code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone imp... | 100 |
'''simple docstring'''
import math
class __A :
'''simple docstring'''
def __init__(self , A=0 ) -> Dict: # a graph with Node 0,1,...,N-1
"""simple docstring"""
_a = n
_a = [
[math.inf for j in range(0 , A )] for i in range... | 211 | 0 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_SCREAMING_SNAKE_CASE : Optional[int] ... | 92 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_SCREAMING_SNAKE_CASE : Optional[Any] = "docs/source/en/_toctree.yml"
def UpperCamelCase_( snake_case : Optional[Any] ):
'''simple docstring'''
snake_case_ ... | 92 | 1 |
import csv
import tweepy
# Twitter API credentials
a =""""""
a =""""""
a =""""""
a =""""""
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> None:
# authorize twitter, initialize tweepy
__lowerCamelCase : Tuple = tweepy.OAuthHandler(lowerCamelCase__ ... | 73 |
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 A_ ( SCREAMING_SNAKE_CASE ):
... | 73 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A_ = abs(UpperCAmelCase__ )
A_ = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
A... | 364 |
'''simple docstring'''
import sys
__lowerCamelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'... | 101 | 0 |
"""simple docstring"""
import datasets
__lowerCamelCase = "\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n ... | 221 | """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,
genera... | 221 | 1 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def __UpperCamelCase ( lowercase__ : dict ) -> tuple:
'''simple do... | 357 |
from math import factorial, pi
def __UpperCamelCase ( lowercase__ : float , lowercase__ : int = 30 ) -> float:
'''simple docstring'''
if not isinstance(lowercase__ , (int, float) ):
raise ValueError("""maclaurin_sin() requires either an int or... | 28 | 0 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_ar... | 77 | """simple docstring"""
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def a_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int] , _lo... | 77 | 1 |
"""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 __SCREAMING_SNAKE_CASE ( _Uppe... | 310 |
"""simple docstring"""
import torch
from transformers import AutoModel
class __SCREAMING_SNAKE_CASE ( torch.nn.Module ):
'''simple docstring'''
def __init__( self : Dict , __a : Tuple="sayef/fsner-bert-base-uncased" ) -> Dict:
super(__a , ... | 310 | 1 |
'''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,
ConditionalDetrForObje... | 344 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
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 I... | 344 | 1 |
'''simple docstring'''
lowerCAmelCase__ = {
'''meter''': '''m''',
'''kilometer''': '''km''',
'''megametre''': '''Mm''',
'''gigametre''': '''Gm''',
'''terametre''': '''Tm''',
'''petametre''': '''Pm''',
'''exametre''': '''Em''',
'''zettametre''': '''Zm''',
'''yottamet... | 353 |
'''simple docstring'''
def _A ( A__ = 1000 ):
"""simple docstring"""
__lowercase , __lowercase = 1, 1
__lowercase = 2
while True:
__lowercase = 0
__lowercase = fa + fa
__lowercase , __lowercase = fa, f
index += 1
for _... | 52 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
fro... | 283 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gy... | 283 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = None
SCREAMI... | 37 |
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase ):
try:
__UpperCAmelCase : Union[str, Any] = float(_UpperCAmelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
__UpperCAmelCase : str = decimal - int(_U... | 37 | 1 |
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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"facebook/deit-base-... | 299 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
... | 299 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
snake_case : Union[str, Any] = namedtuple('''covid_data''', '''cases deaths recovered''')
def __lowercase ( __lowerCAmelCase : str = "https://www.worldometers.info/coronavirus/"... | 109 |
def __lowercase ( __lowerCAmelCase : int ):
if num <= 0:
raise ValueError('Input must be a positive integer' )
a__ = [True] * (num + 1)
a__ = 2
while p * p <= num:
if primes[p]:
for i i... | 109 | 1 |
"""simple docstring"""
from maths.prime_check import is_prime
def _lowercase ( __lowerCAmelCase ) -> Tuple:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = F'''Input value of [number={number... | 132 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase = 5_0_0_0_0_0
