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 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
class UpperCamelCase :
def __init__( self ,__UpperCamelCase ) -> None:
'''simple docstring'''
lowercase_ : Optional[Any] = order
# a_{0} ... a_{k}
lowercase_ : Tuple ... | 213 | """simple docstring"""
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import ... | 213 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE (a__ ):
def __init__( sel... | 190 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER,... | 190 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 329 |
import math
lowerCAmelCase__ :Optional[int] = 1_0
lowerCAmelCase__ :Optional[Any] = 7
lowerCAmelCase__ :Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCAmelCase__ ( a__: int = 2_0 ) -> str:
'''simple docstring'''
_UpperCAmelCase ... | 329 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTransformerConfig... | 238 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 238 | 1 |
'''simple docstring'''
import os
import pytest
from attr import dataclass
UpperCAmelCase = '''us-east-1''' # defaults region
@dataclass
class lowerCAmelCase :
lowerCAmelCase_ = 42
lowerCAmelCase_ = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
... | 141 |
'''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_... | 141 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 72 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class ... | 72 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowerCAmelCase = pytest.mark.integration
@pytest.mark.parametrize('path' , ['paws', 'csv'] )... | 295 |
def _lowerCamelCase( lowercase__ , lowercase__ = " " ) -> list:
'''simple docstring'''
__lowercase= []
__lowercase= 0
for index, char in enumerate(lowercase__ ):
if char == separator:
split_words.append(string[last_index:index] )
__lowercase= index + 1
elif in... | 295 | 1 |
"""simple docstring"""
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/co... | 352 |
from __future__ import annotations
import time
UpperCAmelCase = list[tuple[int, int]]
UpperCAmelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0... | 267 | 0 |
import math
import tensorflow as tf
from packaging import version
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = tf.convert_to_tensor(snake_case )
_lowerCAmelCase = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype... | 82 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __lowerCAmelCase ( lowerCamelCase__ ):
# to overwrite at feature extractactor specifi... | 82 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( a_ : Optional[int] , a_ : Any , ... | 357 |
"""simple docstring"""
def __lowerCamelCase ( a_ : str ) -> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(a_ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("do... | 239 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from ... | 60 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 | 1 |
'''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, At... | 98 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCAmelCase = "\\n\n"
_lowerCAmelCase = "\nPerplexity (PPL) is one of the most co... | 98 | 1 |
'''simple docstring'''
import os
from pathlib import Path
def lowerCamelCase ( ) -> Dict:
from torch.utils.cpp_extension import load
lowercase_ : int = Path(UpperCAmelCase__ ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
low... | 239 | '''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Dict = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
excep... | 239 | 1 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils ... | 309 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
debug_laun... | 309 | 1 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str ) -> bool:
"""simple docstring"""
lowerCAmelCase_ : Tuple = len(lowerCAmelCase__ ) + 1
lowerCAmelCase_ : List[Any] ... | 224 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase_ : Tuple = [0] * len(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = []
lowerCA... | 224 | 1 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
lowerCamelCase_ = logging... | 34 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 34 | 1 |
'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state im... | 272 | '''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 272 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common i... | 360 |
'''simple docstring'''
from __future__ import annotations
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE : Tuple=None):
_A : Any = data
_A : Optional[Any] =... | 227 | 0 |
'''simple docstring'''
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... | 80 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Any = logging.get_logger(__name__)
a__ : str = {
'SCUT-DLVCLab/lilt-roberta-en-base': (
'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/res... | 80 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_SCREAMING_SNAKE_CASE = '''examples/'''
_SCREAMING_SNAKE_CASE = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''... | 217 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformer... | 217 | 1 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
_A = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def lowercase_ ( ) -> List[str]:
lowerCAmelCase__ : List[str] = Github(os.envir... | 242 |
"""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_seed
from accele... | 242 | 1 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
"""simple docstring"""
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError('The length of profit and weight mus... | 254 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, 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 Mod... | 254 | 1 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 68 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> list[int]:
lowercase__: List[str] = 0
lowercase__: Dict = len(snake_case ) - 1
while i < j:
if nums[i] + nums[j] == target:
... | 196 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.tes... | 27 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, 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 M... | 27 | 1 |
"""simple docstring"""
from __future__ import annotations
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ , UpperCAmelCase__ = set(lowerCamelCase ), [start]
while stack:
UpperCAmelCase__ = stack.pop()
