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 operator as op
def lowerCAmelCase_ ( __a ) -> Tuple:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =[]
lowerCamelCase__: Tuple =lambda __a , __a : int(x / y ) # noqa: E731 integer division operation
lowerCamelCase__: T... | 59 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_... | 20 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def a_ ( lowerCamelCase : Any ):
# vision encoder
if "img_encoder.pos_embed" in name:
lower... | 513 |
'''simple docstring'''
def a_ ( lowerCamelCase : Tuple , lowerCamelCase : Tuple ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
lowerCAmelCase = (boundary[1] - boundary[0]) / steps
lowerCAmelCase = boundary[0]
lowerCAmelCase... | 513 | 1 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE_ = 1_000_003
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = len(_lowerCAmelCase )
__lowerCAmelCase = len(_lowerCAmelCase )
... | 465 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
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
from ..models.a... | 465 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
class UpperCamelCase_ ( _lowerCamelCase ):
def __init__( self , lowerCAmelCase_=None , **lowerCAmelCase_ ) -> Dict:
warni... | 541 |
def lowerCamelCase__ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : int , UpperCamelCase__ : List[str] , UpperCamelCase__ : str ) -> List[Any]:
... | 541 | 1 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def __snake_case ( UpperCAmelCase_ : int ):
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
... | 675 |
from __future__ import annotations
class __snake_case :
"""simple docstring"""
def __init__( self , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
__snake_case , __snake_case ... | 268 | 0 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ :Union[str, Any] = logging.get_logger(__name__)
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
... | 243 |
import random
def lowercase_ (A : int ):
snake_case__ : List[str] = num - 1
snake_case__ : Union[str, Any] = 0
while s % 2 == 0:
snake_case__ : Any = s // 2
t += 1
for _ in range(5 ):
... | 243 | 1 |
'''simple docstring'''
def lowercase_ ( __A : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowercase : str =[0] * len(__A )
lowercase : Union[str, Any] =[]
lowercase : str =[]
lowercase : List[Any] =0
for... | 94 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_co... | 545 | 0 |
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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, V... | 522 | import random
from typing import Any
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
for _ in range(len(lowercase ) ):
__lowercase = random.randint(0 , len(lowercase ) - 1 )
__lowercase = random.... | 522 | 1 |
"""simple docstring"""
import numpy as np
def lowercase__ ( lowerCAmelCase__ : Union[str, Any] ) -> Dict:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 642 |
"""simple docstring"""
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCam... | 589 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"shi-lab... | 489 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = range(2, 20 + 1)
_SCREAMING_SNAKE_CASE = [10**k for k in range(ks[-1] + 1)]
_SCREAMING_SNAKE_CASE = {}
def __a(SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Any , SC... | 489 | 1 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
)
| 621 |
"""simple docstring"""
def lowercase__ ( lowerCamelCase, lowerCamelCase ):
return abs(lowerCamelCase ) if a == 0 else greatest_common_divisor(b % a, lowerCamelCase )
def lowercase__ ( lowerCamelCase, lowerCamelCase ):
while y: # --> when y=0 ... | 621 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.u... | 42 |
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.te... | 42 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : List[Any] = logging.get_logger(__name__)
a__ : str = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class UpperCAmel... | 601 |
'''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, PyTorchBen... | 601 | 1 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __lowercase ( a : int ) -> int:
__snake_case : Optional[int] =prime_factors(a )
if is_square_free(a ):
return -1 if len(a ) % 2 el... | 715 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( a : int , a : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise Value... | 497 | 0 |
def UpperCamelCase ( lowercase_ ) -> list:
'''simple docstring'''
for i in range(len(lowercase_ ) - 1 , 0 , -1 ):
lowercase__ : Union[str, Any] = False
for j in range(lowercase_ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
lower... | 12 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get... | 695 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MC... | 704 |
import math
class _lowerCAmelCase :
def __init__( self , _UpperCamelCase=0 ) -> Tuple: # a graph with Node 0,1,...,N-1
lowerCAmelCase_ = n
lowerCAmelCase_ = [
[math.inf for j in range(0 , _UpperCamelCase )] for i ... | 279 | 0 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have... | 290 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRo... | 324 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[int] = {
'''configuration_mobilebert''': [
'''MOBILEBERT_PRETRA... | 711 | '''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class _lowercase :
'''simple docstring'''
def __init__( self : Tuple ) -> Any:
__lowerCAmelCase = {}
def a ... | 330 | 0 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
snake_case__ = get_tests_dir('''fixtures/spiece.model''')
@... | 395 |