_UpperCAmelCase, _UpperCAmelCase = os.path.split(__file__)
_UpperCAmelCase = os.path.join(RESULTS... | 173 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotA... | 63 | import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNet... | 63 | 1 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
impo... | 165 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-... | 260 | 0 |
import fire
from utils import calculate_rouge, save_json
def a( A : Any , A : int , A : Tuple=None , **A : Union[str, Any] ) -> Any:
"""simple docstring"""
a = [x.strip() for x in open(A ).readlines()]
a = [x.strip() for x in open(A )... | 71 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowercase: List[Any] = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd... | 71 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = analyze_text(lowerCamelCase__ )
__SCREAMING_SNAKE_CASE = list("... | 100 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.ba... | 130 | 0 |
"""simple docstring"""
import json
import sys
def lowercase ( _snake_case : Optional[int] , _snake_case : Optional[int] ) ->List[str]:
"""simple docstring"""
with open(_snake_case , encoding='''utf-8''' ) as f:
__snake_case : Optiona... | 24 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
SCREAMING_SNAKE_CASE : int = datasets.utils.logging.get_logger(__name__)
@dataclass
cla... | 24 | 1 |
from functools import lru_cache
@lru_cache
def UpperCamelCase_( _snake_case : List[str] ):
"""simple docstring"""
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __... | 218 |
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 = lo... | 39 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__lowerCAmelCase : str =5_0_0_0_0
__lowerCAmelCase : List[Any] =5_0_0_0
__lowerCAmelCase , __lowerCAmelCase : Optional[Any] =os.p... | 32 | """simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
__lowerCAmelCase : Optional[Any] =logging.getLogger(__name__)
@dataclass
class ... | 32 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_tor... | 126 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
fro... | 159 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, ... | 360 | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 143 | 0 |
from __future__ import annotations
from collections import namedtuple
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> tuple:
'''simple docstring'''
UpperCAmelCase = namedtuple('''result''' , '''name value''' )
if (vol... | 273 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url... | 273 | 1 |
"""simple docstring"""
import random
from .binary_exp_mod import bin_exp_mod
def _A ( lowercase , lowercase=10_00 ):
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
a =... | 215 |
"""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.testi... | 215 | 1 |
'''simple docstring'''
def __lowerCamelCase ( __snake_case : int, __snake_case : int ) -> int:
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(__snake_case, x % y )
def __lowerCamelCase ( __snake_case ... | 134 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : List[str] = ... | 134 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ra... | 140 |
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 transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 140 | 1 |
'''simple docstring'''
def snake_case_ (_a : int ):
UpperCAmelCase = (1 + 2_4 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def snake_case_ (_a : int = 5_0_0_0 ):
UpperCAmelCase = [(i * (3 * i - 1)) // 2 for i in range(1 , ... | 34 |
'''simple docstring'''
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,
LMSDiscreteSchedule... | 75 | 0 |
"""simple docstring"""
import torch
from torch import nn
class lowercase( nn.Module ):
'''simple docstring'''
def __init__( self: Any, a_: List[str], a_: Union[str, Any], a_: int, a_: int, a_: List[Any]=1, a_: Union[str, Any]=Fa... | 132 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
_snake_case : Optional[Any] = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def UpperCAmelCase__ (snake_case__ : int = 50_00 ):
"""simple docstring"""
... | 132 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_c... | 104 | 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
A : Tuple = datasets.utils.logging.get_logger(__nam... | 118 | 0 |
'''simple docstring'''
from collections.abc import Callable
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] , snake_case_ : Callable | None = None ):
