code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from PIL import Image
def lowerCamelCase( a__ ,a__):
def brightness(a__) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''')
return img.point(_lowerCamelCase)
if __name... | 691 | """simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
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 ImageProcessingSavingTe... | 646 | 0 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : List[str] =logging.get_l... | 700 | """simple docstring"""
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... | 197 | 0 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
lowerCAme... | 39 |
'''simple docstring'''
import numpy as np
class lowercase__ :
'''simple docstring'''
def __init__( self ):
_SCREAMING_SNAKE_CASE : List[Any] = (0, 0)
_SCREAMING_SNAKE_CASE : List[str] = None
_SCREAMING_SNAKE_CASE ... | 533 | 0 |
'''simple docstring'''
import math
import unittest
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
... | 438 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}
class UpperCAm... | 438 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def lowercase_ ( self ):
__snake_case : Dict = [
'safety_checker/p... | 576 | from ....configuration_utils import PretrainedConfig
from ....utils import logging
__magic_name__ = logging.get_logger(__name__)
# TODO: upload to AWS
__magic_name__ = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/reso... | 576 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Conditiona... | 569 |
from cva import destroyAllWindows, imread, imshow, waitKey
def __lowerCamelCase ( snake_case__ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = img.shape[0], img.shape[1]
# converting each pixel's c... | 569 | 1 |
'''simple docstring'''
from math import ceil
def lowerCAmelCase ( UpperCamelCase__ : int = 1_0_0_1 ):
"""simple docstring"""
__UpperCAmelCase = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__UpperCAmelCase = 2 *... | 262 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .token... | 37 | 0 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_co... | 129 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__lowerCAmelCase = "sshleifer/bart-tiny-random"... | 129 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils... | 332 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class a :
"""simple docstring"""
UpperCamelCase_ : float
UpperCamelCase_ : TreeNode | None = None
UpperCamelCase_ : TreeNode | None = None
... | 332 | 1 |
import os
import sys
lowerCamelCase : Tuple = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoM... | 702 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Ac... | 290 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase__ ( _A):
... | 2 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCAmelCase__ ( a , a ):
"""simple docstring"""
@register_to_config
de... | 627 | 0 |
"""simple docstring"""
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
f... | 292 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configura... | 292 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : List[str] = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Informer... | 447 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from u... | 447 | 1 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
... | 719 | '''simple docstring'''
from __future__ import annotations
from statistics import mean
def __lowerCAmelCase ( a_ , a_ , a_ ) -> list[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = [0] * no... | 179 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"""huggingface/informer-tourism-monthly""": (
"""https://huggingface.co... | 234 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __magic_name__ ( __UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = "Speech2TextFeatureExtractor"
SCREAMIN... | 234 | 1 |
from ...processing_utils import ProcessorMixin
class __magic_name__ ( A__ ):
lowercase : List[str] ='''WhisperFeatureExtractor'''
lowercase : Any ='''WhisperTokenizer'''
def __init__( self : Optional[int] , UpperCamelCase__ ... | 703 |
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> list[int]:
UpperCAmelCase = int(lowerCamelCase_ )
# Initialize Result
UpperCAmelCase = []
# Traverse through all denomination
for denomination in reversed(lowerCamelCase_ ):
... | 457 | 0 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_snake_case = logging.get_logger(__name__)
class UpperCAmelCase_ ( a):
def __init__( self, *__a, **__a):
'''simple docstring'''
warnings.w... | 500 |
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, D... | 500 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowercase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowerCamelCase : Dict = [('''size''', ctypes.... | 711 |
from __future__ import annotations
a_ :str = 8.988e9 # units = N * m^s * C^-2
def a ( A__ , A__ , A__ , A__ ) -> dict[str, float]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = abs(chargea * chargea )
... | 250 | 0 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
... | 395 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = r'''
Args:
input_ids (`torch.LongTensor` of shape `(b... | 395 | 1 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
a__: Optional[Any] = [10, 20, 30, 40, 50, 60... | 217 | """simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 10**-10 ) ->float:
a__: int = a
