code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCAmelCase : Optional[int] ="""src/transformers"""... | 197 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> np.array:
lowerCAmelCase__ : Dict = F'''{sampling_rate}'''
lowerCAmelCase__... | 212 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_lowerCamelCase : Optional[int] = mf_kn... | 365 |
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import A... | 12 | 0 |
'''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_UpperCamelCase : Dict ... | 304 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
_UpperCamelCase : Any = logging.getLogger(__name__)
class snake_case__ ( UpperCamelCase):
a_ = "masked_bert"
def __init__( self : str , _A : ... | 304 | 1 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
lowercase_ = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
lowercase_ = typing.Union[np.floataa, int, float] # noqa: ... | 11 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from d... | 11 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__A = logging.get_logger(__name__)
# TODO: upload to AWS
__A = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.js... | 135 | """simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 135 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForCondi... | 371 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_SCREAMING_SNAKE_CASE : Any = False
class _snake_case ... | 92 | 0 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
A : Any = "examples/"
A : Optional[Any] = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__versio... | 57 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class A__ ( unittest.TestCase ):
... | 127 | 0 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met... | 96 |
'''simple docstring'''
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 96 | 1 |
"""simple docstring"""
import numpy as np
import datasets
snake_case__ : Dict = '''
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.
... | 60 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase_ ... | 12 | 0 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _a ( SCREAMING_SNAKE_CASE_ : str ):
__... | 102 |
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
UpperCamelCase__ = datasets.logging.get_logger(__name__)
UpperCamelCase__ = """\
@InProceedings{moosavi2019minimum,
... | 102 | 1 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
lowerCAmelCase__ = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
lowerCAmelCase__ = typing.Union[np.floataa, int, float] # noqa: UP007
def _UpperCAmelCase (Up... | 11 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowerCAmelCase__ ( a):
'''simple docstring''... | 11 | 1 |
import os
import pytest
from attr import dataclass
_snake_case : Dict = 'us-east-1' # defaults region
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
a_ = 42
a_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
a_ =... | 358 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict ) -> List[str]:
__lowerCAmelCase = name
__lowerCAmelCase = value
... | 207 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int , lowercase : int ) -> str:
"""simple docstring"""
while b:
snake_case ,snake_case : int = b, a % b
return a
def __lowerCAmelCase ( lowercase : int... | 203 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase... | 92 | 0 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random... | 96 |
'''simple docstring'''
lowerCAmelCase: Union[str, Any] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
low... | 96 | 1 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""snap-research/efficientformer-l1-300""": (
"""https... | 96 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( lowercase__ , lowercase__ ):
# Validation
if not isinstance(lowercase__ , lowercase__ ) or len(lowercase__ ) == 0:
raise ValueError('the string shou... | 96 | 1 |
"""simple docstring"""
def lowercase__ ( lowercase_ ,lowercase_ ) -> str:
"""simple docstring"""
if not (isinstance(lowercase_ ,lowercase_ ) and isinstance(lowercase_ ,lowercase_ )):
raise ValueError("longest_common_substring() takes two s... | 310 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 310 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : Tuple = {
"""configuration_rember... | 102 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
SCREAMING_SNAKE_CASE : Dict = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true p... | 102 | 1 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCAmelCase :Optional[int] = 3
def lowerCamelCase ( lowerCAmelCase : int ):
"""simple docstring"""
print('Generating primitive r... | 353 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
... | 275 | 0 |
"""simple docstring"""
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def low... | 25 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
... | 207 | 0 |
"""simple docstring"""
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
if density <= 0:
raise ValueError('Impossible fluid density')
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus')
return (bulk_modulus / density) ** 0.5
if __name__ == "__main... | 353 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
... | 327 | 0 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
"... | 96 |
"""simple docstring"""
import socket
def _snake_case ( ):
_lowerCamelCase : List[Any] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_lowerCamelCase : Union[str, Any] = socket.gethostname()
_lowerCamelCase : Li... | 96 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> str | Literal[False]:
"""simple docstring"""
a = list(snake_case_ )
a = list(snake_case_ )
a ... | 366 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
UpperCamelCase__ : Any = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers... | 330 | 0 |
