code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''... | 14 |
a = 8.314_462 # Unit - J mol-1 K-1
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
ret... | 518 | 0 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class A_ ( __lowercase ):
'''simple docstr... | 712 |
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def _lowerCamelCase ( __A : Optional[int] ) -> str:
return 1 / (1 + np.exp(-z... | 186 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common impo... | 48 |
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def lowerCamelCase ( _UpperCamelCase : list[int] , _UpperCamelCase : list[int] , _UpperCamelCase : int ) -> list[int]:
'''simple docstring'''
__UpperCAmelCase : List[Any] ... | 139 | 0 |
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("To use the rich extension, install rich with `pip install rich`")
| 702 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase__ = TypeVar("T")
class A_ ( Generic[T] ):
'''simple docstring'''
UpperCAmelCase_ : deque[T]... | 695 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart im... | 349 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A : str = {
"""configuration_layoutlmv3""": [
... | 349 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _snake_case ( _snake_case : str = "https://www.worldometers.info/coronavirus" ):
lowerCAmelCase : str = BeautifulSoup(requests.get(_snake_case ).text , '''html.parser''' )
lowerCAmelCas... | 717 |
"""simple docstring"""
def _snake_case ( _snake_case : float , _snake_case : list[float] ):
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty'''... | 637 | 0 |
'''simple docstring'''
def __lowercase ( __SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.te... | 582 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
SCREA... | 582 | 1 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenc... | 84 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 84 | 1 |
import doctest
from collections import deque
import numpy as np
class _A :
def __init__( self ):
_UpperCAmelCase = [2, 1, 2, -1]
_UpperCAmelCase = [1, 2, 3, 4]
def UpperCAmelCase ( self ):
... | 518 |
a = 8.314_462 # Unit - J mol-1 K-1
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
ret... | 518 | 1 |
def _lowercase ( _SCREAMING_SNAKE_CASE : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
__A : Tuple = set()
# Replace all the whitespace in our sentence
__A : Optional[Any] = inp... | 703 | """simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lice... | 237 | 0 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : int = []
__UpperCAmelCase : Optional[int] = []
__UpperCAmelCase : Union[str, Any] = ... | 77 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 77 | 1 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class __SCREAMING_SNAKE_CASE ( _a , unittest.TestCase ):
snake_case : Optional[int] = ... | 700 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
... | 548 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCom... | 459 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
fr... | 459 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = {}
... | 131 |
from math import pi, sqrt
def a__ ( snake_case ):
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math range error''' )
elif num - int(snake_case ) not in (0, 0.5):
raise NotImplemen... | 131 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import ... | 150 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler... | 124 | 0 |
'''simple docstring'''
class UpperCAmelCase :
def __init__(self : int , A__ : str = "" , A__ : bool = False ) -> Any:
# Mapping from the first character of the prefix of the node
lowercase = {}
# A node will be a leaf if the tree contai... | 715 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common imp... | 459 | 0 |
def __lowerCAmelCase ( UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Tuple ) -> bool:
lowerCamelCase_ = len(snake_case__ )
lowerCamelCase_ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value... | 272 |
import numpy as np
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ = 1E-12 , snake_case__ = 100 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(snake_case__ )[0] == np.shape(snake_case__ )[1]
# Ensure proper... | 193 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common imp... | 716 |
'''simple docstring'''
import os
import sys
import unittest
lowerCAmelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_m... | 39 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
lowercase__ : Optional[int] = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfi... | 390 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : Optional[int] = {
"sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.j... | 390 | 1 |
from math import isqrt, loga
def _SCREAMING_SNAKE_CASE ( snake_case_ : int ):
__magic_name__ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case_ , snake_case_ ):
... | 678 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
exce... | 678 | 1 |
from collections import deque
from math import floor
from random import random
from time import time
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self ) -> List[str]:
_a : Any = {}
def ... | 14 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-b... | 14 | 1 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class _A :
def __init__( self ):
"""simple docstring"""
lowercase = psutil.Process()
