code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
a_ = logging.get_logge... | 707 |
"""simple docstring"""
import argparse
import copy
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : Any ):
"""simple docstring"""
snake_case_ : List[Any] = {}
with open(SCREAMING_SNAKE_CASE__ ) as f:
for line in f:
if line... | 48 | 0 |
"""simple docstring"""
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDime... | 708 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
a_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs... | 48 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def SCREAMING_SNAKE_CASE__ ... | 709 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
... | 48 | 0 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from ac... | 710 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
a_ = logging.getLogger(__name__)
class __lowercase ( _UpperCAmelCase):
"""simple docstri... | 48 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FIL... | 711 |
"""simple docstring"""
import random
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
snake_case_ : Union[str, Any] = num - 1
snake_case_ : List[str] = 0
while s % 2 == 0:
snake_case_ : ... | 48 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : List[str] ):
"""simple docstring"""
snake_case_ : int = 1
snake_case_ : List[str] = 2
while i * i <= n:
snake_case_ : int = 0
while ... | 712 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokeni... | 48 | 0 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/micros... | 713 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 48 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : list ):
"""simple docstring"""
snake_case_ : Optional[int] = len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , SCREAMING_SNAKE_CASE__ ):
snake_case_ : Tuple ... | 714 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_ava... | 48 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
),
... | 715 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase)
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
... | 48 | 0 |
"""simple docstring"""
import torch
from transformers import AutoModel
class __lowercase ( torch.nn.Module):
"""simple docstring"""
def __init__(self , lowercase__="sayef/fsner-bert-base-uncased" ):
super(lowercase__ , self ).__init__()
... | 716 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channe... | 48 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
a_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model output... | 717 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggi... | 48 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import Heun... | 718 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __lowercase :
"""simple docstring"""
def __init__(self , lowercase__ ):
snake_case_ : Union[str, Any] = data
s... | 48 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax impo... | 719 |
"""simple docstring"""
from manim import *
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
def __UpperCamelCase (self ):
snake_case_ : Union[str, Any] = Rectangle(height=0.5 , width=0.5 )
snake_c... | 48 | 0 |
"""simple docstring"""
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int | float | str , SCREAMING_SNAKE_CASE__ : int | float | str ):
"""simple docstring"""
if nth_term == "":
return [""]
snake_case_ : List[... | 720 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] ):
"""simple docstring"""
... | 48 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import... | 721 |
"""simple docstring"""
import random
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : bool = False ):
"""simple docstring"""
snake_case_ : dict = {i: [] for i in range(SCREAMING... | 48 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils imp... | 700 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.js... | 48 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
snake_case_ : Union[str, Any] = [], []
while len(SCREAMING_SNAKE_CASE__ ) > 1:
snake_case_ : int = min(SCREAMIN... | 701 |
"""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 Thread... | 48 | 0 |
"""simple docstring"""
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path impo... | 702 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a_ = logging.getLogger(__name__)
if __name__ == "__main__... | 48 | 0 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : List[Any] , SC... | 703 |
"""simple docstring"""
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__... | 48 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] ):
snake_case_ : Optional[Any] = 0
snake_case_ : int = len(SCREAMING_SNAKE_CASE__ ) - 1
while left <= right:
# avoid d... | 704 |
"""simple docstring"""
from copy import deepcopy
class __lowercase :
"""simple docstring"""
def __init__(self , lowercase__ = None , lowercase__ = None ):
if arr is None and size is not None:
snake_case_ : str = size
... | 48 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : Dict ):
"""simple docstring"""
snake_case_ : Union[str, Any] = []
snake_case_ : str = set({"""(""", """[""", """{"""} )
snake_case_ : List[str] = set(... | 705 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : list ):
"""simple docstring"""
snake_case_ : Optional[int] = len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , SCREAMING_SNAKE_CASE__ ):
