code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from string import ascii_lowercase, ascii_uppercase
def a_ ( _lowerCAmelCase : List[str] ):
'''simple docstring'''
if not sentence:
return ""
lowercase__ : List[str] = dict(zip(__SCREAMING_SNAKE_CASE , __SC... | 704 | """simple docstring"""
import math
def a_ ( _lowerCAmelCase : int = 100 ):
'''simple docstring'''
lowercase__ : Union[str, Any] = sum(i * i for i in range(1 , n + 1 ) )
lowercase__ : str = int(math.pow(sum(range(1 , n + ... | 645 | 0 |
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def a_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple ):
'''simple docstring'''
lowercase__ : Dict = [0] * no... | 705 | """simple docstring"""
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.num... | 645 | 0 |
"""simple docstring"""
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_ ( __lowercase):
def _UpperCAmelCase ( self ) -> Dict:
return [
{"col_1": 3, "col_2": "a"},
... | 706 | """simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTraining... | 645 | 0 |
"""simple docstring"""
import functools
def a_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] ):
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or not all(isinstance(_SCREAMING_SNAKE_CASE ... | 707 | """simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vi... | 645 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, sl... | 708 | """simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
re... | 645 | 0 |
"""simple docstring"""
import sys
import turtle
def a_ ( _lowerCAmelCase : tuple[float, float] , _lowerCAmelCase : tuple[float, float] ) -> int:
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def a_ ( _lowerCAme... | 709 | """simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfor... | 645 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase : Optional[int] ={
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_ava... | 710 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],... | 645 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : int = 1000 ):
'''simple docstring'''
lowercase__ : int = 3
lowercase__ : Any = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
... | 711 | """simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBe... | 645 | 0 |
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a_ ( _lowerCAmelCase : Optional... | 712 | """simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, 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_common import Mode... | 645 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
_UpperCamelCase : Union[str, Any] = list[list[float | int]]
def a_ ( _lowerCAmelCase : Matrix , _lowerCAmelCase : Matrix ):
''... | 713 | """simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
... | 645 | 0 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCAmelCase_ :
pass
| 714 | """simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import Conf... | 645 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_... | 715 | """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-2.0... | 645 | 0 |
"""simple docstring"""
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,... | 716 | """simple docstring"""
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from ac... | 645 | 0 |
"""simple docstring"""
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 717 | """simple docstring"""
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Any = [0] * len(_lowerCAmelCase )
for i in range(1 , len(_lowerCAmelCase ) ):
# use last results for better performance - dynamic progra... | 645 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
... | 718 | """simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 645 | 0 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf... | 719 | """simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 645 | 0 |
"""simple docstring"""
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 : ... | 720 | """simple docstring"""
from collections.abc import Sequence
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmel... | 645 | 0 |
"""simple docstring"""
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_t... | 721 | """simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import I... | 645 | 0 |
"""simple docstring"""
import math
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : Tuple = [True] * n
lowercase__ : Optional[Any] = False
lowercase__ : Dict = False
lowercase__ : ... | 700 | """simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a_ ( _lowerCAmelCase : dict ):
... | 645 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a)
class UpperCAmelCase_ ( _a):
# `task` is not a ClassVar since we want it to be part of the `asdict` output f... | 701 | """simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tr... | 645 | 0 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_UpperCamelCase : Any = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_UpperCamelCase : str = typing.Union[np.floataa, int... | 702 | """simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : List[Any] ... | 645 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase , _lowerCAmelCase ):
'''simple docstring'''
lowercase__ : Any = len(_lowerCAmelCase )
lowercase__ : List[str] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each a... | 703 | """simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_ba... | 645 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
lowercase__ : Any = gray_code_sequence_strin... | 704 | """simple docstring"""
import math
def a_ ( _lowerCAmelCase : int = 100 ):
'''simple docstring'''
lowercase__ : Union[str, Any] = sum(i * i for i in range(1 , n + 1 ) )
lowercase__ : str = int(math.pow(sum(range(1 , n + ... | 645 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
_UpperCamelCase : int = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
_UpperCamelCase : Tuple ... | 705 | """simple docstring"""
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.num... | 645 | 0 |
"""simple docstring"""
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
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers... | 706 | """simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTraining... | 645 | 0 |
