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 |
|---|---|---|---|---|
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 _lowerCAmelCase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
d... | 231 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW models at https://huggingface.co... | 231 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHe... | 350 |
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> int:
"""simple docstring"""
a = ''''''
for i in table:
res += inp[i - 1]
return res
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> int:
"""simple docstring"""
... | 330 | 0 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"vocab_file": "vocab.json",
... | 45 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : str , lowerCAmelCase__ : list[str] ) -> str:
__a = ''''''
for word_or_phrase in separated:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise Exception('''join(... | 45 | 1 |
"""simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __SCREAMING_SNAKE_CASE ( A_ , A_ = True , A_ = math.inf , A_ = -math.inf , A_ = math.inf , A_ = -math.inf , A_ = False , A_ =... | 74 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, Li... | 74 | 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-2.0
#
... | 47 |
from math import isqrt
def _lowerCamelCase( lowercase__ ) -> bool:
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowercase__ ) + 1 ) )
def _lowerCamelCase( lowercase__ = 1_0**6 ) -> int:
'''simple docstring'''
__... | 295 | 0 |
"""simple docstring"""
import os
_A = {'I': 1, 'V': 5, 'X': 1_0, 'L': 5_0, 'C': 1_0_0, 'D': 5_0_0, 'M': 1_0_0_0}
def UpperCAmelCase ( a_ ):
'''simple docstring'''
lowerCamelCase : int = 0
lowerCamelCase : Dict = 0
while index < ... | 362 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_A = logging.get_logger('transformers.models.speecht5')
def UpperCAmelCase ( a_, a_, a_ ):
... | 205 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
UpperCAmelCase_ = logging.get_logger(__... | 201 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
l... | 201 | 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
_A = logging.getLogger(__name__)
def _Uppe... | 366 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
_A = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: ... | 117 | 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_se... | 71 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __a ( __UpperCamelCase ... | 196 | 0 |
"""simple docstring"""
def snake_case ( A__ ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(A__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 253 |
"""simple docstring"""
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class UpperCamelCase_ (__A ):
__magic_name__ = '''M-CLIP'''
def __init__( self : Any , lowerCAmelCase_ : str=1_024 , lowerCAmelCase_ : str=76... | 253 | 1 |
"""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
snake_case__ : Tuple = logging.get_lo... | 60 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class SCREAMING_SNAKE_CASE (datasets.BuilderConfig ):
lower... | 190 | 0 |
'''simple docstring'''
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaCo... | 370 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main impo... | 129 | 0 |
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,
DistilBertForMaskedLM,
DistilB... | 262 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_UpperCAmelCase : Dict ={
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie-m-large_pytorch""": """https... | 262 | 1 |
import qiskit
def lowerCamelCase_ ( _a : int , _a : int ):
'''simple docstring'''
UpperCAmelCase_ : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
UpperCAmelCase_ : Tuple ... | 355 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''shi-labs/dinat-mini-in1k-224''': '''http... | 59 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case_ = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_avail... | 24 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ = 100 ):
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__mai... | 100 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTest... | 365 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
A__ : str = logging.get_logger(__name__)
class lowercase__ ( snake_case__ ):
def __init__( self : Optional[Any] , *snak... | 209 | 0 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = (KDPMaDiscreteScheduler,)
lo... | 7 |
"""simple docstring"""
from __future__ import annotations
lowerCamelCase__ = list[tuple[int, int]]
lowerCamelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0... | 86 | 0 |
"""simple docstring"""
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch... | 239 |
"""simple docstring"""
from torch import nn
class _SCREAMING_SNAKE_CASE( nn.Module ):
def __init__( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> List[str]:
"""simple docstring"""
super()... | 239 | 1 |
"""simple docstring"""
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def UpperCamelCase_ ( lowerCAmelCase__ : Tuple , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__... | 224 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if number > 0:
raise ValueError("input must be a negative integer" )
__SCREAMING_SNAKE_CASE = len(bin(lowerCAmelCase_ )[3:] )
__SCREAMING_SNAKE_CASE = ... | 54 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_lowerCamelCase = ["""image_processor""", """tokenizer"""]
_lowerCamelCase = """ViTImagePr... | 367 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a_ = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
