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 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenizatio... | 85 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase ( l... | 331 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise Opt... | 365 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from trans... | 249 | 0 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A ( _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : List[str] = {
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''Машинное обучение ... | 182 | import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY... | 182 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert im... | 358 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def lowerCamelCase__... | 96 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase :Optional[int] = {
'''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONF... | 331 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase :str = {'''configuration... | 331 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Dict:
'''simple docstring'''
if "cls... | 365 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any:
'''simple docstring'''
if (
(cp >= 0x4e00 and cp <= 0x9fff)
... | 313 | 0 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _a ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
A = [('''size''', ctypes.c_int), ('''v... | 147 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a : Optional[Any] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ... | 147 | 1 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseM... | 12 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """M... | 12 | 1 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class lowerCAmelCase__ :
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=No... | 93 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowercase ... | 93 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, w... | 37 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 37 | 1 |
"""simple docstring"""
from __future__ import annotations
A_ = '''Muhammad Umer Farooq'''
A_ = '''MIT'''
A_ = '''1.0.0'''
A_ = '''Muhammad Umer Farooq'''
A_ = '''contact@muhammadumerfarooq.me'''
A_ = '''Alpha'''
import re
from html.parser import... | 64 | import math
def lowerCAmelCase_ ( __A ) -> bool:
'''simple docstring'''
return math.sqrt(__A ) * math.sqrt(__A ) == num
def lowerCAmelCase_ ( __A ) -> bool:
'''simple docstring'''
UpperC... | 65 | 0 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
rene... | 364 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
UpperCAmelCase = '''\
@misc{chen2021evaluating,
title={Evalu... | 172 | 0 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def lowerCAmelCase__ ( a__: int = 3 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if isinstance(a__ , a__ ):
rais... | 329 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __a ( UpperCAmelCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
"""... | 329 | 1 |
'''simple docstring'''
from datetime import datetime
import requests
def __magic_name__ ( A ) -> bytes:
snake_case = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
snake_case = requests.get(base_url + url ).json()[0]['urls'][0]['src']
retur... | 332 |
'''simple docstring'''
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing... | 332 | 1 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Any = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
'snap-research/efficientformer-l1-300': (
... | 25 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 66 | 0 |
"""simple docstring"""
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__UpperCamelCase : List[str] = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH A... | 371 |
"""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
__UpperCamelCase : Dict = logging.... | 309 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or path... | 323 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docs... | 323 | 1 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
__A = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
('''t... | 360 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 277 | 0 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class UpperCAmelCase__ ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int] ,_a : Optional[int] = 16 ... | 271 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_co... | 215 | 0 |
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 SchedulerMixin
class UpperCamelCase__ ( _snake_case , _snake_case ):
"""simple docstring"""
... | 363 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, Bit... | 257 | 0 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ ) -> bool:
_a : List[str] = 0
for ch in input_str:
_a : Optional[Any] = ord(lowerCAmelCase_ )
_a : Tuple = pow(2 , lowerCAmelCase_ )
# If w... | 89 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A_ :List[str] = [
'''word... | 71 | 0 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
lowercase : Optional[int] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0... | 160 |
'''simple docstring'''
import os
import sys
import unittest
lowercase : List[str] = 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, cr... | 160 | 1 |
'''simple docstring'''
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 accelerat... | 2 |
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_tor... | 122 | 0 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class _A ( SCREAMING_SNAKE_CAS... | 358 |
'''simple docstring'''
import unittest
from transformers import MraConfig, 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, floats_tensor, ids_tensor, r... | 16 | 0 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassific... | 130 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils impo... | 130 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( a__ ):
'''simple docstring'''
lowerCAmelCase : int = ["""image_processor""", """token... | 363 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = 1.6021E-19 # units = C
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> tuple[str, float]:
if (conductivity... | 302 | 0 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODE... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerC... | 66 | 1 |
'''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, random_atten... | 358 |
'''simple docstring'''
from torch import nn
def _snake_case ( _SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif a... | 187 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : Any = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://hug... | 335 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad ):
... | 335 | 1 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.sch... | 44 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transfor... | 44 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
... | 146 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_configur... | 146 | 1 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 ConfigT... | 360 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = (IPNDMScheduler,)
UpperCAmelCase :... | 271 | 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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from tran... | 104 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal... | 187 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_A = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
exc... | 137 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn a... | 137 | 1 |
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.d... | 4 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class A_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 322 | 0 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCAmelCase__ : List[str] ='''src/diffusers'''
# Matches is_xxx_available()
UpperCAmelCase__ : int =re.compile(r''... | 367 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _lowercase ( ) -> str:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with... | 262 | 0 |
'''simple docstring'''
def __magic_name__( lowerCamelCase):
if any(not isinstance(lowerCamelCase, lowerCamelCase) or x < 0 for x in sequence):
raise TypeError('''Sequence must be list of non-negative integers''')
for _ in range(len(lowerCamelCase)):
for i, (rod_u... | 174 |
'''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 dataset... | 174 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _a ( lowerCamelCase: List[Any] ) -> List[Any]:
'''simple docstring'''
__A ... | 351 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : str = logging.get_logger(__name__)
snake_case__ : Optional[int] = {
'google/pix2struct-textcaps-base':... | 250 | 0 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import... | 24 | 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_mvp import MvpTokenizer
_U... | 140 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
... | 174 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = '''T5Config... | 174 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTes... | 88 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def ... | 88 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__: str ={"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():
raise OptionalDepende... | 371 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def UpperCamelCase ( _A ):
"""simple docstring"""
if not isinstance(_A, _A ):
raise TypeError("""Undefined for non-integers""" )
elif precision < 1:
raise ValueError(""... | 138 | 0 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> str:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class A__ ... | 212 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> list:
if len(SCREAMING_SNAKE_CASE_ ) <= 1:
return [tuple(SCREAMING_SNAKE_CASE_ )]
lowerCAmelCase__ : Optional[Any] = []
def generate(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
... | 212 | 1 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
def __init__( self , *A ... | 176 |
def SCREAMING_SNAKE_CASE__ ( lowercase = 1000 ) -> int:
snake_case : Optional[int] = 3
snake_case : List[Any] = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return resu... | 176 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowercase : Any = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
ra... | 27 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
__A : Optional[int] = "M-CLIP"
def __init__( self , __A=1024 , __A=768 , **... | 283 | 0 |
from ....utils import logging
lowerCAmelCase__ : Union[str, Any] =logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case_ ):
'''simple docstring'''
def __init__( self , _A , _A=None , _A=2_048 ):
'''simple docstr... | 350 |
import os
def __lowercase ( a__ = "input.txt" ) -> int:
with open(os.path.join(os.path.dirname(a__ ) , a__ ) ) as input_file:
__SCREAMING_SNAKE_CASE = [
[int(a__ ) for element in line.split(',' )]... | 118 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
UpperCAmelCase = logging.get_logger(__name__)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if isinstance(__SCREAMING_SNAKE_CASE , np.ndarray ):
return lis... | 195 |
import pprint
import requests
UpperCAmelCase = '''https://zenquotes.io/api'''
def UpperCAmelCase_ ( ):
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def UpperCAmelCase_ ( ):
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ ... | 195 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
... | 357 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils imp... | 167 | 0 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
while b:
UpperCamelCase__ , UpperCamelCase__ : int = b, a % b
return a
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase... | 189 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx ... | 189 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, 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 .tokeni... | 161 |
"""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_extractio... | 161 | 1 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = 10, _UpperCAmelCase = 22 ) -> int:
'''simple docstring'''
lowerCAmelCase : List[str] = range(1, _UpperCAmelCase )
lowerCAmelCase : str = range(1, _UpperCAmelCase )
return sum(
1 for power in ... | 138 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( SCREAMING_SNAKE_CASE_ ):
