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 |
|---|---|---|---|---|
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str ) -> List[Any]:
assert x is not None
assert y is not None
__lowercase = len(SCREAMING_SNAKE_CASE )
__lowercase = len(SCREAMING_SNAKE_CASE )... | 325 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
SCREAMING_SNAKE_CASE__ = get_logger(__name__)
class A__ ( enum.Enum ):
lowerCAmelCase__ : Dict = "all_checks"
lowerCAmelCase__ : ... | 325 | 1 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTeste... | 261 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_A = {
'''configuration_encodec''': [
'''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EncodecConfig''',
],
'''feature_extraction_encodec''': ['''... | 261 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
_a : List[Any] = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'token... | 44 | """simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
_a : Dict = datasets.utils.logging.get_logger(__name__)
@dataclass
class __A... | 44 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLI... | 63 | from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
... | 63 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set... | 144 |
"""simple docstring"""
A__ : Optional[int] = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
... | 144 | 1 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _UpperCAmelC... | 366 | import re
def lowerCAmelCase__ ( a__ ) ->str:
'''simple docstring'''
if len(re.findall("[ATCG]" , a__ ) ) != len(a__ ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" , "TAGC" ) )
if __name__ == "__main__":
import doctes... | 63 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : dict ) ->set:
'''simple docstring'''
__A : List[str] = set()
# edges = list of graph's edges
__A : Optional[int] = get_edges(snake_case_ )
# While there are still elem... | 179 |
"""simple docstring"""
import unittest
import numpy as np
import requests
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_inpu... | 179 | 1 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Con... | 369 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _lowercase ( UpperCamelCase_ ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = int(number**0.5 )
return number == sq * sq
def _lowercase ... | 169 | 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 ):
... | 95 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnaly... | 269 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = (EulerDiscreteScheduler,)
lowerCamelCase_ ... | 192 | from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=__A ):
'''simple docstring'''
lowerCamelCase_ = ['''onnx''']
def __init__( self , *lowercase , **lowercase ):
"""simple docstring"""
require... | 192 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
_lowerCamelCase: Union[str, Any] = (IPNDMScheduler,)
_lowerCamelCase: ... | 74 | from __future__ import annotations
class A :
def __init__(self : Union[str, Any] , __UpperCAmelCase : list[list[int]] ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = TypeError(
"Matrices must be for... | 65 | 0 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_UpperCAmelCase : List[Any] = False
class __lowerCAmelCase ( unittest.TestCase):
de... | 158 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_UpperCAmelCase : Optional[int] = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
_UpperCAmelCase : i... | 158 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamel... | 181 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ = {
'''configuration_distilbert'... | 181 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__Upp... | 74 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' whe... | 74 | 1 |
"""simple docstring"""
def lowerCAmelCase__ ( _UpperCamelCase : int ) -> bool:
"""simple docstring"""
if number < 0:
raise ValueError('number must not be negative' )
return number & (number - 1) == 0
if __name__ == "__main__":
impor... | 150 | """simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self , lowerCAmelCase ):
"""simple docstring"""
snake_case ... | 150 | 1 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCamelCase__ =logging.get_logger(__name__)
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
def __init__( self , *__lowerCamelCase ... | 325 |
import os
import unicodedata
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__ =logging.get_logger(__name__)
UpperCamelCase__ ... | 325 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def UpperCamelCase( __UpperCamelCase : Union[str, Any] ):
lowerC... | 103 |
'''simple docstring'''
def _UpperCamelCase ( __A ) -> int:
'''simple docstring'''
UpperCamelCase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _UpperCamelCase ( __A = 100 ) ... | 80 | 0 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_availab... | 353 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def __lowerCAmelCase (_UpperCamelCase ):
@wraps(_UpperCamelCase )
def _inner_fn(*_UpperCamelCase , **_UpperCamelCase ):
warnings.warn(
(F"'{fn.__name__}' is experimental and might be subject to br... | 182 | 0 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase ( A__):
_lowerCAmelCase : Dict = (UnCLIPScheduler,)
def _snake_case ( self : Optional[int] , **lowercase_ : ... | 264 |
from __future__ import annotations
__a = []
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->bool:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
if board[row][i] == 1:
return False
for i in r... | 337 | 0 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Tuple:
