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"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> list[int]:
'''simple docstring'''
lowercase : Tuple = 0
lowercase : int = len(_UpperCAmelCase ) - 1
... | 255 |
"""simple docstring"""
def lowercase__ ( ) -> str:
'''simple docstring'''
lowercase : List[str] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(_UpperCAmelCase )[-10:]
if __name__ == "__main__":
pr... | 255 | 1 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
... | 255 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers... | 255 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase = 4 ) -> list[list[int]]:
'''simple docstring'''
lowercase : Optional[Any] = abs(_UpperCAmelCase ) or 4
return [[1 + x + y * row_size for x in ra... | 255 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMSchedule... | 255 | 1 |
"""simple docstring"""
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pega... | 255 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfi... | 255 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_UpperCamelCase: Any = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig... | 255 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, ... | 255 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_dev... | 255 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
_UpperCamelCase: str = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of ... | 255 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTra... | 255 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: List[Any] = logging.get_logger(__name__)
_UpperCamelCase: int = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
... | 255 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, 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_ava... | 255 |
"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase: Tuple = logging.get_logger(__name__)
_Upper... | 255 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_... | 255 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
'''simple docstring'''
_enforce_args(_UpperCAmelCase , _UpperCAmelCase )
if n == 0:
return 0
lowercase : Tuple = float('-inf' ... | 255 | 1 |
"""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,
AutoModelFo... | 255 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('Input value must be an \'int\' type' )
lowercase : str = 0
... | 255 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_... | 255 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase = None ) -> list[list[str]]:
'''simple docstring'''
lowercase : str = word_bank or []
# create a table
lowercase ... | 255 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase: Dict = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_availab... | 255 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase: List[str] = {}
try:
if not is_s... | 255 | 1 |
"""simple docstring"""
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
_UpperCamelCase... | 255 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> list[int]:
'''simple docstring'''
lowercase : Tuple = 0
lowercase : int = len(_UpperCAmelCase ) - 1
... | 255 | 1 |
"""simple docstring"""
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
_UpperCamelCase: List[str] = 'scheduler_config.json'
class a__ (... | 255 |
"""simple docstring"""
_UpperCamelCase: Dict = [
9_9_9,
8_0_0,
7_9_9,
6_0_0,
5_9_9,
5_0_0,
4_0_0,
3_9_9,
3_7_7,
3_5_5,
3_3_3,
3_1_1,
2_8_8,
2_6_6,
2_4_4,
2_2_2,
2_0_0,
1_9_9,
1_7_7,
1_5_5,
... | 255 | 1 |
"""simple docstring"""
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_UpperCamelCase: O... | 255 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_com... | 255 | 1 |
"""simple docstring"""
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> np.ndarray:
'''simp... | 255 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_comm... | 255 | 1 |
"""simple docstring"""
from math import pi, sqrt, tan
def lowercase__ ( _UpperCAmelCase ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length... | 255 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class a__ ( unittest.TestCase ):
def lowercase ( se... | 255 | 1 |
"""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 imp... | 255 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase ) -> None:
'''simple docstring'''
lowercase : Union[str, Any] = generate_pascal_triangle(_UpperCAmelCase )
for row_idx in range(_UpperCAmelCase ):
# Print left spaces
... | 255 | 1 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils... | 255 |
"""simple docstring"""
from collections.abc import Sequence
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase = False ) -> float:
'''simple docstring'''
if not arr:
return 0
lowercase : Tuple = 0 if allow_empty_suba... | 255 | 1 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def _a ( a :List[Any] ) -> Optional[int]:
a = []
... | 0 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
'''simple docstring'''
lowercase : Optional[int] = len(_UpperCAmelCase ) + 1
lowercase : Any = len(_UpperCAmelCase ) + 1
# dp i... | 255 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_ten... | 1 |
"""simple docstring"""
import math
def lowercase__ ( _UpperCAmelCase = 1_00 ) -> int:
'''simple docstring'''
lowercase : List[str] = sum(i * i for i in range(1 , n + 1 ) )
lowercase : Dict = int(math.pow(sum(range(1 ... | 255 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requir... | 2 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTra... | 255 | 0 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 3 |
"""simple docstring"""
