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 |
|---|---|---|---|---|
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number | (1 << position)
def _A ( lowercase , lowercase ):
"""simple docstring"""
return number & ~(1 << position)
def _A ... | 81 |
from PIL import Image
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(UpperCamelCase__ ) -> int:
return int(128 + factor *... | 285 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/c... | 82 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 285 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowercase__ ( metaclass=lowercase ):
lowercase__ = ["""note_seq"""]
def __init__( self : Tuple ,*lowerCamelCase__ : Union[str, Any] ,**lowerCamelCase__ : Tuple ):
'''... | 83 |
class lowercase :
def __init__( self , snake_case , snake_case , snake_case ):
snake_case_ = name
snake_case_ = value
snake_case_ = weight
def __repr__( self ):
return F'''{self.__class_... | 285 | 0 |
"""simple docstring"""
import baseaa
def _snake_case ( lowercase__ : str ) -> bytes:
'''simple docstring'''
return baseaa.aaaencode(string.encode("""utf-8""" ) )
def _snake_case ( lowercase__ : bytes ) -> str:
... | 84 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = {}
sn... | 285 | 0 |
'''simple docstring'''
from __future__ import annotations
import requests
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
snake_case_ = f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(s... | 85 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if edge <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge... | 285 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_C... | 86 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 285 | 0 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow a... | 87 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase ( lowercase_ ):
@staticmethod
@abstractmethod
def a ( snake_case ):
raise NotImplementedError()
@abstractmethod
def a ( self ):
... | 285 | 0 |
def a__ ( A_ ):
'''simple docstring'''
if len(A_ ) <= 1:
return [tuple(A_ )]
__magic_name__ = []
def generate(A_, A_ ):
__magic_name__ = [0] * n
res.append(tuple(A_ ) )
__magic_name__ =... | 88 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 285 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=_UpperCamelCase ):
lowerCAmelCase : str = ['note_seq']
def __init__( self : Tuple ,*_UpperCAmelCase : List[Any] ,**_UpperCAmelCase... | 89 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Optional[int] = 5_0000
_UpperCAmelCase : Dict = 5000
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = os.path.split(__fi... | 285 | 0 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 90 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case_ ... | 285 | 0 |
"""simple docstring"""
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
re... | 91 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 285 | 0 |
from __future__ import annotations
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : np.ndarray ):
__lowerCAmelCase , __lowerCAmelCase = np.shape(SCREAMING_SNAKE_CASE_ )
if rows != columns:
__lowerCAmelCase = (
... | 92 |
import os
import numpy
import onnx
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = a.name
snake_case_ = b.name
snake_case_ = ''
snake_case_ = ''
snake... | 285 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_lowercase : Dict = 5_0_0_0_0
_lowercase : Union[str, Any] = 5_0_0_0
_lowercase , _lowercase : List[str] ... | 93 |
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(UpperCamelCase__ ... | 285 | 0 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...... | 94 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return "".join(sorted(UpperCamelCase__ ) )
def __lowerCamelCase ( UpperCamelCase__ ):
... | 285 | 0 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,... | 95 |
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
... | 285 | 0 |
"""simple docstring"""
lowercase__ = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def _snake_case ( lowercase__ ):
_lowerCamelCase : Optional[int] = 0
while number:
# Increased Speed Slightly by checkin... | 96 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 285 | 0 |
'''simple docstring'''
from typing import Any
def a ( __a ) -> list[Any]:
'''simple docstring'''
if not input_list:
return []
UpperCamelCase__ :str = [input_list.count(__a ) for value in input_list]
UpperCamelCase__ :Dict = max(__a )... | 97 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dat... | 285 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_... | 98 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( lowercase_ , unittest.TestCase ):
__SCRE... | 285 | 0 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
defau... | 99 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simp... | 285 | 0 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_n... | 100 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,11... | 285 | 0 |
from random import shuffle
import tensorflow as tf
from numpy import array
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = int(lowerCAmelCase__ )
assert noofclusters < len(lowerCAmelCase__ )
# Find out the dimension... | 101 |
def __lowerCamelCase ( ):
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_UpperCAmelCase : Union[str, Any] = generate_large_matrix()
_UpperCAmelCase : Tuple = (
[[4,... | 285 | 0 |
"""simple docstring"""
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.nump... | 102 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_UpperCAmelCase : D... | 285 | 0 |
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=UpperCamelCase_ ):
_a = ['''sentencepiece''']
def __init__( self : Optional[int] , *A_ : List[str] , **A_ : List[Any]):
requires_b... | 103 |
from PIL import Image
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(UpperCamelCase__ ) -> int:
