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 |
|---|---|---|---|---|
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def lowercase_ ( _lowerCamelCase : Union[str, Any]):
return 1 / (1 + np.exp(-z))
def lowercas... | 87 |
import math
import sys
def __a ( SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
if number != int(SCREAMING_SNAKE_CASE ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''th... | 333 | 0 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
UpperCAmelCase__ : List[str] =logging.get_logger(__name__)
class __lowercase ( a ):
def __init__( self , *UpperCAmelCase_ , **UpperCAmelCase_ ):
warnings.war... | 371 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowercase ( _UpperCAmelCase = "isbn/0140328726" ) -> dict:
lowerCamelCase =olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("... | 262 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase :Dict = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
... | 206 |
'''simple docstring'''
from __future__ import annotations
import math
def a ( lowerCamelCase__ ):
'''simple docstring'''
if num <= 0:
A_ : List[Any] = f'{num}: Invalid input, please enter a positive integer.'
raise ValueError(lowerCamelCase__ )
A_ : Dict = ... | 206 | 1 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __lowercase ( a__ , a__ , a__ , a__ , ) -> list[float]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE =... | 118 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
c... | 118 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_ut... | 92 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
a : int = '''<<<<<<< This should probably be modified because it mention... | 105 | 0 |
from math import isqrt, loga
def lowerCAmelCase_ (lowerCAmelCase__: int ):
"""simple docstring"""
UpperCAmelCase_: Dict = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 82 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
f... | 82 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 320 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A_ ( _lowerCAmelCase : Dict ):
"""simple docstring"""
if (
(cp >= 0x4e00 and cp <= 0x9fff)
... | 320 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( SCR... | 181 |
"""simple docstring"""
def _lowerCAmelCase ( ):
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = 1
UpperCAmelCase = 2
while i * i <= n:
UpperC... | 181 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowerCAmelCase_ ( ) -> str:
"""simple docstring"""
lowerCamelCase__: Optional[int] =9
lowerCamelCase__: Union[str, Any] =[
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
... | 10 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A__ ( UpperCamelCase ):
A = [
"encoder.version",
"decoder.version",
"model.encoder.version"... | 292 | 0 |
"""simple docstring"""
def snake_case (A_ :list[list[int | float]] ):
'''simple docstring'''
a : Union[str, Any] = len(lowerCAmelCase__ )
a : Any = len(matrix[0] )
a : int = min(lowerCAmelCase__ , lowerCAmelCase__... | 366 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_UpperCamelCase : Optional[Any] = re.compile(r'\b(a|an|the)\b', re.UNICODE)
_UpperCamelCase : str = None
def snake_case ():
... | 186 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
UpperCAmelCase = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
UpperCAmelCase = n - k
# Calculate C(n,k)
for i in ran... | 78 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resi... | 78 | 1 |
"""simple docstring"""
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_ut... | 359 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
UpperCamelCase = '__DUMMY_TRANSFORMERS_USER__'
UpperCamelCase = 'Dummy User'
UpperCamelCase = 'hf_hZEmnoOEY... | 221 | 0 |
from math import factorial
def a_ ( SCREAMING_SNAKE_CASE__ : int = 100 ):
'''simple docstring'''
return sum(int(SCREAMING_SNAKE_CASE__ ) for x in str(factorial(SCREAMING_SNAKE_CASE__ ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the N... | 199 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A ( nn.Module ):
UpperCamelCase__ : int
UpperCamelCase__ : int
UpperCamelCase__ : fl... | 199 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
... | 350 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCamelCase : int = get_test... | 186 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase_... | 24 |
def lowerCAmelCase ( lowerCAmelCase_ = 10**9 )-> int:
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Optional[int] = 2
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : str = 0
lowerCAmelCase_ : str = 0
whi... | 262 | 0 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMi... | 15 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class UpperCAmelCase_ ( enum.Enum ... | 15 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImage... | 118 | from functools import lru_cache
@lru_cache
def a__ ( __UpperCamelCase ):
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 118 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger... | 313 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
... | 313 | 1 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
A__ = logging.ge... | 82 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A__ = TypeVar("""_T""")
class __lowerCAmelCase ( Generic[_T] ):
