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 |
|---|---|---|---|---|
import argparse
lowerCamelCase__ : int = """docs/source/_static/js/custom.js"""
def UpperCamelCase ( lowercase_ ) -> Dict:
'''simple docstring'''
with open(lowercase_ , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase__ : Optional[int] ... | 12 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import... | 12 | 1 |
import math
class UpperCamelCase__ :
def __init__(self : Union[str, Any] , snake_case_ : Any=0 ): # a graph with Node 0,1,...,N-1
__a : Optional[Any] = n
__a : Tuple = [
[math.inf for j in range(0 , _UpperCam... | 715 |
from manim import *
class UpperCamelCase__ ( __lowercase ):
def lowerCAmelCase (self : Any ):
__a : Dict = Rectangle(height=0.5 , width=0.5 )
__a : Optional[Any] = Rectangle(height=0.46 , width=0.46 ).set_st... | 326 | 0 |
def a_ ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str] ):
'''simple docstring'''
... | 464 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A ( UpperCamelCase_ ):
UpperCamelCase__ : List[str] =(PNDMScheduler,)
UpperCamelCase__ : Dict =(('num_inference_steps', 50),)
def lowerCamelCase ... | 464 | 1 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments | 383 |
import numpy as np
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self :List[str] ):
UpperCamelCase__ :List[str] = (0, 0)
UpperCamelCase__ :Dict = None
UpperCamelCase__ :List[str] = 0
UpperCamelCase__ :... | 383 | 1 |
def lowerCAmelCase__ ( UpperCamelCase_ : int )-> int:
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise TypeError('''only integers accepted as input''' )
else:
A__ = str(abs(UpperCamelCase_ ) )
A__ = ... | 632 |
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 (
AutoTokeni... | 632 | 1 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def _lowerCAmelCase ( __magic_name__ : List[str] ) -> Any:
# getting number of pixels in the image
lowercase , lowercase : str =img.shape[0], img.shape[1]
# convert... | 88 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Optional[Any]=None ):
'''simple docstring'''
... | 88 | 1 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def SCREAMING_SNAKE_CASE__ ( ) -> Tuple:
A__ , A__ : Union[str, Any] =9, 1_4 # noqa: F841
A__ : List[Any] =[
[0, 1, 4],
[0, 7, 8],
... | 416 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ... | 416 | 1 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
fr... | 116 |
import os
from datetime import datetime as dt
from github import Github
SCREAMING_SNAKE_CASE_ = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def __lowercase ( ) -> Optional[int]:
'''simple docstring'''
SCREAMING_S... | 116 | 1 |
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 (_lowerCAmelCase , ... | 81 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def SCREAMING_SNAKE_CASE ( ) -> str:
UpperCamelCase__ : Tuple = 9
UpperCamelCase__ : Optional[int] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
... | 228 | 0 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class UpperCamelCase ( nn.Module ):
__UpperCamelCase =42
__UpperCamelCase =jnp.floataa
def UpperCamelCase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = nn.Conv(
... | 673 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecat... | 673 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowerCAmelCase_ = TypeVar('''T''')
class __lowerCAmelCase ( Generic[T] ):
def __init__(self , __magic_name__ , __magic_name__ ) -> None:
''... | 60 |
import string
import numpy
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
return b if a == 0 else greatest_common_divisor(b % a , UpperCAmelCase_ )
class lowercase_ :
__lowerCamelCase = str... | 443 | 0 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : str ) ->... | 680 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __UpperCAmelCase ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
__snake_case = math.sqrt... | 680 | 1 |
'''simple docstring'''
import numpy as np
class lowerCAmelCase__ :
def __init__( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ : Union[str, Any] = (0, 0)
lowerCamelCase_ : Optional[Any] = None
lowerCamelC... | 501 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : List[str] = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/resol... | 501 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta im... | 717 |
'''simple docstring'''
from itertools import permutations
def _UpperCamelCase ( UpperCamelCase__ ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
UpperCAmelCase__... | 113 | 0 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def lowerCAmelCase__ ( a__: List[Any] , a__: List[Any] ) -> Union[str, Any]:
'''simple docstring'''
... | 618 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availabl... | 618 | 1 |
'''simple docstring'''
import itertools
import string
from collections.abc import Generator, Iterable
def _A ( A ,A ) -> Generator[tuple[str, ...], None, None]:
lowercase : List[Any] = iter(__SCREAMING_SNAKE_CASE )
