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 |
|---|---|---|---|---|
'''simple docstring'''
from math import ceil
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
snake_case_ = list(range(0, lowerCAmelCase__ ) )
snake_case_ = [item for sublist in list(de... | 640 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A_ = {
"""configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}
try:
... | 29 | 0 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class lowercase( __a ):
'''simple docstring'''
def UpperCamelCase_ ( self: int, a_: Any=None, a_: Optional[int]=None, a_: Dict=None, **a_: D... | 713 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
if not isinstance(snake_case__ , snake_case__ ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
_snake_case : Dict = 0
... | 28 | 0 |
def lowerCamelCase__ ( _A ):
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError("check_bouncy() accepts only integer arguments" )
snake_case_ = str(_lowerCamelCase )
snake_case_ ... | 376 |
"""simple docstring"""
from __future__ import annotations
from random import random
class A_ :
def __init__( self: List[str] ,__lowerCAmelCase: int | None = None ):
'''simple docstring'''
_lowerCamelCase : Any = value
_lowerC... | 46 | 0 |
def lowerCamelCase__ ( _lowerCamelCase = 50 ) ->int:
_UpperCAmelCase =[1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_numb... | 592 |
# 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 ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
... | 592 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common impo... | 149 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared ... | 149 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
# See all V... | 160 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_( metaclass=SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
__lowercase : List[str] = ['''torch''']
def __init__( self ,*__UpperCAmelCase ,**__UpperCAmelCase ... | 160 | 1 |
def __lowercase ( snake_case ):
"""simple docstring"""
if not isinstance(snake_case, snake_case ):
__magic_name__ :Union[str, Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(snake_case )
if number < 0:
return ... | 0 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
... | 0 | 1 |
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()
lowercase__ =[
'word_embeddings_layernorm.weight',
'word_... | 326 |
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... | 326 | 1 |
import requests
from bsa import BeautifulSoup
def __lowerCamelCase (UpperCAmelCase__ : str = "AAPL" ):
SCREAMING_SNAKE_CASE = F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(UpperCAmelCase__ ... | 403 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
lowercase_ = 42
lowercase_ = 42
class A ... | 22 | 0 |
from bisect import bisect
from itertools import accumulate
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :Tuple , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :List[Any] , SCREAMING_SNAKE_CASE :Optional[int] ) -> Any:
__lowerCAmelCase : List[Any] = ... | 714 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
W... | 240 | 0 |
'''simple docstring'''
def UpperCAmelCase ( UpperCAmelCase__ : Dict):
lowerCamelCase : Union[str, Any] = 0
lowerCamelCase : Optional[Any] = len(UpperCAmelCase__)
for i in range(n - 1):
for j in range(i + 1 , UpperCAm... | 320 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
A = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 320 | 1 |
'''simple docstring'''
from __future__ import annotations
def A_ ( snake_case , snake_case ):
SCREAMING_SNAKE_CASE:List[str] = set(snake_case ), [start]
while stack:
SCREAMING_SNAKE_CASE:Any = stack.pop()
explored.add(snake_case )
# D... | 711 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import flo... | 465 | 0 |
from math import pi
def UpperCamelCase_ ( __a , __a ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 37 |
"""simple docstring"""
import os
import sys
a_ = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Au... | 76 | 0 |
from __future__ import annotations
from collections.abc import Iterator
class __snake_case :
"""simple docstring"""
def __init__( self : str ,lowerCAmelCase__ : int ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : List[str] =... | 717 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = list(snake_case__)
lowerCAmelCase_ : Tuple = list(snake_case__)
lowerCAmel... | 683 | 0 |
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 : Any = logging.get_logger(__name__)
snake_case : Opti... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 | 1 |
# Imports
import numpy as np
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a=None , _a=None , _a=None , _a=None , _a=None ) -> Union[str, Any]:
self.set_matricies(red=_a , green=_a , blu... | 578 |
def __UpperCAmelCase ( __a : int ,__a : Optional[int] ) -> List[Any]:
"""simple docstring"""
_a : Tuple = (boundary[1] - boundary[0]) / steps
_a : List[str] = boundary[0]
_a : Tuple = boundary[1]
_a : Tuple ... | 578 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase : str = get_tests_dir(... | 686 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
logging... | 686 | 1 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class lowercase :
"""simple docstring"""
def __init__( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = ''''''
UpperCamelCase__ ... | 721 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
... | 280 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __magic_name__ ( _lowerCamelCase: list ) -> int:
'''simple docstring'''
if not postfix_notation:
return 0
lowerCAmelCase = {'''+''', '''-''', '''*''', '''/'''}
lowerCAmelCase ... | 535 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from tr... | 535 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ : Any = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 699 | def UpperCamelCase__ ( A__ ) -> list[int]:
if length <= 0 or not isinstance(A__ , A__ ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(A__ )]
if __name__ == "__main__":
print(hexagonal_numbers(lengt... | 699 | 1 |
import pytest
import datasets
# Import fixture modules as plugins
a_ :Union[str, Any] = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def a ( A__ , A__ ) -> Union[str, Any]:
'''simple docstring'''
for item in items:
if a... | 35 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__a : Any = logging.get_logger(__name__)
class __lowercase ( lowercase_ ):
'''simple docstring'''
def __init__( self : Union[str, Any] , *Upper... | 637 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
lowerCamelCase__ = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}
try:
if not is_to... | 82 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',
'''TableTransform... | 82 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Optional[int] ={
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if not is_to... | 399 |
'''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... | 399 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCamelCase : Optional[Any] = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokeniz... | 712 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase : int = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'T... | 118 | 0 |
'''simple docstring'''
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 688 |
'''simple docstring'''
import inspect
import unittest
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ) -> Dict:
'''simple docstring'''
try:
import diffusers ... | 688 | 1 |
'''simple docstring'''
import os
import sys
import transformers
A_ = "3"
print("Python version:", sys.version)
print("transformers version:", transformers.__version__)
try:
import torch
print("Torch version:", torch.__version__)
print("Cuda available:", torch.cu... | 705 |
'''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,
re... | 465 | 0 |
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,
... | 164 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , a = 6):
lowercase__ : Node | None = None
lowercase__ : Node | None = None
self.create_linked_list(a)
def snake_case_ ( self , a)... | 164 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] = logging.get_logger(__name__)
_A : str = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/u... | 718 |
import math
def _a ( UpperCAmelCase ) -> str:
"""simple docstring"""
lowerCamelCase__ : List[Any] = 0
lowerCamelCase__ : List[Any] = 0
while num > 0:
lowerCamelCase__ : Tuple = num % 8
lowerCamelCas... | 130 | 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 lowerCAmelCase_ :
"""simple docstring"""
__UpperCamelCase : int
... | 223 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCAmelCase__ : Optional[Any] = {'UserAgent': UserAgent().random}
def lowercase_ ( _snake_case ):
SCREAMIN... | 223 | 1 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoin... | 704 |
import requests
__A = "" # <-- Put your OpenWeatherMap appid here!
__A = "https://api.openweathermap.org/data/2.5/"
def lowerCamelCase_ ( UpperCamelCase__ : str = "Chicago" , UpperCamelCase__ : str = APPID ) -> dict:
"""simple docst... | 167 | 0 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbedding... | 169 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( _a ):
_a = (DDIMParallelScheduler,)
_a = (('eta', 0.0), ('num_inference_steps', 50))
... | 169 | 1 |
from typing import List
import numpy as np
def A (__A : dict ) -> int:
"""simple docstring"""
UpperCAmelCase_ = {key: len(__A ) for key, value in gen_kwargs.items() if isinstance(__A , __A )}
if len(set(lists_lengths.values() )... | 169 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class __snake_case ( a , a ):
... | 169 | 1 |
from PIL import Image
def _A ( __magic_name__ ):
lowercase__ , lowercase__ = image.size
lowercase__ = 0
lowercase__ = image.load()
for i in range(__magic_name__ ):
for j in range(__magic_name__ ):
lowercase__ = pixels[j, i]
... | 655 |
_snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def _A ( __magic_name__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''a bytes-like object is re... | 655 | 1 |
lowercase : Optional[int] = {str(digit): digit**5 for digit in range(1_0)}
def UpperCAmelCase_ ( _UpperCAmelCase ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_UpperCAmelCase ) )
def UpperCAmelCase_ ( ):
return sum(
numbe... | 584 | 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 TFModelTeste... | 584 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import... | 673 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configura... | 673 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
low... | 703 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 102 | 0 |
"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey
def __A (_SCREAMING_SNAKE_CASE ) ->int:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ :Optional[Any] = img.shape[0], img.shape[1]
# converting each pixel's color ... | 93 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class _snake_case ( a_ ):
SCREAMING_SNAKE_CA... | 284 | 0 |
'''simple docstring'''
import operator
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = None ):
SCREAMING_SNAKE_CASE_ :str = operator.lt if reverse else operator.gt
SCREAMING_SNAKE_CASE_ :int = solution ... | 233 |
'''simple docstring'''
from math import sqrt
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE = 100_0000 ):
SCREAMING_SNAKE_CASE_ :int = 0
SCREAMING_SNAKE_CASE_ :int = 0
SCREAMING_SNAKE_CASE_ :int
while num_cuboids <= limit:
max_cuboid_size += 1
for su... | 233 | 1 |
"""simple docstring"""
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/... | 134 |
"""simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
__A ... | 134 | 1 |
A_ = 8.3144598