a_ : int = (KDPMaDiscreteScheduler,)
a_ : List[str] =... | 243 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_f... | 351 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''face... | 184 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttent... | 241 |
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_avai... | 241 | 1 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_... | 46 |
'''simple docstring'''
import sys
def _lowerCAmelCase ( lowercase ) -> List[str]:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = [[0 for x in range(lowercase )] for x in range(lowercase )]
__lowerCAmelCase = [[0 for x in range... | 46 | 1 |
"""simple docstring"""
import copy
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
from ..auto import CONFIG_MAPPING
UpperCAmelCase : List[str]... | 115 |
"""simple docstring"""
import numpy as np
UpperCAmelCase : Optional[Any] = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class lowerCamelCase__ :
"""simple d... | 115 | 1 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCAmelCase ( a_ ) -> Dict:
"""simple docstring"""
__A = [
"encoder.version",
"decoder.version",
"model.enc... | 355 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake... | 124 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A :
__magic_name__ = 42
__magic_name__ = None
__magic_name... | 3 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a__ : Optional[Any] = logging.getLogger(__name__)
class UpperCAmelCase__ :
de... | 349 | 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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProce... | 270 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if not (isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )):
raise ValueError("""longest_comm... | 270 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,... | 158 | 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,
)
from transformers.testing_utils import DUMMY_UN... | 343 | 0 |
from math import ceil
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = 1_001 ) -> int:
'''simple docstring'''
lowerCAmelCase : Optional[Any] = 1
for i in range(1, int(ceil(n / 2.0 ) ) ):
lowerCAmelCase : str = 2 * i + 1
lowerC... | 323 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) ... | 323 | 1 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :list[int] , lowerCAmelCase__ :list[int] , lowerCAmelCase__ :int ) -> tuple[float, list[float]]:
'''simple docstring'''
_a = list(range(le... | 168 |
'''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/... | 168 | 1 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokeni... | 362 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip2QForm... | 184 | 0 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 100 * 2**20, 900 * 2**20] )
d... | 95 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
UpperCAmelCase : int = logging.get_logger(__name__)
... | 95 | 1 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class UpperCamelCase_ :
def __init__( self , snake_case__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase = data
Up... | 248 |
"""simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase = True , lowerCAmelCase = math.inf , lowerCAmelCase = -math.inf , lowerCAmelCase = math.inf , lowerCAmelCase = -math.inf ,... | 248 | 1 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as... | 166 |
'''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 _UpperCamelCase ( A ):
... | 166 | 1 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__A = '''\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Langua... | 357 |
from __future__ import annotations
import math
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if not scores:
... | 75 | 0 |
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,
)
from transformers.testing_utils import DUMMY... | 262 |
import logging
from transformers.configuration_utils import PretrainedConfig
__a = logging.getLogger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[int] = 'masked_bert'
def __init__( self : Optional[int] , ... | 30 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCamelCase_ : str = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAI... | 215 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
lowerCamelCase_ : List[Any] = TypeVar("""T""")
class __A ( Generic[T] ):
"""simple docstring"""
def __init__( self , __A ... | 215 | 1 |
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, ids_tensor... | 130 | '''simple docstring'''
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def __UpperCAmelCase ... | 145 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
... | 179 |
'''simple docstring'''
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modul... | 179 | 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"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAU... | 132 | 0 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__a: Optional[Any] = 637_8137.0
__a: Dict = 635_6752.31_4245
__a: int = 6_37_81_37
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCa... | 214 | '''simple docstring'''
from manim import *
class UpperCAmelCase ( a__ ):
'''simple docstring'''
def _lowerCAmelCase( self ) -> List[Any]:
lowercase__ : int = Rectangle(height=0.5 , width=0.5 )
lowercase__ : Optional[int] = Re... | 214 | 1 |
'''simple docstring'''
import math
def a ( __a , __a = 0 , __a = 0 ) -> list:
'''simple docstring'''
UpperCamelCase__ :Dict = end or len(__a )
for i in range(__a , __a ):
UpperCamelCase__ :Tuple = i
UpperCamelCase__ ... | 97 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case = ''''''
__snake_case = ''''''
__snake_case = ''''''
__snake_case = ''''''
def a ( __a ) -> None:
'''simple docstring'''
UpperCame... | 97 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase ... | 363 |
'''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,
)
from transformers.... | 98 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Tuple = logging.get_logger(__name__)
UpperCAmelCase__ : int = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-96... | 121 |
'''simple docstring'''
