code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( UpperCamelCase : List[str] , UpperCamelCase : Dict ) -> int:
if len(UpperCamelCase ) <= 1 or n <= 1:
return
insert_next(UpperCamelCase , n - 1 )
rec_ins... | 273 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenize... | 62 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""tokenization_biogp... | 277 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = os.path.join(args.tf_model_dir , "parameters... | 62 | 0 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__UpperCAmelCase = datasets.load_iris()
__UpperCAmelCase = np.array(data['data'])
__UpperCAmelCase = np.array(data['target'])
__UpperCAmelCase = d... | 600 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.u... | 62 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class lowerCamelCase ( _UpperC... | 462 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_common import ConfigTester
from .... | 62 | 0 |
"""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,
AutoMo... | 473 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_... | 62 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
... | 649 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : int = 6 ):
SCREAMING_SNAKE_CASE : Node | None = None
SCREA... | 62 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
class __lowerCAmelCase :
... | 283 |
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return 1 if input_a == input_a else 0
def lowerCamelCase__ ( ):
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ... | 62 | 0 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
fr... | 196 |
import math
import flax.linen as nn
import jax.numpy as jnp
def lowerCamelCase__ ( lowercase , lowercase , lowercase = 1 , lowercase = 1 , lowercase = 1.0E4 , lowercase = False , lowercase = 1.0 , ):
"""simple docstring"""
assert timesteps.ndim == 1, "Timesteps sh... | 62 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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_configuration_common import ConfigTester
from ...t... | 579 |
# 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 ... | 62 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFe... | 213 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( lowerCAmelCase , unittest.Tes... | 62 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( ) -> Dict:
_lowerCAmelCase : List[str] = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_lowerCAmelCase : Dict = 6
_lowerCAmelCase : List[Any] = 1
_lowerCAmelCase : int = 19_01
... | 384 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import loggin... | 62 | 0 |
"""simple docstring"""
def __magic_name__ ( UpperCamelCase : Optional[int] ) -> Optional[int]:
if not all(char in '01' for char in bin_string ):
raise ValueError('Non-binary value was passed to the function' )
if not bin_string:
raise ValueError('Emp... | 273 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""JukeboxPriorConfig""",
"""Jukeb... | 62 | 0 |
"""simple docstring"""
UpperCAmelCase__ = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface... | 277 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
snake_case = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and M... | 62 | 0 |
from math import sqrt
def __UpperCamelCase ( lowercase__ : Tuple ) -> Any:
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase_ : Any ... | 600 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : float... | 62 | 0 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
lowerCAmelCase = 0
lowerCAmelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0... | 462 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tok... | 62 | 0 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transfo... | 473 |
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE : Optional[int] = str(abs(lowercase ) )
SCREAMING_SNAKE... | 62 | 0 |
'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...im... | 649 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (... | 62 | 0 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __lowerCAmelCase ( a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ['''image_processor''', '''tokenizer''']
_SCREAMING_SNAKE_C... | 283 |
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,
MobileViTImageProcessor,
)
from tran... | 62 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Tuple = {
"configuration_convbert": ["CONVBERT_PRETRAIN... | 196 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
snake_case = datasets.load_iris()
snake_case = np.array(data["""data"""])
snake_case = np.array(data["""target"""])
snake_case = data["""... | 62 | 0 |
import argparse
import struct
import unittest
class lowerCamelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase ):
UpperCAmelCase_ = data
# Initialize hash values
UpperCAmelCase_ = [
0x... | 579 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophe... | 62 | 0 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class a ( lowercase__ ):... | 63 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in ... | 63 | 1 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
a : Any = logging.get_logger(__name__)
class a ( lowercase__ ):
"""simple docstring"""
def __init__( self : List[Any] ... | 63 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = ... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : List[Any] , __lowerCamelCase : Union[str, Any] ):
__UpperCAmelCase : Tuple = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( ... | 63 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 63 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : Optional[int] = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrain... | 63 |
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_avai... | 63 | 1 |
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_vision_available
from ...tes... | 63 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 63 | 1 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_at... | 63 |
from __future__ import annotations
a : Optional[Any] = [True] * 1_000_001
a : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a : Optional[Any] = False
... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : float , __lowerCamelCase : float ):
return round(float(moles / volume ) * nfactor )
def lowerCamelCase__ ( __lowerCamelCase : float , __lowerC... | 63 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 | 1 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def lowerCamelCase__ ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : str=... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 | 1 |
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:
from transforme... | 63 |
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 OptionalDe... | 63 | 1 |
import math
import sys
import cva
import numpy as np
def lowerCamelCase__ ( __lowerCamelCase : np.ndarray , __lowerCamelCase : float ):
