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_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Tuple = {
'configuration_distilbert': [
... | 57 | '''simple docstring'''
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def snake_case_ ( __snake_case : Union[str, Any] , __snake_case : Union[str, Any]=False) -> Optional[int]:
lowerCAmelCase_ = ... | 274 | 0 |
'''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... | 714 |
'''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 : Optional[int] = logging.get_logger(__name__)
__A... | 187 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Dict = {
"""shi-labs/dinat-mini-in1k-22... | 302 |
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 FlaxSchedulerMixin
@flax.struct.dataclas... | 302 | 1 |
from string import ascii_lowercase, ascii_uppercase
def lowerCamelCase__ ( a : str ) -> str:
"""simple docstring"""
if not sentence:
return ""
a__ :str = dict(zip(a , a ) )
return lower_to_upper.get(sentence[0] , sentence[0] ) + sentence[1:]
if __name_... | 373 |
import datasets
from .evaluate import evaluate
snake_case__ = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
'''
snake... | 373 | 1 |
"""simple docstring"""
def a__ ( lowerCAmelCase , lowerCAmelCase ) -> Optional[int]:
UpperCAmelCase__ : Optional[int] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def a__ ( ... | 182 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils impo... | 182 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
__A : int = get_logger(__name__)
class UpperCAmelCase_ ( enum.Enum ):
'''simple docstring'''
a__ = '''all_check... | 706 |
from __future__ import annotations
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
create_state_space_tree(SCREAMING_SNAKE_CASE , [] , 0 , [0 for i in range(len(SCREAMING_SNAKE_CASE ) )] )
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE , SCREAMIN... | 450 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils... | 40 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''',... | 40 | 1 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
a = _symbol_database.Default(... | 713 |
from typing import Dict, Iterable, 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_for... | 650 | 0 |
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 _lowercase ( self ):
'''simple docst... | 130 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtract... | 130 | 1 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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 ... | 716 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__A = logging.get_logger(__name__)
def __A (_SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=... | 560 | 0 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow #... | 362 |
_UpperCAmelCase : Any = 6_5521
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = 1
snake_case_ = 0
for plain_chr in plain_text:
snake_case_ = (a + or... | 362 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeat... | 275 | """simple docstring"""
# Lint as: python3
import itertools
import os
import re
UpperCAmelCase__ = re.compile(r"""([A-Z]+)([A-Z][a-z])""")
UpperCAmelCase__ = re.compile(r"""([a-z\d])([A-Z])""")
UpperCAmelCase__ = re.compile(r"""(?<!_)_(?!_)""")
UpperCAmelCase__ = re.compile(r"""(_{2,... | 275 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class snake_case_ ( unittest.TestCase ):
def __A ( self ):
SCREAMING_SNAKE_CASE_ : Optional[int] = [
'safety_checker/pytorch_model.bin',
'safety... | 345 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__: Dict = logging.get_logger(__name__)
lowerCAmelCase__: Optional[Any] = {
"snap-research/efficientformer-l1-300": (
"https://huggingface.co/snap-research/e... | 345 | 1 |
import importlib
import inspect
import os
import re
# 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
__SCREAMING_SNAKE_CASE ="""src/transformers"""
# This is to make sure the transformers mo... | 89 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedde... | 89 | 1 |
import random
from typing import Any
def UpperCamelCase ( _a ) -> list[Any]:
'''simple docstring'''
for _ in range(len(_a ) ):
lowercase_ :Union[str, Any] = random.randint(0 , len(_a ) - 1 )
lowercase_ :... | 257 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMa... | 257 | 1 |
'''simple docstring'''
import numpy as np
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : Optional[int] = int(np.ceil((x_end - xa) / h ... | 640 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_tor... | 640 | 1 |
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational impor... | 4 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase : Dict ={"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
t... | 316 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : List[str] = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptTokenizer"],... | 546 |
from __future__ import annotations
import requests
def _lowerCAmelCase ( UpperCamelCase__: str ) -> dict:
"""simple docstring"""
A = f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(UpperCamelCase__ ).json()
def ... | 546 | 1 |
'''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_schedule_with_warmup, se... | 689 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
impor... | 689 | 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/LICENSE-2... | 39 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Union[str, Any] = {
