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 |
|---|---|---|---|---|
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...f... | 69 |
from jiwer import compute_measures
import datasets
__a :List[Any] = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measu... | 86 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase = {
"configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperC... | 712 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_UpperCamelCase = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
"tokenization_gpt_neox_j... | 583 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor... | 33 |
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
# Check if the input is valid
if not len(_lowerCAmelCase ) == len(_lowerCAmelCase ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equatio... | 684 | 0 |
'''simple docstring'''
def _lowerCAmelCase( _lowerCAmelCase ) -> int:
snake_case__ : list[list[int]] = [[0 for _ in range(_lowerCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
snake_case__ : Tuple = 1
for n in range(m + 1 ... | 718 |
'''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_batch_size
from ..... | 301 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_... | 98 |
'''simple docstring'''
import warnings
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
UpperCAmelCase_ : int = logging.get_logger(__name... | 120 | 0 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configur... | 612 |
"""simple docstring"""
def lowerCAmelCase ( UpperCamelCase_: Optional[int] , UpperCamelCase_: str ) -> List[Any]:
'''simple docstring'''
_a = (boundary[1] - boundary[0]) / steps
_a = boundary[0]
_a = boundary[1]
_a = ... | 612 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_a = get_logger(__name__)
_a = r"\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n Indices o... | 481 |
from __future__ import annotations
_a = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class __A :
'''simple docstring'''
def __init__( self , __lowerCAmelCas... | 481 | 1 |
"""simple docstring"""
__A : Optional[int] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowercase ( UpperCamelCase : bytes ):
"""simple docstring"""
# Make sure the supplied data is a bytes-like object
if not isinstance(UpperC... | 595 | """simple docstring"""
__A : int = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def lowercase ( UpperCamelCase : st... | 595 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class _A ( __lowercase ):
def __init__( self : str , __magic_name__ : Optional[int] , __magic_n... | 26 |
def lowerCAmelCase__ ( a__: list ) -> list:
'''simple docstring'''
if len(a__ ) < 2:
return collection
def circle_sort_util(a__: list , a__: int , a__: int ) -> bool:
_UpperCAmelCase = Fa... | 618 | 0 |
"""simple docstring"""
def lowerCamelCase_(__SCREAMING_SNAKE_CASE = 100 )-> int:
_SCREAMING_SNAKE_CASE : Any = set()
_SCREAMING_SNAKE_CASE : List[Any] = 0
_SCREAMING_SNAKE_CASE : Tuple = n + 1 # maximum limit
for a in range(2 , __S... | 635 | """simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration... | 635 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ : List[Any] = {
"""configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}
... | 13 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> str:
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_terms=10))
| 699 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _lowerCAmelCase :
def __init__(self , lowercase = None ):
if components is None:
A_ : Tuple = []
A_ : L... | 686 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
fr... | 686 | 1 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 117 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 691 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 701 |
'''simple docstring'''
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"):
lowercase_ = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling... | 352 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
S... | 28 | """simple docstring"""
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
... | 277 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 60 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__snake_case :int = ''''''
... | 60 | 1 |
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 transformers.utils.import_utils import i... | 462 |
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
lowerCAmelCase = datasets.logging.get_logger(__name__)
lowerCAmelCase = """\
@InProceedings{moosavi2019minimum,
author = ... | 462 | 1 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
_lowerCamelCase : Tuple = 6_3_7_8_1_3_7.0
_lowerCamelCase : Dict = 6_3_5_6_7_5_2.3_1_4_2_4_5
_lowerCamelCase : int = 6_3_7_8_1_3_7
def _Upp... | 196 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFea... | 196 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCAmelCase( __lowerCamelCase ):
def wrapper(*__lowerCamelCase , **__lowerCamelCase ):
__a = timeit.default_timer()
... | 559 | import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # no... | 559 | 1 |
