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'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : int = logging.get_logger(__name__... | 679 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrate... | 679 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from trans... | 515 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_p... | 515 | 1 |
from math import factorial, pi
def __UpperCAmelCase ( lowerCamelCase_ : float , lowerCamelCase_ : int = 30 ) -> float:
"""simple docstring"""
if not isinstance(lowerCamelCase_ , (int, float) ):
raise ValueError('maclaurin_sin() requires ... | 105 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
'''configuration_whisper''': ['''WHISPER_PRETRAINE... | 105 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Ba... | 487 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class a__ ( __magic_name__ ):
lowercase_ = ["image_processor", "feature_extractor"]
lowercase_ = "TvltImageProcessor"
lowercase_ = "TvltFeatureExtractor"
def __init__( self : Tuple , Up... | 487 | 1 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
lowerCamelCase_ = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""t... | 330 | """simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def _lowerCamelCase( a , a = "cpu" , a = None ):
__a = torch.load(a , map_location=a )
for k, v in tqdm(state_dict.items() ):
if not isinstance(a , tor... | 528 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig... | 182 |
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,
)
snake_case : str = {
'configuration_albert': ['ALBERT_P... | 182 | 1 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
pass
| 316 |
'''simple docstring'''
def lowerCamelCase_ ( A_ = 3 , A_ = 7 , A_ = 1_00_00_00 ):
__lowerCamelCase = 0
__lowerCamelCase = 1
for current_denominator in range(1 , limit + 1 ):
__lowerCamelCase = current_denominator * numerator // denominator
... | 316 | 1 |
from __future__ import annotations
from typing import Any
class __UpperCamelCase :
'''simple docstring'''
def __init__( self , UpperCAmelCase_ ):
lowerCAmelCase = num_of_nodes
lowerCAmelCase = []
lowerCAmelCase =... | 33 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
UpperCAmelCase_ =datasets.utils.loggi... | 33 | 1 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCamelCase__ : List[Any] = '''src/transformers'''
UpperCamelCase__ : ... | 105 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def A ( _A, _A ):
"""simple docstring"""
snake_case_ :List[str] = list(_A )
snake_case_ :Any = list(_A )
snake_cas... | 584 | 0 |
def A_ ( snake_case : int , snake_case : list ) -> Optional[Any]:
'''simple docstring'''
_enforce_args(snake_case , snake_case )
if n == 0:
return 0
__UpperCamelCase = float('''-inf''' )
for i in range(1 , n + 1 ):
... | 702 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def A_ ( snake_case : int ) -> int:
'''simple docstring'''
def is_in_circle(snake_case : float , snake_case : float ) -> bool:
... | 451 | 0 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase : Union[str, Any] ... | 663 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterM... | 663 | 1 |
def A ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
UpperCAmelCase_ = len(__UpperCAmelCase )
UpperCAmelCase_ = max(__UpperCAmelCase )
... | 701 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"bert-base-uncased": "https://huggingface.co/bert-ba... | 561 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import loggin... | 692 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__A : str = {
"configuration_audio_spectrogram_transformer": [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAIN... | 275 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Any = logging.get_logger(__name__)
__lowercase : List[Any] = {
"facebook/wav2vec2-base-960h"... | 712 | """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,
... | 93 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('One and only one a... | 42 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case_ : Tuple = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""... | 595 | 0 |
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 U... | 715 |
from collections import deque
class __lowerCamelCase :
def __init__( self , __snake_case , __snake_case , __snake_case ) -> None:
"""simple docstring"""
UpperCAmelCase: Tuple = process_name # process name
... | 166 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> str:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ... | 69 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 683 | 0 |
import sys
_snake_case = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"668966489504452445231617318564030987111... | 413 |
_snake_case = [
"DownloadConfig",
"DownloadManager",
"DownloadMode",
"StreamingDownloadManager",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 413 | 1 |
'''simple docstring'''
import numpy as np
import qiskit
def _a ( _lowerCamelCase = 8 , _lowerCamelCase = None ) -> str:
"""simple docstring"""
__snake_case : Any = np.random.default_rng(seed=_lowerCamelCase )
... | 26 |
'''simple docstring'''
from __future__ import annotations
import math
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
"""simple docstring"""
if depth < 0:
raise V... | 26 | 1 |
import numpy
