code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = str(_UpperCamelCase )
return len(_UpperCamelCase ) == 9 and set(_UpperCamelCase ) == set('''123456789''' )
def lowercase_ ( ):
'''simple docstring'''
for bas... | 639 |
a : Any = [
'''Audio''',
'''Array2D''',
'''Array3D''',
'''Array4D''',
'''Array5D''',
'''ClassLabel''',
'''Features''',
'''Sequence''',
'''Value''',
'''Image''',
'''Translation''',
'''TranslationVariableLanguages''',
]
from .audio import Audio
from .f... | 639 | 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
snake_case_ : Union[str, Any] = logging.get_logger(__n... | 350 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __snake_case ( _UpperCAmelCase : Optional[int], _UpperCAmelCase : Tuple, _UpperCAmelCase : Any):
UpperCamelCase = 0
if start < end:... | 350 | 1 |
def UpperCamelCase_ ( __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...""")
UpperCamelCase : Union[str, Any] = ... | 37 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 314 | 0 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_... | 694 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : float , snake_case__ : float , snake_case__ : float ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('... | 694 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __a ( A__ , A__ , A__ ... | 649 |
'''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... | 422 | 0 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _SCREAMING_SNAKE_CASE (lowercase__, lowercase__ ... | 574 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : List[Any] ={
"configuration_pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
}
try:
if not is_torch_available... | 574 | 1 |
'''simple docstring'''
import os
from distutils.util import strtobool
def a__ ( a__ , a__ ):
"""simple docstring"""
for e in env_keys:
__SCREAMING_SNAKE_CASE = int(os.environ.get(a__ , -1 ) )
if val >= 0:
return val
return de... | 627 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfi... | 627 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_... | 666 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet i... | 666 | 1 |
from bisect import bisect
from itertools import accumulate
def lowerCAmelCase_ ( A_ ,A_ ,A_ ,A_):
UpperCamelCase__: Tuple = sorted(zip(A_ ,A_) ,key=lambda A_: x[0] / x[1] ,reverse=A_)
UpperCamelCase__ , UpperCamelCase__: Tuple = [i[0] for i in... | 380 |
import logging
from transformers import PretrainedConfig
A__: Dict = logging.getLogger(__name__)
A__: List[Any] = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
... | 380 | 1 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfor... | 710 | """simple docstring"""
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _snake_case ( __snake_case ):
"""simple docstring"""
a = "M-CLIP"
def __init__( self : Optional[Any] , _A : List[s... | 635 | 0 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : float , lowerCAmelCase__ : int ):
if digit_amount > 0:
return round(number - int(lowerCAmelCase__ ) , lowerCAmelCase__ )
return number - int(lowerCAmelCase__ )
if __name__ == "__main__":
prin... | 688 |
'''simple docstring'''
def __a ( lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : int ):
a__ : List[str] = len(lowerCAmelCase__ )
a__ : int = [[0] * n for i in range(lowerCAmelCase__ )]
for i in rang... | 688 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 715 |
'''simple docstring'''
from manim import *
class lowerCamelCase__ ( __lowerCamelCase ):
"""simple docstring"""
def lowerCAmelCase_ ( self : Optional[Any] ):
a__ = Rectangle(height=0.5 ,width=0.5 )
... | 394 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_... | 14 |
from math import factorial
def _lowerCAmelCase ( lowerCAmelCase_ :int = 20 )->int:
'''simple docstring'''
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n /... | 283 | 0 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTo... | 127 |
import re
import string
import numpy as np
import datasets
lowercase_: Optional[Any] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
lowercase_: Optional[int] = '\... | 127 | 1 |
_a: str = 6_5521
def __lowerCAmelCase ( A ):
UpperCAmelCase_ = 1
UpperCAmelCase_ = 0
for plain_chr in plain_text:
UpperCAmelCase_ = (a + ord(_snake_case )) % MOD_ADLER
UpperCAmelCase_ = (b + a) % MOD_ADLER
return... | 162 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKIN... | 181 | 0 |
from __future__ import annotations
snake_case_ : int = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCamelCase( a__ ,a__ ,a__ ,a__ ,a__ ,):
_SCREAMING_SNAKE_CASE =[
[0 for col in range(len(grid[0]))] for row in r... | 191 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : Optional[Any] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-rob... | 191 | 1 |
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_ = '''▁'''
lowerCA... | 60 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require... | 60 | 1 |
import os
from collections.abc import Iterator
def lowercase_ ( _A : str = "." ):
"""simple docstring"""
for dir_path, dir_names, filenames in os.walk(_A ):
lowerCamelCase__ : Union[str, Any] = [d for d in dir_names if d != "scripts" and d[0] not... | 5 |
import os
def lowercase_ ( _A : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_A ) , _A ) ) as input_file:
lowerCamelCase__ : List[Any] = [
[int(_A ) for element in line.split("," ... | 5 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversat... | 34 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
SCREAMING_SNAKE_CASE_ = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value... | 34 | 1 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
a = logging.get_logger(__name__)
a = {
"post_extract_proj": "feature_projection.project... | 175 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class _A :
def __init__( self , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = list_of_points
