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 |
|---|---|---|---|---|
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common i... | 31 |
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 Arr... | 113 | 0 |
def lowerCAmelCase ( _lowerCAmelCase : int , _lowerCAmelCase : int ):
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(_lowerCAmelCase , x % y )
def lowerCAmelCase ( _lowerCAmelCase : int , _lowerCAmelCase ... | 717 |
import math
def lowerCAmelCase ( _lowerCAmelCase : int ):
"""simple docstring"""
UpperCAmelCase__ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
def lowerCAmelCase ( _lowerCAmelCase : float = 1... | 364 | 0 |
"""simple docstring"""
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Lis... | 690 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ ) -> list[int]:
"""simple docstring"""
A = [0 for i in range(len(UpperCamelCase__ ) )]
# initialize interval's left pointer and right pointer
A , A = 0, 0
for i in ran... | 690 | 1 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__A : Tuple = collections.namedtuple("""_Datasets""", ["""train"... | 450 |
from __future__ import annotations
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
create_state_space_tree(SCREAMING_SNAKE_CASE , [] , 0 , [0 for i in range(len(SCREAMING_SNAKE_CASE ) )] )
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE , SCREAMIN... | 450 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import requests
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, pr... | 102 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( UpperCAmelCase_ ... | 126 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
... | 718 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils im... | 408 | 0 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging... | 414 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging... | 414 | 1 |
"""simple docstring"""
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalG... | 706 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class a ( unittest.TestCase ):
def UpperCamelCase ( self : Tuple ) -> str:
lowerCamelCase_ = [10, 20, 30, 40, 50, 60]
lowerCamelCase_... | 137 | 0 |
from __future__ import annotations
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list[int]:
"""simple docstring"""
if num <= 0:
UpperCamelCase_ = f"{num}: Invalid input, please enter a positive integer."
raise ValueError(S... | 628 |
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 lowerCAmelCase( SCREAMING_SNAKE_CAS... | 628 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : int = logging.get_logger(__name__)
UpperCAmelCase__ : Dict = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.c... | 702 |
"""simple docstring"""
def lowercase_ ( _snake_case ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[str] = 1
SCREAMING_SNAKE_CASE__ : List[Any] = 1
while repunit:
SCREAMING_SNAKE_CASE_... | 545 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE :Dict = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl""": ["""CTRLT... | 628 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 628 | 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
... | 718 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
'configuration_albert': ['ALBERT_PRETRAINE... | 142 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 158 |
'''simple docstring'''
from __future__ import annotations
def A_ ( __SCREAMING_SNAKE_CASE : list , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ) -> list:
__SCREAMING_SNAKE_CASE : Optio... | 158 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
... | 707 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConfig",
"Blip2VisionCon... | 301 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _low... | 89 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_... | 57 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Tuple = logging.get_logger(__name__)
class __A (__magic_name__ ):
snake_case :Optional[int] = "encoder-decoder"
snake_case :List[Any] ... | 706 | '''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
impor... | 10 | 0 |
from math import factorial
def __a ( __UpperCAmelCase : Dict = 100 ) -> int:
"""simple docstring"""
return sum(int(UpperCamelCase__ ) for x in str(factorial(UpperCamelCase__ ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the... | 488 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'''configuration_electra''': ['''ELECTRA_PRETRAINE... | 657 | 0 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( SCRE... | 207 |
def SCREAMING_SNAKE_CASE( __UpperCamelCase ) -> float:
a__ : Optional[Any] = 0
while len(__UpperCamelCase ) > 1:
a__ : str = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
a__ : List[str] = file... | 207 | 1 |
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_params imp... | 70 |
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,
PILImageResa... | 471 | 0 |
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(_SCREAMING_SNAKE_CASE ) * abs(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) | 705 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _A ( _lowercase , _lowercase ):
'''simple docstring'''
@register_to_config
def __init__( self : Optional[Any] , *,
... | 655 | 0 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCamelCase : Optional[int] = models.Sequent... | 248 | import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Union[s... | 248 | 1 |