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, DistributedT... | 395 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Optional[Any] = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
"""Inst... | 655 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Reg... | 655 | 1 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
A__ : Dict = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
A__ : Optional[Any] = typing.Union[np.floataa, int, float] # noqa: UP00... | 13 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : list[int] ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError('List is empty' )
__lowercase =sum(lowercase__ ) / len(lowercase__ ) # Calculate ... | 119 | 0 |
from math import pi
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 38 |
from PIL import Image
def A_ ( _lowerCAmelCase ) -> Image:
UpperCamelCase , UpperCamelCase : List[Any] = image.size
UpperCamelCase : Union[str, Any] = 0
UpperCamelCase : List[str] = image.load()
for i in range(_lowerCAmelCase ):
for j in range... | 38 | 1 |
"""simple docstring"""
import math
import os
import sys
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """"""
try:
with open(__lowerCAmelCase , """rb""" ) as binary_file:
SCREAMING_SNA... | 680 |
"""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 :Tupl... | 680 | 1 |
'''simple docstring'''
def _a ( _SCREAMING_SNAKE_CASE : Any ):
_SCREAMING_SNAKE_CASE = [0] * len(_SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = [1] * len(_SCREAMING_SNAKE_CASE )
for values in graph.values():
for i in values:
... | 493 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrat... | 493 | 1 |
'''simple docstring'''
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,
StableDiffusionP... | 664 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __snake_case ( lowerCamelCase_ : Any , lowerCamelCase_ : int , lowerCamelCase_ : Optional[Any] ):
'''simple docstring'''
... | 664 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case_ ( __a , unittest.TestCas... | 706 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case_ = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:
if not is_torch_a... | 262 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def lowerCamelCase ( _snake_case : float ,_snake_case : float ,_snake_case : float ):
'''simple docstring'''
if (resistance, reactance, impedance).count... | 267 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"C... | 267 | 1 |
'''simple docstring'''
UpperCamelCase_ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def lowerCamelC... | 703 | '''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {'''vocab_file''': '... | 320 | 0 |
import requests
def __lowercase ( snake_case, snake_case ):
"""simple docstring"""
__magic_name__ :Dict = {'''Content-Type''': '''application/json'''}
__magic_name__ :Union[str, Any] = requests.post(snake_case, json={'''text''': message_body}, headers=snake_case ... | 0 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowerCamelCase =(3, 9, -1_1, 0, 7, 5, 1, -1)
lowerCamelCase =(4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class _lowerCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CAS... | 285 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class UpperCAmelCase ( ... | 704 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple... | 139 | 0 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 73 | from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCAmelCase__ :
"""simple docstring"""
lowerCAmelCase__ : int
lowerCAmelCase__ : TreeNode | None = None
lowerCAmelCase__ : TreeNode | None ... | 221 | 0 |
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : float ) -> float:
"""simple docstring"""
if edge <= 0 or not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError("""Length must be a positive.""" )
return 3 * ((25 + 10 * (5 ** (1 /... | 379 | from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : List[Any] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''In... | 379 | 1 |
# Algorithm for the pigeonhole sorting
def _SCREAMING_SNAKE_CASE ( lowercase : str ):
'''simple docstring'''
lowerCamelCase_ = min(lowercase ) # min() finds the minimum value
lowerCamelCase_ = max(lowercase ) # max() finds the ... | 70 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class lowerCAmelCase ( ... | 247 | 0 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _lowerCamelCase ( lowercase : Dict = 3 ) -> List[str]:
'''simple docstring'''
if isinstance(_lowerCamelCase ... | 717 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
lowerCAmelCase_ : List[Any] = [
'good first issue',
'feature request',
'wip',
]
def _lowerCamelCase ( ) -> Dict:
_a = Github(os.environ["GITH... | 521 | 0 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
__lowerCamelCase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0,... | 288 |
'''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_featu... | 288 | 1 |
"""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 transformers... | 716 |
"""simple docstring"""
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 91 | 0 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __lowerCAmelCase ... | 584 |
"""simple docstring"""
__UpperCAmelCase : List[str] = {str(digit): digit**5 for digit in range(10)}
def A ( _A ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_A ) )
def A ( ):
"""simple docstring"""
return... | 584 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor im... | 719 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start... | 129 | 0 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
snake_case_ = '''scheduler_config.json'''
class SCREAMING_SNAKE_CASE__ (_UpperCAmelCase ):
__lowerCamelCase : List[An... | 164 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
a : List[str] = logging.get_logger(__name__)
