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 |
|---|---|---|---|---|
def lowerCAmelCase_ ( __A ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ = []
UpperCAmelCase__ = []
UpperCAmelCase__ = {
"^": 3,
"*": 2,
"/": 2,
"%": 2,
"+": 1,
... | 486 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase (__lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase_ = ["image_processor", "tokenizer"]
UpperC... | 663 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCamelCase ( __lowerCamelCase , __lowerCamelCase ):
@register_to_config
def __init__( sel... | 462 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCamelCase : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 663 | 0 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class _lowerCamelCase ( __lowerCamelCase ... | 590 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_... | 663 | 0 |
def _lowercase ( __lowerCamelCase : list ,__lowerCamelCase : list ,__lowerCamelCase : int ) -> int:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE__ ) != len(SCREAMING_SNAKE_CASE__ ):
raise ValueError('''The length of prof... | 344 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
# TODO: upload to AWS
_lowerCamelCase : str = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co... | 663 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
fro... | 633 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import loa... | 663 | 0 |
'''simple docstring'''
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""... | 665 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 663 | 0 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class __a ( __lowerCamelCase ):
__snake_case : List[str] = ["""input_id... | 600 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowerCamelCase : List[str] = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization... | 663 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : List[str] = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPCon... | 121 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobert... | 663 | 0 |
'''simple docstring'''
from PIL import Image
def snake_case_ ( _lowerCAmelCase : Image , _lowerCAmelCase : int ) -> Image:
UpperCAmelCase : List[str] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCAmelCase : int ... | 127 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowerCamelCase (__lowerCamelCase ):
"""... | 663 | 0 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _lowercase ( ):
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as orig... | 131 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Tuple = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig'''... | 663 | 0 |
'''simple docstring'''
from __future__ import annotations
__SCREAMING_SNAKE_CASE = 1_0
def __a ( lowerCAmelCase__ : list[int] ):
a__ : Optional[Any] = 1
a__ : Union[str, Any] = max(SCREAMING_SNAKE_CASE__ )
while placement <= max_... | 688 |
import os
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
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCa... | 663 | 0 |
def lowerCAmelCase_ ( __A, __A ) -> float:
'''simple docstring'''
def get_matched_characters(__A, __A ) -> str:
UpperCAmelCase__ = []
UpperCAmelCase__ = min(len(_stra ), len(_stra ) ) // 2
for ... | 486 |
from random import shuffle
import tensorflow as tf
from numpy import array
def _a ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] ... | 663 | 0 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
""" Distillat... | 462 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'''The ... | 663 | 0 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils im... | 590 |
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 import TokenizerTesterM... | 663 | 0 |
_SCREAMING_SNAKE_CASE : Optional[Any] = '''Tobias Carryer'''
from time import time
class UpperCamelCase__ :
def __init__( self : int, __lowerCamelCase : Any, __lowerCamelCase : Dict, __lowerCamelCase : str, __lowerCamelCase : int=int(time() ) ) ... | 344 |
from functools import lru_cache
def _a ( SCREAMING_SNAKE_CASE__ : int ) -> set:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = set()
while i *... | 663 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( ) ->Optional[Any]:
'''simple docstring'''
a : int = 0
for i in range(1 , 1001 ):
total += i**i
return str(SCREAMING_SNAKE_CASE__ )[-10:]
if __name__ == "__main__":
... | 633 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCamelCase (unittest.TestCase ):
"... | 663 | 0 |
'''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__magic_name__ = 299_792_458
# Symbols
__magic_name__ = symbols('ct x y z')
def lowerCamelCase ( lowerCamelCase : float):
if velocity > c:
... | 665 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase : List[str] = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 663 | 0 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 600 |
from collections.abc import Callable
import numpy as np
def _a ( SCREAMING_SNAKE_CASE__ : Callable , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float ) -> ... | 663 | 0 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_uti... | 121 |
def _a ( SCREAMING_SNAKE_CASE__ : List[Any]=2_81_23 ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
... | 663 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
Wav... | 127 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED... | 663 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def _lowercase ( lowerCamelCase__ : int = 1_500_000 ):
_a = defaultdict(SCREAMING_SNAKE_CASE__ )
_a = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in... | 131 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common... | 663 | 0 |
'''simple docstring'''
import os
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
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAM... | 688 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCamelCase (__lowerCamelCase ):
... | 663 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase__ = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}
try:
if not is_tor... | 486 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase (__lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase_ = ["image_processor", "tokenizer"]
UpperC... | 663 | 0 |
lowerCAmelCase = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
lowerCAmelCase = [{'''ty... | 462 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCamelCase : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 663 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.... | 590 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_... | 663 | 0 |