# Stores actual heap items.
snake_case__ : list = ... | 43 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import ... | 43 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
a_ =["""keras_nlp"""]
def __init__( self : Optional[int] , *_a : Tupl... | 208 |
'''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
f... | 208 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenize... | 371 | from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...ut... | 78 | 0 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = {}
UpperCamelCase = job["started_at"]
UpperCamelCase = job["com... | 153 |
"""simple docstring"""
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = set(_SCREAMING_SNAKE_CASE ), [start]
while stack:
UpperCamelCase = stack.pop()
... | 153 | 1 |
from __future__ import annotations
def _a ( UpperCAmelCase , UpperCAmelCase ) -> list[tuple[int, int]]:
"""simple docstring"""
lowerCamelCase__ : Dict = position
lowerCamelCase__ : Optional[Any] = [
(y + 1, x + 2),
... | 368 |
from math import factorial
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , A : Dict , A : Any ) ->Optional[Any]:
lowerCamelCase__ : Tuple = real
if isinstance(A , A ):
... | 265 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}
class SCREAMING_SNAKE_CASE ( ... | 302 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert impo... | 302 | 1 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
snake_case__ : Optional[Any] = False
class snake_case_( unittest.T... | 355 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_te... | 314 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase ( _lowerCamelCase , ... | 215 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transf... | 215 | 1 |
def __UpperCAmelCase ( __a : int ) -> bool:
"""simple docstring"""
if not isinstance(__a ,__a ):
_a : Any = F"""Input value of [number={number}] must be an integer"""
raise TypeError(__a )
if number < 0:
... | 15 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
a__ = ['''small''', '''medium''', '''large''']
a__ = '''lm_head.decoder.weight'''
a__ = '''lm_head.weight'''
def __UpperCAmelCase ( __a : str ,__a : str ) -> ... | 15 | 1 |
"""simple docstring"""
import math
def snake_case__ ( __lowerCamelCase : list , __lowerCamelCase : int = 0 , __lowerCamelCase : int = 0 ):
"""simple docstring"""
lowerCamelCase__ : List[str] =end or len(__lowerCamelCase )
for i in range(__lowerCamelCa... | 238 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {"""configuration_xglm""": ["""... | 59 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
if not... | 175 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 175 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class _lowerCAmelCase ( a ):
"""simp... | 293 |
"""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_M... | 293 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase ... | 370 |
'''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.... | 184 | 0 |
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
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
... | 65 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case : Optional[int] = {
"configuration_distilbert": [
"DISTILBERT_PRETRAI... | 123 | 0 |
from __future__ import annotations
from math import ceil, floor, sqrt
def snake_case( __magic_name__ = 2_00_00_00 ) -> int:
'''simple docstring'''
lowercase : list[int] = [0]
lowercase : int
for idx in range(1 , ceil(... | 116 |
import math
def snake_case( __magic_name__ ) -> bool:
'''simple docstring'''
lowercase : Union[str, Any] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__magic_name__ )
def sna... | 116 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
def snake_case__ ( _A: Tuple , _A: Optional[int] , _A: Tuple , _A: int , _A: List[str] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase = f"{file}... | 272 | '''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def snake_case__ ( _A: str ) -> str:
'''simple docstring'''
if not sentence:
return ""
lowerCAmelCase = dict(zip(_A , _A ) )
return lower_to_upper.get(sentence[0] , sentence[0] ) ... | 272 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
Diffusio... | 368 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import U... | 307 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, id... | 264 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__a = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,
}
def __lo... | 337 | 0 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch... | 363 |
from __future__ import annotations
from typing import Any
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : list[Any] ):
create_state_space_tree(SCREAMING_SNAKE_CASE__ , [] , 0 )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : list[Any] , SCR... | 117 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : List[str] = len(_lowerCAmelCase ) + 1
lowercase__ : Any = len(_lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i]... | 77 |
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : Optional[int] = set()
# edges = list of graph's edges
lowerCAmelCase_ : List[str] = get_edges(lowerCAmelCase_ )
# While there are still elements in edges list, take an arbitrary ed... | 262 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''googl... | 364 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __lowerCamelCase ( __magic_name__ : List[Any] ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def __lowerCamelCase ... | 42 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models... | 74 |
"""simple docstring"""
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : int ,A_ : int ) -> Union[str, Any]:
A = n
A = [None] * self.n
A = 0 # index of the first element
A = ... | 74 | 1 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__snake_case : int = """http://www.mo... | 122 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_ut... | 122 | 1 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
