code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distribut... | 39 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = []
if len(SCREAMING_SNAKE_CASE__ ) == 1:
return [nums.copy()]
for _ in range(len(SCREAMING_SNAKE_CASE__ ) ):
snake_case_ = nums.pop(0 )
snake_case_ ... | 8 | 0 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpo... | 24 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_xglm''': ['''XGLM... | 8 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Optional[int]:
__lowerCamelCase : Union[str, Any] = len(SCREAMING_SNAKE_CASE__ )
# We need to create solution object to save path.
__lowerCamelCase : Tuple = ... | 73 |
from ..utils import DummyObject, requires_backends
class snake_case_ ( metaclass=__A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = ["note_seq"]
def __init__( self : Optional[int] , *_UpperCamelCase : ... | 8 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ... | 119 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at... | 8 | 0 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
UpperCamelCase_ = _symbol... | 345 |
from __future__ import annotations
from math import pi, sqrt
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
r... | 8 | 0 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentPa... | 243 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return x + 2
class snake_case_ ( unittest.TestCase ):
'''simple docst... | 8 | 0 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
SCREAMING_SNAKE_CASE : int = models.S... | 102 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as ... | 8 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda... | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __SCREAMING_SNAKE_CASE (*SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = list(SCREAMI... | 8 | 0 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_=7 )-> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : T... | 215 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return [ord(SCREAMING_SNAKE_CASE__ ) - 96 for elem in plain]
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return "".join(chr(elem + 96 ) for elem in encoded ... | 8 | 0 |
import gc
import threading
import time
import psutil
import torch
class lowercase__ :
def __init__( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = psutil.Process(... | 182 |
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(SCREAMING_SNAKE_CASE__ )
else:
if x == 0: ... | 8 | 0 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
_lowerCamelCase : Any = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": ["export", "... | 336 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ ... | 8 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeniza... | 39 |
from __future__ import annotations
from collections.abc import Generator
def __SCREAMING_SNAKE_CASE ():
snake_case_ = {}
snake_case_ = 2
while True:
snake_case_ = factor_map.pop(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if factor:
... | 8 | 0 |
from collections.abc import Sequence
def lowerCamelCase__ ( snake_case_ : Union[str, Any] , snake_case_ : Optional[Any] = False ) -> Dict:
if not arr:
return 0
__snake_case = 0 if allow_empty_subarrays else float('''-inf''' ... | 24 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', ... | 8 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a =logging.get_logger(__name__)
a ={
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main... | 73 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | 8 | 0 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
__UpperCAmelCase ... | 119 |
from collections import deque
from .hash_table import HashTable
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : int , *_UpperCamelCase : int , **_UpperCamelCase : Tuple ) ->Tuple:
super().__init__(... | 8 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
UpperCamelCase_ = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https://... | 345 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = len(SCREAMING_SNAKE_CASE__ )
