... | 67 | 0 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class _snake_case (tf.keras.layers.Layer ):
def __init__( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case=1 ,_snake_case=False ,**_snake_case ):
super().__init__(**_... | 351 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fro... | 67 | 0 |
import itertools
import math
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples ... | 352 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import Ba... | 67 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , ) -> float:
"""simple docstring"""
UpperCA... | 353 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING... | 67 | 0 |
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def a__ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 ) == ... | 354 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
"""simple docstring"""
UpperCAmelCase_ : Tuple = [randint(-10_00 ... | 67 | 0 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
_lowerCamelCase = {
"""facebook/maskforme... | 355 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
... | 67 | 0 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def a__ ( _SCREAMING_SNAKE_CASE : List[str] ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ : str = [
"encoder.... | 356 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_lowerCamelCase = datasets.utils.logging.get_logger(__name__)
class _snake_case (folder_based_builder.FolderBasedBui... | 67 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
UpperCAmelCase_ : Dict = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def a__ ( _SCREAMING_SNAKE_CASE : int = 1_00 ... | 357 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
UpperCAmelCase_ : str = len(_SCREAMING_SNAKE_CASE )
for _ in range(_SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 ... | 67 | 0 |
'''simple docstring'''
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_DOCSTRING,
... | 358 |
'''simple docstring'''
from collections.abc import Sequence
def a__ ( _SCREAMING_SNAKE_CASE : Sequence[float] , _SCREAMING_SNAKE_CASE : float ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(_SCREAMING_SNAKE_CASE ) )... | 67 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_lowerCamelCase = True
except (ImportError, ModuleNotFoundError):
_lowerCamelCase = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""... | 359 |
'''simple docstring'''
import os
from pathlib import Path
def a__ ( ) -> Union[str, Any]:
"""simple docstring"""
from torch.utils.cpp_extension import load
UpperCAmelCase_ : Union[str, Any] = Path(_SCREAMING_SNAKE_CASE ).resolve().parent.parent.... | 67 | 0 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def a__ ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : bool = True , _SCREAMING_SNAKE_CASE : float = math.inf , _SCREAMING_SNAKE_C... | 360 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def a__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ) -> str | Literal[False]:
"""simple docstring"""
UpperCAmelCase... | 67 | 0 |
'''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class _snake_case (__SCREAMING_SNAKE_CASE):
__A : List[Any] ="EncodecFeatureExtractor"
__A : int =("... | 361 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def a__ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 ... | 67 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_low... | 362 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def a__ ( _SCREAMING_SNAKE_CASE : str = "" , ) -> bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ... | 67 | 0 |
'''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, TruncationSt... | 363 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def a__ ( ) -> None:
"""simple docstring"""
... | 67 | 0 |
'''simple docstring'''
import logging
import os
from .state import PartialState
class _snake_case (logging.LoggerAdapter):
@staticmethod
def UpperCamelCase__ ( _snake_case ):
UpperCAmelCase_ : Union[str, Any] = PartialState()
retu... | 364 |
'''simple docstring'''
import re
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
if len(re.findall("[ATCG]" , _SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("Invalid Strand" )
re... | 67 | 0 |
'''simple docstring'''
from math import factorial
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k" )
... | 365 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Union[str, Any] =["torch", "torchsde"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["torc... | 67 | 0 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowerCamelCase = False
t... | 366 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, Table... | 67 | 0 |
def a__ ( _SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
"""simple docstring"""
UpperCAmelCase_ : List[Any] = []
UpperCAmelCase_ : Dict = set({"(", "[", "{"} )
UpperCAmelCase_ : Any = set({")", "]", "}"}... | 367 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowerCamelCase = logging.get_... | 67 | 0 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_lower... | 368 |
'''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
from ...te... | 67 | 0 |
'''simple docstring'''
import re
def a__ ( _SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
if len(re.findall("[ATCG]" , _SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("Invalid Strand" )
re... | 369 |
'''simple docstring'''
from __future__ import annotations
_lowerCamelCase = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def a__ ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING... | 67 | 0 |
'''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,
resi... | 370 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONF... | 67 | 0 |
'''simple docstring'''
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
class _snake_case (__SCREAMING_SNAKE_CASE):
__A : Tupl... | 371 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.j... | 67 | 0 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_lowerCamelCase = collections.namedtupl... | 350 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(_SCREAMING_SNAKE_CASE : int , _SCREAMING_SN... | 67 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase = {
"""configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 351 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fro... | 67 | 0 |
class _snake_case :
def __init__( self ,_snake_case ):
# we need a list not a string, so do something to change the type
UpperCAmelCase_ : str = arr.split("," )
def UpperCamelCase__ ( self ):
UpperCAmelC... | 352 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import Ba... | 67 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 353 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING... | 67 | 0 |
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