code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import math
def lowerCAmelCase__ ( lowerCamelCase_ : str ,lowerCamelCase_ : str):
'''simple docstring'''
lowerCAmelCase__ : int = len(_lowerCAmelCase)
lowerCAmelCase__ : List[str] = int(math.floor(math.sqrt(_lowerCAmelCase)))
low... | 129 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
SCREAMING_SNAKE_CASE_ = logging.get_logge... | 301 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root o... | 20 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'''configuration_roberta''': ['''... | 301 | 0 |
'''simple docstring'''
import re
def _UpperCamelCase ( __A ) -> Optional[Any]:
'''simple docstring'''
if len(re.findall("[ATCG]" , _lowerCAmelCase ) ) != len(_lowerCAmelCase ):
raise ValueError("Invalid Strand" )
return dna.tran... | 80 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
SCREAMING_SNAKE_CASE_ = pytest.mark.integration
@pytest.mark.para... | 301 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_ava... | 309 |
"""simple docstring"""
def lowercase (_lowerCAmelCase = 100_0000 ):
__lowerCAmelCase = 1
__lowerCAmelCase = 1
__lowerCAmelCase = {1: 1}
for inputa in range(2 , _lowerCAmelCase ):
__lowerCAmelCase = 0
__lowerCA... | 301 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from trans... | 258 |
"""simple docstring"""
import sys
import turtle
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ):
my_pen.up()... | 301 | 0 |
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(i... | 307 |
"""simple docstring"""
def lowercase (_lowerCAmelCase ):
__lowerCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__lowerCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , _lowe... | 301 | 0 |
import argparse
from collections import defaultdict
def lowerCamelCase__ ( A__ : Any , A__ : List[Any] , A__ : int , A__ : Dict , A__ : Union[str, Any] ):
'''simple docstring'''
__lowerCamelCase = f'{file... | 12 |
"""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_dense_index,
)
imp... | 301 | 0 |
'''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 Threade... | 31 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calcula... | 301 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a__ ( A__ ):
lowerCamelCase : Optional[Any] =["image_processor", "tokenizer"]
lowerCamelCase : Optional[Any] ="ViTImageProc... | 67 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImagePr... | 301 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CO... | 268 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CA... | 301 | 0 |
'''simple docstring'''
from math import pi, sqrt
def __UpperCAmelCase ( a_: str ):
if num <= 0:
raise ValueError("math domain error" )
if num > 1_71.5:
raise OverflowError("math range error" )
elif num - int(_lowerCAmelCase ) not in (0, 0.5):
ra... | 145 |
"""simple docstring"""
from __future__ import annotations
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = []
create_all_state(1 , _lowerCAmelCase , _lowerCAmelCase , [] , _lowerCAmelCase )
return result
def lower... | 301 | 0 |
import math
def lowerCAmelCase__ ( lowerCamelCase_ : Union[str, Any]):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are ... | 129 |
"""simple docstring"""
import os
from pathlib import Path
def lowercase ():
from torch.utils.cpp_extension import load
__lowerCAmelCase = Path(_lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
__lowerCAmelCase = [
... | 301 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTes... | 20 |
"""simple docstring"""
from __future__ import annotations
from statistics import mean
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = [0] * no_of_processes
__lowerCAmelCase = [0] * no_of_processes
# Initializ... | 301 | 0 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
a__ : Tuple = logging.get_logger(__name__)
a__ : Union[str, Any] = {name: getattr(... | 80 |
"""simple docstring"""
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
SCREAMING_SNAKE_CASE_ = get_... | 301 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : Tuple , _lowerCamelCase : Optional[Any] ) -> int:
_lowerCAmelCase : int = """"""
for i in table:
res += inp[i - 1]
return res
def _UpperCAmelCase ( _lowerCamelCase : List[str] ) ->... | 309 |
"""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 te... | 301 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __a ( ) ->Tuple:
"""simple docstring"""
A = {
"""repo_name""": ["""test_repo1""", """test_re... | 258 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import Re... | 301 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : List[str] = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 307 |
"""simple docstring"""
import math
def lowercase (_lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 301 | 0 |
from math import isqrt, loga
def lowerCamelCase__ ( A__ : Union[str, Any] ):
'''simple docstring'''
__lowerCamelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j i... | 12 |
"""simple docstring"""
import os
from distutils.util import strtobool
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
