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 |
|---|---|---|---|---|
class a :
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase : list ) -> None:
__snake_case : str = set_counts
__snake_case : Union[str, Any] = max(lowerCamelCase ... | 81 |
from __future__ import annotations
def A ( lowercase__ : int ) -> list[int]:
UpperCamelCase__ :Union[str, Any] = [True] * limit
UpperCamelCase__ :int = False
UpperCamelCase__ :Optional[Any] = False
UpperCamelCase__ :str = True
for i in range(3 , int... | 45 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ ):
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
... | 82 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 45 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wa... | 83 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A ( lowercase__ : dict ) -> tuple:
return (data["data"], d... | 45 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = ["""image_processor""", """tokenizer"""]
_UpperCamelCase : Union[str, Any] ... | 84 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A ( lowercase__ : Optional[int] ) -> Optional[Any]:
UpperCamelCase__ :Union[str, Any] = {}
UpperCamelCase... | 45 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _a ( lowercase__ : bytes , lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = f'''{sampling_rate}'''
SCREAMING_... | 85 |
def A ( lowercase__ : int ) -> Optional[Any]:
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def A ( lowercase__ : Union[str, Any] , lowercase__ : Dict , lowercase__ : str ) -> List[str]:
if i >= h:
return
# If first element is smaller than the last the... | 45 | 0 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models... | 86 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase = "."
# Internal TensorFlow ops that can be s... | 45 | 0 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICAT... | 87 |
from __future__ import annotations
def A ( lowercase__ : str , lowercase__ : list[str] | None = None , lowercase__ : dict[str, float] | None = None , lowercase__ : bool = False , ) -> tuple[int, float, str]:
UpperCamelCase__ :Dict = cipher_alphabet or [chr(lowercase__ ) for ... | 45 | 0 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
UpperCAmelCase = namedtuple(
"""_TestComma... | 88 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
def __init__( self :Union[str, Any] , *lo... | 45 | 0 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_mod... | 89 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import D... | 45 | 0 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A , A , A , A , A , ) -> None:
lowerCAmelCase__ = len(A )
# If row is equal to the size of the board it means there are a queen in... | 90 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
de... | 45 | 0 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.t... | 91 |
def A ( lowercase__ : int ) -> bool:
if num < 0:
return False
UpperCamelCase__ :int = num
UpperCamelCase__ :int = 0
while num > 0:
UpperCamelCase__ :Optional[int] = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":... | 45 | 0 |
'''simple docstring'''
from typing import List, Union
import numpy as np
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 PIL import Image
from ..image_utils... | 92 |
from __future__ import annotations
def A ( lowercase__ : list[int] ) -> bool:
return len(set(lowercase__ ) ) == len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 45 | 0 |
"""simple docstring"""
import random
class _lowerCAmelCase :
"""simple docstring"""
@staticmethod
def snake_case ( __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Any = [ord(__UpperCAmelCase ) for i in text]
... | 93 |
from __future__ import annotations
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self :List[Any] , lowerCamelCase__ :int = 0 ):
UpperCamelCase__ :List[str] = key
def __a ( self :Optional[Any] , lowerCamelCase__... | 45 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
SCREAMING_SNAKE_CASE = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
SCREAMING_SNAKE_CASE = ... | 94 |
import random
def A ( lowercase__ : Dict , lowercase__ : str , lowercase__ : Optional[Any] ) -> int:
UpperCamelCase__ :List[Any] = a[left_index]
UpperCamelCase__ :Dict = left_index + 1
for j in range(left_index + 1 , lowercase__ ):
if a[j] < pivot:
UpperCamelC... | 45 | 0 |
"""simple docstring"""
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class UpperCamelCase_ (__A ):
__magic_name__ = ''''''
__magic_name__ = (
None # protocol passe... | 95 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"shi-labs/dinat-mini-in1k-224": "https:/... | 45 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.j... | 96 |
def A ( lowercase__ : int , lowercase__ : int ) -> int:
return int(input_a == input_a == 0 )
def A ( ) -> None:
print("""Truth Table of NOR Gate:""" )