from __future__ import annotations
def a__ ( a ) -> int:
if not nums:
return 0
A_ : Tuple = nums[0]
A_ : int = 0
for num in nums[1:]:
A_ , A_ : Union[str, Any] = (
... | 236 | import heapq
def a__ ( a ) -> set[int]:
A_ : list[list] = []
# 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 priority queue, s... | 236 | 1 |
'''simple docstring'''
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import hu... | 11 |
__UpperCamelCase = 2_5_6
# Modulus to hash a string
__UpperCamelCase = 1_0_0_0_0_0_3
def UpperCamelCase_( _A :str , _A :str )-> bool:
UpperCamelCase__ = len(_A )
UpperCamelCase__ = len(_A )
if p_len > t_len:
return False
UpperCamelCa... | 551 | 0 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__SCREAMING_SNAKE_CASE = numpy.array([0, 0])
__SCREAMING_SNAKE_CASE = numpy.array([0.5, 0.8_66_02_54])
__SCREAMING_SNAKE_CASE = ... | 395 |
"""simple docstring"""
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def A_ ( __lowercase ):
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set() )
@pytest.fixture
def A_ ( __lowercase )... | 395 | 1 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_devic... | 32 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowerCamelCase : Optional[int] = logging... | 587 | 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,
StableDiffusionXLImg... | 566 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transf... | 566 | 1 |
from __future__ import annotations
def UpperCamelCase ( _UpperCAmelCase : str , _UpperCAmelCase : list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
_lowercase : Dict = word_bank or []
# create a table
_lowercase : int... | 461 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as ... | 461 | 1 |
def _a ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__UpperCamelCase , n - 1 , __UpperCamelCase ) * a) % mod
else:
a_ : Any = binary_exponentiation(__Upp... | 715 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''facebook/convnextv2-tiny-1k-224''':... | 478 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 552 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowercase = {
'''iou_prediction_head.lay... | 659 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : str =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional[int] ={
'''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/confi... | 72 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Tuple ={
'''google/pix2struct-textcaps-b... | 72 | 1 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def UpperCAmelCase__ (snake_case__ : int = 8 ):
"""simple docstring"""
_snake_case : Dict = ascii_lette... | 609 |
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> bool:
"""simple docstring"""
__lowerCamelCase = 0
for ch in input_str:
__lowerCamelCase = ord(UpperCamelCase__ )
__lowerCamelCase = pow(2 , UpperCa... | 469 | 0 |
"""simple docstring"""
def UpperCamelCase ( _A : Dict = 10 ) -> str:
if not isinstance(_A , _A ) or n < 0:
raise ValueError("""Invalid input""" )
lowercase : List[str] = 10**n
lowercase : Optional[int] = 28_433 * (pow(2... | 706 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCamelCase ( _A , _A , _A = 1 / sqrt(2 ) ) -> IIRFilter:
lowercase : Optional[int] = tau * frequency / samplerate
lowercase ... | 348 | 0 |
def A ( snake_case__ : int = 10**12 ) -> Tuple:
'''simple docstring'''
__snake_case = 1
__snake_case = 0
__snake_case = 1
__snake_case = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator +... | 313 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, l... | 603 | 0 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCamelCase_ :
pass
| 541 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get... | 541 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/... | 290 |
from collections.abc import Callable
def lowerCamelCase__ ( __lowerCAmelCase : Callable[[float], float] , __lowerCAmelCase : float , __lowerCAmelCase : float ):
"""simple docstring"""
lowerCAmelCase_ = a
lowerCAmelCase_ =... | 290 | 1 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgume... | 11 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : float ):
"""simple docstring"""
return 10 - x * x
def A_ ( _lowerCAmelCase : float , _lowerCAmelCase : float ):
"""simple docstring"""
if equation(_lowerCAmelCase ) *... | 11 | 1 |
"""simple docstring"""
from math import pi
def _lowerCamelCase ( __a, __a ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10)) | 626 |
"""simple docstring"""
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.di... | 626 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : Optional[Any] = {
"... | 486 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : List[Any] = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise ... | 486 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
_lowercase = '''
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
into account true and false pos... | 659 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientSt... | 583 | 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, require_cuda
f... | 72 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutp... | 72 | 1 |
__snake_case = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100_000)]
def _lowercase ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase = 0
while number:
# Increased Speed Slightly by checking every... | 386 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.m... | 386 | 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 UpperCAmelCase__ ( __lowerCAmelCase ):
"""s... | 715 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor... | 682 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline | 327 |
import unittest
from transformers import DonutProcessor
lowercase : Optional[int] = "naver-clova-ix/donut-base"
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCamelCase ( self ) -> Optional[int]:
... | 327 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {}
try:
if not is_sentencepiece_available():
raise... | 599 |
'''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/LI... | 599 | 1 |
"""simple docstring"""
class _lowerCAmelCase :
def __init__( self , UpperCamelCase__ = "" , UpperCamelCase__ = False ) -> Any:
'''simple docstring'''
snake_case : dict[str, RadixNode] = {}
# A node will be a leaf if the tree con... | 178 | def _snake_case ( lowerCAmelCase : str , lowerCAmelCase : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = len(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[Any] = len(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : ... | 216 | 0 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
_a: int = """<<<<<<< This should probably be modified because it mentions: """
_a: Union[str, Any] = """======... | 268 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a: List[Any] = logging.get_logger(__name__)
_a: List[str] ... | 268 | 1 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_a : str = {
# 1536-bit
5: {
... | 689 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block... | 689 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase_ ( lowercase__ , lowercase__):
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative")
elif capacitance <= 0:
raise ValueError("Capacitance cannot be 0 or... | 706 |
'''simple docstring'''
class lowercase :
'''simple docstring'''
def __init__( self : Union[str, Any] ) -> None:
'''simple docstring'''
lowerCamelCase__ = {} # Mapping from char to TrieNode
lowerCamelCase__ = False
def a_... | 187 | 0 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class a ( unittest.TestCase ):
def lowerCAmelCase_ ( self ... | 277 | """simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase = False ):
"""simple docstring"""
if radian_mode:
return [magnitude * cos(... | 277 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : List[str] = logging.get_logger(__name__)
__lowerCAmelCase : str = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
# See all CANIN... | 709 |
from __future__ import annotations
def UpperCAmelCase_ ( __lowerCAmelCase ) -> int:
if not nums:
return 0
__lowercase : List[Any] = nums[0]
__lowercase : Union[str, Any] = 0
for num in nums[1:]:
__lowercase , __lowercase : ... | 284 | 0 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.ma... | 590 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 590 | 1 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCamelCase__ :
def UpperCAmelCase__ ( self : Tuple , UpperCamelCase__ : List[Any] ):
'''sim... | 704 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a = TypeVar('T')
class UpperCamelCase__ ( Generic[T] ):
__SCREAMING_SNAKE_CASE : deque[T] # Cache store of keys
__SCREAMING_SNAKE_CASE : set[T] # Ref... | 650 | 0 |
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,
WavaVecaFeatureExtrac... | 12 |
from collections import defaultdict
from math import ceil, sqrt
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE = 1_000_000 , _SCREAMING_SNAKE_CASE = 10 ) -> int:
"""simple docstring"""
_A = defaultdict(_SCREAMING_SNAKE_CASE )
... | 27 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase: Any = logging.get_logger(__name__)
lowerCAmelCase: List[Any] = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.j... | 705 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
lowerCAmelCase: List[str] = TypeVar('T')
def lowerCamelCase__ ( _A ):
return (position - 1) // 2
def lowerCamelCase__ ( _A ):
return (2 * position) + 1
def ... | 195 | 0 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sq... | 44 | def A__ ( snake_case_ : float , snake_case_ : float ):
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import do... | 64 | 0 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_u... | 700 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
SCREAMING_SNAKE_CASE_ = logging.get_logger(__na... | 579 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenizati... | 67 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 | 0 |
import functools
def UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
_A= len(lowerCAmelCase_ )
_A= len(lowerCAmelCase_ )
@functools.cache
def min_distance(lowerCAmelCase_ , lowerCAmelCase... | 709 | import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoMo... | 476 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
# See all ... | 18 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] ... | 348 | 0 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
UpperCamelCase__ = logging.getLogger(... | 634 |
def UpperCAmelCase__ ( lowercase__ , lowercase__ ) -> int:
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("""String lengths must match!""" )
__lowercase = 0
for chara, chara in zip(lowercase__ , lowercase__ ):