lowercase_ : Union[str, Any] = logging.getLogger(__name__)
if is_torch_tpu_available(check... | 133 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
'''sim... | 133 | 1 |
from __future__ import annotations
import os
from typing import Any
import requests
lowercase_ = """https://api.github.com"""
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_ = BASE_URL + """/user"""
# https://g... | 269 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_we... | 269 | 1 |
"""simple docstring"""
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
a_ = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
a_ = [file for file in fil... | 249 |
import doctest
from collections import deque
import numpy as np
class A_ :
'''simple docstring'''
def __init__(self ) -> None:
__UpperCAmelCase = [2, 1, 2, -1]
__UpperCAmelCase = [1, 2, 3, 4]
def lowerCAmelCase_ (self ... | 333 | 0 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A( UpperCamelCase , unittest.... | 357 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import Prio... | 208 | 0 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = (UnCLIPScheduler,)
def _snake_case ( self , **lowercase ) -... | 46 |
"""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,
... | 249 | 0 |
"""simple docstring"""
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
__A = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.... | 108 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
__A = "src/transformers"
# Matches is_xxx_available()
__A = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__A ... | 108 | 1 |
'''simple docstring'''
import math
import sys
def a__ ( lowercase : int ) -> int:
"""simple docstring"""
if number != int(lowercase ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise Valu... | 324 |
'''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 .t... | 324 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cu... | 123 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase ( UpperCamelCase__ ):
__lowercase = (PNDMScheduler,)
__lowercase = (("""num_inference_steps""", 50),)
def ... | 123 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[Any] = logging.get_logger(__name__)
_snake_case : List[str] = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/conf... | 292 |
"""simple docstring"""
from math import isqrt, loga
def A__ ( UpperCamelCase ):
A = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCamelCase , UpperC... | 292 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load... | 131 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
def get_matched_characters(_UpperCAmelCase , _UpperCAmelCase ) -> str:
__a = []
__a = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
... | 131 | 1 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
lowerCAmelCase__ = datasets.load_iris()
lowerCAmelCase__ = np.array(data['''data'''])
lowerCAmelCase__ = np.array(data['''target'''])
lowerCA... | 153 |
"""simple docstring"""
import re
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = re.compile(r"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$" )
if match := re.search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return match.string == phone
r... | 153 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from .... | 358 |
import numpy
class a :
"""simple docstring"""
def __init__( self : str , lowerCamelCase : numpy.ndarray , lowerCamelCase : numpy.ndarray ) -> None:
__snake_case : Any = input_array
# Random initial weights are a... | 134 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'hustvl/... | 61 |
def UpperCamelCase (lowercase_: int = 10 ) -> str:
if not isinstance(lowercase_ , lowercase_ ) or n < 0:
raise ValueError("""Invalid input""" )
A__ : List[str] = 10**n
A__ : Any = 28433 * (pow(2 , 7830457 , lowercase_ ))... | 192 | 0 |
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 AcceleratorState
from accelerate.test_util... | 362 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_UpperCAmelCase = {
'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}
try:
if not is_torch_availabl... | 232 | 0 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def a__ ( snake_case , snake_case=1 ):
"""simple docstring"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split('''.''' )[n_shave_prefix_segments:] )
els... | 303 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class __UpperCa... | 303 | 1 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE_ ... | 368 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
A_ : Union[str, Any] =logging.get_logger(__name__)
class __a ( lowerCAmelCase__ ):
def __init__( self , *a__ , **a__ ):
... | 80 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Optional[Any] = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig',
],
}
try:
... | 142 |
def _a ( UpperCAmelCase , UpperCAmelCase ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
lowerCamelCase__ : List[str] = str(bin(UpperCAmelCase ) )[2:] # remove ... | 142 | 1 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors im... | 369 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import... | 144 | 0 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__snake_case = logging.get_logger(_... | 97 |
'''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 ImageProcessingSavingTestMixin, prepare... | 97 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase :List[str] = logging.get_logger(__name__)
__UpperCAmelCase :Tuple = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.... | 368 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common i... | 240 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : float ):
if edge <= 0 or not isinstance(_snake_case , _snake_case ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def _snake_case ( _snake_... | 60 | import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A ( unittest.TestCase ):