print("""| Input 1 | Input 2 | Output |""" )
print(f"""| 0 | 0 | {nor_gate(0 , 0 )} |""" )
p... | 45 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}
class l... | 97 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 45 | 0 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stabl... | 98 |
import math
def A ( lowercase__ : Tuple , lowercase__ : Union[str, Any] ) -> Optional[Any]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(lowercase__ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:... | 45 | 0 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def a (lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
__a = s.rsplit(lowerCAmelCase__ , lowerCAmelCase__ )
return new.join(... | 99 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_te... | 45 | 0 |
class __snake_case :
'''simple docstring'''
def __init__( self , A_ , A_=None , A_=None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = data
SCREAMING_SNAKE_CASE__ = previous
SCREAMING_SNAKE_CASE__ ... | 100 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
UpperCamelCase = False
class lowerCAmelCase_ ( unittest.TestCase ... | 45 | 0 |
from collections.abc import Callable
import numpy as np
def a__ ( A__, A__, A__, A__, A__ ):
SCREAMING_SNAKE_CASE_ : Dict = int(np.ceil((x_end - xa) / step_size ) )
SCREAMING_SNAKE_CASE_ : int = np.zeros((n + 1,) )
SC... | 101 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
... | 45 | 0 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__magic_name__ : int = logging.get_logger(__name__) # pylint: disable=in... | 102 |
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 torchvision.transforms.functional import I... | 45 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tok... | 103 |
from __future__ import annotations
def A ( lowercase__ : int ) -> list[int]:
UpperCamelCase__ :Union[str, Any] = [True] * limit
UpperCamelCase__ :int = False
UpperCamelCase__ :Optional[Any] = False
UpperCamelCase__ :str = True
for i in range(3 , int... | 45 | 0 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base i... | 104 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 45 | 0 |
import os
import numpy
import onnx
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = a.name
SCREAMING_SNAKE_CASE_ : Dict = ... | 105 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A ( lowercase__ : dict ) -> tuple:
return (data["data"], d... | 45 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( _lowerCamelCase ):
A_ : Optio... | 106 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A ( lowercase__ : Optional[int] ) -> Optional[Any]:
UpperCamelCase__ :Union[str, Any] = {}
UpperCamelCase... | 45 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class lowercase_ ( _UpperCamelCase ):
"""simple docstring"""
__lowerCAmelCase = "WhisperFeatureExtractor"
__lowerCAmelCase = "WhisperTokenizer"
def __init__( self : Any, UpperC... | 107 |
def A ( lowercase__ : int ) -> Optional[Any]:
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def A ( lowercase__ : Union[str, Any] , lowercase__ : Dict , lowercase__ : str ) -> List[str]:
if i >= h:
return
# If first element is smaller than the last the... | 45 | 0 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...t... | 108 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase = "."
# Internal TensorFlow ops that can be s... | 45 | 0 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
__SCREAM... | 109 |
from __future__ import annotations
def A ( lowercase__ : str , lowercase__ : list[str] | None = None , lowercase__ : dict[str, float] | None = None , lowercase__ : bool = False , ) -> tuple[int, float, str]:
UpperCamelCase__ :Dict = cipher_alphabet or [chr(lowercase__ ) for ... | 45 | 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 (
... | 110 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
def __init__( self :Union[str, Any] , *lo... | 45 | 0 |
'''simple docstring'''
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
lowerCAmelCase_ = Mapping[str, np.ndarray]
lowerCAmelCase_ = Mapping[str, Any] ... | 531 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import D... | 45 | 0 |
'''simple docstring'''
import math
def lowerCamelCase ( lowerCAmelCase : Tuple , lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(lowe... | 561 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
de... | 45 | 0 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def a__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : complex , _SCREAMING_SNAKE_CASE : str = "x" , _SCREAMING_SNAKE_CASE : float = 10**-10... | 71 |
def A ( lowercase__ : int ) -> bool:
if num < 0:
return False
UpperCamelCase__ :int = num
UpperCamelCase__ :int = 0
while num > 0:
UpperCamelCase__ :Optional[int] = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":... | 45 | 0 |
a : str = {
