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 |
|---|---|---|---|---|
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAu... | 23 |
"""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 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenize... | 139 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Con... | 139 | 1 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutput... | 159 |
'''simple docstring'''
from math import sqrt
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = 0
for i in range(1 , int(sqrt(lowerCamelCase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowerCamelCase_ ):
total += i + n // i
elif i == sqrt(low... | 379 | 0 |
def __lowerCamelCase ( __lowerCAmelCase : str , __lowerCAmelCase : str ) -> int:
if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
raise ValueError("""String lengths must match!""" )
__UpperCamelCase : Optional[Any] ... | 515 |
def __lowerCamelCase ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> int:
while second != 0:
__UpperCamelCase : int = first & second
first ^= second
__UpperCamelCase : Dict = c << 1
return first
if __... | 515 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaa... | 503 |
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 (
IMAGENET_STANDARD_MEAN,
I... | 503 | 1 |
'''simple docstring'''
from __future__ import annotations
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , lowercase__ ) -> None:
SCREAMING_SNAKE_CASE : List[Any] = order
# a_{0} ... ... | 179 | '''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,
Compos... | 179 | 1 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __UpperCAmelCase ( tf.keras.layers.Layer ):
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=1 , ... | 274 | '''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArgume... | 274 | 1 |
from cva import destroyAllWindows, imread, imshow, waitKey
def A ( _UpperCAmelCase : Optional[Any] ) -> List[str]:
'''simple docstring'''
# getting number of pixels in the image
_UpperCAmelCase , _UpperCAmelCase = img.shape[0], img.shape[1]
# convertin... | 639 |
import qiskit
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> qiskit.result.counts.Counts:
'''simple docstring'''
_UpperCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q regis... | 639 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_availa... | 633 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 569 | 0 |
"""simple docstring"""
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
UpperCAmelCase__ ="\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n a... | 442 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,... | 442 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 88 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelFor... | 525 | 0 |
"""simple docstring"""
from PIL import Image
def _lowerCamelCase( a ):
__a , __a = image.size
__a = 0
__a = image.load()
for i in range(a ):
for j in range(a ):
__a = pixels[j, i]
... | 67 | """simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _lowerCamelCase( a , a , a ):
__a = OmegaConf.load(a )
__a = torch.load(a , map_location... | 67 | 1 |
"""simple docstring"""
from functools import lru_cache
def __magic_name__ ( _lowerCamelCase: Union[str, Any] ) -> int:
'''simple docstring'''
lowerCAmelCase = 2
lowerCAmelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(SCR... | 535 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class lowercase__ ( _snake_case ):
'''simple docstring'''
@require_torch
def ... | 533 | 0 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__SCREAMING_SNAKE_CASE = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
"""... | 701 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import Gra... | 17 | 0 |
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase = logging.g... | 466 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Seque... | 466 | 1 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate im... | 69 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers impo... | 69 | 1 |
def lowerCAmelCase__(__snake_case ,__snake_case ) -> List[str]:
'''simple docstring'''
if not len(A__ ) == len(A__ ) == 3:
raise ValueError('''Please enter a valid equation.''' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError('''Both... | 481 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers... | 95 | 0 |
'''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 _lowerCAmelCase ( UpperCam... | 705 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
fr... | 159 | 0 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def _lowercase ( SCREAMING_SNAKE_CASE_ : Any ):
"""simple docstring"""
UpperCam... | 386 |
from __future__ import annotations
def _lowercase ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if partitions <= 0:
raise ValueError("""partitions must be a positive number!""" )
if partitions > nu... | 386 | 1 |
import heapq
def lowerCAmelCase_ ( __a ) -> set[int]:
"""simple docstring"""
lowerCamelCase__: list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq w... | 437 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__A = datasets.utils.logging.get_logger(__name__)
@dataclass
class _SCREAMING_SNAKE_CASE ( d... | 437 | 1 |
__snake_case = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def _lowercase ( UpperCamelCase_ ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 0
while number:
# Increased Speed Slightly by checking every 5 digit... | 472 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __versi... | 472 | 1 |
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 526 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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
#
# Unl... | 526 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = OrderedDict(
[
... | 569 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : List[str] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", "... | 705 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECK... | 25 | 0 |
import sys
import turtle
def snake_case_ (__A : tuple[float, float] , __A : tuple[float, float] ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def snake_case_ (__A : tuple[float, float] , __A : tuple[float, float] , __A : tuple... | 651 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_... | 651 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class a ( lowercase ):
