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 .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, In... | 84 | import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY... | 182 | 0 |
"""simple docstring"""
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_a = object()
# For specifying empty leaf dict `{}`
_a = object()
... | 100 |
"""simple docstring"""
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fr... | 100 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __a ( __UpperCamelCase ):
__low... | 196 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test... | 196 | 1 |
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
lowercase : Dict = logging.get_logger(__name__)
lowercase : int = {'vocab_... | 151 |
import random
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : float , _lowerCamelCase : bool = False) -> dict:
'''simple docstring'''
__UpperCamelCase : dict = {i: [] for i in range(_low... | 151 | 1 |
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.tes... | 94 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import V... | 0 | 0 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _A ( datasets.BeamBasedBuilder ):
... | 226 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepa... | 226 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jn... | 110 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 ... | 313 | 0 |
import logging
import os
from .state import PartialState
class A (logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def a_ ( __lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
A_... | 276 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | 276 | 1 |
import torch
from transformers import AutoModel
class _A( torch.nn.Module ):
"""simple docstring"""
def __init__( self , _A="sayef/fsner-bert-base-uncased" ):
super(__a , self ).__init__()
__A : int = AutoModel.from_pretraine... | 280 |
"""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
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ ... | 153 | 0 |
import functools
def _A (__a , __a ) -> List[Any]:
"""simple docstring"""
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) or not all(isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for day in days ):
raise Va... | 370 |
"""simple docstring"""
from __future__ import annotations
import queue
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Tuple , lowercase_ : Optional[int]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ ... | 318 | 0 |
'''simple docstring'''
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)... | 4 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase( __a ):
'''simple docstring'''
lowercase__ = ["image_processor", "tokenizer"]
lowercase__... | 64 | 0 |
"""simple docstring"""
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import r... | 263 |
"""simple docstring"""
def A ( snake_case :int ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
UpperCamelCase : Union[str... | 263 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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... | 160 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''': ['''Lu... | 160 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
P... | 249 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase : Any = {"configuration_vit_mae": ["VIT_MAE_PRETRAINED_CONF... | 249 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageV... | 122 |
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 lowercase_ ( __SCREAMING_SNAKE_CASE ):
A__... | 122 | 1 |
import qiskit
def A_ ( snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : str = qiskit.Aer.get_backend("""aer_simulator""" )
UpperCamelCase : Any = qiskit.QuantumCircuit(4 ,2 )
# ... | 359 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, 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 M... | 27 | 0 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
lowerCamelCase : Any = logging.get_logger(__name__)
class A( UpperCamelCase ):
'''simple docstring'''
def __init__( self : Dict , *A_... | 204 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 204 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__SCREAMING_SNAKE_CASE : Optional[int] = '''\
'''
__SCREAMING_SNAKE_CASE : Any ... | 363 |
"""simple docstring"""
import argparse
from collections import defaultdict
def lowerCAmelCase_( lowercase_ : str , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : str , lowercase_ : str ) -> Optional[int]:
_low... | 73 | 0 |
import pprint
import requests
UpperCamelCase__ = 'https://zenquotes.io/api'
def lowerCAmelCase_ ( ) -> list:
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def lowerCAmelCase_ ( ) -> ... | 65 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCamelCase__ = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
... | 65 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase_ )
class A_ ( lowerCAmelCase_ ):
_lowerCamelCase : str ... | 350 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : list ) -> list:
'''simple docstring'''
_UpperCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
_UpperCAmelCase = True
... | 156 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] = {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/ma... | 88 |
import re
import string
import numpy as np
import datasets
__lowerCAmelCase : Optional[int] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
__lowerCAmelCase : Optional... | 88 | 1 |
"""simple docstring"""
from math import isqrt, loga
def lowercase ( A_ )-> list[int]:
'''simple docstring'''
