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"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _SCREAMING_SNAKE_CASE ( yaml.SafeLoader ):
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
... | 84 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase : List[str] = """src/transformers"""
lowercase : ... | 99 | 0 |
from collections import defaultdict
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
... | 371 |
from __future__ import annotations
import os
from typing import Any
import requests
_UpperCAmelCase : int = """https://api.github.com"""
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
_UpperCAmelCase : Dict = BASE_URL + """/user"""... | 200 | 0 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
... | 70 |
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
... | 70 | 1 |
"""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
__A = logging.get_logger(__name__)
__A = {
'''microsoft/beit-base-... | 64 | """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'''],
'''tokenization_xlm''': ['... | 64 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class lowercase:
'''simple docstring'''
def __init__( self: Tuple, a_: int ):
'''simple docstring'''
_snake_case : Option... | 64 |
def lowerCAmelCase ( _lowerCAmelCase : str ):
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(_lowerCAmelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 169 | 0 |
def UpperCamelCase ( _a ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError('''Input must be a positive integer''' )
lowercase_ :Optional[int] = [True] * (num + 1)
lowercase_ :Optional[Any] = 2
... | 252 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common imp... | 252 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
A : int = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , *__a , ... | 57 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Dict = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/m... | 270 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__magic_name__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_ava... | 152 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {}
try:
if not is_sentencepiece_available():
raise Optio... | 152 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_t... | 240 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Hugg... | 240 | 1 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
lowerCAmelCase__ :Dict = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: '''))... | 185 |
from importlib import import_module
from .logging import get_logger
lowerCAmelCase__ :Optional[Any] = get_logger(__name__)
class __a :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> List[Any]:
"""simple docstring"""
... | 185 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_f... | 56 |
def snake_case__ ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError('String lengths must match!' )
lowercase__ : Union[str, Any] = ... | 214 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ : Tuple = {'''configuration_encoder_decoder''': ['''EncoderDecoderCon... | 248 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionMod... | 248 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__a = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike ... | 30 |
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern t... | 30 | 1 |
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_camembert imp... | 65 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available()... | 65 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vis... | 98 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : str = 0
for ch in input_str:
_SCREAMING_SNAKE_CASE : Optional[Any] = ord(SCREAMING_SNAKE_CASE__ )
_SCREAMING_SNAK... | 200 | 0 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
... | 151 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PI... | 151 | 1 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 64 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available()... | 64 | 1 |
import os
def _UpperCamelCase () -> Optional[int]:
"""simple docstring"""
UpperCamelCase__ = os.path.dirname(os.path.realpath(__a ) )
UpperCamelCase__ = os.path.join(__a , """triangle.txt""" )
with open(__a ) ... | 355 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
),
# See a... | 87 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtr... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : list , __lowerCamelCase : int = 0 ) -> list:
_snake_case = length or len(__lowerCamelCase )
_snake_case = False
for i in range(length - 1 ):
if list_data[i] > lis... | 288 | 1 |
def __UpperCamelCase ( lowercase__ : int , lowercase__ : Tuple ) -> str:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = (boundary[1] - boundary[0]) / steps
lowerCAmelCase_ : int = boundary[0]
lowerCAmelCase_ : str ... | 362 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __a ( __UpperCa... | 28 | 0 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from... | 152 |
'''simple docstring'''
from __future__ import annotations
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
if not nums:
return 0
SCREAMING_SNAKE_CASE__ : Dict =nums[0]
SCREAMING_SNAKE_CAS... | 152 | 1 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A : Union[str, Any] = logging.get_logger(__name__)
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Optional... | 354 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A : Optional[Any] = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAECon... | 276 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A__ : Any = {
"""configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_M... | 185 |
'''simple docstring'''
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
A__ : Tuple = logging.get_logger(__name__)
A__ : List[str] ... | 185 | 1 |
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
if is_torch_availab... | 355 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, loa... | 238 | 0 |
from cva import destroyAllWindows, imread, imshow, waitKey
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ , a_ : Tuple = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(a__):
for j in range(a__):