def __init__( self , _snake_case = None ):
"""simple docstring"""
_lowerCAmelCase = list(... | 82 | 1 |
import numpy as np
from PIL import Image
def __lowerCAmelCase ( lowercase : np.ndarray , lowercase : int , lowercase : int ) -> np.ndarray:
"""simple docstring"""
snake_case : Tuple = np.array(__snake_case )
if arr.shape[0] != ... | 356 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
fro... | 112 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = (IPNDMScheduler,)
lowerCAmelCase__ = (('num_inference_steps', 5... | 181 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
UpperCamelCase__ = True
except (ImportError, ModuleNotFoundError):
UpperCamelCase__ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', qu... | 181 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagem... | 365 |
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_torch_available():
i... | 145 | 0 |
"""simple docstring"""
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_mode... | 100 |
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,
rescale,
resize,
to_channel_dim... | 186 | 0 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def lowercase ( A_ , A_ , A_ , A_ , )-> tuple[float | int, list[tuple[int, int]]]:
'''simple docstring'''
a : Optional[int] = gr... | 363 |
"""simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerato... | 226 | 0 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
__lowercase : int = None
def snake_case_ ( self):
__SCREAMING_SNAKE_CAS... | 100 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class U... | 221 | 0 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Acceler... | 362 |
'''simple docstring'''
import socket
def a ( ) -> Dict:
'''simple docstring'''
UpperCamelCase__ :int = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
UpperCamelCase__ :List[Any] = socket.gethostname()
UpperCamelCase__ ... | 219 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggi... | 339 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json"... | 186 | 0 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__A : Tuple = False
__A : List[str] = True
__A : Optional[int] = False
if __name__ == "__main__":
__A : int... | 368 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__A : List[Any] = logging.get_logger(__name__)
__A : List[Any] = [
['attention', 'attn'],
['encoder_attention'... | 49 | 0 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
SCREAMI... | 15 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = {}
__A = job["started_at"]
__A = job["completed_at"]
__A = date_parser.parse(a_ )
... | 15 | 1 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def lowerCAmelCase_ ( UpperCamelCase_ = 100 ) -> int:
UpperCamelCa... | 328 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_UpperCAmelCase = transforms.Comp... | 328 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a__ : Tuple = logging.get_logger(__name__)
a__ : Optional[Any] = {'''vo... | 313 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a__ : Any = logging.... | 313 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import ... | 371 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE ) ->int:
"""simple docstring"""
lowerCAmelCase__ :list[list[int]] = [[0 for _ in range(_SCREAMING_SNAKE_CASE )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowerCAmelCase__ :str ... | 254 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot ... | 3 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( _lowerCamelCase: list[int] , _lowerCamelCase: list[int] , _lowerCamelCase: int ):
__SCREAMING_SNAKE_CASE : List[Any] = list(range(len(_lowerCamelCase ) ) )
__... | 112 | 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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
... | 354 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
fro... | 222 | 0 |
'''simple docstring'''
def snake_case_ (_a : int ):
if not isinstance(_a , _a ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negativ... | 34 | '''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration... | 145 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : Tuple = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
tr... | 363 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, rand... | 164 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def _UpperCamelCase ( __A , __A , __A ) -> dict[str, float]:
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueEr... | 80 |
from collections import deque
from .hash_table import HashTable
class __lowercase (_UpperCAmelCase ):
def __init__( self , *A_ , **A_ ) ->int:
'''simple docstring'''
super().__init__(*A_ , **A_ )
def UpperCamelCase__ ( s... | 275 | 0 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
return str(UpperCAmelCase_ ) == str(UpperCAmelCase_ )[::-1]
def UpperCamelCase( UpperCAmelCase_ ):
return int(UpperCAmelCase_ ) + int(str(UpperCAmelCase_ )[::-1] )
def UpperCamelCase( UpperCAme... | 364 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A_ ... | 280 | 0 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import Ten... | 21 | from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__lowerCamelCase : str = input('''Enter image url: ''').strip()
print(F"""Downloading image from {url} ...""")