# Degree determines the flexibility of the curve.
# Degre... | 175 | 1 |
'''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
UpperCamelCase__: int = "sched... | 127 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__: str = logging.get_lo... | 127 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
A : Union[str, Any] = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
A : str = requests.get(u... | 714 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCa... | 5 | 0 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> List[str]:
__lowerCAmelCase: Optional[Any] ... | 346 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils imp... | 119 | 0 |
"""simple docstring"""
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batc... | 21 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n ... | 21 | 1 |
"""simple docstring"""
import os
def __UpperCAmelCase ( __UpperCamelCase = "matrix.txt" ):
with open(os.path.join(os.path.dirname(__snake_case ) , __snake_case ) ) as in_file:
__lowercase : int = in_file.read()
__lowercase : Tuple ... | 76 | 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
from transformers import (
... | 10 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate... | 717 | from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def A ( _lowercase ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = analyze_text(_lowercase )
SCREAMING_SNAKE_CASE ... | 34 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = len(SCREAMING_SNAKE_CASE__ ) // 2
# choose the middle 3 elements
snake_case_ = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0... | 39 |
from PIL import Image
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ ):
def brightness(lowerCAmelCase__ ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )... | 428 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = 100 , ) -> float:
UpperCAmelCase = x_start
UpperCAmelCase = ... | 710 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__lowerCamelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is... | 457 | 0 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerCAmelCase : Optional[Any] = logging.getLogger(__name__)
if __name_... | 202 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : List[Any] = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all ... | 587 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi... | 718 |
'''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/LICEN... | 156 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline... | 565 |
'''simple docstring'''
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase ) -> Any:
lowerCAmelCase__ : str = n
lowerCAmelCase__ : Optional[Any] = [None] * self.n
lowerCAmelCase__ : Tuple = 0 ... | 565 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase : int = logging.get_logger(__name__)
UpperCamelCase : Option... | 91 |
"""simple docstring"""
from statistics import mean, stdev
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ = 3 ) -> list:
"""simple docstring"""
A = min(UpperCamelCase__ )
A = max(UpperCamelCase__ )
# normalize data
return [round((x - x_min)... | 91 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A__ ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = [
'encoder.version',
'decoder.version... | 589 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
f... | 589 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __lowerCAmelCase ( __UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
snake_cas... | 21 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
snake_case_ : Any = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
... | 21 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : int = 0 ):
"""simple docstring"""
__lowercase = length or len(UpperCamelCase__ )
__lowercase = False
for i in range(length - 1 ):
if l... | 616 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCAmelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None ):
... | 616 | 1 |
'''simple docstring'''
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
"stable diffusion controlnet",
"0.22.0",
"Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable... | 719 | '''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import t... | 10 | 0 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
__lowercase , __lowercase = head.next, head
while fast and fast.next:
__lowercase = fast.next.next
__lowercase = slow.next
__lowe... | 80 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def a_ ( lowerCamelCase : Optional[Any] ):
return choice(lowerCamelCase )
def a_ ( lowerCamelCase : list[int] , lowerCamelCase : int... | 133 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"huggingface/time-series-transformer-tourism-monthly": (
"https://huggingface.co/huggingface/time-s... | 583 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class... | 583 | 1 |
def __a ( __UpperCAmelCase : int ) -> Any:
"""simple docstring"""
return 10 - x * x
def __a ( __UpperCAmelCase : List[str] , __UpperCAmelCase : Dict ) -> int:
"""simple docstring"""
if equation(UpperCamelCase_... | 488 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''YituTech/conv-bert-ba... | 657 | 0 |
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 tensorfl... | 125 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : list[int | str] ):
"""simple docstring"""
create_state_space_tree(lowerCAmelCase_ , [] , 0 , [0 for i in range(len(lowerCAmelCase_ ) )] )
def _A ( l... | 125 | 1 |
import os
def _UpperCamelCase ():
"""simple docstring"""
with open(os.path.dirname(a__ ) + """/grid.txt""" ) as f:
UpperCamelCase__ = [] # noqa: E741
for _ in range(20 ):
l.append([int(a__ ) fo... | 619 |
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.