a_ : Any ... | 248 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...... | 248 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTextConfig',
... | 363 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils impo... | 189 | 0 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import Polynomial... | 65 | import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied f... | 65 | 1 |
'''simple docstring'''
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCAmelCase : O... | 358 |
'''simple docstring'''
lowerCAmelCase : List[str] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface... | 25 | 0 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
lowercase__ = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: impr... | 151 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import Dif... | 151 | 1 |
'''simple docstring'''
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'philschmid/bart-large-cnn-samsum'
lowercase = (
'This is a tool that summarizes an English ... | 362 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1_3_3_7 , num_e... | 338 | 0 |
'''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
lowercas... | 3 | import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case_ ( __A ):
__A :... | 87 | 0 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_comm... | 324 |
"""simple docstring"""
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_sentencepiec... | 324 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.... | 34 |
'''simple docstring'''
_lowerCamelCase : int = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
... | 28 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'microsoft/fo... | 355 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early... | 302 | 0 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 39 |
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 unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = '''▁'''
__Upper... | 103 |
import re
def UpperCamelCase ( snake_case__ : str ) -> str:
if len(re.findall('[ATCG]' , snake_case__ ) ) != len(snake_case__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main... | 103 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 1_000_000 ) -> int:
_snake_case = limit + 1
_snake_case = [0] * limit
for first_term in range(1 , __A ):
for n in range(__A , __A , __A ):
_snake_case = first_term + n / first_term
if commo... | 42 |
import argparse
import datetime
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : str = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wed... | 232 | 0 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils impor... | 54 | """simple docstring"""
from __future__ import annotations
import math
def lowercase ( a__ : int ) -> list[int]:
if num <= 0:
_UpperCamelCase = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(a__ )
_Upp... | 54 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class _a (... | 110 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowerCamelCase : int ={
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_... | 189 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase_ ( __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 == ... | 156 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE :Dict = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDepende... | 156 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase ( a__ , unittest.TestCase ):
lowercase = C... | 213 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
... | 25 | 0 |
"""simple docstring"""
SCREAMING_SNAKE_CASE = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "Friday",
6: "Saturday",
}
... | 230 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/... | 230 | 1 |
import sys
snake_case : int = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''6689664895044... | 94 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_visio... | 220 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import Aut... | 367 |
from manim import *
class a ( __lowerCamelCase ):
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
snake_case__ : Optional[int] = Rectangle(he... | 44 | 0 |
"""simple docstring"""
import qiskit
def a__ ( snake_case__ = 2 ) -> qiskit.result.counts.Counts:
lowerCamelCase = qubits
# Using Aer's simulator
lowerCamelCase = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circuit acting on t... | 291 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : List[str] = {
"""asapp/sew-d-tiny-100k""": """h... | 291 | 1 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase : int = logging.get_logger(__name__)
UpperCAmelCase : Union[str, Any] ... | 320 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCAmelCase : str = logging.get_logger(__name__)
class lowerCamelCase__ ( A ):
"""simple docstring"""
def _... | 320 | 1 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : List[str] = logging.get_logger(__name__)
# TODO Update this
lowerCamelCase_ : ... | 81 |
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 SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerC... | 302 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
__A = logging.get_logger(__name__)
__A = {'''vocab... | 64 | """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 _snake_case ( a__ ):
def __init__( self : Option... | 64 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A__ : Any = {
'''configuration_owlvit''': [
'''OWLVIT_P... | 103 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transfor... | 103 | 1 |
"""simple docstring"""
from math import factorial
def a_ ( _lowercase , _lowercase , _lowercase ):
if successes > trials:
raise ValueError('''successes must be lower or equal to trials''' )
if trials < 0 or successes < 0:
... | 128 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _a ( _lowerCAmelCase ):
UpperCamelCase = ['''image_processor''', '''feature_extractor''']
UpperCamelCase = '''TvltImageProcessor'''
UpperCamelCase = '''TvltFe... | 128 | 1 |
"""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_mobilebert import MobileBertTokenizer
a__ : int = logging.get_logg... | 54 |
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [0] * no_of_processes
__SCREAMING_SNAKE_CASE ... | 54 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :int = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :str = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
... | 56 |
