code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowerCAmelCase__ ( _lowerCamelCase ):
def __init__( self : Any , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : U... | 106 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__lowercase : Optional[int] = logging.getLogger(__name__)
class lowerCAmelCase ( a ):
... | 142 | 0 |
'''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_distilbert import DistilBertTokenizer
_lowercase : Dict =logging.get_logger... | 721 |
'''simple docstring'''
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_lowercase : Optional[Any] =re.compile(R"\b(a|an|the)\b", re.UNICODE)
_lowercase : Dict =None
def __UpperCAmelCase ( ) -> List[st... | 574 | 0 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
10: "a",
11: "b",
12: "c",
13: "d",
14: "e",
15: "f",
}
def __a(SCREAMING_SNAKE_CASE_ : float ... | 18 |
import numpy as np
def __a ( __UpperCAmelCase : np.ndarray ) -> np.ndarray:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __a ( __UpperCAmelCase : np.ndarray ) -> np.ndarray:
"""simple docstring"""
... | 488 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :Union[str, Any] = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus... | 119 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def UpperCAmelCase_ ( _... | 119 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaP... | 173 |
'''simple docstring'''
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
f... | 173 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a__ ( A__ ):
@staticmethod
@abstractmethod
def lowerCamelCase_ ( _lowerCamelCase :ArgumentParser ):
'''simple docstring'''
raise NotImple... | 395 |
"""simple docstring"""
from __future__ import annotations
def A_ ( __lowercase ):
UpperCamelCase_ : List[Any] =0.00
UpperCamelCase_ : Dict =0
for resistor in resistors:
if resistor <= 0:
UpperCamelCase_ : List[str] =F'''Resistor at index {index} ha... | 395 | 1 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
A : Any = 0
A : List[Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
... | 140 | import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def a__ ( __UpperCamelCase ):
SCREAMING_SNA... | 140 | 1 |
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCamelCase__ : Optional[int] = logging.getLogger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
__a : Optional[Any] = "masked_bert"
def __init__( self ... | 685 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> Union[str, Any]:
"""simple docstring"""
def is_in_circle(lowerCamelCase_ : float , ... | 685 | 1 |
"""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... | 621 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeli... | 573 | 0 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers im... | 439 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import Schedul... | 439 | 1 |
def UpperCamelCase (lowercase_: int ) -> int:
if not isinstance(lowercase_ , lowercase_ ):
A__ : int = f"""Input value of [number={number}] must be an integer"""
raise TypeError(lowercase_ )
if number < 1:
A__ : Any = f"""Input value of [number={number}] m... | 456 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
A_ : str = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])
def UpperCamelCase (lowercase_: Union[str, Any] ) ->... | 456 | 1 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 ... | 285 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : list, _lowerCAmelCase : list, _lowerCAmelCase : int ):
"""simple docstring"""
_a = len(_lowerCAmelCase )
_a = [[0] * n for i in range(_lowerCAmelCase )]
for i i... | 285 | 1 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
A__ : Any = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
i... | 13 |
'''simple docstring'''
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
... | 261 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase : int ={
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
"tokenization_m2m_1... | 15 | from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase : List[Any] =300 # TEMPERATURE (unit = K)
def A__ ( __A , __A , __A , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("""Dono... | 15 | 1 |
from __future__ import annotations
snake_case_ = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
snake_case_ = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def snake_case__ ( SCREAMING_SNAKE_CASE_ : list[float] ):
'''simple doc... | 164 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , a = 6):
lowercase__ : Node | None = None
lowercase__ : Node | None = None
self.create_linked_list(a)
def snake_case_ ( self , a)... | 164 | 1 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from... | 520 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> int:
"""simple docstring"""
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError("""The length of profit and weight must be same.""" )
if max_weight <= 0:
raise Va... | 520 | 1 |
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 transformers.testing_utils import r... | 290 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"C... | 267 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ : Dict = logging.get_logger(__name__)
UpperCAmelCase__ : Union[str, Any] = {
'kssteven/... | 706 |
import argparse
import copy
def _A( UpperCamelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__lowercase = {}
with open(UpperCamelCase__ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbour... | 362 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""caidas/swin2sr-classicalsr-x2-64""": (
