code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common impor... | 197 | def __lowercase ( lowerCamelCase : str ):
UpperCamelCase_ : Dict = 0
for ch in input_str:
UpperCamelCase_ : Tuple = ord(lowerCamelCase )
UpperCamelCase_ : str = pow(2 , lowerCamelCase )
# If we already turned on bit for current character's unicode
... | 175 | 0 |
from copy import deepcopy
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase = None , UpperCAmelCase = None ) -> None:
'''simple docstring'''
if arr is None and size is not None:
lowercase_ ... | 356 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 297 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_at... | 44 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_SCREAMING_SNAKE_CASE = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig"... | 343 | 0 |
from math import pow, sqrt
def __lowerCamelCase ( *lowerCAmelCase__ ):
lowerCAmelCase__ = len(lowerCAmelCase__ ) > 0 and all(value > 0.0 for value in values )
return result
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
re... | 367 | import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAme... | 119 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
# See all CANI... | 104 |
'''simple docstring'''
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar('''KT''')
lowerCAmelCase__ = TypeVar('''VT''')
class lowercase_ (Generic[KT, VT] ):
"""simple docstring"""
def __init... | 104 | 1 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class UpperCamelCase :
"""simple docstring"""
def __init__( self : Dict , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Union[str, Any] , UpperCA... | 356 | '''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase ( a_ , unittest.TestCase ):
"""simple docstring""... | 345 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase ( lowercase_ ... | 213 | """simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.... | 213 | 1 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_lowercase : str = datasets.load_iris()
_lowercase : Optional[Any] = np.array(data['data'])
_lowercase : Optional... | 368 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : List[Any] = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
... | 86 | 0 |
"""simple docstring"""
lowerCAmelCase_ = 'Alexander Joslin'
import operator as op
from .stack import Stack
def __UpperCAmelCase ( __lowerCamelCase ) -> int:
lowercase__ : str = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-... | 16 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
f... | 347 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
'configuration_blenderbot': [
'BLENDERBOT_PRETRAINED_CONFIG_ARCH... | 119 | import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Distr... | 119 | 1 |
import math
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Any = f"Input value of [number={number}] must be an integer"
raise TypeError(_UpperCAmelCase )
if number < 1:
S... | 13 |
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = [0] * len(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: List[Any] = []
SCREAMING_SNAKE_CASE_: str = []
SCREAMING_SNAKE_CASE_: List[str] = 0
for values in graph.values():
... | 13 | 1 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_visi... | 355 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/... | 230 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""facebook/c... | 52 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=Fa... | 99 | 0 |
from __future__ import annotations
def snake_case_(_UpperCamelCase ) -> bool:
"""simple docstring"""
_snake_case = str(_UpperCamelCase )
return len(_UpperCamelCase ) == 9 and set(_UpperCamelCase ) == set('''123456789''' )
def snake_case_() ->... | 278 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependenc... | 278 | 1 |
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
__a = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__a = 1
... | 302 |
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 (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 302 | 1 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1337 , num_examples=4... | 355 |
import os
def A_ ( ) -> int:
UpperCamelCase : List[str] = os.path.dirname(os.path.realpath(_lowerCAmelCase ) )
UpperCamelCase : Any = os.path.join(_lowerCAmelCase , "triangle.txt" )
with open(_lowerCAmelCase ) as f:
UpperCamelCase ... | 140 | 0 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_comm... | 217 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class snake_case ( __snake_case ):
# to overwrite at featu... | 217 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( lowercase_ = 10_00 ) -> int:
A__ = 3
A__ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(f'{solution() = }')
... | 230 |
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRober... | 230 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCamelCase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTok... | 275 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class UpperCAmelCase :
def __init__( self : str , __snake_case : Any ) -> str:
_lowerCAmelCase = str(id_ )
_lo... | 70 | 0 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Confi... | 370 |
def __lowercase ( __lowerCAmelCase : Optional[Any] ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7... | 109 | 0 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def SCREAMING_SNAKE_CASE ( ) -> str:
_lowerCAmelCase : Optional[Any] = ArgumentParser(... | 44 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