class a_ :
def __init_... | 555 | 0 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,... | 652 |
lowercase_ : Tuple = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/tr... | 652 | 1 |
import functools
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = len(__snake_case )
snake_case_ = len(__snake_case )
@functools.cache
def min_distance(UpperCamelCa... | 362 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping th... | 361 | 0 |
from collections.abc import Callable
import numpy as np
def __UpperCamelCase ( a, a, a, a, a) ->np.ndarray:
lowerCamelCase__ = int(np.ceil((x_end - xa) / step_size))
lowerCamelCase__ = np.zeros((n + 1,))
lowerCamelCase__ = ya
... | 360 |
def __UpperCamelCase ( a = 100) ->int:
lowerCamelCase__ = (n * (n + 1) // 2) ** 2
lowerCamelCase__ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"""{solution() = }""")
| 360 | 1 |
"""simple docstring"""
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 dif... | 224 |
"""simple docstring"""
import sys
from collections import defaultdict
class lowerCAmelCase__ :
def __init__( self : List[str] ):
_snake_case = []
def lowercase ( self : Optional[Any] , _lowerCamelCase : ... | 224 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCamelCase : List[str] = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
tr... | 134 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_f... | 134 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def snake_case_ (UpperCa... | 22 | '''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCS... | 244 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regre... | 713 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_lowerCamelCase : List[str] = (720, 1280) # Height, Width
_lowerCamelCase : Optional[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
_lower... | 177 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extracti... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> int:
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_st... | 69 | 1 |
'''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_backbo... | 411 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
lowerCamelCase__ = ... | 411 | 1 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
lowercase = logging.get_logger(__name__)
class __lowerCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ... | 211 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from ... | 211 | 1 |
'''simple docstring'''
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,
... | 714 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : int = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available()... | 47 | 0 |
from math import factorial
UpperCAmelCase__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def A ( snake_case__ : int ) -> int:
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise TypeError('Pa... | 313 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
UpperCAmelCase__ : Union[str, Any] = logging.getLogger(__name__)
class __lowercase ... | 313 | 1 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.mod... | 714 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_en... | 557 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class UpperCAmelCase_ :
def __init__( self , UpperCamelCase_ = None ) -> None:
if components is N... | 76 | 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__)
_UpperCAmelCase = {
"""google/bit-50""": """https... | 558 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a : int = logging.get_logger(__name__)
a : ... | 593 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
a : int = collections.namedtuple('_... | 593 | 1 |
'''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
@datacla... | 13 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
A__ : Optional[Any] = tuple[int, int]
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ... | 13 | 1 |
"""simple docstring"""
def __magic_name__ ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : list[list[int]] ):
def update_area_of_max_square(_lowerCamelCase : int , _lowerCamelCase : int ) -> int:
# BASE CASE
... | 63 |
"""simple docstring"""
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : str = 0
__... | 63 | 1 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _UpperCamelCase ( SCREAMING_SNAKE_CASE):
'''simple docstring'''
_snake_case = '''M-CLIP'''
def __init__( self , a_=1_0_2_4 , a_... | 372 |
'''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 is_torch_a... | 372 | 1 |
import os
def _lowerCAmelCase ( __lowerCAmelCase ) -> List[Any]:
"""simple docstring"""
snake_case__ : Dict = len(grid[0] )
snake_case__ : Optional[Any] = len(__lowerCAmelCase )
snake_case__ : int = 0
snake_case__ : List... | 219 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers... | 219 | 1 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessin... | 59 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Dat... | 691 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import req... | 489 |
'''simple docstring'''
import math
def __a(SCREAMING_SNAKE_CASE_ : int = 100 ):
'''simple docstring'''
_lowerCAmelCase = sum(i * i for i in range(1 , n + 1 ) )
_lowerCAmelCase = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
... | 489 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAM... | 34 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import ... | 109 | 0 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_... | 83 | import numpy as np
import qiskit
def snake_case ( snake_case__ :int = 8 , snake_case__ :int | None = None) -> str:
_A = np.random.default_rng(seed=snake_case__)
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we ... | 83 | 1 |
