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 |
|---|---|---|---|---|
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import Pr... | 721 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,) -> float | int:
'''simple docstring'''
for nxt, d in graph[v]... | 29 | 0 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@requir... | 700 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """ClapFeatureExtractor"""
lowerCAmelCase_ = ("""RobertaTokenizer""", """RobertaToken... | 29 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
class __A ( lowercase__ ):
'''simple docs... | 701 |
from __future__ import annotations
import unittest
from transformers import 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_tensor, random_attention_mask
from ...test_pip... | 29 | 0 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
_a = {
"n_samples": 64,
"horizon": 32,
"num_inference_steps": 20,
"n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network
"scale_grad_by_std": True,
"sca... | 702 |
import string
from math import logaa
def lowerCAmelCase__(__snake_case ,__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = document.translate(
str.maketrans('''''' ,'''''' ,string.punctuation ) ).replace('''\n''' ,'''''' )
lower... | 29 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 703 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
"convert_funnel_origin... | 29 | 0 |
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 (
AutoProcessor,
BertTokeniz... | 704 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_a = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",
... | 29 | 0 |
'''simple docstring'''
def lowerCAmelCase__(__snake_case ) -> List[Any]:
'''simple docstring'''
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(__snake_case ,__snake_case ):
raise TypeError('''Input value must be a \'int... | 705 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_atten... | 29 | 0 |
from collections import deque
def lowerCAmelCase__(__snake_case ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase__ = len(snake_case__ )
lowerCamelCase__ = deque()
lowerCamelCase__ = [False for ... | 706 |
from math import sqrt
def lowerCAmelCase__(__snake_case ) -> bool:
'''simple docstring'''
assert isinstance(__snake_case ,__snake_case ) and (
number >= 0
), "'number' must been an int and positive"
lowerCamelCase__ = True
# 0 and 1 are none primes.
... | 29 | 0 |
import math
import qiskit
def lowerCAmelCase__(__snake_case = 1 ,__snake_case = 1 ,__snake_case = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(UpperCAmelCase__ ,UpperCAmelCase__ )
or isinstance(UpperCAmelCase__ ,UpperCAmelCase__ ... | 707 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None ) -> None:
'''simple docstring'''
if start is None:
lowerCamelCase__ = 0
if end is None:
lowerCamelCase__ = len(__snake_case ) - 1... | 29 | 0 |
def lowerCAmelCase__(__snake_case = 10 ,__snake_case = 22 ) -> int:
'''simple docstring'''
lowerCamelCase__ = range(1 ,_lowercase )
lowerCamelCase__ = range(1 ,_lowercase )
return sum(
1 for power in powers for base in bases if len(str(bas... | 708 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> float:
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('... | 29 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __A ( unittest.TestCase , UpperCAmelCase__ ):
'''simple docstring'''
def __lowerCamelCase ( self ):
'''simple docstring'''
lowe... | 709 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCAmelCase__(__snake_case ) -> Union[str, Any]:
'''simple docstring'''
def wrapper(*__snake_case ,**__snake_case ):
lo... | 29 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if i... | 710 |
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
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]
lowerCa... | 29 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_a = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation a... | 711 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import loggin... | 29 | 0 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> Dict:
'''simple docstring'''
lo... | 712 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_a = datasets.logging.get_logger(__name__)
_a = "\\n@InProceedings{moosavi2019minimum,\n author = { Nafise Sadat Moosavi, Leo B... | 29 | 0 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __A ( UpperCAmelCase__ , UpperCAmelCase__ ):
'''simple docstring'''
@register_to_config
def __init__( self , *,
... | 713 |
# This is the module that test_patching.py uses to test patch_submodule()
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 j... | 29 | 0 |
class __A :
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = name
lowerCamelCase__ = value
lowerCamelCase__ ... | 714 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_a = logging.get_logger(__name__)
class __A :
'''simple docstring'''
lowerCAmelCase_ = None
@experimental
def lowerCAmelCase__(__sna... | 29 | 0 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSche... | 715 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __A ... | 29 | 0 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> Optional[int]:
'''simple docstring'''
... | 716 |
_a = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
_a = [{"type": "code", "content": INSTALL_CONTENT}]