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> float:
"""simple docstring"""
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
raise Excepti... | 479 | import logging
from transformers import PretrainedConfig
A_ = logging.getLogger(__name__)
A_ = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}
class __lowercase ... | 479 | 1 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__A = None
try:
import msvcrt
except ImportError:
__A = None
try:
import fcntl
except ImportError:
__A = None
# Backward compati... | 93 |
import os
import sys
import unittest
lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
... | 230 | 0 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATAS... | 721 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_ver... | 307 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_A = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''PoolFormerConfig''',
'''PoolFormerOnnxConfig... | 431 |
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 accelerate import Accelerator, ... | 431 | 1 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 244 |
"""simple docstring"""
def _snake_case ( lowerCamelCase__ : str ) -> list:
lowerCamelCase_ : Union[str, Any] =[0] * len(lowerCamelCase__ )
for i in range(1 , len(lowerCamelCase__ ) ):
# use last results for better pe... | 244 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : str , __snake_case : str ) -> int:
if len(__snake_case ) != len(__snake_case ):
raise ValueError('String lengths must match!' )
__A : Optional[Any] = 0
... | 8 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
# TODO Update this
__snake_case : Union[str, Any] = ... | 215 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ = {'''tokenization_byt5''': ['''ByT5Tokenizer''']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()['''__fil... | 711 | import os
import time
import numpy as np
import onnxruntime as ort
UpperCAmelCase_ = '''1'''
UpperCAmelCase_ = '''0'''
UpperCAmelCase_ = '''1'''
UpperCAmelCase_ = ort.SessionOptions()
UpperCAmelCase_ = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Cr... | 264 | 0 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
A = '''\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew and
Dor... | 125 |
import math
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> list:
__lowerCamelCase : Union[str, Any] = [True] * n
__lowerCamelCase : List[Any] = False
__lowerCamelCase : int = False
__lowerCamelCase : An... | 652 | 0 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowerCamelCase_ : Dict = HfApi()
lowerCamelCase_ : str = {}
# fmt: off
lowerCamelCase_ : Tuple = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.... | 345 |
import os
def __lowercase( ) -> Tuple:
with open(os.path.dirname(__snake_case ) + '/grid.txt' ) as f:
__snake_case = [] # noqa: E741
for _ in range(20 ):
l.append([int(__snake_case ) for x in f.readline().split()] )
... | 345 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''I... | 657 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=lowerCamelCase ):
lowercase_ : Dict = ['''torch''', '''torchsde''']
def __init__( self , *a_ , **a_ ) -> Optional[int]:
requires_backends(self ,... | 657 | 1 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
impo... | 106 |
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
@require_tokenizers
@require_sentencepiece
@slow # see ... | 106 | 1 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
A = logging.get_logger(__name__)
class lowercase__ ( __SCREAMI... | 475 |
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__ ( uni... | 475 | 1 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class ... | 157 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase (__lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase_ = ["image_processor", "tokenizer"]
UpperCA... | 157 | 1 |
'''simple docstring'''
from string import ascii_uppercase
a : str = {str(ord(c) - 55): c for c in ascii_uppercase}
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> str:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int = 1_00_00_00 ) -> int:
__snake_case = 1
__snake_case = 1
__snake_case = {1: 1}
for inputa in range(2 , _UpperCAmelCase ):
__snake_case = 0
__snake_case = inputa
... | 69 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__lowerCamelCase = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available... | 667 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> float:
_validate_point(UpperCAmelCase__ )
_validate_point(UpperCAmelCase__ )
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError("""B... | 667 | 1 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/con... | 467 |
'''simple docstring'''
def a__ ( UpperCamelCase_ : int | float | str ):
try:
UpperCAmelCase__ :Union[str, Any] = float(UpperCamelCase_ )
except ValueError:
raise ValueError('''Please enter a valid number''' )
UpperCAmelCase__ :List[str] ... | 467 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _low... | 462 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _lowerCamelCase ( UpperCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = '''M-CLIP'''
def __init__( self , __SCREAMING_SNAKE_CASE=1_0_2_4 , ... | 462 | 1 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
return x + 2
class _snake_case ( unittest.TestCase):
def A__ ( self : Union[str, Any... | 413 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCamelCase_ : List[Any] = logging.get_logger(__name__)
class lowerCamelCase__ ( __lowerCamelCase ):
"""simple docstring"""
def ... | 331 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, tor... | 713 | 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,
TFAutoModel... | 699 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TimeSeriesTransformerConfig""",
],... | 472 |
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
'''simple docstring'''
return "\n".join(
