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 |
|---|---|---|---|---|
# Function to print upper half of diamond (pyramid)
def _a ( lowerCAmelCase )-> Tuple:
for i in range(0 , __lowercase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(' ' , end='' )
for _ in range(0 , i + 1 ): # prin... | 360 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
ne... | 383 | 0 |
"""simple docstring"""
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
... | 709 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImagePro... | 137 | 0 |
def _a ( lowercase__ : str ):
'''simple docstring'''
if n_term == "":
return []
SCREAMING_SNAKE_CASE__ : list = []
for temp in range(int(lowercase__ ) ):
series.append(f'''1/{temp + 1}''' if series else '1' )
return series
if ... | 85 |
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 datasets.config import MAX_SHARD_SIZE
from datas... | 343 | 0 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list:
UpperCamelCase = len(__UpperCamelCase )
UpperCamelCase = []
for i in range(len(__UpperCamelCase ) - pat_len + 1 ):
Upp... | 704 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'SenseTime/deformable-detr... | 35 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {"vocab_file": "spiece.model... | 66 |
from __future__ import annotations
UpperCamelCase = tuple[int, int, int]
UpperCamelCase = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
UpperCamelCase = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# -------------------------- default selection --------... | 66 | 1 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
_lowercase ... | 30 | '''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
lowercase_ : str ... | 30 | 1 |
'''simple docstring'''
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... | 432 |
'''simple docstring'''
from __future__ import annotations
from random import random
class snake_case :
"""simple docstring"""
def __init__( self : Tuple , __A : int | None = None ):
__UpperCamelCase = value
__UpperCamelCase = random()
__UpperC... | 399 | 0 |
"""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... | 361 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simp... | 361 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import B... | 232 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int = 2_0_0_0_0_0_0 ):
"""simple docstring"""
snake_case_ : Optional[Any] = [0 for i in range(n + 1 )]
snake_case_ : int = 1
snake_case_ : s... | 480 | 0 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_A : List[str] = logging.getLogger()
def __low... | 712 |
class a :
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : str = "" , SCREAMING_SNAKE_CASE_ : bool = False ):
# Mapping from the first character of the prefix of the node
__lowerCamelCase: dict[str, RadixNode] = {}
# A node wi... | 189 | 0 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, i... | 252 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAm... | 689 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils... | 711 |
'''simple docstring'''
from __future__ import annotations
def _a ( _lowercase : int ):
'''simple docstring'''
__UpperCAmelCase : str = [True] * limit
__UpperCAmelCase : Tuple = False
__UpperCAme... | 266 | 0 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import... | 13 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : List[str] = {
... | 444 | 0 |
'''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:... | 56 |
'''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosi... | 56 | 1 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
A_ : Any ... | 483 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
lowercase : int = logging.getLogger(__name__)
class _a (a__ ):
'''simple docstring'''
lowerCAmelCase_ : Union[str, A... | 116 | 0 |
"""simple docstring"""
import math
import unittest
def lowercase ( __snake_case : Any ):
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# ... | 701 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : int = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARC... | 141 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline | 256 |
from __future__ import annotations
from typing import Any
class A( UpperCamelCase ):
'''simple docstring'''
pass
class A:
'''simple docstring'''
def __init__( self : List[str] , A_ : Any ) -> None:
""... | 70 | 0 |
"""simple docstring"""
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorSta... | 714 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ (_UpperCAmelCase ):
A__ : Union[str, Any] = (PNDMScheduler,)
A__ : Optional[int] = (('''num_inferenc... | 612 | 0 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase = logging.getLogger(__name__)
class UpperCamelCase__ ( ... | 104 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_A = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def A_ ( __SCREAMING_SNAKE_CASE : str = "mumbai" ) -> Generator[t... | 158 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from tran... | 342 | """simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrate... | 342 | 1 |
from __future__ import annotations
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ = None , lowerCamelCase_ = None ):
if start is None:
A : Union[str, Any] = 0
if end is None:
A : Union[str, Any]... | 542 |
import os
def __UpperCAmelCase( ):
with open(os.path.dirname(lowercase_ ) + '''/p022_names.txt''' ) as file:
_lowerCamelCase : Optional[int] = str(file.readlines()[0] )
_lowerCamelCase : List[Any] = names.replace('''"''' , '''''' ).split(... | 114 | 0 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logg... | 297 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"facebook/xmod-base": "https://huggingface.co/faceb... | 297 | 1 |
'''simple docstring'''
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils i... | 372 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
fro... | 264 | 0 |
import random
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase , lowerCamelCase = [], [], []
for element in data:
if element < pivot:
less.append(UpperC... | 484 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
a_ : Optional[int] = (3, 9, -1_1, 0, 7, 5, 1, -1)
a_ : str = (4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class lowerCamelCase__ :
"""simple d... | 484 | 1 |
'''simple docstring'''
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> np.ndarray:
return input_array.reshape((input_array.size, 1) )
... | 288 |
'''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 A__ ( _snake_case ):
lo... | 288 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase : Optional[Any] ={
"""configuration_bridgetower""": [
"""BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BridgeTowerConfig"... | 715 |
UpperCAmelCase : Any =0 # The first color of the flag.