"""simple docstring"""
_UpperCamelCase : int = {
"meter": "m",
"kilometer": "km",
"megametre": "Mm",
"gigametre": "Gm",
"terametre": "Tm",
"petametre": "Pm",
"exametre": "Em",
"zettametre": "Zm",
"yottametre": "Ym",
}
# Exponent of the factor(meter)
_Upper... | 707 | """simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vi... | 645 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf ... | 708 | """simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
re... | 645 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_UpperCamelCase : Any = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understandi... | 709 | """simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfor... | 645 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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, ... | 710 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],... | 645 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
_UpperCamelCase : Dict ... | 711 | """simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBe... | 645 | 0 |
"""simple docstring"""
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_UpperCamelCase : int = {
"tiny.en": "https://openaipublic.... | 712 | """simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, 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_common import Mode... | 645 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase : Tuple = {
"configuration_xmod": [
"XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XmodConfig",
"Xmod... | 713 | """simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
... | 645 | 0 |
"""simple docstring"""
def a_ ( ):
'''simple docstring'''
lowercase__ : Dict = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
lowercase__ : Optional[Any] = 6
lowercase__ : Optional[int] = 1
lowercase__ : Optional[Any... | 714 | """simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import Conf... | 645 | 0 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoToke... | 715 | """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-2.0... | 645 | 0 |
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : List[Any] ... | 716 | """simple docstring"""
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from ac... | 645 | 0 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a_ ( _lowerCAmelCase : Union[str, Any] ):
'''simple docstring'''
for param in module.parameters():
lowercase__ : List[str] ... | 717 | """simple docstring"""
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Any = [0] * len(_lowerCAmelCase )
for i in range(1 , len(_lowerCAmelCase ) ):
# use last results for better performance - dynamic progra... | 645 | 0 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class UpperCAmelCa... | 718 | """simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 645 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
lowercase__ : Any = f"""Input value of [number={number}] must be an integer"""
raise TypeEr... | 719 | """simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 645 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : list ):
'''simple docstring'''
if len(_lowerCAmelCase ) <= 1:
return lst
lowercase__ : Dict = 1
while i < len(_lowerCAmelCase ):
if lst[i - 1] <= ... | 720 | """simple docstring"""
from collections.abc import Sequence
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmel... | 645 | 0 |
"""simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : list[list[int]] = []
lowercase__ : list[int] = []
low... | 721 | """simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import I... | 645 | 0 |
"""simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
_UpperCamelCase : st... | 700 | """simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a_ ( _lowerCAmelCase : dict ):
... | 645 | 0 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import I... | 701 | """simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tr... | 645 | 0 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoencode... | 702 | """simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : List[Any] ... | 645 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_UpperCamelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
def __init__( self , *a , **a ) -> Non... | 703 | """simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_ba... | 645 | 0 |
"""simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : list[float] ):
'''simple docstring'''
lowercase__ : str = 0.0_0
lowercase__ : int = 0
for resistor in resistors:
if resistor <= 0:... | 704 | """simple docstring"""
import math
def a_ ( _lowerCAmelCase : int = 100 ):
'''simple docstring'''
lowercase__ : Union[str, Any] = sum(i * i for i in range(1 , n + 1 ) )
lowercase__ : str = int(math.pow(sum(range(1 , n + ... | 645 | 0 |
"""simple docstring"""
from __future__ import annotations
import bisect
def a_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : int , _lowerCAmelCase : int = 0 , _lowerCAmelCase : int = -1 ):
'''simple docstring'''
if hi < 0... | 705 | """simple docstring"""
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.num... | 645 | 0 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_UpperCamelCase : Optional[int] = [
"Prosecutor: \"No videos were used in the crash investigation\" German ... | 706 | """simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTraining... | 645 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase : int = logging.get_logger(__name__)
_UpperCamelCase : Union[str, Any] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/go... | 707 | """simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vi... | 645 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase : str = logging.get_logger(__name__)
_U... | 708 | """simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
re... | 645 | 0 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tenso... | 709 | """simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfor... | 645 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class UpperCAmelCase_ :
lowerCamelCase__ : str = field(