lowercase = ... | 714 | """simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniza... | 197 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timeste... | 94 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : Any = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',
],... | 64 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _a ( UpperCAmelCase ) -> T... | 713 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...t... | 130 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tens... | 629 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_uti... | 106 | 0 |
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
UpperCamelCase__ : List[str] = []
UpperCamelCase__ : str = []
UpperCamelCase__ : str = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
... | 702 |
from __future__ import annotations
from collections.abc import Callable
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 1_0_0 , ):
UpperCamelCase__ : Union[str, Any] = x_start
UpperCamelCase__ : List[Any] = ... | 462 | 0 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_swit... | 32 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , __a ):
__lowerCAmelCase = str(id_ )
__lowerCAmelCase = No... | 636 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowercase__ ={
'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
'tokenization_gpt_neox_japan... | 705 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def __UpperCamelCase ( lowerCAmelCase__ : Dataset , lowerCAmelCase__ : Dict[str, str] ... | 326 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> int:
return int((input_a, input_a).count(0 ) != 0 )
def _UpperCamelCase ( ) -> None:
assert nand_gate(0 ,0 ) == 1
assert nand_gate(0 ,1 ) == 1
assert nand_gate(1 ,0 ) ... | 42 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Tuple = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
... | 623 | 0 |
"""simple docstring"""
import numpy as np
def lowerCamelCase ( _snake_case ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 720 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ):
return sum(i for i in range(1 ,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
UpperCamelCase__ = int(input('E... | 254 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identif... | 42 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | 0 |
def SCREAMING_SNAKE_CASE ( ):
snake_case__ : Dict = 0
for i in range(1 , 1001 ):
total += i**i
return str(snake_case_ )[-10:]
if __name__ == "__main__":
print(solution())
| 721 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__lowerCamelCase : Union[str, Any] = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.... | 25 | 0 |
"""simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : list ) -> List[str]:
_UpperCAmelCase : int = len(_lowerCAmelCase )
for _ in range(_lowerCAmelCase ):
for i in range(_ % 2, arr_size - 1, 2 ):
if arr[i + 1] < arr[i]:
_U... | 238 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version impor... | 128 | 0 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase... | 592 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
snake_case__ : Tuple = logging.get_logger(__name__)
class _a ( A__ ):
"""simple docstring"""
def __init__( self , *_snake_case , **_snake_case... | 592 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
SCREAMI... | 89 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
SCREAMING_SNAKE_CASE : int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
SCREAMING_SNAKE_CASE : Dict = [file for fil... | 89 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHe... | 444 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageRes... | 444 | 1 |
from typing import List
import numpy as np
def __snake_case ( _UpperCamelCase ) -> int:
_a = {key: len(UpperCamelCase__ ) for key, value in gen_kwargs.items() if isinstance(UpperCamelCase__ , UpperCamelCase__ )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeEr... | 487 | from graphs.minimum_spanning_tree_kruskal import kruskal
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCamelCase__ = 9
UpperCamelCase__ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
... | 240 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
UpperCamelCase = logging.get_logger(__name__)
def __lowerCamelCase ( snake_case__ ) -> List[int]:
"""simple docstring"""
if isinstance(snake_case__... | 707 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 569 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction... | 698 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def __A ( a_ : Dict , a_ : int , a_ : str , a_ : Optional[Any]=None )-> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = (path or []) + [u]
for v in gr... | 698 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : ... | 187 |
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowercase ( _lowerCa... | 187 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ : Optional[int] = logging.get_logger(__name__)
A__ : str = {
"""camembert-base... | 13 |
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,
... | 304 | 0 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODE... | 709 | from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class lowercase_ ( __snake_case ):
def __i... | 580 | 0 |
from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase__ : List[str] =3_00 # TEMPERATURE (unit = K)
def a__ ( A__, A__, A__, ):
if donor_conc <= 0:
raise ValueError('Donor concentration should be positi... | 101 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''... | 14 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def _lowercase ( lowercase__ ):
