code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def __lowerCamelCas... | 241 |
"""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,
)
lowercase__ = {
"""configuration_clip"""... | 241 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_in... | 351 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase ... | 187 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 264 | """simple docstring"""
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/resolv... | 290 | 0 |
import copy
import re
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = 'hp'
lowerCAmelCase__ = {}
lowerCAmelCase__ = None
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase , lowercase ) -> Tuple:
lowerCamelCase_ = prefix
... | 47 |
from sklearn.metrics import recall_score
import datasets
__A ='''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
'''
__A ='''
Arg... | 47 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_a... | 76 |
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , a : Any ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = data
SCREAMING_SNAKE_CASE : int = None
... | 76 | 1 |
"""simple docstring"""
import os
from collections.abc import Iterator
def UpperCAmelCase__ ( lowerCAmelCase__ :str = "." ) -> str:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowerCAmelCase__ ):
lowercase = [d for d in dir_na... | 359 | """simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def UpperCAmelCase__ ( lowerCAmelCase__ :Union[str, Any] ) -> Dict:
'''simple docstring'''
if "img... | 32 | 0 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
A = ("""dense.weight""... | 258 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unord... | 258 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase_ = (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A :
"""simple docstring... | 361 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 1000 ) -> int:
'''simple docstring'''
A__ = 3
A__ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
res... | 282 | 0 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipe... | 28 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
lowercase__ : List[str] = {
"""huggingface/infor... | 224 | 0 |
from __future__ import annotations
import numpy as np
def _lowercase ( UpperCamelCase_ ) -> Optional[int]:
'''simple docstring'''
return np.maximum(0 , _A )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 355 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_... | 169 | 0 |
def A ( _lowercase ):
if n == 1 or not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return 0
elif n == 2:
return 1
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = [0, 1]
for i in range(2 ,... | 182 |
import pytest
__UpperCAmelCase : Optional[Any] = "__dummy_dataset1__"
__UpperCAmelCase : List[str] = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\":... | 111 | 0 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,... | 258 | import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_... | 258 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForS... | 308 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_lowerCamelCase ) , ... | 308 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ : Optional[int] = logging.get_logger(__name__)
lowerCamelCase_ : Dict = {
"""bert-base-unc... | 197 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ : Dict = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}
try:
if not is_torch_ava... | 197 | 1 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase = "x", lowerCamelCase = 1_0**-1_0, lowerCamelCase = 1, ):
__lowerCAmelCase = symbols(lowerCamelCase)
... | 174 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : Optional[Any] = {
"""config... | 174 | 1 |
def lowerCAmelCase_ (lowerCAmelCase__: Tuple = 1_0_0 ):
"""simple docstring"""
UpperCAmelCase_: Optional[int] = (n * (n + 1) // 2) ** 2
UpperCAmelCase_: Optional[int] = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name... | 358 |
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 : Dict = datasets.logging.get_logger(__name__)
a : Any = '\\n@InProceedings{moosavi2019minimum,\n ... | 82 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_ut... | 279 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCAmelCase_ = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d3... | 279 | 1 |
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 ( __l... | 44 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
... | 250 |
# Algorithm for the pigeonhole sorting
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : List[Any] = min(a__) # min() finds the minimum value
a_ : List[str] = max(a__) # max() finds the maximum value
a_ : str = max_val - min_val + 1 ... | 248 | 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex... | 364 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ = None ) -> List[str]:
lowerCAmelCase__ = value
... | 228 | 0 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : int=1 ):
"""simple docstring""... | 93 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : Optional[int] = {
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingfa... | 32 | 0 |
"""simple docstring"""
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def snake_case ... | 253 |
"""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 ..ut... | 253 | 1 |
