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 |
|---|---|---|---|---|
def _lowerCamelCase( lowercase__ ) -> list:
'''simple docstring'''
def merge(lowercase__ , lowercase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merg... | 295 |
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_common import Mo... | 295 | 1 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class _UpperCamelCase ( _A ):
'''simple docstring'''
@require_torch
def lowerCAmelCase__ ( self : List[Any] ):
... | 223 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.ut... | 223 | 1 |
import os
def _UpperCAmelCase ( ):
with open(os.path.dirname(SCREAMING_SNAKE_CASE__ ) + '/p022_names.txt' ) as file:
__UpperCamelCase =str(file.readlines()[0] )
__UpperCamelCase =names.replace('"' , '' ).split(',' )
names.sort()
... | 62 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = {name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}
de... | 340 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __lowerCamelCase (UpperCAmelCase__ : int ):
if (
(cp >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b... | 369 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
''... | 206 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
... | 229 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : Tuple = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP... | 218 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
... | 112 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __lowerCAmelCase ( lowercase : Union[str, Any] , lowercase : Optional[int]=False ) -> Union[str, Any]:
"""simple docstrin... | 112 | 1 |
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_uti... | 101 |
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
lowercase__ :List[Any] = logging.get_logger(__name__)
lowercase__ :Optional[Any] ... | 101 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class UpperCamelCase ( snake_case ):
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmel... | 363 |
"""simple docstring"""
UpperCAmelCase: str = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.g... | 336 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ):
'''simple docstring'''
lowercase__ : Union[str, Any] = namedtuple('result' ... | 77 | """simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 77 | 1 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a_ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=Non... | 368 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
... | 183 | 0 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__a = logging.get_logger(__name__)
__a = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
# See all M-CTC-T models at https:... | 66 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _snake_case ( A__ ):
_lowercase : int = '''Speech2TextFeatureExtractor'''
_lowercase : List[Any] = '''Speech2TextTokenizer'''
def __init__( ... | 137 | 0 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
Up... | 370 |
# 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
# sinc... | 262 | 0 |
"""simple docstring"""
import math
def _snake_case ( lowercase__ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 a... | 96 | import numpy as np
import datasets
_snake_case = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mah... | 157 | 0 |
'''simple docstring'''
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_... | 353 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, 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_modeli... | 187 | 0 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import ... | 340 |
from collections import defaultdict
from math import gcd
def _a ( UpperCamelCase_ : int = 1_500_000 ) -> int:
"""simple docstring"""
lowerCAmelCase__ = defaultdict(UpperCamelCase_ )
lowerCAmelCase__ = 2
while 2 * euclid_m ... | 340 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCamelCase_ : str = logging.get_logger(__name__)
class __A ( _SCREAMING_SNAKE_CASE ):
... | 364 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ ... | 215 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaT... | 94 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
lowerCamelCase :Tuple = logging.get_logger(__name... | 206 | 0 |
from math import sqrt
def UpperCAmelCase_ ( __UpperCAmelCase : int ) -> int:
SCREAMING_SNAKE_CASE_ = 0
for i in range(1 , int(sqrt(__UpperCAmelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(__UpperCAmelCase ):
... | 355 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[int] ) -> int:
if not is_accelerate_available():
return method
SCREAMING_SNAKE_... | 210 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: int ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" )
return sum(
divi... | 112 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
) | 112 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ : Dict = logging.get_logger(__name__)
A_ : List[st... | 349 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ ... | 349 | 1 |
from manim import *
class snake_case_ ( lowerCamelCase__ ):
def __UpperCamelCase ( self : int ) -> Union[str, Any]:
lowercase__ : List[Any] = Rectangle(height=0.5 , width=0.5 )
lowercase__ : Tuple = Rec... | 87 |
import numpy
# List of input, output pairs
_lowerCamelCase : Dict = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
_lowerCamelCase : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
_lowerCame... | 336 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
lowerCAmelCase_ : Union[str, Any] = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.js... | 369 |
"""simple docstring"""
from __future__ import annotations
import math
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3... | 248 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Any = logging.get_logger(__name__)
a__ : Dict = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/re... | 80 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_... | 183 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_comm... | 321 | """simple docstring"""
# 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
#
... | 321 | 1 |
import warnings
from functools import wraps
from typing import Callable
def A (__A : Callable ) -> Callable:
"""simple docstring"""
@wraps(__A )
def _inner_fn(*__A : Dict , **__A : int ):
warnings.warn(
... | 51 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] =logging.get_logger(__name__)
_UpperCAmelCase : str ={
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