... | 710 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],... | 645 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : Any = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def a_ ( _lowerCAmelCase : int ):
''... | 711 | """simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBe... | 645 | 0 |
"""simple docstring"""
from __future__ import annotations
class UpperCAmelCase_ :
def __init__( self , a , a ) -> Union[str, Any]:
lowercase__ : Any = text, pattern
lowercase__ : List[str] = len(a ), len(a )... | 712 | """simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, 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_common import Mode... | 645 | 0 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_sim... | 713 | """simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
... | 645 | 0 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def a_ ( _lowerCAmelCase : Iterable[str] , _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : Any = iter(_lowerCAmelCase )
... | 714 | """simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import Conf... | 645 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_UpperCamelCase : Dict = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormer... | 715 | """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-2.0... | 645 | 0 |
"""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, get_up... | 716 | """simple docstring"""
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from ac... | 645 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, 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_comm... | 717 | """simple docstring"""
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Any = [0] * len(_lowerCAmelCase )
for i in range(1 , len(_lowerCAmelCase ) ):
# use last results for better performance - dynamic progra... | 645 | 0 |
"""simple docstring"""
import os
from pathlib import Path
def a_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : List[str] ):
'''simple docstring'''
lowercase__ : Optional[Any] ... | 718 | """simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 645 | 0 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def a_ ( ):
'''simple docstring'''
print('Making key files...' )
make_key_files('rsa' , 10... | 719 | """simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 645 | 0 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class Up... | 720 | """simple docstring"""
from collections.abc import Sequence
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmel... | 645 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase : int = {
"configuration_perceiver": ["PERCEIVER_PRETRAINED... | 721 | """simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import I... | 645 | 0 |
"""simple docstring"""
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class U... | 700 | """simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a_ ( _lowerCAmelCase : dict ):
... | 645 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 701 | """simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tr... | 645 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def a_ ( _lowerCAmelCase : List[Any] ):
... | 702 | """simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : List[Any] ... | 645 | 0 |
"""simple docstring"""
_UpperCamelCase : int = range(2, 20 + 1)
_UpperCamelCase : Union[str, Any] = [10**k for k in range(ks[-1] + 1)]
_UpperCamelCase : dict[int, dict[int, list[list[int]]]] = {}
def a_ ( _lowerCAmelCase , _lowerCAmelCa... | 703 | """simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_ba... | 645 | 0 |
"""simple docstring"""
from functools import reduce
_UpperCamelCase : Tuple = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"125406987471585238630507156932909632952... | 704 | """simple docstring"""
import math
def a_ ( _lowerCAmelCase : int = 100 ):
'''simple docstring'''
lowercase__ : Union[str, Any] = sum(i * i for i in range(1 , n + 1 ) )
lowercase__ : str = int(math.pow(sum(range(1 , n + ... | 645 | 0 |
"""simple docstring"""
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
_UpperCamelCase : Optional[Any] = TypeVar("T")
class UpperCAmelCase_ ( Generic[T]):
def __init__( self , a = True ) -> None:
... | 705 | """simple docstring"""
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.num... | 645 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase : Any = logging.get_logger(__name__)
_UpperCamelCase : Union[str, Any] ... | 706 | """simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTraining... | 645 | 0 |
"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
_UpperCamelCase : Tuple = logging.get_logger(__name__)
_UpperCamelCase : Any = "T5Confi... | 707 | """simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vi... | 645 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floa... | 708 | """simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
re... | 645 | 0 |
"""simple docstring"""
from math import isqrt, loga
def a_ ( _lowerCAmelCase : int ) -> int:
'''simple docstring'''
lowercase__ : Optional[Any] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_... | 709 | """simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfor... | 645 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCamelCase : Optional[Any] ={"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIV... | 710 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],... | 645 | 0 |
"""simple docstring"""
class UpperCAmelCase_ :
def __init__( self , a = "" , a = False ) -> None:
# Mapping from the first character of the prefix of the node
lowercase__ : dict[str, RadixNode] = {}
# A node will be a leaf if th... | 711 | """simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBe... | 645 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError('Input... | 712 | """simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, 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_common import Mode... | 645 | 0 |
"""simple docstring"""
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 UpperCAmelCase_ ( _a):
def __init__( sel... | 713 | """simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
... | 645 | 0 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : int = 10**9 ):
'''simple docstring'''
lowercase__ : Union[str, Any] = 1