def A (__A : int ) -> list:
"""simple docstring"""
UpperCAmelCase_ = int(__A )
if n_element < 1:
UpperCAmelCase_ = ValueError('''a should be a positive number''' )
raise my_error
... | 51 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : int = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "Deber... | 51 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowerCamelCase_ ( _a ):
... | 211 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''Bei... | 211 | 1 |
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=_lowerCamelCase ):
lowerCAmelCase__ = ["""flax"""]
def __init__(self :List[str] , *_UpperCamelCase :int , **_UpperCamelCase :Dict )-> List[str]:
requires_backends(self ... | 117 |
from __future__ import annotations
def _a ( lowerCamelCase: list[float] , lowerCamelCase: Tuple ) -> List[str]:
'''simple docstring'''
print(F"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(lowerCamelCase ):
... | 117 | 1 |
import argparse
import os
import re
_UpperCAmelCase = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_UpperCAmelCase = re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+Ord... | 367 | import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
... | 192 | 0 |
"""simple docstring"""
import socket
def lowerCamelCase__ ( ) -> Optional[int]:
lowerCamelCase_ = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCamelCase_ = socket.gethostname()
lowerCamelCase_ = ... | 183 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : Dict = {... | 107 | 0 |
def _a ( ) -> Any:
'''simple docstring'''
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def _a ( SCREAMING_SNAKE_CASE__ : Any ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, A... | 361 |
class lowerCamelCase :
"""simple docstring"""
def __init__( self : int, _UpperCAmelCase : Dict, _UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CAS... | 191 | 0 |
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_channel_dimension_format,
)... | 259 | '''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : ... | 31 | 0 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
lowerCAmelCase : List[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ... | 251 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A_( A : List[Any]):
UpperCamelCase = [
'encoder.version',
'decoder.version',
... | 251 | 1 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_... | 42 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, r... | 82 | 0 |
import random
def UpperCAmelCase ( a_, a_, a_ ):
'''simple docstring'''
lowerCamelCase : Union[str, Any] = a[left_index]
lowerCamelCase : Tuple = left_index + 1
for j in range(left_index + 1, a_ ):
if a[j] < pivot:
... | 359 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( __UpperCAmelCase ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'ChineseCLIPImageProcessor'
lo... | 205 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformer... | 55 |
"""simple docstring"""
from math import pow
def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , ) -> tuple[int, int]:
if current_sum =... | 288 | 0 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[Any] = {
'''snap-research/efficientformer-l1-300''': (
'''h... | 218 |
import datasets
from .evaluate import evaluate
_SCREAMING_SNAKE_CASE : Union[str, Any] = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
bookt... | 218 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( __UpperCAmelCase: int , __UpperCAmelCase: int ) -> list[list[int]]:
UpperCamelCase__ : list[list[int]] = []
create_all_state(1 , __UpperCAmelCase , __UpperCAmelCase , [] , __... | 201 |
import random
def lowerCAmelCase_ ( __UpperCAmelCase: list , __UpperCAmelCase: Optional[int] ) -> tuple:
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : List[Any] = [], [], []
for element in data:
if element < pivot:
... | 201 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cac... | 352 |
"""simple docstring"""
def a__ ( snake_case__ , snake_case__ ) -> int:
return number | (1 << position)
def a__ ( snake_case__ , snake_case__ ) -> int:
return number & ~(1 << position)
def a__ ( snake_case__ , snake_case__ ) -> int:
return num... | 168 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase ( A__ ):
"""simple docstring"""
def __init__( self ... | 97 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , ... | 321 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_A = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenizatio... | 369 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precisi... | 212 | 0 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
A__ = """\
"""
A__ = """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It ... | 82 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MA... | 249 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licens... | 86 |
"""simple docstring"""
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_availabl... | 86 | 1 |
'''simple docstring'''
from __future__ import annotations
_A : List[Any] ='''Muhammad Umer Farooq'''
_A : Union[str, Any] ='''MIT'''
_A : List[Any] ='''1.0.0'''
_A : List[str] ='''Muhammad Umer Farooq'''
_A : str ='''contact@muhammadu... | 41 |
"""simple docstring"""
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> list:
'''simple docstring'''
