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 argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGenerat... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where d... | 44 | 1 |
import os
def _lowerCAmelCase ( ) -> int:
"""simple docstring"""
snake_case__ : Optional[Any] = os.path.dirname(os.path.realpath(__lowerCAmelCase ) )
snake_case__ : Any = os.path.join(__lowerCAmelCase , '''triangle.txt''' )
... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase = 50 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for bloc... | 44 | 1 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import Tokeni... | 44 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ = False
class a ( unittest.TestCase ):
... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multip... | 44 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...uti... | 44 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 | 1 |
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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transfor... | 44 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 | 1 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
... | 44 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
A__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class a ( data... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : str = sorted(string.lower() )
r... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> Optional[int]: # noqa: E741
"""simple docstring"""
snake_case__ : Union[str, Any] = len(__lowerCAmelCase )
snake_case__ : Union[str, Any] = 0
snake_case__ : List[Any] = [0]... | 44 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 44 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCAmelCase ( ) -> List[Any]:
"""simple docstring"""
snake_case__ : Any = ArgumentParser(
... | 44 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 | 1 |
# 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.pipeline_contro... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = '''▁'''
A__ ... | 44 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 1 |
import numpy as np
class a :
def __init__( self :int ):
snake_case__ : Optional[Any] = (0, 0)
snake_case__ : Optional[int] = None
snake_case__ : Tuple = 0
snake_case__ : Union[str, Any] =... | 44 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import C... | 44 | 1 |
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,
AutoModelForSequenceClas... | 44 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = ... | 44 | 1 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from trans... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorte... | 44 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a :
def __init__( self :Any ,__lowercase :Collection[float] | None = None ):
if components is None:
snake_case_... | 44 |
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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transfor... | 44 | 1 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
... | 44 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A__ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sm... | 44 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://h... | 44 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 44 | 1 |
from __future__ import annotations
class a :
def __init__( self :Optional[int] ,__lowercase :int ):
snake_case__ : str = data
snake_case__ : Node | None = None
snake_case__ : Node | None = None
... | 44 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ = logging.ge... | 44 | 1 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTest... | 44 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 44 | 1 |
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 a ( unittest.TestCase ):... | 44 |
from manim import *
class a ( __lowerCamelCase ):
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
snake_case__ : Optional[int] = Rectangle(he... | 44 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
... | 44 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 44 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _lowerCAmelCase ( __lowerCAmelCase )... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where d... | 44 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless ... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase = 50 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for bloc... | 44 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 44 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ = False
class a ( unittest.TestCase ):
... | 44 | 1 |
from __future__ import annotations
A__ = 10
def _lowerCAmelCase ( __lowerCAmelCase ) -> list[int]:
"""simple docstring"""
snake_case__ : Union[str, Any] = 1
snake_case__ : Union[str, Any] = max(__lowerCAmelCase )
... | 44 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case__ : Any = mf... | 44 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
'''AltCLIPTe... | 44 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 | 1 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available(... | 44 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...t... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : str = sorted(string.lower() )
r... | 44 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ = logging.ge... | 44 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 44 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
... | 44 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 | 1 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class a :
def __init__( self :Tuple ,__lowercase :int ... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCamelCase )
class a ( __lowerCamelCase ):
# `task` is not a ClassVar since we want it to be part of the `as... | 44 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase = 1000000 ) -> int:
"""simple docstring"""
snake_case__ : List[str] = 1
snake_case__ : str = 1
snake_case__ : Dict = {1: 1}
for inputa in range(2 , __lowerCAmelCase ):
... | 44 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import C... | 44 | 1 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
A__ = '''▁'''
... | 44 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = ... | 44 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
