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'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipeli... | 63 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, ... | 257 | 0 |
def __lowercase ( _UpperCamelCase ) ->list:
"""simple docstring"""
for i in range(len(a__ ) - 1, 0, -1 ):
lowercase : Union[str, Any] = False
for j in range(a__, 0, -1 ):
if unsorted[j] < unsorted[j - 1]:
... | 337 |
def __lowercase ( a__ ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a Perfect number or not...''')
lowerCAmelCase__ : Optional[Any] =int(input(''... | 257 | 0 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase_ : str = '''src/transformers'''
# This is to make sure the... | 91 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowerCAmelCase__ : List[Any] =input('''Enter image url: ''').strip()
print(F'''Downloading image from {url} ...''')
lowerCAmelCase__ : int =BeautifulSoup(requests.get(u... | 257 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'... | 260 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, Fl... | 257 | 0 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : List[str] ):
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a P... | 64 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __lowercase ( a__ ) -> Tuple:
__SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
... | 257 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def _a ( ):
__lowerCAmelCase = 9
__lowerCAmelCase = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2,... | 92 |
def __lowercase ( a__=2_81_23 ) -> List[str]:
__SCREAMING_SNAKE_CASE = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] ... | 257 | 0 |
"""simple docstring"""
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
__snake_case = [
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deformable_detr/cuda/m... | 320 |
from math import factorial
def __lowercase ( a__ = 1_00 ) -> int:
return sum(int(a__ ) for x in str(factorial(a__ ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 257 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 254 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STA... | 257 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_t... | 84 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Any =logging.get_logger(__name__)
lowerCAmelCase__ : str ={
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
'''https://huggingfac... | 257 | 0 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
SCREAMING_SNAKE_CASE : Optional[int] = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
titl... | 21 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, p... | 257 | 0 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__( lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = False, ):
__lowerCAmelCase = cipher_alphabet or [chr(a__) for i in range(9_7, 1_2_3)]
# If the argument is None or th... | 174 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTra... | 257 | 0 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Union[str, Any] , __a : Tuple ):
_a = list_of_points
... | 63 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece... | 257 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __SCREAMING_SNAKE_CASE ( UpperCamelCase_ ):
A : List[str] = '''Speech2TextFeatureExtractor'''
A : List[str] = '''Speech2TextTokenizer'''
... | 337 |
from heapq import heappop, heappush
import numpy as np
def __lowercase ( a__ , a__ , a__ , a__ , ) -> tuple[float | int, list[tuple[int, int]]]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = grid.shape
__SCREAMING_SNAKE_CASE ... | 257 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {}
class lowerCAmelCase__ ( UpperCamelCase_ ):
'''simple docstring'''... | 91 |
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 import (
AutoTokeni... | 257 | 0 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
... | 260 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 257 | 0 |
"""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 floa... | 64 |
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 import cached_property
from .... | 257 | 0 |
from __future__ import annotations
from typing import Any
def _a ( SCREAMING_SNAKE_CASE_ : Any ):
if not postfix_notation:
return 0
__lowerCAmelCase = {"+", "-", "*", "/"}
__lowerCAmelCase = []
for token in postfix_notation:
... | 92 |
def __lowercase ( a__ , a__ ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 257 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import re... | 320 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : List[str] = '''Speech2TextFeatureExtractor'''
UpperCamelCase__ : List[str] = ... | 257 | 0 |
'''simple docstring'''
class _A :
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : Dict = {} # Mapping from char to TrieNode
__UpperCAmelCase : Optional[int] = False
def __A ( self ... | 254 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , _A = 16 , _A = 88 , _A = None , _A... | 257 | 0 |
'''simple docstring'''
def __a ( UpperCAmelCase = 100 ) ->int:
"""simple docstring"""
A = n * (n + 1) * (2 * n + 1) / 6
A = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f"{solution() = ... | 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'''
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : int , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] ):
A = None
A ... | 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 __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_lowerCamelCase : List[Any] = {
'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
'susnato/ernie-m-l... | 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 itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowerCamelCase : Dict = '\\n@misc{chen2021evaluating,\n title... | 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 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 |
'''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 torch
from torch import nn
class __UpperCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__(self : int , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[... | 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 , UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
def update_area_of_max_square(UpperCAmelCase , UpperCAmelCase ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
A = up... | 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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCam... | 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_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase : List[str] = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderCo... | 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'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase : Optional[int] = get_tests_dir('fixtures/... | 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 dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __UpperCAmelCase ( A__ )... | 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 math
def __a ( UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
A = len(UpperCAmelCase )
A = int(math.floor(math.sqrt(UpperCAmelCase ) ) )
A = 0
while arr[min(UpperCAmelCas... | 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
from math import pi
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and o... | 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 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 |
'''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 random
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Tuple:
"""simple docstring"""
A = a[left_index]
A = left_index + 1
for j in range(left_index + 1 , UpperCAmelCase ):
if a[j] < pivo... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_lowerCamelCase : Union[str, Any] = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_availab... | 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 argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Dict:
"""s... | 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 diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __UpperCAmelCase :
'''simple docstring'''
pass
| 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 os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
A = ("""dense.weight""... | 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 json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers... | 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 argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCam... | 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 os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta im... | 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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(_... | 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 json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_uti... | 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_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Optional[Any] = {