"""simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : list[list[int]] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : set ):
__a , __a = len(_lowerCAmelCase ), len(grid[0] )
if (
min(... | 173 | """simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
__A = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into acco... | 173 | 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/LIC... | 143 |
'''simple docstring'''
def A_ ( snake_case , snake_case ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
SCREAMING_SNAKE_CASE:int = str(bin(snake_case ) )[2:] # remove the leading "0b"
SCREAMING_SNAKE_CASE:Dict = str(bi... | 143 | 1 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__a : Optional[Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def _SCREAMING_SNAKE_CASE ... | 700 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def _SCREAMING_SNAKE_CASE ( __lowercase : str , __lowercase : float | Decimal , __lowercase : float = 1_0**-1_0 ) -> float:
"""simple doc... | 199 | 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 PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"vocab_file": "spm_char... | 325 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
... | 325 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 700 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from trans... | 353 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_r... | 9 |
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
r... | 207 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
lowerCamelCase__ = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
(... | 716 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowercase_ ( SCREAMING_SNAKE_CASE : str = "laptop" ):
"""simple docstring"""
snake_case__ : Dict =F'''https://www.amazon.in/laptop/s?k={product}'''
... | 408 | 0 |
from __future__ import annotations
class a :
'''simple docstring'''
def __init__( self : Optional[Any] , __snake_case : list[list[int]] ):
UpperCAmelCase_ = TypeError(
'''Matrices must be formed from a list of zer... | 144 |
def _lowerCAmelCase ( lowerCAmelCase_ :List[str] , lowerCAmelCase_ :int )->Optional[int]:
'''simple docstring'''
snake_case_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _lowe... | 283 | 0 |
def a__ (__lowercase :int = 1000 ) -> int:
_A : Optional[Any] = 1, 1
_A : Optional[int] = 2
while True:
_A : Optional[int] = 0
_A : str = fa + fa
_A : Optional[int] = ... | 713 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase : Dict =logging.get_logger(__name__)
_UpperCamelCase : Optional[Any] ={
'facebook/xmod-bas... | 332 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> None:
lowercase : Dict = generate_pascal_triangle(SCREAMING_SNAKE_CASE__ )
for row_idx in range(SCREAMING_SNAKE_CASE__ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):... | 336 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowercase : Tuple = 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 refe... | 336 | 1 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase_ : Dict = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_ : Optional[Any] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _SCREAMING_SNAKE_CASE :
s... | 590 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArgum... | 590 | 1 |
from __future__ import annotations
def lowerCAmelCase ( UpperCamelCase__ : list[int] , UpperCamelCase__ : list[int] , UpperCamelCase__ : list[int] , UpperCamelCase__ : list[list[str]] , UpperCamelCase__ : int , ) -> None:
"""simpl... | 202 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class a :
def __init__( self , _lowerCAmelCase ):
"""simple docstring"""
if isinstance(_lowerCAmel... | 202 | 1 |
def _lowercase ( lowercase__ ):
__lowerCAmelCase : Any = generate_pascal_triangle(lowercase__ )
for row_idx in range(lowercase__ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=''' ''' )
# Print row v... | 715 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise ... | 583 | 0 |
'''simple docstring'''
def A_ ( snake_case , snake_case ):
SCREAMING_SNAKE_CASE:Any = [1]
for i in range(2 , snake_case ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
SCREAMING_SNAKE_CASE:Tuple = ... | 143 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"camembert-base": "https://hu... | 143 | 1 |
import numpy as np
from PIL import Image
def snake_case ( snake_case__ , snake_case__ , snake_case__) -> Dict:
_A = np.array(snake_case__)
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array is not a square matrix""")
... | 721 | _SCREAMING_SNAKE_CASE = {
'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
'V': '...-'... | 83 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __magic_name__ ( lowercase , lowercase , lowercase ) -> Optional[int]:
"""simple docstring"""
lowercase_ : Dict = Au... | 458 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __magic_name__ ( ) -> str:
"""simple docstring"""
lowercase_ : Optional[int] = ArgumentP... | 458 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class lowerCamelCase_ :
_lowerCamelCase : Optional[int] = 42
_lowerCamelCase : List[Any] = None