# List of input, output pairs
lowerCAmelCase__ : int = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowerCAmelCase__ : Tuple = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
lowerCAmelCase__ : Dict = ... | 707 | from collections import namedtuple
lowerCAmelCase__ : Union[str, Any] = namedtuple('''from_to''', '''from_ to''')
lowerCAmelCase__ : Tuple = {
'''cubicmeter''': from_to(1, 1),
'''litre''': from_to(0.0_01, 10_00),
'''kilolitre''': from_to(1, 1),
'''gallon''': from_to(0.0_04_54... | 699 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
if depth < 0:
raise ValueError("""De... | 65 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureEx... | 91 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a__( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = '''ClapFeatureExtractor'''
UpperCAmelCase_ : List[Any] = (... | 707 | '''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 605 | 0 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _a ( lowerCamelCase = "laptop" ):
lowerCamelCase : Union[str, Any] = F'''https://www.amazon.in/laptop/s?k={product}'''
lowerCamelCase : Optional[int] ... | 681 |
import copy
import random
from transformers import CLIPTokenizer
class A__ ( __SCREAMING_SNAKE_CASE):
def __init__( self , *__magic_name__ , **__magic_name__ ):
super().__init__(*__magic_name__ , **__magic_name__ )
lowerCamelCase : Dict ... | 681 | 1 |
import qiskit
def UpperCAmelCase__ ( lowerCamelCase_ : int = 2 ):
__a : Optional[Any] = qubits
# Using Aer's simulator
__a : List[Any] = qiskit.Aer.get_backend('aer_simulator' )
# Creating a Quantum Circuit acting on the q register
__a :... | 577 |
def UpperCAmelCase__ ( lowerCamelCase_ : int ):
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError('Input must be an integer' )
if input_num <= 0:
raise ValueError('Input must be positive' )
return sum(
d... | 577 | 1 |
'''simple docstring'''
def __snake_case ( lowerCamelCase_ : list[list[int | float]] ):
'''simple docstring'''
__magic_name__ = len(lowerCamelCase_ )
__magic_name__ = len(matrix[0] )
__magic_name__ = min(lowerCamelCase_ , lowerCamelCase_ ... | 664 |
'''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 UpperCamelCase_ ( unittest.TestCa... | 664 | 1 |
"""simple docstring"""
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,
ConditionalD... | 716 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf... | 147 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers... | 261 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' ,[
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num_jobs''': 1}, [range(10 ... | 481 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase__ : List[Any] = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GP... | 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
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperC... | 0 | 1 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
assert x is not None
assert y is not None
lowercase__ = len(_SCREAMING_SNAKE_CASE )
lowercase__ = len(_SCREAMING_SNAKE_CASE )
# declaring the array f... | 235 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 235 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCamelCase ( lowerCamelCase_ : list , lowerCamelCase_ : int ):
"""simple docstring"""
if len(lowerCamelCase_ ) <= 1 or n <= 1:
return
insert_next(lowerCamelCase_ , n -... | 389 | '''simple docstring'''
from collections import defaultdict
def _lowerCamelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = first_str.lower().strip()
UpperCAmelCase_ :... | 389 | 1 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import C... | 66 | lowercase__ : Union[str, Any] = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .dat... | 312 | 0 |
"""simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
UpperCAmelCase = 4
UpperCAmelCase = 3
class UpperCAmelCase_ ( _... | 342 | """simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
UpperCAmelCase = logging.get_logger(__name__)
... | 342 | 1 |
'''simple docstring'''
def __a ( A__ = 1000 ) -> int:
lowerCAmelCase = 3
lowerCAmelCase = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__... | 649 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 649 | 1 |
"""simple docstring"""
from __future__ import annotations
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Tuple ,A_ : str ,A_ : str ) -> Tuple:
A , A = text, pattern
A , A = len(... | 702 |
"""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
_lowercase = logging.get_logger(__name__)
_lowercase = {
... | 22 | 0 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __l... | 358 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __l... | 358 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...t... | 690 |
"""simple docstring"""
def _a ( UpperCAmelCase__ = 10**9 ) -> int:
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
... | 690 | 1 |
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 import (
ProphetNetForConditionalGen... | 592 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | 592 | 1 |
'''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_logging
... | 707 | '''simple docstring'''
from __future__ import annotations
import time
import numpy as np
snake_case__ : List[Any] = [8, 5, 9, 7]
snake_case__ : str = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
snake_case__ : Any = [
... | 389 | 0 |
'''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... | 507 |