__lowerCamelCase : Any = BeautifulSoup(requests.get(url).conten... | 219 | 0 |
import unittest
import numpy as np
from datasets import load_dataset
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... | 122 |
import qiskit
def _UpperCamelCase ( UpperCamelCase_ : int , UpperCamelCase_ : int ) -> qiskit.result.counts.Counts:
"""simple docstring"""
lowerCAmelCase__ = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acti... | 122 | 1 |
import re
import string
import numpy as np
import datasets
UpperCAmelCase_ : Dict = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
UpperCAmelCase_ : Any = '''
Args:
... | 38 |
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 transformers import (
Effic... | 49 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def UpperCAmelCase__ ( lowerCAmelCase__ :Any , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Any ) ->... | 32 | """simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _A ( lowerCAmelCase ):
def __init_... | 32 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ = {
"""configuration_bridgetower""": [
"""BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BridgeTowerCon... | 289 | """simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availa... | 289 | 1 |
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
... | 362 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rou... | 282 | 0 |
"""simple docstring"""
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils imp... | 255 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: List[Any] = logging.get_logger(__name__)
_UpperCamelCase: int = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
... | 255 | 1 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transform... | 288 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case_ ( snake_case = 3 ) -> qiskit.result.counts.Counts:
if isinstance(snake_case , snake_case ):
raise... | 288 | 1 |
"""simple docstring"""
import flax.linen as nn
import jax
import jax.numpy as jnp
class _UpperCAmelCase ( nn.Module ):
'''simple docstring'''
lowerCamelCase__ =42
lowerCamelCase__ =jnp.floataa
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake... | 102 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class ... | 222 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
... | 328 |
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase_ ( ) -> Tuple:
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=UpperCamelCase_ , default="biencoder-nq-de... | 328 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https://huggingface.co/models?f... | 10 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
def _A ( lowercase__ ):
lowercase__ ... | 164 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__:Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__:Union[str, Any] = {
"""facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/co... | 356 | """simple docstring"""
import math
def _lowerCamelCase( a ):
__a = []
__a = 2
__a = int(math.sqrt(a ) ) # Size of every segment
__a = [True] * (end + 1)
__a = []
while start <= end:
if... | 268 | 0 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] ):
'''simple docstring'''
U... | 346 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> float:
if days_between_payments <= 0:
raise ValueError('days_between_payments must be > 0' )
if daily_interest_rate < 0:
raise ValueError('daily_interest_rate must... | 280 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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_tenso... | 17 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cas... | 17 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 122 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = '''▁'''
_A = {'''vocab_file''': ''... | 122 | 1 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import... | 312 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = ["keras_nlp"]
def __init__( self, *lowerCAmelCase__, **lowerCAmelCase__) -> int:
requires... | 312 | 1 |
import math
import flax.linen as nn
import jax.numpy as jnp
def SCREAMING_SNAKE_CASE_ ( __A : jnp.ndarray , __A : int , __A : float = 1 , __A : float = 1 , __A : float = 1.0e4 , __A : bool = False , __A : float = 1.0 , ) -> jnp.ndarray:
... | 32 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTester... | 32 | 1 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
... | 364 |
"""simple docstring"""
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 _SCREAMING_SNAKE_CASE( A ):
... | 239 | 0 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql... | 145 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class S... | 282 | 0 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
... | 370 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: float | Decimal , lowerCAmelCase: float = 10**-10 ) -> float:
_UpperCAmelCase : ... | 189 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : Optional[int] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : List[Any] ) -> List[Any]:
# Return True if there is node that has not iterated.
... | 288 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
UpperCAmelCase__ = '1'
UpperCAmelCase__ = '0'
UpperCAmelCase__ = '1'
UpperCAmelCase__ = ort.SessionOptions()
UpperCAmelCase__ = ort.GraphOptimizationLevel.ORT_D... | 288 | 1 |
"""simple docstring"""
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.spectr... | 351 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> list[int]:
snake_case_ = len(_SCREAMING_SNAKE_CASE )
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(i + 1 , _SCREAMING_SNAKE_CASE ):
if numbers[j] < numbers[i]:
... | 233 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from ... | 328 |
from math import factorial
def A_ ( snake_case : int = 100 ) -> int:
'''simple docstring'''
return sum(int(snake_case ) for x in str(factorial(snake_case ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: "... | 328 | 1 |
"""simple docstring"""
from typing import Any
import numpy as np
def lowercase__(A ) ->bool:
"""simple docstring"""
return np.array_equal(A , matrix.conjugate().T )
def lowercase__(A , A ) ->Any:
... | 354 |
"""simple docstring"""
import os
from pathlib import Path
def lowercase__() ->List[Any]:
"""simple docstring"""
from torch.utils.cpp_extension import load
lowercase__ : Any= Path(A ).resolve().parent.parent.parent / "kerne... | 150 | 0 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase : Tuple = logging.get_logger(__name__)
class lowerCamelCase__ ( __A ):
... | 115 |
"""simple docstring"""
from __future__ import annotations
class UpperCamelCase_ :
def __init__( self : Any , lowerCAmelCase_ : int ) -> None:
UpperCAmelCase_ : Any = data
UpperCAmelCase_ : Node | None = None
UpperCAmelCase_ ... | 268 | 0 |
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():
from .tokenization_camembert import... | 277 |
from __future__ import annotations
def __a ( lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase_= []
UpperCAmelCase_= []
UpperCAmelCase_= 0
UpperCAmelCase_= s... | 277 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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_tens... | 17 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
_a = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive... | 17 | 1 |
"""simple docstring"""
from collections import namedtuple
_lowercase : Dict = namedtuple("from_to", "from_ to")
_lowercase : List[Any] = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.001, 1_0_0_0),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.00_454... | 272 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_lowercase : Any = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_... | 272 | 1 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 312 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 312 | 1 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.dat... | 370 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class _lowerCAmelCase :
@property
def _a (self... | 135 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
snake_case : str = TypeVar('''T''')
snake_case : str = TypeVar('''U''')
class snake_case_ (Generic[T, U] ):
def __init__( self ... | 240 |
import math
def __lowercase ( __lowerCAmelCase : int ):
a__ = [True] * n
a__ = False
a__ = False
a__ = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
a__ ... | 240 | 1 |
'''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_a... | 354 |
'''simple docstring'''
from __future__ import annotations
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> list[int]:
A_ : int = 0
A_ : str = len(lowerCamelCase__ ) - 1
while i < ... | 4 | 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''',
'''tokenizer_co... | 212 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : Tuple ={
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''': ['''LukeTokenizer'''],... | 189 | 0 |
import os
import sys
import unittest
__a = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_te... | 235 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_... | 235 | 1 |
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, Bli... | 235 |
import requests
a__ = '''YOUR API KEY'''
def __UpperCAmelCase ( __a : str ,__a : str = giphy_api_key ) -> list:
"""simple docstring"""
_a : Optional[Any] = '''+'''.join(query.split() )
_a : Union[str, Any] = F"""https://api.giphy.co... | 235 | 1 |
from __future__ import annotations
def __lowercase ( __lowerCAmelCase : float , __lowerCAmelCase : float , __lowerCAmelCase : float , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('You cannot supply more or les... | 109 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : Optional[int] = logging.get_logger(__name__)
snake_case : Union[str, Any] = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.j... | 109 | 1 |
"""simple docstring"""
from math import factorial
def lowercase_ ( _lowerCamelCase: int = 100 ) -> int:
'''simple docstring'''
return sum(map(_UpperCamelCase , str(factorial(_UpperCamelCase ) ) ) )
if __name__ == "__main__":
print(solut... | 135 | """simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( _UpperCamelCase : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# ... | 150 | 0 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from tran... | 367 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCAmelCase : List[str] = (UnCLIPScheduler,)
def ... | 11 | 0 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging a... | 45 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""enc... | 330 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
fro... | 350 |
'''simple docstring'''
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 96 | 0 |
'''simple docstring'''
def snake_case__ ( _A: int = 1000 ) -> int:
'''simple docstring'''
lowerCAmelCase = 3
lowerCAmelCase = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name... | 272 | '''simple docstring'''
import os
import string
import sys
__lowercase = 1 << 8
__lowercase = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 2_7,
'''up''': 6_5 + ARROW_KEY_FLAG,
'''down''': 6_6 + ARROW_KEY_FLAG,
'''right''': 6_7 + ARROW_KEY_FLAG,
... | 272 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
snake_case_ = logging... | 238 |
from __future__ import annotations
snake_case_ = [True] * 1000001
snake_case_ = 2
while i * i <= 1000000:
if seive[i]:
for j in range(i * i, 1000001, i):
snake_case_ = False
i += 1
def lowerCamelCase__ ( s... | 238 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_a = logging.get_logger(__name__) # pylint: disable=invalid-name
class A_ (lowercase__ ):
... | 61 | """simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 135 | 0 |
def _A ( __magic_name__ , __magic_name__ ):
if not isinstance(__magic_name__ , __magic_name__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(__magic_name__ , __magic_name__ ) or not number >= 1:
raise ValueError(
"starting number must be\n ... | 362 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_snake_case = namedtuple("""covid_data""", """cases deaths recovered""")
def _A ( __magic_name__ = "https://www.worldometers.info/coronavirus/" ):
lowercase__ = "//div[@class = \"maincount... | 201 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import requi... | 27 |
'''simple docstring'''
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 4 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.... | 365 | import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowercase = '''▁'''
__lowercase ... | 105 | 0 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __UpperCAmelCase ( __a : Union[str, Any] ) -> int:
"""simple docstring"""
_a : List[str] = FileLock(str(tmpdir / '''foo.lock''' ) )
_a : Tuple = F... | 235 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 235 | 1 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int , _... | 357 |
"""simple docstring"""
__UpperCamelCase : Dict = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCamelCase : str = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : dict[int, list[int]] , _UpperCAmelCase : ... | 309 | 0 |
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_model... | 109 |
"""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.utils impor... | 109 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = "Speech2TextFeatureExtractor"
SCREAMING_SNAKE_CAS... | 336 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase: Opt... | 336 | 1 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __lowerCAmelCa... | 156 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class lowerCAmelCase__ ... | 11 | 0 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStr... | 360 |
'''simple docstring'''
def UpperCamelCase ( _lowerCamelCase : List[Any] , _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : Tuple ):
# Return True if there is node that has not iterated.
A__ = [False] * len(_lowerCamelCase )
A__ = ... | 123 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str , snake_case__ : int ):
"""simple docstring"""
_snake_case : Union[str, Any] = Path(snake_case__ )
... | 64 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( lowercase__ , lowercase__ ):
# Validation
if not isinstance(lowercase__ , lowercase__ ) or len(lowercase__ ) == 0:
raise ValueError('the string shou... | 96 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AutoformerConfig',
],
}... | 366 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase(__UpperCamelCase ) -> bool:
_lowerCAmelCase =str(__UpperCamelCase )
return n == n[::-1]
def _lowerCamelCase(__UpperCamelCase = 1000000 ) -> str:
_lowerCAmelCase =0
for i in range(1 ... | 341 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :float , snake_case_ :float ):
if mass < 0:
raise ValueError('''The mass of a body cannot be negative''' )
return 0.5 * mass * abs(snake_case_ ) * abs(snake_case_ )
if __name__ == "__main__":
import doctest
... | 332 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class _UpperCAmelCase :
def __init__( self : Union[str, Any] , _lowercase : Optional[Any] ):
__UpperCAmelCase = str(id_ )
__UpperCAmelCase = None
... | 332 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_te... | 348 |
# 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 ... | 348 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
a :Union[str, Any] = logging.get_logger(__name__)
class __a (__lowerCamelCase):
'''simple docstring'''
def __init__( self , *_a , **_a ) -> ... | 132 |
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 ... | 201 | 0 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import ... | 31 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from t... | 31 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils impor... | 84 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class ... | 105 | 0 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
... | 164 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowerCAmelCa... | 164 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : int = 0 ) -> list:
'''simple docstring'''
A__ = length or len(SCREAMING_SNAKE_CASE__ )
A__ = False
for i in range(length - 1 ):
... | 7 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Config... | 309 | 0 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def __snake_case ( _UpperCAmelCase ):
__a = int(_UpperCAmelCase )
__a , __a , ... | 350 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaa... | 131 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.