code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from collections.abc import Callable
import numpy as np
def lowerCamelCase ( a_ , a_ , a_ , a_ , a_ ) -> np.array:
lowerCAmelCase_ = int(np.ceil((x_end - xa) / step_size ) )
lowerCAmelCase_ = np.zeros((n + 1,) )
... | 318 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
d... | 318 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotA... | 640 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class _UpperCAmelCase ( snake_case ):
_... | 640 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer... | 622 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 622 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import In... | 188 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import In... | 188 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
... | 194 |
from statistics import mean
import numpy as np
def __a ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
a__ = 0
# Number of processes finished
a__ = 0
# Displays the finished process.
# If it is 0, the performance is... | 194 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImagePr... | 320 | '''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
UpperCamelCase_ = loggin... | 320 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'configuration_blenderbot': [
'BLENDERBOT_PRETRAINED_CONFI... | 300 |
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 lowercase__ ( _UpperCAme... | 472 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,... | 564 |
'''simple docstring'''
def __A ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = 0
__SCREAMING_SNAKE_CASE ... | 564 | 1 |
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self) -> None:
UpperCamelCase__ : dict[str, TrieNode] = {} # Mapping from char to TrieNode
UpperCamelCase__ : Any = False
def lowerCAmelCase__ ( self , ... | 410 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def _lowercase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
UpperCamelCase__ : Dict = {
'en': 'Machine learning i... | 410 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : List[str] =logging.get_logger(__name__)
_lowercase : int ={
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''studio-o... | 704 |
import operator as op
_lowercase : Optional[int] ="""scaler.pt"""
_lowercase : List[Any] ="""pytorch_model"""
_lowercase : Tuple ="""random_states"""
_lowercase : Tuple ="""optimizer"""
_lowercase : Dict ="""scheduler"""
_lowercase ... | 412 | 0 |
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 443 |
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 SCREAMING_SNAKE_CASE ( a_ ... | 651 | 0 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _snake_case ( ctypes.Structure ):
"""simple docstring"""
a = [("size", ctypes.c_int), ("visible... | 719 | """simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCAmelCase_ = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''attention... | 635 | 0 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, requir... | 197 | import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
SCRE... | 197 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main... | 143 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class __lowercase :
_lowerCAmelCase = None
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_... | 143 | 1 |
def __magic_name__ ( __lowerCAmelCase : str , __lowerCAmelCase : list[str] ) -> str:
__lowerCamelCase = ''''''
for word_or_phrase in separated:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise Exception('''join() a... | 298 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : Dict = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=N... | 298 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVeca... | 637 |
"""simple docstring"""
from __future__ import annotations
class snake_case_:
def __init__( self : int , UpperCamelCase_ : str , UpperCamelCase_ : str ):
lowerCAmelCase, lowerCAmelCase : List[str] = text, pattern
lowerCAmelCas... | 637 | 1 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {name: getattr(transformers, name + """Fast""") for name in SLOW_T... | 225 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase : List[str] = logging.get_logger(__name__)
lowerCAmelCase : List[Any] = {
'facebook/convnextv2-tiny-1k-... | 511 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class A :
'''simple docstring'''
def __init__(self , _UpperCAmelCase ) -> None:
__UpperCamelCase : List[Any] = value
... | 399 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_lowerCAmelCase = logging.get_logger(__name__)
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
... | 399 | 1 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
lowerCAmelCase_ = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
('''kernel''',... | 60 |
"""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.... | 93 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
_SCREAMING_SNAKE_CASE = {'''vocab_file''': '''vocab.txt'''... | 56 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_SCREAMING_SNAKE_CASE = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
... | 56 | 1 |
import math
def A ( lowercase__ : Tuple , lowercase__ : Union[str, Any] ) -> Optional[Any]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(lowercase__ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:... | 45 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
c... | 7 | 0 |
'''simple docstring'''
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_b... | 712 |
