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'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 89 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase__ = {"processing_layoutxlm": ["Layou... | 151 | 0 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 26 |
from math import ceil, sqrt
def _a ( a :int = 1_000_000 ) -> int:
a = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
a = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
a = 1
... | 26 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 49 |
"""simple docstring"""
import datasets
__SCREAMING_SNAKE_CASE : Tuple = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n ... | 347 | 0 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCamelCase__ : str = 0b101100111... | 164 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(... | 164 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
A : Tuple = False
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def ... | 118 |
"""simple docstring"""
def _A ( lowercase = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
a =set()
# Replace all the whitespace in our sentence
a =input_str.replace(''' ''' , '''''' )
for alpha in ... | 81 | 0 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
__UpperCAmelCase = datasets.logging.get_logger(__name__)
__UpperCAmelCase = '''\
@InProceedings{moosavi2019minimum,
au... | 42 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__UpperCAmelCase = logging.getLogger(__name... | 42 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase__ ):
'''simple docstring'''
def __init__( self : Dic... | 296 |
"""simple docstring"""
import os
_a = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Union[str, Any] = 0
UpperCAmelCase_ : List[str] = 0
while index < len(__lowerCamelCase... | 61 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import Patchi... | 360 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
fr... | 91 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__ ):
__UpperCamelCase : Union[str, Any] = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
retur... | 298 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_... | 298 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE_ = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
raise Optiona... | 351 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, lo... | 189 | 0 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowerCAmelCase ( __UpperCamelCase ):
def __init__( self : List[Any] ... | 50 |
'''simple docstring'''
from __future__ import annotations
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
if not nums:
return 0
SCREAMING_SNAKE_CASE__ : Dict =nums[0]
SCREAMING_SNAKE_CAS... | 152 | 0 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__UpperCamelCase : Optional[Any] = '''scheduler_config.j... | 350 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : list[str] | None = None ):
lowerCAmelCase = word_bank or []
# create a table
lowerCAmelCase = len(_UpperCAmelCase ) + 1
lowerCAmelCase ... | 309 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ ... | 32 |
import unittest
from transformers import LiltConfig, 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 import ModelTester... | 32 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Co... | 353 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencep... | 308 | 0 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
_snake_case = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
raise Import... | 26 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
i... | 26 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalD... | 352 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokeniz... | 328 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
def _A ( lowercase__ ):
lowercase__ ... | 164 |
'''simple docstring'''
import os
import sys
__A = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassificatio... | 164 | 1 |
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, AutoTokenizer
@require_torch
@require_sentencepiece
@r... | 357 |
def lowerCAmelCase_ ( snake_case_ = 1000 ):
_A : List[Any] = 3
_A : Tuple = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
... | 343 | 0 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __UpperCAmelCase ( _lowerCamelCase ):
@require_torch
def lowerCamelCase ... | 42 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
lowercase : Optional[Any] = False
class __... | 42 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ :Optional[Any] = {
'''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}
try:
if not is_torch_available():
... | 357 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import... | 245 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
sma... | 47 |
"""simple docstring"""
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
UpperCAmelCase_ : Optional[int... | 91 | 0 |
"""simple docstring"""
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def UpperCAmelCase__ ( lowerCAmelCase__ :Tuple , lowerCAmelCase__ :List[str] ) -> Union[str, Any]:
'... | 353 | """simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _A ( lowerCAmelCase ):
def __init_... | 32 | 0 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = PhobertTok... | 19 |
lowerCamelCase : List[Any] =[
