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"""
def _snake_case ( UpperCamelCase : int = 1000000 ):
UpperCAmelCase : Tuple = 1
UpperCAmelCase : Optional[Any] = 1
UpperCAmelCase : Any = {1: 1}
for inputa in range(2 , UpperCamelCase ):
UpperCAmelCase : Any = 0
UpperCAmelCase : T... | 109 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
A: Optional[Any] = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv p... | 109 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
fro... | 225 |
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 impor... | 225 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : str = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TimeSeriesTransformer... | 50 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ = 1_00_00_00 ) -> int:
__lowerCamelCase = set(range(3 , UpperCamelCase__ , 2 ) )
primes.add(2 )
for p in range(3 , UpperCamelCase__ , 2 ):
if p not in primes:
continue
pri... | 67 | 0 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class a_ ( a__ , unittest.TestCase ):
"""simple do... | 19 |
from math import pi, sqrt, tan
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def UpperCAmelCase_( a__ , a__ , a__ ):
""... | 19 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ : Dict = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Config'''... | 38 |
"""simple docstring"""
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
... | 91 | 0 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
lowercase_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2)... | 353 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from d... | 11 | 0 |
"""simple docstring"""
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLIC... | 167 |
"""simple docstring"""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowercase ( __UpperCAmelCase , __... | 167 | 1 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class snake_case ( datasets.BuilderConfig ):
SCREAMING_SNAKE_CASE... | 108 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a__... | 108 | 1 |
from abc import ABC, abstractmethod
from typing import List, Optional
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self ):
'''simple docstring'''
# test for the above condition
self.test()
def lowerCamelCase ( self ):
'''simple docstring'''
... | 330 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import A... | 330 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
"configuration_longformer": [
"LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Longfo... | 359 |
'''simple docstring'''
UpperCAmelCase_ = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
UpperCAmelCase__ = 0
while number:
# Increased Speed Slightly by checking ... | 61 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'facebook/xmod-base': 'https://hu... | 290 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-hoppe... | 290 | 1 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenize... | 277 |
import warnings
from functools import wraps
from typing import Callable
def __a ( lowerCAmelCase_ : Callable ) -> Callable:
'''simple docstring'''
@wraps(lowerCAmelCase_ )
def _inner_fn(*lowerCAmelCase_ : List[Any] ,**lowerCAmelCase_ : Tuple ... | 277 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
... | 151 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
... | 151 | 1 |
"""simple docstring"""
__A = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def _lowerCamelCase() -> Optional[Any]:
_lowerCAmelCase =input("""Enter message: """ )
_lowerCAmelCase =input("""Enter key [alphanumeric]: """ )
_lowerCAmelCase =input("""Encrypt/Decrypt [e/d]: """ )
if ... | 371 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = '▁'
... | 341 | 0 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowerCAmelCase__ = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ :
"""simple... | 108 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , ... | 108 | 1 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPi... | 365 |
import socket
def a__ ( ):
SCREAMING_SNAKE_CASE_ : Dict = socket.socket(socket.AF_INET, socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE_ : Any = socket.gethostname()
SCREAMING_SNAKE_CASE_ : List[str] = 1_2_3_1_2
sock.... | 162 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
SCREAMING_SNAKE_CASE_ = coefficient_matrix.shape
SCREAMI... | 118 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_com... | 255 | 0 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
lowercase_ = "Usage of script: script_name <size_of_canvas:int>"
lowercase_ = [0] * 1_00 + [1] * 10
random.shuffle(choice)
def __lowerCAmelCase ( __Up... | 367 | import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.j... | 20 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_avail... | 297 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/git-base""": """https://huggingface.co/mi... | 296 | 0 |
"""simple docstring"""
def _A ( UpperCamelCase_ : str, UpperCamelCase_ : str) -> float:
'''simple docstring'''
def get_matched_characters(UpperCamelCase_ : str, UpperCamelCase_ : str) -> str:
__lowercase = []
__lowercase = min(len... | 144 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 144 | 1 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAM... | 334 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "vocab.txt"}
_lowerCamelC... | 334 | 1 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> List[str]:
lowercase__: List[str] = np.max(lowerCamelCase_ , axis=-1 , keepdims=lowerCamelCase_ )
lowercase__: List[Any] = np.exp(outputs ... | 357 | """simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
__A = "<<<<<<< This should probably be modified because it mentions: "
__A = "=... | 2 | 0 |
def lowercase( UpperCamelCase_ ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase = [0] * len(lowerCAmelCase__ )
UpperCamelCase = []
UpperCamelCase = [1] * len(lowerCAmelCase__ )
for values in graph.values():
for i in values:
indegree[i] +... | 343 |
"""simple docstring"""
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 Uppe... | 224 | 0 |
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = OmegaConf.load(__UpperCamelCas... | 351 | from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
... | 305 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 45 |
"""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
lowercase_ = logging.get_logger(__name__)
lowercase_... | 45 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : int = logging.get_logger(__name__)
lowerCamelCase : List[str] = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-g... | 352 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@requi... | 306 | 0 |
"""simple docstring"""
import os
import sys
import unittest
__lowercase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( #... | 40 |
import random
from typing import Any
def a_ ( _A ) -> list[Any]:
"""simple docstring"""
for _ in range(len(_A ) ):
snake_case__ = random.randint(0 , len(_A ) - 1 )
snake_case__ = random.randin... | 307 | 0 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __UpperCamelCase :
def __init__( self , __a , __a , __a , __a , __a , __a=0.2 , __a=0.2 ):
'''simple docstring'''
... | 294 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 294 | 1 |
"""simple docstring"""
_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 = ... | 61 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float(moles / volume ) * nfactor )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return round(float((moles * 0.0821 * temperature) / (volume) ) )
def ... | 61 | 1 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ..... | 363 |
"""simple docstring"""
import itertools
import math
def _snake_case ( lowercase__ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 1 | 0 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_tr... | 61 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : int = f"""{file}_{class_name}_{test_name}"""
done_test[_id] += 1
with ope... | 61 | 1 |
'''simple docstring'''
def _A (lowerCAmelCase__ ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeErro... | 370 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_com... | 104 | 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, prepare_image_inputs
if is_torch_available():
import torch
... | 348 | from collections.abc import Sequence
from queue import Queue
class a__ :
def __init__( self : int,_A : List[Any],_A : Optional[Any],_A : Optional[int],_A : int=None,_A : List[str]=None ):
"""simple docstring"""
... | 18 | 0 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = F"{sampling_rate}"
UpperCamelCase = "1... | 359 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if... | 244 | 0 |
def lowercase_ ( _A : int | float | str ):
"""simple docstring"""
try:
lowerCamelCase__ : Optional[Any] = float(_A )
except ValueError:
raise ValueError("Please enter a valid number" )
lowerCamelCase__ : Union[... | 184 |
class _lowercase :
"""simple docstring"""
def __init__( self : List[Any] , __lowerCamelCase : int ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = size
lowerCamelCase__ : Lis... | 184 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if number < 0:
raise ValueError("number must not be negative" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 195 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [0 for i in range(r + 1 )]
# nc0 = 1
__SCREAMING_SNAKE_CASE = 1
for i in range(1 , n + 1... | 195 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A_ = 50_00_00
A_ , A_ = os.path.split(__file__)
A_ = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS... | 64 |
'''simple docstring'''
import argparse
from collections import defaultdict
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_snake_case = f"""{fil... | 341 | 0 |
import pprint
import requests
SCREAMING_SNAKE_CASE_:str = """https://zenquotes.io/api"""
def __UpperCamelCase ( ) -> Tuple:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __UpperCamelCase (... | 365 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_:Optional[int] = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas""": ["""... | 115 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowerCamelCase_ : str = TypeVar("""T""")
