code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 1000 ):
lowerCAmelCase = -1
lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
lowerCAmelCase = (n * n - 2 * a * n) // (2 * n ... | 4 |
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
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_... | 205 | 0 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 531 |
from collections import namedtuple
UpperCAmelCase = namedtuple("""from_to""", """from_ to""")
UpperCAmelCase = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1000),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00_454, 264.172),
"""cubicyard""": from_to(0... | 531 | 1 |
import sys
import turtle
def _snake_case (__lowercase , __lowercase):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def _snake_case (__lowercase , __lowercase , __lowercase , __lowercase , ):
my_pen.up()
my_pen.goto(v... | 23 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class a__:
def __init__( self : List[Any] , __snake_case : str ):
if isinstanc... | 526 | 0 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils ... | 110 |
from manim import *
class UpperCamelCase ( snake_case__ ):
"""simple docstring"""
def A( self : Dict ) -> Tuple:
'''simple docstring'''
A = Rectangle(height=0.5 ,width=0.5 )
A = Rectangle(height=0.46 ,width=0.46 ).set_stroke(wi... | 110 | 1 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase__ ) -> bool:
'''simple docstring'''
if num < 0:
return False
a__ = num
a__ = 0
while num > 0:
a__ = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ =... | 232 | """simple docstring"""
__magic_name__ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def _lowerCamelCase ( ) -> None:
'''simple docstring'''
a__ = input('Enter message: ' )
a__ = input('Enter key [alphanumeric]: ' )
a__ = input('Encrypt/Decrypt [e/d]: ' ... | 232 | 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_barthez impor... | 702 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwr... | 590 | 0 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
UpperCAmelCase : List[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class _A( snake_case__ ... | 239 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : int = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
try:
if not is_torch_available():
... | 239 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_sa... | 371 |
_UpperCAmelCase = {
'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': 'ABBB... | 371 | 1 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
__snake_case : Any = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy ... | 293 |
import pytest
import datasets
# Import fixture modules as plugins
__snake_case = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def _A ( _lowercase , _lowercase ) -> Tuple:
"""simple docstring"""
for item in ... | 1 | 0 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase_ = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def ... | 490 |
'''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 IterableDatase... | 490 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class lowerCAmelCase__ ( unittest.TestCase , __magic_name__ ):
'''simple docstring'''
def __UpperCamelCase ( self ):
'''simple d... | 184 |
def __lowerCAmelCase ( A , A , A , A ):
# Return True if there is node that has not iterated.
UpperCAmelCase_ = [False] * len(A )
UpperCAmelCase_ = []
queue.append(A )
UpperCAmelCase_ = True
while queue:
UpperCAmelCase_ ... | 162 | 0 |
'''simple docstring'''
import datasets
_lowerCAmelCase = "\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n ... | 318 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( lowercase : List[str] , lower... | 318 | 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,
)
__lowercase : List[Any] = {
'''configuration_owlvit''': [
... | 36 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCam... | 18 | 0 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPIN... | 459 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : List[str] = logging.get_logger(__name__)
__lowerCamelCase : str = {
... | 459 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
... | 687 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self: int ):
_lowerC... | 687 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : str , __lowerCamelCase : str ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] =get_failure_array(__lowerCamelCase )
# 2) St... | 718 |
'''simple docstring'''
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,
Ada... | 331 | 0 |
from collections import Counter
from timeit import timeit
def _SCREAMING_SNAKE_CASE ( lowercase : str = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(' ' , '' ).lower() ).values() ) < 2
def _SCREAMING_S... | 70 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : str = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_... | 578 | 0 |
import unittest
from typing import Dict, List, Optional, Union
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_i... | 76 |
# 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... | 76 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if i... | 543 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def a__ ( snake_case__ ) -> Dict[str, torch.Tensor]:
lowerCamelCase = []
lowerCamelCase = [... | 543 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