# We need to create solution object to save path.
snake_case_ = [[0 for _ in range(SCREAMING_SNAKE_CASE__ )] for _ in range(SCREAM... | 8 | 0 |
"""simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class snake_case (... | 243 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError('''Undefined for non-integers''' )
elif pre... | 8 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils imp... | 102 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class snake_case_ ( __A ):
'''simple docstring'''
def __init_... | 8 | 0 |
'''simple docstring'''
SCREAMING_SNAKE_CASE_: List[str] ='\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'... | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase_ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 8 | 0 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = np.max(SCREAMING_SNAKE_CASE__ , axis=-1 , keepdims=SCREAMING_SN... | 215 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowerCAmelCase_ = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''... | 8 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_... | 182 |
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 8 | 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_verbosity_info()
_... | 336 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prette... | 8 | 0 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {'''vocab_fi... | 39 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = []
if len(SCREAMING_SNAKE_CASE__ ) == 1:
return [nums.copy()]
for _ in range(len(SCREAMING_SNAKE_CASE__ ) ):
snake_case_ = nums.pop(0 )
snake_case_ ... | 8 | 0 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 24 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_xglm''': ['''XGLM... | 8 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Optional[int]:
if length <= 0 or not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(SCREAMING_SNAKE_CASE__ )]
if __nam... | 73 |
from ..utils import DummyObject, requires_backends
class snake_case_ ( metaclass=__A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = ["note_seq"]
def __init__( self : Optional[int] , *_UpperCamelCase : ... | 8 | 0 |
def UpperCamelCase ( snake_case__ : Optional[int] ) -> Optional[int]:
UpperCamelCase : Dict = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 119 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at... | 8 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
'''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_M... | 345 |
from __future__ import annotations
from math import pi, sqrt
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
r... | 8 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from... | 243 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return x + 2
class snake_case_ ( unittest.TestCase ):
'''simple docst... | 8 | 0 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE : Dict = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
b... | 102 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as ... | 8 | 0 |
'''simple docstring'''
from math import pi
def lowerCAmelCase_ ( snake_case_ : Dict , snake_case_ : str ) -> Tuple:
'''simple docstring'''
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __SCREAMING_SNAKE_CASE (*SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = list(SCREAMI... | 8 | 0 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_a... | 215 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return [ord(SCREAMING_SNAKE_CASE__ ) - 96 for elem in plain]
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return "".join(chr(elem + 96 ) for elem in encoded ... | 8 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__UpperCamelCase : str = logging.get_logger(__name__)
class lowercase__ ( __A):
def __init__( self : Tuple , *UpperCamelCase__ : Any , ... | 182 |
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(SCREAMING_SNAKE_CASE__ )
else:
if x == 0: ... | 8 | 0 |
def a__ ( UpperCAmelCase : int ) -> str:
if len(SCREAMING_SNAKE_CASE__ ) <= 1:
return lst
UpperCAmelCase : Optional[int] = 1
while i < len(SCREAMING_SNAKE_CASE__ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
UpperCAmelCase , UpperCAmelCase ... | 336 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ ... | 8 | 0 |
def __A ( __lowerCAmelCase = 1_000 )-> int:
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 39 |
from __future__ import annotations
from collections.abc import Generator
def __SCREAMING_SNAKE_CASE ():
snake_case_ = {}
snake_case_ = 2
while True:
snake_case_ = factor_map.pop(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if factor:
... | 8 | 0 |
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 lowerCamelCase__ ( snake_case_ : Any , ... | 24 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', ... | 8 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> int:
if num < 0:
return False
__lowerCamelCase : List[str] = num
__lowerCamelCase : Union[str, Any] = 0
while num > 0:
__lowerCamelCase : Union[str, Any] = rev_num * 1_0 + (num % 1_... | 73 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | 8 | 0 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
fr... | 119 |
from collections import deque
from .hash_table import HashTable
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : int , *_UpperCamelCase : int , **_UpperCamelCase : Tuple ) ->Tuple:
super().__init__(... | 8 | 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_ = {
'''facebook/xlm-roberta-xl''': '''https:... | 345 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = len(SCREAMING_SNAKE_CASE__ )