lowercase_ = """Usage of script: script_name <size_of_canvas:int>"""
lowercase_ = [0] * 100 + [1] * 10
random.shuffle(choice)
def lowerCamelCase ... | 58 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : Dict = {
'''google/bigbird-robe... | 356 |
from functools import lru_cache
@lru_cache
def a_ ( __lowercase : int ) -> int:
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 130 | 0 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 270 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def __magic_name__ ( __lowerCAmelCase : dict , __lowerCAmelCase : str , __lowerCAmelCase : set , __lowerCAmelCase : set , __lowerCAmelCase : dict , __lowerCAmelCase : dict , __lowerCAmelCase : ... | 270 | 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 to have a n... | 355 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
... | 265 | 0 |
"""simple docstring"""
def lowercase ( A_ , A_ , A_ )-> int:
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(_A , n - 1 , _A ) * a) % mod
else:
a : Optional[int] ... | 40 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
fr... | 307 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( a_ : List[str] , a_ : Union[str, Any] , a_ : Optional[Any] ) -> tuple[float, list[float]]:
__SCREAMING_SNAKE_CASE :Optional[Any] = list(ra... | 364 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class _SCREAMING_SNAKE_CASE( A ):
... | 239 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
UpperCAmelCase = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''... | 141 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __UpperCamelCase ( lowercase__ : Optional[int], lowercase__ : str, lowercase__ : int ):
'''simple docstring'''
__... | 141 | 1 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def ... | 285 |
import math
from datetime import datetime, timedelta
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> datetime:
lowercase : Any = year % 19
lowercase : Optional[int] = year % 4
lowercase : Any = year % 7
lowercase ... | 285 | 1 |
from collections import UserDict
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... | 49 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> float:
if days_between_payments <= 0:
raise ValueError('days_between_payments must be > 0' )
if daily_interest_rate < 0:
raise ValueError('daily_interest_rate must... | 280 | 0 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
... | 266 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowerCAmelCase_ ( _lowercase : ... | 266 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class __magi... | 89 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ..... | 235 | 0 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__A = logging.get_logger(__name__)
def __A ( ):
... | 75 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class SCREAMING_SNAKE_CASE ... | 75 | 1 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 343 | import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_SCREAMING_SNAKE_CASE = (
"""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""",
""... | 343 | 1 |
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCAmelCase__ ( a__: Callable[[int | float], int | float] , a__: int | float , a__: int | float , a__: int = 1_0_0 , ) -> float:
'''simple docstring'''
_UpperCAmelCa... | 185 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __a ( UpperCAmelCase ):
_a : Union[List[np.ndarray], torch.... | 185 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase__ (lowe... | 48 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNeta... | 244 | 0 |
SCREAMING_SNAKE_CASE__ : int = [0, 2, 4, 6, 8]
SCREAMING_SNAKE_CASE__ : Dict = [1, 3, 5, 7, 9]
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : list[int] , __lowerCAmelCase : int ... | 339 |
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE__ : Any = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __magic_name__ ( ) -> Any:
... | 339 | 1 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import... | 178 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common impo... | 178 | 1 |
'''simple docstring'''
lowerCAmelCase__ = '''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
lowerCAmelCase__ = [{'''type''': '''code''', '''content''': IN... | 371 |
'''simple docstring'''
lowerCAmelCase__ = {
"joule": 1.0,
"kilojoule": 1000,
"megajoule": 100_0000,
"gigajoule": 10_0000_0000,
"wattsecond": 1.0,
"watthour": 3600,
"kilowatthour": 360_0000,
"newtonmeter": 1.0,
"calorie_nutr": 4186.8,
"kilocalorie_nutr": 418_6800... | 52 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a_ ( _lowerCAmelCase , unittest.TestCase ):
__A ... | 223 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Optional[int] =logging.get_logger(__name__)
lowerCAmelCase : Tuple ={
'''huggingface/autoform... | 223 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] ={
'configuration_vision_text_dual_encoder': ['VisionTextDualEncoderConfig'],
'processing_vi... | 94 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCamelCase__ ( nn.Module):
'''simple docstring'''
snake_case_ =42
snake_case_ =jnp.floataa
def lowerCAmelCase__ (self ) -> List[str]:
"""simple docstring"""
lowerCAmelCase... | 94 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCAmelCase : List[str] ={
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCL... | 237 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__lowerCAmelCase : Any =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402... | 237 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def __lowerCAmelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )-> np.ndarray:
"""simple docstring... | 267 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> Tuple:
"""simple docstring"""
if (
(cp >= 0X4E00 and cp <= 0X9FFF)
or (cp >= 0X3... | 267 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase)
class snake_case__ ( UpperCamelCase):
a_ = field(default="aud... | 304 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMS... | 253 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ : Tuple = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartCo... | 301 |
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 transformers
from transformers impo... | 301 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
# See all ViT MAE models at https://huggingface.co/models?filter=... | 330 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : int = logging.get_logger(__name__)
lowerCamelCase : List[Any] = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-c... | 2 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 49 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
loggin... | 49 | 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 KandinskyVaaPriorPipeline, PriorTransform... | 302 |
# 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 appli... | 302 | 1 |
from math import factorial
def UpperCamelCase ( _A = 20 ):
"""simple docstring"""
__magic_name__ : Any = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
__magic_name__ : Any = n... | 352 |
__magic_name__: List[Any] = "\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"
__magic_name__: Dict = [{"type": "code", "content": INSTALL_CONTENT}... | 138 | 0 |
"""simple docstring"""
import functools
from typing import Any
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
# Validation
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or len(_UpperCamelCase ) == 0:
raise ValueError('the string should be not empty string' )
i... | 86 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
... | 74 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : int , A__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase_ ( ):
'''simple docstring'''
assert or_gate(0 ,... | 367 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__A : Any = {"vocab_file": "vocab.txt", "tokenizer_... | 89 | 0 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
snake_case_ : List[Any] = numpy.array([0, 0])
snake_case_ : Optional[Any] = numpy.array([0.5, 0.8_66_02_54])
snake_case_ : int = nu... | 83 |
def __UpperCamelCase ( _lowerCAmelCase ) -> list:
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(_lowerCAmelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 116 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_UpperCamelCase ) )
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'... | 259 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = [False] * len(_UpperCamelCase )
__lowerCAmelCase = []
queue.append(_UpperCamelCase )
__... | 259 | 1 |
lowerCAmelCase__ = [
(1_0_0_0, '''M'''),
(9_0_0, '''CM'''),
(5_0_0, '''D'''),
(4_0_0, '''CD'''),
(1_0_0, '''C'''),
(9_0, '''XC'''),
(5_0, '''L'''),
(4_0, '''XL'''),
(1_0, '''X'''),
(9, '''IX'''),
(5, '''V'''),
(4, '''IV'''),
(1, '''I'''),
]
def __low... | 130 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils impo... | 130 | 1 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
A_ :List[Any] = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def A ( a_... | 367 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | 245 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class A ( __UpperCAmelCase ):
__snake_case = 'Wav2Vec2FeatureExtractor... | 278 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A ( __UpperCAmelCase ):
__snake_case = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE__ ( self, **UpperCamelCase__ ):
"""simple docstring"""
lo... | 278 | 1 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _A (__a , __a , __a = "x" , __a = 10**-10 , __a = 1 , ) -> complex:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = symb... | 318 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev... | 318 | 1 |
'''simple docstring'''
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_availab... | 134 |
'''simple docstring'''
def __lowerCamelCase ( __snake_case : int, __snake_case : int, __snake_case : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(__snake_case : int, __snake_case : int ) -> in... | 134 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from t... | 365 |
from itertools import count
def _A ( __magic_name__ = 50 ):
lowercase__ = [1] * min_block_length
for n in count(__magic_name__ ):
fill_count_functions.append(1 )
for block_length in range(__magic_name__ , n + 1 ):
for block_start in range(n - block_length ):
... | 201 | 0 |
"""simple docstring"""
class a :
def __init__( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: dict[str, TrieNode] ={} # Mapping from char to TrieNode
SCREAMING_SNAKE_CASE_: int =False
... | 173 |
"""simple docstring"""
def a__ ( lowerCAmelCase , lowerCAmelCase ) -> bool:
UpperCAmelCase__ : Any = len(lowerCAmelCase )
UpperCAmelCase__ : List[Any] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum o... | 171 | 0 |
'''simple docstring'''
from math import sqrt
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict =0
for i in range(1, int(sqrt(UpperCamelCase__ ) + 1 ) ):
... | 222 |
'''simple docstring'''
import math
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
return math.sqrt(UpperCamelCase__ ) * math.sqrt(UpperCamelCase__ ) == num
def _a( UpperCamelCase__ : int ):
... | 222 | 1 |
