code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69 | 1 |
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE = (KDPMaDiscreteScheduler,)
__S... | 69 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def __UpperCAmelCase ( _UpperCAm... | 69 | 1 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers imp... | 69 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple = {
'''huggingface/autoformer-tourism-monthly''': '''https:... | 69 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : List[Any] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 69 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a : List[Any] = get_tests_dir('''... | 69 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__snake_case =... | 69 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
... | 69 | 1 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a : List[Any] = get_tests_dir('''... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__snake_case =... | 69 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> list:
__snake_case = len(_UpperCAmelCase )
__snake_case = []
for i in range(len(_UpperCAmelCase ) - pat_len + 1 ):
__snake_case = True
... | 69 | 1 |
'''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,
)
a : Union[str, An... | 69 |
'''simple docstring'''
a : Dict = range(2, 20 + 1)
a : Optional[int] = [10**k for k in range(ks[-1] + 1)]
a : dict[int, dict[int, list[list[int]]]] = {}
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : D... | 69 | 1 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE = """"""
__SCREAMING_SNAKE_CASE... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : List[Any]=2_81_23 ) -> str:
__snake_case = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
... | 69 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def __UpperCAmelCase ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : bool , _UpperCAmelCase : list[int] , _UpperCAmelCase : float ) -> int:
if depth < 0:
... | 69 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
... | 69 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : list[int] , _UpperCAmelCase : int ) -> bool:
__snake_case = len(_UpperCAmelCase )
__snake_case = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum o... | 69 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Tuple , a_ : Optional[int]=None , a_ : int=None ):
"""simple docstring"""
__s... | 69 | 1 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __UpperCAmelCase ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any] ) -> str:... | 69 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : List[Any] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 69 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[Any] , a_ : int , a_ : MutableSequence[float] ):
"""simpl... | 69 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=Tr... | 69 | 1 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
a : List[str] = [
[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],
... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int = 1_00_00_00 ) -> int:
__snake_case = 1
__snake_case = 1
__snake_case = {1: 1}
for inputa in range(2 , _UpperCAmelCase ):
__snake_case = 0
__snake_case = inputa
... | 69 | 1 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMSche... | 69 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE = """SpeechT5FeatureExtractor"""
__SCREAMING_SNAKE_CASE = """SpeechT5Tokenizer"""
def __init__( self : List... | 69 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load... | 69 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def A ( self : Optional[Any] , a_ : str ):
""... | 69 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import Paddin... | 69 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a :... | 69 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a : Union[str, Any] = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHI... | 69 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers imp... | 69 | 1 |
'''simple docstring'''
import math
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> list:
__snake_case = [True] * n
__snake_case = False
__snake_case = False
__snake_case = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
_... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_st... | 69 | 1 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
a : str = TypeVar('''KEY''')
a : Tuple = TypeVar('''VAL''')
@dataclass(frozen=_UpperCamelCase , slots=_... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> str:
return " ".join(
"".join(word[::-1] ) if len(_UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.tes... | 69 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils ... | 69 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_att... | 69 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> int:
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError("String lengths must match!" )
__snake_case = 0
for chara, chara in z... | 69 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> Optional[int]:
... | 69 | 1 |
'''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 imp... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69 | 1 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_att... | 69 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def __UpperCAmelCase ( _UpperCAm... | 69 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Optional[Any] = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Gra... | 69 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple = {
'''huggingface/autoformer-tourism-monthly''': '''https:... | 69 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a : List[str] = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig'... | 69 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a : List[Any] = get_tests_dir('''... | 69 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , a_ : int , a_ : int , a_ : float = 0 ):
