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"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
_A : Optional[int] = ... | 202 |
"""simple docstring"""
def __magic_name__ ( __snake_case : list ) -> list:
if len(__snake_case ) < 2:
return collection
def circle_sort_util(__snake_case : list , __snake_case : int , __snake_case : int ) -> bool:
... | 202 | 1 |
"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import nump... | 336 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase: Opt... | 336 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
... | 59 |
"""simple docstring"""
from __future__ import annotations
def lowercase (snake_case__ : list , snake_case__ : int , snake_case__ : int , snake_case__ : int ) -> list:
'''simple docstring'''
lowerCAmel... | 155 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase__(A , A ) ->list[list[int]]:
"""simple docstring"""
lowercase__ : list[list[int]]= []
create_all_state(1 , A , A , [] , A )
return resul... | 150 |
"""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
a : Union[str, Any] = Fals... | 150 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase_ )
class lowerCamelCase__ ( lowerCamelCase_ ):
a__ : ... | 148 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
__A = "path-to-your-trained-model"
__A = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
__A = "A photo of sks dog in a bucket"
__A ... | 148 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = [0] * len(lowercase )
for i in range(1 ,len(lowercase ) ):
# use last results for better performance - dynamic programming
_UpperCAmelCase = prefix_result[i ... | 369 | """simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blo... | 30 | 0 |
def lowercase_ ( _A : int , _A : list ):
"""simple docstring"""
_enforce_args(_A , _A )
if n == 0:
return 0
lowerCamelCase__ : Union[str, Any] = float("-inf" )
for i in range(1 , n + 1 ):
... | 184 |
class _lowercase :
"""simple docstring"""
def __init__( self : Any , __lowerCamelCase : int ):
'''simple docstring'''
lowerCamelCase__ : List[str] = n
lowerCamelCase__ : Union[str, Any] ... | 184 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
class lowercase__ ( _SCREAMING_SNAKE_CASE):
def __init__( self : str , *UpperCamelCase__ : Tuple... | 366 | import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def A ( _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : str = ('''dense.weight''', '''attention.self.query''', '''attention.self.... | 258 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class A__ ( UpperCAmelCase__ ):
def __UpperCAmelCase ( self :Optional[Any] ) -> int:
... | 276 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 276 | 1 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__UpperCamelCase : Un... | 357 |
"""simple docstring"""
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''')):
ra... | 74 | 0 |
'''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_ = logging.get_logger(__name__)
UpperCamelCase_ = {"""vocab_fi... | 309 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a_ :
def __init__( self ):
_lowerCAmelCase : Any = """"""
_lowerCAmelCase : List[Any] = """"""
_lowerCAmelCase ... | 309 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _a ( unittest.TestCase ):... | 356 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowerCAmelCase = '<<<<<<< This should probably be modified because it mentions: '
lowerCAmelCase = '=======... | 93 | 0 |
'''simple docstring'''
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 ... | 145 | '''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 .... | 145 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = [1]
for i in range(2 ,lowercase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
_UpperCAmelCase = []... | 30 | """simple docstring"""
import csv
import tweepy
# Twitter API credentials
UpperCAmelCase__ = """"""
UpperCAmelCase__ = """"""
UpperCAmelCase__ = """"""
UpperCAmelCase__ = """"""
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
#... | 30 | 1 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __A ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
_UpperCamelCase : Dict ... | 44 | """simple docstring"""
from __future__ import annotations
_a : List[str] = 10
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[int] ) -> list[int]:
_lowerCAmelCase : Optional[int] = 1
_lowerCAmelCase : Union[str, Any] ... | 44 | 1 |
import copy
import re
class A_ :
lowerCAmelCase__ = """hp"""
lowerCAmelCase__ = {}
lowerCAmelCase__ = None
@classmethod
def _lowerCAmelCase (cls :Dict , _UpperCamelCase :Optional[Any] , _UpperCamelCase ... | 250 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : List[str] = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Trajecto... | 250 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case : Optional[int] = {
'''configuration_blenderbot''': [
'''BLENDE... | 94 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'ClapFeatureExtractor'
SCREAMING_SNAKE_CASE__ = ('RobertaTokenizer', 'RobertaTokenizerFast')
def __init__( ... | 94 | 1 |
from __future__ import annotations
from typing import Generic, TypeVar
lowercase__ : Tuple = TypeVar("T")
class a__ ( Generic[T] ):
def __init__( self , A ) -> None:
'''simple docstring'''
a = data
a = self
... | 354 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
lowercase__ : str = TypeVar("T")
class a__ ( Generic[T] ):