'''simple docstring'''
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_... | 161 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/spiece.mode... | 92 | 0 |
__A = {
"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",
"o": "ABBAB",
"p": "A... | 360 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_dat... | 348 | 0 |
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCamelCase_( snake_case : Optional[i... | 85 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm... | 81 | 0 |
"""simple docstring"""
from typing import Any
class __lowerCamelCase :
def __init__(self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = data
_lowerCAmelCase = None
class __lowerCamelCase :
def __init__(self... | 317 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 317 | 1 |
import math
def _lowercase ( lowercase__ , lowercase__ ):
return math.pow(lowercase__ , 2 ) - a
def _lowercase ( lowercase__ ):
return 2 * x
def _lowercase ( lowercase__ ):
__lowerCAmelCase : str = 2.0
while start... | 275 |
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_STAND... | 275 | 1 |
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
lowercase__ = re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
class __snak... | 203 | """simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __a ( _SCREAMING_SNAKE_CASE ) ->Tuple:
a__: Tuple = {}
a__: Tuple = job['started_at']
a__: int = ... | 203 | 1 |
"""simple docstring"""
from collections import defaultdict
class a__ :
def __init__( self , _a , _a ):
lowercase : List[Any] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are ... | 202 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
_A : Optional[Any] = 1_00
_A : Optional[int] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_A : int
for prime in range(3, ceil(NUM_PRIMES**... | 202 | 1 |
"""simple docstring"""
UpperCamelCase_ = {str(digit): digit**5 for digit in range(10)}
def UpperCamelCase ( UpperCAmelCase ) ->Union[str, Any]:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCAmelCase ) )
def UpperCamelCase ( ) ->Any... | 360 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IM... | 303 | 0 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenizati... | 206 | """simple docstring"""
from sklearn.metrics import recall_score
import datasets
UpperCAmelCase__ = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN... | 289 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A : Tuple = logging.get_logger(__name__)
A : Dict = {
'facebook/convnextv2-tiny-1k-224': 'https://... | 351 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
A : List[Any] = logging.get_logger(__name__)
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__(self : List[Any] , *_UpperCAm... | 146 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tok... | 266 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
if not postfix_notation:
return 0
__A = {'''+''', '''-''', '''*''', '''/'''}
__A = []
for token in postfix_n... | 266 | 1 |
class _lowercase :
"""simple docstring"""
def __init__(self ):
"""simple docstring"""
a = {} # Mapping from char to TrieNode
a = False
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
for word in words:
... | 355 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from... | 71 | 0 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requir... | 285 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : Any = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
... | 283 | 0 |
'''simple docstring'''
class __magic_name__ :
def __init__( self : Optional[Any] ):
lowercase_ : int = """"""
lowercase_ : str = """"""
lowercase_ : Dict = []
def SCREAMING_SNAKE_CASE_ ( self ... | 354 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowercase : Union[str, Any] = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise Optional... | 21 | 0 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR... | 195 |
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 (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 195 | 1 |
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class lowerCamelCase__ ( snake_case ):
def __init__( self ,*A ,**A ):
super().__init__(*A ,**A )
UpperCAmelCase = {}
def _UpperCamelCase ... | 234 |
"""simple docstring"""
_UpperCamelCase = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCamelCase = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCamelCase = {
0: """Sunday""",
1: """Monday""",
2: """Tuesday""",
3: """Wednesday""",
4: """Thursday""",
5: ""... | 234 | 1 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 247 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 247 | 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,
StableDiffusionXLImgaImgPipeline,
UNetaDCo... | 297 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUn... | 297 | 1 |
"""simple docstring"""
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
if not (isinstance(snake_case__ , snake_case__ ) and isinstance(snake_case__ , snake_case__ )):
raise ValueError("""longest_common_substring() takes two strings for in... | 165 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Dict = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerC... | 165 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_blenderbot''': [
''... | 369 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
f... | 154 | 0 |
def A ( _lowerCamelCase = 50 ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start ... | 36 |
'''simple docstring'''
import math
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase__ : Optional[Any]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