# We need to create solution object to save path.
snake_case_ = [[0 for _ in range(SCREAMING_SNAKE_CASE__ )] for _ in range(SCREAM... | 8 | 0 |
"""simple docstring"""
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... | 243 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError('''Undefined for non-integers''' )
elif pre... | 8 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 102 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class snake_case_ ( __A ):
'''simple docstring'''
def __init_... | 8 | 0 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def lowerCAmelCase_ ( snake_case_ : Union[str, Any] , snake_case_ : int , snake_case_ : List[Any]=None , **snake_case_ : List[Any] ) -> Union[str, Any]:
'''sim... | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase_ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:... | 8 | 0 |
'''simple docstring'''
from decimal import Decimal, getcontext
from math import ceil, factorial
def snake_case_ ( lowerCAmelCase_ )-> List[str]:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeE... | 215 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowerCAmelCase_ = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''... | 8 | 0 |
import pytest
__UpperCamelCase : Any = '__dummy_dataset1__'
__UpperCamelCase : str = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.j... | 182 |
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 8 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_lowerCamelCase : Optional[int] = ... | 336 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prette... | 8 | 0 |
import re
def __A ( __lowerCAmelCase )-> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = re.compile(
R'^(?:0|94|\+94|0{2}94)' R'7(0|1|2|4|5|6|7|8)' R'(-| |)' R'\d{7}$' )
return bool(re.search(SCREAMING_SNAKE_CASE__ , SCREAM... | 39 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = []
if len(SCREAMING_SNAKE_CASE__ ) == 1:
return [nums.copy()]
for _ in range(len(SCREAMING_SNAKE_CASE__ ) ):
snake_case_ = nums.pop(0 )
snake_case_ ... | 8 | 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... | 24 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_xglm''': ['''XGLM... | 8 | 0 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.ut... | 73 |
from ..utils import DummyObject, requires_backends
class snake_case_ ( metaclass=__A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = ["note_seq"]
def __init__( self : Optional[int] , *_UpperCamelCase : ... | 8 | 0 |
import random
class lowerCAmelCase_ :
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> tuple[list[int], list[int]]:
UpperCamelCase : List[str] = [ord(_UpperCamelCase ) for i in text]
UpperCamelCase : List[Any] = []... | 119 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at... | 8 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( _a : Optional[Any] ): # This function is recursive
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = len(SCREAMING_SNAKE_CASE__ )
# If the array contains only one element, we return it (it's the stop ... | 345 |
from __future__ import annotations
from math import pi, sqrt
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif capacitance <= 0:
r... | 8 | 0 |
"""simple docstring"""
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
UpperCamelCase_ = logging.get_logger(__name__)
def UpperCamelCase ( UpperCAmelCase , Upp... | 243 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return x + 2
class snake_case_ ( unittest.TestCase ):
'''simple docst... | 8 | 0 |
"""simple docstring"""
from __future__ import annotations
SCREAMING_SNAKE_CASE : Any = list[list[int]]
# assigning initial values to the grid
SCREAMING_SNAKE_CASE : List[Any] = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0... | 102 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as ... | 8 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeli... | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __SCREAMING_SNAKE_CASE (*SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = list(SCREAMI... | 8 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Dict = logging.get_logger(__name__)
class lowercase ( __A ):
"""simple docstring"""
UpperCAmelCase = "encoder-decoder"
UpperCAmelCase ... | 215 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return [ord(SCREAMING_SNAKE_CASE__ ) - 96 for elem in plain]
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
return "".join(chr(elem + 96 ) for elem in encoded ... | 8 | 0 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
class lowercase__ ( __A):
UpperCamelCase_ ... | 182 |
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(SCREAMING_SNAKE_CASE__ )
else:
if x == 0: ... | 8 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[Any] = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
... | 336 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ ... | 8 | 0 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,... | 39 |
from __future__ import annotations
from collections.abc import Generator
def __SCREAMING_SNAKE_CASE ():
snake_case_ = {}
snake_case_ = 2
while True:
snake_case_ = factor_map.pop(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if factor:
... | 8 | 0 |
from numpy import exp, pi, sqrt
def lowerCamelCase__ ( snake_case_ : int , snake_case_ : List[str] = 0.0 , snake_case_ : Any = 1.0 ) -> Tuple:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __... | 24 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', ... | 8 | 0 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_... | 73 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | 8 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-lar... | 119 |
from collections import deque
from .hash_table import HashTable
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : int , *_UpperCamelCase : int , **_UpperCamelCase : Tuple ) ->Tuple:
super().__init__(... | 8 | 0 |
def lowerCamelCase_ ( _a : Any ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(SCREAMING_SNAKE_CASE__ , (list, tuple) ) or not all(
isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for number in numbers ):
rai... | 345 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = len(SCREAMING_SNAKE_CASE__ )
