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 |
|---|---|---|---|---|
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : complex , lowerCamelCase_ : str = "x" , lowerCamelCase_ : float = 10**-10 , lowerCamelCase_ : ... | 105 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**s... | 105 | 1 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = len(lowerCamelCase_ ) // 2
# choose the middle 3 elements
SCREAMING_SNAKE_CASE_ ... | 105 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[float] ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = 0.0_0
SCREAMING_SNAKE_CASE_ : Tuple = 0
for resistor in resistors:
if... | 105 | 1 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def __UpperCAmelCase ( lowerCamelCase_ : dict ) -> tu... | 105 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Any , lo... | 105 | 1 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ,snake_case__=sys.... | 105 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 105 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake_case_... | 105 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
'''configuration_whisper''': ['''WHISPER_PRETRAINE... | 105 | 1 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
UpperCamelCase__ : str =... | 105 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , ... | 105 | 1 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCa... | 105 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> bool:
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a Perfect number ... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE_ : Dict = 1... | 105 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401... | 105 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = list_of_points
# Degree determines the flexibility of the curve.
... | 105 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
... | 105 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez im... | 105 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tenso... | 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 105 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : Any , lowerCamelCase_ : Any ... | 105 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 105 | 1 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCamelCase__ : Any = logging.get_logg... | 105 |
import os
import numpy
import onnx
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = a.name
SCREAMING_SNAKE_CASE_ : Dict = ... | 105 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ : List[Any] = get_tests_... | 105 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : Option... | 105 | 1 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 105 |
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 TFModelTesterMixin, floats_tensor, ids_tenso... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> int:
"""simple docstring"""
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE_ : List[str] = F'Input value of [number={number}] must be an integer'
raise Type... | 105 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['d... | 105 | 1 |
from typing import Any
import numpy as np
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray ) -> bool:
"""simple docstring"""
return np.array_equal(lowerCamelCase_ , matrix.conjugate().T )
def __UpperCAmelCase ( lowerCamelCase_ ... | 105 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 105 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : List[Any] = {
'''roberta-base''... | 105 |
import os
import sys
import unittest
UpperCamelCase__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dumm... | 105 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase_ ( lowerCamelCase_ ):
__a : Any = ["image_processor", "tokenizer"]
__a : Tuple = "ChineseCLIPImageProces... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tup... | 105 | 1 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCamelCase__ : List[str] = '''src/diffusers'''
# Matches is_xxx_available()
UpperCamelCase__ : Optional[int] = ... | 105 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
Fla... | 105 | 1 |
import math
import qiskit
def __UpperCAmelCase ( lowerCamelCase_ : int = 1 , lowerCamelCase_ : int = 1 , lowerCamelCase_ : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(lowerCamelCase_ , low... | 105 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, ... | 105 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = list_of_points
# Degree determines the flexibility of the curve.
... | 105 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.mo... | 105 | 1 |
from __future__ import annotations
class lowerCAmelCase_ :
def __init__( self ,snake_case__ = 0 ):
SCREAMING_SNAKE_CASE_ : Optional[int] = key
def snake_case ( self ,snake_case__ ,snake_case__ ):
assert isinstance(snake_case__ ... | 105 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**s... | 105 | 1 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as... | 105 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[float] ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = 0.0_0
SCREAMING_SNAKE_CASE_ : Tuple = 0
for resistor in resistors:
if... | 105 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : Any = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBli... | 105 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Any , lo... | 105 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_ut... | 105 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 105 | 1 |
from math import factorial, pi
def __UpperCAmelCase ( lowerCamelCase_ : float , lowerCamelCase_ : int = 30 ) -> float:
"""simple docstring"""
if not isinstance(lowerCamelCase_ , (int, float) ):
raise ValueError('maclaurin_sin() requires ... | 105 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
'''configuration_whisper''': ['''WHISPER_PRETRAINE... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> str:
"""simple docstring"""
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(lowerCamelCase_ , lowerCam... | 105 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , ... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
SCREAMING_SNAKE_CASE_ : list = []
for temp in range(int(lowerCamelCase_ ) ):
series.append(F'1/{temp + 1}' if series else '1' )... | 105 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake... | 105 | 1 |
import os
import sys
import unittest
UpperCamelCase__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dumm... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE_ : Dict = 1... | 105 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['d... | 105 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = list_of_points