while True:
a__: ... | 217 | 1 |
from math import factorial, pi
def __lowerCAmelCase ( _UpperCamelCase : float , _UpperCamelCase : int = 30 ) -> float:
'''simple docstring'''
if not isinstance(_UpperCamelCase , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or float for theta... | 439 |
# 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 applica... | 439 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, O... | 53 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_commo... | 53 | 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
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/levit-1... | 496 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstri... | 104 | 0 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = False
if __name__ == "__main__":
SCRE... | 707 |
from __future__ import annotations
import math
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int ) -> float:
__lowercase = u
for i in range(1 , SCREAMING_SNAKE_CASE ):
__lowercase = temp * (u - i)
ret... | 688 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: int ):
assert (
isinstance(_lowerCamelCase , _lowerCamelCase ) and number_of_steps > 0
), F"number_of_steps needs to be positive integer, your input {number_of_steps}"
if number_of_steps == 1:
return 1
__SCREAMIN... | 578 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCAmelCase_ ( _lowerCamelCase: str , _lowerCamelCase: float | Decimal , _lowerCamelCase: float = 10**-10 ):
__SCREAMING_SNAKE_CASE :... | 578 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''google/bigbird-roberta-base''': '''https://huggingfa... | 712 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
Up... | 565 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
clas... | 102 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tra... | 447 | 0 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __lowerCAmelCase ( __UpperCa... | 21 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n ... | 21 | 1 |
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
... | 663 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_A = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_ ( snake_case ):
... | 158 | 0 |
import numpy as np
def A ( _lowercase ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34 | from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def A ( _lowercase ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = analyze_text(_lowercase )
SCREAMING_SNAKE_CASE ... | 34 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 366 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def lowerCamelCase( SCREAMING_SNAKE_CASE_ ) -> str:
A_ = int(SCREAMING_SNA... | 366 | 1 |
"""simple docstring"""
from torch import nn
class __snake_case ( nn.Module ):
def __init__( self , lowercase , lowercase) -> List[str]:
'''simple docstring'''
super().__init__()
a__: Optional[Any] = class_size
a__: Any ... | 217 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->str:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a__: int = F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE )}'
raise ValueError(_SCREAMING_SNAK... | 217 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX m... | 91 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_ve... | 232 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/... | 63 |
"""simple docstring"""
import math
import sys
import cva
import numpy as np
def __magic_name__ ( _lowerCamelCase : np.ndarray , _lowerCamelCase : float ):
# For applying gaussian function for each element in matrix.
__a : int = math.... | 63 | 1 |
def a (lowerCAmelCase__ = 1_000_000 ):
__a = 1
__a = 1
__a = {1: 1}
for inputa in range(2 , lowerCAmelCase__ ):
__a = 0
__a = inputa
while True:
if number in counters:
counter += counters[number]
break... | 99 |
# 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 re... | 99 | 1 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. ... | 389 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : str , lowerCamelCase_ : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(lowerCamelCase_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from docte... | 389 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : float ,lowerCAmelCase_ : float ,lowerCAmelCase_ : float ) -> dict[str, float]:
"""simple docstring"""
if (voltage, current, resistance).count(0 ) != 1:
... | 220 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : Tuple ,lowerCAmelCase_ : Optional[int] ,lowerCAmelCase_ : Union[str, Any] ,lowerCAmelCase_ : Optional[Any]=1024 ... | 220 | 1 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 712 |
"""simple docstring"""
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWith... | 422 | 0 |
"""simple docstring"""
from collections import defaultdict
class lowerCamelCase :
'''simple docstring'''
def __init__(self , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Dict = total # total no of tasks ... | 182 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAv... | 182 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
snake_case_ : Optional[Any] ... | 205 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tra... | 205 | 1 |
def _a ( lowerCAmelCase = 10 )-> List[Any]:
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or n < 0:
raise ValueError('Invalid input' )
SCREAMING_SNAKE_CASE_ = 10**n
SCREAMING_SNAKE_CASE_ = 28433 * (pow(2 , 7830457 , U... | 360 |