UpperC... | 700 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common im... | 254 | 0 |
'''simple docstring'''
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, torc... | 92 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config... | 39 | 0 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
... | 718 |
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
A ... | 277 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowercase__ ={'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}
try:
if not is_tokenizers_availa... | 521 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ =logging.get_logger(__name__)
lowercase__ ={
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu... | 521 | 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
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""faceboo... | 720 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"camembert-base": "https://huggingface.co/c... | 413 | 0 |
"""simple docstring"""
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class UpperCamelCase_ (__A ):
__magic_name__ = ''''''
__magic_name__ = (
None # protocol passe... | 95 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__SCREAMING_SNAKE_CASE = {'tokenization_bertweet': ['BertweetTokenizer']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
__SCREAMING_SNAKE_CASE = _Laz... | 220 | 0 |
import qiskit
def __lowercase ( UpperCAmelCase__ = 2 ):
"""simple docstring"""
__lowerCAmelCase = qubits
# Using Aer's simulator
__lowerCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
# Creating a Quantum Circuit acting on the q register
... | 703 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 102 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils impo... | 536 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testin... | 70 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transfo... | 711 |
'''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 ... | 692 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test... | 695 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import ... | 354 | 0 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgu... | 704 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDC... | 504 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , ) -> list[float]:
'''simple docstring'''
lowercase__ , lowercase__... | 12 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--ch... | 433 | 0 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
... | 720 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def snake_case_ (__A : str = "" ) -> dict[str, float]:
__lowerCAmelCase : str = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
__lowerCAmelCase : ... | 218 | 0 |
"""simple docstring"""
from ... import PretrainedConfig
SCREAMING_SNAKE_CASE = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class __a ( _lowerCAmelCase ):
UpperCamelCase_ : Dict = NEZHA_PRETRAIN... | 554 |
"""simple docstring"""
def lowerCamelCase__ ( UpperCAmelCase_ = 60_08_51_47_51_43 )-> int:
"""simple docstring"""
try:
UpperCamelCase = int(UpperCAmelCase_ )
except (TypeError, ValueError):
raise TypeError("P... | 554 | 1 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : List[Any] = '''▁'''
_lowe... | 707 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Optional[int] = {
'''configuration_blenderbot''': [
... | 647 | 0 |
'''simple docstring'''
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 UpperCAmelCase ( a_ , a_ )... | 683 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _snake_case = None ) -> Optional[int]:
_UpperCamelCase : int = value
_UpperCamelCase : Node | ... | 683 | 1 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, Prop... | 189 |
from __future__ import annotations
_A : List[str] = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
... | 189 | 1 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase__ ( UpperCAmelCase_ ):
'''simple docstring'''
A_ : Union[str, Any] = (CMStochasticIterativeScheduler,)
... | 533 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase_ = {
"""configuration_clip""": [
"""CLIP_PR... | 411 | 0 |
"""simple docstring"""
from __future__ import annotations
class _lowerCamelCase :
def __init__( self : Any , lowerCamelCase_ : list[list[int]] ):
"""simple docstring"""
_lowercase : Tuple = TypeError(
'Matrices must be formed ... | 283 | """simple docstring"""
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
_lowercase : ... | 283 | 1 |
from __future__ import annotations
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
__lowerc... | 80 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ... | 80 | 1 |
import re
import subprocess
import sys
__UpperCAmelCase = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__UpperCAmelCase = subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode('utf-8').split()
__UpperCAmelCase = '|'.join(sys.ar... | 719 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
... | 220 | 0 |
'''simple docstring'''
from pathlib import Path
import fire
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : List[str] = Path(UpperCamelCase__ )
UpperCAmelCase__ : Optional[int] = Path(... | 407 |
'''simple docstring'''
import numpy as np
def _UpperCamelCase ( UpperCamelCase__ ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 407 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( _lowercase ):
a = """SpeechT5FeatureExtractor"""
a = """SpeechT5Tokenizer"""
def __init__( self: List[str] , UpperCamelC... | 631 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Optional[Any] =logging.get_logger(__name__)
_A : Optional[int] ={
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/res... | 631 | 1 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __UpperCAmelCase ( a_: List[Any] ):
for param in module.parameters():
_UpperCAmelCase : Union[str, Any] = False
def __UpperCAmelCase ( ):
_UpperCAmelCas... | 494 | '''simple docstring'''
def __UpperCAmelCase ( ):
_UpperCAmelCase : List[str] = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_UpperCAmelCase : Optional[Any] = 6
_UpperCAmelCase : Union[str, Any] = 1
_UpperCAmelCase : Optional[int] = 1_901
... | 494 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (UpperCamelCase : List[str] , UpperCamelCase : Optional[int] ):
'''simple docstring'''
if len(UpperCamelCase ) <= 1 or n <= 1:
return
insert_next(UpperC... | 711 |