'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
'V': '...-', ... | 556 |
from __future__ import annotations
def A ( lowercase__ : list[int] ) -> bool:
return len(set(lowercase__ ) ) == len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 45 | 0 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __a ( A__ : Optional[Any] , A__ : str=7 ):
SCREAMING_SNAKE_CASE = None
if token is not None:
SCREAMING_SNAKE_CASE = ... | 16 |
from __future__ import annotations
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self :List[Any] , lowerCamelCase__ :int = 0 ):
UpperCamelCase__ :List[str] = key
def __a ( self :Optional[Any] , lowerCamelCase__... | 45 | 0 |
def a ( A__ : int ) -> str:
"""simple docstring"""
if number > 0:
raise ValueError('input must be a negative integer' )
_lowercase =len(bin(lowercase__ )[3:] )
_lowercase =bin(abs(lowercase__ ) - (1 << binary_number_length) )[... | 291 |
import random
def A ( lowercase__ : Dict , lowercase__ : str , lowercase__ : Optional[Any] ) -> int:
UpperCamelCase__ :List[Any] = a[left_index]
UpperCamelCase__ :Dict = left_index + 1
for j in range(left_index + 1 , lowercase__ ):
if a[j] < pivot:
UpperCamelC... | 45 | 0 |
"""simple docstring"""
from __future__ import annotations
def a ( __snake_case : list[int], __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ :Tuple = 0
UpperCAmelCase_ :Any = len(lowercase__ ) - 1
while i < j:
... | 608 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"shi-labs/dinat-mini-in1k-224": "https:/... | 45 | 0 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
... | 385 |
def A ( lowercase__ : int , lowercase__ : int ) -> int:
return int(input_a == input_a == 0 )
def A ( ) -> None:
print("""Truth Table of NOR Gate:""" )
print("""| Input 1 | Input 2 | Output |""" )
print(f"""| 0 | 0 | {nor_gate(0 , 0 )} |""" )
p... | 45 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterM... | 663 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 45 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _A (__a ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = np.shape(lowercase__ )
if rows != columns:
SCREAMING_SNAKE_CASE_... | 512 |
import math
def A ( lowercase__ : Tuple , lowercase__ : Union[str, Any] ) -> Optional[Any]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(lowercase__ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:... | 45 | 0 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCamelCase_ ( __UpperCamelCase ):
"... | 479 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_te... | 45 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _snake_case:
def __init__(self : Tuple , a : int , a : int , a : float = 0 ) -> Union[str, Any]:
"""simple docstring"""
A__ = row, col... | 531 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
UpperCamelCase = False
class lowerCAmelCase_ ( unittest.TestCase ... | 45 | 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
from ..table impo... | 561 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
... | 45 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTe... | 71 |
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 torchvision.transforms.functional import I... | 45 | 0 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils... | 556 |
from __future__ import annotations
def A ( lowercase__ : int ) -> list[int]:
UpperCamelCase__ :Union[str, Any] = [True] * limit
UpperCamelCase__ :int = False
UpperCamelCase__ :Optional[Any] = False
UpperCamelCase__ :str = True
for i in range(3 , int... | 45 | 0 |
def __a ( A__ : int = 50 ):
SCREAMING_SNAKE_CASE = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 16 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 45 | 0 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Bac... | 291 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A ( lowercase__ : dict ) -> tuple:
return (data["data"], d... | 45 | 0 |
"""simple docstring"""
from math import factorial
def a ( __snake_case : int, __snake_case : int ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(lowercase__ ) // (f... | 608 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A ( lowercase__ : Optional[int] ) -> Optional[Any]:
UpperCamelCase__ :Union[str, Any] = {}
UpperCamelCase... | 45 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Tuple = logging.get_logger(__name__)
__lowerCamelCase : Any = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/r... | 385 |
def A ( lowercase__ : int ) -> Optional[Any]:
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def A ( lowercase__ : Union[str, Any] , lowercase__ : Dict , lowercase__ : str ) -> List[str]:
if i >= h:
return
# If first element is smaller than the last the... | 45 | 0 |
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 torchvision.transforms.funct... | 663 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase = "."