if len(__lowerCAmelCase ) != 2 or len(a[0] ) != 2 or len(__lowerCAmelCase ... | 136 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( A_ , A_ )-> list[str]:
'''simple docstring'''
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_bytes:
raise ... | 368 |
"""simple docstring"""
import sys
import turtle
def lowercase ( A_ , A_ )-> tuple[float, float]:
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase ( A_ , A_ , A_ , A_ , ... | 226 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class a :
def __init__( self : Union[str, Any] ):
_UpperCAmelCase = {}
def lowerCAmelCase_ ( self : Optional[int] , __lowerCAmelCase ... | 289 | """simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
... | 289 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
... | 158 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import... | 158 | 1 |
def UpperCamelCase_( lowerCamelCase_ ) -> list:
if len(lowerCamelCase_ ) < 2:
return collection
def circle_sort_util(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> bool:
_lowercase : Any = False
if low == high:
... | 21 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowerCamelCase ( metaclass=lowercase__ ):
'''simple docstring'''
A_ : Optional[Any] = ["""flax""", """transformers"""]
def __init__( self : Union[str, Any] , *_A : ... | 331 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
A: int = 5_0_0_0_0
A: List[Any] = 5_0_0_0
A , A: Tuple = os.path.split(__file__)
A: Dict ... | 351 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 76 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae... | 37 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__na... | 37 | 1 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
lowerCAmelCase__ : Tuple = Lock()
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmel... | 37 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
lowerCAmelCase__ : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __UpperCamelCase ( ):
... | 37 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diff... | 312 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class _a :
"""simple docstring"""
@property
def __A ... | 312 | 1 |
import unittest
import numpy as np
from transformers import AlbertConfig, 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.numpy as jnp
from tr... | 366 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = '''▁'''
lowerCAm... | 121 | 0 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _lowerCamelCase ( lowercase : Union[str, Any] , lowercase : int , lowerc... | 63 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
... | 74 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
... | 361 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_... | 329 | 0 |
from heapq import heappop, heappush
import numpy as np
def __UpperCamelCase ( _A : np.ndarray , _A : tuple[int, int] , _A : tuple[int, int] , _A : bool , ) ->tuple[float | int, list[tuple[int, int]]]:
"""simple docstring"""
lowerCamelCase_ , lowerCamelCase... | 154 |
# Copyright 2022 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 applica... | 154 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffuse... | 368 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
UpperCAmelCase_ : Any = """examples/"""
UpperCAmelCase_ : Optional[int] = {
"""examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check... | 318 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__UpperCAmelCase = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSp... | 323 |
'''simple docstring'''
from __future__ import annotations
__UpperCAmelCase = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"... | 323 | 1 |
def A ( _UpperCAmelCase : int ) -> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
_UpperCAmelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCAmelCase =... | 290 |
UpperCAmelCase__ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : Optional[int] , _Upper... | 290 | 1 |
'''simple docstring'''
def _a( UpperCamelCase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any =len(__UpperCAmelCase )
for _ in range(__UpperCAmelCase ):
for i in range(_ % 2, arr_size -... | 152 | """simple docstring"""
__A = [
(1_0_0_0, "M"),
(9_0_0, "CM"),
(5_0_0, "D"),
(4_0_0, "CD"),
(1_0_0, "C"),
(9_0, "XC"),
(5_0, "L"),
(4_0, "XL"),
(1_0, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) ->... | 177 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
lowerCAmelCase : Tuple =logging.get_logger(__name__)
def UpperCAmelCase_ ( __lowerCamelCase : Union[tf.Tensor, np.ndarray] ):
if isinstance(__low... | 353 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : int | float | str ):
try:
lowercase_ :Optional[int] = float(__lowerCamelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
lowercase_ :Dict = ... | 147 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHI... | 249 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Opt... | 249 | 1 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__lowercase = '''\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and... | 105 | from __future__ import annotations
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 valu... | 105 | 1 |
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 182 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : List[str] ={