return int(128 + factor *... | 285 | 0 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {'''vocab_file... | 104 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 285 | 0 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
fr... | 105 |
class lowercase :
def __init__( self , snake_case , snake_case , snake_case ):
snake_case_ = name
snake_case_ = value
snake_case_ = weight
def __repr__( self ):
return F'''{self.__class_... | 285 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor,... | 106 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = {}
sn... | 285 | 0 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__lowerCAmelCase : Any = logging.get... | 107 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if edge <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge... | 285 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Any = size
... | 108 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 285 | 0 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A: Tuple = logging.get_logger(__name__)
A: List[Any] ... | 109 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase ( lowercase_ ):
@staticmethod
@abstractmethod
def a ( snake_case ):
raise NotImplementedError()
@abstractmethod
def a ( self ):
... | 285 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transfo... | 144 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 285 | 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():
... | 68 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Optional[int] = 5_0000
_UpperCAmelCase : Dict = 5000
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = os.path.split(__fi... | 285 | 0 |
class _snake_case :
def __init__( self , _a , _a , _a ):
__magic_name__ : Dict = name
__magic_name__ : Tuple = value
__magic_name__ : List[str] = weight
def __repr__( self ):
return f'''{self.__class__.__name__}({self... | 281 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case_ ... | 285 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""shi-labs/dinat-mini-in1k-2... | 256 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 285 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowercase__ ( lowercase_ ):
'''... | 200 |
import os
import numpy
import onnx
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = a.name
snake_case_ = b.name
snake_case_ = ''
snake_case_ = ''
snake... | 285 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> Union[str, Any]:
if n_term == "":
return []
UpperCAmelCase : str = []
for temp in range(int(UpperCamelCase__ ) ):
series.append(F'''1/{temp + 1}''' if series... | 265 |
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(UpperCamelCase__ ... | 285 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A : int = logging.get_logger(__name__)
class _lowercase ( lowercase_):
"""simple docstring"""
def __init__( self : Dict... | 184 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return "".join(sorted(UpperCamelCase__ ) )
def __lowerCamelCase ( UpperCamelCase__ ):
... | 285 | 0 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def __lowerCAmelCase (__lowerCAmelCase ):
_UpperCAmelCase : Dict = year % 19
_UpperCAmelCase : int = year % 4
_UpperCAmelCase : Optional[Any] = ... | 234 |
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
... | 285 | 0 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = """▁"""
lowercase_... | 205 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 285 | 0 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help... | 340 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dat... | 285 | 0 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsM... | 123 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( lowercase_ , unittest.TestCase ):
__SCRE... | 285 | 0 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from tr... | 144 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simp... | 285 | 0 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
lowerCAmelCase__ = 2_0_4_8
lowerCAmelCase__ = 4_0_9_6
lowerCAmelCase__ = 4_2
lowerCAmelCase__ = os.environ.pop("""PROCESS_TRAIN""", """false""")
lowerCAmelCase__ = {"""null""": 0, ""... | 68 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,11... | 285 | 0 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class _snake_case ( lowercase_ ):
def __init__( self , _a ... | 281 |
def __lowerCamelCase ( ):
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_UpperCAmelCase : Union[str, Any] = generate_large_matrix()
_UpperCAmelCase : Tuple = (
[[4,... | 285 | 0 |
"""simple docstring"""
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimens... | 256 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_UpperCAmelCase : D... | 285 | 0 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE... | 200 |
from PIL import Image
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(UpperCamelCase__ ) -> int:
return int(128 + factor *... | 285 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase , UpperCAmelCase : Any = np.shape(UpperCamelCase__ )
if rows != columns:
UpperCAmelCase : Any ... | 265 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 285 | 0 |
from typing import Dict, Optional
import numpy as np
import datasets
A : List[str] = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or mult... | 184 |
class lowercase :
def __init__( self , snake_case , snake_case , snake_case ):
snake_case_ = name
snake_case_ = value
snake_case_ = weight
def __repr__( self ):
return F'''{self.__class_... | 285 | 0 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datas... | 234 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = {}
sn... | 285 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from ... | 205 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if edge <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge... | 285 | 0 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialF... | 340 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 285 | 0 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Trai... | 123 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase ( lowercase_ ):