for e in env_keys:
__lowerCAmelCase = int(os.environ.get(_lowerCAmelCase , -1 ) )
if val >= 0:
return val
... | 301 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda,... | 31 |
"""simple docstring"""
def lowercase (_lowerCAmelCase = 100_0000 ):
__lowerCAmelCase = [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 ):
... | 301 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoC... | 67 |
"""simple docstring"""
from math import isqrt, loga
def lowercase (_lowerCAmelCase ):
__lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , _lowerCAmelCase , ... | 301 | 0 |
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def snake_case ( A__ ):
return getitem, k
def snake_case ( A__ ,A__ ):
return setitem, k, v
def snake_case ( A__ ):
... | 268 |
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...t... | 301 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: str, a_: List[Any] ):
_UpperCAmelCase : Union[str, Any] = []
create_all_state(1, _lowerCAmelCase, _lowerCAmelCase, [], _lowerCAmelCase )
return result... | 145 |
"""simple docstring"""
from math import pi, sqrt
def lowercase (_lowerCAmelCase ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(_lowerCAmelCase ) not in (0, 0.5):
... | 301 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase__ ( ... | 129 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
SCREAMING_SNAKE_CASE_ = logging.get_logge... | 301 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
def get_matched_characters(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> str:
lowercase : Tuple = []
lowercase : Tuple = ... | 20 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'''configuration_roberta''': ['''... | 301 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModel... | 80 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
SCREAMING_SNAKE_CASE_ = pytest.mark.integration
@pytest.mark.para... | 301 | 0 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class a_ (A__ ):
def __UpperCamelCase ( self , snake_case_ ):
with open(snake_case_ , encoding="""utf-8""" ) as inpu... | 309 |
"""simple docstring"""
def lowercase (_lowerCAmelCase = 100_0000 ):
__lowerCAmelCase = 1
__lowerCAmelCase = 1
__lowerCAmelCase = {1: 1}
for inputa in range(2 , _lowerCAmelCase ):
__lowerCAmelCase = 0
__lowerCA... | 301 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Optional[int] = logging.get_logg... | 258 |
"""simple docstring"""
import sys
import turtle
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ):
my_pen.up()... | 301 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
__UpperCamelCase : List[str] = logging.g... | 307 |
"""simple docstring"""
def lowercase (_lowerCAmelCase ):
__lowerCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__lowerCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , _lowe... | 301 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
}
... | 12 |
"""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_dense_index,
)
imp... | 301 | 0 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
impo... | 31 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calcula... | 301 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ ) -> List[Any]:
if len(_lowerCAmelCase ) <= 1:
return lst
__lowerCamelCase = 1
while i < len(_lowerCAmelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__lowerCamelCase , _... | 67 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImagePr... | 301 | 0 |
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mo... | 268 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CA... | 301 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_visio... | 145 |
"""simple docstring"""
from __future__ import annotations
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = []
create_all_state(1 , _lowerCAmelCase , _lowerCAmelCase , [] , _lowerCAmelCase )
return result
def lower... | 301 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def lowerCAmelCase__ ( lowerCamelCase_ : List[str] ,lowerCamelCase_ : Union[str, Any] ,lowerCamelCase_ : int ,lowe... | 129 |
"""simple docstring"""
import os
from pathlib import Path
def lowercase ():
from torch.utils.cpp_extension import load
__lowerCAmelCase = Path(_lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
__lowerCAmelCase = [
... | 301 | 0 |
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 : str = logging.get_logger(__name__)
lowercase : Dict = {
... | 20 |
"""simple docstring"""
from __future__ import annotations
from statistics import mean
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = [0] * no_of_processes
__lowerCAmelCase = [0] * no_of_processes
# Initializ... | 301 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerT... | 80 |
"""simple docstring"""
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
SCREAMING_SNAKE_CASE_ = get_... | 301 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class a_ (unittest.TestCase ... | 309 |
"""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 te... | 301 | 0 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase ) ->Tuple:
"""simple docstring"""
if len(_lowerCAmelCase ) == 0:
return array
A , A = min(_lowerCAmelCase ), max(_lowerCAmelCase )
# Compute the variables
A ... | 258 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import Re... | 301 | 0 |
from __future__ import annotations
__UpperCamelCase : Tuple = """Muhammad Umer Farooq"""
__UpperCamelCase : List[Any] = """MIT"""
__UpperCamelCase : Any = """1.0.0"""
__UpperCamelCase : List[Any] = """Muhammad Umer Farooq"""
__UpperCamelCa... | 307 |
"""simple docstring"""
import math
def lowercase (_lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 301 | 0 |
class lowerCamelCase__:
def __init__( self: Union[str, Any] ):
__lowerCamelCase = """"""
__lowerCamelCase = """"""
__lowerCamelCase = []
def lowerCAmelCase__ ( self: int , UpperCamelCase_: int , UpperCamel... | 12 |
"""simple docstring"""
import os
from distutils.util import strtobool
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
for e in env_keys:
__lowerCAmelCase = int(os.environ.get(_lowerCAmelCase , -1 ) )
if val >= 0:
return val
... | 301 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : str = 50 ) -> int:
"""simple docstring"""
_UpperCAmelCase : str = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ... | 31 |
"""simple docstring"""
def lowercase (_lowerCAmelCase = 100_0000 ):
__lowerCAmelCase = [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 ):
... | 301 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ = 10_00 ) -> Union[str, Any]:
__lowerCamelCase = -1
__lowerCamelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
__lowerCamelCase = (n * n - 2... | 67 |
"""simple docstring"""
from math import isqrt, loga
def lowercase (_lowerCAmelCase ):
__lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , _lowerCAmelCase , ... | 301 | 0 |
"""simple docstring"""
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.kan... | 268 |
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...t... | 301 | 0 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn ... | 145 |
"""simple docstring"""
from math import pi, sqrt
def lowercase (_lowerCAmelCase ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(_lowerCAmelCase ) not in (0, 0.5):
... | 301 | 0 |
import os
def lowerCAmelCase__ ( lowerCamelCase_ : Union[str, Any]):
'''simple docstring'''
lowerCAmelCase__ : str = len(grid[0])
lowerCAmelCase__ : Tuple = len(_lowerCAmelCase)
lowerCAmelCase__ : Tuple = 0
low... | 129 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
SCREAMING_SNAKE_CASE_ = logging.get_logge... | 301 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
if len(_lowerCAmelCase ) < 2:
return collection
def circle_sort_util(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> bool:
lowercase : Dict =... | 20 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'''configuration_roberta''': ['''... | 301 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionP... | 80 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
SCREAMING_SNAKE_CASE_ = pytest.mark.integration
@pytest.mark.para... | 301 | 0 |
'''simple docstring'''
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
UpperCamelCase_ = get_tests_dir("""fix... | 309 |
"""simple docstring"""
def lowercase (_lowerCAmelCase = 100_0000 ):
__lowerCAmelCase = 1
__lowerCAmelCase = 1
__lowerCAmelCase = {1: 1}
for inputa in range(2 , _lowerCAmelCase ):
__lowerCAmelCase = 0
__lowerCA... | 301 | 0 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
__lowerCAmelCase = (DDPMParallelScheduler,)
def A (self : Lis... | 258 |
"""simple docstring"""
import sys
import turtle
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ):
my_pen.up()... | 301 | 0 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def a_ ( _A , _A = True , _A = math.inf , _A = -math.inf , _A = math.inf , _A = -math.inf , _A = False , _A = 100 , _A = 0.01... | 307 |
"""simple docstring"""
def lowercase (_lowerCAmelCase ):
__lowerCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__lowerCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , _lowe... | 301 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_co... | 12 |
"""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_dense_index,
)
imp... | 301 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandin... | 31 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calcula... | 301 | 0 |
'''simple docstring'''
from __future__ import annotations
class a__ :
def __init__( self : List[Any] , a : Tuple ):
"""simple docstring"""
__lowerCamelCase = order
# a_{0} ... a_{k}
__lowerCamelCase = [1.0] + [0.0] * order
... | 67 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImagePr... | 301 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common... | 268 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CA... | 301 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAm... | 145 |
"""simple docstring"""
from __future__ import annotations
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = []
create_all_state(1 , _lowerCAmelCase , _lowerCAmelCase , [] , _lowerCAmelCase )
return result
def lower... | 301 | 0 |