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',... | 53 | 0 |
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
print("\nThe shortest path matrix using Floyd Warshall algorithm\n" )
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
if dist[i][j] != float("inf" ):
print(int... | 325 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import ... | 325 | 1 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from tra... | 32 | import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
' Distillatio... | 175 | 0 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase_( a__ , a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int ... | 19 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
a__ : Any = TypeVar('''T''')
def UpperCAmelCase_( a__ ):
"""simple docstring"""
return (position - 1) // 2
def UpperCAmelCase_( a__ ):
"""simple docs... | 19 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import... | 75 |
"""simple docstring"""
from __future__ import annotations
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> None:
'''simple docstring'''
UpperCAmelCase : Any = data
UpperCAmelCase : Node | None = None
UpperCAmelCase : ... | 109 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
sk... | 370 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase__ : Union[str, Any] = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_ber... | 301 | 0 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class lowerCamelCase (_snake_case ):
'''... | 29 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess... | 29 | 1 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInp... | 75 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models... | 75 | 1 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class _snake_case ( __snake_case ):
'''simple docstring'''
def __init__( self: int ,*lowerCamelCase_: List[s... | 345 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
UpperC... | 345 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch,... | 52 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowerCAmelCase__ ... | 52 | 1 |
"""simple docstring"""
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,
StableDiffusion... | 293 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 293 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Tuple = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not is_tokenizers_av... | 305 | from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None ... | 305 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
snake_case_ : ... | 83 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForce... | 186 | 0 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before token... | 362 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_A = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
' Distillation'
)
)
pars... | 117 | 0 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
lowerCAmelCase__ : Any = 'us-east-1' # defaults region
@dataclass
class snake_case :
"""simple docstring"""
snake_case__ = 42
snake_case__ = "arn:aws:iam::558105141721:role/sagemak... | 98 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case : List[Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptToken... | 284 | 0 |
from datetime import datetime
import requests
def _lowercase ( _UpperCAmelCase ) -> bytes:
lowerCamelCase ="""https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
lowerCamelCase =requests.get(base_url + url ).json()[0]["""urls"""][0]["""src"""]
return re... | 362 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
UpperCAmelCase__ : List[Any] =logging.get_logger(_... | 262 | 0 |
'''simple docstring'''
def __snake_case ( ):
lowerCamelCase_ = []
lowerCamelCase_ = 1
while len(UpperCAmelCase_ ) < 1E6:
constant.append(str(UpperCAmelCase_ ) )
i += 1
lowerCamelCase_ = "".join(UpperCAmelCase_ )
return (
int(constant[0] )
* int(... | 55 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( UpperCAmelCase_ : int ):
lowerCamelCase_ = 2
lowerCamelCase_ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(UpperCAmelCase_ )
if n > 1:
fac... | 55 | 1 |
'''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... | 170 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 170 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__A =logging.get_logger(__name__)
__A ={'''vocab_file''': '''vocab.txt'... | 19 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_mod... | 19 | 1 |
'''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_lowercase = 299792458
# Symbols
_lowercase , _lowercase , _lowercase , _lowercase = symbols("""ct x y z""")
def A (__lowerCamelCase :float ):
... | 358 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
... | 229 | 0 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from tr... | 208 | import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowercase__ : Any = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/model... | 338 | 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,
convert_to_rgb,
get_resize_output_image_size,
norm... | 354 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ : str = logging.get_logger(__name__)
lowerCamel... | 215 | 0 |
def UpperCamelCase ( _A, _A, _A, _A, _A ):
"""simple docstring"""
if index == number_of_items:
return 0