UpperCamelCase__ = 10
def _UpperCamelCase (a__ :int , a__ :int , a__ :list[int] , a__ :int ... | 619 | 1 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
... | 718 |
'''simple docstring'''
import math
def __lowercase () -> None:
"""simple docstring"""
__lowerCamelCase : Optional[int] = input("""Enter message: """ )
__lowerCamelCase : Optional[Any] = int(input(f"Enter key [2-{len(_lowercase ) - 1}]: ... | 483 | 0 |
import random
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :float , SCREAMING_SNAKE_CASE :bool = False ) -> dict:
__lowerCAmelCase : Any = {i: [] for i in range(UpperCAmelCase_ )}
# if prob... | 504 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__na... | 104 | 0 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class UpperC... | 477 | """simple docstring"""
import numpy as np
def lowercase__( __SCREAMING_SNAKE_CASE : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 477 | 1 |
"""simple docstring"""
from math import pi, sqrt, tan
def lowercase__ ( snake_case_ :float ):
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def lowercase__ ( ... | 49 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( UpperCAmelCase_ ) ->int:
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise TypeError('Input value ... | 368 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNe... | 472 |
import math
class UpperCAmelCase__ :
"""simple docstring"""
def lowercase_ ( self : int , __lowerCamelCase : list[list[float]] , __lowerCamelCase : list[int] ) -> int:
SCREAMING_SNAKE_CASE__ = 0.0
SCREAMING_SNAKE_CASE__ = 0.0
f... | 472 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import... | 542 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transforme... | 542 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase : Tuple = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_vis... | 711 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
"""microsoft/git-base""": """https://huggingfa... | 308 | 0 |
import datasets
UpperCAmelCase_ : str = '''\
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holger
... | 17 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class A__( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__SCREAM... | 482 | 0 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transf... | 717 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowercase__( A ):
snake_case__ : List[str] = []
embed... | 303 | 0 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin i... | 488 |
"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transform... | 179 | 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
snake_case_ : str =logging.get_logger(__name__)
snake_case_ : i... | 205 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__ )
class a__ ( lowerCAmelCase__ ):
# `task` is not a ClassVar since we want it to be pa... | 205 | 1 |
"""simple docstring"""
from __future__ import annotations
lowercase__ = 10
def __magic_name__ ( _lowerCamelCase : list[int] ):
__a : List[str] = 1
__a : str = max(_lowerCamelCase )
while placement <= max_digit:
... | 581 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase__ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise ... | 581 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _lowercase ( lowercase__):
"""simple docstring"""
A__ = "Speech2TextFeatureExtractor"
A__ = "Speech2TextTokenizer"
... | 5 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 5 | 1 |
import numpy as np
def A__ ( lowerCamelCase ) -> np.array:
return 1 / (1 + np.exp(-vector ))
def A__ ( lowerCamelCase ) -> np.array:
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.te... | 548 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils... | 548 | 1 |
def A__ ( __A , __A ):
'''simple docstring'''
_lowerCamelCase : int = word.split()
def justify(__A , __A , __A ) -> str:
_lowerCamelCase : List[str] = max_width - width
_lowerCamelCase : Any = len(__A )
... | 15 | from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase : List[Any] =300 # TEMPERATURE (unit = K)
def A__ ( __A , __A , __A , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("""Dono... | 15 | 1 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : list[int] ):
if not numbers:
return 0
if not isinstance(__lowerCAmelCase , (list, tuple) ) or not all(
isinstance(__lowerCAmelCase , __lowerCAmelCase ) for number in numbers ):
... | 50 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def A__ ( __lowerCAmelCase : Any ):
# This defines a "chinese character" as anything in the CJK Unicode block:
... | 50 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_util... | 556 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
... | 556 | 1 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_a... | 467 |
'''simple docstring'''
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__lowerCamelCase = logging.get_logger(_... | 467 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ , lowerCamelCase_) -> float:
UpperCamelCase__ : Dict = sorted(numsa + numsa)
UpperCamelCase__, UpperCamelCase__ : Dict = ... | 6 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_a... | 6 | 1 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowercase ( __lowerCamelCase : str = "isbn/0140328726" ) -> dict:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = olid.strip().stri... | 344 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
Auto... | 344 | 1 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def a_ ( _lowerCAmelCase : List[Any] ):
'''simple docstring'''
return choice(_lowerCAmelCase )