a : List[str] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j... | 226 |
"""simple docstring"""
def lowercase ( A_ , A_ )-> float:
'''simple docstring'''
def get_matched_characters(A_ , A_ ) -> str:
a : Optional[int] = []
a : List[Any] = min(len(_stra ) , len(_st... | 226 | 1 |
from __future__ import annotations
import numpy as np
def _lowerCAmelCase ( __lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
snake_case__ : Any = np.shape(__lowerCAmelCase )
if rows != columns:
snake_case_... | 230 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_im... | 121 | 0 |
"""simple docstring"""
import math
def lowercase__ ( lowercase_ ) -> bool:
"""simple docstring"""
return math.sqrt(lowercase_ ) * math.sqrt(lowercase_ ) == num
def lowercase__ ( lowercase_ ) -> bool:
"""simple docstring"""
... | 310 |
"""simple docstring"""
from typing import Any
def lowercase__ ( lowercase_ ) -> list[Any]:
"""simple docstring"""
if not input_list:
return []
_UpperCamelCase : Dict = [input_list.count(lowercase_ ) for value in input_list]
_UpperCamelCa... | 310 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Dict:
"""simple docstring"""
_UpperCamelCase = u
for i in range(1, UpperCamelCase__ ):
_UpperCame... | 194 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def lowerCamelCase_ (UpperCamelCase__ : float ):
if num <= 0:
raise ValueError('''math domain error''' )
return quad(UpperCamelCase__ , 0 , UpperCamelCase__ , ... | 263 | 0 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __UpperCamelCase ( lowercase__ : Tuple , lowercase__ : int=None ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ ... | 28 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixi... | 28 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _snake_case ( _snake_case ):
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
with open(_lowerCamelCase , encoding='''utf-8''' ) as input_f... | 94 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( UpperCAmelCase_ : str ):
"""simple docstring"""
return "".join(sorted(UpperCAmelCase_ ) )
def __lowerCamelCase ( UpperCAmelCase_ : str... | 94 | 1 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # 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 six # no... | 368 |
'''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
if ... | 280 | 0 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (... | 29 |
import logging
from transformers import PretrainedConfig
a__ = logging.getLogger(__name__)
a__ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class UpperCAmelCase_ ( __... | 235 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unispeec... | 333 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pil... | 333 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowercase :
__lowercase : List[Any] = 42
__lowercase : Any = None
__lowercase : Union[str, Any] = None
_UpperCAmelC... | 222 |
"""simple docstring"""
from math import factorial
def _A (__a = 20 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
SCREAMING_SNAKE_CASE_ ... | 91 | 0 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def a_ ( __snake_case : int ) -> Any:
"""simple docstring"""
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0,... | 365 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERendere... | 6 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pip... | 92 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBack... | 64 | 0 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class A__ ( yaml.SafeLoader ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: str , _SCREAMING_SNAKE_CASE: Union[str, Any... | 367 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def _lowercase ( __snake_case ,__snake_case = 2 ,__snake_case = 1 ,__snake_case = 3 ,) -> int | None:
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
... | 58 | 0 |
"""simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_... | 197 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = [
'''encoder.version''',
... | 56 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE_: Tuple ={
'configuration_cpmant': ['CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP... | 364 | '''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE_: List[str] =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: List[Any] ={
't5-small': 'https://... | 106 | 0 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1 / sqrt(2 ) ) -> IIRFilter:
snake_case__ : int = tau * frequency / samplerate... | 35 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import ... | 35 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__A = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles... | 367 |
from math import factorial
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(_UpperCamelCase ) // (factorial(_UpperCam... | 278 | 0 |
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (A ) -> bool:
"""simple docstring"""
return len(set(A ) ) == len(A )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 2 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import Ta... | 2 | 1 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
UpperCAmelCase__ : Union[str, Any] =logging.get_logger(__name__)
class __A ( SCREAMING_SNAKE_CASE__ ):
def __init__( self , *UpperCAmelCase_ , **UpperCAmelCase_ ):
... | 359 |
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
UpperCAmelCase__ : Optional[int] =logging.get_logger(__name__)
... | 262 | 0 |
def _a ( UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[Any] ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ = [0 for i in range(r + 1 )]