snake_case_ : Tuple ... | 48 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
... | 706 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
_A : Union[str, Any] = ["""image_processor""", ""... | 48 | 0 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgument... | 707 |
"""simple docstring"""
import argparse
import copy
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : Any ):
"""simple docstring"""
snake_case_ : List[Any] = {}
with open(SCREAMING_SNAKE_CASE__ ) as f:
for line in f:
if line... | 48 | 0 |
"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
... | 708 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
a_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs... | 48 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils ... | 709 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
... | 48 | 0 |
"""simple docstring"""
import argparse
import json
import subprocess
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
snake_case_ : List[str] = []
snake_case_ : Dict = (
... | 710 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
a_ = logging.getLogger(__name__)
class __lowercase ( _UpperCAmelCase):
"""simple docstri... | 48 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not is_torch_available():
rais... | 711 |
"""simple docstring"""
import random
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
snake_case_ : Union[str, Any] = num - 1
snake_case_ : List[str] = 0
while s % 2 == 0:
snake_case_ : ... | 48 | 0 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : str ... | 712 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokeni... | 48 | 0 |
"""simple docstring"""
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 __lowercase ... | 713 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 48 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
snake_case_ : Tuple = int(SCREAMING_SNAKE_CASE__ )
if decimal in (0, 1): # Exit cases for the recursion
return str(SCREAMING_SNAKE_CASE__ )
... | 714 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_ava... | 48 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int = 5_0 ):
"""simple docstring"""
snake_case_ : Optional[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 ... | 715 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase)
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
... | 48 | 0 |
"""simple docstring"""
import numpy as np
class __lowercase :
"""simple docstring"""
def __init__(self , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None ):
self.set_matricies(red=lowercase__ , gr... | 716 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channe... | 48 | 0 |
"""simple docstring"""
import importlib
import inspect
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_config_docstrings.py
a_ = '''src/transformers'''
# This is to make sure the transformers module imp... | 717 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggi... | 48 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 718 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __lowercase :
"""simple docstring"""
def __init__(self , lowercase__ ):
snake_case_ : Union[str, Any] = data
s... | 48 | 0 |
"""simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __lowercase ( _UpperCAmelCase):
"""simple docstrin... | 719 |
"""simple docstring"""
from manim import *
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
def __UpperCamelCase (self ):
snake_case_ : Union[str, Any] = Rectangle(height=0.5 , width=0.5 )
snake_c... | 48 | 0 |
"""simple docstring"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionSch... | 720 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] ):
"""simple docstring"""
... | 48 | 0 |
"""simple docstring"""
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a_ = 6378137.0
a_ = 6356752.314245
a_ = 6378137
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : flo... | 721 |
"""simple docstring"""
import random
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : bool = False ):
"""simple docstring"""
snake_case_ : dict = {i: [] for i in range(SCREAMING... | 48 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandi... | 700 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.js... | 48 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : list[int] ):
"""simple docstring"""
snake_case_ : Dict = len(SCREAMING_SNAKE_CASE__ )
for i in range(SCREAMING_SNAKE_CASE__ ):
for j in range(i + 1 , ... | 701 |
"""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 Thread... | 48 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/config.json''',
'''google/fnet-large''': '''https://huggingfa... | 702 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a_ = logging.getLogger(__name__)
if __name__ == "__main__... | 48 | 0 |
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
a_ = re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')
@total_ordering
@dataclass
class __lowercase :
"""simp... | 703 |
"""simple docstring"""
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__... | 48 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_s... | 704 |
"""simple docstring"""
from copy import deepcopy
class __lowercase :
"""simple docstring"""
def __init__(self , lowercase__ = None , lowercase__ = None ):
if arr is None and size is not None:
snake_case_ : str = size
... | 48 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {
'''configuration_conditional_detr''': [
'''CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ConditionalDetrCo... | 705 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : list ):
"""simple docstring"""
snake_case_ : Optional[int] = len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , SCREAMING_SNAKE_CASE__ ):
snake_case_ : Tuple ... | 48 | 0 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 706 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
_A : Union[str, Any] = ["""image_processor""", ""... | 48 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 707 |
"""simple docstring"""
import argparse
import copy
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : Any ):
"""simple docstring"""
snake_case_ : List[Any] = {}
with open(SCREAMING_SNAKE_CASE__ ) as f:
for line in f:
if line... | 48 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_... | 708 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
a_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs... | 48 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase)
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
... | 709 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
... | 48 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from .... | 710 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
a_ = logging.getLogger(__name__)
class __lowercase ( _UpperCAmelCase):
"""simple docstri... | 48 | 0 |
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Con... | 711 |
"""simple docstring"""
import random
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
snake_case_ : Union[str, Any] = num - 1
snake_case_ : List[str] = 0
while s % 2 == 0:
snake_case_ : ... | 48 | 0 |
"""simple docstring"""
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/mask... | 712 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokeni... | 48 | 0 |
"""simple docstring"""
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, sl... | 713 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 48 | 0 |
"""simple docstring"""
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class... | 714 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_ava... | 48 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( ):
"""simple docstring"""
snake_case_ : Optional[Any] = 0
for i in range(1 , 1_0_0_1 ):
total += i**i
return str(SCREAMING_SNAKE_CASE__ )[-1_0:]
if __name__ == "__main__":
print(solu... | 715 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase)
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
... | 48 | 0 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.uti... | 716 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channe... | 48 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class __lowercase :
"""simple docstring"""
def __init__(self , lowercase__ = None ):
if components is ... | 717 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggi... | 48 | 0 |
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
def __init__(self , *lowercase__ , **lowercase__ ):
super().__init__(*lowercase__ , **lowercase_... | 718 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __lowercase :
"""simple docstring"""
def __init__(self , lowercase__ ):
snake_case_ : Union[str, Any] = data
s... | 48 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import Tokenizer... | 719 |
"""simple docstring"""
from manim import *
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
def __UpperCamelCase (self ):
snake_case_ : Union[str, Any] = Rectangle(height=0.5 , width=0.5 )
snake_c... | 48 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
a_ = True
except (ImportError, ModuleNotFoundError):
a_ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def SCREAMING_SNAKE_CASE__... | 720 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] ):
"""simple docstring"""
... | 48 | 0 |
"""simple docstring"""
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_... | 721 |
"""simple docstring"""
import random
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : bool = False ):
"""simple docstring"""
snake_case_ : dict = {i: [] for i in range(SCREAMING... | 48 | 0 |
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_... | 49 | import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase... | 49 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""kssteven/ibert-roberta-base""": """https://huggi... | 49 | import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import wr... | 49 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import... | 49 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 49 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaM... | 49 | import os
from math import logaa
def _lowerCamelCase ( a_ : str = "base_exp.txt"):
lowerCamelCase :float = 0
lowerCamelCase :Optional[int] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(a_) , a_))):
lowerCamelCase , lowe... | 49 | 1 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning th... | 49 | def _lowerCamelCase ( a_ : list):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''')
for cell_n in range(1 , len(grid[0])):
grid[0][cell_n] += grid[0][cell_n - 1]
lowerCamelCase :Any = grid[0]
for ... | 49 | 1 |
from __future__ import annotations
import math
def _lowerCamelCase ( a_ : int):
if num <= 0:
lowerCamelCase :Union[str, Any] = F"{num}: Invalid input, please enter a positive integer."
