code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__: Op... | 23 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase_ )
class UpperCamelCase__ ( lowercase_ ):
"""simple doc... | 224 | 0 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> list[int]:
'''simple docstring'''
lowerCAmelCase : Any = int(_UpperCAmelCase )
# Initialize Result
lowerCAmelCase : Dict = []
# Traverse through all denomination
fo... | 323 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) ... | 323 | 1 |
import re
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
return [char.split() for char in re.split(r'''[^ a-z A-Z 0-9 \s]''' , str_ )]
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
__lowercase = ... | 210 | from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__a : List[str] = Lock()
def UpperCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ,... | 210 | 1 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interl... | 285 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> int:
assert (
isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and number_of_steps > 0
), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
if number_of_steps == 1:
re... | 285 | 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():
... | 125 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case_ : Union[str, Any] = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix... | 125 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.command... | 38 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e impo... | 38 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onn... | 87 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__UpperCAm... | 84 | 0 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_tim... | 267 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> Tuple:
"""simple docstring"""
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3... | 267 | 1 |
"""simple docstring"""
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pyte... | 153 | import math
import random
def lowerCAmelCase_ ( __A, __A = False ) -> float:
'''simple docstring'''
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
UpperCamelCase__ = 0.0_... | 65 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE : Optional[Any] = {'''configuration_deit''': ['''DEIT_PRETRAINED_CON... | 369 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
SCREAMING_SNAKE_CASE : List[str] = False
class __lowerCamelCase... | 317 | 0 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
UpperCamelCase = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""stor... | 186 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {
"""configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", "... | 186 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : List[Any] = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
if not is_torch_av... | 130 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
'''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
# See ... | 130 | 1 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 284 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_... | 284 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase__( __SCREAMING_SNAKE_CASE : Union[str, Any] ):
return [ord(_lowerCAmelCase ) - 96 for elem in plain]
def lowercase__( __SCREAMING_SNAKE_CASE : Tuple ):
return "".join(chr(elem + 96 ) for ... | 371 | """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_att... | 321 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : list[int] , lowercase : list[int] ) -> None:
_a = len(lowercase )
print("The following activities are selected:" )
# The first activity is always selected
_a = 0
print... | 63 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_UpperCAmelCase : Optional[int] ="""src/transformers"""
_Up... | 262 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCamelCase : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotA... | 114 |
'''simple docstring'''
from math import factorial
def _lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : float ) -> float:
"""simple docstring"""
if successes > trials:
raise ValueError('successes mu... | 114 | 1 |
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> int:
'''simple docstring'''
assert x is not None
assert y is not None
UpperCamelCase = len(UpperCamelCase_ )
UpperCamelCase = len(UpperCamelCase_ )
# declaring the array for storing the dp values
Uppe... | 343 | import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UN... | 343 | 1 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaToken... | 348 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
... | 348 | 1 |
"""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 ... | 126 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
... | 126 | 1 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__lowerCAmelCase = logging.get_logger(__name__)
class __a ( _UpperCAmelCase ):
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> List[Any... | 355 |
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_inpu... | 288 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import Heun... | 132 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_... | 132 | 1 |
"""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 ImageProcessingSavingTestM... | 166 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = ... | 166 | 1 |
"""simple docstring"""
import logging
from transformers import PretrainedConfig