(1000, '''M'''),
(900, '''CM'''),
(500, '''D'''),
(400, '''CD'''),
(100, '''C'''),
(90, '''XC'''),
(50, '''L'''),
(40, '''XL'''),
(10, '''X'''),
(9, '''IX'''),
(5, '''V'''),
(4, '''IV'''),
(1, '''I'''),... | 189 | 0 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class A ( __snake_case ):
__magic_name__ = (CMStochasticIterativeScheduler,)
__magic_name__ = 10
... | 311 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : List[str] = 2
A : Dict = []
while i * i <= n:
if n % i:
i += 1
else:... | 311 | 1 |
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
__UpperCamelCase : List[Any] = int(_lowerCamelCase )
# Initialize Result
__UpperCamelCase : Any = []
# Traverse through all denomination
... | 298 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
UpperCamelCase_ = 0
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,... | 309 | 0 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str ) -> Any:
"""simple docstring"""
UpperCamelCase , UpperCamelCase :Dict = analyze_text(l... | 371 |
import string
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str ) -> None:
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
UpperCamelCase :List[str] = """"""
for symbol in message:
if symbol in string.ascii_uppercase:
UpperC... | 62 | 0 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase__ : str = logging.get_logger(__name__)
UpperCAmelCase__ : int = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t... | 25 |
# 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 req... | 308 | 0 |
"""simple docstring"""
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,
)
_lowercase : Optional[int] ... | 86 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_fla... | 86 | 1 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Tuple = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-l... | 55 |
from __future__ import annotations
from collections.abc import Callable
def A_ ( snake_case : Callable[[int | float], int | float] , snake_case : int | float , snake_case : int | float , snake_case : int = 100 , ) ... | 328 | 0 |
import operator as op
lowerCAmelCase_ = """scaler.pt"""
lowerCAmelCase_ = """pytorch_model"""
lowerCAmelCase_ = """random_states"""
lowerCAmelCase_ = """optimizer"""
lowerCAmelCase_ = """scheduler"""
lowerCAmelCase_ = """pytorch_model.bin"""
lowerCAmel... | 260 |
from __future__ import annotations
lowerCAmelCase_ = """#"""
class _lowerCAmelCase :
'''simple docstring'''
def __init__( self : Any ):
'''simple docstring'''
_snake_case : dict = {}
def UpperCamelCase_ ( self : Optional[int]... | 260 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
A : Optional[Any] = sorted(numsa + numsa )
A, A : Dict = divmod(len(UpperCa... | 3 | def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> bool:
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(UpperCamelCase_ ) )
def lowercase( UpperCamelCase_ ,... | 343 | 0 |
"""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
_snake_c... | 365 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_snake_case : List[str] = namedtuple(
"_TestCommandArgs",
[
"da... | 134 | 0 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgu... | 179 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase__ : List[Any] = """\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath... | 245 | 0 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A ( ) -> tuple[list[int], int]:
'''simple docstring'''
_UpperCAmelCase = [randint(-1_000 , 1_000 ) for i in range(10 )]
_Up... | 290 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from... | 290 | 1 |
lowerCAmelCase__ : Any = {
'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',
'huggingface-hub':... | 143 |
import unittest
from transformers import LiltConfig, 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 import ModelTester... | 32 | 0 |
import math
import sys
def lowerCamelCase__ ( A__ : str ):
'''simple docstring'''
__lowerCamelCase = """"""
try:
with open(A__ , """rb""" ) as binary_file:
__lowerCamelCase = binary_file.read()
... | 29 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that... | 29 | 1 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( lowercase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = (CMStochasticIterativeScheduler,)
SCREA... | 311 |
'''simple docstring'''
def lowercase ( __magic_name__ ):
'''simple docstring'''
if number > 0:
raise ValueError("input must be a negative integer" )
UpperCAmelCase : List[Any] = len(bin(__magic_name__ )[3:] )
UpperCAmelCase... | 311 | 1 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils imp... | 365 |
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 = logging.get_logger(__name__)
def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamel... | 173 | 0 |
"""simple docstring"""
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ = logging.get_logger(__name__)
class lowercase( __a ):
... | 64 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase__ ( A_ , unittest.TestCase ):
"""simple docstring"""
UpperCAm... | 62 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ ):
"""simple docstring"""
lowe... | 354 |
"""simple docstring"""
from typing import Any
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = data
lowerCAmelCas... | 133 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