"""https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/r... | 80 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class __magic... | 358 | 0 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be consid... | 186 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A_ ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , _A) -> List[str]:... | 186 | 1 |
"""simple docstring"""
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ... | 82 |
def _lowercase ( __UpperCamelCase : list ):
snake_case__ = False
while is_sorted is False: # Until all the indices are traversed keep looping
snake_case__ = True
for i in range(0 , len(__UpperCamelCase ) - 1 , 2 ): # iterating o... | 214 | 0 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast... | 687 |
'''simple docstring'''
from __future__ import annotations
import requests
__snake_case : Union[str, Any] = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category c... | 687 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder... | 546 | '''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patc... | 546 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowerca... | 58 | '''simple docstring'''
from __future__ import annotations
lowercase_ = 10
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =1
_lowerCAmelCase =max(a__ )
while placement <= max_digit:
# declare and initializ... | 58 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'roberta-base': 'http... | 220 |
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : str ,lowerCAmelCase_ : str ) -> list:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] =len(lowerCAmelCase_ )
SCREAMING_SNAKE_CASE_ : List[Any] =[]
for i ... | 220 | 1 |
from typing import Any
def A ( lowercase ) -> list[Any]:
'''simple docstring'''
if not input_list:
return []
UpperCamelCase = [input_list.count(lowercase ) for value in input_list]
UpperCamelCase = max(lowercase ) # Gets the maximum count in the input list.
# Get... | 713 |
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,
)... | 3 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data imp... | 70 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
Ber... | 70 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
__lowerCamelCase : Any = namedtuple("""covid_data""", """cases deaths recovered""")
def A__ ( _a : str = "https://www.worldometers.info/coronavirus/" ):
'''simple docstring'''
... | 448 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
__lowerCamelCase : List[str] = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", ... | 448 | 1 |
'''simple docstring'''
from typing import Any
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ):
_validation(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ... | 585 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from tra... | 308 | 0 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
Ef... | 702 |
'''simple docstring'''
# 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/LICE... | 40 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Dict = logging.get_logger(__name__)
class __magic_name__ ( _lowercase ):
UpperCamelCase_ = '''encoder-decoder'''
UpperCamelCase_ = True
... | 353 | """simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCAme... | 420 | 0 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 703 |
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_vision_available
from ...test_c... | 193 | 0 |
"""simple docstring"""
lowerCAmelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def lowercase__ ( ):
_SCREAMING_SNAKE_CASE : str = input('Enter message: ' )
_SCREAMING_SNAKE_CASE : Optional[int] = input('Enter key [alphanumeric]: ' )
_SCREAMING_... | 621 |
"""simple docstring"""
def lowercase__ ( lowerCamelCase, lowerCamelCase ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 621 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, Train... | 16 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .forma... | 16 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 273 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common ... | 273 | 1 |
UpperCamelCase = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
UpperCamelCase = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperC... | 152 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
return getitem, k
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return setitem, k, v... | 152 | 1 |
from __future__ import annotations
from fractions import Fraction
def __UpperCAmelCase ( __a : int ,__a : int ) -> bool:
"""simple docstring"""
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
... | 14 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
UpperCamelCase__ = False
try:
UpperCamelC... | 620 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = [
['''attention''', '... | 61 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'''configuration_distilbert'... | 61 | 1 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class lowercase__ ( unittest.TestCase ):
"""simple docstring"""
def _a ( self ... | 102 | '''simple docstring'''
class _lowercase :
'''simple docstring'''
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE__ : int ) -> None:
__lowerCAmelCase = size
__lowerCAmelCase = [0] * size
__lowerCAmelCase = [0] * size
... | 427 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_snake_case )
class A_ ( _snake_case ):
'''simple docstring'''
... | 695 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
fr... | 695 | 1 |