F'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5... | 472 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : Optional[Any] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingf... | 702 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
snake_case_ : Optional[Any] = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_AR... | 191 | 0 |
import unittest
import numpy as np
from transformers import RobertaConfig, 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():
from transformers.mo... | 354 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
lowercase_ = datasets.load_iris()
lowercase_ = np.array(data['''data'''])
lowercase_ = np.array(data['''target'''])
lowercase_ ... | 354 | 1 |
'''simple docstring'''
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __lowerCamelCase ( __snake_case : Dataset, __snake_case : Dict[str, str] ) -> Any:
... | 687 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case : List[str] = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeB... | 687 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCamelCase ( metaclass=__snake_case):
__lowerCamelCase = ["torch", "torchsde"]
def __init__(self , *lowerCamelCase__ , **lowerCamelCase__ ):
... | 574 |
"""simple docstring"""
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( UpperCamelCase : Any , UpperCamelCase : int , UpperCamelCase : Any ):
A__ = {
"""en""": "... | 574 | 1 |
"""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... | 505 |
"""simple docstring"""
class lowercase_ :
'''simple docstring'''
def __init__( self : List[str] , _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : Dict ):
_A = name
_A = value
_A = weight
... | 505 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
A = list[tuple[int, int]]
A = [
[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,... | 52 | def _UpperCamelCase ( snake_case__, snake_case__ ) -> str:
if not isinstance(snake_case__, snake_case__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(snake_case__, snake_case__ ) or not number >= 1:
raise V... | 382 | 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()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvai... | 637 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : list[int] , _snake_case : int ):
if len(_snake_case ) == 0:
return False
lowerCAmelCase : List[Any] = len(_snake_case ) // 2
if a_list[midpoint] ... | 637 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import In... | 112 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvisio... | 88 | 0 |
import re
import string
import numpy as np
import datasets
__lowerCamelCase : Optional[int] = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
__lowerCamelCase : Tuple ... | 707 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCamelCase : Optional[int] = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_roc... | 457 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCAmelCase = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
... | 180 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transforme... | 421 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=__lowerCamelCase ):
lowerCamelCase_ =['onnx']
def __init__( self : Any , *__lowerCAmelCase : List[Any] , **__lowerCAmelCase : str) -> Tuple:
req... | 703 | '''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCAmelCase_ : Union[str, Any] =... | 461 | 0 |
"""simple docstring"""
def lowercase_ ( ) -> int:
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(__UpperCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
... | 299 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_A = logging.get_logger(__name__)
class _lowerCamelCase ( a_ ):
def __init__( self : str , *UpperCamelCase : int , **Upper... | 299 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten... | 716 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Any = logging.get_logger(__name__)
lowercase : str = {
'''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
# See all Do... | 114 | 0 |
"""simple docstring"""
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 _snake_case ( lowercase... | 630 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ... | 630 | 1 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCamelCase : Optional[int] ) -> Dict:
"""simple docstring"""
__magic_name__ : Tuple = []
__magic_name__ : Dict = set({'''(''', '''[''', '''{'''} )
__magic_name__ : str = ... | 719 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
A = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@require... | 147 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multi... | 39 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
... | 638 | 0 |
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__ ( __lowerCAmelCase : Any ): # picklable for multiprocessing
ret... | 714 |
'''simple docstring'''
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_... | 9 | 0 |
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in va... | 15 |
"""simple docstring"""
import os
import sys
a_ = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Au... | 76 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
_UpperCAmelCase : List[Any] ... | 3 |
from abc import ABC, abstractmethod
from typing import List, Optional
class lowercase ( _SCREAMING_SNAKE_CASE ):
def __init__( self ) -> Optional[Any]:
"""simple docstring"""
# test for the above condition
self.test()
def __UpperCamelCase ( self ) -> ... | 3 | 1 |
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
a__ : Dict = logging.get_logger(__name__)
a__ : Dict = {... | 622 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _lowerCAmelCase ( ):
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from... | 622 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelC... | 721 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrai... | 79 | 0 |
def UpperCamelCase__ ( _A: float , _A: int ):