raise ValueError(a_)
lowerCamelCase :Any = [True] * (... | 49 | import math
def _lowerCamelCase ( a_ : int):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number... | 49 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_Up... | 49 | import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_av... | 49 | 1 |
def _lowerCamelCase ( a_ : str):
lowerCamelCase :Union[str, Any] = 0
for ch in input_str:
lowerCamelCase :Optional[Any] = ord(a_)
lowerCamelCase :List[Any] = pow(2 , a_)
# If we already turned on bit for current character's ... | 49 | from maths.prime_factors import prime_factors
def _lowerCamelCase ( a_ : int):
if not isinstance(a_ , a_):
lowerCamelCase :Tuple = F"Input value of [number={number}] must be an integer"
raise TypeError(a_)
if number < 1:
raise ValueError('''Input ... | 49 | 1 |
import torch
from diffusers import StableDiffusionPipeline
A__ = """path-to-your-trained-model"""
A__ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
A__ = """A photo of sks dog in a bucket"""
A__ = ... | 49 | import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
... | 49 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
... | 49 | def _lowerCamelCase ( a_ : int = 4_00_00_00):
lowerCamelCase :Dict = [0, 1]
lowerCamelCase :Optional[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1])
if fib[i + 2] > n:
break
i += 1
lowerCamelCase :Dict = ... | 49 | 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... | 49 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available(... | 49 | 1 |
def _lowerCamelCase ( a_ : list):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''')
for cell_n in range(1 , len(grid[0])):
grid[0][cell_n] += grid[0][cell_n - 1]
lowerCamelCase :Any = grid[0]
for ... | 49 | import numpy
class _lowerCAmelCase :
def __init__( self : Dict , __snake_case : numpy.ndarray , __snake_case : numpy.ndarray ):
lowerCamelCase :Dict = input_array
# Random initial weights are assigned where first argument... | 49 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAv... | 49 | def _lowerCamelCase ( a_ : str , a_ : str):
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :int = [[False for _ in range(m + 1)] for _ in range(n + 1)]
lowerCamelCase :Optional[Any] = ... | 49 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = ['image_processor', 'tokenizer']
_UpperCAmelCase = 'CLIPImageProcessor'
... | 49 | import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 49 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.model... | 49 | import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import p... | 49 | 1 |
from itertools import product
def _lowerCamelCase ( a_ : int , a_ : int):
lowerCamelCase :List[Any] = sides_number
lowerCamelCase :List[Any] = max_face_number * dice_number
lowerCamelCase :Dict = [0] * (max_total + 1)
lower... | 49 | import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = ''
_UpperCAmelCase = (
None # p... | 49 | 1 |
import unittest
import numpy as np
def _lowerCamelCase ( a_ : np.ndarray , a_ : np.ndarray , a_ : np.ndarray , a_ : np.ndarray | None = None , ):
lowerCamelCase :List[str] = np.shape(a_)
lowerCamelCase :str = np.shape(a... | 49 | import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...t... | 49 | 1 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def _lowerCamelCase ( a_ : ndarray):
return np.dot(a_ , a_)
class _lowerCAmelCase :
def __init__( self : Any , *,
__snake_case ... | 49 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A__ = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Layo... | 49 | 1 |
import numpy as np
import datasets
A__ = """
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mah... | 49 | import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 49 | 1 |
def _lowerCamelCase ( a_ : int = 1_00):
lowerCamelCase :List[str] = n * (n + 1) * (2 * n + 1) / 6
lowerCamelCase :Tuple = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares)
if __name__ == "__main__":
print(F'{solution() = }')
| 49 | import operator as op
def _lowerCamelCase ( a_ : Tuple):
lowerCamelCase :int = []
lowerCamelCase :List[str] = lambda a_ , a_: int(x / y) # noqa: E731 integer division operation
lowerCamelCase :Optional[int] = {
'''^''': op.... | 49 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
"""funnel-transformer/small-base"... | 49 | import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fai... | 49 | 1 |
def _lowerCamelCase ( a_ : int , a_ : int):
return base * power(a_ , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print("""Raise base to the power of exponent using recursion...""")
