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 inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impor... | 167 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Union[str, Any] = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_... | 3 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def a__ ( a = "isbn/0140328726" ) -> dict:
A_ : Optional[Any] = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
if new_olid.count... | 721 | 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 UpperCAmelCase ( self ):
"""simple docs... | 236 | 0 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def _SCREAMING_SNAKE_CASE (A , A , A , A=None ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = (path or []) + [u]
for v in graph[u]:
if visited_edge[u... | 460 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _SCREAMING_SNAKE_CASE (A ) -> Dict:
"""simple docstring"""
lowercase__ = os.path.join(args.tf_model_dir , ''... | 460 | 1 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def a__ ( *_SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Optional[Union[Dict, Any]] = None , _SCREAMING_SNAKE_CASE : ... | 702 |
'''simple docstring'''
from math import factorial
_lowerCamelCase = {str(d): factorial(d) for d in range(10)}
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(_SCREAMING_SNAKE_CASE ... | 323 | 0 |
"""simple docstring"""
from __future__ import annotations
def A_ ( snake_case__ ) -> bool:
_UpperCamelCase :List[str] = str(snake_case_ )
return len(snake_case_ ) == 9 and set(snake_case_ ) == set('''123456789''' )
def A_ ( ) -> int | None:
for... | 355 |
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 | 0 |
from collections import Counter
from timeit import timeit
def __UpperCAmelCase ( a_ = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(' ' , '').lower()).values()) < 2
def __UpperCAmelCase ( a_ = ""):
if len(a_) == 0:
return True
s... | 607 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tes... | 607 | 1 |
'''simple docstring'''
from __future__ import annotations
from cmath import sqrt
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> tuple[complex, complex]:
"""simple docstring"""
if a == 0:
raise ValueError("""C... | 26 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_... | 26 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a = logging.get_logger(__name__)
a = {
"""microsoft/focalnet-tiny""": """https://hug... | 382 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTCo... | 382 | 1 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
l... | 413 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
# See all Donut... | 507 | 0 |
import os
def SCREAMING_SNAKE_CASE ( ) -> List[Any]:
lowerCamelCase__ : int = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
lowerCamelCase__ : Any = os.path.join(_UpperCAmelCase , 'triangle.txt' )
with open(_UpperCAmelCase ) as f:
l... | 702 |
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 | 0 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = BeautifulSoup(requests.get(lowercase , params=lowercase ).content , "html.parser" )
SCRE... | 62 | """simple docstring"""
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.pipeli... | 473 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Toke... | 680 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : str = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],... | 680 | 1 |
"""simple docstring"""
import string
import numpy
def snake_case ( _a: int , _a: int )-> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , _a )
class _a :
a_ : str = string.ascii_uppercase + s... | 510 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 510 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowercase__ ), 'Tatoeba directory ... | 713 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor impor... | 23 | 0 |
'''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
lowercase_ = logging.get_logger(__name__)
lowercase_ = {... | 11 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Th... | 11 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value... | 701 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__A : Dic... | 398 | 0 |
def __a ( lowerCAmelCase_ : int = 10_00 ) -> int:
'''simple docstring'''
UpperCAmelCase_, UpperCAmelCase_= 1, 1
UpperCAmelCase_= 2
while True:
UpperCAmelCase_= 0
UpperCAmelCase_= fa + fa
UpperCAmelCase_, UpperCAmelCase_... | 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 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixi... | 715 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__lowercase = """\
@misc{chen2021eval... | 135 | 0 |
'''simple docstring'''
def lowercase__ ( __lowercase : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
__UpperCamelCase = 0
__UpperCamelCase = len(__lowercase ) - 1
__UpperCamelCase = 0
while index >= 0:
... | 399 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : Tuple = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Opti... | 98 | 0 |
from __future__ import annotations
def a__ ( snake_case , snake_case ):
"""simple docstring"""
# Checks if the entire collection has been sorted
if len(snake_case ) <= 1 or n <= 1:
return
insert_next(snake_case , n - 1 )
rec_insertion_sort(snake_case ... | 718 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things... | 131 | 0 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def a (lowerCAmelCase__ ):
... | 99 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase_ = {
"""configuration_clip""": [
"""CLIP_PR... | 411 | 0 |
from PIL import Image
def __lowerCAmelCase ( UpperCamelCase ) -> Image:
lowerCAmelCase__ : Any = image.size
lowerCAmelCase__ : Dict = 0
lowerCAmelCase__ : Optional[Any] = image.load()
for i in range(UpperCamelCase ):
for j in r... | 704 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 470 | 0 |
def a__ ( lowercase__ = 1_0 , lowercase__ = 2_2 ):
'''simple docstring'''
UpperCAmelCase_ =range(1 , lowercase__ )
UpperCAmelCase_ =range(1 , lowercase__ )
return sum(
1 for power in powers for base in bases if len(... | 54 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase : Any = lo... | 116 | 0 |
"""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,
... | 712 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _snake_case ( ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = HfArgumentParser(lowercase__ )
lower... | 256 | 0 |
from __future__ import annotations
__UpperCamelCase : Optional[Any] = list[list[int]]
# assigning initial values to the grid
__UpperCamelCase : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
... | 80 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowercase ( SCREAMING_SNAKE_CASE ) -> list[list[float]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since... | 205 | 0 |
"""simple docstring"""
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 a_ ( _UpperC... | 19 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
a : str = True
except (ImportError, ModuleNotFoundError):
a : List[str] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def _UpperCamelCase ( ... | 19 | 1 |
"""simple docstring"""
import requests
__lowerCamelCase = '' # <-- Put your OpenWeatherMap appid here!
__lowerCamelCase = 'https://api.openweathermap.org/data/2.5/'
def a ( __UpperCAmelCase : str = "Chicago" , __UpperCAmelCase : str = AP... | 96 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apach... | 96 | 1 |
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, BlipaProcessor, Bli... | 127 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_: int = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
... | 127 | 1 |
"""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 transformers import Au... | 65 |
"""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_avail... | 65 | 1 |
"""simple docstring"""
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 UpperCamelCa... | 463 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def snake_case ( A__ ):
return np.dot(A__ ,A__ )
class UpperCamelCase_ :
def __init__( self : int , *,
lowerCAmelCase_ : ... | 463 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a__ : Optional[int] = logging.getLogger(__name__)
class UpperCAmelCase_ :
def __init__( self ):
"... | 188 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( _lowercase : list[float] , _lowercase : Tuple ) -> int:
'''simple docstring'''
print(f"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(_lowercase ):
pri... | 266 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_... | 719 |
import math
import unittest
def _lowercase ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < n... | 181 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :Tuple =logging.get_logger(__name__)
__snake_case :str ={
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
'microsoft/markuplm-large... | 106 |
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.transforms.functional impo... | 282 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
_lowerCamelCase : List[str] = ... | 710 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
_lowerCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
... | 361 | 0 |
'''simple docstring'''
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTraini... | 474 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
lowerCamelCase = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def _A ( ):
"""simple docstring"""
__lowercase =os.path.dirname(os.path.realpath(_lowerCAmelCase ... | 474 | 1 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import A... | 664 |
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : Optional[Any] )->Any: # noqa: E741
