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 |
|---|---|---|---|---|
"""simple docstring"""
import baseaa
def A ( snake_case__ ):
'''simple docstring'''
return baseaa.aaaencode(string.encode("""utf-8""" ) )
def A ( snake_case__ ):
'''simple docstring'''
return baseaa.aaadecode(_SCREAMING_SNAKE_CASE ).deco... | 165 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-... | 260 | 0 |
lowercase__ : List[Any] = frozenset(
[
'''prompt''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
]
)
lowercase__ : ... | 338 |
"""simple docstring"""
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_UpperCAmelCase = 6
_UpperCAmelCase = 1
_UpperCAmelCase = 1901
_UpperCAmelCase ... | 260 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import float... | 34 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % ... | 260 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
__snake_case : Tuple = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))... | 172 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transfor... | 260 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@r... | 258 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAm... | 260 | 0 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __lowerCAmelCase ( unittest.TestCase ):
__lowerCamelCase = inspect.getf... | 82 |
"""simple docstring"""
import random
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = a[left_in... | 260 | 0 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase ( _A ):
SCREAMING_SNAKE_CASE_ : List[Any] = (DDPMScheduler,)
def A ( self : ... | 33 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__A : Union[str, Any] = "\\n\n"
__A : Any = "\nPerplexity... | 260 | 0 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __a ( ) ->List[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
... | 290 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A : int = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE ... | 260 | 0 |
def A_ ( _lowerCAmelCase ) -> Dict:
for i in range(len(_SCREAMING_SNAKE_CASE ) - 1 , 0 , -1 ):
UpperCamelCase : Optional[int] = False
for j in range(_SCREAMING_SNAKE_CASE , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
UpperC... | 52 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(_SCREAMING_SNAKE_CASE ):
if lst[i - 1] <= lst... | 260 | 0 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import l... | 43 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
fr... | 260 | 0 |
def _SCREAMING_SNAKE_CASE ( lowercase : Tuple , lowercase : List[str] , lowercase : List[str] , lowercase : Union[str, Any] ):
'''simple docstring'''
lowerCamelCase_ = [False] * len(_SCREAMING_SNAKE_CASE )
low... | 204 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _a ( lowerCAmelCase):
... | 260 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ : Optional[int] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try... | 165 |
"""simple docstring"""
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _a ( lowerCAmelCase):
"""simple docstring"""
def lowercase__ ( self : List[Any] , __Upper... | 260 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Optional[Any] = logging.get_logger(__name__)
lowercase__ : Tuple = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/ma... | 338 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict ... | 260 | 0 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets impo... | 34 |
"""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
__A : Tuple = logging.get_logger(__name__)
_... | 260 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( ) -> List[str]:
'''simple docstring'''
__snake_case : Optional[Any] = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
__snake_case : Optional[int] = 6
__snake_case : int = 1
... | 172 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_see... | 260 | 0 |
'''simple docstring'''
def __a ( UpperCAmelCase ) ->Dict:
"""simple docstring"""
if len(_SCREAMING_SNAKE_CASE ) < 2:
return collection
def circle_sort_util(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> bool:
A = False
if low == high:
r... | 258 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : set ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAm... | 260 | 0 |
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 jnp
@slow
@requ... | 82 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
... | 260 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( __snake_case : list[int] ):
if not nums:
return 0
lowercase_ : int = nums[0]
lowercase_ : List[str] = 0
for num in nums[1:]:
lowercase_ , ... | 33 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..... | 260 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def __a ( _SCREAMING_SNAKE_CASE ) ->Any:
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 p... | 290 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str = " " ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 0
for index, char in enumerate(_SCREAMING_SNAKE_CASE )... | 260 | 0 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__lowerCamelCase : int = collections.namedtuple("""_Datasets""", ... | 52 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmel... | 260 | 0 |
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_processi... | 43 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
while cur > 1:
# Find the maximum number in arr
_UpperCAmelCase = arr.index(m... | 260 | 0 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE ( lowercase : Optional[Any] , lowercase : An... | 204 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = rg... | 260 | 0 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def A ( snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = rgb[:, :, 0], rgb[:, :, 1]... | 165 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-... | 260 | 0 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMIN... | 338 |
"""simple docstring"""
def lowercase ( ):
'''simple docstring'''
_UpperCAmelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_UpperCAmelCase = 6
_UpperCAmelCase = 1
_UpperCAmelCase = 1901
_UpperCAmelCase ... | 260 | 0 |
'''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_common import... | 34 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % ... | 260 | 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
_a : Union[str, Any]= "\\n\n"
_a : Any= "\nPerplexity (PPL) is one of the m... | 172 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transfor... | 260 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMSchedu... | 258 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAm... | 260 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
A__ = logging.get_logger(__name__)
A__ = {"vocab_f... | 82 |
"""simple docstring"""
import random
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = a[left_in... | 260 | 0 |
"""simple docstring"""
def lowercase ( __snake_case : str , __snake_case : List[str] , __snake_case : int , __snake_case : Dict ):
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_S... | 33 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__A : Union[str, Any] = "\\n\n"
__A : Any = "\nPerplexity... | 260 | 0 |
"""simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE ) ->List[str]:
a__: Union[str, Any] = hex_num.strip()
if not hex_num:
raise ValueError('No value was passed to the function' )
a__: Tuple = hex_num[0] == '-'
if is_negative:
a__: List[Any] = hex_nu... | 290 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__A : int = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def lowercase ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE ... | 260 | 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_tenso... | 52 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return lst
_UpperCAmelCase = 1
while i < len(_SCREAMING_SNAKE_CASE ):
if lst[i - 1] <= lst... | 260 | 0 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Any = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
__UpperCamelCase :Tuple = parser.add_subp... | 43 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
fr... | 260 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
Auto... | 204 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _a ( lowerCAmelCase):
... | 260 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
A_ : Any = logging.get_logger(__name__)
class lowerCamelCase (A__ ):
def __init__( self : Union[str, Any] , *__UpperCAmelCase : ... | 165 |
"""simple docstring"""
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _a ( lowerCAmelCase):
"""simple docstring"""
def lowercase__ ( self : List[Any] , __Upper... | 260 | 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_allow_in... | 338 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Dict ... | 260 | 0 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...t... | 34 |
"""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
__A : Tuple = logging.get_logger(__name__)
_... | 260 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
_a : List[Any]= logging.getLogger(__name__)
def __UpperCAmelCase ( ) -> Optional[Any]:
'''simple docstring'''
... | 172 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_see... | 260 | 0 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
_lowerCamelCase : Optional[Any] = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trai... | 258 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : list[list[int]] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : set ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAm... | 260 | 0 |
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
A__ = logging.get_... | 82 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
... | 260 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _lowerCamelCase( a ):
... | 261 | """simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s""",
datefmt="""%m/%d/%Y %H:%M:%S""",
level=logging.INFO,
)
SCREAMING_SNAKE_CASE__... | 261 | 1 |
"""simple docstring"""
def _lowerCamelCase( a ):
if length <= 0 or not isinstance(a , a ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(a )]
if __name__ == "__main__":
print(hexagonal_numbers(len... | 261 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__:List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not... | 261 | 1 |
"""simple docstring"""
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case__ ( snake_case_ ):
_snake_case : Tuple = (EulerDiscreteScheduler,)
_snake_case : Optional[Any]... | 261 | """simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case__ :
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase = 0 ):