ex... | 98 | """simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tra... | 98 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt models at https://huggingface.co/models?fi... | 257 |
__UpperCAmelCase = [
(10_00, "M"),
(9_00, "CM"),
(5_00, "D"),
(4_00, "CD"),
(1_00, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = {'''... | 257 | 1 |
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
from transformers import (
AutoConfig,
A... | 36 | """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 | 0 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, Im... | 358 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slo... | 62 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A =TypeVar('T')
A =TypeVar('U')
class _a ( Generic[T, U] ):
def __init__( self : str , lowercase : T | None , lowercase : U | Non... | 34 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test im... | 34 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
Bas... | 357 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self ... | 348 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.c... | 72 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : float , _lowercase : float ) ->float:
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'''{price_plus_tax(100, 0.25) = }''')
... | 105 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if n... | 229 |
'''simple docstring'''
from __future__ import annotations
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = order
# a_{0} ... a_{k}
_low... | 229 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 10_00 ):
"""simple docstring"""
_a = -1
_a = 0
for a in range(1, n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_a = (n * n - 2 * a * n)... | 320 |
"""simple docstring"""
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
... | 320 | 1 |
"""simple docstring"""
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=log... | 367 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowerCAmelCase ... | 112 | 0 |
import random
from .binary_exp_mod import bin_exp_mod
def a ( lowerCamelCase_ , lowerCamelCase_=1000 ):
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowercase__ = n - 1
lowercase__ = ... | 207 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
... | 207 | 1 |
from __future__ import annotations
class __lowerCAmelCase :
def __init__( self , lowerCAmelCase__ ) -> str:
'''simple docstring'''
a__ : int =TypeError(
"Matrices must be formed from a list of ze... | 148 |
def _A ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1_000 ):
"""simple docstring"""
a__ : Any =1
a__ : Any =0
for divide_by_number in range(SCREAMING_SNAKE_CASE , digit + 1 ):
a__ : list[int] =[]
a_... | 148 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "https://www.worldometers.info/coronavirus" ):
__a : List[Any] = BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text , 'html.parser' ... | 27 |
'''simple docstring'''
import torch
from transformers import AutoModel
class __UpperCamelCase ( torch.nn.Module ):
def __init__( self , __a="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
super(__a , self ).__init__()
__a ... | 27 | 1 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 242 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : list[int] ):
'''simple docstring'''
if not nums:
return 0
_UpperCAmelCase : Tuple =nums[0]
_UpperCAmelCase : int ... | 242 | 1 |
'''simple docstring'''
from typing import Any
import numpy as np
def __snake_case( _lowerCAmelCase ) -> bool:
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Any:
snake_case__ : ... | 35 |
import os
# Precomputes a list of the 100 first triangular numbers
lowerCAmelCase__ = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : str = os.path.dirname(os.path.realpath(lowerCamelCase__ )... | 130 | 0 |
"""simple docstring"""
def UpperCAmelCase ( a_ = 400_0000 ):
'''simple docstring'''
lowerCamelCase : Optional[int] = [0, 1]
lowerCamelCase : List[str] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
... | 360 |
"""simple docstring"""
def UpperCAmelCase ( a_ ):
'''simple docstring'''
return str(a_ ) == str(a_ )[::-1]
def UpperCAmelCase ( a_ ):
'''simple docstring'''
return int(a_ ) + int(str(a_ )[::-1] )
def UpperCAmelCase ( a_ = 1_0000 ... | 205 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__snake_cas... | 203 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : Union[str, Any] ) -> List[str]:
"""simple docstring"""
snake_case : List[str] = len(lowercase )
for i in range(length - 1 ):
snake_case : List[str] = i
for k in... | 203 | 1 |
import re
import string
import numpy as np
import datasets
_lowerCAmelCase :str = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
_lowerCAmelCase :Tuple = '\nArgs:\n predictions: List of... | 358 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 68 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Dict = {
'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-m... | 333 |
import math
import sys
def __a ( SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
if number != int(SCREAMING_SNAKE_CASE ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''th... | 333 | 1 |
'''simple docstring'''
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
... | 242 |
'''simple docstring'''
from __future__ import annotations
import bisect
def lowerCamelCase__ ( __lowerCamelCase : list[int] , __lowerCamelCase : int , __lowerCamelCase : int = 0 , __lowerCamelCase : int = -1 ):
'''simple docstring'''
if hi < ... | 242 | 1 |
def lowerCamelCase__ ( a__ : str ) -> str:
return "".join(chr(ord(a__ ) - 32 ) if """a""" <= char <= """z""" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 122 |
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 = {'''vocab_file''': ''... | 122 | 1 |
"""simple docstring"""
import math
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Dict = input("""Enter message: """ )
UpperCamelCase : Optional[int] = int(input(f'Enter key [2-{len(snake_case_ ) - 1}]: ' ) )
UpperC... | 27 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, Auto... | 27 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
return "".join(sorted(snake_case ) )
def UpperCamelCase_( snake_case ... | 85 |
"""simple docstring"""
import math
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
if initial_intensity < 0:
raise ValueError('''The value of intensity cannot be negative''' )
# handling of negative values of initial intensi... | 249 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
if depth < 0:
raise ValueError('Depth cannot be less than 0' )
