code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : ... | 258 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
_lowerCamelCase : List[Any] = 6_3_7_8_1_3_7.0
_lowerCamelCase : List[Any] = 6_3_5_6_7_5_2.3_1_4_2_4_5
_lowerCamelCase : Optional[int] = 637_8137
def __a ( UpperCAmelCase , UpperCAmelCase ... | 258 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils imp... | 258 |
'''simple docstring'''
import os
import sys
import unittest
_lowerCamelCase : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
... | 258 | 1 |
'''simple docstring'''
def __a ( UpperCAmelCase ) ->int:
"""simple docstring"""
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" )
return sum(
... | 258 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase , UpperCAmelCase ) ->Tuple:
"""simple docstring"""
if len(UpperCAmelCase ) <= 1 or n <= 1:
return
insert_next(UpperCAmelCase , n - 1 )
rec_insertion_sort(UpperCAmelCase , n - ... | 258 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase : Any = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel ... | 258 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unord... | 258 | 1 |
'''simple docstring'''
from __future__ import annotations
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : Optional[Any] , _lowerCAmelCase : list[list[int]] ):
A = TypeError(
"""Matrices must be formed from a list ... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __a ( ) ->None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 ... | 258 | 1 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
def get_matched_characters(UpperCAmelCase , UpperCAmelCase ) -> str:
A = []
A = min(len(_stra ) , len(_stra ) ) // 2
for i, l in... | 258 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import ... | 258 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from dat... | 258 |
'''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():
import onnxrunti... | 258 | 1 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Optional[Any]:
"""simple docstring"""
if index == r:
for j in range(UpperCAmelCase ):
print(data[j] , end=""" """ )
... | 258 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 258 | 1 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase , UpperCAmelCase ) ->set[str]:
"""simple docstring"""
A , A = set(UpperCAmelCase ), [start]
while stack:
A = stack.pop()
explored.add(UpperCAmelCa... | 258 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCamelCase : int = 10
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAm... | 258 | 1 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
_lowerCamelCase : Optional[int] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-val... | 258 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_... | 258 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Bac... | 258 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 258 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
def __init__(self : Optional[int] , _lowerCAmelCase : Tuple , _lowerCAmelCase... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / densit... | 258 | 1 |
'''simple docstring'''
from typing import Any
import numpy as np
def __a ( UpperCAmelCase ) ->bool:
"""simple docstring"""
return np.array_equal(UpperCAmelCase , matrix.conjugate().T )
def __a ( UpperCAmelCase , UpperCAmelCase ) ->Any:
"""... | 258 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( A__ ... | 258 | 1 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __a ( UpperCAmelCase , UpperCAmelCase ) ... | 258 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def __a ( UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
return np.maximum(0 , UpperCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 258 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import... | 258 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_configura... | 258 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Optional[int] = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
def get_matched_characters(UpperCAmelCase , UpperCAmelCase ) -> str:
A = []
A = min(len(_stra ) , len(_stra ) ) // 2
for i, l in... | 258 | 1 |
'''simple docstring'''
def __a ( UpperCAmelCase = 600851475143 ) ->int:
"""simple docstring"""
try:
A = int(UpperCAmelCase )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError(... | 258 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
_lowerCamelCase : List[str] = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n jour... | 258 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tok... | 258 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __UpperCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
... | 258 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : ... | 258 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def __a ( UpperCAmelCase = "" , ) ->bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def __a ... | 258 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogp... | 258 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
'facebook/encodec_24khz': '... | 258 | 1 |
'''simple docstring'''
def __a ( UpperCAmelCase ) ->bool:
"""simple docstring"""
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 258 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARC... | 258 | 1 |
'''simple docstring'''
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
pass
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
pass
class __UpperCAmelCase :
'''simple docstring'''
def __init__(s... | 258 |
'''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 __UpperCAmelCase ( unittest.TestCase ):
'''simple d... | 258 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __UpperCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
... | 258 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
_lowerCamelCase : List[Any] = 6_3_7_8_1_3_7.0
_lowerCamelCase : List[Any] = 6_3_5_6_7_5_2.3_1_4_2_4_5
_lowerCamelCase : Optional[int] = 637_8137
def __a ( UpperCAmelCase , UpperCAmelCase ... | 258 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 258 |
'''simple docstring'''
import os
import sys
import unittest
_lowerCamelCase : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
... | 258 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : List[Any] = {
'... | 258 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase , UpperCAmelCase ) ->Tuple:
"""simple docstring"""
if len(UpperCAmelCase ) <= 1 or n <= 1:
return
insert_next(UpperCAmelCase , n - 1 )
rec_insertion_sort(UpperCAmelCase , n - ... | 258 | 1 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
def __lt__(self : Tuple , _lowerCAmelCase... | 258 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unord... | 258 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase : str = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __a ( ) ->None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 ... | 258 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.mode... | 258 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import ... | 258 | 1 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
_lowerCamelCase : Any = 'naver-clova-ix/donut-base'
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def A (self : Optional[int] ):
A = ... | 258 |
'''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():
import onnxrunti... | 258 | 1 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __a ( ) ->None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 ... | 258 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 258 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 258 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCamelCase : int = 10
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAm... | 258 | 1 |
'''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
_lowerCamelCase : Dict = [
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw ... | 258 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_... | 258 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
_lowerCamelCase : List[str] = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n jour... | 258 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 258 | 1 |
'''simple docstring'''
import requests
def __a ( UpperCAmelCase , UpperCAmelCase ) ->None:
"""simple docstring"""
A = {"""Content-Type""": """application/json"""}
A = requests.post(UpperCAmelCase , json={"""text""": message_body} , head... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / densit... | 258 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : List[Any] = {}
try:
if not is_sentencepiece_available():
... | 258 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( A__ ... | 258 | 1 |
'''simple docstring'''
import numpy as np
import qiskit
def __a ( UpperCAmelCase = 8 , UpperCAmelCase = None ) ->str:
"""simple docstring"""
A = np.random.default_rng(seed=UpperCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we ... | 258 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def __a ( UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
return np.maximum(0 , UpperCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 258 | 1 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_configura... | 258 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_configura... | 258 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def __a ( ) ->Generator[int, None, None]:
"""simple docstring"""
A = {}
A = 2
while True:
A = factor_map.pop(UpperCAmelCase , Upp... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
def get_matched_characters(UpperCAmelCase , UpperCAmelCase ) -> str:
A = []
A = min(len(_stra ) , len(_stra ) ) // 2
for i, l in... | 258 | 1 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowerCamelCase : str = Lock()
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ... | 258 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
_lowerCamelCase : List[str] = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n jour... | 258 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowerCamelCase : Optional[Any] = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
... | 258 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __UpperCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
... | 258 | 1 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 0 , UpperCAmelCase = 0 ) ->int:
"""simple docstring"""
A = right or len(UpperCAmelCase ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
el... | 258 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def __a ( UpperCAmelCase = "" , ) ->bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def __a ... | 258 | 1 |
'''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 __UpperCAmelCase ( unittest.TestCase ):
'''simple d... | 258 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
'facebook/encodec_24khz': '... | 258 | 1 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase ) ->list[int]: # This function is recursive
"""simple docstring"""
A = len(UpperCAmelCase )
# If the array contains only one element, we return it (it's the stop condition of... | 258 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARC... | 258 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
_lowerCamelCase... | 258 |
'''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 __UpperCAmelCase ( unittest.TestCase ):
'''simple d... | 258 | 1 |
'''simple docstring'''
from timeit import timeit
_lowerCamelCase : Optional[Any] = {
'MALAYALAM': True,
'String': False,
'rotor': True,
'level': True,
'A': True,
'BB': True,
'ABC': False,
'amanaplanacanalpanama': True, # "a man a plan a canal panama"
}
# Ensure our test ... | 258 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
_lowerCamelCase : List[Any] = 6_3_7_8_1_3_7.0
_lowerCamelCase : List[Any] = 6_3_5_6_7_5_2.3_1_4_2_4_5
_lowerCamelCase : Optional[int] = 637_8137
def __a ( UpperCAmelCase , UpperCAmelCase ... | 258 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featur... | 258 |
'''simple docstring'''
import os
import sys
import unittest
_lowerCamelCase : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
... | 258 | 1 |
'''simple docstring'''
from math import factorial
def __a ( UpperCAmelCase = 20 ) ->int:
"""simple docstring"""
A = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
A = n // 2
return int(factorial(Uppe... | 258 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase , UpperCAmelCase ) ->Tuple:
"""simple docstring"""
if len(UpperCAmelCase ) <= 1 or n <= 1:
return
insert_next(UpperCAmelCase , n - 1 )
rec_insertion_sort(UpperCAmelCase , n - ... | 258 | 1 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
