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 |
|---|---|---|---|---|
import math
from numpy import inf
from scipy.integrate import quad
def lowerCamelCase_ ( _a ):
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
return quad(__lowerCamelCase , 0 , __lowerCamelCase , a... | 131 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_a = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://h... | 61 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a__ : Any = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARC... | 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a__ : List[str] = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],... | 195 | 0 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
lowercase__ :str = logging.get_logger(__name__)
class lowercase ( SCREAMING_SNAKE_CASE__ ):
def __init__( self ,*A__ ,**A__):
warnings.warn(
'''The ... | 101 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = ('''dense.weight''', '''attention.self.qu... | 101 | 1 |
# 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 applicabl... | 356 | import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeniza... | 165 | 0 |
"""simple docstring"""
def _lowercase ( __snake_case ,__snake_case ) -> list:
__lowerCAmelCase : Optional[int] = word.split()
def justify(__snake_case ,__snake_case ,__snake_case ) -> str:
__lowerCAmelCase : Tuple = ... | 269 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --npr... | 269 | 1 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 36 |
import argparse
import copy
def lowerCAmelCase__ ( _a : List[Any] ):
snake_case_ : List[Any] = {}
with open(_a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case_ : int = []
_list.append([line.spl... | 36 | 1 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.ut... | 30 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 30 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Dict = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json',
# S... | 371 |
import baseaa
def lowerCamelCase__ (_UpperCAmelCase):
return baseaa.aaaencode(string.encode('utf-8'))
def lowerCamelCase__ (_UpperCAmelCase):
return baseaa.aaadecode(_UpperCAmelCase).decode('utf-8')
if __name__ == "__main__":
import doctest
doctest.testmod()
| 327 | 0 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer... | 313 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 ... | 313 | 1 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case = 1_00_00_00 ) -> int:
"""simple docstring"""
_UpperCamelCase = 1
_UpperCamelCase = 1
_UpperCamelCase = {1: 1}
for inputa in range(2, __snake_case ... | 100 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_a = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONF... | 100 | 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
_A = logging.get_logger(__name__)
_A = {
'''google/vit-base-patch16-224''': '''https://hu... | 278 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = args.log_outputs
lowerCAmelCase_ = ... | 278 | 1 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__a = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))
print('''Googling.....''')
... | 173 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__a = 50_00_00
__a , __a = os.path.split(__file__)
__a = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get... | 173 | 1 |
def lowerCAmelCase_ ( snake_case_ ):
_A : Optional[int] = set()
# edges = list of graph's edges
_A : int = get_edges(snake_case_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add h... | 26 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FN... | 26 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
... | 351 | """simple docstring"""
from math import pi
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 312 | 0 |
from cva import destroyAllWindows, imread, imshow, waitKey
def a ( A__ : int ) -> Optional[int]:
"""simple docstring"""
_lowercase , _lowercase =img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for ... | 205 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common im... | 205 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def A_ ( ) -> None:
print("Making key files..." )
make_key_files("rsa" , 1024 )
print("Key files generation successful."... | 140 |
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
__lowerCamelCase : List[str] = logging.get_logger(__name__)
__lowerCamelCase : Any = r"""
Args:
input_ids (... | 140 | 1 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
A__: Tuple = 100
A__: Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A__: int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
pr... | 149 |
import os
from datetime import datetime as dt
from github import Github
A__: int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def... | 149 | 1 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase__ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase__ = [ord(letter) for letter in string.ascii_lo... | 83 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixi... | 83 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
A__ : str ={'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
rai... | 70 | """simple docstring"""
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
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"""vocab_fil... | 256 | 0 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCamelCase( UpperCAmelCase_ ):
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCAmelCase_ , UpperCAmelC... | 280 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowercase__ = TypeVar("KEY")
lowercase__ = TypeVar("VAL")