if len(_UpperCamelCase ) == 0:
raise ValueEr... | 182 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.... | 182 | 1 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def lowercase ( lowerCAmelCase__ : Tuple , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str]=None , **lowerCAmelCase__ : Dict ) -> int:
__a = ... | 45 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCamelCase__ ( _lowerCamelCase : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 ... | 183 | 0 |
'''simple docstring'''
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,
... | 364 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnles... | 220 | 0 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
_lowercase : str = 3
def snake_case_ ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
pr... | 93 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTok... | 70 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
if len(UpperCAmelCase_ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(UpperCAmelCase_ )
or ... | 280 |
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def UpperCamelCase( ):
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=UpperCAmelCase_ , default='biencoder-nq-dev.json' ... | 280 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
pass
class __magic_name__ :
'''simple docstring'''
def __init__( self , _a ):
... | 291 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
... | 291 | 1 |
'''simple docstring'''
import numpy as np
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] ):
"""simple docstring"""
UpperCAmelCase__ = (0, 0)
UpperCAmelCase__ = None
UpperCAmelCase__ ... | 61 |
'''simple docstring'''
UpperCAmelCase_ = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
UpperCAmelCase__ = 0
while number:
# Increased Speed Slightly by checking ... | 61 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json"""... | 126 |
"""simple docstring"""
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
lowerCAmelCase = TypeVar("""T""")
class A_ ( Generic[T] ):
... | 126 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE... | 346 |
'''simple docstring'''
from random import randint, random
def _lowerCamelCase ( lowercase : int , lowercase : int , lowercase : int , lowercase : bool = False , lowercase : bool = False , lowercase : int =... | 346 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__A = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT5_PRETRAIN... | 177 | """simple docstring"""
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __U... | 177 | 1 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class UpperCAmelCase_ ( datasets.BuilderConfig ):
__SCREAMING_SNAKE_CASE ... | 202 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCAmelCase : Dict = "src/transformers"
# This is to make sure the... | 202 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : Union[str, Any] = logging.get_logger(__name__)
__lowercase : Any = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-aud... | 27 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils imp... | 27 | 1 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCa... | 359 |
'''simple docstring'''
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE_ ( __A : Sequence[int] | None = None ) -> int:
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
_SCREAMING_SNAKE_CASE = nums[0]
for i in range(1 , ... | 111 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transforme... | 203 | from manim import *
class A ( UpperCAmelCase_ ):
def lowercase_ (self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase__ ... | 65 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__A = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'... | 352 |
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_configuration_common import ... | 277 | 0 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE__ , unitt... | 38 |
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if edge <= 0 or not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def UpperCame... | 101 | 0 |
"""simple docstring"""
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
a__ : List[Any] = {
'''tiny.en''': '''https://openaipubl... | 195 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
class UpperCamelCase_ ( Upper... | 195 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
fr... | 160 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''': ['''Lu... | 160 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markup... | 352 |
"""simple docstring"""
from __future__ import annotations
SCREAMING_SNAKE_CASE = "#"
class UpperCAmelCase_ :
def __init__( self : Dict ) -> None:
'''simple docstring'''
A__ = {}
def __magic_name__ ( self : Optional[Any] ,... | 230 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Any = u
for i in range(1, __lowerCamelCase ):
UpperCAmelCase_ : int = temp * (u - i)
return temp
def ... | 61 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_a = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://h... | 61 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowerCAmelCase ( _snake_case ):
__lower... | 363 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 201 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a_ = False
class UpperCAmelCase_ ( unittest... | 249 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers imp... | 249 | 1 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
lowercase_ = 'http://www.mocksite.com/file1.txt'
lowercase_ ... | 368 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxMo... | 194 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ = {
'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}
try:
if not is_torch_available():
raise Optio... | 76 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case : str = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagToke... | 240 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenizatio... | 362 |
__UpperCAmelCase : str = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
__UpperCAmelCase : Dict ... | 315 | 0 |
'''simple docstring'''
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subpro... | 346 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 313 | 0 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : str = logging.get_logger(__name__)
lowercase : Dict = {
