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 |
|---|---|---|---|---|
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def A ( _A ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 584 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 641 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncode... | 366 | """simple docstring"""
import math
def lowercase_ ( _lowerCamelCase: int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0,... | 366 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, 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,
... | 289 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def __snake_case ( SCREAMING_SNAKE_CASE__ : Optional[int] ) -> List[str]:
'''simple ... | 289 | 1 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : An... | 145 |
'''simple docstring'''
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_UpperCAmelCase : Tuple = False
class __magic_name__ ( unittest.TestCase ):
... | 145 | 1 |
from math import sqrt
def SCREAMING_SNAKE_CASE ( __UpperCamelCase = 1_00_00_00) -> int:
a = 0
a = 0
a = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1):
... | 515 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface ... | 515 | 1 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
_A : Any = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
def __init__( self : str , *A : T... | 130 |
import numpy as np
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] ) ->int:
lowerCamelCase__ : Optional[Any] = (0, 0)
lowerCamelCase__ : Dict = None
lowerCamelCase__ : Optional[int] ... | 130 | 1 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _a ( unittest.TestCase ):
... | 43 | import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
A_ ... | 604 | 0 |
from __future__ import annotations
def A_ ( A__ , A__ , A__ , ) -> Dict:
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )
elif stress < 0:
raise ValueError('Stress cannot be n... | 711 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def A_ ( A__ ) -> float:
return np.dot(A__ , A__ )
class A__ :
"""simple docstring"""
def __init__( self , *,
lowercase = np.inf... | 392 | 0 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from dat... | 237 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils impo... | 237 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformer... | 713 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def snake_case_ (__A : Union[str, Any] ) -> Any:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class SCREAMING_SNAKE_CASE ( ... | 218 | 0 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __a ( A__ : Tuple , A__ : Any , A__ : List[Any] , A__ ... | 16 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 105 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return 1 if input_a == input_a else 0
def _snake_case ( ):
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1... | 707 |
"""simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_m... | 359 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = 10**12 ):
"""simple docstring"""
lowerCAmelCase__ : str = 1
lowerCAmelCase__ : str = 0
lowerCAmelCase__ : Any = 1
lowerCAmelCase__ : Tuple = 1
whi... | 565 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.uti... | 283 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase = {
"""configuration_layoutlmv3""": [
... | 713 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( snake_case__ , snake_case__ ... | 634 |
'''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... | 634 | 1 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__snake_case = logging.get_logger(__name__)
class lowercase__ ( _UpperCAmelCase ):
def __init__( self : Any , *UpperCAmelCase_ : Any , **Upper... | 400 |
import math
from datetime import datetime, timedelta
def _lowercase ( UpperCamelCase_ ) -> datetime:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = year % 19
SCREAMING_SNAKE_CASE__ = year % 4
SCREAMING_SNAKE_CASE__ = year % 7
SCREAMING_SN... | 400 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_par... | 675 |
'''simple docstring'''
import os
import sys
import unittest
a_ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import c... | 675 | 1 |
class _UpperCamelCase :
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
__lowercase = name
__lowercase = val
def __str__( self ) -> Dict... | 721 | from __future__ import annotations
def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
if b == 0:
return (1, 0)
((__lowercase) , (__lowercase)) = extended_euclid(lowercase , a % b )
__lowercase = ... | 522 | 0 |
'''simple docstring'''
def __lowercase ( __lowercase , __lowercase ) -> Any:
'''simple docstring'''
_A = (boundary[1] - boundary[0]) / steps
_A = boundary[0]
_A = boundary[1]
_A = make_points(__lowercase , __lowerca... | 330 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowerCamelCase_ = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block'''... | 330 | 1 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly f... | 385 |
'''simple docstring'''
import doctest
from collections import deque
import numpy as np
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> None:
A_ : Any = [2, 1, 2, -1]
A_ : Tuple = [1, 2, 3, 4]
d... | 385 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixi... | 482 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowerCAmelCase_ ( __A : List[str] ):
... | 329 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def __lowerCamelCase ( _UpperCamelCase : Any , _UpperCamelCase : List[str] , _UpperCamelCase : List[Any] , _UpperCamelCase : int ):
... | 43 | '''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase__ : Tuple = pytest.mark.integration
@pytest.mark.parametrize('... | 43 | 1 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
a : Dict = False
class lowercase(unittest.TestCase ):
... | 273 |
"""simple docstring"""
from math import ceil
def __magic_name__ ( UpperCamelCase : int = 1001 ) -> int:
a__ = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
a__ = 2 * i + 1
a__ = 2 * i
a__ = total + 4 * odd**2 - 6 * even
... | 273 | 1 |
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
_A = get_logger(__name__)
_A = R"\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n Indices of input... | 279 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
"feature_extraction_wav2ve... | 279 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE = {'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''Yol... | 502 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {
'''configuration_longformer''': [
... | 502 | 1 |
"""simple docstring"""
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,
Be... | 717 |
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_s... | 374 | 0 |
"""simple docstring"""
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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from trans... | 19 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : List[Any] = {'''conf... | 549 | 0 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowerca... | 37 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 37 | 1 |
'''simple docstring'''
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class lowercase__ :
'''simple docstring'''
A_ : Optional[Any] = None
def UpperCAmelCase_ ( self ):
... | 533 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
UpperCAmelCase__ : Union[str, Any] = logging.getLogger(__name__)
class __lowercase ... | 313 | 0 |
from __future__ import annotations
def a (_lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = None ):
if start is None:
SCREAMING_SNAKE_CASE_ = 0
if end is None:
SCREAMING_SNAKE_CASE_ = len(_lowerCamelCase ) - 1
... | 717 |
def a (_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = [], []
while len(_lowerCAmelCase ) > 1:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = min(_lowerCAmelCase ), max(_lowerCAmelCase )
start.append(_lowerCAmel... | 89 | 0 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_t... | 40 |
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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = '''▁'''
__UpperCAmelCase =... | 40 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
... | 711 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( UpperCamelCase : list[list[float]] ):
UpperCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works... | 359 | 0 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
Aut... | 191 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import... | 191 | 1 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Thread... | 721 |
'''simple docstring'''
def snake_case_ ( a__ : int = 1_00 ):
"""simple docstring"""
__lowercase = 0
__lowercase = 0
for i in range(1 ,n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints*... | 163 | 0 |
"""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__ = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CON... | 626 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowerCamelCase ( __a = "isbn/0140328726" ):
SCREAMING_SNAKE_CASE_ = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
if n... | 626 | 1 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..mo... | 452 | 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, FlaxMT... | 452 | 1 |
def a__ ( _UpperCamelCase : int ):
__lowerCamelCase = 1
__lowerCamelCase = 2
while i * i <= n:
__lowerCamelCase = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
... | 175 |
from __future__ import annotations
def a__ ( _UpperCamelCase : list[float] ):
if len(_UpperCamelCase ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
raise ValueError('''All values must be gre... | 175 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = OrderedDict(
[
... | 675 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
lowerCAmelCase = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()["""__file... | 675 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelF... | 405 |
'''simple docstring'''
from __future__ import annotations
class A__ :
def __init__( self : Optional[int] , _a : int ) -> None:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =order
# a_{0} ... a_{k}
_SCREAMING_S... | 405 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutpu... | 706 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from ... | 613 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_f... | 165 |
# 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 b... | 165 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( __a , u... | 721 |
'''simple docstring'''
def __A ( lowerCAmelCase_ ):
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_UpperCAmelCase ... | 156 | 0 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__a: Tuple = '''sshleifer/bart-tiny-random'''
__a: ... | 108 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCamelCase : List[Any] = logging.getLogger(__name__)
class lowerCAmelCase__ ( __magic_name__ ):
''... | 184 | 0 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,... | 703 |
"""simple docstring"""
import baseaa
def lowercase_ ( _snake_case ):
return baseaa.baaencode(string.encode("""utf-8""" ) )
def lowercase_ ( _snake_case ):
return baseaa.baadecode(_snake_case ).decode("""utf-8""" )
if __name__ == "__main__":
UpperCAmelC... | 545 | 0 |
'''simple docstring'''
from typing import Any
class _snake_case :
'''simple docstring'''
def __init__( self: Tuple , __UpperCamelCase: str ) -> Tuple:
__magic_name__ : Dict = data... | 436 |
from math import factorial
def SCREAMING_SNAKE_CASE__ ( lowercase = 20 ) -> int:
snake_case : Dict = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case : Dict = n // 2
return int(factorial(lowercase... | 587 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTes... | 283 | """simple docstring"""
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
_lowercase : ... | 283 | 1 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def lowerCamelCase__ ... | 73 |
"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _A ( __lowercase , __lowercase=None ):
"""simple docstring"""
lowerCamelCase__ = No... | 129 | 0 |
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(lowerCamelCase_ , n - 1 , lowerCamelCase_ ) * a) % mod
else:
Upper... | 457 |
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 __magic_name__ ( A__, unittest.Test... | 457 | 1 |
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=__snake_case):
'''simple docstring'''
lowerCamelCase : Any = ["torch", "torchsde"]
def __init__( self , *a__ , **a__ ) -> Tuple:
... | 455 |
from __future__ import annotations
import math
def _UpperCamelCase ( lowerCAmelCase_ ) ->bool:
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 pr... | 377 | 0 |
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> bool:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(lowercase_ )
_SCREAMING_SNAKE_CASE = len(lowercase_ )
_SCREAMING_SNAKE_CASE = [[False for _ in range(... | 720 |
import numpy as np
def __lowerCamelCase ( snake_case__ ) -> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 569 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSeque... | 339 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a... | 339 | 1 |
