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 os
def lowercase ( ) -> Union[str, Any]:
__a = os.path.dirname(os.path.realpath(lowerCAmelCase__ ) )
__a = os.path.join(lowerCAmelCase__ , '''triangle.txt''' )
with open(lowerCAmelCase__ ) as f:
__a... | 45 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : str ) -> list:
if n_term == "":
return []
__a = []
for temp in range(int(lowerCAmelCase__ ) ):
series.append(f'''1/{temp + 1}''' if series else '''1''' )
return series
if __name... | 45 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
__UpperCamelCase = '''2020.9.26'''
__UpperCamelCase = '''xcodz-dot, cclaus, dhruvmanila'''
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAme... | 312 | """simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import Mvp... | 312 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ : int = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Con... | 324 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 324 | 1 |
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()
lowerCAmelCase__ = logging.g... | 119 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ['TransfoXLCorpus', '... | 119 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 54 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils im... | 315 | 0 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class UpperCamelCase__( unittest.TestCase ):
def a__( self : Optional[int] )-> List[Any]:
"""simple docstring"""
UpperCAmelCase ... | 91 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch... | 91 | 1 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_config_docstrings.py
a_ ... | 330 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_para... | 330 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from... | 196 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase : List[Any] =logging.get_logger(__name__)
lowerCamelCase : Optional[Any] =... | 196 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a_ : Union[str, Any] = {
"""configuration_trocr""": ["""TROCR_PRE... | 75 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> bool:
if len(snake_case ) == 0:
return False
lowercase__: Any = len(snake_case ) // 2
if a_list[midpoint] == item:
return True
... | 196 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ = {
"configuration_al... | 354 |
"""simple docstring"""
from __future__ import annotations
import math
class __lowerCamelCase :
'''simple docstring'''
def __init__( self : Dict , a_ : int ):
lowerCAmelCase_ : Union[str, Any] = size
# approximate the ove... | 161 | 0 |
'''simple docstring'''
from collections import deque
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] , __UpperCAmelCase : str , __UpperCAmelCase : int , __UpperCAmelCase : int ):
'''simple docstring'''
... | 79 |
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
a : L... | 114 | 0 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers impor... | 46 |
'''simple docstring'''
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxrun... | 46 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.model... | 109 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
e... | 264 | 0 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__lowerCamelCase : Dict = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapm... | 358 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Any = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
"""Instr... | 286 | 0 |
'''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 TensorForm... | 134 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__snake_case : Any = pytest.mark.integration
@pytest.mark.parametrize("""p... | 134 | 1 |
import operator as op
_lowerCamelCase : int = "scaler.pt"
_lowerCamelCase : Any = "pytorch_model"
_lowerCamelCase : int = "random_states"
_lowerCamelCase : str = "optimizer"
_lowerCamelCase : Dict = "schedu... | 369 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : str = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_AR... | 99 | 0 |
from __future__ import annotations
import math
__a :Tuple = '2020.9.26'
__a :Any = 'xcodz-dot, cclaus, dhruvmanila'
def __snake_case ( __UpperCamelCase : float ,__UpperCamelCase : float ,__UpperCamelCase : float ,__UpperCa... | 312 |
def __snake_case ( __UpperCamelCase : int = 1000 ):
"""simple docstring"""
return sum(e for e in range(3 ,__UpperCamelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"{solution() = }") | 312 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 370 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
__a = 42
__a = 42
class lowerCAmelCase__ :
... | 40 | 0 |
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,
)
fr... | 119 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCAmelCase = {
'''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
'''tokenization_transfo_xl'''... | 119 | 1 |
"""simple docstring"""
import copy
import re
class _UpperCAmelCase :
"""simple docstring"""
a_ = '''hp'''
a_ = {}
a_ = None
@classmethod
def lowercase ( cls : str , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict ) -> ... | 355 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.... | 207 | 0 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import Tokeni... | 91 |
"""simple docstring"""
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