... | 291 | 0 |
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
a : List[str] = [
# tf -> hf
('''/''', '''.'''),
(... | 105 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCamelCase : List[Any] = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import w... | 47 | 0 |
"""simple docstring"""
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 Opti... | 11 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( lowerCAmelCase__ : list[int] , lowerCAmelCase__ : list[int] , lowerCAmelCase__ : list[int] , lowerCAmelCase__ : list[list[str]] , lowerCAmelCase__ : int , ) -> None:
__a ... | 11 | 1 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase=None ... | 124 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import... | 124 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax impor... | 212 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational... | 212 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'facebook/convnextv... | 266 |
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ... | 266 | 1 |
'''simple docstring'''
from typing import Any
class A__ :
def __init__( self : List[str] , _a : Any ) -> Optional[int]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =data
_SCREAMING_SNAKE_CASE =None
def __repr... | 360 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class A__ ( A__ ):
A__ = 'MCTCTFeatureExtractor'
A__ = 'AutoTokenizer'
def __init__( self : Optional[Any] , _a : Optional[int] ... | 114 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__UpperCAmelCase = {
'sample_size': 32,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 2,
'num_... | 29 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def A_ ( ) -> List[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.rais... | 52 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def A_ ( _lowerCAmelCase ) -> Tuple:
Up... | 365 |
from scipy.stats import spearmanr
import datasets
__lowerCamelCase : List[str] = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive... | 140 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : Tuple = {
'''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
... | 138 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__A : Tuple = logging.get_logger(__name__)
__A : List[Any] = R'''
Args:
input_ids (`torch.LongTensor` o... | 138 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :int = 1000 ):
_lowerCAmelCase = 2**power
_lowerCAmelCase = str(__lowerCamelCase )
_lowerCAmelCase = list(__lowerCamelCase )
_lowerCAmelCase = 0
for i in list_num:
sum_of_num += int... | 370 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_available():
... | 229 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_SCREAMING_SNAKE_CASE = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotA... | 327 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_SCREAMING_SNAKE_CASE = 50_00_00
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = os.path.split(__file__)
_SCREAMING_SNAKE_CASE ... | 327 | 1 |
def A ( __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ = False
while is_sorted is False: # Until all the indices are traversed keep looping
UpperCAmelCase_ = True
for i in range(0 , l... | 363 |
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,
B... | 344 | 0 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE__ :
def __init__(self : str , a__ : int = 0 ):
"""simple docstring"""
__snake_case = key
def a (self : str , a__ ... | 24 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self , _A , _A , _A ):
... | 257 | 0 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_c... | 169 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCa... | 169 | 1 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common ... | 14 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE)
class lowerCamelCase__( __SCREAMING_SNAKE_CASE):
UpperCAmelCase__ : str = fi... | 361 |
from math import ceil, sqrt
def lowerCamelCase__ ( A__ : int = 1000000 ):
'''simple docstring'''
__lowerCamelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__lowerCamelCase ... | 29 | 0 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A : Any = logging... | 118 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers... | 162 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> bool:
'''simple docstring'''
__s... | 364 | """simple docstring"""
from __future__ import annotations
import os
from typing import Any
import requests
_a : int= "https://api.github.com"
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
_a : Dict= BASE_URL + "/user"
... | 95 | 0 |
def lowerCAmelCase_ ( __A ) -> str:
'''simple docstring'''
UpperCAmelCase__ = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowerCAme... | 65 | import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowerCAmelCase_ ( __A ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ = [
"encoder.version",
"dec... | 65 | 1 |
from __future__ import annotations
from typing import Generic, TypeVar
__UpperCAmelCase = TypeVar('''T''')
class lowerCamelCase__ ( Generic[T] ):
def __init__( self : Any , _a : T ):
a__: List[str] =data
a__: List[Any] ... | 354 |
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 = logging.get_logger(__name__)
_... | 42 | 0 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from tra... | 313 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 | 1 |
"""simple docstring"""
from math import factorial