'''configuration_distilbert''': ... | 39 | 1 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_... | 88 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase ( ):
assert and_gate(0 ,0 ) == 0
assert and_gate(0 ,1 ) == 0
assert and_gate(1 ,0 ) == 0
assert and_gate(1 ,1 ) == 1
if... | 110 | 0 |
__lowerCamelCase : Dict = '''
# 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
'''
__lowerCamelCase : ... | 721 |
from sklearn.metrics import mean_squared_error
import datasets
__lowerCamelCase : List[str] = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel,... | 501 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@req... | 51 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wava... | 92 | 0 |
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_torch_available():
impo... | 571 |
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ ( __UpperCamelCase ):
"""simple docstring"""
A = ['''image_processor''', '''feature_extractor''']
A = '''TvltImageProcessor'''
A = '''TvltFeatureExtractor'''
def __init__( self ,... | 571 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSche... | 399 |
'''simple docstring'''
def lowercase__ ( __lowercase : int , __lowercase : Tuple , __lowercase : Tuple ) -> Any:
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__lowerca... | 399 | 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, InputFeatures,... | 716 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def a__ ( lowerCAmelCase : List[str] ):
'''simple docstring'''
def wrapper(*lowerCAmelCase : Any , ... | 660 | 0 |
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> float:
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""" )
return 0.5 * mass * abs(__snake_case ) * abs(__snake_case )
if __name__ == "__main__":
import doctest
docte... | 108 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerC... | 1 | 0 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ : int = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase_ : Optional[int] = ... | 497 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ : Optional[Any] = logging.get_logger(__n... | 497 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 662 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ ) -> list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCamelCase__ )
# If the array contains only one element, we return it (it's th... | 690 | 0 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
a__ : Optional[Any] = 'scheduler_config.json'
... | 720 |
"""simple docstring"""
from __future__ import annotations
import math
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCAmelCase__ : int ) -> None:
__SCREAMING_SNAKE_CASE = size
# approximate ... | 553 | 0 |
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 onnxruntime as ort
... | 268 |
from __future__ import annotations
class __snake_case :
"""simple docstring"""
def __init__( self , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
__snake_case , __snake_case ... | 268 | 1 |
"""simple docstring"""
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xforme... | 491 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/co... | 491 | 1 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE : int = {
'''E''': 1_2.7_0,
'''T''': 9.0_6,
'''A''': 8.1_7,
'''O''': 7.5_1,
'''I''': 6.9_7,
'''N''': 6.7_5,
... | 260 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import ... | 260 | 1 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelin... | 703 |
from math import isclose, sqrt
def lowerCAmelCase_ (lowercase__ : float , lowercase__ : float , lowercase__ : float ) -> tuple[float, float, float]:
'''simple docstring'''
lowerCAmelCase__ = point_y / 4 / point_x
low... | 288 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import ... | 19 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase__ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
... | 19 | 1 |
'''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 ... | 98 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__UpperCAmelCase = False
class a__ ... | 98 | 1 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ = 1000 ) -> int:
_a : int = 2**power
_a : Any = str(lowerCAmelCase_ )
_a : Any = list(lowerCAmelCase_ )
_a : Union[str, Any] = 0
for i in list_num:
sum_of_num += int(lowerCAmelCase_ ... | 358 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__lowerCAmelCase = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'... | 358 | 1 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase ( nn.Module ):
def __init__( self , _a = 16 , _a = 88 , _a = None , _a = 1 , _a = 0.0 , _a = 32 , _a = None , _a = False , _a = None , ... | 54 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf... | 54 | 1 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed... | 97 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
_SCREAMING_SNAKE_CASE = TypeVar("T")
class SCREAMING_SNAKE_CASE_ ( Generic[T] ):
"""simpl... | 181 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def __magic_name__ ( __snake_case : list[float] ) -> int:
return np.maximum(0 , UpperCamelCase__ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [... | 720 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_A : Union[str, Any] = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"... | 518 | 0 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase : List[str] = logging.get_logger(__name__)
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self , *lowercase , **lowerc... | 302 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Dict = {