'''simple docstring'''
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
__lowerCamelCase = logging.get_logger(_... | 467 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDete... | 719 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common impo... | 324 | 0 |
import math
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
# Copied from diffusers.sched... | 327 |
def UpperCAmelCase_ (_lowerCAmelCase : int = 60_08_51_47_51_43 ):
try:
__UpperCamelCase : Optional[Any] = int(_lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter... | 327 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipe... | 551 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_... | 551 | 1 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 97 |
from __future__ import annotations
def a ( snake_case__: Optional[int] , snake_case__: Optional[int] , snake_case__: Any , snake_case__: Optional[int] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
lowercase_ = (l + r) // 2
if v[m] >=... | 97 | 1 |
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,
... | 705 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowerCamelCase : Tuple = datasets.logging.get_logger(__name__)
_lowerCamelCase : Any ... | 157 | 0 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_co... | 59 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
__A = transforms.Compose... | 59 | 1 |
import argparse
import datetime
def lowercase_ (A : str ):
snake_case__ : int = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturday',
... | 243 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since ... | 243 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_... | 472 |
'''simple docstring'''
def _A ( ):
'''simple docstring'''
A__ = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
A__ = 6
A__ = 1
A__ = 1901
A__ = 0
while year < 2001:
day += 7
if (year % 4 == 0 and... | 531 | 0 |
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_snake_case = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerro... | 701 |
from math import factorial, pi
def __lowerCamelCase ( _lowercase , _lowercase = 30 ) -> float:
if not isinstance(_lowercase , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or float for theta' )
if not isinstance(_lowercase , _lowercase ) ... | 170 | 0 |
from typing import Any
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , __lowercase) -> List[str]:
__UpperCamelCase :List[str] = data
__UpperCamelCase :Optional[Any] = None
def __repr__( self) ... | 167 | """simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__A = 637_8137.0
__A = 635_6752.31_4245
__A = 6_3_7_8_1_3_7
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAme... | 586 | 0 |
def UpperCAmelCase_ ( UpperCAmelCase__ ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError("""Input value must be an 'int' type""" )
lowercase_ = 0
while number:
position += 1
number >>= 1
return position
i... | 717 |
# 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 required by applic... | 650 | 0 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class lowercase ( pl.LightningModule):
"""simple docstring"""
def __init__( self : List[str] , __UpperCAmelCase :... | 593 |
'''simple docstring'''
def lowerCamelCase__ ( __lowerCamelCase : str ):
'''simple docstring'''
_UpperCAmelCase : Dict =''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowerCame... | 446 | 0 |
class _a :
"""simple docstring"""
def __init__( self: Dict , __lowerCamelCase: Union[str, Any] = "" , __lowerCamelCase: Any = False ):
'''simple docstring'''
UpperCamelCase__: dict[str, RadixNode] = {}
# A node will be a leaf if... | 721 |
def lowerCAmelCase_ ( ):
for n in range(1 ,1_00_00_00):
yield n * (n + 1) // 2
def lowerCAmelCase_ ( A_):
UpperCamelCase__: int = 1
UpperCamelCase__: Dict = 2
while i * i <= n:
UpperCamelCase__: Any ... | 221 | 0 |
import requests
from bsa import BeautifulSoup
def __magic_name__ ( SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus" ) -> dict:
_lowercase : Optional[Any] = BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE ).text , 'html.parser' )
... | 66 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCAmelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCAmelCase : list[int] = [ord(letter) for letter in string.ascii_low... | 239 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType... | 481 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEAT... | 481 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_ = {"""vocab_file""": ""... | 175 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __a ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
__snake_case : Optional[Any] = [("""size""", ctypes.c_int),... | 600 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers... | 645 | """simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tr... | 645 | 1 |
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.0... | 579 |
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, StableU... | 413 | 0 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCA... | 312 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> bool:
UpperCAmelCase__ : List[Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 312 | 1 |
def __lowercase ( lowerCamelCase : int ):
if num <= 0:
raise ValueError('Input must be a positive integer' )
UpperCamelCase_ : int = [True] * (num + 1)
UpperCamelCase_ : str = 2