_a = {
"{processor_class}": "FakeProcessorC... | 29 | 0 |
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_image_inputs
if is_torch_available():
... | 717 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_a = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise ... | 29 | 0 |
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"
),
# See all TrOCR models at https... | 718 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_a = logging.get_logger(__name__)
class __A ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ... | 29 | 0 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_a = collections.namedtuple("_Datasets", ["train", "validation", "test"])
... | 719 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> Any:
'''simple docstring'''
lowerCamelCase__ = {
'''en''': '''Machine learning is great, isn\'t it?''',
... | 29 | 0 |
from __future__ import annotations
import math
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> Dict:
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(_UpperCamelCase )... | 720 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a = logging.get_logger(__name__)
class __A ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCa... | 29 | 0 |
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": "https://huggingface.co/funnel... | 721 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,) -> float | int:
'''simple docstring'''
for nxt, d in graph[v]... | 29 | 0 |
_a = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_a = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_a = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "Friday",
6: "Saturday",
}
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ... | 700 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """ClapFeatureExtractor"""
lowerCAmelCase_ = ("""RobertaTokenizer""", """RobertaToken... | 29 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get... | 701 |
from __future__ import annotations
import unittest
from transformers import 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_tensor, random_attention_mask
from ...test_pip... | 29 | 0 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __A ( a__ ):
'''simple docstring'''
def __lt__( self , __lowerCAmelCase ):
'''simple docstring'''
... | 702 |
import string
from math import logaa
def lowerCAmelCase__(__snake_case ,__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = document.translate(
str.maketrans('''''' ,'''''' ,string.punctuation ) ).replace('''\n''' ,'''''' )
lower... | 29 | 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 __A ( unittest.TestCase ):
'... | 703 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
"convert_funnel_origin... | 29 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
if not is_torch_available():
raise O... | 704 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_a = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",
... | 29 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, 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 .tok... | 705 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_atten... | 29 | 0 |
import os
def lowerCAmelCase__() -> List[str]:
'''simple docstring'''
with open(os.path.dirname(lowercase_ ) + '''/p022_names.txt''' ) as file:
lowerCamelCase__ = str(file.readlines()[0] )
lowerCamelCase__ =... | 706 |
from math import sqrt
def lowerCAmelCase__(__snake_case ) -> bool:
'''simple docstring'''
assert isinstance(__snake_case ,__snake_case ) and (
number >= 0
), "'number' must been an int and positive"
lowerCamelCase__ = True
# 0 and 1 are none primes.
... | 29 | 0 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compressio... | 707 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None ) -> None:
'''simple docstring'''
if start is None:
lowerCamelCase__ = 0
if end is None:
lowerCamelCase__ = len(__snake_case ) - 1... | 29 | 0 |
class __A :
'''simple docstring'''
def __init__( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = size
lowerCamelCase__ = [0] * size
lowerCamelCase__ = [0] * size
@staticmethod
de... | 708 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> float:
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('... | 29 | 0 |
_a = "Alexander Joslin"
import operator as op
from .stack import Stack
def lowerCAmelCase__(__snake_case ) -> str:
'''simple docstring'''
lowerCamelCase__ = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
lowerCamelCase__ ... | 709 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCAmelCase__(__snake_case ) -> Union[str, Any]:
'''simple docstring'''
def wrapper(*__snake_case ,**__snake_case ):
lo... | 29 | 0 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benc... | 710 |
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
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]
lowerCa... | 29 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_a = logging.get_logger(__name__)
class __A ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , *__lowerCAmelCase ,... | 711 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import loggin... | 29 | 0 |
from collections.abc import Sequence
from queue import Queue
class __A :
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None ):
'''simple docstring... | 712 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_a = datasets.logging.get_logger(__name__)