def __lowerCamelCase ( __snake_case : int, __snake_case : int, __snake_case : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(__snake_case : int, __snake_case : int ) -> in... | 134 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 358 |
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,
Tra... | 300 | 0 |
from collections.abc import Generator
from math import sin
def lowerCAmelCase__( lowercase : str ) -> bytes:
if len(UpperCamelCase__ ) != 32:
raise ValueError("Input must be of length 32" )
__snake_case : Any = B""
for i in [3, 2, 1, 0]:
little_endian += stri... | 326 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Huggi... | 273 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmel... | 116 |
lowerCAmelCase_ = range(2, 20 + 1)
lowerCAmelCase_ = [10**k for k in range(ks[-1] + 1)]
lowerCAmelCase_ = {}
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
l... | 116 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None ):
if start is None:
_UpperCAmelCase : List[Any] = 0
if end is None:
_Upper... | 234 |
'''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_bert import BertTokenizer
lowerCamelCase__ = logging.get_logger(__n... | 234 | 1 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
... | 364 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_A : Optional[Any] =typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_A : Optional[int] =typing.U... | 129 | 0 |
def A ( a_ = 1_000 ) -> int:
return sum(e for e in range(3 ,a_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"{solution() = }")
| 71 |
"""simple docstring"""
import qiskit
def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> qiskit.result.counts.Counts:
A__ = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
A__ = qiskit.QuantumCircuit(lower... | 247 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...... | 135 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase :Dict = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 135 | 1 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassificatio... | 256 | """simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_... | 256 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A_ = 50_00_00
A_ , A_ = os.path.split(__file__)
A_ = os.path.join(RESULTS_BASEPATH, '''results''', RESU... | 132 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int = 2 , snake_case__ : int = 1 , snake_case__ : int = 3 , ):
"""simple docstring"""
if num < 2:
... | 132 | 1 |
"""simple docstring"""
import os
import string
import sys
a :Union[str, Any] = 1 << 8
a :Optional[Any] = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_... | 132 |
"""simple docstring"""
import sys
from collections import defaultdict
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ):
__lowerCAmelCase = []
def snake_case ( self , __a ):
return self.node_position[vertex]
d... | 57 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_availa... | 371 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _lowerCAmelCase :
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[str]:
if dst_width < 0 or dst... | 270 | 0 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not is_accelerate_available():
return method
SCREAMING_SNAKE_CASE : Any... | 313 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...fe... | 26 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from... | 352 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils... | 128 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''microsoft/focalnet-tiny'... | 283 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONA... | 122 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__UpperCAmelCase = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'... | 42 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__UpperCAmelCase = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be... | 42 | 1 |
def lowerCAmelCase__( lowercase : int ) -> 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
__snake_case : Optional[int] = gray_code_sequence_string(lo... | 326 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging... | 326 | 1 |
"""simple docstring"""
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 = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def _snake_case ( _snake_c... | 271 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_availa... | 271 | 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/licens... | 28 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModul... | 28 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowercase : str = {
"""configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRET... | 225 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine impor... | 225 | 1 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def a__ ( A_ ): # picklable for multiprocessing
'''simple docstri... | 88 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _UpperCAmelCase :
a__ : int
a__ : Node | None = None
a_... | 332 | 0 |
def UpperCamelCase( lowercase_ ) -> Dict:
'''simple docstring'''
snake_case_ = [0] * len(lowercase_ )
snake_case_ = []
snake_case_ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i... | 34 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
s... | 34 | 1 |
def __lowercase ( _UpperCamelCase ) ->List[Any]:
"""simple docstring"""
lowercase : Union[str, Any] = len(A_ )
for i in range(length - 1 ):
lowercase : Tuple = i
for k in range(i + 1, A_ ):
if col... | 337 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Dict = {
'''... | 106 | 0 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
__UpperCAmelCase = HfApi()
__UpperCAmelCase = {}
# fmt: off
__UpperCAmelCase = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
... | 139 |
def snake_case_ () -> List[Any]:
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def snake_case_ (__A : Dict ) -> Tuple:
__lowerCAmelCase : Optional[int] = 1
__lowerCAmelCase : Optional[int] = ... | 139 | 1 |
'''simple docstring'''
_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... | 254 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if n... | 254 | 1 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
lowerCAmelC... | 116 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. an... | 116 | 1 |
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