snake_case__ : List[str] = logging.get_logger... | 171 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMING_SNAKE_CASE_ (a__ ):
... | 171 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.gener... | 373 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Option... | 690 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ :Union[str, Any] = logging.get_logger(__name__)
a_ :Dict = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# See all XGLM models at https://huggingfac... | 719 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClas... | 250 | 0 |
'''simple docstring'''
from datetime import datetime
import requests
def UpperCamelCase_ ( A__ : str ):
'''simple docstring'''
lowerCAmelCase_ : str = """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
lowerCA... | 275 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 275 | 1 |
import math
UpperCAmelCase__ : Any =10
UpperCAmelCase__ : Optional[Any] =7
UpperCAmelCase__ : List[str] =BALLS_PER_COLOUR * NUM_COLOURS
def _lowercase ( _UpperCAmelCase = 20 ) -> str:
lowerCamelCase =math.comb(_UpperCAmelCase , ... | 269 |
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> list[str]:
return [sentence[i : i + ngram_size] for i in range(len(_UpperCAmelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 269 | 1 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( a__ : Any ,a__ : Optional[Any] ) ... | 17 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderb... | 278 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
... | 712 |
import os
# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE__ = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def SCREAMING_SNAKE_CASE_ ( ):
'''simple docstring'''
lowercase_ = os.path.dirname(os.path.realpath(__lowerCamelCase ) )
lo... | 601 | 0 |
"""simple docstring"""
import argparse
import json
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_sche... | 96 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a_ ( ) -> Optional[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with ... | 686 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( _snake_case : int ):
_lowercase = [0] * len(_snake_case )
_lowercase = []
_lowercase = []
_lowercase = 0
for values in graph.values():
for i in values:
indegree[i]... | 227 | """simple docstring"""
def __UpperCAmelCase ( _snake_case : list, _snake_case : list, _snake_case : int ):
if len(_snake_case ) != len(_snake_case ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise Value... | 227 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCamelCase = {
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M1... | 96 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transforme... | 96 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _a :
'''simple docstring'''
UpperCamelCase__ = 42
UpperCamelCase__ = None
UpperCamelCase__ = No... | 120 |
"""simple docstring"""
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
UpperCamelCase = {
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie-m-l... | 120 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow,... | 66 |
from PIL import Image
def __magic_name__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Image:
def brightness(SCREAMING_SNAKE_CASE ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError('level m... | 66 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
A =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is the... | 358 |
'''simple docstring'''
from typing import Any
class _a :
def __init__( self : int , lowercase : Any ):
'''simple docstring'''
UpperCAmelCase = data
UpperCAmelCase = None
class _a :
def __init__( se... | 358 | 1 |
from manim import *
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def _a ( self ):
lowerCAmelCase_: List[str] = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_: List[str] = Rectangle(height=0.4_6 , ... | 613 | import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a : List[str] = False
class _lowercase ( unittest.TestCase ):
... | 613 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_A : int =logging.get_logger(__name__)
_A : Tuple ={
'''Intel/dpt-large''': '''https://huggingface.co/Intel... | 631 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 631 | 1 |
__snake_case : int = {
"""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-build... | 540 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 540 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCamelCase = {'configuration_encoder_decoder': ['EncoderDecoderCo... | 363 |
"""simple docstring"""
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_m... | 363 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,... | 31 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availab... | 542 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_... | 310 |
"""simple docstring"""
import os
def A_ ( ):
'''simple docstring'''
with open(os.path.dirname(_lowercase ) + """/grid.txt""" ) as f:
snake_case_ :Optional[int] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowercase ... | 310 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impo... | 434 | """simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->tuple[complex, complex]:
if a == 0:
raise ValueError('''Coefficient \'a\' must not be z... | 434 | 1 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class a :
