code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
def lowerCAmelCase ( lowerCAmelCase_ )-> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
lowerCAmelCase_ : Tuple = gray_code_s... | 262 |
from __future__ import annotations
import requests
def lowerCAmelCase ( lowerCAmelCase_ )-> dict:
lowerCAmelCase_ : List[Any] = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(lowerCAmelCase_ ).json()
def lo... | 262 | 1 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def SCREAMING_SNAKE_CASE( __lowercase = 3 ) -> qiskit.result.counts.Counts:
if isinstance(__lowercase , __lowercase ... | 334 |
'''simple docstring'''
import requests
UpperCamelCase = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='''
def SCREAMING_SNAKE_CASE( __lowercase ) -> None:
# fetching a list of articles in json format
A: Tuple = requests.get(_NE... | 334 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def _A ( A__ , A__ ):
"""simple docstring"""
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
raise ValueError('''Capacitance ca... | 104 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if principal <= 0:
raise Exception("Principal borrowed must be > 0" )
if rate_per_annum < 0:
... | 108 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
lowerC... | 176 |
def SCREAMING_SNAKE_CASE__ ( ) -> Dict:
snake_case : Optional[int] = []
snake_case : Tuple = 1
while len(lowercase ) < 1E6:
constant.append(str(lowercase ) )
i += 1
snake_case : int = """""".join(lowercase )
... | 176 | 1 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 58 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.se... | 58 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | 364 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TimeSeriesTransformerConfig''... | 22 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvail... | 59 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class UpperCAmelCase ( A_ ):
... | 59 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffuser... | 164 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, rand... | 164 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case__ ( lowerCAmelCase_ = 3 ):
"""simple docstring"""
if isinstance(lowerCAmelCase_, lowerCAmelCase_ ):
raise TypeError('nu... | 334 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils... | 334 | 1 |
"""simple docstring"""
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase_ ... | 367 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = ... | 253 | 0 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__snake_case = logging.get_logger(__name__)
class lowercase__ ( _UpperCAmelCase ):
def __init__( self : int , *UpperCAmelCase_ : Optional[Any] , **Up... | 176 |
import random
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ ) -> tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = [], [], []
for element in data:
if element < pivot:
le... | 176 | 1 |
"""simple docstring"""
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_unor... | 369 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAmel... | 168 | 0 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : str = [
["""attention""",... | 13 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range... | 22 | 0 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentP... | 161 |
"""simple docstring"""
def __lowerCamelCase ( __UpperCamelCase ) -> bool:
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not..... | 161 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__A = logging.get_logger(__name__)
__A ... | 164 |
'''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 _A ( lowercase__ ):
lowercase__ ... | 164 | 1 |
'''simple docstring'''
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
fro... | 355 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Tuple = {"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}
try:
if not is_torch_avai... | 264 | 0 |
'''simple docstring'''
class A :
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
A : List[Any] = name
A ... | 3 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__)
class _A ( __magic_name__):
# `task` is not a ClassVar since we want it to be part of the `asdic... | 253 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def _snake_case ( UpperCAmelCase_ : int ):
if num <= 0:
A__ = F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(UpperCAmelCase_ ... | 69 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE_ : List[str] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n a... | 69 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils i... | 185 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
a_ : str = ... | 168 | 0 |
lowercase_ = {
"""joule""": 1.0,
"""kilojoule""": 1000,
"""megajoule""": 100_0000,
"""gigajoule""": 10_0000_0000,
"""wattsecond""": 1.0,
"""watthour""": 3600,
"""kilowatthour""": 360_0000,
"""newtonmeter""": 1.0,
"""calorie_nutr""": 4186.8,
"""kilocalorie_nutr""": ... | 371 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json""... | 224 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers im... | 161 |
'''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def snake_case ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )-> np.ndarray:
... | 161 | 1 |
"""simple docstring"""