A__ = int(input("""Enter the base: """).strip())
A__ ... | 49 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""andreasmadsen/efficient_mlm_m0.40""": (
... | 49 | 1 |
def _lowerCamelCase ( a_ : str , a_ : str):
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :int = [[False for _ in range(m + 1)] for _ in range(n + 1)]
lowerCamelCase :Optional[Any] = ... | 49 | import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase... | 49 | 1 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transform... | 49 | import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import wr... | 49 | 1 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diff... | 49 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 49 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
"""TableTran... | 49 | import os
from math import logaa
def _lowerCamelCase ( a_ : str = "base_exp.txt"):
lowerCamelCase :float = 0
lowerCamelCase :Optional[int] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(a_) , a_))):
lowerCamelCase , lowe... | 49 | 1 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def _lowerCamelCase ( a_ : List[str]):
lowerCamelCase :List[str] = args.pruning_method
lowerCamelCase :Tuple = args.t... | 49 | def _lowerCamelCase ( a_ : list):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''')
for cell_n in range(1 , len(grid[0])):
grid[0][cell_n] += grid[0][cell_n - 1]
lowerCamelCase :Any = grid[0]
for ... | 49 | 1 |
def _lowerCamelCase ( ):
return [list(range(10_00 - i , -10_00 - i , -1)) for i in range(10_00)]
A__ = generate_large_matrix()
A__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, ... | 49 | import math
def _lowerCamelCase ( a_ : int):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number... | 49 | 1 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from tra... | 49 | import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_av... | 49 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModel... | 49 | from maths.prime_factors import prime_factors
def _lowerCamelCase ( a_ : int):
if not isinstance(a_ , a_):
lowerCamelCase :Tuple = F"Input value of [number={number}] must be an integer"
raise TypeError(a_)
if number < 1:
raise ValueError('''Input ... | 49 | 1 |
from math import ceil
def _lowerCamelCase ( a_ : int = 10_01):
lowerCamelCase :Union[str, Any] = 1
for i in range(1 , int(ceil(n / 2.0))):
lowerCamelCase :Any = 2 * i + 1
lowerCamelCase :str = 2 * i
lowerCamelCase... | 49 | import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
... | 49 | 1 |
def _lowerCamelCase ( a_ : float , a_ : float):
if density <= 0:
raise ValueError('''Impossible fluid density''')
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''')
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doct... | 49 | def _lowerCamelCase ( a_ : int = 4_00_00_00):
lowerCamelCase :Dict = [0, 1]
lowerCamelCase :Optional[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1])
if fib[i + 2] > n:
break
i += 1
lowerCamelCase :Dict = ... | 49 | 1 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _lowerCAmelCase :
pass
| 49 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available(... | 49 | 1 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase... | 49 | import numpy
class _lowerCAmelCase :
def __init__( self : Dict , __snake_case : numpy.ndarray , __snake_case : numpy.ndarray ):
lowerCamelCase :Dict = input_array
# Random initial weights are assigned where first argument... | 49 | 1 |
def _lowerCamelCase ( a_ : int):
if p < 2:
raise ValueError('''p should not be less than 2!''')
elif p == 2:
return True
lowerCamelCase :int = 4
lowerCamelCase :List[str] = (1 << p) - 1
for _ in range(p - 2):
lowerCamelCase :Uni... | 49 | def _lowerCamelCase ( a_ : str , a_ : str):
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :int = [[False for _ in range(m + 1)] for _ in range(n + 1)]
lowerCamelCase :Optional[Any] = ... | 49 | 1 |
from math import sqrt
def _lowerCamelCase ( a_ : int):
lowerCamelCase :List[Any] = 0
for i in range(1 , int(sqrt(a_) + 1)):
if n % i == 0 and i != sqrt(a_):
total += i + n // i
elif i == sqrt(a_):
total += i
return total - n
... | 49 | import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 49 | 1 |
class _lowerCAmelCase :
def __init__( self : int , __snake_case : int , __snake_case : List[Any]=None , __snake_case : List[str]=None ):
lowerCamelCase :Union[str, Any] = data
lowerCamelCase :str = ... | 49 | import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import p... | 49 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cased""": """ht... | 49 | import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = ''
_UpperCAmelCase = (
None # p... | 49 | 1 |
def _lowerCamelCase ( a_ : list):
lowerCamelCase :str = len(a_)
for _ in range(a_):
for i in range(_ % 2 , arr_size - 1 , 2):
if arr[i + 1] < arr[i]:
lowerCamelCase , lowerCamelCase :Optional[Any] = arr[i + 1], arr[i]
... | 49 | import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...t... | 49 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ ... | 49 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A__ = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Layo... | 49 | 1 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import... | 49 | import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 49 | 1 |
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