code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from math import factorial
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ = 20 ):
snake_case_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ = n // 2
return int(factorial(SCREAMING_SNAKE_CASE__ ) / ... | 39 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_to... | 52 | 0 |
'''simple docstring'''
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__lowerCamelCase : List[Any] = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.p... | 418 |
'''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 im... | 418 | 1 |
from math import isclose, sqrt
def __UpperCAmelCase ( __a : float ,__a : float ,__a : float ) -> tuple[float, float, float]:
"""simple docstring"""
_a : Any = point_y / 4 / point_x
_a : Dict = 2 * normal_gradi... | 14 |
def __UpperCAmelCase ( __a : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
_a : list = []
for temp in range(int(__a ) ):
series.append(F"""1/{temp + 1}""" if series else '''1''' )
retu... | 14 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'configuration_maskformer_swin': ['Ma... | 712 |
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCamelCase__ ( A__ : int = 2000000 ):
'''simple docstring'''
__lowerCamelCase = [0]
__lowerCamelCase = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 80 | 0 |
"""simple docstring"""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XL... | 636 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Config... | 678 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp... | 275 | """simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
return [ord(lowercase ) - 96 for elem in plain]
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
return "".join(chr(elem + 96 ) for e... | 275 | 1 |
from __future__ import annotations
class snake_case :
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase_ : int ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = order
# a_{0} ... a_{k}
SC... | 393 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import... | 59 | 0 |
"""simple docstring"""
# 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.... | 612 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase ( UpperCamelCase_: ndarray ) -> float:
'''simple docstring'''
return np.dot(UpperCamelCase_ , Uppe... | 612 | 1 |
a_ :Optional[Any] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
a_ :Any = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
a_ :Any = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "Friday",
6: "Saturday",
}
def lowercase_ (A : int , A : ... | 478 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ :Optional[int] = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetC... | 478 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _lowerCamelCase ( ):
lowerCamelCase :Optional[int] = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''p... | 718 | import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 49 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',... | 503 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, l... | 503 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int ) -> List[str]:
"""simple docstring"""
assert (
isinstance(_lowerCAmelCase, _lowerCAmelCase ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {numbe... | 704 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_ut... | 285 | 0 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCamelCase = logging.get_logger(__name__... | 243 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowerCamelCase ( a ):
"""simple docstring"""
UpperCAmelCase_ : Dict ="ClapFeatureExtractor"
UpperCAmelCase_ : Union[str, Any] =("RobertaToken... | 243 | 1 |
'''simple docstring'''
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_ava... | 709 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _SCREAMING_SNAKE_CASE ( yaml.SafeLoader ):
'''simple docstring'''
def A ( self : List[str] , lowercase : List[Any] ... | 265 | 0 |
from math import isqrt
def _a ( lowerCAmelCase )-> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowerCAmelCase ) + 1 ) )
def _a ( lowerCAmelCase = 10**6 )-> int:
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ ... | 360 |
from math import isqrt
def _a ( lowerCAmelCase )-> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowerCAmelCase ) + 1 ) )
def _a ( lowerCAmelCase = 10**6 )-> int:
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ ... | 360 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Dict = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
'JukeboxVQVAEConfig... | 717 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
Dist... | 571 | 0 |
"""simple docstring"""
import os
def lowercase__ ( ) -> Optional[int]:
lowerCAmelCase__ : List[Any] = os.path.join(os.path.dirname(lowerCamelCase ) , "num.txt" )
with open(lowerCamelCase ) as file_hand:
return str(sum(in... | 308 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE_ ... | 300 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any = TypeVar("""T""")
class __lowerCAmelCase( Generic[T] ):
def __init__( self : int , SCREAMING_SNAKE_CASE : int ):
... | 707 |
'''simple docstring'''
from PIL import Image
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE_ :List[Any] = (259 * (level + 255)) / (255 * (259 - level))
def contrast(SCREAMING_SNAKE_CASE ) -> int:
return int(128 + f... | 233 | 0 |
'''simple docstring'''
import math
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
# C... | 119 |
def A_ ( _lowerCAmelCase ) -> bool:
UpperCamelCase : List[Any] = 0