def a_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase ... | 645 | """simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import I... | 645 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_a... | 92 |
"""simple docstring"""
from __future__ import annotations
import math
def _lowerCamelCase ( __a ):
if num <= 0:
SCREAMING_SNAKE_CASE_ = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(__a )
SCREAMING_SNAKE_CASE_ = [True] * (num + 1)
... | 626 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : Union[str, Any] =logging.ge... | 197 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ ( lowerCAmelCase__ :list[float] , lowerCAmelCase__ :list[float] ) -> float:
'''simple docstring'''
lowercase = sorted(numsa + numsa )
lowercase , l... | 197 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED... | 458 |
"""simple docstring"""
import string
def _UpperCAmelCase ( __lowerCamelCase : str ) -> str:
_snake_case = ''''''
for i in sequence:
_snake_case = ord(__lowerCamelCase )
if 65 <= extract <= 90:
output += chr(1_55 - extract )
elif 97 <= extract <=... | 224 | 0 |
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ : int =len(__UpperCAmelCase )
SCREAMING_SNAKE_CASE_ : List[str] =[0] * len_array
if len_array > 0:
... | 153 |
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : list[int] ,lowerCAmelCase_ : int ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neigh... | 153 | 1 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowerCAmelCase_ = TypeVar("""KEY""")
lowerCAmelCase_ = TypeVar("""VAL""")
@dataclass(frozen=UpperCAmelCase_ , slots=UpperCAmelCase_ )
class _lowerCAmelCas... | 411 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
lowerCAmelCase_ = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))
pr... | 411 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that wi... | 709 | 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_ ( __lowercase ):
UpperCAmelCase = (... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}
try:
if not is_torch_available():
... | 500 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _a ( UpperCAmelCase ) -> Any:
"""simple docstring"""
return getitem, k
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Union[s... | 315 | 0 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class a__( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase="" , __lowerCAmelCase="train"):
"""simple docstring"""
... | 605 | '''simple docstring'''
from __future__ import annotations
import time
__lowercase = list[tuple[int, int]]
__lowercase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, ... | 605 | 1 |
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def Upp... | 13 |
def SCREAMING_SNAKE_CASE__ ( snake_case__ :str ) -> list:
_lowercase = [0] * len(snake_case__ )
for i in range(1 , len(snake_case__ ) ):
# use last results for better performance - dynamic programming
_lowercase = prefix_result[i - 1]
w... | 67 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[Any] = logging.get_logger(__name__)
_snake_case : Optional[Any] = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/... | 712 |
def a_ ( lowerCAmelCase_ : str, lowerCAmelCase_ : str ):
if not (isinstance(lowerCAmelCase_, lowerCAmelCase_ ) and isinstance(lowerCAmelCase_, lowerCAmelCase_ )):
raise ValueError('longest_common_substring() takes two strings for inputs' )
__lowerCAmelCase ... | 421 | 0 |
'''simple docstring'''
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
"""stable diffusion controlnet""",
"""0.22.0""",
"""Importing `StableD... | 378 |
'''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/LICENS... | 236 | 0 |
from typing import Dict, Optional
import numpy as np
import datasets
_lowercase = '''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentati... | 713 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin... | 683 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""caidas/swin2sr-classicalsr-x2-64""": (
"""https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/r... | 80 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = """▁"""
snake_c... | 507 | 0 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
lowerCAmelCase : Dict = argparse.ArgumentParser()
parser.add_argument(
"""--checkp... | 720 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __magic_name__ ... | 533 | 0 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class _snake_case ( _UpperCAmelCase ):
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
'''simple docstring'''
super().__init__(*_lowerCAmelCase , **... | 284 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def SCREAMING_SNAKE_CASE ( lowercase_ : np.ndarray , lowercase_ : tuple[int, int] , lowercase_ : tuple[int, int] , lowercase_ : bool , ):
lowercase , lowercase = grid.shape
... | 588 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
... | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tok... | 1 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_... | 78 |
"""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,... | 49 | 0 |
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
if is_torch_available():
import torch
if is_vision_availabl... | 60 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
__snake_case :List[str] = HfApi()
__snake_case :str = {}
# fmt: off
__snake_case :Optional[Any] = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7... | 60 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 258 | '''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configura... | 209 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers... | 91 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
f... | 91 | 1 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = 9.8_0665