... | 91 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A: List[Any] = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"Gro... | 76 |
"""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_convbert import ConvBertTokenizer
A: int = logging.get_logger(__name__)
... | 76 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __a ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
lowercase__: List[str] = [
'safety_ch... | 196 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_mode... | 196 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class a__ :
def __init__( self , A = 6 ) -> None:
'''simple docstring'''
a = None
a = None
self.create_linked_list(A )
def... | 368 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> list[int]:
a = 2
a = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__UpperCamelCase)
if n... | 180 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extractio... | 89 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings... | 161 | 0 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, requi... | 356 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_... | 328 | 0 |
"""simple docstring"""
import torch
def UpperCAmelCase__ ( ):
'''simple docstring'''
if torch.cuda.is_available():
lowerCAmelCase = torch.cuda.device_count()
else:
lowerCAmelCase = 0
print(F'Successfully ran on {... | 46 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torc... | 46 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(_SCREAMING_SNAKE_CASE : int , _SCREAMING_SN... | 67 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
UpperCAmelCase_ : str = len(_SCREAMING_SNAKE_CASE )
for _ in range(_SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 ... | 67 | 1 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.uti... | 43 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
lowerCamelCase_ : List[str] = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def UpperCAmelCase__ ( ):
"""simple docstring"""
A_ : Union[str, Any] = os... | 286 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_te... | 60 |
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_availab... | 60 | 1 |
'''simple docstring'''
snake_case_ : str = 9.8_06_65
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = g ):
if fluid_density <= 0:
raise ValueError('Impossible fluid density' )
if volume < 0:
raise ValueError('Impossible Object... | 83 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 99 | 0 |
'''simple docstring'''
import argparse
_SCREAMING_SNAKE_CASE = "docs/source/_static/js/custom.js"
def __lowerCamelCase ( __lowerCAmelCase : Dict ) -> int:
with open(__lowerCAmelCase , encoding="""utf-8""" , newline="""\n""" )... | 3 |
'''simple docstring'''
def __lowerCamelCase ( __lowerCAmelCase : int ) -> int:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
... | 3 | 1 |
def lowerCamelCase ( ):
'''simple docstring'''
return 1
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return 0 if x... | 43 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
__lowercase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 40 | 0 |
"""simple docstring"""
def a_ ( _lowercase ):
def merge(_lowercase , _lowercase ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
... | 361 |
"""simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def a_ ( _lowercase , _lowercase , _l... | 128 | 0 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
B... | 0 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig... | 207 | 0 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_a : List[Any] = datasets.load_iris()
_a : Dict = np.array(data['data'])
_a : int = np.array(data['target'])
_a : ... | 359 | """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_avai... | 126 | 0 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _a , _a):
# Load checkpoint
SCREAMING_SNAKE_CA... | 76 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
... | 76 | 1 |
'''simple docstring'''
import os
def __magic_name__ ( ) -> Any:
'''simple docstring'''
snake_case_ = os.path.dirname(os.path.realpath(__UpperCAmelCase ) )
snake_case_ = os.path.join(__UpperCAmelCase, '''triangle.txt''' )
with open(__Uppe... | 72 |
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_C... | 72 | 1 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
a__ : Optional[int] = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from... | 161 | def snake_case ( snake_case__ :str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
_A = set()
# Replace all the whitespace in our sentence
_A = input_str.replace(""" """ , """""")
for alpha in input_str:
if "a" <= alpha.l... | 180 | 0 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly forma... | 11 |
"""simple docstring"""
from math import factorial, radians
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : int = 18 , lowerCAmelCase__ : int = 10 ) -> float:
__a = angle_in_degrees - ((angle_in_degrees // 3_60.0) * 3_60.0)
# Co... | 11 | 1 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def A ( self : ... | 28 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Any = logging.get_logger(__name__)
lowercase__ : Tuple = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"... | 328 | 0 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
... | 362 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_para... | 23 | 0 |