# Internal TensorFlow ops that can be s... | 45 | 0 |
"""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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.ut... | 512 |
from __future__ import annotations
def A ( lowercase__ : str , lowercase__ : list[str] | None = None , lowercase__ : dict[str, float] | None = None , lowercase__ : bool = False , ) -> tuple[int, float, str]:
UpperCamelCase__ :Dict = cipher_alphabet or [chr(lowercase__ ) for ... | 45 | 0 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class UpperCamelCase_ ( __UpperCamelCase ):
"""simple docstring"""... | 479 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
def __init__( self :Union[str, Any] , *lo... | 45 | 0 |
'''simple docstring'''
from math import sqrt
def _A ( UpperCAmelCase ):
'''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 mu... | 531 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import D... | 45 | 0 |
'''simple docstring'''
import argparse
import json
import subprocess
def lowerCamelCase ( lowerCAmelCase : List[str] , lowerCAmelCase : List[Any] ):
"""simple docstring"""
__magic_name__ : str = []
__magic_name__ : List[str] ... | 561 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
de... | 45 | 0 |
'''simple docstring'''
def a__ ( ) -> int:
"""simple docstring"""
return 1
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def a__ ( ... | 71 |
def A ( lowercase__ : int ) -> bool:
if num < 0:
return False
UpperCamelCase__ :int = num
UpperCamelCase__ :int = 0
while num > 0:
UpperCamelCase__ :Optional[int] = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":... | 45 | 0 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
a : int = TypeVar('T')
class _a ( Generic[T] ):
def __init_... | 556 |
from __future__ import annotations
def A ( lowercase__ : list[int] ) -> bool:
return len(set(lowercase__ ) ) == len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 45 | 0 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def __a ( A__ : str , A__ : str , A__ : Optional[str] = None ):
if version.parse(hfh.__version__ ).release < version.parse("0.11.0" )... | 16 |
from __future__ import annotations
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self :List[Any] , lowerCamelCase__ :int = 0 ):
UpperCamelCase__ :List[str] = key
def __a ( self :Optional[Any] , lowerCamelCase__... | 45 | 0 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class __lowerCAmelCase :
def __init__( self , lowerCAmelCase = None ) -> Dict:
'''simple docstring'''
if c... | 291 |
import random
def A ( lowercase__ : Dict , lowercase__ : str , lowercase__ : Optional[Any] ) -> int:
UpperCamelCase__ :List[Any] = a[left_index]
UpperCamelCase__ :Dict = left_index + 1
for j in range(left_index + 1 , lowercase__ ):
if a[j] < pivot:
UpperCamelC... | 45 | 0 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transf... | 608 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"shi-labs/dinat-mini-in1k-224": "https:/... | 45 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__lowerCamelCase : int = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""... | 385 |
def A ( lowercase__ : int , lowercase__ : int ) -> int:
return int(input_a == input_a == 0 )
def A ( ) -> None:
print("""Truth Table of NOR Gate:""" )
print("""| Input 1 | Input 2 | Output |""" )
print(f"""| 0 | 0 | {nor_gate(0 , 0 )} |""" )
p... | 45 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] ... | 663 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 45 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_f... | 512 |
import math
def A ( lowercase__ : Tuple , lowercase__ : Union[str, Any] ) -> Optional[Any]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(lowercase__ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:... | 45 | 0 |
_a : List[Any] = {
'joule': 1.0,
'kilojoule': 10_00,
'megajoule': 1_00_00_00,
'gigajoule': 10_00_00_00_00,
'wattsecond': 1.0,
'watthour': 36_00,
'kilowatthour': 3_60_00_00,
'newtonmeter': 1.0,
'calorie_nutr': 41_86.8,
'kilocalorie_nutr': 4_18_68_00.00,
... | 479 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_te... | 45 | 0 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
lowerCAmelCase_ = TypeVar('''T''')
def _A ( UpperCAmelCase ):
'''simple docstring'''
return (position - 1) // 2
def _A ... | 531 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
UpperCamelCase = False
class lowerCAmelCase_ ( unittest.TestCase ... | 45 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase ( lowerCAmelCase : Callable[[int | float], int | float] , lowerCAmelCase : int | float , lowerCAmelCase : int | float ... | 561 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
... | 45 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _snake_case (__SCREAMING_SNAKE_CASE):
__A : int =(PNDMScheduler,)
__A : Any =(("""num_inference_steps""", 50),)
def ... | 71 |
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 torchvision.transforms.functional import I... | 45 | 0 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoT... | 556 |
from __future__ import annotations
def A ( lowercase__ : int ) -> list[int]:
UpperCamelCase__ :Union[str, Any] = [True] * limit
UpperCamelCase__ :int = False
UpperCamelCase__ :Optional[Any] = False
UpperCamelCase__ :str = True
for i in range(3 , int... | 45 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__A : str = logging.get_logger(__name__) # pylint: disable=invalid-name
class _SCREAMING_SNAKE_CASE ( ... | 16 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 45 | 0 |
from string import ascii_uppercase
lowercase_ = {str(ord(c) - 5_5): c for c in ascii_uppercase}
def a ( A__ : int , A__ : int ) -> str:
"""simple docstring"""
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('int() can\'t co... | 291 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A ( lowercase__ : dict ) -> tuple:
return (data["data"], d... | 45 | 0 |
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKI... | 608 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A ( lowercase__ : Optional[int] ) -> Optional[Any]:
UpperCamelCase__ :Union[str, Any] = {}
UpperCamelCase... | 45 | 0 |
def A__ ( _a : int ):
'''simple docstring'''
if num < 0:
return False
snake_case__ : int =num
snake_case__ : int =0
while num > 0:
snake_case__ : Optional[int] =rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_nu... | 385 |
def A ( lowercase__ : int ) -> Optional[Any]:
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def A ( lowercase__ : Union[str, Any] , lowercase__ : Dict , lowercase__ : str ) -> List[str]:
if i >= h:
return
# If first element is smaller than the last the... | 45 | 0 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
fro... | 663 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase = "."