"""simple docstri... | 69 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
... | 69 | 1 |
'''simple docstring'''
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__snake_case =... | 69 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( _UpperCAmelCase : dict , _UpperCAmelCase : str ) -> set[str]:
__snake_case , __snake_case = set(_UpperCAmelCase ), [start]
while stack:
__snake_case = stack.pop(... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> list:
__snake_case = len(_UpperCAmelCase )
__snake_case = []
for i in range(len(_UpperCAmelCase ) - pat_len + 1 ):
__snake_case = True
... | 69 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
fro... | 69 |
'''simple docstring'''
a : Dict = range(2, 20 + 1)
a : Optional[int] = [10**k for k in range(ks[-1] + 1)]
a : dict[int, dict[int, list[list[int]]]] = {}
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : D... | 69 | 1 |
'''simple docstring'''
import os
import jsonlines
import numpy as np
from tqdm import tqdm
a : int = 2_048
a : Optional[int] = 4_096
a : Dict = 42
a : Optional[int] = os.environ.pop('''PROCESS_TRAIN''', '''false''')
a... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : List[Any]=2_81_23 ) -> str:
__snake_case = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
... | 69 | 1 |
'''simple docstring'''
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 TFModelTesterMix... | 69 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
... | 69 | 1 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
a : int = log... | 69 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Tuple , a_ : Optional[int]=None , a_ : int=None ):
"""simple docstring"""
__s... | 69 | 1 |
'''simple docstring'''
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 69 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : List[Any] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 69 | 1 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
a : List[Any] = '''\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pr... | 69 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=Tr... | 69 | 1 |
'''simple docstring'''
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dic... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int = 1_00_00_00 ) -> int:
__snake_case = 1
__snake_case = 1
__snake_case = {1: 1}
for inputa in range(2 , _UpperCAmelCase ):
__snake_case = 0
__snake_case = inputa
... | 69 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
fr... | 69 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE = """SpeechT5FeatureExtractor"""
__SCREAMING_SNAKE_CASE = """SpeechT5Tokenizer"""
def __init__( self : List... | 69 | 1 |
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VAL... | 69 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def A ( self : Optional[Any] , a_ : str ):
""... | 69 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_st... | 69 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a :... | 69 | 1 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transform... | 69 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers imp... | 69 | 1 |
'''simple docstring'''
import numpy as np
def __UpperCAmelCase ( _UpperCAmelCase : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
def __UpperCAmelCase ( _UpperCAmelCase : np.array ) -> np.array:
return vector * sigmoid(1.702 * vector... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_st... | 69 | 1 |
'''simple docstring'''
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> str:
return " ".join(
"".join(word[::-1] ) if len(_UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.tes... | 69 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMix... | 69 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_att... | 69 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a : Optional[int] = logging.get_logger(__name__)
a : Tuple ... | 69 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> Optional[int]:
... | 69 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
a : Any = TypeVar('''KT''')
a : Optional[int] = TypeVar('''VT''')
class SCREAMING_SNAKE_CASE__ ( Generic[KT, VT] ):
... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69 | 1 |
'''simple docstring'''
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
a : int = logging.get_logger(__name__)
a : Any = r'''
Arg... | 69 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def __UpperCAmelCase ( _UpperCAm... | 69 | 1 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def __init__( self : int , a_ : str="" , a_ : List[Any]="train" ):... | 69 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple = {
'''huggingface/autoformer-tourism-monthly''': '''https:... | 69 | 1 |
'''simple docstring'''
import cmath
import math
def __UpperCAmelCase ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> complex:
__snake_case = math.radians(_UpperCAmelCase )
__snake_cas... | 69 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a : List[Any] = get_tests_dir('''... | 69 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_tes... | 69 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
... | 69 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
a : List[Any] ... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__snake_case =... | 69 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("Input value must be an 'int' type" )
__snake_case = 0
while number:
position += 1
num... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> list:
__snake_case = len(_UpperCAmelCase )
__snake_case = []
for i in range(len(_UpperCAmelCase ) - pat_len + 1 ):