"""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 | 0 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class a__ ( tf.keras.optimizers.schedules.LearningRa... | 718 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from ... | 355 | 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_... | 41 |
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = len(lowerCamelCase_ )
lowercase__ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not... | 183 | 0 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from da... | 716 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
if not is_torch_availab... | 351 | 0 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
a__ : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a__ : list[int] = [ord(letter) f... | 51 |
"""simple docstring"""
import math
def UpperCamelCase ( _lowerCAmelCase : int ) -> str:
_UpperCAmelCase : Any = 0
_UpperCAmelCase : Dict = 0
while num > 0:
_UpperCAmelCase : str = num % 8
_UpperC... | 238 | 0 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequ... | 294 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_se... | 294 | 1 |
"""simple docstring"""
import baseaa
def _a ( UpperCAmelCase__ ) -> bytes:
return baseaa.aaaencode(string.encode('''utf-8''' ) )
def _a ( UpperCAmelCase__ ) -> str:
return baseaa.aaadecode(UpperCAmelCase__ ).decode('''utf-8''' )
if __n... | 482 |
"""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 Backb... | 482 | 1 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPU... | 713 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
raise OptionalDependencyN... | 677 | 0 |
'''simple docstring'''
import sys
import turtle
def UpperCAmelCase ( lowerCamelCase_ :tuple[float, float] , lowerCamelCase_ :tuple[float, float] ):
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def UpperCAmelCase ( lowerCamelCase_ :tuple[float, fl... | 334 |
'''simple docstring'''
__A : List[Any] = {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: 'a',
11: 'b',
12: 'c',
13: 'd',
14: 'e',
15: 'f',
}
def UpperCAmelCase ( lowerCamelCase_ :float ... | 334 | 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_blenderbot': [
'BLENDERBOT_PRETRAINED_CONFIG_AR... | 719 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'facebook/data2vec-text-base': 'https://hug... | 185 | 0 |
"""simple docstring"""
from __future__ import annotations
import bisect
def UpperCAmelCase ( A__: list[int] , A__: int , A__: int = 0 , A__: int = -1 ) -> int:
if hi < 0:
__lowerCamelCase : Tuple = len(A__ )
while lo... | 594 |
"""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
cla... | 594 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_ou... | 706 | '''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compos... | 179 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration... | 379 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_commo... | 199 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> float:
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
__SCREAMING_SNAKE_CASE = sum(__UpperCAmelCase... | 13 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 13 | 1 |
"""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... | 346 |
from __future__ import annotations
def _lowerCAmelCase ( A__ , A__ = None ):
lowercase__ = word_bank or []
# create a table
lowercase__ = len(A__ ) + 1
lowercase__ = []
for _ in range(A__ ):
table.append([] )
# seed value
lowercas... | 622 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase__ )
class __A ( lowerCamelCase__ ):
"""simple docstring"""
UpperCAmelCase__ = f... | 613 |
def __UpperCAmelCase( lowercase_ , lowercase_ , lowercase_ ):
return round(float(moles / volume ) * nfactor )
def __UpperCAmelCase( lowercase_ , lowercase_ , lowercase_ ):
return round(float((moles * 0.0_8_2_1 * temperature) / (volume) ) ... | 613 | 1 |
'''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def UpperCAmelCase__ ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Union[str, Any]=10_00 ) -> Union[str, Any]:
if n < 2:
return False
if n % 2 == 0:
ret... | 13 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCamelCase : List[Any] ={
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
... | 228 | 0 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
SCREAMING_SNAKE_CASE__ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
"""simple docstring"""
lowerCAmelCase__ =... | 601 |
from typing import Any
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase_ = data
lowercase_ = None
class __lowerCamelCase ... | 601 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
"""simple docstring"""
a = HfArgumentParser(snake_case_ )
a = parser.parse_args_into_dataclasses()[0]
a... | 387 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> float:
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(snake_case_ ) )
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> float:
"""s... | 387 | 1 |
'''simple docstring'''
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase__ = ... | 411 |
'''simple docstring'''
from math import factorial
def _SCREAMING_SNAKE_CASE( snake_case_ : int , snake_case_ : int ) ->int:
'''simple docstring'''
# If either of the conditions are true, the function is being asked
... | 411 | 1 |
from collections import defaultdict
from math import gcd