while True:
lowercase : str = tupl... | 701 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.w... | 425 | 0 |
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
UpperCamelCase__ : int = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
UpperCamelCase__ : Optional[Any] = 0
... | 285 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate... | 285 | 1 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
UpperCAmelCase_ : Optional[Any] = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear"... | 710 |
import requests
def UpperCamelCase ( _A : str , _A : str )-> None:
"""simple docstring"""
A__ = {"Content-Type": "application/json"}
A__ = requests.post(_A , json={"text": message_body} , headers=_A )
if re... | 232 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ :str = logging.get_logger(__name__)
a_ :Tuple = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class lowercase ( _UpperCAmelCase ):
lowerCamelCase : Tuple = '''ct... | 35 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class snake_case ( pl.LightningModule ):
"""simple docstring"""
def __init... | 675 | 0 |
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.mod... | 714 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_con... | 319 | 0 |
import fire
from utils import calculate_rouge, save_json
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_, snake_case_=None, **snake_case_ ) -> Union[str, Any]:
A__ : Optional[Any] =[x.strip() for x in open(snake_case_ ).readlines()]
A__ :... | 416 |
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... | 416 | 1 |
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def lowercase (snake_case__ : Union[str, Any] ) -> Dict:
'''simple docstring'''
lowerCAmelCase = min(snake_case__ ) # min() finds the minimum value
lowerCAmelCase = max(snake_case__ ) # max()... | 529 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from a... | 529 | 1 |
"""simple docstring"""
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
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = ... | 535 |
"""simple docstring"""
UpperCAmelCase = 8.3_144_598
def __magic_name__ ( _lowerCamelCase: float, _lowerCamelCase: float ) -> float:
'''simple docstring'''
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
raise Exce... | 535 | 1 |
"""simple docstring"""
from collections import defaultdict
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : str ,_lowerCamelCase : str ) -> bool:
_lowerCAmelCase : List[Any] = first_str.lower().strip()
_lowerCAmelCase : Union[str, Any] = second_str.lower().strip()
#... | 663 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 1000000 ) -> int:
_lowerCAmelCase : List[str] = [i - 1 for i in range(limit + 1 )]
for i in range(2 ,limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i ,limit + 1 ,_lowerCamelCase ):... | 663 | 1 |
import itertools
import string
from collections.abc import Generator, Iterable
def _lowercase ( __UpperCamelCase : Optional[Any] , __UpperCamelCase : List[Any] ):
snake_case__ = iter(UpperCAmelCase_ )
while True:
snake_case__ = tuple(itertools... | 214 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transf... | 554 | 0 |
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = len(SCREAMING_SNAKE_CASE )
A_ = len(SCREAMING_SNAKE_CASE )
A_ = (
first_str_length if first_str_length > s... | 705 |
from __future__ import annotations
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE ) == 0:
return []
A_ ,A_ = min(SCREAMING_SNAKE_CASE ), max(SCREAMING_SNAKE_CASE )
A_ ... | 563 | 0 |
"""simple docstring"""
from manim import *
class A__ ( __lowercase):
"""simple docstring"""
def a__ ( self: Optional[Any] )-> int:
lowerCamelCase : List[Any] = Rectangle(height=0.5 , width=0.5 )
lowerCamelCase ... | 222 |
"""simple docstring"""
from __future__ import annotations
def snake_case ( UpperCamelCase__ : list[int] , UpperCamelCase__ : int ) -> list[list[int]]:
lowerCamelCase : list[list[int]] = []
lowerCamelCase : list[int] = []
... | 222 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def __a ( A__ = 100_0000 , A__ = 10 ) -> int:
lowerCAmelCase = defaultdict(A__ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_w... | 708 |
'''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... | 159 | 0 |
"""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 (
SegformerConfig,
SegformerForImageClassification,
SegformerForSeman... | 265 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar('''KEY''')
lowerCAmelCase__ = TypeVar('''VAL''')
@dataclass(frozen=lowerCamelCase__ , slots=lowerCamelCase... | 41 | 0 |
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCamelCase_ , int(b / 2 ) ) * actual_power(lowerCamelCase_ , int(b / 2 ) )
else:
return... | 423 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | 423 | 1 |
from __future__ import annotations
def A ( _SCREAMING_SNAKE_CASE ) -> None:
create_state_space_tree(_SCREAMING_SNAKE_CASE ,[] ,0 ,[0 for i in range(len(_SCREAMING_SNAKE_CASE ) )] )