@staticmethod
@abstractmethod
def a ( snake_case ):
raise NotImplementedError()
@abstractmethod
def a ( self ):
... | 285 | 0 |
"""simple docstring"""
import numpy as np
A__ : Optional[Any] = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w"""... | 144 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 285 | 0 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class a__ ( nn.Module ):
"""simple d... | 68 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Optional[int] = 5_0000
_UpperCAmelCase : Dict = 5000
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = os.path.split(__fi... | 285 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenizat... | 281 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case_ ... | 285 | 0 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dum... | 256 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 285 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device... | 200 |
import os
import numpy
import onnx
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = a.name
snake_case_ = b.name
snake_case_ = ''
snake_case_ = ''
snake... | 285 | 0 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
... | 265 |
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(UpperCamelCase__ ... | 285 | 0 |
import socket
def lowercase_ ( ):
"""simple docstring"""
lowerCamelCase__ : List[str] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase__ : Optional[int] = socket.gethostname()
lowerCamelCase__ : Optional[Any] =... | 184 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return "".join(sorted(UpperCamelCase__ ) )
def __lowerCamelCase ( UpperCamelCase__ ):
... | 285 | 0 |
'''simple docstring'''
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_devi... | 234 |
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
... | 285 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
lowercase_ = (7_2_0, 1_2_8_0) # Height, Width
lowercase_ = (0.4, 0.6) # if height or width lower than this scale, drop it.
lowercase_ = 1 / 1_0_0
l... | 205 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 285 | 0 |
def _a ( UpperCamelCase_ : int ) -> int:
"""simple docstring"""
assert column_title.isupper()
lowerCAmelCase__ = 0
lowerCAmelCase__ = len(UpperCamelCase__ ) - 1
lowerCAmelCase__ = 0
while index >= 0:... | 340 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dat... | 285 | 0 |
from ..utils import DummyObject, requires_backends
class a (metaclass=lowercase_ ):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self : str , *lowerCamelCase : An... | 123 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( lowercase_ , unittest.TestCase ):
__SCRE... | 285 | 0 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
A__ : List[Any] = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
... | 144 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simp... | 285 | 0 |
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 import (
BarkCoarseConfig,
B... | 68 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,11... | 285 | 0 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( _snake_case : Dict ) -> int:
'''simple docstring'''
def is_in_circle(_snake_case : Any , _snake_case : List[str] ) -> ... | 281 |
def __lowerCamelCase ( ):
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_UpperCAmelCase : Union[str, Any] = generate_large_matrix()
_UpperCAmelCase : Tuple = (
[[4,... | 285 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterM... | 256 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_UpperCAmelCase : D... | 285 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Optional[int] = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
... | 200 |
from PIL import Image
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(UpperCamelCase__ ) -> int:
return int(128 + factor *... | 285 | 0 |
'''simple docstring'''
import numpy
class UpperCamelCase_ :
def __init__( self , A , A ) -> Optional[int]:
UpperCAmelCase : Union[str, Any] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previou... | 265 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 285 | 0 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate im... | 184 |
class lowercase :
def __init__( self , snake_case , snake_case , snake_case ):
snake_case_ = name
snake_case_ = value
snake_case_ = weight
def __repr__( self ):
return F'''{self.__class_... | 285 | 0 |
'''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, _... | 234 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = {}
sn... | 285 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformer... | 205 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if edge <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge... | 285 | 0 |
def _a ( UpperCamelCase_ : Tuple ) -> Tuple:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 340 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 285 | 0 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_param... | 123 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase ( lowercase_ ):
@staticmethod
@abstractmethod
def a ( snake_case ):
raise NotImplementedError()
@abstractmethod
def a ( self ):
... | 285 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
A__ : int = logging.get_logger(__name__)
class lowercase__ ( lowercase_ ):
def __init__( self : List[Any] , ... | 144 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 285 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any ) -> Optional[Any]:
'''simple docstring'''
return 1_0 - x * x
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: List[Any] ) -> Union[str, Any]:
'''simple... | 68 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Optional[int] = 5_0000
_UpperCAmelCase : Dict = 5000
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = os.path.split(__fi... | 285 | 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
from ... | 281 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case_ ... | 285 | 0 |
"""simple docstring"""