# Degree determines the flexibility of the curve.
... | 105 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase__ : List[Any] = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCamelCase__ : List[str] ... | 105 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez im... | 105 | 1 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCAmelCase_ :
def __init__( self ,snake_case__=2 ,snake_case__=3 ,snake_case__=64 ,snake_case__=None ):
SCREAMING_SNAKE... | 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 105 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
UpperCamelCase__ : int = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
# This is th... | 105 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 105 | 1 |
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,
)
from . import Bas... | 105 |
import os
import numpy
import onnx
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = a.name
SCREAMING_SNAKE_CASE_ : Dict = ... | 105 | 1 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
def __UpperCAmelCase ( lowerCamelCase_ : Tup... | 105 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : Option... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : int = 10_00 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 3
SCREAMING_SNAKE_CASE_ : List[str] = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif... | 105 |
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 TFModelTesterMixin, floats_tensor, ids_tenso... | 105 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : ... | 105 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['d... | 105 | 1 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCam... | 105 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 105 | 1 |
import unittest
from transformers import BigBirdConfig, 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
from transformers.models.big_bird.... | 105 |
import os
import sys
import unittest
UpperCamelCase__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dumm... | 105 | 1 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
f... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tup... | 105 | 1 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
... | 105 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
Fla... | 105 | 1 |
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 lowerCAmelCase_ ( lowerCamelCase_ ):
... | 105 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, ... | 105 | 1 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def __UpperCAmelCase ( ) ... | 105 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.mo... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> int:
"""simple docstring"""
assert isinstance(lowerCamelCase_ , lowerCamelCase_ ), F'The input value of [n={number}] is not an integer'
if number == 1:
return 2
elif number < 1:
SCREAMING_SN... | 105 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**s... | 105 | 1 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[float] ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = 0.0_0
SCREAMING_SNAKE_CASE_ : Tuple = 0
for resistor in resistors:
if... | 105 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[float] ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = 0.0_0
SCREAMING_SNAKE_CASE_ : Tuple = 0
for resistor in resistors:
if... | 105 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCamelCase__ : str = logging.get_logger(__name__)
UpperCamelCase__ : str = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json... | 105 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Any , lo... | 105 | 1 |
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))'''))
| 105 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 105 | 1 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
UpperCamelCase__ : str = False
UpperCamelCase__ : List[str] = True
UpperCamelCase__ : Optional[Any] = False
if __name_... | 105 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
'''configuration_whisper''': ['''WHISPER_PRETRAINE... | 105 | 1 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
UpperCamelCase__ : Optional[int] = logging.getLogger()
def __UpperCAmelC... | 105 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , ... | 105 | 1 |
from manim import *
class lowerCAmelCase_ ( lowerCamelCase_ ):
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : List[Any] = Rectangle(height=0.5 ,width=0.5 )
SCREAMING_SNAKE_CASE_ : Optional[Any] = Rectangle(height=0.46 ,wid... | 105 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : int = 1_00_00_00 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = 1
SCREAMING_SNAKE_CASE_ : str = 1
SCREAMING_SNAKE_CASE_ : Tuple = {1: 1}
for inputa in rang... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE_ : Dict = 1... | 105 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Any , lo... | 105 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = list_of_points
# Degree determines the flexibility of the curve.
... | 105 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : Optional[int] = logging.get_logger(__name__)
UpperCamelCase__ : Union[str, Any] = {
'''... | 105 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez im... | 105 | 1 |
import math
def __UpperCAmelCase ( lowerCamelCase_ : float , lowerCamelCase_ : float ) -> float:
"""simple docstring"""
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values... | 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 105 | 1 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ... | 105 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 105 | 1 |
from math import pow
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int , ) -> tuple[int, int]:
"""simple docstring"""
if ... | 105 |
import os
import numpy
import onnx
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = a.name
SCREAMING_SNAKE_CASE_ : Dict = ... | 105 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : Union[str, Any] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise... | 105 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : Option... | 105 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase__ : Union[str, Any] = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DP... | 105 |
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 TFModelTesterMixin, floats_tensor, ids_tenso... | 105 | 1 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import req... | 105 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['d... | 105 | 1 |
import argparse
import json
import subprocess
def __UpperCAmelCase ( lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = []
SCREAMING_SNAKE_CASE_ ... | 105 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 105 | 1 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configu... | 105 |
import os
import sys
import unittest
UpperCamelCase__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dumm... | 105 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipeline... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tup... | 105 | 1 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
U... | 105 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
Fla... | 105 | 1 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowerCAm... | 105 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, ... | 105 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ... | 105 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.mo... | 105 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowerCAmelCase_ ( lowerC... | 105 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**s... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_d... | 105 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[float] ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = 0.0_0
SCREAMING_SNAKE_CASE_ : Tuple = 0
for resistor in resistors:
if... | 105 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez im... | 105 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Any , lo... | 105 | 1 |
from typing import Any
def __UpperCAmelCase ( lowerCamelCase_ : list ) -> list[Any]:
"""simple docstring"""
if not input_list:
return []
SCREAMING_SNAKE_CASE_ : Any = [input_list.count(lowerCamelCase_ ) for value in input_list]
SCREAMIN... | 105 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 105 | 1 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : str = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase__ : Any = {
'''facebook/esm-1b''': '''http... | 105 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
'''configuration_whisper''': ['''WHISPER_PRETRAINE... | 105 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : Option... | 105 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , ... | 105 | 1 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__ : Tuple = 10
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCa... | 105 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake... | 105 | 1 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __UpperCAmelCase ( ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
SC... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE_ : Dict = 1... | 105 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAva... | 105 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = list_of_points