lowerCAmelCase : Any = {str(d): factorial(d) for d in range(10)}
def a__ ( snake_case__ ) -> int:
return sum(DIGIT_FACTORIAL[d] for d in str(snake_case__ ) )
def a__ ( ) -> int:
lowerCa... | 168 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def a__ ( snake_case__ ) -> Optional[Any]:
lowerCamelCase = {}
lowerCamelCase = job["""started_at"""]
lowerCamelCase = job["""comple... | 168 | 1 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.... | 338 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase__ : Any = logging.get_logger(__name__)
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ... | 121 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterM... | 363 | """simple docstring"""
import math
import unittest
from transformers import BioGptConfig, 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_m... | 149 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils imp... | 301 |
"""simple docstring"""
from __future__ import annotations
from statistics import mean
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = [0] * no_of_processes
__lowerCAmelCase = [0] * no_of_processes
# Initializ... | 301 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase :List[Any] = {}
try:
if not is_sentencepiece_available()... | 361 |
'''simple docstring'''
def a ( lowerCamelCase__ ):
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod() | 135 | 0 |
import requests
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> None:
"""simple docstring"""
A : Any = {"""Content-Type""": """application/json"""}
A : str = requests.post(_lowerCAmelCase , json={"""text""": message... | 116 |
from __future__ import annotations
from math import ceil, floor, sqrt
def __UpperCamelCase ( _lowerCAmelCase = 200_0000 ) -> int:
"""simple docstring"""
A : list[int] = [0]
A : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
... | 116 | 1 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCAmelCase = ... | 98 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def Up... | 98 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (A = 1_000 ) -> int:
"""simple docstring"""
return sum(e for e in range(3 , A ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 2 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : List[Any] = TypeVar("T")
def __magic_name__ ( __lowerCAmelCase : int ) -> int:
return (position - 1) // 2
def __magic_name__ ( __lowerCAmelCase : ... | 270 | 0 |
lowercase : Union[str, Any] = 0 # The first color of the flag.
lowercase : int = 1 # The second color of the flag.
lowercase : int = 2 # The third color of the flag.
lowercase : Tuple = (red, white, blue)
def ... | 36 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A : Tuple = ['image_processor', 'tokenizer']
A : Tuple = 'AutoIm... | 36 | 1 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowercase: Optional[Any] = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C": 2.78,
"U": 2.... | 227 |
from __future__ import annotations
from typing import Any
def snake_case( __magic_name__ ) -> None:
'''simple docstring'''
create_state_space_tree(__magic_name__ , [] , 0 )
def snake_case( __magic_name__ , __magic_name__ , ... | 308 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis... | 56 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [0] * len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Any = [1] * len(__lowerCAmelCase ... | 56 | 1 |
"""simple docstring"""
import torch
def _SCREAMING_SNAKE_CASE ( ) ->Optional[Any]:
'''simple docstring'''
if torch.cuda.is_available():
a : List[Any] = torch.cuda.device_count()
else:
a : Any = 0
print(F"""Successfully ran ... | 105 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A ... | 10 | 0 |
"""simple docstring"""
from __future__ import annotations
a : Optional[Any] = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _SCREAMING_SNAKE_CASE ( _lowercase : list[list[int]] , _lowercase :... | 79 |
"""simple docstring"""
import math
def _SCREAMING_SNAKE_CASE ( _lowercase : int = 100 ) ->int:
'''simple docstring'''
a : Dict = sum(i * i for i in range(1 , n + 1 ) )
a : Tuple = int(math.pow(sum(range(1 , n + 1... | 79 | 1 |
"""simple docstring"""
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class snake_case ( lowerCamelCase_ ):
SCREAMING_SNAKE_CASE_ : Dict = """philschmid/bart-large-cnn-samsum"""
SCREAMING_SNAKE_CASE_ : int ... | 217 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : int = 10_00 ) -> int:
SCREAMING_SNAKE_CASE = 2**power
SCREAMING_SNAKE_CASE = 0
while n:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = ... | 113 | 0 |
'''simple docstring'''
import numpy as np
a : str = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class a :
def __init__( self : Dict ):
snake_case_ ... | 72 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class a ( unittest.TestCase ):
def A_ ( self : List[Any] ):
snake_case_ = [
'''safety_checker/pytorch_model.bin''',
... | 72 | 1 |
import baseaa
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> bytes:
"""simple docstring"""
return baseaa.aaaencode(string.encode('utf-8' ) )
def lowerCamelCase_ ( UpperCamelCase__ : bytes ... | 90 |
from math import pi, sqrt, tan
def lowerCamelCase_ ( UpperCamelCase__ : float ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * s... | 90 | 1 |
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [0, 2, 4, 6, 8]
SCREAMING_SNAKE_CASE__ : Dict = [1, 3, 5, 7, 9]
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : list[int] , __lower... | 351 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, Fl... | 339 | 0 |