def __lowerCAmelCase ( _UpperCamelCase : int = 1_50_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = defaultdict(_UpperCamelCase )
SCREAMING_SNAKE_CASE = 2
while 2 * euclid_m * (euclid_m +... | 439 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floa... | 439 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddin... | 51 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCamelCase__ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that g... | 51 | 1 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and ... | 75 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAna... | 75 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 106 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__UpperCamelCase : int = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": [... | 106 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_u... | 304 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow,... | 304 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase__ ( a__ , a__ , a__ , a__ ):
'''s... | 58 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']... | 58 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def a_ ( lowerCamelCase : Dict , lowerCamelCase : bool = True , lowerCamelCase : float = math.inf , lowerCamelCase : ... | 133 |
'''simple docstring'''
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def a_ ( lowerCamelCase : Union[str, Any] , lowerCamelC... | 133 | 1 |
'''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def _a ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : List[Any]=() , _SCREAMING_S... | 702 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import v... | 493 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines... | 95 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''huggingface/informer-tourism-monthly''': (
'''https://hugg... | 95 | 1 |
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 accelerate imp... | 278 |
import numpy as np
__lowerCAmelCase :Dict = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class _a:
def __init__( self ) -> No... | 278 | 1 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCAmelCase__ = 'src/transformers'
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _imp... | 596 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, 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... | 596 | 1 |
'''simple docstring'''
def A_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 0 ) ->int:
lowercase_ = right or len(SCREAMING_SNAKE_CASE_ ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_... | 603 | '''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _a ( __a ):
"""simple docstring"""
def __init__( self : ... | 603 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Any:
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
__SCR... | 109 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_ve... | 439 | 0 |
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 transformers import MarianMTModel
from t... | 711 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from tra... | 205 | 0 |
_lowerCAmelCase: Tuple = 'Alexander Joslin'
import operator as op
from .stack import Stack
def _lowercase( __a : str ):
a__ ={'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
a__ =Stack()
a__ =Stack()
for i in equation:
... | 20 |
def __magic_name__ ( SCREAMING_SNAKE_CASE = 50 ) -> int:
_lowercase : Optional[int] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in... | 66 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case_ = {
'configuration_mobilenet_v2': [
'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileNetV2Config',
'Mobi... | 388 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_availab... | 388 | 1 |
'''simple docstring'''
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
A = ... | 125 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config... | 580 | 0 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _A ( unittest.TestCase ):
... | 712 |
# flake8: noqa
# Lint as: python3
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_progr... | 175 | 0 |
"""simple docstring"""
import os
import numpy
import onnx
def _snake_case ( _snake_case : Optional[Any] , _snake_case : Any ) -> Dict:
'''simple docstring'''
_A = a.name
_A = b.name
_A = ''
_A = ''
_A = ... | 7 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
"configuration_efficientformer": [
"EFFICIENTFORMER... | 510 | 0 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 173 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig"... | 173 | 1 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __a ( __UpperCamelCase ):
__snake_case : Optional[int] = """EncodecFeatureExtractor"""
__snake_case : Tuple = ("""T5Tokenizer""", """T5To... | 600 |
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 TokenizerTesterMi... | 600 | 1 |
UpperCAmelCase__ : List[Any] ={0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCAmelCase__ : Union[str, Any] ={0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> lis... | 720 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCAmelCase__ : Tuple =version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _lowercase (... | 269 | 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, prepare_image_inputs
if is_torch_available():
import tor... | 496 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_di... | 483 | 0 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__lowerCamelCase = '''sshleifer/bart-tiny-rand... | 707 |