'''simple docstring'''
from collections.abc import Callable
def SCREAMING_SNAKE_CASE_ ( __A : Callable[[float], float] , __A : float , __A : float ) -> float:
_SCREAMING_SNAKE_CASE = a
_SCREAMING_SNAKE_CASE = b
if function(__A ) == 0: # o... | 418 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}
c... | 418 | 1 |
'''simple docstring'''
import heapq
def lowerCamelCase_ ( UpperCamelCase__ : dict ):
'''simple docstring'''
UpperCamelCase__ = []
# for each node and his adjacency list add them and the rank of the node to queue
# using hea... | 719 | import csv
import tweepy
# Twitter API credentials
lowercase = """"""
lowercase = """"""
lowercase = """"""
lowercase = """"""
def lowerCamelCase_ ( UpperCamelCase__ : str ):
'''simple docstring'''
... | 591 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TY... | 43 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONF... | 258 | 0 |
'''simple docstring'''
def UpperCAmelCase ( A : str ):
SCREAMING_SNAKE_CASE : Dict = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
SCREAMING_SNAKE_CASE : Tuple ... | 464 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename ... | 464 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/... | 76 |
"""simple docstring"""
__A : Optional[int] = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
fro... | 602 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.ut... | 711 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extracti... | 331 | 0 |
'''simple docstring'''
from math import factorial
def lowercase__( _UpperCamelCase : int , _UpperCamelCase : int )-> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k" )
return factorial(... | 138 |
'''simple docstring'''
def lowercase__( _UpperCamelCase : int = 100 )-> int:
"""simple docstring"""
_UpperCamelCase = set()
_UpperCamelCase = 0
_UpperCamelCase = n + 1 # maximum limit
for a in range(2 , _UpperCamelCase ):
for... | 138 | 1 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionM... | 672 |
'''simple docstring'''
a : Tuple = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : str = 0
while number:
# Increased Speed Slightly by checking ev... | 672 | 1 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit... | 63 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...ut... | 234 | 0 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase_ : Union[str, Any] = '''#'''
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Any ) -> None:
"""simple docstring"""
A_ = {}
def ... | 482 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tra... | 482 | 1 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 359 |
a__ = [0, 2, 4, 6, 8]
a__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( a : int , a : int , a : list[int] , a : int ):
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i ... | 654 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def snake_case_ ( lowerCAmelCase_ : bytes , lowerCAmelCase_ : int ):
__lowercase : List[str] = F"{sampling_rate}"
__low... | 649 |
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_modeling_common import Mo... | 649 | 1 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Tuple=None , _SCREAMING_S... | 225 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Tuple=None , _SCREAMING_S... | 225 | 1 |
import warnings
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 _Upp... | 328 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseCon... | 328 | 1 |
'''simple docstring'''
import requests
__SCREAMING_SNAKE_CASE : Tuple ='https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : str ):
'''simple docstring'''
A: List[Any] = reques... | 135 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__SCREAMING_SNAKE_CASE : str =TypeVar('KEY')
__SCREAMING_SNAKE_CASE : Dict =TypeVar('VAL')
@dataclass(frozen=snake_case_ , ... | 135 | 1 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> "list[int]":
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
SCREAMING_SNAKE_CASE = [0] * (upper_limit + 1)
# Base cas... | 327 |
"""simple docstring"""
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.t... | 327 | 1 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class __lowerCamelCase :
def __init__(self ):
'''simple docstring'''
_lowerCAmelCase = psutil.Process()
_lowerCAmelCase = False
def A__ ... | 156 |
"""simple docstring"""
import os
def __UpperCAmelCase ( snake_case_ : str = "input.txt" ) -> int:
"""simple docstring"""
with open(os.path.join(os.path.dirname(snake_case_ ) , snake_case_ ) ) as input_file:
_lowerCAmelCase = [
[int(sna... | 156 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : int , UpperCamelCase : int ):
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowercase ( ):
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert xn... | 700 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_rembert": ["REMBER... | 595 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class a ( a__ ):
snake_case__ = '''SpeechT5FeatureExtractor'''
snake_case__ = '''SpeechT5Tokenizer'''
def __init__( self , _snake_case , _snake_case ):
... | 4 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils i... | 215 | 0 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges ... | 169 |
def A (__A : list , __A : list , __A : int , __A : int , __A : int ) -> int:
"""simple docstring"""
if index == number_of_items:
return 0
UpperCAmelCase_ = 0
... | 169 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoMo... | 476 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils ... | 476 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_encodec''': [