@dataclass(frozen=_snake_case , slots=_snake_case )
cla... | 280 | 1 |
lowercase : str = 256
# Modulus to hash a string
lowercase : List[Any] = 1000003
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> bool:
lowercase : Optional[int] = len(SCREAMING_SNAKE_CASE__ )
lo... | 20 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https://huggingface.co/... | 340 | 0 |
def A ( __UpperCAmelCase , __UpperCAmelCase ) -> int:
'''simple docstring'''
while a != 0:
UpperCAmelCase_ , UpperCAmelCase_ = b % a, a
return b
def A ( __UpperCAmelCase , __UpperCAmelCase ) -> int:
... | 344 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase_ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase_ = [ord(letter) for letter in string.ascii_lowercase]
UpperCam... | 344 | 1 |
'''simple docstring'''
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def _lowerCAmelCase ( _UpperCamelCase : Dict , _UpperCamelCase : Any=False ) -> Optional[Any]:
"""simple docstring"""
... | 47 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_... | 46 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
"processing_vi... | 66 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : List[Any] = {
"BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/conf... | 66 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''huggingface/informer-tourism-monthly''': (
'''https://huggingface.co/hug... | 74 |
"""simple docstring"""
def snake_case_ ( A_ : list[list[float]] ):
'''simple docstring'''
_lowerCamelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(A_ ):
if len(A_ ) < i... | 72 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
def lowercase (SCREAMING_SNAKE_C... | 38 |
"""simple docstring"""
import operator as op
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> int:
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = lambda SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int(x / y ) # noqa: E731 i... | 38 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[Any] = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/... | 105 |
"""simple docstring"""
from __future__ import annotations
from math import pi
def _SCREAMING_SNAKE_CASE ( _lowercase : float , _lowercase : float , _lowercase : float ) ->dict[str, float]:
'''simple docstring'''
... | 105 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline | 116 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _A ( _lowerCamelCase ):
_UpperCamelCase : Tuple = (PNDMScheduler,)
_UpperCamelCase : Optional[int] = (('''num_inference_steps'''... | 116 | 1 |
'''simple docstring'''
from __future__ import annotations
lowercase : Any = 8.988E9 # units = N * m^s * C^-2
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A , __A ) -> dict[str, float]:
_snake_case = abs(chargea * chargea )
if (force, chargea, charg... | 42 |
"""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 | 0 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def lowerCamelCase__ ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[int] ... | 242 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fl... | 242 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipe... | 290 | """simple docstring"""
from math import pow, sqrt
def __a ( *_SCREAMING_SNAKE_CASE ) ->bool:
a__: Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) > 0 and all(value > 0.0 for value in values )
return result
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 290 | 1 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
lowerCamelCase__ = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'''text-classification''',
... | 22 |
def A():
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCamelCase__ = generate_large_matrix()
lowerCamelCase__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7... | 22 | 1 |
"""simple docstring"""
import os
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : List[str] = os.path.dirname(os.path.realpath(A_ ) )
_lowerCamelCase : Dict = os.path.join(A_, '''triangle.txt''' )
... | 72 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def snake_case_ ( A_ : Any ):
'''simple docstring'''
_lowerCamelCase : Any = [
'''encoder.ve... | 72 | 1 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
snake_case_ = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
# Mark tests as "unit"... | 364 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from dif... | 181 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def __lowercase ( __lowercase ) -> int:
'''simple docstring'''
if not postfix_notation:
return 0
_A = {"+", "-", "*", "/"}
_A = []
for t... | 79 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 79 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
... | 368 |
import re
from filelock import FileLock
try:
import nltk
lowercase : List[str] = True
except (ImportError, ModuleNotFoundError):
lowercase : Tuple = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def... | 225 | 0 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( _lowerCAmelCase , unittest.TestCase ):
A = CTRLTok... | 147 |
import random
def lowerCAmelCase_ (lowerCAmelCase__: int ):
"""simple docstring"""
UpperCAmelCase_: Optional[Any] = num - 1
UpperCAmelCase_: Optional[int] = 0
while s % 2 == 0:
UpperCAmelCase_: Union[str, Any] ... | 147 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Any = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class lowe... | 362 |
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
if len(lowerCAmelCase_ ) != len(lowerCAmelCase_ ):
raise ValueError("""String lengths must match!""" )
__lowercase : str = 0
for chara, chara in zip(lowerCAmelCase_ ... | 306 | 0 |