__lowerCAmelCase : Union[str, Any] = args.pruning_method
__lowerCAmelCase : List[Any] = ... | 701 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"huggingface/time-series-transformer-tourism-monthly": (
"https://huggingface.co/huggingface/time-s... | 583 | 0 |
"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import Paddin... | 77 |
'''simple docstring'''
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 ..pipe... | 138 | 0 |
'''simple docstring'''
def A_( A : float , A : float):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"""{price_plus_tax(1_00, 0.25) = }""")
print(f"""{price_plus_tax(125.50, 0.05) = }""")
| 719 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'c... | 432 | 0 |
from manim import *
class _SCREAMING_SNAKE_CASE ( a_ ):
'''simple docstring'''
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = Rec... | 16 |
'''simple docstring'''
import argparse
import os
# New Code #
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_sched... | 653 | 0 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {"vocab_file": "vocab.json", "merges_file": "merges.txt"... | 677 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
UpperCamelCase = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
UpperCamelCase = typing.Union[np.floataa, int, float] # noqa: UP007
def __magic_name__ ( SC... | 677 | 1 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class lowercase ( __SCREAMING_SNAKE_CASE ):
_a = DistilBertTokeni... | 307 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float ) -> float:
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be >... | 695 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_Up... | 701 |
_UpperCAmelCase : List[Any] = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre"""... | 188 | 0 |
from math import pi
def UpperCAmelCase_ ( __UpperCAmelCase : int , __UpperCAmelCase : int ) -> float:
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10)) | 31 |
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 transformers.configuration_... | 298 | 0 |
from cva import destroyAllWindows, imread, imshow, waitKey
def _lowerCAmelCase ( UpperCamelCase__: Dict ) -> Optional[Any]:
"""simple docstring"""
A , A = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(_snake_case ... | 711 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class _UpperCamelCase :
"""simple docstring"""
@property
def _U... | 546 | 0 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# ful... | 42 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_av... | 190 | 0 |
"""simple docstring"""
import re
def a_ ( lowerCamelCase ):
if len(re.findall('[ATCG]' , lowerCamelCase ) ) != len(lowerCamelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import... | 632 | """simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class snake_case ( ctypes.Structure ):
"""simple docstring"""
snake_case__ = [("size... | 632 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_ ( metaclass=snake_case_ ):
'''simple docstring'''
_snake_case = ['''flax''']
def __init__( self , *snake_case_ , **snake_case_ ) ... | 465 |
'''simple docstring'''
def lowerCamelCase ( __lowerCamelCase : int ) ->int:
assert (
isinstance(__lowerCamelCase , __lowerCamelCase ) and number_of_steps > 0
), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
if number_of_steps == 1:
... | 314 | 0 |
import math
class A :
'''simple docstring'''
def a_ ( self : str , __lowerCAmelCase : list[list[float]] , __lowerCAmelCase : list[int] ) -> int:
"""simple docstring"""
A__ = ... | 247 |
def __lowerCamelCase ( __a :int ) -> list[int]:
"""simple docstring"""
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
A__ = [True] * (num + 1)
A__ = 2
while p * p <= num:
if primes[p]... | 247 | 1 |
class lowercase__:
"""simple docstring"""
def __init__( self : Tuple , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Optional[Any]:
lowercase_ = name
lowercase_ = val
def __str__( self : Union[str... | 97 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
f... | 319 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
lowerCamelCase_ : Dict = False
class _lowerCamelCase (unittest... | 345 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowerCamelCase (lowerCamelCase ):
... | 345 | 1 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise TypeError('''only integers accepted as input''' )
else:
_a = ... | 22 | '''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class a__ :
@property
def SCREAMING_SNAKE_CASE__ ... | 546 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ : Union[str, Any] = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfi... | 451 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def A_ ( snake_case : int ) -> int:
'''simple docstring'''
def is_in_circle(snake_case : float , snake_case : float ) -> bool:
... | 451 | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers impo... | 89 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
A__ = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'''
... | 252 | 0 |
'''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase = 100_0000 ):
lowercase__ : Tuple = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , UpperCAmelCase ):
phi[j] -= phi[j] // i
re... | 705 | '''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerT... | 428 | 0 |
"""simple docstring"""
def lowercase_ ( _lowercase : Optional[Any] , _lowercase : Optional[int] , _lowercase : List[Any] ):
'''simple docstring'''
UpperCAmelCase : Any = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * commo... | 595 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
