code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditiona... | 134 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class lowerCamelCase :
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : MutableSequence[float] ) ->... | 134 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Any = logging.get_logger(__name__)
A__ : Optional[int] = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decis... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A__ : int = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
... | 0 | 0 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> str:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ) or not number >= 1:
rais... | 21 |
import numpy
# List of input, output pairs
UpperCAmelCase : str = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCAmelCase : Optional[int] = (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase ... | 95 | 0 |
"""simple docstring"""
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.me... | 354 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _snake_case ( lowercase__ : bool = True , *lowercase__ : Optional[int] , **lowercase__ : s... | 1 | 0 |
"""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/LICENSE-2.0... | 54 |
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=__snake_case ):
'''simple docstring'''
A__ : Tuple = ["flax"]
def __init__( self: str ,*lowerCamelCase_: int ,**lowerCamelCase_: List[str] ) -> str:
requires... | 345 | 0 |
from __future__ import annotations
import pandas as pd
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = [0] * no_of_processes
SCREAMING_SNAKE_CASE_ = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in ... | 363 |
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 BitConfig, Bit... | 257 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get... | 284 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 284 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(__lowerCAmelCase ) == 0:
raise ValueError("""Input list must be a non... | 353 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/mic... | 255 | 0 |
import math
from numpy import inf
from scipy.integrate import quad
def _a ( UpperCamelCase_ : float ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError("math domain error" )
return quad(UpperCamelCase_ , 0 , Upp... | 340 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils i... | 340 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_c... | 246 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
"configuration_mobilebert": [
"MOBI... | 246 | 1 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase__ : List[Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def UpperCamelCase__ ... | 143 | import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCAmelCase__ : str = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S ... | 143 | 1 |
"""simple docstring"""
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Optional[int] , SCREAM... | 133 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : list[int] ):
'''simple docstring'''
lowerCAmelCase : str = len(SCREAMING_SNAKE_CASE )
for i in range(SCREAMING_SNAKE_CASE ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE ):
if nu... | 133 | 1 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 46 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmel... | 260 | 0 |
"""simple docstring"""
import torch
def _snake_case ( ) -> Union[str, Any]:
if torch.cuda.is_available():
lowerCamelCase_ : int =torch.cuda.device_count()
else:
lowerCamelCase_ : List[str] =0
print(F"""Successfully ... | 209 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoe... | 209 | 1 |
class lowerCamelCase__ :
def __init__(self , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None ) -> Dict:
_lowercase =data
_lowercase =previous
_lowercase =next_node
def __str__(self ) -> ... | 5 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__UpperCamelCase : Dict = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}
def ... | 146 | 0 |
"""simple docstring"""
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class _UpperCamelCa... | 259 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 259 | 1 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
# TODO Update this
__A : Optional[Any] = {
"""facebook/esm-1b""": """https:/... | 138 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available... | 95 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE : Optional[int] = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltC... | 360 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE : str = logging.getLogger()
def ... | 84 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity... | 222 |
import os
import re
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 : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase ... | 222 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {}
class lowerCamelCase (__lowerCamelCase ):
"""simple ... | 191 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class lowerCamelCase :
"""simple docstring"""
... | 191 | 1 |
'''simple docstring'''
import os
import sys
import unittest
__snake_case = 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_mod... | 97 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__A : List[Any] = {
'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
'tokenization_gpt_n... | 154 | 0 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_visio... | 359 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Tuple = {
'configuration_distilbert': [
'... | 294 | 0 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : int = prime_factors(__a )
if is_square_free(__a ):
return -1 if len(__a ) % 2 else 1
return 0
if __name... | 327 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
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.
