code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def A_ ( A__ , A__ , A__=1024 , A__=1024 , A__=False , **A__ ) -> List[str]:
a__ : int = AutoTokeniz... | 99 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase__ ( __snake_case, __snake_case ) -> float:
"""simple docstring"""
_UpperCamelCase = sorted(numsa + numsa )
_UpperCamelCase , _UpperCamelCase = divmod(len(... | 194 | 0 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class _lowerCamelCase:
def UpperCamelCase ( self, lowerCamelCase) -> int:
"""simple docstring"""
raise NotImplementedError()
... | 84 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
SCREAMING_SNAKE_CASE : int = logging.get_log... | 84 | 1 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokeniz... | 25 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _a ( metaclass=__a ):
__a : int = ["""flax""", """transformers"""]
def __init__( self : Optional[Any] , *lowercase : str , **lowercase : List[Any] ):
... | 34 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A_ :
'''simple docstring'''
__snake_case = 42
__snake_case = None
__snake_case = None
... | 358 |
from __future__ import annotations
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Optional[Any] = 0.00
__lowerCamelCase : Tuple = 0
for resistor in resistors:
if resistor <= 0:
__lowerCamelCase : Union[str, Any] = f'Resistor at inde... | 194 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : str = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN m... | 83 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
snake_case_ : Any = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def A__ ( ):
... | 83 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A ( pl.LightningModule ):
def __init__( self, UpperCamelCase__ ):
"""simple docstring"""
super().... | 369 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_A = logging.get_l... | 167 | 0 |
"""simple docstring"""
lowerCamelCase_ : List[Any] = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_ava... | 81 |
"""simple docstring"""
lowerCamelCase_ : int = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install gi... | 81 | 1 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
lowercase = logging.getLogger(... | 35 | from __future__ import annotations
from collections import Counter
from random import random
class __lowercase :
'''simple docstring'''
def __init__( self : List[Any] ):
UpperCamelCase__ = {}
def A_ ( self : ... | 35 | 1 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import ... | 218 | '''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __lowerCAmelCase ( UpperCamelCase__ ) -> list[list[float]]:
__lowerCamelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementat... | 67 | 0 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ra... | 354 |
lowercase__ :List[str] = 6_5521
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = 1
lowercase = 0
for plain_chr in plain_text:
lowercase = (a + ord(lowerCAmelCase__ )) % MOD_ADLER
lowerca... | 97 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
_lowercase : Optional[int] = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json... | 239 |
"""simple docstring"""
def A ( snake_case :list[int] , snake_case :int ) -> bool:
__UpperCamelCase = len(snake_case )
__UpperCamelCase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by... | 316 | 0 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
lowerCAmelCase: List[str] = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
... | 370 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowerCAmelCase: Any = argparse.ArgumentParser()
parser.add_argument('--dump_... | 96 | 0 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassif... | 245 |
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 ConfigTester
from ..... | 245 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCAmelCase( __lowerCamelCase ):
return (data["data"... | 353 | from ..utils import DummyObject, requires_backends
class a__ ( metaclass=__snake_case ):
A__ : List[Any] = ['torch', 'transformers', 'onnx']
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> Any:
requires_backends(... | 197 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.... | 121 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils im... | 121 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase : Optional[int] = get_logger(__name__)
class __lowercase (enum.Enum ):
"""simple docstring"""
_snake_case = ... | 176 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : str = {
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', '... | 176 | 1 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
par... | 136 |
"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
UpperCAmelCase : Optional[Any] = "T5... | 136 | 1 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
_lowerCAmelCase : int = [
['''attention''', '''at... | 360 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
... | 70 | 0 |
from string import ascii_lowercase, ascii_uppercase
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not sentence:
return ""
SCREAMING_SNAKE_CASE : int = dict(zip(a__ , a__ ) )
return lower_to_upper.get(sentence[0] , sent... | 313 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 | 1 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
... | 257 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, Bit... | 257 | 1 |
import math
import os
import sys
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
__a = """"""
try:
with open(_SCREAMING_SNAKE_CASE , """rb""" ) as binary_file:
__a = binary_file.read()
for dat i... | 302 |
# 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 appli... | 302 | 1 |
"""simple docstring"""
__A = 9.80_665
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = g ) -> Any:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise ValueError("""Impossible Object volume"""... | 355 |
"""simple docstring"""
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase=1 ) -> Tuple:
if n_shave_prefix_segments >= 0:
return ".".join(path.split(""".""" )[n_shave... | 341 | 0 |
"""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
impor... | 33 |
"""simple docstring"""
def lowercase ( __snake_case : Optional[int] ):
lowercase_ : int = 0
lowercase_ : Optional[Any] = len(__snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , __snake_case ):
if arr[i] > arr[j]:
... | 33 | 1 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...te... | 371 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_UpperCamel... | 234 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __A ( unittest.TestCase , ... | 209 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.... | 79 | 0 |
'''simple docstring'''
from math import factorial
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise V... | 322 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_ou... | 322 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
lowerCamelCase_ = TypeVar('''T''')
def __magic_name__ ( __a : int ):
'''simple docstring'''
return (position - 1) // 2
def __magic_name__ ( __a : ... | 244 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNeta... | 244 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import ... | 357 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase__ :str = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfi... | 97 | 0 |
"""simple docstring"""
from importlib import import_module
from .logging import get_logger
UpperCAmelCase : Any = get_logger(__name__)
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] , UpperCamelCase : List[A... | 115 |
"""simple docstring"""
import re
def lowerCamelCase ( _UpperCamelCase : str ) -> str:
'''simple docstring'''
if len(re.findall("""[ATCG]""" , _UpperCamelCase ) ) != len(_UpperCamelCase ):
raise ValueError("""Invalid Str... | 115 | 1 |
import math
class A__ :
"""simple docstring"""
def a_ ( self , __snake_case , __snake_case ):
snake_case = 0.0
snake_case = 0.0
for i in range(len(__snake_case ) ):
da += math.pow((sample[i] - weights[... | 213 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor... | 213 | 1 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to ... | 124 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _SCREAMING_SNAKE_CASE ( _a ):
def __init__( self : List[Any] , __lowerCamelCase : Callable , __low... | 38 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( __A, __A, __A ) -> tuple[float, list[float]]:
'''simple docstring'''
UpperCAmelCase__ = list(range(len(__A ) ) )
UpperCAmelCase__ = [v / w for v, w in zi... | 143 | import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
Up... | 143 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
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 ... | 72 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer... | 151 | 0 |
"""simple docstring"""
def lowercase__ ( snake_case_ :List[str] ):
__UpperCAmelCase = 1
__UpperCAmelCase = 2
while i * i <= n:
__UpperCAmelCase = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
... | 358 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_fla... | 86 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if... | 46 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import P... | 46 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_a = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try... | 23 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 23 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
fr... | 298 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : Union[str, Any] ) -> List[str]:
"""simple docstring"""
snake_case : List[str] = len(lowercase )
for i in range(length - 1 ):
snake_case : List[str] = i
for k in... | 203 | 0 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase_ : ... | 223 |
def A__ ( lowerCamelCase ) -> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(lowerCamelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 223 | 1 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare... | 141 |
'''simple docstring'''
import datasets
UpperCAmelCase = '''\
@InProceedings{conneau2018xnli,
author = "Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and Schwenk, Holge... | 141 | 1 |
"""simple docstring"""
import math
def _lowerCAmelCase ( UpperCamelCase_ = 100 ):
__SCREAMING_SNAKE_CASE = sum(i * i for i in range(1 , n + 1 ) )
__SCREAMING_SNAKE_CASE = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_... | 255 |
"""simple docstring"""
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 ImageProcessingSavingT... | 255 | 1 |
from timeit import timeit
_UpperCAmelCase : Union[str, Any] = {
"""MALAYALAM""": True,
"""String""": False,
"""rotor""": True,
"""level""": True,
"""A""": True,
"""BB""": True,
"""ABC""": False,
"""amanaplanacanalpanama""": True, # "a man a plan a canal panama"
}
#... | 50 |
'''simple docstring'''
import heapq
import sys
import numpy as np
UpperCamelCase = tuple[int, int]
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : List[Any] ) -> str:
'''simple ... | 319 | 0 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snake_case = logging.getLogger(__name__)
if __nam... | 112 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snake_case = logging.getLogger(__name__)
if __nam... | 112 | 1 |
def UpperCAmelCase ( a_ , a_ , a_ ) -> Optional[int]:
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a_ , n - 1 , a_ ) * a) % mod
else:
__A = binary_exponentiation(a_ , n / 2 , a_ ... | 15 |
from PIL import Image
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(UpperCamelCase__ ) -> int:
return int(128 + factor *... | 285 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied... | 368 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_we... | 269 | 0 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.p... | 1 |
'''simple docstring'''
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
_UpperCamelCase = ver... | 254 | 0 |
'''simple docstring'''
import logging
import os
from .state import PartialState
class lowerCAmelCase_ ( logging.LoggerAdapter ):
@staticmethod
def _snake_case ( SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Optional[int]:
... | 365 |
'''simple docstring'''
from collections import deque
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNA... | 334 | 0 |
import math
def lowercase__ ( __snake_case : int ):
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2... | 29 |
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.utils import loggi... | 119 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
... | 363 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from tr... | 3 | 0 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def lowercase ( lowerCAmelCase__ : Dataset , lowerCAmelCase__ :... | 45 |
"""simple docstring"""