lowerCAmelCase = logging.getLogger(__name__)
lowerCAmelCase = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/reso... | 126 |
"""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__)
lowerCAmel... | 126 | 1 |
import random
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> tuple:
lowerCamelCase , lowerCamelCase , lowerCamelCase =[], [], []
for element in data:
if element < pivot:
less.append(_UpperCAmelCase )
elif element > pivot:
... | 262 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
... | 262 | 1 |
import argparse
import os
import re
import packaging.version
lowerCAmelCase__ = """examples/"""
lowerCAmelCase__ = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(R"""^__vers... | 68 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultis... | 265 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ... | 350 |
"""simple docstring"""
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
... | 313 | 0 |
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
UpperCAmelCase__ = datasets.utils.logging.get_logger(_... | 5 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if not is_torch_available():
raise Opti... | 5 | 1 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urlli... | 180 |
import math
import sys
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> int:
if number != int(__UpperCamelCase):
raise ValueError("the value of input must be a natural number")
if number < 0:
raise ValueError("the value of input must not be a negative number")
... | 180 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
UpperCamelCase_ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('''''', '''|''', '''|'''),
... | 345 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def lowercase (SCREAMING_SNAKE_CASE_ : Callable[[int | float], int | float] , SCREAMING_SNAKE_CASE_ : int | float , SCREAMING_SNAKE_CASE_ : int | float , SCREAMING_SNAKE_CASE_ :... | 113 | 0 |
'''simple docstring'''
def __A ( lowerCAmelCase_ , lowerCAmelCase_ ):
if digit_amount > 0:
return round(number - int(lowerCAmelCase__ ) , lowerCAmelCase__ )
return number - int(lowerCAmelCase__ )
if __name__ == "__main__":
print(decimal_isolate(1.5_3, 0))
print(decimal_isolate(3_5.3... | 359 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def __A ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = None , l... | 170 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def lowercase__ ( __UpperCamelCase )-> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 321 |
import string
def UpperCamelCase (lowercase_: str ) -> None:
for key in range(len(string.ascii_uppercase ) ):
A__ : Dict = """"""
for symbol in message:
if symbol in string.ascii_uppercase:
A__ : Dict = string.ascii_uppercase.find(lowercase... | 192 | 0 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, ... | 240 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
__UpperCAmelCase :Dict = log... | 240 | 1 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
UpperCAmelCase = input('''Enter image url: ''').strip()
print(F'''Downloading image from {url} ...''')
UpperCAmelCase = BeautifulSoup(requests.get(u... | 141 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_g... | 141 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTe... | 268 | """simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__:Tu... | 268 | 1 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data i... | 109 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: List[str] = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CL... | 109 | 1 |
"""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_torch_avail... | 24 |
"""simple docstring"""
def lowercase ( _snake_case : int ) ->str:
"""simple docstring"""
if number > 0:
raise ValueError('''input must be a negative integer''' )
__snake_case : Any = len(bin(_snake_case )[3:] )
__snake_case : List[Any] ... | 24 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 289 | """simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class a :
def __init__( self : Union[str, Any] ):
_UpperCAmelCase = {}
def lowerCAmelCase_ ( self : Optional[int] , __lowerCAmelCase ... | 289 | 1 |
__UpperCAmelCase = [
(10_00, "M"),
(9_00, "CM"),
(5_00, "D"),
(4_00, "CD"),
(1_00, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = {'''... | 257 |
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, FlaxMTaF... | 257 | 1 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
UpperCAmelCase = len(_UpperCAmelCase )
UpperCAmelCase = [[0] * n for i in range(_UpperCAmelCase )]
for i in range(_UpperCAmelCase ):
Up... | 273 |
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 ... | 226 | 0 |
"""simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def SCREAMI... | 2 | """simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Config... | 2 | 1 |
import json
import sys
def UpperCAmelCase ( a_ , a_ ) -> Dict:
"""simple docstring"""
with open(lowerCAmelCase__ , encoding="utf-8" ) as f:
__A = json.load(lowerCAmelCase__ )
__A = ['''<details>''', '''<summary>Show updated benchmarks!</summary>'... | 15 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> int:
UpperCAmelCase__ : Tuple = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def a__ ( lowerCAmelCase__ = 1_00 ) -> int:
Up... | 181 | 0 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> set[str]:
lowerCAmelCase__ : Optional[Any] = set(SCREAMING_SNAKE_CASE_ ), [start]
while stack:
lowerCAmelCase__ : List[Any] = stac... | 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"""
from __future__ import annotations
import math
def lowerCamelCase ( _UpperCamelCase : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or numbe... | 115 |
"""simple docstring"""
from importlib import import_module
from .logging import get_logger
UpperCAmelCase : Any = get_logger(__name__)
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] , UpperCamelCase : List[A... | 115 | 1 |
'''simple docstring'''
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchE... | 160 |
'''simple docstring'''
import torch
from transformers import AutoModel
class __UpperCAmelCase ( torch.nn.Module ):
def __init__( self , lowerCAmelCase_="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
super(lowerCAmelCase_ , self ).... | 160 | 1 |
def _snake_case ( lowerCAmelCase : Any ):
"""simple docstring"""
return " ".join(
"".join(word[::-1] ) if len(lowerCAmelCase__ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroir... | 18 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer,... | 101 | 0 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
lowercase__ = n - k
# Calculate C(n,k)
for i in range(SCREAMING_SNAKE... | 93 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_seri... | 93 | 1 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class _a :
"""simple docstring"""
def __init__( self : Optional[Any] )->Any:
_UpperCAmelCase = {}
def lowercase... | 260 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A : int = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE ... | 260 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase : List[str] ):
'''simple docstring'''
stooge(__lowerCAmelCase , 0 , len(__lowerCAmelCase ) - 1 )
return arr
def _lowerCAmelCase ( UpperCAmelCase : List[Any] , UpperCAmel... | 350 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/li... | 157 | 0 |
'''simple docstring'''
def lowerCamelCase ( __lowerCamelCase : int = 1000 ) ->int:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 1, 1
_SCREAMING_SNAKE_CASE = []
for i in range(1 , n + 1 ):
_SCREAMING_SNAKE_CASE = prev_n... | 58 |
'''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 OptionalDep... | 58 | 1 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
... | 352 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class a__( tf.keras.optimizers.schedules.LearningRateSchedu... | 96 | 0 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase = 10 , UpperCAmelCase = 22 ) ->int:
"""simple docstring"""
a_ = range(1 , UpperCAmelCase )
a_ = range(1 , UpperCAmelCase )
return sum(
1 for power in powers for base in bases ... | 243 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils ... | 243 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
__lowerCAm... | 5 |
'''simple docstring'''
def UpperCAmelCase_ (__a : str ):
"""simple docstring"""
_a : List[Any] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_a : Optional[int] = ''
_a : List[str] = ''
# append each c... | 5 | 1 |
'''simple docstring'''
import baseaa
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
return baseaa.baaencode(string.encode("""utf-8""" ) )
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
... | 70 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_ver... | 176 | 0 |
def a_ ( __lowercase : int , __lowercase : float , __lowercase : float ) -> float:
return round(float(moles / volume ) * nfactor )
def a_ ( __lowercase : float , __lowercase : float , __lowercase : float ) -> float:
return round(... | 130 |
def a_ ( __lowercase : int = 50_000_000 ) -> int:
_snake_case = set()
_snake_case = int((limit - 24) ** (1 / 2) )
_snake_case = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ... | 130 | 1 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _a ( unittest.TestCase ):
def __snake_case (self ) -> None:
UpperCAmelCase_: Union[str, Any] =... | 147 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a : List[str] = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionE... | 147 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase : int = logging.get_logger(__name__)
lowerCamelCase : List[str] = {'vocab_file': 'sentencepiece... | 356 |
def SCREAMING_SNAKE_CASE__ ( ) -> Dict:
snake_case : Optional[int] = []
snake_case : Tuple = 1
while len(lowercase ) < 1E6:
constant.append(str(lowercase ) )
i += 1
snake_case : int = """""".join(lowercase )
... | 176 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__SCREAMING_SNAKE_CASE :Union[str, Any] = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_A... | 22 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unisp... | 320 | 0 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowercase =100
lowercase =set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowercase =42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.d... | 242 |
'''simple docstring'''
from __future__ import annotations
import bisect
def lowerCamelCase__ ( __lowerCamelCase : list[int] , __lowerCamelCase : int , __lowerCamelCase : int = 0 , __lowerCamelCase : int = -1 ):
'''simple docstring'''
if hi < ... | 242 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHI... | 69 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
snake_case__ : Union[str, Any] = 500000
snake_case__ , snake_case__ : Optional[Any] = os.path.split(... | 117 | 0 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __a ( ):