""... | 86 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
lowerCamelCase__ = """us-east-1""" # defaults region
@dataclass
class A__ :
A_ : str
A_ : Union[str, Any] = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
A_ : ... | 86 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
snake_case = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( A_ ):
'''simple docstring'''
def __init__( self : int , UpperCAmelCase_ : str=None , **UpperCAmelCase_ : Optional[... | 368 |
from math import sqrt
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
for i in range(1 , int(sqrt(lowercase ) + 1 ) ):
if n % i == 0 and i != sqrt(lowercase ):
total += i + n // i
elif i ==... | 319 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a: Optional[Any] = logging.get_logger(__name__)
__a: List[str] = {
"""huggingface/informer-tourism-monthly""": (
"""https://huggingfac... | 198 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a: Dict = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl""": ["""CTRLToke... | 198 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timeste... | 296 |
"""simple docstring"""
from collections import defaultdict
from math import gcd
def _lowerCAmelCase ( UpperCAmelCase__ : int = 1_5_0_0_0_0_0 ) ->int:
A__ : defaultdict = defaultdict(UpperCAmelCase__ )
A__ : Any = 2
while 2... | 296 | 1 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __lowercase (lowercase_ ):
def __init__( self , A_ , A_ = None ... | 275 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
__snake_case : Union[str, Any] ... | 134 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
"""microsoft/unispeech-sat-base-100h-libri-ft""": (
"""https://huggi... | 256 |
from collections import deque
from .hash_table import HashTable
class lowerCamelCase_ ( _A ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : int , **__lowerCamelCase : Tuple ) -> Optional[Any]:
su... | 256 | 1 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
if length <= 0 or not isinstance(_lowercase, _lowercase ):
raise ValueError("""Length must be a positive integer.""" )
return [n * (2 * n - 1) for n in range(_lowercase )]
if __name__ == "__main__... | 66 |
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoToken... | 311 | 0 |
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__(self , *_lowerCamelCase , **_lowerCamelCase ):
"""simple docstring"""
super().__init__(*_low... | 166 |
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
impo... | 166 | 1 |
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallbac... | 29 |
def lowercase__ ( __snake_case : int , __snake_case : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCAmelCase_ : Tuple = str(bin... | 29 | 1 |
"""simple docstring"""
__UpperCamelCase : Any = '''Tobias Carryer'''
from time import time
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Tuple ,lowercase_ : List[Any] ,lowercase_ : int ,lowercase_ ... | 355 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, Li... | 74 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class _a ( unitte... | 34 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
SCREAMING_SNAKE_CASE_: Optional[int] =str(bin(lowercase ) )[2:] # remove the leading "0b"
... | 173 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {'vocab_file': 'sentencepiece.model'... | 365 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from fl... | 23 | 0 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...util... | 209 |
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
lowercase_ : Optional[i... | 133 | 0 |
"""simple docstring"""
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase ) ->... | 366 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase(__UpperCamelCase ) -> bool:
_lowerCAmelCase =str(__UpperCamelCase )
return n == n[::-1]
def _lowerCamelCase(__UpperCamelCase = 1000000 ) -> str:
_lowerCAmelCase =0
for i in range(1 ... | 341 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lo... | 40 |
'''simple docstring'''
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme impor... | 319 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()... | 360 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase : List[Any] =logging.get_logger(__name__)
lowerCamelCase : Optional[Any] =... | 196 | 0 |
"""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_model... | 241 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
lowercase__ = TypeVar("""_T""")
class __lowerCamelCase ( Generic[_T] ):
'''simple docstring'''
def __init__( self : Optional[int] , a_ : Iterable[_T] | None = No... | 241 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : List[Any] = logging.get_logger(__name__)
__snake_case : Any = {
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resol... | 362 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils i... | 122 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""f... | 256 | """simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffu... | 256 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 290 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenizati... | 290 | 1 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = """M-CLIP"""
def __init__( self : Union[str, Any] , _lowerCAm... | 166 |