UpperCAmelCase : Optional[int] =1 # The second color of the flag.
UpperCAmelCase : Optional[Any] =2 # The third color of the flag.
UpperCAmelCase : Union[str, Any] =(red, white, blue)
def _low... | 504 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase : List[Any] ={"configuration_plbart": ["PLBART_PRETRAIN... | 440 |
'''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 Deco... | 440 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.mo... | 47 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from... | 47 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 131 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case : int = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",... | 131 | 1 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
A = True
except ImportError:
A ... | 708 | import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCamelCase ( UpperCamelCase : Union[str, Any] , UpperCamelCase : Dict , UpperCamelCase : Union[str, Any] , UpperCamelCase : Optio... | 234 | 0 |
import unittest
import numpy as np
def UpperCAmelCase__ ( __snake_case , __snake_case , __snake_case , __snake_case = None , ) -> np.ndarray:
_A = np.shape(__A )
_A = np.shape(__A )
_A = np.shape(__A )
if sha... | 317 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCAmelCase = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__UpperCAmelC... | 651 | 0 |
'''simple docstring'''
import argparse
import collections
import os
import re
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_table.py
_SCREAMING_SNAKE_CASE = "src/t... | 709 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("check_bouncy() accepts only integer arguments" )
_lowerCAmelCase = str(SCREAMING_SNA... | 489 | 0 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
A__ : Optional[int] = logging.get_logger(__name__)
class __magic_name__ ( SCREAMING_SNAKE_CASE__ ):
def __init__( self , A_=None , **A_ ) -> Tuple:
... | 353 |
"""simple docstring"""
def _lowerCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
stooge(_UpperCamelCase , 0 , len(_UpperCamelCase ) - 1 )
return arr
def _lowerCAmelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple ... | 353 | 1 |
from __future__ import annotations
from typing import TypedDict
class __SCREAMING_SNAKE_CASE ( _A ):
_UpperCAmelCase : List[str] = 4_2
_UpperCAmelCase : str = 4_2
def _a ( UpperCAmelCase ) -> List[str]:
"""simpl... | 710 |
from __future__ import annotations
def _a ( UpperCAmelCase ) -> bool:
"""simple docstring"""
lowerCamelCase__ : List[Any] = len(UpperCAmelCase )