# Internal TensorFlow ops that can be s... | 45 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.... | 512 |
from __future__ import annotations
def A ( lowercase__ : str , lowercase__ : list[str] | None = None , lowercase__ : dict[str, float] | None = None , lowercase__ : bool = False , ) -> tuple[int, float, str]:
UpperCamelCase__ :Dict = cipher_alphabet or [chr(lowercase__ ) for ... | 45 | 0 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_a : Tuple = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, r... | 479 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
def __init__( self :Union[str, Any] , *lo... | 45 | 0 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _snake_case( UpperCA... | 531 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import D... | 45 | 0 |
'''simple docstring'''
from math import sqrt
def lowerCamelCase ( lowerCAmelCase : int = 100_0000 ):
"""simple docstring"""
__magic_name__ : int = 0
__magic_name__ : int = 0
__magic_name__ : int
while num_cuboids <= limit:
max_cubo... | 561 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
de... | 45 | 0 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_lowerCamelCase = """\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no co... | 71 |
def A ( lowercase__ : int ) -> bool:
if num < 0:
return False
UpperCamelCase__ :int = num
UpperCamelCase__ :int = 0
while num > 0:
UpperCamelCase__ :Optional[int] = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":... | 45 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 556 |
from __future__ import annotations
def A ( lowercase__ : list[int] ) -> bool:
return len(set(lowercase__ ) ) == len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 45 | 0 |
__A : Tuple = range(2, 2_0 + 1)
__A : Any = [1_0**k for k in range(ks[-1] + 1)]
__A : Tuple = {}
def __a ( A__ : List[Any] , A__ : Tuple , A__ : str , A__ : Optional[Any] ):
SCREAMING_SNAKE_CASE = sum(a_i[j] for... | 16 |
from __future__ import annotations
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self :List[Any] , lowerCamelCase__ :int = 0 ):
UpperCamelCase__ :List[str] = key
def __a ( self :Optional[Any] , lowerCamelCase__... | 45 | 0 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def a ( A__ : Dict , A__ : Optional[int] , A__ : Any , A__ : List[str] ) ... | 291 |
import random
def A ( lowercase__ : Dict , lowercase__ : str , lowercase__ : Optional[Any] ) -> int:
UpperCamelCase__ :List[Any] = a[left_index]
UpperCamelCase__ :Dict = left_index + 1
for j in range(left_index + 1 , lowercase__ ):
if a[j] < pivot:
UpperCamelC... | 45 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta... | 608 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"shi-labs/dinat-mini-in1k-224": "https:/... | 45 | 0 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ..... | 385 |
def A ( lowercase__ : int , lowercase__ : int ) -> int:
return int(input_a == input_a == 0 )
def A ( ) -> None:
print("""Truth Table of NOR Gate:""" )
print("""| Input 1 | Input 2 | Output |""" )
print(f"""| 0 | 0 | {nor_gate(0 , 0 )} |""" )
p... | 45 | 0 |
def _a ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
return int(input_a == input_a == 0 )
def _a ( ) -> None:
'''simple docstring'''
print("Trut... | 663 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 45 | 0 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:... | 512 |
import math
def A ( lowercase__ : Tuple , lowercase__ : Union[str, Any] ) -> Optional[Any]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(lowercase__ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:... | 45 | 0 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import... | 479 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_te... | 45 | 0 |
'''simple docstring'''
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def _A ( UpperCAmelCase = "" ):
'''simple docstring'''
A__ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
A__ = ... | 531 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
UpperCamelCase = False
class lowerCAmelCase_ ( unittest.TestCase ... | 45 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def lowerCamelCase ( lowerCAmelCase : Optional[int] ):