'''simple docstring'''
_snake_case : Any = tuple[float, float, float]
_snake_case : Optional[int] = tuple[float, float, float]
def snake_case_ (UpperCamelCase : Pointad , UpperCamelCase : Pointad ):
'''simple docstring'''
... | 377 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, D... | 323 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since ... | 323 | 1 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
_lowerCamelCase =version.parse(version.parse(torch.__version__).... | 705 |
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if not len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa[0] == equationa[... | 252 | 0 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Tuple = (IPNDMScheduler,)
_lowerCamelCase : List[st... | 86 |
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,
)
__a :Dict = {'configuration_xglm': ['XGLM_PRETRAIN... | 86 | 1 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> int:
snake_case_ = len(_SCREAMING_SNAKE_CASE )
snake_case_ = len(matrix[0] )
snake_case_ = min(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for row i... | 715 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
't... | 2 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
... | 582 |
'''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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feat... | 582 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forwa... | 708 |
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 i... | 97 | 0 |
"""simple docstring"""
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test... | 104 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingface.co/... | 388 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_configura... | 489 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPegasusConfig",
... | 489 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class UpperCAmelCase_ ... | 14 |
class A__ :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE ={}
def __UpperCamelCase ( self :... | 691 | 0 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be chec... | 493 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_snake_case : Optional[Any] = {
"""configuration_rag""": ["""RagConfig"""],
"""retrieval_rag""": ["""RagRetriever"""],
"""token... | 493 | 1 |
'''simple docstring'''
from collections.abc import Callable
class A__ :
def __init__( self : Any , _a : Callable | None = None ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[]
# Stores indexes of each item f... | 405 |
def __UpperCamelCase ( lowerCAmelCase__ : int = 4_0_0_0_0_0_0 ):
__a : Tuple = [0, 1]
__a : str = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
__a : int = 0
for j in range(len... | 521 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split,... | 365 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
... | 365 | 1 |
'''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, ids_tensor, random_a... | 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6'''))
def lowerCAmelCase (__A):
"""simple docstring"""
_a = credit_card_number
_a ... | 11 | 1 |
import re
def A__ ( _a : Union[str, Any] ):
'''simple docstring'''
snake_case__ : Tuple =re.compile(
R"""^(?:0|94|\+94|0{2}94)""" R"""7(0|1|2|4|5|6|7|8)""" R"""(-| |)""" R"""\d{7}$""" )
return bool(re.search(_a , _a ) )
if __name__ == "__main__"... | 701 |
from string import ascii_uppercase
__lowerCamelCase : List[Any] = {str(ord(c) - 55): c for c in ascii_uppercase}
def A__ ( _a : int , _a : int ):
'''simple docstring'''
if isinstance(_a , _a ):
raise TypeError("""int() can't convert non-string with ... | 448 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def lowercase (SCREAMING_SNAKE_CASE_ : Any ) -> Optional[Any]:
# vision encoder
if "img_enc... | 247 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_token... | 247 | 1 |
from typing import Dict, Iterable, 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... | 718 | '''simple docstring'''
from __future__ import annotations
__snake_case : str = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class __UpperCAmelCase :
'''simple doc... | 174 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
a ... | 493 |
_SCREAMING_SNAKE_CASE : List[str] = {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
_SCREAMING_SNAKE_CASE : str = ['''a''', '''b''', '''c''', '''d''', '''e''']
def UpperCAmelCase_ ( _A , _A , _A ):
'''simple... | 493 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowercase = logging.get_logger(__na... | 607 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
lowercase = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
lowercase = requests.get(url, headers={"... | 607 | 1 |
"""simple docstring"""
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
UpperCAmelCase_ : List[str] = """Usage of script: script_name <size_of_canvas:int>"""
UpperCAmelCase_ : List[Any] = [0] * 100... | 512 |
import math
def A ( lowercase__ : Tuple , lowercase__ : Union[str, Any] ) -> Optional[Any]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(lowercase__ )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:... | 45 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
lowercase : int = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.... | 584 | from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...ut... | 584 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin... | 108 |
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 snake_case_ (lowerCamelCase_ ):
UpperCAmelCase__ : ... | 335 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerC... | 11 |
'''simple docstring'''
import math
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
... | 11 | 1 |
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))")) | 32 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 603 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 712 |
"""simple docstring"""
def snake_case ( _a: int )-> int:
'''simple docstring'''
lowerCamelCase__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def snake_case ( _a: int )-> int:
'... | 659 | 0 |
'''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.preprocess... | 432 |
'''simple docstring'''
def UpperCamelCase ( a ) -> str:
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 432 | 1 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowercase_ : Optional[int] = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear_1.weight... | 107 | import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