def __init__( self : Dict ) -> None:
lowerCamelCase_ = [2, 1, 2, -1]
lowerCamelCase_ = [1, 2, 3, 4]
def... | 710 |
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase : int , _lowerCamelCase : int ) -> int:
while a != 0:
lowerCamelCase_ , lowerCamelCase_ = b % a, a
return b
def lowerCamelCase__ ( _lowerCamelCase : int , _lo... | 137 | 0 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_mode... | 550 |
import os
from distutils.util import strtobool
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
for e in env_keys:
snake_case = int(os.environ.get(UpperCamelCase_ ,-1 ) )
if val >= 0... | 550 | 1 |
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 import ConfigTester
from ...test_modelin... | 472 |
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = []
for data in source_data:
for i, el in enumerate(_A ):
if len(_A ) < i + 1:
data_lists.append([] )
data_lists[i]... | 472 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.ut... | 553 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__SCREAMING_SNAKE_CASE = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_tran... | 553 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import loggin... | 711 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from .... | 165 | 0 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_a... | 109 |
'''simple docstring'''
import os
import sys
_SCREAMING_SNAKE_CASE = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequ... | 366 | 0 |
import os
def SCREAMING_SNAKE_CASE( ) -> str:
with open(os.path.dirname(__UpperCamelCase ) + "/p022_names.txt" ) as file:
a__ : Optional[Any] = str(file.readlines()[0] )
a__ : Optional[int] = names.replace("\"" , "" ).split("," )
names... | 207 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basi... | 207 | 1 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class lowercase__ ( snake_case_ ):
'''simple docstring'''
def __init__( self , lowerCamelCase__="" , lowerCamelCase__="train" ):
... | 212 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''', [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''', num_bytes=1337, num_examples=42, ... | 212 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
A_ : Tuple = logging.get_logger(__name__)
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple d... | 710 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''... | 32 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[str] = logging.get_logger(__name__)
__lowercase : Dict = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
... | 422 |
"""simple docstring"""
import random
def _lowerCamelCase ( lowerCamelCase__ : Tuple , lowerCamelCase__ : Dict , lowerCamelCase__ : str ):
lowercase__ : List[Any] = a[left_index]
lowercase__ : List[Any] = left_index + 1
for j in ... | 200 | 0 |
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_pytorch_model
from transformers.... | 416 |
def _A ( _UpperCamelCase , _UpperCamelCase ):
return number | (1 << position)
def _A ( _UpperCamelCase , _UpperCamelCase ):
return number & ~(1 << position)
def _A ( _UpperCamelCase , _UpperCamelCase ):
return number ^ (1 << position)
def _A ( _UpperC... | 416 | 1 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class lowercase__( UpperCAmelCase , uni... | 97 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.tra... | 482 | 0 |
"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers i... | 715 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attenti... | 491 | 0 |
from manim import *
class __snake_case ( SCREAMING_SNAKE_CASE ):
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = Rectangle(height=0.5 ,width=0.5 )
lowerCAmelCase__ = Rectangle(height=0.46 ,width=0.46 ).set_str... | 193 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( snake_case ):
lowerCamelCase_ = (CMStochasticIterativeScheduler,)
lowerCamelCase_ = 1_0
def _UpperCAmelCase ( ... | 256 | 0 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
UpperCAmelCase_ = ... | 718 |
from collections.abc import Iterable
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar('''_T''')
class __SCREAMING_SNAKE_CASE ( Generic[_T] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__ = None ):
"""simple docstring"""
... | 519 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class snake_case_ ( __UpperCamelCase ... | 351 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from... | 351 | 1 |
from __future__ import annotations
def __UpperCamelCase ( A ):
if len(A ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
raise ValueError('''All values must be grea... | 469 | import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__magic_name__ =logging.get_logger(__name__) # pylint: disable=invalid-name
class _A ( __UpperCamelCase ... | 469 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
"convert_funn... | 59 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
... | 59 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://hugg... | 707 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
... | 44 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def UpperCAmelCase__ ( UpperCAmelCase_ : int = 1_50_00_00 ) -> int:
__lowerCamelCase : defaultdict = defaultdict(UpperCAmelCase_ )
__lowerCamelCase : Any ... | 13 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
A__ : Optional[Any] = tuple[int, int]