'''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 702 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeq... | 598 | 0 |
def __snake_case ( _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 1000 ) -> int:
A_ : Optional[int] = 1
A_ : int = 0
for divide_by_number in range(_lowerCAmelCase , digit + 1 ):
A_ : list[i... | 454 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def __snake_case ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any]=1 ) -> Any:
if n_shave_prefix_segments >= 0:
return ".".join(path.spli... | 454 | 1 |
'''simple docstring'''
from __future__ import annotations
import requests
def UpperCamelCase_ ( snake_case_ : str ) -> dict:
'''simple docstring'''
__lowerCAmelCase = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
ret... | 330 | '''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_util... | 330 | 1 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def A__ ( lowercase: str = "" ) -> dict[str, float]:
A : str =url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
A : Tuple =BeautifulSoup(requests.get(low... | 305 | from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_lowercase : str =(
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowercase : list[int] =[ord(letter) for letter in string.ascii_lowercas... | 305 | 1 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_ava... | 720 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelC... | 517 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowerCAmelCase (__UpperCamelCase ):
'''simple docstring''... | 584 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
fro... | 584 | 1 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
UpperCAmelCase_ = {
'''E''': 12.70,
'''T''': 9.06,
'''A''': 8.17,
'''O''': 7.51,
'''I''': 6.97,
'''N''': 6.75,
'''S''': 6.33,
'''H''': 6.09,
'''R''': 5.99,
'''D''': 4.25,
'''L''': ... | 721 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch... | 519 | 0 |
"""simple docstring"""
from collections import deque
class lowercase__ :
'''simple docstring'''
def __init__( self : int , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : int )... | 82 |
"""simple docstring"""
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
... | 82 | 1 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : int = 1 , UpperCAmelCase__ : int = 1_0_0_0 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :List[Any] = 1
SCREAMING_SNAKE_CASE__ :List[str] = 0
... | 718 | '''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , Upp... | 320 | 0 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import C... | 346 |
"""simple docstring"""
from functools import lru_cache
def a__ ( __SCREAMING_SNAKE_CASE ) -> set:
__lowerCAmelCase: Any = 2
__lowerCAmelCase: Optional[Any] = set()
while i * i <= n:
if n % i:
i += 1
else:
n... | 346 | 1 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
_UpperCamelCase : Optional[Any] =logging.get_logg... | 575 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCamelCase : Tuple ={
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}... | 575 | 1 |
import os
import sys
import unittest
lowercase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init... | 74 |
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 __lowercase ( UpperCamelCase ):
"""simple d... | 605 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase ) -> float:
if not nums:
raise ValueError("""List is empty""" )
return sum(__lowerCAmelCase ) / len(__lowerCAmelCase )
if __name__ == "__main__":
impor... | 12 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 12 | 1 |
def UpperCamelCase( __UpperCamelCase : int ):
if length <= 0 or not isinstance(snake_case__ ,snake_case__ ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(snake_case__ )]
if __name__ == "__main__":
print(hexagonal_... | 171 |
"""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_util... | 543 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regre... | 709 |
'''simple docstring'''
import argparse
import json
import subprocess
def UpperCamelCase ( lowercase_ : List[Any] , lowercase_ : Tuple ) -> Union[str, Any]:
'''simple docstring'''
lowercase =[]
lowercase =(
f'curl -H "Accept: application/vnd.github+... | 145 | 0 |
from __future__ import annotations
import math
class _A :
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Dict = size
# approximate the overall size of segment tree with given value
snake_ca... | 36 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCAmelCase__ :int = logging.get_logger(__name__)
def lowerCAmelCase__ ( a__: Dict ) -> List[str]:
'''simple ... | 618 | 0 |
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 import ena... | 702 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_availabl... | 535 | 0 |
def UpperCamelCase ( ) -> List[str]:
UpperCamelCase : Optional[Any] = 0
for i in range(1 , 1001 ):
total += i**i
return str(snake_case__ )[-10:]
if __name__ == "__main__":
print(solution())
| 40 |
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 import SequenceFeatureExtractio... | 40 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAG... | 704 | from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase : List[Any] =logging.get_logger(__na... | 661 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Tuple = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class SCR... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int = 1_00_00_00 ) -> int:
__snake_case = 1
__snake_case = 1
__snake_case = {1: 1}
for inputa in range(2 , _UpperCAmelCase ):
__snake_case = 0
__snake_case = inputa
... | 69 | 1 |
__lowercase : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609_344,
"knot": 1.852,
}
__lowercase : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.277_777_778,
"mph": 0.621_371_192,
"knot": 0.539_956_803,
}
def lowercase ( __A : ... | 315 |
def lowercase ( __A : Dict ) -> Optional[Any]:
'''simple docstring'''
snake_case : Union[str, Any] = len(__A )
for i in range(length - 1 ):
snake_case : Dict = i
for k in range(i + 1 , __A ):
if collection[k] <... | 315 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_... | 433 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_av... | 433 | 1 |
'''simple docstring'''
from PIL import Image
def _lowerCamelCase( UpperCamelCase__ : Image , UpperCamelCase__ : int ) -> Image:
A : Tuple = (259 * (level + 255)) / (255 * (259 - level))
def contrast(UpperCamelCase__ : int ) -> int:
return... | 537 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if i... | 537 | 1 |
def _A ( __snake_case :int ) -> int:
"""simple docstring"""
assert isinstance(__snake_case , __snake_case ), f'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
__SCREAMING_SNAKE_CASE = f'''The inp... | 693 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_snake_case : str = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
def __init__( self, *_a, **_a ) -> ... | 693 | 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 snake_case ( UpperCAmelCase ):
__mag... | 720 |
"""simple docstring"""
# 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
#... | 118 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase__( UpperCAmelCase ):
... | 97 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__snake_case = logging.ge... | 472 | 0 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSeq... | 714 |
'''simple docstring'''
def lowerCAmelCase_ ( __A : int = 1_00 ):
'''simple docstring'''
snake_case: List[str] = n * (n + 1) * (2 * n + 1) / 6
snake_case: List[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
... | 692 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ... | 273 |
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_property
from ...... | 146 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class a__ :
'''simple docstring'''
def __init__( self , _A ):
"""simple docstring"""
__lowerCAmelCase = data
__lowerCAmelCase ... | 705 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 552 | 0 |
'''simple docstring'''
import math
def A_( A : int):
UpperCamelCase = 0
UpperCamelCase = 0
while num > 0:
UpperCamelCase = num % 8
UpperCamelCase = octal + (remainder * math.floor(math.pow(10 , A... | 3 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE__ :
def __init__( self )-> Dict:
'''simple docstring'''
UpperCamelCase = ... | 3 | 1 |
from __future__ import annotations
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) < k or k < 0:
raise ValueError('Invalid Input' )
snake_case_ = snake_case_ = sum(array[:k] )
for i in range(len(lowercase__ ) - k ):
snake_case_ = current_sum - ar... | 703 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolv... | 46 | 0 |
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __UpperCamelCase ( _A ):
return 1 / (1 + np.exp(-z ))
def __UpperCamelCase ( _A , _A ... | 431 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore... | 235 | 0 |
import argparse
import datetime
def UpperCAmelCase_ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE__ ={
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": """Thursday""",
"""5""": """Friday""",
... | 705 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNo... | 588 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'hustvl/yolos-smal... | 685 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__UpperCAmelCase = '''src/transformers'''
__UpperCAmelCase = '''docs/source... | 40 | 0 |
# 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 app... | 571 |
def UpperCamelCase__ ( _A: int ):
'''simple docstring'''
if not isinstance(_A , _A ):
__lowerCamelCase = f'''Input value of [number={number}] must be an integer'''
raise TypeError(_A )
if number < 0:
... | 571 | 1 |
"""simple docstring"""
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
def __init__... | 247 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase ( lowerCamelCase_ ... | 247 | 1 |
def _lowerCAmelCase( __A ):
UpperCAmelCase = len(__A )
UpperCAmelCase = len(matrix[0] )
UpperCAmelCase = min(__A , __A )
for row in range(__A ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements... | 1 |
def _lowerCAmelCase( __A ):
if not isinstance(__A , __A ):
raise TypeError("only integers accepted as input" )
else:
UpperCAmelCase = str(abs(__A ) )
UpperCAmelCase = [list(__A ) for char in range(len(__A ) )]
for index in range(len(__A ) ):
... | 1 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,... | 173 |
'''simple docstring'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
lowerCAmelCase_ = None
try:
import msvcrt
except ImportError:
lowerCAmelCase_ = None
try:
import fcntl
except ImportError:
lowerCAmelCase_ = None
# ... | 173 | 1 |
from ... import PretrainedConfig
lowercase_: int = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class lowercase__ (__lowerCamelCase ):
__UpperCamelCase : Dict = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
__U... | 714 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if i... | 127 | 0 |
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