class __A ( Generic[T] ):
"""simple docstring"""
... | 81 | import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config... | 43 | 0 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
... | 250 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _a ( lowerCamelCase: List[Any] ) -> List[Any]: # pi... | 250 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ : List[str] = logging.get_logger(__name__)
A__ : str = {
'''google/bigbird-roberta-base''': '''https://... | 103 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCAmelCase_ = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''KD 6... | 279 | 0 |
"""simple docstring"""
lowerCAmelCase__ = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609344,
"knot": 1.852,
}
lowerCAmelCase__ = {
"km/h": 1.0,
"m/s": 0.277777778,
"mph": 0.621371192,
"knot": 0.539956803,
}
def a__ ( SCREAMING_SNAKE_CASE : float... | 133 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( lowercase ... | 133 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __snake_case ( _lowercase):
snake_case__ : List[Any] = "Speech2TextFeatureExtractor"
snake_case__ : Union[str, Any] = "Speech2Te... | 72 |
__UpperCAmelCase : int = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, A... | 111 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pi
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> dict[str, float]:
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ... | 365 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Any:
'''simple docstring'''
if (
(cp >= 0x4e00 and cp <= 0x9fff)
... | 313 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feat... | 11 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 13 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def _snake_case ( lowercase__ : float , lowercase__ : float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError("""Inductance cann... | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , ... | 1 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNe... | 239 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_: Union[str, Any] =logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__(self : int , *__a : Dict , *... | 1 | 0 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : list ):
"""simple docstring"""
lowerCamelCase__ : Dict =len(__lowerCamelCase )
for _ in range(__lowerCamelCase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i +... | 368 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def snake_case__ ( __lowerCamelCase : jnp.ndarray , __lowerCamelCase : int , __lowerCamelCase : float = 1 , __lowerCamelCase : float = 1 , __lowerCamelCase :... | 272 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCAmelCase_ ( ... | 22 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''xlm-roberta-base''': '''https://huggin... | 310 | 0 |
import socket
def __UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
A : int = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
A : Dict = socket.gethostname()
A : int = 1_2312
sock.connect((host, port)... | 361 |
import socket
def __UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
A : str = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
A : Union[str, Any] = socket.gethostname()
A : Dict = 1_2312
sock.conn... | 115 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE : Optional[Any] = {
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
... | 102 |
"""simple docstring"""
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 Tokenize... | 102 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __lowercase ):
... | 15 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_uti... | 15 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
a =TypeVar("""T""")
class A_ ( Generic[T] ):
def __init__( self : List[str] ,SCREAMING_SNAKE_CASE__ : list[T] ,SCREAMING_SNAKE_CASE__ : Callabl... | 73 |
'''simple docstring'''
A__: Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+http... | 276 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLN... | 185 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __a ( UpperCAmelCase ):
_a : Union[List[np.ndarray], torch.... | 185 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def lowerCAmelCase_ ( snake_case_ : float , snake_case_ : float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or... | 1 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
UpperCAmelCase_ = str(bin(snake_cas... | 1 | 1 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
... | 357 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
... | 238 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str , snake_case__ : int ):
"""simple docstring"""
_snake_case : Union[str, Any] = Path(snake_case__ )
... | 64 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoencoder... | 132 | 0 |
def UpperCamelCase ( ):
snake_case : Tuple = []
snake_case : Tuple = 1
while len(__lowerCamelCase ) < 1E6:
constant.append(str(__lowerCamelCase ) )
i += 1
snake_case : List[Any] = "".j... | 10 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__lowerCamelCase = """."""