'''kssteven/ibert-roberta-base''': '''https://huggi... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorte... | 44 | 1 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = ... | 44 |
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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transfor... | 44 | 1 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class a :
__lowerCAmelCase : torch.Tensor # [batch_size x 3]
__lowerCAmelCase : torch.Tensor # [batch_size x 3]
__lowerCAmelCase : torch.Tensor # [batch_size x... | 44 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A__ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sm... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase = 1000 ) -> int:
"""simple docstring"""
snake_case__ , snake_case__ : List[Any] = 1, 1
snake_case__ : Union[str, Any] = 2
while True:
snake_case__ : Optional[int] = 0
... | 44 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 44 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class a ( nn.Module ):
__lowerCAmelCase : int
__lowerCAmelCase : jnp.dtype = jnp.floataa
def __lowerCamelCase ( self :Optional[int] ):
snake_case__ : List[str... | 44 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ = logging.ge... | 44 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {
'''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHI... | 44 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 44 | 1 |
from math import asin, atan, cos, radians, sin, sqrt, tan
A__ = 6378137.0
A__ = 6356752.314245
A__ = 637_8137
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
"""simple docstr... | 44 |
from manim import *
class a ( __lowerCamelCase ):
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
snake_case__ : Optional[int] = Rectangle(he... | 44 | 1 |
import enum
import shutil
import sys
A__ , A__ = shutil.get_terminal_size()
A__ = {'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}
class a ( enum.Enum ):
__lowerCAmelCase : Tuple = 0
__lowerCA... | 44 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 44 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ = False
class a ( unittest.TestCase ):
... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where d... | 44 | 1 |
A__ = '''
# 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 git+https://github.com/huggingface/transformers.git
'''
... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase = 50 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for bloc... | 44 | 1 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'''... | 44 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ = False
class a ( unittest.TestCase ):
... | 44 | 1 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
... | 44 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 | 1 |
A__ = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
A__ = [
... | 44 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _lowerCAmelCase ( __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
c... | 44 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 | 1 |
from collections.abc import Callable
import numpy as np
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> np.ndarray:
"""simple docstring"""
snake_case__ : List[Any] = int(np.c... | 44 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
def wrapper(*__lowerCAmelCase , **__lowerCAmelC... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : str = sorted(string.lower() )
r... | 44 | 1 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_F... | 44 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 44 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDepend... | 44 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 | 1 |
from __future__ import annotations
from statistics import mean
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
"""simple docstring"""
snake_case__ : Dict = [0] * no_of_processes
snake_case__ : ... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where d... | 44 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import C... | 44 | 1 |
import itertools
import string
from collections.abc import Generator, Iterable
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> Generator[tuple[str, ...], None, None]:
"""simple docstring"""
snake_case__ : Dict = iter(__lowerCAmelCase )... | 44 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = ... | 44 | 1 |
from ...processing_utils import ProcessorMixin
class a ( __lowerCamelCase ):
__lowerCAmelCase : Any = """WhisperFeatureExtractor"""
__lowerCAmelCase : Optional[int] = """WhisperTokenizer"""
def __init__( self :Tuple ,__lowercase... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorte... | 44 | 1 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def _lowerC... | 44 |
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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transfor... | 44 | 1 |
import argparse
import json
from tqdm import tqdm
def _lowerCAmelCase ( ) -> List[str]:
"""simple docstring"""
snake_case__ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=__... | 44 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A__ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sm... | 44 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_nu... | 44 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 44 | 1 |
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
class a ( ... | 44 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ = logging.ge... | 44 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_availa... | 44 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 44 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 |
from manim import *
class a ( __lowerCamelCase ):
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
snake_case__ : Optional[int] = Rectangle(he... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(__lowerCAmelCase ) == 0:
raise Va... | 44 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 44 | 1 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where d... | 44 | 1 |
import math
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
"""simple docstring"""
if (