def _lowercase ( __lowerCamelCase : Optional[Any] ,__lowerCamelCase : List[str] ) -> Any:
'''simple docstring'''
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(SCREAMING_SNAKE_CASE__ ):
for j... | 344 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
# TODO: upload to AWS
_lowerCamelCase : str = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co... | 663 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch... | 633 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import loa... | 663 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : list , lowerCamelCase : list , lowerCamelCase : int):
A_ : Any = len(SCREAMING_SNAKE_CASE__)
A_ : Union[str, Any] = [[0] * n for i in range(SCREAMING_SNAKE_CASE__)]
for i in range(... | 665 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 663 | 0 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as or... | 600 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowerCamelCase : List[str] = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization... | 663 | 0 |
from __future__ import annotations
from typing import Any
class snake_case__ :
'''simple docstring'''
def __init__( self : Dict , lowerCAmelCase_ : int = 6 ) -> None:
UpperCAmelCase_ = None
Uppe... | 121 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobert... | 663 | 0 |
'''simple docstring'''
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme... | 127 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowerCamelCase (__lowerCamelCase ):
"""... | 663 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def _lowercase ( lowerCamelCase__ : Any, lowerCamelCase__ : Tuple, lowerCamelCase__ : List[str], lowerCamelCase__ : Tuple ):
_a = ... | 131 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Tuple = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig'''... | 663 | 0 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipe... | 688 |
import os
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
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCa... | 663 | 0 |
from __future__ import annotations
import os
from collections.abc import Mapping
UpperCamelCase__ = tuple[int, int]
class A :
def __init__(self : Any , __UpperCAmelCase : set[int] , __UpperCAmelCase : Mapping[EdgeT, int] ) -> None:
"""simple d... | 486 |
from random import shuffle
import tensorflow as tf
from numpy import array
def _a ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] ... | 663 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:
if not is_torch_available():
... | 462 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'''The ... | 663 | 0 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
if len(SCREAMING_SNAKE_CASE__ ) == 0:
return False
A_ : str = len(SCREAMING_SNAKE_CASE__ ) // 2
if a_list[midpoint] == item:
return True
if it... | 590 |
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 import TokenizerTesterM... | 663 | 0 |
def _lowercase ( __lowerCamelCase : int ,__lowerCamelCase : int ,__lowerCamelCase : int ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase__ : Union[str, Any] =... | 344 |
from functools import lru_cache
def _a ( SCREAMING_SNAKE_CASE__ : int ) -> set:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = set()
while i *... | 663 | 0 |
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info... | 633 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCamelCase (unittest.TestCase ):
"... | 663 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configur... | 665 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase : List[str] = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 663 | 0 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__UpperC... | 600 |
from collections.abc import Callable
import numpy as np
def _a ( SCREAMING_SNAKE_CASE__ : Callable , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float ) -> ... | 663 | 0 |
class snake_case__ :
'''simple docstring'''
def __init__( self : List[Any] ) -> Any:
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
UpperCAmelCase_ = {}
def UpperCamelCase ( ... | 121 |
def _a ( SCREAMING_SNAKE_CASE__ : List[Any]=2_81_23 ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
... | 663 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def snake_... | 127 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Optional[Any] = {
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED... | 663 | 0 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class A ( __lowerCamelCase ):
__UpperCAmelCase ... | 131 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common... | 663 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils... | 688 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCamelCase (__lowerCamelCase ):
... | 663 | 0 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, t... | 486 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase (__lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase_ = ["image_processor", "tokenizer"]
UpperC... | 663 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowerCAmelCase = ''''''
lowerCAmelCase = ''''''
lowerCAmelCase = ''''''
lowerCAmelCase = 1 # (0 is vertical, 1 is horizontal)
def __SCREAMING_SNAKE_CASE ( ) -> ... | 462 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCamelCase : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 663 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class _lowerCamelCase ( __lowerCamelCase ):
"""simple docstrin... | 590 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_... | 663 | 0 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Optional[int] = argparse.ArgumentParser()
parser.add_argument("""--dump_path"""... | 344 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
# TODO: upload to AWS
_lowerCamelCase : str = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co... | 663 | 0 |
"""simple docstring"""
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class __UpperCamelCase :
... | 633 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import loa... | 663 | 0 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __lowerCAmelCase :
'''simple docstring'''
pass
| 665 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_co... | 663 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCamelCase )
class __a ( __lowerCamelCase ):
__snake_case : Dict = field(default="""automatic-spe... | 600 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowerCamelCase : List[str] = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization... | 663 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common i... | 121 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobert... | 663 | 0 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .uti... | 127 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowerCamelCase (__lowerCamelCase ):
"""... | 663 | 0 |