# We need to create solution object to save path.
lowerCamelCase__ : Any = [[0 for _ ... | 130 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers... | 311 |
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
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_... | 311 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __UpperCamelCase ( __lowerCamelCase : Optional[int] ) -> Dict:
... | 700 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
... | 276 | 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
fro... | 82 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase (_snake_case ):
'''simple docstring'''
_snake_case : Tuple = ['''image_processor''', '''tokenizer''']
... | 406 | 0 |
import math
import random
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
__lowerCamelCase = 0.02
def UpperCAmelCase__ (... | 703 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 667 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
'studio-ousia/luke-large': 'https://hug... | 43 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCAmelCase = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui ... | 43 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Tuple = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalD... | 94 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 200) -> int:
'''simple docstring'''
__UpperCamelCase : Any = [1, 2, 5, 10, 20, 50, 100, 200]
__UpperCamelCase : Any = [0] * (pence + 1)
__UpperCamelCas... | 94 | 1 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNA... | 84 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"huggingface/informer-tourism-monthly": (
"https://huggingface.co/huggingface/informer-tourism-monthly/r... | 307 | 0 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def _lowerCamelCase ( UpperCAmelCase_ : int, UpperCAmelCase_ : int ) -> bool:
"""simple docstring"""
return (
num != den and num % 10 ==... | 562 |
"""simple docstring"""
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn... | 562 | 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
from ...utils.backbone_utils import BackboneConfigMixin, get_al... | 58 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
fro... | 649 | 0 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCAmelCase__ :
'''simple docstring'''
pass
| 714 |
from math import sqrt
def a ( _UpperCAmelCase : int ):
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
__UpperCAmelCase : Optional[int] ... | 241 | 0 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCamelCase ( lowercase__ : str ) -> str:
'''simple docstring'''
return "".join(sorted(lowercase__ ) )
def __UpperCamelCase ( lowercase__ : ... | 600 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __a ( unittest.TestCase ):
@require_torch
def A (... | 600 | 1 |
from manim import *
class lowercase ( _UpperCAmelCase ):
'''simple docstring'''
def lowercase__ ( self : List[str] ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE__ : ... | 717 |
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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybrid... | 250 | 0 |
"""simple docstring"""
import math
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 , _SCREAMING_SNAKE_CASE = 0 ) ->list:
"""simple docstring"""
lowerCAmelCase__ :List[str] = end or len(_SCREAMING_SNAKE_CASE )
for i in range(_SCREAMING_SNAKE_CASE , _... | 93 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 63 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class __lowerCAmelCase ( A ):
U... | 701 |
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
while second != 0:
_UpperCAmelCase = first & second
first ^= second
_UpperCAmelCase = c << 1
return first
if __name__ == "__main__... | 639 | 0 |
'''simple docstring'''
from statistics import mean, stdev
def a__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int = 3 ) -> list:
"""simple docstring"""
UpperCAmelCase_ : Dict = min(_SCREAMING_SNAKE_CASE )
UpperCAmelCase... | 71 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
class a__( snake_case__ ):
def __init__( self , *_UpperCAmelCase , **_Upp... | 538 | 0 |
"""simple docstring"""
def __snake_case ( SCREAMING_SNAKE_CASE: int = 200 ):
"""simple docstring"""
_lowerCAmelCase = [1, 2, 5, 10, 20, 50, 100, 200]
_lowerCAmelCase = [0] * (pence + 1)
_lowerCAmelCase = 1 # base case: 1 way to make 0... | 491 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_snake_case = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parse... | 491 | 1 |
"""simple docstring"""
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 _lowerCAmelCase ( a_ ):
... | 93 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar('''KEY''')
__lowerCamelCase : int = TypeVar('''VAL''')
@dataclas... | 653 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-tr... | 186 |
import argparse
import os
import re
SCREAMING_SNAKE_CASE = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE = re.compile(R'[A-Z_]+_MAP... | 186 | 1 |
"""simple docstring"""
import math
def lowerCamelCase_( _lowerCamelCase ) -> list[int]:
'''simple docstring'''
_lowerCamelCase : Any = []
_lowerCamelCase : Optional[int] = 2
_lowerCamelCase : Tuple = int(math.sqrt(_lowerC... | 46 |
import re
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(_A , _A ) )
if __name__ == "__main__":
_A = '''0094702343221'... | 431 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Tuple =logging.get_logger(__name__)
class _UpperCAmelCase ( UpperCAmelCase_ ):
... | 707 | """simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _UpperCAmelC... | 558 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = '▁'
