code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def l... | 167 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowercase_ ( _UpperCAmelCase = "" ):
"""simple docstring"""
A_ : Optional[int] = url or '''https://www.imdb.com/chart/top/... | 167 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import... | 288 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __a ( tf.keras.layers.Layer ):
def __init__( self , ... | 288 | 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
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
... | 100 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 283 | 0 |
import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
from transformers.configur... | 354 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''ClapFeatureExtractor'''
UpperCamelCase_ : Any = ... | 319 | 0 |
def __SCREAMING_SNAKE_CASE ( snake_case_ = 1000 ):
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 133 |
"""simple docstring"""
import random
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> bool:
'''simple docstring'''
lowercase_ = num - 1
lowercase_ = 0
while s % 2 == 0:
lowercase_ = s // 2
t += 1
for _ in range(5 ... | 136 | 0 |
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
from diffusers... | 355 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCAmelCase :Union[str, Any] = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
... | 240 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVec... | 106 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import ... | 3 | 0 |
'''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.
_A : Optional[int] =10
def SCREAMING_SNAKE_CASE_ (Upper... | 368 |
'''simple docstring'''
from torch import nn
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> Dict:
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
... | 129 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
... | 288 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
UpperCAmelCase__ = '1'
UpperCAmelCase__ = '0'
UpperCAmelCase__ = '1'
UpperCAmelCase__ = ort.SessionOptions()
UpperCAmelCase__ = ort.GraphOptimizationLevel.ORT_D... | 288 | 1 |
"""simple docstring"""
from __future__ import annotations
class snake_case__ :
def __init__( self , lowerCamelCase ):
__a = data
__a = None
__a = None
def _lowerCamelCase( a ): # In Order traversal of the tree
if ... | 268 | """simple docstring"""
from __future__ import annotations
def _lowerCamelCase( a , a , a , a , a , ):
__a = len(a )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
... | 268 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __lowercase ( unittest.TestCase ):
"""si... | 13 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE( __lowercase ) -> bool:
if len(__lowercase ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
... | 319 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
... | 124 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 124 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCamelCase = ''''''
UpperCamelCase = ''''''
UpperCamelCase = ''''''
UpperCamelCase = 1 # (0 is vertical, 1 is horizontal)
def lowercase_ ( ):
lower... | 87 |
import argparse
snake_case : int = '''docs/source/_static/js/custom.js'''
def __lowercase ( __lowerCAmelCase : Optional[Any] ):
with open(__lowerCAmelCase , encoding='utf-8' , newline='\n' ) as f:
a__ = f.readlin... | 240 | 0 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
__SCREAMING_SNAKE_CASE : List[Any] = TypeVar('_T')
class lowercase_ ( Generic[_T] ):
def __init__( self , lowercase_ = None ):
_snake_case : ... | 371 | import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def snake_case (__lowercase ) -> str:
'''simple docstring'''
_snake_case : int = args.pruning_method
_snake_case : List[Any] ... | 284 | 0 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
SCREAMING_SNAKE_CASE :Optional[Any] = yaml.safe_load(
'\\nname: ""\nallow_empty: false\nallow_empty_text: true\nsubsections:\n... | 15 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''... | 129 | 0 |
"""simple docstring"""
import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def ... | 370 | """simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determ... | 203 | 0 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
fro... | 268 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class Uppe... | 268 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_atte... | 266 |
def lowerCAmelCase_ ( _lowercase : int) -> int:
"""simple docstring"""
if not isinstance(_lowercase , _lowercase):
raise TypeError("""only integers accepted as input""")
else:
a__ : Any = str(abs(_lowercase))
... | 266 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase : Any = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not is_torch... | 124 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> float:
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
snake_case : Optional[Any] = sum(lowercase ) / len(lowercase ) # Calculate the average
return sum(abs(x -... | 124 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
Ten... | 356 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int = 100_0000 ) -> int:
'''simple docstring'''