for i in range(0 , UpperCamelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
... | 129 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_uti... | 129 | 1 |
'''simple docstring'''
import unittest
from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_attenti... | 47 |
'''simple docstring'''
import copy
import re
class A__ :
A__ = 'hp'
A__ = {}
A__ = None
@classmethod
def A ( cls : Optional[Any] , _a : Optional[Any] , _a : Any ) -> Union[str, Any]:
'''simple docstring'''
... | 47 | 1 |
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : List[Any] ):
__UpperCamelCase =0
__UpperCamelCase =len(lowercase__ )
for i in range(n - 1 ):
for j in range(i + 1 , lowercase__ ):
if arr[i] > arr[j]:
... | 351 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
loggi... | 117 | 0 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def UpperCamelCase_( lowerCamelCase_ ) -> str:
return "".join(sorted(lowerCamelCase_ ) )
def UpperCamelCase_( lowerCamelCase_ ) -> list[str]:
return word_by_signature[signatur... | 21 | """simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowercase ( a__ : str , a__ : bool = True , a__ : float = math.inf , a__ : float = -math.inf , a__ : float = math.inf , a__ : float = -math.inf , a__ : bool = False , a__... | 256 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipel... | 150 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a : Any = """src/diffusers"""
# Matches is_xxx_available()
a : Opt... | 150 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=lowerCamelCase__):
'''simple docstring'''
snake_case_ =["""torch"""]
def __init__(self ,*__lowerCamelCase ,**__lowerCamelCase ) -> List[Any]:
"""simple docstring"""
requ... | 129 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase__ ( lowerCamelCase__):
'''simp... | 129 | 1 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, ... | 364 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestC... | 120 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import ... | 277 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxFo... | 13 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@requi... | 356 | """simple docstring"""
import functools
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> int:
# Validation
if not isinstance(UpperCAmelCase , UpperCAmelCase ) or not all(isinstance(UpperCAmelCase , UpperCAmelCase ) for day in days ):
raise ValueError('The ... | 312 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class A :
def __init__( self , SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
A : Optional[Any] = ... | 3 |
import random
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ ) -> tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = [], [], []
for element in data:
if element < pivot:
le... | 176 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extrac... | 338 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampl... | 338 | 1 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
_... | 93 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Union[str, Any] = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE... | 93 | 1 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> List[str]:
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
_a : Any = str(bin(A__ ) )[2:] # remove the leading "0b"
_a : str... | 357 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load... | 107 | 0 |
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
SCREAMING_SNAKE_CASE : Optional[int] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SN... | 313 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple d... | 313 | 1 |
'''simple docstring'''
import requests
def a__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ : Any = {"""Content-Type""": """application/json"""}
UpperCAmelCase_... | 359 |
'''simple docstring'''
import os
from pathlib import Path
def a__ ( ) -> Union[str, Any]:
"""simple docstring"""
from torch.utils.cpp_extension import load
UpperCAmelCase_ : Union[str, Any] = Path(_SCREAMING_SNAKE_CASE ).resolve().parent.parent.... | 67 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFM... | 84 |
from math import factorial
UpperCAmelCase__ = {str(digit): factorial(digit) for digit in range(10)}
def _a ( a :int ) -> int:
if not isinstance(a , a ):
raise TypeError('''Parameter number must be int''' )
if number < 0:
raise ValueError('''Parameter numb... | 0 | 0 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load... | 165 | def lowercase( UpperCamelCase_ ) -> list[list]:
'''simple docstring'''
UpperCamelCase = current_set.copy()
for row_index, row in enumerate(UpperCamelCase_ ):
UpperCamelCase = row[0]
for column_index, column in enumerate(UpperCamelCase_ ):
if magnitude == 0:
... | 165 | 1 |
'''simple docstring'''
from itertools import product
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int ) -> list[int]:
'''simple docstring'''
_UpperCAmelCase = sides_number
_UpperCAmelCase = max_face_nu... | 22 |
import unittest
from knapsack import knapsack as k
class __a ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
lowercase__: List[Any] = 0
lowercase__: List[Any] = [0]
lowe... | 196 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _A ( A__ ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not ... | 52 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
... | 52 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 201 |
'''simple docstring'''
UpperCAmelCase = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
UpperCAmelCas... | 141 | 0 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