# See all ViT MAE mo... | 262 | 0 |
from manim import *
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
def UpperCamelCase_ ( self ) -> Any:
_SCREAMING_SNAKE_CASE : List[Any] = Rectangle(height=0.5 , width=0.5 )
_SCREAMI... | 325 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCamelCase__ (__lowerCamelCase ):
return DownloadCommand(args.model, args.cache_dir, args.force, args.trust_remote_code )
class lowerCAmelCase__( __lowercase ):
'''simp... | 325 | 1 |
"""simple docstring"""
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
_a : Optional[int]= [
# (stable-diffusion, HF Diffusers)
("time_embed.0.we... | 172 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
B... | 187 | 0 |
from math import pi
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
return 2 * pi * radius * (angle / 3_6_0)
if __name__ == "__main__":
print(arc_length(90, 10))
| 350 | from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase__ = 300 # TEMPERATURE (unit = K)
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ):
if donor_conc <= 0:
raise ValueError('Dono... | 119 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from tran... | 271 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Optional[Any] = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_C... | 215 | 0 |
import math
import unittest
def lowerCAmelCase_ ( __lowerCamelCase ):
assert isinstance(__lowerCamelCase , __lowerCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 134 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedul... | 134 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .te... | 184 | from ..utils import DummyObject, requires_backends
class _UpperCamelCase ( metaclass=_UpperCAmelCase ):
"""simple docstring"""
__a : Dict = ['''keras_nlp''']
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Tuple:
'''si... | 210 | 0 |
"""simple docstring"""
import itertools
import math
def _snake_case ( lowercase__ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 1 |
"""simple docstring"""
import itertools
import math
def _snake_case ( lowercase__ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 1 | 1 |
'''simple docstring'''
import numpy as np
def __lowerCamelCase ( _lowercase ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 265 |
'''simple docstring'''
from PIL import Image
def __lowerCamelCase ( _lowercase , _lowercase ) -> Image:
def brightness(_lowercase ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must b... | 265 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentPars... | 364 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_uti... | 327 | 0 |
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 Accelera... | 282 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snak... | 248 | 0 |
"""simple docstring"""
import math
import sys
def __lowercase ( _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 == ... | 155 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ : str = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP... | 155 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=SCREAMING_SNAKE_CASE__ ):
_lowerCamelCase = ['transformers', 'torch', 'note_seq']
def __init__( self : Dict, *lowerCAmelCase : Dict, **lo... | 255 |
"""simple docstring"""
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, ... | 255 | 1 |
'''simple docstring'''
import torch
from transformers import AutoModel
class _a ( torch.nn.Module ):
def __init__( self ,_SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased" ) -> List[str]:
super(_SCREAMING_SNAKE_CASE ,self ).__init__()
... | 142 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Union[str, Any] = {'''configuration_plbart''': ['... | 142 | 1 |
from manim import *
class A__ ( lowerCAmelCase__ ):
def a__ ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase = Rectangle(height=0.5 , width=0.5 )
__lowercase = Rectangle(height=0.25 , w... | 325 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class A__ :
def __init__( se... | 325 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = """▁"""
UpperCAmelCas... | 362 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvai... | 267 | 0 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_SCREAMING_SNAKE_CASE : Dict = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_SCREAMING_SNAKE_CASE : Optional[Any] = typing... | 85 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCon... | 119 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 364 |
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_modeling_common import Model... | 141 | 0 |
'''simple docstring'''
def __lowerCamelCase ( __snake_case : list[int] ) -> list[int]:
"""simple docstring"""
A__ : Tuple =len(__snake_case )
for i in range(__snake_case ):
for j in range(i + 1, __snake_case ... | 134 |
'''simple docstring'''
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 Model... | 134 | 1 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
a_ = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto acc... | 359 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for no... | 291 | 0 |
'''simple docstring'''
import itertools
import math
def lowerCAmelCase_ ( snake_case_ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# ... | 1 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_: Union[str, Any] =logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__(self : int , *__a : Dict , *... | 1 | 1 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_c... | 356 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax imp... | 96 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
lowercase__ : List[str] = """2020.9.26"""
lowercase__ : Optional[int] = """xcodz-dot, cclaus, dhruvmanila"""
def UpperCamelCase_ ( lowerCAmelCase__ : float , lowerCAmel... | 224 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAmelCase = logging.get_logger(__na... | 256 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
f... | 227 |
'''simple docstring'''
A : Dict = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def lowerCAmelCase__ ( lowerCamelCase : str ):
_A : Dict = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
_A : St... | 227 | 1 |
"""simple docstring"""
def lowercase (snake_case__ : list[list[int | float]] ) -> int:
'''simple docstring'''
lowerCAmelCase = len(snake_case__ )
lowerCAmelCase = len(matrix[0] )
lowerCAmelCase = min(... | 155 |
"""simple docstring"""
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 a... | 155 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