'''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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _snake_c... | 344 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_im... | 311 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclas... | 302 | 0 |
from __future__ import annotations
from math import gcd
def lowerCamelCase__ ( snake_case_ : Optional[Any] , snake_case_ : Any = 2 , snake_case_ : Optional[Any] = 1 , snake_case_ : str = 3 , ) -> int | None:
if num < 2:
... | 712 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ = {
'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_AR... | 388 | 0 |
def a_ ( UpperCamelCase_ : str , UpperCamelCase_ : str ) -> list:
"""simple docstring"""
lowerCamelCase = len(UpperCamelCase_ )
lowerCamelCase = []
for i in range(len(UpperCamelCase_ ) - pat_len + 1 ):
lowerCamelCase = True
for j in r... | 246 |
from __future__ import annotations
from typing import Any
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : str , __snake_case : int = 6 ) -> None:
'''simple docstring'''
lowerCamelCase ... | 246 | 1 |
'''simple docstring'''
def a_ ( lowerCamelCase : Tuple , lowerCamelCase : Tuple ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
lowerCAmelCase = (boundary[1] - boundary[0]) / steps
lowerCAmelCase = boundary[0]
lowerCAmelCase... | 716 |
'''simple docstring'''
def a_ ( lowerCamelCase : Tuple , lowerCamelCase : Tuple ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
lowerCAmelCase = (boundary[1] - boundary[0]) / steps
lowerCAmelCase = boundary[0]
lowerCAmelCase... | 513 | 0 |
"""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
#
... | 650 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 650 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from... | 721 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[str] = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {
'''facebook/encodec_24khz''': '''https://huggi... | 516 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_co... | 545 |
"""simple docstring"""
import argparse
import copy
def A ( __snake_case: Union[str, Any] ) -> Tuple:
"""simple docstring"""
__magic_name__ = {}
with open(__snake_case ) as f:
for line in f:
i... | 545 | 1 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import ... | 706 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : Optional[int] = {
"""configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Timesforme... | 317 | 0 |
"""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... | 574 | """simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCAmelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCAmelCase_ = None
def lowerCamelCase_()-> List[str]:
_SCREAMING_SNAK... | 338 | 0 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCam... | 337 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDete... | 337 | 1 |
from PIL import Image
def _a ( SCREAMING_SNAKE_CASE__ : Image , SCREAMING_SNAKE_CASE__ : int ) -> Image:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = (2_59 * (level + 2_55)) / (2_55 * (2_59 - level))
... | 663 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCamelCase (__lowerCamelCase ):
... | 663 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__A = {
'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}
try:
if not is_torch_available():
... | 708 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
... | 62 | 0 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (DDPMParallelScheduler,)
def UpperCamelCase ( self ... | 591 |
'''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_sentencepiece_available():
from .to... | 591 | 1 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__snake_case : Optional[Any] = datasets.logging.get_logger(__name__)
__snake_case : Optional[Any] = """\
@inproceedings{bleurt,
title={BLEURT: Learning Robust ... | 365 |
import numpy as np
from PIL import Image
def _UpperCamelCase ( UpperCamelCase_ : np.ndarray , UpperCamelCase_ : int , UpperCamelCase_ : int ) -> np.ndarray:
"""simple docstring"""
lowerCAmelCase__ = np.array(UpperCamel... | 365 | 1 |
import copy
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 ..auto import CONFIG_MAPPING
_UpperCamelCase = logging.get_logger(__name__)
_... | 492 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, Flax... | 492 | 1 |
'''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 transfo... | 338 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule... | 338 | 1 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_... | 600 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCAmelCase = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not is_torch_available():
rais... | 600 | 1 |
'''simple docstring'''
def snake_case( __magic_name__ = 50 ) -> int:
'''simple docstring'''
lowercase : str = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ... | 709 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common i... | 596 | 0 |
# 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 required by appli... | 157 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResamp... | 157 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase ={
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
"""co... | 261 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
__UpperCAmelCase ="""https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
__UpperCAmelCase ... | 261 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_available():
raise... | 88 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
... | 456 | 0 |