"""
_SCREAMING_SNAKE_CASE = """
... | 327 | 1 |
"""simple docstring"""
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
... | 56 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [0] * len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Any = [1] * len(__lowerCAmelCase ... | 56 | 1 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 277 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
lowercase : str = """\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Sing... | 20 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Union[str, Any] = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPegasusConfig",
"BigBirdPe... | 366 |
def A_ ( A__ , A__ , A__ ) -> float:
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(A__ , A__ ):
... | 225 | 0 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionC... | 347 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't ... | 347 | 1 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_availab... | 351 |
"""simple docstring"""
from collections.abc import Generator
from math import sin
def UpperCAmelCase__ (snake_case__ : bytes ):
"""simple docstring"""
if len(snake_case__ ) != 32:
raise ValueError("""Input must be of length 32""" )
_snake_case : Optional[int] ... | 132 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-base''': ''... | 39 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase = "" , UpperCAmelCase = False ):
"""simple docstring"""
_UpperCAmelCase = {}
# A node will be a leaf if the tree contai... | 39 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_... | 173 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamel... | 173 | 1 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizer... | 56 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class l... | 267 | 0 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : O... | 17 | '''simple docstring'''
import baseaa
def __UpperCAmelCase ( a_: str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __UpperCAmelCase ( a_: bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
... | 17 | 1 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils impor... | 256 |
'''simple docstring'''
from __future__ import annotations
class A__ :
def __init__( self , UpperCamelCase__=None ) -> Any:
'''simple docstring'''
A_ = data
A_ = None
def __repr__( self ) -> List[str]:... | 162 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _A ( ):
"""simple docstring"""
__lowercase = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
__lowercase = parser.add_s... | 52 |
'''simple docstring'''
def _A ( A__ = 1000 ):
"""simple docstring"""
__lowercase , __lowercase = 1, 1
__lowercase = 2
while True:
__lowercase = 0
__lowercase = fa + fa
__lowercase , __lowercase = fa, f
index += 1
for _... | 52 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__snake_case = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must... | 310 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase (_a , unittest.TestCa... | 310 | 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 ..pipeline... | 52 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : Lis... | 52 | 1 |
'''simple docstring'''
class a_ :
'''simple docstring'''
def __init__( self , A , A ) -> int:
_SCREAMING_SNAKE_CASE = name
_SCREAMING_SNAKE_CASE = val
def __str__( self ) -> Dict:
return f'{self.__class__.__name__}({self.... | 58 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging... | 51 | 0 |
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset... | 31 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
... | 31 | 1 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def __snake_case ( UpperCAmelCase_ : Any ):
# getting number of pixels in the image
lowerCamelCase_ ,lowerCamelCase_ = img.shape[0], img.shape[1]
# converting each pixel's color to its... | 55 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( UpperCAmelCase_ : int ):
lowerCamelCase_ = 2
lowerCamelCase_ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(UpperCAmelCase_ )
if n > 1:
fac... | 55 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json''',
}
... | 358 |
"""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_ima... | 188 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase_ ( _snake_case ):
SCREAMING_SNAKE_CASE__ : Tuple = [True] * limit
SCREAMING_SNAKE_CASE__ : List[Any] = False
SCREAMING_SNAKE_CASE__ : int ... | 25 | import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_as... | 180 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = hex_num.strip()
if not hex_num:
raise ValueError("No value was passed to the function" )
UpperCamelCase_ = hex_num[0] == "-"
if is_negative:
Up... | 359 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
for param in module.parameters():
UpperCamelCase_ = False
def lowerCAmelCase_ ( ) -> Dict:
UpperCamelCa... | 328 | 0 |
from __future__ import annotations
__a = 1.6021E-19 # units = C
def a ( snake_case__: float , snake_case__: float , snake_case__: float , ):
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
... | 30 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Tuple = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 56 | 0 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCamelCase : Any = "."
if __name__ == "__main__":
UpperCamelCase : Optional[int] = os.path.join(REPO_PATH, "utils/document... | 366 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import ... | 263 | 0 |
# 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/LICENSE-2.0
#
# Unless required by app... | 50 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_comm... | 50 | 1 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ) -> int:
return x if y == 0 else greatest_common_divisor(_UpperCAmelCase ,x % y )
def a_ ( _UpperCAmelCase : int ,_UpperCAmelCase : int ) -> ... | 0 |
'''simple docstring'''
import math
def a_ ( _UpperCAmelCase : int ) -> list:
__snake_case : Optional[Any] = [True] * n
__snake_case : Optional[int] = False
__snake_case : Dict = False
__snake_case : List[Any] = True
... | 0 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__A : int = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''... | 138 |
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 : Any = datasets.utils.logging.get_logger(__name__)
@dataclass
class __A ( datasets.Bui... | 138 | 1 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : Tuple = {}
SCREAMING_SNAKE_CASE_ : Any = job['started_at']
SCREAMING_SNAKE_CASE_ :... | 358 |
from __future__ import annotations
def a__ ( A__ ):
return len(set(A__ ) ) == len(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 162 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environmen... | 210 | import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__a : List[Any] = logging.getLogger(__name__)