_lowerCamelCase : Any = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), ... | 258 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unord... | 258 | 1 |
'''simple docstring'''
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] ... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __a ( ) ->None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 ... | 258 | 1 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __UpperCAmelCase ( A__ , A__ ):
... | 258 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import ... | 258 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowerCamelCase : List[Any] = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagToke... | 258 |
'''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():
import onnxrunti... | 258 | 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-... | 258 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 258 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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 ... | 258 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCamelCase : int = 10
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAm... | 258 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_c... | 258 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_... | 258 | 1 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def __a ( UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
return np.maximum(0 , UpperCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 258 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 258 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {
... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / densit... | 258 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbone... | 258 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( A__ ... | 258 | 1 |
'''simple docstring'''
def __a ( UpperCAmelCase = 50 ) ->int:
"""simple docstring"""
A = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length... | 258 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def __a ( UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
return np.maximum(0 , UpperCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 258 | 1 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 258 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_configura... | 258 | 1 |
'''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 ( UpperCAmelCase ) ->tuple:
... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
def get_matched_characters(UpperCAmelCase , UpperCAmelCase ) -> str:
A = []
A = min(len(_stra ) , len(_stra ) ) // 2
for i, l in... | 258 | 1 |
'''simple docstring'''
from datetime import datetime
import requests
def __a ( UpperCAmelCase ) ->bytes:
"""simple docstring"""
A = """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
A = requests.get(base_url + url ).j... | 258 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
_lowerCamelCase : List[str] = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n jour... | 258 | 1 |
'''simple docstring'''
import math
def __a ( UpperCAmelCase ) ->bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not prime... | 258 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __UpperCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
... | 258 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Union[st... | 258 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def __a ( UpperCAmelCase = "" , ) ->bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def __a ... | 258 | 1 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokeniz... | 258 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
'facebook/encodec_24khz': '... | 258 | 1 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __a ( UpperCAmelCase , UpperCAmelCase ) ->np.ndarray:
"""simple docstring"""
A = math.sqrt(UpperCAmelCase )
A = 1 / (sigma * math.sqrt(2 * math.pi ))
return... | 258 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARC... | 258 | 1 |
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __a ( UpperCAmelCase , UpperCAmelCase ... | 258 |
'''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 __UpperCAmelCase ( unittest.TestCase ):
'''simple d... | 258 | 1 |
'''simple docstring'''
from collections.abc import Callable
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : Any , _lowerCAmelCase : Callable | None = None ):
# Stores actual heap items.
A = []
# Stores indexes of e... | 258 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
_lowerCamelCase : List[Any] = 6_3_7_8_1_3_7.0
_lowerCamelCase : List[Any] = 6_3_5_6_7_5_2.3_1_4_2_4_5
_lowerCamelCase : Optional[int] = 637_8137
def __a ( UpperCAmelCase , UpperCAmelCase ... | 258 | 1 |
'''simple docstring'''
import itertools
import math
def __a ( UpperCAmelCase ) ->bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples o... | 258 |
'''simple docstring'''
import os
import sys
import unittest
_lowerCamelCase : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
... | 258 | 1 |
'''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 __U... | 258 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase , UpperCAmelCase ) ->Tuple:
"""simple docstring"""
if len(UpperCAmelCase ) <= 1 or n <= 1:
return
insert_next(UpperCAmelCase , n - 1 )
rec_insertion_sort(UpperCAmelCase , n - ... | 258 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase : Union[str, Any] = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MA... | 258 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unord... | 258 | 1 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase , UpperCAmelCase ) ->list[str]:
"""simple docstring"""
if partitions <= 0:
raise ValueError("""partitions must be a positive number!""" )
if partitions > number_of_bytes:
raise ValueE... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __a ( ) ->None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 ... | 258 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( A__ ... | 258 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import ... | 258 | 1 |
'''simple docstring'''
from ... import PretrainedConfig
_lowerCamelCase : str = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
__lowerCAmelCase = ... | 258 |
'''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():