def __init__( self ... | 180 | 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
UpperCAmelCase : str = logging.get_logger(__name__)
class lowerCAmelCase__ ( ... | 267 |
'''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
UpperCAmelCase : str = logging.get_logger(__name__)
class lowerCAmelCase__ ( ... | 267 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 285 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _snake_case( SCREAMING_SNAKE_CASE__ , SCR... | 285 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def a_ ( _lowerCAmelCase : str ):
'''simple docstring'''
lowercase__ : int = args.pruning_method
... | 77 | """simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class UpperCAmelCase_ :
def __init__( self , a ) -> List[str]:
if isinstance(a ... | 77 | 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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageR... | 333 | # Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def lowercase_ ( _lowerCamelCase : List[str]):
return 1 / (1 + np.exp(-z))
def lowercase_ ... | 333 | 1 |
"""simple docstring"""
import argparse
lowerCamelCase_ : int = """docs/source/_static/js/custom.js"""
def _A ( lowercase ):
"""simple docstring"""
with open(lowercase , encoding='''utf-8''' , newline='''\n''' ) as f:
... | 81 | '''simple docstring'''
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 transform... | 31 | 0 |
from scipy.stats import spearmanr
import datasets
_snake_case : Optional[Any] = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive ... | 134 |
from scipy.stats import spearmanr
import datasets
_snake_case : Optional[Any] = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive ... | 134 | 1 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __lowercase ( a__ ) -> Any:
if not is_accelerate_available():
return method
__SCREAMING_SNAKE_CASE = version.parse(acceler... | 257 | import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
Up... | 65 | 0 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 325 |
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
UpperCamelCase__ ='src/di... | 325 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize... | 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_flax_utils import... | 86 | 1 |
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
from ...test_backbone_common i... | 355 |
# 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 applicab... | 19 | 0 |
"""simple docstring"""
class __snake_case :
def __init__( self : Optional[int] ):
"""simple docstring"""
_lowerCamelCase : dict[str, TrieNode] = {} # Mapping from char to TrieNode
_lowerCamelCase : Tuple = False
... | 72 |
"""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_conf... | 106 | 0 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_... | 183 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_nllb_moe': [
'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
'NllbMoeConfig',
]
... | 183 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _A ( __A ):
_SCREAMING_SNAKE_CASE : List[str] = "Speech2TextFeatureExtractor"
_SCREAMING_SNAKE_CASE : Optional[Any] = "Speech2TextToke... | 254 | import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as P... | 140 | 0 |
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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybrid... | 370 |
import math
def __lowerCamelCase ( UpperCAmelCase_ : float , UpperCAmelCase_ : float ):
"""simple docstring"""
return math.pow(UpperCAmelCase_ , 2 ) - a
def __lowerCamelCase ( UpperCAmelCase_ : float ):
"""simple docstring"""... | 281 | 0 |
from scipy.stats import pearsonr
import datasets
__lowerCAmelCase : int = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assum... | 88 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_SCREAMING_SNAKE_CASE = namedtuple("""covid_data""", """cases deaths recovered""")
def SCREAMING_SNAKE_CASE__ ( __a = "https://www.worldometers.info/coronavirus/" ):
snake_case_ ... | 327 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequen... | 265 |
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Dict:
"""simple docstring"""
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 *... | 265 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
return int((input_a, input_a).count(0 ) == 0 )
def __UpperCAmelCase ( ):
assert and_gate(0, 0 ) == 0
assert and_gate(0, 1 ) == 0
assert and_gate(1, 0 ) ==... | 145 | '''simple docstring'''
from __future__ import annotations
__a = list[tuple[int, int]]
__a = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0... | 145 | 1 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : List[str] = {'''vocab_file'''... | 358 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case : Optional[int] = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig'... | 109 | 0 |
"""simple docstring"""
def lowercase () -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
lowerCAmelCase = 6
lowerCAmelCase = 1
lowerCAmelCase = 1_901
... | 155 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : str ) -> str:
'''simple docstring'''
return " ".join(
"".join(word[::-1] ) if len(__lowercase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
im... | 22 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
def lowercase_ ( _lowerCamelCase: str , _lowerCamelCase: str , _lowerCamelCase: int ) -> str:
'''simple docstring'''
__lowerCamelCase : List[Any] = Path(_lowerCamelCase )
__lowerCamelC... | 363 | """simple docstring"""
def lowercase_ ( _lowerCamelCase: int = 100 ) -> int:
'''simple docstring'''
__lowerCamelCase : Optional[Any] = set()
__lowerCamelCase : Union[str, Any] = 0
__lowerCamelCase : Optional[Any] ... | 64 | 0 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : str ) -> Any:
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(SCREAMING_SNAKE_CASE ):
for j in range(SCREAMING_SNAKE_CASE ... | 325 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing... | 325 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
if len(UpperCamelCase ) < 2:
return collection
def circle_sort_util(UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> bool:
lowerCAmelCase_... | 184 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip2QForm... | 184 | 1 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
_lowercase = '''\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two class... | 74 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils... | 334 | 0 |
"""simple docstring"""
from __future__ import annotations
UpperCAmelCase = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
UpperCAmelCase = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowerCamelCase (a_ :list[float]) -> list[float]:
... | 359 |
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.config... | 172 | 0 |
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,
TrainerCallback,
TrainingArgume... | 26 |
from __future__ import annotations
lowerCamelCase__ : Optional[int] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCamelCase__ : List[Any] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def UpperCAmelCase_ ( __UpperCAmelCase : list[fl... | 225 | 0 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_... | 258 | import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def A ( _lowercase ):
return (data["data"], data["target"])
def ... | 258 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__lowerCamelCase : Union[str, Any] = TypeVar('''T''')
def _snake_case ( lowerCAmelCase : int ):
"""simple docstring"""
return (position - 1) // 2
def _snake_case ( lower... | 18 |
import socket
def _a ( ):
"""simple docstring"""
lowercase__ = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowercase__ = socket.gethostname()
lowercase__ = 1_23_12
sock.connect((host, port) )
sock.send... | 110 | 0 |
def A ( a_ ) -> "list[int]":
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
__UpperCamelCase : Optional[Any] =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__UpperCamelCase : Tuple =1
if ... | 245 |
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 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
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 To... | 75 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
... | 75 | 1 |
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock imp... | 359 |
'''simple docstring'''
import math
import sys
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : str ) -> str:
'''simple docstring'''
UpperCamelCase__ = ""
try:
with open(_UpperCamelCase , "rb" ) as binary_file:
UpperC... | 31 | 0 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
A =Tru... | 34 |
from __future__ import annotations
from math import ceil, floor, sqrt
def __UpperCamelCase ( _lowerCAmelCase = 200_0000 ) -> int:
"""simple docstring"""
A : list[int] = [0]
A : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
... | 116 | 0 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def a_ ( __lowercase : str ) -> None:
_snake_case , _snake_case = analyze_text(__lowercase )
_snake_case = list(' ' + ascii_lowercase )
... | 354 |
def a_ ( __lowercase : List[Any] ) -> Tuple:
_snake_case = len(__lowercase )
for i in range(length - 1 ):
_snake_case = i
for k in range(i + 1 , __lowercase ):
if collection[k] < collection[least]:
_snake_case = k
... | 130 | 0 |
"""simple docstring"""
import random
from typing import Any
def a_ ( lowerCamelCase ):
for _ in range(len(lowerCamelCase ) ):
UpperCAmelCase__ = random.randint(0 , len(lowerCamelCase ) - 1 )
UpperCAmelCase__ = ... | 98 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowerCamelCase_ = ['''small''', '''medium''', '''large''']
lowerCamelCase_ = '''lm_head.decoder.weight'''
lowerCamelCase_ = '''lm_head.weight'''
def __magic_name__ ( __a : str ... | 244 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : List[Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Tuple ) ->... | 277 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import... | 277 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class UpperCamelCase_ :
'''simple docstring'''
UpperCAmelCase__ = 42
UpperCAmelCase__ = No... | 14 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if len(lowerCamelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be greater tha... | 21 | 0 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_avail... | 62 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Optional[int] ) -> Any:
"""simple docstring"""
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
... | 62 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case_ (lowerCamelCase_ ):
@staticmethod
@abstractmethod
def lowerCamelCase__( __snake_case :ArgumentParser ) -> str:
raise NotImplementedError()
@abstractmet... | 240 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Hugg... | 240 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case_ )
class _lowerCAmelCase ( snake_case_ ):
