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"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowercase ( _UpperCAmelCase)... | 480 |
"""simple docstring"""
a_ = 256
# Modulus to hash a string
a_ = 1000003
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ):
"""simple docstring"""
snake_case_ : str = len(SCREAMING... | 480 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 289 |
'''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 ... | 289 | 1 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor im... | 654 |
a__ = [0, 2, 4, 6, 8]
a__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( a : int , a : int , a : list[int] , a : int ):
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i ... | 654 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 715 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class SCREAMING_SNAKE_CASE_ (pl.LightningModule ):
'''simple docstring'''
def __init__( self : Any , __a ... | 171 | 0 |
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
class a ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase__ : Tuple=None , **lowerCamelC... | 332 |
from ..utils import DummyObject, requires_backends
class a ( metaclass=__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = ['note_seq']
def __init__( self : Dict , *lowerCamelCase__ : int , **lowerCamelC... | 332 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {}
class __lowercase ( a__ ):
_lowerCAmelCase = "llama"
_lowerCAmelCase = ["past_key_va... | 143 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_... | 143 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : int = 1000000 ):
"""simple docstring"""
A__ : Optional[Any] =set(range(3 , UpperCamelCase , 2 ) )
primes.add(2 )
for p in range(3 , UpperCamelCase , 2 ):
if p no... | 656 | """simple docstring"""
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
if num <= 0:
raise ValueError("Input must be a positive integer" )
A__ : Union[str, Any] =[True] * (num + 1)
A__ : Union[str, Any] =2
while p *... | 656 | 1 |
'''simple docstring'''
from __future__ import annotations
def _A ( A ,A ,A ,) -> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif electron_conc < 0:
raise ValueError(... | 425 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...mode... | 425 | 1 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCamelCase ( lowerCamelCase : dict , lowerCamelCase : str , lowerCamelCase : set , lowerCamelCase : set , lowerCamelCase : dict , lowerCamelCase : dict ... | 665 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
... | 666 | 0 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
re... | 171 |
import fire
from utils import calculate_rouge, save_json
def __lowerCamelCase ( A__ : Union[str, Any] , A__ : Optional[int] , A__ : Dict=None , **A__ : Dict ) -> str:
lowerCamelCase_ : Union[str, Any] = [x.strip() for x in open(A__ ).readlines()]
lowerCamelCase_ : ... | 171 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
a_ = {'tokenization_herbert': ['HerbertTokenizer']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable(... | 296 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_ima... | 296 | 1 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
A__ : List[str]= None
try:
import msvcrt
except ImportError:
A__ : Dict= None
try:
import fcntl
except ImportError:
A__ : Dict= No... | 20 |
"""simple docstring"""
# Copyright 2022 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... | 20 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def __a ( __UpperCAmelCase , __UpperCAmelCase=1000 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
a__ = n - 1
a__ = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(... | 194 |
import inspect
import unittest
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( ... | 655 | 0 |
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int ):
'''simple docstring'''
lowercase_ = [[0 for _ in range(__lowerCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowercase_ = 1
for n in range(m + 1 ):
for k in range(1 , __lowerCamelCase ... | 601 |
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_ ( __lowerCamelCase: Optional[int] , __lowerCamelCase... | 601 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
'''facebook/s2t-small-librispeech-asr''': (
'''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
),
# See all... | 272 |
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''', '''LayoutLMv2... | 272 | 1 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
UpperCamelCase = """\
@inproceedings{popovic-2015-chrf,
title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
author = \"Popovi{\'c}, Maja\",
b... | 120 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_I... | 120 | 1 |
"""simple docstring"""
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'):
__snake_case = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Res... | 200 |
"""simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _lowerCamelCase ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : str , lowerCamelCase__ : ... | 200 | 1 |
import os
import string
import sys
__UpperCamelCase : Optional[Any] = 1 << 8
__UpperCamelCase : Any = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY... | 641 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
class lowerCAmelCase__( snake_case__ ):
'''simple docstring'''
A_ : Union[str, ... | 641 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class a_ :
def __init__( self : Union[str, Any] , snake_case__ : int ):
lowerCAmelCase__ = num_of_nodes
lowerCAmelCase__ = []
lowerCAmelCase__ = {}