_UpperCAmelCase = limit + 1
_UpperCAmelCase = [0] * limit
for first_term in range(1 , __lowercase ):
for... | 156 | 0 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase__ = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n ... | 151 |
def a_ ( lowerCAmelCase_ : str, lowerCAmelCase_ : int ):
return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 284 | 0 |
import fire
from utils import calculate_rouge, save_json
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None , **_UpperCAmelCase ) -> Optional[int]:
lowerCamelCase__ : Any = [x.strip() for x in open(_UpperCAmelCase ).readl... | 45 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = {
"""vocab_file""": """vo... | 45 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils impor... | 16 |
"""simple docstring"""
def __lowerCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
snake_case : Dict = []
snake_case : List[Any] = 1
while len(lowercase ) < 1e6:
constant.append(str(lowercase ) )
i += 1
... | 203 | 0 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
_A = logging.getLogger(__name__)
class lowerCamelCase ( A_ ):
def __init__(self ... | 357 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling... | 137 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class snake_case :
'''simple docstring'''
def __init__( self : Dict, _lowerCamelCase : int, _lowerCamelCase : MutableSequence[float] ):
'''simple docstring'''
... | 266 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowercase_ = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n ... | 266 | 1 |
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_... | 260 |
from __future__ import annotations
lowerCAmelCase_ = []
def lowerCamelCase_ ( lowerCAmelCase: list[list[int]] , lowerCAmelCase: int , lowerCAmelCase: int )-> bool:
for i in range(len(lowerCAmelCase ) ):
if board[row][i] == 1:
return False
for i... | 260 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=a__ )
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
... | 25 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __lowerCAmelCase ( lowerCAmelCase_ ):
... | 156 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__A : Optional[Any] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and... | 323 |
__A : Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
__A : List[Any] = [
... | 323 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers ... | 45 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : list ) -> List[Any]:
_enforce_args(lowerCAmelCase__ , lowerCAmelCase__ )
if n == 0:
return 0
__a = float('''-inf''' )
for i in range(1 , n ... | 45 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedToken... | 52 |
'''simple docstring'''
lowerCAmelCase__ = {
"joule": 1.0,
"kilojoule": 1000,
"megajoule": 100_0000,
"gigajoule": 10_0000_0000,
"wattsecond": 1.0,
"watthour": 3600,
"kilowatthour": 360_0000,
"newtonmeter": 1.0,
"calorie_nutr": 4186.8,
"kilocalorie_nutr": 418_6800... | 52 | 1 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tr... | 324 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import ... | 137 | 0 |
def a ( SCREAMING_SNAKE_CASE_ : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
UpperCamelCase : Any = set()
# Replace all the whitespace in our sentence
UpperCamelCase : Unio... | 315 |
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_r... | 315 | 1 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__A : str = re.compile(r"\b(a|an|the)\b", re.UNICODE)
__A : List[str] = None
def lowercase ( ... | 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
while cur > 1:
# Find the maximum number in arr
_UpperCAmelCase = arr.index(m... | 260 | 1 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class lowercase_ ( __lowercase , unittest.TestCase ):
UpperCamelCase_ : Dict = DownBlockaD # ... | 278 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
Pi... | 278 | 1 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__UpperCAmelCase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of th... | 323 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCAmelCase = 0
__UpperCAmelCase = [
[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... | 323 | 1 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_... | 166 |
"""simple docstring"""
_A = range(2, 20 + 1)
_A = [10**k for k in range(ks[-1] + 1)]
_A = {}
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> int:
UpperCAmelCase__ : List[str] = sum(a_i[j] for j in... | 166 | 1 |
__lowerCamelCase : List[str] = 8.3_1_4_4_5_9_8
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0... | 52 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : Optional[int] = int(_lowerCAmelCase )
if decimal in (0, 1): # Exit cases for the recursion
return str(_lowerCAmelCase )
UpperCamelCase , UpperCamelCase : Dict = divmod(_lowerCAmelCase , 2 )... | 52 | 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
_... | 212 |
"""simple docstring"""
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tes... | 212 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
_A = set()
# Replace all the whitespace in our sentence
_A = input_str.replace(' ' ... | 315 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transfor... | 315 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