lowercase__ : Union[str, Any] = 2
lowercase__ : Tuple = 0
lowercase__ : Union[str, Any]... | 714 | """simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import Conf... | 645 | 0 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCAmelCase_ :
lowerCamelCase__ : List[Any] = None
def _UpperCAmelCase ( self ) -> int:
lowercase__ ... | 715 | """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-2.0... | 645 | 0 |
"""simple docstring"""
from random import randint, random
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = False , _lowerCAmelCase ... | 716 | """simple docstring"""
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from ac... | 645 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def ... | 717 | """simple docstring"""
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : Any = [0] * len(_lowerCAmelCase )
for i in range(1 , len(_lowerCAmelCase ) ):
# use last results for better performance - dynamic progra... | 645 | 0 |
"""simple docstring"""
import os
def a_ ( ):
'''simple docstring'''
with open(os.path.dirname(_lowerCAmelCase ) + '/p022_names.txt' ) as file:
lowercase__ : Union[str, Any] = str(file.readlines()[0] )
lowercase__ : Tuple = ... | 718 | """simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 645 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCamelCase : Tuple = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/c... | 719 | """simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 645 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : Optional[Any] = {
"configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", ... | 720 | """simple docstring"""
from collections.abc import Sequence
def a_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def a_ ( _lowerCAmel... | 645 | 0 |
"""simple docstring"""
from math import isqrt
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 , isqrt(_lowerCAmelCase ) + 1 ) )
def a_ ( _lowerCAmelCase : int = 10**6 ):
... | 721 | """simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import I... | 645 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase : int = logging.get_logger(__name__)
_UpperCamelCase : Dict = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config... | 700 | """simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a_ ( _lowerCAmelCase : dict ):
... | 645 | 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 a_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[Any] , ... | 701 | """simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tr... | 645 | 0 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atten... | 702 | """simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCamelCase : Dict = logging.get_logger(__name__)
_UpperCamelCase : List[Any] ... | 645 | 0 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common imp... | 703 | """simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_ba... | 645 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def a_ ( _lowerCAmelCase : List[Any] ):
'''simple docstring'''
return choice(_lowerCAmelCase )
def a_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase ... | 704 | """simple docstring"""
import math
def a_ ( _lowerCAmelCase : int = 100 ):
'''simple docstring'''
lowercase__ : Union[str, Any] = sum(i * i for i in range(1 , n + 1 ) )
lowercase__ : str = int(math.pow(sum(range(1 , n + ... | 645 | 0 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def a_ ( _lowerCAmelCase : Namespace ):
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_c... | 705 | """simple docstring"""
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.num... | 645 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCamelCase : int = {
"configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_M... | 706 | """simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTraining... | 645 | 0 |
"""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 VaeImage... | 707 | """simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vi... | 645 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase : str = logging.get_logger(__name__)
_UpperCamelCase : List[Any] = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin... | 708 | """simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
re... | 645 | 0 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:... | 709 | """simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfor... | 645 | 0 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
_UpperCamelCase : Tuple ="\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classificat... | 710 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCamelCase : str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],... | 645 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_UpperCamelCase : Any = logging.get_logger(__name__)
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : Lis... | 711 | """simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBe... | 645 | 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
_UpperCamelCase : int = {
"configuration_efficientnet": [
"EFFICIENTNET_PRE... | 712 | """simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, 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_common import Mode... | 645 | 0 |
"""simple docstring"""
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
... | 713 | """simple docstring"""
from __future__ import annotations
def a_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
... | 645 | 0 |
"""simple docstring"""
from __future__ import annotations
class UpperCAmelCase_ :
def __init__( self , a ) -> Dict:
lowercase__ : Optional[Any] = TypeError(
'Matrices must be formed from a list of zero or more lists containing at '
... | 714 | """simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import Conf... | 645 | 0 |
"""simple docstring"""
import functools
from typing import Any
def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : list[str] ):
'''simple docstring'''
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or len(_lowerCAmelCase ) == 0:
... | 715 | """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-2.0... | 645 | 0 |
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