code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class lowerCamelCase__ ( __a):
SCREAMING_SNAKE_CASE__ = DistilB... | 5 |
'''simple docstring'''
class _a :
def __init__( self : Any ):
'''simple docstring'''
UpperCAmelCase = {} # Mapping from char to TrieNode
UpperCAmelCase = False
def A ( self : int , lowercase : list[st... | 34 | 0 |
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_common import Tok... | 184 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/confi... | 34 | 0 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...... | 39 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, 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,... | 34 | 0 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 26 |
'''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,
AutoModelForMultipleCh... | 34 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import loggin... | 63 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImage... | 34 | 0 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
if not isinstance(_a, _a ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" )
return sum(
divisor for d... | 66 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effect... | 34 | 0 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutp... | 52 |
'''simple docstring'''
import os
def snake_case_ ():
UpperCAmelCase = os.path.join(os.path.dirname(_a ) , '''num.txt''' )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
... | 34 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTes... | 173 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
... | 34 | 0 |
"""simple docstring"""
import logging
from transformers import PretrainedConfig
__UpperCamelCase = logging.getLogger(__name__)
__UpperCamelCase = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/confi... | 69 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : ... | 34 | 0 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = s.rsplit(_a , _a )
retur... | 82 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging... | 34 | 0 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampl... | 86 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : Dict , _a : str , _a : Optional[Any] , _a : List[str] ): # noqa: E741
while r - l > 1:
UpperCAmelCase = (l + r) // 2
if v[m] >= key:
... | 34 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase__ ... | 5 |
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with... | 34 | 0 |
from ...processing_utils import ProcessorMixin
class _lowercase ( __a):
"""simple docstring"""
A__ = """SpeechT5FeatureExtractor"""
A__ = """SpeechT5Tokenizer"""
def __init__( self : List[str] , ... | 184 |
'''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... | 34 | 0 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> Dict:
""... | 39 |
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ (_a : Union[str, Any] , _a : List[Any] ):
for e in env_keys:
UpperCAmelCase = int(os.environ.get(_a , -1 ) )
if val >= 0:
return ... | 34 | 0 |
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
class lowercase ... | 26 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A... | 34 | 0 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
... | 63 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *lowercase : str , **lowercase : List[Any] ):
... | 34 | 0 |
"""simple docstring"""
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
while a != 0:
snake_case_, snake_case_ :Tuple = b % a, a
return b
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
if gcd(_a, _a... | 66 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from ... | 34 | 0 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__lowerCamelCase : str = logging.get_logger(__name__)
class A__ ( __a ):
def __init__( self , *A_ , **A_ ):
'''simple docstrin... | 52 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A =input('Enter image url: ').strip()
print(f"""Downloading image from {url} ...""")
A =BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL ... | 34 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, 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,
... | 173 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_u... | 34 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
__UpperCamelCase = TypeVar('''T''')
__UpperCamelCase = TypeVar('''U''')
class UpperCamelCase ( Generic[T, U] ):
def __init__( self, ... | 69 |
'''simple docstring'''
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, _conc... | 34 | 0 |
from numpy import exp, pi, sqrt
def _UpperCAmelCase ( snake_case , snake_case = 0.0 , snake_case = 1.0 ):
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 82 |
'''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 im... | 34 | 0 |
"""simple docstring"""
lowerCamelCase__ = """Tobias Carryer"""
from time import time
class A__ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=int(time() ) ): # noqa: B008
__lowerCAme... | 86 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A =[
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',... | 34 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avail... | 5 |
'''simple docstring'''
class _a :
def __init__( self : Any ):
'''simple docstring'''
UpperCAmelCase = {} # Mapping from char to TrieNode
UpperCAmelCase = False
def A ( self : int , lowercase : list[st... | 34 | 0 |
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def lowercase_ ( _A : Any ):
"""simple docstring"""
return (torch.arange(state.num_processes ) + 1.0 + (stat... | 184 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/confi... | 34 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVe... | 39 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, 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,... | 34 | 0 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
... | 26 |
'''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,
AutoModelForMultipleCh... | 34 | 0 |
'''simple docstring'''
from PIL import Image
def _lowerCamelCase ( lowercase : Image , lowercase : int ) -> Tuple:
_a = (259 * (level + 255)) / (255 * (259 - level))
def contrast(lowercase : int ) -> int:
return int(128 + fa... | 63 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImage... | 34 | 0 |
"""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."