a_ = CTRL... | 207 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class _UpperCAmel... | 207 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_A = Lock()
def __UpperCamelCase ( _A , _A , _A , _A , _A , _A , _A ):
global process_lock
# we perform n swaps since after n swaps we know we are s... | 278 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase ( _A = 3 ):
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
... | 278 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE... | 60 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 60 | 1 |
'''simple docstring'''
def _A ( _lowerCAmelCase ):
"""simple docstring"""
if len(_lowerCAmelCase ) <= 1:
return [tuple(_lowerCAmelCase )]
__lowercase =[]
def generate(_lowerCAmelCase , _lowerCAmelCase ):
__lowe... | 166 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = ... | 166 | 1 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def lowercase () -> List[str]:
SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=__lowerC... | 371 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils... | 38 | 0 |
import math
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
lowerCAmelCase__ : Optional[Any] = len(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Dict = int(math.floor(math.sqrt(SCREAMING_SNAKE_CASE_ ) ) ... | 212 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class A__ :
@property
def _lowerCamelCase ... | 212 | 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 ...... | 357 | """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, D... | 95 | 0 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOut... | 207 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_ava... | 207 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class UpperCamelCase__:
def __init__( self : int , lowerCAmelCase : Collection[float] | None = None )-> None:
... | 356 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : List[Any] = ["image_processor", "tokenizer"]
__magic_name__ : Tuple = "ViTIm... | 91 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class snake_case_( a__ ):
__UpperCamelCase = '''SpeechT5FeatureExtractor'''
__UpperCamelCase = '''SpeechT5Tokenizer'''
def __init__( self : List[str] , UpperCamelCase_ : Tuple ... | 60 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import trans... | 60 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''... | 103 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines... | 103 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 43 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _SCREAMING_SNAKE_CASE ( _a ):
def __init__( self : List[Any] , __lowerCamelCase : Callable , __low... | 38 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def SCREAMING_SNAKE_CASE__ ( *lowercase ) -> Optional[int]:
if not isinstance(lowercase ,lowercase ):
snake_case : str = list(lowercase )
... | 176 |
def SCREAMING_SNAKE_CASE__ ( lowercase = 1000 ) -> int:
snake_case : Optional[int] = 3
snake_case : List[Any] = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return resu... | 176 | 1 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> str:
'''simple docstring'''
lowerCAmelCase : Any ... | 138 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available... | 95 | 0 |
import torch
from transformers import AutoModel
class A__ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self , lowercase="sayef/fsner-bert-base-uncased") -> Optional[int]:
'''simple docstring'''
super(lowercase , self).__init__(... | 225 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, ... | 225 | 1 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case__(UpperCAmelCase__ ):
"""simple docstring""... | 130 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCAmelCase_ : Optional[Any] = datasets.logging.get_logger(__name__)
UpperCAmelCase_ : List[str] ... | 91 | 0 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def lowerCAmelCase_ ( _snake_case : List[str] , _snake_case : Optional[Any] ) -> List[Any]:
'''simple docstring'''
__magi... | 366 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 41 | 0 |
from typing import Any
import numpy as np
def UpperCamelCase( __UpperCamelCase : np.ndarray ):
return np.array_equal(__UpperCamelCase ,matrix.conjugate().T )
def UpperCamelCase( __UpperCamelCase : np.ndarray ,__UpperCamelCase : np.ndarray ):
lowerCAmelCase_ : Dict... | 103 |
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
A__ : int = logging.get_logger(__name__)
A__ : Optional[int] = {
'''facebook... | 103 | 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.or... | 3 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __lowerCamelCase (... | 3 | 1 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
im... | 176 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase__ ( nn.Module ):
A__ : int
A__ : int
A__ : float =0.0
A__ : int =... | 176 | 1 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention... | 208 |
def _SCREAMING_SNAKE_CASE ( lowercase : Tuple , lowercase : Dict , lowercase : List[str] , lowercase : Dict , lowercase : Dict , lowercase : List[str] ):
'''simple docstring'''