_lowerCamelCase : Any = None
def __SCREAMING_SN... | 705 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.... | 403 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 1000 ):
lowerCAmelCase = -1
lowerCAmelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
lowerCAmelCase = (n * n - 2 * a * n) // (2 * n ... | 4 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : ... | 4 | 1 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
... | 182 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case : Optional[int] = logging.get_logger(__name__)
snake_case : Union[str, Any] = {
'facebook/data... | 182 | 1 |
import os
import sys
import unittest
A__ : List[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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, fin... | 183 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
im... | 183 | 1 |
a_ = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Versi... | 702 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDC... | 622 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class __UpperCamelCase :
def __init__( self , __a ):
'''simple docstring'''
__a : Optional[Any] = []
self.adlist.append(
{'value': '', 'next_s... | 476 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import flo... | 673 | 0 |
"""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_avai... | 703 | """simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 674 | 0 |
"""simple docstring"""
def A_ (__a , __a ):
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers" )
A_ = str(bin(__a ) )
binary_number += "0" * shift_amount
return binary_numbe... | 115 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfo... | 115 | 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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 707 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A__ ( nn.Module ):
'''simple docstring'''
snake_case__ = 42
... | 410 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( ) -> list[list[int]]:
"""simple docstring"""
return [list(range(1000 - i, -1000 - i, -1 ) ) for i in range(1000 )]
__UpperCamelCase : List[Any] = generate_large_matrix()
__UpperCam... | 448 |
'''simple docstring'''
import os
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE__: str = "matrix.txt" ) -> int:
"""simple docstring"""
with open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__ ), SCREAMING_SNAKE_CASE__ ) ) as in_file:
... | 448 | 1 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE__:Union[str, Any] = parse(importlib.metadata.version("""torch"""))
def _lowerCamelCase( a , a , a ... | 67 | """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... | 67 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
... | 28 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tok... | 28 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase ( snake_case_ , unittest.TestCase ):
__UpperCAmelC... | 117 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""facebook/xmod-base""... | 117 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import... | 124 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipe... | 124 | 1 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
loggin... | 715 |
class UpperCamelCase:
def __init__( self : Any ) -> Any:
'''simple docstring'''
__snake_case = 0
__snake_case = 0
__snake_case = {}
def SCREAMING_SNAKE_CASE_ ( self : Dict , SCREAMING_SN... | 473 | 0 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pip... | 218 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class _a ( UpperCamelCase__):
"""simple docstring"""
UpperCa... | 380 | 0 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ... | 713 |
import fire
from utils import calculate_rouge, save_json
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case=None , **__snake_case ) -> List[str]:
_UpperCAmelCase = [x.strip() for x in open(__snake_case ).readlines()]
_UpperCA... | 402 | 0 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
... | 633 |
"""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_flax_a... | 633 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_avai... | 716 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( _UpperCAmelCase : list[int]):
UpperCamelCase = len(_UpperCAmelCase) // 2
# choose the middle 3 elements
UpperCamelCase = lst[m - 1 : m + 2]
# if middle element is peak
if th... | 350 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
a_ = logging.get_logger(__name__)
class __lowerCAmelCase ( __UpperCamelCase ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''sim... | 175 |
def __UpperCamelCase ( lowercase__ : int , lowercase__ : int , lowercase__ : int ) -> float:
'''simple docstring'''
lowerCAmelCase_ : Tuple = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
... | 600 | 0 |
'''simple docstring'''
def _a ( ):
snake_case : List[str] =[]
snake_case : int =1
while len(lowerCamelCase_ ) < 1e6:
constant.append(str(lowerCamelCase_ ) )
i += 1
snake_case : Tuple =''''''.join(lowerCamelCase_ ... | 716 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_lo... | 136 | 0 |
