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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPS... | 356 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__A = R'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documenta... | 75 | 0 |
import os
__A = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
def __A ( _lowercase ):
'''simple docstring'''
_A = 0
_A = 0
while index < len(_lowercase ) - 1:
_A = SYMBOLS[numerals[index]]
_A... | 357 |
from __future__ import annotations
import math
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if not scores:
... | 75 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 358 |
__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 = [
999,
976,
952,
928,
... | 75 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: List[Any] , __A: list[tuple[float, float]] ) -> Optional[Any]:
_A = list_of_points
... | 359 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 75 | 0 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMR... | 360 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstrin... | 75 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __A ( _lowercase ):
'''simple docstring'''
if "cls_token" in name:
_A = name.replace('''cls_token''' ... | 361 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIP... | 75 | 0 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __A ( _lowercase , _lowercase=0 ):
'''simple docstring'''
return sorted(_lowercase , key=lambda... | 362 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavav... | 75 | 0 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: Any , *__A: Union[str, Any] ... | 363 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = ('''dense.weight''', '''attention.self.query''', '''attenti... | 75 | 0 |
import itertools
import math
def __A ( _lowercase ):
'''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 numbers, all multiples of 3 ar... | 364 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@require_torch
def __A ( self: D... | 75 | 0 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTV... | 365 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
__A = yaml.safe_load(
'\\nname: ""\nallow_empty: false\nallow_empty_text: true\nsubsections:\n - name: "Dataset Card for X" # ... | 75 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__A = logging.get_logger(__name__)
__A = ... | 366 |
import os
__A = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
def __A ( _lowercase ):
'''simple docstring'''
_A = 0
_A = 0
while index < len(_lowercase ) - 1:
_A = SYMBOLS[numerals[index]]
_A... | 75 | 0 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...... | 367 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 75 | 0 |
from math import isqrt
def __A ( _lowercase ):
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 , isqrt(_lowercase ) + 1 ) )
def __A ( _lowercase = 10**6 ):
'''simple docstring'''
_A = 0
_A ... | 368 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase )... | 75 | 0 |
"""simple docstring"""
from torch import nn
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
def __init__( self: List[Any] , __A: Dict , __A: int ) -> Dict:
super().__init__()
_A = clas... | 369 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__A = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # no... | 75 | 0 |
from __future__ import annotations
import requests
__A = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited... | 370 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__A = input('Enter image url: ').strip()
print(f'Downloading image from {url} ...')
__A = BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the co... | 75 | 0 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , ):
'''simple docstring'''
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
e... | 371 |
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 transformers.models... | 75 | 0 |
import os
import pytest
from attr import dataclass
__A = 'us-east-1' # defaults region
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
A_ = 42
A_ = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
A_ = {
... | 350 |
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 SCREAMING_SNAKE_CASE ... | 75 | 0 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""sim... | 351 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
... | 75 | 0 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax", "transformers"]
def __init__( self: List[Any] , *__A: Any , **__A: Optional[int] ) ->... | 352 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: List[Any] , *__A: Union[str, An... | 75 | 0 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Tuple , __A: int=2 , __A: List[Any]=3 , ... | 353 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
'configuration_owlvit': [
'OWLVIT_PRETRAINED_CON... | 75 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleCh... | 354 |
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 sagemaker.huggingf... | 75 | 0 |
__A = 8.3_144_598
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
raise Exception('''Molar mass cannot be less than or equal t... | 355 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IM... | 75 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__A = R'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model ... | 356 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__A = R'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documenta... | 75 | 0 |
__A = 'Input must be a string of 8 numbers plus letter'
__A = 'TRWAGMYFPDXBNJZSQVHLCKE'
def __A ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
_A = f"""Expected string as input, found {type(_lowerc... | 357 |
from __future__ import annotations
import math
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if not scores:
... | 75 | 0 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHe... | 358 |
__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 = [
999,
976,
952,
928,
... | 75 | 0 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
... | 359 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 75 | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTest... | 360 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstrin... | 75 | 0 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
if mass < 0:
raise ValueError('''The mass of a body cannot be negative''' )
return 0.5 * mass * abs(_lowercase ) * abs(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmo... | 361 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIP... | 75 | 0 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