__lowercase = HUGGINGFACE_HUB_CACHE
__lowercase = """config.json"""
__lowercase = """diffusion_pytorch_model.bin"""
__lowercase = """diffusion_flax_mode... | 360 |
"""simple docstring"""
from __future__ import annotations
import queue
class _A :
"""simple docstring"""
def __init__( self : List[str] , __UpperCAmelCase : Union[str, Any]):
a : Optional[Any] = data
... | 226 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDepende... | 97 |
"""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 Acc... | 106 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
... | 190 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
excep... | 190 | 1 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
__lowercase = logging.get_logger(__name__)
class a... | 272 | '''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def ... | 272 | 1 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import wr... | 366 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase (_UpperCAmelCase ,unittest.TestCase ):
__snake_case : int ... | 125 | 0 |
'''simple docstring'''
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessi... | 1 | '''simple docstring'''
import math
def lowerCAmelCase_ ( ) -> None:
'''simple docstring'''
UpperCAmelCase_ = input("Enter message: " )
UpperCAmelCase_ = int(input(f"""Enter key [2-{len(snake_case_ ) - 1}]: """ ) )
UpperCAmelCase_ ... | 1 | 1 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCAmelCase : Dict = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 363 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTra... | 251 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase_ : str = 0
for ch in input_str:
lowerCAmelCase_ : Dict = ord(_UpperCamelCase )
lowerCAmelCase_ : str = pow(2... | 120 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils... | 337 | 0 |
'''simple docstring'''
def UpperCAmelCase ( lowerCamelCase_ :int , lowerCamelCase_ :int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def UpperCAmelCase ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert an... | 8 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
__A : Dict = 'src/transformers'
# Matches is_xxx_available()
__A : Dict = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__A : Any = re.comp... | 8 | 1 |
from timeit import timeit
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
lowerCAmelCase : Any = 0
while number:
number &= number - 1... | 138 | """simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 77 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ..... | 367 | """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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
... | 85 | 0 |
"""simple docstring"""
from math import sqrt
def _snake_case ( lowercase__ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 84 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
d... | 84 | 1 |
from math import ceil, sqrt
def __lowercase ( __lowerCAmelCase : int = 1_0_0_0_0_0_0 ):
a__ = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
a__ = max(ceil(sqrt(outer_width**2 ... | 109 |
from __future__ import annotations
def __lowercase ( __lowerCAmelCase : list[int] , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : int ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction =... | 109 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase... | 241 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.ber... | 71 | 0 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase = get_tests_dir('fixtures/test_... | 187 |
'''simple docstring'''
import numpy
class __snake_case:
'''simple docstring'''
def __init__( self , A_ , A_ ) -> None:
lowerCAmelCase = input_array
# Random initial weights are assigned where first argument is the
# number of nodes... | 187 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Any:
__lowerCamelCase : Optional[Any] = AutoConfig.from_pretraine... | 73 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class A_ :
def __init__( self : List[str] ,SCREAMING_SNAKE_CASE__ : list[tuple[float, float]]):
__lowerCamelCase : Union[str, Any] = list_of_points
# Degree d... | 73 | 1 |
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class UpperCAmelCase ( __a ):
'''simple docstring'''
lowerCamelCase_ = '''facebook/bart-large-mnli'''
lowerCamelCase_ = (
'''This is ... | 369 | import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {"... | 192 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : List[str] ... | 14 |
"""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
fr... | 260 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeli... | 353 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_ ):
_A : Tuple = 0
if start < end:
_A : Tuple = randint(snake_case_,snake_case_ )
... | 343 | 0 |
"""simple docstring"""
from math import factorial, pi
def snake_case_ ( A_ : Union[str, Any], A_ : Tuple = 30 ):
'''simple docstring'''
if not isinstance(A__, (int, float) ):
raise ValueError('''maclaurin_sin() requires either an ... | 72 |
'''simple docstring'''
from PIL import Image
def __lowerCamelCase ( A__ , A__ ) -> Image:
"""simple docstring"""
def brightness(A__ ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueEr... | 28 | 0 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttenti... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:... | 291 | 0 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...util... | 290 | """simple docstring"""
from math import pi, sqrt, tan
def __a ( _SCREAMING_SNAKE_CASE ) ->float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowerCamelCase_ : str = {