if index == r:... | 208 | 1 |
from __future__ import annotations
from math import pow, sqrt
def UpperCAmelCase_ ( __UpperCAmelCase : float , __UpperCAmelCase : float , __UpperCAmelCase : float ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
... | 225 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__na... | 225 | 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 : List[Any] = logging.get_... | 368 |
'''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 : int = logging.get_logger... | 25 | 0 |
from __future__ import annotations
def UpperCAmelCase ( a_ ) -> float:
"""simple docstring"""
if not nums:
raise ValueError("List is empty" )
return sum(a_ ) / len(a_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 15 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , ) -> tuple[float | int, list[tuple[int, int]]]:
lowerCamel... | 41 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, 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_fl... | 188 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
... | 188 | 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... | 3 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
lowercase : 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 th... | 3 | 1 |
__A = [0, 2, 4, 6, 8]
__A = [1, 3, 5, 7, 9]
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
if remaining_length == 0:
if digits[0] == 0 or digits[... | 351 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.... | 254 | 0 |
'''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 n... | 208 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
E... | 208 | 1 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ... | 366 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _Upper... | 40 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, B... | 5 |
"""simple docstring"""
import math
import unittest
def lowercase_ ( _snake_case ):
assert isinstance(_snake_case ,_snake_case ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 25 | 0 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
lowerCamelCase = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.Image.Res... | 367 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowercase__ :
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = None
... | 241 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': [... | 188 |
from ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=lowerCamelCase__ ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = ['torch']
def __init__( self, *lowercase_, **lowercase_ ) -> List[str]:
"""s... | 188 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 352 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ ='''WhisperFeatureExtractor'''
a__ ='''WhisperTokenizer'''
def __init__( self , A , A ) -> Any:
super().__i... | 68 | 0 |
"""simple docstring"""
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import to... | 17 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenizat... | 254 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : List[str] = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
... | 369 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[str] = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPeg... | 89 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available(... | 76 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase ... | 40 | 0 |
from __future__ import annotations
lowerCamelCase_ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
class ... | 350 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 34 | 0 |
import string
import numpy
def A_ ( A__ , A__ ) -> int:
return b if a == 0 else greatest_common_divisor(b % a , A__ )
class A__ :
"""simple docstring"""
__A : Union[str, Any] = string.ascii_uppercase + string.digits
# This cipher takes alphanu... | 99 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, Be... | 241 | 0 |
"""simple docstring"""
import operator as op
def __lowerCamelCase ( a_ : Dict ) -> str:
__SCREAMING_SNAKE_CASE :Optional[Any] = []
__SCREAMING_SNAKE_CASE :str = lambda a_ , a_ : int(x / y ) # noqa: E731 in... | 239 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"microsoft/xprophetnet-large-wiki100-cased": (
"htt... | 239 | 1 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
_snake_case = [0] * len(SCREAMING_SNAKE_CASE_ )
for i in range(1 , len(SCREAMING_SNAKE_CASE_ ) ):
# use last results for better performance - dynamic programming
... | 341 |
def lowerCAmelCase__ ( ) -> Any:
'''simple docstring'''
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> Any:
'''simple docstring'''
A__ = 1
A__ ... | 68 | 0 |
def UpperCamelCase ( ) ->int:
"""simple docstring"""
return 1
def UpperCamelCase ( UpperCAmelCase ) ->int:
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def UpperCamelCase ( UpperCAmelCase ) ->int:
"""simple docstring"""
return... | 353 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class snake_case ( unittest.TestCase ... | 303 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConf... | 39 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> str | Literal[False]:
_a : Optional[int] = list(lowerCAmelCase_ )