# nc0 = 1
lowerCAmelCase__ = 1
for i in range(1 , ... | 340 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET... | 325 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__magic_name__: int = logging.get_logger(__name__)
__magic_name__: Tuple = {
"vocab_file": "vocab.json",... | 138 |
from manim import *
class snake_case__ ( _lowerCAmelCase ):
def __magic_name__ ( self ) -> Dict:
__magic_name__ : int = Rectangle(height=0.5 , width=0.5 )
__magic_name__ : Optional[int] = Rectangle(hei... | 138 | 1 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availa... | 14 |
"""simple docstring"""
class A_ :
"""simple docstring"""
def __init__( self :List[str] , lowercase_ :int , lowercase_ :Optional[int]=None , lowercase_ :List[str]=None ) -> str:
UpperCAmelCase = data
UpperCAm... | 78 | 0 |
"""simple docstring"""
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
A_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6:... | 296 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class ... | 296 | 1 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __snake_case ( tf.keras.layers.Layer ):
def __init__( self... | 103 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A__ : List[str] = logging.get_logger(__name__)
class __snake_case ( UpperCamelCase_ ,UpperCamelCase... | 103 | 1 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ac... | 355 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __lt__( self : Dict , lowerCAmelCase_ :... | 301 | 0 |
from __future__ import annotations
_a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
... | 39 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS... | 264 | 0 |
def _SCREAMING_SNAKE_CASE ( lowercase : List[Any] = 1_00_00_00 ):
'''simple docstring'''
lowerCamelCase_ = 1
lowerCamelCase_ = 1
lowerCamelCase_ = {1: 1}
for inputa in range(2 , _lowerCAmelCase ):
... | 354 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this ... | 208 | 0 |
import math
def _a ( a :int ) -> bool:
return math.sqrt(a ) * math.sqrt(a ) == num
def _a ( a :int ) -> bool:
a = 0
a = n
while left <= right:
a = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
... | 0 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : Union[str, Any] = {
'''configuration_chinese_clip''': [
'''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ChineseCLIPConfig''... | 18 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
_UpperCAmelCase : Union[str, Any] = logging.get_logge... | 364 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import... | 158 | 0 |
"""simple docstring"""
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__A = TypeVar("T")
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> List[str]:
return (position - 1) // 2
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> str:
r... | 177 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_... | 11 | 0 |
'''simple docstring'''
import os
def lowercase_ ( ):
"""simple docstring"""
with open(os.path.dirname(lowerCAmelCase__ ) + """/grid.txt""" ) as f:
__UpperCAmelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(lowerCAmelCase__ ) for x ... | 354 |
'''simple docstring'''
from collections.abc import Sequence
def lowercase_ ( lowerCAmelCase__ : Sequence[int] | None = None ):
"""simple docstring"""
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
__UpperCAmelCase ... | 16 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
Stabl... | 288 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availa... | 288 | 1 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
lowerCamelCase_ : Tuple = logging.get_logger(__name__)
class _UpperCamelCase... | 368 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def A__ ( ) -> Union[str, Any]:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
... | 223 | 0 |
'''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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils i... | 83 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 80 | 0 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironmen... | 361 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
lowerCAmelCase__ = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",... | 244 | 0 |
def _UpperCAmelCase (UpperCamelCase__ : int , UpperCamelCase__ : int ):
while a != 0:
_A , _A : Optional[Any] = b % a, a
return b
def _UpperCAmelCase (UpperCamelCase__ : int , UpperCamelCase__ : in... | 11 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
# See all ViT MAE models at https://huggingf... | 11 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.nump... | 179 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : str , UpperCamelCase : Any ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def snake_case_ (UpperCamelCase : Any , UpperCamelCase : str=0 ):... | 179 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ = {
"configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
"tokenization_mvp": ... | 100 |
"""simple docstring"""
__magic_name__ = "Tobias Carryer"
from time import time
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=int(time())): # noqa: B008... | 100 | 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,
... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Squ... | 1 | 0 |
from itertools import product
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = sides_number
snake_case_ = max_face_number * dice_number
snake_case_ = [0] * (max_total + 1)
snake_case_ = 1
snake_case_ = rang... | 8 |
'''simple docstring'''
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(chec... | 93 | 0 |