_a = "\\n@InProceedings{moosavi2019minimum,\n author = { Nafise Sadat Moosavi, Leo B... | 29 | 0 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case=5 ) -> Union[str, Any]:
'''simple docstring'''
assert masked_input.count('''<mask>''' ) == 1
lowerCamelCase__ ... | 713 |
# This is the module that test_patching.py uses to test patch_submodule()
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 j... | 29 | 0 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_a = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
" final seco... | 714 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_a = logging.get_logger(__name__)
class __A :
'''simple docstring'''
lowerCAmelCase_ = None
@experimental
def lowerCAmelCase__(__sna... | 29 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dime... | 715 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __A ... | 29 | 0 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_a : Dict = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"susnato/ernie-m-large_pytorch": "https://huggingface.c... | 716 |
_a = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
_a = [{"type": "code", "content": INSTALL_CONTENT}]
_a = {
"{processor_class}": "FakeProcessorC... | 29 | 0 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor... | 717 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_a = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise ... | 29 | 0 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ = 0
lowerCamelCase__ = len(_lowerCamelCase ) - 1
while i < j:
if nums[i] + nums[j] == target:
... | 718 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_a = logging.get_logger(__name__)
class __A ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ... | 29 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DU... | 719 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> Any:
'''simple docstring'''
lowerCamelCase__ = {
'''en''': '''Machine learning is great, isn\'t it?''',
... | 29 | 0 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings''' ,set() )
@pytest.f... | 720 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a = logging.get_logger(__name__)
class __A ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCa... | 29 | 0 |
from ..utils import DummyObject, requires_backends
class __A ( metaclass=lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = ["torch", "scipy"]
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
'''simple docstring... | 721 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,) -> float | int:
'''simple docstring'''
for nxt, d in graph[v]... | 29 | 0 |
from math import factorial, pi
def lowerCAmelCase__(__snake_case ,__snake_case = 30 ) -> List[str]:
'''simple docstring'''
if not isinstance(__snake_case ,(int, float) ):
raise ValueError('''maclaurin_sin() requires either an int or float for theta''' )
if not is... | 700 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """ClapFeatureExtractor"""
lowerCAmelCase_ = ("""RobertaTokenizer""", """RobertaToken... | 29 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available... | 701 |
from __future__ import annotations
import unittest
from transformers import 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_tensor, random_attention_mask
from ...test_pip... | 29 | 0 |
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,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHea... | 702 |
import string
from math import logaa
def lowerCAmelCase__(__snake_case ,__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = document.translate(
str.maketrans('''''' ,'''''' ,string.punctuation ) ).replace('''\n''' ,'''''' )
lower... | 29 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeni... | 703 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
"convert_funnel_origin... | 29 | 0 |
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": "https://huggingface.co/funnel... | 704 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_a = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",
... | 29 | 0 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ... | 705 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_atten... | 29 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class __A ( __lowe... | 706 |
from math import sqrt
def lowerCAmelCase__(__snake_case ) -> bool:
'''simple docstring'''
assert isinstance(__snake_case ,__snake_case ) and (
number >= 0
), "'number' must been an int and positive"
lowerCamelCase__ = True
# 0 and 1 are none primes.
... | 29 | 0 |
import json
import os
from typing import 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",
"merges_file": "merges.txt",
}
_a = {
"vocab_fi... | 707 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None ) -> None:
'''simple docstring'''
if start is None:
lowerCamelCase__ = 0
if end is None:
lowerCamelCase__ = len(__snake_case ) - 1... | 29 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, ... | 708 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> float:
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('... | 29 | 0 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
def lowerC... | 709 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCAmelCase__(__snake_case ) -> Union[str, Any]:
'''simple docstring'''