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
UpperCAmelCase = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesyst... | 256 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 285 | 0 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case_ ( SCREAMING_SNAKE_CASE__ = 3 ):
"""simple docstring"""
if isinstance(UpperCamelCase__ , UpperCamel... | 200 |
import os
import numpy
import onnx
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = a.name
snake_case_ = b.name
snake_case_ = ''
snake_case_ = ''
snake... | 285 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 265 |
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return vector * sigmoid(UpperCamelCase__ ... | 285 | 0 |
from collections.abc import Callable
import numpy as np
def lowercase_ ( _A : str , _A : Optional[Any] , _A : Any , _A : Optional[int] , _A : Optional[Any] ):
"""simple docstring"""
lowerCamelCase__ : Union[str, An... | 184 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return "".join(sorted(UpperCamelCase__ ) )
def __lowerCamelCase ( UpperCamelCase__ ):
... | 285 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def ... | 234 |
from __future__ import annotations
import numpy as np
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case_ = (
... | 285 | 0 |
def a ( A__ : int ) -> List[Any]:
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise TypeError('only integers accepted as input' )
else:
_lowercase =str(abs(UpperCamelCas... | 205 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 285 | 0 |
import random
def _a ( UpperCamelCase_ : List[str] , UpperCamelCase_ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = [], [], []
for element in data:
... | 340 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dat... | 285 | 0 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_snake_case : Optional[int] = """scheduler_config.json"""
class a (lowercase_ ... | 123 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( lowercase_ , unittest.TestCase ):
__SCRE... | 285 | 0 |
"""simple docstring"""
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
A__ : Dict = (
"""This metric will be remove... | 144 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simp... | 285 | 0 |
import qiskit
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] = 2 ) -> str:
'''simple docstring'''
A__ = qubits
# Using Aer's simulator
A__ = qiskit.Aer.get_backend("aer_simulator" )
# Creating a Quantum Circuit acting on the q register
... | 68 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,11... | 285 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def lowerCAmelCase_ ( _snake_case : Tuple ) -> Union[str, Any]:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
__magic_name__ ... | 281 |
def __lowerCamelCase ( ):
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_UpperCAmelCase : Union[str, Any] = generate_large_matrix()
_UpperCAmelCase : Tuple = (
[[4,... | 285 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.js... | 256 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_UpperCAmelCase : D... | 285 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) !... | 200 |
from PIL import Image
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(UpperCamelCase__ ) -> int:
return int(128 + factor *... | 285 | 0 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
impo... | 265 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 285 | 0 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepa... | 184 |
class lowercase :
def __init__( self , snake_case , snake_case , snake_case ):
snake_case_ = name
snake_case_ = value
snake_case_ = weight
def __repr__( self ):
return F'''{self.__class_... | 285 | 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,
)
lowerCamelCase__ = {
... | 234 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = {}
sn... | 285 | 0 |
def a ( A__ : List[str] ) -> List[Any]:
"""simple docstring"""
_lowercase =len(UpperCamelCase__ )
while cur > 1:
# Find the maximum number in arr
_lowercase =arr.index(max(arr[0:cur] ) )
#... | 205 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if edge <= 0 or not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge... | 285 | 0 |
from __future__ import annotations
from typing import Any
class lowercase__ :
def __init__( self , __UpperCAmelCase = 6 )-> Tuple:
'''simple docstring'''
lowerCAmelCase__ = None
lowerCAmelCase__ = None
self.create_linked... | 340 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 285 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case : List[Any] = logging.get_logger(__name__)
_snake_case : Tuple = {
"""google/bit-50""": """... | 123 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase ( lowercase_ ):
@staticmethod
@abstractmethod
def a ( snake_case ):
raise NotImplementedError()
@abstractmethod
def a ( self ):
... | 285 | 0 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
... | 144 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 285 | 0 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 68 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_UpperCAmelCase : Optional[int] = 5_0000
_UpperCAmelCase : Dict = 5000
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = os.path.split(__fi... | 285 | 0 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case : int = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
raise ... | 281 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case_ ... | 285 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.co/microsoft/unispee... | 256 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 285 | 0 |
'''simple docstring'''
from functools import reduce
UpperCAmelCase_ : int = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""125406987471585238630507156932909... | 200 |
import os
import numpy
import onnx
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = a.name
snake_case_ = b.name
snake_case_ = ''
snake_case_ = ''
snake... | 285 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.