... | 28 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
_SCREAMING_SNAKE_CASE = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
_SCREAMING_SNAKE_CASE ... | 81 | def snake_case ( ) -> Any:
for n in range(1 , 1_000_000):
yield n * (n + 1) // 2
def snake_case ( snake_case__ :Dict) -> Optional[Any]:
_A = 1
_A = 2
while i * i <= n:
_A ... | 81 | 1 |
'''simple docstring'''
from math import sqrt
def snake_case__ ( _A: int = 1000000 ) -> int:
'''simple docstring'''
lowerCAmelCase = 0
lowerCAmelCase = 0
lowerCAmelCase = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides ... | 272 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
fro... | 168 | 0 |
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
A : Tuple = int(__lowerCamelCase )
if decimal in (0, 1): # Exit cases for the recursion
return str(__lowerCamelCase )
A , A : Any = divmod(__lowerCamelC... | 368 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 115 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : str = {
"""configuration_owlvit""": [
... | 52 |
def A_ ( _lowerCAmelCase = 50 ) -> int:
UpperCamelCase : List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_co... | 52 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatt... | 187 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
... | 187 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a_ ( lowerCamelCase : Tuple , lowerCamelCase : ... | 4 |
def __UpperCamelCase ( _A = 1000000 ):
lowerCAmelCase_ = 1
lowerCAmelCase_ = 1
lowerCAmelCase_ = {1: 1}
for inputa in range(2 , _A ):
lowerCAmelCase_ = 0
lowerCAmelCase_ = inputa
while True:
... | 278 | 0 |
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 transfor... | 351 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
... | 125 | 0 |
from typing import Dict, Optional
import numpy as np
import datasets
lowerCamelCase = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-clas... | 199 |
import os
import pytest
from attr import dataclass
lowerCamelCase = 'us-east-1' # defaults region
@dataclass
class A :
UpperCamelCase__ : str
UpperCamelCase__ : Dict ='arn:aws:iam::558105141721:role/sagemaker_execution_role'
UpperCamelCase__ : int ... | 199 | 1 |
from __future__ import annotations
from collections.abc import Generator
def lowerCAmelCase_ ( ) -> Generator[int, None, None]:
"""simple docstring"""
a__ : dict[int, int] = {}
a__ : Union[str, Any] = 2
while True:
a_... | 371 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class snake_case__ (A__ ):
"""simple docstring"""
def __init__( self , __lowercase , __lowercase ) -> int:
"""simple docstring"""
... | 266 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch... | 193 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a__: List[str] = False
class SCREAMING_SNAKE_CASE... | 193 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: Union[str, Any] = logging.get_logger(__name__)
_UpperCamelCase: Union[str, Any] = {
'sayakpaul/vit-msn-base': 'https://huggingface.c... | 368 |
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if ... | 53 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Optional[int]:
if length <= 0 or not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(SCREAMING_SNAKE_CASE__ )]
if __nam... | 73 |
from ..utils import DummyObject, requires_backends
class snake_case_ ( metaclass=__A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = ["note_seq"]
def __init__( self : Optional[int] , *_UpperCamelCase : ... | 8 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Union[str, Any] = {
"""configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}
try:
if not is_torc... | 355 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
dep... | 286 | 0 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class a ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self: ... | 335 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_... | 335 | 1 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
fro... | 29 |
UpperCAmelCase_ = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
UpperCAmelCase_ = ['a', 'b', 'c', 'd', 'e']
def lowerCamelCase__ ( A__ : Union[str, Any] , A__ : Optional[int] , A__ : str ):
'''simple docstring'''
__lowerCamelCase ... | 29 | 1 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusi... | 56 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
lowerCamelCase = (
"""This metric will be removed from the lib... | 166 | 0 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_mod... | 126 | """simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
... | 126 | 1 |
import math
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> float:
'''simple docstring'''
if initial_intensity < 0:
raise ValueError('''The value of intensity cannot be negative''' )
# handling of negative values of initial inten... | 348 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def a ( A__ : Dict ) -> str:
"""simple docstring"""
_lowercase ={}
_lowercase =job['started_at']
_lowercase =job['com... | 205 | 0 |
"""simple docstring"""
from math import factorial
A: dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def _snake_case ( UpperCamelCase : int ):
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise TypeError("""Parameter number m... | 76 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( UpperCamelCase : str ):
def decorator(UpperCamelCase : Optional[int] ):
UpperCAmelCase : List[Any] = getattr(UpperCamelCase , """handle_key""" ,... | 76 | 1 |
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