"""hustvl/... | 302 | 1 |
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDeco... | 707 |
"""simple docstring"""
import baseaa
def lowerCamelCase__ ( _lowerCamelCase ):
'''simple docstring'''
return baseaa.aaaencode(string.encode('utf-8' ) )
def lowerCamelCase__ ( _lowerCamelCase ):
'''simple docstring'''
return baseaa.aaadecod... | 16 | 0 |
import os
def __magic_name__ ( lowerCAmelCase_ = "input.txt"):
'''simple docstring'''
with open(os.path.join(os.path.dirname(lowerCAmelCase_) , lowerCAmelCase_)) as input_file:
lowerCamelCase_ : Dict = [
[int(lowerCAmelCase_) for element in l... | 250 |
import unittest
import numpy as np
from transformers import RoFormerConfig, 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.numpy a... | 250 | 1 |
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,
)
UpperCAmelCase_ = pytest.mark.integration
@pytest.mark.parametrize('''path''' , ['''paws''... | 664 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCAmelCase_ = {"UserAgent": UserAgent().random}
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : Dict )->dict:
_lowerCAmelCase = script.conte... | 664 | 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 ... | 141 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
SCREAMING_SNAKE_CASE : List[str] = numpy.array([0, 0])
SCREAMING_SNAKE_CASE : Tuple = numpy.array([0.5, 0.8660254])
SCREAMING_SNAKE_CASE : Tup... | 141 | 1 |
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
class __lowercase (_Uppe... | 714 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 583 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['image_processor', 'tokenizer']
lowercase : List[Any] = 'ViTImageProces... | 499 |
"""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 REGE... | 499 | 1 |
"""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()
... | 255 |
"""simple docstring"""
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
UpperCAmelCase =get_logger(__name__)
UpperCAmelCase =R"\n Args:\n input_ids (`jnp.ndarray` of... | 255 | 1 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
__snake_case : Tuple = BeautifulSoup(requests.get(__lowerCamelCase , params=__lowerCamelCase ).content , "html.parser" )
__... | 81 |
def __lowerCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
if index == number_of_items:
return 0
_lowercase: str = 0
_lowercase: Tuple = 0
_lowercase: int = knapsack(__magic_name__ , __magic... | 226 | 0 |
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... | 708 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCas... | 188 | 0 |
def __lowerCamelCase ( __a :int = 1 , __a :int = 1_0_0_0 ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for divide_by_number in range(__a , digit + 1 ):
A__ = []
A__ = numerator
for _ in range(1 ... | 176 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
... | 176 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : List[str] = {
"Intel/dpt-large": "https://huggingfa... | 700 |
import warnings
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 __magic_name__ ( __lowerCAmelCase):
A:... | 106 | 0 |
lowercase_ : str = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 64 | from __future__ import annotations
from collections import deque
class _lowerCamelCase :
def __init__( self , lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__: list[dict]= []
self.adlist.append(
{'''value''': '''''', '''next_states''': [], '''fail_state''': 0,... | 64 | 1 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowerCAmelCase_ ( __UpperCAmelCase: Dict ) -> Optional[int]:
UpperCamelCase__ : Dict = {}
UpperCamelCase__ : Dict = job['... | 705 |
# Lint as: python3
import itertools
import os
import re
UpperCAmelCase_ = re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCAmelCase_ = re.compile(R'([a-z\d])([A-Z])')
UpperCAmelCase_ = re.compile(R'(?<!_)_(?!_)')
UpperCAmelCase_ = re.compile(R'(_{2,})')
U... | 369 | 0 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase__ ( A_: Dict , A_: Optional[int] , A_: Tuple ) -> Tuple:
... | 68 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_AR... | 406 | 0 |
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : List[Any] = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : Optional[Any] ,*SCREAMING_SNAKE_CASE__ : Optional[int] ,**... | 337 |
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : List[Any] = ['''sentencepiece''']
def __init__( self : Any ,*SCREAMING_SNAKE_CASE__ : List[str] ,**SCREAMING_SNAKE_CASE__ : str):
... | 337 | 1 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
low... | 433 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
UpperCAmelCase ... | 433 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 702 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCamelCase ( pl.LightningModule ):
"""simple docstring"""
def __init__( self , UpperCAmelCase ) ... | 601 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def __magic_name__ ( _lowerCamelCase: Optional[Any] ) -> int:
'''simple docstring'''
lowerCAmelCase = [
'''encoder.version''',
... | 535 |
"""simple docstring"""
def __magic_name__ ( _lowerCamelCase: Tuple, _lowerCamelCase: Any ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase = [1]
for i in range(2, _lowerCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * ... | 535 | 1 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class UpperCAmelCase ( __A ):
'''simple docstring'''
