code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return str(lowerCamelCase ) == str(lowerCamelCase )[::-1]
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return int(lowerCamelCase ) + int(str(lowerCamelCase )[::-1]... | 80 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase_ : Dict = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 115 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase ):
__snake_case : int = abs(__lowerCamelCase )
__snake_case : Dict = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def lowerCAmelCase_ ( __lowerCa... | 81 |
"""simple docstring"""
import operator
def A_ (__a , __a = False , __a = None ):
'''simple docstring'''
A_ = operator.lt if reverse else operator.gt
A_ = solution or []
if not arr:
return solution
A_ = [arr.pop(0 )]
... | 115 | 0 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids... | 82 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 115 | 0 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditio... | 83 |
"""simple docstring"""
def A_ (__a ):
'''simple docstring'''
A_ = len(__a )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A_ = arr[mi::-1] + ar... | 115 | 0 |
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
from accelerate import Accelerator
from accelerate.test... | 84 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 115 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'Speech2TextFeatureExtractor'
lowercase_ = 'Speech2TextTokenizer'
def __init__( self : Optional[Any] , a_ : O... | 85 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fro... | 115 | 0 |
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
@data... | 86 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not ... | 115 | 0 |
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 import Accelerator, ... | 87 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_util... | 115 | 0 |
"""simple docstring"""
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""": ["""WH... | 88 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : ... | 115 | 0 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class _lowerCamelCase:
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=0.2, lowerCamelCase=0.2) -> str:
"""simple docstring"""
_lowerc... | 89 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a ):
'''simple docstring'''
A_ = 0.00
A_ = 0
for resistor in resistors:
if resistor <= 0:
A_ = f'Resistor at index {index} has a negative or zero value!'
... | 115 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import... | 90 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a , __a = None , __a = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = len(__a ) - 1
if start >= end:
return
A_ ... | 115 | 0 |
"""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/LICENSE-2.0
#... | 91 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import... | 115 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _lowerCAmelCase ( __magic_name__ : int , __magic_name__ : int , __magic_name__ : bool , __magic_name__ : list[int] , __magic_name__ : float ) -> int:
if de... | 92 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
__magic_name__ :int
__magic_name__ :int
class _... | 93 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase_ : List[str] = {
'''yjernite/retribert-base-uncased''': (
'... | 115 | 0 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedCo... | 94 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 115 | 0 |
"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
lowerCamelCase_ = logging.getLog... | 95 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 115 | 0 |
"""simple docstring"""
def a ( __UpperCAmelCase : str ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
__magic_name__: Optional[Any] = sorted(string... | 96 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfo... | 115 | 0 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def a ( snake_case__: str , snake_case__: str ):
'''simple docstring'''
lowercase_ = list(snake_case__ )
lowercase_ = list(snake_case__ )
lowercase... | 97 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availabl... | 115 | 0 |
'''simple docstring'''
def a__ ( lowercase : List[str], lowercase : Tuple ) -> int:
"""simple docstring"""
_UpperCamelCase = [0 for i in range(r + 1 )]
# nc0 = 1
_UpperCamelCase = 1
for i in range(1, n + 1 ):
... | 98 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Dict = {'''configuration_plbart''': ['''PLBART_PRETR... | 115 | 0 |
def a ():
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
SCREAMING_SNAKE_CASE = generate_large_matrix()
SCREAMING_SNAKE_CASE = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
... | 99 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import Con... | 115 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_con... | 100 |
"""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 | 0 |
lowerCAmelCase__ : Any ={
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o'... | 101 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A_ (__a , __a , __a , __a , __a ):
'''simple d... | 115 | 0 |
"""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.activations import gelu_new, gelu_python, get_activation
@require_torch
class ... | 102 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase_ : Dict = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 115 | 0 |
"""simple docstring"""
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme... | 103 |
"""simple docstring"""
import operator
def A_ (__a , __a = False , __a = None ):
'''simple docstring'''
A_ = operator.lt if reverse else operator.gt
A_ = solution or []
if not arr:
return solution
A_ = [arr.pop(0 )]
... | 115 | 0 |
"""simple docstring"""
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
pass
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
pass
class UpperCamelCase__ :
"""simp... | 104 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 115 | 0 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, ... | 105 |
"""simple docstring"""
def A_ (__a ):
'''simple docstring'''
A_ = len(__a )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A_ = arr[mi::-1] + ar... | 115 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :str =logging.get_logger(__name__)
__snake_case :int ={
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https... | 106 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 115 | 0 |
'''simple docstring'''
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 transf... | 107 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fro... | 115 | 0 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