def UpperCAmelCase ( ):
"""simple docstring"""
__lowercase = argparse.ArgumentParser(
... | 210 | 1 |
def __lowerCamelCase ( UpperCAmelCase_ : str , UpperCAmelCase_ : str ):
"""simple docstring"""
if not (isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and isinstance(UpperCAmelCase_ , UpperCAmelCase_ )):
raise ValueError('''longest_common... | 352 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
snake_case : Any = pytest.mark.integration
@pytest.mark.parametrize('''path''' , ['''paws''', ''... | 281 | 0 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : str = {name: getatt... | 47 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__UpperCamelCase ) )
def ... | 321 | 0 |
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.to... | 31 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : list ) -> float:
'''simple docstring'''
UpperCamelCase__ = 0
while len(_UpperCamelCase ) > 1:
UpperCamelCase__ = 0
# Consider two files with minimum cost to be me... | 31 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case :int = logging.get_logger(__name__)
__snake_case :Optional[Any] =... | 49 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""c... | 293 | 0 |
"""simple docstring"""
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.u... | 233 |
"""simple docstring"""
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_uti... | 233 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
... | 33 |
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0_0 ):
lowercase_ , lowercase_ : str = 1, 1
lowercase_ : List[str] = 2
while True:
lowercase_ : Tuple = 0
lowercase_ : List[Any] = ... | 33 | 1 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''M-CLIP'''
def __init__( self : List[str] ,... | 368 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorc... | 37 | 0 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_snake_case = TypeVar('KEY')
_snake_case = TypeVar('VAL')
@dataclass(frozen=snake_case_ , slots=snake_cas... | 294 |
"""simple docstring"""
import cva
import numpy as np
class UpperCamelCase :
def __init__( self : Optional[int] , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> Dict:
if k in (0.0_4, 0.0_6):
... | 294 | 1 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 365 |
import baseaa
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str ) -> bytes:
return baseaa.baaencode(string.encode('''utf-8''' ) )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : bytes ) -> str:
return baseaa.baadecode(__UpperCam... | 177 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : Optional[int] , __lowerCamelCase : str , __lowerCamelCase : Optional[int] ):
if days_between_payments <= 0:
raise ValueError("""days_between_payments must be > 0""" )... | 114 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, to... | 248 | 0 |
import os
import numpy
import onnx
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]:
__lowerCamelCase : List[Any] = a.name
__lowerCamelCase : Union[str, Any] = b.name
__lowerCamelCase : str = ''
__lowerC... | 113 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a ={
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Ro... | 113 | 1 |
"""simple docstring"""
from collections import defaultdict
def lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
__A = 1
__A = True
for v in tree[start]:
if v not in visited:
ret += dfs(__UpperCamelCase )
if ret % 2 == 0:
cuts.append(__... | 266 |
"""simple docstring"""
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import To... | 266 | 1 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, tor... | 256 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
O... | 256 | 1 |
"""simple docstring"""
def __lowercase ( snake_case_ : Optional[Any] ) ->Union[str, Any]:
'''simple docstring'''
__A : int = 0
__A : List[Any] = len(snake_case_ )
for i in range(n - 1 ):
for j in range(i + 1 ,s... | 179 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from... | 179 | 1 |
'''simple docstring'''
import sys
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : Optional[Any] = len(lowerCamelCase__ )
A_ : Union[str, Any] = [[0 for x in range(lowerCamelCase__ )] for x in range(lowerCamelCase__ )]
A_ : ... | 135 |
'''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 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstr... | 284 |
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.data import Dataset
from tr... | 225 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase ( UpperCAmelCase__ : NDArray[floataa] , UpperCAmelCase__ : NDArray[floataa] , UpperCAmelCase__ : list[int] , UpperCAmelCase_... | 21 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ ... | 21 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Di... | 5 |
from typing import Any
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> list:
"""simple docstring"""
_validation(
__snake_case , __snake_case , __snake_case , ... | 5 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """... | 354 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
"""simple docstring"""
UpperCAmelCase_ : Tuple = [randint(-10_00 ... | 67 | 0 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class _lowerCAmelCase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
d... | 231 |
from __future__ import annotations
import math
def lowerCAmelCase_ ( _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:
# Negati... | 170 | 0 |
import argparse
import copy
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = {}
with open(_UpperCAmelCase) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
SCREAMING_SNAKE_CASE = []
_list.append([line.split()[1... | 350 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decode... | 327 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ..... | 131 |
def _A ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[int] ):
UpperCamelCase :Tuple = len(SCREAMING_SNAKE_CASE__ )
print('''The following activities are selected:''' )
# The first activity is always selected
UpperCamelCase ... | 259 | 0 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class A__ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowercase =... | 368 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import U... | 307 | 0 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class lowerCAmelCase__ ( tf.keras.optimi... | 234 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available(... | 234 | 1 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def A (*__A : str ) -> Optional[int]:
"""simple docstring"""
if not isinstance(a_ , a_... | 366 |
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