'configuration_lxmert': ['LXMERT_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 json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTes... | 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 json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker... | 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 sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
_lowerCamelCase : Any = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
_lowerCamelCase : Dic... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase : List[Any] = {
'configuration_chinese_clip': [
'CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ChineseCL... | 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 gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer... | 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 math import isqrt, loga
def __a ( UpperCAmelCase ) ->list[int]:
"""simple docstring"""
A = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , 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'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __a ( ) ->Any:
"""simple docstring"""
A = ArgumentParser(
description=(
... | 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 random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_lowerCamelCase : List[Any] = HfApi()
_lowerCamelCase : int = {}
# fmt: off
_lowerCamelCase : Tuple = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0... | 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 argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
Adam... | 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 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 |
'''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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Any = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise... | 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 import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_lowerCamelCase : Tuple = datasets.load_iris()
_lowerCamelCase : Union[str, Any] = np.array(data['data'])
_lowerCamelCase : Union[str, Any... | 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 subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
@require_torch
def A ... | 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 numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_lowerCamelCase : Tuple = pd.read_csv('sample_data.csv', header=None)... | 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 sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_... | 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 gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.ka... | 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'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : List[Any] = {
'configuration_roformer': ['ROFORME... | 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 re
def __a ( UpperCAmelCase ) ->bool:
"""simple docstring"""
A = re.compile(R"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$""" )
if match := re.search(UpperCAmelCase , UpperCAmelCase ):
return match.string == phone
return False
... | 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 argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowerC... | 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 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 |
'''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 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 |
'''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'''
_lowerCamelCase : str = 256
# Modulus to hash a string
_lowerCamelCase : Dict = 100_0003
def __a ( UpperCAmelCase , UpperCAmelCase ) ->bool:
"""simple docstring"""
A = len(UpperCAmelCase )
A = len(UpperC... | 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 inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .s... | 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 argparse
import os
import re
_lowerCamelCase : Dict = 'src/transformers'
# Pattern that looks at the indentation in a line.
_lowerCamelCase : Union[str, Any] = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
_lowerCamelCase : 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'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __a ( ) ->Optional[Any]:
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rena... | 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 __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_c... | 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_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : List[str] = {
'configuration_distilbert': [
... | 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'''
import numpy as np
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Tuple:
"""simple docstring"""
A = int(np.ceil((x_end - xa) / h ) )
A = np.zeros((n + 1,) ... | 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_torch_available
_lowerCamelCase : Optional[int] = {
'configuration_upernet': ['UperNetConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotA... | 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'''
_lowerCamelCase : Union[str, Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers... | 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 math import factorial
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : int ):
A = real
if isinstance(_lowerCAmelCase ... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See ... | 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 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 |
'''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 argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_lowerCamelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument('--dump_... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : str = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
... | 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 json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensor... | 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 ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 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
import math
from collections import Counter
from string import ascii_lowercase
def __a ( UpperCAmelCase ) ->None:
"""simple docstring"""
A , A = analyze_text(UpperCAmelCase )
A = ... | 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'''
from __future__ import annotations
from typing import Any
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
pass
class __UpperCAmelCase :
'''simple docstring'''
def __init__(self : Any , _lowerCAmelC... | 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'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_lowerCamelCase : int = re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
... | 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 dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, L... | 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 re
from filelock import FileLock
try:
import nltk
_lowerCamelCase : Tuple = True
except (ImportError, ModuleNotFoundError):
_lowerCamelCase : List[str] = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet... | 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'''
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 |
'''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
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_p... | 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 json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 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'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 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 __future__ import annotations
_lowerCamelCase : int = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class __UpperCAmelCase :
'''simple docstrin... | 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 itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extractio... | 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 collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbon... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCase : str = {
... | 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'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
... | 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 math import pow
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ) ->tuple[int, int]:
"""simple docstring"""
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_su... | 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 ...processing_utils import ProcessorMixin
class __UpperCAmelCase ( A__ ):
'''simple docstring'''
__lowerCAmelCase = '''SpeechT5FeatureExtractor'''
__lowerCAmelCase = '''SpeechT5Tokenizer'''
def __init__(self : List... | 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'''
_lowerCamelCase : List[Any] = {str(digit): digit**5 for digit in range(10)}
def __a ( UpperCAmelCase ) ->int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCAmelCase ) )
def __a ( ) ->... | 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 sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm ... | 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 |
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