def _SCREAMING... | 644 | """simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__lowerCAmelCase : Optional[Any] = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 644 | 1 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
A: int = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ :
def __init... | 713 |
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
A: List[An... | 359 | 0 |
def _snake_case (__lowercase):
UpperCamelCase_ = 1
for i in range(1 , num + 1):
fact *= i
return fact
def _snake_case (__lowercase):
UpperCamelCase_ = 0
while number > 0:
UpperCamelCase_ = number % 10
sum_of_di... | 23 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import Generation... | 603 | 0 |
"""simple docstring"""
import math
def lowerCamelCase_ ( _lowerCamelCase ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(_lowerCamelCase )
... | 708 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 696 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=_SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["transformers", "torch", "note_seq"]
def __init__( self : Tuple , *_lowerCAmelCase : List[... | 31 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
A : List[Any] = argparse.ArgumentParser()
parser.add_argument('''--dump_path''', defau... | 176 | 0 |
'''simple docstring'''
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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImag... | 712 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Optional[Any] = logging.get_logger(__name__)
lowercase_ : int = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-sant... | 653 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( snake_case__ ):
'''simple docstring'''
a_ = ['''image_processor''', '''tokenizer''']
a_ = '''ChineseCLIPImageProcessor'''
a_ ... | 424 | class __A :
'''simple docstring'''
def __init__( self ):
_lowerCAmelCase : Dict = ""
_lowerCAmelCase : Optional[Any] = ""
_lowerCAmelCase : List[Any] = []
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ... | 424 | 1 |
'''simple docstring'''
import logging
from transformers import PretrainedConfig
a_ : str = logging.getLogger(__name__)
a_ : List[str] = {
"bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summ... | 532 |
'''simple docstring'''
from typing import Any
def _A (lowerCAmelCase__ :list ) -> list[Any]:
'''simple docstring'''
if not input_list:
return []
_a = [input_list.count(lowerCAmelCase__ ) for value in input_list]
_a ... | 532 | 1 |
"""simple docstring"""
class lowercase__ :
def __init__( self : Any , snake_case__ : Any , snake_case__ : Optional[Any] ):
lowerCamelCase_ : str =name
lowerCamelCase_ : Tuple =val
def __str__( self : str ):
return ... | 153 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class lowerCA... | 247 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : ... | 126 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class _UpperCamelCase ( tf.keras.layers.Layer ):
'''sim... | 126 | 1 |
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,
Au... | 678 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
... | 678 | 1 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_com... | 486 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoCo... | 486 | 1 |
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, TensorFlowBenchmarkArguments
@require_tf
c... | 157 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
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_params import (
TEXT_G... | 157 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A ={
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNot... | 704 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TF... | 241 | 0 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..mod... | 41 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def _A ( A__ ):
"""simple docstring"""
__lowercase = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.... | 41 | 1 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __lowerCamelCase :
def __init__( self ,... | 20 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
A__ : Any= TypeVar("""T""")
class __lowerCamelCase ( Generic[T] ):
def __init__( self , snake_case_ ) -> None:
UpperCamelCase__ = data
... | 20 | 1 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
A_ = logging.getLogger(__name__)
if is_torch_tp... | 143 |
'''simple docstring'''
def A_ ( snake_case = 1000 ):
SCREAMING_SNAKE_CASE:Tuple = 2**power
SCREAMING_SNAKE_CASE:Optional[int] = str(snake_case )
SCREAMING_SNAKE_CASE:int = list(snake_case )
SCREAMING_SNAKE_CASE:Optional[Any] = 0
for i in l... | 143 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _snake_case ,_snake_case = None ):
UpperCAmelCase__ : List[Any] = word_bank or []
# create a table
UpperCAmelCase__ : int = len(_snake_case ) + 1
UpperCAmelCase__ : ... | 254 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbon... | 254 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=lowerCamelCase__ ):
"""simple docstring"""
__UpperCAmelCase = ["""transformers""", """torch""", """note_seq"""]
def __init__( self : Optional... | 347 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 454 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a_ :Tuple = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
try:
if not is_torch_available():
raise OptionalDep... | 709 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
a_ :s... | 250 | 0 |
import inspect
import unittest
class lowerCAmelCase_ ( unittest.TestCase ):
def snake_case ( self ):
try:
import diffusers # noqa: F401
except ImportError:
assert False
def snake_case ( self ):
import diffusers
... | 105 |
def _A ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list ):
_enforce_args(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if n == 0:
return 0
UpperCamelCase :Union[str, Any] = float('''-inf''' )
for i in range(1 , n + 1 ... | 658 | 0 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__a: int = (7_20, 12_80) # Height, Width
__a: Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
__a: Optional[int] = ... | 428 | '''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ):
lowercase__ , lowercase__ : Tuple = position
lowercase__ : int = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y ... | 428 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->tuple[np.ndarray, np.ndarray]:
_lowerCamelCase, _lowerCamelCase : List[str] = np.shape(SCREAMING_SNAKE_CASE_ )
if rows != columns:
... | 434 | """simple docstring"""
from collections.abc import Callable
import numpy as np
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->np.array:
_lowerCamelCase : Tuple = ... | 434 | 1 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/lice... | 645 | """simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfor... | 645 | 1 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import... | 445 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : Optional[int] = logging.get_logger(__name__)
snake_case : Optional[Any] = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/... | 445 | 1 |
"""simple docstring"""
def __magic_name__ ( __snake_case : int = 1000 ) -> int:
lowercase , lowercase : Optional[Any] = 1, 1
lowercase : int = []
for i in range(1 , n + 1 ):
lowercase : ... | 518 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : int = logging.get_logger(__name__)
_A : int = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
cl... | 518 | 1 |
from __future__ import annotations
import numpy as np
def _A ( _lowercase ) -> int:
"""simple docstring"""
return np.maximum(0 , _lowercase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 1 |
"""simple docstring"""
def UpperCAmelCase_ ( __a : float , __a : float ):
'''simple docstring'''
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
retur... | 437 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
UpperCamelCase : Dict = logging.get_logger(__name__)
class UpperCamelCase__ (UpperCamelCase__ ):
'''simple docstring'''
def __init__( self ,*_lowerCAmelCas... | 714 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 9 | 0 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def a_ ( lowerCAmelCase_ : np.ndarray ):
return input_array.reshape((input_array.size, 1) )
def a_ ( lowerCAmelCase_ : np... | 53 | import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetT... | 524 | 0 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowercase = logging.get_logg... | 713 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase):
'''simple ... | 150 | 0 |
"""simple docstring"""
from __future__ import annotations
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = False , ):
UpperCAmelCase_ = cipher_alphabet or [chr(lowerCAmelCase__ ) for i in... | 82 | import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # no... | 559 | 0 |
def __lowercase ( snake_case ):
"""simple docstring"""
if len(snake_case ) <= 1:
return [tuple(snake_case )]
__magic_name__ :str = []
def generate(snake_case, snake_case ):
if k == 1:
res.append(tuple(arr[:] ) )
return
... | 180 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
SCREAMING_SNAKE_CASE__ : List[str] = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""tex... | 180 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=a_):
a_ = ['''flax''']
def __init__( self : List[Any] , *_A : Tuple , **_A : Optional[int] ) -> str:
requires_backends(self , ['... | 541 |
'''simple docstring'''
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__ = {
'''facebook/xlm-roberta... | 186 | 0 |
def lowerCamelCase__ ( ):
"""simple docstring"""
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = 1
SCREAMING_SNAKE_CASE : ... | 488 |
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 488 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__UpperCamelCase : int = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author... | 248 | def A ( _lowercase = 1_000_000 ):
SCREAMING_SNAKE_CASE : Optional[int] = set(range(3 , _lowercase , 2 ) )
primes.add(2 )
for p in range(3 , _lowercase , 2 ):
if p not in primes:
continue
... | 248 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 481 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_confi... | 481 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftFormerOnnxConfig"... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalDepen... | 259 | 0 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class... | 712 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
__lowerCamelCase : Optional[An... | 363 | 0 |
"""simple docstring"""
from math import isqrt
def __snake_case ( SCREAMING_SNAKE_CASE: int ):
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(__UpperCamelCase ) + 1 ) )
def __sn... | 580 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _lowercase ( unittest.TestCase ):
_lowerCamelCase ... | 490 | 0 |
from ..utils import DummyObject, requires_backends
class lowercase__( metaclass=snake_case__ ):
'''simple docstring'''
snake_case__ = ['''torch''']
def __init__( self , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE) -> int:
... | 582 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class lowercase__( tf.keras.optimizers.schedules.LearningRateSchedule ):
''... | 582 | 1 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTester... | 263 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowercase__ =argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type... | 263 | 1 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def lowerCamelCase_( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )-> tuple[float | int, list[tuple[int, int]]]:
_SCREAMING_SNAKE_CASE : int... | 716 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''... | 635 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A__ (snake_case : Dict ... | 279 |
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:... | 279 | 1 |
"""simple docstring"""
from math import isqrt, loga
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
A__ : Optional[int] =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in r... | 715 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : Optional[Any] = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP... | 595 | 0 |
"""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_subprocess_async, requir... | 227 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCamelCase ( lowercase_ : float , lowercase_ : float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
e... | 72 | 0 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
... | 714 |
"""simple docstring"""
import requests
snake_case = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='''
def snake_case ( lowerCAmelCase_ ) -> None:
# fetching a list of articles in json format
_snake_case = requests.get(_NEWS_... | 404 | 0 |
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = [0 for i in range(len(_SCREAMING_SNAKE_CASE ) )]
# initialize interval's left pointer and right pointer
__lowercase , __lowercase = 0, 0
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
# case when cu... | 402 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
snake_case__ : Optional[Any] = logging.get_logger(__name__)
class _A ( _lowercase ):
'''simple docstring'''
def __init__( self : Dict , *lowerCam... | 402 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ : Any = {'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnn... | 710 |
"""simple docstring"""
from collections import Counter
from timeit import timeit
def A_ ( UpperCAmelCase__ = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(' ' , '' ).lower() ).values() ) < 2
def A_ ( UpperCAmelCase... | 509 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> List[Any]:
lowerCamelCase_ = ''
for i in table:
res += inp[i - 1]
return res
def _UpperCamelCase ( __UpperCamelCase ) -> Tuple:
return data[1:] + data[0]
... | 42 |
from math import sqrt
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
assert isinstance(a_ , a_ ) and (
number >= 0
), "'number' must been an int and positive"
__A = True
# 0 and 1 are none primes.
if number <= 1:
__... | 55 | 0 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
UpperCAmelCase_ : Union[str, Any] = logging... | 176 |
"""simple docstring"""
from __future__ import annotations
import requests
def _A (__a ) -> dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(__a ... | 176 | 1 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowercase_ : List[Any] = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=s... | 64 |
import math
def A_ ( _UpperCAmelCase ):
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 primes
return False
... | 671 | 0 |
"""simple docstring"""
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 lowercase__( __SCREAMING_SNAKE_CASE : Dataset , __SCREAMING_SNAKE_CASE : Dict... | 477 | """simple docstring"""
import numpy as np
def lowercase__( __SCREAMING_SNAKE_CASE : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 477 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_f... | 460 |
'''simple docstring'''
from math import sqrt
def _SCREAMING_SNAKE_CASE (A = 1_000_000 ) -> int:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
lowercase__ = 42
while num_cuboids <= limit:
max_cuboid_size += 1
... | 460 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase (__UpperCamelCase : str , __UpperCamelCase : Dict ):
"""simple docstring"""
__UpperCamelCase =sorted(numsa + numsa )
__UpperCamelCase =divmod(len(__UpperCamelCase ) , ... | 714 | """simple docstring"""
def lowerCAmelCase (__UpperCamelCase : int = 1_0_0_0_0_0_0 ):
"""simple docstring"""
__UpperCamelCase =1
__UpperCamelCase =1
__UpperCamelCase ={1: 1}
for inputa in range(2 , __UpperCamelCase ):
__Upper... | 296 | 0 |
from collections import defaultdict
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _A , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = total # total no of tasks (N)
# DP table will have a ... | 148 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ : Any ={
'''configuration_mask2former''': [
'''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Mask2FormerConfig''',
],
}
... | 148 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
'''configuration_mobilebert''': [
''... | 680 |
'''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 format,... | 680 | 1 |
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
lowercase_ = logging.get_logger(__name__)
lowercase_ =... | 74 |
"""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 lowercase__ ( lowerCamelCase : int , ... | 308 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.ut... | 223 |
'''simple docstring'''
import os
from pathlib import Path
def _UpperCamelCase ( ) -> Tuple:
'''simple docstring'''
from torch.utils.cpp_extension import load
UpperCamelCase__ = Path(__A ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
... | 223 | 1 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def __lowerCAmelCase ( __UpperCamelCase : dict , __UpperCamelCase : str , __UpperCamelCase : set , __UpperCamelCase : set , __Upp... | 58 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__lowerCAmelCase : Tuple ... | 58 | 1 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _SCREAMING_SNAKE_CASE ( __snake_case ):