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class lowerCamelCase__ ( A__):
'''simple docstring'''
snake_case_ ="""facebook/bart-large-mnli"""
snake_case_ =(
"""This is a tool that classifies an English ... | 129 |
"""simple docstring"""
import os
from pathlib import Path
def lowercase ():
from torch.utils.cpp_extension import load
__lowerCAmelCase = Path(_lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
__lowerCAmelCase = [
... | 301 | 0 |
import tensorflow as tf
from ...tf_utils import shape_list
class __snake_case ( tf.keras.layers.Layer ):
def __init__( self ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case=1 ,snake_case=False ,**snake_case ):
'''simple docstring'''
super().__init... | 20 |
"""simple docstring"""
from __future__ import annotations
from statistics import mean
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = [0] * no_of_processes
__lowerCAmelCase = [0] * no_of_processes
# Initializ... | 301 | 0 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMSched... | 80 |
"""simple docstring"""
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
SCREAMING_SNAKE_CASE_ = get_... | 301 | 0 |
'''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_simp... | 309 |
"""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 te... | 301 | 0 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : Opt... | 258 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import Re... | 301 | 0 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavi... | 307 |
"""simple docstring"""
import math
def lowercase (_lowerCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 301 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ = {'tokenization_byt5': ['ByT5Tokenizer']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _imp... | 12 |
"""simple docstring"""
import os
from distutils.util import strtobool
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
for e in env_keys:
__lowerCAmelCase = int(os.environ.get(_lowerCAmelCase , -1 ) )
if val >= 0:
return val
... | 301 | 0 |
'''simple docstring'''
import math
from collections.abc import Callable
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] ... | 31 |
"""simple docstring"""
def lowercase (_lowerCAmelCase = 100_0000 ):
__lowerCAmelCase = [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 ):
... | 301 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__UpperCAmelCase ={
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"S... | 67 |
"""simple docstring"""
from math import isqrt, loga
def lowercase (_lowerCAmelCase ):
__lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , _lowerCAmelCase , ... | 301 | 0 |
"""simple docstring"""
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class UpperCamelCase_ (A__ ... | 268 |
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...t... | 301 | 0 |
'''simple docstring'''
import os
from math import logaa
def __UpperCAmelCase ( a_: Optional[Any] = "base_exp.txt" ):
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Any = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(_lowerCAmelCase ),... | 145 |
"""simple docstring"""
from math import pi, sqrt
def lowercase (_lowerCAmelCase ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(_lowerCAmelCase ) not in (0, 0.5):
... | 301 | 0 |
class lowerCamelCase__ : # Public class to implement a graph
'''simple docstring'''
def __init__(self ,__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase ) -> None:
"""simple docstring"""
lowerCAmelCase__ : List[str] = row
lowerCAmelCase... | 129 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
SCREAMING_SNAKE_CASE_ = logging.get_logge... | 301 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)... | 20 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'''configuration_roberta''': ['''... | 301 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common im... | 80 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
SCREAMING_SNAKE_CASE_ = pytest.mark.integration
@pytest.mark.para... | 301 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config... | 309 |
"""simple docstring"""
def lowercase (_lowerCAmelCase = 100_0000 ):
__lowerCAmelCase = 1
__lowerCAmelCase = 1
__lowerCAmelCase = {1: 1}
for inputa in range(2 , _lowerCAmelCase ):
__lowerCAmelCase = 0
__lowerCA... | 301 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Dict = {
'configuration_layoutlmv3': [
... | 258 |
"""simple docstring"""
import sys
import turtle
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ):
my_pen.up()... | 301 | 0 |
def a_ ( _A = 200 ) -> List[Any]:
"""simple docstring"""
snake_case__ = [1, 2, 5, 10, 20, 50, 100, 200]
snake_case__ = [0] * (pence + 1)
snake_case__ = 1 # base case: 1 way to make 0 pence
for coin in coin... | 307 |
"""simple docstring"""
def lowercase (_lowerCAmelCase ):
__lowerCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__lowerCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , _lowe... | 301 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( A__ : Union[str, Any] ):
'''simple docstring'''