'''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __lowerCamelCase ( __snake_case : int ) -> int:
"""simple docstring"""
A__ : List[Any] =prime_factors(__snake_case )
if is_squar... | 358 |
'''simple docstring'''
def __lowerCamelCase ( __snake_case : int ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
A__ : Any =4
A__ : ... | 136 | 0 |
'''simple docstring'''
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
lowerCAmelCase_ : Dict = logging.getLogger(__name__)
def _lowerCamelCase ( ) -> Optional[int]:
_a = argparse.ArgumentParser(... | 63 |
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = len(snake_case_ )
for i in range(snake_case_ ):
for j in range(i + 1 , snake_case_ ):
if numbers[j] < numbers[i]:
... | 133 | 0 |
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Tuple=0 ):
"""simple docstring"""
if name is None... | 350 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.tes... | 51 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 315 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
while b:
UpperCamelCase__ , UpperCamelCase__ : int = b, a % b
return a
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase... | 189 | 0 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
a__ = logging.getLogger(__name__)
@dataclass
class Uppe... | 365 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
a__ = yaml.safe_load(
'''\
name: ""
allow_empty: false
allow_empty_text: true
subsections:
- name: "Dataset Card for X"... | 15 | 0 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
log... | 280 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _A:
"""simple docstring"""
def __init__( self , _A = None ):
if components is None:
__A : int = []
... | 280 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCamelCase : str = logging.get_logger(__name__)
class __lowercase (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self , *A , **A ) -> ... | 367 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : List[str] = {
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_torch_available():
raise Optiona... | 176 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
create_state_space_tree(UpperCamelCase__ , [] , 0 )
def lowerCAmelCase__ ( UpperCamelCase... | 294 |
"""simple docstring"""
import cva
import numpy as np
class UpperCamelCase :
def __init__( self : Optional[int] , UpperCAmelCase__ : float , UpperCAmelCase__ : int ) -> Dict:
if k in (0.0_4, 0.0_6):
... | 294 | 1 |
"""simple docstring"""
import os
from pathlib import Path
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : str = {
"""en""": """Machine learning is great, isn\'t it?""",
"""r... | 353 |
"""simple docstring"""
import pprint
import requests
UpperCAmelCase: Tuple = """https://zenquotes.io/api"""
def __SCREAMING_SNAKE_CASE ( ):
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __SCREAMING_SNAKE_CASE ( ):
return requests.ge... | 336 | 0 |
"""simple docstring"""
# Copyright 2023 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
#... | 241 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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
#
... | 241 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
UpperCAmelCase_ = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}
try:... | 295 |
from cva import destroyAllWindows, imread, imshow, waitKey
def lowerCamelCase__ ( UpperCamelCase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
_snake_case , _snake_case = img.shape[0], img.shape[1]
# converting each pixel's colo... | 295 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVe... | 39 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_=1 ) -> Dict:
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.' )[n_s... | 89 | 0 |
'''simple docstring'''
from timeit import timeit
UpperCAmelCase = {
'MALAYALAM': True,
'String': False,
'rotor': True,
'level': True,
'A': True,
'BB': True,
'ABC': False,
'amanaplanacanalpanama': True, # "a man a plan a canal panama"
}
# Ensure our test d... | 187 |
'''simple docstring'''
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': (
... | 187 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
defau... | 52 | '''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __A ( UpperCame... | 1 | 0 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class UpperCamelCase__( a__ ):
def a__( self : Optional[Any] , lowerCAmelCase : List[Any]=None , lowerCAmelCase : Dict=None , lowerCAmelCase : Tuple=None ... | 369 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class UpperCamelCase__( nn.Module ):
__magic_name__ : int
__magic_name__ : int
... | 91 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Enco... | 99 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_snake_case = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm'... | 283 | 0 |
'''simple docstring'''
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (DDIMParallelScheduler,)
_SCREAMING_... | 249 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Union[str, Any] = {
"c... | 249 | 1 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from uni... | 48 |
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> int:
return x if y == 0 else greatest_common_divisor(_SCREAMING_SNAKE_CASE ,x % y )
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> int:
return (x * y) // greatest_common_divisor(_SCRE... | 48 | 1 |
"""simple docstring"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers im... | 371 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = ... | 308 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : Optional[int] = logging.get_logger(__name__)