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_... | 354 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_a = logging.get_logge... | 23 | 0 |
'''simple docstring'''
from __future__ import annotations
class UpperCAmelCase :
def __init__( self : Dict , __snake_case : int ) -> Tuple:
_lowerCAmelCase = order
# a_{0} ... a_{k}
_lowerC... | 70 |
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> int:
'''simple docstring'''
def update_area_of_max_square(UpperCamelCase_ , UpperCamelCase_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
SCREAMING_SN... | 176 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=lowercase ):
UpperCAmelCase : Any = ["""onnx"""]
def __init__(self : Union[str, Any] , *_A : int , **_A : Tuple) -> Union[... | 95 | """simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, D... | 95 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _SCREAMING_SNAKE_CASE ( unittest.TestCase , _a ):
def _A ( self : Union[str, Any] ):
UpperCamelCase :Union[str, Any] = load_tool("""text-classification... | 38 |
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
lowercase_ : str = 'scheduler_config.json'
class __lowerCAmelCase ( UpperCAmelCase__ ):
snake_case_ : List... | 133 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> Optional[Any]:
snake_case : Tuple = AutoConfig.from_pretrained(__lowerCam... | 351 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ... | 176 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 238 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.... | 238 | 1 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
"""kakaobrain/align-base""": """http... | 35 | 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,
)
lowercase = {
"""configuration_xlm_roberta""... | 35 | 1 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
ren... | 130 |
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 | 1 |
import requests
_A = 'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str ):
# fetching a list of articles in json format
__UpperCamelCase =requests.get(_NEWS_API + bbc_news_api_key ).json()
#... | 117 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
_A = logging.get_logger(__name__)
_A = {'vocab_file': 'vocab.txt', 'token... | 117 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import Blend... | 328 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_op... | 293 | 0 |
"""simple docstring"""
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, requir... | 230 |
"""simple docstring"""
from typing import Any
class UpperCAmelCase_ :
def __init__( self : Optional[Any] , snake_case_ : Any ) -> List[str]:
'''simple docstring'''
A__ = data
A__ = None
def __repr__( self : Optional[int] ... | 230 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
a :List[str] = 50_000
a :Union[str, Any] = 5_000
a ,a :Dict = os.path.split(__file__)
a :Dict = os.path.join(RESULTS_BASEPATH, "resu... | 132 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_... | 11 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transf... | 229 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowercase = datasets.logging.get_logger(__name__)
_lowercase = """\
@InProceedings{moosavi2019minimum,
author ... | 229 | 1 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def lowercase ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Tuple ) -> Optional[int]:
_... | 45 |
from __future__ import annotations
def UpperCamelCase ( _A ): # This function is recursive
"""simple docstring"""
__magic_name__ : str = len(_A )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
... | 342 | 0 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__snake_case = datasets.load_iris()
__snake_case = np.array(data['''data'''])
__snake_case = np.array(data['''target'''])
__snake_case... | 153 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__snake_case = 500000
__snake_case ,__snake_case = os.path.split(__file__)
__snake_case = os.path.join(RESULTS_BASEPATH, '''results''... | 153 | 1 |
"""simple docstring"""
from torch import nn
class __lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , _a , _a ):
super().__init__()
__a = class_size
__a = embed_size
# self.mlp1 = nn.Li... | 45 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def snake_case_ ( lowerCAmelCase_ )-> typing.Counter[int]:
'''simple docstring'''
_UpperCAmelCase : typing.Counter[int] = Counter()
for base in ra... | 215 | 0 |
"""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_con... | 112 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch... | 112 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case ( _lowerCAmelCase ):
'''simple docstring'''
@staticmethod
@abstractmethod
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : ArgumentParser ):
'''simple... | 266 |
"""simple docstring"""
import re
def lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
return [char.split() for char in re.split(r'''[^ a-z A-Z 0-9 \s]''' , str_ )]
def lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
__A ... | 266 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowercase_ = JukeboxTokenizer
lowercase_ = {
"artist": "Zac Brown Band",
"g... | 273 |
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_configuration_common import ConfigTester... | 273 | 1 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
UpperCamelCase : Any = (boundary[1] - boundary[0]) / steps
UpperCamelCase : List[Any] = boundary[0]
UpperCamelCase : List[st... | 52 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceF... | 52 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test,... | 362 |