a =logging.get_logger(__name__) # pylint: disable=invalid-name
class A_ ( SCREAMING_SNAKE_CASE ):
def __... | 113 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFC... | 113 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase_ )
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
_UpperCAmelCase : str = fiel... | 142 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
... | 142 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, lo... | 135 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
lowerCamelCase :List[Any] = list[list[float | int]]
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : int = len(lowerCamelCas... | 135 | 1 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import... | 172 |
lowerCamelCase_ = frozenset(
[
'''prompt''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
]
)
lowerCamelCase_ = frozen... | 244 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=UpperCamelCase__ ):
"""simple docstring"""
_snake_case : Optional[int] = ["""speech"""]
def __init__( self : Tuple , *lowerCAm... | 368 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def a__ ( lowercase : str ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = img.shape[0], img.shape[1]
# converting each pixel's color to its nega... | 287 | 0 |
"""simple docstring"""
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,
resiz... | 25 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase ( ... | 141 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert import Alb... | 361 |
from __future__ import annotations
def __lowerCamelCase ( __magic_name__ : str , __magic_name__ : Any , __magic_name__ : List[Any] , __magic_name__ : str ): # noqa: E741
while r - l > 1:
a__: Any =(l + r) // 2
if v[m] >... | 42 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_a = logging.get_logger(__name__)
_a = {
"""... | 209 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Dict = logging.get_logger(__name__)
def a__ ( snake_case__ ) ... | 291 | 0 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow #... | 200 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase... | 200 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
't5-small': 'https://huggingface.co/t5-small/resolve/main/confi... | 17 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class lower... | 96 | 0 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def A(__a: List[str] , __a: str=None ):
lowerCAmelCase_ = None
if token is not None:
lowerCAmelCase_ = {"""Accept""": """application... | 354 |
import datasets
lowerCamelCase__ = '''\
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
... | 22 | 0 |
def a( A : float , A : int ) -> float:
"""simple docstring"""
if digit_amount > 0:
return round(number - int(A ) , A )
return number - int(A )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(de... | 227 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Any = logging.get_logger(__name__)
a : Dict = {
'roberta-base': 'https://huggingface.co/roberta-b... | 358 |
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
a : Dict = datasets.logging.get_logger(__name__)
a : Any = '\\n@InProceedings{moosavi2019minimum,\n ... | 82 | 0 |
"""simple docstring"""
import os
from math import logaa
def lowercase (SCREAMING_SNAKE_CASE_ : str = "base_exp.txt" ) -> int:
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 0
for i, line in enumerate(open(os.path.join(os.path.d... | 113 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowercase (SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any=False ) -> str:
SCREAMING_SNAKE_CASE = ... | 113 | 1 |
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,
t... | 308 |
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 : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : ... | 308 | 1 |
"""simple docstring"""
import qiskit
def lowercase_ ( _lowerCamelCase: int , _lowerCamelCase: int ) -> qiskit.result.counts.Counts:
'''simple docstring'''
__lowerCamelCase : Optional[Any] = qiskit.Aer.get_backend("aer_simulator" )
... | 135 | """simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTok... | 135 | 1 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
A_ : Optional[int] = logging.getLogger(__name__)
A_ : List[Any] = 50 # max width of l... | 363 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
A_ : List[Any] = {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}
try:
if not is_tokenizers_avail... | 141 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_... | 308 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, Hf... | 308 | 1 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class UpperCamelCase ( lowercase__ ):
''... | 360 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_... | 252 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torc... | 310 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 42 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=SCREAMING_SNAKE_CASE__ ):
lowercase_ : Optional[Any] =['''note_seq''']
def __init__( self ,*A__ ,**A__):
requires_backends(self ,['''note_seq'''])
... | 356 |
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, Dist... | 97 | 0 |
'''simple docstring'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelera... | 200 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : str = [10, 20, 30, 40, 50, 60]
... | 200 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class ... | 363 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def lowerCamelCase (a_ :List[Any]) -> ... | 172 | 0 |
import os
from datetime import datetime as dt
from github import Github
_snake_case = [
"good first issue",
"feature request",
"wip",
]
def lowerCAmelCase_ ( ):
_A : Dict = Github(os.environ["""GITHUB_TOKEN"""] )
_A : Union[str... | 26 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_inf... | 22 | 0 |
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = '''\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/tr... | 367 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ : Union[str, Any] = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:
if not... | 62 | 0 |
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... | 50 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""sail/poolformer_s12""": """https... | 82 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A_ ( nn.Module ):
_lowerCamelCase : Union[str, Any] = 42
_lowerCamelCase ... | 353 |
'''simple docstring'''