_lowerCAmelCase = len(_SCREAMING_SNAKE_CASE )
_lowerCAmelCase = 0
_lowerCAmelCase = [0] * n
_lowerCAmelCase = [False] * n
_lowerCAmelCase = [False] * n
def d... | 664 | 1 |
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_available()... | 558 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_avail... | 61 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase__ : Optional[Any] ) ->int:
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""" )
for cell_n in range(1, len(grid[0] ) ):
... | 712 |
"""simple docstring"""
from timeit import timeit
def _lowerCAmelCase ( UpperCAmelCase__ : int ) ->int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
A__ : Optional[int] = 0
while number:
... | 498 | 0 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__ : Optional[int] = 10
def lowerCAmelCase_ ( _lowerCamelCase: list[int] ):
__SCREAMING_SNAKE_CASE : Any = 1
__SCREAMING_SNAKE_CASE : str = max(_lowerCamelCa... | 578 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( _lowerCamelCase: int ):
__SCREAMING_SNAKE_CASE : Dict = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCamelCase )
def lowerCAmelCase_ ( _lowerCame... | 578 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( snake_case_ : float , snake_case_ : float ) -> float:
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(snake_case_ ) * abs(snake_case_ )
if __na... | 220 |
'''simple docstring'''
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 = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Im... | 220 | 1 |
from math import factorial
def SCREAMING_SNAKE_CASE_ ( _snake_case :int = 100 ) -> int:
return sum(int(_snake_case ) for x in str(factorial(_snake_case ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 2 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCAmelCase = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE... | 409 | 0 |
'''simple docstring'''
import string
from math import logaa
def _lowerCAmelCase( UpperCAmelCase_ : str , UpperCAmelCase_ : str ) -> int:
lowerCAmelCase__ = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ).rep... | 211 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=_A ):
'''simple docstring'''
A__ = ['''flax''', '''transformers''']
def __init__( self : List[str] , *__A : int , ... | 211 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 54 |
"""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 __snake_case ( __A : Union[str, Any] , __A : Any , __A : ... | 265 | 0 |
'''simple docstring'''
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
_UpperCAmelCase : Tuple... | 721 |
'''simple docstring'''
from __future__ import annotations
_UpperCAmelCase : str = 10
def UpperCamelCase ( lowercase_ : list[int] ) -> list[int]:
'''simple docstring'''
lowercase =1
lowercase =max(lowercase_ )
while placement <= max_digit:
# declare... | 145 | 0 |
'''simple docstring'''
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 672 |
'''simple docstring'''
import numpy as np
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return vector * sigmoid(1.702 *... | 672 | 1 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
a__ : Tuple = 0
a__ : List[Any] = [
[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... | 235 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
a__ : int = transforms.Comp... | 235 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( ):
return [
a * b * (1_0_0_0 - a - b)
for a in range(1 , 9_9_9 )
for b in range(__snake_case , 9_9_9 )
if (a * a + b * b == (1_0_0_0 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'''{so... | 107 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Any:
lowercase__ = [0] * len(_SCREAMING_SNAKE_CASE )
lowercase__ = []
lowercase__ = [1] * len(_SCREAMING_SNAKE_CASE )
for values in graph.values():
for i in values:
... | 235 | 0 |
'''simple docstring'''
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,
get_resize_output_image_size,
normalize,
rescale,
resiz... | 719 |
'''simple docstring'''
from statistics import mean, stdev
def snake_case_ ( a__ : list ,a__ : int = 3 ):
"""simple docstring"""
__lowercase = min(a__ )
__lowercase = max(a__ )
# normalize data
return [round((x - x_... | 163 | 0 |
def lowerCAmelCase_ ( _lowercase : list , _lowercase : int , _lowercase : int = 0 , _lowercase : int = 0) -> int:
"""simple docstring"""
a__ : str = right or len(_lowercase) - 1
if left > right:
return -1
elif list_dat... | 136 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipel... | 136 | 1 |
'''simple docstring'''
import math
def __a ( __lowerCamelCase : Tuple , __lowerCamelCase : List[str] ) -> List[Any]:
'''simple docstring'''
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(__lowerCame... | 461 | '''simple docstring'''
from __future__ import annotations
def __a ( __lowerCamelCase : int | str ) -> bool:
'''simple docstring'''
lowercase_ = str(__lowerCamelCase )
return n == n[::-1]
def __a ( __lowerCamelCase : int = 1_000_000 ) -> Optional[int]:... | 461 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : List[str] = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.jso... | 410 |
__UpperCAmelCase : int = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100_000)]
def lowerCamelCase_ ( UpperCamelCase_ ):
_a : Optional[Any] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
su... | 471 | 0 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import ... | 636 | import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import conv... | 636 | 1 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def UpperCAmelCase__ ( lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Dict , ) -> List[str]:
'''simple... | 359 |
def __lowerCAmelCase ( A , A ):
UpperCAmelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __lowerCAmelCase ( A , A , A ):
UpperCAmelCase_ = 0
while b > 0:
if b & 1:
UpperCAmelCase_ =... | 162 | 0 |
'''simple docstring'''
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
__lowercase = {
"g... | 707 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowercase = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Config... | 305 | 0 |
from copy import deepcopy
class __A:
def __init__( self , _snake_case = None , _snake_case = None ) -> None:
'''simple docstring'''
if arr is None and size is not None:
__a = size
__a = [0] * size
... | 219 |
def __lowerCAmelCase ( a__ , a__ ) -> None:
__a = len(a__ )
print('''The following activities are selected:''' )
# The first activity is always selected
__a = 0
print(a__ , end=''',''' )
# Consider rest of the activitie... | 219 | 1 |
'''simple docstring'''
__lowerCamelCase : str = 8.314_4598
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
... | 708 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tes... | 459 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def lowerCAmelCase__ ( a__: Union[str, Any] ) -> Any:
'''simple docstring'''
_UpperCAmelCase = SwinConf... | 618 |
from sklearn.metrics import matthews_corrcoef
import datasets
a__ : Any = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true a... | 622 | 0 |
'''simple docstring'''
def __snake_case (__UpperCAmelCase , __UpperCAmelCase ):
"""simple docstring"""
_enforce_args(__UpperCAmelCase , __UpperCAmelCase )
if n == 0:
return 0
lowerCamelCase_ : Optional[Any] = float('''-inf''' )
for i in range(1 ... | 418 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]... | 418 | 1 |
import os
def lowerCamelCase__ ( __A :str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(__A ) ,__A ) ) as input_file:
__snake_case = [
[int(__A ) for element in line.split(""... | 268 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transform... | 268 | 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
_UpperCAmelCase : List[Any] =logging.get_logger(__name__... | 702 |
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_xlnet import... | 619 | 0 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def __magic_name__ ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
UpperCam... | 606 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def _UpperCAmelCase ( A , A=7 ):
'''simple docstring'''
UpperCAmelCase__ =None
if token is not None:
UpperCAmelCase__ ... | 625 | 0 |
def __lowerCAmelCase ( __magic_name__ ):
if len(__magic_name__ ) < 2:
return collection
def circle_sort_util(__magic_name__ , __magic_name__ , __magic_name__ ) -> bool:
_lowercase: int = False
if low == high:
return swapped
_lowercase: str = ... | 206 |
_SCREAMING_SNAKE_CASE : dict[tuple[int, int, int], int] = {}
def __lowerCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__ ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
... | 206 | 1 |
def __a ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE : int = [0] * len(__snake_case )
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE : Union[str, Any] = []
SCREAMING_SNAKE_CASE : s... | 352 |
def _A ( __snake_case :bytes ) -> str:
"""simple docstring"""
return "".join([hex(__snake_case )[2:].zfill(2 ).upper() for byte in list(__snake_case )] )
def _A ( __snake_case :str ) -> bytes:
"""simple docstring"""
if (len(__sna... | 693 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbon... | 368 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__magic_name__ : List[str] = (
"""This metric will be removed... | 368 | 1 |
A : Optional[Any] = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstring"""
lowercase__ = 0
while number:
# Increased Speed Slightly by checkin... | 15 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''roberta-base''': '''https://huggingface.co/roberta-b... | 282 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
... | 452 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__lowercase = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ASTC... | 452 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Conditiona... | 328 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not ... | 328 | 1 |
"""simple docstring"""
from ....utils import logging
_A = logging.get_logger(__name__)
class lowerCamelCase (_SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[Any] , _snake_case : Optional[int] , _snake_case : ... | 538 | """simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabe... | 538 | 1 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMix... | 87 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_lowerCamelCase : int = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ , low... | 87 | 1 |
"""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_s... | 48 |
"""simple docstring"""
import random
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : bool = False ):
"""simple docstring"""
snake_case_ : dict = {i: [] for i in range(SCREAMING... | 48 | 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,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 644 | """simple docstring"""
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
return abs(lowerCamelCase__ ) if a == 0 else greatest_common_divisor(b % a , lowerCamelCase__ )
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCa... | 644 | 1 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prep... | 555 |
'''simple docstring'''
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_singl... | 555 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 646 |
# Lint as: python3
import itertools
import os
import re
_lowercase = re.compile(r'''([A-Z]+)([A-Z][a-z])''')
_lowercase = re.compile(r'''([a-z\d])([A-Z])''')
_lowercase = re.compile(r'''(?<!_)_(?!_)''')
_lowercase = re.compile(r'''(_{2,})''')
_lowercase = r'''^\w+(\.\w+)*... | 157 | 0 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __a ( __lowerCamelCase : int ) -> bool:
'''simple docstring'''
lowercase_ = int(number**0.5 )
return number == sq * sq
def __a ( __lowerCamelCase ... | 461 | '''simple docstring'''
from __future__ import annotations
def __a ( __lowerCamelCase : int | str ) -> bool:
'''simple docstring'''
lowercase_ = str(__lowerCamelCase )
return n == n[::-1]
def __a ( __lowerCamelCase : int = 1_000_000 ) -> Optional[int]:... | 461 | 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... | 108 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Tuple = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
... | 623 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_m... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 87 | 0 |
import numpy as np
from transformers import Pipeline
def _lowerCAmelCase ( UpperCamelCase__: Optional[Any] ) -> Optional[int]:
"""simple docstring"""
A = np.max(UpperCamelCase__ , axis=-1 , keepdims=UpperCamelCase__ )
A = np.exp(out... | 641 |
# 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 applic... | 641 | 1 |
import math
def lowercase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] = 100 ) -> int:
_snake_case : Union[str, Any] = sum(i * i for i in range(1 , n + 1 ) )
_snake_case : Union[str, Any] = int(math.pow(sum(range(1 , n + 1 )... | 710 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_ca... | 198 | 0 |
from sklearn.metrics import fa_score
import datasets
SCREAMING_SNAKE_CASE__ : Dict = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
SCREAMING_SNAKE_CASE__ : Tuple ... | 85 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ : int = 2_00_00_00 ) -> int:
_lowercase = [0 for i in range(n + 1 )]
_lowercase = 1
_lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_lis... | 287 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if i... | 720 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = '▁'
_UpperCAme... | 240 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exce... | 76 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
__A = """"""
__A = """"""
__A = """"""
__A = """"""
def __A (_SCREAMING_SNAKE_CASE ) ->None:
"""simple docstring"""
lowerCAmelCase__ :Any ... | 93 | 0 |
def snake_case__ ( ):
A : Any = 0
for i in range(1 , 1001 ):
total += i**i
return str(lowerCamelCase_ )[-10:]
if __name__ == "__main__":
print(solution())
| 423 |
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
return x if y == 0 else greatest_common_divisor(lowerCamelCase_ , x % y )
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
return (x * y) // greatest_common_divisor(lowerCa... | 423 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : Any ={
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
... | 54 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCamelCase__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
... | 225 | 0 |