def wrapper(*__snake_case ,**__snake_case ):
lo... | 29 | 0 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class __A ( nn.Module ... | 710 |
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
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]
lowerCa... | 29 | 0 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transforme... | 711 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import loggin... | 29 | 0 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mode... | 712 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_a = datasets.logging.get_logger(__name__)
_a = "\\n@InProceedings{moosavi2019minimum,\n author = { Nafise Sadat Moosavi, Leo B... | 29 | 0 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def lowerCAmelCase__(__snake_case ) -... | 713 |
# This is the module that test_patching.py uses to test patch_submodule()
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 j... | 29 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 714 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_a = logging.get_logger(__name__)
class __A :
'''simple docstring'''
lowerCAmelCase_ = None
@experimental
def lowerCAmelCase__(__sna... | 29 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowerCAmelCase__(__snake_case ,__snake_case ) -> np.array:
'''simple docstring'''
lowerCamelCase__ = F'{sampling_rate}'
lowerCamelCase__ ... | 715 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __A ... | 29 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils im... | 716 |
_a = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
_a = [{"type": "code", "content": INSTALL_CONTENT}]
_a = {
"{processor_class}": "FakeProcessorC... | 29 | 0 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case ) -> set[str]:
'''simple docstring'''
lowerCamelCase__ = set(__snake_case ), [start]
while stack:
lowerCamelCase__ = stack.pop()
explored.add(__snake_case ... | 717 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_a = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise ... | 29 | 0 |
from ...processing_utils import ProcessorMixin
class __A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
lowerCAmelCase_ = ["""image_processor""", """feature_extractor"""]
lowerCAmelCase_ = """TvltImageProcessor"""
lowerCAmelCase_ = """TvltF... | 718 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_a = logging.get_logger(__name__)
class __A ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ... | 29 | 0 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKI... | 719 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> Any:
'''simple docstring'''
lowerCamelCase__ = {
'''en''': '''Machine learning is great, isn\'t it?''',
... | 29 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,... | 720 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a = logging.get_logger(__name__)
class __A ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCa... | 29 | 0 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_t... | 721 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,) -> float | int:
'''simple docstring'''
for nxt, d in graph[v]... | 29 | 0 |
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 tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 700 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """ClapFeatureExtractor"""
lowerCAmelCase_ = ("""RobertaTokenizer""", """RobertaToken... | 29 | 0 |
from math import isqrt
def lowerCAmelCase__(__snake_case ) -> str:
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 ,isqrt(lowerCamelCase_ ) + 1 ) )
def lowerCAmelCase__(__snake_case = 10**6 ) -> Optional[Any]:
... | 701 |
from __future__ import annotations
import unittest
from transformers import 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_tensor, random_attention_mask
from ...test_pip... | 29 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureExtractor"],
"processing_mctct": ["MCTCTProce... | 702 |
import string
from math import logaa
def lowerCAmelCase__(__snake_case ,__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = document.translate(
str.maketrans('''''' ,'''''' ,string.punctuation ) ).replace('''\n''' ,'''''' )
lower... | 29 | 0 |
'''simple docstring'''
from cva import destroyAllWindows, imread, imshow, waitKey
def lowerCAmelCase__(__snake_case ) -> Tuple:
'''simple docstring'''
lowerCamelCase__ = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in rang... | 703 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
"convert_funnel_origin... | 29 | 0 |
from __future__ import annotations
from typing import TypedDict
class __A ( _UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = 42
lowerCAmelCase_ = 42
def lowerCAmelCase__(__snake_case ) -> Optional[Any]:
'''simple docs... | 704 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_a = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",
... | 29 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise OptionalDepe... | 705 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_atten... | 29 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_a = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAva... | 706 |
from math import sqrt
def lowerCAmelCase__(__snake_case ) -> bool:
'''simple docstring'''
assert isinstance(__snake_case ,__snake_case ) and (
number >= 0
), "'number' must been an int and positive"