def a__ ( __SCREAMING_SNAKE_CASE ) -> str:
return " ".join(
"".join(word[::-1] ) if len(__SCREAMING_SNAKE_CASE ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long... | 108 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.vers... | 108 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_UpperCamelCase : Dict = (7_2_0, 1_2_8_0) # Height, Width
_UpperCamelCase : Optional[Any] = (0.4, 0.6) # if height or width lower than this scal... | 220 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_pro... | 264 | 0 |
'''simple docstring'''
import random
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : float , __A : bool = False ) -> dict:
_SCREAMING_SNAKE_CASE = {i: [] for i in range(__A )}
# if probability is greater or equal than 1, then generate a complete graph
if probab... | 360 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ =... | 111 | 0 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
fro... | 69 | """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
__UpperCamelCase = logging.get_logger(__name__)
__Up... | 69 | 1 |
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 UpperCAmelCase__ ( A_ ):
UpperCAm... | 350 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_A = {'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}
try:
... | 117 | 0 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str ) -> str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 38 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
lowercase__ : List[str] = loggi... | 224 | 0 |
from __future__ import annotations
from math import pi
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argum... | 356 |
print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))
| 285 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers... | 108 |
"""simple docstring"""
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ = "" , snake_case__ = False ):
"""simple docstring"""
lowerCAmelCase : dict[str, RadixNode] = {}
... | 108 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : List[Any] = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
... | 355 |
"""simple docstring"""
def A ( snake_case :int ) -> int:
__UpperCamelCase = abs(snake_case )
__UpperCamelCase = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def A ( snake_case :int ) -> int:
__UpperCamelCase = abs(snake_case )
... | 263 | 0 |
from statistics import mean
import numpy as np
def __magic_name__ ( __a : Union[str, Any] , __a : Any , __a : Optional[int] , __a : Dict ):
'''simple docstring'''
UpperCamelCase__ = 0
# Number of processes finished
... | 244 |
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... | 111 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
),... | 357 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__:Optional[Any] = {
'configuration_roberta_prelayernorm': [
'ROBE... | 261 |
import os
snake_case__ : Any = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
def _a ( lowerCamelCase: str ) -> int:
'''simple docstring'''
__A = 0
__A = 0
while i... | 117 | 0 |
# 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_cards - useful... | 119 | import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAme... | 119 | 1 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Trai... | 123 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase ( lowercase_ ):
@staticmethod
@abstractmethod
def a ( snake_case ):
raise NotImplementedError()
@abstractmethod
def a ( self ):
... | 285 | 0 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
log... | 353 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_ ):
_A : Tuple = 0
if start < end:
_A : Tuple = randint(snake_case_,snake_case_ )
... | 343 | 0 |
def A (__A : int , __A : Optional[int] ) -> int:
"""simple docstring"""
UpperCAmelCase_ = [0 for i in range(r + 1 )]
# nc0 = 1
UpperCAmelCase_ = 1
for i in range(1 , n + 1 ):
... | 51 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :List[str] = logging.get_logger(__name__)
_lowerCAmelCase :Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiua... | 263 | 0 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils im... | 319 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 app... | 319 | 1 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_lowerCamelCase : Union[str, Any] = logging.get_logger("transformers.models.speecht5")
def ... | 28 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_u... | 34 | 0 |
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configurat... | 271 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a = [8, 5, 9, 7]
a = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, ... | 271 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effic... | 119 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effic... | 119 | 1 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _a ... | 360 |
'''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__UpperCAmelCase... | 240 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ = 100 ):
'''simple docstring'''
A : List[str] = 0
A : List[str] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
ret... | 3 | 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 BackboneTesterMixin
from ...test_config... | 343 | 0 |
"""simple docstring"""
def _A ( lowercase = 10_00 ):
"""simple docstring"""
a =1, 1
a =2
while True:
a =0
a =fa + fa
a =fa, f
index += 1
for _ in str(A__ ):
i += 1
if... | 351 |
"""simple docstring"""
from __future__ import annotations
def _A ( lowercase , lowercase , lowercase , ):
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2... | 215 | 0 |
'''simple docstring'''
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_... | 319 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase=None , **__lowercase ) -> Any:
A: Any = [x.strip() for x in open(__lowercase ... | 319 | 1 |
from __future__ import annotations
def _snake_case( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[str] ) -> int: # noqa: E741
... | 282 |
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 calculate_bleu, calculate_rou... | 282 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
__lowerCAmelCase = [
"""VerificationMode""",
"""Version""",
"""disable_progress_bar""",
"""enable_progress_bar""",
"""is_progress_bar_enabled""",
"""experimental""",
]
from .info_utils import VerificationMode
from .logg... | 271 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__lowerCAmelCase = TypeVar("""T""")
class UpperCAmelCase__ ( Generic[T] ):
"""simple docstring"""
def __init__( self : Tuple ,_a :... | 271 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class _lowercase ( unittest.TestCase ):
pass
@nightly
@require_tor... | 362 | def __lowercase ( lowerCamelCase : int ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise TypeError('only integers accepted as input' )
else:
UpperCamelCase_ : Any = str(abs(lowerCamelCase ) )
UpperCamelCase_ : Any = [list(lowerCamelCase ) ... | 50 | 0 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def a ( ):
'''simple docstring'''
print('''Making key files...''' )
make_key_files('''rsa''' , 1024 )
print('''Key ... | 207 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slo... | 240 | 0 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
lowerCAmelCase_ = """scheduler_config.json"""
class _lowerCAmelCase ( ... | 260 |
def lowerCamelCase_ ( lowerCAmelCase: int )-> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 1 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from d... | 84 |
'''simple docstring'''
from torch import nn
class lowercase ( nn.Module ):
"""simple docstring"""
def __init__( self ,a_ ,a_ ) -> List[Any]:
super().__init__()
_UpperCAmelCase : Dict = class_size
_UpperCAmelCase : Union[str,... | 215 | 0 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
def merge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
... | 60 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 60 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
fro... | 282 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_lowerCamelCase : Dict = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
def __init__( self : Tuple ,... | 282 | 1 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
UpperCamelCase__ : Tuple = 100
UpperCamelCase__ : str = set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCamelCase__ : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if pr... | 330 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncodin... | 330 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ :List[Any] = logging.get_logger(__name__)
a_ :Optional[int] = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.js... | 277 |
# 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 app... | 50 | 0 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
lowercase : List[str] = logging.getLogger()
def UpperCAmelCase_ (_lowerC... | 171 |
def UpperCAmelCase_ (_lowerCAmelCase : list ):
if len(_lowerCAmelCase ) <= 1:
return lst
__UpperCamelCase : Dict = 1
while i < len(_lowerCAmelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__UpperCamelCase , __UpperCamelCa... | 171 | 1 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class _a ( logging.LoggerAdapter):
"""simple docstring"""
@staticmethod
def lowercase__ ( __UpperCamelCase : List[str] )->str:
_UpperCAmelCase = PartialStat... | 260 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transfor... | 260 | 1 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a ( a__ ):
snake_case__ = (D... | 309 |
"""simple docstring"""
import re
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
if len(re.findall('[ATCG]' , _UpperCAmelCase ) ) != len(_UpperCAmelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main_... | 309 | 1 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 60 |
"""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,
)
import trans... | 60 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
create_state_space_tree(UpperCamelCase__ , [] , 0 , [0 for i in range(len(UpperCamelCase__ ) )] )
d... | 324 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_comm... | 324 | 1 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
a_ = 100
a_ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
a_ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(pri... | 330 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 330 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPegasusConfig",
... | 359 |
'''simple docstring'''
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(_... | 83 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExtractor""... | 171 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_A = 10