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREA... | 311 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Optional[Any] = (KDPM... | 311 | 1 |
'''simple docstring'''
def __lowerCAmelCase ( a_ = 10 , a_ = 22 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = range(1 , a_ )
SCREAMING_SNAKE_CASE : List[Any] = range(1 ... | 715 | '''simple docstring'''
def __lowerCAmelCase ( a_ = 1000 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = 2**power
SCREAMING_SNAKE_CASE : Any = str(a_ )
SCREAMING_SNAKE_CASE : int ... | 179 | 0 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __ve... | 491 |
# Copyright 2022 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 appli... | 491 | 1 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCAmelCase__( _UpperCAmelCase ):
'''simple docstring'''
def __init... | 702 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutp... | 381 | 0 |
from itertools import permutations
def snake_case (UpperCAmelCase__ ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
UpperCamelCase_: Optional[int] = [7, 1_1... | 57 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...... | 367 | 0 |
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, to... | 642 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[Any] = {
"google/umt5-small": "https://huggingface.co/google... | 642 | 1 |
def __magic_name__ ( __a : int , __a : int ):
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
UpperCamelCase__ = str(bin(__a ) )
binary_number += "0" * shif... | 513 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
UpperCamelCase__ = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari... | 227 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __magic_name__ ( lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
for param in module.parameters():
UpperCamelCase = False
def __magic_name__ ( ... | 711 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __magic_name__ ( lowercase_ = "isbn/0140328726" ) -> dict:
'''simple docstring'''
UpperCamelCase = olid.strip().strip("/" ) # Remove le... | 414 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regr... | 278 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 67 | 0 |
"""simple docstring"""
import math
def UpperCamelCase ( ) ->None:
"""simple docstring"""
a_ = input("Enter message: " )
a_ = int(input(F'''Enter key [2-{len(UpperCAmelCase ) - 1}]: ''' ) )
a_ = input("Encryption/Decryption [e/d]: " )
if m... | 708 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->list:
"""simple docstring"""
a_ = False
while is_sorted is False: # Until all the indices are traversed keep looping
a_ = True
for i in range(0 , len(UpperCAmelCase ) - 1 , 2 ... | 210 | 0 |
'''simple docstring'''
import os
import sys
import unittest
_a : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402... | 168 | '''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _lowercase ( lowerCamelCase__ ) -> int:
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , ... | 168 | 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__":
_UpperCAmelCase : str = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: "... | 188 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, ... | 188 | 1 |
"""simple docstring"""
from __future__ import annotations
def a__ ( SCREAMING_SNAKE_CASE : list[float] , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
print(f"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(S... | 645 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : int ="Speech2TextFeatureExtractor"
a : int ="Speech... | 645 | 1 |
"""simple docstring"""
import socket
def lowerCAmelCase_( ) -> Dict:
_lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_lowerCamelCase = socket.gethostname()
_lowerCamelCase = 1_23_12
sock.connect((host, port) )
... | 623 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 623 | 1 |
def A ( _lowerCamelCase ):
'''simple docstring'''
return "".join([hex(_lowerCamelCase )[2:].zfill(2 ).upper() for byte in list(_lowerCamelCase )] )
def A ( _lowerCamelCase ):
'''simple docstring'''
if (len(_lowerCame... | 500 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
Data... | 500 | 1 |
import os
from distutils.util import strtobool
def _lowercase ( lowercase__ , lowercase__ ):
'''simple docstring'''
for e in env_keys:
__lowerCAmelCase : Union[str, Any] = int(os.environ.get(lowercase__ , -1 ) )
if val >= 0:
... | 707 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}
... | 583 | 0 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _lowerCAmelCase ( lowerCamelCase__ : Optional[int] ) -> List[str]:
_SCREAMING_SNAKE_CASE : List[str] = o... | 572 | import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__magic_name__ =argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help=''... | 415 | 0 |
"""simple docstring"""
def snake_case (A_ :int = 6_0_0_8_5_1_4_7_5_1_4_3 ):
'''simple docstring'''
try:
a : str = int(A_ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Para... | 118 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def snake_case (A_ :int = 1_0_0_0_0_0_0 , A_ :int = 1_0 ):