__lowerCamelCase = len(_lowerCAmelCase )
# We need to create solution object to save path.
__lowerCamelCase = [[0 for _ in range(... | 12 |
"""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_dense_index,
)
imp... | 301 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_UpperCAmelCase : str = 1
_UpperCAmelCase : int ... | 31 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calcula... | 301 | 0 |
'''simple docstring'''
import sys
def __lowerCAmelCase ( UpperCamelCase__ ) -> List[str]:
__lowerCamelCase = len(_lowerCAmelCase )
__lowerCamelCase = [[0 for x in range(_lowerCAmelCase )] for x in range(_lowerCAmelCase )]
__lowerCamelCase = [[0 for x in range(_lowerCAmel... | 67 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImagePr... | 301 | 0 |
"""simple docstring"""
def snake_case ( A__ ):
UpperCAmelCase_ : List[str] = current_set.copy()
for row_index, row in enumerate(_lowerCAmelCase ):
UpperCAmelCase_ : Dict = row[0]
for column_index, column in enumerate(_lowerCAmelCase ):
... | 268 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CA... | 301 | 0 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEF... | 145 |
"""simple docstring"""
from __future__ import annotations
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = []
create_all_state(1 , _lowerCAmelCase , _lowerCAmelCase , [] , _lowerCAmelCase )
return result
def lower... | 301 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=A__)
class lowerCamelCase__ ( A__):
'''simple docstring'''
snake_case_ =field(default="""audio-clas... | 129 |
"""simple docstring"""
import os
from pathlib import Path
def lowercase ():
from torch.utils.cpp_extension import load
__lowerCAmelCase = Path(_lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
__lowerCAmelCase = [
... | 301 | 0 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_toke... | 20 |
"""simple docstring"""
from __future__ import annotations
from statistics import mean
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = [0] * no_of_processes
__lowerCAmelCase = [0] * no_of_processes
# Initializ... | 301 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
... | 302 |
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_channel_dimension_format,
)
fr... | 302 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : int ):
""... | 302 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class SC... | 302 | 1 |
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def lowerCAmelCase__ ( ):
"""simple docstring"""
assert nand_gate(0 , 0 ) == 1
assert... | 302 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
lowerCamelCase__ = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v1""": """https://huggin... | 302 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowerCamelCase__ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
def __init__( self : int , *__lowercase : Dict ... | 302 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase__ = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlipConfig""... | 302 | 1 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
__a = {... | 302 |
class SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : Union[str, Any] ):
'''simple docstring'''
__a = val
__a = None
__a = None
def UpperCamelCase_ ... | 302 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase__ = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_vision_avail... | 302 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impo... | 302 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : List[A... | 302 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerC... | 302 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {}
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] ='llama'
__lowerCamelCase ... | 302 |
import string
import numpy
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , _SCREAMING_SNAKE_CASE )
class SCREAMING_SNAKE_CASE :
__lowerCame... | 302 | 1 |
import math
import sys
import cva
import numpy as np
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
__a = math.sqrt(_SCREAMING_SNAKE_CASE )
__a = 1 / (sigma * math.sqrt(2 * math.... | 302 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/r... | 302 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
lowerCamelCase__ = """\
@inproceedings{popovic-2015-chrf,
title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
author = \"Popovi{\'c}, Maja\",
booktitle = \"Proceedings of t... | 302 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 302 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""microsoft/focalnet-tiny""": """https://huggi... | 302 |
from __future__ import annotations
lowerCamelCase__ = """#"""
class SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] ):
'''simple docstring'''
__a = {}
def UpperCamelCase_ ( self : Optional[Any... | 302 | 1 |
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if length <= 0 or not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError("""Length must be a positive integer.""" )
return [n * (2 * n - 1) for n in range(_SCREAMI... | 302 |
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... | 302 | 1 |
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 lowerCAmelCase__ ( _SCREAMING_SNAKE_CAS... | 302 |
from functools import lru_cache
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
__a = 2
__a = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(_SCREAMIN... | 302 | 1 |
# 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 thi... | 302 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()... | 302 | 1 |
# Copyright 2023 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... | 302 |
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_albert impo... | 302 | 1 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from .... | 302 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerCamelCase : Optional[int] =(IPNDMScheduler,)
__lowerCamelCase : int =(('num_inference_steps', 50),)... | 302 | 1 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_z... | 302 |
from __future__ import annotations
lowerCamelCase__ = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
class... | 302 | 1 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as comput... | 302 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransform... | 302 | 1 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor... | 302 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
... | 302 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_to... | 302 |
# Copyright 2023 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... | 302 | 1 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@re... | 302 |
import random
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
__a , __a , __a = [], [], []
for element in data:
if element < pivot:
less.append(_SCREAMING_SNAKE_CASE ... | 302 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.