def lowerCAmelCase__ ( a__ = 50 ) ->str:
'''simple docstring'''
_UpperCamelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] ... | 547 |
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 import ConfigTester
from .... | 643 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARC... | 544 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...tes... | 544 | 1 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_availabl... | 566 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : int = logging.get_logger(__name__)
snake_case : Optional[Any] = {
'microsoft/git-base': 'https://huggingface.co/microsoft/git-... | 566 | 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,
)
lowercase_ : List[str] ... | 716 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
fro... | 295 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase: str = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', ... | 192 | 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
if is_torch_available():
import torch
if is_vision_available... | 192 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identi... | 624 |
'''simple docstring'''
def _A ( ):
"""simple docstring"""
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def _A ( A__ ):
"""simple docstring"""
__lowercase = 1
__lowercase = 2
while i * i <= n:
__lowercase = ... | 624 | 1 |
"""simple docstring"""
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import f... | 610 |
"""simple docstring"""
lowercase__ = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowercase__ = [{"""type""": """code""", """content""": INSTALL_CONTENT}... | 610 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowercase : Union[str, Any] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: int , _lowerCAmelCase: Any=None , **_lowerCAmelCase: Op... | 711 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_ut... | 550 | 0 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def UpperCamelCase_ ( lowerCAmelCase__ = 8 ):
"""simple docstring"""
_lowerCAmelCase : str = ascii_letters + digits + punctuation
return ... | 424 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a ni... | 443 | 0 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :List[str] = DownBlockaD # noqa F40... | 720 |
import math
import tensorflow as tf
from packaging import version
def A_ ( _lowerCAmelCase ) -> Any:
UpperCamelCase : List[Any] = tf.convert_to_tensor(_lowerCAmelCase )
UpperCamelCase : Any = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype )... | 38 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase : Tuple = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase : Optional[int] = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
... | 139 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : int = 1_0_0_0_0_0_0 ) -> int:
'''simple docstring'''
__UpperCAmelCase : List[str] = 1
__UpperCAmelCase : Union[str, Any] = 1
__UpperCAmelCase : Optional[Any] = {1: 1}
... | 139 | 1 |
from __future__ import annotations
import typing
from collections import Counter
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : int ) -> typing.Counter[int]:
UpperCAmelCase_ : typing.Counter[int] = Counter()
for base in range(1, max_perimeter + 1 ):
... | 713 |
'''simple docstring'''
import sys
import turtle
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : tuple[float, float], SCREAMING_SNAKE_CASE__ : tuple[float, float] ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCamelCase... | 644 | 0 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
... | 219 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__magic_name__ : Any = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_... | 615 | 0 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
a_ = '''examples/'''
a_ = {
'''examples''': (re.compile(r'''^check_min_version\(\"[^\"]+\"\)\s*$''', re.MULTILINE), '''check_min_version(\"VERSION\")\n'''),
'''init''': (re.compile(r'''^__version__\s+... | 709 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
... | 48 | 0 |
"""simple docstring"""
from torch import nn
def _snake_case ( snake_case__ : Union[str, Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'Unsupported activation function: {act_f... | 91 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, g... | 400 | 0 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[str]:
"""simple docstring"""
A__ = {
'''en''': '''Machine learning is... | 177 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import f... | 177 | 1 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_... | 682 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.te... | 682 | 1 |
"""simple docstring"""
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = (EulerDiscre... | 711 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 12 | 0 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCAmelCase_ ( unittest.TestCa... | 529 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
__lowerCAmelCase : Dict = logging.getLogger(__name__)
... | 529 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase__ ( A : Union[str, Any] , A : Any ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
... | 700 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 50 | 0 |
"""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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbo... | 7 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def snake_case ( ) -> Generator[int, None, None]:
lowerCamelCase : dict[int, int] = {}
lowerCamelCase : str = 2
while True:
lowerCamelCase ... | 222 | 0 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowercase_ = input("Enter image url: ").strip()
print(F'''Downloading image from {url} ...''')