__a: int = TypeVar('''T''')
__a: Any = Union[List[T], Tuple[T, ...]]
__a: Optional[Any] = Union[T, List[T], Dict[str, T]]
__a: Any = Union[str, bytes, os.PathLike] | 108 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not ... | 115 | 0 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( __UpperCAmelCase , __UpperC... | 109 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_util... | 115 | 0 |
def lowerCamelCase__ ( snake_case_ : List[str] , snake_case_ : Union[str, Any] ) -> Any:
_validate_point(__a )
_validate_point(__a )
if len(__a ) != len(__a ):
raise ValueError('''Both points must be in the same n-dimensional space''' )
r... | 592 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : ... | 115 | 0 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tok... | 37 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a ):
'''simple docstring'''
A_ = 0.00
A_ = 0
for resistor in resistors:
if resistor <= 0:
A_ = f'Resistor at index {index} has a negative or zero value!'
... | 115 | 0 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from hu... | 240 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a , __a = None , __a = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = len(__a ) - 1
if start >= end:
return
A_ ... | 115 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def UpperCamelCase__ ( lowerCAmelCase ):
... | 207 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import... | 115 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __magic_name__ ( _lowercase , unittest.TestC... | 271 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 0 |
'''simple docstring'''
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.pipeli... | 13 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase_ : List[str] = {
'''yjernite/retribert-base-uncased''': (
'... | 115 | 0 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
a_ :List[Any] = logging.get_logger(__name__)
class snake_case__ ( _lowercase )... | 478 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 115 | 0 |
from timeit import timeit
lowerCamelCase__ = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
as... | 547 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 115 | 0 |
'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from acce... | 135 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfo... | 115 | 0 |
'''simple docstring'''
import operator
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple = False , _SCREAMING_SNAKE_CASE : List[Any] = None ) -> Any:
"""simple docstring"""
UpperCAmelCase_ : Tuple = operato... | 71 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availabl... | 115 | 0 |
def __snake_case ( __magic_name__ = 1000 ):
'''simple docstring'''
lowercase = 3
lowercase = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
... | 441 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Dict = {'''configuration_plbart''': ['''PLBART_PRETR... | 115 | 0 |
def lowerCamelCase__ ( snake_case_ : Union[str, Any] ) -> Optional[int]:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(__a , __a ):
raise TypeError('''Input value must be a \'int\' type''' )
retu... | 592 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import Con... | 115 | 0 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fr... | 37 |
"""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 | 0 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def lowerCamelCase_ ( UpperCamelCa... | 240 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A_ (__a , __a , __a , __a , __a ):
'''simple d... | 115 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTeste... | 207 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase_ : Dict = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 115 | 0 |
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_ = {
'''distilbert-base-uncased''': '''https://hu... | 271 |
"""simple docstring"""
import operator
def A_ (__a , __a = False , __a = None ):
'''simple docstring'''
A_ = operator.lt if reverse else operator.gt
A_ = solution or []
if not arr:
return solution
A_ = [arr.pop(0 )]
... | 115 | 0 |
'''simple docstring'''
from math import factorial
def UpperCAmelCase__ ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[Any] ) -> List[str]:
if n < k or k < 0:
raise ValueError('Please enter positive integers for n and k where n >= k' ... | 13 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 115 | 0 |
import torch
from diffusers import StableDiffusionPipeline
a_ :int = '''path-to-your-trained-model'''
a_ :int = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
a_ :Union[str, Any] = '''A photo of sks dog in a bucket'''
a_ :str = pipe(prompt, num_infere... | 478 |
"""simple docstring"""
def A_ (__a ):
'''simple docstring'''
A_ = len(__a )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A_ = arr[mi::-1] + ar... | 115 | 0 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import l... | 547 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 115 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE : int ={
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBert... | 135 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fro... | 115 | 0 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput... | 71 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not ... | 115 | 0 |
import unittest
from knapsack import greedy_knapsack as kp
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE( self :Dict ) ->Optional[Any]:
lowercase = [10, 20, 30, 40, 5... | 441 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_util... | 115 | 0 |
def lowerCamelCase__ ( snake_case_ : int ) -> Optional[Any]:
__snake_case = len(__a )
for i in range(__a ):
for j in range(i + 1 , __a ):
if numbers[j] < numbers[i]:
__snake_case , __snake_case = numbers[j], numbers[... | 592 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : ... | 115 | 0 |
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, TrainingArguments... | 37 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a ):
'''simple docstring'''
A_ = 0.00
A_ = 0
for resistor in resistors:
if resistor <= 0:
A_ = f'Resistor at index {index} has a negative or zero value!'