'''simple docstring'''
from datetime import datetime
import requests
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase ='https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
__lowercase =requests.get(base_url + url ).json()... | 166 | 1 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def __lowercase ( __lowerCAmelCase : Tuple ):
a__ = int(__lowerCAmelCase ... | 109 |
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 import logging
logging.set_... | 109 | 1 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import t... | 221 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config... | 74 | 0 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ = 10_00 ) -> int:
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = 1, 1
UpperCAmelCase__ : Tuple = 2
while True:
UpperCAmelCase__ : List[Any] = 0
... | 299 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
B... | 299 | 1 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
Chan... | 229 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter:
Upper... | 23 | 0 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __A ( _lowercase ):
"""simple docstring"""
def lowercase__ ( self : Optional[Any] , UpperCAmelCase_ : str ):
with open(__UpperCam... | 360 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, lo... | 323 | 0 |
def __UpperCAmelCase ( __a : Any ) -> Union[str, Any]:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
_a : Union[str, Any] = sorted(string.lower() )
return ... | 235 |
'''simple docstring'''
from math import factorial, radians
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 18 , _SCREAMING_SNAKE_CASE = 10 ):
_snake_case = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from... | 341 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCAmelCase : List[str] = {
"""yjernite/retribert-base-uncased""": (
"""https://huggin... | 148 |
def _A ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F"""{price_plus_tax(100, 0.2_5) = }""")
print(F"""{price_plus_tax(1_2_5.5_0, 0.0... | 148 | 1 |
def UpperCAmelCase_ ( __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : List[Any] ) -> str:
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
SCREAMING_SNAKE_CASE_ = str(bin(__UpperCAmel... | 225 |
def snake_case_ ( snake_case , snake_case ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 196 | 0 |
from __future__ import annotations
from collections import namedtuple
def UpperCamelCase (lowercase_: float , lowercase_: float , lowercase_: float ) -> tuple:
A__ : Optional[Any] = namedtuple("""result""" , """name value""" )
if (voltage, current, power).coun... | 359 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : int = OrderedDict(
[
# Ba... | 141 | 0 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils impor... | 345 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_A = '''\
'''
_A = '''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined ... | 122 | 0 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 3, _lowerCAmelCase : int = 7, _lowerCAmelCase : int = 1_00_00_00 ):
"""simple docstring"""
_a = 0
_a = 1
for current_denominator in range(1, limit + 1 ):
... | 368 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def A_ ( _lowerCAmelCase : Callable, _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float, _lowerCAmelCase : float ):
"""simp... | 153 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_util... | 290 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/mai... | 290 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''ucla... | 188 |
"""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_mo... | 188 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
A: int = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def __init__( self , *_SCREAMING_SNAKE_CASE ... | 109 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A: Any = datasets.utils.loggi... | 109 | 1 |
'''simple docstring'''
from __future__ import annotations
import pandas as pd
def a__ ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
UpperCAmelCase_ ... | 67 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : list ) -> list:
"""simple docstring"""
UpperCAmelCase_ : str = len(_SCREAMING_SNAKE_CASE )
for _ in range(_SCREAMING_SNAKE_CASE ):
for i in range(_ % 2 , arr_size - 1 ... | 67 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_... | 30 |
def A_ ( a ):
"""simple docstring"""
return "".join(chr(ord(a ) - 3_2 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 253 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 9, 14 # noqa: F841
__SCREAMING_SNAKE_CA... | 331 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCAmelCase : Optional[int] = {
'yjernite/retribert-base-uncased': (
'https... | 331 | 1 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCAmelCase__ (lowerCAmelCase_ = "" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
... | 54 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..contr... | 323 | 0 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='''session''' )
def _A ( ):
""... | 359 |
'''simple docstring'''
def _A ( A__ ):
"""simple docstring"""
stooge(A__ , 0 , len(A__ ) - 1 )
return arr
def _A ( A__ , A__ , A__ ):
"""simple docstring"""
if i >= h:
return