__snake_case = True
... | 69 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : list[int] ) -> list[list[int]]:
__snake_case = []
if len(_UpperCAmelCase ) == 1:
return [nums.copy()]
for _ in range(len(_UpperCAmelCase ) ):
__snake_case = nums.pop(0 )
... | 69 |
'''simple docstring'''
a : Dict = range(2, 20 + 1)
a : Optional[int] = [10**k for k in range(ks[-1] + 1)]
a : dict[int, dict[int, list[list[int]]]] = {}
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : D... | 69 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def __init__( self : Optional[int] , a_ : int , a_ : str ):
... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : List[Any]=2_81_23 ) -> str:
__snake_case = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
... | 69 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[Any] , a_ : Optional[Any]=2 , a_ ... | 69 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
... | 69 | 1 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
... | 69 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Tuple , a_ : Optional[int]=None , a_ : int=None ):
"""simple docstring"""
__s... | 69 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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_configuration_co... | 69 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : List[Any] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 69 | 1 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class SCREAMING_SNAKE_CASE__ ( tf.keras.layers.Layer ):
def __init__( self : str , a_ : str , a_ : Optional[int] , a_ : Optional[Any] , ... | 69 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=Tr... | 69 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a : int = ge... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int = 1_00_00_00 ) -> int:
__snake_case = 1
__snake_case = 1
__snake_case = {1: 1}
for inputa in range(2 , _UpperCAmelCase ):
__snake_case = 0
__snake_case = inputa
... | 69 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTok... | 69 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE = """SpeechT5FeatureExtractor"""
__SCREAMING_SNAKE_CASE = """SpeechT5Tokenizer"""
def __init__( self : List... | 69 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : int ) -> float:
if principal <= 0:
raise Exception("Principal borrowed must be > 0" )
if rate_per_annum < 0:
raise Exception("Rate... | 69 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def A ( self : Optional[Any] , a_ : str ):
""... | 69 | 1 |
'''simple docstring'''
from typing import 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 BatchF... | 69 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a :... | 69 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMod... | 69 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers imp... | 69 | 1 |
'''simple docstring'''
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,
WEIG... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_st... | 69 | 1 |
'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from ... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> str:
return " ".join(
"".join(word[::-1] ) if len(_UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.tes... | 69 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, f... | 69 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_att... | 69 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int = 50 ) -> int:
__snake_case = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_len... | 69 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> Optional[int]:
... | 69 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniza... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69 | 1 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a : int = '''__DUMMY_TRANSFORMERS_USER__'''
a : str = '''Dummy User'''
a ... | 69 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def __UpperCAmelCase ( _UpperCAm... | 69 | 1 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pi... | 69 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple = {
'''huggingface/autoformer-tourism-monthly''': '''https:... | 69 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device... | 69 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a : List[Any] = get_tests_dir('''... | 69 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
... | 69 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
... | 69 | 1 |
'''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 (
... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__snake_case =... | 69 | 1 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def A ( self : Optional[Any] , a_ : str ):
""... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> list:
__snake_case = len(_UpperCAmelCase )
__snake_case = []
for i in range(len(_UpperCAmelCase ) - pat_len + 1 ):
__snake_case = True
... | 69 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class SCREAMING_SNAKE_CASE__ :
__SCREAMING_SNAKE_CASE = 42 # [batch_size x 3]
__SCREAMING_SNAKE_CASE = 42 # [batch_size x 3]
__SCREAMING_SNAKE... | 69 |
'''simple docstring'''
a : Dict = range(2, 20 + 1)
a : Optional[int] = [10**k for k in range(ks[-1] + 1)]
a : dict[int, dict[int, list[list[int]]]] = {}
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : D... | 69 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : List[str] = {}
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : List[Any]=2_81_23 ) -> str:
__snake_case = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
... | 69 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class SC... | 69 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