def lowerCAmelCase_ ( snake_case_ = 1,snake_case_ = 1000 ):
_A : Optional[int] = 1
_A : List[Any] = 0
for divide_by_number in range(snake_case_,digit + 1 ):
_A : list[int] = []
_A : Any = numerator
for _ ... | 54 |
def lowerCAmelCase_ ( snake_case_ = 1000000 ):
_A : Any = limit + 1
_A : Tuple = [0] * limit
for first_term in range(1,snake_case_ ):
for n in range(snake_case_,snake_case_,snake_case_ ):
_A : Optional[int] = first_term +... | 54 | 1 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowerCamelCase_ ( lower... | 147 |
from __future__ import annotations
__lowerCAmelCase = []
def _lowercase ( a__ : list[list[int]] , a__ : int , a__ : int ) -> bool:
"""simple docstring"""
for i in range(len(a__ ) ):
if board[row][i] == 1:
return False
for i ... | 147 | 1 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCAmelCase_ ( tf.keras.optimizers.schedu... | 709 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def __snake_case ( lowercase : int = 1_000_000 , lowercase : int = 10 ):
snake_case_ = defaultdict(lowercase )
for outer_width in range(3 , (t_limit // 4) + 2 ... | 420 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a__ ( unittest... | 227 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCAmelCase ( snake_case : BertModel , snake_case : str , snake_case : str ):
_lowerCAmelCa... | 227 | 1 |
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class __UpperCAmelCase ( _lowerCamelCase ):
'''simple docstring'''
def __init__( self , *_A , **_A ):
'''simple docstring'''
super().__init__(*_A , **_A )
_SCREAMING_SN... | 721 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : int = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETR... | 165 | 0 |
import argparse
import datetime
def a (lowerCAmelCase__ ):
__a = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": """Thursday""",
"""5""": """Friday""",
"""6""": """Saturday"""... | 99 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from t... | 109 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a_ = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftFormerOnnxCon... | 708 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"configuration_longformer": [
"LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"LongformerConfig"... | 375 | 0 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_... | 654 |
import argparse
import json
import subprocess
def lowerCAmelCase__ ( a__: List[str] , a__: Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = (
F'''curl -H "Accept... | 618 | 0 |
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_av... | 131 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( snake_case , snake_case , snake_case , snake_case ):
"""sim... | 131 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
tem... | 36 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from s... | 303 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> float:
_validate_point(lowerCamelCase__ )
_validate_point(lowerCamelCase__ )
if len(lowerCamelCase__ ) != len(lowerCamelCase__ ):
raise ValueError('Both points must be in the same n-dimensional space' ... | 337 |
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""": ["""XLMTokenizer"""],
}
... | 337 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
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
from ..pipeline_pa... | 646 | """simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp... | 646 | 1 |
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : Optional[Any] , __A : int ):
snake_case__ : List[str] = n
snake_case__ : Optional[int] = [None] * self.n
snake_case__ : int = ... | 25 |
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 ... | 25 | 1 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
f... | 91 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with p... | 91 | 1 |
"""simple docstring"""
def lowercase__ ( snake_case_ :dict ):
__UpperCAmelCase = set()
# edges = list of graph's edges
__UpperCAmelCase = get_edges(snake_case_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and a... | 397 |
"""simple docstring"""
def lowercase__ ( snake_case_ :List[Any] , snake_case_ :str , snake_case_ :Tuple , snake_case_ :Any , snake_case_ :Union[str, Any] , snake_case_ :List[Any] ):
if index == r:
for j in range(snake_case_ ):
print(data[j] , en... | 397 | 1 |
'''simple docstring'''
import sys
from pathlib import Path
__snake_case = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy impo... | 451 |
'''simple docstring'''
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/r... | 13 | 0 |
"""simple docstring"""
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
lowerCAmelCase : Any = HfArgumentParser(InitializationArguments)
lowerCAmelCase : int = ... | 533 |
"""simple docstring"""
def a__ ( snake_case__ = 1_00_00_00 ) -> int:
lowerCamelCase = 1
lowerCamelCase = 1
lowerCamelCase = {1: 1}
for inputa in range(2 , snake_case__ ):
lowerCamelCase = 0
lowerCamelCase ... | 533 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from datasets import load_dataset
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 ImageProcess... | 633 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import... | 207 | 0 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ ( _A ):
'''simple docstring'''
a__ = (PNDMScheduler,)
a__ = (("num_inference_steps", 50),)
def ... | 17 |
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""",
... | 17 | 1 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing... | 221 | import torch
from torch import nn
class UpperCAmelCase__ ( nn.Module ):
"""simple docstring"""