# 1536-bit
5: {
"""prime""": int(
"""F... | 223 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( _A , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Tu... | 223 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> List[str]:
__lowerCamelCase : List[Any] = len(lowerCamelCase__ )
while cur > 1:
# Find the maximum number in arr
__lowerCamelCase : List[Any] = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi... | 73 |
'''simple docstring'''
import re
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
snake_case_ = re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(__UpperCAmelCase, __U... | 56 | 0 |
'''simple docstring'''
from __future__ import annotations
import queue
class lowerCAmelCase__ :
def __init__( self : List[str] , lowerCamelCase__ : Optional[int] ) ->List[str]:
'''simple docstring'''
_UpperCAme... | 322 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
lowerCamelCase__ = list[list[float | int]]
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase : int = len(__lowerCAmelCase )
_Up... | 322 | 1 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCAmelCase__ = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase_ ):
"""simple docstring"""
a : List[str] ="masked_bert"
de... | 108 | from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_... | 219 | 0 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class a ( Upper... | 363 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils impo... | 189 | 0 |
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 _A :
@property
def UpperCAmelCase ( self ):
"""simple do... | 253 |
from jiwer import compute_measures
import datasets
lowerCAmelCase : Tuple = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation... | 253 | 1 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
A_ : Any =logging.getLogger(__name__)
class __a ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : List[Any] = "masked_bert"
def __init__( self , ... | 80 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> list[int]:
if num <= 0:
raise ValueError('Input must be a positive integer' )
_lowerCamelCase = [True] * (num + 1)
_lowerCamelCase = 2
while p * p <= num:
... | 80 | 1 |
'''simple docstring'''
# Copyright 2021 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/... | 349 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a__ : Optional[Any] = logging.getLogger(__name__)
class UpperCAmelCase__ :
de... | 349 | 1 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __magic_name__ ( ) -> Dict:
'''simple docstring'''
snake_case_ = 9, 14 # noqa: F841
snake_case_ = [
[0, 1, 4],
... | 364 |
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.model... | 72 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils ... | 210 | # Copyright 2022 The HuggingFace Team and The OpenBMB 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 ... | 210 | 1 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
_SCREAMING_SNAKE_CASE : Optional[int] = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def _lowerCAmelCase ( UpperCAmelCase : Optional[int] , Upper... | 157 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
_SCREAMING_SNAKE_CASE : List[Any] = """1"""
_SCREAMING_SNAKE_CASE : Union[str, Any] = """0"""
_SCREAMING_SNAKE_CASE : List[str] = """1"""
_SCREAMING_SNAKE_CASE : Optional[int] ... | 157 | 1 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = prime_factors(_UpperCamelCase )
if is_square_free(_UpperCamelCase ):
... | 57 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
A : Any = "examples/"
A : Optional[Any] = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__versio... | 57 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpT... | 199 |
'''simple docstring'''
from timeit import timeit
def _A ( snake_case ) -> int:
if number < 0:
raise ValueError("the value of input must not be negative" )
_lowercase : Union[str, Any] = 0
while number:
number &= number - 1
result += 1
... | 199 | 1 |
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCamelCase_ ( UpperCamelCase__ : int = 200_0000 ) -> int:
"""simple docstring"""
__lowerCamelCase = [0]
__lowerCamelCase = 42
... | 90 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optiona... | 176 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {'vocab_file': 'vocab.json', 'merges_... | 356 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
fr... | 235 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
UpperCamelCase = loggi... | 319 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTe... | 319 | 1 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import ... | 112 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowerCAmelCase ... | 112 | 1 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class A_ (datasets.BuilderConfig ):
UpperCAmelCase__ = None
class A_ (datasets.ArrowBasedBuilder ):
... | 273 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokeniz... | 188 |
"""simple docstring"""
# 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.apach... | 188 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=_lowerCamelCase):
A_ : Tuple = ['transformers', 'torch', 'note_seq']
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
requires_backends(s... | 86 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-pre'''... | 244 | 0 |
# 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 UpperCAmelCase_ ( __UpperCAmelCase : Optional[int] ) -> Tuple:
return 1 / (1 + np.exp(-z ... | 210 |
import argparse
import os
import re
lowerCamelCase__ : str = 'src/transformers'