while b:
_A ,_A = b, a % b
return a
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
return a if b == 0 else euclidean_gcd_recursive... | 362 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavav... | 75 | 0 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = '▁'
__A = {'vocab_file': 'vocab.txt',... | 363 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = ('''dense.weight''', '''attention.self.query''', '''attenti... | 75 | 0 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def __A ( _lowercase ):
'''s... | 364 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
@require_torch
def __A ( self: D... | 75 | 0 |
def __A ( _lowercase = 4_00_00_00 ):
'''simple docstring'''
_A = []
_A ,_A = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_lowercase )
_A ,_A = b, a + b
return sum(_lowercase )
if __name... | 365 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
__A = yaml.safe_load(
'\\nname: ""\nallow_empty: false\nallow_empty_text: true\nsubsections:\n - name: "Dataset Card for X" # ... | 75 | 0 |
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 Fl... | 366 |
import os
__A = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
def __A ( _lowercase ):
'''simple docstring'''
_A = 0
_A = 0
while index < len(_lowercase ) - 1:
_A = SYMBOLS[numerals[index]]
_A... | 75 | 0 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ... | 367 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 75 | 0 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_... | 368 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase )... | 75 | 0 |
"""simple docstring"""
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Optional[Any] ) -> Tuple:
_A = {}
def __A ( self: Dict ) -> None:
print(self.vertex )
for i in self.... | 369 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__A = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # no... | 75 | 0 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
__A = HUGGINGFACE_HUB_CACHE
__A = 'config.json'
__A = 'diffusion_pytorch_model.bin'
__A = 'diffusion_flax_model.msgpack'
__A = 'model.onnx'
__A = 'diffusion_pytorch_mode... | 370 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__A = input('Enter image url: ').strip()
print(f'Downloading image from {url} ...')
__A = BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the co... | 75 | 0 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_c... | 371 |
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 transformers.models... | 75 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
A: Any = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def __init__( self , *_SCREAMING_SNAKE_CASE , **_S... | 76 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 76 | 1 |
"""simple docstring"""
import os
def _snake_case ( ):
with open(os.path.dirname(UpperCamelCase ) + """/p022_names.txt""" ) as file:
UpperCAmelCase : str = str(file.readlines()[0] )
UpperCAmelCase : Optional[int] = names.replace("""\"""" , """""" ... | 76 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 76 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
A: Optional[Any] = version.p... | 76 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pi... | 76 | 1 |
"""simple docstring"""
from __future__ import annotations
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE = 0 ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Dict = key
def SCREAMING_SNAKE_CASE ( self ... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : dict ):
UpperCAmelCase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
UpperCAmelCase : set[int] = set()
return any(
node not in visited and depth_f... | 76 | 1 |
"""simple docstring"""
A: int = {str(digit): digit**5 for digit in range(1_0)}
def _snake_case ( UpperCamelCase : int ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCamelCase ) )
def _snake_case ( ):
return sum(
number
f... | 76 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( UpperCamelCase : str ):
def decorator(UpperCamelCase : Optional[int] ):
UpperCAmelCase : List[Any] = getattr(UpperCamelCase , """handle_key""" ,... | 76 | 1 |
"""simple docstring"""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ , Upper... | 76 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 76 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number | (1 << position)
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number & ~(1 << position)
def _snake_case ... | 76 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
Diffusion... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : Tuple , UpperCamelCase : Tuple , UpperCamelCase : Tuple=False ):
if isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase , UpperCamelCase ):
UpperCAmelCase :... | 76 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( UpperCamelCase : list , UpperCamelCase : int ):
# Checks if the entire collection has been sorted
if len(UpperCamelCase ) <= 1 or n <= 1:
return
insert_next(UpperCamelCase , n - 1 ... | 76 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
A: List[Any] = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( enum.Enum ):
__lowerCAmelCase : Dict = 'all_... | 76 | 1 |
"""simple docstring"""
A: int = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggin... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the sa... | 76 | 1 |
"""simple docstring"""
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
f... | 76 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( UpperCamelCase : list[list[float]] ):
UpperCAmelCase : int = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implem... | 76 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: Any = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerC... | 76 |
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAK... | 76 | 1 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _snake_case ( UpperCamelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(UpperCamelCase ):
UpperCAmelCase : List[Any] = [d for d in dir_names if d != """scripts""" and d[0] not in... | 76 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : Dict = """"""
try:
with open(UpperCamelCase , """rb""" ) as binary_file:
UpperCAmelCase : str = binary_file.read()
for dat in data:
UpperC... | 76 | 1 |
"""simple docstring"""
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered... | 76 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
A: Optional[Any] = logging.get_logger(__name__)