__a , __a = row, column
__a = [[default_value for c in rang... | 261 | 1 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
SCREAMING_SNAKE_CASE__:str = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any memb... | 261 | """simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data im... | 261 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
res... | 261 | """simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnP... | 261 | 1 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
SCREAMING_SNAKE_CASE__:s... | 261 | """simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
SCREAMING_SNAKE_CASE__:List[Any] = log... | 261 | 1 |
"""simple docstring"""
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
SCREAMING_SNAKE_CASE__:List[str] ... | 261 | """simple docstring"""
from collections import Counter
from timeit import timeit
def _lowerCamelCase( a = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def _lowerCamelCase( a = "" ):
if len(a ... | 261 | 1 |
"""simple docstring"""
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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we ha... | 261 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_com... | 261 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _lowerCamelCase( a ):
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class snake_case__ ( snake_case_ ):... | 261 | """simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case__ ( snake_case_, snake_case_ ):
@register_to_config
def __init__( ... | 261 | 1 |
"""simple docstring"""
import numpy as np
def _lowerCamelCase( a ):
return 1 / (1 + np.exp(-vector ))
def _lowerCamelCase( a ):
return vector * sigmoid(1.7_02 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 261 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
SCREAMING_SNAKE_CASE__:List[str] = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """Lon... | 261 | 1 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_config... | 261 | """simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase( a ... | 261 | 1 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _lowerCamelCase( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ... | 261 | """simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, ... | 261 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__:Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__:Tuple = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/... | 261 | """simple docstring"""
import random
def _lowerCamelCase( a , a , a ):
__a = a[left_index]
__a = left_index + 1
for j in range(left_index + 1 , a ):
if a[j] < pivot:
__a , __a = a[i], a[j]
... | 261 | 1 |
"""simple docstring"""
def _lowerCamelCase( a ):
if not isinstance(a , a ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
__a = 0
while number:
# This way we arrive at next set bit (next 1) instead of l... | 261 | """simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _lowerCamelCase( a ):
return getitem, k
def _lowerCamelCase( a , a ):
return setitem, k, v
def _lowerCamelCase(... | 261 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
class snake_case__ :
def __init__( self , lowerCamelCase ):
__a = []
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []} )
f... | 261 | """simple docstring"""
import copy
import re
class snake_case__ :
_snake_case : Dict = """hp"""
_snake_case : List[str] = {}
_snake_case : int = None
@classmethod
def a__ ( cls , lowerCamelCase , lowerCamelCase ):
__a = prefix
... | 261 | 1 |
"""simple docstring"""
import functools
def _lowerCamelCase( a , a ):
__a = len(a )
__a = len(a )
@functools.cache
def min_distance(a , a ) -> int:
# if first word index is overflow - delete all from the second wor... | 261 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__:int = logging.get_logger(__name__)
class snake_case__ ( snake_case_ ):
_snake_case : O... | 261 | 1 |
"""simple docstring"""
import socket
def _lowerCamelCase( ):
__a = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__a = socket.gethostname()
__a = 1_2_3_1_2
sock.connect((host, port) )
sock.send(b"Hello server!" )
... | 261 | """simple docstring"""
def _lowerCamelCase( a = 1_0_0_0 ):
__a = 3
__a = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
a += 1
return re... | 261 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__:Optional[Any] = {"""configuration_plbart""": ["""PLBART_PR... | 261 | """simple docstring"""
import operator
def _lowerCamelCase( a , a = False , a = None ):
__a = operator.lt if reverse else operator.gt
__a = solution or []
if not arr:
return solution
__a = [arr.pop(0 )]
for i, i... | 261 | 1 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 261 | """simple docstring"""
import unittest
from transformers import BertGenerationConfig, 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_co... | 261 | 1 |
"""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 .token... | 261 | """simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.p... | 261 | 1 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_g... | 261 | """simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
log... | 261 | 1 |
"""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
@dataclass... | 261 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__:str = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CO... | 261 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__:Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__:List[str] = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.c... | 261 | """simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s""",