... | 716 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( lowerCamelCase_):
assert column_title.isupper()
a__ = 0
a__ = len(lowerCamelCase_) - 1
a__ = 0
while index >= 0:
a__ = (ord(column_title[index]) - 64) * pow(26 , ... | 200 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _lowerCamelCase ( datasets.BeamBasedBuilder ):
def UpperCamelCase_ ( self ) -> Optiona... | 64 | """simple docstring"""
def lowerCamelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'''{price_plus_tax(100, 0.25) = }''')
print(F'''{price_plus_tax(125.50, 0.05) = }''')
... | 530 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 8 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE = parse(importlib.metadata.version('torch'))
def lowercase_ ( __A : Union[str, Version] , ... | 8 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Optional[int] , _UpperCAmel... | 82 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCamelCase ( lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowerCAmelCase__ , lowerCAmelCa... | 521 | 0 |
def _UpperCamelCase ( lowerCAmelCase_ ) ->str:
return "".join([hex(lowerCAmelCase_ )[2:].zfill(2 ).upper() for byte in list(lowerCAmelCase_ )] )
def _UpperCamelCase ( lowerCAmelCase_ ) ->bytes:
# Check data validity, following RFC3548
# https://www.ietf.org/r... | 717 |
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 _lowercase ( self : ... | 627 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
... | 103 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case =logging.get_logger(__name__)
__snake_case ={
"""bigcode/gpt_bigcode-santacoder""": """https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/conf... | 133 | 0 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class __A ( enum.Enum ):
__A ... | 269 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
UpperCAmelCase__ : List[Any] =list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
UpperCAmelCase__ : Dict ... | 269 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCAmelCase ( __a : int = 3 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if isinstance(__a ,__a ):
... | 14 |
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from ut... | 448 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..ima... | 700 |
import os
def __UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
with open(os.path.dirname(__a ) + '''/grid.txt''' ) as f:
_a : str = [] # noqa: E741
for _ in range(20 ):
l.append([int(__a ) for x in f.read... | 578 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vis... | 58 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvaila... | 684 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqa... | 355 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
snake_case_ = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def ... | 355 | 1 |
def a ( snake_case__: list ):
'''simple docstring'''
lowercase_ = len(snake_case__ )
for _ in range(snake_case__ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
lowercase_ , lowercase_ = arr[i ... | 97 |
from __future__ import annotations
from math import pi, sqrt
def a ( snake_case__: float , snake_case__: float ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
rais... | 97 | 1 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__a = re.compile(R"""^(?P<major>\d+)""" R"""\.(?P<minor>\d+)""" R"""\.(?P<patch>\d+)$""")
@total_ordering
@dataclass
class __lo... | 627 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configur... | 627 | 1 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def A_ ( _lowerCAmelCase ) -> int:
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def A_ ( _lowerCAmelCase ) -> ... | 629 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils... | 629 | 1 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
_SCREAMING_SNAKE_CASE : Any = logging.getLogger()
def __lowerCAmelCase (... | 712 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import... | 206 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI... | 74 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __UpperCamelCa... | 74 | 1 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_ca... | 701 |
import os
from datetime import datetime as dt
from github import Github
A : Union[str, Any] = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def _lowe... | 473 | 0 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class a__ ( UpperCamelCase_ ):
snake_case__ = '''MCTCTFeatureExtractor'''
snake_case__ = '''AutoTokenizer'''
def __init__(... | 227 |
"""simple docstring"""
import math
from collections.abc import Callable
def UpperCAmelCase ( snake_case : Callable[[float], float] , snake_case : float , snake_case : float ):
_lowerCAmelCase:float = xa
_lowerCAmelCase:float ... | 227 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeniza... | 701 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common imp... | 511 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
Ski... | 2 |
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__ = {
'andreasmadsen/efficient_mlm_m0.40': (
... | 503 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A_( A , A , ... | 486 |
from torch import nn
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , __lowercase : List[str] , __lowercase : Dict ):
'''simple docstring'''
super()._... | 486 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not ... | 7 | '''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] = {
... | 262 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 711 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
... | 582 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.