... | 634 | 1 |
'''simple docstring'''
UpperCAmelCase_ : List[Any] = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
UpperC... | 44 |
from __future__ import annotations
def _UpperCAmelCase ( a__):
'''simple docstring'''
if len(a__) == 0:
return []
a_ , a_ : List[Any] = min(a__), max(a__)
a_ : Tuple = int(max_value - min_value) + 1
a_ : list[list] = ... | 540 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common i... | 701 | """simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__lowercase = 299_792_458
# Symbols
__lowercase , __lowercase , __lowercase , __lowercase = symbols('''ct x y z''')
def low... | 296 | 0 |
'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available()... | 111 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlipConfig',
'BlipTextConfi... | 417 | 0 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoCon... | 318 |
'''simple docstring'''
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutpu... | 318 | 1 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __lowerCAmelCase ( UpperCAmelCase_ , unittest.TestCase ):
... | 9 |
import math
def UpperCAmelCase ( UpperCAmelCase )-> int:
'''simple docstring'''
if not isinstance(UpperCAmelCase ,UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCA... | 393 | 0 |
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase = " "):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
for index, char in enumerate(_UpperCAmelCase):
if char == separator:
split_words.append(string[last_index:index])
SCREAMING_SNAKE_CASE ... | 707 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _snake_case ( A__ ):
_lowercase : Union[str, Any] ... | 444 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase :Tuple = {
'''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE... | 667 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : ... | 4 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( UpperCamelCase_ ):
a_ : Optional[Any] = (EulerDiscreteScheduler,)
a_ : Tuple = 10
def A_... | 712 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING... | 142 | 0 |
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 TaTokenizer
else:
SCREAMI... | 419 |
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.dataclass
cla... | 681 | 0 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class A_ :
"""simple docstring"""
def __init__( self : Tuple ,__A : int=2 ,__A : List[Any]=3 ,__A ... | 535 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
... | 535 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __l... | 33 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
... | 678 | 0 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __UpperCamelCase ( __lowerCamelCase : Optional[int] ) -> int:
'''simple docstring'''
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_d... | 721 |
'''simple docstring'''
lowercase__ = 256
# Modulus to hash a string
lowercase__ = 1_000_003
def __UpperCamelCase ( __lowerCamelCase : str , __lowerCamelCase : str ) -> bool:
'''simple docstring'''
_a = len(__lowerCamelCase )
_a ... | 276 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decis... | 519 |
from __future__ import annotations
from typing import Any
class __UpperCamelCase :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0 ) -> None:
a__ , a__ = row, column
a__ = [[default_va... | 194 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : Dict = logging.get_logger(__name__)
snake_case__ : int = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class _a ( _A ):
... | 717 |
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 OptionalDependencyNotAvailable()
... | 592 | 0 |
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 OptionalDependencyNo... | 12 |
"""simple docstring"""
import requests
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = {"""Content-Type""": """application/json"""}
UpperCAmelCase__ : Optional[Any] = requ... | 65 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : str, lowerCamelCase : Any, lowerCamelCase : Tuple )-> None:... | 703 |
"""simple docstring"""
def snake_case__ ( __lowerCamelCase : list[int] ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(__lowerCamelCase , (list, tuple) ) or not all(
isinstance(__lowerCamelCase , __lowerCamelCase ) for number in num... | 625 | 0 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
fr... | 7 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from... | 200 | 0 |
"""simple docstring"""
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
a : Tuple = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weigh... | 701 |
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available... | 31 | 0 |
"""simple docstring"""
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 torc... | 65 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowercase : Optional[int] = ["small", "medium", "large"]
lowercase : Optional[int] = "lm_head.decoder.weight"
lowercase : List[Any] = "lm_head.weight"
def ... | 495 | 0 |
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=_lowerCamelCase ):
a = ['''speech''']
def __init__( self , *a__ , **a__ ):
requires_backends(self , ["""speech"""] )
class ... | 481 |
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 i... | 481 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowerCamelCase_ = TypeVar('''T''')
lowerCamelCase_ = Union[List[T], Tuple[T, ...]]