"""simple docstring"""
from string import ascii_uppercase
__lowercase = {char: i for i, char in enumerate(ascii_uppercase)}
__lowercase = dict(enumerate(ascii_uppercase))
def lowercase ( A_ , A_ )-> str:
'''simple docstring'''
... | 40 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.u... | 40 | 1 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowerCamelCase : Optional[Any] = loggi... | 191 |
from __future__ import annotations
import time
_lowerCamelCase : Tuple = list[tuple[int, int]]
_lowerCamelCase : int = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],... | 191 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature... | 55 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case ... | 55 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_torch_available():
raise OptionalDepende... | 356 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_torch_available():
raise OptionalDepende... | 78 | 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_video_inputs
if is_torch_... | 42 |
import re
def UpperCamelCase ( _A ):
"""simple docstring"""
__magic_name__ : List[Any] = re.compile(R"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$""" )
if match := re.search(_A, _A ):
return match.string == phone
return False
if __nam... | 342 | 0 |
"""simple docstring"""
def _a ( _snake_case ):
"""simple docstring"""
UpperCAmelCase = []
if len(__a ) == 1:
return [nums.copy()]
for _ in range(len(__a ) ):
UpperCAmelCase = nums.pop(0 )
... | 350 |
"""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,
)
_UpperCamelCase = {"""configu... | 234 | 0 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vi... | 122 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_A = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
try:
if not is_torch_available():
... | 122 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''config... | 356 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def snake_case_ ( A_ : Optional[int] ):
'''simple docstring'''
def wrapper(*A_ : int, **A_ : List[s... | 175 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowercase : Optional[int] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDepende... | 238 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_lowercase : Any = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__... | 238 | 1 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger... | 143 | import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
Up... | 143 | 1 |
from __future__ import annotations
import math
__A = "2020.9.26"
__A = "xcodz-dot, cclaus, dhruvmanila"
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] , ... | 90 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def A_ ( snake_case ):
return np.maximum(0 , snake_case )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 139 | 0 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Tuple = {'vocab_file': 'vocab.json'}
a_ : Tuple = {
'vocab_file': {
... | 362 |
from scipy.stats import pearsonr
import datasets
a_ : Optional[int] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption t... | 327 | 0 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
lowerCamelCase_ = '''
import os
'''
lowerCamelCase_ = '''
def foo():
import os
return False
'''
lowerCamelCase_ = '''
def foo():
def bar():
... | 79 |
import os
import sys
lowercase__ :Tuple = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTok... | 101 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...s... | 358 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"""The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionInpaintPipeline` instead.""... | 100 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__UpperCamelCase = 5_0000
__UpperCamelCase = 5000
__UpperCamelCase , __UpperCamelCase = os.path.split(__file__)
__UpperCamelCase = os.pa... | 69 | """simple docstring"""
import sys
from collections import defaultdict
class UpperCamelCase :
def __init__( self) -> Optional[int]:
snake_case_ = []
def a_ ( self, lowerCAmelCase__) -> Any:
return self.node_pos... | 69 | 1 |
'''simple docstring'''
import functools
def UpperCamelCase_( snake_case : List[str] , snake_case : List[str] ):
'''simple docstring'''
if not isinstance(snake_case , snake_case ) or not all(isinstance(snake_case , snake_case ) ... | 358 |
'''simple docstring'''
def UpperCamelCase_( snake_case : int = 1_0_0_0 ):
'''simple docstring'''
snake_case_ = 3
snake_case_ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a %... | 92 | 0 |
'''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__ : str = {"""vocab_file""": """vocab.txt""", """tokenizer_file"""... | 185 |
'''simple docstring'''
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
impor... | 185 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Tuple = logging.get_logger(__name__)
UpperCAmelCase : Tuple = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/d... | 331 |
'''simple docstring'''
class lowerCAmelCase__ : # Public class to implement a graph
"""simple docstring"""
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : list[list[bool]] ) -> None:
"""simple... | 331 | 1 |
'''simple docstring'''
import os
import sys
import unittest
lowercase : Dict = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, ... | 42 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Any = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLI... | 42 | 1 |
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
while b:
_UpperCAmelCase , _UpperCAmelCase = b, a % b
return a