)
| 66 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effect... | 34 | 0 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available... | 52 |
'''simple docstring'''
import os
def snake_case_ ():
UpperCAmelCase = os.path.join(os.path.dirname(_a ) , '''num.txt''' )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
... | 34 | 0 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __magic_name__ ( lowercase = "" ):
SCREAMING_SNAKE_CASE_: int =url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
SCREAMING_SNAKE_CAS... | 173 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
... | 34 | 0 |
"""simple docstring"""
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
__UpperCamelC... | 69 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : ... | 34 | 0 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A__ = """."""
if __name__ == "__main__":
A__ = os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
A__ = []
A__... | 82 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging... | 34 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get... | 86 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : Dict , _a : str , _a : Optional[Any] , _a : List[str] ): # noqa: E741
while r - l > 1:
UpperCAmelCase = (l + r) // 2
if v[m] >= key:
... | 34 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCAmelCase_ ( __snake_case ) -> int:
"""sim... | 5 |
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with... | 34 | 0 |
from __future__ import annotations
def lowercase_ ( _A : list , _A : int | None = None , _A : int | None = None ):
"""simple docstring"""
if start is None:
lowerCamelCase__ : str = 0
if end is None:
low... | 184 |
'''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... | 34 | 0 |
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
_a ... | 39 |
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ (_a : Union[str, Any] , _a : List[Any] ):
for e in env_keys:
UpperCAmelCase = int(os.environ.get(_a , -1 ) )
if val >= 0:
return ... | 34 | 0 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformer... | 26 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A... | 34 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int , lowercase : int ) -> str:
return base * power(_a , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...')
... | 63 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *lowercase : str , **lowercase : List[Any] ):
... | 34 | 0 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
if nth_term == "":
return [""]
snake_case_ :Union[str, Any] = int(_a )
snake_case_ :Any = int(_a )
snake_case_ :str = ... | 66 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from ... | 34 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPI... | 52 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A =input('Enter image url: ').strip()
print(f"""Downloading image from {url} ...""")
A =BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL ... | 34 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
while b:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Union[str, Any] =b, a % b
return a
def __magic_name__ ( lowercase , lowercase ):
return a if b == 0 ... | 173 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_u... | 34 | 0 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, ... | 69 |
'''simple docstring'''
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, _conc... | 34 | 0 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance < 0:
... | 82 |
'''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 im... | 34 | 0 |
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase ):
if number < 0:
raise ValueError('number must not be negative' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod() | 86 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A =[
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',... | 34 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConf... | 5 |
'''simple docstring'''
class _a :
def __init__( self : Any ):
'''simple docstring'''
UpperCAmelCase = {} # Mapping from char to TrieNode
UpperCAmelCase = False
def A ( self : int , lowercase : list[st... | 34 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Tuple = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCH... | 184 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/confi... | 34 | 0 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_ava... | 39 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, 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,... | 34 | 0 |
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_ ):
_A : Dict = multiprocessing.Manager()
_A : Optional[int] ... | 26 |
'''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,
AutoModelForMultipleCh... | 34 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : Any =... | 63 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImage... | 34 | 0 |
"""simple docstring"""
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
__a = get_tests_dir("fixtures/test_sentencep... | 66 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effect... | 34 | 0 |
def A_ ( _lowerCAmelCase ) -> Optional[int]:
UpperCamelCase : Optional[int] = []
UpperCamelCase : Dict = set({"(", "[", "{"} )
UpperCamelCase : Any = set({")", "]", "}"} )
UpperCamelCase : List[Any] = {"{": "}", "[": "]", "(": ")"}
... | 52 |
'''simple docstring'''
import os
def snake_case_ ():
UpperCAmelCase = os.path.join(os.path.dirname(_a ) , '''num.txt''' )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
... | 34 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {
"""configuration_distilbert""": [
... | 173 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
... | 34 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase ( unittest.TestCase ):
... | 69 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : ... | 34 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __a ):
def __init__( self , *_snake_case , **_snake_case ):
"""simp... | 82 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging... | 34 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester... | 86 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : Dict , _a : str , _a : Optional[Any] , _a : List[str] ): # noqa: E741
while r - l > 1:
UpperCAmelCase = (l + r) // 2
if v[m] >= key:
... | 34 | 0 |
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def UpperCAmelCase_ ( __snake... | 5 |
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with... | 34 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : int = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"GroupViTOnnxConfig",
... | 184 |
'''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... | 34 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 39 |
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ (_a : Union[str, Any] , _a : List[Any] ):
for e in env_keys:
UpperCAmelCase = int(os.environ.get(_a , -1 ) )
if val >= 0:
return ... | 34 | 0 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( ):
_A :... | 26 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A... | 34 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase_ : Union[str, Any] = {
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/re... | 63 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *lowercase : str , **lowercase : List[Any] ):
... | 34 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDe... | 66 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from ... | 34 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {
"""faceboo... | 52 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A =input('Enter image url: ').strip()
print(f"""Downloading image from {url} ...""")