... | 426 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( a : list[int] , a : int , a : int , a : int ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[index... | 700 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFo... | 126 | 0 |
'''simple docstring'''
lowerCAmelCase: Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
lowerCAmelCase: Dict = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def lowerCamelCase__ ( _A , _A , _A ):
a : Union[str, Any] = True
a : List[Any]... | 526 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_comm... | 526 | 1 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils... | 713 |
"""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, require_flax
if is_fl... | 158 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case : List[str] = {
'configuration_efficient... | 571 |
"""simple docstring"""
__snake_case : Optional[Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def a_ ( __a , __a , __a , __a ... | 571 | 1 |
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int ):
if n == 1 or not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return 0
elif n == 2:
return 1
else:
__UpperCamelCase =[0, 1]
for i in range(2 ,... | 682 |
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
return 1 if input_a == input_a else 0
def _UpperCAmelCase ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 ... | 682 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
fro... | 79 |
"""simple docstring"""
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common impo... | 549 | 0 |
from typing import Any
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
snake_case_ : Dict = data
snake_case_ : Tuple = None
def __repr__( self... | 114 |
def lowerCAmelCase__ ( _a : str , _a : int ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
snake_case_ : Optional[Any] = (boundary[1] - boundary[0]) / steps
snake_case_ : str = boundary[0]
snake_case_ ... | 114 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __UpperCamelCase ( A__ ):
__A : UNetaD... | 32 |
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 Ac... | 386 | 0 |
"""simple docstring"""
class __UpperCamelCase :
def __init__( self , lowerCAmelCase__ ) -> Optional[Any]:
a : Optional[int] = arr.split("," )
def __a ( self ) -> Tuple:
a : Union[str... | 712 |
"""simple docstring"""
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation imp... | 31 | 0 |
__lowerCamelCase : int = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym"... | 385 |
def a__ ( _UpperCamelCase : str ):
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
__lowerCamelCase = sorted(string.lower() )
return len(_UpperCamelCase ) == len(set(_UpperCamelCase ) )
... | 175 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case :
a_ : int
a_ : TreeNode | None = None
a_ : TreeNode | None = None
UpperCamelCase_ = ... | 721 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-emb... | 210 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/co... | 536 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
__lowerCAmelCase ... | 536 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCAmelCase: List[str] , lowerCAmelCase: Optional[int] , lowerCAmelCase: Dict , lowerCAmelCase: Tuple )-> List[Any]: # noqa: E741
while r - l > 1:
_snake_case : Dict = (l + r) // 2... | 709 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_t... | 669 | 0 |
def a_ ( SCREAMING_SNAKE_CASE__ : Tuple ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] =[]
_lowerCamelCase : Any =[]
_lowerCamelCase : Union[str, Any] ={
'^': 3,
'*': 2,
'/... | 464 |
from timeit import timeit
lowerCamelCase = {
'MALAYALAM': True,
'String': False,
'rotor': True,
'level': True,
'A': True,
'BB': True,
'ABC': False,
'amanaplanacanalpanama': True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is... | 464 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
f... | 613 |
import pytest
import datasets
# Import fixture modules as plugins
_lowerCamelCase = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def __UpperCAmelCase( lowercase_ , lowercase_ ):
# Mark tests as "unit" by default if not marked as "integration" ... | 613 | 1 |
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove the lea... | 15 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __UpperCamelCase () -> Optional[Any]:
lowercase__ = {
'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
... | 235 | 0 |
class a__ :
def __init__( self , lowercase__ , lowercase__ , lowercase__ ) -> Tuple:
__A = None
__A = None
__A = graph
self._normalize_graph(lowercase__ , lowercase__ )
... | 714 |
from __future__ import annotations
def UpperCAmelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if not nums:
return 0
__A = nums[0]
__A = 0
for num in nums[1:]:
__A , __A = (
max_excluding + num,
... | 205 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-... | 247 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : str ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError('String must only contain alphabetic characters.' )
SCREAMING_SNAKE_CASE = sorted(string.lower() ... | 247 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a__ ( __a ):
@staticmethod
@abstractmethod
def __magic_name__ ( _a ):
raise NotImplementedError()
@abstractmethod
def __magic_name__ ( self ... | 706 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_A : int = logging.get_logger(__name__)
class a__ ( a_ ):
def __init__( self , *_a , **_a ):
warnings.warn(