# Degree determines the flexibility of the curve.
... | 105 | 1 |
UpperCamelCase__ : Optional[Any] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __UpperCAmelCase ( lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : ... | 105 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez im... | 105 | 1 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_to... | 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 105 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird i... | 105 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : int = 10 , lowerCamelCase_ : int = 10_00 , lowerCamelCase_ : bool = True ) -> int:
"""simple docstring"""
assert (
isinstance(lowerCamelCase_ , lowerCamelCase_ )
and isinstance(lower... | 105 |
import os
import numpy
import onnx
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = a.name
SCREAMING_SNAKE_CASE_ : Dict = ... | 105 | 1 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> Tuple:
"""simple docstring"""
SCR... | 105 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : Option... | 105 | 1 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = value
SCREAMING_SNAKE_CASE_ : Node | None = None
SCREAMING_... | 105 |
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 TFModelTesterMixin, floats_tensor, ids_tenso... | 105 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str , **lowerCamelCase_ : List[str] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNA... | 105 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['d... | 105 | 1 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLay... | 105 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/ma... | 105 | 1 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowerCAmelCase_ ( nn.Module ):
__a : int
__a : int
__a : float ... | 105 |
import os
import sys
import unittest
UpperCamelCase__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dumm... | 105 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : str ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ord(lowerCamelCase_ )
SCREAMING_SNAKE_CASE_ : Tup... | 105 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 105 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
Fla... | 105 | 1 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_availabl... | 105 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, ... | 105 | 1 |
import math
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> list:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = [True] * n
SCREAMING_SNAKE_CASE_ : Any = False
SCREAMING_SNAKE_CASE_ : List[Any] = False
SCRE... | 105 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.mo... | 105 | 1 |
import os
import numpy
import onnx
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = a.name
SCREAMING_SNAKE_CASE_ : Dict = ... | 105 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**s... | 105 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase__ : Dict = logging.getLogger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def ... | 105 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[float] ) -> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = 0.0_0
SCREAMING_SNAKE_CASE_ : Tuple = 0
for resistor in resistors:
if... | 105 | 1 |
from maths.prime_check import is_prime
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> int:
"""simple docstring"""
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
SCREAMING_SNAKE_CASE_ : Dict = F'Input value of [number={number... | 105 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Any , lo... | 105 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 105 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 105 | 1 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
UpperCamelCase__ : List[Any] = importlib.util.find_spec('''s3fs''') is not None
if _has_safs:
from .safil... | 105 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
'''configuration_whisper''': ['''WHISPER_PRETRAINE... | 105 | 1 |
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 import (
AlbertTokenizer,
AutoTokeniz... | 105 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCAmelCase ( lowerCamelCase_ : np.ndarray , lowerCamelCase_ : np.ndarray ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , ... | 105 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __UpperCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : float = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
SCREA... | 105 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake... | 105 | 1 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : list[str] | None = None , lowerCamelCase_ : dict[str, float] | None = None , lowerCamelCase_ : bool = False , ) -> tuple[int, float, str]:
... | 105 |
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE_ : Dict = 1... | 105 | 1 |
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 lowerCAmelCase_ ( lowerCamelCas... | 105 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowerCAmelCase_ :
def __init__( self ,snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = list_of_points
# Degree determines the flexibility of the curve.
... | 105 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(... | 105 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez im... | 105 | 1 |
from __future__ import annotations
import typing
from collections import Counter
def __UpperCAmelCase ( lowerCamelCase_ : int ) -> typing.Counter[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : typing.Counter[int] = Counter()
for base in ra... | 105 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Optional[int] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}... | 105 | 1 |
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 TFModelTesterMixin, floats_tensor, ids_tenso... | 105 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 105 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.