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.... | 156 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 308 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case( ) -> int:
'''simple docstring'''
lowercase : List[str] = ArgumentP... | 308 | 1 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __lowercase :
'''simple docstring'''
def _... | 350 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig'... | 188 | 0 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def lowerCamelCase__ ( _a , _a):
return math.sqrt(sum(pow(a - b , 2) for a, b in zip(_a , _a)))
def lowerCamelCase__ ( _a , _a):
if dataset.ndim != value_array... | 76 |
'''simple docstring'''
UpperCAmelCase = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
UpperCAmelCas... | 141 | 0 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
... | 350 | from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def lowercase( UpperCamelCase_ = True , *UpperCamelCase_ , **UpperCamelCase_ ) -> int:
'''simple docstring'''
if not is_tqdm_available():
... | 165 | 0 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
A__ : Union[str, Any] = {
'''n_samples''': 64,
'''horizon''': 32,
'''num_inference_steps''': 20,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not use value network
... | 103 |
"""simple docstring"""
_UpperCamelCase : List[str] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
_UpperCame... | 220 | 0 |
"""simple docstring"""
def __lowerCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : Dict = 1
while len(__UpperCamelCase ) < 1e6:
constant.append(str(__UpperCamelCase ) )
i... | 161 |
"""simple docstring"""
def __lowerCamelCase ( __UpperCamelCase ) -> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
lowerCAmelCase_ : Optional[Any] = gray_code_sequenc... | 161 | 1 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMSched... | 144 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
__snake_case = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def a ( ) -> List[s... | 97 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : int = """▁"""
lowerCAmelCase : Optional[Any] = {"""... | 359 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
... | 127 | 0 |
"""simple docstring"""
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
... | 54 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from tra... | 172 | 0 |
from ...configuration_utils import PretrainedConfig
class _UpperCAmelCase ( _UpperCAmelCase ):
"""simple docstring"""
a_ = "bert-generation"
def __init__( self : List[Any] , lowerCAmelCase_ : Optional[int]=5_0_3_5_8 , lowerCAmelCase_ : Dict=1_0_2_4 , lowerCAmelCas... | 352 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a_ ( lowerCAmel... | 207 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require... | 15 |
from __future__ import annotations
import math
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ ) -> None:
__UpperCamelCase =size
# approximate the overall size of segment tree with given value
__UpperCame... | 62 | 0 |
from __future__ import annotations
def _lowerCAmelCase ( UpperCAmelCase : list[float] , UpperCAmelCase : list[float] ):
'''simple docstring'''
UpperCamelCase__ : List[str] =sorted(numsa + numsa )
UpperCamelCase__ : Optional[A... | 360 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_SCREAMING_SNAKE_CASE : str = False
class __a (... | 157 | 0 |
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
_a : str = {
'facebook/maskformer-swin-base-ade... | 44 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTes... | 156 | 0 |
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _A ( UpperCamelCase_ : Tuple, UpperCamelCase_ : Optional[Any], UpperCamelCase_ : Optional[Any]) -> Optional[Any]:
'''simple ... | 144 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, ... | 144 | 1 |
"""simple docstring"""
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowercase ( A_ )-> List[Any]:
'''simple docstring'''
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_w... | 40 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUI... | 188 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 366 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 160 | 0 |
'''simple docstring'''
import os
import sys
import unittest
_A : List[str] =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies impo... | 41 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowerCamelCase (nn.Module ):
lowerCamelCase__ : int
lowerCame... | 165 | 0 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class __a:
"""simple docstring"""
lowerCAmelCase = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Model name or path of model to be trained.'''} )
lowerCA... | 235 |
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,
)
f... | 235 | 1 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConf... | 161 |
'''simple docstring'''
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xf... | 161 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
_A = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SPEECHT5_PRETRAINED_HIFIGAN_CONFI... | 370 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowercase_ ( A__ ) -> bool:
"""simple docstring"""
snake_case = int(number**0.5 )
return number == sq * sq
def lowercase_ ( A__ ,... | 137 | 0 |
import unittest
from knapsack import greedy_knapsack as kp
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = [10, 20, 30, 40, 50, 60]
__a = [2, 4, 6, 8, 10, 12]
_... | 6 |
from __future__ import annotations
_SCREAMING_SNAKE_CASE : Optional[int] = []
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
for i in range(len(UpperCamelCase_ ) ):
if board[ro... | 127 | 0 |
"""simple docstring"""
from __future__ import annotations
UpperCAmelCase__ = 'Muhammad Umer Farooq'
UpperCAmelCase__ = 'MIT'
UpperCAmelCase__ = '1.0.0'
UpperCAmelCase__ = 'Muhammad Umer Farooq'
UpperCAmelCase__ = 'contact@muhammadumerfarooq.me'
UpperCAmelCase__ ... | 370 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
__a = 42
__a = 42
class lowerCAmelCase__ :
... | 40 | 0 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet i... | 41 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __lowerCamelCase ( ) -> tuple[list[int], int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [randint(-10_00 ... | 306 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __UpperCamelCase ( ... | 294 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Tuple = {
'configuration_distilbert': [
'... | 294 | 1 |
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