def _snake_case ( Up... | 76 | 1 |
"""simple docstring"""
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
UpperCAmelCase : str = data
UpperCAmelCase : Optional[Any] = None
def __repr... | 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 |
"""simple docstring"""
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 versi... | 76 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _snake_case ( UpperCamelCase : np.ndarray ):
return input_array.reshape((input_array.size, 1) )
def _snake_cas... | 76 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
A: int = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_to... | 76 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
A: int = logging.get_logger(__name__)
... | 76 | 1 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
A: Union[str, Any] = datasets.logging.get_logger(__name__)
A: List[str] = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and... | 76 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
... | 76 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorTyp... | 76 |
"""simple docstring"""
import baseaa
def _snake_case ( UpperCamelCase : str ):
return baseaa.aaaencode(string.encode("""utf-8""" ) )
def _snake_case ( UpperCamelCase : bytes ):
return baseaa.aaadecode(UpperCamelCase ).decode("""utf-8""" )
i... | 76 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( UpperCamelCase : int = 4 ):
UpperCAmelCase : Dict = abs(UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(UpperCamelCase )] for y in range(UpperCamelCase )]
def _s... | 76 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _snake_case ( UpperCamelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(UpperCamelCase ):
UpperCAmelCase : List[Any] = [d for d in dir_names if d != """scripts""" and d[0] not in... | 76 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...... | 76 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import loggi... | 76 | 1 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : str = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCAmelCase : List[Any] = """"""
UpperCAmelCase : str = """"""
# append each character + "|... | 76 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE = 6 ) -> None:
'''simple docstring'''
UpperCAmelCase : Node | None = None
UpperCAmelCase ... | 76 | 1 |
"""simple docstring"""
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_to... | 76 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 76 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case ( UpperCamelCase : np.ndarray , UpperCamelCase : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(UpperCamelCas... | 76 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 76 | 1 |
"""simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spect... | 76 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pi... | 76 | 1 |
"""simple docstring"""
from timeit import timeit
A: Union[str, Any] = {
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama"
}
# Ensure our test data i... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : dict ):
UpperCAmelCase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
UpperCAmelCase : set[int] = set()
return any(
node not in visited and depth_f... | 76 | 1 |
"""simple docstring"""
from __future__ import annotations
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : List[str] = text, ... | 76 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( UpperCamelCase : str ):
def decorator(UpperCamelCase : Optional[int] ):
UpperCAmelCase : List[Any] = getattr(UpperCamelCase , """handle_key""" ,... | 76 | 1 |
"""simple docstring"""
from collections import Counter
from timeit import timeit
def _snake_case ( UpperCamelCase : str = "" , ):
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _snake_case ( UpperCam... | 76 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 76 | 1 |
"""simple docstring"""
A: Optional[Any] = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number | (1 << position)
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number & ~(1 << position)
def _snake_case ... | 76 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A: Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDepende... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : Tuple , UpperCamelCase : Tuple , UpperCamelCase : Tuple=False ):
if isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase , UpperCamelCase ):
UpperCAmelCase :... | 76 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_... | 76 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
A: List[Any] = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( enum.Enum ):
__lowerCAmelCase : Dict = 'all_... | 76 | 1 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list ):
UpperCAmelCase : Any = len(UpperCamelCase )
for i in range(1 , UpperCamelCase ):
UpperCAmelCase : Dict = collection[i]
UpperCAmelCase : Optional[int] = 0
UpperCAmelC... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the sa... | 76 | 1 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fr... | 76 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( UpperCamelCase : list[list[float]] ):
UpperCAmelCase : int = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implem... | 76 | 1 |
"""simple docstring"""
A: Dict = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def _snake_case ( UpperCamelCase : dict , UpperCamelCase : Optional[int] ... | 76 |
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAK... | 76 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
loggi... | 76 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : Dict = """"""
try:
with open(UpperCamelCase , """rb""" ) as binary_file:
UpperCAmelCase : str = binary_file.read()
for dat in data:
UpperC... | 76 | 1 |
"""simple docstring"""
class SCREAMING_SNAKE_CASE__ :
def __init__( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Dict = 0
UpperCAmelCase : Any = 0
UpperCAmelCase : Tuple = {}
def SCREAMING_SNAKE_CASE ( ... | 76 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
A: Optional[Any] = logging.get_logger(__name__)
def _snake_case ( Up... | 76 | 1 |
"""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 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
A... | 76 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _snake_case ( UpperCamelCase : np.ndarray ):
return input_array.reshape((input_array.size, 1) )
def _snake_cas... | 76 | 1 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pi... | 76 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