lowerCAmelCase__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def ... | 72 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCAmelCase :Dict ... | 240 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers... | 240 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ :Optional[Any] = logging.get_logger(__name__)
a_ :List[Any] = {
"shi-labs/dinat-mini-in1k-224": "... | 277 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case__ ( lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"... | 277 | 1 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def __magic_name__ ( __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = np.max(__UpperCAmelCase, axis=-1, keepdims=__UpperCAmelCase )
snake_case_ = ... | 72 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@datacl... | 72 | 1 |
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Tuple ... | 132 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a :str = "\\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 ... | 132 | 1 |
from numpy import exp, pi, sqrt
def lowerCAmelCase__ ( lowerCamelCase_ : List[Any] ,lowerCamelCase_ : float = 0.0 ,lowerCamelCase_ : float = 1.0):
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2) * exp(-((x - mu) ** 2) / (2 * sigma**2))
if __name__ == "__main__":
... | 351 |
def lowerCAmelCase__ ( lowerCamelCase_ : int = 1000000):
'''simple docstring'''
lowerCAmelCase__ : int = set(range(3 ,lowerCamelCase_ ,2))
primes.add(2)
for p in range(3 ,lowerCamelCase_ ,2):
if p not in primes:
continue
primes.differ... | 94 | 0 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = [
'... | 104 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase :List[Any] = {'configuration_opt': ['OPT_PRETRAINED_CON... | 263 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase = ... | 352 |
"""simple docstring"""
from maths.prime_check import is_prime
def a__ ( lowerCAmelCase__ ):
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = f"""Input value of [number={number}] must be an integer"""
... | 241 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : List[str] = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
]
}
try:
if n... | 2 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (A ) -> int:
"""simple docstring"""
if not isinstance(A , A ):
raise TypeError('''only integers accepted as input''' )
else:
lowercase__ = str(abs(A ) ... | 2 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 366 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : List[Any] = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
... | 89 | 0 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTest... | 334 | """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
... | 44 | 0 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB 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.apach... | 154 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
f... | 154 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ = {'''configuration_glpn''': ['''GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GLPNConfig''']}
try:
if not is_vision_available():... | 309 |
def _SCREAMING_SNAKE_CASE ( a ) -> Tuple:
__A , __A : Optional[Any] = [], []
while len(a ) > 1:
__A , __A : Any = min(a ), max(a )
start.append(a )
end.append(a )
collection.remove(a )
collec... | 280 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : int ):
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(UpperCAme... | 157 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_avail... | 157 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
Upper... | 38 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A... | 204 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar("""T""")
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> int:
"""simple docstring"""
return (position - 1) // 2
def __lowerCAmelCase (SCREAMING_SNAKE_CAS... | 360 |
import numpy as np
import datasets
UpperCAmelCase = """
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mah... | 267 | 0 |
'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
__lowercase : List[Any] = logging.get_logger(__name__)
class __UpperCamelCase :
A_ = None
@experimental
def lowerCamelC... | 27 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.... | 27 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class __magic_name__ :
UpperCamelCase__ = None
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCam... | 362 | '''simple docstring'''
def lowerCamelCase ( ) -> Dict:
lowercase_ : Union[str, Any] = []
lowercase_ : Tuple = 1
while len(UpperCAmelCase__ ) < 1e6:
constant.append(str(UpperCAmelCase__ ) )
i += 1
lowerc... | 21 | 0 |
# Lint as: python3
import itertools
import os
import re
lowerCamelCase_ = re.compile(r'''([A-Z]+)([A-Z][a-z])''')
lowerCamelCase_ = re.compile(r'''([a-z\d])([A-Z])''')
lowerCamelCase_ = re.compile(r'''(?<!_)_(?!_)''')
lowerCamelCase_ = re.compile(r'''(_{2,})''')
lowerCamelCa... | 244 |
from __future__ import annotations
def __magic_name__ ( __a : list[int] , __a : int ):
'''simple docstring'''
if len(__a ) == 0:
return False
UpperCamelCase__ = len(__a ) // 2
if a_list[midpoint] == item:
return T... | 244 | 1 |
"""simple docstring"""
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_t... | 112 |
"""simple docstring"""
import string
def __lowerCAmelCase ( lowercase : str ) -> str:
"""simple docstring"""
snake_case : List[str] = ""
for i in sequence:
snake_case : Optional[Any] = ord(lowercase )
if 65 <= extract <= 90... | 112 | 1 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("""repo_id""" , ["""canonical_dataset_name""", """org-name/dataset-name"""] )
@pytest.mark.parametrize("""path""" , ["""filename.csv""", """filename with bl... | 35 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
... | 41 | 0 |
'''simple docstring'''
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
... | 43 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.uti... | 43 | 1 |
from sklearn.metrics import fa_score
import datasets
snake_case_ = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
snake_case_ = '\nArgs:\n predictions (`list` of `int`)... | 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 __future__ import annotations
import math
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : bool , SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : float ) ... | 360 |
from collections.abc import Sequence