import onnxrunti... | 258 | 1 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git w... | 258 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 258 | 1 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
__lowerCAmelCase = ''''''
__lowerCAme... | 258 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCamelCase : int = 10
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAm... | 258 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[Any] = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Trajector... | 258 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_... | 258 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowerCamelCase : List[str] = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['T... | 258 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 258 | 1 |
'''simple docstring'''
import numpy
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : Any , _lowerCAmelCase : numpy.ndarray , _lowerCAmelCase : numpy.ndarray ):
A = input_array
# Random initial weights are... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / densit... | 258 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_cha... | 258 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( A__ ... | 258 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class __UpperCAmelCase (... | 258 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def __a ( UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
return np.maximum(0 , UpperCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 258 | 1 |
'''simple docstring'''
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... | 258 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_configura... | 258 | 1 |
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
def get_matched_characters(UpperCAmelCase , UpperCAmelCase ) -> str:
A = []
A = min(len(_stra ) , len(_stra ) ) // 2
for i, l in... | 258 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import Patchin... | 258 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
_lowerCamelCase : List[str] = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n jour... | 258 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_... | 258 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __UpperCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
... | 258 | 1 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __UpperCAmelCase ( A__ , A__ ):
'''simple docstring'''
... | 258 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def __a ( UpperCAmelCase = "" , ) ->bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def __a ... | 258 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
_lowerCamelCase : Any = list[tuple[int, int]]
_lowerCamelCase : Union[str, Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0... | 258 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
'facebook/encodec_24khz': '... | 258 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 258 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARC... | 258 | 1 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase ) ->bool:
"""simple docstring"""
A = str(UpperCAmelCase )
return n == n[::-1]
def __a ( UpperCAmelCase = 1000000 ) ->Union[str, Any]:
"""simple doc... | 258 |
'''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 __UpperCAmelCase ( unittest.TestCase ):
'''simple d... | 258 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if... | 258 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
_lowerCamelCase : List[Any] = 6_3_7_8_1_3_7.0
_lowerCamelCase : List[Any] = 6_3_5_6_7_5_2.3_1_4_2_4_5
_lowerCamelCase : Optional[int] = 637_8137
def __a ( UpperCAmelCase , UpperCAmelCase ... | 258 | 1 |
'''simple docstring'''
def __a ( UpperCAmelCase ) ->list:
"""simple docstring"""
if len(UpperCAmelCase ) <= 1:
return lst
A = 1
while i < len(UpperCAmelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
A , A = lst[i], lst[i - 1]... | 258 |
'''simple docstring'''
import os
import sys
import unittest
_lowerCamelCase : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
... | 258 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_lowerCamelCase : List[str] = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer L... | 258 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase , UpperCAmelCase ) ->Tuple:
"""simple docstring"""
if len(UpperCAmelCase ) <= 1 or n <= 1:
return
insert_next(UpperCAmelCase , n - 1 )
rec_insertion_sort(UpperCAmelCase , n - ... | 258 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers impor... | 258 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unord... | 258 | 1 |
'''simple docstring'''
import os
import sys
import unittest
_lowerCamelCase : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __a ( ) ->None:
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 ... | 258 | 1 |
'''simple docstring'''
def __a ( UpperCAmelCase ) ->str:
"""simple docstring"""
return "".join([hex(UpperCAmelCase )[2:].zfill(2 ).upper() for byte in list(UpperCAmelCase )] )
def __a ( UpperCAmelCase ) ->bytes:
"""simple docstring"""
i... | 258 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import ... | 258 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __a ( UpperCAmelCase ) ->str:
"""simple docstring"""
return "".join(sorted(UpperCAmelCase ) )
def __a ( UpperCAmelCase ) ->list... | 258 |
'''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():
import onnxrunti... | 258 | 1 |
'''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
from transfo... | 258 |
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 258 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Dict = [
['attenti... | 258 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCamelCase : int = 10
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAm... | 258 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARC... | 258 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_... | 258 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from ... | 258 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 258 | 1 |
'''simple docstring'''
import argparse
import datetime
def __a ( UpperCAmelCase ) ->str:
"""simple docstring"""
A = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": """Thursday""... | 258 |
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / densit... | 258 | 1 |
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