__magic_name__ : Union[str, Any] = 0
__magic_name__ : str = 0
__magic_name__ : Optional[int... | 342 |
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_backbone_common import Backbone... | 342 | 1 |
import logging
import os
from .state import PartialState
class UpperCAmelCase ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def lowerCAmelCase_ ( lowercase ):
"""simple docstring"""
A_ : List[Any] ... | 192 | import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_UpperCAmelCase = logging.get_logger(__name__)
_Uppe... | 192 | 1 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _A ( *UpperCamelCase_ : List[Any]) -> List[str]:
'''simple docstring'''
if not isinstance(UpperCamelCase_, UpperCamelCase_):
__lowerca... | 17 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCa... | 191 | 0 |
"""simple docstring"""
from math import factorial, radians
def UpperCamelCase_ ( lowerCAmelCase__ : float , lowerCAmelCase__ : int = 18 , lowerCAmelCase__ : int = 10 ) -> float:
"""simple docstring"""
lowerCAmelCase_ ... | 368 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def UpperCamelCase_ ( lowerCAmelCase__ : int ) -> bool:
"""simple docstring"""
lowerCAmelCase_ : int = int(num... | 289 | 0 |
"""simple docstring"""
def snake_case_ ( A_ : int ):
'''simple docstring'''
if num < 0:
return False
_lowerCamelCase : int = num
_lowerCamelCase : int = 0
while num > 0:
_lowerCamelCase ... | 72 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCam... | 199 | 0 |
"""simple docstring"""
def __lowerCamelCase ( a_ : int ) -> list[int]:
if num <= 0:
raise ValueError('''Input must be a positive integer''' )
__SCREAMING_SNAKE_CASE :Dict = [True] * (num + 1)
__SCREAMING_SNAKE_CAS... | 239 |
"""simple docstring"""
def __lowerCamelCase ( a_ : Union[str, Any] , a_ : Optional[Any] ) -> Union[str, Any]:
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __lowerCamelCase ( a_ : Optional[int] ,... | 239 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
while b:
lowercase : Optional[Any] = b, a % b
return a
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
return a if b ==... | 20 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__UpperCAmelCase =... | 119 | 0 |
'''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK... | 242 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase ={
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxCo... | 242 | 1 |
import math
def UpperCAmelCase__ ():
"""simple docstring"""
snake_case = input('''Enter message: ''' )
snake_case = int(input(F'''Enter key [2-{len(UpperCamelCase_ ) - 1}]: ''' ) )
snake_case = input('''Encryption/Decryption [e/d]: ''' ... | 127 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached... | 127 | 1 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __snake_case ( _SCREAMING_SNAKE_... | 353 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers ... | 89 | 0 |
'''simple docstring'''
def lowercase__ ( __lowercase : list[int] ) -> int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(__lowercase , (list, tuple) ) or not all(
isinstance(__lowercase , __lowercase ... | 53 |
'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules... | 53 | 1 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ : int = logging.get_logger(__na... | 369 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mod... | 289 | 0 |
'''simple docstring'''
import math
class __a :
def UpperCAmelCase__ ( self : List[str] , __magic_name__ : list[list[float]] , __magic_name__ : list[int] ) -> int:
"""simple docstring"""
UpperCAmelCase_... | 125 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class __a :
def __init__( self : int , __magic_name__ : int , __magic_name__ : MutableSequence[float] ) -> None:
""... | 125 | 1 |
"""simple docstring"""
__A = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
__A = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
__A ... | 254 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__A = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
__A = _Lazy... | 254 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logg... | 339 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a_ :Any = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mi... | 277 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiec... | 352 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class snake_case ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , *__UpperCAmelCase , **__Uppe... | 303 | 0 |
class lowercase__ :
def __init__( self : List[str] , UpperCAmelCase_ : List[str] ):
# we need a list not a string, so do something to change the type
SCREAMING_SNAKE_CASE__ = arr.split(',' )
def A_ ( self : Tuple ):
... | 176 |
import os
def _lowercase ( ) -> List[str]:
'''simple docstring'''
with open(os.path.dirname(UpperCamelCase_ ) + '/p022_names.txt' ) as file:
SCREAMING_SNAKE_CASE__ = str(file.readlines()[0] )
SCREAMING_SNAKE_CASE__ = names.replace('"' ... | 176 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def a( A : Optional[Any] ) -> Tuple:
"""sim... | 361 |
# using dfs for finding eulerian path traversal
def a( A : int , A : Optional[Any] , A : Any , A : Optional[int]=None ) -> List[str]:
"""simple docstring"""
a = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
... | 71 | 0 |
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