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lo... | 171 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_a : List[Any] = ... | 363 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tenso... | 46 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils impor... | 316 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE )... | 316 | 1 |
def __lowerCamelCase (UpperCAmelCase__ : str ):
SCREAMING_SNAKE_CASE = len(A__ )
for i in range(length - 1 ):
SCREAMING_SNAKE_CASE = i
for k in range(i + 1 , A__ ):
if collection[k] < collection[least]:
... | 358 | from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
def __low... | 206 | 0 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowercase : list[int | str] ) -> None:
"""simple docstring"""
create_state_space_tree(lowercase, [], 0, [0 for i in range(len(lowercase ) )] )
def a__ ( ... | 324 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention... | 324 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_ )
class a ( lowerCamelCase_ ):
snake_case__ = field(def... | 366 |
"""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]' when... | 309 | 0 |
class lowercase :
def __init__( self ,A__):
lowercase = n
lowercase = [None] * self.n
lowercase = 0 # index of the first element
lowercase = 0
lowercase = 0
def __len__( ... | 101 |
'''simple docstring'''
from math import pi
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ ):
return 2 * pi * radius * (angle / 3_6_0)
if __name__ == "__main__":
print(arc_length(90, 10))
| 83 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase = "The quick brown fox jumps over the lazy dog" , ) -> bool:
a = set()
# Replace all the whitespace in our sentence
a = input_str.replace(" " , "")
for alpha in input_str:
if "a" <= alpha.lower() <= "z"... | 366 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
g... | 180 | 0 |
"""simple docstring"""
import numpy as np
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = int(np.ceil((x_end - xa) / h ) )
__lowerCAmelCase = np.z... | 57 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase )
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = field(def... | 46 | 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... | 329 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 329 | 1 |
'''simple docstring'''
from typing import List
import numpy as np
def _lowerCAmelCase ( _UpperCamelCase : dict ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE ={key: len(_UpperCamelCase ) for key, value in gen_kwargs.items() if isinstance(_UpperC... | 47 |
'''simple docstring'''
class _lowerCAmelCase ( __UpperCAmelCase ):
pass
class _lowerCAmelCase ( __UpperCAmelCase ):
pass
class _lowerCAmelCase :
def __init__(self ):
A_ : List[Any] = [
[],
[],
[],
]
def _a (self ... | 206 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __Up... | 150 |
"""simple docstring"""
from __future__ import annotations
class __UpperCAmelCase:
"""simple docstring"""
def __init__( self , snake_case__ ):
'''simple docstring'''
lowercase__ : List[Any]= data
l... | 150 | 1 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .t... | 38 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : list[float] ) -> Dict:
return np.maximum(0 , _lowerCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 309 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> set[str]:
"""simple docstring"""
A__ , A__ = set(lowercase_ ), [start]
while stack:
A__ = stack.pop()
explored.add(... | 231 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
"""simple docstring"""
create_state_space_tree(lowercase_ , [] , 0 )
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ... | 231 | 1 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_... | 215 | import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class a ( unittest.TestCase ):
"""simple docstring"""
lowerCamelCase :Tuple = JukeboxTokenizer
lowerCamelCase :str = {
'''artist... | 180 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_avai... | 181 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ = {
"""configuration_rember... | 181 | 1 |
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 is_t... | 329 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ :int = logging.get_logger(__name__)
lowerCAmelCase__ :Optional[Any] = {
'''facebook/data2vec-... | 329 | 1 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class snake_case_( unittest.TestCase ):
def lowerCamelCase__ ( self : List[Any] ):
lowerCAmelCase : Optional[int] = 0
lowerCAmelCase : List[str] = [... | 314 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case__ : Tuple = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerCon... | 314 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers impo... | 150 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
t... | 150 | 1 |
import math
import sys
def lowercase_ ( _lowerCamelCase : int):
if number != int(_lowerCamelCase):
raise ValueError("the value of input must be a natural number")
if number < 0:
raise ValueError("the value of input must not be a negative number")
if number == 0:
... | 354 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase = {
'''configuration_mask2former''': [
'''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Mask2FormerConfig''',