for ch in input_str:
UpperCamelCase : Optional[Any] = ord(_lowerCAmelCase )
UpperCamelCase : Optional[Any] = pow(2 , _lowerCAmelCase )
# If we already turned on bit for ... | 629 | 0 |
from collections.abc import Sequence
from queue import Queue
class __A :
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__=None ):
lowerCamelCase__ : int = start
lowerCa... | 96 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
fro... | 96 | 1 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_dif... | 559 | # limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class a__ ( __snake_case ):
def __init__( self , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
super().__ini... | 559 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_lowerCAmelCase : Dict = ["small", "medium", "large"]
_lowerCAmelCase : Dict = "lm_head.decoder.weight"
_lowerCAmelCase : List[Any] = "lm_head.weight"
def lowerCAmelCase ( _lowerCAmelCas... | 712 |
import torch
from transformers import AutoModel
class _UpperCamelCase ( torch.nn.Module ):
def __init__( self :str , lowerCamelCase :Tuple="sayef/fsner-bert-base-uncased" ) -> int:
super(lowerCamelCase , self ).__init__()
UpperCAmelCase__ = ... | 364 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Union[str, Any] , ) -> fl... | 13 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase ) -> str:
return " ".join(
"""""".join(word[::-1] ) if len(__UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("""Hey wo... | 299 | 0 |
class snake_case_ :
'''simple docstring'''
def __init__( self, A_ ) -> None:
UpperCAmelCase__ =size
UpperCAmelCase__ =[0] * size
UpperCAmelCase__ =[0] * size
@staticmethod
def __UpperCAmelCase ( A_ ) -> int:
return index |... | 700 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-... | 510 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.... | 641 |
_lowercase : Dict = {
"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": "... | 641 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINT... | 390 | lowercase_ = {str(digit): digit**5 for digit in range(10)}
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(__SCREAMING_SNAKE_CASE ) )
def __lowerCAmelCase ... | 390 | 1 |
"""simple docstring"""
import math
def A_ (__a ):
'''simple docstring'''
A_ = [True] * n
A_ = False
A_ = False
A_ = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
A_ = i * 2
while i... | 115 |
"""simple docstring"""
import argparse
import os
import re
UpperCamelCase_ : Any = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_ : Optional[int... | 115 | 1 |
'''simple docstring'''
from __future__ import annotations
a__ = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
a__ = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __UpperCAmelCase ( __a : str ) -> list[float]:
"""simple docstring"""
... | 710 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a__ = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation of ... | 578 | 0 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils i... | 184 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( __A : List[str] , __A ... | 184 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaP... | 715 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
_a = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
_a = set()
return any(
node not in visited and depth_first_search(__A ... | 352 | 0 |
import math
def UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
if (
not isinstance(__UpperCamelCase , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('''power_factor must be a valid float value... | 493 |
def UpperCamelCase( __UpperCamelCase : Any ):
if not head:
return True
# split the list to two parts
lowerCAmelCase_ , lowerCAmelCase_ : Any = head.next, head
while fast and fast.next:
lowerCAmelCase_ : List[Any] = fast.next.next
lowerCA... | 171 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionConfig',
... | 702 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_c... | 503 | 0 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __snake_case ( ctypes.Structure ):
__lowerCAmelCase = [("""size""", ctyp... | 368 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentPars... | 636 | 0 |
'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from to... | 721 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_extr... | 640 | 0 |
"""simple docstring"""
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import ve... | 674 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICEN... | 674 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case : Any = logging.get_logger(__name__)
_snake_case : int = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingface.co/CarlC... | 493 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import v... | 493 | 1 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 3 , __lowerCAmelCase = 7 , __lowerCAmelCase = 100_0000 ) -> Optional[Any]:
snake_case__ = 0
snake_case__ = 1
for current_denominator in range(1 , limit + 1 ):
snake_case__ = current_denominator * num... | 33 |
from math import sqrt
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even nu... | 362 | 0 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
fro... | 176 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _A (__a ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CAS... | 176 | 1 |
import os
def __magic_name__ ( ):
'''simple docstring'''
with open(os.path.dirname(__a ) + """/p022_names.txt""" ) as file:
UpperCamelCase__ = str(file.readlines()[0] )
UpperCamelCase__ = names.replace("""\"""" , """""" ... | 513 |