'''simple docstring'''
import requests
def __a ( UpperCAmelCase , UpperCAmelCase ) ->None:
"""simple docstring"""
A = {"""Content-Type""": """application/json"""}
A = requests.post(UpperCAmelCase , json={"""text""": message_body} , head... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / densit... | 258 | 1 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state i... | 25 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subpr... | 25 | 1 |
from copy import deepcopy
class lowercase__ :
'''simple docstring'''
def __init__( self, __magic_name__ = None, __magic_name__ = None ) -> None:
"""simple docstring"""
if arr is None and size is not None:
UpperCam... | 201 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils imp... | 201 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE ={
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_M... | 351 | """simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlow... | 321 | 0 |
"""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_ ( _lowerCAmelCase : int, _lowerCAmelCase : List[Any], ... | 320 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
... | 320 | 1 |
"""simple docstring"""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __lowercase ( _Up... | 188 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
A = TypeVar('''T''')
class __lowercase ( Generic[T] ):
'''simple docstring'''
__low... | 188 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_on... | 85 |
'''simple docstring'''
def UpperCamelCase_( snake_case : Optional[int] , snake_case : Optional[int] ):
'''simple docstring'''
snake_case_ = [0 for i in range(r + 1 )]
# nc0 = 1
snake_case_ = 1
for i in ... | 85 | 1 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class lowerCamelCase_ ( _A ):
'''simple docstring'''
def __init__( self : ... | 256 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ... | 256 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, O... | 233 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_inf... | 233 | 1 |
"""simple docstring"""
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class UpperCamelCase :
@property
de... | 95 | """simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCamelCase ( lowercase ):
@require_torch
def _lowercase (self... | 95 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_UpperCamelCase = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
p... | 275 |
def _lowercase ( lowercase__ , lowercase__ ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCAmelCase : int = str(bin(lowercase__ ) )[2:] # remove the leading "0b"
__lowerCAmelCase : Any... | 275 | 1 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( __snake_case : Optional[int] ) -> Optional[int]:
"""simple docstring"""
for i in range(0, lowerCAmelCase__ ):
for _ in range(0, n - i - 1 ): # printing s... | 359 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( __snake_case : list[int] ) -> bool:
"""simple docstring"""
return len(set(__snake_case ) ) == len(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 136 | 0 |
from collections import defaultdict
def lowercase_ ( _A : int ):
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = 1
lowerCamelCase__ : Dict = True
for v in tree[start]:
if v not in visited:
ret +=... | 184 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : Optional[Any] = {
"xlm-roberta-base": "... | 184 | 1 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOC... | 350 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class snake_case__ (ctypes.Structure ):
"""simple docstring"""
__lowerCAmelCase :Dict = [("size", c... | 266 | 0 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 3 ):
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
raise TypeError('number of qubi... | 195 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
... | 195 | 1 |
# using dfs for finding eulerian path traversal
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase , _lowercase=None ) -> List[str]:
'''simple docstring'''
UpperCAmelCase_ : Dict = (path or []) + [u]
for v in graph... | 353 |
from math import ceil
def lowerCamelCase__ ( _lowercase = 1001 ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
UpperCAmelCase_ : List[Any] = 2 * i +... | 235 | 0 |
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
@require_tokenizers
class _SC... | 38 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = {
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''... | 174 | 0 |
'''simple docstring'''
import argparse
a_ : int = """docs/source/_static/js/custom.js"""
def a_ ( __snake_case : Tuple ) -> List[str]:
"""simple docstring"""
with open(__snake_case , encoding='''utf-8''' , newline='''\n... | 6 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a_ : Any = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE... | 6 | 1 |
'''simple docstring'''
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 lowercase_ ( unittest.TestCase ):
def __a ( self ):
... | 80 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
... | 309 | 0 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from... | 56 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number | (1 << position)
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
return number & ~(1 << position)
def _lowercase ( __lower... | 56 | 1 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def A_ ( _lowercase = "isbn/0140328726" ):
'''simple docstring'''