... | 7 | 0 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def lowercase_ ( _lowerCamelCase : bool = True , *_lowerCamelCase : int , **_lowerCamelCase : Optional[Any]):
if not is... | 87 | import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = R'''
Args:
input_ids (`torch.LongT... | 87 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : List[Any] = {
"""configuration_owlvit... | 352 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageRes... | 286 | 0 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
UpperCAmelCase_ : str = datasets.utils.loggi... | 38 |
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()
except OptionalDependencyNotAvailable:
from ...utils.du... | 38 | 1 |
from math import pi, sqrt
def snake_case_ ( lowerCAmelCase_ )-> float:
'''simple docstring'''
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(lowerCAmelCa... | 365 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
class ... | 349 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE( __lowercase ) -> int:
assert (
isinstance(__lowercase , __lowercase ) and number_of_steps > 0
), F"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if number_of_s... | 319 |
'''simple docstring'''
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list:
if len(__lowercase ) != 2 or len(a[0] ) != 2 or len(__lowercase ) != 2 or len(b[0] ) != 2:
raise Excepti... | 319 | 1 |
"""simple docstring"""
__A : Optional[Any] = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
... | 57 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase_... | 57 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def _UpperCamelCase ( __A , __A , __A , __A ) -> str:
'''simple docstring'''
UpperCamelCase__ = s.r... | 80 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __A ) -> float:
'''simple docstring'''
UpperCamelCase__ = 0.00
UpperCamelCase__ = 0
for resistor in resistors:
if resistor <= 0:
... | 80 | 1 |
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_image_... | 71 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, 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 ConfigTester
from... | 71 | 1 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
' Distillatio... | 175 | import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from dataset... | 175 | 1 |
lowercase__ : List[Any] = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p"... | 358 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCamelCase__ ) , """Tato... | 180 | 0 |
from math import sqrt
def lowerCAmelCase_ ( _lowercase : Tuple) -> int:
"""simple docstring"""
a__ : str = 0
for i in range(1 , int(sqrt(_lowercase) + 1)):
if n % i == 0 and i != sqrt(_lowercase):
total += i + n // i... | 170 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
... | 171 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, ... | 351 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__lowerCamelCase : Dict = logging.get_logger("""transformers.models.speecht5""")
def A_ ( _lowerCAmelCase , _lowerCAmelCase ... | 140 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a_... | 68 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerCAmelCase__ = get_tests_dir(... | 68 | 1 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __A ( ... | 323 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
i... | 323 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCAmelCase : Any = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'configuration_data2vec... | 107 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''camembert-base'... | 97 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _UpperCAmelCase ( unittest.TestCase )... | 68 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from .... | 68 | 1 |
"""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 model through reduction of a normal pre-trained model, but keeping the
... | 288 |
import pprint
import requests
UpperCAmelCase = '''https://zenquotes.io/api'''
def UpperCAmelCase_ ( ):
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def UpperCAmelCase_ ( ):
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ ... | 195 | 0 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswerin... | 366 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Tuple = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.... | 218 | 0 |
from math import isclose, sqrt
def a_ ( _A , _A , _A ) -> tuple[float, float, float]:
"""simple docstring"""
snake_case__ = point_y / 4 / point_x
snake_case__ = 2 * normal_gradient / (1 + normal_gradient * normal_... | 307 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
clas... | 307 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_... | 368 |
from __future__ import annotations
def A ( lowercase , lowercase ) -> tuple[int, int]:
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCamelCase) , (UpperCamelCase)) = extended_euclid(lowercase , a % b )
UpperCamelCase = a // b
return (y, x - k ... | 110 | 0 |
"""simple docstring"""
from string import ascii_uppercase
_a = {str(ord(c) - 55): c for c in ascii_uppercase}
def __a ( __lowerCamelCase, __lowerCamelCase ):
if isinstance(__lowerCamelCase, __lowerCamelCase ):
raise TypeError("int() can't convert non-string with explicit b... | 61 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before token... | 240 | 0 |
from __future__ import annotations
from statistics import mean
def __lowerCAmelCase ( a__ , a__ , a__ ) -> list[int]:
__a = [0] * no_of_processes
__a = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(_... | 371 |
from typing import List
from .keymap import KEYMAP, get_character
def __lowerCAmelCase ( a__ ) -> List[str]:
def decorator(a__ ):
__a = getattr(a__ , '''handle_key''' , [] )
handle += [key]
setattr(a__ , '''handle_key''' , a__ ... | 33 | 0 |
"""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_ima... | 160 |
"""simple docstring"""
from statistics import mean
import numpy as np
def __A ( a_ :list , a_ :list , a_ :list , a_ :int) -> list:
__a : Any = 0
# Number of processes finished
__a : Union[str, Any] = ... | 160 | 1 |
'''simple docstring'''
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common im... | 311 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( __snake_cas... | 311 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available():
... | 339 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32acce... | 339 | 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 _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelC... | 356 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCAmelCase = 10
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase... | 184 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.