"""simple docstring"""
def _A ( lowercase = 2_00_00_00 ):
"""simple docstring"""
a =[0 for i in range(n + 1 )]
a =1
a =1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 215 | 0 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class lower... | 11 |
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 OptionalDependencyNotAvai... | 11 | 1 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from t... | 368 | '''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[... | 21 | 0 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
_A : Union[str, Any] =['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamel... | 41 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a__( unittest.TestCase ):
... | 272 | 0 |
"""simple docstring"""
from __future__ import annotations
lowercase__ : Optional[Any] = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class a__ :
def __init__( sel... | 352 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowercase__ : Dict = [
["attention", "attn"],
... | 180 | 0 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Pa... | 312 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __snake_case ( __UpperCamelCase : NDArray[floataa] ,__UpperCamelCase : NDArray[floataa] ,__UpperCamelCase : list[int] ,__UpperCamelCase ... | 312 | 1 |
'''simple docstring'''
import math
import os
import sys
def __lowerCamelCase ( __lowerCAmelCase : str ) -> str:
snake_case = """"""
try:
with open(__snake_case , """rb""" ) as binary_file:
snake_case = binary_fi... | 355 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __lowerCamelCase ( __lowerCA... | 3 | 0 |
def UpperCamelCase ( __lowercase : int = 1_00_00_00 ):
'''simple docstring'''
A_ : Any = set(range(3 ,__lowercase ,2 ) )
primes.add(2 )
for p in range(3 ,__lowercase ,2 ):
if p not in primes:
continue
primes.difference_update(set(... | 140 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class __magic_name__ ( __lowerCAmelCase):
def __init__( self :... | 146 | 0 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __lowerCamelCase ( lowerCamelCase__ : int ... | 66 |
UpperCAmelCase : Tuple = "Tobias Carryer"
from time import time
class __lowercase :
"""simple docstring"""
def __init__( self , A , A , A , A=int(time() ) ) -> Optional[int]: # noqa: B008
'''simple docstring'''
... | 66 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironmen... | 273 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
i... | 20 | 0 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowerCAmelCase : Union[str, Any] = logging... | 308 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencep... | 308 | 1 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class UpperCAm... | 62 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase ... | 82 | 0 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _UpperCAmelCase ( datasets.BuilderConfig):
_lowerCAmelCase : ... | 369 |
"""simple docstring"""
def __lowercase ( _a = 4_000_000 ):
snake_case_ : Dict = []
snake_case_, snake_case_ : List[str] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_a )
snake_case_, snake_case_ : str = b,... | 155 | 0 |
import math
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
__A = 0
__A = 0
while num > 0:
__A = num % 8
__A = octal + (remainder * math.floor(math.pow(1_0 , a_ ) ))
counter += 1
__A = math.floor(nu... | 15 |
'''simple docstring'''
import os
import pytest
from attr import dataclass
_snake_case = 'us-east-1' # defaults region
@dataclass
class a__ :
_SCREAMING_SNAKE_CASE : str
_SCREAMING_SNAKE_CASE : str = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
_SCREAMING... | 250 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : float ) -> float:
'''simple docstring'''
if edge <= 0 or not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ... | 95 | """simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
while a != 0:
__snake_case , __snake_case : Union[str, Any] = b % a, a
return b
def __UpperC... | 95 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class UpperCamelCase__ (pl.LightningModule ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ ... | 48 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,... | 193 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __lowerCamelCase ( ... | 153 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def A_ ( _lowerCAmelCase : Callable, _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float ):
"""simp... | 153 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision... | 81 |
"""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 imp... | 202 | 0 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = "x" , lowerCAmelCase__ = 10**-10 , lowerCAmelCase__ = 1 , ):
... | 359 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .m... | 241 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regress... | 348 | import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__snake_case = logging.get_logger(__name__)
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
... | 348 | 1 |
'''simple docstring'''
from math import sqrt
def UpperCamelCase ( _lowerCamelCase : int ):
assert isinstance(_UpperCamelCase , _UpperCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
A__ = True
# 0 and 1 are none primes.
if nu... | 368 |
'''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 | 0 |
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