"""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 | 1 |
from __future__ import annotations
def UpperCAmelCase_ ( __lowerCAmelCase ) -> int:
if not nums:
return 0
__lowercase : Any = nums[0]
__lowercase : str = 0
for num in nums[1:]:
__lowercase , __lowercase : Optional[int] =... | 156 |
import numpy as np
from PIL import Image
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> np.ndarray:
__lowercase : Optional[int] = np.array(__lowerCAmelCase )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input ... | 156 | 1 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def UpperCamelCase ( a ) -> float:
'''simple docstring'''
return np.dot(a , a )
class _SCREAMING_SNAKE_CASE :
def __init__( ... | 98 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threa... | 98 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __snake_case ( UpperCamelCase_ ):
def UpperCAmelCase__ ( self : List[Any] , A_ : str):
with open(A_ , encoding='''utf-8'... | 103 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_C... | 224 | 0 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import... | 362 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE : Any = (7_2_0, 1_2_8_0) # Height, Width
SCREAMING_SNAKE_CASE : List[str] = (0.4, 0.6) # if height or width lo... | 317 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __A ( unittest.TestCase ):
... | 16 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : List[Any] = logging.get_logger(__name__)
_a : Any = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2-tiny-patch4-win... | 44 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class lowercase ( snake_case__):
"""simple docstring"""
a__... | 277 |
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 AudioPipelineOutput... | 277 | 1 |
"""simple docstring"""
import heapq
def a_ ( lowerCamelCase ):
UpperCAmelCase__ = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min... | 98 | import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large... | 343 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testin... | 72 |
'''simple docstring'''
from datetime import datetime
import requests
def __magic_name__ ( __UpperCAmelCase ) -> bytes:
'''simple docstring'''
snake_case_ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
snake_case_ = requests... | 72 | 1 |
"""simple docstring"""
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... | 64 |
"""simple docstring"""
from math import factorial
A_ = {str(d): factorial(d) for d in range(10)}
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(snake_case__ ) )
def UpperCAmelCas... | 64 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
__snake_case : Union[str, 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}... | 366 |
'''simple docstring'''
import torch
from torch import nn
class lowerCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase... | 136 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_uti... | 128 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils... | 128 | 1 |
"""simple docstring"""
from __future__ import annotations
def A (__A : list[list[int]] ) -> bool:
"""simple docstring"""
UpperCAmelCase_ = len(__A )
# We need to create solution object to save path.
UpperCAmelC... | 359 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstring... | 7 | 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
from .utils import re... | 56 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
a : Union[str, Any] = True
except (ImportError, ModuleNotFoundError):
a : Any = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', q... | 56 | 1 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
... | 363 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, rand... | 164 | 0 |
"""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__ : Optional[int] = logging.get_logger(__name__)
lower... | 98 | """simple docstring"""
import argparse
lowerCAmelCase__ : List[str] = 'docs/source/_static/js/custom.js'
def a_ ( lowerCamelCase ):
with open(lowerCamelCase , encoding='utf-8' , newline='\n' ) as f:
UpperCAmelCase__ = f.readlines()
... | 98 | 1 |
from __future__ import annotations
def __UpperCamelCase ( _A , _A , _A , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
raise ValueError('''Electr... | 167 |
_A = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_186_800.00,
"electronvolt": 1.602... | 167 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A : str = datasets.utils.logging.g... | 305 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co... | 305 | 1 |
"""simple docstring"""
from itertools import permutations
def _lowercase ( __snake_case ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
__lowerC... | 360 |
"""simple docstring"""
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils ... | 58 | 0 |
from collections import deque
from .hash_table import HashTable
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
def __init__( self : Optional[Any], *_snake_case : Optional[Any], **_snake_case : List[Any] ) ->Optional[int]:
super... | 277 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import loggin... | 277 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCAmelCase ( unittest.TestCase):
def lowercase_ ( self : Dict ):
"""simple ... | 357 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common impo... | 31 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def snake_case_ ( A_ : str, A_ : float | Decimal, A_ : float = 10**-10 ):
'''simple docstring'''
_lo... | 72 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def snake_case_ ( A_ : Any ):
'''simple docstring'''
_lowerCamelCase : Any = [
'''encoder.ve... | 72 | 1 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowercase : int = {"UserAgent": UserAgent().random}
def UpperCAmelCase_ (_lowerCAmelCase : Any ):
__UpperCamelCase : Optional[Any] ... | 354 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCAmelCase_ (_lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple ... | 171 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, sl... | 79 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
... | 136 | 0 |
import heapq as hq
import math