... | 706 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""tokenization_biogpt""": ["""BioGptT... | 70 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
... | 387 |
import random
from typing import Any
def __lowerCamelCase ( lowerCamelCase__ : list ):
'''simple docstring'''
for _ in range(len(lowerCamelCase__ ) ):
lowerCamelCase = random.randint(0 , len(lowerCamelCase__ ) - 1 )
lowerCamelCa... | 457 | 0 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : int = 10**9 ) -> int:
__lowercase = 1
__lowercase = 2
__lowercase = 0
__lowercase = 0
__lowercase = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_val... | 688 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( lowerCAmelCase__ ):
lowerCAme... | 688 | 1 |
from __future__ import annotations
from fractions import Fraction
def A ( _lowercase , _lowercase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : List... | 248 | def A ( _lowercase , _lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def A ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
assert and_gate(1 , 1 ) == 1
... | 248 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : List[str] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHI... | 710 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARC... | 697 | 0 |
'''simple docstring'''
import string
def __lowercase (_SCREAMING_SNAKE_CASE :str ):
for key in range(len(string.ascii_uppercase ) ):
SCREAMING_SNAKE_CASE : Union[str, Any] = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:... | 507 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
# See all Donut... | 507 | 1 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_to... | 720 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
lowercase__ =logging.getLogger(__name__)
if __name__ == "__main__":
lowercase__ ... | 326 | 0 |
"""simple docstring"""
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self: List[Any] , UpperCamelCase_: Dict , UpperCamelCase_: Any , UpperCamelCase_: List[str] ):
UpperCamelCase_ =None
UpperCamelCase_ =None
... | 391 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
... | 694 | 0 |
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 a__ ( self : int ) -> List[... | 720 |
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_available():
... | 688 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
... | 349 |
'''simple docstring'''
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 (
BnbQuantizationCon... | 349 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_: List[Any] = logging.get_logger(__name__)
lowercase_: Union[str, Any] = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
... | 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'''
# 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/LIC... | 75 |
'''simple docstring'''
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 ... | 75 | 1 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class A_ (unittest.TestCase ):
"""simple docstring"""
def ... | 714 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from data... | 656 | 0 |
'''simple docstring'''
from __future__ import annotations
import queue
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ ) -> int:
lowerCAmelCase__ = data
lowe... | 90 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 79 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
UpperCAmelCase = logging.get_logger(__name... | 342 | """simple docstring"""
from timeit import timeit
UpperCAmelCase = {
"""MALAYALAM""": True,
"""String""": False,
"""rotor""": True,
"""level""": True,
"""A""": True,
"""BB""": True,
"""ABC""": False,
"""amanaplanacanalpanama""": True, # "a man a plan a canal panama"
}
# Ens... | 342 | 1 |
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowerCamelCase = 300 # TEMPERATURE (unit = K)
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: float , UpperCamelCase__: float , UpperCamelCase__: float , ):
if donor_conc <... | 6 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.... | 83 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__UpperCamelCase : Dict = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", ... | 106 |
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_util... | 106 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'timm_backbone'
def __init__( self , ... | 42 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> float:
lowerCamelCase_ = x
lowerCamelCase_ = y
for step in range(__UpperCamelCase ): # noqa: B0... | 42 | 1 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__lowerCAmelCase : Dict = datasets.utils.logging.get_logger(__name__)
class A ( folder_based_bui... | 708 | '''simple docstring'''
import argparse
import os
import re
import packaging.version
__lowerCAmelCase : Optional[int] = "examples/"
__lowerCAmelCase : Dict = {
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VER... | 654 | 0 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
__A =logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def __init__( self , *lowercase , **lowercase ) -> None:
warnings.warn(
"T... | 463 |
from __future__ import annotations
import math
def lowerCamelCase_ ( lowerCamelCase__ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not... | 463 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Generic, TypeVar
lowerCAmelCase__ : Any = TypeVar("""T""")
class a ( Generic[T] ):
"""simple docstring"""
def __init__( self : Tuple , snake_case_ ... | 711 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def _a ( __lowerCAmelCase : Dict ):
"""simple docstring"""