"facebook/encodec_24khz": "https://huggingface.co/... | 370 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffu... | 160 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Tuple = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeT... | 93 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_c... | 93 | 1 |
def _SCREAMING_SNAKE_CASE ( lowercase : int | float | str ):
'''simple docstring'''
try:
lowerCamelCase_ = float(lowercase )
except ValueError:
raise ValueError('Please enter a valid number' )
lowerCamelCa... | 208 |
def _SCREAMING_SNAKE_CASE ( lowercase : str ):
'''simple docstring'''
if not all(char in '01' for char in bin_string ):
raise ValueError('Non-binary value was passed to the function' )
if not bin_string:
raise ValueError... | 208 | 1 |
from manim import *
class lowerCamelCase_ ( A_ ):
'''simple docstring'''
def UpperCamelCase__ ( self) -> Optional[int]:
__UpperCamelCase :Dict = Rectangle(height=0.5 , width=0.5)
__UpperCamelCase :Optional[Any] = Rectangle(height=0.46 , width=0.4... | 43 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def UpperCamelCase ( __lowerCamelCase : Dict[str, torch.Tensor] ):
snake_case : List[str] = []
snake_case ... | 59 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class... | 281 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
snake_case : List[Any] = get_tests_dir('''fixtures/test_sentenc... | 281 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : Optional[int] = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExtract... | 133 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
f... | 265 | 0 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 76 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def __init__... | 76 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case... | 230 |
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__ = {
'''distilbert-base-uncased''': '''https://huggingfa... | 230 | 1 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
def __init__( self : List[str] , __a : Ca... | 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A__ : List[Any] = logging.get_logger(__name__)
A__ : Tuple = {
'''t5-small''': '''https://huggingfac... | 0 | 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
def lowerCAmelCase_ ( snake_case_ : list , snake_case_ : int | None = None , snake_case_ : int | None = None ) -> None:
'''simple docstring'''
if start is None:
UpperCAmelCase_ = 0
... | 360 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int = 1_00_00_00 ) -> int:
'''simple docstring'''
UpperCAmelCase_ = limit + 1
UpperCAmelCase_ = [0] * limit
for first_term in range(1 , snake_case_ ):
for n in range(snake_cas... | 106 | 0 |
'''simple docstring'''
def __lowercase ( __lowercase ) -> float:
'''simple docstring'''
if edge <= 0 or not isinstance(__lowercase , __lowercase ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2)... | 79 |
'''simple docstring'''
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, lo... | 79 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMo... | 262 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# sinc... | 262 | 1 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from ..... | 59 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=UpperCamelCase__):
"""simple docstring"""
UpperCamelCase__ = ["""flax""", """transformers"""]
def __init__( self: Optional[int] , *__lowerCamelCase: ... | 149 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase ... | 192 | from math import isqrt
def UpperCamelCase ( __lowercase : int ):
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 ,isqrt(__lowercase ) + 1 ) )
def UpperCamelCase ( __lowercase : int = 10**6 ):
'''si... | 192 | 1 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...te... | 82 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowercase ( A__ ):
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCame... | 97 | 0 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors i... | 371 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class lower... | 42 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case__ : str = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''... | 60 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_cas... | 60 | 1 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = []
_UpperCamelCase = []
_UpperCamelCase = {
'''^''': 3,
'''*''': 2,
'''/''':... | 353 |
"""simple docstring"""
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 = {
"""facebook/xlm-rober... | 100 | 0 |
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 had to include /home/niels... | 76 |
def lowerCamelCase__ ( _a , _a):
return int((input_a, input_a).count(1) != 0)
def lowerCamelCase__ ( ):
assert or_gate(0 , 0) == 0
assert or_gate(0 , 1) == 1
assert or_gate(1 , 0) == 1
assert or_gate(1 , 1) == 1
if __name__ == "__main__":
print(... | 76 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See... | 354 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class lowerCamelCase_ :
def __init__( self : List[Any] , _A : int | None = None ):
'''simple docstring'''
UpperCAmelCase__ : List[A... | 299 | 0 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self ... | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"t5-small": "https://huggingface.co/t5-small/resolv... | 0 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_lowercase : Optional[int] = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependen... | 365 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weight... | 272 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFe... | 69 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
... | 106 | 0 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowercase : List[Any] = 10
def UpperCAmelCase_ (_lowerCAmelCase : int , _lowerCAmelCase : i... | 363 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
lowercase... | 171 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: int, a_: int ):
_UpperCAmelCase : list[list[int]] = []
create_all_state(1, a_, a_, [], a_ )
return result
def __UpperCAmelCase ( a_: ... | 145 | '''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__a = {
# 1536-bit
5: {
'prime': int(
... | 145 | 1 |
"""simple docstring"""
def lowercase__ ( snake_case_ :Optional[int] ):
if not head:
return True
# split the list to two parts
__UpperCAmelCase , __UpperCAmelCase = head.next, head
while fast and fast.next:
__UpperCAmelCase = fast.next.next
__Upper... | 86 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import... | 86 | 1 |
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