snake_case_ :str = olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & s... | 66 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
lowerCamelCase = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.... | 166 | 0 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Option... | 49 |
import argparse
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_dummies.py
__A : Union[str, Any] = 'src/diffusers'
# Matches is_xxx_available()
__A : Dict = re.compile(R'is\_([a-z_]*)_available\(\... | 49 | 1 |
from jiwer import compute_measures
import datasets
_SCREAMING_SNAKE_CASE = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluatio... | 180 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_A = logging.get_logger(__name__)
class A ( __UpperCAmelCase ):
def __init__( self, *UpperCamelCase__, **UpperCamelCase__ ):
"""simple docstring"""
warnings.warn... | 278 | 0 |
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 ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWit... | 28 |
from __future__ import annotations
from typing import Any
class __a :
def __init__( self : Dict , UpperCAmelCase : int = 6 ):
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
self.... | 28 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
a_ = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
... | 152 |
'''simple docstring'''
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... | 47 | 0 |
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
UpperCamelCase__ = "scheduler_config.json"
class __SCREAMING_SNAKE_CASE ( _a ... | 360 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common impor... | 87 | 0 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class snake_case ( SCREAMING_SNAKE_CASE_ ):
# to overwrite at featu... | 243 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
lowerCAmelCas... | 212 | 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
fro... | 371 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=SCREAMING_SNAKE_CASE__):
_lowerCamelCase : str = ['torch', 'scipy']
def __init__( self : List[str], *a_ : Optional[int], **a_ : int ):
"""simpl... | 31 | 0 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class lowercase ( ... | 97 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: List[Any] = logging.get_logger(__name__)
_UpperCamelCase: int = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
... | 255 | 0 |
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''',
'''GroupViTO... | 360 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, lo... | 323 | 0 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_... | 105 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformer... | 322 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : list[list[int | float]] ) -> Optional[int]:
UpperCAmelCase_ = len(__UpperCamelCase )
UpperCAmelCase_ = len(matrix[0] )
UpperCAmelCase_ = min(__UpperCamelCase , __UpperCamelCas... | 369 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : float , __UpperCamelCase : float ) -> float:
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
... | 177 | 0 |
'''simple docstring'''
from __future__ import annotations
__SCREAMING_SNAKE_CASE : Optional[int] = list[list[int]]
# assigning initial values to the grid
__SCREAMING_SNAKE_CASE : Matrix = [
[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,... | 31 |
def __SCREAMING_SNAKE_CASE ( snake_case_ = 1000 ):
'''simple docstring'''
_UpperCAmelCase = 2**power
_UpperCAmelCase = 0
while n:
_UpperCAmelCase , _UpperCAmelCase = r + n % 10, n // 10
return r
if _... | 133 | 0 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
f... | 370 |
"""simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state imp... | 24 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
A ... | 305 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transfor... | 305 | 1 |
"""simple docstring"""
class A__ :
def __init__( self , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : List[Any] = size
__lowerCAmelCase : str = [0] * size
__lowerCAmelCase : Any = [0] * size
@staticmethod
def ... | 182 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlo... | 182 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2"... | 195 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase = logging.get_logger(__name__)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = r'\w+[.]\d+'
... | 195 | 1 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from... | 243 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'nielsr/canine-s'... | 243 | 1 |
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,
CTR... | 287 |
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 ={
"""camembert-base""": """https://huggingface.co/camembert... | 287 | 1 |
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffuse... | 359 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .m... | 241 | 0 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 66 | """simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import fr... | 290 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _A ( metaclass=__lowercase ):
lowercase__: int = ['''speech''']
def __init__( self : Tuple , *__magic_name__ : List[Any] , **__magic_name__ : Dict... | 13 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> bool:
"""simple docstring"""
__snake_case : Optional[int] = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def _a ( _lowerCamelCase = 5000 ) -> ... | 13 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.