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
_lowercase : Tuple = logging.get_logger(__name__)
_lowercase : List[Any] = ... | 641 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceCl... | 29 | 0 |
import os
import unicodedata
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 = logging.get_logger(__name__)
__snake_case = ... | 721 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"... | 400 | 0 |
import argparse
import os
import re
import packaging.version
lowercase_ = '''examples/'''
lowercase_ = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__version__\s... | 354 |
from __future__ import annotations
class __a :
def __init__( self : Optional[int] , snake_case_ : int = 0)-> List[str]:
__lowerCAmelCase =key
def UpperCamelCase ( self : Any , snake_case_ : str , snake_case_ : ... | 354 | 1 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
... | 411 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_l... | 411 | 1 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
SCREAMING_SNAKE_CASE : Optional[Any] = 6_37_81_37.0
SCREAMING_SNAKE_CASE : Optional[int] = 6_35_67_52.31_42_45
SCREAMING_SNAKE_CASE : Tuple = 6_378_137
... | 257 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _lowercase ( __UpperCAmelCase ):
_lowerCamelC... | 490 | 0 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def A ( UpperCAmelCase , UpperCAmelCase ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(UpperCAmelCase , UpperCAmelCase ) ) )
de... | 708 |
def A ( UpperCAmelCase ):
return str(UpperCAmelCase ) == str(UpperCAmelCase )[::-1]
def A ( UpperCAmelCase ):
return int(UpperCAmelCase ) + int(str(UpperCAmelCase )[::-1] )
def A ( UpperCAmelCase = 10_000 )... | 278 | 0 |
from __future__ import annotations
A = []
def __UpperCAmelCase ( __A , __A , __A ) -> bool:
'''simple docstring'''
for i in range(len(__A ) ):
if board[row][i] == 1:
return False
... | 475 |
import argparse
import datetime
def __UpperCAmelCase ( __A ) -> str:
'''simple docstring'''
UpperCAmelCase__ = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4"... | 475 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import... | 561 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass... | 561 | 1 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : Any ,a__ : str ,a__ : Dict ) -> int: # noqa: E741
while r - l > 1:
__A : Tuple = (l + r) // 2
if v[m] >= key:
__A : Optional[int] = m
else:
__A : Optio... | 17 | '''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTe... | 435 | 0 |
"""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__ ... | 258 |
"""simple docstring"""
from itertools import count
def _A ( __lowercase = 50 ):
"""simple docstring"""
lowerCamelCase__ = [1] * min_block_length
for n in count(__lowercase ):
fill_count_functions.append(1 )
for block_l... | 258 | 1 |
from __future__ import annotations
from typing import Any
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : float = 0 ) -> None:
SCREAM... | 493 |
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
if len(_A ) <= 1:
return [tuple(_A )]
SCREAMING_SNAKE_CASE__ = []
def generate(_A , _A ):
SCREAMING_SNAKE_CASE__ = [0] * n
res.append(tuple(_A ... | 493 | 1 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 47 |
'''simple docstring'''
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... | 47 | 1 |
def snake_case__ ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(SCREAMING_SNAKE_CASE_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from d... | 164 |
snake_case_ = [
(1_000, '''M'''),
(900, '''CM'''),
(500, '''D'''),
(400, '''CD'''),
(100, '''C'''),
(90, '''XC'''),
(50, '''L'''),
(40, '''XL'''),
(10, '''X'''),
(9, '''IX'''),
(5, '''V'''),
(4, '''IV'''),
(1, '''I'''),
]
def snake_case__ ( ... | 164 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_ID... | 571 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 571 | 1 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data im... | 272 |
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_attention_... | 272 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : Union[str, Any] ) -> Any:
if isinstance(lowercase , lowercase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(lowercase , lowercase ):
raise ... | 714 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
... | 521 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TO... | 257 |
from typing import Any
import numpy as np
def UpperCamelCase ( _a ) -> bool:
'''simple docstring'''
return np.array_equal(_a , matrix.conjugate().T )
def UpperCamelCase ( _a , _a ) -> Any:
'''simple docstring... | 257 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
"studio-ousia/luke-large": "https://h... | 700 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https:/... | 387 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterM... | 50 | import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase ( unittes... | 635 | 0 |
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
return base * power(__lowerCAmelCase , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("Raise base to the power of exponent using recursion...")