def lowerCAmelCase_( lowercase_ : float , lowercase_ : float , lowercase_ : float = g ) -> float:
if fluid_density <= 0:
raise ValueError('''Impossible fluid density''' )
if volu... | 661 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTest... | 661 | 1 |
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 AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tes... | 706 |
def lowercase ( _a = 2000000 ) -> int:
UpperCAmelCase_: List[str] = [0 for i in range(n + 1 )]
UpperCAmelCase_: str = 1
UpperCAmelCase_: Union[str, Any] = 1
for i in range(2 ,int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in ... | 306 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ : Union[str, Any] =logging.get_logger(__name__)
A_ : List[str] ={
"""vocab_file""": """vocab... | 483 |
def lowerCamelCase_ ( UpperCAmelCase__ = 100 ):
"""simple docstring"""
a_ = (n * (n + 1) // 2) ** 2
a_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'''{solution() = }''') | 483 | 1 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_opt... | 525 | # Function to print upper half of diamond (pyramid)
def __A ( _A ):
"""simple docstring"""
for i in range(0 , _A ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
for _ in range(0 , i + 1 ): # printing ... | 525 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowerCAmelCase :
'''simple docstring'''
def __init__(self : List[Any] , UpperCamelCase : Any ):
'''simple docstring''... | 460 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def _SCREAMIN... | 460 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils i... | 479 | import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
A_ = logging.get_logger(__name__)
def __UpperCAmelCase ( UpperCAmelCase )-> Tuple:
... | 479 | 1 |
import heapq
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
UpperCamelCase__ : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works wi... | 285 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase =logging.get_logger(__name__)
lowerCamelCase ={
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}
class _lowerCamelCase ( UpperCamelCase_... | 285 | 1 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCAmelCase = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCAmelCase = typing.Union[np.floataa, int, float] # noqa: UP007
def a__ ( a , ... | 236 | from math import loga
def a__ ( a ) -> int:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(a , a ):
raise TypeError('''Input value must be a \'int\' type''' )
return 0 if (a == 0) els... | 236 | 1 |
class lowerCAmelCase_ :
def __init__( self, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCamelCase : Tuple = n
UpperCamelCase : List[Any] = [None] * self.n
UpperCamelCase : str = 0 # ind... | 40 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class lowerCAmelCase_ ... | 40 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import To... | 231 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
_snake_case = {
... | 231 | 1 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _A :
__a = None
def UpperCAmelCase ( self ):
_UpperCAmelCase = self.feature_extraction_class(**self.f... | 518 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
a = False
try:
a = _is_package... | 518 | 1 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager impor... | 705 |
'''simple docstring'''
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
fro... | 305 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( lowerCAmelCase : list[int] , lowerCAmelCase : int ):
"""simple docstring"""
if len(_UpperCAmelCase ) < k or k < 0:
raise ValueError('Invalid Input' )
__magic_na... | 561 |
import enum
import shutil
import sys
a__ , a__ : int = shutil.get_terminal_size()
a__ : Tuple = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class UpperCAmelCase_ ( enum.Enum ):
__lowerCAmelCase : int = 0
__lowerCAmelCase : Optio... | 188 | 0 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class lowercase :
'''simple docstring'''
def __init__( self : Tuple , __lowerCamelCase : str , __lowerCamelCase : Optional[Any] , __lowerCamelCa... | 187 |
'''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
__A : Optional[int] = logging.get_logger(__name__)
__A... | 187 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitesp... | 19 |
"""simple docstring"""
from PIL import Image
def A_ ( snake_case_ : Image ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : Optional[int] = (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))
def contrast(snake_case_ : int... | 499 | 0 |
"""simple docstring"""
lowercase = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', ... | 720 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase = {
'''configuration_roberta... | 24 | 0 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
SCREAMIN... | 257 |
from __future__ import annotations
def UpperCamelCase ( _a , _a = None , _a = None ) -> None:
'''simple docstring'''
if start is None:
lowercase_ :Optional[int] = 0
if end is None:
lowercase_ :... | 257 | 1 |
"""simple docstring"""
def snake_case_ ( A_ : Any, A_ : Union[str, Any] ):
'''simple docstring'''
_enforce_args(_lowercase, _lowercase )
if n == 0:
return 0
_lowerCamelCase : List[Any] = float('''-inf''' )
... | 704 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowerCAmelCase__ = False
class __snake_case ( unittest.TestCase):
d... | 598 | 0 |
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