A: int = logging.get_logger(__name__)
... | 76 | 1 |
"""simple docstring"""
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 BartForConditional... | 76 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
... | 76 | 1 |
"""simple docstring"""
from typing import Any, Dict, List, Union
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 ..image_utils import load_image
if is_torch_available(... | 76 |
"""simple docstring"""
import baseaa
def _snake_case ( UpperCamelCase : str ):
return baseaa.aaaencode(string.encode("""utf-8""" ) )
def _snake_case ( UpperCamelCase : bytes ):
return baseaa.aaadecode(UpperCamelCase ).decode("""utf-8""" )
i... | 76 | 1 |
"""simple docstring"""
from collections.abc import Sequence
def _snake_case ( UpperCamelCase : Sequence[float] , UpperCamelCase : bool = False ):
if not arr:
return 0
UpperCAmelCase : str = 0 if allow_empty_subarrays else float("""-inf""" )
UpperCAmelCase... | 76 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _snake_case ( UpperCamelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(UpperCamelCase ):
UpperCAmelCase : List[Any] = [d for d in dir_names if d != """scripts""" and d[0] not in... | 76 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: int = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 76 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import loggi... | 76 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
A: Any = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def __init__( self , *_SCREAMING_SNAKE_CASE , **... | 76 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE = 6 ) -> None:
'''simple docstring'''
UpperCAmelCase : Node | None = None
UpperCAmelCase ... | 76 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizer... | 76 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 76 | 1 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
A: List[str] = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def... | 76 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 76 | 1 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
A: Optional[Any] = logging.get_logger(__name__)
A: ... | 76 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pi... | 76 | 1 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : float , UpperCamelCase : float ):
return round(float(moles / volume ) * nfactor )
def _snake_case ( UpperCamelCase : float , UpperCamelCase : ... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : dict ):
UpperCAmelCase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
UpperCAmelCase : set[int] = set()
return any(
node not in visited and depth_f... | 76 | 1 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
A: Dict = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kad... | 76 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( UpperCamelCase : str ):
def decorator(UpperCamelCase : Optional[int] ):
UpperCAmelCase : List[Any] = getattr(UpperCamelCase , """handle_key""" ,... | 76 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
... | 76 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 76 | 1 |
"""simple docstring"""
import requests
def _snake_case ( UpperCamelCase : str , UpperCamelCase : str ):
UpperCAmelCase : Any = {"""Content-Type""": """application/json"""}
UpperCAmelCase : int = requests.post(UpperCamelCase , json={"""text""": me... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number | (1 << position)
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number & ~(1 << position)
def _snake_case ... | 76 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
A: int = logging.get_logger(__name__)
... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : Tuple , UpperCamelCase : Tuple , UpperCamelCase : Tuple=False ):
if isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase , UpperCamelCase ):
UpperCAmelCase :... | 76 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A: Optional[Any] = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 76 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
A: List[Any] = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( enum.Enum ):
__lowerCAmelCase : Dict = 'all_... | 76 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float ):
if days_between_payments <= 0:
raise ValueError("""days_between_payments must be > 0""" )
if daily_intere... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the sa... | 76 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import loggi... | 76 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( UpperCamelCase : list[list[float]] ):
UpperCAmelCase : int = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implem... | 76 | 1 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return int(input_a == input_a == 0 )
def _snake_case ( ):
print("""Truth Table of NOR Gate:""" )
print("""| Input 1 | Input 2 | Output |""" )
print(F"|... | 76 |
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAK... | 76 | 1 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
A: Union[str, Any] = datasets.utils.logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( folder_based_builder.Fol... | 76 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : Dict = """"""
try:
with open(UpperCamelCase , """rb""" ) as binary_file:
UpperCAmelCase : str = binary_file.read()
for dat in data:
UpperC... | 76 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 76 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
A: Optional[Any] = logging.get_logger(__name__)
def _snake_case ( Up... | 76 | 1 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( UpperCamelCase : Dict , ... | 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 |
"""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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils impo... | 76 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _snake_case ( UpperCamelCase : np.ndarray ):
return input_array.reshape((input_array.size, 1) )
def _snake_cas... | 76 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A: List[Any] = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"Gro... | 76 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
A: int = logging.get_logger(__name__)
... | 76 | 1 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def _snake_case ( ):
UpperCAmelCase , UpperCAmelCase : Tuple = 9, 14 # noqa: F841
UpperCAmelCase : str = [
[0, 1, 4],
[0, 7, 8],... | 76 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
... | 76 | 1 |
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