'''simple docstring'''
from math import factorial, radians
def _lowercase ( lowerCamelCase__ : float, lowerCamelCase__ : int = 18, lowerCamelCase__ : int = 10 ):
_a = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
... | 131 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Tuple = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig'''... | 663 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 688 |
import os
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
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCa... | 663 | 0 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def lowerCAmelCase_ ( __A ) -> str... | 486 |
from random import shuffle
import tensorflow as tf
from numpy import array
def _a ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] ... | 663 | 0 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
fr... | 462 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'''The ... | 663 | 0 |
import socket
def _SCREAMING_SNAKE_CASE ( ):
A_ : List[Any] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
A_ : str = socket.gethostname()
A_ : Tuple = 12_312
sock.connect((host, port) )
sock.send(B'''Hello server!''' )
with ... | 590 |
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 import TokenizerTesterM... | 663 | 0 |
def _lowercase ( __lowerCamelCase : int = 1000 ) -> int:
'''simple docstring'''
UpperCamelCase__ : Any = -1
UpperCamelCase__ : Any = 0
for a in range(1 ,n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N ... | 344 |
from functools import lru_cache
def _a ( SCREAMING_SNAKE_CASE__ : int ) -> set:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = set()
while i *... | 663 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : str = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try... | 633 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCamelCase (unittest.TestCase ):
"... | 663 | 0 |
'''simple docstring'''
def __snake_case ( ):
'''simple docstring'''
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(lowerCamelCase_ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":... | 664 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 664 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenizat... | 664 |
'''simple docstring'''
import numpy
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : numpy.ndarray , _lowerCamelCase : numpy.ndarray ) -> None:
__magic_name__ ... | 664 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : int ={
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig',
... | 664 |
'''simple docstring'''
import torch
from transformers import AutoModel
class UpperCamelCase_ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Any , _lowerCamelCase : Optional[int]="sayef/fsner-bert-base-uncased" ) -> List[An... | 664 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class UpperCamelCase_ ( A ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = '''MCTCTFeatureExtractor'''
UpperCAmelCase... | 664 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noq... | 664 | 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.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.TestCa... | 664 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __snake_case ( lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int ):
'''simple docstring'''
__magic_name__ = int(lowerCamelCase_ )
if decimal in (0, 1): # Exit cases for the recursion
return str(lowerCamelCase_ )
__magic_name__ , __magic_name__ ... | 664 |
'''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
class UpperCamelCase_... | 664 | 1 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
... | 664 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threaded... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int = 1000 ):
'''simple docstring'''
__magic_name__ , __magic_name__ = 1, 1
__magic_name__ = 2
while True:
__magic_name__ = 0
__magic_name__ = fa + fa
__mag... | 664 |
'''simple docstring'''
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
fr... | 664 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__magic_name__ : List[Any] ={
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetCo... | 664 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__magic_name__ = str(bin(lowerCamelCase_ ) )[... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : List[Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __snake_case ( lowerCamelCase_ : Union[str, Any] ... | 664 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingf... | 664 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
UpperCAmelCase__ : Optional[Union[str, Path]] = None
UpperCAmelCase__ ... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Union[str, Any] ={'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
... | 664 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase_ ( metaclass=A ):
"""simple docstring"""
UpperCAmelCase__ : int = ['''torch''']
def __init__( self : Tuple , *_lowerCamelCase : int... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : Optional[Any] ={
'configuration_longformer': [
'LONGFORMER_PRETRAINED_CON... | 664 | 1 |
'''simple docstring'''
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 (
MaxLen... | 664 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
__magic_name__ : str ={
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Ima... | 664 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__magic_name__ : str =logging.get_logger(__name__)
class UpperCamelCase_ ( A ):
"""simple docstring"""
def __init__( self : Union[str, Any] ... | 664 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import... | 664 | 1 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def __snake_case ( lowerCamelCase_ : int ):
'''simple docstring'''
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if ... | 664 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( lowerCamelCase_ : list[int] , lowerCamelCase_ : int ):
'''simple docstring'''
if len(lowerCamelCase_ ) < k or k < 0:
raise ValueError("Invalid Input" )
__magic_name__ ... | 664 | 1 |
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def __snake_... | 664 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : int =logging.get_logger(__name__)
__magic_name__ : List[Any] ={}
class UpperCamelCase_ ( A ):
"""simple docstring"""
UpperCAmelCase__ : in... | 664 | 1 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
__magic_name__ : Optional[int] ='\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\... | 664 |
'''simple docstring'''