A =BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL ... | 34 | 0 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
... | 173 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_u... | 34 | 0 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''facebook/encodec_24khz''': '''https://huggingface.co/f... | 69 |
'''simple docstring'''
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, _conc... | 34 | 0 |
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,
AutoTokenizer,
Da... | 82 |
'''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 im... | 34 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A__ ( __a):
A_ : int = ["""image_processor""", """tokenizer"""]
A_ : Union[str, Any] = """ChineseCLIPImageProcessor""... | 86 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A =[
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',... | 34 | 0 |
def UpperCAmelCase_ ( __snake_case ) -> List[str]:
"""simple docstring"""
_lowercase =''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def UpperCAmelCase_ ( __snake_case ) -... | 5 |
'''simple docstring'''
class _a :
def __init__( self : Any ):
'''simple docstring'''
UpperCAmelCase = {} # Mapping from char to TrieNode
UpperCAmelCase = False
def A ( self : int , lowercase : list[st... | 34 | 0 |
import os
def lowercase_ ( ):
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = os.path.join(os.path.dirname(_a ) , "num.txt" )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) ... | 184 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/confi... | 34 | 0 |
import numpy as np
def __A ( __lowerCAmelCase )-> int:
"""simple docstring"""
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 39 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, 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,... | 34 | 0 |
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : Dict = len(_a ) + 1
_A : Tuple = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of... | 26 |
'''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,
AutoModelForMultipleCh... | 34 | 0 |
'''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
lowerCAmelCase_ : List[Any] = logging.get_logger(__name__)
... | 63 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImage... | 34 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"... | 66 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effect... | 34 | 0 |
import unittest
from transformers import MPNetConfig, 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
from ...test_pipeline_mi... | 52 |
'''simple docstring'''
import os
def snake_case_ ():
UpperCAmelCase = os.path.join(os.path.dirname(_a ) , '''num.txt''' )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
... | 34 | 0 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import imag... | 173 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
... | 34 | 0 |
"""simple docstring"""
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORD... | 69 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : ... | 34 | 0 |
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
A__ = logging.get_logger(__name__)
def _UpperCAmelCase ( snake_case , snake_case... | 82 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging... | 34 | 0 |
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_con... | 86 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : Dict , _a : str , _a : Optional[Any] , _a : List[str] ): # noqa: E741
while r - l > 1:
UpperCAmelCase = (l + r) // 2
if v[m] >= key:
... | 34 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowerCamelCase__ ( __a):
def __init__(self , UpperCAmelCase , Upp... | 5 |
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with... | 34 | 0 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQue... | 184 |
'''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... | 34 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __lowerCamelCase ( __a):
"""si... | 39 |
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ (_a : Union[str, Any] , _a : List[Any] ):
for e in env_keys:
UpperCAmelCase = int(os.environ.get(_a , -1 ) )
if val >= 0:
return ... | 34 | 0 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, 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_tenso... | 26 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A... | 34 | 0 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
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 im... | 63 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *lowercase : str , **lowercase : List[Any] ):
... | 34 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
e... | 66 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from ... | 34 | 0 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import Au... | 52 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A =input('Enter image url: ').strip()
print(f"""Downloading image from {url} ...""")
A =BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL ... | 34 | 0 |
"""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,
)
_UpperCAmelCase = {
"""co... | 173 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_u... | 34 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from tr... | 69 |
'''simple docstring'''
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, _conc... | 34 | 0 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEI... | 82 |
'''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 im... | 34 | 0 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowerCamelCase__ = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argum... | 86 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A =[
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',... | 34 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
try:
if not is_torch_available():
raise Option... | 5 |
'''simple docstring'''
class _a :
def __init__( self : Any ):
'''simple docstring'''
UpperCAmelCase = {} # Mapping from char to TrieNode
UpperCAmelCase = False
def A ( self : int , lowercase : list[st... | 34 | 0 |
def lowercase_ ( _A : int ):
"""simple docstring"""
if not isinstance(_a , _a ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() doe... | 184 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/confi... | 34 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''https://huggingface.co/tii... | 39 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, 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,... | 34 | 0 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class lowercase ( __a ):
_a = CustomTokenizer
pass
| 26 |
'''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,
AutoModelForMultipleCh... | 34 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow... | 63 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImage... | 34 | 0 |
"""simple docstring"""
class lowerCamelCase :
'''simple docstring'''
def __init__( self: int ) -> Tuple:
snake_case_ :Optional[Any] = {}
def lowerCAmelCase_ ( self: Tuple ) -> int:
print(self.vertex )
... | 66 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effect... | 34 | 0 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
__lowerCamelCase : List[Any] = get_logger(__name__)
class A__ ( enum.Enum ):
_UpperCAmelCase :Union[str, Any] = """all_checks"""
... | 52 |
'''simple docstring'''
import os
def snake_case_ ():
UpperCAmelCase = os.path.join(os.path.dirname(_a ) , '''num.txt''' )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
... | 34 | 0 |
"""simple docstring"""
import re
import subprocess
import sys
_UpperCAmelCase = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
_UpperCAmelCase = subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode("""utf-8""").split()
_UpperCA... | 173 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
... | 34 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_whisper''': ['''WHISPER_PRETRAIN... | 69 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : ... | 34 | 0 |
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
fro... | 82 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging... | 34 | 0 |
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
__lowerCAmelCase : Any = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in para... | 86 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (_a : Dict , _a : str , _a : Optional[Any] , _a : List[str] ): # noqa: E741
while r - l > 1:
UpperCAmelCase = (l + r) // 2
if v[m] >= key:
... | 34 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, requir... | 5 |
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with... | 34 | 0 |
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