__lowerCAmelCase = {'vocab_file': 'spiece.model'}
__lo... | 585 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowerCAmelCase ( unittest.Te... | 585 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE ... | 717 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
... | 0 | 0 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
lowercase_ = '''h... | 154 |
from math import isqrt
def lowerCAmelCase ( UpperCAmelCase ) ->bool:
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2, isqrt(UpperCAmelCase ) + 1 ) )
def lowerCAmelCase ( U... | 154 | 1 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate... | 481 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
... | 481 | 1 |
"""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_pipe... | 103 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : int = f'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCamelCase__ )
i... | 407 | 0 |
'''simple docstring'''
def _a ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case : int = [False] * len(lowerCAmelCase_ )
_snake_case : Tuple = []
queue.append(lo... | 47 |
'''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,
AutoM... | 47 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_com... | 101 |
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
lowercase_ ... | 291 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
... | 393 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise TypeError("""Input value must be an 'int' type""" )
lowerCAmelCa... | 393 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : List[str] = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
... | 390 |
from math import factorial
def __a ( lowerCAmelCase_ : int = 1_00 ) -> int:
'''simple docstring'''
return sum(int(lowerCAmelCase_ ) for x in str(factorial(lowerCAmelCase_ ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the N... | 593 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase : Tuple = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SqueezeBertConfig""",... | 700 |
from copy import deepcopy
class A__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase__ : list[int] | None = None , lowerCamelCase__ : int | None = None ):
if arr is None and size is not None:
a__ : Uni... | 151 | 0 |
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> int:
assert column_title.isupper()
_lowercase : Optional[Any] = 0
_lowercase : Optional[Any] = len(SCREAMING_SNAKE_CASE ) - 1
_lowercase : Optional[int] = 0
whi... | 66 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 464 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ = {
'''config... | 420 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def __snake_case ( lowercase : int = 1_000_000 , lowercase : int = 10 ):
snake_case_ = defaultdict(lowercase )
for outer_width in range(3 , (t_limit // 4) + 2 ... | 420 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : Any = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Optiona... | 257 | """simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDi... | 277 | 0 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 1000 ) -> int:
'''simple docstring'''
UpperCAmelCase : Dict = 1
UpperCAmelCase : Union[str, Any] = 0
... | 713 |
'''simple docstring'''
UpperCamelCase__: dict[tuple[int, int, int], int] = {}
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> int:
# if we are absent twice, or late 3 conse... | 528 | 0 |
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, prepare_image_inputs
if is_torch_available... | 121 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_AR... | 121 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from t... | 711 |
import re
import string
import numpy as np
import datasets
SCREAMING_SNAKE_CASE_:int = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
SCREAMING_SNAKE_CASE_:Union[str, Any] = """
Args:
pr... | 520 | 0 |
'''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():
from .tok... | 120 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floa... | 147 | 0 |
"""simple docstring"""
def lowercase (snake_case__ : str ) -> str:
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 529 |
"""simple docstring"""
import argparse
import collections
import os
import re
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_table.py
a = 'src/transformers'... | 529 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
els... | 357 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def A_ ( __lowercase ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unic... | 357 | 1 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class lowercase__ ( unittest.TestCase , _Up... | 400 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
... | 400 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils impor... | 76 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1... | 370 | 0 |
import math
import flax.linen as nn
import jax.numpy as jnp
def A ( snake_case :str , snake_case :Optional[Any] , snake_case :Optional[int] = 1 , snake_case :Union[str, Any] = 1 , snake_case :Union[str, Any] = 1.0e4 , snake_case :L... | 710 |
"""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,
)
UpperCamelCase : List[Any] = {
... | 293 | 0 |
from __future__ import annotations
def _lowercase ( lowercase__ , lowercase__ , lowercase__ ):
__lowerCAmelCase : Optional[int] = list(range(len(lowercase__ ) ) )
__lowerCAmelCase : int = [v / w for v, w in zip(lowercase__ , lowercase__ )]
index.... | 492 |
# Copyright (c) 2021-, NVIDIA CORPORATION. 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 ap... | 492 | 1 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : Tuple = logging.get_logger(__name__)
lowerCAmelCase__ : Tuple = {
... | 632 | """simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | 632 | 1 |
lowerCamelCase_ : Tuple = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Expo... | 548 |