snake_case : Optional[int] = {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/m... | 94 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
W... | 300 | 0 |
from timeit import timeit
lowerCamelCase__ = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a plan a canal panama"
}
# Ensure our test data is valid... | 63 | from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase ):
'''simple docstring'''
__A = ['''torch''']
def __init__( self : Any , *lowercase_ : Optional[int] , **lowercase_ : Optional[Any]) -> Opti... | 63 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTex... | 172 | """simple docstring"""
def __UpperCAmelCase ( ) -> int:
'''simple docstring'''
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(UpperCAmelCase_ , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
... | 172 | 1 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __UpperCamelCase ( ) ->Optional[Any]:
"""simple docstring"""
lowerCamelCase_ , lowerCamelCase_ =9, 14 # noqa: F841
lowerCamelCase_ =[
... | 49 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@req... | 49 | 1 |
"""simple docstring"""
A_ = {}
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed... | 64 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCAmelCase ( ):
"""simple docstring"""
UpperCAmelCase__ = [randint(-1000 , 1000 ) for i in range(10 )]
UpperCAmelCase__ ... | 169 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 1000000 ) -> int:
"""simple docstring"""
_lowerCAmelCase = limit + 1
_lowerCAmelCase = [0] * limit
for first_term in range(1 , snake_case_ ):
for n in range(sn... | 317 |
"""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/LICENSE... | 317 | 1 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
__lowerCAmelCase = '''src/transformers'''
# Matches is_xxx_available()
__lowerCAmelCase = re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
__lowerCAmelCase ... | 89 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, r... | 82 | 0 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
lowercase__ = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correla... | 280 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark... | 280 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : Dict = {
'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json',
'RWKV/rwkv-4-430m-pile': 'https://huggingface.co/RWKV/rwkv-4-... | 6 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
"""simple docstring"""... | 13 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import... | 370 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_avail... | 232 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A__ :
A__ = 42
A__ = None
A__ = None
def _lowerCAmelCase ( ) ... | 47 |
'''simple docstring'''
class A__ :
def __init__( self : Union[str, Any] , _a : int ) -> None:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =size
_SCREAMING_SNAKE_CASE =[0] * size
_SCREAMING_SNAKE_CASE =[0] * ... | 47 | 1 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def UpperCamelCase_( snake_case__: List[Any] , snake_case__: List[str] , snake_case__: Dict , snake_case__: Optional[int] ) -> Optional[int]:
UpperCAmelCase__ = {
'en': 'Machine learni... | 335 |
# flake8: noqa
# Lint as: python3
_UpperCamelCase = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disab... | 335 | 1 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class A__ ( __magic_name__ ):
# to overwrite at feature extractactor specific... | 212 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)... | 212 | 1 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__a :Optional[Any] = logging.get_logger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : List[str] , *UpperCAme... | 356 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__a :Optional[Any] = logging.get_logger(__name__)
__a :Any = {... | 329 | 0 |
def a ( A__ : Any ) -> Union[str, Any]:
"""simple docstring"""
return str(__SCREAMING_SNAKE_CASE ) == str(__SCREAMING_SNAKE_CASE )[::-1]
def a ( A__ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
... | 205 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase = logging.get_logger(__name__)
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = r'\w+[.]\d+'
... | 195 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : str ) -> str:
return "".join(chr(ord(lowerCAmelCase__ ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
... | 11 |
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from trans... | 11 | 1 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"vocab_file": "vocab.json",
"tokenizer_config_f... | 217 |
"""simple docstring"""
import os
def __UpperCAmelCase ( ) -> int:
with open(os.path.dirname(__lowerCamelCase ) + '''/p022_names.txt''' ) as file:
lowercase__ : List[Any] = str(file.readlines()[0] )
lowercase__ : Dict = names.replace(... | 16 | 0 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowercase ( unittest.TestCase... | 350 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig'... | 188 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProces... | 63 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _lowerCamelCase ( lowercase : Union[str, Any] , lowercase : int , lowerc... | 63 | 1 |
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 ConfigTester
from ...test_modeling_common import ModelTes... | 371 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A =logging.... | 47 | 0 |
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