lowerCamelCase__ = True
# 0 and 1 are none primes.
... | 29 | 0 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import Au... | 707 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None ) -> None:
'''simple docstring'''
if start is None:
lowerCamelCase__ = 0
if end is None:
lowerCamelCase__ = len(__snake_case ) - 1... | 29 | 0 |
import math
def lowerCAmelCase__(__snake_case ) -> Tuple:
'''simple docstring'''
return math.sqrt(lowerCAmelCase__ ) * math.sqrt(lowerCAmelCase__ ) == num
def lowerCAmelCase__(__snake_case ) -> Optional[int]:
'''simple docstring'''
... | 708 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> float:
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('... | 29 | 0 |
_a = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
_a = [{"type": "code", "content": INSTAL... | 709 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCAmelCase__(__snake_case ) -> Union[str, Any]:
'''simple docstring'''
def wrapper(*__snake_case ,**__snake_case ):
lo... | 29 | 0 |
from math import sqrt
def lowerCAmelCase__(__snake_case ) -> Optional[Any]:
'''simple docstring'''
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 pr... | 710 |
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
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]
lowerCa... | 29 | 0 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import Mode... | 711 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import loggin... | 29 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __A ( unittest.TestCase ... | 712 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_a = datasets.logging.get_logger(__name__)
_a = "\\n@InProceedings{moosavi2019minimum,\n author = { Nafise Sadat Moosavi, Leo B... | 29 | 0 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, 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 ... | 713 |
# This is the module that test_patching.py uses to test patch_submodule()
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 j... | 29 | 0 |
class __A :
'''simple docstring'''
def __init__( self , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = size
lowerCamelCase__ = [0] * size
lowerCamelCase__ = [0] * size
@staticmethod
de... | 714 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_a = logging.get_logger(__name__)
class __A :
'''simple docstring'''
lowerCAmelCase_ = None
@experimental
def lowerCAmelCase__(__sna... | 29 | 0 |
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 __A ( lowerCAmelCase ):
'''simple docstring'''
def __init__( se... | 715 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __A ... | 29 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Any = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InformerConfig""",
],
}
... | 716 |
_a = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
_a = [{"type": "code", "content": INSTALL_CONTENT}]
_a = {
"{processor_class}": "FakeProcessorC... | 29 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}
try:
if not ... | 717 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_a = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise ... | 29 | 0 |
class __A : # Public class to implement a graph
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = row
lowerCamelCase__ = c... | 718 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_a = logging.get_logger(__name__)
class __A ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ... | 29 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( low... | 719 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> Any:
'''simple docstring'''
lowerCamelCase__ = {
'''en''': '''Machine learning is great, isn\'t it?''',
... | 29 | 0 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
... | 720 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a = logging.get_logger(__name__)
class __A ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCa... | 29 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class __A ( unittest.TestCase ):
'''simple docstring'... | 721 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,) -> float | int:
'''simple docstring'''
for nxt, d in graph[v]... | 29 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNeta... | 700 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """ClapFeatureExtractor"""
lowerCAmelCase_ = ("""RobertaTokenizer""", """RobertaToken... | 29 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_a = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise Optio... | 701 |
from __future__ import annotations
import unittest
from transformers import 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_tensor, random_attention_mask
from ...test_pip... | 29 | 0 |
def lowerCAmelCase__(__snake_case ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError('''Input must be a positive integer''' )
lowerCamelCase__ = [True] * (num + 1)
lowerCamelCase__ = 2
while p * p <= num:
if primes[p]:
for... | 702 |
import string
from math import logaa
def lowerCAmelCase__(__snake_case ,__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = document.translate(
str.maketrans('''''' ,'''''' ,string.punctuation ) ).replace('''\n''' ,'''''' )
lower... | 29 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> str:
'''si... | 703 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
"convert_funnel_origin... | 29 | 0 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
_a = (3, 9, -11, 0, 7, 5, 1, -1)
_a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase_ = 42
lowerCAm... | 704 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_a = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",
... | 29 | 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_roformer": ["ROFORMER_PRETRAINED_CONFIG_AR... | 705 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_atten... | 29 | 0 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils... | 706 |
from math import sqrt
def lowerCAmelCase__(__snake_case ) -> bool:
'''simple docstring'''
assert isinstance(__snake_case ,__snake_case ) and (
number >= 0
), "'number' must been an int and positive"
lowerCamelCase__ = True
# 0 and 1 are none primes.
... | 29 | 0 |
def lowerCAmelCase__(__snake_case ) -> str:
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 707 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None ) -> None:
'''simple docstring'''
if start is None:
lowerCamelCase__ = 0
if end is None:
lowerCamelCase__ = len(__snake_case ) - 1... | 29 | 0 |
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 __A ... | 708 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> float:
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('... | 29 | 0 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 709 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCAmelCase__(__snake_case ) -> Union[str, Any]:
'''simple docstring'''
def wrapper(*__snake_case ,**__snake_case ):
lo... | 29 | 0 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
_a = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
raise ImportWarning(
... | 710 |
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
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]
lowerCa... | 29 | 0 |
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