if __name__ == "__main__":
__lowerCamelCase = os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
... | 10 | 1 |
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 (
FlaxForcedBOSTokenLogi... | 300 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from fla... | 60 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["image_processor", "tokenizer"]
lowercase_ = "ChineseCLIPImageProcess... | 210 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@staticmethod
@abstractmethod
def lowerCAmelCase_ ( _lowerCAmelCase : ArgumentParser ):
raise NotImplementedError()
... | 210 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
A ={'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig'... | 34 | def lowerCAmelCase_ ( __A, __A ) -> None:
'''simple docstring'''
UpperCAmelCase__ = len(__A )
print("The following activities are selected:" )
# The first activity is always selected
UpperCAmelCase__ = 0
print... | 65 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCo... | 371 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( lowerCAmelCase , unittest.TestCase ):
_a : ... | 285 | 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 a_ ( l... | 98 | """simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def a_ ( lowerCamelCase ):
return np.dot(lowerCamelCase , lowerCamelCase )
class snake_case :
"""simple docstring"""
def __... | 98 | 1 |
'''simple docstring'''
def __lowercase ( __lowercase ) -> bool:
'''simple docstring'''
_A = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
_A = set()
return any(
node not in visited and depth_first_se... | 365 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {'''configuration_... | 174 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_avai... | 316 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't b... | 316 | 1 |
_A = [
'''DownloadConfig''',
'''DownloadManager''',
'''DownloadMode''',
'''StreamingDownloadManager''',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 368 |
def __UpperCamelCase ( _A ):
if length <= 0 or not isinstance(_A , _A ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(_A )]
if __name__ == "__main__":
print(hexagonal_numbers(length=5))
print(hexagonal_numbers(... | 167 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__A : Dict = logging.get_logger(__name__)
class _UpperCAmelCase ( _A ):
def __init__( self : ... | 33 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
SCREAMING_SNAKE_CASE__ : Union[str, Any] = re.compile(r"\b(a|an|the)\b", re.UNICODE)
SCREAMING_SNAKE_CASE__ : int = None
def __magic_name__ ( ) -> str:
_... | 270 | 0 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 357 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compos... | 322 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class lowercase__ :
def __init__( self : Any , snake_case__ : int ):
lowerCamelCase_ : Any =num_of_nodes
lowerCamelCase_ : list[list... | 144 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
A__ : Any = True
except (ImportError, ModuleNotFoundError):
A__ : str = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', q... | 144 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Optiona... | 52 |
'''simple docstring'''
from math import sqrt
def _A ( A__ ):
"""simple docstring"""
assert isinstance(A__ , A__ ) and (
number >= 0
), "'number' must been an int and positive"
__lowercase = True
# 0 and 1 are none primes.
if number <= 1:
__lowercase ... | 52 | 1 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__UpperCAmelCase = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18... | 29 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import pa... | 53 | 0 |
"""simple docstring"""
from __future__ import annotations
import queue
class _lowerCamelCase :
def __init__(self , __a ) -> int:
UpperCamelCase = data
UpperCamelCase = None
UpperCamelCase = None
def a__ ( ):
"""simple docstring""... | 369 |
"""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/licenses/LICEN... | 244 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__UpperCamelCase : Union[str, Any] = 5_0_0_0_0_0
__UpperCamelCase : Optional[Any] = os.path.split(__file__)
__UpperC... | 106 | """simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a)
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : str = field(default="language-modeling" , metad... | 77 | 0 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCAmelCase : List[str] = 10
def SCREAMING_SNAKE_CASE__ ( snake_case :... | 298 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Dict = logging.get_logger(__name__)
_lowerCAmelCase : Union[str, Any] = {
"""snap-research/efficientformer-l1-300""": (
... | 298 | 1 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import... | 195 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shape... | 195 | 1 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections import Counter
def _snake_case ( UpperCamelCase : int ):
UpperCAmelCase : List[Any] = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(UpperC... | 362 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : Dict = """"""
try:
with open(UpperCamelCase , """rb""" ) as binary_file:
UpperCAmelCase : str = binary_file.read()
for dat in data:
UpperC... | 76 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import... | 236 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_UpperCAmelCase : Dict = {"tokenization_tapex": ["TapexTokenizer"]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_UpperCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["... | 236 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[Any] = s.rsplit(_UpperCAmelCase , _Upp... | 127 |
def A_ ( _UpperCAmelCase = 10**9 ):
SCREAMING_SNAKE_CASE_: List[str] = 1
SCREAMING_SNAKE_CASE_: Optional[int] = 2
SCREAMING_SNAKE_CASE_: int = 0
SCREAMING_SNAKE_CASE_: Dict = 0
SCREAMING_SNAKE_CASE_: List[str] = 0