from collections.abc import Iterator
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase : List[Any]... | 351 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(sorted(SCREAMING_SNAKE_CASE_ ) )
def a ( SCREAMIN... | 315 | 0 |
from __future__ import annotations
import time
_UpperCAmelCase : Optional[int] = list[tuple[int, int]]
_UpperCAmelCase : Dict = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, ... | 285 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 7 | 0 |
from collections import defaultdict
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
A_ : Optional[Any] = 1
A_ : Union[str, Any] = True
for v in tree[start]:
if v not in visited:
ret += dfs(lowerCamelCase_ )
if ret % 2 == 0:
cuts.append(lowerCamelCase_ )
re... | 367 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available()... | 65 | 0 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
f... | 113 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
def _A ( lowercase__ ):
lowercase__ ... | 164 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowercase ( _UpperCAmelCase = "isbn/0140328726" ) -> dict:
lowerCamelCase =olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("... | 358 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
... | 262 | 0 |
"""simple docstring"""
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
... | 167 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCa... | 167 | 1 |
from __future__ import annotations
import numpy as np
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return np.maximum(0 , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 366 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transforme... | 244 | 0 |
import os
import re
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
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelC... | 50 |
'''simple docstring'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowercase_ = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": operator.ge,
""">""": ... | 58 | 0 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTeste... | 222 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : List[str] ):
'''simple docstring''... | 222 | 1 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
fro... | 6 | '''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : list ) -> list:
"""simple docstring"""
_UpperCAmelCase : List[Any] = len(_UpperCAmelCase )
for _ in range(_UpperCAmelCase ):
for i in range(_ % 2 , arr_siz... | 31 | 0 |
'''simple docstring'''
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase=False):
if isinstance(lowerCamelCase, lowerCamelCase) and isinstance(lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = len(set_a.intersection(lowerCamelCase))
... | 367 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
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 ImageProces... | 9 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE models at https://huggingfa... | 61 |
"""simple docstring"""
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 lowerCamelCase ( lowerCAmelCase__ , unitt... | 171 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import ... | 370 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 320 | 0 |
from collections import deque
from math import floor
from random import random
from time import time
class _A :
def __init__( self : int ) -> List[Any]:
"""simple docstring"""
lowercase : Optional[Any] = {}
... | 308 |
"""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 OptionalDepe... | 315 | 0 |
'''simple docstring'''
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM... | 101 |
'''simple docstring'''
import sys
__lowerCamelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'... | 101 | 1 |
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(snake_case , x % y )
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
return (x * y) // greatest_c... | 82 | import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied f... | 65 | 0 |
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...t... | 364 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils impo... | 14 | 0 |
"""simple docstring"""
def lowercase_ ( _snake_case ):
assert column_title.isupper()
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
SCREAMING_SNAKE_CASE__ : List[str] = len(lowerCAmelCase_ ) - 1
SCREAMING_SNAKE_CASE__ ... | 25 |
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 to... | 262 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 5 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
__lowerCAmelCase = datasets.logging.get_logger(__name__)
__lowerCAmelCase = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavi... | 5 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 62 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__UpperCAmelCase = [[] for _ in range(SCREAMING_SNAKE_CASE )]
__UpperCAmelCase = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t... | 333 | 0 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logg... | 351 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCamelCase_ ( _UpperCamelCase ) -> tuple:
... | 279 | 0 |
def A ( lowercase ) -> list:
'''simple docstring'''
UpperCamelCase = len(lowercase )
for _ in range(lowercase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
UpperCamelCase , UpperCamelCase = arr[i + 1], arr[i]
return arr... | 222 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A ( lowercase ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase = [
'encoder.version',
'decoder.version',
'model.encoder.versi... | 222 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAv... | 366 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transforme... | 244 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
f... | 288 |
from importlib import import_module
from .logging import get_logger
__lowerCAmelCase : str =get_logger(__name__)
class _lowercase :
'''simple docstring'''
def __init__( self :List[Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :str=None ) -> int:
... | 9 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from t... | 359 |
# Copyright 2021 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/LICENSE-2.0
#
# U... | 14 | 0 |
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