'''simple docstring'''
a : defaultdict = defaultdict(A_ )
for outer_width in range(3 , (t_limit // 4) + 2... | 118 | 1 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def __A ( a_ :np.ndarray) -> np.ndarray:
__a , __a , __a : Dict = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_9_8_9 * r + 0.5_8_7... | 52 |
"""simple docstring"""
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_fl... | 139 | 0 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase = get_tests_dir("""fixtures/test_sentencepiece_with_bytefallba... | 591 | import functools
from typing import Any
def lowerCamelCase_ ( UpperCamelCase__ : str, UpperCamelCase__ : list[str] ):
'''simple docstring'''
if not isinstance(UpperCamelCase__, UpperCamelCase__ ) or len(UpperCamelCase__ ) == 0:
raise Valu... | 591 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenc... | 13 |
def _UpperCamelCase ( lowerCAmelCase_ ) ->int:
if n == 1 or not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return 0
elif n == 2:
return 1
else:
UpperCAmelCase = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + s... | 377 | 0 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class _A ( __UpperCAmelCase ):
SCREAMING_SNAKE_CA... | 716 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ =logging.get_logger(__name__)
__magic_name__ ={
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
# See all ViT MAE models at https://h... | 469 | 0 |
'''simple docstring'''
import argparse
import copy
def A_ ( _lowerCAmelCase : str ):
"""simple docstring"""
_lowerCamelCase : Optional[Any] = {}
with open(_lowerCAmelCase ) as f:
for line in f:
if line.split()[0] not i... | 44 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : List[Any] = {
'configuration_mobilebert': [
'MOBILEBERT_PRET... | 44 | 1 |
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,
PILImageResam... | 588 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder impor... | 588 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def UpperCamelCase_( snake_case__: int = 3 ) -> qiskit.result.counts.Counts:
if isinstance(snake_case__ , snake_case__ ):
raise TypeError('number of q... | 146 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( _UpperCamelCase , unittest.... | 146 | 1 |
from __future__ import annotations
import pandas as pd
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ):
UpperCamelCase_ : List[Any] = [0] * no_of_proce... | 703 | import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Any , _SCREA... | 138 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def __lowercase (_lowercase, _lowercase ) -> list:
"""simple docstring"""
if len(_lowercase ) != 2 or len(a[0] ) != 2 or len(_lowercase ) != 2 or len(b[0] ) != 2:
raise Exception("""Matrices are not 2x2"""... | 150 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def __lowercase (_lowercase, _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ) -> np.ndarray:
"""simple docstring"""
# prepare kernel
... | 150 | 1 |
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
SCREAMING_SNAKE_CASE_:Any = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
... | 702 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_:Optional[int] = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}
try:
if not is_tor... | 520 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase ( snake_case__):
""... | 593 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_... | 593 | 1 |
from statistics import mean
import numpy as np
def __lowercase ( __lowerCAmelCase : list , __lowerCAmelCase : list , __lowerCAmelCase : list , __lowerCAmelCase : int ):
a__ = 0
# Number of processes ... | 657 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 657 | 1 |
from __future__ import annotations
a : Optional[int] = list[tuple[int, int]]
a : 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],
[1, 0, 1, 0, 0, 0... | 63 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 | 1 |
'''simple docstring'''
import warnings
from .generation import TFGenerationMixin
class lowerCamelCase__( snake_case_ ):
# warning at import time
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be removed in Transformer... | 339 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
snake_case : str = list[list[float | int]]
def lowercase__ ( __UpperCamelCase : Matrix , __UpperCamelCase : Matrix ):
'''simple docstring'''
__lowerc... | 339 | 1 |
def __lowerCamelCase ( _lowerCAmelCase ) -> list:
_UpperCAmelCase = len(_lowerCAmelCase )
for i in range(1 , _lowerCAmelCase ):
_UpperCAmelCase = collection[i]
_UpperCAmelCase = 0
_UpperCAmelCase = i - 1
while l... | 684 |
import requests
__lowerCAmelCase = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def __lowerCamelCase ( _lowerCAmelCase ) -> None:
# fetching a list of articles in json format
_UpperCAmelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()... | 684 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : List[str] = logging.get_logger(__name__)