# We need to create solution object to save path.
snake_case_ = [[0 for _ in range(SCREAMING_SNAKE_CASE__ )] for _ in range(SCREAM... | 8 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[Any] =logging.get_logger(__name__)
__lowerCAmelCase : List[str] ={
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve... | 9 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import to... | 9 | 1 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__lowerCAmelCase : Optional[int] ='\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Mode... | 9 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _lowercase ( unittes... | 9 | 1 |
import argparse
import datetime
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': '''... | 9 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _lowercase ( A__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_... | 9 | 1 |
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_proce... | 9 |
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__=False ):
if isinstance(lowercase__ , lowercase__ ) and isinstance(lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : List[str] = len(set_a.intersectio... | 9 | 1 |
def _UpperCamelCase ( ):
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(lowercase__ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f"""{solution(... | 9 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__lowerCAmelCase : Optional[int] ='\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Mode... | 9 | 1 |
def _UpperCamelCase ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : List[str] = F'''Input value of [number={number}] must be an integer'''
raise TypeError(lowercase__ )
if number < 0:
... | 9 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__lowerCAmelCase : Dict =logging.get_logger(__name__)
def _UpperCamelCase ( lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : ... | 9 | 1 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : ... | 9 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__lowerCAmelCase : List[Any] =datasets.load_iris()
__lowerCAmelCase : Tuple =np.array(data['data'])
__lowerCAmelCase : Dict =np.array(data['target'])
__lowerCAmelCase... | 9 | 1 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 9 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nes... | 9 | 1 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : List[str] = {}
__SCREAMING_SNAKE_CASE : Optional[Any] = job['''started_at''']
__SCR... | 9 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Any ={'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAv... | 9 | 1 |
from collections import defaultdict
from math import ceil, sqrt
def _UpperCamelCase ( lowercase__ = 1000000 , lowercase__ = 10 ):
__SCREAMING_SNAKE_CASE : defaultdict = defaultdict(lowercase__ )
for outer_width in range(3 , (t_limit // 4) + 2... | 9 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _lowercase ( A__ ):
'''simple docstring'''
def __init__( ... | 9 | 1 |
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = ['''flax''', '''transformers''']
def __init__( self :Tuple , *lowerCAmelCase__ :Optional[Any] , **lowerC... | 9 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeniz... | 9 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : Union[str, Any] =logging.get_logger(__name__)
__lowerCAmelCase : List[Any] ={
'roberta-base': 'https://hu... | 9 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase : Optiona... | 9 | 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 import Accelerator, Di... | 9 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__lowerCAmelCase : Optional[Any] ='\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n an... | 9 | 1 |
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = len(lowercase__ )
for i in range(1 , lowercase__ ):
__SCREAMING_SNAKE_CASE : Tuple = collection[i]
__SCREAMING_SNAKE_CASE : ... | 9 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from... | 9 | 1 |
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_output_features_output_indices
__lowerC... | 9 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _UpperCamelCase ( *lowercase__ , lowercase__ = None , lowercase__=True , lowercase__=2 ):
from .. import __version__
__SCREAMING_SNAKE_CASE ... | 9 | 1 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__lowerCAmelCase : Dict =(3, 9, -1_1, 0, 7, 5, 1, -1)
__lowerCAmelCase : int =(4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class _lowercase :
'''simple docstring'''
S... | 9 |
from __future__ import annotations
import bisect
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ = 0 , lowercase__ = -1 ):
if hi < 0:
__SCREAMING_SNAKE_CASE : Union[str, Any] = len(lowercase__ )
while lo < ... | 9 | 1 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __magic_name__( self :Dict ) -> Dict:
__... | 9 |
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 _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __magi... | 9 | 1 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _UpperCamelCase ( ... | 9 |
from importlib import import_module
from .logging import get_logger
__lowerCAmelCase : str =get_logger(__name__)
class _lowercase :
'''simple docstring'''
def __init__( self :List[Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :str=None ) -> int:
... | 9 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 9 |
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.te... | 9 | 1 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _UpperCamelCase ( lowercase__ ):
return getitem, k
def _UpperCamelCase ( lowercase__ , lowercase__ ):
return setitem, k, v
d... | 9 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__lowerCAmelCase : Union[str, Any] ={