from datetime import datetime as dt
import os
from github import Github
__lowerCamelCase = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def _a ( ):
a_ : List[str] ... | 478 | 0 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( lowercase_ ) -> Union[str, Any]: # picklable for multiprocessing
... | 326 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = OrderedDict(
[
# Base mod... | 326 | 1 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
... | 713 |
'''simple docstring'''
lowercase__ : List[Any] = '''Input must be a string of 8 numbers plus letter'''
lowercase__ : Optional[Any] = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def _lowerCAmelCase ( __snake_case : str ) -> bool:
if n... | 338 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Dict = {
'k... | 55 |
'''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,
BartFor... | 689 | 0 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
lowercase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( a__ ):
def __init__( self , *lowerCAmelCase , **lowerCAmelCas... | 703 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging ... | 380 | 0 |
# 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_ ( T... | 10 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (... | 412 | 0 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> bool:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError('''check_bouncy() accepts only integer arguments''' )
UpperCAmelCase__ : Optional[Any] = str(lowerCAmelCase__ )... | 701 |
'''simple docstring'''
from timeit import timeit
def a__ ( lowerCAmelCase__ ) -> int:
if number < 0:
raise ValueError('''the value of input must not be negative''' )
UpperCAmelCase__ : Tuple = 0
while number:
number &= number - 1
... | 312 | 0 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Accelerat... | 5 |
def lowerCAmelCase_ ( __UpperCAmelCase: float ) -> float:
return 10 - x * x
def lowerCAmelCase_ ( __UpperCAmelCase: float , __UpperCAmelCase: float ) -> float:
# Bolzano theory in order to find if there is a root between a and b
... | 253 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAIN... | 406 |
"""simple docstring"""
from __future__ import annotations
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_ ):
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return []
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = min(SCREAMING_SNAKE_CASE_ ), max(SCREAMING_SNAKE_CA... | 406 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_... | 652 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar("""T""")
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] , _lowerCAmelCase : T ) -... | 80 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
_... | 153 |
from __future__ import annotations
__SCREAMING_SNAKE_CASE = '#'
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self ):
SCREAMING_SNAKE_CASE_ : dict ={}
def __lowerCamelCase ( self , __UpperCAmelCase ):
... | 153 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 100 , ) -> float:
_a : List[Any] = x_start
_a : Union[str, Any] ... | 358 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__lowerCAmelCase = logging.get_logger(__name__)
class __magic_name__ ( _UpperCamelCase ):
def __init__( self : Optional[int] ,*_UpperCAmelCase... | 358 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : Optional[int] = logging.... | 630 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class UpperCamelCase__ ( tf.keras.layers.Layer ):... | 630 | 1 |
def A__ ( _a : List[str] ):
'''simple docstring'''
return 10 - x * x
def A__ ( _a : Any , _a : str ):
'''simple docstring'''
if equation(__SCREAMING_SNAKE_CASE ) * equation(__SCREAMING_SNAKE_CASE ) >= 0:
raise Value... | 385 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> List[Any]: # noqa: E741
while r - l > 1:
__lowerCAmelCase: str ... | 346 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
UpperCamelCase__ : str = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**snake_case__ ):
... | 685 |
def __UpperCAmelCase ( lowerCamelCase_ : int = 10_00 ) -> int:
"""simple docstring"""
return sum(e for e in range(3 , lowerCamelCase_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 685 | 1 |
"""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 ... | 178 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( snake_case_ ):
__UpperCAmelCase : List[str] = (KDPMaDiscreteScheduler,)
__Upp... | 178 | 1 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case : Tuple = {
'vo... | 687 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impo... | 687 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
snake_case_ : str = logging.get_logger(__name__)
class snake_case_ ( _lowerCAmelCase ):
'''simple docstring'''
def __init__( self : str , *__magic_... | 488 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class ... | 554 | 0 |
from __future__ import annotations
_snake_case = 'Muhammad Umer Farooq'
_snake_case = 'MIT'
_snake_case = '1.0.0'
_snake_case = 'Muhammad Umer Farooq'
_snake_case = 'contact@muhammadumerfarooq.me'
_snake_case = 'Alpha'
import re
from html.parser import HTMLPar... | 715 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxCon... | 567 | 0 |
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