snake_case : Union[str, Any] = {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/config.json''',
'''google/fnet-l... | 657 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 657 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
snake_case__ : List[Any] = {
"""configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 402 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"... | 80 | 0 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
... | 714 |
lowerCamelCase__ = [
"DownloadConfig",
"DownloadManager",
"DownloadMode",
"StreamingDownloadManager",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 202 | 0 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def _lowercase ( a__ : np.ndarray ) -> np.ndarray:
"""simple docstring"""
return input_array.reshape((input_array.size, 1) )
de... | 147 |
from __future__ import annotations
__lowerCAmelCase = []
def _lowercase ( a__ : list[list[int]] , a__ : int , a__ : int ) -> bool:
"""simple docstring"""
for i in range(len(a__ ) ):
if board[row][i] == 1:
return False
for i ... | 147 | 1 |
"""simple docstring"""
import numpy as np
def snake_case (A_ :np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def snake_case (A_ :np.array ):
'''simple docstring'''
return vector * sigmoid(1.702 * vector )
if __name__ == "__ma... | 715 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from ut... | 118 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils impo... | 49 |
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_model_forward
f... | 226 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig... | 708 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
Musi... | 404 | 0 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import... | 453 |
"""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,
)
if is_sentencepiece_available():
from ..ta.toke... | 453 | 1 |
class a :
def __init__( self , _lowerCAmelCase = "" , _lowerCAmelCase = False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE: dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
__SCREAMING... | 707 |
from manim import *
class a ( __lowercase ):
def snake_case_ ( self ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE: str = Rectangle(height=0.5 , width=0.5 )
__SCREAMING_SNAKE_CASE: Any = Rectan... | 146 | 0 |
'''simple docstring'''
import math
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : Union[str, Any] = []
A_ : int = 2
A_ : Tuple = int(math.sqrt(lowerCamelCase__ ) ) # Size of every segment
A_ : List[str] ... | 667 |
'''simple docstring'''
import math
lowerCamelCase :int = 1_0
lowerCamelCase :List[Any] = 7
lowerCamelCase :Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def a ( lowerCamelCase__ = 20 ):
'''simple docstring'''
A_ : ... | 667 | 1 |
"""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 : Any = {... | 100 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase : List[Any] = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperC... | 100 | 1 |
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_t... | 59 |
'''simple docstring'''
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | 541 | 0 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
A... | 592 |
import numpy as np
def lowerCamelCase__ ( _lowerCamelCase ) ->np.ndarray:
return 1 / (1 + np.exp(-vector ))
def lowerCamelCase__ ( _lowerCamelCase ) ->np.ndarray:
return vector * sigmoid(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod... | 592 | 1 |
from math import factorial, pi
def a_ ( __magic_name__ , __magic_name__ = 30 ) -> float:
"""simple docstring"""
if not isinstance(__magic_name__ , (int, float) ):
raise ValueError('''maclaurin_sin() requires either an int or float for thet... | 598 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class a_ ( unittest.... | 598 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase )-> List[str]:
'''simple docstring'''
__UpperCAmelCase = 0
# if input_string is "aba" than new_input_string become "a|b|a"
__UpperCAmelCase = """"""
__UpperCAmelCase = """"""
# append each character + ... | 714 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _lowerCAmelCase ( _lowerCAmelCase = 3 )-> qiskit.result.counts.Counts:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ... | 617 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
... | 115 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , snake_case__ ):
'''simple docstring'''
_lowerCAmel... | 444 | 0 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : List[str] = [int(__lowerCAmelCase ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(__lowerCAmelCase ) == 4 and all(0 <= int(__lowerCAmelCase... | 717 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
... | 12 | 0 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def A__ ( A__ , A__ , A__ ) -> Any:
'''simple docstring'''
_UpperCAmelCase ... | 426 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vi... | 426 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : int = {