lowercase_ = BeautifulSoup(requests.get(url).content, "html.parser")
# The ima... | 390 | import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTR... | 390 | 1 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 391 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, ... | 231 | 0 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
_UpperCamelCase : Tuple = logging.get_l... | 118 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 118 | 1 |
'''simple docstring'''
import sys
from collections import defaultdict
class a__ :
def __init__( self : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase = []
def SCREAMING_SNAKE_CASE__ ( self : List[... | 546 | '''simple docstring'''
import numpy as np
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> str:
__lowerCamelCase = int(np.ceil((x_end - xa) / h ) )
... | 546 | 1 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , l... | 719 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class __lowercase ( __snake_case ):... | 627 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffu... | 22 |
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
fro... | 598 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | 714 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for t... | 352 | 0 |
"""simple docstring"""
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase ( snake_case_ , unittest.TestCase ):
__UpperCAmelCase... | 178 |
"""simple docstring"""
__snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCAmelCase ( ) -> None:
"""simple docstring"""
snake_case : str = input("Enter message: " )
snake_case : Tuple = input("Enter key [alphanumeric]: " ... | 178 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase_ ( _a):
def __init__( self : List[Any] , __A : i... | 373 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 373 | 1 |
from __future__ import annotations
def _lowercase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Dict:
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array... | 410 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
lowerCAmelCase : List[Any] = {
'xlm-mlm-en-2048': 'https://hu... | 511 | 0 |
import requests
from bsa import BeautifulSoup
def _UpperCAmelCase (UpperCamelCase_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = BeautifulSoup(requests.get(UpperCamelCase_ ).text , """html.... | 710 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_comm... | 196 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : Tuple = logging.get_logger(__name__)
_lowerCAmelCase : Optional[Any] = {
... | 246 |
from collections import deque
def a_ ( UpperCamelCase_ : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase = len(UpperCamelCase_ )
lowerCamelCase = deque()
lowerCamelCase = [False for _ in range(UpperCamelCase_ )]
... | 246 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimensi... | 712 |
'''simple docstring'''
def UpperCAmelCase ( UpperCAmelCase__ : int = 10_00):
lowerCamelCase : Optional[Any] = 2**power
lowerCamelCase : str = str(UpperCAmelCase__)
lowerCamelCase : Union[str, Any] = list(UpperCAmelCase__)
... | 449 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONF... | 407 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case (... | 407 | 1 |
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : int = 100):
UpperCamelCase = n * (n + 1) * (2 * n + 1) / 6
UpperCamelCase = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares)
if __name__ == "__main__":
print(F'''{solution() = }''')
... | 721 |
'''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 ( ):
UpperCamelCase = ArgumentParser(
description=(
'''PyTorch TP... | 350 | 0 |
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,
DistilB... | 226 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
'configuration_vision_text_dual_encoder': ['VisionTextDualEncoderCon... | 226 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
# See all Donut models at https://hug... | 142 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
UpperCamelCase_ = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models... | 142 | 1 |
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