while p * p <= num:
if primes[p]:
for i in range(p * p , num + 1 , lowerCamelCas... | 417 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threaded... | 664 | 0 |
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowerCAmelCase : str = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow(''''''... | 718 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __lowerCAmelCase ( lowerCamelCase : bytes , lowerCamelCase : int ):
'''simple docstring'''
__lowerCAmelCase = f''... | 39 | 0 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , S... | 39 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Tr... | 39 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase__ ():
_SCREAMING_SNAKE_CASE : Tuple = [randint(-1000, 1000 ) for i in range(10 )]
_SCREAMING_SNAKE_CASE : int ... | 381 |
from __future__ import annotations
UpperCamelCase__ =[True] * 100_0001
UpperCamelCase__ =2
while i * i <= 100_0000:
if seive[i]:
for j in range(i * i, 100_0001, i):
UpperCamelCase__ =False
i += 1
def lowerCamelCase__ (__lowerCamelCase ):... | 381 | 1 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class A_ :
def __init__( self: Optional[Any] ,__lowerCAmelCase: Optional[Any] ):
'''simple docstring'''
_lowerCamelCase : Any = str(id_ )... | 46 |
'''simple docstring'''
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_... | 577 | 0 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params... | 583 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class... | 583 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : str =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Union[str, Any] ={
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bi... | 434 | """simple docstring"""
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->Tuple:
if height >= 1:
move_tower(height - 1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CA... | 434 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ : List[Any] = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not ... | 496 | '''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : int = logging.get_logger(__name__)
UpperCamelCase__ : Optional[Any] = {'vocab_file': 'vocab.json'}
UpperCamelCase_... | 496 | 1 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowerCAmelCase_ : List[Any] = {
'n_samples': 64,
'horizon': 32,
'num_inference_steps': 20,
'n_guide_steps': 2, # can set to 0 for f... | 527 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffu... | 527 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extracti... | 331 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
lowercase =[int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowerCamelCase__ ( ):
'''simple docstring'''
_UpperCAmelCase : int =os.path.dirname(os.path.realpath(__low... | 331 | 1 |
def A__ ( lowercase: Any, lowercase: List[Any], lowercase: List[Any]=False ) -> Dict:
if isinstance(lowercase, lowercase ) and isinstance(lowercase, lowercase ):
A : int =len(set_a.intersection(lowercase ) )
if alternati... | 305 | import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase : Any =get_tests_dir('''fixtures/test_sentencepiece_with_bytefallb... | 305 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
__lowerCamelCase = ["image_proce... | 85 |
"""simple docstring"""
a : List[Any] = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def lowercase__(A ) ->bytes:
"""simple docstring"""
if not isinstance(A , A ):
lowercase__ ... | 85 | 1 |
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.util... | 175 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logg... | 647 | 0 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
A = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def ... | 449 |
'''simple docstring'''
def UpperCAmelCase ( UpperCAmelCase__ : list[int]):
if not nums: # Makes sure that the list is not empty
raise ValueError('List is empty')
lowerCamelCase : int = sum(UpperCAmelCase__) / len(UpperCAmelCase__) # Calculate the... | 449 | 1 |
'''simple docstring'''
def __lowercase ( __lowercase , __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_... | 330 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LIC... | 143 | 0 |
"""simple docstring"""
from __future__ import annotations
UpperCAmelCase =list[tuple[int, int]]
UpperCAmelCase =[
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, ... | 718 |
"""simple docstring"""
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowerCamelCase = (DDPMParallelScheduler,)
... | 255 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : List[Any] = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class __UpperCam... | 334 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowercase ( SCREAMING_SNAKE_CASE__ : Any ) -> str:
return getitem, k
def lowercase ( SCREAMING_SNAKE_CASE__ : List[Any] , ... | 477 | 0 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundf... | 720 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppToken... | 381 | 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... | 244 | '''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = (DDPMScheduler,)
def _A ( self : ... | 244 | 1 |
def _snake_case ( __snake_case , __snake_case ):
return abs(__snake_case ) if a == 0 else greatest_common_divisor(b % a , __snake_case )
def _snake_case ( __snake_case , __snake_case ):
while y: # --> when y=0 then loop will terminate and return x as final GCD.