def _A ( _lowercase , _lowercase ) -> str:
"""simple docstring"""
if not (isinstance(_lowercase , _lowercase ) and isinstance(_lowercase , _lowercase )):
raise ValueError('longest_common_substring() takes two strings for inputs' )
... | 310 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''xlm-roberta-base''': '''https://huggin... | 310 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A__ : Optional[int] = logging.get_logger(__name__)
A__ : Any = {
'shi-labs/dinat-mini-i... | 209 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : List[Any] = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise... | 209 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark im... | 63 |
def _lowercase ( lowercase__ ):
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
__lowerCAmelCase : int = sorted(string.lower() )
return len(lowercase__ ) == len(set(lowerca... | 275 | 0 |
"""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... | 371 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : List[Any] = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConf... | 326 | 0 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
a_ = """\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew and
Dorr, Bonnie and
Schwartz, R... | 330 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, ... | 327 | 0 |
'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__A : Any = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understand... | 89 |
'''simple docstring'''
import requests
def UpperCamelCase_ ( A__ : str , A__ : str ):
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = {"""Content-Type""": """application/json"""}
lowerCAmelCase_ : Uni... | 89 | 1 |
from collections import defaultdict
class a :
def __init__( self :List[Any] ,__lowercase :Union[str, Any] ,__lowercase :int ):
snake_case__ : str = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initia... | 230 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@requ... | 330 | 0 |
"""simple docstring"""
import math
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase=0 ): # a graph with Node 0,1,...,N-1
__a : Tuple = n
__a : Tuple = [
... | 188 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
A = TypeVar('''T''')
class __lowercase ( Generic[T] ):
'''simple docstring'''
__low... | 188 | 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.ut... | 209 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
... | 209 | 1 |
import os
def lowerCamelCase_ ( ):
with open(os.path.dirname(lowerCamelCase__ ) + "/grid.txt" ) as f:
lowerCamelCase_ = [] # noqa: E741
for _ in range(2_0 ):
l.append([int(lowerCamelCase__ ) for x in f.readline().split()] )
lowerCamelCase_ ... | 47 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__=False ):
lowerCamelCase_ = OmegaConf.load(lowerCamelCase__ )
if display:
print(yaml.dump(Omega... | 47 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridC... | 13 |
from maths.prime_factors import prime_factors
def lowerCAmelCase__( lowercase : int ) -> int:
if not isinstance(lowercase , lowercase ):
__snake_case : Optional[int] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(lowercase )
i... | 326 | 0 |
'''simple docstring'''
import enum
import shutil
import sys
__a , __a = shutil.get_terminal_size()
__a = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class A__ ( enum.Enum ):
"""simple docstring"""
UpperCamelCase_ : Tuple = 0
U... | 353 | '''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (Eu... | 17 | 0 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 89 |
'''simple docstring'''
from typing import Any
class __magic_name__ :
def __init__( self : List[Any] ,_UpperCAmelCase : Any ):
_a : List[Any] = data
_a : Union[str, Any] = None
def __repr__( ... | 89 | 1 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm... | 291 |
"""simple docstring"""
a_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",... | 291 | 1 |
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 ModelTesterMixin, ids_tensor, ra... | 188 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __magic_name__ :
'''simple docstring'''
def __init__( self, lowercase_ ) -> List[str]:
"""simple docstring"""
a__ =data
a__ =[0X67452301, 0Xefcda... | 188 | 1 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : Optional[Any] = x
UpperCAmelCase__ : Optional[int] = ... | 283 |
'''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 _Upper... | 283 | 1 |
'''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 RealmTokeni... | 47 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase : int = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available()... | 47 | 1 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Confi... | 355 |
def UpperCAmelCase__ ( lowerCamelCase ):
if not isinstance(lowerCamelCase, lowerCamelCase ):
raise TypeError("Input value must be an 'int' type" )
lowercase :int = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
impo... | 158 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TableTransformerConfig",
"TableTransformerOnnx... | 26 |
"""simple docstring"""
import baseaa
def _A ( UpperCamelCase_ : str) -> bytes:
'''simple docstring'''
return baseaa.baaencode(string.encode("utf-8"))
def _A ( UpperCamelCase_ : bytes) -> str:
'''simple docstring'''
return baseaa.baadecode(UpperCamelCa... | 17 | 0 |
"""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 diffuse... | 181 |
"""simple docstring"""
from math import factorial, radians
def _lowerCAmelCase ( lowercase_ , lowercase_ = 18 , lowercase_ = 10 ):
UpperCAmelCase = angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)
# Converting from degrees to radians
Up... | 181 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
lowerCAmelCase : Dict = logging.get_logger(__name__)
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
... | 291 |