def A ( _UpperCAmelCase : int , ... | 370 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"facebook/xmod-base": "https://huggingface.co/facebo... | 290 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_av... | 109 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A: str = {
"configuration_distilbert": [
"DISTILBERT_PRETRAI... | 109 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : List[Any] = {
"facebook/xmod-base": "http... | 365 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCamelCase ( _A : List[Any] , _A : Tuple , _A : Un... | 49 | 0 |
import math
from collections.abc import Iterator
from itertools import takewhile
def __lowerCamelCase ( UpperCAmelCase_ : int ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or numb... | 94 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rando... | 94 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=SCREAMING_SNAKE_CASE_ ):
a_ : Any = ["""note_seq"""]
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase) ->Union[str, Any]:
requ... | 366 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common i... | 303 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.uti... | 1 | '''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condition... | 1 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
lowerCamelCase_ = TypeVar("T")
class _SCREAMING_SNAKE_CASE( Generic[T] ):
def __init__( self ,SCREAMING_SNAKE_CASE__ ) -> None:
... | 239 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 239 | 1 |
'''simple docstring'''
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class A_ ( A__ ):
def __init__( self : Union[s... | 22 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def UpperCAmelCase_ ( ):
'''simple docstring'''
raise RuntimeError('''CUDA out of memory.''' )
class ... | 314 | 0 |
"""simple docstring"""
from PIL import Image
def lowerCamelCase_ (UpperCamelCase__ : Image ):
_UpperCAmelCase : int = image.size
_UpperCAmelCase : List[Any] = 0
_UpperCAmelCase : int = image.load()
for i in range(UpperCame... | 365 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class _UpperCAmelCase :
'''simple docstring'''
a__ ... | 68 | 0 |
'''simple docstring'''
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 UpperCAmelCase_ :
"""simple docstring"""
def __in... | 35 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _a (__magic_name__ , __mag... | 192 | 0 |
'''simple docstring'''
UpperCamelCase__ : int = {str(digit): digit**5 for digit in range(10)}
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a_ ) )
def Upper... | 164 |
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,... | 164 | 1 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""kakaobrain/align-base""": """https://h... | 303 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerca... | 303 | 1 |
'''simple docstring'''
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowercase__ : str = False
class __lowerCAmelCase ( unittest.TestCase ):
... | 365 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Optional[Any] = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
... | 287 | 0 |
def A_ ( snake_case : int = 50 ) -> int:
'''simple docstring'''
__UpperCamelCase = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
... | 328 |
def A_ ( snake_case : str ) -> int:
'''simple docstring'''
assert column_title.isupper()
__UpperCamelCase = 0
__UpperCamelCase = len(snake_case ) - 1
__UpperCamelCase = 0
while index >= 0:
__UpperCamelC... | 328 | 1 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
class __snake_case :
def __init__( self : s... | 159 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase : Optional[Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetrConfig",... | 159 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_te... | 85 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', ... | 8 | 0 |
"""simple docstring"""
def _lowerCamelCase( a ):
__a = len(a )
for i in range(a ):
for j in range(i + 1 , a ):
if numbers[j] < numbers[i]:
__a , __a = numbers[j], numbers[i]
retu... | 368 | """simple docstring"""
SCREAMING_SNAKE_CASE__:Any = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": ""... | 268 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 228 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 228 | 1 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available... | 103 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''facebook/data2vec-text-base''': '''https://hugg... | 103 | 1 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def A ( _SCREAMING_SNAKE_CASE ) -> float:
return np.dot(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
class UpperCamelCase__ :
'''simple docstring'''
... | 48 |
def A ( _SCREAMING_SNAKE_CASE ) -> list:
if n_term == "":
return []
lowerCamelCase : list = []
for temp in range(int(_SCREAMING_SNAKE_CASE ) ):
series.append(f'''1/{temp + 1}''' if series else "1" )
return s... | 48 | 1 |
def lowerCAmelCase__ ( a__: Optional[Any] , a__: Optional[int] ) -> str:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
_UpperCAmelCase = str(bin(a__ ) )
binary_number... | 360 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from .... | 185 | 0 |
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoToken... | 311 |
'''simple docstring'''
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 )... | 311 | 1 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
# TODO Update this
_A = {
'facebook/esm-1b': 'https://huggingface.co/faceb... | 371 |
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_A = re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
cl... | 205 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tenso... | 237 |