datefmt="""%m/%d/%Y %H:%M:%S""",
level=logging.INFO,
)
SCREAMING_SNAKE_CASE__... | 261 | 1 |
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
SCREAMING_SNAKE_CASE__:List[str] = ... | 261 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__:List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not... | 261 | 1 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case__ ( snake_case_ ):
_snake_case : Optional[Any] = (UnCLIPScheduler,)
def a__ ( self , **lowerCamelCase ):
__a = ... | 261 | """simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case__ :
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase = 0 ):
__a , __a = row, column
__a = [[default_value for c in rang... | 261 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__:List[str] = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
... | 261 | """simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data im... | 261 | 1 |
"""simple docstring"""
import random
def _lowerCamelCase( a , a ):
__a , __a , __a = [], [], []
for element in data:
if element < pivot:
less.append(a )
elif element > pivot:
grea... | 261 | """simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnP... | 261 | 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... | 261 | """simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
SCREAMING_SNAKE_CASE__:List[Any] = log... | 261 | 1 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def _lowerCamelCase( a = 1_0_0_0_0_0_0 , a = 1_0 ):
__a = defaultdict(a )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width ... | 261 | """simple docstring"""
from collections import Counter
from timeit import timeit
def _lowerCamelCase( a = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def _lowerCamelCase( a = "" ):
if len(a ... | 261 | 1 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case__ ( snake_case_ ):
_snake_case : List[Any] = (KDPMaDiscreteScheduler,)
_snake_case : Union[str... | 261 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_com... | 261 | 1 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _lowerCamelCase( a , a , a ):
__a = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
... | 261 | """simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case__ ( snake_case_, snake_case_ ):
@register_to_config
def __init__( ... | 261 | 1 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
SCREAMING_SNAKE_CASE__:Optional[int] = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__:Optional[int] = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart... | 261 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
SCREAMING_SNAKE_CASE__:List[str] = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """Lon... | 261 | 1 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case__ ( snake_case_, snake_case_ ):
@register_to_config
def __init__( ... | 261 | """simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase( a ... | 261 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
SCREAMING_SNAKE_CASE__:Tuple = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAI... | 261 | """simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, ... | 261 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
SCREAMING_SNAKE_CASE__:Any = version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def ... | 261 | """simple docstring"""
import random
def _lowerCamelCase( a , a , a ):
__a = a[left_index]
__a = left_index + 1
for j in range(left_index + 1 , a ):
if a[j] < pivot:
__a , __a = a[i], a[j]
... | 261 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase( a ):
__a = [True] * limit
__a = False
__a = False
__a = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
__a = ... | 261 | """simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _lowerCamelCase( a ):
return getitem, k
def _lowerCamelCase( a , a ):
return setitem, k, v
def _lowerCamelCase(... | 261 | 1 |
"""simple docstring"""
from __future__ import annotations
SCREAMING_SNAKE_CASE__:Union[str, Any] = 8.988e9 # units = N * m^s * C^-2
def _lowerCamelCase( a , a , a , a ):
__a = abs(chargea * chargea )
if (force, chargea, chargea, distance).c... | 261 | """simple docstring"""
import copy
import re
class snake_case__ :
_snake_case : Dict = """hp"""
_snake_case : List[str] = {}
_snake_case : int = None
@classmethod
def a__ ( cls , lowerCamelCase , lowerCamelCase ):
__a = prefix
... | 261 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_avail... | 261 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__:int = logging.get_logger(__name__)
class snake_case__ ( snake_case_ ):
_snake_case : O... | 261 | 1 |
"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def _lowerCamelCase( a , a=None ):
__a = None
if token is not None:
__a = {"Accept": "... | 261 | """simple docstring"""
def _lowerCamelCase( a = 1_0_0_0 ):
__a = 3
__a = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
a += 1
return re... | 261 | 1 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_availabl... | 261 | """simple docstring"""
import operator
def _lowerCamelCase( a , a = False , a = None ):
__a = operator.lt if reverse else operator.gt
__a = solution or []
if not arr:
return solution
__a = [arr.pop(0 )]
for i, i... | 261 | 1 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _lowerCamelCase( ):