"""simple docstring"""
from __future__ import annotations
def a__ ( snake_case__ , snake_case__ ) -> bool:
if len(snake_case__ ) == 0:
return False
lowerCamelCase = len(snake_case__ ) // 2
if a_list[midpoint] == item:
return True
if item < a... | 291 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
A__ : Optional[Any] = logging.get_logger(__name__)
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : Union[str, Any] , ... | 0 |
'''simple docstring'''
import math
def a_ ( _UpperCAmelCase : int ) -> list:
__snake_case : Optional[Any] = [True] * n
__snake_case : Optional[int] = False
__snake_case : Dict = False
__snake_case : List[Any] = True
... | 0 | 1 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClass... | 283 |
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
_snake_case = logging.get_logger(__name__)
_snake_case = {
... | 283 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
lowerCAmelCase__ :str = ... | 185 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def lowerCAmelCase__ ( a__: Sequence[float] , a__: int , a__: int ) -> tuple[int | None, int | None, float]:
'''simple docstring'''
... | 185 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Tuple = logging.get_logger(__name__)
lowerCamelCase : Dict = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# S... | 2 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import... | 158 | 0 |
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_tf_commo... | 366 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEF... | 348 | 0 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig... | 181 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ = {
'''configuration_re... | 181 | 1 |
_lowerCamelCase : Optional[int] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> List[st... | 363 |
def SCREAMING_SNAKE_CASE ( lowercase_ = 50 ) -> int:
"""simple docstring"""
A__ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in ra... | 231 | 0 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self... | 0 |
import math
def _a ( a :int ) -> list:
a = [True] * n
a = False
a = False
a = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
a = i * 2
while index < n:
a = False
a = index + i
a = ... | 0 | 1 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoC... | 59 |
from scipy.stats import spearmanr
import datasets
UpperCamelCase_ = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations impl... | 59 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : dict ) -> bool:
__lowerCamelCase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
__lowerCamelCase : set[int] = set()
return any(
... | 185 |
'''simple docstring'''
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
A__ : List[str] = get_logger(__name__)
A__ : str = R"""
Args:
input_ids (`jnp.ndarray` ... | 185 | 1 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 204 | from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_prope... | 204 | 1 |
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))'''))
| 38 | __snake_case = '''Input must be a string of 8 numbers plus letter'''
__snake_case = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def lowerCAmelCase_ ( __lowerCAmelCase )-> bool:
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
... | 348 | 0 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase_( lowercase_ : str , low... | 369 |
"""simple docstring"""
import os
from collections.abc import Iterator
def lowerCAmelCase_( lowercase_ : str = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(lowercase_ ):
_lowerCamelCase = [d for d in dir_names if d != '''scripts''' and... | 73 | 0 |
import pytest
import datasets
# Import fixture modules as plugins
snake_case_ : Tuple = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def A (__A : Optional[int] , __A : Any ) -> Optional[int]:
"""simple ... | 51 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interl... | 231 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get... | 362 |
def _a ( UpperCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
raise TypeError('''only integers accepted as input''' )
else:
lowerCamelCase__ : Any = str(abs(UpperCAmelCase ) ... | 265 | 0 |
def UpperCamelCase ( __lowerCamelCase : int ):
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
raise TypeError("Input value must be a 'int' type" )
return... | 59 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def UpperCamelCase ( __lowerCamelCase : np.ndarray ):
return input_array.reshape((input_array.size, 1) )
def UpperCamelCase ... | 59 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A_ ( snake_case_ : Optional[int] ,snake_case_ : str ,snake_case_ : Union[str, Any] ,snake_... | 368 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : flo... | 27 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@requ... | 204 |
def _SCREAMING_SNAKE_CASE ( lowercase : int = 10_00 ):
'''simple docstring'''
lowerCamelCase_ = 2**power
lowerCamelCase_ = 0
while n:
lowerCamelCase_ , lowerCamelCase_ = r + n % 10, n // 10
return ... | 204 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class _snake_case ( nn.Module ):
lowerCamelCase__: int
lowerCamelCase__: jnp.dtype = jnp.floataa
def _lowerCamelCase ( self: Tuple ) -> Union[str, Any]:
__UpperCAmelCase... | 342 | import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import... | 342 | 1 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
lowerCAmelCase_ = None
try:
import msvcrt
except ImportError:
lowerCAmelCase_ = None
try:
import fcntl
except ImportError:
lowerCAmelCase_ = None
# Backward compatibility
# ----------------... | 279 |
from bisect import bisect
from itertools import accumulate
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Union[str, Any]:
__lowerCamelCase : Optional[Any] = sorted(zip(lowerCamelCase__ , lowerCamelCase__ ... | 73 | 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_xlnet import... | 138 |
def UpperCamelCase ( _A = 1, _A = 1000 ):
"""simple docstring"""
__magic_name__ : Optional[int] = 1
__magic_name__ : Dict = 0
for divide_by_number in range(_A, digit + 1 ):
__magic_name__ : ... | 138 | 1 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets i... | 256 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __lowerCamelCase ( ) -> Any:
raise RuntimeError("""CUDA out of memory.""" )
c... | 265 | 0 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __snake_case ( a ):
def __init__( self : List[str] , _snake_case : Dict="" , _snake_case : List[Any]="train"):
"""simple do... | 361 |
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, BertConfig, GPTaConfig
from trans... | 7 | 0 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
__UpperCamelCase : Optional[int] = {
'''n_samples''': 6_4,
'''horizon''': 3_2,
'''num_inference_steps''': 2_0,
'''n_guide_steps''': 2, # can set to 0 fo... | 106 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVeca... | 27 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils im... | 365 | """simple docstring"""
import os
from pathlib import Path
def lowerCAmelCase ():
"""simple docstring"""
from torch.utils.cpp_extension import load
__UpperCamelCase =Path(__UpperCamelCase ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
__Uppe... | 85 | 0 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class snake_case__ ( nn.Module ):
lowercase__ : int
lowercase__ : jnp.dtype = jnp.floataa
def __magic_name__ ( self ) -> Dict:
__magic_name__ : Optional[int] ... | 342 |
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 transformers.utils i... | 342 | 1 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 107 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __magic_name__ :
lowerCAmelCase : str = field(
... | 107 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_t... | 138 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__A : Any = TypeVar('''T''')
class __A ( Generic[T] ):
def __init__( self : Dict , UpperCAmelCase_ : list[T] , UpperCAmelCase_ : Callable[[T, ... | 138 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@... | 366 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
... | 7 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
... | 100 |
def _snake_case( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any ) -> Optional[int]:
'''simple docstring'''
global f # a global dp tabl... | 7 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a )
class __lowerCAmelCase ( _a ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON seriali... | 279 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transf... | 279 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"vocab_file": "sentenc... | 290 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def UpperCamelCase_( snake_case : Callable ):
'''simple docstring'''
@wraps(snake_case )
def _inner_fn(*snake_case : Optional[int] , **snak... | 85 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transfor... | 299 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProc... | 299 | 1 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
fr... | 107 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class snake_case__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str ... | 107 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=SCREAMING_SNAKE_CASE_ ):
a_ : Any = ["""note_seq"""]
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase) ->Union[str, Any]:
requ... | 303 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ) ->tuple[float | int, list[tuple[int, int]]]:
"""simple docstring"""
a_ , a_ = grid.shape
a_ = [-1, ... | 303 | 1 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstri... | 201 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from... | 7 | 0 |
"""simple docstring"""
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _a ( _SCREAMING_SNAKE_CASE ) -> List[Tuple[int, ...]]:
... | 368 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.u... | 233 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> tuple[float, list[float]]:
"""simple docstring"""
snake_case_ : Dict = list(range(len(_UpperCamelCase ) ) )
snake_case_ : Dict ... | 279 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCAmelCase_ = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d3... | 279 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils imp... | 370 |
def _a ( SCREAMING_SNAKE_CASE__ : int = 50_00_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = set()
SCREAMING_SNAKE_CASE__ : Dict = int((limit - 24) ** (1 / 2) )
SCREAMING_SNAK... | 191 | 0 |
'''simple docstring'''
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : List[Any] , lowerCamelCase_ : int , lowerCamelCase_ : int=None , lowerCamelCase_ : Tuple=None ):
'''simple docstring'''
SCREAMIN... | 323 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class UpperCamelCase__ ( tf.keras.layers.Layer ):
"... | 323 | 1 |
"""simple docstring"""
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_pa... | 364 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _a ( _SCREAMING_SNAKE_CASE = 8 ) -> str:
snake_case_ = ascii_letters + digits + punctuation
return "".join(secre... | 233 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltFor... | 334 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase =2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sm... | 334 | 1 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class UpperCamelCase_ (__A ):
__magic_name__ = '''M-CLIP'''
def __init__( self : Any , lowerCAmelCase_ : str=1_024 , lowerCAmelCase_ : str=768 , **lowerCAme... | 364 |
"""simple docstring"""
import math
import sys
import cva
import numpy as np
def snake_case ( A__ ,A__ ):