"Th... | 518 | 0 |
'''simple docstring'''
__UpperCAmelCase = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformer... | 90 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioG... | 582 | 0 |
import math
def lowerCAmelCase ( snake_case__ : int )-> bool:
assert isinstance(snake_case__ , snake_case__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
... | 608 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCamelCase ( unittest.TestCase ):
"""simple docst... | 608 | 1 |
class UpperCamelCase :
'''simple docstring'''
def __init__( self , UpperCamelCase_ , UpperCamelCase_=None , UpperCamelCase_=None ):
lowercase_ :Dict = data
lowercase_ :Dict = previous
lowercase_ :in... | 257 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils... | 180 | 0 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase__ (unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : Dict ):
snake_case__ : List[str] = [
"""saf... | 127 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_: Optional[int] = logging.get_logger(__name__)
lowercase_: Optional[int] = {
'facebook/encodec_24khz': 'https://hugging... | 127 | 1 |
"""simple docstring"""
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFea... | 224 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedu... | 224 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetne... | 537 |
'''simple docstring'''
def _lowerCamelCase( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : str ) -> Optional[int]:
A : Optional[int] = 0
A : str = len(UpperCamelCase__ ) - 1
while left <= right:
# avoid divi... | 537 | 1 |
'''simple docstring'''
import math
import qiskit
def A__ ( UpperCAmelCase_ = 1 , UpperCAmelCase_ = 1 , UpperCAmelCase_ = 1 ):
if (
isinstance(_UpperCAmelCase , _UpperCAmelCase )
or isinstance(_UpperCAmelCase , _UpperCAmelCase )
or isi... | 195 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def __snake_case ( _UpperCAmelCase : int, _UpperCAmelCase : Dict, _UpperCAmelCase : Any=None, **_UpperCAmelCase : List[Any]):
UpperCamelCase = [x.strip() f... | 212 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
... | 489 |
'''simple docstring'''
from __future__ import annotations
def __a(SCREAMING_SNAKE_CASE_ : list[float] , SCREAMING_SNAKE_CASE_ : list[float] ):
'''simple docstring'''
_lowerCAmelCase = sorted(numsa + numsa )
_lowerCAmelCase , _lowerCAmelCase = div... | 489 | 1 |
"""simple docstring"""
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
_lowerCAmelCase : str = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight''... | 46 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
_lowerCAmelCase = ''''''
_lowerCAmelCase = ''''''
_lowerCAmelCase = ''''''
_lowerCAmelCase = 1 # (0 is vertical, 1 is horizontal)
def _SCREAMING_SNAKE_CASE ( ):
... | 565 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
]
}
try:
if not is_torch_available():
... | 714 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
... | 133 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_avail... | 315 |
from __future__ import annotations
def _a ( UpperCAmelCase ) -> None:
"""simple docstring"""
create_state_space_tree(UpperCAmelCase , [] , 0 , [0 for i in range(len(UpperCAmelCase ) )] )
def _a ( UpperCAmelCase , UpperCAmelCase , ... | 315 | 1 |
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_dimension_format,
)
from... | 102 |
def __lowercase ( UpperCAmelCase__ = 10 , UpperCAmelCase__ = 1_000 , UpperCAmelCase__ = True ):
"""simple docstring"""
assert (
isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
and isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
... | 102 | 1 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = '▁'
snake_cas... | 592 |
def lowerCamelCase__ ( snake_case_ : Dict=2_8123 ) -> Tuple:
__snake_case = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
__... | 592 | 1 |
from collections import defaultdict
from math import ceil, sqrt
def _lowercase ( SCREAMING_SNAKE_CASE_ : int = 1_000_000 , SCREAMING_SNAKE_CASE_ : int = 10 ):
"""simple docstring"""
UpperCamelCase = defaultdict(SCREAMING_SNAKE_CASE_ )
... | 702 |
import torch
from transformers import AutoModel
class UpperCAmelCase ( torch.nn.Module ):
def __init__( self : int , __magic_name__ : List[Any]="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
... | 181 | 0 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> tuple[complex, complex]:
if a == 0:
raise ValueError('''Coefficient \'a\' must not be zero.''' )
_... | 482 |
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 imp... | 606 | 0 |
"""simple docstring"""
import qiskit
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :Optional[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
snake_case_ :str = qiskit... | 707 |
"""simple docstring"""
from __future__ import annotations
__a = list[tuple[int, int]]
__a = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, ... | 310 | 0 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def UpperCamelCase ( _lowerCamelCase : Optional[int] , _lowerCamelCase : int ):
# ===== initialization =====
A__ ... | 440 |
'''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 OptionalDependencyNotAv... | 440 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
e... | 423 |
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
return x if y == 0 else greatest_common_divisor(lowerCamelCase_ , x % y )
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
return (x * y) // greatest_common_divisor(lowerCa... | 423 | 1 |
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