... | 115 | 0 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowercase = logging.getLogger(__name__)
lowercase = 5_0 # max width of ... | 240 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a , __a = None , __a = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = len(__a ) - 1
if start >= end:
return
A_ ... | 115 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
f... | 207 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import... | 115 | 0 |
def lowerCAmelCase_ ( lowercase: Any ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase: Dict = len(__a )
while cur > 1:
# Find the maximum number in arr
_UpperCamelCase: int = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
_UpperCamelCase: A... | 271 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[Any] = {
'''configuration_trajectory_transformer''': [
'''TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Tra... | 13 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase_ : List[str] = {
'''yjernite/retribert-base-uncased''': (
'... | 115 | 0 |
import argparse
from collections import defaultdict
import yaml
a_ :Any = '''docs/source/en/_toctree.yml'''
def lowercase_ (A : Tuple ):
snake_case__ : Tuple = defaultdict(__a )
snake_case__ : Optional[Any] = []
snake_case__ : ... | 478 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 115 | 0 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( a__ ) ->Optional[int]:
'''simple docstring'''
... | 547 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 115 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[str] =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int ={
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-m... | 135 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfo... | 115 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_availabl... | 71 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availabl... | 115 | 0 |
def __snake_case ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
lowercase = ""
for word_or_phrase in separated:
if not isinstance(__a , __a ):
raise Exception("join() accepts only strings to be joined" )
... | 441 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Dict = {'''configuration_plbart''': ['''PLBART_PRETR... | 115 | 0 |
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_imag... | 592 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import Con... | 115 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase : int = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIV... | 37 |
"""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 | 0 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
lowercase = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'''conv... | 240 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A_ (__a , __a , __a , __a , __a ):
'''simple d... | 115 | 0 |
'''simple docstring'''
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class ... | 207 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase_ : Dict = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 115 | 0 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers imp... | 271 |
"""simple docstring"""
import operator
def A_ (__a , __a = False , __a = None ):
'''simple docstring'''
A_ = operator.lt if reverse else operator.gt
A_ = solution or []
if not arr:
return solution
A_ = [arr.pop(0 )]
... | 115 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.... | 13 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 115 | 0 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( _lowercase , unittest.TestCase ):
... | 478 |
"""simple docstring"""
def A_ (__a ):
'''simple docstring'''
A_ = len(__a )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A_ = arr[mi::-1] + ar... | 115 | 0 |
import math
def lowerCAmelCase__ ( a__ , a__ = 0 , a__ = 0 ) ->Any:
'''simple docstring'''
_UpperCamelCase = end or len(__a )
for i in range(__a , __a ):
_UpperCamelCase = i
_UpperCamelCase = array[i]
while temp_index ... | 547 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 115 | 0 |
'''simple docstring'''
import pprint
import requests
__SCREAMING_SNAKE_CASE : Tuple ='''https://zenquotes.io/api'''
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def _S... | 135 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fro... | 115 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger... | 71 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not ... | 115 | 0 |
def __snake_case ( __magic_name__ ):
'''simple docstring'''
lowercase = []
lowercase = []
lowercase = {
"^": 3,
"*": 2,
"/": 2,
"%": 2,
"+": 1,
"-": 1,
} # Priority of e... | 441 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_util... | 115 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : Any , snake_case_ : Union[str, Any] = None , snake_case_ : Union[str, Any] = None ) -> Dict:
if start is None:
__snake_case = 0
if end is None:
... | 592 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : ... | 115 | 0 |
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 TFModel... | 37 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a ):
'''simple docstring'''
A_ = 0.00
A_ = 0
for resistor in resistors:
if resistor <= 0:
A_ = f'Resistor at index {index} has a negative or zero value!'