'''simple docstring'''
from __future__ import annotations
class UpperCAmelCase :
def __init__( self :Optional[int] , lowercase_ :int )-> None:
A__ = order
# a_{0} ... a_{k}
A__ = [1.0] + [0.0] * order
# b_{0} ... b_{k}
A__ = [... | 237 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
"""microsoft/markuplm... | 359 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 269 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class a__ :
def __init__( self , _a ):
lowercase : Any = data
lowercase : Node | None = None
class... | 202 |
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
from transformers.utils import logging
logging.... | 103 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
lowercase_ = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
lowercase_ = requests.ge... | 224 |
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_extraction_utils import FeatureEx... | 224 | 1 |
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMM... | 97 |
'''simple docstring'''
def a ( __a , __a ) -> int:
'''simple docstring'''
if len(__a ) != len(__a ):
raise ValueError('''String lengths must match!''' )
UpperCamelCase__ :Union[str, Any] = 0
for chara, chara in zip(__a , __a ):
... | 97 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = 0
UpperCAmelCase = len(A )
for i in range(n - 1 ):
for j in range(i + 1 , A ):
... | 91 |
'''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
fr... | 91 | 1 |
import math
class __snake_case :
'''simple docstring'''
def __init__( self : Any , A : List[str]=0 ): # a graph with Node 0,1,...,N-1
__snake_case: Any = n
__snake_case: int = [
[math.inf for j in r... | 111 |
from statistics import mean
import numpy as np
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> list:
__snake_case: List[Any] = 0
# Number of processes finished
__snake_case: Unio... | 111 | 1 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ):
"""simple docstring"""
lowercase : Any = ''
lowercase ... | 171 |
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,
PILImageResampli... | 171 | 1 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers i... | 45 |
'''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 transformers import AutoProcessor, BlipaProc... | 166 | 0 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
__A : Any = logging.getLogger(__name__)
class _SCREAMING_SNAKE_CASE :
def __init__( self )-> Union[str, Any]:
... | 369 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__A : List[str] = logging.getLogger(__name__)
def __UpperCamelCase ( ) ->int:
"""simple docstring"""
lowerCamelCase_ =argparse.ArgumentPa... | 49 | 0 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = "cpu" , SCREAMING_SNAKE_CASE = None ) -> None:
'''simple docstring'''
__UpperCAmelCase = torch.load(SCREAMING_SNA... | 333 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ : Tuple = logging.get_logger(__name__)
class A_ ( _a ):
'''simple docstring'''
... | 333 | 1 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE = 1000 ):
_snake_case, _snake_case = 1, 1
_snake_case = 2
while True:
_snake_case = 0
_snake_case = fa + fa
_snake_case, _snake_case ... | 364 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
__lowerCAmelCase = logging.getLogger(__name__)
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCAmelCase_ = "masked_bert"
def __i... | 270 | 0 |
"""simple docstring"""
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
M... | 77 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""MvpToken... | 82 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : bytes ) -> str:
'''simple docstring'''
return "".join([hex(_UpperCamelCase )[2:].zfill(2 ).upper() for byte in list(_UpperCamelCase )] )
def SCREAMING_SNAKE_CASE__( ... | 362 |
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _co... | 31 | 0 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 146 | '''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class _lowercase :
'''simple docstring'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE... | 229 | 0 |
def lowerCAmelCase_ ( __a , __a ) -> List[str]:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__a , int(b / 2 ) ) * actual_power(__a , int(b / 2 ) )
else:
return a * actual_power(__a , int(b / 2 ) ) * actu... | 273 |
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_envi... | 273 | 1 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if... | 91 |
"""simple docstring"""
import random
from typing import Any
def _A (__a ) -> list[Any]:
"""simple docstring"""
for _ in range(len(__a ) ):
SCREAMING_SNAKE_CASE_ : Optional[int] = random.randint(0 , len(__a ) - 1 )
... | 91 | 1 |
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
lowerCAmelCase__ = 2048
lowerCAmelCase__ = 4096
lowerCAmelCase__ = 42
lowerCAmelCase__ = os.environ.pop('''PROCESS_TRAIN''', '''false''')
lowerCAmelCase__ = {'''null''': 0, '''... | 175 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = [randint(-10_00, 10_00 ) fo... | 175 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import F... | 119 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,... | 128 | 0 |
'''simple docstring'''
import math
import flax.linen as nn
import jax.numpy as jnp
def _lowerCamelCase ( lowercase : jnp.ndarray , lowercase : int , lowercase : float = 1 , lowercase : float = 1 , lowercase : float = 1.0... | 346 |
'''simple docstring'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
lowerCAmelCase_ : Union[str, Any] = None
try:
import msvcrt
except ImportError:
lowerCAmelCase_ : Tuple = None
try:
import fcntl
e... | 346 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.