'''simple docstring'''
if digit_amount > 0:
return round(number - int(_A ) , _A )
return number - int(_A )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(deci... | 479 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class UpperCamelCase_ ( datasets.BeamBasedBuilder ):
"""simple docstring"""
def lowerCa... | 479 | 1 |
def A ( lowercase , lowercase , lowercase ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(lowerca... | 3 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A ( lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase = int(lowercase )
assert noofclusters < len(lowercase )
# Find out the dimensionality
UpperCamelCase ... | 3 | 1 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE = 100 ) -> int:
"""simple docstring"""
_A = n * (n + 1) * (2 * n + 1) / 6
_A = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(... | 27 |
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
if not isinstance(__a , __a ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
SCREAMING_SNAKE_CASE : int ... | 258 | 0 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase=False ):
'''simple docstring'''
A_ : Tuple = OmegaConf.load(_lowerCAmelCase )
if display:
prin... | 481 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,):
'''simple docstring'''
A_ , A_ : int = coefficient_matrix.shape... | 481 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identif... | 497 | import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME,... | 216 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : Tuple = logging.get_logger(__name__)
lowerCamelCase__ : Optional[Any] = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/se... | 706 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | 495 | 0 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def lowerCAmelCase__( lowercase : bool = True , *lowercase : Union[str, Any] , **lowercase : List[str] ) -> List[str]... | 243 |
import tensorflow as tf
from ...tf_utils import shape_list
class _lowerCamelCase ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=1... | 243 | 1 |
"""simple docstring"""
import math
class A__ :
"""simple docstring"""
def a__ ( self: str , __a: list[list[float]] , __a: list[int] )-> int:
lowerCamelCase : Dict = 0.0
lowerCamelCase : Tuple = 0.0
f... | 42 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atten... | 42 | 1 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec... | 2 |
'''simple docstring'''
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCamelCase : Dict = logg... | 310 | 0 |
'''simple docstring'''
import numpy as np
import qiskit
def __UpperCAmelCase ( _UpperCAmelCase : int = 8 , _UpperCAmelCase : int | None = None ) -> str:
__snake_case = np.random.default_rng(seed=_UpperCAmelCase )
# Roughly 25% of the qubits will contribute t... | 680 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("dataset_size" , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize("input_in_memory_max_size" , ["default", 0, 1_00 * 2**20, 9_00 *... | 680 | 1 |
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_roberta import RobertaTokenizer
lower... | 547 | import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.modeling_fl... | 547 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=lowerCamelCase_ ):
a_: int = ["""note_seq"""]
def __init__( self : Union[str, Any] , *lowerCamelCase_ : str , **lowerCamelCase_ : List[str] ):
... | 713 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regr... | 149 | 0 |
# Algorithm for the pigeonhole sorting
def _SCREAMING_SNAKE_CASE ( lowercase : str ):
'''simple docstring'''
lowerCamelCase_ = min(lowercase ) # min() finds the minimum value
lowerCamelCase_ = max(lowercase ) # max() finds the ... | 70 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_snake_case = "scheduler_config.json"
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 1
lowe... | 500 | 0 |
"""simple docstring"""
import numpy as np
class a :
def __init__( self ):
UpperCAmelCase__ : Optional[Any] = (0, 0)
UpperCAmelCase__ : int = None
UpperCAmelCase__ : str = 0
UpperCAmelCase__ : List[... | 254 |
"""simple docstring"""
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 ge... | 254 | 1 |
from manim import *
class _snake_case ( UpperCAmelCase_ ):
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : str = Rectangle(height=0.5 , width=0.5)
lowercase__ : Tuple = Rectangle(height=0.4_6 , width=0.4_6).set_stroke(w... | 12 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, v... | 12 | 1 |
import math
def snake_case_ ( lowercase__ : int ):
'''simple docstring'''
_lowerCAmelCase =math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(a_ )
def snake_case_ ( lowercase__ : float = 1 / 1_23_45... | 718 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, ... | 149 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
lowerCAmelCase__: Optional[Any] = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
lowerCAmelCase__: Union[str, Any]... | 345 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images... | 460 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cac... | 118 |
"""simple docstring"""
def snake_case (A_ :int , A_ :int ):
'''simple docstring'''
return base * power(A_ , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...')
_UpperCamelCase : A... | 118 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
lowerCamelCase = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSp... | 474 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""facebook/xm... | 474 | 1 |
'''simple docstring'''
def a ( __a , __a ) -> int:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = 0
UpperCamelCase__ :int = len(snake_case__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sort... | 700 |
'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils imp... | 280 | 0 |
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