],
... | 333 | 0 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase ( __a , unittest.TestCase ):
_lowercase =PhobertTokenizer
_lowercase =... | 231 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ ( __lowerCAmelCase : str = "" ):
"""simple docstring"""
lowerCAmelCase_ = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
lowerCAmelCase_ = BeautifulSo... | 231 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokeniza... | 26 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def _a ( a :Tuple ) -> int:
a = tmp_path / '''file... | 26 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ = {
'''configuration_distilbert'... | 181 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def a__ ( lowerCAmelCase__ ) -> Optional[Any]:
UpperCAmelCase__ : Any = min(lowerCAmelCase__ ) # min() finds the minimum value
UpperCAmelCase__ : Optional[int] = ... | 181 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Optional[Any] = logging.get_logger(__name__)
class UpperCamelCase__( __SCREAMING_SNAKE_CASE ):
__magic_name__ : Union[str, Any] = "timm_backbone"
... | 351 |
'''simple docstring'''
def lowerCamelCase__ ( A : int , A : int ):
'''simple docstring'''
while a != 0:
UpperCAmelCase , UpperCAmelCase = b % a, a
return b
def lowerCamelCase__ ( A : int , ... | 91 | 0 |
import unittest
from knapsack import knapsack as k
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self : Tuple ) -> Dict:
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = [0]
... | 314 |
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( _A = "AAPL" ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
SCREAMING_SNAKE_CASE__ = BeautifulSoup(requests.get(_A ).text , ''... | 314 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers... | 315 |
def a ( SCREAMING_SNAKE_CASE_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
UpperCamelCase : Any = set()
# Replace all the whitespace in our sentence
UpperCamelCase : Unio... | 315 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
'https://huggingface.co/CarlCochet/trajectory-transformer-... | 64 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Tr... | 333 | 0 |
"""simple docstring"""
def _a ( SCREAMING_SNAKE_CASE = 10_00 ):
"""simple docstring"""
lowercase__ , lowercase__ = 1, 1
lowercase__ = 2
while True:
lowercase__ = 0
lowercase__ = fa + fa
lowercase__ , lowercase... | 363 |
from collections.abc import Sequence
from queue import Queue
class _a :
def __init__( self: Tuple , UpperCamelCase_: Optional[int] , UpperCamelCase_: int , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Union[str, Any]=None , U... | 93 | 0 |
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArgume... | 26 |
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_enforce_args(snake_case_,snake_case_ )
if n == 0:
return 0
_A : Tuple = float("""-inf""" )
for i in range(1,n + 1 ):
_A : str = max(
snake_case... | 26 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ): # noqa: E741
while r - l > 1:
_UpperCAmelCase : Any = (l + r) // 2
if v[m] >= key:
_UpperCAmelCase : Any ... | 370 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_d... | 322 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( __A, __A, __A=None ) -> List[str]:
... | 65 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCAmelCase_ : Any = logging.get_logger(__name__)
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
def __ini... | 91 | 0 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging... | 235 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeli... | 235 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig'... | 322 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_a = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _a ( SCREAMING_SNAKE_CA... | 322 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCAmelCase = "The quick brown fox jumps over the lazy dog" , ):
'''simple docstring'''
UpperCAmelCase = set()
# Replace all the whitespace in our sentence
UpperCAmelCase = input_str.replace(""" ... | 248 |
"""simple docstring"""
from __future__ import annotations
import math
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3... | 248 | 1 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytess... | 26 |
'''simple docstring'''
_lowercase : int = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def snake_case_ ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
lowercase_ : Optional[int] = ... | 93 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from t... | 363 |
'''simple docstring'''
import math
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> int:
'''simple docstring'''
_UpperCAmelCase : str = len(lowerCAmelCase_ )
_UpperCAmelCase : List[str] = int(math.floor(math.sqrt(lowerC... | 349 | 0 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
lowercase : Optional[Any] = False
class __... | 42 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 322 | 0 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_UpperCamelCase: Any... | 366 |
"""simple docstring"""
from collections.abc import Sequence
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase = False ) -> float:
'''simple docstring'''
if not arr:
return 0
lowercase : List[str] = 0 if al... | 53 | 0 |
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