'''simple docstring'''
from __future__ import annotations
def A (__lowerCamelCase :list[int] ):
if len(__lowerCamelCase ) == 0:
return array
_lowerCAmelCase , _lowerCAmelCase = min(__lowerCamelCase ), max(__lowerCamelCase )
# Compute the variables
... | 716 |
'''simple docstring'''
from __future__ import annotations
def A (__lowerCamelCase :list[int] ):
if len(__lowerCamelCase ) == 0:
return array
_lowerCAmelCase , _lowerCAmelCase = min(__lowerCamelCase ), max(__lowerCamelCase )
# Compute the variables
_lowerCAm... | 162 | 0 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __A :
def __init__( self : Dict ) -> Optional[Any]:
__magic_name__: Dict = {}
def lowe... | 96 |
"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a :str = 637_8137.0
a :Optional[Any] = 635_6752.31_4245
a :List[Any] = 6_378_137
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ,... | 680 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_d... | 716 | """simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase : Dict =logging.get_logge... | 237 | 0 |
'''simple docstring'''
def A_ ( _lowerCamelCase : list ):
if len(__A ) < 2:
return collection
def circle_sort_util(_lowerCamelCase : list , _lowerCamelCase : int , _lowerCamelCase : int ) -> bool:
_lowerCAmelCase = False
... | 309 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class A_ ( ... | 485 | 0 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if (
(cp >= 0x4e00 and cp <= 0x9fff)
or (cp >= 0x3400 and cp... | 719 |
'''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():
... | 79 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_mod... | 94 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : Optional[Any] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kineti... | 602 | 0 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 711 |
import sys
from collections import defaultdict
class snake_case__ :
def __init__( self : List[Any] ):
snake_case__ : Dict = []
def UpperCAmelCase__ ( self : List[str] , _lowerCamelCase : Tuple ):
... | 303 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''facebook/convnextv2-tiny-1k... | 351 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def lowerCAmelCase_ ( *__A ) -> Dict:
'''simple docstring'''
if not isinstance(__A, __A ):
UpperCAmelCase__ = list(_... | 486 | 0 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import loggin... | 243 |
def lowercase_ (A : Optional[int]=2_8_1_2_3 ):
snake_case__ : Any = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * ... | 243 | 1 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def _lowerCamelCase ( UpperCAmelCase_ : Any, UpperCAmelCase_ : Any, UpperCAmelCase_ : List[Any], UpperCAmelCase_ : List[str] ) -> int:
... | 104 | '''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A ( pl.LightningModule ):
def __init__( self : Dict , __a : List[str] ... | 262 | 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 __a ( A_ , unittest.TestCase ):... | 708 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils i... | 97 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : List[str] = logging.get_logger(__name__)
snake_case : List[Any] = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
... | 566 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
snake_case : Optional[Any] = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
snake_case... | 566 | 1 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __lowerCamelCase ( __a :str = "laptop" ) -> DataFrame:
"""simple docstring"""
A__ = F'https://www.amazon.in/lapto... | 247 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate imp... | 247 | 1 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__UpperCAmelCase = [
"good first issue",
"feature request",
"wip",
]
def lowerCAmelCase_ ( ):
'''simple docstring'''
snake_case: Tuple = Github... | 329 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__UpperCAmelCase = TypeVar("KEY")
__UpperCAmelCase = TypeVar("VAL")
@dataclass(frozen=snake_case , slots=snake_case )
class SCREAMING... | 329 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_co... | 255 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ={
"google/umt5-small": "https://huggingfa... | 255 | 1 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __lowerCamelCase ( ... | 61 |
def SCREAMING_SNAKE_CASE__ ( snake_case_ = "The quick brown fox jumps over the lazy dog", ) -> bool:
"""simple docstring"""
a = set()
# Replace all the whitespace in our sentence
a = input_str.replace(''' ''', '''''' )
for alpha in input_str:
if... | 387 | 0 |
def UpperCAmelCase ( lowercase__ : int ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicative_persistence() doe... | 412 |
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_auto impo... | 412 | 1 |
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