__lowerCAmelCase : str = int(i... | 702 |
from math import factorial
__lowerCAmelCase : Dict = {str(d): factorial(d) for d in range(10)}
def UpperCAmelCase_ ( __lowerCAmelCase ) -> int:
return sum(DIGIT_FACTORIAL[d] for d in str(__lowerCAmelCase ) )
def UpperCAmelCase_ ( ) -> int:
__lowercas... | 284 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
... | 202 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase : List[str] = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
i... | 202 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_di... | 599 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class __UpperCAmelCase ( UpperCamelCase__ ):
'''simple docstring'''
def __... | 599 | 1 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transfor... | 433 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--ch... | 433 | 1 |
"""simple docstring"""
A__ : int = 8.3144598
def _lowerCAmelCase ( _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
raise Exception('''Molar mass c... | 272 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channe... | 272 | 1 |
"""simple docstring"""
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
... | 49 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_ver... | 51 | 0 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : str = "isbn/0140328726" ) -> dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE... | 719 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class SCREAMING_SNAKE_CASE... | 68 | 0 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __lowerCamelCase ( UpperCAmelCase_ ... | 368 |
'''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 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ : List[Any] ... | 204 | '''simple docstring'''
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[int] = logging.get... | 204 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@f... | 131 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested... | 131 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
__A = logging.get_logger(__name__)
class _sna... | 701 | """simple docstring"""
import os
def lowercase_ ( ) -> List[str]:
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = os.path.dirname(os.path.realpath(_lowerCamelCase ) )
__lowerCamelCase : int = os.path.join(_lowerCamelCase , ... | 366 | 0 |
"""simple docstring"""
def __A ( a_ :Optional[Any]) -> Optional[int]:
__a : Any = []
__a : Union[str, Any] = []
__a : Dict = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
... | 52 |
"""simple docstring"""
import unittest
from knapsack import greedy_knapsack as kp
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCamelCase ( self ):
__a : Optional[int] ... | 52 | 1 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase , UpperCAmel... | 715 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a: Optional[Any] = logging.get_logger(__name__)
__a: str = {
'''google/bit-50''': '''https://... | 402 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 74 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __snake_case ( SCREAMING_SNAKE_CASE: int ):
"""simple docstring"""
_lowerCAmelCase = int(number**0.5 )
... | 580 | 0 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _A ( A__ , A__ , A__ , A__=5 ):
"""simple docstring"""
assert masked_input.count('''<mask>''' ) == 1
__lowercase = torch.tensor(tokenizer.encode(A__ , add_special_toke... | 710 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
lowerCAmelCase__ = (720, 1280) # Height, Width
lowerCAmelCase__ = (0.4, 0.6) # if height or width lower than this scale, drop it.
lowerCAmelCase... | 624 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@da... | 82 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_fla... | 325 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE ) < k or k < 0:
raise ValueError('Invalid Input' )
U... | 20 |
"""simple docstring"""
import sys
from collections import defaultdict
class __lowerCamelCase :
def __init__( self ) -> Tuple:
UpperCamelCase__ = []
def SCREAMING_SNAKE_CASE__ ( self , snake_case_ ) -> List[str]:
... | 20 | 1 |
"""simple docstring"""
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked be... | 182 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__snake_case : Dict = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'toke... | 215 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ):
if nth_term == "":
return [""]
lowercase__ : Union[str, Any] = int(UpperCAmelCase )
lowercase__ : Union[str, Any] = int(UpperCAmelCase )
lowe... | 428 | '''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a: Optional[int] = logging.get_logger(__name__)
__a: int = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/ef... | 428 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[str] =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] ={
'''microsoft/trocr-base-handwritten''': (
'''https://huggingface.co/micro... | 428 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ , lowercase__ = position
lowercase__ = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
... | 43 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __UpperCAmelCase ( u... | 606 | '''simple docstring'''
import argparse
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
from ... | 606 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def lowerCAmelCase_ ( snake_case_ : Sequence[int] | None = None ) -> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
Up... | 78 |
def __snake_case ( _lowerCAmelCase : list , _lowerCAmelCase : list , _lowerCAmelCase : int ) -> int:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:... | 454 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 710 |
'''simple docstring'''
def _UpperCAmelCase ( a : list ) -> list:
"""simple docstring"""
for i in range(len(a ) - 1 , 0 , -1 ):
lowercase_ : Any = False
for j in range(a , 0 , -1 ):
... | 7 | 0 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
_lowerCamelCase : List[str] = logging.getLogger(__name__)
... | 686 |
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def lowerCamelCase ( _UpperCamelCase : list[int] , _UpperCamelCase : list[int] , _UpperCamelCase : int ) -> list[int]:
'''simple docstring'''
__UpperCAmelCase : List[Any] ... | 139 | 0 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have ... | 458 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProce... | 458 | 1 |
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