# `task` is not a ClassVar since we want... | 364 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is... | 112 | 0 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __magic_name__ ( __lowerCAmelCase):
def __init__( self : Dict , *lowerCamelCase__ ... | 146 |
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 _a ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Any ... | 146 | 1 |
"""simple docstring"""
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... | 354 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
@register_to_config
def __init__( sel... | 110 | 0 |
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 = logging.get_logger(__name__)
_UpperCamelCase = '''▁'''
_UpperCamelCase... | 326 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ : str =JukeboxTokenizer
UpperCAmelCase_ : Tuple ={... | 326 | 1 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
... | 31 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from t... | 31 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''microsoft/beit-base-patch16-224-pt22k''': (
... | 278 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class lowerCAmelCase :
def __init__( self : List[str] , __lowercase : Collection[float] | None = None ):
... | 141 | 0 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.t... | 351 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
loggi... | 117 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
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 Imag... | 41 |
from ... import PretrainedConfig
_SCREAMING_SNAKE_CASE : Dict = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
a = NEZHA_PRETRAINE... | 314 | 0 |
'''simple docstring'''
def _a ( _lowercase : int = 600851475143 ):
'''simple docstring'''
try:
__UpperCAmelCase : str = int(_lowercase )
except (TypeError, ValueError):
raise TypeError('''Parameter n must b... | 240 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 240 | 1 |
import inspect
import unittest
class lowercase ( unittest.TestCase ):
def __UpperCamelCase ( self ) -> Any:
"""simple docstring"""
try:
import diffusers # noqa: F401
except ImportError:
assert False
def __UpperCamelCase ( self ) -> Dict:
"""simp... | 222 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A ( lowercase ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase = [
'encoder.version',
'decoder.version',
'model.encoder.versi... | 222 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-b-16x2-kinetics400/resolv... | 355 |
"""simple docstring"""
import math
def _lowerCAmelCase ( lowercase_ ):
assert isinstance(lowercase_ , lowercase_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 181 | 0 |
"""simple docstring"""
import numpy as np
def _A ( lowercase , lowercase , lowercase = 1E-12 , lowercase = 1_00 , ):
"""simple docstring"""
assert np.shape(lowercase )[0] == np.shape(lowercase )[1]
# Ensure proper dimensi... | 81 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase = "cpu", lowerCamelCase = None ):
lowercase :Optional[Any] = torch.load(lowerCamelCase, map_location=lowerCamelCase )
for k, v in tqdm(state_dict.items... | 236 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(Fal... | 366 |
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 __magic_name__ ... | 308 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
SCREAMING_SNAKE_CASE :List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :int = {
'''speechbrain/m-ctc-t-large''': '''https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/... | 159 |
from math import pi
def _lowerCAmelCase ( lowerCAmelCase_ :int , lowerCAmelCase_ :int )->float:
'''simple docstring'''
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 159 | 1 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from ... | 64 | """simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 64 | 1 |
from __future__ import annotations
import time
import numpy as np
_UpperCamelCase = [8, 5, 9, 7]
_UpperCamelCase = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_UpperCamelCase = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
... | 275 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class _UpperCAmelCase :
def __init__( self : Union[str, Any] , _lowercase : Optional[Any] ):
__UpperCAmelCase = str(id_ )
__UpperCAmelCase = None
... | 332 | 0 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import ... | 164 |
'''simple docstring'''
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_t... | 164 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
... | 224 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase_ : Tuple = [0] * len(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = []
lowerCA... | 224 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_util... | 203 | """simple docstring"""
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class... | 203 | 1 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : int = {
"facebook/encodec_24khz... | 21 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if len(lowerCamelCase_ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be greater tha... | 21 | 1 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
... | 355 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline... | 27 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
from typing import Any
import requests
_a = "https://api.github.com"
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
_a = BASE_URL + "/user"