... | 69 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMI... | 69 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Tuple , a_ : Optional[int]=None , a_ : int=None ):
"""simple docstring"""
__s... | 69 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE = """SpeechT5FeatureExtractor"""
__SCREAMING_SNAKE_CASE = """SpeechT5Tokenizer"""
def __init__( self : List... | 69 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : List[Any] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 69 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils impo... | 69 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=Tr... | 69 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Tuple = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class SCR... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int = 1_00_00_00 ) -> int:
__snake_case = 1
__snake_case = 1
__snake_case = {1: 1}
for inputa in range(2 , _UpperCAmelCase ):
__snake_case = 0
__snake_case = inputa
... | 69 | 1 |
import random
def __lowercase ( snake_case, snake_case, snake_case ):
"""simple docstring"""
__magic_name__ :Tuple = a[left_index]
__magic_name__ :str = left_index + 1
for j in range(left_index + 1, snake_case ):
if a[j] < pivot:
__magic_n... | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE = """SpeechT5FeatureExtractor"""
__SCREAMING_SNAKE_CASE = """SpeechT5Tokenizer"""
def __init__( self : List... | 69 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import... | 1 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def A ( self : Optional[Any] , a_ : str ):
""... | 69 | 0 |
import unittest
import numpy as np
from datasets import load_dataset
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_in... | 2 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a :... | 69 | 0 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAm... | 3 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers imp... | 69 | 0 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[Any] ):
lowerCAmelCase = args.pruning_method
lowerCAmelCase = args.thresho... | 4 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_st... | 69 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():... | 5 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> str:
return " ".join(
"".join(word[::-1] ) if len(_UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.tes... | 69 | 0 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: str = "AAPL" ):
SCREAMING_SNAKE_CASE__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
SCREAMING_SNAKE_CASE__ = BeautifulSoup(requests.get(UpperCamelCase__ ... | 6 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_att... | 69 | 0 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''vocab_file''': '''vocab.json''',
... | 7 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> Optional[int]:
... | 69 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...te... | 8 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69 | 0 |
import cva
import numpy as np
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , _snake_case : float , _snake_case : int ):
"""simple docstring"""
if k in (0.04, 0.06):
... | 9 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def __UpperCAmelCase ( _UpperCAm... | 69 | 0 |
def _snake_case ( __snake_case = 10**12 ):
_UpperCamelCase = 1
_UpperCamelCase = 0
_UpperCamelCase = 1
_UpperCamelCase = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_num... | 10 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple = {
'''huggingface/autoformer-tourism-monthly''': '''https:... | 69 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowercase_ = logging.get_logger(__name__)
class __A ( A ):
'''simple docstring'''
def __init__(self , *A , **A ) -> None:
"... | 11 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a : List[Any] = get_tests_dir('''... | 69 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configurat... | 12 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
... | 69 | 0 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def UpperCAmelCase__ ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : str , ... | 13 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__snake_case =... | 69 | 0 |
def __UpperCAmelCase ( __a : int ,__a : int ) -> str:
"""simple docstring"""
if not isinstance(__a ,__a ):
raise ValueError('''iterations must be defined as integers''' )
if not isinstance(__a ,__a ) or not number >= 1:
... | 14 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> list:
__snake_case = len(_UpperCAmelCase )
__snake_case = []
for i in range(len(_UpperCAmelCase ) - pat_len + 1 ):
__snake_case = True
... | 69 | 0 |
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_available():
import jax.numpy as jn... | 15 |
'''simple docstring'''
a : Dict = range(2, 20 + 1)
a : Optional[int] = [10**k for k in range(ks[-1] + 1)]
a : dict[int, dict[int, list[list[int]]]] = {}
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : D... | 69 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
... | 16 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : List[Any]=2_81_23 ) -> str:
__snake_case = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
... | 69 | 0 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase_ ( _lowercase , unittest.TestCas... | 17 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
... | 69 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_bi... | 18 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Tuple , a_ : Optional[int]=None , a_ : int=None ):
"""simple docstring"""
__s... | 69 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _Upper... | 19 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : List[Any] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 69 | 0 |
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