def lo... | 65 | 0 |
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxrunti... | 352 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowercase__ ( l... | 310 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (A , A , A , A ) -> int:
"""simple docstring"""
lowercase__ = [False] * len(A )
lowercase__ = []
queue.append(A )
lowercase__ = True
while queue:
... | 2 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMix... | 28 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : str = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "C... | 368 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sag... | 308 | 0 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
i... | 295 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
... | 244 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
... | 357 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 160 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@r... | 220 |
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_attention_mas... | 306 | 0 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1_337 , num_examples=42 , dataset_name="m... | 370 |
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 Accele... | 151 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder impor... | 278 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''google/vit-base-patch16-224''': '''https://hu... | 278 | 1 |
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = word.split()
def justify(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> str:
... | 369 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteSchedul... | 324 | 0 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
__snake_case : List[Any] = logging.get_logger(__name__)
class A__ ( l... | 269 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
A__ : Any = '''EncodecFeatureExtractor'''
A__ : ... | 156 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
UpperCamelCase : List[str] ... | 345 | '''simple docstring'''
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impo... | 345 | 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,
rescale,
resize,
to_channel_d... | 309 |
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''': 1,
} # Priority of each operator
SCREAMING_SNAKE_CASE_ ... | 299 | 0 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
... | 50 | import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTester... | 50 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int, _lowerCAmelCase : int ):
"""simple docstring"""
return number | (1 << position)
def A_ ( _lowerCAmelCase : int, _lowerCAmelCase : int ):
"""simple docstring"""
re... | 320 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class __lowerCamelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_a = {}
def _UpperCAmelCase ( s... | 320 | 1 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.te... | 362 | '''simple docstring'''
def lowerCamelCase ( ) -> Dict:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
lowerc... | 21 | 0 |
from datetime import datetime as dt
import os
from github import Github
UpperCamelCase = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def lowercase_ ( ):
lowercase_... | 87 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
... | 316 | 0 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common impo... | 34 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase_ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
lowerCamelCase_ : Dict = 'all_checks'
lowerCamelCas... | 34 | 1 |
_UpperCAmelCase = tuple[float, float, float]
_UpperCAmelCase = tuple[float, float, float]
def UpperCamelCase ( __lowercase : Pointad ,__lowercase : Pointad ):
'''simple docstring'''
A_ : List[str] = end_pointa[0] - end_pointa[0]
A_ : ... | 140 | import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_UpperCAmelCase = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""kernel"""... | 140 | 1 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv... | 217 |
'''simple docstring'''
def _lowerCAmelCase ( lowerCamelCase_ : int = 6_0_0_8_5_1_4_7_5_1_4_3 ):
try:
__lowercase = int(lowerCamelCase_ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n ... | 217 | 1 |
def A (__A : Dict , __A : int ) -> Optional[int]:
"""simple docstring"""
if not isinstance(__A , __A ):
raise ValueError('''iterations must be defined as integers''' )
if not isinstance(__A , __A ) o... | 51 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try... | 110 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main... | 370 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTeste... | 307 | 0 |
'''simple docstring'''
import pytest
__snake_case = '''__dummy_dataset1__'''
__snake_case = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", ... | 97 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
__snake_case = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def a ( ) -> List[s... | 97 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
... | 367 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( __snake_cas... | 311 | 0 |
'''simple docstring'''
import qiskit
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE = 2 ):
_snake_case = qubits
# Using Aer's simulator
_snake_case = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circuit acting on the q register
... | 341 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@re... | 341 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowercase : List[Any] = logging.get_logger(__name__)
class __snake_case ( lowerCAmelCase ):
def __init__( self ,*snake_case ,**snake_case ):
'''simple docstring'''
... | 285 |
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
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : List[Any] = {
... | 285 | 1 |
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