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 argparse
import json
import os
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_... | 665 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _... | 665 | 1 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : int ... | 665 |
'''simple docstring'''
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 665 | 1 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict =9, 1_4 # noqa: F84... | 665 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from .... | 665 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
... | 665 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : ... | 665 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not is... | 665 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e impo... | 665 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 665 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class ... | 665 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : str, UpperCamelCase__ : str, Upp... | 665 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =size
SCREAMING_SNAKE_CASE__ : List[Any] =[0] ... | 665 | 1 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
a_ = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kern... | 665 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from... | 665 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _a( ):
'''simple docstring'''
with offline(OfflineSimulatio... | 700 |
'''simple docstring'''
from math import isqrt
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =[True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
... | 665 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_util... | 701 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
... | 665 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_to... | 702 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow... | 665 | 0 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
a_ = 1_0_0
a_ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
a_ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes... | 703 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_... | 665 | 0 |
'''simple docstring'''
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_ti... | 704 |
'''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 BaseTransformersCL... | 665 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"kssteven/ibe... | 705 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils im... | 665 | 0 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Optional[int], UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] ... | 706 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a_ = TypeVar('T')
class __SCREAMING_SNAKE_CASE ( Generic[T] ):
snake_case_ = 42 # Cache store of keys
snake_cas... | 665 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
a_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( UpperCAmelCase_ ):
def __init__( self : Option... | 707 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE... | 665 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, Tenso... | 708 |
'''simple docstring'''
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =len(UpperCam... | 665 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_uti... | 709 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_... | 665 | 0 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = (UnCLIPScheduler,)
def __magic_name__ ( self : ... | 710 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = JukeboxTokenizer
snake_case_ = {
""... | 665 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """timm_backbone"""
def __init__( s... | 711 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at htt... | 665 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils impor... | 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', ... | 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'V... | 713 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _a( UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : list[int], UpperCamelCase_... | 665 | 0 |
'''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_chan... | 714 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _... | 665 | 0 |
'''simple docstring'''
a_ = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transforme... | 715 |
'''simple docstring'''
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 665 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'vocab_file': 'vocab.json',
'merg... | 716 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from .... | 665 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_ima... | 717 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : ... | 665 | 0 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def _a( UpperCamelCase__ : str, UpperCamelCase__ : float | Decimal, UpperCamelCase__ : float = 1_0**-1_0 ):
... | 718 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e impo... | 665 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a_ = False
class __SCREAMING_SNAKE_CASE ( u... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class ... | 665 | 0 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WE... | 720 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =size
SCREAMING_SNAKE_CASE__ : List[Any] =[0] ... | 665 | 0 |
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =(1 + 2_4 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def _a( UpperCamelCase__ : int = 5_0_0_0 ):
'... | 721 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from... | 665 | 0 |
import copy
import re
class __SCREAMING_SNAKE_CASE :
snake_case_ = '''hp'''
snake_case_ = {}
snake_case_ = None
@classmethod
def __magic_name__ ( cls : List[str] , __lowercase : Union[str, An... | 700 |
'''simple docstring'''
from math import isqrt
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =[True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
... | 665 | 0 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
... | 701 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
... | 665 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common impor... | 702 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow... | 665 | 0 |
'''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... | 703 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_... | 665 | 0 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
a_ = 1_0_0
a_ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
a_ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes... | 704 |
'''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 BaseTransformersCL... | 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =len(UpperCamelCase__ )
for i in range(n - 1 ... | 705 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils im... | 665 | 0 |
'''simple docstring'''
import numpy as np
def _a( UpperCamelCase__ : np.array ):
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 706 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a_ = TypeVar('T')
class __SCREAMING_SNAKE_CASE ( Generic[T] ):
snake_case_ = 42 # Cache store of keys
snake_cas... | 665 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : str = 6 ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Tupl... | 707 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE... | 665 | 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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTex... | 708 |
'''simple docstring'''
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =len(UpperCam... | 665 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __SCREAMING_SNAKE_CASE :
def __init__( self : int , __lowercase : Optional[int]=2 ... | 709 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_... | 665 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''... | 710 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = JukeboxTokenizer
snake_case_ = {
""... | 665 | 0 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from tra... | 711 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at htt... | 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not ... | 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', ... | 665 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def _a( UpperCamelCase__ : Dict ):
'''simple docstring'''
return ConvertCommand(
args.mode... | 713 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _a( UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : list[int], UpperCamelCase_... | 665 | 0 |
'''simple docstring'''
import os
from pathlib import Path
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple ={
... | 714 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _... | 665 | 0 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mas... | 715 |
'''simple docstring'''
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 665 | 0 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a( UpperCamelCase__ : List[Any], UpperCamelCase__ : Dict, UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SC... | 716 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from .... | 665 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import Gene... | 717 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : ... | 665 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
... | 718 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e impo... | 665 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common imp... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class ... | 665 | 0 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
a_ = '\nimport os\n'
a_ = '\ndef foo():\n import os\n return False\n'
a_ = '\ndef foo():\n def bar():\n if True:\n import os\n return Fal... | 720 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =size
SCREAMING_SNAKE_CASE__ : List[Any] =[0] ... | 665 | 0 |
import numpy as np
def _a( UpperCamelCase__ : np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 721 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from... | 665 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDepen... | 700 |