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ... | 13 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__lowerCAmelCase = logging.get_logger(__name__)
__low... | 709 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase = get_tests_dir('''fixtures/spiece.mo... | 335 | 0 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowerCamelCase_ :
def __init__( self , lowerCamelCase_ ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = data
_UpperCamelCase = [0x6745_2301, 0x... | 147 |
"""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
#
... | 698 | 0 |
'''simple docstring'''
import math
def __UpperCAmelCase ( a_: int ):
_UpperCAmelCase : Union[str, Any] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(a_ )
def __UpperCAmelCase ( a_: float ... | 257 | '''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__a = ... | 257 | 1 |
from collections.abc import Iterable
from typing import Generic, TypeVar
A_ : Union[str, Any] = TypeVar('_T')
class A_ ( Generic[_T] ):
'''simple docstring'''
def __init__(self , lowercase__ = None ) -> Optional[int]:
__UpperCAmelCase ... | 303 |
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 im... | 623 | 0 |
def lowercase ( __A : int ) -> bool:
'''simple docstring'''
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a Perfect number or not...''')
__lowercase :... | 315 |
def lowercase ( __A : Dict ) -> Optional[Any]:
'''simple docstring'''
snake_case : Union[str, Any] = len(__A )
for i in range(length - 1 ):
snake_case : Dict = i
for k in range(i + 1 , __A ):
if collection[k] <... | 315 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
... | 235 |
# 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
#
# Unl... | 235 | 1 |
'''simple docstring'''
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class UpperCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
a... | 714 |
'''simple docstring'''
from __future__ import annotations
def __lowerCAmelCase ( lowerCamelCase : list ):
'''simple docstring'''
if not nums:
raise ValueError("List is empty" )
return sum(lowerCamelCase ) / len(lowerCamelCase )
if __name__ == "__main__":
im... | 39 | 0 |
'''simple docstring'''
import os
from math import logaa
def __magic_name__ ( __UpperCAmelCase = "base_exp.txt" ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for i, line in enumerate(open(os.path.join(os.... | 109 |
'''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/facebook/esm-1b/re... | 109 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Optional[Any] = logging.get_logger(__name__)
_lowercase : Union[str, Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small... | 30 | '''simple docstring'''
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 ... | 30 | 1 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned... | 493 |
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase ( unittest.TestCase ):
def A( self):
__UpperCAmelCase : Optional[Any] = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
__UpperCAmelCase : str = [2, 4, 6, 8, 1_0, 1_2]
__UpperCAmelCase ... | 462 | 0 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMo... | 700 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case__ : int = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0... | 637 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFI... | 480 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
@require_torch
... | 480 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__snake_case : Optional[Any] = logging.get_lo... | 705 |
'''simple docstring'''
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...tes... | 687 | 0 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCamelCase_ = logging.getLogger(__name__)
class a_ ( a_ ):
'''simple docstring'''
... | 318 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output... | 318 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : List[Any] = logging.get_logger(__name__)
a__ : Union[str, Any] = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/confi... | 719 |
def snake_case (UpperCamelCase : int ):
'''simple docstring'''
lowerCamelCase__ = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 235 | 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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/... | 201 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__lowerCAmelCase = 4
__lowerCAmelCase = 3
class lowerCamelCase ( __lowerCa... | 201 | 1 |
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Dict = [int(snake_case__) for i in ip_va_address.split(".") if i.isdigit()]
return len(snake_case__) == 4 and all(0 <= int(snake_case__) <= 2_54 for octet in octets)
if __name__ == "__main__":
_lowercase = input()... | 683 |
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {}
... | 683 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWit... | 128 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version impor... | 128 | 1 |
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/xlnet-large-ca... | 714 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch... | 97 | 0 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def A ( snake_case__ , snake_case__=7 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = None
if token ... | 196 |
"""simple docstring"""
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
A_ : Union[str, Any] = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
... | 196 | 1 |