def lowerCamelCase_ ( _a : int , _a : int ):
'''simple docstring'''
while a != 0:
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = b % a, a
return b
def lowerCamelCase_ ( _a : int , _a : int ):
'... | 322 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ... | 322 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def __snake_case ( __UpperCamelCase : bool = True ,*__UpperCamelCase : Tuple ,**__UpperCamelCase : Dict ):
"""simple docstring"""
... | 86 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
A = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multiling... | 320 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ ={
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIV... | 703 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase__ =8.9_8_8E9 # units = N * m^s * C^-2
def _a ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> dict[str, float]:
__SCREAMING_SNAKE_CASE = abs(c... | 690 | 0 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowercase__ : List[str] = {
'''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,
'''S''': 6.3_3,
'''H''': 6.0_9,
'''... | 312 | lowercase__ : Union[str, Any] = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .dat... | 312 | 1 |
'''simple docstring'''
import gc
import threading
import time
import psutil
import torch
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
snake_case: Any = psutil.Process()
snake_case:... | 692 |
'''simple docstring'''
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils ... | 692 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAme... | 651 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""camembert-base""": """https://hu... | 651 | 1 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowercase = logging.getLogger(__name__)
class A_ :
def... | 468 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def _lowerCAmelCase ( __lowerCamelCase:Optional[int] ):
'''simple docstring'''
return choice(__lowerCamelCase )
def _lowerCAmelCase ( __lowerCamelCase:list[... | 468 | 1 |
"""simple docstring"""
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mo... | 409 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import Autoencod... | 409 | 1 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def UpperCamelCase ( _A = True , *_A , **_A ) -> List[str]:
if not is_tqdm_available():
rai... | 348 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
_lowerCAmelCase = logging.getLogger()
@unittest.skip("""Temporaril... | 348 | 1 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__UpperCamelCase : Any = logging.get_logger(__name__)
__UpperCamelCase : List[str] = {... | 468 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, Imag... | 313 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp ... | 417 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase__ ( snake... | 417 | 1 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class ... | 204 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt... | 204 | 1 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import C... | 706 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''andreasmadsen/efficient_mlm_m0.40''': (
'''https://hug... | 325 | 0 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE = (DDPMParallelScheduler,)
def A ( self : Tu... | 69 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_att... | 69 | 1 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _A ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase : List[Any] = 0
if start < end:
_UpperCAmelCase : List[Any] = randint(lowerCAmelCase_ , l... | 703 |
from __future__ import annotations
def _A ( _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase : list[list[int]] = []
_UpperCAmelCase : list[int] = []
_UpperCAmelCase : List[Any] = 0
_UpperCAmelCase : str = sum(_UpperCamelCase )
cr... | 416 | 0 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
... | 64 |
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_alig... | 259 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case :Optional[int] = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 60 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
__snake_case :List[str] = HfApi()
__snake_case :str = {}
# fmt: off
__snake_case :Optional[Any] = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7... | 60 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __A ( unittest.TestCase ):
def A__ ( self :Tuple ):
'''simple docstring'''
debug_launcher(test_s... | 21 |
from __future__ import annotations
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
A , A : int = np.shape(_lowerCAmelCase )
if rows != columns:
A : Union[str, Any] = ... | 662 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( __lowerCamelCase:str ):
'''simple docstring'''
return "".join(chr(ord(__lowerCamelCase ) - 3_2 ) if "a" <= char <= "z" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
tes... | 468 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixi... | 468 | 1 |
from __future__ import annotations
import math
def UpperCAmelCase__ ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : bool , lowerCamelCase_ : list[int] , lowerCamelCase_ : float ):
if dep... | 47 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagem... | 376 | 0 |
'''simple docstring'''
import os
import string
import sys
lowerCamelCase_ : str = 1 << 8
lowerCamelCase_ : int = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'''right... | 701 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ : str = logging.get_logger(__name__)
lowerCamelCase_ : Optional[int] = {
'''SenseTime/deformable-detr''': '''ht... | 265 | 0 |
"""simple docstring"""
class lowercase__ : # Public class to implement a graph
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) -> None:
_lowerCamelCase : str = row
_lowerCamelCase : Dict = col
... | 88 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config""... | 74 | 0 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
lowercase_ = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
'bicubic': ... | 700 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase , __lowerCamelCase : List[str] = len(SCREAMING_SNAKE_CASE__ ), len(grid[0] )
if (
min(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE... | 230 | 0 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class snake_case_ ( low... | 34 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