# If first element is smaller than the last then swa... | 52 | 0 |
"""simple docstring"""
def UpperCamelCase__ ( lowercase__ : str , lowercase__ : int ):
snake_case : Any = word.split()
def justify(lowercase__ : list , lowercase__ : int , lowercase__ : int ) -> str:
snake_case :... | 148 |
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_de... | 148 | 1 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.fea... | 93 |
lowerCAmelCase = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squa... | 93 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__UpperCamelCase : Any = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise Option... | 146 |
'''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_im... | 141 | 0 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __ma... | 353 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class a__ ( _... | 9 | 0 |
import datasets
from .evaluate import evaluate
_a = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
... | 39 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase__ = '''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and F... | 153 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.js... | 152 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transfo... | 152 | 1 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike... | 188 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase = {
'''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''... | 188 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ ):
if not nums:
return 0
UpperCAmelCase : Optional[int] = nums[0]
UpperCAmelCase : List[str] = 0
for num in nums[1:]:
UpperCAmelCase : List[str] = (
max_excl... | 358 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCAmelCase_ , int(b / 2 ) ) * actual_power(UpperCAmelCase_ , int(b / 2 ) )
else:
return a * actual_power(UpperCAmelCase_ , in... | 280 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"camembert-base": "ht... | 67 | '''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 67 | 1 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Any ):
UpperCamelCase_: List[str] = get_activation("""swish""" )... | 351 |
def A__ ( lowerCamelCase ) -> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(lowerCamelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 223 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCamelCase ( ):
"""simple docstring"""
__magic_name__ , __magic_name__ : Dict = 9, 14 # noqa: F841
__magic_name__ : Union[... | 331 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCAmelCase :Tuple = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'... | 331 | 1 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
lowerCamelCase_ = '''__DUMMY_TRANSFORMERS_USER__'''
lowerCamelCase_ = '''Dummy User'''
lowerCamelCase_ = '''hf_hZEmnoO... | 360 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],
}
t... | 178 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
lowercase... | 96 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def A_ ( ) -> List[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.rais... | 52 | 0 |
def __lowerCamelCase ( lowerCamelCase__ : int , lowerCamelCase__ : int ):
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCamelCase__ , int(b / 2 ) ) * actual_power(lowerCamelCase__ , int(b ... | 66 |
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
UpperCAmelCase : str =... | 66 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_lowercase : int = logging.get_logger(__name__)
class lowerCAmelCase__ ( lowerCamelCase_ ):
def __init__( self ... | 93 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowercase : int = logging.get_logger(__name__)
_lowercase : List[Any] ... | 93 | 1 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the roo... | 370 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See all ... | 175 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( snake_case__ = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
A : Optional[Any] = BeautifulSoup(requests.get(snake_case__ ... | 3 |
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__=False ):
if isinstance(lowercase__ , lowercase__ ) and isinstance(lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : List[str] = len(set_a.intersectio... | 9 | 0 |
'''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_unorder... | 98 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object(... | 98 | 1 |
'''simple docstring'''
import re
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''', str_ )]
def _a( UpperCamelCase__ : str ):
... | 152 |
'''simple docstring'''
from __future__ import annotations
import math
a_ = '2020.9.26'
a_ = 'xcodz-dot, cclaus, dhruvmanila'
def _a( UpperCamelCase__ : float, UpperCamelCase__ : float, UpperCamelCase__ : float, UpperCamelCase__ : float, UpperCamelCase_... | 152 | 1 |
"""simple docstring"""
from manim import *
class __a ( snake_case__ ):
"""simple docstring"""
def _lowerCAmelCase ( self : List[Any] ):
UpperCamelCase__ : int =Rectangle(height=0.5 , width=0.5 )
UpperCamelCase__ : Lis... | 371 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
_SCREAMING_SNAKE_CASE : Optional[int] = """path-to-your-trained-model"""
_SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
... | 157 | 0 |
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