def _lowerCAmelCase( __A : float , __A : float , __A : int ):
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(lowerC... | 708 |
lowerCAmelCase__ = {
"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": "ABBBA",
... | 1 | 0 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
b... | 483 | '''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.c... | 262 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.uti... | 366 | """simple docstring"""
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class _snake_case :
snake_case__ = None
snake_case__ = False
snake_case__ = False
snake_case__ = False
snake_c... | 366 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( _lowerCamelCase : Optional[int] , _lowerCamelCase : Any ):
# Checks if the entire collection has been sorted
if len(_lowerCamelCase ) <= 1 or n <= 1:
return
insert_next(_lowerCamelCa... | 440 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__:Optional[Any] = {"""configuration_reformer""": ["""REFORMER_PR... | 528 | 0 |
'''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():
from .... | 718 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
... | 270 | 0 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self, *A, **A ):
'''simple doc... | 28 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
UpperCam... | 28 | 1 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: ... | 670 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Optional[int] ):
Up... | 670 | 1 |
"""simple docstring"""
import argparse
import json
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_w... | 58 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def lowercase (*SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[Union[Dict, Any]] = None , ... | 247 | 0 |
import unittest
from knapsack import knapsack as k
class __snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = 0
lowerCAmelCase__ = [0]
lowerCAmelCase__ = [0]
lowerC... | 703 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ = "x" , snake_case__ = 10**-10 , snake_case__ = 1 , ) -> complex:
"""simple docstring"""
lowerCAmelCase... | 604 | 0 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Any:
SCR... | 100 |
# 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
cl... | 455 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
... | 391 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__magic_name__ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|", "|"),
... | 391 | 1 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase( ... | 152 | '''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, D... | 152 | 1 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_... | 718 |
from __future__ import annotations
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) ->dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise ValueError("Resistance c... | 592 | 0 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class _a ( unittest.TestCase ):
def __snake_case (self ) -> List[Any]:
UpperCAmelCase_: Optional[int] = [
"""safety_checker/pyto... | 556 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 556 | 1 |
"""simple docstring"""
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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def SCREAMING_SNAKE_CASE__ ... | 709 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
... | 48 | 0 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''facebook/encodec_24khz''': '''https://... | 426 |
import unittest
from knapsack import greedy_knapsack as kp
class _A ( unittest.TestCase ):
def __a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase : Dict = [10, 20, 30, 40, ... | 217 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( a : int ) ->list[int]:
snake_case = [True] * limit
snake_case = False
snake_case = False
snake_case = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ... | 711 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exc... | 44 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_t... | 6 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_lowerCamelCase = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 6 | 1 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase_ ( lowercase_ ):
SCREAMING_SNAKE_CASE_ : Tuple... | 709 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
UpperCAmelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowercase_ ):
def __init__( self : List[Any] , *UpperCAmelCase_ ... | 416 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( UpperCamelCase__ ):
_lowercase : str = ['''image_processor''', '''tokenizer''']
_lowercase : Any = '''CLIPImagePro... | 43 |
from __future__ import annotations
def lowerCamelCase__ ( __A :list[float] ,__A :Union[str, Any] ):
"""simple docstring"""
print(F'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(__A ):
print(F'{i}\t\t{d}' )
... | 268 | 0 |
import math
import unittest
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> bool:
assert isinstance(__lowerCAmelCase , __lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
... | 713 |
from string import ascii_lowercase, ascii_uppercase
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> str:
if not sentence:
return ""
snake_case__ = dict(zip(__lowerCAmelCase , __lowerCAmelCase ) )
return lower_to_upper.get(sentence[0]... | 208 | 0 |
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