im... | 107 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate impor... | 21 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _snake_case ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[int] ) -> List[Any]:
... | 433 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
A__ : Dict = logging.get_logger(__name__)
class lowercase__ ( snake_case__ ):
def __init__( self : Dict , *snake_case__ : ... | 244 |
"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization... | 244 | 1 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__A : Optional[Any] = logging.get_logger(__name__)
class lowerCamelCase( __snake_case ):
'''simple docstring'''
def __init__( self , ... | 27 |
SCREAMING_SNAKE_CASE :Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
SCREA... | 628 | 0 |
from cva import destroyAllWindows, imread, imshow, waitKey
def _a ( lowerCamelCase__ ) -> Any:
# getting number of pixels in the image
lowerCamelCase_ : List[str] = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(lowe... | 719 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
rai... | 144 | 0 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if i... | 34 | import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase=None ):
__a = None
if token is not None:
__a = {'Accep... | 559 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
... | 232 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Distributed... | 232 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__UpperCAmelCase = datasets.load_iris()
__UpperCAmelCase = np.array(data['data'])
__UpperCAmelCase = np.array(data['target'])
__UpperCAmelCase = d... | 600 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.u... | 62 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe... | 700 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"bert-base-uncased": "https://huggingface.co/bert-base-uncas... | 526 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : List[Any] = {
"""configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 676 |
'''simple docstring'''
def a_ ( __snake_case : str , __snake_case : str ) -> str:
"""simple docstring"""
lowerCamelCase_ =len(__snake_case )
lowerCamelCase_ =len(__snake_case )
lowerCamelCase_ =(
first_str_length if first_str_length... | 676 | 1 |
"""simple docstring"""
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,... | 251 |
"""simple docstring"""
def lowercase__ ( lowerCAmelCase__ : str , lowerCAmelCase__ : list[str] ) -> str:
'''simple docstring'''
a__ : List[str] = ""
for word_or_phrase in separated:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
... | 251 | 1 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
... | 33 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline... | 33 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow,... | 418 |
'''simple docstring'''
__lowerCamelCase : int = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
... | 418 | 1 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase_ = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCamelCase ( UpperCAmelCase__ : list[list[int]] , UpperCAmelCase__ : list[int] , UpperCAmelCase__ ... | 209 | '''simple docstring'''
import numpy as np
import qiskit
def lowerCamelCase ( UpperCAmelCase__ : int = 8 , UpperCAmelCase__ : int | None = None ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :Union[str, Any] = np.random.default_rng(seed=U... | 209 | 1 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from f... | 721 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common imp... | 218 | 0 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
... | 167 | import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERA... | 167 | 1 |
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, TableTransformerConfig, TableTransformerForObjectDetection
from... | 706 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase = [ord(letter) for letter in string.ascii_lowercase]
Upp... | 152 | 0 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, Ag... | 103 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : List[str] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 304 | 0 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to ... | 266 |
'''simple docstring'''
def _a ( _lowercase : List[str] ):
'''simple docstring'''
__UpperCAmelCase : int = len(_lowercase )
while cur > 1:
# Find the maximum number in arr
__UpperCAmelCase : Union[s... | 266 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLPN models at ... | 91 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( snake_case__ : tuple[int, int] , snake_case__ : int ):
A , A = position
A = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
... | 91 | 1 |
def snake_case( __magic_name__ ) -> list[int]:
'''simple docstring'''
lowercase : Dict = [0 for i in range(len(__magic_name__ ) )]
# initialize interval's left pointer and right pointer
lowercase , lowercase ... | 596 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also ... | 596 | 1 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
... | 308 | """simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __UpperCAmelCase (unittest.TestCase , __A ):
'''simple docstring'''
def lowerCamelCase ( self ):
'''simple docstring''... | 363 | 0 |
def UpperCamelCase_ ( a_ = 100 ) ->int:
A =set()
A =0
A =n + 1 # maximum limit
for a in range(2 , a_ ):
for b in range(2 , a_ ):
A =a**b # calculates the current power
collect_powers.add(a_ ) # adds the result to the set
return len(a_ )
if ... | 689 |
def UpperCamelCase_ ( a_ , a_ ) ->list[int]:
A =int(a_ )
# Initialize Result
A =[]
# Traverse through all denomination
for denomination in reversed(a_ ):
# Find denominations
while int(a_ ) >= int(a_ ):
total_value -= int(a_ )
answer.append(a_ ) # Appen... | 689 | 1 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
log... | 600 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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 Backb... | 687 | 0 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class UpperCamelCase__ :
def __init__( self , UpperCamelCase__ = None ):
if components is None:
A__ : Optional[int] = []
A__ : D... | 701 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 55 | 0 |
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