... | 369 | 0 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_lowerCAmelCase : Union[str, Any] ... | 386 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> list[str]:
'''simple docstring'''
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_byte... | 386 | 1 |
'''simple docstring'''
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_fast import BertTokenizerFast
from .tokenization_dpr ... | 435 | '''simple docstring'''
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> str:
'''simple docstring'''
_UpperCamelCase : str = [0 for i in range(r + 1 )]
# nc0 = 1
_UpperCamelCase : List[Any] = 1
for i in ra... | 435 | 1 |
def lowerCAmelCase_ ( ) -> int:
UpperCamelCase_ = 0
for i in range(1 , 1001 ):
total += i**i
return str(lowerCAmelCase_ )[-10:]
if __name__ == "__main__":
print(solution())
| 703 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class _UpperCamelCase ( lowerCAmelCase_ ):
... | 371 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_lowerCAmelCase = {
"""configuration_trocr""": ["""TROCR... | 259 |
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase ):
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_lowerCAmelCase : Any = 1
_lowerCAmelCase : Optional[Any] = 1
while repunit:
_lowerCA... | 259 | 1 |
import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a... | 559 |
import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a... | 559 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__magic_name__ : str =logging.get_logger(__name__)
class UpperCamelCase_ ( A ):
"""simple docstring"""
def __init__( self : Union[str, Any] ... | 664 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import... | 664 | 1 |
from __future__ import annotations
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase ) // 2
# choose the middle 3 elements
SCREAMING_SNAKE_CASE_ = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three... | 597 |
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,
require_torch,
... | 597 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_devic... | 544 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( _A = "AAPL" ) -> str:
lowercase : Optional[Any] = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
lowercase : str = BeautifulSoup(requests.get(... | 264 | 0 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowerCAmelCase... | 66 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import ... | 66 | 1 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowercase = logging.getLogger(__name__)
class a_ ( UpperCAmelCase__ ):
def __init__( self :... | 356 |
'''simple docstring'''
import numpy
class a_ :
def __init__( self : Any , __lowerCAmelCase : numpy.ndarray , __lowerCAmelCase : numpy.ndarray ):
__snake_case = input_array
# Random initial weights are assigned where first argument is th... | 356 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_a : int = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFo... | 719 | '''simple docstring'''
# 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 t... | 10 | 0 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import ... | 83 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 35 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class lowerCamelCase (__lowerCamelCase ):
"""simple docstring"""
... | 713 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
'''facebook/data2vec-base-960h''': '''https://hugg... | 157 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
... | 407 |
'''simple docstring'''
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,
DistilBe... | 407 | 1 |
'''simple docstring'''
from itertools import count
def __lowerCAmelCase (__lowerCAmelCase = 50 ):
_UpperCAmelCase : int = [1] * min_block_length
for n in count(__lowerCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(__lowerCAmel... | 718 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
... | 40 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelera... | 544 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuest... | 242 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : int = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConfig",
"Bli... | 712 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 272 | 0 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niel... | 412 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
... | 412 | 1 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shar... | 122 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> float:
return base * power(__lowerCamelCase , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...... | 122 | 1 |
from __future__ import annotations
import requests
__magic_name__ : List[str] = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_ca... | 615 |
from random import shuffle
import tensorflow as tf
from numpy import array
def lowerCAmelCase_ ( __a , __a ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] =int(__a )
assert noofcl... | 258 | 0 |
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 : Any = logging.get_logger(__name__)
clas... | 151 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A__ ( A__ ):
"""simple docstring"""
_lowercase =... | 151 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def UpperCamelCase__ ( __magic_name__ : str , __magic_name__ : bool = True , __magic_name__ : float = math.inf , __magic_name__ : float = -math.i... | 38 |
from collections import Counter
from timeit import timeit
def _A ( SCREAMING_SNAKE_CASE : str = "" , ):
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def _A ( SCREAMING_SNAKE_CASE : str = "... | 563 | 0 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
_UpperCamelCase : Tuple ="\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classificat... | 710 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],... | 645 | 0 |
import warnings
from .generation import TFGenerationMixin
class lowerCAmelCase__ ( __lowercase ):
# warning at import time
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be removed in Transform... | 612 |
from collections.abc import Sequence
def __A(lowerCAmelCase = None ) -> int:
"""simple docstring"""
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
_UpperCamelCase = nums[0]
for i in range(1 , len(lowerCAmelCase ) ):
_U... | 612 | 1 |
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_m... | 719 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
lowerCamelCase_ : List[str] = TypeVar('''T''')
lowerCamelCase_ : Optional[int] = TypeVar('''U''')
class _SCREAMING_SNAKE_CASE ( Generic[T, U] ... | 265 | 0 |
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