_a ... | 89 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class SCREAMING_SNAKE_CASE__ ( snake_case_ ):
"""simple docstring"""
a : str
a : int
def a__ ( SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
... | 350 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt... | 133 | 0 |
'''simple docstring'''
def snake_case_ (_a : int ):
if number < 0:
raise ValueError('''number must not be negative''' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34 |
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with... | 34 | 1 |
from __future__ import annotations
def _lowerCamelCase( lowercase__ ) -> list[int]: # This function is recursive
'''simple docstring'''
__lowercase= len(lowercase__ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
i... | 304 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 304 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase : Optional[Any] = {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torch_ava... | 239 | '''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class __magic_name__ :
def __init__( self : str , lowercase_ : Dict ):
if isinst... | 239 | 1 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter... | 70 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __snake_case ( ) -> tuple[list[int], int]:
A_ : Dict = [randint(-1000 , 1000 ) for i in range(10 )]
A_ : List[str] = randint(-5000 ,... | 70 | 1 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import ... | 303 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer,... | 303 | 1 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
# load base model
_UpperCamelCase : ... | 236 |
'''simple docstring'''
from __future__ import annotations
def A__ ( UpperCAmelCase_ ):
if not nums:
return 0
_UpperCamelCase : Any = nums[0]
_UpperCamelCase : Optional[int] = 0
for num in nums[1:]:
_UpperCamelCase , ... | 236 | 1 |
'''simple docstring'''
def UpperCamelCase_( snake_case : int = 1_0_0_0 ):
'''simple docstring'''
snake_case_ = -1
snake_case_ = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a... | 85 |
def __SCREAMING_SNAKE_CASE ( snake_case_ ):
'''simple docstring'''
_UpperCAmelCase = len(snake_case_ )
for i in range(snake_case_ ):
for j in range(i + 1 , snake_case_ ):
if numbers[j] < numbers[i]:
... | 133 | 0 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,... | 155 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_CO... | 155 | 1 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as... | 304 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase)
class snake_case__ ( UpperCamelCase):
a_ = field(default="language-modeling" , ... | 304 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int ) -> int:
"""simple docstring"""
_lowerCAmelCase = [1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 0, 0, 0
_lowerCAmelCase = ugly_nums[... | 317 |
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(snake_case_ : int ) -> int:
if target < 0:
r... | 317 | 1 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_poin... | 70 |
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = len(lowerCAmelCase )
for i in range(length - 1 ):
_lowerCAmelCase = i
for k in rang... | 70 | 1 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterM... | 361 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_t... | 141 | 0 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
lowercase :Union[str, Any] = [0 for i in range(r + 1 )]
# nc0 = 1
lowercase :Tuple = 1
for i in range(1, n + 1 ):
# to compute current row from previous row.
lowercase :Union[str, Any] = mi... | 236 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.t... | 236 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_ ( metaclass=lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : str = ["""flax""", """transformers"""]
def __init__( self , *lowerCAmelCa... | 149 | """simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transform... | 149 | 1 |
"""simple docstring"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowercase (snake_case__ : str , snake_case__ : List[str] , snake_case__ : Union[str, Any] , sn... | 155 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def lowercase () -> Dict:
'''simple docstring'''
lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--src_path""" , type=s... | 155 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, Blip... | 349 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case_ ( )-> int:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] ... | 349 | 1 |
def lowercase ( SCREAMING_SNAKE_CASE__ : int ) -> int:
_snake_case : Optional[int] = [1]
_snake_case , _snake_case , _snake_case : Optional[Any] = 0, 0, 0
_snake_case : str = ugly_nums[ia] * 2
_snake_case : Dict = ugly_nums[ia] *... | 317 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
a__ = logging.get_logger(__name__)
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def __init__( self : Any , ... | 317 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = len(UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = len(UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]... | 361 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
if not i... | 255 | 0 |
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