__magic_name__ : Dict =8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def __snake_case ( lowerCamelCase_ : float , lowerCamelCase_ : float , lowerCamelCase_ : float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
... | 664 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is... | 664 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__magic_name__ : List[Any] =logging.getLogger(__name__)
class UpperCamelCase_ ( A ):
"""simple docst... | 664 | 1 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fr... | 664 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : int , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : ... | 664 | 1 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, 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
from ...test_modeling_common impor... | 664 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__magic_na... | 664 | 1 |
'''simple docstring'''
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__magic_name__ : Optional[Any] =Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # ... | 664 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.TestCa... | 664 | 1 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( lowerCamelCase_ : tuple[int, int] , lowerCamelCase_ : int ):
'''simple docstring'''
__magic_name__ , __magic_name__ = position
__magic_name__ = [
(y + 1, x + 2)... | 664 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from tr... | 664 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class UpperCamelCase_ ( A ):
"""simple docstring"""
def __init__( self : Any , _lowerCamelCase : int , _lowerCamelCase ... | 664 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 664 | 1 |
'''simple docstring'''
from collections import namedtuple
__magic_name__ : List[str] =namedtuple('from_to', 'from_ to')
__magic_name__ : Tuple ={
'cubicmeter': from_to(1, 1),
'litre': from_to(0.0_0_1, 10_00),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_0_4_5_4, 2_6_4.1_7_2),
'cubi... | 664 |
'''simple docstring'''
import numpy
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Union[str, Any] , _lowerCamelCase : numpy.ndarray , _lowerCamelCase : numpy.ndarray ) -> None:
__magic_name__ ... | 664 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase_ ( A ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = ['''image_processor''', '''tokenizer''']
Uppe... | 664 |
'''simple docstring'''
import torch
from transformers import AutoModel
class UpperCamelCase_ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self : Any , _lowerCamelCase : Optional[int]="sayef/fsner-bert-base-uncased" ) -> List[An... | 664 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not ... | 664 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noq... | 664 | 1 |
'''simple docstring'''
__magic_name__ : List[Any] ={
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: 'a',
11: 'b',
12: 'c',
13: 'd',
14: 'e',
15: 'f',
}
def __snake_case ( lowerCamelCase_ : ... | 664 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __snake_case ( lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
... | 664 | 1 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRCont... | 664 |
'''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
class UpperCamelCase_... | 664 | 1 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : int =logging.get_logger(__name__)
__magic_name__ : List[str] ={
'microsoft/xprophetnet-large-wiki100-cased': (
'https://hugging... | 664 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threaded... | 664 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, ... | 664 |
'''simple docstring'''
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
fr... | 664 | 1 |
'''simple docstring'''
__magic_name__ : str ='0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_f... | 664 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__magic_name__ = str(bin(lowerCamelCase_ ) )[... | 664 | 1 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_g... | 664 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingf... | 664 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Union[str, Any] ={'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
... | 664 | 1 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__magic_name__ : Optional[Any] =logging.getLogger()
@unittest.skip('''Temporar... | 664 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : Optional[Any] ={
'configuration_longformer': [
'LONGFORMER_PRETRAINED_CON... | 664 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__magic_name__ : List[Any] =logging.get_logger(__name__)
__magic_name__ : str ={
'post_extract_proj': '... | 664 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
__magic_name__ : str ={
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Ima... | 664 | 1 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
__magic_name__ : Optional[int] ='\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL:... | 664 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import... | 664 | 1 |
'''simple docstring'''
from math import ceil, sqrt
def __snake_case ( lowerCamelCase_ : int = 100_0000 ):
'''simple docstring'''
__magic_name__ = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__magic_name_... | 664 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( lowerCamelCase_ : list[int] , lowerCamelCase_ : int ):
'''simple docstring'''
if len(lowerCamelCase_ ) < k or k < 0:
raise ValueError("Invalid Input" )
__magic_name__ ... | 664 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : list ):
'''simple docstring'''
__magic_name__ = 0
while len(lowerCamelCase_ ) > 1:
__magic_name__ = 0
# Consider two files with minimum cost to be merged
for _ in range(2 )... | 664 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : int =logging.get_logger(__name__)
__magic_name__ : List[Any] ={}
class UpperCamelCase_ ( A ):
"""simple docstring"""
UpperCAmelCase__ : in... | 664 | 1 |
'''simple docstring'''
__magic_name__ : List[Any] ={
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
__magic_name__ : int ... | 664 |
'''simple docstring'''
__magic_name__ : Dict =8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def __snake_case ( lowerCamelCase_ : float , lowerCamelCase_ : float , lowerCamelCase_ : float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
... | 664 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase_ ( A , unittest.TestCase ):... | 664 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__magic_name__ : List[Any] =logging.getLogger(__name__)
class UpperCamelCase_ ( A ):
"""simple docst... | 664 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.