# For applying gaussian function for each element in matrix.
__UpperCAmelCase : int = math.sqrt(__lo... | 63 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 | 1 |
from manim import *
class a ( lowercase__ ):
"""simple docstring"""
def UpperCAmelCase ( self : List[Any] ) -> List[str]:
__UpperCAmelCase : List[Any] = Rectangle(height=0.5 , width=0.5 )
__UpperCAmelCase :... | 63 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import... | 63 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Tuple = {
"configuration_blenderbot_small": [
"BL... | 63 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : List[str] = {"configuration_xg... | 63 | 1 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def lowerCamelCase__ ( __lowerCamelCase : Any ): ... | 63 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 | 1 |
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 import DPRContextEnc... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__UpperCAmelCase : int = [True] * (num + 1)
__UpperCAmelCase : Tuple = 2
while p * p <= num:
if primes[p]:
... | 63 | 1 |
import re
from filelock import FileLock
try:
import nltk
a : Optional[int] = True
except (ImportError, ModuleNotFoundError):
a : Union[str, Any] = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.down... | 63 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 | 1 |
a : List[str] = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
a : Tuple ... | 63 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepi... | 63 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaIm... | 63 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth <... | 63 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_... | 63 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 | 1 |
a : Union[str, Any] = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features impor... | 63 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/ma... | 63 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib... | 63 | 1 |
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_propert... | 63 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 1 |
a : Optional[int] = 9.8_06_65
def lowerCamelCase__ ( __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : float = g ):
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
i... | 63 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
... | 63 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in ... | 63 | 1 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPrior... | 63 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = ... | 63 | 1 |
from __future__ import annotations
from random import random
class a :
"""simple docstring"""
def __init__( self : Optional[int] , __lowercase : int | None = None ) -> Any:
__UpperCAmelCase : Optional[Any] = value
... | 63 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 63 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class a ( unittest.... | 63 |
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_avai... | 63 | 1 |
a : str = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
a : Any = [{"type": "code", "content": INSTALL_CONTENT}]
a : str ... | 63 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 63 | 1 |
from __future__ import annotations
import requests
a : List[Any] = set(
"approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categor... | 63 |
from __future__ import annotations
a : Optional[Any] = [True] * 1_000_001
a : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a : Optional[Any] = False
... | 63 | 1 |
import torch
from diffusers import DiffusionPipeline
class a ( lowercase__ ):
"""simple docstring"""
def __init__( self : Optional[Any] , __lowercase : Dict , __lowercase : Union[str, Any] ) -> Any:
super().__init__()
... | 63 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__UpperCAmelCase : int = [True] * (num + 1)
__UpperCAmelCase : Tuple = 2
while p * p <= num:
if primes[p]:
... | 63 |
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 OptionalDe... | 63 | 1 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
a : str = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH AH QH",
"TS KS 5S 9S AC",
"KD 6S... | 63 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : List[Any] = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_... | 63 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import... | 63 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : int = logging.get_logger(__name__)
a : Union[str, Any] ... | 63 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : List[str] = {"configuration_xg... | 63 | 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... | 63 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 | 1 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : List[str]=False ):
__UpperCAmelCase : Tuple = OmegaConf.l... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__UpperCAmelCase : int = [True] * (num + 1)
__UpperCAmelCase : Tuple = 2
while p * p <= num:
if primes[p]:
... | 63 | 1 |
import qiskit
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int ):
__UpperCAmelCase : Optional[int] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__UpperCAme... | 63 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 | 1 |
import functools
def lowerCamelCase__ ( __lowerCamelCase : list[int] , __lowerCamelCase : list[int] ):
# Validation
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not all(isinstance(__lowerCamelCase , __lowerCamelCase ... | 63 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepi... | 63 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils impor... | 63 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth <... | 63 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
a : str = logging.get_logger(__name__)
class a ( lowercase__ ):
"""simple docstring"""
def __init__( self : Optional[Any] , *_... | 63 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 | 1 |
from collections.abc import Iterable
from typing import Any
class a :
"""simple docstring"""
def __init__( self : Tuple , __lowercase : int | None = None ) -> str:
__UpperCAmelCase : int = value
__UpperCAmelCase ... | 63 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : list[int] ):
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
__UpperCAmelCase : List[str] = sum(__lowerCamelCase ) / len(__lowerCamelCase ) # Calculate the ... | 63 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib... | 63 | 1 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slo... | 63 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : list[list[int]] ):
def update_area_of_max_square(__lowerCamelCase : int , __lowerCamelCase : int ) -> int:
# BASE CASE
if row >= row... | 63 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
... | 63 | 1 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