from __future__ import annotations
import time
lowerCamelCase_ : Union[str, Any] = list[tuple[int, int]]
lowerCamelCase_ : str = [
[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],
[1, 0, 1, 0,... | 548 | 1 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@req... | 615 |
"""simple docstring"""
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_ba... | 615 | 1 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowerCamelCase = logging.get_logger("""transformers.models.speecht5""")
def a__ ( lowerCAmelCase__ ... | 82 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : str = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'... | 614 | 0 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE__ = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def lowercase__ ( )-> Union[str, Any]:
UpperCamelCase = os.path.dirname(o... | 718 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> str:
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__UpperCamelCase , __Up... | 35 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils... | 71 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCamelCase = {
"""configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT... | 71 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
A__ : List[str] = logging.get_logger(__name__)
class lowercase__ ( lowerCamelCase__ ):
def __init__( self : Dict , ... | 702 |
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
A__ : ... | 244 | 0 |
"""simple docstring"""
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 lowerCamelCase__ ( A ):
... | 139 |
"""simple docstring"""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCamelCase__ ( A , A ):
"... | 139 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
resca... | 710 |
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase : int , _lowerCamelCase : int ) -> int:
while a != 0:
lowerCamelCase_ , lowerCamelCase_ = b % a, a
return b
def lowerCamelCase__ ( _lowerCamelCase : int , _lo... | 137 | 0 |
"""simple docstring"""
def _a ( _snake_case = 100_0000 ):
"""simple docstring"""
UpperCAmelCase = set(range(3 , _snake_case , 2 ) )
primes.add(2 )
for p in range(3 , _snake_case , 2 ):
if p not in primes:
... | 341 |
"""simple docstring"""
import operator as op
def _a ( _snake_case ):
"""simple docstring"""
UpperCAmelCase = []
UpperCAmelCase = lambda _snake_case , _snake_case : int(x / y ) # noqa: E731 integer division operation
Upper... | 341 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__lowerCAmelCase : int = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm"... | 284 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):... | 284 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_t... | 21 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerat... | 261 | 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 ap... | 448 |
def A__ ( _a : list ):
'''simple docstring'''
if len(_a ) <= 1:
return [tuple(_a )]
snake_case__ : Optional[int] =[]
def generate(_a : int , _a : list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , _a )
for i i... | 448 | 1 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 422 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Optional[int] = logging.get_logger(__name__)
__lowercase : Optional[int] = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/... | 422 | 1 |
"""simple docstring"""
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class... | 222 |
"""simple docstring"""
from manim import *
class __a ( lowerCAmelCase__ ):
def snake_case_ ( self ):
_lowerCamelCase = Rectangle(height=0.5 , width=0.5 )
_lowerCamelCase = Rectangle(height=0.46 , width=0.46 )... | 222 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase : str = logging.get_logger(__name__)
lowerCamelCase : List[str] ... | 70 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
... | 426 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switchi... | 388 |
import warnings
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
snake_case_ = logging.get_logger(__name__)
snake_case_ ... | 388 | 1 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
UpperCamelCase_: Union[str, Any] = str(bin(__a ) )
binary_number += "0" * shift_amount
retur... | 548 |
"""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.u... | 437 | 0 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __magic_name__ ( unit... | 89 |
def a (_lowerCAmelCase ):
if number > 0:
raise ValueError('''input must be a negative integer''' )
SCREAMING_SNAKE_CASE_ = len(bin(_lowerCAmelCase )[3:] )
SCREAMING_SNAKE_CASE_ = bin(abs(_lowerCAmelCase ) - (1 << binary_number_length)... | 89 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = ... | 1 |
'''simple docstring'''
import random
from typing import Any
def __lowercase ( __lowercase ) -> list[Any]:
'''simple docstring'''
for _ in range(len(__lowercase ) ):
_A = random.randint(0 , len(__lowercase ) - 1 )
_A = random... | 330 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> int:
return 1 if input_a == input_a else 0
def __UpperCAmelCase ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xno... | 717 |
"""simple docstring"""
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... | 122 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetectio... | 617 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.test... | 323 | 0 |
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 import AutoImageProcessor, ResNe... | 207 |
from collections.abc import Callable
def SCREAMING_SNAKE_CASE( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> float:
a__ : float = a
a__ : float = b
if function(__UpperCamelCase ) == 0: # one of the a or b is a root for the function
... | 207 | 1 |
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