def __magic_name__ ( __a : list[int] ):
'''simple docstring'''
UpperCamelCase__ = len(__a )
for i in range(__a ):
for j in range(i + 1 , __a ):
if numbers[j] < numbers[i]:
UpperCamelCase__ , UpperCam... | 513 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__A = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app ... | 560 |
"""simple docstring"""
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = None
... | 560 | 1 |
import re
import string
import numpy as np
import datasets
__lowerCAmelCase : Dict ='\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
__lowerCAmelCase : str ='\nArgs:... | 696 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Dict =logging.get_logger(__name__)
__lowerCAmelCase : List[Any] ={
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class _... | 696 | 1 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('''--dump_path''', default=None, typ... | 48 |
"""simple docstring"""
from manim import *
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
def __UpperCamelCase (self ):
snake_case_ : Union[str, Any] = Rectangle(height=0.5 , width=0.5 )
snake_c... | 48 | 1 |
from typing import Any
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not input_list:
return []
UpperCAmelCase_ : Any = [input_list.count(_lowercase ) for value in input_list]
UpperCAmelCase_ : Dict = max(_lowercase ) # Gets the ... | 30 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
snake_case__ = logging.get_logger(__name__)
class UpperCAmelCase ( __lowerCamelCase ):
def __init__( self : Optional[Any] , *lowerCAmelCase : ... | 583 | 0 |
from math import pow, sqrt
def _SCREAMING_SNAKE_CASE ( *_lowerCamelCase : float) -> bool:
'''simple docstring'''
__UpperCamelCase : Tuple = len(_lowerCamelCase) > 0 and all(value > 0.0 for value in values)
return result
... | 94 |
import re
import string
import numpy as np
import datasets
lowercase : List[str] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
lowercase : List[str] = '\... | 94 | 1 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Tuple ) -> Any:
"""simple docstring"""
if len(_a ) == 0:
return False
lowerCAmelCase = len(_a ... | 433 |
a_ = frozenset(
[
'prompt',
'height',
'width',
'guidance_scale',
'negative_prompt',
'prompt_embeds',
'negative_prompt_embeds',
'cross_attention_kwargs',
]
)
a_ = frozenset(['prompt', 'negative_prompt'])
a_ = frozenset... | 25 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
A: Optional[Any] = {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
"albert-large-... | 7 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: int = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerCon... | 7 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( ... | 92 |
'''simple docstring'''
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
lowerCAmelCase_ : str = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default... | 414 | 0 |
"""simple docstring"""
import json
import sys
def snake_case ( UpperCamelCase__ : Any , UpperCamelCase__ : Tuple ) -> Tuple:
with open(UpperCamelCase__ , encoding="""utf-8""" ) as f:
lowerCamelCase : str = json.load(UpperCamel... | 42 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase :Optional[int] = logging.get_logger(__name__)
__lowerCamelCase :List[str] = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/real... | 42 | 1 |
'''simple docstring'''
A__ : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.6_0_9_3_4_4,
"knot": 1.8_5_2,
}
A__ : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.2_7_7_7_7_7_7_7_8,
"mph": 0.6_2_1_3_7_1_1_9_2,
"knot": 0.5_3_9_9_5_6_8_0_3,
}
def ... | 13 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, re... | 405 | 0 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'p... | 363 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import Squad... | 363 | 1 |
"""simple docstring"""
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase ( a ):
"""simple docstring"""
__lowercase :Optional[Any] = (EulerDisc... | 142 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase ( a ):
"""simple docstring"""
__lowercase :Optional[int] = ["image_processor", "tokenizer"]
__low... | 142 | 1 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration... | 700 |
"""simple docstring"""
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 = {
'''facebook/de... | 505 | 0 |
from __future__ import annotations
def __UpperCAmelCase ( __A , __A = None , __A = None ) -> None:
'''simple docstring'''
if start is None:
UpperCAmelCase__ = 0
if end is None:
UpperCAmelCase... | 475 |
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 = {
"facebook/deit-base-distilled-patch16-22... | 475 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {'configuration_... | 712 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Padd... | 8 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscr... | 176 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configu... | 501 | 0 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pat... | 701 | """simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-ba... | 477 | 0 |
'''simple docstring'''
# 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
#
... | 98 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
SCREAMING_SNAKE_CASE_ = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relie... | 465 | 0 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A... | 707 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowercase__( __UpperCamelCase: bytes ,__UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CA... | 508 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-... | 42 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class __lowercase (_UpperCAmelCase ):