a : List[Any] = "http://www.moc... | 63 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in ... | 63 | 1 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers... | 63 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = ... | 63 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a : ... | 63 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 63 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Dict = {
"configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
"tokenization_mvp": ["MvpT... | 63 |
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_avai... | 63 | 1 |
from __future__ import annotations
import numpy as np
def lowerCamelCase__ ( __lowerCamelCase : list[float] ):
return np.maximum(0 , __lowerCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 63 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : str , __lowerCamelCase : list[str] ):
__UpperCAmelCase : Any = """"""
for word_or_phrase in separated:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise Exception("""join() a... | 63 |
from __future__ import annotations
a : Optional[Any] = [True] * 1_000_001
a : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a : Optional[Any] = False
... | 63 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
"sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
# See all V... | 63 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 | 1 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fro... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 | 1 |
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 OptionalDe... | 63 |
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 OptionalDe... | 63 | 1 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be... | 63 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 | 1 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess... | 63 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import... | 63 | 1 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
a : List[str] = l... | 63 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : List[str] = {"configuration_xg... | 63 | 1 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_... | 63 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 | 1 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCamelCase__ ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : Optional[int]=None ):
__Upp... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__UpperCAmelCase : int = [True] * (num + 1)
__UpperCAmelCase : Tuple = 2
while p * p <= num:
if primes[p]:
... | 63 | 1 |
import numpy as np
def lowerCamelCase__ ( __lowerCamelCase : np.array ):
return 1 / (1 + np.exp(-vector ))
def lowerCamelCase__ ( __lowerCamelCase : np.array ):
return vector * sigmoid(1.7_0_2 * vector )
if __name__... | 63 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 | 1 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class a ( lowercase__... | 63 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepi... | 63 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
a : Dict = TypeVar("T")
def lowerCamelCase__ ( __lowerCamelCase : int ):
return (position - 1) // 2
def lowerCamelCase__ ( _... | 63 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth <... | 63 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
a : int = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={... | 63 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 | 1 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
a : Any = ... | 63 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 | 1 |
from math import pow
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , ):
if current_sum == needed_sum:
# If the s... | 63 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib... | 63 | 1 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here ... | 63 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 1 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCamelCase__ ( __lowerCamelCase : str , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
if v... | 63 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : list ):
if len(__lowerCamelCase ) <= 1:
return lst
__UpperCAmelCase : Optional[Any] = 1
while i < len(__lowerCamelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__UpperCAmelCase ,... | 63 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in ... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : Dict , __lowerCamelCase : Tuple ):
__UpperCAmelCase : Optional[Any] = """"""
for i in table:
res += inp[i - 1]
return res
def lowerCamelCase__ ( __lowerCamelCase : Tuple ... | 63 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = ... | 63 | 1 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( lowercase__ , unittest.TestCase ):
"""simple docstring"""
... | 63 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 63 | 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 ):
"""simple docstring"""
def UpperCAmelCase ( self : List[Any] ) -> Any:
... | 63 |
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_avai... | 63 | 1 |
a : List[Any] = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"... | 63 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 63 | 1 |
import numpy
# List of input, output pairs
a : Tuple = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
a : List[str] = (((515, 22, 13), 555), ((61, 35, 49), 150))
a : Optional[Any] = ... | 63 |
from __future__ import annotations
a : Optional[Any] = [True] * 1_000_001
a : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a : Optional[Any] = False
... | 63 | 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 a ( lowercase__ ):
... | 63 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 | 1 |
# 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/licenses/LICENSE-2.0
#
# Unless... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 | 1 |
from manim import *
class a ( lowercase__ ):
"""simple docstring"""
def UpperCAmelCase ( self : Optional[Any] ) -> str:
__UpperCAmelCase : Tuple = Rectangle(height=0.5 , width=0.5 )
__UpperCAmelCase : ... | 63 |
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 OptionalDe... | 63 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky... | 63 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
a : str = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
... | 63 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import... | 63 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Optional[Any] = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingface.co/mi... | 63 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : List[str] = {"configuration_xg... | 63 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNo... | 63 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 | 1 |
import os
def lowerCamelCase__ ( __lowerCamelCase : Any ):
__UpperCAmelCase : Tuple = len(grid[0] )
__UpperCAmelCase : int = len(__lowerCamelCase )
__UpperCAmelCase : Dict = 0
__UpperCAmelCase : List[str... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__UpperCAmelCase : int = [True] * (num + 1)
__UpperCAmelCase : Tuple = 2
while p * p <= num:
if primes[p]:
... | 63 | 1 |
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