def _snake_case( SCREAMING_SNAKE_CASE__ : Sequence[int] | None = None ) -> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
A__ ... | 282 | 0 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBe... | 76 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',... | 294 | 0 |
def UpperCamelCase ( _A ):
"""simple docstring"""
if not isinstance(_A, _A ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
__magic_name__ : str = 0
while number:
# This way we arriv... | 138 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def UpperCamelCase ( _A, _A ):
"""simple docstring"""
return math.sqrt(sum(pow(a - b, 2 ) for a, b in zip(_A, _A ) ) )
def UpperCamelCase ( _A, _A ):
... | 138 | 1 |
import pprint
import requests
snake_case__ : Tuple = 'https://zenquotes.io/api'
def _a ( ) -> list:
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def _a ( ) -> list:
... | 117 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
snake_case__ : List[Any] = logging.get_logg... | 117 | 1 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : ... | 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Dict = {
'''configuration_blenderbot... | 73 | 0 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from... | 6 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAIN... | 101 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
UpperCamelCase : int = """\
"""
UpperCamelCase : str = """
Perplexity (PPL)... | 345 | '''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def SCREAMING_SNAKE_CASE__ ( snake_case : str... | 345 | 1 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
def lowerCamelCase__ ( A__ : Any ):
'''simple d... | 12 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import A... | 12 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
... | 280 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCamelCase( UpperCAmelCase_ , UpperCAm... | 280 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( snake_case__ ):
_lowercase : ... | 322 |
_a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _a ( SCREAMING_SNAKE_CAS... | 322 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 299 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except... | 299 | 1 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def lowerCAmelCase_ ( snake_case_=Non... | 26 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.ro... | 26 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hug... | 371 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.j... | 67 | 0 |
# Copyright 2021 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 ap... | 137 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
a_ : Optional[Any] = logging.get_logger(__n... | 137 | 1 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__lowerCamelCase = datasets.load_iris()
__lowerCamelCase = np.array(data["data"])
__lowerCamelCase = np.... | 359 | """simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
... | 154 | 0 |
import inspect
import unittest
from transformers import ViTMSNConfig
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 ConfigTester
from ...test... | 280 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase : Optional[int] = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenizat... | 280 | 1 |
'''simple docstring'''
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 _UpperCamelCase ( A ):... | 361 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance... | 48 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelC... | 50 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 100_0000 ) -> int:
lowerCamelCase__ : int = limit + 1
lowerCamelCase__ : Optional[Any] = [0] * limit
for first_term in range(1 , _UpperCAmelCase ):
for n in range(_UpperCAmelCase , _Upper... | 50 | 1 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class a_ :
@property
def __a ( self :Union[str, Any]) -> Any:
... | 344 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and ... | 344 | 1 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_uno... | 80 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_lowercase : Any = (7_2_0, 1_2_8_0) # Height, Width
_lowercase : List[Any] = (0.4, 0.6) # if height or width ... | 93 | 0 |
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 __lowerCamelCase ( __snake_case ):
def __init__( self , lowerC... | 34 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def _UpperCamelCase ( UpperCamelCase__ = None ):
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
UpperCAmelCase__ : Dict = nums[0... | 163 | import qiskit
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> qiskit.result.counts.Counts:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =qiskit.Aer.get_backend('''aer_simulator''' )
UpperCAmelCase : List[str] ... | 348 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_env... | 62 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase_ : str = get_tests_dir('''fixture... | 62 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemak... | 110 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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_mod... | 110 | 1 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class lowercase :
"""simple docstring"""
def __init__( self ) -> Dict:
_UpperCAmelCase : int = {}
def _snake_case (... | 369 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 349 | 0 |
from math import loga
def A ( _lowercase ):
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError('''Input value must be a \'int\' type''' )
return 0 if... | 182 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self ):
_lowerCAmelCase : Any = """"""
_lowerCAmelCase : List[Any] = """"""
_lowerCAmelCase ... | 309 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCAmelCase : List[str] =logging.get_logger(__name__)
class UpperCAmelCase ( UpperCamelCase__ ):
def __init__( self :List[str] , *lower... | 123 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tens... | 123 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __lowerCamelCase ... | 28 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_commo... | 28 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceCl... | 366 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowercase__ : List[Any] = parse(importlib.metadata.version('torch'))
def a__ ( lowercase : Union[str, Version], lo... | 287 | 0 |
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