def UpperCamelCase__ ( self ) ->List[Any]:
'''simple docstring'''
return [
{"col_1": 3, "col_2": "a"},
... | 492 | 0 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE ="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowercase__( __SCREAMING_SNAKE_CASE : bytes ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNA... | 477 | """simple docstring"""
import numpy as np
def lowercase__( __SCREAMING_SNAKE_CASE : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 477 | 1 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import Token... | 590 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Dict = [0 for i in range(r + 1 )]
# nc0 = 1
A_ : Tuple = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
A_ : str = mi... | 590 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class snake_case_ ( unittest.TestCase ):
def __A ( self ):
... | 712 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__: Optional[int] = "src/transformers"
# This is to make sure the trans... | 311 | 0 |
def __UpperCamelCase ( lowercase__ : str , lowercase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = len(lowercase__ )
lowerCAmelCase_ : int = len(lowercase__ )
lowerCAmelCase_ ... | 600 |
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
__snake_case : Optional[Any] = [("""size""", ctypes.c_int),... | 600 | 1 |
'''simple docstring'''
# 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... | 710 |
'''simple docstring'''
from __future__ import annotations
import math
def _lowerCamelCase( UpperCamelCase__ : int ) -> list[int]:
if num <= 0:
A : str = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(UpperCamelCase__ ... | 537 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}
class __lowerCAmelCase ( ... | 291 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalD... | 291 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase = {
'''configuratio... | 704 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( __UpperCamelCase : str , __UpperCamelCase : dict ):
lowercase = BeautifulSoup(requests.get(__UpperCamelCase , params=__UpperCamelCase ).content , '''html.parser''' ... | 396 | 0 |
'''simple docstring'''
from math import factorial
lowercase__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def a__ ( lowercase : int ) -> int:
"""simple docstring"""
if not isinstance(lowercase, lowercase ):
r... | 98 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtra... | 112 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@requi... | 361 |
"""simple docstring"""
# Function to print upper half of diamond (pyramid)
def lowercase_ ( _UpperCAmelCase ):
"""simple docstring"""
for i in range(0 , _UpperCAmelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end=''''... | 361 | 1 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_colla... | 225 |
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()
lowerCamelCase__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Uni... | 225 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowercase_ = "\\n\n"
lowercase_ = "\nPerplexity (PPL) is one of the most common metrics for eval... | 352 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCAmelCase (__A , __A , __A , __A , __A):
"""simple docstring"""
if depth < 0:
raise ValueError('''Depth cannot be less than 0''')
if not scores:
raise V... | 352 | 1 |
from __future__ import annotations
from typing import Any
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> None:
create_state_space_tree(__snake_case , [] , 0 )
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case ) -> ... | 108 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
... | 42 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fr... | 712 |
from manim import *
class __UpperCamelCase ( _lowercase ):
"""simple docstring"""
def _UpperCAmelCase ( self ) -> Dict:
a__ = Rectangle(height=0.5 , width=0.5 )
a__ = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
a__ ... | 148 | 0 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
__snake_case : List[str] = '\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 ... | 215 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCamelCase ( lowercase_ , lowercase_ ):
... | 215 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
SCREAMING_SNAKE_CASE__ = {"vocab_file": "vocab.txt", "tokenizer_file"... | 104 | """simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def snake_cas... | 104 | 1 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __snake_case ( lowerCAmelCase : list[list[float]] ):
__UpperCAmelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this impleme... | 396 | '''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_UpperCamelCase : Any = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Tran... | 396 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowerCAmelCase ( __a ):
def __a ( self , _UpperCamelCase ) -> Optional[Any]:
with open(_UpperCamelCase , encoding="utf-8" ) as input_f... | 279 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _lowerCAmelCase ( __a , __a ):
@register... | 279 | 1 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 298 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class lowerCAmelCase__ :
def __init__( self : Optional[int] ) -> Optional[int]:
__lowerCamelCase = ''''''
__lowerCamelCase = ''''''
__lowerCamelCase ... | 298 | 1 |
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 ModelTesterMixin, ids_tensor