# https://github.com/sett... | 17 |
# limitations under the License.
# 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 .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecat... | 336 | 0 |
from __future__ import annotations
from collections.abc import Generator
def UpperCAmelCase__ ( ) -> Generator[int, None, None]:
'''simple docstring'''
lowercase = {}
lowercase = 2
while True:
lowercase = ... | 371 | """simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
fr... | 32 | 0 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( __a ):
_lowercase =''''''
_lowercase =(
None # protocol passed in prefix to the url. ex: "gzip", for gzi... | 231 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_A = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Spee... | 231 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F40... | 358 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
... | 262 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def _a ( UpperCAmelCase ) -> tuple:
"""simp... | 142 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfi... | 28 | 0 |
'''simple docstring'''
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
... | 360 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
cl... | 31 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase : str = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SPEECHT... | 13 | '''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
def update_area_of_max_square(UpperCAmelCase , UpperCAmelCase ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
lowercase__ : int = update_area_of_max_s... | 198 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
_A = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_... | 359 |
"""simple docstring"""
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
... | 212 | 0 |
from math import factorial
A__ : str = {str(d): factorial(d) for d in range(10)}
def UpperCamelCase( __UpperCamelCase : int ):
return sum(DIGIT_FACTORIAL[d] for d in str(__UpperCamelCase ) )
def UpperCamelCase( ):
lowerCAmelCase_ : str = 7 * factorial(9 ) + 1
ret... | 103 | """simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 77 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[str] = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
"XCLIPVisi... | 363 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Optional[Any] = {
"configuration_funnel": ["FUNNEL_PRETRAI... | 326 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ... | 166 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqCon... | 166 | 1 |
import torch
from diffusers import DiffusionPipeline
class lowercase_ ( lowercase ):
'''simple docstring'''
def __init__( self : List[str] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[int] ) ... | 26 |
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 impo... | 26 | 1 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 60 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_f... | 60 | 1 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def __... | 28 |
from math import ceil
def __UpperCamelCase ( lowercase__ : int = 1001 ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[str] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
lowerCAmelCase_ : Optional[Any] = 2 ... | 28 | 1 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = int(number**0.5 )
return number == sq * sq
def __UpperCAmelCase (... | 289 | """simple docstring"""
import requests
UpperCAmelCase__ = """""" # <-- Put your OpenWeatherMap appid here!
UpperCAmelCase__ = """https://api.openweathermap.org/data/2.5/"""
def __UpperCAmelCase ( lowercase = "Chicago" ,lowercase = APPID ):
"""simple docstring"""
... | 289 | 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_unorde... | 321 | """simple docstring"""
def lowercase__( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : int ):
def count_of_possible_combinations(__SCREAMING_SNAKE_CASE : int ) -> int:
if target < 0... | 321 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _snake_case ( lowerCAmelCase__ ... | 85 |
import random
from .binary_exp_mod import bin_exp_mod
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1000 ) -> List[str]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowerCamelCase :... | 48 | 0 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if... | 189 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class a ( UpperCAmelCase ):
_lowercase = ["image_proc... | 189 | 1 |
"""simple docstring"""
from random import randint, random
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False , __UpperCamelCase = False , __UpperCamelCase = 5 , ):
"""simple docstring"""
_... | 266 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
imp... | 266 | 1 |
'''simple docstring'''
import warnings
from .generation import TFGenerationMixin
class a__ ( UpperCAmelCase__ ):
# warning at import time
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be removed i... | 237 | '''simple docstring'''
__UpperCAmelCase ="ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __lowerCAmelCase ( ) -> None:
__lowerCamelCase = input('''Enter message: ''' )
__lowerCamelCase = input('''Enter key [alphanumeric]: ''' )
__lowerCamelCase = input... | 237 | 1 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : List[Any] , ... | 325 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class _A ( unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : List[Any]):
a : str = 0
a : Op... | 40 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def UpperCAmelCase ( UpperCamelCase__ ):
"""simple docstring"""
return choice(UpperCamelCase__ )
def UpperCAmelCase ( UpperCamelCase__ , ... | 154 | """simple docstring"""
class UpperCamelCase__:
def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Dict:
A__ = None
A__ = None
A__ = graph
... | 154 | 1 |
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_sentencepiece,
req... | 52 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder impo... | 52 | 1 |
'''simple docstring'''
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessin... | 366 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 270 | 0 |
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