__a = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"path": ["test_1.p... | 261 | """simple docstring"""
import unittest
from transformers import BertGenerationConfig, 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_co... | 261 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanti... | 261 | """simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.p... | 261 | 1 |
"""simple docstring"""
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCamelCase( a , a , a , a , a ):
# load base model
__a = StableDiffusionPipeline.from_pretrained(a , tor... | 261 | """simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
log... | 261 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 261 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__:str = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CO... | 261 | 1 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__:Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__:int = ... | 261 | """simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s""",
datefmt="""%m/%d/%Y %H:%M:%S""",
level=logging.INFO,
)
SCREAMING_SNAKE_CASE__... | 261 | 1 |
"""simple docstring"""
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase( a , a , a ):
# Initialise PyTorch model
__a = TaConf... | 261 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__:List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not... | 261 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_t... | 261 | """simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case__ :
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase = 0 ):
__a , __a = row, column
__a = [[default_value for c in rang... | 261 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_... | 261 | """simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data im... | 261 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__:List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not... | 261 | """simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnP... | 261 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__:int = logging.get_logger(__name__)
SCREAMING_SN... | 261 | """simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
SCREAMING_SNAKE_CASE__:List[Any] = log... | 261 | 1 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
log... | 261 | """simple docstring"""
from collections import Counter
from timeit import timeit
def _lowerCamelCase( a = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def _lowerCamelCase( a = "" ):
if len(a ... | 261 | 1 |
"""simple docstring"""
def _lowerCamelCase( ):
__a = 0
for i in range(1 , 1_0_0_1 ):
total += i**i
return str(a )[-1_0:]
if __name__ == "__main__":
print(solution())
| 261 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_com... | 261 | 1 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTest... | 261 | """simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case__ ( snake_case_, snake_case_ ):
@register_to_config
def __init__( ... | 261 | 1 |
"""simple docstring"""
def _lowerCamelCase( a , a ):
__a = len(a ) + 1
__a = len(a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given p... | 261 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
SCREAMING_SNAKE_CASE__:List[str] = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """Lon... | 261 | 1 |
"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, 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_co... | 261 | """simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase( a ... | 261 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__:Optional[int] = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not i... | 261 | """simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, ... | 261 | 1 |
"""simple docstring"""
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_uti... | 261 | """simple docstring"""
import random
def _lowerCamelCase( a , a , a ):
__a = a[left_index]
__a = left_index + 1
for j in range(left_index + 1 , a ):
if a[j] < pivot:
__a , __a = a[i], a[j]
... | 261 | 1 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_prope... | 261 | """simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _lowerCamelCase( a ):
return getitem, k
def _lowerCamelCase( a , a ):
return setitem, k, v
def _lowerCamelCase(... | 261 | 1 |
"""simple docstring"""
def _lowerCamelCase( a , a ):
if len(a ) != len(a ):
raise ValueError("String lengths must match!" )
__a = 0
for chara, chara in zip(a , a ):
if chara != chara:
count... | 261 | """simple docstring"""
import copy
import re
class snake_case__ :
_snake_case : Dict = """hp"""
_snake_case : List[str] = {}
_snake_case : int = None
@classmethod
def a__ ( cls , lowerCamelCase , lowerCamelCase ):
__a = prefix
... | 261 | 1 |
"""simple docstring"""
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... | 261 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__:int = logging.get_logger(__name__)
class snake_case__ ( snake_case_ ):
_snake_case : O... | 261 | 1 |
"""simple docstring"""
import operator
def _lowerCamelCase( a , a = False , a = None ):
__a = operator.lt if reverse else operator.gt
__a = solution or []
if not arr:
return solution
__a = [arr.pop(0 )]
for i, i... | 261 | """simple docstring"""
def _lowerCamelCase( a = 1_0_0_0 ):
__a = 3
__a = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
a += 1
return re... | 261 | 1 |
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