"configuration_electra": ["ELECTRA_PRETRAINED_CONFIG... | 710 |
import requests
def UpperCamelCase ( _A : str , _A : str )-> None:
"""simple docstring"""
A__ = {"Content-Type": "application/json"}
A__ = requests.post(_A , json={"text": message_body} , headers=_A )
if re... | 232 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> str:
snake_case__ : List[str] = [0] * len(snake_case__ )
snake_case__ : int = []
snake_case__ : Optional[int] = [1] * len(snake_case__ )
for ... | 374 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailab... | 40 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import ... | 256 |
"""simple docstring"""
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A , __A ) -> Optional[Any]:
lowerCAmelCase_ :Union[str, Any] = name
lowerCAmelCase_ :str = val
def __str__( self ) -> Di... | 256 | 1 |
'''simple docstring'''
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 i... | 173 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 632 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __SCREAMING_SNAKE_CASE ( _lowerCAmelCase ):
@staticmethod
@abstractmethod
def lowercase_ ( __UpperCAmelCase :ArgumentParser ) -> Tuple:
""... | 121 | """simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_si... | 121 | 1 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
A__ = get_tests_dir("""fixtures/spiece.model""")
@req... | 166 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A , _A=False ):
if isinstance(_A , _A ) and isinstance(_A , _A ):
a : Tuple = len(set_a.intersection(_A ) )
if alternative_union:
a : Union[str, Any] = len(_A ... | 526 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderToke... | 209 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
SCREAMING_SNAKE_CASE = {'UserAgent': UserAgent().random}
def a (lowerCAmelCase__ ):
__a = script.contents[0]
__a ... | 209 | 1 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_SCREAMING_SNAKE_CASE = 10
def _snake_case (_snake_case : int , _snake_case : int , ... | 181 |
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,
BertTokeni... | 181 | 1 |
'''simple docstring'''
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_... | 513 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case ={
"""configuration_efficientformer""": [
"""EFFICIENT... | 513 | 1 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__ ) -> Optional[Any]:
# In... | 382 | import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
... | 382 | 1 |
'''simple docstring'''
class a__ :
def __init__( self : Optional[Any] , lowerCamelCase_ : int , lowerCamelCase_ : List[str] , lowerCamelCase_ : Union[str, Any] ):
a_ : int = name
a_ ... | 720 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowerCamelCase = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''K... | 478 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : str = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
... | 461 |
def UpperCamelCase ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_lowercase : List[str] = [int(_UpperCAmelCase ) for i in ip_va_address.split("." ) if i.isdigit()]
return len(_UpperCAmelCase ) == 4 and all(0 <= int(_UpperCAmelCase ) <= 254 ... | 461 | 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,
DDIMScheduler,
EulerAncestralDiscreteSchedule... | 122 |
"""simple docstring"""
from math import sqrt
def __UpperCAmelCase ( __lowerCamelCase = 1_00_00_00 ) -> int:
lowercase__ : int = 0
lowercase__ : int = 0
lowercase__ : int
while num_cuboids <= limit:
max_cuboid_size += 1
... | 122 | 1 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils impo... | 72 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_snake_case ... | 340 | 0 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __UpperCamelCase ( _A : Dict , _A : Tuple=False ) ->List[str]:
"""simple docstring"""
lowerCamelCase_ =OmegaConf.load(_A )
... | 75 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Optional[Any] = {
'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class _SCRE... | 75 | 1 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
... | 28 |
"""simple docstring"""
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_avai... | 682 | 0 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
UpperCamelCase_ : List[Any] = logging.get_logger(__name__)
UpperCamelCase_ : ... | 702 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tra... | 482 | 0 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
snake_case__ : List[Any]... | 23 |
"""simple docstring"""
def snake_case__ ( _lowerCamelCase ) ->str:
"""simple docstring"""
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7... | 575 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.mo... | 715 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a: Optional[Any] = logging.get_logger(__name__)
__a: str = {
'''google/bit-50''': '''https://... | 402 | 0 |
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