... | 115 | 0 |
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 PaddingStrategy, logging
... | 240 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a , __a = None , __a = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = len(__a ) - 1
if start >= end:
return
A_ ... | 115 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Dict ={
'''configuration_xmod''': [
'''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XmodConfig''',
''... | 207 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import... | 115 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_... | 271 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 0 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
A__ : Optional[int] = logging.getLogger(__name__)
class UpperCAmelCase_ :
"""simple docs... | 13 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ : Any = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase_ : List[str] = {
'''yjernite/retribert-base-uncased''': (
'... | 115 | 0 |
def lowercase_ (A : Dict , A : List[str] ):
snake_case__ : Tuple = 0
snake_case__ : Tuple = len(__a ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] =... | 478 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 115 | 0 |
lowerCamelCase__ = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ImportWarning(
'''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
... | 547 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 115 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import... | 135 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfo... | 115 | 0 |
'''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
d... | 71 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availabl... | 115 | 0 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbo... | 441 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Dict = {'''configuration_plbart''': ['''PLBART_PRETR... | 115 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def a (self : Optional[int] ):
"""simple d... | 592 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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_configuration_common import Con... | 115 | 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 AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_d... | 37 |
"""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 | 0 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INF... | 240 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A_ (__a , __a , __a , __a , __a ):
'''simple d... | 115 | 0 |
'''simple docstring'''
import math
import qiskit
def UpperCamelCase__ ( lowerCAmelCase = 1 , lowerCAmelCase = 1 , lowerCAmelCase = 1 ):
"""simple docstring"""
if (
isinstance(__a , __a )
or isinstance(__a... | 207 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase_ : Dict = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 115 | 0 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextE... | 271 |
"""simple docstring"""
import operator
def A_ (__a , __a = False , __a = None ):
'''simple docstring'''
A_ = operator.lt if reverse else operator.gt
A_ = solution or []
if not arr:
return solution
A_ = [arr.pop(0 )]
... | 115 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransfor... | 13 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 115 | 0 |
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_available():
import to... | 478 |
"""simple docstring"""
def A_ (__a ):
'''simple docstring'''
A_ = len(__a )
while cur > 1:
# Find the maximum number in arr
A_ = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A_ = arr[mi::-1] + ar... | 115 | 0 |
lowerCamelCase__ = {
'''meter''': '''m''',
'''kilometer''': '''km''',
'''megametre''': '''Mm''',
'''gigametre''': '''Gm''',
'''terametre''': '''Tm''',
'''petametre''': '''Pm''',
'''exametre''': '''Em''',
'''zettametre''': '''Zm''',
'''yottametre''': '''Ym''',
}
# Exponent of the... | 547 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 115 | 0 |
'''simple docstring'''
from __future__ import annotations
__SCREAMING_SNAKE_CASE : List[str] =[]
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : List[str] , lowerCamelCase__ : List[str] , lowerCamelCase__ : Union[str, Any] ):
'''simple docstring'''
... | 135 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
fro... | 115 | 0 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
_lowerCamelCase = ''''''
_lowerCamelCase = ''''''
_lowerCamelCase = ''''''
_lowerCamelCase = 1 # (0 is vertical, 1 is horizontal)
... | 71 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not ... | 115 | 0 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
... | 441 |
"""simple docstring"""
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_util... | 115 | 0 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ..... | 592 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : ... | 115 | 0 |
from pathlib import Path
import fire
def UpperCamelCase_ ( __a , __a , __a ) -> Any:
a__ : Dict = Path(__a )
a__ : Optional[int] = Path(__a )
dest_dir.mkdir(exist_ok=__a )
for path in src_dir.iterdir():
a__ : List[str] =... | 37 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a ):
'''simple docstring'''
A_ = 0.00
A_ = 0
for resistor in resistors:
if resistor <= 0:
A_ = f'Resistor at index {index} has a negative or zero value!'
... | 115 | 0 |
from ....utils import logging
lowercase = logging.get_logger(__name__)
class __lowercase ( _lowercase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , _a : Dict , _a : int=None , _a : Dict=2_04... | 240 |
"""simple docstring"""
from __future__ import annotations
def A_ (__a , __a = None , __a = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = len(__a ) - 1
if start >= end:
return
A_ ... | 115 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_u... | 207 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import... | 115 | 0 |
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