snake_case__ : int = args.pruning_method
... | 502 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ ):
lowerCamelCase_ = generate_pascal_triangle(lowerCAmelCase__ )
for row_idx in range(lowerCAmelCase__ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=''' ''' )
# Pri... | 29 |
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 import Batch... | 563 | 0 |
from __future__ import annotations
UpperCamelCase = '''#'''
class __UpperCAmelCase :
def __init__( self: Optional[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {}
def Uppe... | 716 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class ... | 569 | 0 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
... | 50 |
"""simple docstring"""
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=loggi... | 510 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class lowerCamelCase_ :
def __init__( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any ... | 464 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def UpperCAmelCase ( A : Union[str, Any] , A : Optional[int] ... | 464 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__magic_name__ = TypeVar('''T''')
def UpperCAmelCase__( __UpperCAmelCase : int ):
return (position - 1) // 2
def UpperCAmelCase__( __UpperCAmelCase : in... | 576 | from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__magic_name__ = 10
def UpperCAmelCase__( __UpperCAmelCase : int , __UpperCAmelCase : int... | 576 | 1 |
from math import isqrt, loga
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[int]:
'''simple docstring'''
__UpperCAmelCase : str = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 675 |
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 = {
"""bert-base-uncased""": """https://huggingface... | 675 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils ... | 96 |
"""simple docstring"""
def a ( __UpperCAmelCase : List[Any] ) -> str:
__magic_name__: Optional[int] = [0] * len(__UpperCAmelCase )
__magic_name__: str = []
__magic_name__: Any = []
__magic_name__: Union[... | 96 | 1 |
'''simple docstring'''
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 transf... | 631 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaCon... | 631 | 1 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def A ( snake_case__ = 3 ):
'''simple docstring'''
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE... | 196 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ... | 476 | 0 |
import requests
__magic_name__ ='''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='''
def __UpperCamelCase ( A ):
# fetching a list of articles in json format
UpperCamelCase__ = requests.get(_NEWS_API + bbc_news_api_key ).json()
... | 469 | def __UpperCamelCase ( A = 600851475143 ):
try:
UpperCamelCase__ = int(A )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter n must be ... | 469 | 1 |
from math import ceil
def UpperCAmelCase_ ( _UpperCAmelCase , _UpperCAmelCase ):
lowerCamelCase_: Any = list(range(0 , __SCREAMING_SNAKE_CASE ) )
lowerCamelCase_: List[Any] = [item for sublist in list(device_map.values() ) for item in sublist... | 423 |
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 TokenizerTesterMixin
UpperCAmel... | 410 | 0 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _snake_case ( pl.LightningModule ):
def __init__( self ,UpperCamelCase ) -> Dict:
... | 57 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
fro... | 57 | 1 |
'''simple docstring'''
A__ : List[Any] = [
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager impor... | 13 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : int = 10_00 ) -> int:
__lowerCamelCase : Union[str, Any] = 3
__lowerCamelCase : Dict = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
... | 13 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
... | 710 |
'''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
a_ =... | 92 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase ) -> list[int]:
"""simple docstring"""
return [ord(_UpperCamelCase ) - 96 for elem in plain]
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""... | 60 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class _lowerCAmelCase ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self : Any , *UpperCamel... | 411 | 0 |
'''simple docstring'''
import string
def __snake_case ( _UpperCAmelCase : str):
for key in range(len(string.ascii_uppercase)):
UpperCamelCase = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
UpperCamelCase = strin... | 709 |
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : Optional[int]):
UpperCamelCase = []
UpperCamelCase = []
UpperCamelCase = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''': 1,
} ... | 350 | 0 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = JukeboxTokenizer
SCREAMING_SNAKE_CASE__ : List[A... | 696 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():... | 466 | 0 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common imp... | 423 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs... | 423 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a )
class _a ( _a ):
'''simple docstring'''
A :Dict = field(default="language-modeling" , meta... | 191 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusio... | 429 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 448 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( _a : int , _a : Any , _a : Union[str, Any] , _a : ... | 448 | 1 |
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