'''simple docstring'''
import math
from collections.abc import Callable
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> float:
__lowerCamelCase = xa
__lowerCamelCase = xa
while True:
if x_n == x_na or function(UpperCamelCase__ )... | 67 | '''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 67 | 1 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : str ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError("String must only contain alphabetic characters." )
snake_case : Any = sorted(string.lower(... | 368 |
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccum... | 112 | 0 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import Ba... | 60 |
"""simple docstring"""
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... | 60 | 1 |
def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive")
lowercase__ : Tuple = str(bin(_lowerCamelCase))[2:] # remove the leading "0b"
lowercase__ : List... | 333 | import argparse
import datetime
def lowercase_ ( _lowerCamelCase : str):
lowercase__ : Optional[Any] = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturda... | 333 | 1 |
'''simple docstring'''
import argparse
lowercase : Dict = 'docs/source/_static/js/custom.js'
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
with open(snake_case__ , encoding='''utf-8''' , newline='''\n''' ) as f:
... | 3 |
'''simple docstring'''
import os
def lowerCAmelCase_ ( ):
'''simple docstring'''
A : List[Any] = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(... | 3 | 1 |
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin... | 361 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main... | 3 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A_ = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """D... | 64 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
... | 128 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/... | 55 |
'''simple docstring'''
import itertools
import string
from collections.abc import Generator, Iterable
def a_ ( lowerCamelCase : Iterable[str] , lowerCamelCase : int ):
lowerCAmelCase = iter(lowerCamelCase )
while True:
lowerCAmelCase... | 55 | 1 |
def __lowerCAmelCase ( a__ , a__ , a__ ) -> list:
__a = len(a__ )
__a = [[0] * n for i in range(a__ )]
for i in range(a__ ):
__a = y_points[i]
for i in range(2 , a__ ):
for j in range(a__ , a__ ):
... | 6 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerat... | 126 | 0 |
"""simple docstring"""
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import... | 350 | import numpy
# List of input, output pairs
a_ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
a_ = (((515, 22, 13), 555), ((61, 35, 49), 150))
a_ = [2, 4, 1, 5]
a_ = len(train_data)
a_ = 0.009
def __lowercase ( lowe... | 50 | 0 |
"""simple docstring"""
from math import ceil
def snake_case_ ( A_ : Dict, A_ : Tuple ):
'''simple docstring'''
_lowerCamelCase : List[Any] = list(range(0, A_ ) )
_lowerCamelCase : List[Any] = [item for s... | 72 |
"""simple docstring"""
def snake_case_ ( A_ : list[int], A_ : str ):
'''simple docstring'''
_lowerCamelCase : Tuple = int(A_ )
# Initialize Result
_lowerCamelCase : Dict = []
# Traverse through all deno... | 72 | 1 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils im... | 122 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
__snake_case : Any = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def _UpperCamelCase ( UpperCamelCase_ : str = "mumbai" ... | 122 | 1 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thin... | 11 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
# See all ViT MAE models at https://huggingf... | 11 | 1 |
def UpperCAmelCase_ (_lowerCAmelCase : int = 1_00 ):
__UpperCamelCase : Tuple = n * (n + 1) * (2 * n + 1) / 6
__UpperCamelCase : List[str] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{s... | 171 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 171 | 1 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, S... | 78 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
UpperCamelCase__: Tuple = numpy.array([0, 0])
UpperCamelCase__: Union[str, Any] = numpy.array([0.5, 0.8660254])
... | 23 | 0 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__a = datasets.utils.logging.get_logger(__name__)
@dataclass
class __a( datasets.BuilderConfig ):
... | 360 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImagePro... | 235 | 0 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase_ ( lowerCamelCase__ ):
... | 0 |
'''simple docstring'''
from math import ceil, sqrt
def lowerCAmelCase_ ( _lowerCamelCase: int = 1_00_00_00 ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__SCREAMING_SNAKE_C... | 112 | 0 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None ):
if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ... | 356 |
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 __lowercase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
"... | 127 | 0 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__UpperCAmelCase = str(bin(SCREAMING_SNAKE_CASE ) )[2:] # r... | 333 |
import math
import sys
def __a ( SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
if number != int(SCREAMING_SNAKE_CASE ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''th... | 333 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