# Pattern that looks at the indentation in a line.
lowerCamelCase__ : Tuple = re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase__ : ... | 210 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeli... | 215 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = (IPNDMScheduler,)
UpperCAmelCase = (("""num_inferen... | 215 | 1 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
Aut... | 359 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
... | 127 | 0 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def a_ ( lowerCAmelCase_ : int = 8 ):
__lowerCAmelCase = ascii_letters + digits + punctuation
return "".join(secrets.choice(lowerCAmelCase_ ) for _ in range(l... | 284 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case : List[Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptToken... | 284 | 1 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common im... | 364 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _... | 89 | 0 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCAmelCase (... | 17 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Any, UpperCAmelCase__ : int ):
__lowercase = num_of_nodes
__lowercase = []
__lo... | 17 | 1 |
import unittest
from transformers import 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 ModelTesterMixin, ids_tensor
from ... | 127 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
... | 127 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_A : str =logging.get_logger(__name__)
_A : List[str] ={
... | 41 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome... | 41 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _UpperCAmelCase ( _lowerCAmelCase ):
@staticmethod
@abstractmethod
def a ( _lowercase : ArgumentParser ):
raise NotImplementedError()
@abst... | 366 |
"""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,
)
_lowercase : Tuple ... | 86 | 0 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if ver... | 29 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acceler... | 29 | 1 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> Any:
snake_case : ... | 176 |
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def SCREAMING_SNAKE_CASE__ ( ) -> None:
assert or_gate(0 ,0 ) == 0
assert or_gate(0 ,1 ) == 1
assert or_gate(1 ,0 ) ==... | 176 | 1 |
from math import factorial
_lowerCamelCase : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
if not isinstance(lowercase_ , lowercase_ ):
... | 14 |
"""simple docstring"""
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_... | 263 | 0 |
"""simple docstring"""
class __A :
'''simple docstring'''
def __init__( self : List[Any] ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
lowercase__ : Dict = arr.split(''',''' )
... | 362 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_... | 302 | 0 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import ... | 143 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A = logging.get_logger(__name__)
_A = {
"""SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-detr/r... | 242 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transf... | 150 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a : Union[str, Any] = Fals... | 150 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase : Any = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
# Se... | 252 |
import re
import string
import numpy as np
import datasets
UpperCAmelCase : List[str] = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
UpperCAmelCase : str = "\nArgs:\n predi... | 252 | 1 |
from sklearn.metrics import mean_squared_error
import datasets
__UpperCAmelCase = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Pre... | 367 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers... | 139 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available,... | 29 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowercase_ = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _snake_case( SCREAMING_SNAKE_CA... | 7 | 0 |
"""simple docstring"""
def UpperCamelCase_( _snake_case : int , _snake_case : int ):
"""simple docstring"""
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase_( ):
"""simple docstring"""
... | 358 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowerCAmelCase : Tuple = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C"... | 308 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.