def __init__( self: int , __lowerCAmelCase: List[Any] , __lowerCAmelCase: str , __lowerCAmelCase: int , __lowerCAmelCase: List[Any] ... | 221 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
A = {
'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRA... | 109 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import ... | 109 | 1 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : Tuple = int(number**0.5 )
return number == sq * sq
def A ( _lowercase , _lowercase , _lowercase ... | 248 |
import unittest
from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_attention_mask
if is_tor... | 53 | 0 |
from __future__ import annotations
class snake_case :
def __init__( self : Any , a_ : int )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = data
SCREAMING_SNAKE_CASE__ : Node | None = None
SCREAMING_SNAKE_CA... | 636 | import math
def _a ( lowercase__ : int ):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif... | 636 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
return abs(_SCREAMING_SNAKE_CASE ) if a == 0 else greatest_common_divisor(b % a , _SCREAMING_SNAKE_CASE )
def a__ ... | 71 |
'''simple docstring'''
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_... | 71 | 1 |
'''simple docstring'''
def __lowercase (_lowercase ) -> None:
"""simple docstring"""
__lowerCamelCase : List[Any] = generate_pascal_triangle(_lowercase )
for row_idx in range(_lowercase ):
# Print left spaces
for _ in range(num_rows... | 483 |
'''simple docstring'''
import math
def __lowercase () -> None:
"""simple docstring"""
__lowerCamelCase : Optional[int] = input("""Enter message: """ )
__lowerCamelCase : Optional[Any] = int(input(f"Enter key [2-{len(_lowercase ) - 1}]: ... | 483 | 1 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_A : Optional[Any] = lo... | 427 | '''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
_A : Any = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
... | 427 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
... | 355 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from ... | 355 | 1 |
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self :List[str] ):
UpperCamelCase__ :List[str] = """"""
UpperCamelCase__ :Optional[Any] = """"""
UpperCamelCase__ :Union[str, Any] = []
def __a ( self ... | 45 |
from __future__ import annotations
def A ( lowercase__ : int ) -> list[int]:
UpperCamelCase__ :Union[str, Any] = [True] * limit
UpperCamelCase__ :int = False
UpperCamelCase__ :Optional[Any] = False
UpperCamelCase__ :str = True
for i in range(3 , int... | 45 | 1 |
lowercase_ = """Tobias Carryer"""
from time import time
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Optional[Any] , _A : Optional[Any] , _A : List[Any] , _A : Any , _A : ... | 131 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}
class __UpperCamelCase ( lowerCAme... | 131 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A__ ( ):
SCREAMING_SNAKE_CASE__: Optional[Any]= [randint(-1_000 , 1_000 ) for i in range(10 )]
SCREAMING_SNAKE_CASE__: Union[str, Any]= randint(-5_000 ... | 64 |
import math
import unittest
def __UpperCAmelCase ( __A ) -> bool:
'''simple docstring'''
assert isinstance(__A , __A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
... | 475 | 0 |
import math
def A__( __lowerCAmelCase ):
_snake_case : int = [True] * n
_snake_case : str = False
_snake_case : int = False
_snake_case : Optional[int] = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
_snake_case ... | 652 |
import functools
def A__( __lowerCAmelCase , __lowerCAmelCase ):
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not all(isinstance(__lowerCAmelCase , __lowerCAmelCase ) for day in days ):
raise ValueError('The parameter days should be a list of i... | 652 | 1 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCAmelCase__( __UpperCAmelCase : Optional[int] ):
return x + 2
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
... | 576 |
"""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_video_inp... | 573 | 0 |
"""simple docstring"""
def a ( __UpperCAmelCase : str , __UpperCAmelCase : int ) -> list[str]:
return [sentence[i : i + ngram_size] for i in range(len(__UpperCAmelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
f... | 213 |
"""simple docstring"""
from __future__ import annotations
def a ( __UpperCAmelCase : list[int] , __UpperCAmelCase : int ) -> list[int]:
__magic_name__: int = 0
__magic_name__: List[str] = len(__UpperCAme... | 213 | 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... | 639 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to t... | 639 | 1 |
from ..utils import DummyObject, requires_backends
class lowercase__ ( metaclass=_UpperCAmelCase ):
'''simple docstring'''
a : Optional[int] = ["flax"]
def __init__( self, *__magic_name__, **__magic_name__ ) -> Optional[int]:
... | 718 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, ... | 369 | 0 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class lowercase__ ( UpperCamelCase_):
UpperCamelCase_ = """MCTCTFeatureExtractor"""
UpperCamelCase_ = """AutoTokenizer"""
def __init__( self : Optiona... | 248 | import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A ( _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : List[Any] = OmegaConf.load(_lowercase )
SCREAMING_SNAKE_... | 248 | 1 |
def snake_case ( snake_case__ :Dict , snake_case__ :Dict) -> Dict:
_A = 0
_A = len(__lowerCAmelCase) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]... | 709 | import heapq
def snake_case ( snake_case__ :dict) -> set[int]:
_A = []
# 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... | 83 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...te... | 594 |
'''simple docstring'''
import qiskit
def a_ ( _UpperCAmelCase : int = 2 ) -> qiskit.result.counts.Counts:
__snake_case : Union[str, Any] = qubits
# Using Aer's simulator
__snake_case : List[Any] = qiskit.Aer.get_backend('aer_simulato... | 286 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerO... | 338 |
'''simple docstring'''
def _lowerCAmelCase ( __snake_case : Union[str, Any] , __snake_case : Tuple ) -> Union[str, Any]:
__A : Tuple = [0 for i in range(r + 1 )]
# nc0 = 1
__A : Dict = 1
for i in range(1 , ... | 338 | 1 |
lowercase__ : Dict = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCamelCase__ ( _A , _A , _A , _A ):
'''simple docst... | 376 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
... | 376 | 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
lowercase__ : Dict = logging.get_logger(__name__)
lowercase_... | 711 |
"""simple docstring"""
from statistics import mean, stdev
def __lowercase ( _a , _a = 3 ):
snake_case_ : Optional[int] = min(_a )
snake_case_ : str = max(_a )
# normalize data
return [round((x - x_min) / (x_max - x_min) , _a ) for x in data... | 485 | 0 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
_UpperCamelCase = 3
def lowerCAmelCase__( lowercase : int ) -> Optional[Any]:
print("Generating primitive root of p" )
while True:
__snake_case : ... | 243 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase__ ( metaclass=lowercase__ ):
"""simple docstring"""
__UpperCAmelCase : List[str] = ['''keras_nlp''']
def __init__( self : Union[str, Any] ,*_a : List[An... | 229 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_att... | 573 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import B... | 573 | 1 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_tr... | 578 |
import numpy as np
def A__ ( _a : np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 385 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformer... | 714 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_... | 103 | 0 |
"""simple docstring"""
import math
def _UpperCamelCase ( UpperCamelCase = 100 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Any = sum(i * i for i in range(1 , n + 1 ) )
__UpperCAmelCase : Union[str, Any] =... | 77 |
import math
def a_ ( lowerCAmelCase_ : list, lowerCAmelCase_ : int ):
__lowerCAmelCase = len(lowerCAmelCase_ )
__lowerCAmelCase = int(math.floor(math.sqrt(lowerCAmelCase_ ) ) )
__lowerCAmelCase = 0
while arr... | 53 | 0 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _UpperCAmelCase ( unittest.TestCase):
__lowercase : int = JukeboxTokenizer
__lowercase : Any = {
"""artist""": """Zac Brow... | 713 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def a__ ( a : Namespace ):
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_du... | 87 | 0 |
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:
if not is_vis... | 332 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()... | 332 | 1 |
"""simple docstring"""
def a ( __UpperCAmelCase : int | float | str ) -> tuple[int, int]:
try:
__magic_name__: Dict = float(__UpperCAmelCase )
except ValueError:
raise ValueError("""Please enter a valid nu... | 704 |
"""simple docstring"""
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,
sl... | 213 | 0 |
from maths.prime_factors import prime_factors
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowercase = F'Input value of [number={number}] must be an integer'
raise TypeError(_UpperCamelCase )
... | 639 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 639 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __lowerCAmelCase ( ) -> Any:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
... | 703 | from sklearn.metrics import fa_score
import datasets
a_ = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
a_ = """
Args:
predictions (`list` of `int`): Predicted labels.
r... | 286 | 0 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
UpperCamelCase__ = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari... | 227 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCAmelCase ( snake_case : BertModel , snake_case : str , snake_case : str ):
_lowerCAmelCa... | 227 | 1 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def lowerCamelCase ( UpperCAmelCase__ : int ) -> int:
'''simple docstring'''
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * ... | 705 | '''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import versio... | 320 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Any = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/r... | 149 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase : List[str] = {
'''configuration_conditional_detr''': [
'''CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Condition... | 149 | 1 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
... | 717 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _snake_case ( lowerCAmelCase_ ):
""... | 305 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common imp... | 138 |
'''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_squeezebert import SqueezeBertTokenizer
snake_case_ : Optional[int] = l... | 138 | 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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_ou... | 709 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a__ = get_tests_dir('''fixtu... | 566 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.