def lowercase__ ( ) -> str:
'''simple docstring'''
lowercase : List[str] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(_UpperCAmelCase )[-10:]
if __name__ == "__main__":
pr... | 255 | 0 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
__snake_case =[
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0,... | 4 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers... | 255 | 0 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ... | 5 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMSchedule... | 255 | 0 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
A : Union[str, Any] = datasets.load_iris()
A : int = np.array(data['data'])
A : Optional[Any] = np.array(data['target'])
A : int = data['target_names']
A ... | 6 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfi... | 255 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ =... | 7 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, ... | 255 | 0 |
from numpy import exp, pi, sqrt
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 0.0 , SCREAMING_SNAKE_CASE__ = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import docte... | 8 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
_UpperCamelCase: str = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of ... | 255 | 0 |
from __future__ import annotations
def _UpperCamelCase ( lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : list[list[int]] = []
create_all_state(1 , lowercase__ , lowercase__ , [] , lowercase__ )
return res... | 9 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: List[Any] = logging.get_logger(__name__)
_UpperCamelCase: int = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
... | 255 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https://huggingface.co/models?f... | 10 |
"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase: Tuple = logging.get_logger(__name__)
_Upper... | 255 | 0 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import ... | 11 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
'''simple docstring'''
_enforce_args(_UpperCAmelCase , _UpperCAmelCase )
if n == 0:
return 0
lowercase : Tuple = float('-inf' ... | 255 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils impo... | 12 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('Input value must be an \'int\' type' )
lowercase : str = 0
... | 255 | 0 |
# Algorithm for the pigeonhole sorting
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optional[Any] = min(_UpperCAmelCase ) # min() finds the minimum value
SCREAMING_SNAKE_CASE_: Tuple = max(_UpperCAmelCase ) # max() finds the maximum value
SCREA... | 13 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase = None ) -> list[list[str]]:
'''simple docstring'''
lowercase : str = word_bank or []
# create a table
lowercase ... | 255 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
"""microsoft/swinv2-tiny-patch4-window8-256""": (
"""https://huggingface.co/microsoft/sw... | 14 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase: List[str] = {}
try:
if not is_s... | 255 | 0 |
from sklearn.metrics import recall_score
import datasets
SCREAMING_SNAKE_CASE :Union[str, Any] = '\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and... | 15 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> list[int]:
'''simple docstring'''
lowercase : Tuple = 0
lowercase : int = len(_UpperCAmelCase ) - 1
... | 255 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCAmelCase_ = log... | 16 |
"""simple docstring"""
_UpperCamelCase: Dict = [
9_9_9,
8_0_0,
7_9_9,
6_0_0,
5_9_9,
5_0_0,
4_0_0,
3_9_9,
3_7_7,
3_5_5,
3_3_3,
3_1_1,
2_8_8,
2_6_6,
2_4_4,
2_2_2,
2_0_0,
1_9_9,
1_7_7,
1_5_5,
... | 255 | 0 |
"""simple docstring"""
import pytest
_a = '__dummy_dataset1__'
_a = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL... | 17 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_com... | 255 | 0 |
import os
from distutils.util import strtobool
def _snake_case ( lowerCAmelCase : Dict , lowerCAmelCase : Tuple ):
"""simple docstring"""
for e in env_keys:
SCREAMING_SNAKE_CASE_ : Dict = int(os.environ.get(lowerCAmelCase , -1 ) )
if val >= 0:
retur... | 18 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_comm... | 255 | 0 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_... | 19 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class a__ ( unittest.TestCase ):
def lowercase ( se... | 255 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
lowercase , lowercase : Union[str, Any] = len(SCREAMING_SNAKE_CASE__ ), len(grid[0] )
if (
min(SCREAMING_SNAKE_CAS... | 20 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase ) -> None:
'''simple docstring'''
lowercase : Union[str, Any] = generate_pascal_triangle(_UpperCAmelCase )
for row_idx in range(_UpperCAmelCase ):
# Print left spaces
... | 255 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : ... | 21 |
"""simple docstring"""
from collections.abc import Sequence
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase = False ) -> float:
'''simple docstring'''
if not arr:
return 0
lowercase : Tuple = 0 if allow_empty_suba... | 255 | 0 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A_ ( tf.keras.layers.Layer ):
def __init__( se... | 22 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
'''simple docstring'''
lowercase : Optional[int] = len(_UpperCAmelCase ) + 1
lowercase : Any = len(_UpperCAmelCase ) + 1
# dp i... | 255 | 0 |
'''simple docstring'''
import math
import sys
def snake_case_ ( _lowerCAmelCase : str ) -> str:
UpperCAmelCase : List[Any] = ''''''
try:
with open(_lowerCAmelCase , '''rb''' ) as binary_file:
Upp... | 23 |
"""simple docstring"""
import math
def lowercase__ ( _UpperCAmelCase = 1_00 ) -> int:
'''simple docstring'''
lowercase : List[str] = sum(i * i for i in range(1 , n + 1 ) )
lowercase : Dict = int(math.pow(sum(range(1 ... | 255 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available... | 24 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTra... | 255 | 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 ... | 25 |
"""simple docstring"""
def lowercase__ ( ) -> str:
'''simple docstring'''
lowercase : List[str] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(_UpperCAmelCase )[-10:]
if __name__ == "__main__":
pr... | 255 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 26 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers... | 255 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "https://www.worldometers.info/coronavirus" ):
__a : List[Any] = BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text , 'html.parser' ... | 27 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMSchedule... | 255 | 0 |
'''simple docstring'''
_lowerCamelCase : str = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def __lowerCamelCase ( A__ ) -> bytes:
"""simple docstring"""
# Make sure the supplied data is a bytes-like object
if not is... | 28 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfi... | 255 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'huggingface/time-series-transformer-tourism-monthly': (
'https:/... | 29 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, ... | 255 | 0 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__a = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(),... | 30 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
_UpperCamelCase: str = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of ... | 255 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : int = {}
try:
if not is_sentencepiece_available():
... | 31 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: List[Any] = logging.get_logger(__name__)
_UpperCamelCase: int = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
... | 255 | 0 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
UpperCAmelCase_ : List[str] = 'scheduler_config.json'
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
... | 32 |
"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase: Tuple = logging.get_logger(__name__)
_Upper... | 255 | 0 |
"""simple docstring"""
import os
import numpy
import onnx
def lowercase ( __snake_case : Tuple , __snake_case : Union[str, Any] ):
lowercase_ : str = a.name
lowercase_ : List[str] = b.name
lowercase_ : List[str] ... | 33 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
'''simple docstring'''
_enforce_args(_UpperCAmelCase , _UpperCAmelCase )
if n == 0:
return 0
lowercase : Tuple = float('-inf' ... | 255 | 0 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def snake_case_ (_a : str , _a : float = 0.0 , _a : float = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
... | 34 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('Input value must be an \'int\' type' )
lowercase : str = 0
... | 255 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if n == 1 or not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return 0
elif n == 2:
return 1
else:
snake_case__ : Optional[int] = [0, 1]
fo... | 35 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase = None ) -> list[list[str]]:
'''simple docstring'''
lowercase : str = word_bank or []
# create a table
lowercase ... | 255 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase: List[str] = {}
try:
if not is_s... | 255 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_lowerCAmelCa... | 37 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> list[int]:
'''simple docstring'''
lowercase : Tuple = 0
lowercase : int = len(_UpperCAmelCase ) - 1
... | 255 | 0 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int ) -> bool:
"""simple docstring"""
UpperCamelCase :Optional[Any] = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int = 5000 ) -> int:... | 38 |
"""simple docstring"""
_UpperCamelCase: Dict = [
9_9_9,
8_0_0,
7_9_9,
6_0_0,
5_9_9,
5_0_0,
4_0_0,
3_9_9,
3_7_7,
3_5_5,
3_3_3,
3_1_1,
2_8_8,
2_6_6,
2_4_4,
2_2_2,
2_0_0,
1_9_9,
1_7_7,
1_5_5,
... | 255 | 0 |
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.... | 39 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_com... | 255 | 0 |
"""simple docstring"""
import argparse
import copy
def lowercase ( A_ )-> Optional[int]:
'''simple docstring'''
a : Optional[Any] = {}
with open(A_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neig... | 40 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_comm... | 255 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : str =logging.get_logger(__name__)
_A : int ={
'''... | 41 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class a__ ( unittest.TestCase ):
def lowercase ( se... | 255 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 1_000_000 ) -> int:
_snake_case = limit + 1
_snake_case = [0] * limit
for first_term in range(1 , __A ):
for n in range(__A , __A , __A ):
_snake_case = first_term + n / first_term
if commo... | 42 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase ) -> None:
'''simple docstring'''
lowercase : Union[str, Any] = generate_pascal_triangle(_UpperCAmelCase )
for row_idx in range(_UpperCAmelCase ):
# Print left spaces
... | 255 | 0 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase :str = 9, 14 # noqa: F841
__UpperCamelCase :Dict = [
[0, 1, 4],