# For applying gaussian function for each element in matrix.
UpperCAmelCase_ : int = math.sqrt(A__ )
UpperCAmelCase_ : Tuple = 1 / (sigma ... | 253 | 0 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def _A (__a , __a ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = iter(lowerCAmelCase_ )
while True:
SCREAMING_SN... | 91 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffus... | 233 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class _UpperCamelCase :
'''simple docstring'''
__UpperCAmelCas... | 366 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : str = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available(... | 259 | 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 ... | 38 |
"""simple docstring"""
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 ... | 191 | 0 |
'''simple docstring'''
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : Optional[int] = (DDIMParallelScheduler,)
__magic_name__ : Any = (("eta", 0.0), ("num_... | 91 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowerCamelCase__ ( A : int , A : int , A : int , A : int , A : int , A ... | 91 | 1 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
if i >= h:
return
... | 96 |
lowerCamelCase : Optional[Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def snake_case_ ( ):
__lowercase : List[str] = input("""Enter message: """ )
__lowercase : int = input("""Enter key [alphanumeric]: """ )
__lowercase : ... | 233 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCamelCase_ (UpperCamelCase__ : int ):
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, ... | 353 |
"""simple docstring"""
_lowerCAmelCase :List[str] = 0 # The first color of the flag.
_lowerCAmelCase :Optional[Any] = 1 # The second color of the flag.
_lowerCAmelCase :List[Any] = 2 # The third color of the flag.
_lowerCAmelCase :Any = (red, white, blue)
def lowerCamelCase_ ... | 68 | 0 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_UpperCAmelCase : Dict ='\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu ... | 262 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
lowerCAmelCase : List[Any] = datasets.load_iris()
lowerCAmelCase : List[str] = np.array(data['data'])
lowerCAmelCase : Any = np.array(dat... | 253 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase : List[Any] = {
"""microsoft/swinv2-tiny-patch4-window8-256""": (
"""https://huggingface.co/microso... | 371 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ... | 148 | 0 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__lowerCAmelCase : Dict =(3, 9, -1_1, 0, 7, 5, 1, -1)
__lowerCAmelCase : int =(4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class _lowercase :
'''simple docstring'''
S... | 9 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _A ( SCREAMING_SNAKE_CASE__ : str = "isbn/0140328726" ):
UpperCamelCase :Optional[int] = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashe... | 259 | 0 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_UpperCAmelCase : Any = """src/diffusers"""
# Matches is_xxx_available()
_UpperCAmelC... | 9 |
'''simple docstring'''
# Imports
import numpy as np
class a__ :
"""simple docstring"""
def __init__(self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None ):
self.set_matricies... | 9 | 1 |
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
UpperCAmelCase_ : List[str] = re.compile(r"""^(?P<major>\d+)""" r"""\.(?P<minor>\d+)""" r"""\.(?P<patch>\d+)$""")
@to... | 91 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class ... | 91 | 1 |
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # ... | 356 |
"""simple docstring"""
import math
UpperCamelCase_ = 10
UpperCamelCase_ = 7
UpperCamelCase_ = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase ( UpperCAmelCase = 20 ) ->str:
"""simple docstring"""
a_ = math.comb(UpperCAmelCase , UpperCAmelCase )
a_ = math.comb... | 303 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusion... | 67 |
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
lowerCAmelCase__ = get_tests_dir(... | 68 | 0 |
UpperCAmelCase_ : Union[str, Any] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : int = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : int = {
0: """Sunday""",
1: """Monday""",
2: """Tuesday""",
3: """Wednesday""",
4: """Thursday""",
5: """Frid... | 351 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start... | 120 | 0 |
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