'''simple docstring'''
from math import isqrt
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =[True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
... | 665 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __lowercase : Collect... | 701 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
... | 665 | 0 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
a_ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a_ = [ord(letter) for letter in string.ascii_lowercase]
a_ ... | 702 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow... | 665 | 0 |
'''simple docstring'''
import argparse
import os
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_s... | 703 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_... | 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDe... | 704 |
'''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 BaseTransformersCL... | 665 | 0 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from .... | 705 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils im... | 665 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
a_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : int , ... | 706 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a_ = TypeVar('T')
class __SCREAMING_SNAKE_CASE ( Generic[T] ):
snake_case_ = 42 # Cache store of keys
snake_cas... | 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ = {
'configuration_ow... | 707 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE... | 665 | 0 |
'''simple docstring'''
from __future__ import annotations
def _a( UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Optional[int], ):
'''simple docstring'''
if (electron_conc, hole_conc, intrin... | 708 |
'''simple docstring'''
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =len(UpperCam... | 665 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_t... | 709 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_... | 665 | 0 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
a_ = False
try:
a_ = ... | 710 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = JukeboxTokenizer
snake_case_ = {
""... | 665 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : list[Any] =[]... | 711 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at htt... | 665 | 0 |
'''simple docstring'''
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
... | 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', ... | 665 | 0 |
'''simple docstring'''
from __future__ import annotations
def _a( UpperCamelCase__ : int = 4 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =abs(__snake_case ) or 4
return [[1 + x + y * row_size f... | 713 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _a( UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : list[int], UpperCamelCase_... | 665 | 0 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __SCREAMING_SNAKE_CASE :
def __magic_name__ ( self : str , __lowercase : List[str] ) -> List[... | 714 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _... | 665 | 0 |
'''simple docstring'''
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:... | 715 |
'''simple docstring'''
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 665 | 0 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from tra... | 716 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from .... | 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
if not isinstance(UpperCamelCase__, UpperCamelCase__ ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise... | 717 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : ... | 665 | 0 |
'''simple docstring'''
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
a_ = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
... | 718 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e impo... | 665 | 0 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class ... | 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str =int(lowercase_ )
if n_element < 1:
SCREAMING_SNAKE_CASE__ : Optional[int] =ValueError('''a ... | 720 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =size
SCREAMING_SNAKE_CASE__ : List[Any] =[0] ... | 665 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE )... | 721 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from... | 665 | 0 |
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 ver... | 700 |
'''simple docstring'''
from math import isqrt
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =[True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
... | 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : Tuple, UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ )
print('''The following activities are se... | 701 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
... | 665 | 0 |
'''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 __SCREAMING_SNAK... | 702 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow... | 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}
try:
if not is_torch_availa... | 703 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_... | 665 | 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://huggingface.co/... | 704 |
'''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 BaseTransformersCL... | 665 | 0 |
'''simple docstring'''
from maths.prime_check import is_prime
def _a( UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
if not isinstance(__snake_case, __snake_case ):
SCREAMING_SNAKE_CASE__ : int =f"I... | 705 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils im... | 665 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, requi... | 706 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a_ = TypeVar('T')
class __SCREAMING_SNAKE_CASE ( Generic[T] ):
snake_case_ = 42 # Cache store of keys
snake_cas... | 665 | 0 |
'''simple docstring'''
import argparse
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
from... | 707 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE... | 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : Dict ):
'''simple docstring'''
if not isinstance(_snake_case, _snake_case ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueErr... | 708 |
'''simple docstring'''
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =len(UpperCam... | 665 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'nielsr/canine-s': 2_0_4_8,
}
# Unicode defines 1,114,112 total “codepoints... | 709 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_... | 665 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPExcept... | 710 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = JukeboxTokenizer
snake_case_ = {
""... | 665 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
'''shi-labs/dinat-mini-in1k-224''': '... | 711 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at htt... | 665 | 0 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _a( UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
def is_in_circle(UpperCamelCase__... | 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', ... | 665 | 0 |
'''simple docstring'''
import functools
from typing import Any
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
if not isinstance(lowerCamelCase_, lowerCamelCase_ ) or len(lowerCamelCase_ ) =... | 713 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _a( UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : list[int], UpperCamelCase_... | 665 | 0 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
a_ = False
try:
a_ = _is_package_availa... | 714 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _... | 665 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_back... | 715 |
'''simple docstring'''
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] ... | 665 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : Tuple ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cann... | 716 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from .... | 665 | 0 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __SCREAMING_SNAKE_CASE :
def __init__( self : Any ) -> int:
SCREAMING_SNAKE_CASE__ : Dict =''''''
... | 717 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : ... | 665 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
a_ = logging.g... | 718 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e impo... | 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"... | 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class ... | 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def _a( UpperCamelCase__ : str ):
... | 720 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =size
SCREAMING_SNAKE_CASE__ : List[Any] =[0] ... | 665 | 0 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __SCREAMING_SNAKE_CASE ( _snake_case ):
def __magic_name__ ( self : List[Any] , __lowercase : ... | 721 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from... | 665 | 0 |
def _a( UpperCamelCase__ : int = 1_0**9 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =1
SCREAMING_SNAKE_CASE__ : Tuple =2
SCREAMING_SNAKE_CASE__ : Tuple =0
SCREAMING_SN... | 700 |
'''simple docstring'''
from math import isqrt
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =[True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
... | 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Optional[Any] =float(_lowerCAmelCase )
except ValueError:
raise ValueError('''P... | 701 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
... | 665 | 0 |
'''simple docstring'''
import os
import sys
a_ = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSeq... | 702 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow... | 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"configuration_electra": ["ELECTRA_PRETRAIN... | 703 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_... | 665 | 0 |
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