from ..... | 376 |
# 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
deprecate... | 376 | 1 |
def lowerCAmelCase__( lowercase : str , lowercase : str = " " ) -> List[Any]:
__snake_case : Any = []
__snake_case : List[str] = 0
for index, char in enumerate(lowerCAmelCase__ ):
if char == separator:
split_words.ap... | 243 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 521 | 0 |
"""simple docstring"""
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
UpperCamelCase = [
os.path.join(os.path.dirname(__file__), dirname)
... | 700 |
"""simple docstring"""
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn... | 562 | 0 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib ... | 213 |
'''simple docstring'''
def lowerCAmelCase_ ( a : int ):
a__ = generate_pascal_triangle(a )
for row_idx in range(a ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=' ' )
... | 394 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCAmelCase_ ( A__ , ... | 714 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import B... | 573 | 0 |
'''simple docstring'''
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = 0 ) -> List[Any]:
"""simple docstring"""
__snake_case : int = right or len(_lowerCamelCase ) - 1
if left > right... | 26 | def __UpperCamelCase ( A ):
if len(A ) < 2:
return collection
def circle_sort_util(A , A , A ) -> bool:
UpperCamelCase__ = False
if low == high:
return swapped
UpperCamelCase__ = low
... | 415 | 0 |
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 a__ ( A_, A_ ):
'''simple docstring'''
assert isinstance(A_, A_ ... | 718 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokeniz... | 76 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : str =logging.get_logger(__name__)
__lowerCAmelCase : Unio... | 359 | """simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def Up... | 359 | 1 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
... | 626 |
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
i... | 626 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_te... | 385 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
... | 385 | 1 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _lowerCamelCase ( *lowerCamelCase__ : Optional[int] ):
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
lowercase__ : Op... | 128 |
"""simple docstring"""
import socket
def _lowerCamelCase ( ):
lowercase__ : str = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowercase__ : int = socket.gethostname()
lowercase__ : Optional[Any] = 1_23_12
sock.connect((host, port) )
sock.send... | 128 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_SCREAMING_SNAKE_CASE = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
_SCREAMING_SNAKE_CASE = ... | 18 |
"""simple docstring"""
def snake_case__ ( _lowerCamelCase, _lowerCamelCase ) ->Dict:
"""simple docstring"""
assert x is not None
assert y is not None
__lowercase : Optional[int] = len(_lowerCamelCase )
__lowercase : Union[str, Any] ... | 575 | 0 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case ( ) -> Any:
"""simple docstring"""
UpperCamelCase_ : int = ... | 707 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from t... | 543 | 0 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
de... | 594 |
"""simple docstring"""
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase( lowercase__ ):
'''simple docstring'''
__a : int = (DDPMParallelScheduler,)
def snake_case_ ( self , **__a... | 594 | 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, Timestep... | 709 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> list[str]:
'''simple docstring'''
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_byte... | 386 | 0 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
__UpperCAmelCase : Tuple = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__lowerCamelCase )
if numb... | 63 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.mod... | 616 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
snake_case_ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and m... | 718 |
class SCREAMING_SNAKE_CASE__ :
def __init__(self : Optional[Any] ):
"""simple docstring"""
__snake_case = {}
def a (self : str ):
"""simple docstring"""
print(self.vertex )
... | 388 | 0 |
"""simple docstring"""
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__magic_name__ : L... | 102 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : Dict = sorted(zip(SCREA... | 102 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ... | 403 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.... | 403 | 1 |
"""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():
from PIL import Imag... | 153 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()... | 153 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
__UpperCamelCase ... | 712 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Union[str, Any] = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAIN... | 458 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from d... | 657 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def UpperCamelCase_( _snake_case : Optional[Any] ):
"""simple docstring"""
... | 242 | 0 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/hugg... | 720 |
'''simple docstring'''
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
a : Optional[Any] = "<<<<<<< This should probably be modified because it mentions: "
a : ... | 609 | 0 |
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