"""simple docstring"""
__magic_name__ : Optional[int] ... | 561 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
... | 45 | 0 |
'''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
_lowerCamelCase = logging.get_logger(__name__)
_lowerCam... | 71 |
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 torchvision.transforms.functional import I... | 45 | 0 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also sa... | 556 |
from __future__ import annotations
def A ( lowercase__ : int ) -> list[int]:
UpperCamelCase__ :Union[str, Any] = [True] * limit
UpperCamelCase__ :int = False
UpperCamelCase__ :Optional[Any] = False
UpperCamelCase__ :str = True
for i in range(3 , int... | 45 | 0 |
import json
import sys
def __a ( A__ : Optional[Any] , A__ : Any ):
with open(lowercase__ , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE = json.load(lowercase__ )
SCREAMING_SNAKE_CASE = ["""<details>""", """<summary>Show... | 16 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 45 | 0 |
from collections.abc import Sequence
def a ( A__ : Sequence[int] | None = None ) -> int:
"""simple docstring"""
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
_lowercase =nums[0]
for i in range... | 291 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A ( lowercase__ : dict ) -> tuple:
return (data["data"], d... | 45 | 0 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Token... | 608 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def A ( lowercase__ : Optional[int] ) -> Optional[Any]:
UpperCamelCase__ :Union[str, Any] = {}
UpperCamelCase... | 45 | 0 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
__lowerCamelCase : Optional[int] ... | 385 |
def A ( lowercase__ : int ) -> Optional[Any]:
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def A ( lowercase__ : Union[str, Any] , lowercase__ : Dict , lowercase__ : str ) -> List[str]:
if i >= h:
return
# If first element is smaller than the last the... | 45 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def _a ( SCREAMING_SNAKE_CASE__ : dict ) -> ... | 663 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase = "."
# Internal TensorFlow ops that can be s... | 45 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common impo... | 512 |
from __future__ import annotations
def A ( lowercase__ : str , lowercase__ : list[str] | None = None , lowercase__ : dict[str, float] | None = None , lowercase__ : bool = False , ) -> tuple[int, float, str]:
UpperCamelCase__ :Dict = cipher_alphabet or [chr(lowercase__ ) for ... | 45 | 0 |
from __future__ import annotations
def UpperCamelCase__ ( _A: int ):
'''simple docstring'''
__lowerCamelCase = 2
__lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
n /... | 479 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
def __init__( self :Union[str, Any] , *lo... | 45 | 0 |
'''simple docstring'''
from __future__ import annotations
def _A ( UpperCAmelCase ):
'''simple docstring'''
if not nums:
return 0
A__ = nums[0]
A__ = 0
for num in nums[1:]:
A__ = (
max_excluding + num,
... | 531 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import D... | 45 | 0 |
'''simple docstring'''
class _lowerCamelCase : # Public class to implement a graph
'''simple docstring'''
def __init__( self : List[Any] , _A : int , _A : int , _A : list[list[bool]] ) -> str:
__magic_name__ : List[str] = row
__magic... | 561 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
de... | 45 | 0 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_lowerCamelCase ... | 71 |
def A ( lowercase__ : int ) -> bool:
if num < 0:
return False
UpperCamelCase__ :int = num
UpperCamelCase__ :int = 0
while num > 0:
UpperCamelCase__ :Optional[int] = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":... | 45 | 0 |
import os
def lowerCAmelCase_ (lowerCAmelCase__: str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(lowercase__ ) , lowercase__ ) ) as input_file:
UpperCAmelCase_: Any = [
[int(lowercase__ ) for ... | 556 |
from __future__ import annotations
def A ( lowercase__ : list[int] ) -> bool:
return len(set(lowercase__ ) ) == len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 45 | 0 |
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