lowerCamelCase_ = Union[T, List[T], Dict[str, T]]
lowerCamelCase_ = Union[str, bytes, os.Path... | 95 |
def __UpperCAmelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Optional[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE_ : Dict = 1... | 105 | 0 |
'''simple docstring'''
import torch
from transformers import AutoModel
class SCREAMING_SNAKE_CASE__ ( torch.nn.Module ):
def __init__( self , lowercase__="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
... | 68 |
'''simple docstring'''
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ) -> float:... | 68 | 1 |
def SCREAMING_SNAKE_CASE__ ( snake_case__ :list , snake_case__ :list ) -> float:
_validate_point(snake_case__ )
_validate_point(snake_case__ )
if len(snake_case__ ) != len(snake_case__ ):
raise ValueError('Both points must be in the same n-dimensional space' )
... | 67 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : float , __UpperCamelCase : list[float] ) -> float:
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty''' )
... | 144 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environ... | 531 |
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> int:
"""simple docstring"""
assert column_title.isupper()
snake_case_ = 0
snake_case_ = len(SCREAMING_SNAKE_CASE ) - 1
snake_case_ = 0
while index >= 0:
snake_case_ = (ord(column_title[ind... | 531 | 1 |
"""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-2... | 95 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class lowerCamelCas... | 201 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__lowerCamelCase : str = 637_8137.0
__lowerCamelCase : List[Any] = 635_6752.31_4245
__lowerCamelCase : int = 637_8137
def A_ ( _lowerCAmelCase , _lowerCAmelCase ... | 703 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lo... | 38 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _lowerCAmelCase ( lowercase : int , lowercase : int , lowercase : bool , lowercase : list[int] , lowercase : float ) ->int:
"""simple docstring"""... | 161 |
'''simple docstring'''
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_... | 161 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ... | 721 |
"""simple docstring"""
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
UpperCamelCase__ = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowerCAmelCase_( SCREAMING_SNAKE_CASE = 50_00 ) -> int:
... | 20 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
A_ = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
try:
if not is_tokenizers_availab... | 604 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 604 | 1 |
from functools import reduce
UpperCamelCase__ : Any = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043... | 486 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def A_( A ):
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_... | 486 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase : List[Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['Bio... | 529 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Dict = logging.get_logger(__name__)
__lowerCAmelCase : int = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/m... | 529 | 1 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , ... | 230 |
from math import pow, sqrt
def UpperCamelCase__ ( *SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : int = len(SCREAMING_SNAKE_CASE__ ) > 0 and all(value > 0.0 for value in values )
return result
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return (
... | 230 | 1 |
"""simple docstring"""
def __a ( a ):
"""simple docstring"""
if not isinstance(a, a ):
raise ValueError("check_bouncy() accepts only integer arguments" )
_a = str(a )
_a = "".join(sorted(a ) )
... | 388 |
"""simple docstring"""
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__SCREAMING_SNAKE_CASE = """... | 388 | 1 |
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,
... | 709 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( lowercase_ , lowercase_ ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 259 | 0 |
"""simple docstring"""
from itertools import product
def lowerCAmelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ):
"""simple docstring"""
__lowercase = sides_number
__lowercase = max_face_number * dice_number
__lowe... | 616 |
"""simple docstring"""
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def lowerCAmelCase_ ( UpperCamelCase__ : np.ndarray , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : int... | 616 | 1 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
__A = "Usage of script: script_name <size_of_canvas:int>"
__A = [0] * 100 + [1] * 10
random.shuffle(choice)
def lowerCAmelCase_ ( __a ) ->... | 437 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = ... | 437 | 1 |
"""simple docstring"""
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__lowerCamelCase = datasets.logging.get_logger(__name__)
__lowerCamelCase = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Ro... | 96 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_bar... | 97 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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_tens... | 646 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __UpperCamelCase ( _A : Dict ) -> int:
... | 646 | 1 |
"""simple docstring"""
def __snake_case ( ) -> Tuple:
"""simple docstring"""
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def __snake_case ( UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
A = 1
A ... | 690 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
UpperCamelCase : str = logging.ge... | 690 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configura... | 522 | from ...processing_utils import ProcessorMixin
class _UpperCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
__a : Tuple = '''SpeechT5FeatureExtractor'''
__a : Optional[Any] = '''SpeechT5Tokenizer'''
def __init__( self , lowerCAmelCas... | 522 | 1 |
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