'<': operator.lt,
'<=': operator.le,
'==': operator.eq,
'!=': operator.ne,
'>=': operator.ge,
'>': operator.gt,
}
def ... | 9 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Any ={'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAv... | 9 |
from __future__ import annotations
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : Dict = 0.00
__SCREAMING_SNAKE_CASE : List[str] = 0
for resistor in resistors:
if resistor <= 0:
__SCREAMING_SNAKE_CASE ... | 9 | 1 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _lowercase ( A__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_... | 9 |
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = ['''keras_nlp''']
def __init__( self :Tuple , *lowerCAmelCase__ :Optional[Any] , **lowerCAmelC... | 9 | 1 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__lowerCAmelCase : Tuple ={
# 1536-bit
5: {
'prime': int(
'FFFFFFF... | 9 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import to... | 9 | 1 |
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : Tuple = 1
__SCREAMING_SNAKE_CASE : Optional[int] = 2
while i * i <= n:
__SCREAMING_SNAKE_CASE : Optional[Any] = 0
while n % i == 0:
... | 9 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _lowercase ( unittes... | 9 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase : Dict ={
'configuration_albert': ['ALBERT_PRETR... | 9 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _lowercase ( A__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_... | 9 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_... | 9 |
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__=False ):
if isinstance(lowercase__ , lowercase__ ) and isinstance(lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : List[str] = len(set_a.intersectio... | 9 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_com... | 9 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__lowerCAmelCase : Optional[int] ='\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Mode... | 9 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase : Optional[Any] ={
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_av... | 9 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__lowerCAmelCase : Dict =logging.get_logger(__name__)
def _UpperCamelCase ( lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : ... | 9 | 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 import Accelerator, Di... | 9 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__lowerCAmelCase : List[Any] =datasets.load_iris()
__lowerCAmelCase : Tuple =np.array(data['data'])
__lowerCAmelCase : Dict =np.array(data['target'])
__lowerCAmelCase... | 9 | 1 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ,... | 9 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nes... | 9 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
loggi... | 9 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Any ={'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAv... | 9 | 1 |
import unittest
from typing import Dict, List, Optional, Union
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_in... | 9 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _lowercase ( A__ ):
'''simple docstring'''
def __init__( ... | 9 | 1 |
# Imports
import numpy as np
class _lowercase :
'''simple docstring'''
def __init__( self :List[Any] , lowerCAmelCase__ :Optional[Any]=None , lowerCAmelCase__ :str=None , lowerCAmelCase__ :Tuple=None , lowerCAmelCase__ :Any=None , lowerCAmelCase__ :A... | 9 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeniz... | 9 | 1 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ('''dense.weight''', '''att... | 9 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase : Optiona... | 9 | 1 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__lowerCAmelCase : Any =logging.get_logger(__name__)
__lowerCAmelCase : List[str] ={'vocab_file': 'vocab.txt'}
__lower... | 9 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__lowerCAmelCase : Optional[Any] ='\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n an... | 9 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase : Optional[int] ={
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_... | 9 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from... | 9 | 1 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__lowerCAmelCase : Tuple =logging.get_logger(__name__)
class _lowercase ( A__ ):
'''simple docstring'''
def __init__( self :List[str] , *low... | 9 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _UpperCamelCase ( *lowercase__ , lowercase__ = None , lowercase__=True , lowercase__=2 ):
from .. import __version__
__SCREAMING_SNAKE_CASE ... | 9 | 1 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
__lowerCAmelCase : Dict ... | 9 |
from __future__ import annotations
import bisect
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ = 0 , lowercase__ = -1 ):
if hi < 0:
__SCREAMING_SNAKE_CASE : Union[str, Any] = len(lowercase__ )
while lo < ... | 9 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowercase ( A__ ):
'''s... | 9 |
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 _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __magi... | 9 | 1 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def _UpperCamelCase ( lowercase__ , lowercase__=False ):
__SCREAMING_SNAKE_CASE : Tuple = OmegaConf.load(lowercase__ )
if display:
... | 9 |
from importlib import import_module
from .logging import get_logger
__lowerCAmelCase : str =get_logger(__name__)
class _lowercase :
'''simple docstring'''
def __init__( self :List[Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :str=None ) -> int:
... | 9 | 1 |
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