while perimeter <= max_perime... | 127 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configu... | 28 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__A = logging.get_logger(__name__)
__A ... | 164 | 0 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_SCREAMING_SNAKE_C... | 157 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : List[Any] = {
"""microsoft/unispeech-large-1500h-cv""": (
... | 157 | 1 |
'''simple docstring'''
_lowercase : str = tuple[float, float, float]
_lowercase : List[Any] = tuple[float, float, float]
def lowerCamelCase ( UpperCAmelCase__ : Pointad , UpperCAmelCase__ : Pointad ) -> Vectorad:
lowercase_ ... | 239 | '''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
_lowercase : List[str] = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matth... | 239 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig',
'BigBirdPegasusOnnxConfig... | 359 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 56 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_... | 97 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class a ( a_ ):
UpperCAmelCase_ : List[Any] =["image_processor", "tokenizer"]
UpperCAmelCase_ : str ="AutoImageProcessor"
UpperCAme... | 220 | 0 |
import os
import numpy
import onnx
def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int] ):
__lowerCAmelCase = a.name
__lowerCAmelCase = b.name
__lowerCAmelCase = ""
__lowerCAmelCase ... | 102 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a__ ( unittest.TestCase ):
@prop... | 102 | 1 |
"""simple docstring"""
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,... | 266 |
"""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, PyTorchBenchmarkArgu... | 266 | 1 |
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, prepare_video_inputs
if is_torch_available():
... | 361 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__SCREAMING_SNAKE_CASE = 637_8137.0
__SCREAMING_SNAKE_CASE = 635_6752.31_4245
__SCREAMING_SNAKE_CASE = 6378137
def UpperCAmelCase ( _lowerCamelCase , _low... | 256 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase__ : Optional[Any] = {
'google/vivit-b-16x2-kinetics400': (
'https... | 25 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_availabl... | 181 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingfa... | 221 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = "Speech2TextFeatureExtractor"
snake_case__ = "Speech2TextTokenizer"
... | 221 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__UpperCAmelCase = TypeVar('T')
class lowerCamelCase (Generic[T] ):
'''simple docstring'''
def __init__( self , _UpperCame... | 29 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __lowercase ( a__ ) -> Tuple:
__SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
... | 257 | 0 |
"""simple docstring"""
import os
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = len(grid[0] )
UpperCAmelCase = len(lowercase_ )
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 0
# Check ve... | 362 |
"""simple docstring"""
from math import factorial, radians
def _lowerCAmelCase ( lowercase_ , lowercase_ = 18 , lowercase_ = 10 ):
UpperCAmelCase = angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)
# Converting from degrees to radians
Up... | 181 | 0 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
UpperCamelCase_ = None
try:
import msvcrt
except ImportError:
UpperCamelCase_ = None
try:
import fcntl
except ImportError:
UpperCamelCase_ = N... | 345 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.te... | 345 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Any =logging.get_logger(__name__)
__lowerCAmelCase : str ={
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve... | 366 | """simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise TypeError("""only integers accepted as input""" )
else:
lowercase ... | 32 | 0 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEI... | 82 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test im... | 34 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( UpperCamelCase_ = 4 ):
__SCREAMING_SNAKE_CASE = abs(UpperCamelCase_ ) or 4
return [[1 + x + y * row_size for x in range(UpperCamelCase_ )] for y in range(UpperCamelCase_ )]
def _lowerCAme... | 255 |
"""simple docstring"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=1024 ):
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE... | 255 | 1 |
from __future__ import annotations
class _lowercase :
"""simple docstring"""
def __init__(self , lowerCamelCase_=None ):
"""simple docstring"""
a = data
a = None
def __repr__(self ):
"""simple docstring"""
a = []
... | 227 |
def a( A : list ) -> list:
"""simple docstring"""
if any(not isinstance(A , A ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(A ) ):
for i, (... | 227 | 1 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[int]:
"""simple docstring"""
if num <= 0:
A__ = f"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(lowercase_ )... | 231 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> Tuple:
"""simple docstring"""
A__ = AutoConfig.from_pretrained(lowercase_ )... | 231 | 1 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class lowerCamelCase__ ( lowerCAmelCase):
def __init__(self , *UpperCAmelCase , **UpperCAmelCase ) ... | 5 | '''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate i... | 198 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feat... | 304 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowerCAmelCase = logging.... | 304 | 1 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test... | 82 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 104 | 0 |
'''simple docstring'''
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from... | 367 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 98 | 0 |
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