... | 359 |
from math import ceil
def lowerCamelCase_ ( lowerCAmelCase: Tuple , lowerCAmelCase: Union[str, Any] )-> str:
_snake_case : Union[str, Any] = list(range(0 , lowerCAmelCase ) )
_snake_case : int = [item for sublist in list(device... | 260 | 0 |
from scipy.stats import pearsonr
import datasets
__UpperCamelCase : Optional[int] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on ... | 182 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingS... | 3 | 0 |
from collections.abc import Sequence
def lowercase ( _SCREAMING_SNAKE_CASE : Sequence[int] | None = None ):
'''simple docstring'''
if nums is None or not nums:
raise ValueError('''Input sequence should not be empty''' )
_UpperCAmelCase ... | 363 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Optional[Any] = {
"configuration_funnel": ["FUNNEL_PRETRAI... | 326 | 0 |
'''simple docstring'''
from __future__ import annotations
a_ : int = """#"""
class snake_case :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
lowerCamelCase_ = {}
def snake_case ( ... | 55 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStr... | 55 | 1 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.model... | 99 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling... | 99 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from t... | 109 |
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_sentencepie... | 50 | 0 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_SCREAMING_SNAKE_CASE = '''
import os
'''
_SCREAMING_SNAKE_CASE = '''
def foo():
import os
return False
'''
_SCREAMING_SNAKE_CASE = '''
def foo():
... | 217 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'''Intel/dpt-large''': '''https://huggingface.... | 217 | 1 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from tr... | 122 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
'''configuration_megatron_bert''': ['''MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegatronBertConfig'''],
}
try:
if not is_torch_available():
raise Opt... | 122 | 1 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowerCAmelCase_ = logging.getLogger(__name__)
... | 279 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowerCAmelCase ( unittest.TestCase ):
@pro... | 279 | 1 |
"""simple docstring"""
_A = """
# 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/transformers.git
"""
_A = [{"""type""": "... | 171 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
_A = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def a__ ( ) -> str:
UpperCAmelCase__ : Union[str, Any] = Github(os.environ["""GITHUB_TOKEN"""] ... | 171 | 1 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase_( lowercase_ : str , low... | 73 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[str] = {
'''configuration_vision_encoder_decoder''': ['''Vis... | 73 | 1 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase_ ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : Tu... | 315 |
import requests
a__ = '''YOUR API KEY'''
def __UpperCAmelCase ( __a : str ,__a : str = giphy_api_key ) -> list:
"""simple docstring"""
_a : Optional[Any] = '''+'''.join(query.split() )
_a : Union[str, Any] = F"""https://api.giphy.co... | 235 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test,... | 352 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_u... | 220 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()... | 69 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached... | 127 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( lowerCAmelCase__="ro" , lowerCAmelCase__="en" , lowerCAmelCase__="wmt16" , lowerCAmelCase__=None ):
'''simple docstring'''
try:
import datasets
except (... | 352 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowe... | 97 | 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 _... | 84 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
_... | 260 | 0 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : str , lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docst... | 352 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__UpperCAmelCase = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("""3.7""... | 139 | 0 |
'''simple docstring'''
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__a = logging.get_logger(__name__)
def __snake_case( _lowerCAmelCase , _lowerCAme... | 35 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
_UpperCamelCase = 4
_UpperCamelCase = 3
class _lowerCamelCase ( a ):
"""s... | 326 | 0 |
__A = 'Input must be a string of 8 numbers plus letter'
__A = 'TRWAGMYFPDXBNJZSQVHLCKE'
def __A ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
_A = f"""Expected string as input, found {type(_lowerc... | 357 |
from __future__ import annotations
import math
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if not scores:
... | 75 | 0 |
from torch import nn
def A_ ( A__ ) -> Dict:
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'Unsupported activation... | 99 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Co... | 99 | 1 |
from datetime import datetime as dt
import os
from github import Github
UpperCamelCase__ = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def _UpperCamelCase ():
"""simple docstring"""
... | 87 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def _UpperCamelCase (a__ :int... | 87 | 1 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections import Counter