... | 43 |
"""simple docstring"""
from collections.abc import Sequence
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase = False ) -> float:
'''simple docstring'''
if not arr:
return 0
lowercase : Tuple = 0 if allow_empty_suba... | 255 | 0 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from trans... | 44 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
'''simple docstring'''
lowercase : Optional[int] = len(_UpperCAmelCase ) + 1
lowercase : Any = len(_UpperCAmelCase ) + 1
# dp i... | 255 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerC... | 45 |
"""simple docstring"""
import math
def lowercase__ ( _UpperCAmelCase = 1_00 ) -> int:
'''simple docstring'''
lowercase : List[str] = sum(i * i for i in range(1 , n + 1 ) )
lowercase : Dict = int(math.pow(sum(range(1 ... | 255 | 0 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .sch... | 46 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTra... | 255 | 0 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowerCamelCase : Tuple = ""
lowerCamelCase : Union[str, Any] = ""
lowerCamelCase : Dict = ""
lowerCamelCase : str = 1 # (0 is vertical, 1 is horizo... | 47 |
"""simple docstring"""
def lowercase__ ( ) -> str:
'''simple docstring'''
lowercase : List[str] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(_UpperCAmelCase )[-10:]
if __name__ == "__main__":
pr... | 255 | 0 |
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None ) -> Tuple:
lowerCamelCase : Tuple = data
lowerCamelCase : str = previous... | 48 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers... | 255 | 0 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :Optional[int] = logging.get_logger(__name__)
__snake_case :Union[str, Any] = {
'''huggingface/autoformer-tourism-monthly''': '''https://huggingface... | 49 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMSchedule... | 255 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : str = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TimeSeriesTransformer... | 50 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfi... | 255 | 0 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGener... | 51 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, ... | 255 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONF... | 52 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
_UpperCamelCase: str = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of ... | 255 | 0 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
a__ : List[str] ='''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv p... | 53 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: List[Any] = logging.get_logger(__name__)
_UpperCamelCase: int = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
... | 255 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 54 |
"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase: Tuple = logging.get_logger(__name__)
_Upper... | 255 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
a_ : Any = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},... | 55 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
'''simple docstring'''
_enforce_args(_UpperCAmelCase , _UpperCAmelCase )
if n == 0:
return 0
lowercase : Tuple = float('-inf' ... | 255 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> Any:
'''simple docstring'''
snake_case_ = [0] * len(__UpperCAmelCase )
snake_case_ = []
snake_case_ = [1] * len(__UpperCAmelCase )
for values in graph.values():
for i ... | 56 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('Input value must be an \'int\' type' )
lowercase : str = 0
... | 255 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
def update_area_of_max_square(_UpperCamelCase , _UpperCamelCase ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__lower... | 57 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase = None ) -> list[list[str]]:
'''simple docstring'''
lowercase : str = word_bank or []
# create a table
lowercase ... | 255 | 0 |
'''simple docstring'''
from math import pi
def lowerCamelCase ( __lowerCamelCase : int , __lowerCamelCase : int ) ->float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 58 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase: List[str] = {}
try:
if not is_s... | 255 | 0 |
import math
def UpperCamelCase ( __lowerCamelCase : float , __lowerCamelCase : float ):
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial intensity
if ang... | 59 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> list[int]:
'''simple docstring'''
lowercase : Tuple = 0
lowercase : int = len(_UpperCAmelCase ) - 1
... | 255 | 0 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class snake_case_:
def __init__( self : str , UpperCamelCase_ : int=None , UpperCamelCase_ : List[str]=None ):
# Input as list
lowerCAmelCase : str = li... | 60 |
"""simple docstring"""
_UpperCamelCase: Dict = [
9_9_9,
8_0_0,
7_9_9,
6_0_0,
5_9_9,
5_0_0,
4_0_0,
3_9_9,
3_7_7,
3_5_5,
3_3_3,
3_1_1,
2_8_8,
2_6_6,
2_4_4,
2_2_2,
2_0_0,
1_9_9,
1_7_7,
1_5_5,
... | 255 | 0 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnode... | 61 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_com... | 255 | 0 |
from __future__ import annotations
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : tuple[int, int] , SCREAMING_SNAKE_CASE__ : int ):
__UpperCamelCase , __UpperCamelCase =position
__UpperCamelCase =[
(y + 1, x + 2),
(y - 1, x +... | 62 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_comm... | 255 | 0 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from tr... | 63 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class a__ ( unittest.TestCase ):