not isinstance(__lowerCAmelCase , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('''pow... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase = 50 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for bloc... | 44 | 1 |
from functools import lru_cache
def _lowerCAmelCase ( __lowerCAmelCase ) -> set:
"""simple docstring"""
snake_case__ : Optional[int] = 2
snake_case__ : int = set()
while i * i <= n:
if n % i:
i += 1
... | 44 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ = False
class a ( unittest.TestCase ):
... | 44 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, r... | 44 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 | 1 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 | 1 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
"""simple docstring"""
snake_case__ ... | 44 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 | 1 |
import itertools
import math
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
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 numb... | 44 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params im... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : str = sorted(string.lower() )
r... | 44 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__lowerCamelCase )
class a ( __lowerCamelCase ):
__lowerCAmelCase : str =... | 44 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 44 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A__ = logging.get_logger(__name__)
A__ = {
'''shi-labs/dinat-mini-in1k-224''': '''https://hu... | 44 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 | 1 |
class a :
def __init__( self :Optional[Any] ):
snake_case__ : str = ''''''
snake_case__ : Union[str, Any] = ''''''
snake_case__ : int = []
def __lowerCamelCase ( self :str ,__lowe... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class a ( __lowerCam... | 44 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
A__ = ... | 44 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import C... | 44 | 1 |
from __future__ import annotations
class a :
def __init__( self :List[str] ,__lowercase :Any=None ):
snake_case__ : Dict = data
snake_case__ : str = None
def __repr__( self :int ):
snake_cas... | 44 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = ... | 44 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class a ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
__lowerCAmelCase : int = [(""... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorte... | 44 | 1 |
from math import factorial
A__ = {str(d): factorial(d) for d in range(10)}
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(__lowerCAmelCase ) )
def _lowerCAmelCase ( ) ... | 44 |
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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transfor... | 44 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
'''configuration_jukebox''': [
'''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''JukeboxConfig''',
'''JukeboxPriorConfig''',
''... | 44 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A__ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sm... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase )
snake_case__ : Dict = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
... | 44 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 44 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
A__ = '''
import os
'''
A__ = '''
def foo():
import os
return False
'''
A__ = '''
def foo():
def bar():
if True:
import os
return False
... | 44 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ = logging.ge... | 44 | 1 |
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 BackboneTesterMixin... | 44 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 44 | 1 |
from collections import defaultdict
class a :
def __init__( self :List[Any] ,__lowercase :Any ,__lowercase :Optional[int] ):
snake_case__ : Dict = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
... | 44 |
from manim import *
class a ( __lowerCamelCase ):
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
snake_case__ : Optional[int] = Rectangle(he... | 44 | 1 |
A__ = {
'''meter''': '''m''',
'''kilometer''': '''km''',
'''megametre''': '''Mm''',
'''gigametre''': '''Gm''',
'''terametre''': '''Tm''',
'''petametre''': '''Pm''',
'''exametre''': '''Em''',
'''zettametre''': '''Zm''',
'''yottametre''': '''Ym''',
}
# Ex... | 44 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 44 | 1 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where d... | 44 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import i... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase = 50 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for bloc... | 44 | 1 |
A__ = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609344,
"knot": 1.852,
}
A__ = {
"km/h": 1.0,
"m/s": 0.277777778,
"mph": 0.621371192,
"knot": 0.539956803,
}
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCas... | 44 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ = False
class a ( unittest.TestCase ):
... | 44 | 1 |
import numpy as np
import qiskit
def _lowerCAmelCase ( __lowerCAmelCase = 8 , __lowerCAmelCase = None ) -> str:
"""simple docstring"""
snake_case__ : List[str] = np.random.default_rng(seed=__lowerCAmelCase )
# Roughly 25% of the qubits will con... | 44 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(che... | 44 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tok... | 44 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 | 1 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 1 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : str = sorted(string.lower() )
r... | 44 | 1 |
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 ModelTesterMixin, ids... | 44 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 44 | 1 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerO... | 44 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 | 1 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
A__ = logging.get_logger(__name__)
def _lowerCAmelCase ( __lowerCAmelCase=None , __lowerCAmelCase=None ) ... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''': ['''LukeTokenizer'''],
}
try:
if n... | 44 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 1 |
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