def a__ ( __SCREAMING_SNAKE_CASE ) -> typing.Counter[int]:
__lowerCAmelCase: typing.Counter[int] = Counter()
for base in range(1 , max_perimeter + 1 ):
... | 217 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from tra... | 217 | 1 |
"""simple docstring"""
from math import factorial
_a = {str(digit): factorial(digit) for digit in range(10)}
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
if not isinstance(A_, A_ ):
raise TypeE... | 358 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"""The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionInpaintPipeline` instead.""... | 100 | 0 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise ValueError('''check_bouncy() accepts only integer arguments''' )
snake_case_ = str(SCREAMING_SNAKE_CASE__ )
snake_case_... | 8 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : int = ''''''
lowerCamelCase_ : str = (
... | 279 | 0 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_UpperCAmelCase : List[Any] = [
"""Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phon... | 200 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def __lowerCamelCase ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == ... | 200 | 1 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> List[Any]:
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set() )
@pytest.fixture... | 73 |
from math import isclose, sqrt
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> tuple[float, float, float]:
__lowerCamelCase : Tuple = point_y / 4 / point_x
__lowerCamelCase : Tuple = 2 * normal_gradient / (1... | 73 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...ut... | 370 |
def _a ( SCREAMING_SNAKE_CASE__ : int = 50_00_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = set()
SCREAMING_SNAKE_CASE__ : Dict = int((limit - 24) ** (1 / 2) )
SCREAMING_SNAK... | 191 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
_UpperCamelCase : int = logging.get_logger(__name__)
class a ( a_ ):
def __init__( self , *_lowerCamelCase , **_lowerCamelCase ):
... | 220 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 220 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def lowerCamelCase_( _lowerCamelCase ) ... | 358 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCamelCase_( _lowerCamelCase ) -> str:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("Undefined for non-integers... | 340 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def UpperCamelCase( __UpperCamelCase : Tuple ):
for param in module.parameters():
lowerCAmelCase_ : Dict = False
def UpperCamelCase( ):
lowerCAmelCase_ : List[Any] = '''cuda''' if... | 103 |
'''simple docstring'''
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, TableTransformer... | 97 | 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_CO... | 362 |
"""simple docstring"""
import os
from math import logaa
def _snake_case ( lowercase__ : str = "base_exp.txt" ) -> int:
'''simple docstring'''
lowerCAmelCase_ :float = 0
lowerCAmelCase_ :Union[str, Any] = 0
for i, line ... | 1 | 0 |
'''simple docstring'''
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:int = set()
# edges = list of graph's edges
SCREAMING_SNAKE_CASE:Any = get_edges(snake_case )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_n... | 139 |
'''simple docstring'''
from __future__ import annotations
A_ = list[list[int]]
# assigning initial values to the grid
A_ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3,... | 139 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 70 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 70 | 1 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = """▁... | 141 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
... | 75 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''xlm-roberta-bas... | 253 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
lowerCamelCase_ = tuple[int, int]
class UpperCamelCase_ :
def __init__( self : List[Any] , lowerCAmelCase_ : set[int] , lowerCAmelCase_ : Mapping[EdgeT,... | 253 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.js... | 87 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''YituTech/conv-bert-base''': '''h... | 87 | 1 |
import unittest
import numpy as np
import requests
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_availabl... | 362 |
from __future__ import annotations
import typing
from collections import Counter
def _a ( a :int ) -> typing.Counter[int]:
a = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(a , max_perimeter + 1 ):
a ... | 26 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class __snake_case ( _lowercase):
def __init__( self : str ):
"""simple docstring"""
self.test()
def SCREAMING_SNAKE_CASE ( self : ... | 72 |
"""simple docstring"""
from collections import defaultdict
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = first_str.lower().strip()
__SCREAMING_SNAKE_CASE = second_str.lower().strip()
# Remove whitespace
__SCREAMING_SNAKE_CA... | 100 | 0 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, s... | 90 |
def __UpperCamelCase ( lowerCAmelCase__ : list ):
def merge(lowerCAmelCase__ : list , lowerCAmelCase__ : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
re... | 90 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.