def lowercase ( se... | 255 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
S... | 64 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase ) -> None:
'''simple docstring'''
lowercase : Union[str, Any] = generate_pascal_triangle(_UpperCAmelCase )
for row_idx in range(_UpperCAmelCase ):
# Print left spaces
... | 255 | 0 |
import os
def lowerCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ = os.path.join(os.path.dirname(__A ), "num.txt" )
with open(__A ) as file_hand:
return str(sum(int(__A ) for line in fil... | 65 |
"""simple docstring"""
from collections.abc import Sequence
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase = False ) -> float:
'''simple docstring'''
if not arr:
return 0
lowercase : Tuple = 0 if allow_empty_suba... | 255 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def A_ ( _lowercase ):
'''simple docstring'''
... | 66 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
'''simple docstring'''
lowercase : Optional[int] = len(_UpperCAmelCase ) + 1
lowercase : Any = len(_UpperCAmelCase ) + 1
# dp i... | 255 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase ={
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapTextC... | 67 |
"""simple docstring"""
import math
def lowercase__ ( _UpperCAmelCase = 1_00 ) -> int:
'''simple docstring'''
lowercase : List[str] = sum(i * i for i in range(1 , n + 1 ) )
lowercase : Dict = int(math.pow(sum(range(1 ... | 255 | 0 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
lowerCAmelCase__ = """__DUMMY_TRANSFORMERS_USER__"""
lowerCAmelCase__ = """Dummy User"""
lowerCAmelCase__ = """hf_hZEmno... | 68 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTra... | 255 | 0 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__UpperCamelCase = datasets.utils.logging.get_logger(__name__)
class UpperCamelCase ( folder_based_builder.FolderBasedBuilderC... | 69 |
"""simple docstring"""
def lowercase__ ( ) -> str:
'''simple docstring'''
lowercase : List[str] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(_UpperCAmelCase )[-10:]
if __name__ == "__main__":
pr... | 255 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__ : str ={'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxC... | 70 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers... | 255 | 0 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def A ( a_ ,a_ ) -> Union[str, Any]:
__UpperCamelCase : List[Any] =... | 71 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMSchedule... | 255 | 0 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def snake_case_ ( A_ : str = "isbn/0140328726" ):
'''simple docstring'''
_lowerCamelCase : List[str] = olid.strip().str... | 72 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfi... | 255 | 0 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
fro... | 73 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, ... | 255 | 0 |
"""simple docstring"""
from typing import List
import numpy as np
def _snake_case ( snake_case__ : dict ):
A = {key: len(snake_case__ ) for key, value in gen_kwargs.items() if isinstance(snake_case__ , snake_case__ )}
if len(set(lists_lengths.values() ) ) > 1:
r... | 74 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
_UpperCamelCase: str = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of ... | 255 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __UpperCamelCase ( unittest.TestCase , lowerCamelCase__ ):
def lowercase__ ( self ):
"""simple docstring"""
... | 75 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: List[Any] = logging.get_logger(__name__)
_UpperCamelCase: int = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
... | 255 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( _a = 4):
SCREAMING_SNAKE_CASE : Optional[Any] = abs(_a) or 4
return [[1 + x + y * row_size for x in range(_a)] for y in range(_a)]
def lowerCamelCase__ ( _a):
return reverse_row(transpose(_a))
# OR.. transpo... | 76 |
"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase: Tuple = logging.get_logger(__name__)
_Upper... | 255 | 0 |
"""simple docstring"""
from collections.abc import Callable
class UpperCAmelCase_ :
def __init__( self , a = None ) -> None:
# Stores actual heap items.
lowercase__ : list = []
# Stores indexes of each item for supporting updates a... | 77 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
'''simple docstring'''
_enforce_args(_UpperCAmelCase , _UpperCAmelCase )
if n == 0:
return 0
lowercase : Tuple = float('-inf' ... | 255 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
snake_case_ = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
... | 78 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('Input value must be an \'int\' type' )
lowercase : str = 0
... | 255 | 0 |
'''simple docstring'''
from __future__ import annotations
lowerCamelCase_ = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __lowercase ( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , ) -> ... | 79 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase = None ) -> list[list[str]]:
'''simple docstring'''
lowercase : str = word_bank or []
# create a table
lowercase ... | 255 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _UpperCamelCase ( __A , __A ) -> list:
'''simple docstring'''
if len(__A ) != 2 or len(a[0] ) != 2 or len(__A ) != 2 or len(b[0] ) != 2:
raise Exception("Matric... | 80 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase: List[str] = {}
try:
if not is_s... | 255 | 0 |
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