... | 718 | from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _snake_case ( __snake_case , __snake_case , __snake_case = 1 / sqrt(2 ) ):
_UpperCamelCase = tau * frequency / samplerate
_UpperCamelCase = sin(__snake_case )
_UpperCamelCase ... | 71 | 0 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str,... | 144 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
... | 144 | 1 |
import math
import sys
def lowerCamelCase( a__):
if number != int(a__):
raise ValueError('''the value of input must be a natural number''')
if number < 0:
raise ValueError('''the value of input must not be a negative number''')
if number == 0:
return 1
_SCREAMIN... | 191 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_u... | 191 | 1 |
def UpperCamelCase ( snake_case__ : int ,snake_case__ : int ,snake_case__ : int ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
__snake_case :int = _modexpt(snake_case__ ,expon... | 455 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
... | 455 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __snake_case ( ):
__a = ArgumentParser(
description=(
'''PyTorch TPU distributed training launch helper utility... | 711 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING... | 60 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"fa... | 197 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def UpperCamelCase__ ( __magic_name__ : str , __magic_name__ : List[Any]=None ) -> Union[str, Any]:
''... | 38 | 0 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchma... | 527 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_=2 , snake_case_=3 , snake_case_=6_4 , snake_case_=Non... | 527 | 1 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_lowercase ... | 5 |
def lowerCAmelCase_ ( __UpperCAmelCase: str ) -> bool:
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def lowerCAmelCase_ ( __UpperCAmelCase: str ) -> bool:
UpperCamelCase__ : Tuple ... | 253 | 0 |
from __future__ import annotations
def _lowerCAmelCase ( _a : list[int] ) -> list[int]: # This function is recursive
lowerCAmelCase_ : List[Any] = len(_a )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
i... | 440 |
UpperCAmelCase_ : str = """Alexander Joslin"""
import operator as op
from .stack import Stack
def _lowerCAmelCase ( _a : str ) -> int:
lowerCAmelCase_ : Any = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
lowerCAmelCas... | 440 | 1 |
"""simple docstring"""
import math
import unittest
def snake_case_ ( A_ : int ):
'''simple docstring'''
assert isinstance(A_, A_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 ... | 83 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
_UpperCAmelCase : str = {
"vocab_file": "vocab.json"... | 668 | 0 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import g... | 223 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __A , __A , __A , __A ) -> None:
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
... | 223 | 1 |
"""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
... | 118 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers i... | 118 | 1 |
'''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
if is_torch_available():
import t... | 415 | '''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_: Dict ={
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Au... | 415 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SN... | 692 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _lowerC... | 692 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 711 |
from __future__ import annotations
def __a ( __UpperCAmelCase , __UpperCAmelCase ):
a__ = get_failure_array(__UpperCAmelCase )
# 2) Step through text searching for pattern
a__ , a__ = 0, 0 # index into text, pattern
while i < len(__UpperCAmelCase... | 148 | 0 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from f... | 7 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 617 | 0 |
from math import factorial
def UpperCAmelCase ( lowercase__ : int = 100 ):
'''simple docstring'''
return sum(map(lowercase__ , str(factorial(lowercase__ ) ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 712 |
def UpperCAmelCase ( lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / de... | 412 | 0 |
"""simple docstring"""
import logging
from transformers import PretrainedConfig
lowerCamelCase__ : Optional[int] = logging.getLogger(__name__)
lowerCamelCase__ : str = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extract... | 238 |
"""simple docstring"""
import argparse
import datetime
def UpperCamelCase ( _lowerCAmelCase : str ) -> str:
_UpperCAmelCase : List[str] = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """We... | 238 | 1 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 9, 14 # noqa: F841
SCREAM... | 538 | """simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase ) -> List[str... | 538 | 1 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_avai... | 657 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
try:
_UpperCAmelCase = float(UpperCamelCase__ )
except ValueError:
raise ValueError("Please enter a valid number" )
_UpperCAmelCase = decimal - int(UpperCamelCase__ )
if fractional_part == ... | 657 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversat... | 695 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerca... | 695 | 1 |
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